def fit(self,):
"""
call the fitter
"""
x = self.spectrum.wavelength
y = self.spectrum.flux
err = self.spectrum.fluxerr
# build initial parameter value list
p0 = []
pinfo = []
for x in self.fit_parameters:
par = x["parinfo"]
for pv in par:
p0.append(pv['value'])
pinfo.append(pv)
# build argument list
fkw={'x':x,'y':y,'err':err}
# call L-M fitter
Z = mpfit.mpfit(fit_function,p0,functkw=fkw,parinfo=pinfo,quiet=True)
if (Z.status <= 0): print 'fit_spectrum.mpfit error message = ', Z.errmsg
# update the fit (parinfo...?), return results
self.result = Z
return Z
def execute(self, stack_time, stack_flux, stack_er):
p0 = [
0.2640, 1.392, -0.1398, 2.54017, 0.001593, 0.9994, 0.000455, 3.52,
0.13, 5.61, 0.78, 0.76, 0.001
]
parinfo = self.getParamInfo()
fa = {"x": stack_time, "y": stack_flux, "err": stack_er}
self.m = mpfit(self.lightcurveFunc, p0, functkw=fa, parinfo=parinfo)
def chi2colorfit(datfile, clobber=False, Nsim=20000):
"""do chi2 fitting to a supernova light
curve using SNANA simulations. Keep color
as a free parameter"""
sn = snana.SuperNova(SNDATA_ROOT + '/SIM/' + datfile)
sn.getClassSim(Nsim=Nsim, clobber=clobber)
#Type Ia chi-squared
mIa = zeros([len(sn.ClassSim.Ia.PTROBS_MIN), len(sn.MJD)])
for MJD, FLT, i in zip(sn.MJD, sn.FLT, range(len(sn.MJD))):
sn.ClassSim.Ia.samplephot(MJD)
mIa[:, i] = sn.ClassSim.Ia.__dict__['%s%i' % (FLT, int(MJD))]
chisqIa = []
Iaparams = []
for i in range(len(sn.ClassSim.Ia.PTROBS_MIN)):
if not len(where(mIa[i, :] == 99)[0]):
fa = {
'x': sn.MJD,
'y': sn.FLUXCAL,
'err': zeros(len(sn.MJD)),
'model_mag': mIa[i, :],
'flt': sn.FLT
}
mvals = mpfit.mpfit(lsfunc_color,
ones(len(unique(sn.FLT))),
functkw=fa)
Iaparams = Iaparams + [mvals.params]
scale = ones(len(sn.FLT))
for f, j in zip(unique(sn.FLT), range(len(unique(sn.FLT)))):
row = where(sn.FLT == f)[0]
scale[row] = mvals.params[j]
model = array(scale) * 10**(-0.4 * (mIa[i, :] - 27.5))
chisqIa = chisqIa + [
sum((model - sn.FLUXCAL)**2 / sn.FLUXCALERR**2)
]
#Type Ib/c chi-squared
mIbc = zeros([len(sn.ClassSim.Ibc.PTROBS_MIN), len(sn.MJD)])
for MJD, FLT, i in zip(sn.MJD, sn.FLT, range(len(sn.MJD))):
sn.ClassSim.Ibc.samplephot(MJD)
mIbc[:, i] = sn.ClassSim.Ibc.__dict__['%s%i' % (FLT, int(MJD))]
chisqIbc = []
Ibcparams = []
count = 0
for i in range(len(sn.ClassSim.Ibc.PTROBS_MIN)):
if not len(where(mIbc[i, :] == 99)[0]):
count = count + 1
fa = {
'x': sn.MJD,
'y': sn.FLUXCAL,
'err': zeros(len(sn.MJD)),
'model_mag': mIbc[i, :],
'flt': sn.FLT
}
mvals = mpfit.mpfit(lsfunc_color,
ones(len(unique(sn.FLT))),
functkw=fa)
Ibcparams = Ibcparams + [mvals.params]
scale = ones(len(sn.FLT))
for f, j in zip(unique(sn.FLT), range(len(unique(sn.FLT)))):
