def test_int_cat_hist_pick_several():
h = bh.Histogram(bh.axis.IntCategory([1, 2, 7], __dict__={"xval": 5}),
storage=bh.storage.Int64())
h.fill(1, weight=8)
h.fill(2, weight=7)
h.fill(7, weight=6)
assert h.view() == approx(np.array([8, 7, 6]))
assert h.sum() == 21
assert h[[0, 2]].values() == approx(np.array([8, 6]))
assert h[[2, 0]].values() == approx(np.array([6, 8]))
assert h[[1]].values() == approx(np.array([7]))
assert h[[bh.loc(1), bh.loc(7)]].values() == approx(np.array([8, 6]))
assert h[[bh.loc(7), bh.loc(1)]].values() == approx(np.array([6, 8]))
assert h[[bh.loc(2)]].values() == approx(np.array([7]))
assert tuple(h[[0, 2]].axes[0]) == (1, 7)
assert tuple(h[[2, 0]].axes[0]) == (7, 1)
assert tuple(h[[1]].axes[0]) == (2, )
assert h.axes[0].xval == 5
assert h[[0]].axes[0].xval == 5
assert h[[0, 1, 2]].axes[0].xval == 5
def test_double(self):
h = (
Hist.new.Reg(10, 0, 1, name="x")
.Reg(10, 0, 1, name="y")
.Double()
.fill(x=[0.5, 0.5], y=[0.2, 0.6])
)
assert h[0.5j, 0.2j] == 1
assert h[bh.loc(0.5), bh.loc(0.6)] == 1
assert isinstance(h[0.5j, 0.5j], float)
# add storage to existing storage
with pytest.raises(Exception):
h.Double()
assert (
Hist(axis.Regular(10, 0, 1, name="x"), "double")._storage_type
== storage.Double
)
assert (
Hist(axis.Regular(10, 0, 1, name="x"), storage="DouBle")._storage_type
== storage.Double
)
assert (
Hist(axis.Regular(10, 0, 1, name="x"), storage.Double())._storage_type
== storage.Double
)
def test_named_access():
"""
Test named access -- whether NamedHist bins can be accessed.
"""
h = NamedHist(axis.Regular(10, -5, 5, name="X", label="x [units]")).fill(
X=np.random.normal(size=1000)
)
assert h[6] == h[bh.loc(1)] == h[1j] == h[0j + 1] == h[-3j + 4] == h[bh.loc(1, 0)]
h[6] = h[bh.loc(1)] = h[1j] = h[0j + 1] = h[-3j + 4] = h[bh.loc(1, 0)] = 0
h = NamedHist(
axis.Regular(50, -5, 5, name="Norm", label="normal distribution"),
axis.Regular(50, -5, 5, name="Unif", label="uniform distribution"),
axis.StrCategory(["hi", "hello"], name="Greet"),
axis.Boolean(name="Yes"),
axis.Integer(0, 1000, name="Int"),
).fill(
Norm=np.random.normal(size=1000),
Unif=np.random.uniform(size=1000),
Greet=["hi"] * 800 + ["hello"] * 200,
Yes=[True] * 600 + [False] * 400,
Int=np.ones(1000),
)
assert h[0j, -0j + 2, "hi", True, 1]
# mismatch dimension
with pytest.raises(Exception):
h[0j, -0j + 2, "hi", True]
def test_bool_axis():
'''
Test bool axis -- whether Bool is enabled and work properly.\
As our bool axis is derived from boost-histogram.axis.Regular,\
we won't test all thoses features. If it is justified as the\
subclass of boost-histogram.axis.Regular, then it should pass\
those axis tests.
