def printsefdinfo (self):
raras = self.raras
solution = self.solution
saps = self.saps
n = len (saps)
maxant = util.apAnt (saps[-1]) + 1
msefds = np.empty (n)
pstds = np.empty (n)
antcounts = np.zeros (maxant)
antprods = np.ones (maxant)
for i in xrange (n):
info = raras[i]
# TODO later: we're ignoring the RARA weighting
# sqrt (2 * 102.4 khz * 10 s)
sefd = 1431 * solution[i] * info[1]
msefds[i] = sefd.mean ()
pstds[i] = 100. * sefd.std () / msefds[i]
ant = util.apAnt (saps[i])
antcounts[ant] += 1
antprods[ant] *= msefds[i]
w = np.where (antcounts == 2)[0]
if w.size == 0:
bestant = -1
else:
bestant = w[np.argmin (antprods[w])]
mmarks = [' | '] * n
smarks = [' | '] * n
sm = np.argsort (msefds)
ss = np.argsort (pstds)
for i in xrange (5):
mmarks[sm[i]] = ' ' * i + '*' + ' ' * (4 - i) + '| '
mmarks[sm[-1 - i]] = ' |' + ' ' * (4 - i) + '*' + ' ' * i
smarks[ss[i]] = ' ' * i + '*' + ' ' * (4 - i) + '| '
smarks[ss[-1 - i]] = ' |' + ' ' * (4 - i) + '*' + ' ' * i
print 'instrument:', self.instr
print
print '%4s %7s %5s %13s %13s' % ('AP', 'SEFD', 'Var.', 'SEFD Rank',
'Var. Rank')
for i in xrange (n):
print '%4s %7.0f %4.1f%% [%s] [%s]' % \
(util.fmtAP (saps[i]), msefds[i], pstds[i], mmarks[i], smarks[i])
print
print 'Median (mean SEFD):', np.median (msefds)
print ' Equiv. TSys:', np.median (msefds) / 153.
if bestant < 0:
print 'No dual-pol antennas'
else:
print 'Best dual-pol antenna:', bestant
def printsefdinfo(self):
raras = self.raras
solution = self.solution
saps = self.saps
n = len(saps)
maxant = util.apAnt(saps[-1]) + 1
msefds = np.empty(n)
pstds = np.empty(n)
antcounts = np.zeros(maxant)
antprods = np.ones(maxant)
for i in xrange(n):
info = raras[i]
# TODO later: we're ignoring the RARA weighting
# sqrt (2 * 102.4 khz * 10 s)
sefd = 1431 * solution[i] * info[1]
msefds[i] = sefd.mean()
pstds[i] = 100. * sefd.std() / msefds[i]
ant = util.apAnt(saps[i])
antcounts[ant] += 1
antprods[ant] *= msefds[i]
w = np.where(antcounts == 2)[0]
if w.size == 0:
bestant = -1
else:
bestant = w[np.argmin(antprods[w])]
mmarks = [' | '] * n
smarks = [' | '] * n
sm = np.argsort(msefds)
ss = np.argsort(pstds)
for i in xrange(5):
mmarks[sm[i]] = ' ' * i + '*' + ' ' * (4 - i) + '| '
mmarks[sm[-1 - i]] = ' |' + ' ' * (4 - i) + '*' + ' ' * i
smarks[ss[i]] = ' ' * i + '*' + ' ' * (4 - i) + '| '
smarks[ss[-1 - i]] = ' |' + ' ' * (4 - i) + '*' + ' ' * i
print 'instrument:', self.instr
print
print '%4s %7s %5s %13s %13s' % ('AP', 'SEFD', 'Var.', 'SEFD Rank',
'Var. Rank')
for i in xrange(n):
print '%4s %7.0f %4.1f%% [%s] [%s]' % \
(util.fmtAP (saps[i]), msefds[i], pstds[i], mmarks[i], smarks[i])
print
print 'Median (mean SEFD):', np.median(msefds)
print ' Equiv. TSys:', np.median(msefds) / 153.
