def plot_ts(x, y, title='', savefig=None):
x_pol, y_pol = util.poly_fit(x, y, order=3)
x_lin, y_lin = linear_fit(x, y)
m, c = linear_fit(x, y, return_coef=True)
fig = plt.figure(figsize=(10,3))
ax = fig.add_subplot((111))
x = x.round(2)
n = y_lin[0] # reference to zero
plt.plot(x, y-n, linewidth=3)
#plt.plot(x_pol, y_pol-n)
plt.plot(x_lin, y_lin-n, 'r')
viz.add_inner_title(ax, 'dh/dt = %.1f cm/yr' % (m*100), loc=4)
viz.add_inner_title(ax, title, loc=2)
#plt.ylim(-0.1, 0.1)
plt.xlim(2003.5, 2009.6)
#plt.xlabel('time')
plt.ylabel('dh (m)')
#fig.autofmt_xdate()
if savefig is not None:
plt.savefig(savefig)
plt.show()
return fig
def plot_ts(x, y, title='', savefig=None):
x_pol, y_pol = util.poly_fit(x, y, order=3)
x_lin, y_lin = linear_fit(x, y)
m, c = linear_fit(x, y, return_coef=True)
fig = plt.figure(figsize=(10, 3))
ax = fig.add_subplot((111))
x = x.round(2)
#n = y_lin[0] # zero at the begining
n = y_lin[0] + (y_lin[-1] - y_lin[0]) / 2. # zero at the middle
plt.plot(x, y - n, linewidth=3)
#plt.plot(x_pol, y_pol-n)
plt.plot(x_lin, y_lin - n, 'r')
viz.add_inner_title(ax, 'dh/dt = %.1f cm/yr' % (m * 100), loc=1)
viz.add_inner_title(ax, title, loc=2)
plt.ylim(-.3, .3)
plt.xlim(2003.5, 2010.1)
#plt.xlabel('years')
plt.ylabel('Elevation change (m)')
#fig.autofmt_xdate()
if savefig is not None:
pass
#plt.savefig(savefig+'_ts.png')
#np.savetxt(savefig+'_ts.txt', np.column_stack((x, y)))
return fig
def plot_ts(ax, x, y, dtime, title='', savefig=None, linewidth=None, color=None, alpha=None, label=True):
#x_pol, y_pol = util.poly_fit(x, y, order=3)
x_lin, y_lin = linear_fit(x, y)
m, c = linear_fit(x, y, return_coef=True)
x = x.round(2)
#n = y_lin[0] # zero at the begining
n = y_lin[0] + (y_lin[-1] - y_lin[0])/2. # zero at the middle
x_dt = fy2dt(x)
ax.plot(x, y-n, linewidth=linewidth, color=color, alpha=alpha)
#plt.plot(x_pol, y_pol-n, **kw)
x_lin_dt = np.sort(fy2dt(x_lin))
ax.plot(x_lin, y_lin-n, linewidth=linewidth-1, color=color, alpha=alpha)
if label:
viz.add_inner_title(ax, 'dh/dt = %.1f cm/yr' % (m*100), loc=1)
viz.add_inner_title(ax, title, loc=2)
plt.ylim(-.28, .28)
plt.xlim(2003.5, 2010.1)
#plt.xlabel('years')
plt.ylabel('Elevation change (m)')
if savefig is not None:
pass
#plt.savefig(savefig+'_ts.png')
#np.savetxt(savefig+'_ts.txt', np.column_stack((x, y)))
return ax
def plot_ts(x, y, title='', savefig=None):
x_pol, y_pol = util.poly_fit(x, y, order=3)
x_lin, y_lin = linear_fit(x, y)
m, c = linear_fit(x, y, return_coef=True)
fig = plt.figure(figsize=(10,3))
ax = fig.add_subplot((111))
x = x.round(2)
#n = y_lin[0] # zero at the begining
n = y_lin[0] + (y_lin[-1] - y_lin[0])/2. # zero at the middle