row = where(sn.FLT == f)[0]
scale[row] = mvals.params[j]
model = array(scale) * 10**(-0.4 * (mIbc[i, :] - 27.5))
chisqIbc = chisqIbc + [
sum((model - sn.FLUXCAL)**2 / sn.FLUXCALERR**2)
]
#Type II chi-squared
mII = zeros([len(sn.ClassSim.II.PTROBS_MIN), len(sn.MJD)])
for MJD, FLT, i in zip(sn.MJD, sn.FLT, range(len(sn.MJD))):
sn.ClassSim.II.samplephot(MJD)
mII[:, i] = sn.ClassSim.II.__dict__['%s%i' % (FLT, int(MJD))]
chisqII = []
IIparams = []
for i in range(len(sn.ClassSim.II.PTROBS_MIN)):
if not len(where(mII[i, :] == 99)[0]):
fa = {
'x': sn.MJD,
'y': sn.FLUXCAL,
'err': zeros(len(sn.MJD)),
'model_mag': mII[i, :],
'flt': sn.FLT
}
mvals = mpfit.mpfit(lsfunc_color,
ones(len(unique(sn.FLT))),
functkw=fa)
IIparams = IIparams + [mvals.params]
scale = ones(len(sn.FLT))
for f, j in zip(unique(sn.FLT), range(len(unique(sn.FLT)))):
row = where(sn.FLT == f)[0]
scale[row] = mvals.params[j]
model = array(scale) * 10**(-0.4 * (mII[i, :] - 27.5))
chisqII = chisqII + [
sum((model - sn.FLUXCAL)**2 / sn.FLUXCALERR**2)
]
print """Type Ia SNe have minimum chi squared: %.2f
computed from %.f SNe""" % (min(chisqIa), len(chisqIa))
print """Type Ibc SNe have minimum chi squared: %.2f
computed from %.f SNe""" % (min(chisqIbc), len(chisqIbc))
print """Type II SNe have minimum chi squared: %.2f
computed from %.f SNe""" % (min(chisqII), len(chisqII))
#now compute the likelihoods
eIa = exp(-array(chisqIa) / 2.)
eIbc = exp(-array(chisqIbc) / 2.)
eII = exp(-array(chisqII) / 2.)
chiIa = sum(exp(-array(chisqIa) / 2.))
chiIbc = sum(exp(-array(chisqIbc) / 2.))
chiII = sum(exp(-array(chisqII) / 2.))
p_Ia = chiIa / (chiIa + chiIbc + chiII)
p_Ibc = chiIbc / (chiIa + chiIbc + chiII)
p_II = chiII / (chiIa + chiIbc + chiII)
print """likelihoods:
Type Ia Type Ib/c Type II
%.4f %.4f %.4f""" % (p_Ia, p_Ibc, p_II)
print """odds ratios:
Type Ia/Ib/c Type Ia/II
%.4f %.4f""" % (chiIa / chiIbc, chiIa / chiII)
Iarow = where(chisqIa == min(chisqIa))[0][0]
Ibcrow = where(chisqIbc == min(chisqIbc))[0][0]
IIrow = where(chisqII == min(chisqII))[0][0]
return mIa, mIbc, mII, eIa / chiIa, eIbc / chiIbc, eII / chiII, Iarow, Ibcrow, IIrow, Iaparams, Ibcparams, IIparams
def annealFit(session, spinwave_domain, spinwave_range, spinwave_range_Err, size=3, k = 100, tMin = .001, tMax = 15, tFactor = .95, MCeveryTime = True, recordKeeperCallback = None):
# Create fitter
fitter = PM.Fitter(session, spinwave_domain, size, k, tMin, tMax, tFactor, MCeveryTime)
#testFile = open("/home/tom/Desktop/annealOutput1.txt", 'w')
#testFile = open("C:\\annealOutput1.txt", 'w')
# Helper function for GetResult
def myfunc(p, y=None, err=None, callback = None):
"""returns Chi Squared to be minimized."""
