def test_categorical_scale_default():
link1 = SankeyLink('a', 'b', type='x')
link2 = SankeyLink('a', 'b', type='y')
link3 = SankeyLink('a', 'b', type='z')
# If no default given, cycles through colours given
s = CategoricalScale('type', palette={'x': 'Red', 'y': 'Green'})
assert s(link1, {}) == 'Red'
assert s(link2, {}) == 'Green'
assert s(link3, {}) == 'Red'
# If default is given, use that for unspecified colours
s = CategoricalScale('type',
palette={
'x': 'Red',
'y': 'Green'
},
default='Black')
assert s(link1, {}) == 'Red'
assert s(link2, {}) == 'Green'
assert s(link3, {}) == 'Black'
# If default is given with a list, don't allow cycling
s = CategoricalScale('type', palette=['Red'], default='Black')
assert s(link1, {}) == 'Red'
assert s(link2, {}) == 'Black'
assert s(link3, {}) == 'Black'
def test_sankey_data_link_json():
link = SankeyLink('a', 'b', type='c', time='d', data={'value': 2}, title='link',
opacity=0.9, color='blue')
# draft JSON Sankey serialisation format
assert link.to_json() == {
'source': 'a',
'target': 'b',
'type': 'c',
'time': 'd',
'data': {
'value': 2,
},
'title': 'link',
'style': {
'opacity': 0.9,
'color': 'blue',
}
}
# format expected by ipysankeywidget
assert link.to_json(format='widget') == {
'source': 'a',
'target': 'b',
'type': 'c',
'time': 'd',
'value': 2,
'title': 'link',
'opacity': 0.9,
'color': 'blue',
}
def test_quantitative_scale_custom_get_color():
link1 = SankeyLink('a', 'b', type='red')
link2 = SankeyLink('a', 'b', type='blue')
class MyScale(QuantitativeScale):
def get_color(self, link, value):
return '{}{}'.format(link.type, value)
s = MyScale('value')
assert s(link1, {'value': 1}) == 'red1.0'
assert s(link2, {'value': 1}) == 'blue1.0'
def test_quantitative_scale_custom_get_palette():
link1 = SankeyLink('a', 'b', type='Reds_9')
link2 = SankeyLink('a', 'b', type='Blues_9')
class MyScale(QuantitativeScale):
def get_palette(self, link):
return self.lookup_palette_name(link.type)
s = MyScale('value')
assert s(link1, {'value': 1}) == '#67000d'
assert s(link2, {'value': 1}) == '#08306b'
def link(src, tgt, original_flows, value, link_type='*', color='#FBB4AE'):
return SankeyLink(source=src,
target=tgt,
type=link_type,
time='*',
data={'value': value},
title=link_type,
color=color,
original_flows=original_flows)
def test_categorical_scale():
link1 = SankeyLink('a', 'b', type='x')
link2 = SankeyLink('a', 'b', type='y')
# Default palette if not specified; same colour used consistently for the
# same link.
s = CategoricalScale('type')
assert s(link1, {}) == '#FBB4AE'
assert s(link1, {}) == '#FBB4AE'
assert s(link2, {}) == '#B3CDE3'
assert s(link1, {}) == '#FBB4AE'
# If custom palette given, it cycles through the colours given. Same colour
# is used consistently.
s = CategoricalScale('type', palette=['Red', 'Green', 'Blue'])
assert s(link1, {}) == 'Red'
assert s(link2, {}) == 'Green'
assert s(link1, {}) == 'Red'
# If the domain is not specified explicitly, colours are assigned in the
# order used. So this gives different results to the test above.
s = CategoricalScale('type', palette=['Red', 'Green', 'Blue'])
assert s(link2, {}) == 'Red'
assert s(link1, {}) == 'Green'
assert s(link2, {}) == 'Red'
# If setting the domain explicitly, then the order the colours are used
# doesn't matter.
s = CategoricalScale('type', palette=['Red', 'Green', 'Blue'])
s.set_domain(['x', 'y'])
assert s(link2, {}) == 'Green'
assert s(link1, {}) == 'Red'
assert s(link2, {}) == 'Green'
# Can pass a dict to set domain as well.
s = CategoricalScale('type',
palette={
'x': 'Blue',
'y': 'Green',
'z': 'Red'
})
assert s(link2, {}) == 'Green'
assert s(link1, {}) == 'Blue'
assert s(link2, {}) == 'Green'
def test_quantitative_scale_custom_get_value():
link = SankeyLink('a', 'b')
class MyScale(QuantitativeScale):
def get_value(self, link, measures):
# subtract value from 1, as an example of a transformation
return 1.0 - measures[self.attr]
s_reversed = MyScale('value')
s_normal = QuantitativeScale('value')
assert s_reversed(link, {'value': 0.2}) == s_normal(link, {'value': 0.8})
def test_sankey_data_link_json():
link = SankeyLink('a',
'b',
type='c',
time='d',
value=2,
title='link',
opacity=0.9,
color='blue')
assert link.to_json() == {
'source': 'a',
'target': 'b',
'type': 'c',
'time': 'd',
'value': 2,
'title': 'link',
# 'style': {
'opacity': 0.9,
'color': 'blue',
# }
}
def test_categorical_scale():
link1 = SankeyLink('a', 'b', type='x')
link2 = SankeyLink('a', 'b', type='y')
s = CategoricalScale('type')
assert s(link1, {}) == '#FBB4AE'
assert s(link1, {}) == '#FBB4AE'
s = CategoricalScale('type', palette=['Red', 'Green', 'Blue'])
assert s(link1, {}) == 'Red'
assert s(link2, {}) == 'Green'
assert s(link1, {}) == 'Red'
s = CategoricalScale('type', palette=['Red', 'Green', 'Blue'])
assert s(link2, {}) == 'Red'
assert s(link1, {}) == 'Green'
assert s(link2, {}) == 'Red'
s = CategoricalScale('type', palette=['Red', 'Green', 'Blue'])
s.set_domain(['x', 'y'])
assert s(link2, {}) == 'Green'
assert s(link1, {}) == 'Red'
assert s(link2, {}) == 'Green'
def test_quantitative_scale():
link = SankeyLink('a', 'b')
s = QuantitativeScale('value', palette='Greys_9')
s.set_domain((0, 10))
assert s(link, {'value': 0}) == '#ffffff'
# XXX not sure this is completely accurate scale
# assert s(link, {'value': 5}) == '#888888'
assert s(link, {'value': 10}) == '#000000'
snorm = QuantitativeScale('property', intensity='value', palette='Greys_9')
snorm.set_domain((0, 1))
assert snorm(link, {
'value': 5.0,
'property': 1.0
}) == s(link, {'value': 2})
def test_sankey_data_link_default_values():
assert SankeyLink('a', 'b').type == None
def test_sankey_data_link_required_attrs():
with pytest.raises(TypeError):
SankeyLink(source='a')
with pytest.raises(TypeError):
SankeyLink(target='a')
def test_sankey_data_json():
data = SankeyData(nodes=[SankeyNode(id='a')],
links=[SankeyLink(source='a', target='a')])
json = data.to_json()
assert json['nodes'] == [n.to_json() for n in data.nodes]
assert json['links'] == [l.to_json() for l in data.links]
