def tui_checkap(args):
import omega as om
if len(args) != 2:
util.die('usage: <datfile> <antpol>')
nc = NoiseCal()
nc.load(args[0])
ap = util.parseAP(args[1])
apidx = nc.saps.index(ap)
if apidx < 0:
util.die('no antpol %s in data file!', util.fmtAP(ap))
sqbps = nc.sqbps
vals = nc.bpdata[:, 1]
modelvals = nc.bpdata[:, 3] * nc.svals
resids = vals - modelvals
runcerts = np.sqrt(1. / nc.bpdata[:, 2] +
(nc.suncerts * nc.bpdata[:, 3])**2)
resids /= runcerts
w = np.where((sqbps[:, 0] == apidx) | (sqbps[:, 1] == apidx))
resids = resids[w]
n = resids.size
mn = resids.mean()
s = resids.std()
md = np.median(resids)
smadm = 1.4826 * np.median(np.abs(resids - md)) # see comment below
print ' Number of samples:', n
print ' Norm. mean residal:', mn
print ' Median:', md
print 'Norm. residual std. dev.:', s
print ' SMADM:', smadm
bins = 50
rng = -5, 5
p = om.quickHist(resids,
keyText='%s Residuals' % util.fmtAP(ap),
bins=bins,
range=rng)
x = np.linspace(-5, 5, 200)
area = 10. / bins * n
y = area / np.sqrt(2 * np.pi) * np.exp(-0.5 * x**2)
p.addXY(x, y, 'Ideal')
p.rebound(False, False)
p.show()
return 0
def tui_checkap (args):
import omega as om
if len (args) != 2:
util.die ('usage: <datfile> <antpol>')
nc = NoiseCal ()
nc.load (args[0])
ap = util.parseAP (args[1])
apidx = nc.saps.index (ap)
if apidx < 0:
util.die ('no antpol %s in data file!', util.fmtAP (ap))
sqbps = nc.sqbps
vals = nc.bpdata[:,1]
modelvals = nc.bpdata[:,3] * nc.svals
resids = vals - modelvals
runcerts = np.sqrt (1./nc.bpdata[:,2] + (nc.suncerts * nc.bpdata[:,3])**2)
resids /= runcerts
w = np.where ((sqbps[:,0] == apidx) | (sqbps[:,1] == apidx))
resids = resids[w]
n = resids.size
mn = resids.mean ()
s = resids.std ()
md = np.median (resids)
smadm = 1.4826 * np.median (np.abs (resids - md)) # see comment below
print ' Number of samples:', n
print ' Norm. mean residal:', mn
print ' Median:', md
print 'Norm. residual std. dev.:', s
print ' SMADM:', smadm
bins = 50
rng = -5, 5
p = om.quickHist (resids, keyText='%s Residuals' % util.fmtAP (ap),
bins=bins, range=rng)
x = np.linspace (-5, 5, 200)
area = 10. / bins * n
y = area / np.sqrt (2 * np.pi) * np.exp (-0.5 * x**2)
p.addXY (x, y, 'Ideal')
p.rebound (False, False)
p.show ()
return 0
def tui_checkabs (args):
import omega as om
if len (args) != 1:
util.die ('usage: <datfile>')
nc = NoiseCal ()
nc.load (args[0])
saps = nc.saps
sqbps = nc.sqbps
vals = nc.bpdata[:,1]
uncerts = nc.bpdata[:,2]**-0.5
modelvals = nc.bpdata[:,3] * nc.svals
muncerts = nc.suncerts * nc.bpdata[:,3]
pg = om.quickPager ([])
for idx in xrange (len (saps)):
w = np.where ((sqbps[:,0] == idx) | (sqbps[:,1] == idx))
p = om.quickXYErr (vals[w], modelvals[w], muncerts[w],
util.fmtAP (saps[idx]), lines=False)
p.setLabels ('Measured variance (Jy²)', 'Modeled variance (Jy²)')
pg.send (p)
pg.done ()
return 0
