def test_chart_infer_types():
data = pd.DataFrame({'x': pd.date_range('2012', periods=10, freq='Y'),
'y': range(10),
'c': list('abcabcabca')})
def _check_encodings(chart):
dct = chart.to_dict()
assert dct['encoding']['x']['type'] == 'temporal'
assert dct['encoding']['x']['field'] == 'x'
assert dct['encoding']['y']['type'] == 'quantitative'
assert dct['encoding']['y']['field'] == 'y'
assert dct['encoding']['color']['type'] == 'nominal'
assert dct['encoding']['color']['field'] == 'c'
# Pass field names by keyword
chart = alt.Chart(data).mark_point().encode(x='x', y='y', color='c')
_check_encodings(chart)
# pass Channel objects by keyword
chart = alt.Chart(data).mark_point().encode(x=alt.X('x'), y=alt.Y('y'),
color=alt.Color('c'))
_check_encodings(chart)
# pass Channel objects by value
chart = alt.Chart(data).mark_point().encode(alt.X('x'), alt.Y('y'),
alt.Color('c'))
_check_encodings(chart)
# override default types
chart = alt.Chart(data).mark_point().encode(alt.X('x', type='nominal'),
alt.Y('y', type='ordinal'))
dct = chart.to_dict()
assert dct['encoding']['x']['type'] == 'nominal'
assert dct['encoding']['y']['type'] == 'ordinal'
def test_selection_to_dict():
brush = alt.selection(type='interval')
# test some value selections
# Note: X and Y cannot have conditions
alt.Chart('path/to/data.json').mark_point().encode(
color=alt.condition(brush, alt.ColorValue('red'),
alt.ColorValue('blue')),
opacity=alt.condition(brush, alt.value(0.5), alt.value(1.0)),
text=alt.condition(brush, alt.TextValue('foo'),
alt.value('bar'))).to_dict()
# test some field selections
# Note: X and Y cannot have conditions
# Conditions cannot both be fields
alt.Chart('path/to/data.json').mark_point().encode(
color=alt.condition(brush, alt.Color('col1:N'), alt.value('blue')),
opacity=alt.condition(brush, 'col1:N', alt.value(0.5)),
text=alt.condition(brush, alt.value('abc'), alt.Text('col2:N')),
size=alt.condition(brush, alt.value(20), 'col2:N')).to_dict()