'''
# Test bool axis
assert isinstance(hist.axis.Bool(name="B"), bh.axis.Regular)
# Test histograms with bool axes
h = hist.NamedHist(hist.axis.Bool(name="S"), hist.axis.Bool(name="L"))
valid_s = [True, True, True, False]
valid_l = [True, True, False, False]
h.fill(L=valid_l, S=valid_s)
assert h[0, 0] == 1
assert h[1, 0] == 1
assert h[1, 1] == 2
s_valid_only = h[{'S': bh.loc(True)}]
assert s_valid_only[0] == 1
assert s_valid_only[1] == 2
l_valid_only = h[{'L': bh.loc(True)}]
assert l_valid_only[0] == 0
assert l_valid_only[1] == 2
def test_shrink_1d():
h = bh.Histogram(bh.axis.Regular(20, 1, 5))
h.fill(1.1)
hs = h[{0: slice(bh.loc(1), bh.loc(2))}]
assert_array_equal(hs.view(), [1, 0, 0, 0, 0])
d = OrderedDict({0: slice(bh.loc(1), bh.loc(2))})
hs = h[d]
assert_array_equal(hs.view(), [1, 0, 0, 0, 0])
def test_str_categories_histogram():
hist = bh.Histogram(bh.axis.StrCategory(["a", "b", "c"]),
storage=bh.storage.Int64())
vals = ["a", "b", "b", "c"]
hist.fill(vals)
assert hist[bh.loc("a")] == 1
assert hist[bh.loc("b")] == 2
assert hist[bh.loc("c")] == 1
def test_double(self):
h = (NamedHist.new.Reg(10, 0, 1, name="x").Reg(
10, 0, 1, name="y").Double().fill(x=[0.5, 0.5], y=[0.2, 0.6]))
assert h[0.5j, 0.2j] == 1
assert h[bh.loc(0.5), bh.loc(0.6)] == 1
assert isinstance(h[0.5j, 0.5j], float)
# add storage to existing storage
with pytest.raises(Exception):
h.Double()
def test_get_1D_slice():
h1 = bh.Histogram(bh.axis.Regular(10, 0, 1))
h2 = bh.Histogram(bh.axis.Regular(5, 0, 0.5))
h1.fill([0.25, 0.25, 0.25, 0.15])
h2.fill([0.25, 0.25, 0.25, 0.15])
assert h1 != h2
assert h1[:5] == h2
assert h1[:bh.loc(0.5)] == h2
assert h1[2:4] == h2[2:4]
assert h1[bh.loc(0.2):bh.loc(0.4)] == h2[bh.loc(0.2):bh.loc(0.4)]
assert len(h1[2:4].view()) == 2
assert len(h1[2:4:bh.rebin(2)].view()) == 1
def _hist_to_result(
hist: bh.Histogram,
nanto: Optional[float] = None) -> "BootstrapEfficiency.Histogram":
numerator = cast(bh.Histogram, hist[bh.loc(1), ...]) # type: ignore
notselected = cast(bh.Histogram, hist[bh.loc(0), ...]) # type: ignore
denominator = numerator + notselected
ratio = numerator / denominator
if nanto is not None:
np.nan_to_num(ratio.view(),
copy=False,
nan=nanto,
posinf=nanto,
neginf=nanto)
return BootstrapEfficiency.Histogram(numerator, denominator, ratio)
def test_repr():
assert repr(bh.loc(2)) == "loc(2)"
assert repr(bh.loc(3) + 1) == "loc(3) + 1"
assert repr(bh.loc(1) - 2) == "loc(1) - 2"
assert repr(bh.underflow) == "underflow"
assert repr(bh.underflow + 1) == "underflow + 1"
assert repr(bh.underflow - 1) == "underflow - 1"
assert repr(bh.overflow) == "overflow"
assert repr(bh.overflow + 1) == "overflow + 1"
assert repr(bh.overflow - 1) == "overflow - 1"
assert repr(bh.rebin(2)) == "rebin(2)"
def test_general_index_access():
"""
Test general index access -- whether Hist can be accessed by index.
