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')
satname = din.satname
time = change_day(din.time, 15) # change all days (e.g. 14,15,16,17) to 15
ts = getattr(din, VAR_TO_CALIBRATE)
err = din.dh_error
n_ad = din.n_ad
n_da = din.n_da
lon = din.lon
lat = din.lat
din.file.close()
t = ap.num2year(time)
if SUBSET: # get subset
ts, lon2, lat2 = ap.get_subset(ap.amundsen, ts, lon, lat)
err, lon2, lat2 = ap.get_subset(ap.amundsen, err, lon, lat)
n_ad, lon2, lat2 = ap.get_subset(ap.amundsen, n_ad, lon, lat)
n_da, lon2, lat2 = ap.get_subset(ap.amundsen, n_da, lon, lat)
lon, lat = lon2, lat2
xx, yy = np.meshgrid(lon, lat)
nt, ny, nx = ts.shape
offset_12 = np.full((ny, nx), np.nan)
offset_23 = np.full((ny, nx), np.nan)
print 'cross-calibrating time series:', VAR_TO_CALIBRATE
isfirst = True
if SAT_NAMES is None:
satnames = np.unique(din.satname)
# iterate over every grid cell (all times)
no_overlap_12 = 0
no_overlap_23 = 0
for i in xrange(ny):
for j in xrange(nx):
if 0:
i, j = ap.find_nearest2(xx, yy, (LON, LAT))
i -= 0
j += 0
print 'grid-cell:', i, j
ts_ij = ts[:, i, j]
if np.isnan(ts_ij).all(): continue
# get all time series (all sats) in one df (per grid-cell)
var = create_df_with_sats(time, ts_ij, satname, SAT_NAMES)
if DETREND:
var = var.apply(detrend)
if FILTER:
var = var.apply(ap.hp_filt, lamb=7, nan=True)
if PLOT_TS and (var.count().sum() > 10):
print 'grid-cell:', i, j
var.plot(linewidth=3, figsize=(9, 3), legend=False)
plt.title('Elevation change, dh (lon=%.2f, lat=%.2f)' %
(xx[i, j], yy[i, j]))
plt.ylabel('m')
plt.show()
# compute offset (if ts overlap)
#---------------------------------------------------
x = pd.notnull(var)
overlap_12 = x['ers1'] & x['ers2']
overlap_23 = x['ers2'] & x['envi']
if np.sometrue(overlap_12):
if SAT_BIAS:
s1 = var['ers1'][overlap_12]
s2 = var['ers2'][overlap_12]
if LINEAR_FIT:
# using linear fit
s1 = s1[s1.notnull() & s2.notnull()]
s2 = s2[s1.notnull() & s2.notnull()]
if len(s1) > 1 and len(s2) > 1:
s1.index, s1[:] = ap.linear_fit(
ap.date2year(s1.index), s1.values)
s2.index, s2[:] = ap.linear_fit(
ap.date2year(s2.index), s2.values)
offset = (s1.values[-1] - s1.values[0]) - (
s2.values[-1] - s2.values[0])
else:
pass
else:
# using absolute values
s1 = ap.referenced(s1, to='first')
s2 = ap.referenced(s2, to='first')
s1[0], s2[0] = np.nan, np.nan # remove first values
offset = np.nanmean(s1 - s2)
#pd.concat((s1, s2), axis=1).plot(marker='o')
else:
offset = np.nanmean(var['ers1'] - var['ers2'])
offset_12[i, j] = offset
else:
no_overlap_12 += 1
if np.sometrue(overlap_23):
if SAT_BIAS:
s2 = var['ers2'][overlap_23]
s3 = var['envi'][overlap_23]
if LINEAR_FIT:
s2 = s2[s2.notnull() & s3.notnull()]
s3 = s3[s2.notnull() & s3.notnull()]
if len(s2) > 1 and len(s3) > 1:
s2.index, s2[:] = ap.linear_fit(
ap.date2year(s2.index), s2.values)
s3.index, s3[:] = ap.linear_fit(
ap.date2year(s3.index), s3.values)
offset = (s2.values[-1] - s2.values[0]) - (
s3.values[-1] - s3.values[0])
else:
pass
else:
s2 = ap.referenced(s2, to='first')
s3 = ap.referenced(s3, to='first')
s2[0], s3[0] = np.nan, np.nan
offset = np.nanmean(s2 - s3)
#pd.concat((s2, s3), axis=1).plot(marker='o')
#plt.show()
else:
offset = np.nanmean(var['ers2'] - var['envi'])
offset_23[i, j] = offset
else:
no_overlap_23 += 1
#---------------------------------------------------
mean_offset_12 = np.nanmean(offset_12)
median_offset_12 = np.nanmedian(offset_12)
mean_offset_23 = np.nanmean(offset_23)
median_offset_23 = np.nanmedian(offset_23)
if SAVE_TO_FILE:
fout = tb.open_file(FNAME_OUT, 'w')
fout.create_array('/', 'lon', lon)
fout.create_array('/', 'lat', lat)
fout.create_array('/', 'offset_12', offset_12)
fout.create_array('/', 'offset_23', offset_23)
fout.close()
if PLOT:
plt.figure()
plt.subplot(211)
offset_12 = ap.median_filt(offset_12, 3, 3)
plt.imshow(offset_12,
origin='lower',
interpolation='nearest',
vmin=-.5,
vmax=.5)
plt.title('ERS1-ERS2')
plt.colorbar(shrink=0.8)
plt.subplot(212)
offset_23 = ap.median_filt(offset_23, 3, 3)
plt.imshow(offset_23,
origin='lower',
interpolation='nearest',
vmin=-.5,
vmax=.5)
plt.title('ERS2-Envisat')
#plt.colorbar(shrink=0.3, orientation='h')
plt.colorbar(shrink=0.8)
plt.figure()
plt.subplot(121)
o12 = offset_12[~np.isnan(offset_12)]
plt.hist(o12, bins=100)
plt.title('ERS1-ERS2')
ax = plt.gca()
ap.intitle('mean/median = %.2f/%.2f m' %
(mean_offset_12, median_offset_12),
ax=ax,
loc=2)
plt.xlim(-1, 1)
plt.subplot(122)
o23 = offset_23[~np.isnan(offset_23)]
plt.hist(o23, bins=100)
plt.title('ERS2-Envisat')
ax = plt.gca()
ap.intitle('mean/median = %.2f/%.2f m' %
(mean_offset_23, median_offset_23),
ax=ax,
loc=2)
plt.xlim(-1, 1)
plt.show()
print 'calibrated variable:', VAR_TO_CALIBRATE
print 'no overlaps:', no_overlap_12, no_overlap_23
print 'mean offset:', mean_offset_12, mean_offset_23
print 'median offset:', median_offset_12, median_offset_23
print 'out file ->', FNAME_OUT
nz, ny, nx = data.shape
if 0: # subset
print 'subsetting...'
region = ap.dotson
data, _, _ = ap.get_subset(region, data, lon, lat)
nt, ny, nx = data.shape # i,j,k = t,y,x
#------------------------------------------------------
if 0: # plot alphas and MSEs (the prediction error curve)
N = 3
x = time
y = data[:,5,2] # 6,2 -> PIG
y = ap.referenced(y, to='mean')
y_pred, lasso = ap.lasso_cv(x, y, cv=10, max_deg=N, max_iter=1e3, return_model=True)
mse = lasso.mse_path_.mean(axis=1)
std = lasso.mse_path_.std(axis=1, ddof=1) / np.sqrt(10)
#plt.plot(np.log(lasso.alphas_), mse)
plt.errorbar(np.log(lasso.alphas_), mse, yerr=std)
plt.vlines(np.log(lasso.alpha_), ymin=mse.min(), ymax=mse.max(), color='r')
plt.xlabel('log(alpha)')
plt.ylabel('10-fold-average MSE')
plt.show()
exit()
#------------------------------------------------------
data = as_frame(data, time, lat, lon)
data = data.apply(ap.referenced, to='mean', raw=True)
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
nz, ny, nx = data.shape
if 0: # subset
print "subsetting..."
