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
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
def cic(self):
# Fit exponential curves to unsupported Pb-210 activity profile and derive
# age and rate assuming constant initial concentration. See example:
# Donnelly and Bertness (2001) PNAS 98: 14218-14223.
# Sometimes background/supported level of Pb-210 is unknown. Just in case,
# this algorithm fits an intercept to the data that predicts the background
# rate. When background samples are present, these intercept values should
# be near zero
# Initialize variables
self.data['initial'] = np.ones(self.max_iter) * np.nan
self.data['rate'] = np.ones(self.max_iter) * np.nan
self.data['intercept'] = np.ones(self.max_iter) * np.nan
self.data['rsquared'] = np.ones(self.max_iter) * np.nan
# Step through the Monte Carlo iterations
for ii in np.arange(self.max_iter):
# Define initial guess at y-intercept
self.temp_unsupport = np.mean(self.data['unsupported']\
[self.data['depth']==np.min(self.data['depth']),ii],axis=0)
# Fit an exponential model to the data, with and without predicting
# a baseline background
if self.bkgrd < 0:
temp_fit = cfit(self.cic_forward,\
self.data['mass'],self.data['unsupported'][:,ii], \
p0=np.array([self.temp_unsupport,self.initial_depo_rate,\
self.initial_depo_intercept]))[0]
self.data['intercept'][ii] = temp_fit[2]
else:
temp_fit = cfit(self.cic_forward_nointercept,\
self.data['mass'],self.data['unsupported'][:,ii], \
p0=np.array([self.temp_unsupport,self.initial_depo_rate]))[0]
self.data['intercept'][ii] = 0
# Extract model parameters
self.data['initial'][ii] = temp_fit[0]
self.data['rate'][ii] = temp_fit[1]
self.data['rsquared'][ii] = np.corrcoef(self.data['unsupported'][:,ii],\
self.cic_forward(self.data['unsupported'][:,ii],\
self.data['initial'][ii],self.data['rate'][ii], \
self.data['intercept'][ii]))[1,0]**2
self.data['age'][:, ii] = self.data['mass'] / self.data['rate'][ii]
# Forward-predict the activity values
self.data['predicted'] = self.data['initial'] * \
np.exp(-self.decay*self.data['age'])
def done(self):
jdelim = args.delimiter if args.delimiter != None else ' '
for curve,i in zip(args.curve, args.curvef):
if len(self.tup) > 0:
args.outfile.write(jdelim.join(self.tup) + jdelim)
popt, pcov = cfit(i, self.xdata, self.ydata, p0=args.params)
try:
pvar = np.diag(pcov)
except:
pvar = [None]
args.outfile.write(jdelim.join(map(str, popt)) + jdelim + curve + jdelim + jdelim.join(map(str, pvar)) + '\n')
def done(self):
jdelim = args.delimiter if args.delimiter != None else ' '
for curve, i in zip(args.curve, args.curvef):
if len(self.tup) > 0:
args.outfile.write(jdelim.join(self.tup) + jdelim)
popt, pcov = cfit(i, self.xdata, self.ydata, p0=args.params)
try:
pvar = np.diag(pcov)
except:
pvar = [None]
args.outfile.write(
jdelim.join(map(str, popt)) + jdelim + curve + jdelim +
jdelim.join(map(str, pvar)) + '\n')
def fit_noise_power(ells,
nls,
ell_fit=5000.,
lknee_guess=2000.,
alpha_guess=-4.0):
''' Fit beam-convolved (i.e. does not know about beam) noise power (uK^2 units) to
an atmosphere+white noise model parameterized by rms_noise, lknee, alpha
ell_fit is the ell above which an average of the nls is taken to estimate
the rms white noise
