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 loadNoiseCalExport (path):
byinstr = {}
for a in pathwords (path):
instr = a[0]
ap = util.parseAP (a[1])
value = float (a[2])
byap = byinstr.setdefault (instr, {})
byap[ap] = value
for instr, byap in byinstr.items ():
default = np.median (byap.values ())
byinstr[instr] = (byap, default)
return byinstr
def loadNoiseCalExport(path):
byinstr = {}
for a in pathwords(path):
instr = a[0]
ap = util.parseAP(a[1])
value = float(a[2])
byap = byinstr.setdefault(instr, {})
byap[ap] = value
for instr, byap in byinstr.items():
default = np.median(byap.values())
byinstr[instr] = (byap, default)
return byinstr
