def _make_parinfo(self, model=None):
self.parinfo = ParinfoList([
Parinfo(parname=name,value=value)
for name,value in zip(model.param_names,model.parameters)])
return self.parinfo, {}
def multinh3fit(self, xax, data, npeaks=1, err=None,
params=(20,20,14,1.0,0.0,0.5),
parnames=None,
fixed=(False,False,False,False,False,False),
limitedmin=(True,True,True,True,False,True),
limitedmax=(False,False,False,False,False,True), minpars=(2.73,2.73,0,0,0,0),
parinfo=None,
maxpars=(0,0,0,0,0,1), quiet=True, shh=True, veryverbose=False, **kwargs):
"""
Fit multiple nh3 profiles (multiple can be 1)
Inputs:
xax - x axis
data - y axis
npeaks - How many nh3 profiles to fit? Default 1 (this could supersede onedgaussfit)
err - error corresponding to data
These parameters need to have length = 6*npeaks. If npeaks > 1 and length = 6, they will
be replicated npeaks times, otherwise they will be reset to defaults:
params - Fit parameters: [tkin, tex, ntot (or tau), width, offset, ortho fraction] * npeaks
If len(params) % 6 == 0, npeaks will be set to len(params) / 6
fixed - Is parameter fixed?
limitedmin/minpars - set lower limits on each parameter (default: width>0, Tex and Tkin > Tcmb)
limitedmax/maxpars - set upper limits on each parameter
parnames - default parameter names, important for setting kwargs in model ['tkin','tex','ntot','width','xoff_v','fortho']
quiet - should MPFIT output each iteration?
shh - output final parameters?
Returns:
Fit parameters
Model
Fit errors
chi2
"""
if parinfo is None:
self.npars = len(params) / npeaks
if len(params) != npeaks and (len(params) / self.npars) > npeaks:
npeaks = len(params) / self.npars
self.npeaks = npeaks
if isinstance(params,np.ndarray): params=params.tolist()
# this is actually a hack, even though it's decently elegant
# somehow, parnames was being changed WITHOUT being passed as a variable
# this doesn't make sense - at all - but it happened.
# (it is possible for self.parnames to have npars*npeaks elements where
# npeaks > 1 coming into this function even though only 6 pars are specified;
# _default_parnames is the workaround)
if parnames is None: parnames = copy.copy(self._default_parnames)
partype_dict = dict(zip(['params','parnames','fixed','limitedmin','limitedmax','minpars','maxpars'],
[params,parnames,fixed,limitedmin,limitedmax,minpars,maxpars]))
# make sure all various things are the right length; if they're not, fix them using the defaults
for partype,parlist in partype_dict.iteritems():
if len(parlist) != self.npars*self.npeaks:
# if you leave the defaults, or enter something that can be multiplied by npars to get to the
# right number of gaussians, it will just replicate
if len(parlist) == self.npars:
partype_dict[partype] *= npeaks
elif len(parlist) > self.npars:
# DANGER: THIS SHOULD NOT HAPPEN!
print "WARNING! Input parameters were longer than allowed for variable ",parlist
partype_dict[partype] = partype_dict[partype][:self.npars]
elif parlist==params: # this instance shouldn't really be possible
partype_dict[partype] = [20,20,1e10,1.0,0.0,0.5] * npeaks
elif parlist==fixed:
partype_dict[partype] = [False] * len(params)
elif parlist==limitedmax: # only fortho, fillingfraction have upper limits
partype_dict[partype] = (np.array(parnames) == 'fortho') + (np.array(parnames) == 'fillingfraction')
elif parlist==limitedmin: # no physical values can be negative except velocity
partype_dict[partype] = (np.array(parnames) != 'xoff_v')
elif parlist==minpars: # all have minima of zero except kinetic temperature, which can't be below CMB. Excitation temperature technically can be, but not in this model
partype_dict[partype] = ((np.array(parnames) == 'tkin') + (np.array(parnames) == 'tex')) * 2.73
elif parlist==maxpars: # fractions have upper limits of 1.0
partype_dict[partype] = ((np.array(parnames) == 'fortho') + (np.array(parnames) == 'fillingfraction')).astype('float')
elif parlist==parnames: # assumes the right number of parnames (essential)
partype_dict[partype] = list(parnames) * self.npeaks
if len(parnames) != len(partype_dict['params']):
raise ValueError("Wrong array lengths AFTER fixing them")
