# read altim print 'reading altim...' f2 = tb.openFile(FILE_ALTIM, 'a') h_altim = f2.root.dh_mean_mixed_const_xcal[:-1,...] # minus last to agree with firn ts t_altim = ap.num2year(f2.root.time_xcal[:-1]) # years lat_altim = f2.root.lat[:] # 1d lon_altim = f2.root.lon[:] # 1d lon_altim = ap.lon_180_360(lon_altim, inverse=True) # get lon/lat only of complete (no gaps) altim ts h_altim = h_altim.sum(axis=0) # 3d -> 2d i_altim, j_altim = np.where(~np.isnan(h_altim)) # 2d indices lonlat_altim = np.column_stack((lon_altim[j_altim], lat_altim[i_altim])) # find nearest lon/lat in the firn model i_firn, j_firn = ap.find_nearest2(lon_firn, lat_firn, lonlat_altim) # find nearest altim times in the firn times k_firn, = ap.find_nearest(t_firn, t_altim) # new firn grid => same altim resolution with original firn time nt, ny, nx = h_firn.shape[0], h_altim.shape[0], h_altim.shape[1] h_firn_new = np.full((nt, ny, nx), np.nan, dtype='f8') # space interpolation (out-of-core) #h_firn_new[:, i_altim, j_altim] = h_firn[:, i_firn, j_firn] h_firn_new = array_to_array(h_firn_new, h_firn, i_altim, j_altim, i_firn, j_firn) del h_firn # 3-month average, and time interpolation
if len(sys.argv) < 2:
raise IOError('no input file!')
fname = sys.argv[1]
fin = nc.Dataset(fname)
d = fin.variables
firn = d['zs']
year = d['time'][:]
lon = d['lon'][:]
lat = d['lat'][:]
ism = d['ism']
j, i = ap.find_nearest2(lon, lat, points)
k, = np.where((year > 1992) & (year < 2012))
'''
plt.figure()
m = ap.viz.make_proj_stere((lon[-1,0], lat[-1,0], lon[0,-1], lat[0,-1]), lat_ts=-90)
px, py = m(lon[i,j], lat[i,j])
m.imshow(firn[1000,...], origin='lower', interpolation='nearest')
m.plot(px, py, 'o')
plt.title('lon, lat = %.3f, %.3f' % (plon, plat))
'''
for ii, jj in zip(i,j):
print lon[jj,ii], lat[jj,ii]
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
# crop firn grid
# i1, j1 = ap.find_nearest2(lons1, lats1, points2)
k1, = np.where((dt1 > dt.datetime(1992, 1, 1)) & (dt1 < dt.datetime(2012, 1, 1)))
# i_min, i_max, j_min, j_max = i1.min(), i1.max(), j1.min(), j1.max()
##lons1, lats1 = lons1[i_min:i_max,j_min:j_max], lats1[i_min:i_max,j_min:j_max]
##firn = firn[k1,i_min:i_max,j_min:j_max]
# lons1, lats1 = lons1[130:160,30:65], lats1[130:160,30:65]
# firn = firn[k1,130:160,30:65]
# dt1 = dt1[k1]
# lons1 = lons1[::-1,:]
# lats1 = lats1[::-1,:]
# indices of cropped grid
i1, j1 = ap.find_nearest2(lons1, lats1, points2)
i2, j2 = ap.find_nearest2(lons2, lats2, points2)
print lons1[-1, 0], lats1[-1, 0], lons1[0, -1], lats1[0, -1]
for i_1, j_1, i_2, j_2 in zip(i1, j1, i2, j2):
x1, y1 = lons1[i_1, j_1].round(3), lats1[i_1, j_1].round(3)
x2, y2 = lons2[i_2, j_2].round(3), lats2[i_2, j_2].round(3)
print "firn lon/lat:", x1, y1
print "elev lon/lat:", x2, y2
if np.alltrue(np.isnan(elev[:, i_2, j_2])):
continue
fig = plt.figure()
print 'reading altim...'
