def test_volume_lambda(self):
'''
Check the dependence of upper limits on volume and lambda.
'''
# volumes to test: these range over 5 orders of magnitude
volumes = numpy.logspace(-3,2,50)
for vol in volumes:
# take a large, fixed set of mu samples to bracket the 1/vol values
mu = numpy.logspace(-5,5,1e5)
# lambda = 0
likely = upper_limit_utils.margLikelihood([vol], [0], mu)
post = likely # uniform prior; NB compute_upper_limit works for unnormalized posteriors
muhi = upper_limit_utils.compute_upper_limit(mu, post, alpha = 0.90)
self.assertTrue( 2.30/vol < muhi < 2.31/vol )
# lambda = 1
likely = upper_limit_utils.margLikelihood([vol], [1], mu)
post = likely # uniform prior
muhi = upper_limit_utils.compute_upper_limit(mu, post, alpha = 0.90)
self.assertTrue( 3.27/vol < muhi < 3.28/vol )
# lambda = infinity
likely = upper_limit_utils.margLikelihood([vol], [1e6], mu)
post = likely # uniform prior
muhi = upper_limit_utils.compute_upper_limit(mu, post, alpha = 0.90)
self.assertTrue( 3.885/vol < muhi < 3.895/vol )
def test_logspacing_rate_upper_limit(self):
'''
Give the upper_limit function some known distributions with known 95% upper limits.
'''
# Exponential
mu = numpy.logspace(-5,2,1e6)
post = numpy.exp(-mu)
muhi = upper_limit_utils.compute_upper_limit(mu, post, alpha = 0.95)
self.assertTrue( abs( muhi - numpy.log(20) ) < 0.0001 ) # get upper limit to 4 decimal places
# Gaussian over positive mu
post = numpy.exp(-(mu**2)/2)
muhi = upper_limit_utils.compute_upper_limit(mu, post, alpha = 0.95)
self.assertTrue( abs(muhi - 1.95996) < 0.0001 ) # get upper limit to 4 decimal places
def test_compute_many_posterior(self):
# for 0 lambda's, volumes add
mu = numpy.linspace(0,100,1e4)
post1 = upper_limit_utils.margLikelihood([5,10,4,6],[0,0,0,0],mu)
post2 = upper_limit_utils.margLikelihood([15,10],[0,0],mu)
post3 = upper_limit_utils.margLikelihood([25],[0],mu)
mu_90_1 = upper_limit_utils.compute_upper_limit(mu,post1/post1.sum(),0.90)
mu_90_2 = upper_limit_utils.compute_upper_limit(mu,post2/post2.sum(),0.90)
mu_90_3 = upper_limit_utils.compute_upper_limit(mu,post3/post3.sum(),0.90)
self.assertTrue( (mu_90_2-mu_90_1) < 0.05 )
self.assertTrue( (mu_90_3-mu_90_1) < 0.05 )
self.assertTrue( (mu_90_3-mu_90_2) < 0.05 )
def test_zero_volume_search(self):
'''
Check that running a search with zero volume has
no effect on upper limits.
'''
# no prior specified, unit volume
mu = numpy.linspace(0,100,1e4)
likely = upper_limit_utils.margLikelihood([1], [0], mu)
post = likely/likely.sum() #uniform prior
muhi_np = upper_limit_utils.compute_upper_limit(mu, post, alpha = 0.90)
# posterior for prior, zero volume
likely = upper_limit_utils.margLikelihood([0],[0], mu)
post *= likely #uniform post for prior
post /= post.sum()
muhi_zv = upper_limit_utils.compute_upper_limit(mu, post, alpha = 0.90)
self.assertTrue( abs(muhi_zv - muhi_np) < 0.01 )
def test_zero_padded_uniform(self):
# Uniform posterior
mumax = 15
mu = numpy.linspace(0,2*mumax,2*1e6)
post = numpy.concatenate((numpy.ones(len(mu)/2),numpy.zeros(len(mu)/2)))
a = 1
muhi = upper_limit_utils.compute_upper_limit(mu, post, alpha = 1)
self.assertTrue( a*mumax - 0.0001 < muhi < a*mumax + 0.0001) # get upper limit to 4 sig figs
def test_uniform_upper_limit(self):
# Uniform posterior
mumax = 15
mu = numpy.linspace(0,mumax,1e6)
post = numpy.ones(len(mu))
alphas = numpy.arange(0.1,1,0.1)
for a in alphas:
muhi = upper_limit_utils.compute_upper_limit(mu, post, alpha = a)
self.assertTrue( a*mumax - 0.0001 < muhi < a*mumax + 0.0001) # get upper limit to 4 decimal places
def test_gaussian_upper_limit(self):
'''
Give the upper_limit function some known distributions with known 95% upper limits.
'''
# Gaussian over *positive* mu: 95% limit is equal to sqrt(2)*erfc^-1(0.05)
mu = numpy.linspace(0,5,1e6)
post = numpy.exp(-(mu**2)/2)
muhi = upper_limit_utils.compute_upper_limit(mu, post, alpha = 0.95)
self.assertTrue( abs(muhi - 1.95996) < 0.0001 ) # get upper limit to 4 decimal places
def test_volume_additivity(self):
'''
For a series of independent searches with 0 lambda,
the search volumes add. Check that upper limits computed
by summing volumes is same as upper limit obtained by
iteratively applying the individual searches.
'''
# volume additivity check
mu = numpy.linspace(0,100,1e4)
likely = upper_limit_utils.margLikelihood([1], [0], mu)
post = likely/likely.sum() #uniform prior
post *= upper_limit_utils.margLikelihood([9], [0], mu) # use post for prior
post /= post.sum()
muhi_1p9 = upper_limit_utils.compute_upper_limit(mu, post, alpha = 0.90)
likely = upper_limit_utils.margLikelihood([10], [0], mu)
post = likely/likely.sum() #uniform prior
muhi_10 = upper_limit_utils.compute_upper_limit(mu, post, alpha = 0.90)
self.assertTrue( abs( muhi_1p9 - muhi_10 ) < 0.01 )
def test_confidence_interval(self):
'''
The confidence interval function computes a minimal width interval.
For a symmetric distribution, this interval is the same as computing
upper and lower limits at half-confidences. For a monotonically
decreasing distribution, this interval is the same as the upper limit
at the same confidence level.
'''
exes = numpy.arange(-10,10,0.01)
post = numpy.exp(-exes**2/2)
lo, hi = upper_limit_utils.confidence_interval_min_width(exes, post, 0.9)
lo2 = upper_limit_utils.compute_lower_limit(exes, post, 0.95)
hi2 = upper_limit_utils.compute_upper_limit(exes, post, 0.95)
self.assertTrue( abs(lo - lo2) < 0.001 )
self.assertTrue( abs(hi - hi2) < 0.001 )
def test_exponential_upper_limit(self):
# Exponential
mu = numpy.linspace(0,15,1e6)
post = numpy.exp(-mu)
muhi = upper_limit_utils.compute_upper_limit(mu, post, alpha = 0.95)
self.assertTrue( abs( muhi - numpy.log(20) ) < 0.0001 ) # get upper limit to 4 decimal places