# used in components. Is this just a hack?
self.parnames = partype_dict['parnames']
parinfo = [ {'n':ii, 'value':partype_dict['params'][ii],
'limits':[partype_dict['minpars'][ii],partype_dict['maxpars'][ii]],
'limited':[partype_dict['limitedmin'][ii],partype_dict['limitedmax'][ii]], 'fixed':partype_dict['fixed'][ii],
'parname':partype_dict['parnames'][ii]+str(ii/self.npars),
'mpmaxstep':float(partype_dict['parnames'][ii] in ('tex','tkin')), # must force small steps in temperature (True = 1.0)
'error': 0}
for ii in xrange(len(partype_dict['params'])) ]
# hack: remove 'fixed' pars
parinfo_with_fixed = parinfo
parinfo = [p for p in parinfo_with_fixed if not p['fixed']]
fixed_kwargs = dict((p['parname'].strip("0123456789").lower(),p['value']) for p in parinfo_with_fixed if p['fixed'])
# don't do this - it breaks the NEXT call because npars != len(parnames) self.parnames = [p['parname'] for p in parinfo]
# this is OK - not a permanent change
parnames = [p['parname'] for p in parinfo]
# not OK self.npars = len(parinfo)/self.npeaks
parinfo = ParinfoList([Parinfo(p) for p in parinfo], preserve_order=True)
#import pdb; pdb.set_trace()
else:
self.parinfo = ParinfoList([Parinfo(p) for p in parinfo], preserve_order=True)
parinfo_with_fixed = None
fixed_kwargs = {}
fitfun_kwargs = dict(kwargs.items()+fixed_kwargs.items())
npars = len(parinfo)/self.npeaks
# (fortho0 is not fortho)
# this doesn't work if parinfo_with_fixed is not None:
# this doesn't work for p in parinfo_with_fixed:
# this doesn't work # users can change the defaults while holding them fixed
# this doesn't work if p['fixed']:
# this doesn't work kwargs.update({p['parname']:p['value']})
def mpfitfun(x,y,err):
if err is None:
def f(p,fjac=None): return [0,(y-self.n_ammonia(pars=p, parnames=parinfo.parnames, **fitfun_kwargs)(x))]
else:
def f(p,fjac=None): return [0,(y-self.n_ammonia(pars=p, parnames=parinfo.parnames, **fitfun_kwargs)(x))/err]
return f
if veryverbose:
print "GUESSES: "
print "\n".join(["%s: %s" % (p['parname'],p['value']) for p in parinfo])
mp = mpfit(mpfitfun(xax,data,err),parinfo=parinfo,quiet=quiet)
mpp = mp.params
if mp.perror is not None: mpperr = mp.perror
else: mpperr = mpp*0
chi2 = mp.fnorm
if mp.status == 0:
raise Exception(mp.errmsg)
for i,p in enumerate(mpp):
parinfo[i]['value'] = p
parinfo[i]['error'] = mpperr[i]
if not shh:
print "Fit status: ",mp.status
print "Fit message: ",mp.errmsg
print "Final fit values: "
for i,p in enumerate(mpp):
print parinfo[i]['parname'],p," +/- ",mpperr[i]
print "Chi2: ",mp.fnorm," Reduced Chi2: ",mp.fnorm/len(data)," DOF:",len(data)-len(mpp)
if any(['tex' in s for s in parnames]) and any(['tkin' in s for s in parnames]):
texnum = (i for i,s in enumerate(parnames) if 'tex' in s)
tkinnum = (i for i,s in enumerate(parnames) if 'tkin' in s)
for txn,tkn in zip(texnum,tkinnum):
if mpp[txn] > mpp[tkn]: mpp[txn] = mpp[tkn] # force Tex>Tkin to Tex=Tkin (already done in n_ammonia)
self.mp = mp
if parinfo_with_fixed is not None:
# self self.parinfo preserving the 'fixed' parameters
# ORDER MATTERS!
for p in parinfo:
parinfo_with_fixed[p['n']] = p
self.parinfo = ParinfoList([Parinfo(p) for p in parinfo_with_fixed], preserve_order=True)
else:
self.parinfo = parinfo
self.parinfo = ParinfoList([Parinfo(p) for p in parinfo], preserve_order=True)
