def _update_mask(self):
# Min and Max for new mask
xstart = self.rect['world'][0]
xend = self.rect['world'][2]
if xstart <= xend: newlist=[xstart,xend]
else: newlist = [xend,xstart]
# Mask or unmask
invmask = None
if self.rect['button'] == 1:
invmask = False
mflg = 'Mask'
elif self.rect['button'] == 3:
invmask = True
mflg = 'UNmask'
asaplog.push(mflg+': '+str(newlist))
asaplog.post()
newmask = self.scan.create_mask(newlist,invert=invmask)
# Logic operation to update mask
if invmask:
self.mask = mask_and(self.mask,newmask)
else:
self.mask = mask_or(self.mask,newmask)
# Plot masked regions
#if self.showmask or not self.once: self._plot_mask()
if self.showmask: self._plot_mask()
def auto_fit(self, insitu=None, plot=False):
"""
Return a scan where the function is applied to all rows for
all Beams/IFs/Pols.
"""
from asap import scantable
if not isinstance(self.data, scantable):
msg = "Data is not a scantable"
raise TypeError(msg)
if insitu is None:
insitu = rcParams["insitu"]
if not insitu:
scan = self.data.copy()
else:
scan = self.data
rows = xrange(scan.nrow())
# Save parameters of baseline fits as a class attribute.
# NOTICE: This does not reflect changes in scantable!
if len(rows) > 0:
self.blpars = []
asaplog.push("Fitting:")
for r in rows:
out = " Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % (
scan.getscan(r),
scan.getbeam(r),
scan.getif(r),
scan.getpol(r),
scan.getcycle(r),
)
asaplog.push(out, False)
self.x = scan._getabcissa(r)
self.y = scan._getspectrum(r)
# self.mask = mask_and(self.mask, scan._getmask(r))
if len(self.x) == len(self.mask):
self.mask = mask_and(self.mask, self.data._getmask(row))
else:
asaplog.push("lengths of data and mask are not the same. " "preset mask will be ignored")
asaplog.post("WARN", "asapfit.fit")
self.mask = self.data._getmask(row)
self.data = None
self.fit()
x = self.get_parameters()
fpar = self.get_parameters()
if plot:
self.plot(residual=True)
x = raw_input("Accept fit ([y]/n): ")
if x.upper() == "N":
self.blpars.append(None)
continue
scan._setspectrum(self.fitter.getresidual(), r)
self.blpars.append(fpar)
if plot:
self._p.quit()
del self._p
self._p = None
return scan
def auto_fit(self, insitu=None, plot=False):
"""
Return a scan where the function is applied to all rows for
all Beams/IFs/Pols.
"""
from asap import scantable
if not isinstance(self.data, scantable):
msg = "Data is not a scantable"
raise TypeError(msg)
if insitu is None: insitu = rcParams['insitu']
if not insitu:
scan = self.data.copy()
else:
scan = self.data
rows = xrange(scan.nrow())
# Save parameters of baseline fits as a class attribute.
# NOTICE: This does not reflect changes in scantable!
if len(rows) > 0: self.blpars = []
asaplog.push("Fitting:")
for r in rows:
out = " Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % (
scan.getscan(r), scan.getbeam(r), scan.getif(r),
scan.getpol(r), scan.getcycle(r))
asaplog.push(out, False)
self.x = scan._getabcissa(r)
self.y = scan._getspectrum(r)
#self.mask = mask_and(self.mask, scan._getmask(r))
if len(self.x) == len(self.mask):
self.mask = mask_and(self.mask, self.data._getmask(row))
else:
asaplog.push('lengths of data and mask are not the same. '
'preset mask will be ignored')
asaplog.post('WARN', 'asapfit.fit')
self.mask = self.data._getmask(row)
self.data = None
self.fit()
x = self.get_parameters()
fpar = self.get_parameters()
if plot:
self.plot(residual=True)
x = raw_input("Accept fit ([y]/n): ")
if x.upper() == 'N':
self.blpars.append(None)
continue
scan._setspectrum(self.fitter.getresidual(), r)
self.blpars.append(fpar)
if plot:
self._p.quit()
del self._p
self._p = None
return scan
def fit(self, row=0, estimate=False):
"""
Execute the actual fitting process. All the state has to be set.
