'George_S',
'George_N',
'Larsen_B',
'Larsen_C',
'Larsen_D',
'Ronne',
'Filchner',
'Brunt',
'Riiser',
'All_Antarctica',
]
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']
'George_S',
'George_N',
'Larsen_B',
'Larsen_C',
'Larsen_D',
'Ronne',
'Filchner',
'Brunt',
'Riiser',
'All_Antarctica',
]
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']
# read firn print 'reading firn...' f1 = nc.Dataset(FILE_FIRN) d = f1.variables h_firn = d['zs'] #[:] t_firn = d['time'][:] # years lon_firn = d['lon'][:] # 2d lat_firn = d['lat'][:] # 2d # 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)
ii, = np.where(~np.isnan(dh[:, i, j]))
if len(ii) < 8:
continue
# TREND
m, c = ap.linear_fit(year, dh[:, i, j], return_coef=True)
dhdt[i, j] = m
if m == 0:
continue
return dhdt
ap.rcparams()
print 'loading data...'
f = tb.openFile(DIR + FNAME)
time = ap.num2year(f.root.time_xcal[:])
lon = f.root.lon[:]
lat = f.root.lat[:]
xed = f.root.x_edges[:]
yed = f.root.y_edges[:]
h0 = f.root.dh_mean_xcal[:]
h = f.root.dh_mean_xcal_interp_short_const[:]
#h = f.root.dh_mean_short_const_xcal[:]
h2 = f.root.dh_mean_xcal_short_const[:]
#h2 = f.root.dh_mean_short_const_xcal[:]
g = f.root.dg_mean_xcal[:]
g2 = f.root.dg_mean_xcal[:]
dt = ap.year2date(time)
# add interpolated grid-cells
ind = np.where(~np.isnan(h2))
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
# read firn
print 'reading firn...'
f1 = nc.Dataset(FILE_FIRN)
d = f1.variables
h_firn = d['zs'] #[:]
t_firn = d['time'][:] # years
lon_firn = d['lon'][:] # 2d
lat_firn = d['lat'][:] # 2d
# 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)
def testSpline(x, y, s, npts):
sp = UnivariateSpline(x, y, s=240)
plt.plot(x,sp(x),'r')
print "splerr", ssqe(sp(x), s, npts)
return sp(x)
def plotPowerSpectrum(y, w):
ft = np.fft.rfft(y)
ps = np.real(ft*np.conj(ft))*np.square(dt)
plt.plot(w, ps)
if __name__ == '__main__':
i, j = 1, 161
f = tb.openFile(DIR + FNAME)
x = ap.num2year(f.root.time_xcal[:])
y = f.root.dh_mean_xcal_interp_short_const[:,i,j]
sigma = np.var(y)
s = y[:]
npts = len(x)
end = x[-1]
dt = end/float(npts)
nyf = 0.5/dt
'''
sigma = 0.5
x = np.linspace(0,end,npts)
r = np.random.normal(scale = sigma, size=(npts))
s = np.sin(2*np.pi*x)#+np.sin(4*2*np.pi*x)
y = s + r
'''
lsm = d1['lsm'][:] #dt1 = ap.year2date(year1) dt1 = year1 f2 = tb.openFile(FILE2) #elev = f2.root.elev[:] elev = f2.root.dh_mean_all[:] time2 = f2.root.time_all[:] 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) f3 = tb.openFile(FILE3) d3 = f3.root.data[:] y3, x3 = d3[:,0], d3[:,1] x3 = ap.lon_180_360(x3, inverse=True) ''' f4 = tb.openFile(FILE4) d4 = f4.root.data[:] y4, x4 = d4[:,0], d4[:,1] x4 = ap.lon_180_360(x4, inverse=True) ''' # crop firn grid #i1, j1 = ap.find_nearest2(lons1, lats1, points2)
def write_slabs(fid, var_name, data):
"""Save 3d array into several slabs, for XDMF."""
g = fid.create_group('/', 'data')
for i, d in enumerate(data):
fid.create_array(g, var_name +'_%02d' % i, d)
ap.rcparams()
# read data
print('loading data...')
fin = tb.open_file(FILE_IN)
data = fin.root.dh_mean_mixed_const_xcal[:]
error = fin.root.dh_error_xcal[:]
time = ap.num2year(fin.root.time_xcal[:])
lon = fin.root.lon[:]
lat = fin.root.lat[:]
xx, yy = np.meshgrid(lon, lat)
nz, ny, nx = data.shape
f1 = tb.open_file(FILE_OFFSET)
offset_12 = f1.root.offset_12[:]
offset_23 = f1.root.offset_23[:]
f1.close()
print('done')
if 0: # (for testing only) subset
print lon.shape, lat.shape, data.shape
i, j = ap.where_isnan('Totten', lon, lat)
data[:,i,j] = np.nan
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