def tui_checkabs(args):
import omega as om
if len(args) != 1:
util.die('usage: <datfile>')
nc = NoiseCal()
nc.load(args[0])
saps = nc.saps
sqbps = nc.sqbps
vals = nc.bpdata[:, 1]
uncerts = nc.bpdata[:, 2]**-0.5
modelvals = nc.bpdata[:, 3] * nc.svals
muncerts = nc.suncerts * nc.bpdata[:, 3]
pg = om.quickPager([])
for idx in xrange(len(saps)):
w = np.where((sqbps[:, 0] == idx) | (sqbps[:, 1] == idx))
p = om.quickXYErr(vals[w],
modelvals[w],
muncerts[w],
util.fmtAP(saps[idx]),
lines=False)
p.setLabels('Measured variance (Jy²)', 'Modeled variance (Jy²)')
pg.send(p)
pg.done()
return 0
def export(self, stream):
solution = self.solution
saps = self.saps
for i in xrange(len(saps)):
print >> stream, self.instr, util.fmtAP(
saps[i]), '%.5e' % solution[i]
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 _format(aps):
if isinstance(aps, int): aps = (aps, )
return '-'.join(util.fmtAP(x) for x in aps)
def tui_checkfit (args):
import omega as om
if len (args) != 1:
util.die ('usage: <datfile>')
nc = NoiseCal ()
nc.load (args[0])
vals = nc.bpdata[:,1]
modelvals = nc.bpdata[:,3] * nc.svals
resids = vals - modelvals
runcerts = np.sqrt (1./nc.bpdata[:,2] + (nc.suncerts * nc.bpdata[:,3])**2)
normresids = resids / runcerts
n = normresids.size
mn = normresids.mean ()
s = normresids.std ()
md = np.median (normresids)
smadm = 1.4826 * np.median (np.abs (normresids - md)) # see comment below
print ' Number of samples:', n
print ' Normalized mean residal:', mn
print ' Median:', md
print 'Normalized std. dev. (should be 1):', s
print ' SMADM:', smadm
# Check for problematic antpols and basepols
saps = nc.saps
sqbps = nc.sqbps
nap = len (saps)
dumbnbp = nap**2
apcounts = np.zeros (nap, dtype=np.int)
apsumsqresids = np.zeros (nap)
bpcounts = np.zeros (dumbnbp, dtype=np.int)
bpsumsqresids = np.zeros (dumbnbp)
for i in xrange (n):
idx1, idx2 = sqbps[i]
apcounts[idx1] += 1
apsumsqresids[idx1] += normresids[i]**2
apcounts[idx2] += 1
apsumsqresids[idx2] += normresids[i]**2
bpidx = idx1 * nap + idx2
bpcounts[bpidx] += 1
bpsumsqresids[bpidx] += normresids[i]**2
aprmsresids = np.sqrt (apsumsqresids / apcounts)
sapresids = np.argsort (aprmsresids)
print
print 'Extreme residual RMS by antpol:'
for i in xrange (5):
idx = sapresids[i]
print ' %10s %8.2f' % (util.fmtAP (saps[idx]), aprmsresids[idx])
print ' ....'
for i in xrange (5):
idx = sapresids[i - 5]
print ' %10s %8.2f' % (util.fmtAP (saps[idx]), aprmsresids[idx])
wbpgood = np.where (bpcounts)[0]
wbpbad = np.where (bpcounts == 0)[0]
bpcounts[wbpbad] = 1
bprmsresids = bpsumsqresids / bpcounts
sbpresids = np.argsort (bprmsresids[wbpgood])
print
print 'Extreme residual RMS by basepol:'
for i in xrange (3):
idx = wbpgood[sbpresids[i]]
ap2 = saps[idx % nap]
ap1 = saps[idx // nap]
print ' %10s %8.2f' % (util.fmtBP ((ap1, ap2)), bprmsresids[idx])
print ' ....'
