def __init__(self):
file2read = netcdf.NetCDFFile("/scratch/general/am8e13/results36km/grid.nc",'r')
bathy = file2read.variables['HFacC']
self.bathy = bathy[:]*1
Z = file2read.variables['Z']
Z = Z[:]*1
self.Z = Z
lat = file2read.variables['XC']
self.lat = lat[:]*1
lon = file2read.variables['YC']
self.lon = lon[:]*1
file1 = '/hpcdata/scratch/am8e13/cs_36km_tutorial/run_input/theta.phc_210_192_50_12'
self.T = komod.mitbin(file1,xdim=192,ydim=210,zdim=50,datatype='float32')
self.T = np.squeeze(self.T,axis=0)
self.T[self.bathy == 0] = np.nan
file1 = '/hpcdata/scratch/am8e13/cs_36km_tutorial/run_input/salt.phc_210_192_50_12'
self.S = komod.mitbin(file1,xdim=192,ydim=210,zdim=50,datatype='float32')
self.S = np.squeeze(self.S,axis=0)
self.S[self.bathy == 0] = np.nan
self.rho = rho(self.S,self.T)
self.rhop = rhop(self.S,self.T)
self.depth = Z
# calculate jmd 95 density
self.rho_jmd = np.zeros_like(self.rho)
for z in range(len(self.Z)):
self.rho_jmd[z,:,:] = densjmd95(self.S[z,:,:],self.T[z,:,:],-9.81*self.Z[z]*1025)
self.title = 'PHC'
self.dataDyn = {}
temp_lv = np.nanmean(np.nanmean(self.T,axis=1),axis=1)
self.dataDyn['theta_lv_mean'] = (np.ones((400,1))*temp_lv)
temp_lv = np.nanmean(np.nanmean(self.S,axis=1),axis=1)
self.dataDyn['salt_lv_mean'] = (np.ones((400,1))*temp_lv)
self.dataDyn['rho_lv_mean'] = rhop(self.dataDyn['salt_lv_mean'],self.dataDyn['theta_lv_mean'])
def readData(self,list_var):
file2read = netcdf.NetCDFFile(self.path+'state.nc','r')
Temp=file2read.variables['Temp']
self.data['T']=Temp[list_var]*1
V=file2read.variables['V']
self.data['V']=V[list_var]*1
U=file2read.variables['U']
self.data['U']=U[list_var]*1
S=file2read.variables['S']
self.data['S']=S[list_var]*1
Eta=file2read.variables['Eta']
self.data['Eta']=Eta[list_var]*1
days=file2read.variables['T']
self.data['days']=days[list_var]*1
self.years = (self.data['days'] - self.data['days'][0])/(60*60*24*360)
file2read.close()
if self.data['T'].shape[3] == 210:
self.grid = "/scratch/general/am8e13/results36km/grid.nc"
self.res = 36
elif self.data['T'].shape[3] == 420:
self.grid = "/scratch/general/am8e13/results18km/grid.nc"
self.res = 18
elif self.data['T'].shape[3] == 840:
self.grid = "/scratch/general/am8e13/results9km/grid.nc"
self.res = 9
file2read = netcdf.NetCDFFile(self.grid,'r')
# Bathy is 1 on land and 0 over sea
hfacc = file2read.variables['HFacC']
self.hfacc = hfacc[:]*1
hfacw = file2read.variables['HFacW']
self.hfacw = hfacw[:]*1
hfacs = file2read.variables['HFacS']
self.hfacs = hfacs[:]*1
lat = file2read.variables['YC']
self.lat = lat[:]*1
lon = file2read.variables['XC']
self.lon = lon[:]*1
depth = file2read.variables['Z']
self.depth = depth[:]*1
X = file2read.variables['X']
self.X = X[:]*1
Y = file2read.variables['Y']
self.Y = Y[:]*1
Z = file2read.variables['Z']
self.Z = Z[:]*1
file2read.close()
self.data['T'][:,self.hfacc==0] = np.nan
self.data['U'][:,self.hfacw==0] = np.nan
self.data['V'][:,self.hfacs==0] = np.nan
self.data['S'][:,self.hfacc==0] = np.nan
self.data['Eta'][:,self.hfacc[0,:,:]==0] = np.nan
self.T = np.nanmean(self.data['T'],axis=0)
self.S = np.nanmean(self.data['S'],axis=0)
self.rho = rho(self.S,self.T)
