def main( orbit_option=orbit_option, do_mcmc=do_mcmc, syst_option=syst_option, \
nwalkers=nwalkers, nsteps=nsteps, nburn=nburn, nthreads=nthreads, \
wlrange=wlrange, period=period, pl_pars=pl_pars, tpars=tpars, \
p4_pars=p4_pars, jd0=jd0, apradius=apradius, \
exptime=exptime, specfile=specfile, \
imfile1=imfile1, path=path ):
plt.ioff()
os.chdir(path)
# extract and prep light curves
lc_dict = prep_lc_dict( specfile, wlrange, syst_option, exptime=exptime, \
period=period, jd0=jd0 )
smalldata = prep_trap_model( specfile, imfile1, wlrange ) / exptime
# first get the right parameter guesses
if syst_option == 'trap':
syst_pars = tpars
elif syst_option == 'poly4':
syst_pars = p4_pars
transit_pars = np.concatenate(( pl_pars, syst_pars ))
print('Running first transit-only mpfit...')
fa = { 'lc_dict':lc_dict, 'wlrange':wlrange, 'smalldata':smalldata, 'orbit_option':orbit_option, 'syst_option':syst_option }
results_t1 = mpfit( resids_mpfit_transit, transit_pars, functkw=fa, quiet=1 )
print('Finding outliers...')
lc_dict = cut_outliers_transit( lc_dict, results_t1.params, wlrange, smalldata )
if do_mcmc == False:
print('Running second transit-only mpfit...')
fa = { 'lc_dict':lc_dict, 'wlrange':wlrange, 'smalldata':smalldata, 'orbit_option':orbit_option, 'syst_option':syst_option }
results_t2 = mpfit( resids_mpfit_transit, results_t1.params, functkw=fa, quiet=1 )
# print results_t2.params
# print results_t2.perror
# add final results to dictionary
lc_dict_transit = add_results2dict( lc_dict, results_t2, wlrange, smalldata, do_mcmc, syst_option, orbit_option, jd0 )
# save pickle file
pickname = 'wasp127_mpfit_{0}_{1}_{2:.2f}-{3:.2f}micron_aprad{4}.pkl'.format( orbit_option, syst_option, wlrange[0], wlrange[1], apradius )
f = open( pickname, 'wb' )
pickle.dump( lc_dict_transit, f )
f.close()
elif do_mcmc == True:
print('Running emcee...')
pars0 = initiate_walkers_trap_trans( nwalkers, results_t1.params )
ndim = len( results_t1.params )
sampler = emcee.EnsembleSampler( nwalkers, ndim, log_likelihood_trap_trans, args=[ lc_dict, wlrange, smalldata ], threads=nthreads )
sampler.run_mcmc( pars0, nsteps )
samples = sampler.chain[:, nburn:, :].reshape((-1, ndim))
fig = corner.corner(samples, labels=[ "RpRs", "tpops", "tpopf", "dts", "dtf","slope", "c"], plot_contours=False)
figname = 'wasp19_triangle_{0}_{1}_{2:.2f}-{3:.2f}micron_aprad{4}.eps'.format( orbit_option, syst_option, wlrange[0], wlrange[1], apradius )
fig.savefig(figname)
plt.close()
#find the 50th, 16th and 84th percentiles)
rprs_mcmc, tpops_mcmc, tpopf_mcmc, dts_mcmc, dtf_mcmc, linm_mcmc, linc_mcmc = [(v[1], v[2]-v[1], v[1]-v[0]) for v in zip(*np.percentile(samples,[16,50,84], axis=0))]
mcmc_results = np.array([ rprs_mcmc, tpops_mcmc, tpopf_mcmc, dts_mcmc, dtf_mcmc, linm_mcmc, linc_mcmc ])
lc_dict_transit = add_results2dict( lc_dict, mcmc_results, wlrange, smalldata, do_mcmc, syst_option, orbit_option, jd0 )
pickname = 'wasp127_mcmcfit_{0}_{1}_{2:.2f}-{3:.2f}micron_aprad{4}.pkl'.format( orbit_option, syst_option, wlrange[0], wlrange[1], apradius )
f = open( pickname, 'wb' )
pickle.dump( lc_dict_transit, f )
f.close()
def test_linfit():
x = N.array([
-1.7237128E+00, 1.8712276E+00, -9.6608055E-01, -2.8394297E-01,
1.3416969E+00, 1.3757038E+00, -1.3703436E+00, 4.2581975E-02,
-1.4970151E-01, 8.2065094E-01
])
y = N.array([
1.9000429E-01, 6.5807428E+00, 1.4582725E+00, 2.7270851E+00,
5.5969253E+00, 5.6249280E+00, 0.787615, 3.2599759E+00, 2.9771762E+00,
4.5936475E+00
])
ey = 0.07 * N.ones(y.shape, dtype='float64')
p0 = N.array([1.0, 1.0], dtype='float64') #initial conditions
pactual = N.array([3.2, 1.78]) #actual values used to make data
parbase = {'value': 0., 'fixed': 0, 'limited': [0, 0], 'limits': [0., 0.]}
parinfo = []
for i in range(len(pactual)):
parinfo.append(copy.deepcopy(parbase))
for i in range(len(pactual)):
parinfo[i]['value'] = p0[i]
fa = {'x': x, 'y': y, 'err': ey}
m = mpfit(myfunctlin, p0, parinfo=parinfo, functkw=fa)
if (m.status <= 0):
print('error message = ', m.errmsg)
assert N.allclose(m.params,
N.array([3.20996572, -1.7709542], dtype='float64'))
assert N.allclose(m.perror,
N.array([0.02221018, 0.01893756], dtype='float64'))
chisq = (myfunctlin(m.params, x=x, y=y, err=ey)[1]**2).sum()
assert N.allclose(N.array([chisq], dtype='float64'),
N.array([2.756284983], dtype='float64'))
assert m.dof == 8
return
def zandtfit(plandata, Keplerlc, Teff, Logg, Metalicity, sigma):
p0 = [
plandata['Impactpar'], plandata['Transitduration'],
(plandata['R_plan/R_star'] / 3), Teff, Logg,
np.log10(Metalicity)
] #inital guess (from kepler mast)
#print p0
fa = {
'Keplerlc': Keplerlc,
'sigma': sigma
} #additional variables to be past to the function
parinfo = [{'value':p0[0], 'fixed':False, 'limited':[True, True], 'limits':[0,2]}, {'value':p0[1], 'fixed':False}, {'value':p0[2], 'fixed':False},\
{'value':p0[3], 'fixed':False, 'limited':[True,True], 'limits':[0,40000]}, {'value':p0[4], 'fixed':False, 'limited':[True,True], 'limits':[0,5.0]},\
{'value':p0[5], 'fixed':False, 'limited':[True,True], 'limits':[-5,1]} ]
m = mpfit(minimisefunction,
p0,
functkw=fa,
quiet=0,
maxiter=25,
parinfo=parinfo,
ftol=0.0001) #run the mpfit for the best fit
#print m #testing
bestfitarray = m.params
errors = m.perror #these need to be properly stored
covar = m.covar #these need to be properly stored
if errors == None:
errors = [0, 0, 0, 0, 0, 0, 0]
#print bestfitarray
return np.abs(
bestfitarray[0]
), bestfitarray[1], bestfitarray[2], bestfitarray[3], bestfitarray[4], (
10**bestfitarray[5]), bestfitarray, errors, covar
def fitSkydipn(x, y, err, val0):
"""
DES: fits a skydip signal-elevation function
3 parameters fitted: opt, tauz, feff
INP: (f array) x = x data
(f array) y = y data
(f array) err=errors on y values
(3xf list) val0 = first guess values, in this order:
[Tatm, tau_z, F_eff]
"""
parname = ['Tatm', 'tau_z', 'F_eff']
p = val0
parinfo = []
for i in range(3):
parinfo.extend([{'parname': parname[i], \
'value': p[i], \
'fixed': 0, \
'limits' : [0.,0.],\
'limited': [0,0]}])
#parinfo[0]['limits'] = [100.,520.]
#parinfo[0]['limited'] = [0,0]
#parinfo[1]['limits'] = [0.,10.] # don't observe at tau>3 !!
#parinfo[2]['limits'] = [0.2,1.0]
fa = {'x': x, 'y': y, 'err': err}
m = mpfit.mpfit(skydipn, p, parinfo=parinfo, functkw=fa, quiet=1, debug=0)
if (m.status <= 0):
raise BoaError, str("mpfit failed: %s" % (m.errmsg))
return m
def fitdata(a4, I, Ierr):
fa = {'x': a4, 'I': I, 'Ierr': Ierr}
parinfo = []
lowerm = []
upperm = []
#[amplitude center fwhm background slope]
mid = a4[I == I.max()]
pfit = N.array([I.max(), mid, (a4[a4.size - 1] - a4[0]) / 4,
I.min(), 0], 'd')
lowerm = [0, -2 * a4[0], 0, 0, 0]
upperm = [
10 * I.max(), 2 * a4[a4.size - 1], (a4[a4.size - 1] - a4[0]),
2 * I.min(0) + 1, 1
]
print lowerm
print upperm
print pfit
for i in range(pfit.size):
## parinfo.append({'value':0., 'fixed':0, 'limited':[1,1],\
## 'limits':[lowerm[i], upperm[i]], 'step':0})
parinfo.append({'value':0., 'fixed':0, 'limited':[1,1],\
'limits':[lowerm[i], upperm[i]], 'step':0})
parinfo[4]['fixed'] = 1
m = mpfit.mpfit(chisqrcalc, pfit, parinfo=parinfo, functkw=fa, quiet=1)
print 'status = ', m.status
if (m.status <= 0): print 'error message = ', m.errmsg
p = N.array(m.params, 'd')
# print 'p ', p
dof = a4.size - p.size
# print 'dof ',dof
std = m.perror * N.sqrt(m.fnorm / dof)
return p, std
def do_mpfit(x,y,covarin,guess,functname=thepolynomial):
# check if covariance or error bars were given
covar=covarin
if np.size(np.shape(covarin)) == 1:
err=covarin
print('err')
#Prepare arguments for mpfit
fa={'x':double(x),'y':double(y),'err':double(err),'functname':functname}
costfct=fdeviates
else:
print('covar')
#Fist do a SVD decomposition
u,s,v=np.linalg.svd(covarin)
#Prepare arguments for mpfit
fa={'x':double(x),'y':double(y),'svdvals':double(s),'v':double(v),'functname':functname}
costfct=chi2svd
#Run MPFIT
mpf = mpfit.mpfit(costfct, guess, functkw=fa, autoderivative=1)
print('Status of the Fit',mpf.status)
print('Chi2=',mpf.fnorm)
print('ndf=',mpf.dof)
print('Fitted params:',mpf.params)
return(mpf,mpf.params,mpf.perror,mpf.covar)
def findcenter(gal_lin):
'''
Function to find the center of continuum of the galaxy
by fitting a Gaussian to the continuum
'''
shape=np.shape(gal_lin)
center=int(shape[0]/2.)
box=[center-150,center+150,800,1000]
sumforgauss=np.sum(gal_lin[box[0]:box[1],box[2]:box[3]],axis=1)
offset=center-150.
xaxis=np.arange(len(sumforgauss))+offset
gerr=np.zeros(len(sumforgauss))+0.5
amp=np.max(sumforgauss)
cen=np.argmax(sumforgauss)+offset
#run mpfit
p0=[amp,cen,10.,30.]
def myfunct(p, fjac=None, x=None, y=None, err=None):
model = p[0] * np.exp(-((x-p[1])**2.)/(2.*p[2]**2.)) + p[3]
status = 0
return([status, (y-model)/err])
fa = {'x':xaxis, 'y':sumforgauss, 'err':gerr}
m=mpfit(myfunct,p0,functkw=fa)
#print (m.params[1])
return int(m.params[1])
def test_linfit():
x=N.array([-1.7237128E+00,1.8712276E+00,-9.6608055E-01,
-2.8394297E-01,1.3416969E+00,1.3757038E+00,
-1.3703436E+00,4.2581975E-02,-1.4970151E-01,
8.2065094E-01])
y=N.array([1.9000429E-01,6.5807428E+00,1.4582725E+00,
2.7270851E+00,5.5969253E+00,5.6249280E+00,
0.787615,3.2599759E+00,2.9771762E+00,
4.5936475E+00])
ey=0.07*N.ones(y.shape,dtype='float64')
p0=N.array([1.0,1.0],dtype='float64') #initial conditions
pactual=N.array([3.2,1.78]) #actual values used to make data
parbase={'value':0., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.]}
parinfo=[]
for i in range(len(pactual)):
parinfo.append(copy.deepcopy(parbase))
for i in range(len(pactual)):
parinfo[i]['value']=p0[i]
fa = {'x':x, 'y':y, 'err':ey}
m = mpfit(myfunctlin, p0, parinfo=parinfo,functkw=fa)
if (m.status <= 0):
print 'error message = ', m.errmsg
assert N.allclose(m.params,N.array([ 3.20996572, -1.7709542 ],dtype='float64'))
assert N.allclose(m.perror,N.array([ 0.02221018, 0.01893756],dtype='float64'))
chisq=(myfunctlin(m.params, x=x, y=y, err=ey)[1]**2).sum()
assert N.allclose(N.array([chisq],dtype='float64'),N.array([2.756284983],dtype='float64'))
assert m.dof==8
return
def gaussfit2d_mp(xgrid, ygrid, zgrid, error, p0, norotation=False):
##xgrid/ygrid are the 2D grid of independent variables on which co calculate the model
##zgrid are the dependent variable measurements to fit to, error is the uncertainty on zgrid.