for i in xrange (7):
idx = wbpgood[sbpresids[i - 7]]
ap2 = saps[idx % nap]
ap1 = saps[idx // nap]
print ' %10s %8.2f' % (util.fmtBP ((ap1, ap2)), bprmsresids[idx])
# Plot the distribution of residuals
bins = 50
rng = -5, 5
p = om.quickHist (normresids, keyText='Residuals', bins=bins, range=rng)
x = np.linspace (-5, 5, 200)
area = 10. / bins * n
y = area / np.sqrt (2 * np.pi) * np.exp (-0.5 * x**2)
p.addXY (x, y, 'Ideal')
p.rebound (False, False)
p.show ()
return 0
def export (self, stream):
solution = self.solution
saps = self.saps
for i in xrange (len (saps)):
print >>stream, self.instr, util.fmtAP (saps[i]), '%.5e' % solution[i]
def _format (aps):
if isinstance (aps, int): aps = (aps, )
return '-'.join (util.fmtAP (x) for x in aps)
def tui_checkfit(args):
import omega as om
if len(args) != 1:
util.die('usage: <datfile>')
nc = NoiseCal()
nc.load(args[0])
vals = nc.bpdata[:, 1]
modelvals = nc.bpdata[:, 3] * nc.svals
resids = vals - modelvals
runcerts = np.sqrt(1. / nc.bpdata[:, 2] +
(nc.suncerts * nc.bpdata[:, 3])**2)
normresids = resids / runcerts
n = normresids.size
mn = normresids.mean()
s = normresids.std()
md = np.median(normresids)
smadm = 1.4826 * np.median(np.abs(normresids - md)) # see comment below
print ' Number of samples:', n
print ' Normalized mean residal:', mn
print ' Median:', md
print 'Normalized std. dev. (should be 1):', s
print ' SMADM:', smadm
# Check for problematic antpols and basepols
saps = nc.saps
sqbps = nc.sqbps
nap = len(saps)
dumbnbp = nap**2
apcounts = np.zeros(nap, dtype=np.int)
apsumsqresids = np.zeros(nap)
bpcounts = np.zeros(dumbnbp, dtype=np.int)
bpsumsqresids = np.zeros(dumbnbp)
for i in xrange(n):
idx1, idx2 = sqbps[i]
apcounts[idx1] += 1
apsumsqresids[idx1] += normresids[i]**2
apcounts[idx2] += 1
apsumsqresids[idx2] += normresids[i]**2
bpidx = idx1 * nap + idx2
bpcounts[bpidx] += 1
bpsumsqresids[bpidx] += normresids[i]**2
aprmsresids = np.sqrt(apsumsqresids / apcounts)
sapresids = np.argsort(aprmsresids)
print
print 'Extreme residual RMS by antpol:'
for i in xrange(5):
idx = sapresids[i]
print ' %10s %8.2f' % (util.fmtAP(saps[idx]), aprmsresids[idx])
print ' ....'
for i in xrange(5):
idx = sapresids[i - 5]
print ' %10s %8.2f' % (util.fmtAP(saps[idx]), aprmsresids[idx])
wbpgood = np.where(bpcounts)[0]
wbpbad = np.where(bpcounts == 0)[0]
bpcounts[wbpbad] = 1
bprmsresids = bpsumsqresids / bpcounts
sbpresids = np.argsort(bprmsresids[wbpgood])
print
print 'Extreme residual RMS by basepol:'
for i in xrange(3):
idx = wbpgood[sbpresids[i]]
ap2 = saps[idx % nap]
ap1 = saps[idx // nap]
print ' %10s %8.2f' % (util.fmtBP((ap1, ap2)), bprmsresids[idx])
print ' ....'
for i in xrange(7):
idx = wbpgood[sbpresids[i - 7]]
ap2 = saps[idx % nap]
ap1 = saps[idx // nap]
print ' %10s %8.2f' % (util.fmtBP((ap1, ap2)), bprmsresids[idx])
# Plot the distribution of residuals
bins = 50
rng = -5, 5
p = om.quickHist(normresids, keyText='Residuals', bins=bins, range=rng)
x = np.linspace(-5, 5, 200)
area = 10. / bins * n
y = area / np.sqrt(2 * np.pi) * np.exp(-0.5 * x**2)
p.addXY(x, y, 'Ideal')
p.rebound(False, False)
p.show()
return 0
