def map_S(S):
# Make a map
rast = spherical.mesh_to_map(X, S.value, 501)
import pylab as pl
pl.clf()
pl.imshow(rast, interpolation='nearest')
pl.colorbar()
def map_S(S):
# Make a map
rast = spherical.mesh_to_map(X,S.value,501)
import pylab as pl
pl.clf()
pl.imshow(rast,interpolation='nearest')
pl.colorbar()
for v in scalar_variables:
pm.Matplot.plot(v)
# Make mean and variance maps
burn = 0
thin = 1
resolution = 501
m1 = np.zeros((resolution/2+1,resolution))
m2 = np.zeros((resolution/2+1,resolution))
nmaps = 0
for i in xrange(burn, M._cur_trace_index, thin):
M.remember(0,i)
# Note, this rasterization procedure is not great. It's using SciPy, which is assuming that the data are on the plane. It would be better to either:
# - Take the finite element representation literally, and evaluate it on the grid
# - Sample to the interior conditional on the finite element representation.
gridded_p = spherical.mesh_to_map(X, M.p.value, resolution)
m1 += gridded_p
m2 += gridded_p**2
nmaps += 1
mean_map = np.ma.masked_array(m1 / nmaps, mask=gridded_p==np.nan)
variance_map = np.ma.masked_array(m2 / nmaps - mean_map**2, mask=gridded_p==np.nan)
pl.figure(len(scalar_variables)+1)
pl.imshow(mean_map[::-1,:], interpolation='nearest',vmin=0,vmax=1)
pl.colorbar()
pl.title('Mean map')
pl.figure(len(scalar_variables)+2)
pl.imshow(variance_map[::-1,:], interpolation='nearest',vmin=0,vmax=1)
pl.colorbar()
