def test_lag_plot(lag):
data = pd.DataFrame({'x': range(10), 'y': range(10)})
# test series input
plot = pdvega.lag_plot(data['x'], lag=lag)
lag_data = utils.get_data(plot)
spec = plot.to_dict()
assert spec['mark'] == 'point'
assert spec['encoding']['x']['type'] == 'quantitative'
assert spec['encoding']['y']['type'] == 'quantitative'
utils.check_encodings(plot, x='y(t)', y='y(t + {0})'.format(lag))
assert lag_data.shape == (data.shape[0] - lag, 2)
# test dataframe input
plot = pdvega.lag_plot(data, lag=lag)
lag_data = utils.get_data(plot)
spec = plot.to_dict()
assert spec['mark'] == 'point'
assert spec['encoding']['x']['type'] == 'quantitative'
assert spec['encoding']['y']['type'] == 'quantitative'
assert spec['encoding']['color']['type'] == 'nominal'
utils.check_encodings(plot,
x='y(t)',
y='y(t + {0})'.format(lag),
color='variable')
assert lag_data.shape == (2 * (data.shape[0] - lag), 3)
def test_parallel_coordinates():
data = pd.DataFrame({'x': range(10),
'y': range(10),
'z': range(10),
'c': list('ABABABABAB')})
plot = pdvega.parallel_coordinates(data, 'c', alpha=0.5)
utils.validate_vegalite(plot.spec)
utils.check_encodings(plot.spec, x='variable', y='value',
color='c', detail='index', opacity=utils.IGNORE)
enc = plot.spec['encoding']
assert plot.spec['mark'] == 'line'
assert enc['x']['type'] == 'nominal'
assert enc['y']['type'] == 'quantitative'
assert enc['color']['type'] == 'nominal'
assert enc['detail']['type'] == 'quantitative'
assert enc['opacity']['value'] == 0.5
df = utils.get_data(plot.spec)
assert set(pd.unique(df['variable'])) == {'x', 'y', 'z'}
plot = pdvega.parallel_coordinates(data, 'c', cols=['x', 'y'])
utils.validate_vegalite(plot.spec)
utils.check_encodings(plot.spec, x='variable', y='value',
color='c', detail='index')
enc = plot.spec['encoding']
assert plot.spec['mark'] == 'line'
assert enc['x']['type'] == 'nominal'
assert enc['y']['type'] == 'quantitative'
assert enc['color']['type'] == 'nominal'
assert enc['detail']['type'] == 'quantitative'
df = utils.get_data(plot.spec)
assert set(pd.unique(df['variable'])) == {'x', 'y'}
def test_df_kde_y():
df = pd.DataFrame({'x': range(10), 'y': range(10)})
plot = df.vgplot.kde(y='y', bw_method='scott')
assert plot.spec['mark'] == 'line'
utils.check_encodings(plot.spec, x=' ', y='Density', color=utils.IGNORE)
data = utils.get_data(plot.spec)
assert set(pd.unique(data['variable'])) == {'y'}
def test_andrews_curves():
data = pd.DataFrame({
'x': range(10),
'y': range(10),
'z': range(10),
'c': list('ABABABABAB')
})
n_samples = 120
n_points = len(data)
plot = pdvega.andrews_curves(data, 'c', samples=120, alpha=0.5)
utils.validate_vegalite(plot)
utils.check_encodings(plot,
x='t',
y=' ',
color='c',
detail='sample',
opacity=utils.IGNORE)
spec = plot.to_dict()
enc = spec['encoding']
assert spec['mark'] == 'line'
assert enc['x']['type'] == 'quantitative'
assert enc['y']['type'] == 'quantitative'
assert enc['color']['type'] == 'nominal'
assert enc['detail']['type'] == 'quantitative'
assert enc['opacity']['value'] == 0.5
df = utils.get_data(plot)
assert len(df) == n_samples * n_points
def test_line_simple():
df = pd.DataFrame({'x': [1, 4, 2, 3, 5], 'y': [6, 3, 4, 5, 2]})
plot = df.vgplot.line()
utils.validate_vegalite(plot.spec)
assert plot.spec['mark'] == 'line'
utils.check_encodings(plot.spec, x='index', y='value', color='variable')
data = utils.get_data(plot.spec)
assert set(pd.unique(data['variable'])) == {'x', 'y'}
def test_bar_stacked():
df = pd.DataFrame({'x': [1, 4, 2, 3, 5], 'y': [6, 3, 4, 5, 2]})
plot = df.vgplot.bar(stacked=True)
utils.validate_vegalite(plot.spec)
assert plot.spec['mark'] == 'bar'
utils.check_encodings(plot.spec, x='index', y='value', color='variable')
data = utils.get_data(plot.spec)
assert set(pd.unique(data['variable'])) == {'x', 'y'}
assert plot.spec['encoding']['y']['stack'] == "zero"
def test_df_area_xy_unstacked():
df = pd.DataFrame({
'x': [1, 4, 2, 3, 5],
'y': [6, 3, 4, 5, 2],
'z': range(5)
})
plot = df.vgplot.area(x='x', y='y', stacked=False)
utils.validate_vegalite(plot.spec)
assert plot.spec['mark'] == 'area'
utils.check_encodings(plot.spec, x='x', y='value', color='variable')
data = utils.get_data(plot.spec)
assert set(pd.unique(data['variable'])) == {'y'}
assert plot.spec['encoding']['y']['stack'] is None
def test_barh_simple():
df = pd.DataFrame({'x': [1, 4, 2, 3, 5], 'y': [6, 3, 4, 5, 2]})
plot = df.vgplot.barh()
utils.validate_vegalite(plot.spec)
assert plot.spec['mark'] == 'bar'
utils.check_encodings(plot.spec,
y='index',
x='value',
color='variable',
opacity=utils.IGNORE)
data = utils.get_data(plot.spec)
assert set(pd.unique(data['variable'])) == {'x', 'y'}
assert plot.spec['encoding']['x']['stack'] is None
def test_df_area_unstacked():
df = pd.DataFrame({'x': [1, 4, 2, 3, 5], 'y': [6, 3, 4, 5, 2]})
plot = df.vgplot.area(stacked=False)
utils.validate_vegalite(plot.spec)
assert plot.spec['mark'] == 'area'
utils.check_encodings(plot.spec,
x='index',
y='value',
color='variable',
opacity=utils.IGNORE)
data = utils.get_data(plot.spec)
assert set(pd.unique(data['variable'])) == {'x', 'y'}
assert plot.spec['encoding']['y']['stack'] is None
assert plot.spec['encoding']['opacity']['value'] == 0.7
