def test_quantitative_x_count_y():
df_count = pd.DataFrame({
"a": [1, 1, 2, 3, 5],
"b": [1.4, 1.4, 2.9, 3.18, 5.3]
})
chart = ChartMetadata(
alt.Chart(df_count).mark_point().encode(alt.X('a'), alt.Y('count()')))
def test_invalid_temporal(): # TODO: move to parse_chart tests???
chart = alt.Chart(df).mark_point().encode(alt.X('quant:T'))
ChartMetadata(chart)
def test_invalid_encodings():
chart_spec = alt.Chart(df).encode(x2='quant').mark_point()
chart = ChartMetadata(chart_spec)
with pytest.raises(ValueError):
_convert(chart)
def test_encoding_not_provided(): # TODO: move to the parse_chart tests
chart_spec = alt.Chart(df).mark_point()
with pytest.raises(ValueError):
chart = ChartMetadata(chart_spec)
def test_timeUnit():
chart = ChartMetadata(
alt.Chart(df).mark_point().encode(alt.X('date(combination)')))
def test_convert_y_success_temporal(column):
chart = ChartMetadata(alt.Chart(df).mark_point().encode(alt.Y(column)))
mapping = _convert(chart)
assert list(mapping['y']) == list(mdates.date2num(df[column].values))
def test_convert_x2_y2_fail_temporal(column):
chart = ChartMetadata(
alt.Chart(df).mark_point().encode(alt.X2(column), alt.Y2(column)))
_convert(chart)
def test_convert_size_success(channel, type):
chart_spec = ChartMetadata(
alt.Chart(df).encode(size='{}:{}'.format(channel, type)).mark_point())
mapping = _convert(chart_spec)
assert list(mapping['s']) == list(df[channel].values)
def test_convert_size_success_nominal():
with pytest.raises(NotImplementedError):
chart_spec = ChartMetadata(
alt.Chart(df).encode(size='nom').mark_point())
_convert(chart_spec)
def test_quantitative_opacity_array():
chart = ChartMetadata(
alt.Chart(df_quant).mark_point().encode(alt.Opacity('alpha')))
_convert(chart)
def test_convert_opacity_fail_temporal(column):
chart = ChartMetadata(
alt.Chart(df).mark_point().encode(alt.Opacity(column)))
_convert(chart)
def test_quantitative_opacity_value():
chart = ChartMetadata(
alt.Chart(df_quant).mark_point().encode(opacity=alt.value(.5)))
def test_quantitative_shape():
chart = ChartMetadata(
alt.Chart(df_quant).mark_point().encode(alt.Shape('shape')))
mapping = _convert(chart)
def test_convert_color_success(channel, dtype):
chart_spec = ChartMetadata(
alt.Chart(df).encode(
color=alt.Color(field=channel, type=dtype)).mark_point())
mapping = _convert(chart_spec)
assert list(mapping['c']) == list(df[channel].values)
def test_convert_x_success(channel):
chart_spec = alt.Chart(df).encode(x=channel).mark_point()
chart = ChartMetadata(chart_spec)
mapping = _convert(chart)
assert list(mapping['x']) == list(df[channel].values)
def test_quantitative_stroke():
chart = ChartMetadata(
alt.Chart(df_quant).mark_point().encode(alt.Stroke('fill')))
_convert(chart)
def test_convert_y_success(channel):
chart_spec = ChartMetadata(alt.Chart(df).encode(y=channel).mark_point())
mapping = _convert(chart_spec)
assert list(mapping['y']) == list(df[channel].values)
def test_convert_stroke_fail_temporal(column):
chart = ChartMetadata(
alt.Chart(df).mark_point().encode(alt.Stroke(column)))
_convert(chart)
def test_convert_y_fail():
with pytest.raises(KeyError):
chart_spec = ChartMetadata(alt.Chart(df).encode(y='b:N').mark_point())
def test_quantitative_x2_y2():
chart = ChartMetadata(
alt.Chart(df_quant).mark_point().encode(alt.X('a'), alt.Y('b'),
alt.X2('c'), alt.Y2('alpha')))
_convert(chart)