def test_sample():
h1 = Hist1D.from_random("norm", bins="10,-5,5", size=1e4)
h2 = Hist1D(h1.sample(size=1e5), bins=h1.edges)
# fitting the ratio of the two should give a horizontal line at y=1
ret = (h1.normalize() / h2.normalize()).fit("slope*x+offset")
assert abs(ret["params"]["slope"]["value"]) < 0.05
assert abs(ret["params"]["offset"]["value"] - 1) < 0.01
def test_lookup():
h = Hist1D.from_random(size=50, bins="7,-3,3", random_state=42)
allclose(h.lookup(h.bin_centers), h.counts)
assert h.lookup([-10.0]) == h.counts[0]
assert h.lookup([10.0]) == h.counts[-1]
assert h.lookup(-10.0) == h.counts[0]
assert h.lookup(10.0) == h.counts[-1]
def test_likelihood():
h = Hist1D(np.arange(5) * 2, bins="10,0,10")
f = "p0+0*x"
ret_chi2 = h.fit(f, draw=False, likelihood=False)
ret_like = h.fit(f, draw=False, likelihood=True)
allclose(ret_chi2["params"]["p0"]["value"], 1.0)
allclose(ret_like["params"]["p0"]["value"], 0.5)
# all relative errors within <1% when counts are large
h = Hist1D.from_random("norm",
params=[0, 1],
size=2000,
random_state=42,
bins="20,-3,3")
ret_chi2 = h.fit("a*np.exp(-(x-mu)**2./(2*sigma**2.))",
draw=False,
likelihood=False)
ret_like = h.fit("a*np.exp(-(x-mu)**2./(2*sigma**2.))",
draw=False,
likelihood=True)
keys = ret_chi2["params"].keys()
ret_chi2_errors = np.array(
[ret_chi2["params"][key]["error"] for key in keys])
ret_like_errors = np.array(
[ret_like["params"][key]["error"] for key in keys])
ret_chi2_values = np.array(
[ret_chi2["params"][key]["value"] for key in keys])
v = (ret_chi2_errors - ret_like_errors) / ret_chi2_values
assert (np.abs(v) < 0.01).mean() == 1.0
def test_extent():
h_full = Hist1D.from_random(
"uniform", params=[-1, 1], size=1e3,
bins="100,-1,1") + Hist1D.from_random(
"uniform", params=[0, +1], size=2e3, bins="100,-1,1")
h_restricted = h_full.restrict(0, 1)
fit_full = h_full.fit("a+b*x", extent=[0, 1], color="C0", draw=False)
fit_restricted = h_restricted.fit("a+b*x", color="C3", draw=False)
# fitted values should be the same since the domain is the same
assert fit_full["params"] == fit_restricted["params"]
# check that the histograms of the fit match the input domain
assert h_restricted._check_consistency(fit_restricted["hfit"])
assert h_full._check_consistency(fit_full["hfit"])
def test_return_func():
h = Hist1D.from_random("uniform")
fit = h.fit("a+b*x")
func = fit["func"]
hfit = fit["hfit"]
xs = h.bin_centers
ys = func(xs)
allclose(ys, hfit.counts)
def test_gaussian():
np.random.seed(42)
for mean, sigma in [[0, 1], [1, 2]]:
h1 = Hist1D.from_random("norm",
params=[mean, sigma],
bins="10,-5,5",
size=1e4,
overflow=False)
for likelihood in [True, False]:
fit = h1.fit("gaus", likelihood=likelihood)
assert abs(fit["params"]["mean"]["value"] - h1.mean()) < 0.2
assert abs(fit["params"]["sigma"]["value"] -
h1.std()) / h1.std() < 0.1
assert (abs(fit["params"]["constant"]["value"] - h1.counts.max()) /
h1.counts.max() < 0.2)
def test_fromrandom():
h = Hist1D.from_random("norm", params=[0, 1], size=1e3, random_state=42)
assert abs(h.mean()) < 0.1
assert 0.9 < h.std() < 1.1