def test_subtract_noise(self):
Q = n.diag(n.arange(1,9))
N = n.identity(8)
a = n.arange(2,17,2)
n_vec = uf.rand_from_covar(N)
Qa = uf.vdot(Q,a)
y = Qa+n_vec
a,ahat,err = qgea.test_recover_alms(y,Q,N,a,num_remov=-4)
_,nhat,err = qgea.test_recover_alms(n_vec,Q,N,a,num_remov=-4)
ahat_better = ahat - nhat
W = qgea.window_fn_matrix(Q,N,num_remov=-4)
self.assertTrue(n.allclose(n.dot(W,a),ahat_better))
def test_recover_gs_ratio(self):
print "GS Ratio"
Q = n.diag(n.arange(1,9))
N = n.identity(8)*50
#N[0,0]=1
a = n.array([16,4,0,0,0,0,0,0])#n.arange(16,1,-2)
print 'a ',a
n_vec = uf.rand_from_covar(N)
Qa = uf.vdot(Q,a)
y = Qa+n_vec
self.assertTrue(y.shape==(8,))
a,ahat,err = qgea.test_recover_alms(y,Q,N,a,num_remov=0)
W = qgea.window_fn_matrix(Q,N,num_remov=0)
Wa = uf.vdot(W,a)
print 'Wa ',Wa
print 'ahat ',ahat
print Wa[0]/Wa[1]
print a[0]/a[1]
self.assertTrue(n.abs(Wa[0]/Wa[1]-a[0]/a[1])<0.1)
def test_recover_wind(self):
print "Window"
num=100
Q = n.diag(n.arange(1,9))
N = n.identity(8)
a = n.arange(2,17,2)
a1 = n.arange(2,17,2)
ahat_avg = 0
for ii in range(num):
n_vec = uf.rand_from_covar(N)
Qa = uf.vdot(Q,a)
y = Qa+n_vec
self.assertTrue(y.shape==(8,))
a,ahat,err = qgea.test_recover_alms(y,Q,N,a,num_remov=-4)
ahat_avg += ahat
self.assertTrue(n.all(y==n.dot(Q,a1)+n_vec))
ahat_avg = ahat_avg/num
W = qgea.window_fn_matrix(Q,N,num_remov=-4)
print uf.vdot(W,a)
print ahat_avg
self.assertTrue(n.all(n.abs(n.dot(W,a)-ahat_avg)<0.1))
def test_recover_gs(self):
print "GS"
num=500
Q = n.diag(n.arange(1,9))
N = n.identity(8)*50
N[0,0]=1
a = n.array([16,0,0,0,0,0,0,0])#n.arange(16,1,-2)
print 'a ',a
ahat_avg = 0
for ii in range(num):
n_vec = uf.rand_from_covar(N)
Qa = uf.vdot(Q,a)
y = Qa+n_vec
self.assertTrue(y.shape==(8,))
a,ahat,err = qgea.test_recover_alms(y,Q,N,a,num_remov=0)
ahat_avg += ahat
ahat_avg = ahat_avg/num
W = qgea.window_fn_matrix(Q,N,num_remov=0)
print uf.vdot(W,a)
print ahat_avg
self.assertTrue(n.abs(uf.vdot(W,a)[0]-ahat_avg[0])<0.1)