def test_add() -> None:
hist1 = BootstrapHistogram(bh.axis.Regular(100, -5.0, 5.0), rng=1234)
hist2 = BootstrapHistogram(bh.axis.Regular(100, -5.0, 5.0), rng=1234)
hist1.fill(np.random.normal(size=1000))
hist2.fill(np.random.normal(size=1000))
a1 = hist1.view()
a2 = hist2.view()
hist3 = hist1 + hist2
assert np.array_equal(hist3.view(), a1 + a2)
def test_divide_by_scalar_samples() -> None:
hist1 = BootstrapHistogram(bh.axis.Regular(100, -5.0, 5.0), rng=1234)
hist1.fill(np.random.normal(size=1000))
scale = 2.0
a1 = hist1.view() / scale
hist3 = hist1 / scale
assert np.array_equal(hist3.view(), a1)
def test_projection() -> None:
numsamples = 100
hist = BootstrapHistogram(
bh.axis.Regular(10, 0.0, 1.0),
bh.axis.Regular(10, 0.0, 1.0),
numsamples=numsamples,
rng=1234,
)
size = 100000
xdata = np.random.uniform(size=size)
ydata = np.random.uniform(size=size)
hist.fill(xdata, ydata)
hist = hist.project(0)
X = hist.view()
mean = np.average(X, axis=1)
std = np.std(X, axis=1)
nbins = len(hist.axes[0])
assert array_almost_equal(mean,
size / nbins,
delta=5.0 * np.sqrt(size / nbins))
assert array_almost_equal(
std,
np.sqrt(size / nbins),
delta=5.0 *
_standard_error_std(size=numsamples, sigma=np.sqrt(size / nbins)),
)
def test_projection2() -> None:
hist = BootstrapHistogram(
bh.axis.Regular(100, -5.0, 5.0),
bh.axis.Regular(100, -5.0, 5.0),
numsamples=10,
rng=1234,
)
size = 100000
x = np.random.normal(loc=0.0, scale=1.0, size=size)
y = np.random.normal(loc=0.0, scale=1.0, size=size)
hist.fill(x, y)
hist = hist.project(0)
y = hist.view()[:, np.random.randint(0, hist.numsamples)]
binwidth = hist.axes[0].edges[1] - hist.axes[0].edges[0]
mean = np.average(hist.axes[0].centers, weights=y)
std = np.average((hist.axes[0].centers - mean)**2, weights=y)
assert array_almost_equal(mean,
0.0,
delta=5.0 * _standard_error_mean(size=size) +
binwidth)
assert array_almost_equal(std,
1.0,
delta=5.0 * _standard_error_std(size=size) +
binwidth)
return
def test_view_property() -> None:
numsamples = 10
nbins = 5
hist = BootstrapHistogram(bh.axis.Regular(nbins, -5.0, 5.0),
numsamples=numsamples,
rng=1234)
view = hist.view()
assert np.array_equal(view, np.zeros(shape=(nbins, numsamples)))
def test_project() -> None:
xax = bh.axis.Regular(2, 0.0, 2.0)
yax = bh.axis.Regular(3, 0.0, 3.0)
hist = BootstrapHistogram(xax, yax, numsamples=9, rng=1234)
assert hist.view(flow=True).shape == (2 + 2, 3 + 2, 9)
assert hist.project(0).view(flow=True).shape == (2 + 2, 9)
assert hist.project(1).view(flow=True).shape == (3 + 2, 9)
assert hist.project(2).view(flow=True).shape == (9, )
assert hist.project(0, 1).view(flow=True).shape == (2 + 2, 3 + 2, 9)
def test_samples() -> None:
numsamples = 100
hist = BootstrapHistogram(bh.axis.Regular(100, 0.0, 1.0),
numsamples=numsamples,
rng=1234)
size = 100000
data = np.random.uniform(size=size)
hist.fill(data)
y = hist.view()
mean = np.average(y, axis=1)
std = np.std(y, axis=1)
nbins = len(hist.axes[0])
assert array_almost_equal(mean,
size / nbins,
delta=5.0 * np.sqrt(size / nbins))
assert array_almost_equal(
std,
np.sqrt(size / nbins),
delta=5.0 *
_standard_error_std(size=numsamples, sigma=np.sqrt(size / nbins)),
)
return
def test_fill_with_empty_weighted_array() -> None:
hist = BootstrapHistogram(bh.axis.Regular(2, 0.0, 2.0),
numsamples=9,
rng=1234)
hist.fill([], weight=[])
assert np.all(hist.view(flow=True) == np.zeros((2 + 2, 9)))