##p0=[normalization, mean1,mean2, stdev1,stdev2,background_const,rotation angle] the starting point for the fit
##set norotation = True to fix the rotation to zero.
fa = {'x': xgrid, 'y': ygrid, 'z': zgrid, 'err': error}
parinfo = [{
'fixed': 0,
'limited': [0, 0],
'limits': [0., 0.]
} for dddd in range(p0.shape[0])]
if norotation == True:
parinfo[6]['fixed'] = 1
p0[6] = 0.0000
thisfit = mpfit.mpfit(gaussian_2d,
p0,
functkw=fa,
quiet=True,
parinfo=parinfo)
fitpars = thisfit.params
fitcov = thisfit.covar
themodel = gaussian_2d(fitpars,
x=xgrid,
y=ygrid,
z=zgrid,
err=error,
model=True)
return fitpars, fitcov, themodel
def fitdata(a4, I, Ierr):
fa = {'x': a4, 'I': I, 'Ierr': Ierr}
parinfo = []
lowerm = []
upperm = []
#[amplitude center fwhm background slope]
mid = a4[I == I.max()]
pfit = N.array([I.max(), mid, (a4[a4.size - 1] - a4[0]) / 4,
I.min(), 0], 'd')
lowerm = [0, -2 * a4[0], 0, 0, 0]
upperm = [
10 * I.max(), 2 * a4[a4.size - 1], (a4[a4.size - 1] - a4[0]),
2 * I.min(0) + 1, 1
]
print lowerm
print upperm
print pfit
for i in range(pfit.size):
## parinfo.append({'value':0., 'fixed':0, 'limited':[1,1],\
## 'limits':[lowerm[i], upperm[i]], 'step':0})
parinfo.append({'value':0., 'fixed':0, 'limited':[1,1],\
'limits':[lowerm[i], upperm[i]], 'step':0})
parinfo[4]['fixed'] = 1
m = mpfit.mpfit(chisqrcalc, pfit, parinfo=parinfo, functkw=fa, quiet=1)
print 'status = ', m.status
if (m.status <= 0): print 'error message = ', m.errmsg
p = N.array(m.params, 'd')
# print 'p ', p
dof = a4.size - p.size
# print 'dof ',dof
std = m.perror * N.sqrt(m.fnorm / dof)
return p, std
def zandtfit(Inclination, Planetaryradius_SI, Keplerlc, Teff, Logg, Metalicity, sigma, Orbitalperiod_SI, KeplerID, Planetnumberer):
Planetaryradius_Earth = (Planetaryradius_SI) / 6.3675E6 #convert the planetary radius to earth radii
p0 = [math.cos(1.570795), Planetaryradius_Earth, Teff, Logg, np.log10(Metalicity)] #inital guess (from kepler mast and headers)
fa = {'Keplerlc':Keplerlc, 'sigma':sigma, 'Orbitalperiod_SI':Orbitalperiod_SI, 'KeplerID':KeplerID, 'Planetnumberer':Planetnumberer} #additional variables to be past to the function
parinfo = [{'value':p0[0], 'fixed':0, 'limited':[1,1], 'limits':[-1,1], 'step':0.01}, {'value':p0[1], 'fixed':0, 'limited':[1,0], 'limits':[0.01,100], 'step':0},\
{'value':p0[2], 'fixed':0, 'limited':[1,1], 'limits':[0,40000], 'step':0}, {'value':p0[3], 'fixed':0, 'limited':[1,1], 'limits':[0,5.0], 'step':0},\
{'value':p0[4], 'fixed':0, 'limited':[1,1], 'limits':[-5,1], 'step':0} ]
m = mpfit(minimisefunction, p0, functkw = fa, quiet = 0, maxiter = 35, parinfo = parinfo, ftol=0.0001) #run the mpfit for the best fit
#print m #testing
bestfitarray = m.params #extract the results
errors = m.perror #these need to be properly stored
covar = m.covar #these need to be properly stored
if errors == None: #i.e. if mpfits is a pain and decides to not return the errors or covariance properly
errors = np.zeros(5) #create an empty errors array
if covar == None: #if no covariance array is produced, create an array of zeros so that there are no problems later
covar = np.zeros((5,5))
#print bestfitarray
Inclination_fit = math.acos(bestfitarray[0]) #Extract the fit inclination
Planetaryradius_SI_fit = bestfitarray[1] * 6.3675E6 #Extract the fit planetary radius and convert it to SI units
Teff_fit = bestfitarray[2] #Extract the fit Teff
Logg_fit = bestfitarray[3] #Extract the fit logg of the surface gravity
Metalicity_fit = (10**bestfitarray[4]) #Extract the metalicity and remove the logg
Mstar_SI, Rstar_SI, Stellardensity_SI = RMrhocalc(Teff_fit, Logg_fit, Metalicity_fit) #Calculate the stellar mass, radius and density
semimajoraxis_SI = semimajoraxiscalc(Orbitalperiod_SI, Mstar_SI) #calculate the semi-major axis
T_dur_SI = Transitdurationcalc(Rstar_SI, Planetaryradius_SI_fit, semimajoraxis_SI, Inclination_fit, Orbitalperiod_SI) #calculate the transit duration
return Inclination_fit, Planetaryradius_SI_fit, Teff_fit, Logg_fit, Metalicity_fit, Mstar_SI, Rstar_SI, Stellardensity_SI, semimajoraxis_SI, T_dur_SI, errors, covar
def _run_mpfit(self):
"""Run an MPFIT regression"""
# Make up the PARINFO list
parinfo = []
for i in range(len(self._guess)):
pdict = {
"value": self._guess[i].copy(),
"fixed": 0,
"limited": self._ilimited[i],
"limits": self._ilimits[i],
}
parinfo.append(pdict)
quiet = 1
if self.debug:
quiet = 0
m = mpfit.mpfit(self._residualsMPFIT, self._guess, parinfo=parinfo, quiet=quiet, debug=self.debug)
self._result = m.params
self._niter = m.niter
if m.perror is not None:
self._stdev = m.perror
else:
self._stdev = zeros(len(self._guess))
if m.covar is not None:
self._covar = m.covar
else:
self._covar = zeros((len(self._guess), len(self._guess)))
self._mpfit = m
def test_gaussfit_fixed():
x = N.array([-1.7237128E+00,1.8712276E+00,-9.6608055E-01,
-2.8394297E-01,1.3416969E+00,1.3757038E+00,
-1.3703436E+00,4.2581975E-02,-1.4970151E-01,
8.2065094E-01])
y = N.array([-4.4494256E-02,8.7324673E-01,7.4443483E-01,
4.7631559E+00,1.7187297E-01,1.1639182E-01,
1.5646480E+00,5.2322268E+00,4.2543168E+00,
6.2792623E-01])
ey=0.5*N.ones(y.shape,dtype='float64')
p0=N.array([0.0, 1.0, 0.0, 0.1],dtype='float64') #initial conditions
pactual=N.array([0.0, 4.70, 0.0, 0.5]) #actual values used to make data
parbase={'value':0., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.]}
parinfo=[]
for i in range(len(pactual)):
parinfo.append(copy.deepcopy(parbase))
parinfo[i]['value'] = p0[i]
parinfo[0]['fixed'] = 1
parinfo[2]['fixed'] = 1
fa = {'x': x, 'y': y, 'err': ey}
m = mpfit(myfunctgauss, p0, parinfo = parinfo, functkw = fa)
if (m.status <= 0):
print 'status = ', m.status
assert N.allclose(m.params, N.array([ 0., 5.059244, 0., 0.479746 ], dtype='float64'))
assert N.allclose(m.perror, N.array([ 0., 0.329307, 0., 0.053804 ], dtype='float64'))
chisq = (myfunctgauss(m.params, x=x, y=y, err=ey)[1]**2).sum()
assert N.allclose(N.array([chisq], dtype='float64'), N.array([15.516134], dtype='float64'))
assert m.dof == 8
return
def test_gaussfit_fixed():
x = N.array([-1.7237128E+00,1.8712276E+00,-9.6608055E-01,
-2.8394297E-01,1.3416969E+00,1.3757038E+00,
-1.3703436E+00,4.2581975E-02,-1.4970151E-01,
8.2065094E-01])
y = N.array([-4.4494256E-02,8.7324673E-01,7.4443483E-01,
4.7631559E+00,1.7187297E-01,1.1639182E-01,
1.5646480E+00,5.2322268E+00,4.2543168E+00,
6.2792623E-01])
ey=0.5*N.ones(y.shape,dtype='float64')
p0=N.array([0.0, 1.0, 0.0, 0.1],dtype='float64') #initial conditions
pactual=N.array([0.0, 4.70, 0.0, 0.5]) #actual values used to make data
parbase={'value':0., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.]}
parinfo=[]
for i in range(len(pactual)):
parinfo.append(copy.deepcopy(parbase))
parinfo[i]['value'] = p0[i]
parinfo[0]['fixed'] = 1
parinfo[2]['fixed'] = 1
fa = {'x': x, 'y': y, 'err': ey}
m = mpfit(myfunctgauss, p0, parinfo = parinfo, functkw = fa)
if (m.status <= 0):
print 'status = ', m.status
assert N.allclose(m.params, N.array([ 0., 5.059244, 0., 0.479746 ], dtype='float64'))
assert N.allclose(m.perror, N.array([ 0., 0.329307, 0., 0.053804 ], dtype='float64'))
chisq = (myfunctgauss(m.params, x=x, y=y, err=ey)[1]**2).sum()
assert N.allclose(N.array([chisq], dtype='float64'), N.array([15.516134], dtype='float64'))
assert m.dof == 8
return
def newfit2( infile, outfile, xcol, ycol, p0, \
constrain=[ {'fixed':0}, {'fixed':0}, {'fixed':0}, {'fixed':0}, {'fixed':0} ] ) :
[x,y] = getdata( infile, xcol, ycol )
err = numpy.ones( len(x), dtype=float )
parinfo = [ {'fixed':0}, {'fixed':0}, {'fixed':0}, {'fixed':0}, {'fixed':0} ]
fa = {'x':x, 'y':y, 'err':err}
m = mpfit.mpfit(myfunc, p0, functkw=fa, parinfo=constrain)
fout = open( outfile, "w" )
fout.write("# %s\n" % outfile )
fout.write("# fit to spectrum %s, xcol=%d, ycol=%d\n" % ( infile, xcol, ycol ) )
fout.write("# final fit:\n")
fout.write("# yavg= %0.5f %d \n" % (m.params[0], constrain[0]['fixed']) )
fout.write("# slope = %0.5f %d\n" % (m.params[1], constrain[1]['fixed']) )
fout.write("# amp = %0.5f %d\n" % (m.params[2], constrain[2]['fixed']) )
fout.write("# v0 = %0.3f %d\n" % (m.params[3], constrain[3]['fixed']) )
fout.write("# FWHM = %0.3f %d\n" % (m.params[4], constrain[4]['fixed']) )
fout.write("#\n")
xavg = 0.5 * (x[0] + x[len(x)-1])
fit = eval('m.params[0] + m.params[1]*(x-xavg) + \
m.params[2]*numpy.exp(-2.772 * pow( (x-m.params[3])/m.params[4], 2.))')
# fit to raw data, including slope
yminusSlope = eval('y - m.params[1]*(x-xavg)')
# raw data with linear slope removed
gfit = eval('m.params[0] + m.params[2]*numpy.exp(-2.772 * pow( (x-m.params[3])/m.params[4], 2.))')
# gaussian fit to data with slope removed
yminusSlopeNorm = eval('yminusSlope/m.params[0]')
# normalized absorption
gfitNorm = eval('gfit/m.params[0]')
for i in range(0,len(x)) :
fout.write(" %10.3f %9.4f %9.4f %9.4f %9.4f %9.5f %9.5f\n" % \
(x[i],y[i],fit[i],yminusSlope[i],gfit[i],yminusSlopeNorm[i],gfitNorm[i] ) )
fout.close()
def gaussfit_mp(xdata, ydata, error, p0, nocurve=False):
fa = {'x': xdata, 'y': ydata, 'err': error}
parinfo = [{
'fixed': 0,
'limited': [0, 0],
'limits': [0., 0.]
