figsize=(16.5, 14))
fig.patch.set_facecolor('white')
# calculate trends
zeros = np.zeros_like(time)
n = 0
for j in range(ncols):
for i in range(nrows):
k = df.columns[n]
print n, k
# polynamial regression
#-------------------------------------------------------------
s = df[k]
m, c = ap.linear_fit_robust(time, s.values, return_coef=True)
x, y = ap.linear_fit_robust(time, s.values, return_coef=False)
pol = np.polyfit(time, s.values, 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,
fig, axs = plt.subplots(nrows, ncols, sharex=False, sharey=False, figsize=(16.5,14))
fig.patch.set_facecolor('white')
# calculate trends
zeros = np.zeros_like(time)
n = 0
for j in range(ncols):
for i in range(nrows):
k = df.columns[n]
print n, k
# polynamial regression
#-------------------------------------------------------------
s = df[k]
m, c = ap.linear_fit_robust(time, s.values, return_coef=True)
x, y = ap.linear_fit_robust(time, s.values, return_coef=False)
pol = np.polyfit(time, s.values, 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
#-------------------------------------------------------------
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
y = sin_data['y'].values
X = dmatrix('C(x, Poly)')
N = 5
w = 1/noise
out = ap.lstsq_cv(x, y, cv=10, max_deg=N, weight=w, randomise=True, return_coef=True)
y_wls, coef, deg, mse, var = out
y_ols = ap.lstsq_cv(x, y, cv=10, max_deg=N, weight=None, randomise=True)
a2 = np.polyfit(x, y, 1, w=None)#w)
y_line = np.polyval(a2, x)
m, c = ap.linear_fit(x, y, return_coef=True)
m2, c2 = ap.linear_fit_robust(x, y, return_coef=True)
out = ap.lasso_cv(x, y, cv=10, max_deg=N, return_model=True)
y_lasso, lasso = out
a = np.append(lasso.intercept_, lasso.coef_)
#y_lasso = np.dot(X[:,:N+1], a)
#dy_lasso = a[1] * X[:,0] + 2 * a[2] * X[:,1] # + 3 * a[3] * X[:,2]
dy_lasso = np.gradient(y_lasso, x[2] - x[1])
print a[1]
print 'coef.:', a
print 'slope:', y_lasso[-1] - y_lasso[0]
plt.figure()
