def test_line(self):
chart = alt.Chart(df_line).mark_line().encode(
alt.X('a'),
alt.Y('b'),
)
fig, _ = convert(chart)
return fig
def test_axis_scale_NotImplemented_quantitative(df, x, type):
chart = alt.Chart(df).mark_point().encode(
alt.X(x, scale=alt.Scale(type=type)), alt.Y('a'))
mapping = convert(chart)
fig, ax = plt.subplots()
ax.scatter(**mapping)
convert_axis(ax, chart)
def test_scatter_temporal(channel):
chart = alt.Chart(df).mark_point().encode(alt.X("years"), channel)
mapping = convert(chart)
mapping['y'] = df['quantitative'].values
fig, ax = plt.subplots()
ax.scatter(**mapping)
return fig
def test_axis_scale_type_y_quantitative(column, type, base, exponent):
chart = alt.Chart(df_quant).mark_point().encode(
alt.X('a'),
alt.Y(column, scale=alt.Scale(type=type, base=base,
exponent=exponent)))
fig, ax = convert(chart)
plt.show()
return fig
def test_axis(df, x, y):
chart = alt.Chart(df).mark_point().encode(alt.X(x), alt.Y(y))
mapping = convert(chart)
fig, ax = plt.subplots()
ax.scatter(**mapping)
convert_axis(ax, chart)
fig.tight_layout()
return fig
def test_axis_formatter_fail():
chart = alt.Chart(df_quant).mark_point().encode(
alt.X('c', axis=alt.Axis(format='-$.2g')),
alt.Y('b', axis=alt.Axis(format='+.3r')))
mapping = convert(chart)
fig, ax = plt.subplots()
ax.scatter(**mapping)
convert_axis(ax, chart)
def test_axis_formatter_temporal():
"""Note: this test is separate from the other test_axis_formatter test because the parametrization created issues
with the filename for the image comparison test."""
chart = alt.Chart(df_temp).mark_point().encode(
alt.X('months:T', axis=alt.Axis(format='%b %Y')),
alt.Y('hrs:T', axis=alt.Axis(format='%H:%M:%S')))
fig, ax = convert(chart)
return fig
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 = alt.Chart(df_count).mark_point().encode(alt.X('a'),
alt.Y('count()'))
mapping = convert(chart)
def test_axis_scale_basic(df, column, scale_type):
chart = alt.Chart(df).mark_point().encode(
alt.X(column, scale=alt.Scale(type=scale_type)), alt.Y('a'))
mapping = convert(chart)
fig, ax = plt.subplots()
ax.scatter(**mapping)
convert_axis(ax, chart)
fig.tight_layout()
return fig
def test_axis_unaggregated_quantitative():
chart = alt.Chart(df_quant).mark_point().encode(
alt.X('a', scale=alt.Scale(domain="unaggregated")),
alt.Y('c', scale=alt.Scale(domain="unaggregated")))
mapping = convert(chart)
fig, ax = plt.subplots()
ax.scatter(**mapping)
plt.close()
convert_axis(ax, chart)
def test_axis_more_than_x_and_y():
chart = alt.Chart(df_quant).mark_point().encode(alt.X('a'),
alt.Y('b'),
color=alt.Color('c'))
mapping = convert(chart)
fig, ax = plt.subplots()
ax.scatter(**mapping)
convert_axis(ax, chart)
fig.tight_layout()
return fig
def test_axis_formatter(df, x, y, format_x, format_y):
chart = alt.Chart(df).mark_point().encode(
alt.X(x, axis=alt.Axis(format=format_x)),
alt.Y(y, axis=alt.Axis(format=format_y)))
mapping = convert(chart)
fig, ax = plt.subplots()
ax.scatter(**mapping)
convert_axis(ax, chart)
fig.tight_layout()
return fig
def test_axis_scale_type_x_quantitative(column, type, base, exponent):
chart = alt.Chart(df_quant).mark_point().encode(
alt.X(column, scale=alt.Scale(type=type, base=base,
exponent=exponent)), alt.Y('a'))
mapping = convert(chart)
fig, ax = plt.subplots()
ax.scatter(**mapping)
convert_axis(ax, chart)
fig.tight_layout()
return fig
def test_axis_tickCount(df, x, tickCount):
chart = alt.Chart(df).mark_point().encode(
alt.X(x, axis=alt.Axis(tickCount=tickCount)),
alt.Y('a', axis=alt.Axis(tickCount=tickCount)))
mapping = convert(chart)
fig, ax = plt.subplots()
ax.scatter(**mapping)
convert_axis(ax, chart)
fig.tight_layout()
return fig
def test_axis_values(df, y, vals):
chart = alt.Chart(df).mark_point().encode(
alt.X('a', axis=alt.Axis(values=[-1, 1, 1.5, 2.125, 3])),
alt.Y(y, axis=alt.Axis(values=vals)))
mapping = convert(chart)
fig, ax = plt.subplots()
ax.scatter(**mapping)
convert_axis(ax, chart)
fig.tight_layout()
return fig
def test_axis_domain(df, x, y, x_dom, y_dom):
chart = alt.Chart(df).mark_point().encode(
alt.X(x, scale=alt.Scale(domain=x_dom)),
alt.Y(y, scale=alt.Scale(domain=y_dom)))
mapping = convert(chart)
fig, ax = plt.subplots()
ax.scatter(**mapping)
convert_axis(ax, chart)
fig.tight_layout()
return fig
def test_axis_zero_quantitative(x, y, zero):
chart = alt.Chart(df_quant).mark_point().encode(
alt.X(x, scale=alt.Scale(zero=zero)),
alt.Y(y, scale=alt.Scale(zero=zero)))
mapping = convert(chart)
fig, ax = plt.subplots()
ax.scatter(**mapping)
convert_axis(ax, chart)
fig.tight_layout()
return fig
def test_convert_y_success_temporal(column):
chart = 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_y_fail():
chart_spec = alt.Chart(df).encode(y='b:N').mark_point()
with pytest.raises(KeyError):
convert(chart_spec)
def test_invalid_temporal():
chart = alt.Chart(df).mark_point().encode(alt.X('quant:T'))
convert(chart)
def test_convert_y_success(channel):
chart_spec = alt.Chart(df).encode(y=channel).mark_point()
mapping = convert(chart_spec)
assert list(mapping['y']) == list(df[channel].values)
def test_encoding_not_provided():
chart_spec = alt.Chart(df).mark_point()
with pytest.raises(ValueError):
convert(chart_spec)
def test_invalid_encodings():
chart_spec = alt.Chart(df).encode(x2='quant').mark_point()
with pytest.raises(ValueError):
convert(chart_spec)
def test_quantitative_scatter(chart):
mapping = convert(chart)
plt.scatter(**mapping)
plt.show()
def test_scatter_temporal(channel):
chart = alt.Chart(df).mark_point().encode(alt.X("years"), channel)
mapping = convert(chart)
mapping['y'] = df['quantitative'].values
plt.scatter(**mapping)
plt.show()
def test_timeUnit():
chart = alt.Chart(df).mark_point().encode(alt.X('date(combination)'))
convert(chart)
def test_convert_size_success(channel, type):
chart_spec = 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_stroke_fail_temporal(column):
chart = alt.Chart(df).mark_point().encode(alt.Stroke(column))
convert(chart)
def test_quantitative_stroke():
chart = alt.Chart(df_quant).mark_point().encode(alt.Stroke('fill'))
convert(chart)
def test_convert_size_success_nominal():
chart_spec = alt.Chart(df).encode(size='nom').mark_point()
with pytest.raises(NotImplementedError):
convert(chart_spec)