region = ap.dotson
data, _, _ = ap.get_subset(region, data, lon, lat)
nt, ny, nx = data.shape # i,j,k = t,y,x
# ------------------------------------------------------
if 0: # plot alphas and MSEs (the prediction error curve)
N = 3
x = time
y = data[:, 5, 2] # 6,2 -> PIG
y = ap.referenced(y, to="mean")
y_pred, lasso = ap.lasso_cv(x, y, cv=10, max_deg=N, max_iter=1e3, return_model=True)
mse = lasso.mse_path_.mean(axis=1)
std = lasso.mse_path_.std(axis=1, ddof=1) / np.sqrt(10)
# plt.plot(np.log(lasso.alphas_), mse)
plt.errorbar(np.log(lasso.alphas_), mse, yerr=std)
plt.vlines(np.log(lasso.alpha_), ymin=mse.min(), ymax=mse.max(), color="r")
plt.xlabel("log(alpha)")
plt.ylabel("10-fold-average MSE")
plt.show()
exit()
# ------------------------------------------------------
data = as_frame(data, time, lat, lon)
data = data.apply(ap.referenced, to="mean", raw=True)
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')
satname = din.satname
time = change_day(din.time, 15) # change all days (e.g. 14,15,16,17) to 15
ts = getattr(din, VAR_TO_CALIBRATE)
err = din.dh_error
n_ad = din.n_ad
n_da = din.n_da
lon = din.lon
lat = din.lat
din.file.close()
t = ap.num2year(time)
if SUBSET: # get subset
ts, lon2, lat2 = ap.get_subset(ap.amundsen, ts, lon, lat)
err, lon2, lat2 = ap.get_subset(ap.amundsen, err, lon, lat)
n_ad, lon2, lat2 = ap.get_subset(ap.amundsen, n_ad, lon, lat)
n_da, lon2, lat2 = ap.get_subset(ap.amundsen, n_da, lon, lat)
lon, lat = lon2, lat2
xx, yy = np.meshgrid(lon, lat)
nt, ny, nx = ts.shape
offset_12 = np.full((ny,nx), np.nan)
offset_23 = np.full((ny,nx), np.nan)
print 'cross-calibrating time series:', VAR_TO_CALIBRATE
isfirst = True
if SAT_NAMES is None:
satnames = np.unique(din.satname)
# iterate over every grid cell (all times)
no_overlap_12 = 0
no_overlap_23 = 0
for i in xrange(ny):
for j in xrange(nx):
if 0:
i, j = ap.find_nearest2(xx, yy, (LON,LAT))
i -= 0
j += 0
print 'grid-cell:', i, j
ts_ij = ts[:,i,j]
if np.isnan(ts_ij).all(): continue
# get all time series (all sats) in one df (per grid-cell)
var = create_df_with_sats(time, ts_ij, satname, SAT_NAMES)
if DETREND:
var = var.apply(detrend)
if FILTER:
var = var.apply(ap.hp_filt, lamb=7, nan=True)
if PLOT_TS and (var.count().sum() > 10):
print 'grid-cell:', i, j
var.plot(linewidth=3, figsize=(9, 3), legend=False)
plt.title('Elevation change, dh (lon=%.2f, lat=%.2f)' % (xx[i,j], yy[i,j]))
plt.ylabel('m')
plt.show()
# compute offset (if ts overlap)
#---------------------------------------------------
x = pd.notnull(var)
overlap_12 = x['ers1'] & x['ers2']
overlap_23 = x['ers2'] & x['envi']
if np.sometrue(overlap_12):
if SAT_BIAS:
s1 = var['ers1'][overlap_12]
s2 = var['ers2'][overlap_12]
if LINEAR_FIT:
# using linear fit
s1 = s1[s1.notnull() & s2.notnull()]
s2 = s2[s1.notnull() & s2.notnull()]
if len(s1) > 1 and len(s2) > 1:
s1.index, s1[:] = ap.linear_fit(ap.date2year(s1.index), s1.values)
s2.index, s2[:] = ap.linear_fit(ap.date2year(s2.index), s2.values)
offset = (s1.values[-1] - s1.values[0]) - (s2.values[-1] - s2.values[0])
else:
pass
else:
# using absolute values
s1 = ap.referenced(s1, to='first')
s2 = ap.referenced(s2, to='first')
s1[0], s2[0] = np.nan, np.nan # remove first values
offset = np.nanmean(s1 - s2)
#pd.concat((s1, s2), axis=1).plot(marker='o')
else:
offset = np.nanmean(var['ers1'] - var['ers2'])
offset_12[i,j] = offset
else:
no_overlap_12 += 1
if np.sometrue(overlap_23):
if SAT_BIAS:
s2 = var['ers2'][overlap_23]
s3 = var['envi'][overlap_23]
if LINEAR_FIT:
s2 = s2[s2.notnull() & s3.notnull()]
s3 = s3[s2.notnull() & s3.notnull()]
if len(s2) > 1 and len(s3) > 1:
s2.index, s2[:] = ap.linear_fit(ap.date2year(s2.index), s2.values)
s3.index, s3[:] = ap.linear_fit(ap.date2year(s3.index), s3.values)
offset = (s2.values[-1] - s2.values[0]) - (s3.values[-1] - s3.values[0])
else:
pass
else:
s2 = ap.referenced(s2, to='first')
s3 = ap.referenced(s3, to='first')
s2[0], s3[0] = np.nan, np.nan
offset = np.nanmean(s2 - s3)
#pd.concat((s2, s3), axis=1).plot(marker='o')
#plt.show()
else:
offset = np.nanmean(var['ers2'] - var['envi'])
offset_23[i,j] = offset
else:
no_overlap_23 += 1
#---------------------------------------------------
mean_offset_12 = np.nanmean(offset_12)
median_offset_12 = np.nanmedian(offset_12)
mean_offset_23 = np.nanmean(offset_23)
median_offset_23 = np.nanmedian(offset_23)
if SAVE_TO_FILE:
fout = tb.open_file(FNAME_OUT, 'w')
fout.create_array('/', 'lon', lon)
fout.create_array('/', 'lat', lat)
fout.create_array('/', 'offset_12', offset_12)
fout.create_array('/', 'offset_23', offset_23)
fout.close()
if PLOT:
plt.figure()
plt.subplot(211)
offset_12 = ap.median_filt(offset_12, 3, 3)
plt.imshow(offset_12, origin='lower', interpolation='nearest', vmin=-.5, vmax=.5)
plt.title('ERS1-ERS2')
plt.colorbar(shrink=0.8)
plt.subplot(212)
offset_23 = ap.median_filt(offset_23, 3, 3)
plt.imshow(offset_23, origin='lower', interpolation='nearest', vmin=-.5, vmax=.5)
plt.title('ERS2-Envisat')
#plt.colorbar(shrink=0.3, orientation='h')
plt.colorbar(shrink=0.8)
plt.figure()
plt.subplot(121)
o12 = offset_12[~np.isnan(offset_12)]
plt.hist(o12, bins=100)
plt.title('ERS1-ERS2')
ax = plt.gca()
ap.intitle('mean/median = %.2f/%.2f m' % (mean_offset_12, median_offset_12),
ax=ax, loc=2)
plt.xlim(-1, 1)
plt.subplot(122)
o23 = offset_23[~np.isnan(offset_23)]
plt.hist(o23, bins=100)
plt.title('ERS2-Envisat')
ax = plt.gca()
ap.intitle('mean/median = %.2f/%.2f m' % (mean_offset_23, median_offset_23),
ax=ax, loc=2)
plt.xlim(-1, 1)
plt.show()
print 'calibrated variable:', VAR_TO_CALIBRATE
print 'no overlaps:', no_overlap_12, no_overlap_23
print 'mean offset:', mean_offset_12, mean_offset_23
print 'median offset:', median_offset_12, median_offset_23
print 'out file ->', FNAME_OUT