def label_ice_shelves(mask):
mask_grounded = 1
mask_floating = 2
nrows,ncols = mask.shape
# identify "ice rises"
mask1 = np.zeros_like(mask)
mask1[mask == mask_grounded] = 2
mask1[nrows/2,ncols/2] = 1
# in continent "ice rises" are 1, everything else is zero
continent = cc.cc(mask1, True)
# incorporate the continental ice sheet in continent
continent[mask == mask_grounded] += 1
# create a mask with ice shelves connected by "ice rises"
mask2 = np.zeros_like(mask)
mask2[mask == mask_floating] = 1
mask2[continent == 2] = 1
# label ice shelves
shelves = cc.cc(mask2, False)
# remove ice rises from ice shelves
shelves[continent == 2] = 0
return continent, shelves
I, t = test_image()
print "Preparing input took %f s" % t
# original
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
plt.imshow(np.ma.array(I,mask=I==0),
interpolation='nearest')
plt.xticks([])
plt.yticks([])
plt.title("original image")
plt.xlabel("land is blue, floating ice is red")
# labeled components
tic = time.clock()
result1 = cc.cc(I)
toc = time.clock()
print "Computing labels took %f s" % (toc - tic)
plt.subplot(1,3,2)
plt.imshow(np.ma.array(result1, mask=result1==0),
interpolation='nearest')
plt.xticks([])
plt.yticks([])
plt.title("labeled image")
plt.xlabel("a unique label for each component")
# "iceberg" identification
tic = time.clock()
result2 = cc.cc(I, identify_icebergs=True)
toc = time.clock()
I, t = test_image()
print "Preparing input took %f s" % t
# original
plt.figure(figsize=(12, 4))
plt.subplot(1, 3, 1)
plt.imshow(np.ma.array(I, mask=I == 0), interpolation='nearest')
plt.xticks([])
plt.yticks([])
plt.title("original image")
plt.xlabel("land is blue, floating ice is red")
# labeled components
tic = time.clock()
result1 = cc.cc(I)
toc = time.clock()
print "Computing labels took %f s" % (toc - tic)
plt.subplot(1, 3, 2)
plt.imshow(np.ma.array(result1, mask=result1 == 0), interpolation='nearest')
plt.xticks([])
plt.yticks([])
plt.title("labeled image")
plt.xlabel("a unique label for each component")
# "iceberg" identification
tic = time.clock()
result2 = cc.cc(I, identify_icebergs=True)
toc = time.clock()
print "Computing labels took %f s" % (toc - tic)
#!/usr/bin/env python
import cc
import netCDF4 as NC
import numpy as np
import pylab as plt
import argparse
parser = argparse.ArgumentParser()
parser.description = "Identify icebergs using a PISM-style mask read from a file."
parser.add_argument("FILE", nargs=1)
options = parser.parse_args()
nc = NC.Dataset(options.FILE[0])
pism_mask = np.squeeze(nc.variables['mask'][:])
pism_mask_grounded = 2
pism_mask_floating = 3
cc_mask = np.zeros_like(pism_mask)
cc_mask[pism_mask == pism_mask_grounded] = 1
cc_mask[pism_mask == pism_mask_floating] = 2
result = cc.cc(cc_mask, True) # result is zero everywhere, except 1 if it is an iceberg
result[pism_mask == pism_mask_grounded] = 2 # mark grounded areas
plt.imshow(result, interpolation='nearest')
plt.title("PISM mask read from %s after identifying icebergs" % options.FILE[0])
plt.show()
