def test_extended_binlh_ww_w2(self):
egauss = Extended(gaussian)
_, minuit = fit_binlh(egauss, self.data, bins=100, bound=(1., 9.), quiet=True,
mean=4., sigma=1., N=1000., weights=self.wdown,
print_level=0, extended=True)
assert_almost_equal(minuit.values['mean'], 5., delta=minuit.errors['mean'])
assert_almost_equal(minuit.values['sigma'], 2., delta=minuit.errors['sigma'])
assert_almost_equal(minuit.values['N'], 2000, delta=minuit.errors['N'])
assert(minuit.migrad_ok())
_, minuit2 = fit_binlh(egauss, self.data, bins=100, bound=(1., 9.), quiet=True,
mean=4., sigma=1., N=1000., weights=self.wdown,
print_level= -1, extended=True, use_w2=True)
assert_almost_equal(minuit2.values['mean'], 5., delta=2 * minuit2.errors['mean'])
assert_almost_equal(minuit2.values['sigma'], 2., delta=2 * minuit2.errors['sigma'])
assert_almost_equal(minuit2.values['N'], 2000., delta=2 * minuit2.errors['N'])
assert(minuit2.migrad_ok())
minuit.minos()
minuit2.minos()
# now error should scale correctly
assert_almost_equal(minuit.errors['mean'] / sqrt(10),
minuit2.errors['mean'],
delta=minuit.errors['mean'] / sqrt(10) / 100.)
assert_almost_equal(minuit.errors['sigma'] / sqrt(10),
minuit2.errors['sigma'],
delta=minuit.errors['sigma'] / sqrt(10) / 100.)
assert_almost_equal(minuit.errors['N'] / sqrt(10),
minuit2.errors['N'],
delta=minuit.errors['N'] / sqrt(10) / 100.)
def test_extended_binlh_ww_w2(self):
egauss = Extended(gaussian)
_, minuit = fit_binlh(egauss, self.data, bins=100, bound=(1., 9.), quiet=True,
mean=4., sigma=1., N=1000., weights=self.wdown,
print_level=0, extended=True)
assert_allclose(minuit.values['mean'], 5., atol=minuit.errors['mean'])
assert_allclose(minuit.values['sigma'], 2., atol=minuit.errors['sigma'])
assert_allclose(minuit.values['N'], 2000, atol=minuit.errors['N'])
assert minuit.migrad_ok()
_, minuit2 = fit_binlh(egauss, self.data, bins=100, bound=(1., 9.), quiet=True,
mean=4., sigma=1., N=1000., weights=self.wdown,
print_level=-1, extended=True, use_w2=True)
assert_allclose(minuit2.values['mean'], 5., atol=2 * minuit2.errors['mean'])
assert_allclose(minuit2.values['sigma'], 2., atol=2 * minuit2.errors['sigma'])
assert_allclose(minuit2.values['N'], 2000., atol=2 * minuit2.errors['N'])
assert minuit2.migrad_ok()
minuit.minos()
minuit2.minos()
# now error should scale correctly
assert_allclose(minuit.errors['mean'] / sqrt(10),
minuit2.errors['mean'],
atol=minuit.errors['mean'] / sqrt(10) / 100.)
assert_allclose(minuit.errors['sigma'] / sqrt(10),
minuit2.errors['sigma'],
atol=minuit.errors['sigma'] / sqrt(10) / 100.)
assert_allclose(minuit.errors['N'] / sqrt(10),
minuit2.errors['N'],
atol=minuit.errors['N'] / sqrt(10) / 100.)
def test_binlh(self):
ngauss = Normalized(gaussian, (1., 9.))
_, minuit = fit_binlh(ngauss, self.data, bins=100, bound=(1., 9.), quiet=True,
mean=4., sigma=1.5, print_level=0)
assert(minuit.migrad_ok())
assert_almost_equal(minuit.values['mean'], 5., delta=3 * minuit.errors['mean'])
assert_almost_equal(minuit.values['sigma'], 2., delta=3 * minuit.errors['sigma'])
def test_binlh(self):
ngauss = Normalized(gaussian, (1., 9.))
