def test_mnprofile():
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('x')
m.draw_mnprofile('x')
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_mnprofile('y')
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_mnprofile('y')
from iminuit import Minuit
def cost(x, y, z):
return (x - 1) ** 2 + (y - x) ** 2 + (z - 2) ** 2
cost.errordef = Minuit.LEAST_SQUARES
m = Minuit(cost, x=0, y=0, z=0)
m.migrad()
m.draw_mnprofile("y")
# definition of the cost function to minimize, examplary chisquare
def chisquare_1d(a, b, c):
resid = ydat - fit_fun(xdat, a, b, c)
return (1.0 / (npts - 1.0)) * np.sum(resid**2 / yerr**2)
print(chisquare_1d(-1.0 / 500, 250.0 / 500.0, 0))
m = Minuit(
chisquare_1d,
a=-1.0 / 750.0, # set start parameter
#limit_a= (limit_lower,limit_upper) # if you want to limit things
#fix_a = "True", # you can also fix it
b=1000.0,
c=200.0,
errordef=1,
print_level=1)
m.migrad(ncall=500000)
#m.minos(), if you need fancy mapping
plt.plot(xdat, ydat)
plt.plot(xdat, fit_fun(xdat, m.values["a"], m.values["b"], m.values["c"]))
# plt.xlim(3500,4000)
plt.show()
m.draw_mnprofile('a', bound=5, bins=100)
plt.show()
