def gp_adjust(lon, lat, gp_x, gp_y):
x,y,z = sphere.spherical_to_cartesian((lon, lat))
az_x, az_y = sphere.polar_to_az(lon, lat)
xc = gp_x.predict([x,y,z])
yc = gp_y.predict([x,y,z])
corr_touch_lon, corr_touch_lat = sphere.az_to_polar(az_x+xc, az_y+yc)
return corr_touch_lon, corr_touch_lat
def gp_predict(gp_x, gp_y, az_x, az_y):
"""Predict the corrected lon, lat of the position given
by az_x, az_y (touch point in azimuthal co-ordinates) using
the Gaussian Process correction method."""
tinput = [az_x, az_y]
xc = gp_x.predict(tinput)
yc = gp_y.predict(tinput)
corr_touch_lon, corr_touch_lat = sphere.az_to_polar(az_x+xc, az_y+yc)
return corr_touch_lon, corr_touch_lat
def gp_offset_shading(calibration, gp_x, gp_y, best_s):
"""Plot the GP offset model for x and y, after removing the constant correction"""
proj = proj_wrapper(pyproj.Proj("+proj=robin"))
plt.figure(figsize=(9,4))
plot_reference_grid(pyproj.Proj("+proj=robin"), labels=False)
xs, ys, zs = [], [], []
us, vs = [], []
test_pts = spiral_layout(2000)
for lon, lat in test_pts:
# gp corrected lon, lat
az_x,az_y = sphere.polar_to_az(lon, lat)
tinput = [az_x, az_y]
xc = gp_x.predict(tinput)
yc = gp_y.predict(tinput)
lonc, latc = sphere.az_to_polar(az_x+xc, az_y+yc)
# constant corrected lon, lat
lons, lats = polar_adjust(lon, lat, best_s)
# compute offset from constant corrected
px,py = proj(lon, lat)
px_c,py_c = proj(lonc, latc)
px_s,py_s = proj(lons, lats)
d = (px_s-px_c)**2 + (py_s-py_c)**2
xs.append(px)
ys.append(py)
if np.sqrt(d)<6e7:
us.append(px_s-px_c)
vs.append(py_s-py_c)
else:
us.append(0)
vs.append(0)
zs.append(np.sqrt(d))
xi = np.linspace(np.min(xs), np.max(xs), 500)
yi = np.linspace(np.min(ys), np.max(ys), 500)
#zi = matplotlib.mlab.griddata(xs, ys, zs, xi, yi, interp='linear')
#levels = np.linspace(np.min(zi), np.max(zi), 30)
#plt.contourf(xi, yi, zi, cmap="bone", levels=levels)
plt.quiver(xs,ys,us,vs,scale=3e7,width=5e-4)