} for dddd in range(p0.shape[0])]
parinfo[2]['limited'] = [1, 0]
parinfo[2]['limits'] = [0.0, 100.]
if nocurve == True:
parinfo[4]['fixed'] = 1
parinfo[5]['fixed'] = 1
p0[4] = 0.0
p0[5] = 0.0
thisfit = mpfit.mpfit(fit_funcmp,
p0,
functkw=fa,
quiet=True,
parinfo=parinfo)
fitpars = thisfit.params
fitcov = thisfit.covar
thecurve = fit_funcmp(fitpars, x=xdata, y=ydata, err=error, model=True)
#pdb.set_trace()
return fitpars, fitcov, thecurve
def test_quadfit():
x = N.array([-1.7237128E+00,1.8712276E+00,-9.6608055E-01,
-2.8394297E-01,1.3416969E+00,1.3757038E+00,
-1.3703436E+00,4.2581975E-02,-1.4970151E-01,
8.2065094E-01])
y = N.array([2.3095947E+01,2.6449392E+01,1.0204468E+01,
5.40507,1.5787588E+01,1.6520903E+01,
1.5971818E+01,4.7668524E+00,4.9337711E+00,
8.7348375E+00])
ey=0.2*N.ones(y.shape,dtype='float64')
p0=N.array([1.0,1.0,1.0],dtype='float64') #initial conditions
pactual=N.array([4.7, 0.0, 6.2]) #actual values used to make data
parbase={'value':0., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.]}
parinfo=[]
for i in range(len(pactual)):
parinfo.append(copy.deepcopy(parbase))
parinfo[i]['value'] = p0[i]
fa = {'x': x, 'y': y, 'err': ey}
m = mpfit(myfunctquad, p0, parinfo = parinfo, functkw = fa)
if (m.status <= 0):
print 'status = ', m.status
print m.perror
assert N.allclose(m.params, N.array([ 4.703829, 0.062586, 6.163087], dtype='float64'))
assert N.allclose(m.perror, N.array([ 0.097512, 0.054802, 0.054433], dtype='float64'))
chisq = (myfunctquad(m.params, x=x, y=y, err=ey)[1]**2).sum()
assert N.allclose(N.array([chisq], dtype='float64'), N.array([5.679323], dtype='float64'))
assert m.dof == 7
return
def gaussfit_mp_gridless(zgrid, error, p0, norotation=False):
##construct the dependent variables
x, y = np.meshgrid(np.arange(zgrid.shape[0], dtype=float),
np.arange(zgrid[1], dtype=float))
##zgrid are the dependent variable measurements to fit to, error is the uncertainty on zgrid.
##p0=[normalization, mean1,mean2, stdev1,stdev2,background_const,rotation angle] the starting point for the fit
##set norotation = True to fix the rotation to zero.
fa = {'x': xgrid, 'y': ygrid, 'z': zgrid, 'err': error}
parinfo = [{
'fixed': 0,
'limited': [0, 0],
'limits': [0., 0.]
} for dddd in range(p0.shape[0])]
##fix rotation if asked to
if norotation == True:
parinfo[6]['fixed'] = 1
p0[6] = 0.0000
thisfit = mpfit.mpfit(gaussian_2d,
p0,
functkw=fa,
quiet=True,
parinfo=parinfo)
fitpars = thisfit.params
fitcov = thisfit.covar
themodel = gaussian_2d(fitpars,
x=xgrid,
y=ygrid,
z=zgrid,
err=error,
model=True)
return fitpars, fitcov, themodel
def fitSkydip(x, y, err, val0, fixT=1):
"""
DES: fits a skydip signal-elevation function
only 2 parameters fitted: opt, tauz
INP: (f array) x = x data
(f array) y = y data
(f array) err=errors on y values
(5xf list) val0 = first guess values, in this order:
[coupling, Tcabin, Tatm, tau_z, F_eff]
"""
parname = ['Coupling', 'Tcabin', 'Tatm', 'tau_z', 'F_eff']
p = val0
parinfo = []
for i in range(5):
parinfo.extend([{'parname': parname[i], \
'value': p[i], \
'fixed': 0, \
'limits' : [0.,0.],\
'limited': [1,1]}])
parinfo[0]['limits'] = [0., 1.]
parinfo[3]['limits'] = [0., 3.] # don't observe at tau>3 !!
if fixT:
parinfo[1]['fixed'] = parinfo[4]['fixed'] = parinfo[2]['fixed'] = 1
else:
parinfo[1]['fixed'] = parinfo[4]['fixed'] = 1
parinfo[1]['limits'] = [200, 320]
parinfo[2]['limits'] = [100, 320]
parinfo[4]['limits'] = [0., 1.]
fa = {'x': x, 'y': y, 'err': err}
m = mpfit.mpfit(skydip, p, parinfo=parinfo, functkw=fa, quiet=1, debug=0)
if (m.status <= 0):
raise BoaError, str("mpfit failed: %s" % (m.errmsg))
return m
def test_quadfit():
x = N.array([-1.7237128E+00,1.8712276E+00,-9.6608055E-01,
-2.8394297E-01,1.3416969E+00,1.3757038E+00,
-1.3703436E+00,4.2581975E-02,-1.4970151E-01,
8.2065094E-01])
y = N.array([2.3095947E+01,2.6449392E+01,1.0204468E+01,
5.40507,1.5787588E+01,1.6520903E+01,
1.5971818E+01,4.7668524E+00,4.9337711E+00,
8.7348375E+00])
ey=0.2*N.ones(y.shape,dtype='float64')
p0=N.array([1.0,1.0,1.0],dtype='float64') #initial conditions
pactual=N.array([4.7, 0.0, 6.2]) #actual values used to make data
parbase={'value':0., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.]}
parinfo=[]
for i in range(len(pactual)):
parinfo.append(copy.deepcopy(parbase))
parinfo[i]['value'] = p0[i]
fa = {'x': x, 'y': y, 'err': ey}
m = mpfit(myfunctquad, p0, parinfo = parinfo, functkw = fa)
if (m.status <= 0):
print 'status = ', m.status
print m.perror
assert N.allclose(m.params, N.array([ 4.703829, 0.062586, 6.163087], dtype='float64'))
assert N.allclose(m.perror, N.array([ 0.097512, 0.054802, 0.054433], dtype='float64'))
chisq = (myfunctquad(m.params, x=x, y=y, err=ey)[1]**2).sum()
assert N.allclose(N.array([chisq], dtype='float64'), N.array([5.679323], dtype='float64'))
assert m.dof == 7
return
def fitSkydipFull(x, y, err, val0):
"""
DES: fits a skydip signal-elevation function
6 parameters fitted:
p[0] = Tatm
p[1] = tau
p[2] = Feff
p[3] = Jy2K
p[4] = offset
p[5] = coupl
"""
parname = ['Tatm', 'tau_z', 'F_eff', 'Jy2K', 'offset', 'coupl']
p = val0
parinfo = []
for i in range(6):
parinfo.extend([{'parname': parname[i], \
'value': p[i], \
'fixed': 0, \
'limits' : [0.,0.],\
'limited': [0,0]}])
fa = {'x': x, 'y': y, 'err': err}
m = mpfit.mpfit(skydipFull,
p,
parinfo=parinfo,
functkw=fa,
quiet=1,
debug=0)
if (m.status <= 0):
raise BoaError, str("mpfit failed: %s" % (m.errmsg))
return m
def testMagModel(self):
f = np.loadtxt("resonatorTestQIMA.txt", usecols=(1,))
I = np.loadtxt("resonatorTestQIMA.txt", usecols=(2,))
Ierr = 0.001*np.ones(len(I))
Q = np.loadtxt("resonatorTestQIMA.txt", usecols=(3,))
Qerr = 0.001*np.ones(len(Q))
res = Resonator(f,I,Ierr,Q,Qerr)
qfp = res.quickFitPrep()
x = qfp['functkw']['x']
p = qfp['p0']
yModel = Resonator.magModel(x,p)
yData = qfp['functkw']['y']
err = qfp['functkw']['err']
mdl = Resonator.magDiffLin(p, fjac=None, x=x, y=yData, err=err)
chi2 = np.power(mdl[1],2).sum()
print "chi2=",chi2
parinfo = qfp['parinfo']
functkw = qfp['functkw']
m = mpfit(Resonator.magDiffLin, p, parinfo=parinfo, functkw=functkw, quiet=1)
print "m=",m
pFit = m.params
yFit = Resonator.magModel(x,pFit)
plt.clf()
plt.plot(x,yData,label="data")
plt.plot(x,yModel,label="first guess model")
plt.plot(x,yFit,label="fit model")
plt.legend(loc='lower right')
title="Q=%.1f f0=%.5f carrier=%.3f depth=%.2f \n slope=%.1f curve=%.1f w=%.1f"%tuple(pFit.tolist())
plt.title(title)
plt.savefig("testMagModel.png")
def ab_fit(src,xd,td,lguess,xmin_id,xmax_id,evmin1,evmax1,evmin2,evmax2):
npar = len(lguess)
guessp = np.array(lguess, dtype='float64')
plimd = [[False,False]]*npar
plims = [[0.,0.]]*npar
parbase = {'value': 0., 'fixed': 0, 'parname': '', 'limited': [0, 0], 'limits': [0., 0.]}
pname = ['bg']*npar
pfix = [False]*npar
count = 1
for i in range(npar):
if(i==0):
pname[i] = 'Tcont'
if( ((i-1)%3 == 0) and (i>0) ):
pname[i] = 'tau'+str(count)
if( ((i-2)%3 == 0) and (i>0) ):
pname[i] = 'v0'+str(count)
if( ((i-3)%3 == 0) and (i>0) ):
pname[i] = 'wid'+str(count)
count = count + 1
parinfo = []
for i in range(len(guessp)):
parinfo.append(copy.deepcopy(parbase))
for i in range(len(guessp)):
parinfo[i]['value'] = guessp[i]
parinfo[i]['fixed'] = pfix[i]
parinfo[i]['parname'] = pname[i]
parinfo[i]['limited'] = plimd[i]
## data and 1-sigma uncertainty ##
x = xd[xmin_id:xmax_id]
y = td[xmin_id:xmax_id]
sg,\
erry = get_tb_sigma(xd, td, evmin1, evmax1, evmin2, evmax2)
erry = erry[xmin_id:xmax_id]
x = x.astype(np.float64)
y = y.astype(np.float64)
erry = erry.astype(np.float64)
fa = {'x':x, 'y':y, 'err':erry}
mp = mpfit(myabfunc, guessp, parinfo=parinfo, functkw=fa, quiet=True)
## Fit values of Tau ##
abp = mp.params
abper = mp.perror
fit = 0.
for i in range(1, len(abp), 3):
fit = fit + abp[i]*np.exp(- ( (x-abp[i+1])/(0.6005612*abp[i+2]))**2)
fit = np.exp(-fit)
# fit = abp[1]*fit
return x,fit,y/abp[0],abp,abper,npar,parbase,pname,parinfo
def onedgaborfit(xax, data, err=None,
params=[0,1,0,1,1,0],fixed=[False,False,False,False,False,False],
limitedmin=[False,False,False,True,True,True],
limitedmax=[False,False,False,False,False,True], minpars=[0,0,0,0,0,0],
maxpars=[0,0,0,0,0,360], quiet=True, shh=True,
veryverbose=False):
"""
Inputs:
xax - x axis
data - y axis
err - error corresponding to data
params - Fit parameters: Height of background, Amplitude, Shift, Width, Wavelength, Phase
fixed - Is parameter fixed?
limitedmin/minpars - set lower limits on each parameter (default: width>0)
limitedmax/maxpars - set upper limits on each parameter
quiet - should MPFIT output each iteration?
shh - output final parameters?
Returns:
Fit parameters
Model
Fit errors
chi2
"""
def mpfitfun(x,y,err):
if err is None:
def f(p,fjac=None): return [0,(y-onedgabor(x,*p))]
else:
def f(p,fjac=None): return [0,(y-onedgabor(x,*p))/err]
return f
if xax == None:
xax = np.arange(len(data))
parinfo = [ {'n':0,'value':params[0],'limits':[minpars[0],maxpars[0]],'limited':[limitedmin[0],limitedmax[0]],'fixed':fixed[0],'parname':"HEIGHT",'error':0} ,
{'n':1,'value':params[1],'limits':[minpars[1],maxpars[1]],'limited':[limitedmin[1],limitedmax[1]],'fixed':fixed[1],'parname':"AMPLITUDE",'error':0},
{'n':2,'value':params[2],'limits':[minpars[2],maxpars[2]],'limited':[limitedmin[2],limitedmax[2]],'fixed':fixed[2],'parname':"SHIFT",'error':0},
{'n':3,'value':params[3],'limits':[minpars[3],maxpars[3]],'limited':[limitedmin[3],limitedmax[3]],'fixed':fixed[3],'parname':"WIDTH",'error':0},
{'n':4,'value':params[4],'limits':[minpars[4],maxpars[4]],'limited':[limitedmin[4],limitedmax[4]],'fixed':fixed[4],'parname':"WAVELENGTH",'error':0},
{'n':5,'value':params[5],'limits':[minpars[5],maxpars[5]],'limited':[limitedmin[5],limitedmax[5]],'fixed':fixed[5],'parname':"PHASE",'error':0}]
mp = mpfit(mpfitfun(xax,data,err),parinfo=parinfo,quiet=quiet)
mpp = mp.params
mpperr = mp.perror
chi2 = mp.fnorm
if mp.status == 0:
raise Exception(mp.errmsg)
if (not shh) or veryverbose:
print "Fit status: ",mp.status
for i,p in enumerate(mpp):
parinfo[i]['value'] = p
print parinfo[i]['parname'],p," +/- ",mpperr[i]
print "Chi2: ",mp.fnorm," Reduced Chi2: ",mp.fnorm/len(data)," DOF:",len(data)-len(mpp)
return mpp,onedgabor(xax,*mpp),mpperr,chi2
def delPuncorr_bestfit(k, delP_over_P):
'''
'''
fa = {'x': k, 'y': delP_over_P}
param_guess = [-4.0]
bestfit = mpfit.mpfit(nongauss_poly_mpfit, param_guess, functkw=fa, quiet=True)
return bestfit.params
def quickFit(self, quiet=1):
qfp = self.quickFitPrep()
m = mpfit(Resonator.magDiffLin,
qfp['p0'],
parinfo=qfp['parinfo'],
functkw=qfp['functkw'],
quiet=quiet)
return m
def mpfitexpr(func, x, y, err , start_params, check=True, full_output=False,
imports=None, **kw):
"""Fit the used defined expression to the data
Input:
- func: string with the function definition
- x: x vector
- y: y vector
- err: vector with the errors of y
- start_params: the starting parameters for the fit
Output:
- The tuple (params, yfit) with best-fit params and the values of func evaluated at x
Keywords:
- check: boolean parameter. If true(default) the function will be checked for sanity
- full_output: boolean parameter. If True(default is False) then instead of best-fit parameters the mpfit object is returned
- imports: list of strings, of optional modules to be imported, required to evaluate the function
Example:
params,yfit=mpfitexpr('p[0]+p[2]*(x-p[1])',x,y,err,[0,10,1])
If you need to use numpy and scipy functions in your function, then
you must to use the full names of these functions, e.g.:
numpy.sin, numpy.cos etc.