'''
from scipy.optimize import curve_fit as cfit
noise_guess = np.sqrt(np.nanmean(
nls[ells > ell_fit])) * (180. * 60. / np.pi)
nlfitfunc = lambda ell, l, a: noise_func(
ell, 0., noise_guess, l, a, dimensionless=False)
popt, pcov = cfit(nlfitfunc, ells, nls, p0=[lknee_guess, alpha_guess])
lknee_fit, alpha_fit = popt
return noise_guess, lknee_fit, alpha_fit
nts = [16, 24, 32, 64, 128, 256]
for nt in nts:
with open("data_{0}.txt".format(nt)) as f:
data = f.read().split('\n')[:-1]
to_num = lambda row: np.asarray(
list(map(lambda x: float(x),
row.split(',')[:-1])))
rho = list(map(to_num, data[0::3]))
u = list(map(to_num, data[1::3]))
p = list(map(to_num, data[2::3]))
x = np.linspace(0., 1., 17)
popt, _ = cfit(func, x, u[-1])
ufs.append(popt[0])
deltas.append(popt[1])
plt.plot(16 / np.asarray(nts), np.asarray(deltas) / np.pi, '.-', label='delta')
plt.xlim(max(16 / np.asarray(nts)), min(16 / np.asarray(nts)))
plt.xticks(16 / np.asarray(nts))
plt.xlabel("c Dt/Dx")
plt.ylabel("delta/pi")
plt.show()
plt.plot(16 / np.asarray(nts), np.asarray(ufs) / 0.1, '.-')
plt.xlim(max(16 / np.asarray(nts)), min(16 / np.asarray(nts)))
plt.xticks(16 / np.asarray(nts))
plt.xlabel("c Dt/Dx")
plt.ylabel("uf/u0")
def main(argv=None):
if argv is None:
npoints = 10000
elif hasattr(argv, "__len__"):
if len(argv) > 1:
npoints = int(argv[1])
else:
print("Usage:")
print("fit [npoints]")
else:
# expected a number
npoints = argv
def gauss(t0, *param0):
param = numpy.array(param0)
t = numpy.array(t0)
dummy = 2.3548200450309493 * (t - param[3]) / param[4]
return param[0] + param[1] * t + param[2] * myexp(-0.5 * dummy * dummy)
def myexp(x):
# put a (bad) filter to avoid over/underflows
# with no python looping
return numpy.exp(x * numpy.less(abs(x), 250)) -\
1.0 * numpy.greater_equal(abs(x), 250)
xx = numpy.arange(npoints, dtype=numpy.float)
yy = gauss(xx, *[10.5, 2, 1000.0, 20., 15])
sy = numpy.sqrt(abs(yy))
parameters = [0.0, 1.0, 900.0, 25., 10]
stime = time.time()
fittedpar, cov, ddict = leastsq(gauss, xx, yy, parameters,
sigma=sy,
left_derivative=False,
full_output=True,
check_finite=True)
etime = time.time()
sigmapars = numpy.sqrt(numpy.diag(cov))
print("Took ", etime - stime, "seconds")
print("Function calls = ", ddict["nfev"])
print("chi square = ", ddict["chisq"])
print("Fitted pars = ", fittedpar)
print("Sigma pars = ", sigmapars)
try:
from scipy.optimize import curve_fit as cfit
SCIPY = True
except ImportError:
SCIPY = False
if SCIPY:
counter = 0
stime = time.time()
scipy_fittedpar, scipy_cov = cfit(gauss,
xx,
yy,
parameters,
sigma=sy)
etime = time.time()
print("Scipy Took ", etime - stime, "seconds")
print("Counter = ", counter)
print("scipy = ", scipy_fittedpar)
print("Sigma = ", numpy.sqrt(numpy.diag(scipy_cov)))
def main(argv):
iniFile = 'input/noise.ini'
config = ConfigParser()
config.read(iniFile)
mass = 1.E14
conc = 3.0
z = 1.0
#noiseT = 1.0
#beam = 5.0
#ellbeam = 7000.
Nclus = 1000.
arcupto = 10.
mss = []
#beams = np.arange(1.0,7.0,0.1)
beams = [float(argv[0])]
noiseT = float(argv[1])
xy = argv[2]
print beams
#sys.exit()
for beam in beams:
ellbeam = 7000
#ellbeam = int(np.sqrt(8.*np.log(2.)) / beam *60. * 180./np.pi)
print "Ellbeam, ", ellbeam
Nsupp = 10000.
px = 0.1
arc = 20.
