def onclick(event):
if event.inaxes != None:
xpx = event.xdata
ypx = event.ydata
try:
out = (xpx, ypx, gbma[ypx, xpx])
if ggt is not None:
#Note matplotlib (0,0) is lower left
#gdal (0,0) is upper left
mx, my = geolib.pixelToMap(xpx, ypx, ggt)
out = (xpx, ypx), (mx, my, gbma[ypx, xpx])
print out
except IndexError:
pass
def plot_point(ex, ey):
if ex > m.shape[2]-1 or ey > m.shape[1]-1:
print "Input coordinates are outside stack extent:"
print ex, ey
print m.shape
else:
v = sample_stack(ex, ey, geoid_offset=geoid_offset, pad=pad)
v_idx = (~np.ma.getmaskarray(v)).nonzero()[0]
v_error = error[v_idx].data
v_source = source[v_idx].data
samp_list = split_sample(v)
#v = m[:,ey,ex]
#if not samp_list:
if v.count() > 0:
c = next(colors)
v_mean = v.mean()
v_valid = v.compressed()
v_rel_valid = v_valid - v_mean
d_valid = np.ma.array(d, mask=np.ma.getmaskarray(v)).compressed()
#Plot trendline
if plot_trend:
vm, r, slope = linregress(v)
vm_rel = vm - v_mean
plt.figure(1)
ax_rel.plot_date(d, vm_rel, label='%0.1f m/yr' % slope, marker=None, markersize=ms, color=c, linestyle='--', linewidth=0.6)
#v_rel_abs_lim = int(max(abs(v_rel.min()), abs(v_rel.max())) + 0.5)
#if np.all(np.abs(ax_rel.get_ylim()) < vn_abs_lim):
# ax_rel.set_ylim(-v_rel_abs_lim, v_rel_abs_lim)
if plot_resid:
plt.figure(3)
r_samp_list = split_sample(r)
for i in r_samp_list:
r_line, = ax_resid.plot_date(i[0], i[1], marker=i[4], alpha=i[5], markersize=ms, color=c, linestyle='None')
if errorbars:
if np.any(i[2] != 0):
ax_resid.errorbar(i[0], i[1], yerr=i[2], color=c, linestyle='None')
plt.draw()
for i in samp_list:
#Don't label, as we want legend to contain trend values
plt.figure(1)
v_rel_line, = ax_rel.plot_date(i[0], i[1]-v_mean, marker=i[4], alpha=i[5], markersize=ms, color=c, linestyle='None')
if errorbars:
if np.any(i[2] != 0):
plt.figure(1)
ax_rel.errorbar(i[0], i[1]-v_mean, yerr=i[2], color=c, linestyle='None')
plt.figure(2)
v_line, = ax_abs.plot_date(i[0], i[1], marker=i[4], alpha=i[5], markersize=ms, color=c, linestyle='None')
if errorbars:
if np.any(i[2] != 0):
plt.figure(2)
ax_abs.errorbar(i[0], i[1], yerr=i[2], color=c, linestyle='None')
plt.figure(2)
#Now draw lines connecting points
v_line, = ax_abs.plot_date(d_valid, v_valid, marker=None, color=c, linestyle='-', linewidth=0.6, alpha=0.5)
plt.draw()
#Don't really need/want lines between points on this one - too busy
plt.figure(1)
v_rel_line, = ax_rel.plot_date(d_valid, v_rel_valid, marker=None, color=c, linestyle='-', linewidth=0.6, alpha=0.5)
if plot_trend:
#Add legend containing trend values
plt.legend(loc='upper right', prop={'size':10})
plt.draw()
#Plot doy
#Need to add title to doy plots
mx, my = geolib.pixelToMap(ex, ey, gt)
title='%0.1f, %0.1f' % (mx, my)
#doy_plot(s.date_list, v, ylabel, title=title)
#create_legend_interactive(ax_abs)
#Now add point to context maps
plt.figure(0)
#Could get fancy here and scale the marker as unfilled square scaled to size of padded sample
ax_pt_list[0].extend(ax_list[0].plot(ex, ey, 'o', color=c))
ax_pt_list[1].extend(ax_list[1].plot(ex, ey, 'o', color=c))
ax_pt_list[2].extend(ax_list[2].plot(ex, ey, 'o', color=c))
ax_pt_list[3].extend(ax_list[3].plot(ex, ey, 'o', color=c))
plt.draw()
if False:
out_fn = 'stack_sample_%0.1f_%0.1f.csv' % (mx, my)
#np.savetxt(out_fn, np.array([d_valid, v_valid, v_error, v_source]).T, fmt='%0.6f, %0.1f, %0.1f, %s', delimiter=',')
np.savetxt(out_fn, np.array([d_valid, v_valid, v_error]).T, fmt='%0.6f, %0.1f, %0.1f', delimiter=',')
def sample_stack(ex, ey, geoid_offset=False, pad=3):
if ex > m.shape[2]-1 or ey > m.shape[1]-1:
print "Input coordinates are outside stack extent:"
print ex, ey
print m.shape
v = None
else:
print "Sampling with pad: %i" % pad
if pad == 0:
v = m[:,ey,ex]
else:
window_x = np.around(np.clip([ex-pad, ex+pad+1], 0, m.shape[2]-1)).astype(int)
window_y = np.around(np.clip([ey-pad, ey+pad+1], 0, m.shape[1]-1)).astype(int)
print window_x
print window_y
v = m[:,window_y[0]:window_y[1],window_x[0]:window_x[1]].reshape(m.shape[0], np.ptp(window_x)*np.ptp(window_y))
#v = v.mean(axis=1)
v = np.ma.median(v, axis=1)
if v.count() == 0:
print "No valid values"
else:
mx, my = geolib.pixelToMap(ex, ey, gt)
print ex, ey, mx, my
print "Count: %i" % v.count()
#Hack to get elevations relative to geoid
#Note: this can be added multiple times if clicked quickly
if geoid_offset:
#geoid_offset = geolib.sps2geoid(mx, my, 0.0)[2]
geoid_offset = geolib.nps2geoid(mx, my, 0.0)[2]
print "Removing geoid offset: %0.1f" % geoid_offset
v += geoid_offset
#Should filter here
#RS1 has some values that are many 1000s of m/yr below neighbors
if filter_outliers:
if True:
med = malib.fast_median(v)
mad = malib.mad(v)
min_v = med - mad*4
f_idx = (v < min_v).filled(False)
if np.any(f_idx):
print med, mad
print "Outliers removed by absolute filter: (val < %0.1f)" % min_v
print timelib.o2dt(d[f_idx])
print v[f_idx]
v[f_idx] = np.ma.masked
if True:
v_idx = (~np.ma.getmaskarray(v)).nonzero()[0]
#This tries to maintain fixed window in time
f = filtlib.rolling_fltr(v, size=7)
#This uses fixed number of neighbors
f = filtlib.rolling_fltr(v[v_idx], size=7)
#f_diff = np.abs(f - v)
#Note: the issue is usually that the velocity values are too low
#f_diff = f - v
f_diff = f - v[v_idx]
diff_thresh = 2000
#f_idx = (f_diff > diff_thresh).filled(False)
#f_idx = (f_diff < diff_thresh).filled(False)
f_idx = np.zeros_like(v.data).astype(bool)
f_idx[v_idx] = (f_diff > diff_thresh)
if np.any(f_idx):
print "Outliers removed by rolling median filter: (val < %0.1f)" % diff_thresh
print timelib.o2dt(d[f_idx])
print v[f_idx]
v[f_idx] = np.ma.masked
return v