plt.plot(x, y-n, linewidth=3)
#plt.plot(x_pol, y_pol-n)
plt.plot(x_lin, y_lin-n, 'r')
viz.add_inner_title(ax, 'dh/dt = %.1f cm/yr' % (m*100), loc=1)
viz.add_inner_title(ax, title, loc=2)
plt.ylim(-.3, .3)
plt.xlim(2003.5, 2010.1)
#plt.xlabel('years')
plt.ylabel('Elevation change (m)')
#fig.autofmt_xdate()
if savefig is not None:
pass
#plt.savefig(savefig+'_ts.png')
#np.savetxt(savefig+'_ts.txt', np.column_stack((x, y)))
return fig
def plot_ts(x, y, title='', savefig=None):
x_pol, y_pol = util.poly_fit(x, y, order=3)
x_lin, y_lin = linear_fit(x, y)
m, c = linear_fit(x, y, return_coef=True)
fig = plt.figure(figsize=(10, 3))
ax = fig.add_subplot((111))
x = x.round(2)
#n = y_lin[0] # reference to zero
nmin = y_lin.min() # reference to center
nmax = y_lin.max()
n = nmin + (nmax - nmin) / 2.
plt.plot(x, y - n, linewidth=3)
plt.plot(x_pol, y_pol - n)
plt.plot(x_lin, y_lin - n)
viz.add_inner_title(ax, 'dh/dt = %.1f cm/yr' % (m * 100), loc=4)
#viz.add_inner_title(ax, 'dAGC/dt = %.1f dB/yr' % (m*100), loc=4)
viz.add_inner_title(ax, title, loc=1)
#plt.ylim(-3.5, 3.5)
plt.xlim(1992, 2012.7)
ax.xaxis.set_ticks(range(1992, 2013, 2))
#plt.xlabel('time')
plt.ylabel('dh [m]')
#plt.ylabel('dAGC [dB]')
fig.autofmt_xdate()
if savefig is not None:
plt.savefig(savefig)
plt.show()
return fig
def plot_ts(x, y, title='', savefig=None):
x_pol, y_pol = util.poly_fit(x, y, order=3)
x_lin, y_lin = linear_fit(x, y)
m, c = linear_fit(x, y, return_coef=True)
fig = plt.figure(figsize=(10,3))
ax = fig.add_subplot((111))
x = x.round(2)
#n = y_lin[0] # reference to zero
nmin = y_lin.min() # reference to center
nmax = y_lin.max()
n = nmin + (nmax - nmin)/2.
plt.plot(x, y-n, linewidth=3)
plt.plot(x_pol, y_pol-n)
plt.plot(x_lin, y_lin-n)
viz.add_inner_title(ax, 'dh/dt = %.1f cm/yr' % (m*100), loc=4)
#viz.add_inner_title(ax, 'dAGC/dt = %.1f dB/yr' % (m*100), loc=4)
viz.add_inner_title(ax, title, loc=1)
#plt.ylim(-3.5, 3.5)
plt.xlim(1992, 2012.7)
ax.xaxis.set_ticks(range(1992, 2013, 2))
#plt.xlabel('time')
plt.ylabel('dh [m]')
#plt.ylabel('dAGC [dB]')
fig.autofmt_xdate()
if savefig is not None:
plt.savefig(savefig)
plt.show()
return fig
def plot_ts(ax, x, y, num=1):
x_pol, y_pol = util.poly_fit(x, y, order=3)
x_lin, y_lin = linear_fit(x, y)
m, c = linear_fit(x, y, return_coef=True)
x = x.round(2)
n = y_lin[0] # reference to zero
ax.plot(x, y-n, linewidth=2.5)
#ax.plot(x_pol, y_pol-n)
ax.plot(x_lin, y_lin-n, 'r')
if num != 7:
viz.add_inner_title(ax, '(%d)' % num, loc=2)
viz.add_inner_title(ax, 'trend = %.1f cm/yr' % (m*100), loc=3)
return ax
def plot_ts(x, y):
x_pol, y_pol = util.poly_fit(x, y, order=3)