f2 = tb.openFile(FILE_ALTIM, 'a')
h_altim = f2.root.dh_mean_mixed_const_xcal[:-1,
...] # minus last to agree with firn ts
t_altim = ap.num2year(f2.root.time_xcal[:-1]) # years
lat_altim = f2.root.lat[:] # 1d
lon_altim = f2.root.lon[:] # 1d
lon_altim = ap.lon_180_360(lon_altim, inverse=True)
# get lon/lat only of complete (no gaps) altim ts
h_altim = h_altim.sum(axis=0) # 3d -> 2d
i_altim, j_altim = np.where(~np.isnan(h_altim)) # 2d indices
lonlat_altim = np.column_stack((lon_altim[j_altim], lat_altim[i_altim]))
# find nearest lon/lat in the firn model
i_firn, j_firn = ap.find_nearest2(lon_firn, lat_firn, lonlat_altim)
# find nearest altim times in the firn times
k_firn, = ap.find_nearest(t_firn, t_altim)
# new firn grid => same altim resolution with original firn time
nt, ny, nx = h_firn.shape[0], h_altim.shape[0], h_altim.shape[1]
h_firn_new = np.full((nt, ny, nx), np.nan, dtype='f8')
# space interpolation (out-of-core)
#h_firn_new[:, i_altim, j_altim] = h_firn[:, i_firn, j_firn]
h_firn_new = array_to_array(h_firn_new, h_firn, i_altim, j_altim, i_firn,
j_firn)
del h_firn
plt.imshow(data[10], origin='lower', interpolation='nearest',
extent=(lon.min(), lon.max(), lat.min(), lat.max()),
aspect='auto')
plt.show()
exit()
'''
if 1: # (yes) filter time
_, data = ap.time_filt(time, data, from_time=1994, to_time=2013)
time, error = ap.time_filt(time, error, from_time=1994, to_time=2013)
dt = ap.year2date(time)
# remove bad grid cells (visual inspection)
if 1:
ii, jj = ap.find_nearest2(xx, yy, lonlat)
k = 0
for i, j in zip(ii, jj):
print k, lonlat[k]
'''
plt.plot(time, data[:,i,j])
plt.show()
'''
data[:,i,j] = np.nan
k += 1
print 'removed grid cells:', k
# (NO) filter ts with xcal offset > 1.5 # offset of dh_mean!
if 0:
print 'xcalib filtering...'
i, j = np.where((offset_12 > 2) | (offset_23 > 2))
if len(sys.argv) < 2:
raise IOError("no input file!")
fname = sys.argv[1]
fin = nc.Dataset(fname)
d = fin.variables
firn = d["zs"]
year = d["time"][:]
lon = d["lon"][:]
lat = d["lat"][:]
ism = d["ism"]
j, i = ap.find_nearest2(lon, lat, points)
k, = np.where((year > 1992) & (year < 2012))
"""
plt.figure()
m = ap.viz.make_proj_stere((lon[-1,0], lat[-1,0], lon[0,-1], lat[0,-1]), lat_ts=-90)
px, py = m(lon[i,j], lat[i,j])
m.imshow(firn[1000,...], origin='lower', interpolation='nearest')
m.plot(px, py, 'o')
plt.title('lon, lat = %.3f, %.3f' % (plon, plat))
"""
for ii, jj in zip(i, j):
print lon[jj, ii], lat[jj, ii]
hpfilt = hpfilt.apply(ap.referenced, to='first')
# smooth fields (sigma between 0.6-7)
print 'smoothing fields...'
hpfilt = hpfilt.T.to_panel()
hpfilt_grad = hpfilt_grad.T.to_panel()
apply_to_panel(ap.gfilter, hpfilt, .7)
apply_to_panel(ap.gfilter, hpfilt_grad, .7)
# plot time series: (1,161), (2,161), (3,161)
if PLOT:
'''
i, j = 1, 161
'''
xx, yy = np.meshgrid(lon, lat)
i, j = ap.find_nearest2(xx, yy, [(116, -67)])
a, b = xx[i,j], yy[i,j]
print a, b
'''
plt.figure()
ax1 = plt.subplot(211)
plt.plot(time, h0[:,i,j], linewidth=2)
ap.intitle('uncorr. elevation', ax=ax1)
ax2 = plt.subplot(212)
plt.plot(time, g[:,i,j], linewidth=2, c='k')
ap.intitle('backscatter', ax=ax2)
#plt.savefig('totten_d1.png')
'''
plt.plot(time, h[:,i,j], linewidth=1, label='seasonal')
#plt.plot(time, h_cycle, linewidth=2)
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
hpfilt = hpfilt.apply(ap.referenced, to='first')
# smooth fields (sigma between 0.6-7)
print 'smoothing fields...'
hpfilt = hpfilt.T.to_panel()
hpfilt_grad = hpfilt_grad.T.to_panel()
apply_to_panel(ap.gfilter, hpfilt, .7)
apply_to_panel(ap.gfilter, hpfilt_grad, .7)
# plot time series: (1,161), (2,161), (3,161)
if PLOT:
'''
i, j = 1, 161
'''
xx, yy = np.meshgrid(lon, lat)
i, j = ap.find_nearest2(xx, yy, [(116, -67)])
a, b = xx[i, j], yy[i, j]
print a, b
'''
plt.figure()
ax1 = plt.subplot(211)
plt.plot(time, h0[:,i,j], linewidth=2)
ap.intitle('uncorr. elevation', ax=ax1)
ax2 = plt.subplot(212)
plt.plot(time, g[:,i,j], linewidth=2, c='k')
ap.intitle('backscatter', ax=ax2)
#plt.savefig('totten_d1.png')
'''
plt.plot(time, h[:, i, j], linewidth=1, label='seasonal')
#plt.plot(time, h_cycle, linewidth=2)
