def plotCls(self,saveFile,keys=None,xlimits=None,ylimits=None,transform=True,showBinnedTheory=False,scaleX='linear',scaleY='linear'):
nsigma = 2.
binCenters = self.binner.getBinCenters()
if transform:
ylab = "$\ell C_{\ell}$"
mult = binCenters
multTh = 1.#binCenters*0.+1.
else:
ylab = "$C_{\ell}$"
mult = binCenters*0.+1.
multTh = 0.#binCenters*0.
pl = Plotter(labelX="$\ell$",labelY=ylab,scaleX=scaleX,scaleY=scaleY)
if keys is None: keys = self.datas.keys()
for key in keys:
dat = self.datas[key]
if dat['covmat'] is None:
#This is a theory curve
ells = np.array(range(len(dat['unbinned'])))
if dat['isFit']:
ls="--"
lw=1
else:
ls="-"
lw=2
base_line, = pl.add(ells,(multTh*(ells-1)+1.)*dat['unbinned'],label=dat['label'],lw=lw,ls=ls)
if dat['isFit']:
pl._ax.fill_between(ells,(multTh*(ells-1)+1.)*dat['unbinned']*(1.-nsigma*dat['amp'][1]/dat['amp'][0]),(multTh*(ells-1)+1.)*dat['unbinned']*(1.+nsigma*dat['amp'][1]/dat['amp'][0]),alpha=0.3, facecolor=base_line.get_color())
if showBinnedTheory:
pl.add(binCenters[:len(dat['binned'])],mult[:len(dat['binned'])]*dat['binned'],
ls='none',marker='x',mew=2,markersize=10,label=dat['label']+' binned')
else:
errs = np.sqrt(np.diagonal(dat['covmat']))
print dat['label']
pl.addErr(binCenters[:len(dat['binned'])],mult[:len(dat['binned'])]*dat['binned'],mult[:len(dat['binned'])]*errs,label=dat['label'],marker='o',elinewidth=2,markersize=10,mew=2,)
[i.set_linewidth(2.0) for i in pl._ax.spines.itervalues()]
pl._ax.tick_params(which='major',width=2)
pl._ax.tick_params(which='minor',width=2)
pl._ax.axhline(y=0.,ls='--')
if not(xlimits is None):
pl._ax.set_xlim(*xlimits)
else:
pl._ax.set_xlim(self.binner.bin_edges[0],self.binner.bin_edges[-1])
if not(ylimits is None): pl._ax.set_ylim(*ylimits)
pl.legendOn(loc='lower left',labsize=10)
pl.done(saveFile)
def fit(self,keyData,keyTheory,amplitudeRange=np.arange(0.1,2.0,0.01),debug=False,numbins=-1):
# evaluate likelihood on a 1d grid and fit to a gaussian
# store fit as new theory curve
width = amplitudeRange[1]-amplitudeRange[0]
Likelihood = lambda x: np.exp(-0.5*self.chisq(keyData,keyTheory,amp=x,numbins=numbins))
Likes = np.array([Likelihood(x) for x in amplitudeRange])
Likes = Likes / (Likes.sum()*width) #normalize
ampBest,ampErr = cfit(norm.pdf,amplitudeRange,Likes,p0=[1.0,0.5])[0]
if debug:
fitVals = np.array([norm.pdf(x,ampBest,ampErr) for x in amplitudeRange])
pl = Plotter()
pl.add(amplitudeRange,Likes,label="likes")
pl.add(amplitudeRange,fitVals,label="fit")
pl.legendOn()
pl.done("output/debug_coreFit.png")
fitKey = keyData+"_fitTo_"+keyTheory
self.datas[fitKey] = {}
self.datas[fitKey]['covmat'] = None
self.datas[fitKey]['binned'] = self.datas[keyTheory]['binned']*ampBest
self.datas[fitKey]['unbinned'] = self.datas[keyTheory]['unbinned']*ampBest
self.datas[fitKey]['label'] = keyData+" fit to "+keyTheory+" with amp "+'{0:.2f}'.format(ampBest)+"+-"+'{0:.2f}'.format(ampErr)
self.datas[fitKey]['amp']=(ampBest,ampErr)
self.datas[fitKey]['isFit'] = True
return fitKey
fileList = glob.glob("output/"+derivRoot+"*.csv")
farr = np.loadtxt("output/"+derivRoot[:-5]+"fCls.csv",delimiter=',')
arrs = {}
for fileN in fileList:
lab = fileN[len("output/"+derivRoot):-4]
arrs[lab] = np.loadtxt(fileN,delimiter=',')
specList = ['TT','EE','BB','TE','KK','KT']
for spec in specList:
if spec=='BB': continue
pls = Plotter(scaleY='log',scaleX='log')
ind = specList.index(spec)
for lab in arrs:
arr = arrs[lab]
y = arr[:,ind]**2./farr[:,ind]**2.
