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[:]
sat = din.satname[:]
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
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]
dh_cor = np.zeros_like(dh)
if np.alltrue(np.isnan(dh)): continue
#---------------------------------------------------------
# pull and correct a chunk of the array at a time
for s in np.unique(sat):
k = np.where(sat == s)
dh_cor[k], R, S = ap.backscatter_corr(dh[k], dg[k],
diff=DIFF, robust=True)
RR[k,i,j] = R
SS[k,i,j] = S
#---------------------------------------------------------
if PLOT_TS:
dh_cor = ap.referenced(dh_cor, to='first')
dh = ap.referenced(dh, to='first')
dg = ap.referenced(dg, to='first')
fig = plot_rs(time, RR[:,i,j], SS[:,i,j])
for s in np.unique(sat):
k = np.where(sat == s)
r, s = np.mean(RR[k,i,j]), np.mean(SS[k,i,j])
try:
fig = plot_ts(time[k], lon[j], lat[i], dh_cor[k],
dh[k], dg[k], r, s, diff=DIFF)
except:
print 'something wrong with ploting!'
print 'dh:', dh
print 'dg:', dg
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)
try:
c1 = din.file.create_carray('/', SAVE_AS_NAME, atom,
(nt,ny,nx), '', filters)
except:
c1 = din.file.getNode('/', SAVE_AS_NAME)
c2 = din.file.create_carray('/', R_NAME, atom,
(nt,ny,nx), '', filters)
c3 = din.file.create_carray('/', S_NAME, atom,
(nt,ny,nx), '', filters)
c1[:,i,j] = dh_cor
if SAVE_TO_FILE:
c2[:] = RR
c3[:] = SS
if PLOT_MAP:
RR = RR[0] # change accordingly
SS = SS[0]
plot_map(lon, lat, np.abs(RR), BBOX, MASK_FILE, mres=1, vmin=0, vmax=1)
plt.title('Correlation Coefficient, R')
plt.savefig('map_r.png')
plot_map(lon, lat, SS, BBOX, MASK_FILE, 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
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
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