#xy = 'TT'
bin_width = 10 #int(beam/px)
Nreals = 5000
Nbins = int(arcupto/bin_width)
scale = px
thetas = np.arange(bin_width*scale/2.,arcupto,scale*bin_width)
lensedClFile = "../cmb-lensing-projections/data/TheorySpectra/ell28k_highacc_lensedCls.dat"
unlensedClFile = "../cmb-lensing-projections/data/TheorySpectra/ell28k_highacc_scalCls.dat"
cmbells,cltt,clee,clbb,clte = Lens.loadCls(lensedClFile)
ucmbells,ucltt,uclee,uclte,dummy = Lens.loadCls(unlensedClFile)
uclbb = clbb.copy()[:len(ucmbells)]
uClsNow = [ucltt,uclee,uclbb,uclte]
uClsFid = [ucltt,uclee,uclbb,uclte]
lClsFid = [cltt,clee,clbb,clte]
template = "../DerivGen/data/order5_lensedCMB_T_beam_cutout_3.fits"
templateMap = liteMap.liteMapFromFits(template)
Lens.makeBinfile("../cmb-lensing-projections/data/bintemp.dat",first=2,last=9000,width=20)
lmin = 2.
lmax = 8000.
if xy=='mv':
NormGen = Lens.AL(templateMap,'TT',ucmbells,uClsNow,ucmbells,uClsFid,cmbells,lClsFid,lmax,lmax,gradCut=2000.)
NormGen.addWhiteNoise(noiseT,np.sqrt(2.)*noiseT,beam,(0,0,lmin,lmax),(lmin,lmax,lmin,lmax))
L1,Nl1,ph = NormGen.binnedNLkk("../cmb-lensing-projections/data/bintemp.dat",inverted=False,halo=True)
NormGen = Lens.AL(templateMap,'EB',ucmbells,uClsNow,ucmbells,uClsFid,cmbells,lClsFid,lmax,lmax,gradCut=2000.)
NormGen.addWhiteNoise(noiseT,np.sqrt(2.)*noiseT,beam,(0,0,lmin,lmax),(lmin,lmax,lmin,lmax))
L2,Nl2,ph = NormGen.binnedNLkk("../cmb-lensing-projections/data/bintemp.dat",inverted=False,halo=True)
assert np.all(L1==L2)
L = L1.copy()
Nl = 1./(1./Nl1+1./Nl2)
else:
NormGen = Lens.AL(templateMap,xy,ucmbells,uClsNow,ucmbells,uClsFid,cmbells,lClsFid,lmax,lmax,gradCut=2000.)
NormGen.addWhiteNoise(noiseT,np.sqrt(2.)*noiseT,beam,(0,0,lmin,lmax),(lmin,lmax,lmin,lmax))
L,Nl,ph = NormGen.binnedNLkk("../cmb-lensing-projections/data/bintemp.dat",inverted=False,halo=True)
pl = Plotter(scaleX='log',scaleY='log')
pl.add(L,Nl)
pl.done('stampNl.png')
#sys.exit()
rads = []
print "Making stamps to determine profile covmat..."
for i in range(Nreals):
#print i
lm = liteMap.makeEmptyCEATemplate(raSizeDeg=arc/60., decSizeDeg=arc/60.,pixScaleXarcmin = px, pixScaleYarcmin=px)
if i==0:
#print lm.data.shape
Npix = lm.data.shape[0]
lfilt = stepFunctionFilterFromLiteMap(lm,ellbeam)
kapmaker = kappaMaker(Cosmology(defaultLCDM),mass,conc,z,storeKap=False)
kapstamp,kaprad = kapmaker.getKappaAndProfile(Npix,scale=px,beam=None,bin_width=bin_width)
# pl = Plotter()
# pl.plot2d(kapstamp)
# pl.done('kappa.png')
# sys.exit()
lm.fillWithGaussianRandomField(L,Nl/Nsupp,bufferFactor = 1)
stamp = lm.data.copy()
stamp = stamp+kapstamp.copy()
stamp = np.nan_to_num(filterDataFromTemplate(stamp,lfilt))
prof = radial_data(stamp,annulus_width=bin_width).mean
#print prof
rads.append(prof)
if i%1000==0: print i
radmeans, covMean, cov, errMean,err,corrcoef = getStats(rads,Nreals)
thstamp = np.nan_to_num(filterDataFromTemplate(kapstamp,lfilt))
thprof = radial_data(thstamp,annulus_width=bin_width).mean
siginv = np.linalg.pinv(cov[:len(thetas),:len(thetas)])
#print siginv
#print radmeans[:len(thetas)]
b = np.dot(siginv,radmeans[:len(thetas)])
chisq = np.dot(radmeans[:len(thetas)],b)
print np.sqrt(chisq*Nclus/Nsupp)
#print radmeans
#print err
pl = Plotter()
pl.addErr(thetas,radmeans[:len(thetas)],yerr=err[:len(thetas)])
pl.add(thetas,kapmaker.kappa(thetas))
pl._ax.set_ylim(-0.01,0.25)
pl._ax.set_xlim(0.0,arcupto)
pl.done('profile.png')
pl = Plotter()
pl.plot2d(corrcoef)
pl.done('corrcoef.png')
pl = Plotter()
pl.plot2d(stamp)
pl.done('stamp.png')
amplitudeRange = np.arange(-1.,2.,0.01)
width = amplitudeRange[1]-amplitudeRange[0]
amplist = []
print "Fitting amplitudes..."
