def test_find_neighbors_adaptive_res():
n_neighbors = 36
n_disp = 5
processes = 4
bounds = [60., 80., -5., 5.]
import glob
fnames = glob.glob('/n/fink1/ggreen/bayestar/output/l70/l70.*.h5')
los_coll = maptools.los_collection(fnames, bounds=bounds,
processes=processes)
nside, pix_idx = los_coll.nside, los_coll.pix_idx
print 'Finding neighbors ...'
neighbor_idx, neighbor_dist = find_neighbors(nside, pix_idx,
n_neighbors)
print 'Done.'
# Highlight a couple of nearest-neighbor sections
pix_val = np.zeros(pix_idx.size)
l = 0.025 * np.pi / 180.
print l
for k in xrange(n_disp):
idx = np.random.randint(pix_idx.size)
pix_val[idx] = 2
pix_val[neighbor_idx[idx, :]] = 1
d = neighbor_dist[idx, :]
print 'Center pixel: %d' % idx
print d
print np.exp(-(d/l)**2.)
print ''
nside_max, pix_idx_exp, pix_val_exp = maptools.reduce_to_single_res(pix_idx,
nside,
pix_val)
size = (2000, 2000)
img, bounds, xy_bounds = hputils.rasterize_map(pix_idx_exp, pix_val_exp,
nside_max, size)
# Plot nearest neighbors
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.imshow(img, extent=bounds, origin='lower', aspect='auto',
interpolation='nearest')
plt.show()
def test_plot_resampled_map():
infname = '/n/home09/ggreen/projects/bayestar/output/resample_test_4.h5'
plot_fname = '/nfs_pan1/www/ggreen/maps/l70/resampled_4'
size = (2000, 2000)
# Load in chain
f = h5py.File(infname, 'r')
loc = f['/location'][:]
chain = f['/Delta_EBV'][:,:,:] # (sample, pixel, slice)
DM_min = f['/Delta_EBV'].attrs['DM_min']
DM_max = f['/Delta_EBV'].attrs['DM_max']
f.close()
nside = loc[:]['nside']
pix_idx = loc[:]['pix_idx']
# Rasterize each sample and plot
import matplotlib.pyplot as plt
for n,sample in enumerate(chain):
print 'Plotting sample %d ...' % n
pix_val = np.sum(sample[:, :12], axis=1)
nside_max, pix_idx_exp, pix_val_exp = maptools.reduce_to_single_res(pix_idx,
nside,
pix_val)
img, bounds, xy_bounds = hputils.rasterize_map(pix_idx_exp, pix_val_exp,
nside_max, size)
fig = plt.figure()
ax = fig.add_subplot(1,1,1)
ax.imshow(img.T, extent=bounds, origin='lower', aspect='auto',
interpolation='nearest')
fname = '%s.%.5d.png' % (plot_fname, n)
fig.savefig(fname, dpi=300)
plt.close(fig)
del img
print 'Done.'