def timeseries_area_threshold(file_path,
var_name,
threshold,
grid,
gtype='t',
time_index=None,
t_start=None,
t_end=None,
time_average=False):
# Read the data
data = read_netcdf(file_path,
var_name,
time_index=time_index,
t_start=t_start,
t_end=t_end,
time_average=time_average)
if len(data.shape) == 2:
# Just one timestep; add a dummy time dimension
data = np.expand_dims(data, 0)
# Convert to array of 1s and 0s based on threshold
data = (data >= threshold).astype(float)
# Now build the timeseries
timeseries = []
for t in range(data.shape[0]):
timeseries.append(area_integral(data[t, :], grid, gtype=gtype))
return np.array(timeseries)
def polynya_mask(grid_path, polynya, mask_file, prec=64):
from plot_latlon import latlon_plot
# Define the centre and radii of the ellipse bounding the polynya
if polynya == 'maud_rise': # Area 2.6 x 10^5 km^2
lon0 = 0.
lat0 = -65.
rlon = 8.
rlat = 2.
elif polynya == 'near_shelf': # Area 2.6 x 10^5 km^2
lon0 = -30.
lat0 = -70.
rlon = 9.
rlat = 2.2
elif polynya == 'maud_rise_big': # Area 6.2 x 10^5 km^2
lon0 = 0.
lat0 = -65.
rlon = 15.
rlat = 2.5
elif polynya == 'maud_rise_small': # Area 0.34 x 10^5 km^2
lon0 = 0
lat0 = -65.
rlon = 2.8
rlat = 0.75
else:
print 'Error (polynya_mask): invalid polynya option ' + polynya
sys.exit()
# Build the grid
grid = Grid(grid_path)
# Set up the mask
mask = np.zeros([grid.ny, grid.nx])
# Select the polynya region
index = (grid.lon_2d - lon0)**2 / rlon**2 + (grid.lat_2d -
lat0)**2 / rlat**2 <= 1
mask[index] = 1
# Print the area of the polynya
print 'Polynya area is ' + str(area_integral(mask, grid) * 1e-6) + ' km^2'
# Plot the mask
latlon_plot(mask_land_ice(mask, grid),
grid,
include_shelf=False,
title='Polynya mask',
figsize=(10, 6))
# Write to file
write_binary(mask, mask_file, prec=prec)
def find_aice_min_max(aice, grid):
total_aice = area_integral(aice, grid, time_dependent=True)
return np.argmin(total_aice), np.argmax(total_aice)