mld_10 = np.zeros(len(range(dayi,dayf,days))) 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_50 = path+'Velocity_CG_0_'+label_50+'_'+str(time[t])+'.csv' file0_25 = path+'Velocity_CG_0_'+label_25+'_'+str(time[t])+'.csv' file0_10 = path+'Velocity_CG_0_'+label_10+'_'+str(time[t])+'.csv' file1 = 'drate_'+label+'_'+str(time[t]) file1_50 = 'drate_'+label_50 file1_25 = 'drate_'+label_25 file1_10 = 'drate_'+label_10 W_50 = lagrangian_stats.read_Scalar(file0_50,zn,yn_50,xn_50) W_25 = lagrangian_stats.read_Scalar(file0_25,zn,yn_25,xn_25) W_10 = lagrangian_stats.read_Scalar(file0_10,zn,yn_10,xn_10) for i in range(len(Xlist_50)): for j in range(len(Ylist_50)): FW_50[j,i,:,t] = (np.gradient(W_50[:,j,i])/dz_50)**2 for i in range(len(Xlist_25)): for j in range(len(Ylist_25)): FW_25[j,i,:,t] = (np.gradient(W_25[:,j,i])/dz_25)**2 for i in range(len(Xlist_10)): for j in range(len(Ylist_10)): FW_10[j,i,:,t] = (np.gradient(W_10[:,j,i])/dz_10)**2 plt.figure(figsize=(4,8))
v = np.zeros((len(Zlist),2))
#for time in range(dayi,dayf,days):
for file in files:
time = int(file)
tlabel = str(time)
while len(tlabel) < 3: tlabel = '0'+tlabel
#
file0 = basename + '_' + str(time) + '.pvtu'
filepath = path+file0
file1 = label+'_' + tlabel
fileout = path + file1
#
print 'opening: ', filepath
#
Rho = lagrangian_stats.read_Scalar('/nethome/jmensa/scripts_fluidity/2D/RST/Density_CG/Density_CG_'+label+'_'+str(time),zn,xn,yn,[time])
#
#
for d in range(9,len(Zlist),100):
if file == files[0]:
v[d,:] = np.nanmin(Rho[d,:,:,0]), np.nanmax(Rho[d,:,:,0])
fig = plt.figure(figsize=(7, 6))
plt.contourf(Xlist/1000.0,Ylist/1000.0,Rho[d,:,:,0],np.linspace(v[d,0],v[d,1],50,endpoint=True),extend='both',cmap=plt.cm.PiYG)
plt.colorbar(ticks=np.linspace(v[d,0],v[d,1],5,endpoint=True))
plt.axis('equal')
plt.xlabel('X (Km)',fontsize=16)
plt.ylabel('Y (Km)',fontsize=16)
plt.title('day '+str(time/4.0),fontsize=16)
plt.savefig('./plot/'+label+'/R_'+file1+'_'+str(Zlist[d])+'_h_snap.eps')
print './plot/'+label+'/R_'+file1+'_'+str(Zlist[d])+'_h_snap.eps'
plt.close()
zn = len(Zlist)
dx = np.diff(Xlist)
for time in range(dayi,dayf,days):
print 'time:', time
tlabel = str(time)
while len(tlabel) < 3: tlabel = '0'+tlabel
#Velocity_CG_m_6e_9.csv
file0_B = path+'Velocity_CG_2_'+label_B+'_'+str(time)+'.csv'
file0_BW = path+'Velocity_CG_2_'+label_BW+'_'+str(time)+'.csv'
file1 = 'Velocity_CG_2_'+label+'_'+str(time)
file1 = 'Velocity_CG_2_'+label+'_'+str(time)
#
T_B = lagrangian_stats.read_Scalar(file0_B,zn,xn,yn)
T_BW = lagrangian_stats.read_Scalar(file0_BW,zn,xn,yn)
FT_B = np.zeros((xn/1,yn))
FT_BW = np.zeros((xn/1,yn))
#
for k in range(1):
for j in range(0,len(Ylist),100):