if lab=='tau': ls = '--'
else: ls = "-"
pls.add(range(arr.shape[0]),y,label=lab,ls=ls)
pls.legendOn(loc='upper right',labsize=8)
pls._ax.set_xlim(20.,4000.)
pls.done("output/d"+spec+".png")
def fitAuto(self,keyData,keyTheory,amplitudeRange=np.arange(0.1,2.0,0.01),constRange=np.arange(0.1,2.0,0.01),debug=False,store=False):
# evaluate likelihood on a 2d grid and fit to a gaussian
# store fit as new theory curve
width = amplitudeRange[1]-amplitudeRange[0]
height = constRange[1]-constRange[0]
Likelihood = lambda x,y: np.exp(-0.5*self.chisqAuto(keyData,keyTheory,amp=x,const=y))
#Likelihood = lambda x,y: -0.5*self.chisqAuto(keyData,keyTheory,amp=x,const=y)
Likes = np.array([[Likelihood(x,y) for x in amplitudeRange] for y in constRange])
ampLike = np.sum(Likes,axis=0)
constLike = np.sum(Likes,axis=1)
ampLike = ampLike / (ampLike.sum()*width) #normalize
constLike = constLike / (constLike.sum()*height) #normalize
ampBest,ampErr = cfit(norm.pdf,amplitudeRange,ampLike,p0=[amplitudeRange.mean(),0.1*amplitudeRange.mean()])[0]
constBest,constErr = cfit(norm.pdf,constRange,constLike,p0=[constRange.mean(),0.1*constRange.mean()])[0]
if debug:
pl = Plotter()
pl.plot2d(Likes)
pl.done("output/like2d.png")
pl = Plotter()
fitVals = np.array([norm.pdf(x,ampBest,ampErr) for x in amplitudeRange])
pl.add(amplitudeRange,ampLike,label="amplikes")
pl.add(amplitudeRange,fitVals,label="fit")
pl.legendOn()
pl.done("output/amplike1d.png")
pl = Plotter()
fitVals = np.array([norm.pdf(x,constBest,constErr) for x in constRange])
pl.add(constRange,constLike,label="constlikes")
pl.add(constRange,fitVals,label="fit")
pl.legendOn()
pl.done("output/constlike1d.png")
#sys.exit()
if not(store):
return constBest,constErr
else:
self.datas[keyData]['binned'] -= constBest
self.datas[keyData]['unbinned'] -= constBest
fitKey = keyData+"_fitTo_"+keyTheory
self.datas[fitKey] = {}
self.datas[fitKey]['covmat'] = None
self.datas[fitKey]['binned'] = self.datas[keyTheory]['binned']*ampBest
self.datas[fitKey]['unbinned'] = self.datas[keyTheory]['unbinned']*ampBest
self.datas[fitKey]['label'] = keyData+" fit to "+keyTheory+" with amp "+'{0:.2f}'.format(ampBest)+"+-"+'{0:.2f}'.format(ampErr)
self.datas[fitKey]['amp']=(ampBest,ampErr)
self.datas[fitKey]['const']=(constBest,constErr)
self.datas[fitKey]['isFit'] = True
return fitKey
zsource = 1.0
myInt.addDeltaNz('delta', zsource)
for i, zwidth in enumerate(np.arange(0.01, 0.1, 0.01)):
myInt.addStepNz('step' + str(i), zsource - zwidth / 2.,
zsource + zwidth / 2.)
print("getting cls..")
pl = Plotter(scaleY='log', scaleX='log')
ellrange = list(range(2, ellmax, 1))
myInt.generateCls(ellrange)
for i, tag in enumerate(sorted(myInt.kernels.keys())):
if tag == "cmb": continue
retcl = myInt.getCl("cmb", tag)
if tag == "delta":
compcl = retcl.copy()
lw = 2
ls = "--"
else:
lw = 1
ls = "-"
pl.add(ellrange, retcl, label=tag, ls=ls, lw=lw)
rat = (retcl / compcl)
ratio = (np.abs(rat[np.logical_and(ellrange > 100., ellrange < 2000.)] -
1.)).max() * 100.
print((tag, ratio, " %"))
pl.legendOn(loc='upper right', labsize=10)
pl.done("output/estcls.png")
print xy,beam
if files==[]:
print "skipping"
continue
print files[0]
noises = []
mss = []
for filen in files:
beam,ms = np.loadtxt(filen,unpack=True)
noise = float(filen[-7:-4])
noises.append(noise)
mss.append(ms)
print "last noise ", noises[-1]
#print mss[-1]
#pl.add(noises,mss,lw=3,label=xy+" "+str(beam) + " arcmin",ls='--',color=col)
pl.add(noises,np.array(mss)*0.01,lw=3,label=str(beam) + " arcmin",ls='-',color=col1)
pl._ax.set_xlim(1.,5.)
#pl._ax.set_ylim(0.,10.)
pl._ax.set_ylim(0.,4.*0.01)
pl.legendOn()
pl.done("beam.png")
