def test_mncontour():
m = Minuit(f1, x=0, y=0, pedantic=False, print_level=1)
m.tol=1e-4
m.migrad()
assert_less(m.fval, 1e-6)
assert_almost_equal(m.values['x'], 1., places=3)
assert_almost_equal(m.values['y'], 1., places=3)
m.minos()
m.draw_mncontour('x','y')
# <markdowncell>
# ###Contour and $\chi^2$/Likelihood profile
# $\chi^2$ and contour can be obtained easily
# <codecell>
#minos contour
xminos, zminos, ctr = m.mncontour('x','z')
fill(*zip(*ctr)) #looks kinda ugly, right?
# <codecell>
#drawing it nicely
m.draw_mncontour('x','z', nsigma=4);
# <codecell>
#or you can get the gridded data
x, y, g, r = m.mncontour_grid('x','z', nsigma=3) # r is the raw data
pcolormesh(x,y,g)
colorbar()
# <codecell>
#1D value Scan
x,y = m.profile('x',subtract_min=True);
plot(x,y) #if you have matplotlib
# <codecell>
from iminuit import Minuit
def f(x, y, z):
return (x - 1)**2 + (y - x)**2 + (z - 2)**2
m = Minuit(f, print_level=0, pedantic=False)
m.migrad()
m.draw_mncontour("x", "y")
# <codecell>
m.minos(); #do m.minos('x') if you need just 1 of them
# <codecell>
m.merrors
# <markdowncell>
# ###Contour and Profile(Scan)
# <codecell>
m.draw_mncontour('x','y') # you can get the raw value using m.mncontour or m.mncontour_grid;
# <codecell>
m.draw_profile('x') # this is 1d evaluation not minos scan;
# <markdowncell>
# ####Initial value, Limit, initial error, fixing
# <codecell>
m = Minuit(f, x=2, y=4, fix_y=True, limit_z=(-10,10), error_z=0.1)
# <codecell>
from iminuit import Minuit
def f(x, y, z):
return (x-1)**2 + (y-x)**2 + (z-2)**2
m = Minuit(f, print_level=0, pedantic=False)
m.migrad()
m.draw_mncontour('x', 'y')
from iminuit import Minuit
def cost(x, y, z):
return (x - 1)**2 + (y - x)**2 + (z - 2)**2
m = Minuit(cost, print_level=0, pedantic=False)
m.migrad()
m.draw_mncontour("x", "y", nsigma=4)
def Hubble_diagram(zcmb,
zhel,
mB,
dmB,
X1,
dX1,
C,
dC,
M_stell,
IDJLA,
exp,
results,
cov_path,
sigma_mu_path,
fixe,
omgM=0.295,
alpha=0.141,
beta=3.101,
Mb=-19.05,
delta_M=-0.070):
"""
Function which make the hubble fit of the given compilation.
inputs :
-omgM: 1st free parameter to be fitted initialized to the 0.295 value if not precised
-alpha: 2nd free parameter to be fitted initialized to the 0.141 value if not precised
-beta: 3rd free parameter to be fitted initialized to the 3.101 value if not precised
-Mb: 4th free parameter to be fitted initialized to the -19.05 value if not precised
-delta_M: 5th free parameter to be fitted initialized to the -0.070 value if not precised
-fixe: when fixe=0 omgK and w are fixe, fixe = 1 w is fixe, fixe =2 omgK is fixe else all parameters are fixe
-zcmb: an array which contains the redshifts of the SNs
-mB: B band peak magnitude of the SNs
-dmB: uncertainty on mB
-X1: SALT2 shape parameter
-dX1: uncertainty on X1Cmu[N.diag_indices_from(Cmu)]
-dC: uncertainty on C
-C: colour parameter
-M_stell: the log_10 host stellar mass in units of solar mass
-IDJLA: index of the SNs from the 740 of jla used for the fit
-results : a file where some results wiil be written
- m : the iminuit object (see doc of iminuit for more information).
-ecarts5 : the residuals of the fit
"""
#check : need to have at least 2 SNe
'''f2,(test,P2,P3,P4,P5) = P.subplots(5, sharex=True, sharey=False, gridspec_kw=dict(height_ratios=[3,1,1,1,1]))'''
if len(zcmb) == 1 or len(zcmb) == 0:
results.write('Not enough data \n')
return 0
#Definition of the Chi2 object
chi2mini = Chi2(zcmb, zhel, mB, dmB, X1, dX1, C, dC, M_stell, IDJLA,
cov_path, sigma_mu_path)
#minimisation of the chi2
'''
the values of the free parameter can be initialized.