This function is motivated by mpfitexpr() from wonderful MPFIT IDL package
written by Craig Markwardt
"""
hash={}
hash['numpy']=numpy
hash['scipy']=scipy
if imports is not None:
for i in imports:
#exec '%s=__import__("%s")'%(a,b) in globals(),locals()
hash[i]= __import__(i)
def myfunc(p,fjac=None,x=None, y=None, err=None):
return [0, eval('(y-(%s))/(err)'%func,hash,locals())]
myre = "(?:[^a-zA-Z_]|^)p\[(\d+)\]"
r = re.compile(myre)
maxp = -1
for m in re.finditer(r,func):
curp = int(m.group(1))
maxp = curp if curp > maxp else maxp
if check:
if maxp == -1:
raise Exception("wrong function format")
if maxp + 1 != len(start_params):
raise Exception("the length of the start_params != the length of the parameter verctor of the function")
fa={'x' : x, 'y' : y,'err' : err}
res = mpfit.mpfit(myfunc,start_params,functkw=fa,**kw)
yfit = eval(func, hash, {'x':x, 'p': res.params})
if full_output:
return (res, yfit)
else:
return (res.params, yfit)
def mpfitexpr(func, x, y, err, start_params, check=True, full_output=False,
imports=None, **kw):
"""Fit the used defined expression to the data
Input:
- func: string with the function definition
- x: x vector
- y: y vector
- err: vector with the errors of y
- start_params: the starting parameters for the fit
Output:
- The tuple (params, yfit) with best-fit params and the values of func evaluated at x
Keywords:
- check: boolean parameter. If true(default) the function will be checked for sanity
- full_output: boolean parameter. If True(default is False) then instead of best-fit parameters the mpfit object is returned
- imports: list of strings, of optional modules to be imported, required to evaluate the function
Example:
params,yfit=mpfitexpr('p[0]+p[2]*(x-p[1])',x,y,err,[0,10,1])
If you need to use numpy and scipy functions in your function, then
you must to use the full names of these functions, e.g.:
numpy.sin, numpy.cos etc.
This function is motivated by mpfitexpr() from wonderful MPFIT IDL package
written by Craig Markwardt
"""
hash = {}
hash['numpy'] = numpy
hash['scipy'] = scipy
if imports is not None:
for i in imports:
#exec '%s=__import__("%s")'%(a,b) in globals(),locals()
hash[i] = __import__(i)
def myfunc(p, fjac=None, x=None, y=None, err=None):
return [0, eval('(y-(%s))/err' % func, hash, locals())]
myre = "[^a-zA-Z]p\[(\d+)\]"
r = re.compile(myre)
maxp = -1
for m in re.finditer(r, func):
curp = int(m.group(1))
maxp = curp if curp > maxp else maxp
if check:
if maxp == -1:
raise Exception("wrong function format")
if maxp + 1 != len(start_params):
raise Exception(
"the length of the start_params != the length of the parameter verctor of the function")
fa = {'x': x, 'y': y, 'err': err}
res = mpfit.mpfit(myfunc, start_params, functkw=fa, **kw)
yfit = eval(func, globals(), {'x': x, 'p': res.params})
if full_output:
return (res, yfit)
else:
return (res.params, yfit)
def main():
# Generate a noisy polynomial
# [off, x1, x2, x3, y1, y2, y3]
pIn = [2.0, 1.5, 0.1, 0.3, 1.0, 2.0, 0.05]
pIn = [1.0, 0.2, 0.0, 0.0, 0.1, 0.0, 0.0]
shape = (200, 200)
X, Y, Z, xyData = genpolydata(pIn, shape, 300, 10.2)
print X.shape
print Y.shape
print Z.shape
print xyData.shape
# raw_input()
# Define an function to evaluate the residual
def errFn(p, fjac=None):
status = 0
# poly_surface' returns the 'rfunc' function and the X,Y data is
# inserted via argument unpacking.
return status, poly_surface(p)(*[Y, X]) - Z
# Fit the data starting from an initial guess
mp = mpfit(errFn, parinfo=inParms, quiet=False)
print
for i in range(len(inParms)):
print "%s = %f +/- %f" % (inParms[i]['parname'],
mp.params[i],
mp.perror[i])
p1 = mp.params
#-------------------------------------------------------------------------#
# Plot the original, fit & residual
fig = pl.figure(figsize=(18,4.3))
ax1 = fig.add_subplot(1,3,1)
cax1 = ax1.imshow(xyData, origin='lower',cmap=mpl.cm.jet)
cbar1=fig.colorbar(cax1, pad=0.0)
ax1.scatter(X, Y, c=Z, s=40, cmap=mpl.cm.jet)
ax1.set_title("Sampled Data")
ax1.set_xlim(0, shape[-1]-1)
ax1.set_ylim(0, shape[-2]-1)
ax1.set_aspect('equal')
ax2 = fig.add_subplot(1,3,2)
xyDataFit = poly_surface(p1, shape)
cax2 = ax2.imshow(xyDataFit, origin='lower', cmap=mpl.cm.jet)
cbar2=fig.colorbar(cax2, pad=0.0)
ax2.set_title("Model Fit")
ax3 = fig.add_subplot(1,3,3)
xyDataRes = xyData - xyDataFit
cax3 = ax3.imshow(xyDataRes, origin='lower', cmap=mpl.cm.jet)
cbar2=fig.colorbar(cax3, pad=0.0)
ax3.set_title("Residual")
pl.show()
def fitGaussian(x, y, err, const=0):
"""
DES: fits a Gaussian to the data using mpfit
INP: (float) x = x data
(float) y = y data
(float) err = array with errors on y
(log) const = should we include a constant term?
"""
p = [1.0, 1.0, 1.0]
if const:
p.append(0.)
# try to guess the parameters quick-and-dirty
p[0] = max(y)
weights = y / sum(y)
p[1] = sum(x * weights)
cutoff_mask = Numeric.where((array(y) > max(y) / 2.), 1, 0)
cutoff = Numeric.compress(cutoff_mask, array(y))
p[2] = max(x) * float(len(cutoff)) / float(len(y))
if const:
p[3] = fStat.f_median(y)
parinfo = [{'value': 0., 'mpprint': 0}] * 3
for i in range(3):
parinfo[i]['value'] = p[i]
if const:
parinfo.append({'value': 0., 'mpprint': 0})
parinfo[3]['value'] = p[3]
fa = {'x': x, 'y': y, 'err': err}
try:
if const:
m = mpfit.mpfit(gaussbase, p, parinfo=parinfo, functkw=fa, quiet=1)
else:
m = mpfit.mpfit(gauss, p, parinfo=parinfo, functkw=fa, quiet=1)
if (m.status <= 0):
raise BoaError, str("mpfit failed: %s" % (m.errmsg))
except:
m.params = [0., 1., 1.]
if const:
m.append(0.)
#self.MessHand.warning("could not fit gaussian")
return m
def onedsinusoidfit(xax, data, err=None,
params=[0,1,0,1],fixed=[False,False,False,False],
limitedmin=[False,False,False,False],
limitedmax=[False,False,False,False], minpars=[0,0,0,0],
maxpars=[0,0,0,0], quiet=True, shh=True,
veryverbose=False):
"""
Inputs:
xax - x axis
data - y axis
err - error corresponding to data
params - Fit parameters: Height of background, Amplitude, Frequency, Phase
fixed - Is parameter fixed?
limitedmin/minpars - set lower limits on each parameter
limitedmax/maxpars - set upper limits on each parameter
quiet - should MPFIT output each iteration?
shh - output final parameters?
Returns:
Fit parameters
Model
Fit errors
chi2
"""
def mpfitfun(x,y,err):
if err is None:
def f(p,fjac=None): return [0,(y-onedsinusoid(x,*p))]
else:
def f(p,fjac=None): return [0,(y-onedsinusoid(x,*p))/err]
return f
if xax == None:
xax = np.arange(len(data))
parinfo = [ {'n':0,'value':params[0],'limits':[minpars[0],maxpars[0]],'limited':[limitedmin[0],limitedmax[0]],'fixed':fixed[0],'parname':"HEIGHT",'error':0} ,
{'n':1,'value':params[1],'limits':[minpars[1],maxpars[1]],'limited':[limitedmin[1],limitedmax[1]],'fixed':fixed[1],'parname':"AMPLITUDE",'error':0},
{'n':2,'value':params[2],'limits':[minpars[2],maxpars[2]],'limited':[limitedmin[2],limitedmax[2]],'fixed':fixed[2],'parname':"FREQUENCY",'error':0},
{'n':3,'value':params[3],'limits':[minpars[3],maxpars[3]],'limited':[limitedmin[3],limitedmax[3]],'fixed':fixed[3],'parname':"PHASE",'error':0}]
mp = mpfit(mpfitfun(xax,data,err),parinfo=parinfo,quiet=quiet)
mpp = mp.params
mpperr = mp.perror
chi2 = mp.fnorm
if mp.status == 0:
raise Exception(mp.errmsg)
if (not shh) or veryverbose:
print "Fit status: ",mp.status
for i,p in enumerate(mpp):
parinfo[i]['value'] = p
print parinfo[i]['parname'],p," +/- ",mpperr[i]
print "Chi2: ",mp.fnorm," Reduced Chi2: ",mp.fnorm/len(data)," DOF:",len(data)-len(mpp)
return mpp,onedsinusoid(xax,*mpp),mpperr,chi2
def disk_bulge_AIM(Iobs,err,psf,guessP,quiet=0,ftol=1.e-10,return_image=False):
#Dictionary to pass parameters to function `handleFunc`
fa = {'y':Iobs,'x':psf,'err':err}
#Possible constraints for each parameter
parinfo= [{'value':0., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.]} for i in range(len(guessP))]
#Pass initial values to parinfo
for i in range(len(guessP)):
parinfo[i]['value'] = guessP[i]
L = guessP[0]
#Set parameter limits and whether fixed
parinfo[0]['fixed'] = 1 #xmax
parinfo[1]['fixed'] = 1 #N
parinfo[2]['limited'] = [1,1] #disk flux
parinfo[2]['limits'] = [0.1,1.e10] #
parinfo[3]['limited'] = [1,1] #bulge flux
parinfo[3]['limits'] = [0.1,1.e10] #
parinfo[4]['limited'] = [1,1] #q (q=10 corresponds to e = 0.82)
parinfo[4]['limits'] = [1.0,20.0]
parinfo[5]['limited'] = [1,1] #phi (there is 2pi degeneracy, but keep to avoid boundaries)
parinfo[5]['limits'] = [0.0,6.283185]
parinfo[6]['limited'] = [1,1] #disk re
parinfo[6]['limits'] = [0.05,3.0*L] #
parinfo[7]['limited'] = [1,1] #bulge re
parinfo[7]['limits'] = [0.05,3.0*L] #
parinfo[8]['limited'] = [1,1]
parinfo[8]['limits'] = [0.5,4.0] #n_b
parinfo[9]['fixed'] = 1 #kappa
parinfo[10]['fixed'] = 1 #Shear 1
parinfo[11]['fixed'] = 1 #Shear 2
parinfo[12]['limited'] = [1,1] #f1
parinfo[12]['limits'] = [-1.0,1.0]
parinfo[13]['limited'] = [1,1] #f2
parinfo[13]['limits'] = [-1.0,1.0]
parinfo[14]['limited'] = [1,1] #g1
parinfo[14]['limits'] = [-1.0,1.0]
parinfo[15]['limited'] = [1,1] #g2
parinfo[15]['limits'] = [-1.0,1.0]
parinfo[16]['limited'] = [1,1] #cx
parinfo[16]['limits'] = [-L,L]
parinfo[17]['limited'] = [1,1] #cy
parinfo[17]['limits'] = [-L,L]
m = mpfit.mpfit(disk_bulge_func,guessP,parinfo=parinfo,functkw = fa,quiet=quiet,ftol=ftol)
if return_image == True:
fit_image = disk_bulge_model(m.params,psf)
return m,fit_image
else:
return m
def __init__(self,f, xdata, ydata, p0=None, sigma=None, fixed=None,limits=None, maxiter=200,quiet=1):
args, varargs, varkw, defaults = inspect.getargspec(f)
self.quiet = quiet
if len(args) < 2:
msg = "Unable to determine number of fit parameters."
raise ValueError(msg)
def general_function(params, fjac=None, xdata=None, ydata=None):
return [0, f(xdata, *params) - ydata]
def weighted_general_function(params,fjac=None, xdata=None, ydata=None, weights=None):
return [0, weights * (f(xdata, *params) - ydata)]
parinfo = [{'value':1., 'fixed':0, 'limited':[0,0], 'limits':[0.,0.]} for i in range(len(args)-1)]
if p0 != None:
for x,y in zip(parinfo,p0):
x['value']=y
if fixed != None:
for x,y in zip(parinfo,fixed):
x['fixed']=y
if limits != None:
for x,y in zip(parinfo,limits):
x['limited']=y[0]
x['limits']=y[1]
#args = (xdata, ydata, f)
if sigma is None:
func = general_function
# print 'here'
# my_functkw = {'xdata':xdata,'ydata':ydata,'function':f}
my_functkw = {'xdata':xdata,'ydata':ydata}
else:
func = weighted_general_function
weights= 1.0/asarray(sigma)
# my_functkw = {'xdata':xdata,'ydata':ydata,'function':f,'weights':weights}
my_functkw = {'xdata':xdata,'ydata':ydata,'weights':weights}
result = mpfit(func, functkw=my_functkw, parinfo = parinfo,quiet=self.quiet,maxiter=maxiter)
self.params = result.params
self.errors = result.perror
self.dof = result.dof
self.chi2 = result.fnorm
self.covar = result.covar
def fit_spec_poly5(xData, yData, dyData, order=5):
"""
Fit a 5th order polynomial to a spectrum. To avoid overflow errors the
X-axis data should not be large numbers (e.g.: x10^9 Hz; use GHz instead).
