# Titles and figure names for each ice shelf

names = ['All Ice Shelves', 'Larsen D Ice Shelf', 'Larsen C Ice Shelf', 'Wilkins & George VI & Stange Ice Shelves', 'Ronne-Filchner Ice Shelf', 'Abbot Ice Shelf', 'Pine Island Glacier Ice Shelf', 'Thwaites Ice Shelf', 'Dotson Ice Shelf', 'Getz Ice Shelf', 'Nickerson Ice Shelf', 'Sulzberger Ice Shelf', 'Mertz Ice Shelf', 'Totten & Moscow University Ice Shelves', 'Shackleton Ice Shelf', 'West Ice Shelf', 'Amery Ice Shelf', 'Prince Harald Ice Shelf', 'Baudouin & Borchgrevink Ice Shelves', 'Lazarev Ice Shelf', 'Nivl Ice Shelf', 'Fimbul & Jelbart & Ekstrom Ice Shelves', 'Brunt & Riiser-Larsen Ice Shelves', 'Ross Ice Shelf']

fig_names = ['total_massloss.png', 'larsen_d.png', 'larsen_c.png', 'wilkins_georgevi_stange.png', 'ronne_filchner.png', 'abbot.png', 'pig.png', 'thwaites.png', 'dotson.png', 'getz.png', 'nickerson.png', 'sulzberger.png', 'mertz.png', 'totten_moscowuni.png', 'shackleton.png', 'west.png', 'amery.png', 'princeharald.png', 'baudouin_borchgrevink.png', 'lazarev.png', 'nivl.png', 'fimbul_jelbart_ekstrom.png', 'brunt_riiserlarsen.png', 'ross.png']

# Limits on longitude and latitude for each ice shelf

# These depend on the source geometry, in this case RTopo 1.05

# Note there is one extra index at the end of each array; this is because

# the Ross region crosses the line 180W and therefore is split into two

lon_min = [-180, -62.67, -65.5, -79.17, -85, -104.17, -102.5, -108.33, -114.5, -135.67, -149.17, -155, 144, 115, 94.17, 80.83, 65, 33.83, 19, 12.9, 9.33, -10.05, -28.33, -180, 158.33]

lon_max = [180, -59.33, -60, -66.67, -28.33, -88.83, -99.17, -103.33, -111.5, -114.33, -140, -145, 146.62, 123.33, 102.5, 89.17, 75, 37.67, 33.33, 16.17, 12.88, 7.6, -10.33, -146.67, 180]

lat_min = [-90, -73.03, -69.35, -74.17, -83.5, -73.28, -75.5, -75.5, -75.33, -74.9, -76.42, -78, -67.83, -67.17, -66.67, -67.83, -73.67, -69.83, -71.67, -70.5, -70.75, -71.83, -76.33, -85, -84.5]

lat_max = [-30, -69.37, -66.13, -69.5, -74.67, -71.67, -74.17, -74.67, -73.67, -73, -75.17, -76.41, -66.67, -66.5, -64.83, -66.17, -68.33, -68.67, -68.33, -69.33, -69.83, -69.33, -71.5, -77.77, -77]

# Observed mass loss (Rignot 2013) and uncertainty for each ice shelf, in Gt/y

obs_massloss = [1325, 1.4, 20.7, 135.4, 155.4, 51.8, 101.2, 97.5, 45.2, 144.9, 4.2, 18.2, 7.9, 90.6, 72.6, 27.2, 35.5, -2, 21.6, 6.3, 3.9, 26.8, 9.7, 47.7]

obs_massloss_error = [235, 14, 67, 40, 45, 19, 8, 7, 4, 14, 2, 3, 3, 8, 15, 10, 23, 3, 18, 2, 2, 14, 16, 34]

# Observed ice shelf melt rates and uncertainty

obs_ismr = [0.85, 0.1, 0.4, 3.1, 0.3, 1.7, 16.2, 17.7, 7.8, 4.3, 0.6, 1.5, 1.4, 7.7, 2.8, 1.7, 0.6, -0.4, 0.4, 0.7, 0.5, 0.5, 0.1, 0.1]

obs_ismr_error = [0.1, 0.6, 1, 0.8, 0.1, 0.6, 1, 1, 0.6, 0.4, 0.3, 0.3, 0.6, 0.7, 0.6, 0.7, 0.4, 0.6, 0.4, 0.2, 0.2, 0.2, 0.2, 0.1]

# Density of ice in kg/m^3

rho_ice = 916

old_time = []

# Check if the log file exists

if exists(log_path):

print 'Reading previously calculated values'

f = open(log_path, 'r')

# Skip the first line (header for time array)

f.readline()

for line in f:

try:

old_time.append(float(line))

except(ValueError):

# Reached the header for the next variable

break

# Set up array for mass loss values at each ice shelf

old_massloss = empty([len(names), len(old_time)])

index = 0

# Loop over ice shelves

while index < len(names):

t = 0

for line in f:

try:

old_massloss[index, t] = float(line)

t += 1

except(ValueError):

# Reached the header for the next ice shelf

break

index += 1

f.close()

# Calculate dA (masked with ice shelf mask) and lon and lat coordinates

print 'Analysing grid'

dA, lon, lat = calc_grid(file_path)

# Read time data and convert from seconds to years

id = Dataset(file_path, 'r')

new_time = id.variables['ocean_time'][:]/(365.25*24*60*60) + add_years

if exists(log_path):

# Concatenate with time values from log file

start_t = len(old_time)

time = old_time

for t in range(size(new_time)):

time.append(new_time[t])

time = array(time)

else:

start_t = 0

time = new_time

# Set up array of mass loss values

massloss = empty([len(names), size(time)])

if exists(log_path):