_, minuit = fit_binlh(ngauss, self.data, bins=100, bound=(1., 9.), quiet=True,
mean=4., sigma=1.5, print_level=0)
assert minuit.migrad_ok()
assert_allclose(minuit.values['mean'], 5., atol=3 * minuit.errors['mean'])
assert_allclose(minuit.values['sigma'], 2., atol=3 * minuit.errors['sigma'])
def test_extended_binlh_ww(self):
egauss = Extended(gaussian)
_, minuit = fit_binlh(egauss, self.data, bins=100, bound=(1., 9.), quiet=True,
mean=4., sigma=1., N=1000., weights=self.wdown,
print_level=0, extended=True)
assert(minuit.migrad_ok())
assert_almost_equal(minuit.values['mean'], 5., delta=minuit.errors['mean'])
assert_almost_equal(minuit.values['sigma'], 2., delta=minuit.errors['sigma'])
assert_almost_equal(minuit.values['N'], 2000, delta=minuit.errors['N'])
def test_extended_binlh_ww(self):
egauss = Extended(gaussian)
_, minuit = fit_binlh(egauss, self.data, bins=100, bound=(1., 9.), quiet=True,
mean=4., sigma=1., N=1000., weights=self.wdown,
print_level=0, extended=True)
assert minuit.migrad_ok()
assert_allclose(minuit.values['mean'], 5., atol=minuit.errors['mean'])
assert_allclose(minuit.values['sigma'], 2., atol=minuit.errors['sigma'])
assert_allclose(minuit.values['N'], 2000, atol=minuit.errors['N'])
def test_extended_binlh(self):
egauss = Extended(gaussian)
_, minuit = fit_binlh(egauss, self.data, bins=100, bound=(1., 9.), quiet=True,
mean=4., sigma=1., N=10000.,
print_level=0, extended=True)
assert minuit.migrad_ok()
assert_allclose(minuit.values['mean'], 5., atol=3 * minuit.errors['mean'])
assert_allclose(minuit.values['sigma'], 2., atol=3 * minuit.errors['sigma'])
assert_allclose(minuit.values['N'], 20000, atol=3 * minuit.errors['N'])
def test_binlh(self):
ngauss = Normalized(gaussian, (1.0, 9.0))
_, minuit = fit_binlh(
ngauss,
self.data,
bins=100,
bound=(1.0, 9.0),
quiet=True,
mean=4.0,
sigma=1.5,
print_level=0,
)
assert minuit.migrad_ok()
assert_allclose(minuit.values["mean"],
5.0,
atol=3 * minuit.errors["mean"])
assert_allclose(minuit.values["sigma"],
2.0,
atol=3 * minuit.errors["sigma"])
def test_extended_binlh_ww(self):
egauss = Extended(gaussian)
_, minuit = fit_binlh(
egauss,
self.data,
bins=100,
bound=(1.0, 9.0),
quiet=True,
mean=4.0,
sigma=1.0,
N=1000.0,
weights=self.wdown,
print_level=0,
extended=True,
)
assert minuit.migrad_ok()
assert_allclose(minuit.values["mean"], 5.0, atol=minuit.errors["mean"])
assert_allclose(minuit.values["sigma"],
2.0,
atol=minuit.errors["sigma"])
assert_allclose(minuit.values["N"], 2000, atol=minuit.errors["N"])
def test_extended_binlh(self):
egauss = Extended(gaussian)
_, minuit = fit_binlh(egauss,
self.data,
bins=100,
bound=(1., 9.),
quiet=True,
mean=4.,
sigma=1.,
N=10000.,
print_level=0,
extended=True)
assert (minuit.migrad_ok())
assert_almost_equal(minuit.values['mean'],
5.,
delta=3 * minuit.errors['mean'])
assert_almost_equal(minuit.values['sigma'],
2.,
delta=3 * minuit.errors['sigma'])
assert_almost_equal(minuit.values['N'],
20000,
delta=3 * minuit.errors['N'])