"""
h = Hist(
axis.Regular(10, -5, 5, name="Ones"),
axis.Regular(10, -5, 5, name="Twos"),
axis.StrCategory(["hi", "hello"], name="Greet"),
axis.Boolean(name="Yes"),
axis.Integer(0, 10, name="Int"),
).fill(
np.ones(10),
np.ones(10) * 2,
["hi"] * 8 + ["hello"] * 2,
[True] * 6 + [False] * 4,
np.ones(10),
)
assert h[1j, 2j, "hi", True, 1] == 6
assert h[{0: 6, 1: 7, 2: bh.loc("hi"), 3: bh.loc(True), 4: bh.loc(1)}] == 6
assert h[0j + 1, -2j + 4, "hi", True, 1] == 6
assert (h[{
"Greet": "hi",
"Ones": bh.loc(1, 0),
1: bh.loc(3, -1),
3: True,
"Int": 1
}] == 6)
assert h[0:10:2j, 0:5:5j, "hello", False, 5]
assert len(h[::2j, 0:5, :, :, :].axes[1]) == 5
assert len(h[:, 0:5, :, :, :].axes[1]) == 5
# wrong loc shortcut
with pytest.raises(Exception):
h[0.5, 1 / 2, "hi", True, 1]
with pytest.raises(Exception):
h[0.5 + 1j, 1 / 2 + 1j, "hi", True, 1]
# wrong rebin shortcut
with pytest.raises(Exception):
h[0:10:0.2j, 0:5:0.5j, "hello", False, 5]
with pytest.raises(Exception):
h[0:10:1 + 2j, 0:5:1 + 5j, "hello", False, 5]
with pytest.raises(Exception):
h[0:10:20j, 0:5:10j, "hello", False, 5]
def test_np_scalars():
hist = bh.Histogram(
bh.axis.Regular(30, 1, 500, transform=bh.axis.transform.log))
hist.fill([7, 7])
hist2 = hist / np.float64(2.0)
assert hist2[bh.loc(7)] == 1.0
hist2 = hist / hist.axes.widths.prod(axis=0)
assert hist2[bh.loc(7)] == approx(1.3467513416439476)
with pytest.raises(ValueError):
hist / np.array([1, 2, 3])
hist /= np.float64(2.0)
assert hist[bh.loc(7)] == 1.0
def test_basic_usage():
# Check hist with only one axis
h = Hist(axis.Regular(10, 0, 1))
h.fill([0.35, 0.35, 0.45])
assert h[2] == 0
assert h[3] == 2
assert h[4] == 1
assert h[5] == 0
# Check hist with two axes
h = Hist(axis.Regular(10, 0, 1), axis.Regular(10, 0, 1))
h.fill([0.35, 0.35, 0.45], [0.65, 0.65, 0.85])
assert h[3, 6] == 2
assert h[4, 8] == 1
assert h[3, 5] == 0
# Checking hist with axis type bool
h = Hist(axis.bool())
h.fill([0, 1, 1])
assert h[0] == 1
assert h[1] == 2
# check if there are exactly two bins (accessing h[2] raises IndexError)
with pytest.raises(IndexError):
assert h[2] == 0
# check if flow is disabled (if view() with or without flow gives the same output)
assert (h.view() == h.view(flow=True)).all()
h = Hist(axis.Regular(10, 0, 1), axis.Regular(10, 0, 1))
h.fill([0.35, 0.35, 0.45], [0.65, 0.65, 0.85])
# Check indexing using dict and bh.loc()
h2 = h[loc(0.35), :]
# Broken in 0.6.2, fixed now
h3 = h[{0: loc(0.35)}]
assert (h2.view() == h3.view()).all()
def fitSliceY(self,
ax=None,
num_slices: int = 10,
NSIMA: int = 3,
fit_range=None,
center: bool = False,
params=None,
plot: bool = True):
if not ax:
ax = plt.gca()
if fit_range:
slices = np.linspace(*fit_range, num_slices)
else:
slices = np.linspace(np.min(self.hist.axes[1]),
np.max(self.hist.axes[1]), num_slices)
width = np.abs(slices[0] - slices[1])
outs = []
ys = []
xst = []
xsb = []
for sl in slices[:-1]:
y_val = (sl + sl + width) / 2
try:
slic = self.hist[:, bh.loc(sl):bh.loc(sl + width):bh.sum]
except ValueError:
continue
temp_hist = Hist1D(boost_hist=slic)
if center:
params = GaussianModel().make_params()
params['center'].set(value=0, min=-0.5, max=0.5)
params['sigma'].set(value=0.1, min=0, max=1.0)
try:
out = temp_hist.fitGaussian(plots=False, params=params)
outs.append(out)
ys.append(y_val)
xst.append(out.params['center'] + NSIMA * out.params['sigma'])
xsb.append(out.params['center'] - NSIMA * out.params['sigma'])
except TypeError:
print(f"Cannot fit from [{sl}, {sl + width}]")
continue
left = Scatter(np.array(xst), np.array(ys))
right = Scatter(np.array(xsb), np.array(ys))
return outs, left, right
def test_2D_get_bin():
h = bh.Histogram(bh.axis.Regular(10, 0, 0.99),
bh.axis.Regular(10, 0, 0.99))
h.fill(0.15, [0.25, 0.25, 0.25, 0.15])
assert h[0, 0] == 0
assert h[0, 1] == 0
assert h[1, 1] == 1
assert h[1, 2] == 3
assert h[bh.loc(0.1), bh.loc(0.2)] == 3
assert h[bh.loc(0) + 1, bh.loc(0.3) - 1] == 3
assert h.view()[1, 2] == h[1, 2]
with pytest.raises(IndexError):
h[1]
def test_metadata_add():
h1 = bh.Histogram(bh.axis.IntCategory([1, 2, 3]),
bh.axis.StrCategory(["1", "2", "3"]))
h2 = bh.Histogram(bh.axis.IntCategory([1, 2, 3]),
bh.axis.StrCategory(["1", "2", "3"]))
h1.fill([1, 2, 1, 2], ["1", "1", "2", "2"])
h2.fill([2, 3, 2, 3], ["2", "2", "3", "3"])
h3 = h1 + h2
assert h1.axes[0].size == 3
assert h2.axes[0].size == 3
assert h3.axes[0].size == 3
assert h1.axes[1].size == 3
assert h2.axes[1].size == 3
assert h3.axes[1].size == 3
assert h3[bh.loc(2), bh.loc("2")] == 2.0
def test_setting(storage):
h = bh.Histogram(bh.axis.Regular(10, 0, 1), storage=storage())
h[bh.underflow] = 1
h[0] = 2
h[1] = 3
h[bh.loc(0.55)] = 4
h[-1] = 5
h[bh.overflow] = 6
assert h[bh.underflow] == 1
assert h[0] == 2
assert h[1] == 3
assert h[bh.loc(0.55)] == 4
assert h[5] == 4
assert h[-1] == 5
assert h[9] == 5
assert h[bh.overflow] == 6
assert_array_equal(h.view(flow=True), [1, 2, 3, 0, 0, 0, 4, 0, 0, 0, 5, 6])
def test_grow_and_add():
h1 = bh.Histogram(bh.axis.IntCategory([], growth=True),
bh.axis.StrCategory([], growth=True))
h2 = bh.Histogram(bh.axis.IntCategory([], growth=True),
bh.axis.StrCategory([], growth=True))
h1.fill([1, 2, 1, 2], ["hi", "hi", "ho", "ho"])
h2.fill([2, 3, 4, 5], ["ho", "ho", "hum", "hum"])
h3 = h1 + h2
assert h1.axes[0].size == 2
assert h2.axes[0].size == 4
assert h3.axes[0].size == 5
assert h1.axes[1].size == 2
assert h2.axes[1].size == 2
assert h3.axes[1].size == 3
assert h3[bh.loc(2), bh.loc("ho")] == 2.0
def slice_to_xy(self, slice_range, density: bool = True):
"""Return the x, y values from a histogram in the range
Useful for plotting in different ways and for fitting
Args:
slice_range (Required) : Range to return x, y slice from
density (bool, optional): Choose to plot y values or density of y values. Defaults to True.
Returns:
Tuple(x, y): Returns a tuple of np arrays for x and y values of histogram
"""
slic = self.hist[bh.loc(slice_range[0]):bh.loc(slice_range[1])]
x = slic.axes[0].centers
if density:
y = slic.view() / np.max(slic.view())
else:
y = slic.view()
return (x, y)
def _loc_shortcut(self, x: Any) -> Any:
"""
Convert some specific indices to location.