if bestant < 0:
print 'No dual-pol antennas'
else:
print 'Best dual-pol antenna:', bestant
def record (self, state):
inp = state.inp
track = self.track
systemps = self.systemps
prefactor = self.prefactor
bps = self.bps
bpfactors = self.bpfactors
if track is None:
track = inp.makeVarTracker ().track ('systemp', 'sdf', 'inttime')
self.track = track
if track.updated ():
nants = inp.getVarInt ('nants')
systemps = self.systemps = inp.getVarFloat ('systemp', nants)
nspect = inp.getVarInt ('nspect')
sdf = inp.getVarDouble ('sdf', nspect)
if nspect > 1:
sdf = sdf[0]
inttime = inp.getVarFloat ('inttime')
prefactor = self.prefactor = 2 * inttime * sdf * 1e9
if bpfactors is None or bpfactors.size != state.nbp:
bpfactors = self.bpfactors = np.empty (state.nbp)
bps = self.bps = np.empty ((state.nbp, 2), dtype=np.int)
ap1, ap2 = mir2bp (inp, state.preamble)
if ap1 == ap2:
# Autocorrelation! Get the RARA (raw autocorrelation
# RMS amplitude)
w = np.where (state.flags)[0]
if w.size == 0:
self.raras.pop (ap1, 0)
else:
self.raras[ap1] = np.sqrt ((state.data.real[w]**2).mean ())
# Get the factor that will go in front of the RARAs for
# jyperk computations. Do this for autocorrs too because
# there's no reason not to.
if state.instr not in self.byinstr:
raise Exception ('need information for instrument ' +
state.instr)
byap, default = self.byinstr[state.instr]
cal1 = byap.get (ap1, default)
cal2 = byap.get (ap2, default)
ant1, ant2 = apAnt (ap1), apAnt (ap2)
tsys1, tsys2 = systemps[ant1 - 1], systemps[ant2 - 1]
bpindex = state.bpindex
bps[bpindex,0] = ap1
bps[bpindex,1] = ap2
bpfactors[bpindex] = prefactor * cal1 * cal2 / (tsys1 * tsys2)
def record(self, state):
inp = state.inp
track = self.track
systemps = self.systemps
prefactor = self.prefactor
bps = self.bps
bpfactors = self.bpfactors
if track is None:
track = inp.makeVarTracker().track('systemp', 'sdf', 'inttime')
self.track = track
if track.updated():
nants = inp.getVarInt('nants')
systemps = self.systemps = inp.getVarFloat('systemp', nants)
nspect = inp.getVarInt('nspect')
sdf = inp.getVarDouble('sdf', nspect)
if nspect > 1:
sdf = sdf[0]
inttime = inp.getVarFloat('inttime')
prefactor = self.prefactor = 2 * inttime * sdf * 1e9
if bpfactors is None or bpfactors.size != state.nbp:
bpfactors = self.bpfactors = np.empty(state.nbp)
bps = self.bps = np.empty((state.nbp, 2), dtype=np.int)
ap1, ap2 = mir2bp(inp, state.preamble)
if ap1 == ap2:
# Autocorrelation! Get the RARA (raw autocorrelation
# RMS amplitude)
w = np.where(state.flags)[0]
if w.size == 0:
self.raras.pop(ap1, 0)
else:
self.raras[ap1] = np.sqrt((state.data.real[w]**2).mean())
# Get the factor that will go in front of the RARAs for
# jyperk computations. Do this for autocorrs too because
# there's no reason not to.
if state.instr not in self.byinstr:
raise Exception('need information for instrument ' +
state.instr)
byap, default = self.byinstr[state.instr]
cal1 = byap.get(ap1, default)
cal2 = byap.get(ap2, default)
ant1, ant2 = apAnt(ap1), apAnt(ap2)
tsys1, tsys2 = systemps[ant1 - 1], systemps[ant2 - 1]
bpindex = state.bpindex
bps[bpindex, 0] = ap1
bps[bpindex, 1] = ap2
bpfactors[bpindex] = prefactor * cal1 * cal2 / (tsys1 * tsys2)