if callback:
callback(p)
#testFile.write('\np: ' + str(p))
#testFile.flush()
fitter.fit_list = p
model = fitter.GetResult()
#print 'y:\n', y, '\n\nmodel:\n', model
#for i in range(len(y)):
# testFile.write(" p: p %3.5f y %3.5f model %3.5f err %3.5f"%(p,y[i],model[i],err[i]) )
result = (y-model)/err
chi_sq = 0
for entry in result:
#print "entry: ", entry
chi_sq += math.pow(entry, 2)
#print '\n\nresult:\n', result
#print "\nchi_sq: " + str(chi_sq)
#testFile.write("\nchi_sq: " + str(chi_sq))
#testFile.flush()
return chi_sq
# Function Keywords
y = spinwave_range
err = spinwave_range_Err
p0 = fitter.fit_list
#testFile.write("initial p: " + str(p0))
#_______________________ Testing _____________________________
#START = -10
#STOP = 10
#step = .01
#FILE = "C:\\CHISQ_points3.txt"
##plot
#handle = open(FILE, 'w')
##handle.write("data: "+ str(y))
##handle.flush()
#for i in range((STOP-START)/step):
#x = START + i*step
#val = myfunc([x], y, err)
#if i != 0:
#handle.write("\n")
#handle.write(str(x) + " " + str(val))
#handle.close()
#sys.exit()
#_____________________________________________________________________
result=anneal(myfunc,p0,args=(y,err, recordKeeperCallback), schedule='simple',lower=fitter.min_range_list,upper=fitter.max_range_list,
maxeval=None, maxaccept=None,dwell=50, maxiter=2000, full_output=1)
print "annealing result: ", result
p = result[0]
myfunc(p, y, err)#To propogate final value
#----Now Localy optimize with mpfit to fine tune the answer -----------
def mpfitfunc(p, fjac=None, y=None, err=None, callback = None):
if callback:
callback(p)
#testFile.write("mpfit: " + str(p))
fitter.fit_list = p
model = fitter.GetResult()
status = 0
print 'y:\n', y, '\n\nmodel:\n', model
#print '\n\ny-model:\n', (y-model)
#print '\n\nerr:\n', err
result = (y-model)/err
print '\n\nresult:\n', result
return [status, result]
fa = {'y':y, 'err':err, 'callback':recordKeeperCallback}
# Set parinfo with the limits and such. Probably don't need
parbase={'value':0., 'limited':[0,0], 'limits':[0.,0.]}
parinfo=[]
p0 = fitter.fit_list
for i in range(len(p0)):
parinfo.append(copy.deepcopy(parbase))
for i in range(len(p0)):
parinfo[i]['value']=p0[i]
if(fitter.min_range_list[i] != np.NINF):
parinfo[i]['limits'][0]=fitter.min_range_list[i]
parinfo[i]['limited'][0] = 1
else:
parinfo[i]['limited'][0] = 0
if fitter.max_range_list[i] != np.PINF:
parinfo[i]['limits'][1] = fitter.max_range_list[i]
parinfo[i]['limited'][1] = 1
else:
parinfo[i]['limited'][1] = 0
# Run mpfit on fitlist with all the jazz.
print "params: ", p0
print "parinfo: ", parinfo
m = mpfit(mpfitfunc, p0, parinfo=parinfo, functkw = fa)
#testFile.write("\nresult: " + str(result))
#testFile.write("\nm: " + str(m))
#testFile.close()
return (m.status, m.params, m.perror)
def fitting(session, spinwave_domain, spinwave_range, spinwave_range_Err, size=3, k = 100, tMin = .001, tMax = 15, tFactor = .95, MCeveryTime = True, recordKeeperCallback = None):
# Create fitter
fitter = PM.Fitter(session, spinwave_domain, size, k, tMin, tMax, tFactor, MCeveryTime)
# Helper function for GetResult
def myfunc(p, fjac=None, y=None, err=None, callback = None):
if callback:
callback(p)
fitter.fit_list = p
model = fitter.GetResult()
status = 0
print 'y:\n', y, '\n\nmodel:\n', model
#print '\n\ny-model:\n', (y-model)
#print '\n\nerr:\n', err
result = (y-model)/err
print '\n\nresult:\n', result
return [status, result]
# Function Keywords
#spinwave_domain is a list of 3D lists:
#-[(x1,y1,z1),(x2,y2,z2),...]
#x must be a 1D numpy array:
#[x1,y1,z1,x2,t2,z2,...]
