def run_minuit():
from minuit import Minuit
m = Minuit(chi2, b=5, a=10)
m.migrad()
m.hesse()
popt = m.values.values()
perr = np.array(m.errors.values())
# In scipy.optimize.curve_fit, the following factor is applied
# cov = cov * sum_sqr / self.nfree
factor = m.fval / (len(x) - 2)
perr *= np.sqrt(factor)
print ('minuit')
print('popt: {0}'.format(popt))
print('perr: {0}'.format(perr))
# Load data
x, y, y_err = load_data()
# Define model
def power_law(x, norm, gamma):
return norm * x ** -gamma
# Define fit statistic
def chi2(norm, gamma):
model = power_law(x, norm, gamma)
chi = (y - model) / y_err
return (chi ** 2).sum()
# Perform fit
m = Minuit(chi2, norm=1e-12, gamma=2)
m.migrad()
m.hesse()
#m.minos()
#print 'matrix:\n', m.matrix()
#print 'errors: ', m.errors
#print 'merrors:', m.merrors
# Report results
package = 'minuit'
gamma, norm = m.values.values()
gamma_err, norm_err = m.errors.values()
chi2 = m.fval
cov = m.covariance[('norm', 'gamma')]
corr = m.matrix(correlation=True)[0][1]
report_results(package, norm, norm_err, gamma, gamma_err,
chi2, cov, corr)
