def test_fromrandom():
mus = [0, 0]
cov = [[1, 0], [0, 1]]
h = Hist2D.from_random("multivariate_normal",
params=[mus, cov],
size=1e4,
random_state=42)
for axis in ["x", "y"]:
assert abs(h.projection(axis).mean()) < 0.1
assert 0.9 < h.projection(axis).std() < 1.1
h = Hist2D.from_random("norm", params=[(2, 2)], bins=50)
assert h.rebin(5).projection("x") == h.projection("x").rebin(5)
def test_sample():
np.random.seed(0)
h1 = Hist2D.from_random(size=1e5)
h2 = Hist2D(h1.sample(1e5), bins=h1.edges)
result = (h1.projection() / h2.projection()).fit("a+b*x")
slope = result["params"]["b"]
assert abs(slope["error"]) > abs(slope["value"])
def test_cumulativelookup():
h = Hist2D.from_random(bins="5,0,5")
assert h.cumulative().counts.max() == h.integral
assert h.cumulative(forwardx=True, forwardy=True).lookup(4.9,
4.9) == h.integral
assert h.cumulative(forwardx=False,
forwardy=False).lookup(0.1, 0.1) == h.integral
assert h.cumulative(forwardx=True,
forwardy=False).lookup(4.9, 0.1) == h.integral
assert h.cumulative(forwardx=False,
forwardy=True).lookup(0.1, 4.9) == h.integral
def test_smooth():
h = Hist2D.from_random(bins="5,0,5")
assert h == h.smooth(window=1, ntimes=1)
assert h == h.smooth(window=1, ntimes=10)
m = np.array([
[2, 2],
])
h = Hist2D(m, bins="5,-0.5,4.5")
h = h.smooth(window=3, ntimes=1)
assert h.lookup(2 + 1, 2) == h.lookup(2 - 1, 2)
assert h.lookup(2, 2 + 1) == h.lookup(2, 2 - 1)
def test_cumulative():
h = Hist2D.from_random(bins="5,0,5")
assert h == h.cumulative(forwardx=None, forwardy=None)