#xList = []
#for point in fit.spinwave_domain:
# xList.append(point[0])
# yList.append(point[1])
# zList.append(point[2])
#x = np.array(xList)
#y = myfunc(fit)
y = spinwave_range
#errVal = .001
#err = [errVal]*len(y)
err = spinwave_range_Err
#fa = {'x':x, 'y':y, 'err':err}
fa = {'y':y, 'err':err, 'callback':recordKeeperCallback}
# Set parinfo with the limits and such. Probably don't need
parbase={'value':0., 'limited':[0,0], 'limits':[0.,0.]}
parinfo=[]
p0 = fitter.fit_list
for i in range(len(p0)):
parinfo.append(copy.deepcopy(parbase))
for i in range(len(p0)):
parinfo[i]['value']=p0[i]
if(fitter.min_range_list[i] != np.NINF):
parinfo[i]['limits'][0]=fitter.min_range_list[i]
parinfo[i]['limited'][0] = 1
else:
parinfo[i]['limited'][0] = 0
if fitter.max_range_list[i] != np.PINF:
parinfo[i]['limits'][1] = fitter.max_range_list[i]
parinfo[i]['limited'][1] = 1
else:
parinfo[i]['limited'][1] = 0
# Run mpfit on fitlist with all the jazz.
print "params: ", p0
print "parinfo: ", parinfo
m = mpfit(myfunc, p0, parinfo=parinfo, functkw = fa)
# Bare Bones Run for when the above (especially keywords x) breaks instantly
#m = mpfit(myfunc, fit.fitlist)
#Return the parameters
return (m.status, m.params, m.perror)
def plot_planet(obj=None,caltype=None,nobs=None):
sectdatapath='../data/FTS/sect_planets/'
ftsdatapath='../data/FTS/'
modelfile=None
mc=False
if obj == 'saturn':
datapath=sectdatapath
nobs=nobs
d_plt_ini=16.
obsid=['1342198279','1342224754','1342247750']
dobs=[17.7,16.7,17.4]
suffix=''
modelfile='spire_JanskyGHz.dat'
ftspsfile='./v11/Saturn/'+obsid[nobs]+ \
'_spectrum_point_HR_unapod_18Apr2013' \
+suffix+'.fits'
dtrue=dobs[nobs]
scale=(dtrue/dobs[0])**2.
nskip=3
munit=1.
uplot=1e-3
unit='kJy'
yrange=[3.,40.]
sim_func=sim_planet
psuf='_v11_'+str(nobs)
if obj == 'mars':
datapath=sectdatapath
d_plt_ini=6.
nobs=nobs
modelfile='MarsModel_OD176.txt'
obsid=['1342193675', '1342197462', '1342198930', \
'1342231056', '1342231059', '1342231062', \
'1342231064', '1342231070', '1342231076', \
'1342231079', '1342231085', '1342245848', \
'1342247563', '1342247571']
dobs=[8.8,6.0,5.4,5.5,5.5,5.5, \
5.5,5.5,5.5,5.5,5.5,8.8,6.6,6.6]
ftspsfile='./v11/Mars/'+obsid[nobs]+'_spectrum_' \
'point_HR_unapod_18Apr2013.fits'
dtrue=dobs[nobs]
scale=(dtrue/5.5)**2.
nskip=2
munit=1e3
uplot=1e-3
unit='kJy'
yrange=[0.5,20.]
sim_func=sim_planet
psuf='_v11_'+str(nobs)
if obj == 'neptune':
datapath=sectdatapath
d_plt_ini=2.
dtrue=2.28
modelfile='0x5000AD87_model_HR.fits'
obsid='1342221703'
ftspsfile='./v10/'+obsid+'_spectrum_' \
'point_HR_unapod_v10.fits'
scale=1.
uplot=1.
unit='Jy'
yrange=[20.,300.]
sim_func=sim_planet
psuf=''
if obj == 'uranus':
datapath=sectdatapath
d_plt_ini=3.
dtrue=3.55
modelfile='5001389B_model_HR.fits'
obsid='1342257307'
ftspsfile='./v10/'+obsid+'_spectrum_' \
'point_HR_unapod_v10.fits'
scale=1.
uplot=1.
unit='Jy'
yrange=[90.,600.]
sim_func=sim_planet
psuf=''
if obj == 'm83' or obj == 'M83':
datapath=ftsdatapath
modelfile=''
d_plt_ini=18.
ftspsfile='M83/HIPE.9.0.2742/unapodize/' \
'1342212345_avgspectrum_HR_unapod_0_6.fits'
ftsexfile='M83/HIPE.9.0.2742/unapodize/' \
'1342212345_avgspectrum_HR_unapod_0_6.fits'
yrange=[0.1,300.]
uplot=1.
unit='Jy'
sim_func=sim_gauss
psuf=''
if obj == 'm82' or obj == 'M82':
datapath=ftsdatapath
modelfile=''
d_plt_ini=22.
