i1 = np.where( t < 1997)
i2 = np.where((t > 1997) & ( t < 2000))
i3 = np.where((t > 2000) & ( t < 2002))
i4 = np.where((t > 2002) & ( t < 2004))
i5 = np.where(t > 2004)
plt.imshow(h0[10,...], extent=(lon[0], lon[-1], lat[0], lat[-1]), origin='lower', interpolation='nearest')
# plot GPS locations
plt.plot(x[i1], y[i1], 'b.', mec='blue', label='1995')
plt.plot(x[i2], y[i2], 'c.', mec='cyan', label='1999')
plt.plot(x[i3], y[i3], 'g.', mec='green', label='2001')
plt.plot(x[i4], y[i4], 'y.', mec='yellow', label='2003')
plt.plot(x[i5], y[i5], 'r.', mec='red', label='2006')
plt.legend(loc=3).draw_frame(False)
ap.intitle('Amery GPS locations', 2)
plt.xlabel('x (km)')
plt.ylabel('y (km)')
plt.grid(True)
plt.gcf().autofmt_xdate()
plt.savefig('gps_locations_amery.png', bbox_inches='tight')
plt.show()
'''
# plot GPS time series
plt.plot(t[i1], h[i1], 'b.', mec='blue')
plt.plot(t[i2], h[i2], 'c.', mec='cyan')
plt.plot(t[i3], h[i3], 'g.', mec='green')
plt.plot(t[i4], h[i4], 'y.', mec='yellow')
plt.plot(t[i5], h[i5], 'r.', mec='red')
ap.intitle('Amery GPS tseries', 2)
yy = np.polyval(pol, time)
#axs[i,j].fill_between(time, s.values+2*r, s.values-2*r, facecolor='0.5',
# edgecolor='w', alpha=0.2)
#axs[i,j].plot(time, zeros, ':', c='0.5', linewidth=0.5)
axs[i,j].plot(time, y, c='0.2', linewidth=0.75, zorder=2)
axs[i,j].plot(time, s.values, 's', c='0.5', markersize=4, zorder=1)
if 0:
axs[i,j].plot(time, yy, c='b', linewidth=1.5, zorder=2)
if 1:
axs[i,j].plot(time, ap.lasso_cv(time, s, max_deg=3), c='b', linewidth=1.75, zorder=4)
# set plots
#-------------------------------------------------------------
if i == 0:
ap.intitle('%s %.2f %s' % (k, m, UNITS), ax=axs[i,j], loc=8,
pad=-1, borderalpha=0.7)
else:
ap.intitle('%s %.2f' % (k, m), ax=axs[i,j], loc=8,
pad=-1, borderalpha=0.7)
if i != nrows-1:
ap.adjust_spines(axs[i,j], ['left'], pad=15)
else:
ap.adjust_spines(axs[i,j], ['left', 'bottom'], pad=15)
axs[i,j].set_xticks([1994, 1997, 2000, 2003, 2006, 2009, 2012])
mn, mx = ap.get_limits(s)
axs[i,j].set_yticks([mn, 0, mx])
axs[i,j].set_ylim(mn, mx)
axs[i,j].set_xlim(1994, 2012)
axs[i,j].tick_params(axis='both', direction='out', length=6, width=1,
labelsize=12)
if 0:
axs[i, j].plot(time, yy, c='b', linewidth=1.5, zorder=2)
if 1:
axs[i, j].plot(time,
ap.lasso_cv(time, s, max_deg=3),
c='b',
linewidth=1.75,
zorder=4)
# set plots
#-------------------------------------------------------------
if i == 0:
ap.intitle('%s %.2f %s' % (k, m, UNITS),
ax=axs[i, j],
loc=8,
pad=-1,
borderalpha=0.7)
else:
ap.intitle('%s %.2f' % (k, m),
ax=axs[i, j],
loc=8,
pad=-1,
borderalpha=0.7)
if i != nrows - 1:
ap.adjust_spines(axs[i, j], ['left'], pad=15)
else:
ap.adjust_spines(axs[i, j], ['left', 'bottom'], pad=15)
axs[i, j].set_xticks([1994, 1997, 2000, 2003, 2006, 2009, 2012])
mn, mx = ap.get_limits(s)
i3 = np.where((t > 2000) & (t < 2002))
i4 = np.where((t > 2002) & (t < 2004))
i5 = np.where(t > 2004)
plt.imshow(h0[10, ...],
extent=(lon[0], lon[-1], lat[0], lat[-1]),