"""
# Lower order limit is a line with slope
if order < 1:
order = 1
if order > 5:
order = 5
# Estimate starting coefficients
C1 = nanmean(np.diff(yData)) / nanmedian(np.diff(xData))
ind = int(np.median(np.where(~np.isnan(yData))))
C0 = yData[ind] - (C1 * xData[ind])
C5 = 0.0
C4 = 0.0
C3 = 0.0
C2 = 0.0
inParms = [{
'value': C5,
'parname': 'C5'
}, {
'value': C4,
'parname': 'C4'
}, {
'value': C3,
'parname': 'C3'
}, {
'value': C2,
'parname': 'C2'
}, {
'value': C1,
'parname': 'C1'
}, {
'value': C0,
'parname': 'C0'
}]
# Set the polynomial order
for i in range(len(inParms)):
if len(inParms) - i - 1 > order:
inParms[i]['fixed'] = True
else:
inParms[i]['fixed'] = False
# Function to evaluate the difference between the model and data.
# This is minimised in the least-squared sense by the fitter
def errFn(p, fjac=None):
status = 0
return status, (poly5(p)(xData) - yData) / dyData
# Use mpfit to perform the fitting
mp = mpfit(errFn, parinfo=inParms, quiet=True)
return mp
def test_rosenbrock():
p0=N.array([-1,1.],dtype='float64') #initial conditions
pactual=N.array([1.,1.]) #actual minimum of the rosenbrock function
m = mpfit(myfunctrosenbrock, p0)
if (m.status <= 0):
print 'error message = ', m.errmsg
assert m.status > 0
assert N.allclose(m.params,pactual)
assert N.allclose(m.fnorm,0)
return
def dlosenv_peakfit_sigma_env_fit_test(cat_corr, n_NN=3, fit='gauss', **kwargs):
""" Linear fit of the best-fit sigma as a function of environment.
Bestfit sigma values are computed for the dLOS distribution in bins of
galaxy environment. Linear fit is done using MPFit.
"""
env_low, env_high, fpeaks, fpeak_errs, sigmas, sigma_errs, amps, nbins = \
dlos_envbin_peakfit(
cat_corr,
n_NN = n_NN,
fit = fit,
**kwargs
)
env_mid = np.array( 0.5 * (env_low + env_high) )
p0 = [ -0.03, 4.0 ] # guess
fa = {'x': env_mid, 'y': sigmas, 'err': sigma_errs}
fit_param = mpfit.mpfit(mpfit_linear, p0, functkw=fa, quiet=1)
best_slope = fit_param.params[0]
best_yint = fit_param.params[1]
print 'Entire range'
print best_slope, best_yint
for range_test in [20.0, 30.0, 40.0, 50.0]:
test_range = np.where(
env_mid < range_test
)
fa = {'x': env_mid[test_range], 'y': sigmas[test_range], 'err': sigma_errs[test_range]}
fit_param = mpfit.mpfit(mpfit_linear, p0, functkw=fa, quiet=1)
best_slope = fit_param.params[0]
best_yint = fit_param.params[1]
print 'dNN < ', str(range_test)
print best_slope, best_yint
return best_slope, best_yint
def test_rosenbrock():
p0 = N.array([-1, 1.], dtype='float64') #initial conditions
pactual = N.array([1., 1.]) #actual minimum of the rosenbrock function
m = mpfit(myfunctrosenbrock, p0)
if (m.status <= 0):
print('error message = ', m.errmsg)
assert m.status > 0
assert N.allclose(m.params, pactual)
assert N.allclose(m.fnorm, 0)
return
def fit_pixel(inputs):
wl,q_array,u_array,sigma_q,sigma_u=inputs
pos=[1,1,10]
funcargs= {'x':wl, 'y':[q_array,u_array],'err':[sigma_q,sigma_u]}
fit=mpfit.mpfit(alpha_function,pos,functkw=funcargs,autoderivative=1,quiet=1)
parms=fit.params
parms[:2]*=1e-7
dparms=fit.perror
dparms[:2]*=1e-7
return np.array([parms,dparms,fit.fnorm])
def Flat_Field( spec_list, flat ):
# This Function divides each spectrum in spec_list by the flat and writes
# The new images as fits files. The output is a list of file names of
# the flat fielded images.
print "\n====================\n"
print 'Flat Fielding Images by Dividing by %s\n' % (flat)
np.seterr(divide= 'warn')
flat_data = fits.getdata(flat)
#If flat is a blue spectrum, find the Littro ghost and add those pixels to the header
if 'blue' in flat.lower():
fit_data = np.median(flat_data[0][75:85],axis=0)
low_index = 1210. #Lowest pixel to search within
high_index = 1650. #highest pixel to search within
fit_data1 = fit_data[low_index:high_index]
fit_pix1 = np.linspace(low_index,low_index+len(fit_data1),num=len(fit_data1))
max_pixel = np.argmax(fit_data1)
fit_data2 = fit_data1[max_pixel-30:max_pixel+30]
guess1 = np.zeros(5)
guess1[0] = np.mean(fit_data2)
guess1[1] = (fit_data2[-1]-fit_data2[0])/len(fit_data2)
guess1[2] = np.amax(fit_data2)
guess1[3] = np.argmax(fit_data2)
guess1[4] = 4.
error_fit1 = np.ones(len(fit_data2))
xes1 = np.linspace(0,len(fit_data2)-1,num=len(fit_data2))
fa1 = {'x':xes1,'y':fit_data2,'err':error_fit1}
fitparams1 = mpfit.mpfit(fitgaussslope,guess1,functkw=fa1,quiet=True)
center_pixel = low_index+max_pixel-30.+fitparams1.params[3]
littrow_ghost = [np.rint(center_pixel-9.),np.rint(center_pixel+9.)]
diagnostic[0:len(fit_pix1),17] = fit_pix1
diagnostic[0:len(fit_data1),18] = fit_data1
diagnostic[0:len(xes1+low_index+max_pixel-30.),19] = xes1+low_index+max_pixel-30.
diagnostic[0:len(gaussslope(xes1,fitparams1.params)),20] = gaussslope(xes1,fitparams1.params)
diagnostic[0,21] = littrow_ghost[0]
diagnostic[1,21] = littrow_ghost[1]
else:
littrow_ghost = 'None'
f_spec_list = []
for spec in spec_list:
spec_data = fits.getdata(spec)
f_spec_data = np.divide(spec_data, flat_data)
f_spec_data[ np.isnan(f_spec_data) ] = 0
print "f"+"%s Mean: %.3f StDev: %.3f" % (spec, np.mean(f_spec_data), np.std(f_spec_data) )
hdu = fits.getheader(spec)
Fix_Header(hdu)
hdu.set('DATEFLAT', datetime.datetime.now().strftime("%Y-%m-%d"), 'Date of Flat Fielding')
hdu.set('LITTROW',str(littrow_ghost),'Littrow Ghost location in Flat')
hdu.append( ('FLATFLD', flat,'Image used to Flat Field.'),
useblanks= True, bottom= True )
NewHdu = fits.PrimaryHDU(data= f_spec_data, header= hdu)
new_file_name= check_file_exist('f'+spec)
NewHdu.writeto(new_file_name, output_verify='warn', clobber= True)
f_spec_list.append(new_file_name)
return f_spec_list
def fit_rmsf(xData, yData, thresh=0.3):
"""
Fit the main lobe of the RMSF with a Gaussian function. Sidelobes beyond
a threshold near the first null are masked out.
"""
# Detect the peak and mask off the sidelobes
msk = detect_peak(yData, thresh)
validIndx = np.where(msk == 1.0)
xData = xData[validIndx]
yData = yData[validIndx]
# Estimate starting parameters
a = 1.0
b = xData[np.argmax(yData)]
w = np.nanmax(xData) - np.nanmin(xData)
# Estimate starting parameters
inParms = [{
'value': a,
'fixed': False,
'parname': 'amp'
}, {
'value': b,
'fixed': False,
'parname': 'offset'
}, {
'value': w,
'fixed': False,
'parname': 'width'
}]
# Function which returns another function to evaluate a Gaussian
def gauss1D(p):
a, b, w = p
gfactor = 2.0 * m.sqrt(2.0 * m.log(2.0))
s = w / gfactor
def rfunc(x):
y = a * np.exp(-(x - b)**2.0 / (2.0 * s**2.0))
return y
return rfunc
# Function to evaluate the difference between the model and data.
# This is minimised in the least-squared sense by the fitter
def errFn(p, fjac=None):
status = 0
return status, gauss1D(p)(xData) - yData
# Use mpfit to perform the fitting
mp = mpfit(errFn, parinfo=inParms, quiet=True)
coeffs = mp.params
return mp.params, mp.status
def dlosenv_peakfit_sigma_env_fit(cat_corr, n_NN=3, fit='gauss', **kwargs):
""" Linear fit of the best-fit sigma as a function of environment.
Bestfit sigma values are computed for the dLOS distribution in bins of
galaxy environment. Linear fit is done using MPFit.
"""
env_low, env_high, fpeaks, fpeak_errs, sigmas, sigma_errs, amps, nbins = \
dlos_envbin_peakfit(
cat_corr,
n_NN = n_NN,
fit = fit,
**kwargs
)
env_mid = np.array( 0.5 * (env_low + env_high) )
if n_NN == 5:
fit_range = np.where(
env_mid < 40.0
)
else:
raise NotImplementedError()
p0 = [ -0.03, 4.0 ] # guess
fa = {'x': env_mid[fit_range], 'y': sigmas[fit_range], 'err': sigma_errs[fit_range]}
fit_param = mpfit.mpfit(mpfit_linear, p0, functkw=fa, quiet=1)
best_slope = fit_param.params[0]
best_yint = fit_param.params[1]
try:
if kwargs['writeout']:
dlos_fpeak_envbin_fit_file = ''.join([
direc('data', cat_corr),
'DLOS_sigma_env_d', str(n_NN), 'NN_bin_bestfit.dat'
])
data_hdr = "Columns : bestfit_slope, bestfit_yint"
data_fmt = ['%10.5f', '%10.5f']
np.savetxt(
dlos_fpeak_envbin_fit_file,
np.c_[best_slope, best_yint],
fmt = data_fmt,
delimiter = '\t',
header = data_hdr
)
except KeyError:
pass
return best_slope, best_yint
def gaussfit2d(im,sig_im,pstart):
xgrid,ygrid = np.meshgrid(np.arange(im.shape[0]),np.arange(im.shape[1]))
# pdb.set_trace()
import mpfit
fa = {'xgrid':xgrid, 'ygrid':ygrid,'im':im,'sig_im':sig_im}
thisfit = mpfit.mpfit(gauss2d_resid,pstart,functkw=fa,quiet=True)
bestresid,bestmod = gauss2d_resid(thisfit.params,xgrid=xgrid,ygrid=ygrid,im=im,sig_im=sig_im,model=True)
return thisfit,bestresid,bestmod
def alignImages(refImage,
imToAlign,
parameterGuess,
parameterLowerLimit,
parameterUpperLimit,
parameterMinStep=[0.05, 2, 2],
model=correlation):
"""
Runs mpfit.py
Inputs:
refImage: image used as a reference, 2D numpy array
imToAlign: imgage to be aligned, 2D numpy array
parameterGuess: initial guess for theta, xshift, yshift
parameterLowerLimit: strict lower limit in parameter space
parameterUpperLimit: strict upper limit in parameter space
model: returns the value to minimize
"""
parinfo = []
for k in range(len(parameterGuess)):
lowLimit = True
upLimit = True
parMinStep = parameterMinStep[k]
if parameterLowerLimit[k] == 'None': lowLimit = False
if parameterUpperLimit[k] == 'None': upLimit = False
fixGuess = False
if parameterLowerLimit[k] == parameterUpperLimit[
k] and parameterLowerLimit != None:
fixGuess = True
par = {
'n': k,
'value': parameterGuess[k],
'limits': [parameterLowerLimit[k], parameterUpperLimit[k]],
'limited': [lowLimit, upLimit],
'fixed': fixGuess,
'step': parMinStep
}
parinfo.append(par)
quiet = True
fa = {'refImage': refImage, 'imToAlign': imToAlign}
# m = mpfit.mpfit(correlation, functkw=fa, parinfo=parinfo, maxiter=1000, xtol=10**(-20),
# ftol=10**(-15), gtol=10**(-30), quiet=quiet)
m = mpfit.mpfit(correlation,
functkw=fa,
parinfo=parinfo,
maxiter=1000,
xtol=10**(-20),
ftol=10**(-20),
gtol=10**(-30),
quiet=quiet)
print('status', m.status, 'errmsg', m.errmsg)
mpPar = m.params
print('parameters:', mpPar, "\n")
return mpPar
def onedgaussfit(xax,data,err=None,params=[0,1,0,1],fixed=[False,False,False,False],limitedmin=[False,False,False,True],
limitedmax=[False,False,False,False],minpars=[0,0,0,0],maxpars=[0,0,0,0],
quiet=True,shh=True):