"""
if isinstance(x, slice):
return slice(
self._loc_shortcut(x.start),
self._loc_shortcut(x.stop),
self._step_shortcut(x.step),
)
elif isinstance(x, complex):
if x.real % 1 != 0:
raise ValueError("The real part should be an integer")
else:
return bh.loc(x.imag, int(x.real))
elif isinstance(x, str):
return bh.loc(x)
else:
return x
def test_pick_multiaxis():
h = bh.Histogram(
bh.axis.StrCategory(["a", "b", "c"]),
bh.axis.IntCategory([-5, 0, 10]),
bh.axis.Regular(5, 0, 1),
bh.axis.StrCategory(["d", "e", "f"]),
storage=bh.storage.Int64(),
)
h.fill("b", -5, 0.65, "f")
h.fill("b", -5, 0.65, "e")
mini = h[[bh.loc("b"), 2], [1, bh.loc(-5)], sum, bh.loc("f")]
assert mini.ndim == 2
assert tuple(mini.axes[0]) == ("b", "c")
assert tuple(mini.axes[1]) == (0, -5)
assert h[[1, 2], [0, 1], sum, bh.loc("f")].sum() == 1
assert h[[1, 2], [1, 0], sum, bh.loc("f")].sum() == 1
assert mini.values() == approx(np.array(((0, 1), (0, 0))))
def test_pick_int_category():
noflow = dict(underflow=False, overflow=False)
h = bh.Histogram(
bh.axis.Regular(10, 0, 10),
bh.axis.Regular(10, 0, 10, **noflow),
bh.axis.IntCategory([3, 5, 7, 12, 13]),
)
vals = np.arange(100).reshape(10, 10)
h[:, :, bh.loc(3)] = vals
h[:, :, bh.loc(5)] = vals + 1
h[:, :, 3] = vals + 100
assert h[0, 1, bh.loc(3)] == 1
assert h[1, 0, bh.loc(5)] == 10 + 1
assert h[1, 1, bh.loc(5)] == 11 + 1
assert h[3, 4, bh.loc(7)] == 0
assert h[3, 4, bh.loc(12)] == 134
assert_array_equal(h[:, :, bh.loc(3)].view(), vals)
assert_array_equal(h[:, :, bh.loc(5)].view(), vals + 1)
assert_array_equal(h[:, :, bh.loc(7)].view(), 0)
def test_str_cat_pick_several():
h = bh.Histogram(bh.axis.StrCategory(["a", "b", "c"]))
h.fill(["a", "a", "a", "b", "b", "c"], weight=0.25)
assert h[[0, 1, 2]].values() == approx(np.array([0.75, 0.5, 0.25]))
assert h[[2, 1, 0]].values() == approx(np.array([0.25, 0.5, 0.75]))
assert h[[1, 0]].values() == approx(np.array([0.5, 0.75]))
assert h[[bh.loc("a"), bh.loc("b"),
bh.loc("c")]].values() == approx(np.array([0.75, 0.5, 0.25]))
assert h[[bh.loc("c"), bh.loc("b"),
bh.loc("a")]].values() == approx(np.array([0.25, 0.5, 0.75]))
assert h[[bh.loc("b"),
bh.loc("a")]].values() == approx(np.array([0.5, 0.75]))
assert tuple(h[[1, 0]].axes[0]) == ("b", "a")
def test_string_requirement():
h = bh.Histogram(
bh.axis.Integer(0, 10),
bh.axis.StrCategory(["1", "a", "hello"]),
storage=bh.storage.Int64(),
)
with pytest.raises(TypeError):
h[bh.loc("1"), bh.loc(1)]
with pytest.raises(TypeError):
h[bh.loc(1), bh.loc(1)]
with pytest.raises(TypeError):
h[bh.loc("1"), bh.loc("1")]
assert h[bh.loc(1), bh.loc("1")] == 0
def test_boost_histogram(selenium):
import unittest
import boost_histogram as bh
h = bh.Histogram(bh.axis.Integer(0, 10))
h.fill([1, 1, 1, 14])
assert h[bh.underflow] == 0
assert h[bh.loc(0)] == 0
assert h[bh.loc(1)] == 3
assert h[bh.overflow] == 1
assert h.sum() == 3
assert h.sum(flow=True) == 4
assert h[sum] == 4
h = bh.Histogram(bh.axis.Regular(10, 0, 10), bh.axis.Boolean())
assert len(h.axes[0]) == 10
assert len(h.axes[1]) == 2
h.fill([0.5, 0.5, 3.5], [True, False, True])
assert h[sum, bh.loc(True)] == 2
assert h[sum, bh.loc(False)] == 1
assert h[0, sum] == 2
assert h[0, bh.loc(True)] == 1
h = bh.Histogram(bh.axis.StrCategory([], growth=True))
h.fill("fear leads to anger anger leads to hate hate leads to suffering".
