def fitter(self,
xax,
data,
err=None,
quiet=True,
veryverbose=False,
debug=False,
parinfo=None,
**kwargs):
"""
Run the fitter using mpfit.
kwargs will be passed to _make_parinfo and mpfit.
Parameters
----------
xax : SpectroscopicAxis
The X-axis of the spectrum
data : ndarray
The data to fit
err : ndarray (optional)
The error on the data. If unspecified, will be uniform unity
parinfo : ParinfoList
The guesses, parameter limits, etc. See
`pyspeckit.spectrum.parinfo` for details
quiet : bool
pass to mpfit. If False, will print out the parameter values for
each iteration of the fitter
veryverbose : bool
print out a variety of mpfit output parameters
debug : bool
raise an exception (rather than a warning) if chi^2 is nan
"""
if parinfo is None:
parinfo, kwargs = self._make_parinfo(debug=debug, **kwargs)
else:
log.debug("Using user-specified parinfo dict")
# clean out disallowed kwargs (don't want to pass them to mpfit)
#throwaway, kwargs = self._make_parinfo(debug=debug, **kwargs)
self.xax = xax # the 'stored' xax is just a link to the original
if hasattr(xax, 'as_unit') and self.fitunits is not None:
# some models will depend on the input units. For these, pass in an X-axis in those units
# (gaussian, voigt, lorentz profiles should not depend on units. Ammonia, formaldehyde,
# H-alpha, etc. should)
xax = copy.copy(xax)
# xax.convert_to_unit(self.fitunits, quiet=quiet)
xax = xax.as_unit(self.fitunits, quiet=quiet, **kwargs)
elif self.fitunits is not None:
raise TypeError("X axis does not have a convert method")
if np.any(np.isnan(data)) or np.any(np.isinf(data)):
err[np.isnan(data) + np.isinf(data)] = np.inf
data[np.isnan(data) + np.isinf(data)] = 0
if np.any(np.isnan(err)):
raise ValueError(
"One or more of the error values is NaN."
" This is not allowed. Errors can be infinite "
"(which is equivalent to giving zero weight to "
"a data point), but otherwise they must be positive "
"floats.")
elif np.any(err < 0):
raise ValueError("At least one error value is negative, which is "
"not allowed as negative errors are not "
"meaningful in the optimization process.")
for p in parinfo:
log.debug(p)
log.debug("\n".join([
"%s %i: tied: %s value: %s" %
(p['parname'], p['n'], p['tied'], p['value']) for p in parinfo
]))
mp = mpfit(self.mpfitfun(xax, data, err),
parinfo=parinfo,
quiet=quiet,
debug=debug,
**kwargs)
mpp = mp.params
if mp.perror is not None: mpperr = mp.perror
else: mpperr = mpp * 0
chi2 = mp.fnorm
if mp.status == 0:
if "parameters are not within PARINFO limits" in mp.errmsg:
log.warn(parinfo)
raise mpfitException(mp.errmsg)
for i, (p, e) in enumerate(zip(mpp, mpperr)):
self.parinfo[i]['value'] = p
self.parinfo[i]['error'] = e
if veryverbose:
log.info("Fit status: {0}".format(mp.status))
log.info("Fit error message: {0}".format(mp.errmsg))
log.info("Fit message: {0}".format(mpfit_messages[mp.status]))
for i, p in enumerate(mpp):
log.info("{0}: {1} +/- {2}".format(self.parinfo[i]['parname'],
p, mpperr[i]))
log.info("Chi2: {0} Reduced Chi2: {1} DOF:{2}".format(
mp.fnorm, mp.fnorm / (len(data) - len(mpp)),
len(data) - len(mpp)))
self.mp = mp
self.mpp = self.parinfo.values
self.mpperr = self.parinfo.errors
self.mppnames = self.parinfo.names
self.model = self.n_modelfunc(self.parinfo,
**self.modelfunc_kwargs)(xax)
log.debug("Modelpars: {0}".format(self.mpp))
if np.isnan(chi2):
if debug:
raise ValueError("Error: chi^2 is nan")
else:
log.warn("Warning: chi^2 is nan")
return mpp, self.model, mpperr, chi2
def fitter(self, xax, data, err=None, quiet=True, veryverbose=False,
debug=False, parinfo=None, **kwargs):
"""
Run the fitter using mpfit.
kwargs will be passed to _make_parinfo and mpfit.