# I don't THINK these are necessary?
#self.parinfo = parinfo
#self.parinfo = ParinfoList([Parinfo(p) for p in self.parinfo])
# need to restore the fixed parameters....
# though the above commented out section indicates that I've done and undone this dozens of times now
# (a test has been added to test_nh3.py)
# this was NEVER included or tested because it breaks the order
#for par in parinfo_with_fixed:
# if par.parname not in self.parinfo.keys():
# self.parinfo.append(par)
self.mpp = self.parinfo.values
self.mpperr = self.parinfo.errors
self.mppnames = self.parinfo.names
self.model = self.n_ammonia(pars=self.mpp, parnames=self.mppnames, **kwargs)(xax)
#if self.model.sum() == 0:
# print "DON'T FORGET TO REMOVE THIS ERROR!"
# raise ValueError("Model is zeros.")
indiv_parinfo = [self.parinfo[jj*self.npars:(jj+1)*self.npars] for jj in xrange(len(self.parinfo)/self.npars)]
modelkwargs = [
dict([(p['parname'].strip("0123456789").lower(),p['value']) for p in pi])
for pi in indiv_parinfo]
self.tau_list = [ammonia(xax,return_tau=True,**mk) for mk in modelkwargs]
return self.mpp,self.model,self.mpperr,chi2
def _make_parinfo(self,
params=None,
parnames=None,
parvalues=None,
parlimits=None,
parlimited=None,
parfixed=None,
parerror=None,
partied=None,
fitunits=None,
parsteps=None,
npeaks=1,
parinfo=None,
names=None,
values=None,
limits=None,
limited=None,
fixed=None,
error=None,
tied=None,
steps=None,
negamp=None,
limitedmin=None,
limitedmax=None,
minpars=None,
maxpars=None,
vheight=False,
debug=False,
**kwargs):
"""
Generate a `ParinfoList` that matches the inputs
This code is complicated - it can take inputs in a variety of different
forms with different priority. It will return a `ParinfoList` (and
therefore must have values within parameter ranges)
"""
# for backwards compatibility - partied = tied, etc.
locals_dict = locals()
for varname in str.split(
"parnames,parvalues,parsteps,parlimits,parlimited,parfixed,parerror,partied",
","):
shortvarname = varname.replace("par", "")
if locals_dict.get(shortvarname) is not None and locals_dict.get(
varname) is not None:
raise ValueError("Cannot specify both {0} and {1}".format(
varname, shortvarname))
input_pardict = {
k: locals_dict.get(k)
for k in str.split(
"parnames,parvalues,parsteps,parlimits,parlimited,parfixed,parerror,partied",
",")
}
_tip = {
'par' + k: locals_dict.get(k)
for k in str.split(
"names,values,steps,limits,limited,fixed,error,tied", ",")
if locals_dict.get(k)
}
input_pardict.update(_tip)
if params is not None and parvalues is not None:
raise ValueError(
"parvalues and params both specified; they're redundant so that's not allowed."
