def check_converge(month, tol=10, add_neighbors=True, log=None):
""" check for convergence, ROI that have been updated
month: int or string
if int, intrepret as a month, else a folder
"""
from pointlike import IntVector
from skymaps import Band
outdir = 'month%02d' % month if type(month) == types.IntType else month
#print '%s:' %outdir
r = roirec(outdir)
if r is None: return
diff = r.loglike - r.prevlike
nisnan = sum(np.isnan(diff))
if nisnan > 0:
print 'warning: %d NaN values in likelihoods: ignoring them' % nisnan
diff[np.isnan(diff)] = 0
dmin, dmax = diff.min(), diff.max()
rmin, rmax = list(diff).index(dmin), list(diff).index(dmax)
changed = set(np.arange(1728)[np.abs(diff) > tol])
print >> log, '\tpass %d: %d changed > %d, min, max: %d(#%d) %d(#%d)' % (
max(r.niter), len(changed), tol, dmin, rmin, dmax, rmax),
if not add_neighbors: return list(changed)
nbrs = set()
b12 = Band(12)
for x in changed:
v = IntVector()
b12.findNeighbors(int(x), v) # int is tricky
for n in v:
nbrs.add(n)
q = list(changed.union(nbrs))
print >> log, ' (total %d)' % len(q)
if log is not None: log.flush()
return q
def smooth(self, a=0.6):
""" simple-minded smooth using nearest neighbors
"""
newvec = [0]*len(self.vec)
band = Band(self.nside)
iv = IntVector() # needed for findNeighbors: first 4 share sides
b=0.25*(1-a)
for i in range(len(self.vec)):
band.findNeighbors(i,iv)
newvec[i] = a*self.vec[i] + b*sum([self.vec[j] for j in iv[:4]])
self.vec= newvec
