def accumulate_power_results(N=100,M=100):
rr = []
for i in range(M):
if np.random.rand()>0.5:
f, p = np.random.rand(2)
pulsed = True
else:
f, p = 0., 0.
pulsed = False
events = bayespf.generate(f, p, N)
phases, fractions, r, P = bayespf.infer(events, n_phase=100, n_frac=101)
rr.append((P,pulsed))
return rr
def test_credible_interval():
M = 200
N = 30
ps = []
for i in range(M):
f, p = np.random.rand(2)
events = bayespf.generate(f, p, N)
phases, fractions, r, P = bayespf.infer(events)
frac_pdf = np.average(r,axis=0)
fi = np.searchsorted(fractions, f/2)
p = np.sum(frac_pdf[:fi])/np.sum(frac_pdf)
p = 2*min(p, 1-p)
ps.append(p)
assert scipy.stats.kstest(ps,lambda x: x)[1] > 0.01
def test_pulsed_probability():
np.random.seed(0)
p_accept = 0.75
M = 50
N = 30
accepted = 0
correct = 0
total = 0
while accepted<M:
if np.random.rand()>0.5:
f, p = np.random.rand(2)
pulsed = True
else:
f, p = 0., 0.
pulsed = False
events = bayespf.generate(f, p, N)
phases, fractions, r, P = bayespf.infer(events, n_phase=100, n_frac=101)
if P>=p_accept:
accepted += 1
if pulsed:
correct += 1
if P<1-p_accept:
accepted += 1
if not pulsed:
correct += 1
total += 1
if accepted!=0:
fac = correct/float(accepted)
else:
fac = "NaN"
print "P = %f\tfraction correct\t%s\t(fraction %g accepted)" % (P, fac, accepted/float(total))
print "cdf value %f" % (scipy.stats.binom(M,p_accept).cdf(correct),)
assert 0.01<scipy.stats.binom(M,p_accept).cdf(correct)