]
print 'loading data...'
fin = tb.openFile(DIR + FILE_IN)
try:
time = ap.num2year(fin.root.time[:])
except:
time = fin.root.time[:]
lon = fin.root.lon[:]
lat = fin.root.lat[:]
d = fin.root.dh_mean_mixed_const_xcal[:]
#d = fin.root.dg_mean_xcal[:]
#e = fin.root.dh_error_xcal[:]
#d = fin.root.n_ad_xcal[:]
nz, ny, nx = d.shape
dt = ap.year2date(time)
if 1: # load area info
df_temp = pd.read_csv('ice_shelf_area4.csv')
df_area = df_temp['sampled_km2']
df_area.index = df_temp['ice_shelf']
if 1:
time, d = ap.time_filt(time, d, from_time=1994, to_time=2013)
#time, e = time_filt(time, e, from_time=1992, to_time=20013)
# area-average
#---------------------------------------------------------------------
df = pd.DataFrame(index=time)
df2 = pd.DataFrame(index=time)
]
print 'loading data...'
fin = tb.openFile(DIR + FILE_IN)
try:
time = ap.num2year(fin.root.time[:])
except:
time = fin.root.time[:]
lon = fin.root.lon[:]
lat = fin.root.lat[:]
d = fin.root.dh_mean_mixed_const_xcal[:]
#d = fin.root.dg_mean_xcal[:]
#e = fin.root.dh_error_xcal[:]
#d = fin.root.n_ad_xcal[:]
nz, ny, nx = d.shape
dt = ap.year2date(time)
if 1: # load area info
df_temp = pd.read_csv('ice_shelf_area4.csv')
df_area = df_temp['sampled_km2']
df_area.index = df_temp['ice_shelf']
if 1:
time, d = ap.time_filt(time, d, from_time=1994, to_time=2013)
#time, e = time_filt(time, e, from_time=1992, to_time=20013)
# area-average
#---------------------------------------------------------------------
df = pd.DataFrame(index=time)
df2 = pd.DataFrame(index=time)
def main():
fname_in = sys.argv[1]
din = GetData(fname_in, 'a')
time = ap.num2date(getattr(din, T_NAME)[:])
lon = din.lon[:]
lat = din.lat[:]
nrows = len(time)
nt, ny, nx = getattr(din, H_NAME).shape # i,j,k = t,y,x
RR = np.empty((nt,ny,nx), 'f8') * np.nan
SS = np.empty((nt,ny,nx), 'f8') * np.nan
if TINT:
intervals = [ap.year2date(tt) for tt in INTERVALS]
if TINT:
print 'using time-interval correlation'
elif TVAR:
print 'using time-variable correlation'
else:
print 'using constant correlation'
print 'processing time series:'
isfirst = True
# iterate over every grid cell (all times): i,j = y,x
#-----------------------------------------------------------------
for i in xrange(ny):
for j in xrange(nx):
print 'time series of grid-cell:', i, j
dh = getattr(din, H_NAME)[:nrows,i,j]
dg = getattr(din, G_NAME)[:nrows,i,j]
if np.alltrue(np.isnan(dh)): continue
#---------------------------------------------------------
if TINT:
# satellite-dependent R and S
dh_cor, RR[:,i,j], SS[:,i,j] = \
ap.backscatter_corr3(dh, dg, time, intervals,
diff=DIFF, robust=True)
elif TVAR:
# time-varying R and S
dh_cor, RR[:,i,j], SS[:,i,j] = \
ap.backscatter_corr2(dh, dg, diff=DIFF,
robust=True, npts=NPTS)
else:
# constant R and S
dh_cor, RR[:,i,j], SS[:,i,j] = \
ap.backscatter_corr(dh, dg, diff=DIFF, robust=True)
#---------------------------------------------------------
# plot figures
if PLOT_TS:
dh_cor = ap.referenced(dh_cor, to='first')
dh = ap.referenced(dh, to='first')
dg = ap.referenced(dg, to='first')
k, = np.where(~np.isnan(RR[:,i,j]))
r = np.mean(RR[k,i,j])
s = np.mean(SS[k,i,j])
fig = plot_rs(time, RR[:,i,j], SS[:,i,j])
fig = plot_ts(time, lon[j], lat[i], dh_cor, dh, dg,
r, s, diff=DIFF)
if fig is None: continue
plt.show()
# save one TS per grid cell at a time
#---------------------------------------------------------
if not SAVE_TO_FILE: continue
if isfirst:
# open or create output file
isfirst = False
atom = tb.Atom.from_type('float64', dflt=np.nan)
filters = tb.Filters(complib='zlib', complevel=9)
c1 = din.file.createCArray('/', SAVE_AS_NAME, atom,
(nt,ny,nx), '', filters)
c2 = din.file.createCArray('/', R_NAME, atom,
(nt,ny,nx), '', filters)
c3 = din.file.createCArray('/', S_NAME, atom,
(nt,ny,nx), '', filters)
c1[:,i,j] = dh_cor
if SAVE_TO_FILE:
c2[:] = RR
c3[:] = SS
if PLOT_MAP:
if TVAR:
# 3D -> 2D
RR = np.mean(RR[~np.isnan(RR)], axis=0)
SS = np.mean(SS[~np.isnan(SS)], axis=0)
plot_map(lon, lat, np.abs(RR), BBOX, MFILE, mres=1, vmin=0, vmax=1)
plt.title('Correlation Coefficient, R')
plt.savefig('map_r.png')
plot_map(lon, lat, SS, BBOX, MFILE, mres=1, vmin=-0.2, vmax=0.7)
plt.title('Correlation Gradient, S')
plt.savefig('map_s.png')
plt.show()
din.file.close()
if SAVE_TO_FILE:
print 'out file -->', fname_in
FILE2 = "/Users/fpaolo/data/shelves/ross_19920715_20111015.h5" # larsenc_19920715_20111015.h5' FILE3 = "/Users/fpaolo/data/coastline/moa_coastfile_ll.h5" FILE4 = "/Users/fpaolo/data/coastline/antarctica_gl_ll.h5" # BBOX = (-71.3854, -73.0697, -58.0593, -63.0486) BBOX = (-126.863, -46.7492, 53.2309, -47.0738) # BBOX = (-33.4259, -46.9022, 147.058, -46.9198) f1 = nc.Dataset(FILE1) d1 = f1.variables firn = d1["zs"] year1 = d1["time"][:] lons1 = d1["lon"][:] # 2d lats1 = d1["lat"][:] # 2d ism = d1["ism"][:] lsm = d1["lsm"][:] dt1 = ap.year2date(year1) # dt1 = year1 f2 = tb.openFile(FILE2) elev = f2.root.elev[:] time2 = f2.root.time[:] lon2 = f2.root.lon[:] # 1d lat2 = f2.root.lat[:] # 1d lon2 = ap.lon_180_360(lon2, inverse=True) lons2, lats2 = np.meshgrid(lon2, lat2) points2 = np.column_stack((lons2.ravel(), lats2.ravel())) # points2 = (lons2[6,4], lats2[6,4]) dt2 = ap.num2date(time2) # dt2 = ap.num2year(time2) """
t4 = dt.datetime(1, 1, 1, 1, 0, 0) t5 = dt.datetime(2013, 1, 1, 0, 0, 0) t6 = dt.datetime(2013, 1, 1, 0, 0, 1) t7 = dt.datetime(2013, 1, 1, 0, 1, 0) t8 = dt.datetime(2013, 1, 1, 1, 0, 0) y1 = ap.date2year(t1) y2 = ap.date2year(t2) y3 = ap.date2year(t3) y4 = ap.date2year(t4) y5 = ap.date2year(t5) y6 = ap.date2year(t6) y7 = ap.date2year(t7) y8 = ap.date2year(t8) d1 = ap.year2date(y1) d2 = ap.year2date(y2) d3 = ap.year2date(y3) d4 = ap.year2date(y4) d5 = ap.year2date(y5) d6 = ap.year2date(y6) d7 = ap.year2date(y7) d8 = ap.year2date(y8) print '(def date -> date2year -> year2date)' print 'original_date converted_date converted_year' print t1, ' ', d1[0], ' ', y1[0] print t2, ' ', d2[0], ' ', y2[0] print t3, ' ', d3[0], ' ', y3[0] print t4, ' ', d4[0], ' ', y4[0] print t5, ' ', d5[0], ' ', y5[0]