Parameters
----------
xax : SpectroscopicAxis
The X-axis of the spectrum
data : ndarray
The data to fit
err : ndarray (optional)
The error on the data. If unspecified, will be uniform unity
parinfo : ParinfoList
The guesses, parameter limits, etc. See
`pyspeckit.spectrum.parinfo` for details
quiet : bool
pass to mpfit. If False, will print out the parameter values for
each iteration of the fitter
veryverbose : bool
print out a variety of mpfit output parameters
debug : bool
raise an exception (rather than a warning) if chi^2 is nan
"""
if parinfo is None:
parinfo, kwargs = self._make_parinfo(debug=debug, **kwargs)
else:
log.debug("Using user-specified parinfo dict")
# clean out disallowed kwargs (don't want to pass them to mpfit)
#throwaway, kwargs = self._make_parinfo(debug=debug, **kwargs)
self.xax = xax # the 'stored' xax is just a link to the original
if hasattr(xax,'as_unit') and self.fitunits is not None:
# some models will depend on the input units. For these, pass in an X-axis in those units
# (gaussian, voigt, lorentz profiles should not depend on units. Ammonia, formaldehyde,
# H-alpha, etc. should)
xax = copy.copy(xax)
# xax.convert_to_unit(self.fitunits, quiet=quiet)
xax = xax.as_unit(self.fitunits, quiet=quiet, **kwargs)
elif self.fitunits is not None:
raise TypeError("X axis does not have a convert method")
if np.any(np.isnan(data)) or np.any(np.isinf(data)):
err[np.isnan(data) + np.isinf(data)] = np.inf
data[np.isnan(data) + np.isinf(data)] = 0
if np.any(np.isnan(err)):
raise ValueError("One or more of the error values is NaN."
" This is not allowed. Errors can be infinite "
"(which is equivalent to giving zero weight to "
"a data point), but otherwise they must be positive "
"floats.")
elif np.any(err<0):
raise ValueError("At least one error value is negative, which is "
"not allowed as negative errors are not "
"meaningful in the optimization process.")
for p in parinfo: log.debug( p )
log.debug( "\n".join(["%s %i: tied: %s value: %s" % (p['parname'],p['n'],p['tied'],p['value']) for p in parinfo]) )
mp = mpfit(self.mpfitfun(xax,data,err),parinfo=parinfo,quiet=quiet,debug=debug,**kwargs)
mpp = mp.params
if mp.perror is not None: mpperr = mp.perror
else: mpperr = mpp*0
chi2 = mp.fnorm
if mp.status == 0:
if "parameters are not within PARINFO limits" in mp.errmsg:
log.warning( parinfo )
raise mpfitException(mp.errmsg)
for i,(p,e) in enumerate(zip(mpp,mpperr)):
self.parinfo[i]['value'] = p
self.parinfo[i]['error'] = e
if veryverbose:
log.info("Fit status: {0}".format(mp.status))
log.info("Fit error message: {0}".format(mp.errmsg))
log.info("Fit message: {0}".format(mpfit_messages[mp.status]))
for i,p in enumerate(mpp):
log.info("{0}: {1} +/- {2}".format(self.parinfo[i]['parname'],
p,mpperr[i]))
log.info("Chi2: {0} Reduced Chi2: {1} DOF:{2}".format(mp.fnorm,
mp.fnorm/(len(data)-len(mpp)),
len(data)-len(mpp)))
self.mp = mp
self.mpp = self.parinfo.values
self.mpperr = self.parinfo.errors
self.mppnames = self.parinfo.names
self.model = self.n_modelfunc(self.parinfo,**self.modelfunc_kwargs)(xax)
log.debug("Modelpars: {0}".format(self.mpp))
if np.isnan(chi2):
if debug:
raise ValueError("Error: chi^2 is nan")
else:
log.warning("Warning: chi^2 is nan")
return mpp,self.model,mpperr,chi2
def fitter(self, xax, data, err=None, quiet=True, veryverbose=False,
debug=False, parinfo=None, **kwargs):
"""
Run the fitter using mpfit.