Parameters:
row: specify the row in the scantable
estimate: auto-compute an initial parameter set (default False)
This can be used to compute estimates even if fit was
called before.
Example:
s = scantable('myscan.asap')
s.set_cursor(thepol=1) # select second pol
f = fitter()
f.set_scan(s)
f.set_function(poly=0)
f.fit(row=0) # fit first row
"""
if ((self.x is None or self.y is None) and self.data is None) or self.fitfunc is None:
msg = "Fitter not yet initialised. Please set data & fit function"
raise RuntimeError(msg)
if self.data is not None:
if self.data._getflagrow(row):
raise RuntimeError, "Can not fit flagged row."
self.x = self.data._getabcissa(row)
self.y = self.data._getspectrum(row)
# self.mask = mask_and(self.mask, self.data._getmask(row))
if len(self.x) == len(self.mask):
self.mask = mask_and(self.mask, self.data._getmask(row))
else:
asaplog.push("lengths of data and mask are not the same. " "preset mask will be ignored")
asaplog.post("WARN", "asapfit.fit")
self.mask = self.data._getmask(row)
asaplog.push("Fitting:")
i = row
out = "Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % (
self.data.getscan(i),
self.data.getbeam(i),
self.data.getif(i),
self.data.getpol(i),
self.data.getcycle(i),
)
asaplog.push(out, False)
self.fitter.setdata(self.x, self.y, self.mask)
if self.fitfunc == "gauss" or self.fitfunc == "lorentz":
ps = self.fitter.getparameters()
if len(ps) == 0 or estimate:
self.fitter.estimate()
fxdpar = list(self.fitter.getfixedparameters())
if len(fxdpar) and fxdpar.count(0) == 0:
raise RuntimeError, "No point fitting, if all parameters are fixed."
if self._constraints:
for c in self._constraints:
self.fitter.addconstraint(c[0] + [c[-1]])
if self.uselinear:
converged = self.fitter.lfit()
else:
converged = self.fitter.fit()
if not converged:
raise RuntimeError, "Fit didn't converge."
self._fittedrow = row
self.fitted = True
return
def fit(self, row=0, estimate=False):
"""
Execute the actual fitting process. All the state has to be set.
Parameters:
row: specify the row in the scantable
estimate: auto-compute an initial parameter set (default False)
This can be used to compute estimates even if fit was
called before.
Example:
s = scantable('myscan.asap')
s.set_cursor(thepol=1) # select second pol
f = fitter()
f.set_scan(s)
f.set_function(poly=0)
f.fit(row=0) # fit first row
"""
if ((self.x is None or self.y is None) and self.data is None) \
or self.fitfunc is None:
msg = "Fitter not yet initialised. Please set data & fit function"
raise RuntimeError(msg)
if self.data is not None:
if self.data._getflagrow(row):
raise RuntimeError, "Can not fit flagged row."
self.x = self.data._getabcissa(row)
self.y = self.data._getspectrum(row)
#self.mask = mask_and(self.mask, self.data._getmask(row))
if len(self.x) == len(self.mask):
self.mask = mask_and(self.mask, self.data._getmask(row))
else:
asaplog.push('lengths of data and mask are not the same. '
'preset mask will be ignored')
asaplog.post('WARN', 'asapfit.fit')
self.mask = self.data._getmask(row)
asaplog.push("Fitting:")
i = row
out = "Scan[%d] Beam[%d] IF[%d] Pol[%d] Cycle[%d]" % (
self.data.getscan(i), self.data.getbeam(i), self.data.getif(i),
self.data.getpol(i), self.data.getcycle(i))
asaplog.push(out, False)
self.fitter.setdata(self.x, self.y, self.mask)
if self.fitfunc == 'gauss' or self.fitfunc == 'lorentz':
ps = self.fitter.getparameters()
if len(ps) == 0 or estimate:
self.fitter.estimate()
fxdpar = list(self.fitter.getfixedparameters())
if len(fxdpar) and fxdpar.count(0) == 0:
raise RuntimeError, "No point fitting, if all parameters are fixed."
if self._constraints:
for c in self._constraints:
self.fitter.addconstraint(c[0] + [c[-1]])
if self.uselinear:
converged = self.fitter.lfit()
else:
converged = self.fitter.fit()
if not converged:
raise RuntimeError, "Fit didn't converge."
self._fittedrow = row
self.fitted = True
return