origin='lower',
interpolation='nearest')
# plot GPS locations
plt.plot(x[i1], y[i1], 'b.', mec='blue', label='1995')
plt.plot(x[i2], y[i2], 'c.', mec='cyan', label='1999')
plt.plot(x[i3], y[i3], 'g.', mec='green', label='2001')
plt.plot(x[i4], y[i4], 'y.', mec='yellow', label='2003')
plt.plot(x[i5], y[i5], 'r.', mec='red', label='2006')
plt.legend(loc=3).draw_frame(False)
ap.intitle('Amery GPS locations', 2)
plt.xlabel('x (km)')
plt.ylabel('y (km)')
plt.grid(True)
plt.gcf().autofmt_xdate()
plt.savefig('gps_locations_amery.png', bbox_inches='tight')
plt.show()
'''
# plot GPS time series
plt.plot(t[i1], h[i1], 'b.', mec='blue')
plt.plot(t[i2], h[i2], 'c.', mec='cyan')
plt.plot(t[i3], h[i3], 'g.', mec='green')
plt.plot(t[i4], h[i4], 'y.', mec='yellow')
plt.plot(t[i5], h[i5], 'r.', mec='red')
ap.intitle('Amery GPS tseries', 2)
plt.xlabel('time')
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
#plt.ylabel('Surface elevation (m)')
plt.ylabel('meters or dB')
plt.savefig('tseries_bs.png', bbox_inches='tight')
plt.show()
'''
# correlation of time series
plt.figure(figsize=(12,5))
ax1 = plt.subplot(121)
df_alt = df_alt0.copy()
alt, firn = df_alt0.unstack().values, df_firn.unstack().values
i, = np.where((~np.isnan(alt)) & (~np.isnan(firn)))
corrcoef = np.corrcoef(alt[i], firn[i])[0,1]
plt.scatter(df_alt.unstack(), df_firn.unstack(), marker='.', color='b', alpha=.5)
plt.title('Individual time series')
ap.intitle('correlation = %.2f' % corrcoef, 4, ax=ax1)
#plt.xlabel('Altimetry, backscatter (dB)')
plt.xlabel('Altimetry dh/dt (m/yr)')
plt.ylabel('Firn model dh/dt (m/yr)')
plt.xlim(-10, 10)
plt.ylim(-2, 2)
#plt.savefig('scatter_bs.png', bbox_inches='tight')
###
ax2 = plt.subplot(122)
corrcoef = np.corrcoef(df_alt.mean(axis=1), df_firn.mean(axis=1))[0,1]
plt.scatter(df_alt.mean(axis=1), df_firn.mean(axis=1), s=70, marker='o', color='b', alpha=.8)
plt.title('Average time series')
ap.intitle('correlation = %.2f' % corrcoef, 4, ax=ax2)
#plt.xlabel('Altimetry, backscatter (dB)')
plt.xlabel('Altimetry dh/dt (m/yr)')
#plt.ylabel('Firn model, elevation (m)')
'''
# correlation of time series
plt.figure(figsize=(12, 5))
ax1 = plt.subplot(121)
df_alt = df_alt0.copy()
alt, firn = df_alt0.unstack().values, df_firn.unstack().values
i, = np.where((~np.isnan(alt)) & (~np.isnan(firn)))
corrcoef = np.corrcoef(alt[i], firn[i])[0, 1]
plt.scatter(df_alt.unstack(),
df_firn.unstack(),
marker='.',
color='b',
alpha=.5)
plt.title('Individual time series')
ap.intitle('correlation = %.2f' % corrcoef, 4, ax=ax1)
#plt.xlabel('Altimetry, backscatter (dB)')
plt.xlabel('Altimetry dh/dt (m/yr)')
plt.ylabel('Firn model dh/dt (m/yr)')
plt.xlim(-10, 10)
plt.ylim(-2, 2)
#plt.savefig('scatter_bs.png', bbox_inches='tight')
###
ax2 = plt.subplot(122)
corrcoef = np.corrcoef(df_alt.mean(axis=1), df_firn.mean(axis=1))[0, 1]
plt.scatter(df_alt.mean(axis=1),
df_firn.mean(axis=1),
s=70,
marker='o',
color='b',
alpha=.8)
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