'''
# TO BE CLEANED
if fixe == 0:
m = Minuit(chi2mini.chi2,
omgM=0.295,
omgK=0,
w=-1,
alpha=0.141,
beta=3.101,
Mb=-19.05,
delta_M=-0.070,
limit_omgM=(0.2, 0.4),
limit_omgK=(-1, 1),
limit_w=(-1.4, 0.4),
limit_alpha=(0.1, 0.2),
limit_beta=(2.0, 4.0),
limit_Mb=(-20., -18.),
limit_delta_M=(-0.1, -0.0),
fix_omgM=False,
fix_omgK=True,
fix_w=True,
fix_alpha=False,
fix_beta=False,
fix_Mb=False,
fix_delta_M=False,
print_level=1)
elif fixe == 1:
# last_iter_args={u'fix_Mb': True, u'limit_w': None, u'fix_delta_M': True, u'error_omgL': 0.1672842193144214, u'error_omgM': 0.11284796124765262, 'delta_M': -0.07040462309885408, u'error_Mb': 0.02559618467415318, u'error_w': 1.0, u'error_alpha': 0.006593439955397084, u'fix_alpha': True, u'limit_alpha': None, 'Mb': -19.038807526119403, u'limit_omgM': None, u'limit_omgL': None, u'limit_Mb': None, 'beta': 3.0999314004965535, u'limit_delta_M': None, u'limit_beta': None, 'alpha': 0.14098769528058613, 'omgM': 0.20020038670359183, 'omgL': 0.561065865545341, u'fix_w': True, u'fix_beta': True, u'fix_omgM': False, u'fix_omgL': False, u'error_delta_M': 0.023111327883836057, u'error_beta': 0.08066293365248206, 'w': -1.0}
# m=Minuit(chi2mini.chi2,**last_iter_args)
# m=Minuit(chi2mini.chi2,omgM=0.2,omgL=0.55,w=-1,alpha=0.141,beta=3.101,Mb=-19.05,delta_M=-0.070,limit_omgM=(0,0.4),limit_omgL=(-1,1),limit_w=(-1.4,0.4),limit_alpha=(0.1,0.2),limit_beta=(2.0,4.0),limit_Mb=(-20.,-18.),limit_delta_M=(-0.1,-0.0),fix_omgM=False,fix_omgL=False,fix_w=True, fix_alpha=False, fix_beta=False, fix_Mb=False, fix_delta_M=False, print_level=1)
m = Minuit(chi2mini.chi2,
omgM=0.2,
omgL=0.55,
w=-1,
alpha=0.141,
beta=3.101,
Mb=-19.05,
delta_M=-0.070,
limit_omgM=(0, 0.4),
limit_omgL=(-1, 1),
limit_w=(-1.4, 0.4),
limit_alpha=(0.1, 0.2),
limit_beta=(2.0, 4.0),
limit_Mb=(-20., -18.),
limit_delta_M=(-0.1, -0.0),
fix_omgM=False,
fix_omgL=False,
fix_w=True,
fix_alpha=True,
fix_beta=True,
fix_Mb=True,
fix_delta_M=True,
print_level=1)
#m=Minuit(chi2mini.chi2,omgM=0.2,omgL=0.55,w=-1,alpha=0.141,beta=3.101,Mb=-19.05,delta_M=-0.070,fix_omgM=False,fix_omgL=False,fix_w=True, fix_alpha=False, fix_beta=False, fix_Mb=False, fix_delta_M=False, print_level=1)
elif fixe == 2:
m = Minuit(chi2mini.chi2,
omgM=0.2,
omgK=0,
w=-1,
alpha=0.141,
beta=3.101,
Mb=-19.05,
delta_M=-0.070,
limit_omgM=(0, 0.4),
limit_omgK=(-1, 1),
limit_w=(-1.4, 0.4),
limit_alpha=(0.1, 0.2),
limit_beta=(2.0, 4.0),
limit_Mb=(-20., -18.),
limit_delta_M=(-0.1, -0.0),
fix_omgM=False,
fix_omgK=True,
fix_w=False,
fix_alpha=False,
fix_beta=False,
fix_Mb=False,
fix_delta_M=False,
print_level=1)
else:
m = Minuit(chi2mini.chi2,
omgM=0.2,
omgK=0,
w=-1,
alpha=0.141,
beta=3.101,
Mb=-19.05,
delta_M=-0.070,
limit_omgM=(0, 0.4),
limit_omgK=(-1, 1),
limit_w=(-1.4, 0.4),
limit_alpha=(0.1, 0.2),
limit_beta=(2.0, 4.0),
limit_Mb=(-20., -18.),
limit_delta_M=(-0.1, -0.0),
fix_omgM=True,
fix_omgK=True,
fix_w=True,
fix_alpha=True,
fix_beta=True,
fix_Mb=True,
fix_delta_M=True,
print_level=1)
m.migrad()
return m
m.hesse()
