# Sectors to consider

sector_names = ['Filchner-Ronne Ice Shelf Cavity', 'Eastern Weddell Region Cavities', 'Amery Ice Shelf Cavity', 'Australian Sector Cavities', 'Ross Sea Cavities', 'Amundsen Sea Cavities', 'Bellingshausen Sea Cavities', 'Larsen Ice Shelf Cavities', 'All Ice Shelf Cavities']

num_sectors = len(sector_names)

# Water masses to consider

wm_names = ['ISW', 'AASW', 'CDW', 'MCDW', 'WW', 'HSSW']

num_watermasses = len(wm_names)

# ROMS vertical grid parameters

theta_s = 7.0

theta_b = 2.0

hc = 250

N = 31

# FESOM mesh parameters

circumpolar = True

cross_180 = False

print 'Processing MetROMS'

# Read ROMS grid variables we need

id = Dataset(roms_grid, 'r')

roms_lon = id.variables['lon_rho'][:,:]

roms_lat = id.variables['lat_rho'][:,:]

roms_h = id.variables['h'][:,:]

roms_zice = id.variables['zice'][:,:]

id.close()

num_lat = size(roms_lat, 0)

num_lon = size(roms_lon, 1)

# Get integrands on 3D grid

roms_dx, roms_dy, roms_dz, roms_z = cartesian_grid_3d(roms_lon, roms_lat, roms_h, roms_zice, theta_s, theta_b, hc, N)

# Get volume integrand

dV = roms_dx*roms_dy*roms_dz

# Read ROMS output

id = Dataset(roms_file, 'r')

roms_temp = id.variables['temp'][0,:,:,:]

roms_salt = id.variables['salt'][0,:,:,:]

id.close()

# Initialise volume of each water mass in each sector

roms_vol_watermass = zeros([num_watermasses, num_sectors])

# Calculate water mass breakdown

for j in range(num_lat):

for i in range(num_lon):

# Select ice shelf points

if roms_zice[j,i] < 0:

# Figure out which sector this point falls into

lon = roms_lon[j,i]

if lon > 180:

lon -= 360

lat = roms_lat[j,i]

if lon >= -85 and lon < -30 and lat < -74:

# Filchner-Ronne

sector = 0

elif lon >= -30 and lon < 65:

# Eastern Weddell region

sector = 1

elif lon >= 65 and lon < 76:

# Amery

sector = 2

elif lon >= 76 and lon < 165 and lat >= -74:

# Australian sector

sector = 3

elif (lon >= 155 and lon < 165 and lat < -74) or (lon >= 165) or (lon < -140):

# Ross Sea

sector = 4

elif (lon >= -140 and lon < -105) or (lon >= -105 and lon < -98 and lat < -73.1):

# Amundsen Sea

sector = 5

elif (lon >= -104 and lon < -98 and lat >= -73.1) or (lon >= -98 and lon < -66 and lat >= -75):

# Bellingshausen Sea

sector = 6

elif lon >= -66 and lon < -59 and lat >= -74:

# Larsen Ice Shelves

sector = 7

else:

print 'No region found for lon=',str(lon),', lat=',str(lat)

break #return

# Loop downward

for k in range(N):

curr_temp = roms_temp[k,j,i]

curr_salt = roms_salt[k,j,i]

curr_volume = dV[k,j,i]

# Get surface freezing point at this salinity

curr_tfrz = curr_salt/(-18.48 + 18.48/1e3*curr_salt)

# Figure out what water mass this is

if curr_temp < curr_tfrz:

# ISW

wm_key = 0

elif curr_salt < 34:

# AASW

wm_key = 1

elif curr_temp > 0:

# CDW

wm_key = 2

elif curr_temp > -1:

# MCDW

wm_key = 3

elif curr_salt < 34.5:

# WW

wm_key = 4

else:

# HSSW

wm_key = 5

# Integrate volume for the right water mass and sector

roms_vol_watermass[wm_key, sector] += curr_volume

# Also integrate total Antarctica

roms_vol_watermass[wm_key, -1] += curr_volume

# Find total volume of each sector by adding up the volume of each

# water mass

roms_vol_sectors = sum(roms_vol_watermass, axis=0)

# Calculate percentage of each water mass in each sector

roms_percent_watermass = zeros([num_watermasses, num_sectors])

for wm_key in range(num_watermasses):

for sector in range(num_sectors):

roms_percent_watermass[wm_key, sector] = roms_vol_watermass[wm_key, sector]/roms_vol_sectors[sector]*100

print 'Processing low-res FESOM'

# Build mesh

elements_lr = fesom_grid(fesom_mesh_lr, circumpolar, cross_180)

id = Dataset(fesom_file_lr, 'r')

temp_nodes_lr = id.variables['temp'][0,:]

salt_nodes_lr = id.variables['salt'][0,:]

id.close()

fesom_vol_watermass_lr = zeros([num_watermasses, num_sectors])

for i in range(len(elements_lr)):

elm = elements_lr[i]

if elm.cavity:

lon = mean(elm.lon)

lat = mean(elm.lat)

if lon >= -85 and lon < -30 and lat < -74:

sector = 0

elif lon >= -30 and lon < 65:

sector = 1

elif lon >= 65 and lon < 76:

sector = 2

elif lon >= 76 and lon < 165 and lat >= -74:

sector = 3

elif (lon >= 155 and lon < 165 and lat < -74) or (lon >= 165) or (lon < -140):

sector = 4

elif (lon >= -140 and lon < -105) or (lon >= -105 and lon < -98 and lat < -73.1):

sector = 5

elif (lon >= -104 and lon < -98 and lat >= -73.1) or (lon >= -98 and lon < -66 and lat >= -75):

sector = 6

elif lon >= -66 and lon < -59 and lat >= -74:

sector = 7

else:

print 'No region found for lon=',str(lon),', lat=',str(lat)

break #return

# Get area of 2D element

area = elm.area()

nodes = [elm.nodes[0], elm.nodes[1], elm.nodes[2]]

