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 plot (self, modelx, dlines=False, xmin=None, xmax=None,
ymin=None, ymax=None, **kwargs):
"""This assumes that `data` is 1D and that `mfunc` takes one argument that
should be treated as the X variable."""
import omega as om
modelx = np.asarray (modelx)
if modelx.shape != self.data.shape:
raise ValueError ('modelx and data arrays must have same shape')
modely = self.mfunc (modelx)
sigmas = self.invsigma**-1 # TODO: handle invsigma = 0
vb = om.layout.VBox (2)
vb.pData = om.quickXYErr (modelx, self.data, sigmas,
'Data', lines=dlines, **kwargs)
vb[0] = vb.pData
vb[0].addXY (modelx, modely, 'Model')
vb[0].setYLabel ('Y')
vb[0].rebound (False, True)
vb[0].setBounds (xmin, xmax, ymin, ymax)
vb[1] = vb.pResid = om.RectPlot ()
vb[1].defaultField.xaxis = vb[1].defaultField.xaxis
vb[1].addXYErr (modelx, self.resids, sigmas, None, lines=False)
vb[1].setLabels ('X', 'Residuals')
vb[1].rebound (False, True)
# ignore Y values since residuals are on different scale:
vb[1].setBounds (xmin, xmax)
vb.setWeight (0, 3)
return vb
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 plot(self,
modelx,
dlines=False,
xmin=None,
xmax=None,
ymin=None,
ymax=None,
**kwargs):
"""Plot the data and model (requires `omega`).
This assumes that `data` is 1D and that `mfunc` takes one argument
that should be treated as the X variable.
"""
import omega as om
modelx = np.asarray(modelx)
if modelx.shape != self.data.shape:
raise ValueError('modelx and data arrays must have same shape')
modely = self.mfunc(modelx)
sigmas = self.invsigma**-1 # TODO: handle invsigma = 0
vb = om.layout.VBox(2)
vb.pData = om.quickXYErr(modelx,
self.data,
sigmas,
'Data',
lines=dlines,
**kwargs)
vb[0] = vb.pData
vb[0].addXY(modelx, modely, 'Model')
vb[0].setYLabel('Y')
vb[0].rebound(False, True)
vb[0].setBounds(xmin, xmax, ymin, ymax)
vb[1] = vb.pResid = om.RectPlot()
vb[1].defaultField.xaxis = vb[1].defaultField.xaxis
vb[1].addXYErr(modelx, self.resids, sigmas, None, lines=False)
vb[1].setLabels('X', 'Residuals')
vb[1].rebound(False, True)
# ignore Y values since residuals are on different scale:
vb[1].setBounds(xmin, xmax)
vb.setWeight(0, 3)
return vb
def plot (self, dlines=False, densemodel=True, xmin=None, xmax=None,
ymin=None, ymax=None, mxmin=None, mxmax=None, **kwargs):
import omega as om
if not densemodel:
modx = self.x
mody = self.modely
else:
if mxmin is None:
mxmin = self.x.min ()
if mxmax is None:
mxmax = self.x.max ()
modx = np.linspace (mxmin, mxmax, 400)
mody = self.modelfunc (modx)
sigmas = self.invsigma**-1 # TODO: handle invsigma = 0
vb = om.layout.VBox (2)
vb.pData = om.quickXYErr (self.x, self.y, sigmas,
'Data', lines=dlines, **kwargs)
vb[0] = vb.pData
vb[0].addXY (modx, mody, 'Model')
vb[0].setYLabel ('Y')
vb[0].rebound (False, True)
vb[0].setBounds (xmin, xmax, ymin, ymax)
vb[1] = vb.pResid = om.RectPlot ()
vb[1].defaultField.xaxis = vb[1].defaultField.xaxis
vb[1].addXYErr (self.x, self.resids, sigmas, None, lines=False)
vb[1].setLabels ('X', 'Residuals')
vb[1].rebound (False, True)
# ignore Y values since residuals are on different scale:
vb[1].setBounds (xmin, xmax)
vb.setWeight (0, 3)
return vb
def report (self, cfg):
import omega as om
import omega.gtk3
from pwkit import ndshow_gtk3
self.all_aps = np.sort (list (self.all_aps))
self.all_bps = sorted (self.all_bps)
self.all_times = np.sort (list (self.all_times))
# Antpols by DDID, time:
data = []
descs = []
for ddid, stats in self.ap_time_stats_by_ddid.iteritems ():
mean, scat = postproc (stats.finish (self.all_times, self.all_aps))
data.append (mean / scat)
descs.append ('DDID %d' % ddid)
print ('Viewing X axis: antpol; Y axis: time; iteration: DDID (~= spwid) ...')
ndshow_gtk3.cycle (data, descs, run_main=True)
# Antpols by DDID, freq:
data = []
descs = []
for ddid, stats in self.ap_spec_stats_by_ddid.iteritems ():
mean, scat = postproc (stats.finish (self.all_aps))
data.append (mean / scat)
descs.append ('DDID %d' % ddid)
print ('Viewing X axis: frequency; Y axis: antpol; iteration: DDID ...')