# """
# Inputs:
# xax - x axis
# data - y axis
# err - error corresponding to data
# params - Fit parameters: Height of background, Amplitude, Shift, Width
# fixed - Is parameter fixed?
# limitedmin/minpars - set lower limits on each parameter (default: width>0)
# limitedmax/maxpars - set upper limits on each parameter
# quiet - should MPFIT output each iteration?
# shh - output final parameters?
# Returns:
# Fit parameters
# Model
# Fit errors
# chi2
# """
def mpfitfun(x,y,err):
if err == None:
def f(p,fjac=None): return [0,(y-onedgaussian(x,*p))]
else:
def f(p,fjac=None): return [0,(y-onedgaussian(x,*p))/err]
return f
if xax == None:
xax = numpy.arange(len(data))
parinfo = [ {'n':0,'value':params[0],'limits':[minpars[0],maxpars[0]],'limited':[limitedmin[0],limitedmax[0]],'fixed':fixed[0],'parname':"HEIGHT",'error':0} ,
{'n':1,'value':params[1],'limits':[minpars[1],maxpars[1]],'limited':[limitedmin[1],limitedmax[1]],'fixed':fixed[1],'parname':"AMPLITUDE",'error':0},
{'n':2,'value':params[2],'limits':[minpars[2],maxpars[2]],'limited':[limitedmin[2],limitedmax[2]],'fixed':fixed[2],'parname':"SHIFT",'error':0},
{'n':3,'value':params[3],'limits':[minpars[3],maxpars[3]],'limited':[limitedmin[3],limitedmax[3]],'fixed':fixed[3],'parname':"WIDTH",'error':0}]
mp = mpfit(mpfitfun(xax,data,err),parinfo=parinfo,quiet=quiet)
mpp = mp.params
mpperr = mp.perror
chi2 = mp.fnorm
if mp.status == 0:
raise Exception(mp.errmsg)
if not shh:
for i,p in enumerate(mpp):
parinfo[i]['value'] = p
print parinfo[i]['parname'],p," +/- ",mpperr[i]
print "Chi2: ",mp.fnorm," Reduced Chi2: ",mp.fnorm/len(data)," DOF:",len(data)-len(mpp)
return mpp,onedgaussian(xax,*mpp),mpperr,chi2
def onedmoffatfit(xax, data, err = None,
params=[0,1,0,1,3],fixed=[False,False,False,False,False],
limitedmin=[False,False,False,True,True],
limitedmax=[False,False,False,False,False], minpars=[0,0,0,0,0],
maxpars=[0,0,0,0,0], quiet=True, shh=True,
veryverbose=False,
vheight=True, negamp=False,
usemoments=False):
def mpfitfun(x,y,err):
if err is None:
def f(p,fjac=None): return [0,(y-onedmoffat(x,*p))]
else:
def f(p,fjac=None): return [0,(y-onedmoffat(x,*p))/err]
return f
if xax == []:
xax = numpy.arange(len(data))
parinfo = [ {'n':0,'value':params[0],'limits':[minpars[0],maxpars[0]],
'limited':[limitedmin[0],limitedmax[0]],'fixed':fixed[0],
'parname':"HEIGHT",'error':0} ,
{'n':1,'value':params[1],'limits':[minpars[1],maxpars[1]],
'limited':[limitedmin[1],limitedmax[1]],'fixed':fixed[1],
'parname':"AMPLITUDE",'error':0},
{'n':2,'value':params[2],'limits':[minpars[2],maxpars[2]],
'limited':[limitedmin[2],limitedmax[2]],'fixed':fixed[2],
'parname':"SHIFT",'error':0},
{'n':3,'value':params[3],'limits':[minpars[3],maxpars[3]],
'limited':[limitedmin[3],limitedmax[3]],'fixed':fixed[3],
'parname':"ALPHA",'error':0},
{'n':4,'value':params[4],'limits':[minpars[4],maxpars[4]],
'limited':[limitedmin[4],limitedmax[4]],'fixed':fixed[4],
'parname':"BETA",'error':0}]
mp = mpfit(mpfitfun(xax,data,err), parinfo=parinfo, quiet=quiet)
mpp = mp.params
mpperr = mp.perror
chi2 = mp.fnorm
try:
dof = len(data) - len(mpp)
except TypeError:
dof = len(data)
if mp.status == 0:
raise Exception(mp.errmsg)
if (not shh) or veryverbose:
print "Fit status: ",mp.status
for i,p in enumerate(mpp):
parinfo[i]['value'] = p
print parinfo[i]['parname'],p," +/- ",mpperr[i]
print "Chi2: ",chi2," Reduced Chi2: ",chi2/dof," DOF:",dof
retax = numpy.arange(xax[0], xax[-1], 0.1)
return mpp, onedmoffat(retax,*mpp), mpperr, [chi2, dof], retax
def onedmoffatfit(xax, data, err = None,
params=[0,1,0,1,3],fixed=[False,False,False,False,False],
limitedmin=[False,False,False,True,True],
limitedmax=[False,False,False,False,False], minpars=[0,0,0,0,0],
maxpars=[0,0,0,0,0], quiet=True, shh=True,
veryverbose=False,
vheight=True, negamp=False,
usemoments=False):
def mpfitfun(x,y,err):
if err is None:
def f(p,fjac=None): return [0,(y-onedmoffat(x,*p))]
else:
def f(p,fjac=None): return [0,(y-onedmoffat(x,*p))/err]
return f
if xax == []:
xax = numpy.arange(len(data))
parinfo = [ {'n':0,'value':params[0],'limits':[minpars[0],maxpars[0]],
'limited':[limitedmin[0],limitedmax[0]],'fixed':fixed[0],
'parname':"HEIGHT",'error':0} ,
{'n':1,'value':params[1],'limits':[minpars[1],maxpars[1]],
'limited':[limitedmin[1],limitedmax[1]],'fixed':fixed[1],
'parname':"AMPLITUDE",'error':0},
{'n':2,'value':params[2],'limits':[minpars[2],maxpars[2]],
'limited':[limitedmin[2],limitedmax[2]],'fixed':fixed[2],
'parname':"SHIFT",'error':0},
{'n':3,'value':params[3],'limits':[minpars[3],maxpars[3]],
'limited':[limitedmin[3],limitedmax[3]],'fixed':fixed[3],
'parname':"ALPHA",'error':0},
{'n':4,'value':params[4],'limits':[minpars[4],maxpars[4]],
'limited':[limitedmin[4],limitedmax[4]],'fixed':fixed[4],
'parname':"BETA",'error':0}]
mp = mpfit(mpfitfun(xax,data,err), parinfo=parinfo, quiet=quiet)
mpp = mp.params
mpperr = mp.perror
chi2 = mp.fnorm
try:
dof = len(data) - len(mpp)
except TypeError:
dof = len(data)
if mp.status == 0:
raise Exception(mp.errmsg)
if (not shh) or veryverbose:
print "Fit status: ",mp.status
for i,p in enumerate(mpp):
parinfo[i]['value'] = p
print parinfo[i]['parname'],p," +/- ",mpperr[i]
print "Chi2: ",chi2," Reduced Chi2: ",chi2/dof," DOF:",dof
retax = numpy.arange(xax[0], xax[-1], 0.1)
return mpp, onedmoffat(retax,*mpp), mpperr, [chi2, dof], retax
def onedvoigtfit(xax, data, err=None,
params = [1, 1, 1, 1, 0, 0],
fixed = [False,False,False,False,True,True],
limitedmin = [False,False,False,False,False,False],
limitedmax = [False,False,False,False,False,False],
minpars=[0,0,0,0,0,0],
maxpars=[0,0,0,0,0,0], quiet=True, shh=True,
veryverbose=False):
def mpfitfun(x,y,err):
if err is None:
def f(p,fjac=None): return [0,(y-onedvoigt(x,*p))]
else:
def f(p,fjac=None): return [0,(y-onedvoigt(x,*p))/err]
return f
if xax == []:
xax = numpy.arange(len(data))
parinfo = [ {'n':0,'value':params[0],'limits':[minpars[0],maxpars[0]],
'limited':[limitedmin[0],limitedmax[0]],'fixed':fixed[0],
'parname':"alphaD",'error':0} ,
{'n':1,'value':params[1],'limits':[minpars[1],maxpars[1]],
'limited':[limitedmin[1],limitedmax[1]],'fixed':fixed[1],
'parname':"alphaL",'error':0},
{'n':2,'value':params[2],'limits':[minpars[2],maxpars[2]],
'limited':[limitedmin[2],limitedmax[2]],'fixed':fixed[2],
'parname':"x_0",'error':0},
{'n':3,'value':params[3],'limits':[minpars[3],maxpars[3]],
'limited':[limitedmin[3],limitedmax[3]],'fixed':fixed[3],
'parname':"A",'error':0},
{'n':4,'value':params[4],'limits':[minpars[4],maxpars[4]],
'limited':[limitedmin[4],limitedmax[4]],'fixed':fixed[4],
'parname':"a_back",'error':0} ,
{'n':5,'value':params[5],'limits':[minpars[5],maxpars[5]],
'limited':[limitedmin[5],limitedmax[5]],'fixed':fixed[5],
'parname':"b_back",'error':0}]
mp = mpfit(mpfitfun(xax,data,err),parinfo=parinfo,quiet=quiet)
mpp = mp.params
mpperr = mp.perror
chi2 = mp.fnorm
dof = len(data)-len(mpp)
if mp.status == 0:
raise Exception(mp.errmsg)
if (not shh) or veryverbose:
print "Fit status: ",mp.status
for i,p in enumerate(mpp):
parinfo[i]['value'] = p
print parinfo[i]['parname'],p," +/- ",mpperr[i]
print "Chi2: ",chi2," Reduced Chi2: ",chi2/dof," DOF:",dof
return mpp,onedvoigt(xax,*mpp),mpperr,[chi2, dof]
def fitGauss(dataList,nBins=201):
hist,histBinEdges = np.histogram(dataList,bins=nBins)
histBinCenters = histBinEdges[0:-1]+np.diff(histBinEdges)/2.
amplitude = 0.95*np.max(hist)
x_offset = histBinCenters[np.argmax(hist)]
sigma = np.std(dataList)*.8
y_offset = 1.e-8
params=[sigma, x_offset, amplitude, y_offset] # First guess at fit params
#errs = np.sqrt(hist) #for poisson distributed data
#errs[np.where(errs == 0.)] = 1.
nListVals = 1.*np.sum(hist)
pvals = hist/nListVals
#assume errors are the multinomial distribution
errs = np.sqrt(nListVals*pvals*(1-pvals))
errs[np.where(errs == 0.)] = 1.