split())
assert h[bh.loc("fear")] == 1
assert h[bh.loc("anger")] == 2
assert h[bh.loc("hate")] == 2
assert h[bh.loc("to")] == 3
# Test exception handling
mean = bh.accumulators.Mean()
with unittest.TestCase().assertRaises(KeyError):
mean["invalid"]
def test_basic_usage():
'''
Test basic usage -- whether NamedHist are properly derived from\
boost-histogram and whether it can be filled by names.
'''
# Test normal NamedHist
h = NamedHist(axis.Regular(10, 0, 1, name="x"))
h.fill(x=[0.35, 0.35, 0.45])
assert h[2] == 0
assert h[3] == 2
assert h[4] == 1
assert h[5] == 0
assert h[{0: 2}] == 0
assert h[{0: 3}] == 2
assert h[{0: 4}] == 1
assert h[{0: 5}] == 0
assert h[{'x': 2}] == 0
assert h[{'x': 3}] == 2
assert h[{'x': 4}] == 1
assert h[{'x': 5}] == 0
# Test multi-axis NamedHist
h = NamedHist(axis.Regular(10, 0, 1, name="x"),
axis.Regular(10, 0, 1, name="y"), axis.Integer(0,
2,
name="z"))
h.fill(x=[0.35, 0.35, 0.35, 0.45, 0.55, 0.55, 0.55],
y=[0.35, 0.35, 0.45, 0.45, 0.45, 0.45, 0.45],
z=[0, 0, 1, 1, 1, 1, 1])
z_one_only = h[{'z': bh.loc(1)}]
assert z_one_only[{'x': 3, 'y': 4}] == 1
assert z_one_only[{'x': 4, 'y': 4}] == 1
assert z_one_only[{'x': 5, 'y': 4}] == 3
assert z_one_only[{'x': 5, 'y': 5}] == 0
assert z_one_only[{'x': 6, 'y': 5}] == 0
assert z_one_only[3, 4] == 1
assert z_one_only[4, 4] == 1
assert z_one_only[5, 4] == 3
assert z_one_only[5, 5] == 0
assert z_one_only[6, 5] == 0
def sort(
self: T,
axis: int | str,
key: (Callable[[int], SupportsLessThan]
| Callable[[str], SupportsLessThan] | None) = None,
reverse: bool = False,
) -> T:
"""
Sort a categorical axis.
"""
with warnings.catch_warnings():
warnings.simplefilter("ignore")
sorted_cats = sorted(self.axes[axis], key=key, reverse=reverse)
# This can only return T, not float, etc., so we ignore the extra types here
return self[{
axis: [bh.loc(x) for x in sorted_cats]
}] # type: ignore[dict-item, return-value]
def test_pick_str_category():
noflow = dict(underflow=False, overflow=False)
h = bh.Histogram(
bh.axis.Regular(10, 0, 10),
bh.axis.Regular(10, 0, 10, **noflow),
bh.axis.StrCategory(["on", "off", "maybe"]),
)
vals = np.arange(100).reshape(10, 10)
h[:, :, bh.loc("on")] = vals
assert h[0, 1, bh.loc("on")] == 1
assert h[1, 0, bh.loc("on")] == 10
assert h[1, 1, bh.loc("on")] == 11
assert h[3, 4, bh.loc("maybe")] == 0
assert_array_equal(h[:, :, bh.loc("on")].view(), vals)
assert_array_equal(h[{2: bh.loc("on")}].view(), vals)
assert_array_equal(h[:, :, bh.loc("off")].view(), 0)
def test_one_sided_slice():
h = bh.Histogram(bh.axis.Regular(4, 1, 5))
h.view(True)[:] = 1
assert h[sum] == 6 # 4 (internal bins) + 2 (flow bins)
assert h[bh.tag.at(-1):bh.tag.