# Loop downward

while True:

if nodes[0].below is None or nodes[1].below is None or nodes[2].below is None:

# Reached the bottom

break

# Calculate average temperature, salinity, and

# layer thickness for this 3D triangular prism

temp_vals = []

salt_vals = []

dz_vals = []

for n in range(3):

temp_vals.append(temp_nodes_lr[nodes[n].id])

salt_vals.append(salt_nodes_lr[nodes[n].id])

temp_vals.append(temp_nodes_lr[nodes[n].below.id])

salt_vals.append(salt_nodes_lr[nodes[n].below.id])

dz_vals.append(abs(nodes[n].depth - nodes[n].below.depth))

# Get ready for next iteration of loop

nodes[n] = nodes[n].below

curr_temp = mean(array(temp_vals))

curr_salt = mean(array(salt_vals))

curr_volume = area*mean(array(dz_vals))

curr_tfrz = -0.0575*curr_salt + 1.7105e-3*sqrt(curr_salt**3) - 2.155e-4*curr_salt**2

if curr_temp < curr_tfrz:

wm_key = 0

elif curr_salt < 34:

wm_key = 1

elif curr_temp > 0:

wm_key = 2

elif curr_temp > -1:

wm_key = 3

elif curr_salt < 34.5:

wm_key = 4

else:

wm_key = 5

fesom_vol_watermass_lr[wm_key, sector] += curr_volume

fesom_vol_watermass_lr[wm_key, -1] += curr_volume

fesom_vol_sectors_lr = sum(fesom_vol_watermass_lr, axis=0)

fesom_percent_watermass_lr = zeros([num_watermasses, num_sectors])

for wm_key in range(num_watermasses):

for sector in range(num_sectors):

fesom_percent_watermass_lr[wm_key, sector] = fesom_vol_watermass_lr[wm_key, sector]/fesom_vol_sectors_lr[sector]*100

print 'Processing high-res FESOM'

elements_hr = fesom_grid(fesom_mesh_hr, circumpolar, cross_180)

fesom_vol_watermass_hr = zeros([num_watermasses, num_sectors])

id = Dataset(fesom_file_hr, 'r')

temp_nodes_hr = id.variables['temp'][0,:]

salt_nodes_hr = id.variables['salt'][0,:]

id.close()

for i in range(len(elements_hr)):

elm = elements_hr[i]

if elm.cavity:

lon = mean(elm.lon)

lat = mean(elm.lat)

if lon >= -85 and lon < -30 and lat < -74:

sector = 0

elif lon >= -30 and lon < 65:

sector = 1

elif lon >= 65 and lon < 76:

sector = 2

elif lon >= 76 and lon < 165 and lat >= -74:

sector = 3

elif (lon >= 155 and lon < 165 and lat < -74) or (lon >= 165) or (lon < -140):

sector = 4

elif (lon >= -140 and lon < -105) or (lon >= -105 and lon < -98 and lat < -73.1):

sector = 5

elif (lon >= -104 and lon < -98 and lat >= -73.1) or (lon >= -98 and lon < -66 and lat >= -75):

sector = 6

elif lon >= -66 and lon < -59 and lat >= -74:

sector = 7

else:

print 'No region found for lon=',str(lon),', lat=',str(lat)

break #return

area = elm.area()

nodes = [elm.nodes[0], elm.nodes[1], elm.nodes[2]]

while True:

if nodes[0].below is None or nodes[1].below is None or nodes[2].below is None:

break

temp_vals = []

salt_vals = []

dz_vals = []

for n in range(3):

temp_vals.append(temp_nodes_hr[nodes[n].id])

salt_vals.append(salt_nodes_hr[nodes[n].id])

temp_vals.append(temp_nodes_hr[nodes[n].below.id])

salt_vals.append(salt_nodes_hr[nodes[n].below.id])

dz_vals.append(abs(nodes[n].depth - nodes[n].below.depth))

nodes[n] = nodes[n].below

curr_temp = mean(array(temp_vals))

curr_salt = mean(array(salt_vals))

curr_volume = area*mean(array(dz_vals))

curr_tfrz = -0.0575*curr_salt + 1.7105e-3*sqrt(curr_salt**3) - 2.155e-4*curr_salt**2

if curr_temp < curr_tfrz:

wm_key = 0

elif curr_salt < 34:

wm_key = 1

elif curr_temp > 0:

wm_key = 2

elif curr_temp > -1:

wm_key = 3

elif curr_salt < 34.5:

wm_key = 4

else:

wm_key = 5

fesom_vol_watermass_hr[wm_key, sector] += curr_volume

fesom_vol_watermass_hr[wm_key, -1] += curr_volume

fesom_vol_sectors_hr = sum(fesom_vol_watermass_hr, axis=0)

fesom_percent_watermass_hr = zeros([num_watermasses, num_sectors])

for wm_key in range(num_watermasses):

for sector in range(num_sectors):

fesom_percent_watermass_hr[wm_key, sector] = fesom_vol_watermass_hr[wm_key, sector]/fesom_vol_sectors_hr[sector]*100

# Print results

for sector in range(num_sectors):

print sector_names[sector]

print 'MetROMS:'

for wm_key in range(num_watermasses):

print str(roms_percent_watermass[wm_key, sector]) + '% ' + wm_names[wm_key]

print 'FESOM low-res:'

for wm_key in range(num_watermasses):

print str(fesom_percent_watermass_lr[wm_key, sector]) + '% ' + wm_names[wm_key]

print 'FESOM high-res:'

for wm_key in range(num_watermasses):