x_lin, y_lin = linear_fit(x, y)
m, c = linear_fit(x, y, return_coef=True)
fig = plt.figure()
ax = fig.add_subplot((111))
x = x.round(2)
n = y_lin[0] # reference to zero
plt.plot(x, y-n, linewidth=2)
plt.plot(x_pol, y_pol-n)
plt.plot(x_lin, y_lin-n)
viz.add_inner_title(ax, 'linear trend = %.1f cm/yr' % (m*100), loc=2)
#plt.ylim(-0.8, 0.8)
plt.xlabel('time')
plt.ylabel('dh (m)')
plt.show()
return fig
def plot_ts(x, y):
x_pol, y_pol = util.poly_fit(x, y, order=3)
x_lin, y_lin = linear_fit(x, y)
m, c = linear_fit(x, y, return_coef=True)
fig = plt.figure()
ax = fig.add_subplot((111))
x = x.round(2)
n = y_lin[0] # reference to zero
plt.plot(x, y - n, linewidth=2)
plt.plot(x_pol, y_pol - n)
plt.plot(x_lin, y_lin - n)
viz.add_inner_title(ax, 'linear trend = %.1f cm/yr' % (m * 100), loc=2)
#plt.ylim(-0.8, 0.8)
plt.xlabel('time')
plt.ylabel('dh (m)')
plt.show()
return fig
def plot_tseries(time2,
lon,
lat,
dh_mean_corr,
dh_mean,
dg_mean,
R,
S,
term='mix'):
#time2 = y2dt(time2)
R = np.mean(R)
S = np.mean(S)
if not np.alltrue(np.isnan(dh_mean[1:])):
# use only non-null and non-zero entries for correlation
ind, = np.where((~np.isnan(dh_mean)) & (~np.isnan(dg_mean)) & \
(dh_mean!=0) & (dg_mean!=0))
t = np.arange(len(dh_mean))
x, y = linear_fit(dg_mean[ind], dh_mean[ind])
x2, y2 = linear_fit_robust(dg_mean[ind], dh_mean[ind])
fig = plt.figure()
ax = fig.add_subplot((111))
plt.plot(dg_mean[ind], dh_mean[ind], 'o')
plt.plot(x, y, linewidth=2, label='lstsq fit')
plt.plot(x2, y2, linewidth=2, label='robust fit')
plt.legend(loc=2).draw_frame(False)
if term == 'mix':
plt.xlabel('dAGC (dB)')
plt.ylabel('dh (m)')
plt.title('Mix-term sensitivity (S = corr grad)')
elif term == 'short':
plt.xlabel('$\Delta$dAGC (dB)')
plt.ylabel('$\Delta$dh (m)')
plt.title('Short-term sensitivity (S = corr grad)')
ax = viz.add_inner_title(ax, 'R = %.2f, S = %.2f' % (R, S), 4)
#-----------------
fig = plt.figure()
plt.subplot((211))
plt.plot(time2, dg_mean, linewidth=2, label='backscatter')
plt.legend().draw_frame(False)
plt.ylabel('dAGC (dB)')
plt.title('lon = %.2f, lat = %.2f' % (lon, lat))
#plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d'))
plt.subplot((212))
plt.plot(time2, dh_mean, 'g', linewidth=2, label='dh_uncorr')
plt.plot(time2, dh_mean_corr, 'r', linewidth=2, label='dh_corr')
plt.legend().draw_frame(False)
plt.ylabel('dh (m)')
#plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d'))
fig.autofmt_xdate()
return fig
def plot_ts_mean(t, Y):
y = np.ma.masked_invalid(Y)
ts = np.mean(np.mean(y, axis=1), axis=1)
#x_pol, y_pol = spline_interp(t, ts, smooth=0.1)
x_pol, y_pol = util.poly_fit(t, ts, order=3)