at(5):sum] == 6 # keeps underflow, keeps overflow
# check that slicing without bh.sum adds removed counts to flow bins
assert_array_equal(h[1:3].view(True), [2, 1, 1, 2])
assert h[0::sum] == 5 # removes underflow, keeps overflow
assert h[:4:sum] == 5 # removes overflow, keeps underflow
assert h[0:4:sum] == 4 # removes underflow and overflow
assert h[bh.loc(1)::sum] == 5 # remove underflow
assert h[:bh.loc(5):sum] == 5 # remove overflow
assert h[bh.loc(1):bh.loc(5):sum] == 4 # removes underflow and overflow
assert h[bh.loc(0)::sum] == 6 # keep underflow
assert h[:bh.loc(10) + 1:sum] == 6 # keep overflow
assert h[bh.loc(0):bh.loc(10) + 1:sum] == 6
def test_named_hist_proxy():
"""
Test named hist proxy -- whether NamedHist hist proxy works properly.
"""
h = NamedHist.new.Reg(10, 0, 1, name="x").Double().fill(x=[0.5, 0.5])
assert h[0.5j] == 2
assert type(h) is NamedHist
with pytest.raises(AttributeError):
NamedHist().new
h = (
NamedHist.new.Reg(10, 0, 1, name="x")
.Reg(10, 0, 1, name="y")
.Double()
.fill(x=[0.5, 0.5], y=[0.2, 0.6])
)
assert h[0.5j, 0.2j] == 1
assert h[bh.loc(0.5), bh.loc(0.6)] == 1
h = NamedHist.new.Bool(name="x").Double().fill(x=[True, True])
assert h[bh.loc(True)] == 2
h = (
NamedHist.new.Bool(name="x")
.Bool(name="y")
.Double()
.fill(x=[True, True], y=[True, False])
)
assert h[True, True] == 1
assert h[True, False] == 1
h = NamedHist.new.Var(range(10), name="x").Double().fill(x=[5, 5])
assert h[5j] == 2
h = (
NamedHist.new.Var(range(10), name="x")
.Var(range(10), name="y")
.Double()
.fill(x=[5, 5], y=[2, 6])
)
assert h[5j, 2j] == 1
assert h[bh.loc(5), bh.loc(6)] == 1
h = NamedHist.new.Int(0, 10, name="x").Double().fill(x=[5, 5])
assert h[5j] == 2
h = (
NamedHist.new.Int(0, 10, name="x")
.Int(0, 10, name="y")
.Double()
.fill(x=[5, 5], y=[2, 6])
)
assert h[5j, 2j] == 1
assert h[bh.loc(5), bh.loc(6)] == 1
h = NamedHist.new.IntCat(range(10), name="x").Double().fill(x=[5, 5])
assert h[5j] == 2
h = (
NamedHist.new.IntCat(range(10), name="x")
.IntCat(range(10), name="y")
.Double()
.fill(x=[5, 5], y=[2, 6])
)
assert h[5j, 2j] == 1
assert h[bh.loc(5), bh.loc(6)] == 1
h = NamedHist.new.StrCat("TF", name="x").Double().fill(x=["T", "T"])
assert h["T"] == 2
h = (
NamedHist.new.StrCat("TF", name="x")
.StrCat("TF", name="y")
.Double()
.fill(x=["T", "T"], y=["T", "F"])
)
assert h["T", "T"] == 1
assert h["T", "F"] == 1