ftspsfile='M82/HIPE.8.0.3384/unapodize/' \
'1342208388_avgspectrum_HR_unapod_0_6.fits'
ftsexfile='M82/HIPE.8.0.3384/unapodize/' \
'1342208388_avgspectrum_HR_unapod_0_6.fits'
yrange=[1.,2000.]
uplot=1.
unit='Jy'
sim_func=sim_gauss
psuf=''
if obj == 'ngc4214' or obj == 'NGC4214':
datapath=ftsdatapath
modelfile=''
d_plt_ini=18.
ftspsfile=obj.upper()+'/HIPE.11.0.2785/unapodize/' \
'1342256082_spectrum_point_HR_unapod.fits'
ftsexfile='M83/HIPE.11.0.2785/unapodize/' \
'1342256082_spectrum_extended_HR_unapod.fits'
yrange=[0.01,20.]
uplot=1.
unit='Jy'
sim_func=sim_gauss
psuf=''
if caltype == 'point':
ftsfile=pyfits.open(datapath+ftspsfile)
if caltype == 'extended':
ftsfile=pyfits.open(datapath+ftsexfile)
ftsfile.verify('silentfix')
ctrxy=array([128,128])
# ======= Read model spectrum of the planet =========
if bool(modelfile):
if modelfile[-4:] != 'fits':
spec_mod=open('../data/FTS/sect_planets/'+modelfile)
modline=spec_mod.readlines()
smfreq=[]
smkjy=[]
for i in range(len(modline)/10-nskip):
ii=i*10+nskip
temp=None
temp=modline[ii].split()
smfreq.append(double(temp[0]))
smkjy.append(double(temp[len(temp)-1]))
mfreq=array(smfreq)
mkjy=array(smkjy).copy()/munit
if obj == 'mars':
mfreq=3e5/array(smfreq)
scalem=0.
for np in range(len(modelwn)):
anp=abs(mfreq-modelwn[np]*30.)
idxnp=where(anp == min(anp))
scalem=scalem+(1e-3*(fluxLellouch[np]+fluxRudy[np])/2.)/ \
mkjy[idxnp[0][0]]
scalem=scalem/float(len(modelwn))
mkjy=scalem*mkjy.copy()
else:
model=pyfits.open('../data/FTS/sect_planets/'+modelfile)
model.verify('silentfix')
nover=len((where(model['SSWD4'].data['wave'] <= \
model['SLWC3'].data['wave'].max()))[0])
mslw=model['SLWC3'].data
mssw=model['SSWD4'].data
nmod=len(mslw['flux'])+len(mssw['flux'])-nover
mfreq=zeros(nmod)
mkjy=zeros(nmod)
mfreq[0:len(mslw['flux'])]= \
mslw['wave'][0:len(mslw['flux'])]
mfreq[len(mslw['flux']):]=mssw['wave'][nover:]
mkjy[0:(len(mslw['flux'])-nover)]= \
mslw['flux'][0:(len(mslw['flux'])-nover)]
mkjy[(len(mslw['flux'])-nover):len(mslw['flux'])]= \
0.5*(mslw['flux'][(len(mslw['flux'])-nover):]+ \
mssw['flux'][0:nover])
mkjy[len(mslw['flux']):]=mssw['flux'][nover:]
# ======== Construct planet light distribution image =========
[chiparam,chierr,dplt]=find_best_d(ftsfile,d_plt_ini, \
caltype=caltype,obj=obj)
prob=exp(-(chiparam-chiparam.min())/chiparam.min())
mindx=where(chiparam == chiparam.min())
mindx=mindx[0][0]
proberr=zeros(len(prob))+1.
# proberr=sqrt((chierr**2.+chiparam[mindx]**2.)) #\
# +(chiparam*chierr[mindx]/chiparam[mindx]**2.)**2.)
figure(10)
plot(dplt,prob,marker='*')
fa={'x':dplt,'y':prob,'err':proberr}
p_info=[{'value':d_plt_ini,'fixed':0, \
'limited':[1,1],'limits':[0.,100.]}, \
{'value':2.,'fixed':0, \
'limited':[1,1],'limits':[0.,100.]}]
params=mpfit.mpfit(fit_likelihood,functkw=fa,maxiter=1000,parinfo=p_info)
dplt_g=dplt[0]+arange(101)*(dplt[-1]-dplt[0])/100.