x_lin, y_lin = linear_fit(t, ts)
m, c = linear_fit(t, ts, return_coef=True)
fig = plt.figure()
ax = fig.add_subplot((111))
n = y_lin[0] # shift
plt.plot(t, ts - n, linewidth=2)
plt.plot(x_pol, y_pol - n)
plt.plot(x_lin, y_lin - n)
viz.add_inner_title(ax, 'linear trend = %.1f cm/yr' % (m * 100), loc=2)
#plt.ylim(ts.min(), ts.max())
plt.xlabel('time')
plt.ylabel('dh (m)')
plt.title('Mean TS')
plt.show()
return fig
def plot_ts_mean(t, Y):
y = np.ma.masked_invalid(Y)
ts = np.mean(np.mean(y, axis=1), axis=1)
#x_pol, y_pol = spline_interp(t, ts, smooth=0.1)
x_pol, y_pol = util.poly_fit(t, ts, order=3)
x_lin, y_lin = linear_fit(t, ts)
m, c = linear_fit(t, ts, return_coef=True)
fig = plt.figure()
ax = fig.add_subplot((111))
n = y_lin[0] # shift
plt.plot(t, ts-n, linewidth=2)
plt.plot(x_pol, y_pol-n)
plt.plot(x_lin, y_lin-n)
viz.add_inner_title(ax, 'linear trend = %.1f cm/yr' % (m*100), loc=2)
#plt.ylim(ts.min(), ts.max())
plt.xlabel('time')
plt.ylabel('dh (m)')
plt.title('Mean TS')
plt.show()
return fig
def plot_tseries(time2, lon, lat, dh_mean_corr, dh_mean, dg_mean, R, S, term='mix'):
#time2 = y2dt(time2)
R = np.mean(R)
S = np.mean(S)
if not np.alltrue(np.isnan(dh_mean[1:])):
# use only non-null and non-zero entries for correlation
ind, = np.where((~np.isnan(dh_mean)) & (~np.isnan(dg_mean)) & \
(dh_mean!=0) & (dg_mean!=0))
t = np.arange(len(dh_mean))
x, y = linear_fit(dg_mean[ind], dh_mean[ind])
x2, y2 = linear_fit_robust(dg_mean[ind], dh_mean[ind])
fig = plt.figure()
ax = fig.add_subplot((111))
plt.plot(dg_mean[ind], dh_mean[ind], 'o')
plt.plot(x, y, linewidth=2, label='lstsq fit')
plt.plot(x2, y2, linewidth=2, label='robust fit')
plt.legend(loc=2).draw_frame(False)
if term == 'mix':
plt.xlabel('dAGC (dB)')
plt.ylabel('dh (m)')
plt.title('Mix-term sensitivity (S = corr grad)')
elif term == 'short':
plt.xlabel('$\Delta$dAGC (dB)')
plt.ylabel('$\Delta$dh (m)')
plt.title('Short-term sensitivity (S = corr grad)')
ax = viz.add_inner_title(ax, 'R = %.2f, S = %.2f' % (R, S), 4)
#-----------------
fig = plt.figure()
plt.subplot((211))
plt.plot(time2, dg_mean, linewidth=2, label='backscatter')
plt.legend().draw_frame(False)
plt.ylabel('dAGC (dB)')
plt.title('lon = %.2f, lat = %.2f' % (lon, lat))
#plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d'))
plt.subplot((212))
plt.plot(time2, dh_mean, 'g', linewidth=2, label='dh_uncorr')
plt.plot(time2, dh_mean_corr, 'r', linewidth=2, label='dh_corr')
plt.legend().draw_frame(False)
plt.ylabel('dh (m)')
#plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d'))
fig.autofmt_xdate()
return fig
ts = mean_t + mean_xy[i,j] + Y[:,i,j]
#x_pol, y_pol = spline_interp(t, ts, smooth=0.1)
