def test_minor_breaks():
n = 10
x = np.arange(n)
# Default
s = scale_xy.scale_x_continuous()
s.train(x)
breaks = s.get_breaks()
minor_breaks = s.get_minor_breaks(breaks)
expected_minor_breaks = (breaks[:-1] + breaks[1:])/2
assert np.allclose(minor_breaks, expected_minor_breaks, rtol=1e-12)
# List
expected_minor_breaks = [2, 4, 6, 8]
s = scale_xy.scale_x_continuous(minor_breaks=expected_minor_breaks)
s.train(x)
breaks = s.get_breaks()
minor_breaks = s.get_minor_breaks(breaks)
assert np.allclose(minor_breaks, expected_minor_breaks, rtol=1e-12)
# Callable
def func(limits):
return np.linspace(limits[0], limits[1], n)
s = scale_xy.scale_x_continuous(minor_breaks=func)
s.train(x)
breaks = s.get_breaks()
minor_breaks = s.get_minor_breaks(breaks)
_breaks = set(breaks)
expected_minor_breaks = [x for x in np.arange(n) if x not in _breaks]
assert np.allclose(minor_breaks, expected_minor_breaks, rtol=1e-12)
assert not (_breaks & set(minor_breaks))
def test_minor_breaks():
n = 10
x = np.arange(n)
# Default
s = scale_xy.scale_x_continuous()
s.train(x)
breaks = s.get_breaks()
minor_breaks = s.get_minor_breaks(breaks)
expected_minor_breaks = (breaks[:-1] + breaks[1:])/2
assert np.allclose(minor_breaks, expected_minor_breaks, rtol=1e-12)
# List
expected_minor_breaks = [2, 4, 6, 8]
s = scale_xy.scale_x_continuous(minor_breaks=expected_minor_breaks)
s.train(x)
breaks = s.get_breaks()
minor_breaks = s.get_minor_breaks(breaks)
assert np.allclose(minor_breaks, expected_minor_breaks, rtol=1e-12)
# Callable
def func(limits):
return np.linspace(limits[0], limits[1], n)
s = scale_xy.scale_x_continuous(minor_breaks=func)
s.train(x)
breaks = s.get_breaks()
minor_breaks = s.get_minor_breaks(breaks)
_breaks = set(breaks)
expected_minor_breaks = [x for x in np.arange(n) if x not in _breaks]
assert np.allclose(minor_breaks, expected_minor_breaks, rtol=1e-12)
assert not (_breaks & set(minor_breaks))
def test_scale_continous_breaks():
x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
breaks = [2, 4, 6, 8, 10]
# Array breaks should not trip up the conditional checks
s1 = scale_x_continuous(breaks=breaks, limits=(1, 10))
s2 = scale_x_continuous(breaks=np.array(breaks), limits=(1, 10))
s1.train(x)
s2.train(x)
assert list(s1.get_breaks()) == list(s2.get_breaks())
def test_changing_scale_transform():
# No warning
with pytest.warns(None):
scale_x_continuous(trans='reverse')
scale_xy.scale_x_reverse(trans='reverse')
scale_xy.scale_x_log10(trans='log10')
# Warnings
with pytest.warns(PlotnineWarning):
scale_xy.scale_x_reverse(trans='log10')
with pytest.warns(PlotnineWarning):
scale_xy.scale_x_datetime(trans='identity')
s = scale_xy.scale_x_reverse()
with pytest.warns(PlotnineWarning):
s.trans = 'log10'
def test_breaks_and_labels_outside_of_limits():
df = pd.DataFrame({'x': range(5, 11), 'y': range(5, 11)})
p = (ggplot(aes('x', 'y'), data=df) + geom_point() +
scale_x_continuous(limits=[7, 9.5],
breaks=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
labels=[
'one', 'two', 'three', 'four', 'five', 'six',
'seven', 'eight', 'nine', 'ten', 'eleven'
]))
assert p == 'breaks_and_labels_outside_of_limits'
def test_setting_limits():
lst = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
s = scale_x_continuous()
s.train(lst)
assert s.limits == (1, 10)
s = scale_x_continuous(limits=(3, 7))
s.train(lst)
assert s.limits == (3, 7)
s = scale_x_continuous(limits=(3, None))
s.train(lst)
assert s.limits == (3, 10)
s = scale_x_continuous(limits=(None, 7))
s.train(lst)
assert s.limits == (1, 7)
s = scale_color.scale_color_hue(limits=tuple('abcdefg'))
s.train(['a', 'b', 'a'])
assert s.limits == tuple('abcdefg')
def test_xy_palette():
s = scale_x_discrete()
value = s.palette(3)
assert (value == 3)
s = scale_y_discrete()
value = s.palette(11.5)
assert (value == 11.5)
s = scale_x_continuous()
value = s.palette(3.63)
assert (value == 3.63)
s = scale_y_continuous()
value = s.palette(11.52)
assert (value == 11.52)