print j
plt.plot(T_B[k,j,:]-np.mean(T_B[k,j,:]))
plt.savefig('./plot/'+label+'/'+file1+'_'+str(Zlist[k])+'_'+str(j)+'_sec.eps',bbox_inches='tight')
plt.close()
print './plot/'+label+'/'+file1+'_'+str(Zlist[k])+'_'+str(j)+'_sec.eps'
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' file1 = 'Velocity_CG_'+label+'_'+str(time) print file1 # # xn_50 = 101 # yn_50 = 101 # xn = 101 # yn = 101 u = lagrangian_stats.read_Scalar(file0u,zn,xn,yn) v = lagrangian_stats.read_Scalar(file0v,zn,xn,yn) # # u = np.squeeze(np.reshape(Vel[:,0],[len(Zlist),len(Xlist),len(Ylist)])) # v = np.squeeze(np.reshape(Vel[:,1],[len(Zlist),len(Xlist),len(Ylist)])) for k in range(len(Zlist)): # u = np.mean(u,0) # v = np.mean(v,0) uk = u[k,:,:] vk = v[k,:,:] # dt = 3600 # s dx = np.mean(np.mean(uk,0),0)*dt
xn = len(Xlist) yn = len(Ylist) zn = len(Zlist) dx = np.diff(Xlist) z = 1 for time in range(dayi,dayf,days): print 'time:', time tlabel = str(time) while len(tlabel) < 3: tlabel = '0'+tlabel #Velocity_CG_m_50_6e_9.csv file0 = path+'Tracer_'+str(z)+'_CG_'+label+'_'+str(time)+'.csv' # T = lagrangian_stats.read_Scalar(file0,zn,xn,yn) T = np.sum(T,0)/3. FT = np.zeros((xn/1,yn)) # for j in range(len(Ylist)): tempfft = scipy.fftpack.fft(T[:,j]**2,xn) FT[:,j] = abs(tempfft)**2 w = scipy.fftpack.fftfreq(xn, dx[1]) # w = scipy.fftpack.fftshift(w) FTp = np.mean(FT,1)/xn # ideal t=0 Theory = T*0 + 3 for j in range(len(Ylist)): tempfft = scipy.fftpack.fft(Theory[:,j]**2,xn)
Ylist = np.linspace(0,2000,yn)# y co-ordinates of the desired array shape
[X,Y] = np.meshgrid(Xlist,Ylist)
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 = np.cumsum(dl)
time = range(dayi,dayf,days)
t = 0
for tt in time:
tlabel = str(tt)
while len(tlabel) < 3: tlabel = '0'+tlabel
#
file1 = basename+'_'+label+'_' + tlabel + '.csv'
print file1
f = open(file1,'wr')
writer = csv.writer(f)
#
Temp = lagrangian_stats.read_Scalar('./RST/'+basename+'/'+basename+'_'+label+'_'+str(tt)+'.csv',zn,xn,yn)
#
for k in range(len(Zlist)):
# determine speed direction
#
for i in range(len(Xlist)):
for j in range(len(Ylist)):
writer.writerow([Xlist[i],Ylist[j],Zlist[k],Temp[k,i,j]])
f.close()
Zlist = -1*np.cumsum(depths)
Xlist = np.linspace(-150000,150000,301)
Ylist = np.linspace(-150000,150000,301)
[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
#
Rho = lagrangian_stats.read_Scalar('./Density_CG/Density_CG_'+label+'_'+str(tt),zn,xn,yn,[tt])
#
for z in range(5,zn,25):
# determine speed direction
#
plt.figure()
plt.contourf(Xlist,Ylist,Rho[z,:,:,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+'.eps')
plt.close()
print './plot/'+label+'/T_'+str(z)+'_'+file1+'.eps'