chi2miniminuit = chi2mini.chi2tot #minimun chi2 value obtain by iminuit
#print(m.values)
#print(m.errors)
omgM = m.args[0]
omgK = m.args[1]
w = m.args[2]
alpha = m.args[3]
beta = m.args[4]
Mb = m.args[5]
delta_M = m.args[6]
#errors obtain by minuit
omgMerr = m.errors.get('omgM')
omgKerr = m.errors.get('omgL')
werr = m.errors.get('w')
alphaerr = m.errors.get('alpha')
betaerr = m.errors.get('beta')
Mberr = m.errors.get('Mb')
delta_Merr = m.errors.get('delta_M')
# cov_omg=m.hesse()
# cov_omg=m.matrix()
if fixe == 1:
# Pcov = P.plot()
# bX,bY,contourXY = m.mncontour('omgM', 'omgL', numpoints=20, sigma=1.0)
# bX,bY,contourXY = m.contour('omgM', 'omgL', bins=20, bound=2)
# contourXY -= chi2miniminuit
# Pcov=m.draw_contour('omgM', 'omgL', bins=20, bound=1)
Pcov = m.draw_mncontour('omgM', 'omgL')
# X,Y=N.meshgrid(N.linspace(0,0.3,1000),N.linspace(0,0.5,1000))
# Pcov = P.contour(bX,bY,contourXY,[1,2,9])
# P.show()
elif fixe == 2:
Pcov = P.plot()
# bins_X,bins_Y,contour_X_Y = m.contour('omgM', 'w', bins=20, bound=1)
m.draw_contour('omgM', 'w', bins=20, bound=1)
P.show()
#write some results in output
results.write('\\begin{tabular}{|c|l|}' + '\n' + '\\hline' + '\n')
results.write('chi2 / d.o.f& %.3f' %
((chi2mini.chi2tot) / (len(IDJLA) - 5)) + ' \\\\ \\hline \n')
results.write('chi2& %.3f' % (chi2mini.chi2tot) + ' \\\\ \\hline \n')
#Computation of the luminosity-distance modulus and its uncertainty for the minimized parameters
mufinal = muexp(mB, X1, C, alpha, beta, Mb, delta_M, M_stell)
dmufinal = dmuexp(dmB, dX1, dC, alpha, beta)
#Computation of the difference between the measured luminosity-distance modulus and the theorical one (residuals)
ecarts5 = ecarts(zcmb, zhel, mufinal, omgM, omgK, w)
#computation of the RMS,mean and their error of the distribution
#std5,std5err = comp_rms(N.array(ecarts5),(len(ecarts5)))
std5 = N.std(ecarts5)
std5err = RMSerr(ecarts5)
mean5 = N.mean(ecarts5)
mean5err = MEANerr(ecarts5)
results.write('mean of the residuals& %.5f' % mean5 + ' \\\\ \\hline \n')
sigma_mean5 = std5 / N.sqrt(len(ecarts5))
results.write('rms& %.4f' % (std5) + ' \\\\ \\hline \n')
#mean = N.mean(ecarts5)
#Computaion of the rms of the fit.
rms, err_rms = comp_rms(N.array(ecarts5), len(mB), err=True, variance=None)
results.write('omgM& %.4f' % (omgM) + ' +/- %.4f' % (omgMerr) +
' \\\\ \\hline \n')
results.write('omgK& %.4f' % (omgK) + ' +/- %.4f' % (omgKerr) +
' \\\\ \\hline \n')
results.write('w& %.4f' % (w) + ' +/- %.4f' % (werr) + ' \\\\ \\hline \n')
results.write('alpha& %.4f' % (alpha) + ' +/- %.4f' % (alphaerr) +
' \\\\ \\hline \n')
results.write('beta& %.4f' % (beta) + ' +/- %.4f' % (betaerr) +
' \\\\ \\hline \n')
results.write('Mb& %.4f' % (Mb) + ' +/- %.4f' % (Mberr) +
' \\\\ \\hline \n')
results.write('delta-M& %.4f' % (delta_M) + ' +/- %.4f' % (delta_Merr) +
' \\\\ \\hline \n')
results.write('\\end{tabular}')
return m
from iminuit import Minuit
def f(x, y, z):
return (x - 1)**2 + (y - x)**2 + (z - 2)**2
m = Minuit(f, print_level=0, pedantic=False)
m.migrad()
m.draw_mncontour('x', 'y')