ndshow_gtk3.cycle (data, descs, run_main=True)
# Antpols by DDID
p = om.RectPlot ()
for ddid, stats in self.ap_stats_by_ddid.iteritems ():
ok, mean, scat = postproc_mask (stats.finish (self.all_aps))
p.addXYErr (np.arange (len (self.all_aps))[ok], mean, scat, 'DDID %d' % ddid)
p.setBounds (-0.5, len (self.all_aps) - 0.5)
p.setLabels ('Antpol number', 'Mean closure phase (rad)')
p.addHLine (0, keyText=None, zheight=-1)
print ('Viewing everything grouped by antpol ...')
p.show ()
# Basepols by DDID
data = []
descs = []
tostatuses = []
def bpgrid_status (pol1, pol2, ants, yx):
i, j = [int (_) for _ in np.floor (yx + 0.5)]
if i < 0 or j < 0 or i >= ants.size or j >= ants.size:
return ''
ni = self._getname (ants[i])
nj = self._getname (ants[j])
if i <= j:
return '%s-%s %s' % (ni, nj, util.pol_names[pol1])
return '%s-%s %s' % (nj, ni, util.pol_names[pol2])
for ddid, stats in self.bp_stats_by_ddid.iteritems ():
mean, scat = postproc (stats.finish (self.all_bps))
nmean = mean / scat
from itertools import izip
pol1, pol2, ants, grid = grid_bp_data (self.all_bps,
izip (self.all_bps, nmean))
data.append (grid)
descs.append ('DDID %d' % ddid)
tostatuses.append (lambda yx: bpgrid_status (pol1, pol2, ants, yx))
print ('Viewing X axis: antpol1; Y axis: antpol2; iteration: DDID ...')
ndshow_gtk3.cycle (data, descs, tostatuses=tostatuses, run_main=True)
# Everything by time
ok, mean, scat = postproc_mask (self.global_stats_by_time.finish (self.all_times))
stimes = self.all_times[ok] / 86400
st0 = int (np.floor (stimes.min ()))
stimes -= st0
p = om.quickXYErr (stimes, mean, scat)
p.addHLine (0, keyText=None, zheight=-1)
p.setLabels ('MJD - %d' % st0, 'Mean closure phase (rad)')
print ('Viewing everything grouped by time ...')
p.show ()
def report(self, cfg):
import omega as om
import omega.gtk3
from pwkit import ndshow_gtk3
self.all_aps = np.sort(list(self.all_aps))
self.all_bps = sorted(self.all_bps)
self.all_times = np.sort(list(self.all_times))
# Antpols by DDID, time:
data = []
descs = []
for ddid, stats in self.ap_time_stats_by_ddid.items():
mean, scat = postproc(stats.finish(self.all_times, self.all_aps))
data.append(mean / scat)
descs.append('DDID %d' % ddid)
print(
'Viewing X axis: antpol; Y axis: time; iteration: DDID (~= spwid) ...'
)
ndshow_gtk3.cycle(data, descs, run_main=True)
# Antpols by DDID, freq:
data = []
descs = []
for ddid, stats in self.ap_spec_stats_by_ddid.items():
mean, scat = postproc(stats.finish(self.all_aps))
data.append(mean / scat)
descs.append('DDID %d' % ddid)
print('Viewing X axis: frequency; Y axis: antpol; iteration: DDID ...')
ndshow_gtk3.cycle(data, descs, run_main=True)
# Antpols by DDID
p = om.RectPlot()
for ddid, stats in self.ap_stats_by_ddid.items():
ok, mean, scat = postproc_mask(stats.finish(self.all_aps))
p.addXYErr(
np.arange(len(self.all_aps))[ok], mean, scat, 'DDID %d' % ddid)
p.setBounds(-0.5, len(self.all_aps) - 0.5)
p.setLabels('Antpol number', 'Mean closure phase (rad)')
p.addHLine(0, keyText=None, zheight=-1)
print('Viewing everything grouped by antpol ...')
p.show()
# Basepols by DDID
data = []
descs = []
tostatuses = []
def bpgrid_status(pol1, pol2, ants, yx):
i, j = [int(_) for _ in np.floor(yx + 0.5)]
if i < 0 or j < 0 or i >= ants.size or j >= ants.size:
return ''
ni = self._getname(ants[i])
nj = self._getname(ants[j])
if i <= j:
return '%s-%s %s' % (ni, nj, util.pol_names[pol1])
return '%s-%s %s' % (nj, ni, util.pol_names[pol2])
for ddid, stats in self.bp_stats_by_ddid.items():
mean, scat = postproc(stats.finish(self.all_bps))
nmean = mean / scat
pol1, pol2, ants, grid = grid_bp_data(self.all_bps,
zip(self.all_bps, nmean))
data.append(grid)
descs.append('DDID %d' % ddid)
tostatuses.append(lambda yx: bpgrid_status(pol1, pol2, ants, yx))
print('Viewing X axis: antpol1; Y axis: antpol2; iteration: DDID ...')
ndshow_gtk3.cycle(data, descs, tostatuses=tostatuses, run_main=True)
# Everything by time
ok, mean, scat = postproc_mask(
self.global_stats_by_time.finish(self.all_times))
stimes = self.all_times[ok] / 86400
st0 = int(np.floor(stimes.min()))
stimes -= st0
p = om.quickXYErr(stimes, mean, scat)
p.addHLine(0, keyText=None, zheight=-1)
p.setLabels('MJD - %d' % st0, 'Mean closure phase (rad)')
print('Viewing everything grouped by time ...')
p.show()