x_pol, y_pol = util.poly_fit(t, ts, order=3)
x_lin, y_lin = linear_fit(t, ts)
m, c = linear_fit(t, ts, return_coef=True)
fig = plt.figure()
ax = fig.add_subplot((111))
n = y_lin[0] # shift
plt.plot(t, ts-n, linewidth=2)
plt.plot(x_pol, y_pol-n)
plt.plot(x_lin, y_lin-n)
viz.add_inner_title(ax, 'linear trend = %.1f cm/yr' % (m*100), loc=2)
#plt.ylim(ts.min(), ts.max())
plt.xlabel('time')
plt.ylabel('dh (m)')
plt.title('Mean TS')
plt.show()
return fig
def plot_grid_and_ts(lon, lat, dhdt, t, dh, (i,j), **kw):
cell = (lon[j], lon[j+1], lat[i], lat[i+1])
plot_grid(lon, lat, dhdt, cell, **kw)
plt.show()
def plot_map(lon, lat, grid, bbox, mfile, mres=1, **kw):
def plot_ts(time2, lon, lat, dh_mean_cor, dh_mean, dg_mean, R, S, diff=True):
if np.alltrue(np.isnan(dh_mean[1:])):
return None
#time2 = y2dt(time2)
R = np.mean(R)
S = np.mean(S)
# use only non-null and non-zero entries for correlation
ind, = np.where((~np.isnan(dh_mean)) & (~np.isnan(dg_mean)) & \
(dh_mean!=0) & (dg_mean!=0))
t = np.arange(len(dh_mean))
if not diff:
x, y = ap.linear_fit(dg_mean[ind], dh_mean[ind])
x2, y2 = ap.linear_fit_robust(dg_mean[ind], dh_mean[ind])
fig = plt.figure()
ax = fig.add_subplot((111))
plt.plot(dg_mean[ind], dh_mean[ind], 'o')
plt.plot(x, y, linewidth=2, label='lstsq fit')
plt.plot(x2, y2, linewidth=2, label='robust fit')
plt.legend(loc=2).draw_frame(False)
plt.xlabel('dAGC (dB)')
plt.ylabel('dh (m)')
plt.title('Mixed-term sensitivity')
else:
dh_mean2 = np.diff(dh_mean)
dg_mean2 = np.diff(dg_mean)
dh_mean2 = np.append(dh_mean2, np.nan)
dg_mean2 = np.append(dg_mean2, np.nan)
x, y = ap.linear_fit(dg_mean2[ind], dh_mean2[ind])
x2, y2 = ap.linear_fit_robust(dg_mean2[ind], dh_mean2[ind])
fig = plt.figure()
ax = fig.add_subplot((111))
plt.plot(dg_mean2[ind], dh_mean2[ind], 'o')
plt.plot(x, y, linewidth=2, label='lstsq fit')
plt.plot(x2, y2, linewidth=2, label='robust fit')
plt.legend(loc=2).draw_frame(False)
plt.xlabel('$\Delta$dAGC (dB)')
plt.ylabel('$\Delta$dh (m)')
plt.title('Short-term sensitivity')
ax1 = viz.add_inner_title(ax, 'corrcoef: R = %.2f' % R, 3)
ax1 = viz.add_inner_title(ax, 'slope: S = %.2f' % S, 4)
plt.savefig('corr.png')
#-----------------
if not diff:
fig = plt.figure()
ax2 = plt.subplot((211))
plt.plot(time2, dh_mean, 'b', linewidth=2, label='dh')
plt.plot(time2, dh_mean_cor, 'r', linewidth=2, label='dh$_{COR}$')
#plt.legend().draw_frame(False)
viz.add_inner_title(ax2, 'dh', 2)
viz.add_inner_title(ax2, 'dh$_{COR}$', 3)
plt.title('lon = %.2f, lat = %.2f' % (lon, lat))
plt.ylabel('m')
#plt.xlim(1992, 2012.1)
#plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d'))