yy=fit_gaussian(dplt_g,params.params[0],params.params[1])
plot(dplt_g,yy)
xlim([dplt[0],dplt[-1]])
ylim([0.,1.2])
xlabel('Diameter (arcsec)')
ylabel('exp(chi-param)')
annotate('$\mu$ = {:.2f} (arcsec)'.format(params.params[0]), \
[dplt_g[0],1.1])
annotate('$\sigma$ = {:.2f} (arcsec)'.format(params.params[1]), \
[dplt_g[0],1.0])
savefig(plotdir+obj+'_d_fit'+psuf+'.png',format='PNG')
close(10)
# dplanet=params.params[0]
dplanet=params.params[0]
planet_mod=sim_func(dplanet,ctrxy)
planet_area=sum(planet_mod)
if obj == 'saturn':
mod1=sim_gauss(12.,ctrxy)
mod1_area=sum(mod1)
mod2=sim_exp(4.,ctrxy)
mod2_area=sum(mod2)
# ======== Correcting the FTS Spectra ==========
figure(0,figsize=[8.,8.])
subplot(111)
title('Correction on the '+obj.capitalize()+' FTS Spectrum',fontsize='large')
# suptitle('$\\theta_{D}=$'+'{:.2f}'.format(params.params[0]) \
# +' $\\theta_{obs}=$'+'{:.2f}'.format(dtrue))
out=pyfits.HDUList()
for i in range(2):
if i == 0:
wn=wn_ssw
beam=beam_ssw
fwhm=fwhm_ssw
rtel=telrsrf[1]
iro='blue'
ni='SSW'
deti='SSWD4'
if i == 1:
wn=wn_slw
beam=beam_slw
fwhm=fwhm_slw
rtel=telrsrf[2]
iro='red'
ni='SLW'
deti='SLWC3'
fts=ftsfile[deti]
if (obj == 'm83' or obj == 'M83') or \
(obj == 'm82' or obj == 'M82'):
fts.data['flux'][:]= \
fts.data['flux'].copy()*cps[deti].data['pointConv']
beam_area=[]
beam_planet=[]
if obj == 'saturn':
beam_mod1=[]
beam_mod2=[]
for j in range(len(wn[0].data)):
beam_area.append(sum(beam[0].data[j,:,:]))
beam_planet.append(sum(beam[0].data[j,:,:]*planet_mod))
if obj == 'saturn':
beam_mod1.append(sum(beam[0].data[j,:,:]*mod1))
beam_mod2.append(sum(beam[0].data[j,:,:]*mod2))
a_beam=None
ps_coup=None
eta_mb=None
a_beam=array(beam_area)
if i == 0:
assw=a_beam.copy()
if i == 1:
aslw=a_beam.copy()
ps_coup=array(beam_planet)
if obj == 'saturn':
pmod1_coup=array(beam_mod1)
pmod2_coup=array(beam_mod2)
x=wn[0].data*30. # change the wave number to GHz
fab=interpolate.interp1d(x,a_beam, \
bounds_error=False,fill_value=a_beam[0])
fps=interpolate.interp1d(x,ps_coup, \
bounds_error=False,fill_value=ps_coup[0])
if obj == 'saturn':
fmod1=interpolate.interp1d(x,pmod1_coup, \
bounds_error=False,fill_value=pmod1_coup[0])
fmod2=interpolate.interp1d(x,pmod2_coup, \
bounds_error=False,fill_value=pmod2_coup[0])
xp=ftsfile[deti].data['wave']
ypa=ftsfile[deti].data['flux']*uplot
rs=rtel.data['rsrf']/cps[deti].data['pointConv']* \
fps(xp)/planet_area
if caltype == 'point':
correction=rtel.data['rsrf']/(rs*cps[deti].data['pointConv'])
if caltype == 'extended':
correction=rtel.data['rsrf']/rs
ypb=ypa.copy()*correction
if obj == 'saturn':
ypb1=ypb/(planet_area/fps(xp)*fmod1(xp)/mod1_area)
ypb2=ypb/(planet_area/fps(xp)*fmod2(xp)/mod2_area)
fill_between(xp,ypb1,ypb2,color='gray',alpha=0.5)
exec('foo'+str(i)+'=ypb1')
exec('bar'+str(i)+'=ypb2')
plot(xp,ypa,'--',color=iro,label=ni+' '+caltype,linewidth=1.)
