def test_add_catplot():
pytest.importorskip('seaborn')
X = generate_counts(n_samples=100)
upset = UpSet(X)
# smoke test
upset.add_catplot('violin')
fig = matplotlib.figure.Figure()
upset.plot(fig)
# can't provide value with Series
with pytest.raises(ValueError):
upset.add_catplot('violin', value='foo')
# check the above add_catplot did not break the state
upset.plot(fig)
X = generate_counts(n_samples=100)
X.name = 'foo'
X = X.to_frame()
upset = UpSet(X, subset_size='count')
# must provide value with DataFrame
with pytest.raises(ValueError):
upset.add_catplot('violin')
upset.add_catplot('violin', value='foo')
with pytest.raises(ValueError):
# not a known column
upset.add_catplot('violin', value='bar')
upset.plot(fig)
# invalid plot kind raises error when plotting
upset.add_catplot('foobar', value='foo')
with pytest.raises(AttributeError):
upset.plot(fig)
def test_plot_smoke_test(kw):
fig = matplotlib.figure.Figure()
X = generate_counts(n_samples=100)
plot(X, fig, **kw)
fig.savefig(io.BytesIO(), format='png')
# Also check fig is optional
n_nums = len(plt.get_fignums())
plot(X, **kw)
assert len(plt.get_fignums()) - n_nums == 1
assert plt.gcf().axes
def test_vertical():
X = generate_counts(n_samples=100)
fig = matplotlib.figure.Figure()
UpSet(X, orientation='horizontal').make_grid(fig)
horz_height = fig.get_figheight()
horz_width = fig.get_figwidth()
assert horz_height < horz_width
fig = matplotlib.figure.Figure()
UpSet(X, orientation='vertical').make_grid(fig)
vert_height = fig.get_figheight()
vert_width = fig.get_figwidth()
assert horz_width / horz_height > vert_width / vert_height
# TODO: test axes positions, plot order, bar orientation
pass
def test_show_counts(orientation):
fig = matplotlib.figure.Figure()
X = generate_counts(n_samples=100)
plot(X, fig)
n_artists_no_sizes = _count_descendants(fig)
fig = matplotlib.figure.Figure()
plot(X, fig, show_counts=True)
n_artists_yes_sizes = _count_descendants(fig)
assert n_artists_yes_sizes - n_artists_no_sizes > 6
fig = matplotlib.figure.Figure()
plot(X, fig, show_counts='%0.2g')
assert n_artists_yes_sizes == _count_descendants(fig)
with pytest.raises(ValueError):
fig = matplotlib.figure.Figure()
plot(X, fig, show_counts='%0.2h')
def test_not_unique(sort_by, sort_categories_by):
kw = {
'sort_by': sort_by,
'sort_categories_by': sort_categories_by,
'subset_size': 'sum',
'sum_over': None
}
Xagg = generate_counts()
df1, intersections1, totals1 = _process_data(Xagg, **kw)
Xunagg = generate_samples()['value']
Xunagg.loc[:] = 1
df2, intersections2, totals2 = _process_data(Xunagg, **kw)
assert_series_equal(intersections1, intersections2, check_dtype=False)
assert_series_equal(totals1, totals2, check_dtype=False)
assert set(df1.columns) == {'_value', '_bin'}
assert set(df2.columns) == {'_value', '_bin'}
assert len(df2) == len(Xunagg)
assert df2['_bin'].nunique() == len(intersections2)
def test_show_counts(orientation):
fig = matplotlib.figure.Figure()
X = generate_counts(n_samples=10000)
plot(X, fig, orientation=orientation)
n_artists_no_sizes = _count_descendants(fig)
fig = matplotlib.figure.Figure()
plot(X, fig, orientation=orientation, show_counts=True)
n_artists_yes_sizes = _count_descendants(fig)
assert n_artists_yes_sizes - n_artists_no_sizes > 6
assert '9547' in get_all_texts(fig) # set size
assert '283' in get_all_texts(fig) # intersection size
fig = matplotlib.figure.Figure()
plot(X, fig, orientation=orientation, show_counts='%0.2g')
assert n_artists_yes_sizes == _count_descendants(fig)
assert '9.5e+03' in get_all_texts(fig)
assert '2.8e+02' in get_all_texts(fig)
fig = matplotlib.figure.Figure()
plot(X, fig, orientation=orientation, show_percentages=True)