)
elif params is not None and parvalues is None:
input_pardict['parvalues'] = params
log.debug("Parvalues = {0}, npeaks = {1}".format(
input_pardict['parvalues'], npeaks))
# this is used too many damned times to keep referencing a dict.
parnames = input_pardict['parnames']
parlimited = input_pardict['parlimited']
parlimits = input_pardict['parlimits']
parvalues = input_pardict['parvalues']
if parnames is not None:
self.parnames = parnames
elif parnames is None and hasattr(
self, 'parnames') and self.parnames is not None:
parnames = self.parnames
elif self.default_parinfo is not None and parnames is None:
parnames = [p['parname'] for p in self.default_parinfo]
input_pardict['parnames'] = parnames
assert input_pardict['parnames'] is not None
if limitedmin is not None:
if limitedmax is not None:
parlimited = list(zip(limitedmin, limitedmax))
else:
parlimited = list(zip(limitedmin, (False, ) * len(parnames)))
elif limitedmax is not None:
parlimited = list(zip((False, ) * len(parnames), limitedmax))
elif self.default_parinfo is not None and parlimited is None:
parlimited = [p['limited'] for p in self.default_parinfo]
input_pardict['parlimited'] = parlimited
if minpars is not None:
if maxpars is not None:
parlimits = list(zip(minpars, maxpars))
else:
parlimits = list(zip(minpars, (False, ) * len(parnames)))
elif maxpars is not None:
parlimits = list(zip((False, ) * len(parnames), maxpars))
elif limits is not None:
parlimits = limits
elif self.default_parinfo is not None and parlimits is None:
parlimits = [p['limits'] for p in self.default_parinfo]
input_pardict['parlimits'] = parlimits
self.npeaks = int(npeaks)
# the height / parvalue popping needs to be done before the temp_pardict is set in order to make sure
# that the height guess isn't assigned to the amplitude
self.vheight = vheight
if ((vheight and len(self.parinfo) == self.default_npars
and len(parvalues) == self.default_npars + 1)):
# if the right number of parameters are passed, the first is the height
self.parinfo = [{
'n': 0,
'value': parvalues.pop(0),
'limits': (0, 0),
'limited': (False, False),
'fixed': False,
'parname': 'HEIGHT',
'error': 0,
'tied': ""
}]
elif vheight and len(self.parinfo) == self.default_npars and len(
parvalues) == self.default_npars:
# if you're one par short, guess zero
self.parinfo = [{
'n': 0,
'value': 0,
'limits': (0, 0),
'limited': (False, False),
'fixed': False,
'parname': 'HEIGHT',
'error': 0,
'tied': ""
}]
elif vheight and len(self.parinfo) == self.default_npars + 1 and len(
parvalues) == self.default_npars + 1:
# the right numbers are passed *AND* there is already a height param
self.parinfo = [{
'n': 0,
'value': parvalues.pop(0),
'limits': (0, 0),
'limited': (False, False),
'fixed': False,
'parname': 'HEIGHT',
'error': 0,
'tied': ""
}]
#heightparnum = (i for i,s in self.parinfo if 'HEIGHT' in s['parname'])
#for hpn in heightparnum:
# self.parinfo[hpn]['value'] = parvalues[0]
elif vheight:
raise ValueError(
'VHEIGHT is specified but a case was found that did not allow it to be included.'
)
else:
self.parinfo = []
log.debug("After VHEIGHT parse len(parinfo): %i vheight: %s" %
(len(self.parinfo), vheight))
# this is a clever way to turn the parameter lists into a dict of lists
# clever = hard to read
temp_pardict = OrderedDict([(
varname, np.zeros(self.npars * self.npeaks, dtype='bool')
) if input_pardict.get(varname) is None else (
varname,
list(input_pardict.get(varname))
) for varname in str.split(
"parnames,parvalues,parsteps,parlimits,parlimited,parfixed,parerror,partied",
",")])
temp_pardict['parlimits'] = parlimits if parlimits is not None else [
(0, 0)
] * (self.npars * self.npeaks)
temp_pardict[
'parlimited'] = parlimited if parlimited is not None else [
(False, False)
] * (self.npars * self.npeaks)
for k, v in temp_pardict.items():
if (self.npars * self.npeaks) / len(v) > 1:
n_components = ((self.npars * self.npeaks) / len(v))
if n_components != int(n_components):
raise ValueError("The number of parameter values is not a "
"multiple of the number of allowed "
"parameters.")