for i in range(Nreals):
prof = rads[i][:len(thetas)]
Likelihood = lambda x: np.exp(-0.5*fchisq(prof,siginv,thprof[:len(thetas)],amp=x))
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]
#print ampBest,ampErr
amplist.append(ampBest)
ms = plotstat( -1.,2.,0.01 , (np.array(amplist)) , "amps",fit=True,scale=np.sqrt(Nclus/Nsupp))
mss.append(ms)
pl = Plotter()
pl.add(beams,mss)
pl.done('beam.png')
np.savetxt("output/m1beamsms"+xy+argv[0]+"_noise"+str(noiseT)+".txt",np.vstack((beams,mss)).transpose())
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
t = (n * 2 * s.h * s.c**2) / ((l**5) * (np.exp(
(s.h * s.c) / (l * s.k * T)) - 1))
return t
plt.plot(wvl, dat)
plt.show()
inp = input("Where to cut:\n")
if inp == 'max':
cut = np.argmax(dat)
else:
cut = wvl.tolist().index(int(inp))
cut = int(cut)
spec = wvl.tolist().index(3500)
val, var = cfit(B, l[cut:], dat[cut:], p0=[1000, 1])
peaks, _ = fp((B(l, val[0], val[1]) - dat), height=0, prominence=0.05)
wvlpk = (peaks * 5 + 1150)
specpk = []
for i in range(len(peaks)):
if wvlpk[i] > 6550 and wvlpk[i] < 6650:
specpk.append(wvlpk[i])
elif wvlpk[i] > 4750 and wvlpk[i] < 4850:
specpk.append(wvlpk[i])
elif wvlpk[i] > 4300 and wvlpk[i] < 4400:
specpk.append(wvlpk[i])
specpk = (np.array(specpk) + (-1150)) / 5
wvlpk = np.array(specpk).astype(int)
plt.plot((peaks * 5 + 1150), dat[peaks], "x")
plt.plot((wvlpk * 5 + 1150), dat[wvlpk], "o")
plt.plot(wvl, B(l, val[0], val[1]) - dat)
def main(argv=None):
if argv is None:
npoints = 10000
elif hasattr(argv, "__len__"):
if len(argv) > 1:
npoints = int(argv[1])
else:
print("Usage:")
print("fit [npoints]")
else:
# expected a number
npoints = argv
def gauss(t0, *param0):
param = numpy.array(param0)
t = numpy.array(t0)
dummy = 2.3548200450309493 * (t - param[3]) / param[4]
return param[0] + param[1] * t + param[2] * myexp(-0.5 * dummy * dummy)
def myexp(x):
# put a (bad) filter to avoid over/underflows
# with no python looping
return numpy.exp(x * numpy.less(abs(x), 250)) -\
1.0 * numpy.greater_equal(abs(x), 250)
xx = numpy.arange(npoints, dtype=numpy.float)
yy = gauss(xx, *[10.5, 2, 1000.0, 20., 15])
sy = numpy.sqrt(abs(yy))
parameters = [0.0, 1.0, 900.0, 25., 10]
stime = time.time()
fittedpar, cov, ddict = leastsq(gauss, xx, yy, parameters,
sigma=sy,
left_derivative=False,
full_output=True,
check_finite=True)
etime = time.time()
sigmapars = numpy.sqrt(numpy.diag(cov))
print("Took ", etime - stime, "seconds")
print("Function calls = ", ddict["nfev"])
print("chi square = ", ddict["chisq"])
print("Fitted pars = ", fittedpar)
print("Sigma pars = ", sigmapars)
try:
from scipy.optimize import curve_fit as cfit
SCIPY = True
except ImportError:
SCIPY = False
if SCIPY:
counter = 0
stime = time.time()
scipy_fittedpar, scipy_cov = cfit(gauss,
xx,
yy,
parameters,
sigma=sy)
etime = time.time()
print("Scipy Took ", etime - stime, "seconds")
print("Counter = ", counter)
print("scipy = ", scipy_fittedpar)
print("Sigma = ", numpy.sqrt(numpy.diag(scipy_cov)))
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