quiet = True
parinfo = [ {'n':0,'value':params[0],'limits':[sigma/10., 10*sigma], 'limited':[True,True],'fixed':False,'parname':"Sigma",'error':0},
{'n':1,'value':params[1],'limits':[x_offset-sigma*2, x_offset+sigma*2],'limited':[True,True],'fixed':False,'parname':"x offset",'error':0},
{'n':2,'value':params[2],'limits':[0.5*amplitude, 2.*amplitude],'limited':[True,True],'fixed':False,'parname':"Amplitude",'error':0},
{'n':3,'value':params[3],'limited':[False,False],'fixed':True,'parname':"y_offset",'error':0}]
fa = {'x':histBinCenters,'y':hist,'err':errs}
m = mpfit.mpfit(gaussian, functkw=fa, parinfo=parinfo, maxiter=1000, quiet=quiet)
if m.status <= 0:
print m.status, m.errmsg
mpp = m.params #The fit params
mpperr = m.perror
for k,p in enumerate(mpp):
parinfo[k]['value'] = p
parinfo[k]['error'] = mpperr[k]
if k==0: sigma = p
if k==1: x_offset = p
if k==2: amplitude = p
if k==3: y_offset = p
def gaussFitFunc(x):
return y_offset + amplitude * np.exp( - (( x - x_offset)**2) / ( 2. * (sigma**2)))
gaussFit = gaussFitFunc(histBinCenters)
resolution = np.abs(x_offset/(2.355*sigma))
return {'gaussFit':gaussFit,'resolution':resolution,'sigma':sigma,'x_offset':x_offset,'amplitude':amplitude,'y_offset':y_offset,'hist':hist,'histBinEdges':histBinEdges,'gaussFitFunc':gaussFitFunc,'histBinCenters':histBinCenters,'parinfo':parinfo}
def minimize(self):
# Set PARINFO structure for all 4 free parameters for mpfit
teff_info = {
'parname': 'Teff',
'limited': [1, 1],
'limits': [3950, 7000],
'step': 100,
'mpside': 2,
'mpprint': 0
}
logg_info = {
'parname': 'logg',
'limited': [1, 1],
'limits': [1.2, 4.9],
'step': 0.10,
'mpside': 2,
'mpprint': 0
}
feh_info = {
'parname': '[M/H]',
'limited': [1, 1],
'limits': [-2.5, 0.7],
'step': 0.05,
'mpside': 2,
'mpprint': 0
}
alpha_info = {
'parname': '[a/Fe]',
'limited': [1, 1],
'limits': [-0.3, 0.4],
'step': 0.05,
'mpside': 2,
'mpprint': 0
}
self.parinfo = [teff_info, logg_info, feh_info, alpha_info]
fa = {'y_obs': self.flux}
# Minimization starts here.
self.m = mpfit(self._myfunct,
xall=self.p0,
parinfo=self.parinfo,
ftol=1e-10,
xtol=1e-8,
gtol=1e-8,
functkw=fa,
maxiter=50,
quiet=1)
self.dof = len(self.flux) - len(self.m.params)
self.params = self._convergence_info(self.m, self.parinfo)
#print(self.params)
#print(self._convergence_info(self.m, self.parinfo))
return self.params
def onedvoigtfit(xax, data, err=None,
params = [1, 1, 1, 1, 0, 0],
fixed = [False,False,False,False,True,True],
limitedmin = [False,False,False,False,False,False],
limitedmax = [False,False,False,False,False,False],
minpars=[0,0,0,0,0,0],
maxpars=[0,0,0,0,0,0], quiet=True, shh=True,
veryverbose=False):
def mpfitfun(x,y,err):
if err is None:
def f(p,fjac=None): return [0,(y-onedvoigt(x,*p))]
else:
def f(p,fjac=None): return [0,(y-onedvoigt(x,*p))/err]
return f
if xax == []:
xax = numpy.arange(len(data))
parinfo = [ {'n':0,'value':params[0],'limits':[minpars[0],maxpars[0]],
'limited':[limitedmin[0],limitedmax[0]],'fixed':fixed[0],
'parname':"alphaD",'error':0} ,
{'n':1,'value':params[1],'limits':[minpars[1],maxpars[1]],
'limited':[limitedmin[1],limitedmax[1]],'fixed':fixed[1],
'parname':"alphaL",'error':0},
{'n':2,'value':params[2],'limits':[minpars[2],maxpars[2]],
'limited':[limitedmin[2],limitedmax[2]],'fixed':fixed[2],
'parname':"x_0",'error':0},
{'n':3,'value':params[3],'limits':[minpars[3],maxpars[3]],
'limited':[limitedmin[3],limitedmax[3]],'fixed':fixed[3],
'parname':"A",'error':0},
{'n':4,'value':params[4],'limits':[minpars[4],maxpars[4]],
'limited':[limitedmin[4],limitedmax[4]],'fixed':fixed[4],
'parname':"a_back",'error':0} ,
{'n':5,'value':params[5],'limits':[minpars[5],maxpars[5]],
'limited':[limitedmin[5],limitedmax[5]],'fixed':fixed[5],
'parname':"b_back",'error':0}]
mp = mpfit(mpfitfun(xax,data,err),parinfo=parinfo,quiet=quiet)
mpp = mp.params
mpperr = mp.perror
chi2 = mp.fnorm
dof = len(data)-len(mpp)
if mp.status == 0:
raise Exception(mp.errmsg)
if (not shh) or veryverbose:
print "Fit status: ",mp.status
for i,p in enumerate(mpp):
parinfo[i]['value'] = p
print parinfo[i]['parname'],p," +/- ",mpperr[i]
print "Chi2: ",chi2," Reduced Chi2: ",chi2/dof," DOF:",dof
return mpp,onedvoigt(xax,*mpp),mpperr,[chi2, dof]
def fitR(energies,bPlot=False,nBins=153,lowerEnergyCutoff=-6000):
mask = energies < lowerEnergyCutoff
energies = energies[mask]
energyHist,energyHistBinEdges = np.histogram(energies,bins=nBins,density=True)
#make some rough guesses for initial params
amplitude = 0.95*np.max(energyHist)
x_offset = energyHistBinEdges[np.argmax(energyHist)]
sigma = np.std(energies)*.8
y_offset = 1.e-8
params=[sigma, x_offset, amplitude, y_offset]
#assume poisson errors in the histogram
errs = np.sqrt(energyHist)
#remove the bad values
errs[np.where(errs == 0.)] = np.inf
#errs[np.where(errs == 0.)] = 1.
quiet = True
parinfo = [ {'n':0,'value':params[0],'limits':[sigma/10., 10*sigma], 'limited':[True,True],'fixed':False,'parname':"Sigma",'error':0},
{'n':1,'value':params[1],'limits':[x_offset-sigma*2, x_offset+sigma*2],'limited':[True,True],'fixed':False,'parname':"x offset",'error':0},
{'n':2,'value':params[2],'limits':[0.5*amplitude, 2.*amplitude],'limited':[True,True],'fixed':False,'parname':"Amplitude",'error':0},
{'n':3,'value':params[3],'limited':[False,False],'fixed':False,'parname':"y_offset",'error':0}]
fa = {'x':energyHistBinEdges[0:-1],'y':energyHist,'err':errs}
m = mpfit.mpfit(gaussian, functkw=fa, parinfo=parinfo, maxiter=1000, quiet=quiet)
if m.status <= 0:
print m.status, m.errmsg
mpp = m.params #The fit params
mpperr = m.perror
for k,p in enumerate(mpp):
parinfo[k]['value'] = p
#print parinfo[k]['parname'],p," +/- ",mpperr[k]
if k==0: sigma = p
if k==1: x_offset = p
if k==2: amplitude = p
if k==3: y_offset = p
gaussfit = y_offset + amplitude * np.exp( - (( energyHistBinEdges[0:-1] - x_offset)**2) / ( 2. * (sigma**2)))
resolution = np.abs(x_offset/(2.355*sigma))
if bPlot:
fig = plt.figure()
ax = fig.add_subplot(111)
ax.step(energyHistBinEdges[0:-1],energyHist)
ax.plot(energyHistBinEdges[0:-1],gaussfit)
return {'gaussfit':gaussfit,'resolution':resolution,'sigma':sigma,'x_offset':x_offset,'amplitude':amplitude,'y_offset':y_offset,'energyHist':energyHist,'energyHistBinEdges':energyHistBinEdges}
def fit_spec_poly5(xData, yData, dyData = None, order=5):
"""
Fit a <=5th order polynomial to a spectrum. To avoid overflow errors the
X-axis data should not be large numbers (e.g.: x10^9 Hz; use GHz instead).
"""
xData = np.array(xData, dtype='f8')
yData = np.array(yData, dtype='f8')
if dyData is None:
dyData = np.ones_like(yData)
else:
dyData = np.array(dyData, dtype='f8')
# Limit the order between 1 and 5
if order<1:
order = 1
if order>5:
order = 5
# Estimate initial coefficients
C1 = nanmedian(np.diff(yData)) / nanmedian(np.diff(xData))
ind = int(np.median(np.where(~np.isnan(yData))))
C0 = yData[ind] - (C1 * xData[ind])
C5 = 0.0
C4 = 0.0
C3 = 0.0
C2 = 0.0
inParms=[ {'value': C5, 'parname': 'C5'},
{'value': C4, 'parname': 'C4'},
{'value': C3, 'parname': 'C3'},
{'value': C2, 'parname': 'C2'},
{'value': C1, 'parname': 'C1'},
{'value': C0, 'parname': 'C0'} ]
# Set the polynomial order
for i in range(len(inParms)):
if len(inParms)-i-1>order:
inParms[i]['fixed'] = True
else:
inParms[i]['fixed'] = False
# Function to evaluate the difference between the model and data.
# This is minimised in the least-squared sense by the fitter
def errFn(p, fjac=None):
status = 0
return status, (poly5(p)(xData) - yData) / dyData
# Use mpfit to perform the fitting
mp = mpfit(errFn, parinfo=inParms, quiet=True)
coeffs = mp.params
return mp.params, mp.status
def brokenpowerfit(xax, data, err=None, alphaguess1=0.0, alphaguess2=-2.0, scaleguess=1.0,
breakpoint=None, quiet=True):
"""
Fit a broken power law (a line in log-space) to data as a function of x
differs from 'plfit' because plfit fits a power law distribution,
this code simply fits a power law
This is a lot more intricate than the simple power law fit, since it involves
fitting two power laws with different slopes
Parameters:
p[0] - scale
p[1] - breakpoint
p[2] - power 1 (xax < breakpoint)
p[3] - power 2 (xax >= breakpoint)
There are 5 parameters (NOT 4) returned because there are two scales that are *NOT*
independent
returns: scale1,scale2,breakpoint,alpha1,alpha2
"""
logdata = np.log10(data)
if err is None: err = np.ones(data.shape,dtype='float')
def brokenpowerlaw(p):
lowerhalf = (np.log10(p[0]) + np.log10(xax)*p[2]) * (xax < p[1])
# find the location at which both functions must be equal
scale2loc = np.argmin(np.abs(xax - p[1]))
scale2 = np.log10(xax[scale2loc])*(p[2] - p[3]) + np.log10(p[0])
upperhalf = (scale2 + np.log10(xax)*p[3]) * (xax >= p[1])
# DEBUG print "scale1: %15g scale2: %15g xaxind: %5i xaxval: %15g lower: %15g upper: %15g" % (p[0],scale2,scale2loc,np.log10(xax[scale2loc]),lowerhalf[scale2loc-1],upperhalf[scale2loc])
return lowerhalf+upperhalf
def mpfitfun(data,err):
def f(p,fjac=None): return [0,np.ravel((brokenpowerlaw(p)-data)/err)]
return f
if breakpoint is None: breakpoint = np.median(xax)
parinfo = [{}, {'mpminstep':xax.min(),'mpmaxstep':xax.max(),'step':xax.min()}, {}, {}]
mp = mpfit.mpfit(mpfitfun(logdata,err),xall=[scaleguess,breakpoint,alphaguess1,alphaguess2],quiet=quiet,parinfo=parinfo)
fitp = mp.params
scale2loc = np.argmin(np.abs(xax - fitp[1]))
scale2 = 10**( np.log10(xax[scale2loc])*(fitp[2] - fitp[3]) + np.log10(fitp[0]) )
fitp = np.array( [fitp[0],scale2] + fitp[1:].tolist() )
return fitp,mp
def fitR(energies):
nBins=153
mask = energies < -6000
energies = energies[mask]
extremeDict = extrema(energies)
energyHist,energyHistBinEdges = np.histogram(energies,bins=nBins,density=True)
amplitude = 0.95*np.max(energyHist)
#x_offset = np.median(phasebins[ind_left:ind_right])
#sigma = 50.
x_offset = energyHistBinEdges[np.argmax(energyHist)]
sigma = np.std(energies)*.8
#print 'sigma: ', sigma
y_offset = 1.e-8
params=[sigma, x_offset, amplitude, y_offset] # First guess at fit params
errs = np.sqrt(energyHist)
errs[np.where(errs == 0.)] = 1.