temp_pardict[k] = list(v) * int(n_components)
# generate the parinfo dict
# note that 'tied' must be a blank string (i.e. ""), not False, if it is not set
# parlimited, parfixed, and parlimits are all two-element items (tuples or lists)
self.parinfo += [{
'n':
ii + self.npars * jj + vheight,
'value':
float(temp_pardict['parvalues'][ii + self.npars * jj]),
'step':
temp_pardict['parsteps'][ii + self.npars * jj],
'limits':
temp_pardict['parlimits'][ii + self.npars * jj],
'limited':
temp_pardict['parlimited'][ii + self.npars * jj],
'fixed':
temp_pardict['parfixed'][ii + self.npars * jj],
'parname':
temp_pardict['parnames'][ii].upper() + "%0i" % int(jj),
'error':
float(temp_pardict['parerror'][ii + self.npars * jj]),
'tied':
temp_pardict['partied'][ii + self.npars * jj]
if temp_pardict['partied'][ii + self.npars * jj] else ""
} for jj in range(self.npeaks)
for ii in range(self.npars)] # order matters!
log.debug("After Generation step len(parinfo): %i vheight: %s "
"parinfo: %s" % (len(self.parinfo), vheight, self.parinfo))
if debug > True:
import pdb
pdb.set_trace()
# special keyword to specify emission/absorption lines
if negamp is not None:
if negamp:
for p in self.parinfo:
if 'AMP' in p['parname']:
p['limited'] = (p['limited'][0], True)
p['limits'] = (p['limits'][0], 0)
else:
for p in self.parinfo:
if 'AMP' in p['parname']:
p['limited'] = (True, p['limited'][1])
p['limits'] = (0, p['limits'][1])
# This is effectively an override of all that junk above (3/11/2012)
# Much of it is probably unnecessary, but it was easier to do this than
# rewrite the above
self.parinfo = ParinfoList([Parinfo(p) for p in self.parinfo])
# New feature: scaleability
for par in self.parinfo:
if par.parname.lower().strip('0123456789') in ('amplitude', 'amp'):
par.scaleable = True
log.debug("Parinfo has been set: {0}".format(self.parinfo))
log.debug("kwargs {0} were passed.".format(kwargs))
assert self.parinfo != []
return self.parinfo, kwargs
def make_parinfo(self,
quiet=True,
npeaks=1,
params=(20, 20, 14, 1.0, 0.0, 0.5),
parnames=None,
fixed=(False, False, False, False, False, False),
limitedmin=(True, True, True, True, False, True),
limitedmax=(False, False, False, False, False, True),
minpars=(2.73, 2.73, 0, 0, 0, 0),
maxpars=(0, 0, 0, 0, 0, 1),
tied=('', ) * 6,
max_tem_step=1.,
**kwargs):
if not quiet:
log.info("Creating a 'parinfo' from guesses.")
self.npars = len(params) / npeaks
if len(params) != npeaks and (len(params) / self.npars) > npeaks:
npeaks = len(params) / self.npars
self.npeaks = npeaks
if isinstance(params, np.ndarray): params = params.tolist()
# this is actually a hack, even though it's decently elegant
# somehow, parnames was being changed WITHOUT being passed as a variable
# this doesn't make sense - at all - but it happened.
# (it is possible for self.parnames to have npars*npeaks elements where
# npeaks > 1 coming into this function even though only 6 pars are specified;
# _default_parnames is the workaround)
if parnames is None: parnames = copy.copy(self._default_parnames)
partype_dict = dict(
zip([
'params', 'parnames', 'fixed', 'limitedmin', 'limitedmax',
'minpars', 'maxpars', 'tied'
], [
params, parnames, fixed, limitedmin, limitedmax, minpars,
maxpars, tied
]))
# make sure all various things are the right length; if they're
# not, fix them using the defaults
# (you can put in guesses of length 12 but leave the rest length 6;
# this code then doubles the length of everything else)
for partype, parlist in partype_dict.iteritems():
if len(parlist) != self.npars * self.npeaks:
# if you leave the defaults, or enter something that can be
# multiplied by npars to get to the right number of
# gaussians, it will just replicate
if len(parlist) == self.npars:
partype_dict[partype] *= npeaks
elif len(parlist) > self.npars:
# DANGER: THIS SHOULD NOT HAPPEN!
log.warn(
"WARNING! Input parameters were longer than allowed for variable {0}"
.format(parlist))
partype_dict[partype] = partype_dict[partype][:self.npars]
elif parlist == params: # this instance shouldn't really be possible
partype_dict[partype] = [20, 20, 1e10, 1.0, 0.0, 0.5
] * npeaks
elif parlist == fixed:
partype_dict[partype] = [False] * len(params)
elif parlist == limitedmax: # only fortho, fillingfraction have upper limits
partype_dict[partype] = (np.array(parnames) == 'fortho'
) + (np.array(parnames)
== 'fillingfraction')
elif parlist == limitedmin: # no physical values can be negative except velocity
partype_dict[partype] = (np.array(parnames) != 'xoff_v')
elif parlist == minpars:
# all have minima of zero except kinetic temperature, which can't be below CMB.
# Excitation temperature technically can be, but not in this model
partype_dict[partype] = (
(np.array(parnames) == 'tkin') +
(np.array(parnames) == 'tex')) * 2.73
elif parlist == maxpars: # fractions have upper limits of 1.0
partype_dict[partype] = (
(np.array(parnames) == 'fortho') +
(np.array(parnames)
== 'fillingfraction')).astype('float')
elif parlist == parnames: # assumes the right number of parnames (essential)
partype_dict[partype] = list(parnames) * self.npeaks
elif parlist == tied:
partype_dict[partype] = [
_increment_string_number(t, ii * self.npars)
for t in tied for ii in range(self.npeaks)
]
if len(parnames) != len(partype_dict['params']):
raise ValueError("Wrong array lengths AFTER fixing them")
# used in components. Is this just a hack?
self.parnames = partype_dict['parnames']
parinfo = [
{
'n':
ii,
'value':
partype_dict['params'][ii],
'limits':
[partype_dict['minpars'][ii], partype_dict['maxpars'][ii]],
'limited': [
partype_dict['limitedmin'][ii],
partype_dict['limitedmax'][ii]
],
'fixed':
partype_dict['fixed'][ii],
'parname':
partype_dict['parnames'][ii] + str(ii / self.npars),
'tied':
partype_dict['tied'][ii],
'mpmaxstep':
max_tem_step *
float(partype_dict['parnames'][ii] in ('tex', 'tkin')
), # must force small steps in temperature (True = 1.0)
'error':
0
} for ii in xrange(len(partype_dict['params']))
]
# hack: remove 'fixed' pars
#parinfo_with_fixed = parinfo
#parinfo = [p for p in parinfo_with_fixed if not p['fixed']]
#fixed_kwargs = dict((p['parname'].strip("0123456789").lower(),
# p['value'])
# for p in parinfo_with_fixed if p['fixed'])
## don't do this - it breaks the NEXT call because npars != len(parnames) self.parnames = [p['parname'] for p in parinfo]
## this is OK - not a permanent change
#parnames = [p['parname'] for p in parinfo]
## not OK self.npars = len(parinfo)/self.npeaks
parinfo = ParinfoList([Parinfo(p) for p in parinfo],
preserve_order=True)
#import pdb; pdb.set_trace()
return parinfo
def multinh3fit(self,
xax,
data,
err=None,
parinfo=None,
quiet=True,
shh=True,
debug=False,
maxiter=200,
use_lmfit=False,
veryverbose=False,
**kwargs):
"""
Fit multiple nh3 profiles (multiple can be 1)
Inputs:
xax - x axis
data - y axis
npeaks - How many nh3 profiles to fit? Default 1 (this could supersede onedgaussfit)
err - error corresponding to data
These parameters need to have length = 6*npeaks. If npeaks > 1 and length = 6, they will
be replicated npeaks times, otherwise they will be reset to defaults:
params - Fit parameters: [tkin, tex, ntot (or tau), width, offset, ortho fraction] * npeaks
If len(params) % 6 == 0, npeaks will be set to len(params) / 6
fixed - Is parameter fixed?
limitedmin/minpars - set lower limits on each parameter (default: width>0, Tex and Tkin > Tcmb)
limitedmax/maxpars - set upper limits on each parameter
parnames - default parameter names, important for setting kwargs in model ['tkin','tex','ntot','width','xoff_v','fortho']
quiet - should MPFIT output each iteration?
shh - output final parameters?