def main():
fname_in = sys.argv[1]
din = GetData(fname_in, 'a')
time = ap.num2date(getattr(din, T_NAME)[:])
lon = din.lon[:]
lat = din.lat[:]
nrows = len(time)
nt, ny, nx = getattr(din, H_NAME).shape # i,j,k = t,y,x
RR = np.empty((nt, ny, nx), 'f8') * np.nan
SS = np.empty((nt, ny, nx), 'f8') * np.nan
if TINT:
intervals = [ap.year2date(tt) for tt in INTERVALS]
if TINT:
print 'using time-interval correlation'
elif TVAR:
print 'using time-variable correlation'
else:
print 'using constant correlation'
print 'processing time series:'
isfirst = True
# iterate over every grid cell (all times): i,j = y,x
#-----------------------------------------------------------------
for i in xrange(ny):
for j in xrange(nx):
print 'time series of grid-cell:', i, j
dh = getattr(din, H_NAME)[:nrows, i, j]
dg = getattr(din, G_NAME)[:nrows, i, j]
if np.alltrue(np.isnan(dh)): continue
#---------------------------------------------------------
if TINT:
# satellite-dependent R and S
dh_cor, RR[:,i,j], SS[:,i,j] = \
ap.backscatter_corr3(dh, dg, time, intervals,
diff=DIFF, robust=True)
elif TVAR:
# time-varying R and S
dh_cor, RR[:,i,j], SS[:,i,j] = \
ap.backscatter_corr2(dh, dg, diff=DIFF,
robust=True, npts=NPTS)
else:
# constant R and S
dh_cor, RR[:,i,j], SS[:,i,j] = \
ap.backscatter_corr(dh, dg, diff=DIFF, robust=True)
#---------------------------------------------------------
# plot figures
if PLOT_TS:
dh_cor = ap.referenced(dh_cor, to='first')
dh = ap.referenced(dh, to='first')
dg = ap.referenced(dg, to='first')
k, = np.where(~np.isnan(RR[:, i, j]))
r = np.mean(RR[k, i, j])
s = np.mean(SS[k, i, j])
fig = plot_rs(time, RR[:, i, j], SS[:, i, j])
fig = plot_ts(time,
lon[j],
lat[i],
dh_cor,
dh,
dg,
r,
s,
diff=DIFF)
if fig is None: continue
plt.show()
# save one TS per grid cell at a time
#---------------------------------------------------------
if not SAVE_TO_FILE: continue
if isfirst:
# open or create output file
isfirst = False
atom = tb.Atom.from_type('float64', dflt=np.nan)
filters = tb.Filters(complib='zlib', complevel=9)
c1 = din.file.createCArray('/', SAVE_AS_NAME, atom,
(nt, ny, nx), '', filters)
c2 = din.file.createCArray('/', R_NAME, atom, (nt, ny, nx), '',
filters)
c3 = din.file.createCArray('/', S_NAME, atom, (nt, ny, nx), '',
filters)
c1[:, i, j] = dh_cor
if SAVE_TO_FILE:
c2[:] = RR
c3[:] = SS
if PLOT_MAP:
if TVAR:
# 3D -> 2D
RR = np.mean(RR[~np.isnan(RR)], axis=0)
SS = np.mean(SS[~np.isnan(SS)], axis=0)
plot_map(lon, lat, np.abs(RR), BBOX, MFILE, mres=1, vmin=0, vmax=1)
plt.title('Correlation Coefficient, R')
plt.savefig('map_r.png')
plot_map(lon, lat, SS, BBOX, MFILE, mres=1, vmin=-0.2, vmax=0.7)
plt.title('Correlation Gradient, S')
plt.savefig('map_s.png')
plt.show()
din.file.close()
if SAVE_TO_FILE:
print 'out file -->', fname_in