plot(xp,ypb,'-',color=iro,linewidth=1.,label=ni+' corrected')
fts.data['flux'][:]=ypb[:]
fts.data['error'][:]=fts.data['error']*correction
out.append(fts)
tempfts=fts.copy()
if bool(modelfile):
if modelfile[-4:] != 'fits':
idx=(mfreq >=xp[0]-5.) & (mfreq <= xp[-1]+5.)
fmod=interpolate.interp1d(mfreq[idx],mkjy[idx], \
bounds_error=False, \
fill_value=mkjy[-1])
tempfts.data['flux'][:]=fmod(xp)*scale/ypb
else:
idx=(mfreq >= xp[0]) & (mfreq <= xp[-1])
tempfts.data['flux'][:]=scale*mkjy[idx]/ypb
tempfts.data['error'][:]=0.
tempfts.data['mask'][:]=0.
tempfts.name='GAMMA'+deti
out.append(tempfts)
if bool(modelfile):
plot(mfreq,scale*mkjy,color='black',label='Model')
if obj == 'mars':
plot(modelwn*30.,scale*fluxLellouch*1e-3,linewidth=0.,marker='+', \
markersize=10.,color='black',markeredgewidth=2., \
label='Lellouch')
plot(modelwn*30.,scale*fluxRudy*1e-3,linewidth=0.,marker='+', \
markersize=10.,color='red',markeredgewidth=2., \
label='Rudy')
xlabel('Frequency (GHz)',fontsize='x-large')
ylabel('Flux Density('+unit+')',fontsize='x-large')
xlim([400.,1600.])
ylim(yrange)
# annotate('{:.2f}'.format(params.params[0]),[450.,0.95*yrange[0]])
axvline(linewidth=3.,color='black')
axhline(linewidth=3.,color='black')
yscale('log')
legend(loc=2)
savefig(filename=plotdir+obj+psuf+'.png',format='PNG')
close(0)
os.system('open %s%s%s.png &'%(plotdir,obj,psuf))
out.writeto(plotdir+obj+'_correct'+psuf+'.fits',clobber=True, \
output_verify='ignore')
return
def annealFit(session, spinwave_domain, spinwave_range, spinwave_range_Err, size=3, k = 100, tMin = .001, tMax = 15, tFactor = .95, MCeveryTime = True, recordKeeperCallback = None):
# Create fitter
fitter = PM.Fitter(session, spinwave_domain, size, k, tMin, tMax, tFactor, MCeveryTime)
#testFile = open("/home/tom/Desktop/annealOutput1.txt", 'w')
#testFile = open("C:\\annealOutput1.txt", 'w')
# Helper function for GetResult
def myfunc(p, y=None, err=None, callback = None):
"""returns Chi Squared to be minimized."""
if callback:
callback(p)
#testFile.write('\np: ' + str(p))
#testFile.flush()
fitter.fit_list = p
model = fitter.GetResult()
#print 'y:\n', y, '\n\nmodel:\n', model
#for i in range(len(y)):
# testFile.write(" p: p %3.5f y %3.5f model %3.5f err %3.5f"%(p,y[i],model[i],err[i]) )
result = (y-model)/err
chi_sq = 0
for entry in result:
#print "entry: ", entry
chi_sq += math.pow(entry, 2)
#print '\n\nresult:\n', result
#print "\nchi_sq: " + str(chi_sq)
#testFile.write("\nchi_sq: " + str(chi_sq))
#testFile.flush()
return chi_sq
# Function Keywords
y = spinwave_range
err = spinwave_range_Err
p0 = fitter.fit_list
#testFile.write("initial p: " + str(p0))
#_______________________ Testing _____________________________
#START = -10
#STOP = 10
#step = .01
#FILE = "C:\\CHISQ_points3.txt"
##plot
#handle = open(FILE, 'w')
##handle.write("data: "+ str(y))
##handle.flush()
#for i in range((STOP-START)/step):
#x = START + i*step
#val = myfunc([x], y, err)
#if i != 0:
#handle.write("\n")
#handle.write(str(x) + " " + str(val))
#handle.close()
#sys.exit()
#_____________________________________________________________________
result=anneal(myfunc,p0,args=(y,err, recordKeeperCallback), schedule='simple',lower=fitter.min_range_list,upper=fitter.max_range_list,
maxeval=None, maxaccept=None,dwell=50, maxiter=2000, full_output=1)
print "annealing result: ", result
p = result[0]
