def find_pairs(dem_stack, tide_list, max_dt=timedelta(450.0)):
ds = dem_stack.get_ds()
line_fn = '/scr/pig_dem_stack/pig_shelf_poly_shean_2011gl_line.shp'
m = geolib.shp2array(line_fn, res=dem_stack.res, extent=dem_stack.extent)
#This is total gl pixels
#max_gl_count = (~m).nonzero()[0].size
pairs = []
dt_list = dem_stack.date_list
min_dz = 0.5
#This is 100 km2
min_count = 2000
min_gl_count = 40
out = []
for i, dt in enumerate(dt_list):
candidate_idx = timelib.get_closest_dt_padded_idx(dt, dt_list, max_dt)
tidediff = tide_list[i] - tide_list[candidate_idx]
valid_candidate_idx = np.abs(tidediff) > min_dz
if np.any(valid_candidate_idx):
for c in candidate_idx[valid_candidate_idx]:
dz = dem_stack.ma_stack[i] - dem_stack.ma_stack[c]
if dz.count() > min_count and (
dz * ~m).nonzero()[0].size > min_gl_count:
if not (c, i) in pairs:
#if dem_stack.source[i] != 'glas' and dem_stack.source[c] != 'glas' and dem_stack.source[i] != 'spirit' and dem_stack.source[c] != 'spirit':
#if 'DG_stereo' in dem_stack.source[i] and 'DG_stereo' in dem_stack.source[c]:
if 'DG' in dem_stack.source[
i] and 'DG' in dem_stack.source[c]:
dt_diff = (dt_list[c] -
dt_list[i]).total_seconds() / 86400.
tide_diff = tide_list[i] - tide_list[c]
title = '%s-%s (%0.1f days) dz=%0.2f m' % (
timelib.print_dt(
dt_list[i]), timelib.print_dt(
dt_list[c]), dt_diff, tide_diff)
print title, i, c
pairs.append((i, c))
flip = 1.0
if tide_diff < 0:
flip = -1.0
o = flip * (dz - np.ma.median(dz))
out.append(o)
f = malib.iv(o,
clim=(-1.0, 1.0),
cmap='RdYlBu',
title=title)
pltlib.shp_overlay(f.gca(),
ds,
line_fn,
color='green')
out = np.ma.array(out)
malib.iv(np.ma.median(out, axis=0), clim=(-1.0, 1.0), cmap='RdYlBu')
return pairs, out
#vy_stack_fn = os.path.join(v_dir, vy_stack_fn)
#vy_stack_fn = '/scr/pig_stack_20151201_tworows_highcount/vel/shelf_clip/20060921_1746_189days_20060521_0950-20061126_0822_alos_mos_Track-Pig06_vy_20150530_1556_22days_20150525_0356-20150605_0356_tsx_mos_track-MayJun15_vy_stack_19_clip.npz'
#vy_stack = malib.DEMStack(stack_fn=vy_stack_fn, save=False, trend=True, med=True, stats=True)
#vy_stack_fn = '/scr/pig_stack_20151201_tworows_highcount/vel_20160225_1km/shelf_clip/20060921_1746_189days_20060521_0950-20061126_0822_alos_mos_Track-Pig06_vy_20151109_1349_90days_20151014_0000-20160112_0000_ls8_mos_All_S1516_vy_stack_22_clip_LinearNDint_121_142_121day.npz'
vy_stack_fn = '/scr/pig_stack_20151201_tworows_highcount/vel_20160225_512m/20060921_1746_189days_20060521_0950-20061126_0822_alos_mos_Track-Pig06_vy_20151109_1349_90days_20151014_0000-20160112_0000_ls8_mos_All_S1516_vy_stack_22_clip_LinearNDint_237_277_121day.npz'
vy_stack = malib.DEMStack(stack_fn=vy_stack_fn, save=False, trend=False, med=False, stats=False, datestack=False)
#vm_stack_dt_list = vx_stack.date_list
vm_stack = np.ma.sqrt(vx_stack.ma_stack**2 + vy_stack.ma_stack**2)
if smbcorr:
smb_fn = '/scr/RACMO2.3_June2015_update/FDM_zs_ANT27_36y_1979-2014_zs_20080102_002_FDM_zs_ANT27_36y_1979-2014_zs_20141230_364_stack_1278.npz'
smb_stack = malib.DEMStack(stack_fn=smb_fn, save=False)
#Shelf mask
m = geolib.shp2array(mask_fn, res=dem_stack.res, extent=dem_stack.extent)
dem_stack.ma_stack[:,m] = np.ma.masked
#Timestep
#dt = timedelta(days=5)
#dt_val = 20
dt_val = 10
vmax = np.percentile(np.ma.median(vm_stack, axis=0), 98)
max_dt_val = int(dem_stack.res/(vmax/365.25))
if dt_val > max_dt_val:
dt_val = max_dt_val
#This was forced for high-res runs, otherwise interval is 2-3 days at 32 m/px
#dt_val = 10
dt = timedelta(days=dt_val)
stack_fn = vx_stack_fn
elif vtype == 'vy':
stack_fn = vy_stack_fn
elif vtype == 'dem':
stack_fn = dem_stack_fn
stack_fn = sys.argv[1]
stack = malib.DEMStack(stack_fn=stack_fn, save=False, trend=True, med=True, stats=True)
#Get times of original obs
t = stack.date_list_o.data
if clip_to_shelfmask:
mask_fn = '/scr/pig_dem_stack/pig_shelf_poly_shean_2011gl.shp'
outermask_fn = '/scr/pig_stack_20151201_tworows_highcount/pig_vel_mask.shp'
m_orig = geolib.shp2array(mask_fn, res=stack.res, extent=stack.extent)
outermask = ~(geolib.shp2array(outermask_fn, res=stack.res, extent=stack.extent))
m = m_orig
#Expand shelf mask 3 km upstream for flux gates
#Should also reduce edge artifacts
import scipy.ndimage
it = int(np.ceil(3000./stack.res))
m = ~(scipy.ndimage.morphology.binary_dilation(~m, iterations=it))
#Need to make sure to sample mask at appropriate res
apply_mask(stack.ma_stack, m)
if vtype == 'vm' or vtype == 'vy' or vtype == 'vx':
apply_mask(stack.ma_stack, outermask)
elif vtype == 'dem':
stack_fn = dem_stack_fn
#stack_fn = sys.argv[1]
stack = malib.DEMStack(stack_fn=stack_fn, save=False, trend=True, med=True, stats=True)
#Get times of original obs
t = stack.date_list_o.data
t = t.astype(int)
t[0] -= 0.1
t[-1] += 0.1
if clip_to_shelfmask:
mask_fn = '/scr/pig_dem_stack/pig_shelf_poly_shean_2011gl.shp'
outermask_fn = '/scr/pig_stack_20151201_tworows_highcount/pig_vel_mask.shp'
m_orig = geolib.shp2array(mask_fn, res=stack.res, extent=stack.extent)
outermask = ~(geolib.shp2array(outermask_fn, res=stack.res, extent=stack.extent))
m = m_orig
#Expand shelf mask 3 km upstream for flux gates
#Should also reduce edge artifacts
import scipy.ndimage
#it = int(np.ceil(4000./stack.res))
it = int(np.ceil(6000./stack.res))
m = ~(scipy.ndimage.morphology.binary_dilation(~m, iterations=it))
#Need to make sure to sample mask at appropriate res
apply_mask(stack.ma_stack, m)
if vtype == 'vm' or vtype == 'vy' or vtype == 'vx':
apply_mask(stack.ma_stack, outermask)