quiet = True
parinfo = [ {'n':0,'value':params[0],'limits':[sigma/10., 10*sigma], 'limited':[True,True],'fixed':False,'parname':"Sigma",'error':0},
{'n':1,'value':params[1],'limits':[x_offset-sigma*2, x_offset+sigma*2],'limited':[True,True],'fixed':False,'parname':"x offset",'error':0},
{'n':2,'value':params[2],'limits':[0.5*amplitude, 2.*amplitude],'limited':[True,True],'fixed':False,'parname':"Amplitude",'error':0},
{'n':3,'value':params[3],'limited':[False,False],'fixed':False,'parname':"y_offset",'error':0}]
fa = {'x':energyHistBinEdges[0:-1],'y':energyHist,'err':errs}
m = mpfit.mpfit(gaussian, functkw=fa, parinfo=parinfo, maxiter=1000, quiet=quiet)
if m.status <= 0:
print m.status, m.errmsg
mpp = m.params #The fit params
mpperr = m.perror
for k,p in enumerate(mpp):
parinfo[k]['value'] = p
#print parinfo[k]['parname'],p," +/- ",mpperr[j]
if k==0: sigma = p
if k==1: x_offset = p
if k==2: amplitude = p
if k==3: y_offset = p
gaussfit = y_offset + amplitude * np.exp( - (( energyHistBinEdges[0:-1] - x_offset)**2) / ( 2. * (sigma**2)))
resolution = np.abs(x_offset/(2.355*sigma))
fig = plt.figure()
ax = fig.add_subplot(111)
ax.step(energyHistBinEdges[0:-1],energyHist)
ax.plot(energyHistBinEdges[0:-1],gaussfit)
return {'gaussfit':gaussfit,'resolution':resolution,'sigma':sigma,'x_offset':x_offset,'amplitude':amplitude,'y_offset':y_offset,'energyHist':energyHist,'energyHistBinEdges':energyHistBinEdges}
def multipeak_fit(x, y, initpars, npeak, parinfo, fit=False, \
err=None, maxiter=200, ftol=1.E-10, gtol=1.E-10, xtol=1.E-10, plot=False, silent=False):
import numpy as np
import matplotlib.pyplot as plt
from mpfit import mpfit
from math_functions import gaussian, polynomial
if err is None:
err = [1.0]*len(x)
quiet = 0
if silent: # quiet = 1 will print no output
quiet = 1
p0 = initpars #p.params
fa = {'x':x, 'y':y, 'err':err, 'npeak':npeak}
# xtol and ftol are set to 1.E-10 by default in MPFIT
p = mpfit(multipeak, p0, functkw = fa, parinfo = parinfo, maxiter=maxiter, ftol=ftol, gtol=gtol, xtol=xtol, quiet=quiet)
#print 'p.niter = ',p.niter # Number of iterations
#print 'p.status = ',p.status # Status. Look at ftol and xtol
#print 'p.params', p.params # Fitted values for the initial parameters
#print 'p.error = ',p.perror # Covariance matrix with formal 1-sigma errors
#print 'p.fnorm = ',p.fnorm # Chi2
#print 'DOF = ',len(x) - len(p.params) # Degrees of freedom
#print 'p.covar = ', p.covar # Covariance matrix (11x11 in this case)
#print 'p = ', p
ft = multipeak(p.params, x = x, y = y, err = err, npeak = npeak, fit=fit)[1]
# Where does the [1] come from? See last line of multipeak
if plot: # Plot data and final model
plt.figure()
plt.plot(x, y, color='black', ls='dashed', lw=1.5)
plt.plot(x, ft, color='purple', ls='solid', lw=1.5)
# Considers a "fourth" peak for Halpha + NII_a + NII_b + Halpha_SNR
color_array = np.array(['blue', 'red', 'darkorange', 'turquoise'])
for np in range(npeak):
plt.plot(x, gaussian(x, p.params[3*np:3*np+3]) + polynomial(x, p.params[3*npeak:]),\
color=color_array[np])
return [p, ft]
def main():
# Generate a noisy polynomial
# [off, x1, x2, x3, y1, y2, y3]
pIn = [2.0, 1.5, 0.1, 0.3, 1.0, 2.0, 0.05]
pIn = [1.0, 0.2, 0.0, 0.0, 0.1, 0.0, 0.0]
shape = (200, 200)
X, Y, Z, xyData = genpolydata(pIn, shape, 300, 10.2)
# Define an function to evaluate the residual
def errFn(p, fjac=None):
status = 0
# poly_surface' returns the 'rfunc' function and the X,Y data is
# inserted via argument unpacking.
return status, poly_surface(p)(*[Y, X]) - Z
# Fit the data starting from an initial guess
mp = mpfit(errFn, parinfo=inParms, quiet=False)
print
for i in range(len(inParms)):
print "%s = %f +/- %f" % (inParms[i]['parname'], mp.params[i],
mp.perror[i])
p1 = mp.params
#-------------------------------------------------------------------------#
# Plot the original, fit & residual
fig = pl.figure(figsize=(18, 4.3))
ax1 = fig.add_subplot(1, 3, 1)
cax1 = ax1.imshow(xyData, origin='lower', cmap=mpl.cm.jet)
cbar1 = fig.colorbar(cax1, pad=0.0)
ax1.scatter(X, Y, c=Z, s=40, cmap=mpl.cm.jet)
ax1.set_title("Sampled Data")
ax1.set_xlim(0, shape[-1] - 1)
ax1.set_ylim(0, shape[-2] - 1)
ax1.set_aspect('equal')
ax2 = fig.add_subplot(1, 3, 2)
xyDataFit = poly_surface(p1, shape)
cax2 = ax2.imshow(xyDataFit, origin='lower', cmap=mpl.cm.jet)
cbar2 = fig.colorbar(cax2, pad=0.0)
ax2.set_title("Model Fit")
ax3 = fig.add_subplot(1, 3, 3)
xyDataRes = xyData - xyDataFit
cax3 = ax3.imshow(xyDataRes, origin='lower', cmap=mpl.cm.jet)
cbar2 = fig.colorbar(cax3, pad=0.0)
ax3.set_title("Residual")
pl.show()
def zandtfit(Inclination, Planetaryradius_SI, Keplerlc, Teff, Logg, Metalicity,
sigma, Orbitalperiod_SI, KeplerID, Planetnumberer):
Planetaryradius_Earth = (
Planetaryradius_SI
) / 6.3675E6 #convert the planetary radius to earth radii
p0 = [
math.cos(1.570795), Planetaryradius_Earth, Teff, Logg,
np.log10(Metalicity)
] #inital guess (from kepler mast and headers)
fa = {
'Keplerlc': Keplerlc,
'sigma': sigma,
'Orbitalperiod_SI': Orbitalperiod_SI,
'KeplerID': KeplerID,
'Planetnumberer': Planetnumberer
} #additional variables to be past to the function
parinfo = [{'value':p0[0], 'fixed':0, 'limited':[1,1], 'limits':[-1,1], 'step':0.01}, {'value':p0[1], 'fixed':0, 'limited':[1,0], 'limits':[0.01,100], 'step':0},\
{'value':p0[2], 'fixed':0, 'limited':[1,1], 'limits':[0,40000], 'step':0}, {'value':p0[3], 'fixed':0, 'limited':[1,1], 'limits':[0,5.0], 'step':0},\
{'value':p0[4], 'fixed':0, 'limited':[1,1], 'limits':[-5,1], 'step':0} ]
m = mpfit(minimisefunction,
p0,
functkw=fa,
quiet=0,
maxiter=35,
parinfo=parinfo,
ftol=0.0001) #run the mpfit for the best fit
#print m #testing
bestfitarray = m.params #extract the results
errors = m.perror #these need to be properly stored
covar = m.covar #these need to be properly stored
if errors == None: #i.e. if mpfits is a pain and decides to not return the errors or covariance properly
errors = np.zeros(5) #create an empty errors array
if covar == None: #if no covariance array is produced, create an array of zeros so that there are no problems later
covar = np.zeros((5, 5))
#print bestfitarray
Inclination_fit = math.acos(bestfitarray[0]) #Extract the fit inclination
Planetaryradius_SI_fit = bestfitarray[
1] * 6.3675E6 #Extract the fit planetary radius and convert it to SI units
Teff_fit = bestfitarray[2] #Extract the fit Teff
Logg_fit = bestfitarray[3] #Extract the fit logg of the surface gravity
Metalicity_fit = (10**bestfitarray[4]
) #Extract the metalicity and remove the logg
Mstar_SI, Rstar_SI, Stellardensity_SI = RMrhocalc(
Teff_fit, Logg_fit,
Metalicity_fit) #Calculate the stellar mass, radius and density
semimajoraxis_SI = semimajoraxiscalc(
Orbitalperiod_SI, Mstar_SI) #calculate the semi-major axis
T_dur_SI = Transitdurationcalc(
Rstar_SI, Planetaryradius_SI_fit, semimajoraxis_SI, Inclination_fit,
Orbitalperiod_SI) #calculate the transit duration
return Inclination_fit, Planetaryradius_SI_fit, Teff_fit, Logg_fit, Metalicity_fit, Mstar_SI, Rstar_SI, Stellardensity_SI, semimajoraxis_SI, T_dur_SI, errors, covar
def linefit(xx,data,err=None,guess=[1,0],quiet=True,**kwargs):
def line(p,x):
return p[0]*x+p[1]
def mpfitfun(x,data,err):
if err == None:
def f(p,fjac=None): return [0,data-line(p,x)]
else:
def f(p,fjac=None): return [0,(data-line(p,x))/err]
return f
mp = mpfit(mpfitfun(xx,data,err),guess,quiet=quiet,**kwargs)
return mp.params
def gaborfit(data,err=None,params=(),fixed=np.repeat(False,9),
limitedmin=[False,False,False,False,True,True,True,True,True],
limitedmax=[False,False,False,False,False,False,True,False,True],
minpars=[0.0, 0.0, 0.0, 0.0, 0.01, 0.01, 0.0, 0.01, 0.0],
maxpars=[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 360.0, 0.0, 360.0],
quiet=True,returnmp=False,return_all=False,returnfitimage=False,**kwargs):
""" gabor params = (height,amplitude,center_x,center_y,width_x,width_y,theta,lambda,phi) """
""" gaussian params=(height, amplitude, center_x, center_y, width_x, width_y, theta) """
if len(params) == 0:
params,_errors = gaussfit(data,limitedmin=limitedmin[:7],limitedmax=limitedmax[:7],\
minpars=minpars[:7],maxpars=maxpars[:7],return_all=True)
spatial_freq = math.sqrt(params[2]**2+params[3]**2)
phase = 0.0
params = np.append(params, np.array([spatial_freq, phase],dtype='float'))
def mpfitfun(data,err):
if err is None:
def f(p,fjac=None): return [0,np.ravel(data-twodgabor(p)\
(*np.indices(data.shape)))]
else:
def f(p,fjac=None): return [0,np.ravel((data-twodgabor(p)\
(*np.indices(data.shape)))/err)]
return f
parinfo = [
{'n':0,'value':params[0],'limits':[minpars[0],maxpars[0]],'limited':[limitedmin[0],limitedmax[0]],'fixed':fixed[0],'parname':"HEIGHT",'error':0},
{'n':1,'value':params[1],'limits':[minpars[1],maxpars[1]],'limited':[limitedmin[1],limitedmax[1]],'fixed':fixed[1],'parname':"AMPLITUDE",'error':0},
{'n':2,'value':params[2],'limits':[minpars[2],maxpars[2]],'limited':[limitedmin[2],limitedmax[2]],'fixed':fixed[2],'parname':"XSHIFT",'error':0},
{'n':3,'value':params[3],'limits':[minpars[3],maxpars[3]],'limited':[limitedmin[3],limitedmax[3]],'fixed':fixed[3],'parname':"YSHIFT",'error':0},
{'n':4,'value':params[4],'limits':[minpars[4],maxpars[4]],'limited':[limitedmin[4],limitedmax[4]],'fixed':fixed[4],'parname':"XWIDTH",'error':0},
{'n':5,'value':params[5],'limits':[minpars[5],maxpars[5]],'limited':[limitedmin[5],limitedmax[5]],'fixed':fixed[5],'parname':"YWIDTH",'error':0},
{'n':6,'value':params[6],'limits':[minpars[6],maxpars[6]],'limited':[limitedmin[6],limitedmax[6]],'fixed':fixed[6],'parname':"ROTATION",'error':0},
{'n':7,'value':params[7],'limits':[minpars[7],maxpars[7]],'limited':[limitedmin[7],limitedmax[7]],'fixed':fixed[7],'parname':"SPATIALFREQ",'error':0},
{'n':8,'value':params[8],'limits':[minpars[8],maxpars[8]],'limited':[limitedmin[8],limitedmax[8]],'fixed':fixed[8],'parname':"PHASE",'error':0} ]
mp = mpfit(mpfitfun(data,err),parinfo=parinfo,quiet=quiet)
if returnmp:
returns = (mp)
elif return_all == 0:
returns = mp.params
elif return_all == 1:
returns = mp.params,mp.perror
if returnfitimage:
fitimage = twodgabor(mp.params)(*np.indices(data.shape))
returns = (returns,fitimage)
return returns
def zandtfit(plandata, Keplerlc, Teff, Logg, Metalicity, sigma):
p0 = [plandata['Impactpar'], plandata['Transitduration'], (plandata['R_plan/R_star']/3), Teff, Logg, np.log10(Metalicity)] #inital guess (from kepler mast)
#print p0
fa = {'Keplerlc':Keplerlc, 'sigma':sigma} #additional variables to be past to the function
parinfo = [{'value':p0[0], 'fixed':False, 'limited':[True, True], 'limits':[0,2]}, {'value':p0[1], 'fixed':False}, {'value':p0[2], 'fixed':False},\
{'value':p0[3], 'fixed':False, 'limited':[True,True], 'limits':[0,40000]}, {'value':p0[4], 'fixed':False, 'limited':[True,True], 'limits':[0,5.0]},\
{'value':p0[5], 'fixed':False, 'limited':[True,True], 'limits':[-5,1]} ]
m = mpfit(minimisefunction, p0, functkw = fa, quiet = 0, maxiter = 25, parinfo = parinfo, ftol=0.0001) #run the mpfit for the best fit
#print m #testing
bestfitarray = m.params
errors = m.perror #these need to be properly stored
covar = m.covar #these need to be properly stored
if errors == None:
errors = [0,0,0,0,0,0,0]
#print bestfitarray
return np.abs(bestfitarray[0]), bestfitarray[1], bestfitarray[2], bestfitarray[3], bestfitarray[4], (10**bestfitarray[5]), bestfitarray, errors, covar