kwargs will be passed to _make_parinfo and mpfit.
Parameters
----------
xax : SpectroscopicAxis
The X-axis of the spectrum
data : ndarray
The data to fit
err : ndarray (optional)
The error on the data. If unspecified, will be uniform unity
parinfo : ParinfoList
The guesses, parameter limits, etc. See
`pyspeckit.spectrum.parinfo` for details
quiet : bool
pass to mpfit. If False, will print out the parameter values for
each iteration of the fitter
veryverbose : bool
print out a variety of mpfit output parameters
debug : bool
raise an exception (rather than a warning) if chi^2 is nan
"""
if parinfo is None:
parinfo, kwargs = self._make_parinfo(debug=debug, **kwargs)
else:
if debug: print "Using user-specified parinfo dict"
# clean out disallowed kwargs (don't want to pass them to mpfit)
#throwaway, kwargs = self._make_parinfo(debug=debug, **kwargs)
self.xax = xax # the 'stored' xax is just a link to the original
if hasattr(xax,'convert_to_unit') and self.fitunits is not None:
# some models will depend on the input units. For these, pass in an X-axis in those units
# (gaussian, voigt, lorentz profiles should not depend on units. Ammonia, formaldehyde,
# H-alpha, etc. should)
xax = copy.copy(xax)
xax.convert_to_unit(self.fitunits, quiet=quiet)
elif self.fitunits is not None:
raise TypeError("X axis does not have a convert method")
if np.any(np.isnan(data)) or np.any(np.isinf(data)):
err[np.isnan(data) + np.isinf(data)] = np.inf
data[np.isnan(data) + np.isinf(data)] = 0
if debug:
for p in parinfo: print p
print "\n".join(["%s %i: tied: %s value: %s" % (p['parname'],p['n'],p['tied'],p['value']) for p in parinfo])
mp = mpfit(self.mpfitfun(xax,data,err),parinfo=parinfo,quiet=quiet,**kwargs)
mpp = mp.params
if mp.perror is not None: mpperr = mp.perror
else: mpperr = mpp*0
chi2 = mp.fnorm
if mp.status == 0:
if "parameters are not within PARINFO limits" in mp.errmsg:
print parinfo
raise mpfitException(mp.errmsg)
for i,(p,e) in enumerate(zip(mpp,mpperr)):
self.parinfo[i]['value'] = p
self.parinfo[i]['error'] = e
if veryverbose:
print "Fit status: ",mp.status
print "Fit error message: ",mp.errmsg
print "Fit message: ",mpfit_messages[mp.status]
for i,p in enumerate(mpp):
print self.parinfo[i]['parname'],p," +/- ",mpperr[i]
print "Chi2: ",mp.fnorm," Reduced Chi2: ",mp.fnorm/len(data)," DOF:",len(data)-len(mpp)
self.mp = mp
self.mpp = self.parinfo.values
self.mpperr = self.parinfo.errors
self.mppnames = self.parinfo.names
self.model = self.n_modelfunc(self.parinfo,**self.modelfunc_kwargs)(xax)
if debug:
print "Modelpars: ",self.mpp
if np.isnan(chi2):
if debug:
raise ValueError("Error: chi^2 is nan")
else:
print "Warning: chi^2 is nan"
return mpp,self.model,mpperr,chi2