def test_get_reds(self):
antpos = np.array([[0.,0,0],[1,0,0],[2,0,0],[3,0,0]])
reds = [[(0,1),(1,2),(2,3)],[(0,2),(1,3)]]
i = Oi.RedundantInfo()
i.init_from_reds(reds,antpos)
reds2 = i.get_reds()
self.assertEqual(reds, reds2)
def test_tofromnpz(self):
i1 = Oi.RedundantInfoLegacy()
i1.fromfile_txt(redinfo_psa32)
i2 = Oi.RedundantInfo()
i1.to_npz(tmpnpz)
i2.from_npz(tmpnpz)
self.assertTrue(i1.get_reds() == i2.get_reds())
def test_basic_getsetitem(self):
i = Oi.RedundantInfo()
i.nAntenna = 32
self.assertEqual(i.nAntenna, 32)
self.assertEqual(i['nAntenna'], 32)
i['nAntenna'] = 64
self.assertEqual(i.nAntenna, 64)
self.assertEqual(i['nAntenna'], 64)
def test_tofrom_reds(self):
i1 = Oi.RedundantInfoLegacy()
i1.fromfile_txt(redinfo_psa32)
reds = i1.get_reds()
antpos = i1.get_antpos()
i2 = Oi.RedundantInfo()
i2.init_from_reds(reds, antpos)
self.assertTrue(np.all(i1.antloc == i2.antloc))
self.assertTrue(np.all(i1.bl2d[i1.ublindex] == i2.bl2d[i2.ublindex]))
def test_large_info_IO(self):
calibrator = Oc.RedundantCalibrator(150)
calibrator.compute_redundantinfo()
calibrator.write_redundantinfo(infotestpath, verbose=VERBOSE)
info2 = Oi.RedundantInfo(filename=infotestpath)
self.assertEqual(calibrator.Info.nAntenna, info2.nAntenna)
self.assertEqual(calibrator.Info.nBaseline, info2.nBaseline)
self.assertEqual(calibrator.Info.get_reds(), info2.get_reds())
os.remove(infotestpath)
def test_init_from_reds(self):
antpos = np.array([[0.,0,0],[1,0,0],[2,0,0],[3,0,0]])
reds = [[(0,1),(1,2),(2,3)],[(0,2),(1,3)]]
i = Oi.RedundantInfo()
i.init_from_reds(reds,antpos)
self.assertEqual(i.nAntenna,4)
self.assertTrue(np.all(i.antloc == antpos))
self.assertEqual(i.nBaseline,5)
self.assertEqual(i.ublcount[0],3)
self.assertEqual(i.ublcount[1],2)
self.assertTrue(np.all(i.ublindex == np.arange(5,dtype=np.int32)))
self.assertTrue(np.all(i.ubl[0] == np.array([1.,0,0],dtype=np.float32)))
self.assertTrue(np.all(i.ubl[1] == np.array([2.,0,0],dtype=np.float32)))
def test_order_data(self):
antpos = np.array([[0.,0,0],[1,0,0],[2,0,0],[3,0,0]])
reds = [[(0,1),(1,2),(2,3)],[(0,2),(1,3)]]
i = Oi.RedundantInfo()
i.init_from_reds(reds,antpos)
dd = {
(0,1):np.array([[0,1j]]),
(1,2):np.array([[0,1j]]),
(2,3):np.array([[0,1j]]),
(2,0):np.array([[0,1j]]),
(1,3):np.array([[0,1j]]),
}
d = i.order_data(dd)
self.assertTrue(np.all(d[...,0] == np.array([[0,1j]])))
self.assertTrue(np.all(d[...,1] == np.array([[0,1j]])))
self.assertTrue(np.all(d[...,2] == np.array([[0,1j]])))
self.assertTrue(np.all(d[...,3] == np.array([[0,1j]]).conj()))
self.assertTrue(np.all(d[...,4] == np.array([[0,1j]])))
# # plt.scatter(uniqueBaselines[:,0]/1.0/Separation, uniqueBaselines[:,1]/1.0/Separation,c=np.minimum(redundancy,100000),s=40)
# plt.scatter(uniqueBaselines[:,0]/1.0/1.5, uniqueBaselines[:,1]/1.0/1.5,s=40,c=np.log10(np.minimum(redundancy,40)))
# plt.xlim(-350,350); plt.ylim(-350,350)
# # plt.colorbar()
# plt.title('Baseline Redundancy')
# #datacursor(display='single',formatter="x={x:.4f}\ny={y:.4f}".format)
# #plt.axis('equal')
# return np.asarray(positions), np.asarray(positions[0:nCore])
# if __name__ == "__main__":
# positions,corePos = HexArray(hexNum = 11, SplitCore = True, SplitCoreOutriggers = True, CutDownTo = None, SpreadOut128 = False)
if True:
import omnical.info as oi
import omnical.arrayinfo as oai
redundantInfo = oi.RedundantInfo()
positions = np.asarray(positions)
reds = oai.compute_reds(positions)
redundantInfo.init_from_reds(reds, positions)
AtransA = redundantInfo.At.dot(redundantInfo.At.T).toarray()
BtransB = redundantInfo.Bt.dot(redundantInfo.Bt.T).toarray()
gainVariances = np.diag(
np.linalg.pinv(AtransA)[0:len(positions), 0:len(positions)])
print "Gain and phase modes that can't be Omnicaled:"
print[
len(XtransX) - np.linalg.matrix_rank(XtransX, tol=1e-10)
for XtransX in [AtransA, BtransB]
]
plt.figure(3)
plt.clf()
plt.xlim(-300, 300)
plt.ylim(-300, 300)
plt.savefig(prefix + 'base.png', dpi=300)
#%%
else:
plt.figure(1, figsize=(8, 8))
plt.clf()
plt.scatter(np.asarray(positions)[:, 0], np.asarray(positions)[:, 1])
plt.xlabel('Position (m)')
plt.ylabel('Position (m)')
plt.axis('square')
if False:
import omnical.info as oi
import omnical.arrayinfo as oai
redundantInfo = oi.RedundantInfo()
positions = np.asarray(positions)
reds = oai.compute_reds(positions)
redundantInfo.init_from_reds(reds, positions)
AtransA = redundantInfo.At.dot(redundantInfo.At.T).toarray()
BtransB = redundantInfo.Bt.dot(redundantInfo.Bt.T).toarray()
gainVariances = np.diag(
np.linalg.pinv(AtransA)[0:len(positions), 0:len(positions)])
print "Gain and phase modes that can't be Omnicaled:"
print[
len(XtransX) - np.linalg.matrix_rank(XtransX, tol=1e-10)
for XtransX in [AtransA, BtransB]
]
#%%
plt.figure(3)