myfunc(p, y, err)#To propogate final value
#----Now Localy optimize with mpfit to fine tune the answer -----------
def mpfitfunc(p, fjac=None, y=None, err=None, callback = None):
if callback:
callback(p)
#testFile.write("mpfit: " + str(p))
fitter.fit_list = p
model = fitter.GetResult()
status = 0
print 'y:\n', y, '\n\nmodel:\n', model
#print '\n\ny-model:\n', (y-model)
#print '\n\nerr:\n', err
result = (y-model)/err
print '\n\nresult:\n', result
return [status, result]
fa = {'y':y, 'err':err, 'callback':recordKeeperCallback}
# Set parinfo with the limits and such. Probably don't need
parbase={'value':0., 'limited':[0,0], 'limits':[0.,0.]}
parinfo=[]
p0 = fitter.fit_list
for i in range(len(p0)):
parinfo.append(copy.deepcopy(parbase))
for i in range(len(p0)):
parinfo[i]['value']=p0[i]
if(fitter.min_range_list[i] != np.NINF):
parinfo[i]['limits'][0]=fitter.min_range_list[i]
parinfo[i]['limited'][0] = 1
else:
parinfo[i]['limited'][0] = 0
if fitter.max_range_list[i] != np.PINF:
parinfo[i]['limits'][1] = fitter.max_range_list[i]
parinfo[i]['limited'][1] = 1
else:
parinfo[i]['limited'][1] = 0
# Run mpfit on fitlist with all the jazz.
print "params: ", p0
print "parinfo: ", parinfo
m = mpfit(mpfitfunc, p0, parinfo=parinfo, functkw = fa)
#testFile.write("\nresult: " + str(result))
#testFile.write("\nm: " + str(m))
#testFile.close()
return (m.status, m.params, m.perror)
def fitting(session, spinwave_domain, spinwave_range, spinwave_range_Err, size=3, k = 100, tMin = .001, tMax = 15, tFactor = .95, MCeveryTime = True, recordKeeperCallback = None):
# Create fitter
fitter = PM.Fitter(session, spinwave_domain, size, k, tMin, tMax, tFactor, MCeveryTime)
# Helper function for GetResult
def myfunc(p, fjac=None, y=None, err=None, callback = None):
if callback:
callback(p)
fitter.fit_list = p
model = fitter.GetResult()
status = 0
print 'y:\n', y, '\n\nmodel:\n', model
#print '\n\ny-model:\n', (y-model)
#print '\n\nerr:\n', err
result = (y-model)/err
print '\n\nresult:\n', result
return [status, result]
# Function Keywords
#spinwave_domain is a list of 3D lists:
#-[(x1,y1,z1),(x2,y2,z2),...]
#x must be a 1D numpy array:
#[x1,y1,z1,x2,t2,z2,...]
#xList = []
#for point in fit.spinwave_domain:
# xList.append(point[0])
# yList.append(point[1])
# zList.append(point[2])
#x = np.array(xList)
#y = myfunc(fit)
y = spinwave_range
#errVal = .001
#err = [errVal]*len(y)
err = spinwave_range_Err
#fa = {'x':x, 'y':y, 'err':err}
fa = {'y':y, 'err':err, 'callback':recordKeeperCallback}
# Set parinfo with the limits and such. Probably don't need
parbase={'value':0., 'limited':[0,0], 'limits':[0.,0.]}
parinfo=[]
p0 = fitter.fit_list
for i in range(len(p0)):
parinfo.append(copy.deepcopy(parbase))
for i in range(len(p0)):
parinfo[i]['value']=p0[i]
if(fitter.min_range_list[i] != np.NINF):
parinfo[i]['limits'][0]=fitter.min_range_list[i]
parinfo[i]['limited'][0] = 1
else:
parinfo[i]['limited'][0] = 0
if fitter.max_range_list[i] != np.PINF:
parinfo[i]['limits'][1] = fitter.max_range_list[i]
parinfo[i]['limited'][1] = 1
else:
parinfo[i]['limited'][1] = 0
# Run mpfit on fitlist with all the jazz.
print "params: ", p0
print "parinfo: ", parinfo
m = mpfit(myfunc, p0, parinfo=parinfo, functkw = fa)
# Bare Bones Run for when the above (especially keywords x) breaks instantly
#m = mpfit(myfunc, fit.fitlist)
#Return the parameters
return (m.status, m.params, m.perror)