ax3 = plt.subplot((212))
plt.plot(time2, dg_mean, 'g', linewidth=2, label='dAGC')
#plt.legend().draw_frame(False)
viz.add_inner_title(ax3, 'dAGC', 3)
plt.ylabel('dB')
#plt.xlim(1992, 2012.1)
#plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d'))
else:
fig = plt.figure()
ax2 = plt.subplot((311))
plt.plot(time2, dh_mean, 'b', linewidth=2, label='dh')
plt.plot(time2, dh_mean_cor, 'r', linewidth=2, label='dh$_{COR}$')
#plt.legend().draw_frame(False)
viz.add_inner_title(ax2, 'dh', 2)
viz.add_inner_title(ax2, 'dh$_{COR}$', 3)
plt.title('lon = %.2f, lat = %.2f' % (lon, lat))
plt.ylabel('m')
#plt.xlim(1992, 2012.1)
#plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d'))
ax3 = plt.subplot((312))
plt.plot(time2, dh_mean2, 'm', linewidth=2, label='$\Delta$dh')
#plt.legend().draw_frame(False)
viz.add_inner_title(ax3, '$\Delta$dh', 3)
plt.ylabel('m')
#plt.xlim(1992, 2012.1)
#plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d'))
ax4 = plt.subplot((313))
plt.plot(time2, dg_mean2, 'c', linewidth=2, label='$\Delta$dAGC')
#plt.legend().draw_frame(False)
viz.add_inner_title(ax4, '$\Delta$dAGC', 3)
plt.ylabel('dB')
#plt.xlim(1992, 2012.1)
#plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d'))
fig.autofmt_xdate()
plt.savefig('ts.png')
return fig
def plot_ts(time2, lon, lat, dh_mean_cor, dh_mean, dg_mean, R, S, diff=True):
if np.alltrue(np.isnan(dh_mean[1:])):
return None
#time2 = y2dt(time2)
R = np.mean(R)
S = np.mean(S)
# use only non-null and non-zero entries for correlation
ind, = np.where((~np.isnan(dh_mean)) & (~np.isnan(dg_mean)) & \
(dh_mean!=0) & (dg_mean!=0))
t = np.arange(len(dh_mean))
if not diff:
x, y = ap.linear_fit(dg_mean[ind], dh_mean[ind])
x2, y2 = ap.linear_fit_robust(dg_mean[ind], dh_mean[ind])
fig = plt.figure()
ax = fig.add_subplot((111))
plt.plot(dg_mean[ind], dh_mean[ind], 'o')
plt.plot(x, y, linewidth=2, label='lstsq fit')
plt.plot(x2, y2, linewidth=2, label='robust fit')
plt.legend(loc=2).draw_frame(False)
plt.xlabel('dAGC (dB)')
plt.ylabel('dh (m)')
plt.title('Mixed-term sensitivity')
else:
dh_mean2 = np.diff(dh_mean)
dg_mean2 = np.diff(dg_mean)
dh_mean2 = np.append(dh_mean2, np.nan)
dg_mean2 = np.append(dg_mean2, np.nan)
x, y = ap.linear_fit(dg_mean2[ind], dh_mean2[ind])
x2, y2 = ap.linear_fit_robust(dg_mean2[ind], dh_mean2[ind])
fig = plt.figure()
ax = fig.add_subplot((111))
plt.plot(dg_mean2[ind], dh_mean2[ind], 'o')
plt.plot(x, y, linewidth=2, label='lstsq fit')
plt.plot(x2, y2, linewidth=2, label='robust fit')
plt.legend(loc=2).draw_frame(False)
plt.xlabel('$\Delta$dAGC (dB)')
plt.ylabel('$\Delta$dh (m)')
plt.title('Short-term sensitivity')