def getDLnMCMB(ells,Nls,clusterCosmology,log10Moverh,z,concentration,arcStamp,pxStamp,arc_upto,bin_width,expectedSN,Nclusters=1000,numSims=30,saveId=None,numPoints=1000,nsigma=8.,overdensity=500.,critical=True,atClusterZ=True):
import flipper.liteMap as lm
if saveId is not None: from orphics.tools.output import Plotter
M = 10.**log10Moverh
cc = clusterCosmology
stepfilter_ellmax = max(ells)
lmap = lm.makeEmptyCEATemplate(raSizeDeg=arcStamp/60., decSizeDeg=arcStamp/60.,pixScaleXarcmin=pxStamp,pixScaleYarcmin=pxStamp)
xMap,yMap,modRMap,xx,xy = fmaps.getRealAttributes(lmap)
lxMap,lyMap,modLMap,thetaMap,lx,ly = fmaps.getFTAttributesFromLiteMap(lmap)
kappaMap,retR500 = NFWkappa(cc,M,concentration,z,modRMap*180.*60./np.pi,winAtLens,overdensity,critical,atClusterZ)
finetheta = np.arange(0.01,arc_upto,0.01)
finekappa,retR500 = NFWkappa(cc,M,concentration,z,finetheta,winAtLens,overdensity,critical,atClusterZ)
kappaMap = fmaps.stepFunctionFilterLiteMap(kappaMap,modLMap,stepfilter_ellmax)
generator = fmaps.GRFGen(lmap,ells,Nls)
bin_edges = np.arange(0.,arc_upto,bin_width)
binner = bin2D(modRMap*180.*60./np.pi, bin_edges)
centers, thprof = binner.bin(kappaMap)
if saveId is not None:
pl = Plotter()
pl.plot2d(kappaMap)
pl.done("output/"+saveId+"kappa.png")
expectedSNGauss = expectedSN*np.sqrt(numSims)
sigma = 1./expectedSNGauss
amplitudeRange = np.linspace(1.-nsigma*sigma,1.+nsigma*sigma,numPoints)
lnLikes = 0.
bigStamp = 0.
for i in range(numSims):
profiles,totstamp = getProfiles(generator,stepfilter_ellmax,kappaMap,binner,Nclusters)
bigStamp += totstamp
stats = getStats(profiles)
if i==0 and (saveId is not None):
pl = Plotter()
pl.add(centers,thprof,lw=2,color='black')
pl.add(finetheta,finekappa,lw=2,color='black',ls="--")
pl.addErr(centers,stats['mean'],yerr=stats['errmean'],lw=2)
pl._ax.set_ylim(-0.01,0.3)
pl.done("output/"+saveId+"profile.png")
pl = Plotter()
pl.plot2d(totstamp)
pl.done("output/"+saveId+"totstamp.png")
Likes = getAmplitudeLikelihood(stats['mean'],stats['covmean'],amplitudeRange,thprof)
lnLikes += np.log(Likes)
width = amplitudeRange[1]-amplitudeRange[0]
Likes = np.exp(lnLikes)
Likes = Likes / (Likes.sum()*width) #normalize
ampBest,ampErr = cfit(norm.pdf,amplitudeRange,Likes,p0=[1.0,0.5])[0]
sn = ampBest/ampErr/np.sqrt(numSims)
snAll = ampBest/ampErr
if snAll<5.: print "WARNING: ", saveId, " run with mass ", M , " and redshift ", z , " has overall S/N<5. \
Consider re-running with a greater numSims, otherwise estimate of per Ncluster S/N will be noisy."
if saveId is not None:
Fit = np.array([np.exp(-0.5*(x-ampBest)**2./ampErr**2.) for x in amplitudeRange])
Fit = Fit / (Fit.sum()*width) #normalize
pl = Plotter()
pl.add(amplitudeRange,Likes,label="like")
pl.add(amplitudeRange,Fit,label="fit")
pl.legendOn(loc = 'lower left')
pl.done("output/"+saveId+"like.png")
pl = Plotter()
pl.plot2d(bigStamp/numSims)
pl.done("output/"+saveId+"bigstamp.png")
np.savetxt("data/"+saveId+"_m"+str(log10Moverh)+"_z"+str(z)+".txt",np.array([log10Moverh,z,1./sn]))
return 1./sn