assert n_artists_yes_sizes == _count_descendants(fig)
assert '95.5%' in get_all_texts(fig)
assert '2.8%' in get_all_texts(fig)
fig = matplotlib.figure.Figure()
plot(X,
fig,
orientation=orientation,
show_counts=True,
show_percentages=True)
assert n_artists_yes_sizes == _count_descendants(fig)
if orientation == 'vertical':
assert '9547\n(95.5%)' in get_all_texts(fig)
assert '283 (2.8%)' in get_all_texts(fig)
else:
assert '9547 (95.5%)' in get_all_texts(fig)
assert '283\n(2.8%)' in get_all_texts(fig)
with pytest.raises(ValueError):
fig = matplotlib.figure.Figure()
plot(X, fig, orientation=orientation, show_counts='%0.2h')
def test_element_size():
X = generate_counts(n_samples=100)
figsizes = []
for element_size in range(10, 50, 5):
fig = matplotlib.figure.Figure()
UpSet(X, element_size=element_size).make_grid(fig)
figsizes.append((fig.get_figwidth(), fig.get_figheight()))
figwidths, figheights = zip(*figsizes)
# Absolute width increases
assert np.all(np.diff(figwidths) > 0)
aspect = np.divide(figwidths, figheights)
# Font size stays constant, so aspect ratio decreases
assert np.all(np.diff(aspect) < 0)
# But doesn't decrease by much
assert np.all(aspect[:-1] / aspect[1:] < 1.1)
fig = matplotlib.figure.Figure()
figsize_before = fig.get_figwidth(), fig.get_figheight()
UpSet(X, element_size=None).make_grid(fig)
figsize_after = fig.get_figwidth(), fig.get_figheight()
assert figsize_before == figsize_after
"""
====================
Vertical orientation
====================
This illustrates the effect of orientation='vertical'.
"""
from matplotlib import pyplot as plt
from upsetplot import generate_counts, plot
example = generate_counts()
plot(example, orientation='vertical')
plt.suptitle('A vertical plot')
plt.show()
plot(example, orientation='vertical', show_counts='%d')
plt.suptitle('A vertical plot with counts shown')
plt.show()
def test_param_validation(kw):
X = generate_counts(n_samples=100)
with pytest.raises(ValueError):
UpSet(X, **kw)
from upsetplot import plot
from upsetplot import UpSet
from upsetplot import generate_counts, generate_samples
from upsetplot.plotting import _process_data
# TODO: warnings should raise errors
def is_ascending(seq):
# return np.all(np.diff(seq) >= 0)
return sorted(seq) == list(seq)
@pytest.mark.parametrize('x', [
generate_counts(),
generate_counts().iloc[1:-2],
])
@pytest.mark.parametrize('sort_by', ['cardinality', 'degree'])
@pytest.mark.parametrize('sort_categories_by', [None, 'cardinality'])
def test_process_data_series(x, sort_by, sort_categories_by):
assert x.name == 'value'
for subset_size in ['auto', 'legacy', 'sum', 'count']:
for sum_over in ['abc', False]:
with pytest.raises(ValueError, match='sum_over is not applicable'):
_process_data(x,
sort_by=sort_by,
sort_categories_by=sort_categories_by,
subset_size=subset_size,
sum_over=sum_over)
# TODO: warnings should raise errors
def is_ascending(seq):
# return np.all(np.diff(seq) >= 0)
return sorted(seq) == list(seq)
def get_all_texts(mpl_artist):
out = [text.get_text() for text in mpl_artist.findobj(Text)]
return [text for text in out if text]
@pytest.mark.parametrize('x', [
generate_counts(),
generate_counts().iloc[1:-2],
])
@pytest.mark.parametrize('sort_by', ['cardinality', 'degree'])
@pytest.mark.parametrize('sort_categories_by', [None, 'cardinality'])
def test_process_data_series(x, sort_by, sort_categories_by):
assert x.name == 'value'
for subset_size in ['auto', 'sum', 'count']:
for sum_over in ['abc', False]:
with pytest.raises(ValueError, match='sum_over is not applicable'):
_process_data(x,
sort_by=sort_by,
sort_categories_by=sort_categories_by,
subset_size=subset_size,
sum_over=sum_over)