Returns:
Fit parameters
Model
Fit errors
chi2
"""
if parinfo is None:
parinfo = self.parinfo = self.make_parinfo(**kwargs)
else:
if isinstance(parinfo, ParinfoList):
if not quiet:
log.info("Using user-specified parinfo.")
self.parinfo = parinfo
else:
if not quiet:
log.info(
"Using something like a user-specified parinfo, but not."
)
self.parinfo = ParinfoList([
p if isinstance(p, Parinfo) else Parinfo(p)
for p in parinfo
],
preserve_order=True)
fitfun_kwargs = dict(
(x, y) for (x, y) in kwargs.items()
if x not in ('npeaks', 'params', 'parnames', 'fixed', 'limitedmin',
'limitedmax', 'minpars', 'maxpars', 'tied',
'max_tem_step'))
if 'use_lmfit' in fitfun_kwargs:
raise KeyError("use_lmfit was specified in a location where it "
"is unacceptable")
npars = len(parinfo) / self.npeaks
def mpfitfun(x, y, err):
if err is None:
def f(p, fjac=None):
return [
0,
(y - self.n_ammonia(pars=p,
parnames=parinfo.parnames,
**fitfun_kwargs)(x))
]
else:
def f(p, fjac=None):
return [
0,
(y - self.n_ammonia(
pars=p, parnames=parinfo.parnames, **fitfun_kwargs)
(x)) / err
]
return f
if veryverbose:
log.info("GUESSES: ")
log.info(str(parinfo))
#log.info "\n".join(["%s: %s" % (p['parname'],p['value']) for p in parinfo])
if use_lmfit:
return self.lmfitter(xax,
data,
err=err,
parinfo=parinfo,
quiet=quiet,
debug=debug)
else:
mp = mpfit(mpfitfun(xax, data, err),
parinfo=parinfo,
maxiter=maxiter,
quiet=quiet,
debug=debug)
mpp = mp.params
if mp.perror is not None: mpperr = mp.perror
else: mpperr = mpp * 0
chi2 = mp.fnorm
if mp.status == 0:
raise Exception(mp.errmsg)
for i, p in enumerate(mpp):
parinfo[i]['value'] = p
parinfo[i]['error'] = mpperr[i]
if not shh:
log.info("Fit status: {0}".format(mp.status))
log.info("Fit message: {0}".format(mpfit_messages[mp.status]))
log.info("Fit error message: {0}".format(mp.errmsg))
log.info("Final fit values: ")
for i, p in enumerate(mpp):
log.info(" ".join(
(parinfo[i]['parname'], str(p), " +/- ", str(mpperr[i]))))
log.info(" ".join(("Chi2: ", str(mp.fnorm), " Reduced Chi2: ",
str(mp.fnorm / len(data)), " DOF:",
str(len(data) - len(mpp)))))
self.mp = mp
self.parinfo = parinfo
self.mpp = self.parinfo.values
self.mpperr = self.parinfo.errors
self.mppnames = self.parinfo.names
self.model = self.n_ammonia(pars=self.mpp,
parnames=self.mppnames,
**fitfun_kwargs)(xax)
indiv_parinfo = [
self.parinfo[jj * self.npars:(jj + 1) * self.npars]
for jj in xrange(len(self.parinfo) / self.npars)
]
modelkwargs = [
dict([(p['parname'].strip("0123456789").lower(), p['value'])
for p in pi]) for pi in indiv_parinfo
]
self.tau_list = [
ammonia(xax, return_tau=True, **mk) for mk in modelkwargs
]
return self.mpp, self.model, self.mpperr, chi2
