def test_redcal_xtalk(self):
antpos = np.array([[0., 0, 0], [1, 0, 0], [2, 0, 0], [3, 0, 0]])
d = {
(1, 2): np.array([[1.]], dtype=np.complex64),
(2, 3): np.array([[1. + 1j]], dtype=np.complex64)
}
x = {
(1, 2): np.array([[0.]], dtype=np.complex64),
(2, 3): np.array([[0. + 1j]], dtype=np.complex64)
}
reds = [[(1, 2), (2, 3)]]
info = Oc.RedundantInfo()
info.init_from_reds(reds, antpos)
m, g, v = Oc.redcal(d, info, xtalk=x, uselogcal=False)
self.assertEqual(g[1][0, 0], 1.)
self.assertEqual(g[2][0, 0], 1.)
self.assertEqual(g[3][0, 0], 1.)
#2D array testing
d = {
(1, 2):
np.array([[1., 2], [3., 4]], dtype=np.complex64),
(2, 3):
np.array([[1. + 1j, 2 + 2j], [3 + 3j, 4 + 4j]], dtype=np.complex64)
}
x = {
(1, 2):
np.array([[0., 2], [2., 3]], dtype=np.complex64),
(2, 3):
np.array([[0. + 1j, 1 + 2j], [2 + 3j, 3 + 4j]], dtype=np.complex64)
}
m, g, v = Oc.redcal(d, info, xtalk=x, uselogcal=False)
self.assertEqual(g[1][0, 0], 1.)
self.assertEqual(g[2][0, 0], 1.)
self.assertEqual(g[3][0, 0], 1.)
self.assertEqual(m['res'][(2, 3)][0][0], 0.)
def test_redcal(self):
#check that logcal give 0 chi2 for all 20 testinfos
for index in xrange(20):
arrayinfopath = os.path.dirname(
os.path.realpath(__file__)) + '/testinfo/test' + str(
index + 1) + '_array_info.txt'
c = Oc.RedundantCalibrator(56)
c.compute_redundantinfo(arrayinfopath, tol=.1)
info = c.Info
npz = np.load(testdata % index)
bls = [tuple(bl) for bl in npz['bls']]
dd = dict(zip(bls, npz['vis']))
m, g, v = Oc.redcal(dd,
info,
removedegen=True,
maxiter=50,
stepsize=.2,
computeUBLFit=True,
conv=1e-5,
uselogcal=True)
calparpath = os.path.dirname(os.path.realpath(
__file__)) + '/testinfo/test' + str(index + 1) + '_calpar.txt'
with open(calparpath) as f:
rawinfo = [[float(x) for x in line.split()] for line in f]
temp = np.array(rawinfo[:-1])
correctcalpar = (np.array(temp[:, 0]) +
1.0j * np.array(temp[:, 1]))
i = g.keys()[0]
scalar = correctcalpar[i].real / g[i].real
for i in xrange(56):
if not g.has_key(i): continue
self.assertAlmostEqual(
np.abs(correctcalpar[i] - g[i] * scalar), 0, 4)
def test_redcal_xtalk(self):
antpos = np.array([[0.,0,0],[1,0,0],[2,0,0],[3,0,0]])
d = {(1,2): np.array([[1.]], dtype=np.complex64), (2,3): np.array([[1.+1j]], dtype=np.complex64)}
x = {(1,2): np.array([[0.]], dtype=np.complex64), (2,3): np.array([[0.+1j]], dtype=np.complex64)}
reds = [[(1,2),(2,3)]]
info = Oc.RedundantInfo(); info.init_from_reds(reds, antpos)
m,g,v = Oc.redcal(d, info, xtalk=x, uselogcal=False)
self.assertEqual(g[1][0,0], 1.)
self.assertEqual(g[2][0,0], 1.)
self.assertEqual(g[3][0,0], 1.)
#2D array testing
d = {(1,2): np.array([[1.,2],[3.,4]],dtype=np.complex64), (2,3): np.array([[1.+1j,2+2j],[3+3j,4+4j]],dtype=np.complex64)}
x = {(1,2): np.array([[0.,2],[2.,3]],dtype=np.complex64), (2,3): np.array([[0.+1j,1+2j],[2+3j,3+4j]],dtype=np.complex64)}
m,g,v = Oc.redcal(d, info, xtalk=x, uselogcal=False)
self.assertEqual(g[1][0,0], 1.)
self.assertEqual(g[2][0,0], 1.)
self.assertEqual(g[3][0,0], 1.)
self.assertEqual(m['res'][(2,3)][0][0],0.)
def test_redcal_xtalk(self):
antpos = np.array([[0.,0,0],[1,0,0],[2,0,0],[3,0,0]])
d = {(1,2): np.array([[1.]], dtype=np.complex64), (2,3): np.array([[1.+1j]], dtype=np.complex64)}
x = {(1,2): np.array([[0.]], dtype=np.complex64), (2,3): np.array([[0.+1j]], dtype=np.complex64)}
reds = [[(1,2),(2,3)]]
info = Oi.RedundantInfo(); info.init_from_reds(reds, antpos)
m,g,v = Oc.redcal(d, info, xtalk=x, uselogcal=False)
self.assertEqual(g[1][0,0], 1.)
self.assertEqual(g[2][0,0], 1.)
self.assertEqual(g[3][0,0], 1.)
def test_redcal(self):
#check that logcal give 0 chi2 for all 20 testinfos
for index in xrange(20):
arrayinfopath = os.path.dirname(os.path.realpath(__file__)) + '/testinfo/test'+str(index+1)+'_array_info.txt'
c = Oc.RedundantCalibrator(56)
c.compute_redundantinfo(arrayinfopath, tol=.1)
info = c.Info
npz = np.load(testdata % index)
bls = [tuple(bl) for bl in npz['bls']]
dd = dict(zip(bls, npz['vis']))
m,g,v = Oc.redcal(dd, info, removedegen=True,maxiter=50,stepsize=.2,computeUBLFit=True,conv=1e-5,uselogcal=True)
calparpath = os.path.dirname(os.path.realpath(__file__)) + '/testinfo/test'+str(index+1)+'_calpar.txt'
with open(calparpath) as f:
rawinfo = [[float(x) for x in line.split()] for line in f]
temp = np.array(rawinfo[:-1])
correctcalpar = (np.array(temp[:,0]) + 1.0j*np.array(temp[:,1]))
i = g.keys()[0]
scalar = correctcalpar[i].real / g[i].real
for i in xrange(56):
if not g.has_key(i): continue
self.assertAlmostEqual(np.abs(correctcalpar[i] - g[i] * scalar), 0, 4)