ax1 = viz.add_inner_title(ax, 'corrcoef: R = %.2f' % R, 3)
ax1 = viz.add_inner_title(ax, 'slope: S = %.2f' % S, 4)
plt.savefig('corr.png')
#-----------------
if not diff:
fig = plt.figure()
ax2 = plt.subplot((211))
plt.plot(time2, dh_mean, 'b', linewidth=2, label='dh')
plt.plot(time2, dh_mean_cor, 'r', linewidth=2, label='dh$_{COR}$')
#plt.legend().draw_frame(False)
viz.add_inner_title(ax2, 'dh', 2)
viz.add_inner_title(ax2, 'dh$_{COR}$', 3)
plt.title('lon = %.2f, lat = %.2f' % (lon, lat))
plt.ylabel('m')
#plt.xlim(1992, 2012.1)
#plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d'))
ax3 = plt.subplot((212))
plt.plot(time2, dg_mean, 'g', linewidth=2, label='dAGC')
#plt.legend().draw_frame(False)
viz.add_inner_title(ax3, 'dAGC', 3)
plt.ylabel('dB')
#plt.xlim(1992, 2012.1)
#plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d'))
else:
fig = plt.figure()
ax2 = plt.subplot((311))
plt.plot(time2, dh_mean, 'b', linewidth=2, label='dh')
plt.plot(time2, dh_mean_cor, 'r', linewidth=2, label='dh$_{COR}$')
#plt.legend().draw_frame(False)
viz.add_inner_title(ax2, 'dh', 2)
viz.add_inner_title(ax2, 'dh$_{COR}$', 3)
plt.title('lon = %.2f, lat = %.2f' % (lon, lat))
plt.ylabel('m')
#plt.xlim(1992, 2012.1)
#plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d'))
ax3 = plt.subplot((312))
plt.plot(time2, dh_mean2, 'm', linewidth=2, label='$\Delta$dh')
#plt.legend().draw_frame(False)
viz.add_inner_title(ax3, '$\Delta$dh', 3)
plt.ylabel('m')
#plt.xlim(1992, 2012.1)
#plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d'))
ax4 = plt.subplot((313))
plt.plot(time2, dg_mean2, 'c', linewidth=2, label='$\Delta$dAGC')
#plt.legend().draw_frame(False)
viz.add_inner_title(ax4, '$\Delta$dAGC', 3)
plt.ylabel('dB')
#plt.xlim(1992, 2012.1)
#plt.gca().xaxis.set_major_formatter(plt.FormatStrFormatter('%d'))
fig.autofmt_xdate()
plt.savefig('ts.png')
return fig
ts = mean_t + mean_xy[i, j] + Y[:, i, j]
#x_pol, y_pol = spline_interp(t, ts, smooth=0.1)
x_pol, y_pol = util.poly_fit(t, ts, order=3)
x_lin, y_lin = linear_fit(t, ts)
m, c = linear_fit(t, ts, return_coef=True)
fig = plt.figure()
ax = fig.add_subplot((111))
n = y_lin[0] # shift
plt.plot(t, ts - n, linewidth=2)
plt.plot(x_pol, y_pol - n)
plt.plot(x_lin, y_lin - n)
viz.add_inner_title(ax, 'linear trend = %.1f cm/yr' % (m * 100), loc=2)
#plt.ylim(ts.min(), ts.max())
plt.xlabel('time')
plt.ylabel('dh (m)')
plt.title('Mean TS')
plt.show()
return fig
def plot_grid_and_ts(lon, lat, dhdt, t, dh, (i, j), **kw):
cell = (lon[j], lon[j + 1], lat[i], lat[i + 1])
plot_grid(lon, lat, dhdt, cell, **kw)
plt.show()
def plot_map(lon, lat, grid, bbox, mfile, mres=1, **kw):