self.rhop = rhop(self.S,self.T)
# calculate jmd 95 density
self.rho_jmd = np.zeros_like(self.rho)
for z in range(len(self.Z)):
self.rho_jmd[z,:,:] = densjmd95(self.S[z,:,:],self.T[z,:,:],-9.81*self.Z[z]*1027.5)
self.V = np.nanmean(self.data['V'],axis=0)
self.U = np.nanmean(self.data['U'],axis=0)
self.Vda = np.nanmean(np.nanmean(self.data['V'],axis=1),axis=0)
self.Uda = np.nanmean(np.nanmean(self.data['U'],axis=1),axis=0)
print 'Data read from '+self.path
def __init__(self):
file2read = netcdf.NetCDFFile("/scratch/general/am8e13/results36km/grid.nc",'r')
bathy = file2read.variables['HFacC']
self.bathy = bathy[:]*1
self.res = 36
Z = file2read.variables['Z']
Z = Z[:]*1
self.Z = Z
lat = file2read.variables['YC']
self.lat = lat[:]*1
lon = file2read.variables['XC']
self.lon = lon[:]*1
file1 = '/scratch/general/am8e13/WOA/WOA05_THETA_210x192x50_arctic'
self.T = komod.mitbin(file1,xdim=192,ydim=210,zdim=50,datatype='float32')
self.T = np.squeeze(self.T,axis=0)
self.T[self.bathy == 0] = np.nan
file1 = '/scratch/general/am8e13/WOA/WOA05_SALT_210x192x50_arctic'
self.S = komod.mitbin(file1,xdim=192,ydim=210,zdim=50,datatype='float32')
self.S = np.squeeze(self.S,axis=0)
self.S[self.bathy == 0] = np.nan
self.rho = rho(self.S,self.T)
self.rhop = rhop(self.S,self.T)
self.depth = Z
# calculate jmd 95 density
# 18 km files
file1 = '/scratch/general/am8e13/WOA/WOA05_THETA_420x384x50_arctic'
self.T18 = komod.mitbin(file1,xdim=384,ydim=420,zdim=50,datatype='float32')
self.T18 = np.squeeze(self.T18,axis=0)
file1 = '/scratch/general/am8e13/WOA/WOA05_SALT_420x384x50_arctic'
self.S18 = komod.mitbin(file1,xdim=384,ydim=420,zdim=50,datatype='float32')
self.S18 = np.squeeze(self.S18,axis=0)
file2read = netcdf.NetCDFFile("/scratch/general/am8e13/results18km/grid.nc",'r')
bathy = file2read.variables['HFacC']
self.bathy = bathy[:]*1
self.T18[self.bathy == 0] = np.nan
self.S18[self.bathy == 0] = np.nan
self.rho18 = rho(self.S18,self.T18)
self.rhop18 = rhop(self.S18,self.T18)
# 9 km files
file1 = '/scratch/general/am8e13/WOA/WOA05_THETA_840x768x50_arctic'
self.T9 = komod.mitbin(file1,xdim=768,ydim=840,zdim=50,datatype='float32')
self.T9 = np.squeeze(self.T9,axis=0)
file1 = '/scratch/general/am8e13/WOA/WOA05_SALT_840x768x50_arctic'
self.S9 = komod.mitbin(file1,xdim=768,ydim=840,zdim=50,datatype='float32')
self.S9 = np.squeeze(self.S9,axis=0)
file2read = netcdf.NetCDFFile("/scratch/general/am8e13/results9km/grid.nc",'r')
bathy = file2read.variables['HFacC']
self.bathy = bathy[:]*1
self.T9[self.bathy == 0] = np.nan
self.S9[self.bathy == 0] = np.nan
self.rho9 = rho(self.S9,self.T9)
self.rhop9 = rhop(self.S9,self.T9)
self.rho_jmd = np.zeros_like(self.rho)
for z in range(len(self.Z)):
self.rho_jmd[z,:,:] = densjmd95(self.S[z,:,:],self.T[z,:,:],-9.81*self.Z[z]*1025)
self.title = 'Woa'
self.dataDyn = {}
temp_lv = np.nanmean(np.nanmean(self.T,axis=1),axis=1)
self.dataDyn['theta_lv_mean'] = (np.ones((400,1))*temp_lv)
temp_lv = np.nanmean(np.nanmean(self.S,axis=1),axis=1)
self.dataDyn['salt_lv_mean'] = (np.ones((400,1))*temp_lv)
self.dataDyn['rho_lv_mean'] = rhop(self.dataDyn['salt_lv_mean'],self.dataDyn['theta_lv_mean'])
