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)
for k, s in zip(names, shelves):
shelf, x, y = ap.get_subset(s, d, lon, lat)
#error, x, y = ap.get_subset(s, e, lon, lat)
A = ap.get_area_cells(shelf[10], x, y)
ts, _ = ap.area_weighted_mean(shelf, A)
#ts2, _ = ap.area_weighted_mean(error, A)
df[k] = ts
#df2[k] = ts2
if 0:
print k
plt.imshow(shelf[10],
extent=(x.min(), x.max(), y.min(), y.max()),
origin='lower',
interpolation='nearest',
aspect='auto')
plt.show()
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)
for k, s in zip(names, shelves):
shelf, x, y = ap.get_subset(s, d, lon, lat)
#error, x, y = ap.get_subset(s, e, lon, lat)
A = ap.get_area_cells(shelf[10], x, y)
ts, _ = ap.area_weighted_mean(shelf, A)
#ts2, _ = ap.area_weighted_mean(error, A)
df[k] = ts
#df2[k] = ts2
if 0:
print k
plt.imshow(shelf[10], extent=(x.min(), x.max(), y.min(), y.max()),
origin='lower', interpolation='nearest', aspect='auto')
plt.show()
# volume
#---------------------------------------------------------------------
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 0:
reg = ap.west
d, lon, lat = ap.get_subset(reg, d, lon, lat)
plt.figure()
plt.imshow(d[10],
extent=(lon.min(), lon.max(), lat.min(), lat.max()),
origin='lower',
interpolation='nearest',
aspect='auto')
plt.grid(True)
plt.show()
sys.exit()
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 time series
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 0:
reg = (207, 216, -78, -76)
d, lon, lat = ap.get_subset(reg, d, lon, lat)
plt.imshow(d[10], extent=(lon.min(), lon.max(), lat.min(), lat.max()),
origin='lower', interpolation='nearest', aspect='auto')
plt.grid(True)
plt.show()
sys.exit()
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)
# bin-area-average time series
df = pd.DataFrame(index=time)
df2 = pd.DataFrame(index=time)
for k, s in zip(names, shelves):
shelf, x, y = ap.get_subset(s, d, lon, lat)
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
print 'loading data...'
fin = tb.openFile(DIR + FILE_IN)
try:
time = ap.num2year(fin.root.time[:])
except:
time = fin.root.time[:]
dt = ap.year2date(time)
lon = fin.root.lon[:]
lat = fin.root.lat[:]
data = fin.root.dh_mean_mixed_const_xcal[:]
nz, ny, nx = data.shape
if 0: # subset (for testing!)
region = ap.larsenc
data, lon, lat = ap.get_subset(region, data, lon, lat)
nt, ny, nx = data.shape # i,j,k = t,y,x
if 1: # load mask
print 'loading mask...'
f = tb.open_file(FILE_MSK, 'r')
x_msk = f.root.x[:]
y_msk = f.root.y[:]
msk = f.root.mask[:]
f.close()
print 'done'
if 1: # 2d -> 1d, x/y -> lon/lat
x_msk, y_msk = np.meshgrid(x_msk, y_msk) # 1d -> 2d
x_msk, y_msk = x_msk.ravel(), y_msk.ravel() # 2d -> 1d
msk = msk.ravel()
print 'loading data...'
fin = tb.open_file(DIR + FILE_IN)
try:
time = ap.num2year(fin.root.time[:])
except:
time = fin.root.time[:]
lon = fin.root.lon[:]
lat = fin.root.lat[:]
data = fin.root.dh_mean_mixed_const_xcal[:]
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')
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
