def bpHist(self):
import omega
n = int(np.ceil(np.log2(len(self.bpData)) + 1))
values = [t[1] for t in self.bpData]
p = omega.quickHist(values, n, keyText='Basepols')
p.setLabels('%s closure' % self.item, 'Number of basepols')
return p
def bpHist (self):
import omega
n = int (np.ceil (np.log2 (len (self.bpData)) + 1))
values = [t[1] for t in self.bpData]
p = omega.quickHist (values, n, keyText='Basepols')
p.setLabels ('%s closure' % self.item, 'Number of basepols')
return p
def valHist(self):
import omega
rms = self.allrms.finish()
n = int(np.ceil(np.log2(rms.size) + 1))
p = omega.quickHist(rms, n, keyText=self.datum)
p.setLabels('%s closure' % self.item, 'Number of %s' % self.datum)
return p
def valHist (self):
import omega
rms = self.allrms.finish ()
n = int (np.ceil (np.log2 (rms.size) + 1))
p = omega.quickHist (rms, n, keyText=self.datum)
p.setLabels ('%s closure' % self.item, 'Number of %s' % self.datum)
return p
def tui_checkcal (args):
import omega as om, scipy.stats as SS
toread = []
uvdatoptions = {}
for arg in args:
if '=' in arg:
key, value = arg.split ('=', 1)
uvdatoptions[key] = value
else:
toread.append (VisData (arg))
if len (toread) < 1:
util.die ('usage: <vis1> [... visn] [uvdat options]')
samples = VectorGrower ()
gen = uvdat.setupAndRead (toread, 'x3', False, **uvdatoptions)
for inp, pream, data, flags in gen:
w = np.where (flags)[0]
if w.size == 0:
continue
data = data[w]
var = 0.5 * (data.real.var (ddof=1) + data.imag.var (ddof=1))
uvar = np.sqrt (1. / (w.size - 1)) * var # uncert in variance msmt
thy = inp.getVariance ()
samples.add ((var - thy) / uvar)
samples = samples.finish ()
n = samples.size
m = samples.mean ()
s = samples.std ()
med = np.median (samples)
smadm = 1.4826 * np.median (np.abs (samples - med)) # see comment below
print ' Number of samples:', n
print 'Mean normalized error (should be 0):', m
print ' Median:', med
print ' Normalized std. dev. (should be 1):', s
print ' SMADM:', smadm
print 'Probability that samples are normal:', SS.normaltest (samples)[1]
bins = 50
rng = -5, 5
p = om.quickHist (samples, keyText='Samples', 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_checkcal(args):
import omega as om, scipy.stats as SS
toread = []
uvdatoptions = {}
for arg in args:
if '=' in arg:
key, value = arg.split('=', 1)
uvdatoptions[key] = value
else:
toread.append(VisData(arg))
if len(toread) < 1:
util.die('usage: <vis1> [... visn] [uvdat options]')
samples = VectorGrower()
gen = uvdat.setupAndRead(toread, 'x3', False, **uvdatoptions)
for inp, pream, data, flags in gen:
w = np.where(flags)[0]
if w.size == 0:
continue
data = data[w]
var = 0.5 * (data.real.var(ddof=1) + data.imag.var(ddof=1))
uvar = np.sqrt(1. / (w.size - 1)) * var # uncert in variance msmt
thy = inp.getVariance()
samples.add((var - thy) / uvar)
samples = samples.finish()
n = samples.size
m = samples.mean()
s = samples.std()
med = np.median(samples)
smadm = 1.4826 * np.median(np.abs(samples - med)) # see comment below
print ' Number of samples:', n
print 'Mean normalized error (should be 0):', m
print ' Median:', med
print ' Normalized std. dev. (should be 1):', s
print ' SMADM:', smadm
print 'Probability that samples are normal:', SS.normaltest(samples)[1]
bins = 50
rng = -5, 5
p = om.quickHist(samples, keyText='Samples', 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_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_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 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