# Fill first start_t timesteps with existing values

massloss[:,0:start_t] = old_massloss[:,:]

print 'Reading data'

# Read melt rate and convert from m/s to m/y

ismr = id.variables['m'][:,:-15,1:-1]*365.25*24*60*60

id.close()

print 'Setting up arrays'

# Set up array of masked area values for each ice shelf

dA_masked = ma.empty([len(names), size(dA,0), size(dA,1)])

# Set up array of conversion factors from mass loss to area-averaged melt

# rate for each ice shelf

factors = empty(len(names))

for index in range(len(names)):

# Mask dA for the current ice shelf (note dA is already masked

# with the global ice shelf mask, so just restrict the indices

# to isolate the given ice shelf)

if index == len(names)-1:

# Ross region is split into two

dA_tmp = ma.masked_where(((lon < lon_min[index]) + (lon > lon_max[index]) + (lat < lat_min[index]) + (lat > lat_max[index]))*((lon < lon_min[index+1]) + (lon > lon_max[index+1]) + (lat < lat_min[index+1]) + (lat > lat_max[index+1])), dA)

else:

dA_tmp = ma.masked_where((lon < lon_min[index]) + (lon > lon_max[index]) + (lat < lat_min[index]) + (lat > lat_max[index]), dA)

dA_masked[index,:,:] = dA_tmp[:,:]

# Calculate area of this ice shelf

area_tmp = sum(dA_masked[index,:,:])

# Calculate conversion factor

factors[index] = 1e12/(rho_ice*area_tmp)

print 'Area of ' + names[index] + ': ' + str(area_tmp) + ' m^2'

# Build timeseries

print 'Calculating variables'

for t in range(start_t, size(time)):

# Loop over ice shelves

for index in range(len(names)):

# Integrate ice shelf melt rate over area to get volume loss

volumeloss = sum(ismr[t-start_t,:,:]*dA_masked[index,:,:])

# Convert to mass loss in Gt/y

massloss[index, t] = 1e-12*rho_ice*volumeloss

# Plot each timeseries

print 'Plotting'

for index in range(len(names)):

# Calculate the bounds on observed mass loss and melt rate

massloss_low = obs_massloss[index] - obs_massloss_error[index]

massloss_high = obs_massloss[index] + obs_massloss_error[index]

ismr_low = obs_ismr[index] - obs_ismr_error[index]

ismr_high = obs_ismr[index] + obs_ismr_error[index]

# Set up plot: mass loss and melt rate are directly proportional (with

# a different constant of proportionality for each ice shelf depending

# on its area) so plot one line with two y-axes

fig, ax1 = subplots()

ax1.plot(time, massloss[index,:], color='black')

# In blue, add dashed lines for observed mass loss

ax1.axhline(massloss_low, color='b', linestyle='dashed')

ax1.axhline(massloss_high, color='b', linestyle='dashed')

# Make sure y-limits won't cut off observed melt rate

ymin = amin([ismr_low/factors[index], massloss_low, amin(massloss[index,:])])

ymax = amax([ismr_high/factors[index], massloss_high, amax(massloss[index,:])])

# Adjust y-limits to line up with ticks

ticks = ax1.get_yticks()

min_tick = ticks[0]

max_tick = ticks[-1]

dtick = ticks[1]-ticks[0]

while min_tick >= ymin:

min_tick -= dtick

while max_tick <= ymax:

max_tick += dtick

ax1.set_ylim([min_tick, max_tick])

# Title and ticks in blue for this side of the plot

ax1.set_ylabel('Basal Mass Loss (Gt/y)', color='b')

for t1 in ax1.get_yticklabels():

t1.set_color('b')

ax1.set_xlabel('Years')

# setp(ax1.get_xticklabels(), fontsize=15)

# setp(ax1.get_yticklabels(), fontsize=15)

ax1.grid(True)

# Twin axis for melt rates

ax2 = ax1.twinx()

# Make sure the scales line up

limits = ax1.get_ylim()

ax2.set_ylim([limits[0]*factors[index], limits[1]*factors[index]])

# In red, add dashed lines for observed ice shelf melt rates

ax2.axhline(ismr_low, color='r', linestyle='dashed')

ax2.axhline(ismr_high, color='r', linestyle='dashed')

# Title and ticks in red for this side of the plot

ax2.set_ylabel('Area-Averaged Ice Shelf Melt Rate (m/y)', color='r')

for t2 in ax2.get_yticklabels():

t2.set_color('r')

# setp(ax2.get_yticklabels(), fontsize=15)

# Name of the ice shelf for the main title

title(names[index])

fig.savefig(fig_names[index])

print 'Saving results to log file'

f = open(log_path, 'w')

f.write('Time (years):\n')

for t in range(size(time)):

f.write(str(time[t]) + '\n')

for index in range(len(names)):

f.write(names[index] + ' Basal Mass Loss\n')

for t in range(size(time)):

f.write(str(massloss[index, t]) + '\n')

# Read grid variables

id = Dataset(file_path, 'r')

lon = id.variables['lon_rho'][:-15,1:-1]

lat = id.variables['lat_rho'][:-15,1:-1]

zice = id.variables['zice'][:-15,1:-1]

id.close()

# Calculate dx and dy in another script

dx, dy = cartesian_grid_2d(lon, lat)

# Calculate dA and mask with zice

dA = ma.masked_where(zice==0, dx*dy)

# Save dimensions

num_lat = size(lon, 0)

num_lon = size(lon, 1)

# Make longitude values go from -180 to 180, not 0 to 360

index = lon > 180

lon[index] = lon[index] - 360