def plot(i):
if i == 2:
p = qplot(x, y, xlab='x', ylab='y')
else:
p = (qplot(x, y, color=colors[i], xlab='x', ylab='y')
+ lims(color=(1, 7))
+ labs(color='color'))
return p + theme_minimal()
def plot(i):
if i == 2:
p = qplot(x, y, xlab='x', ylab='y')
else:
p = (qplot(x, y, color=colors[i], xlab='x', ylab='y')
+ lims(color=(1, 7))
+ labs(color='color'))
return p + theme_minimal()
def plot(i):
c = colors[i]
if i == 2:
p = (qplot(x, y, color=c, xlab='x', ylab='y')
+ lims(color=(1, 7))
+ labs(color='color'))
else:
p = (qplot(x, y, stroke=c, xlab='x', ylab='y')
+ lims(stroke=(1, 7))
+ labs(stroke='stroke'))
return p + theme_minimal()
def plot(i):
c = colors[i]
if i == 2:
p = (qplot(x, y, color=c, xlab='x', ylab='y')
+ lims(color=(1, 7))
+ labs(color='color'))
else:
p = (qplot(x, y, stroke=c, xlab='x', ylab='y')
+ lims(stroke=(1, 7))
+ labs(stroke='stroke'))
return p + theme_minimal()
def plot(i):
return (qplot(x, y, color=colors[i], xlab='x', ylab='y')
+ lims(color=(1, 7))
+ labs(color='color')
+ theme_minimal()
+ _theme
)
def plot(i):
return (qplot(x, y, color=colors[i], xlab='x', ylab='y')
+ lims(color=(1, 7))
+ labs(color='color')
+ theme_minimal()
+ _theme
)
def test_series_labelling():
df = pd.DataFrame({
'x_axis_label': [1, 2, 3],
'y_axis_label': [1, 2, 3],
'color_label': ['a', 'b', 'c']
})
p = qplot(df.x_axis_label, df.y_axis_label, color=df.color_label)
assert p + _theme == 'series_labelling'
def test_multiple_geoms():
n = 3
m = 10
# n stairs of points, each m points high
df = pd.DataFrame({'x': np.repeat(range(n), m),
'y': np.linspace(0, n, n*m)})
p = qplot('factor(x)', 'y', data=df, geom=("boxplot", "point"))
assert p == 'multiple_geoms'
def test_multiple_geoms():
n = 3
m = 10
# n stairs of points, each m points high
df = pd.DataFrame({'x': np.repeat(range(n), m),
'y': np.linspace(0, n, n*m)})
p = qplot('factor(x)', 'y', data=df, geom=("boxplot", "point"))
assert p == 'multiple_geoms'
def plot(i):
if i == 2:
_lims = lims(color=(3, 7))
else:
_lims = lims(color=(1, 7))
return (qplot(x, y, color=colors[i], xlab='x', ylab='y')
+ _lims
+ labs(color='color')
+ theme_minimal()
+ _theme
)
def plot(i):
if i == 2:
_lims = lims(color=(3, 7))
else:
_lims = lims(color=(1, 7))
return (qplot(x, y, color=colors[i], xlab='x', ylab='y')
+ _lims
+ labs(color='color')
+ theme_minimal()
+ _theme
)
def test_range():
p = qplot(x=range(5), y=range(5))
assert p == 'range'
def test_string_arrays():
p = qplot(x='np.arange(5)', y='np.arange(5)')
assert p == 'string-arrays'
def test_arrays():
p = qplot(x=np.arange(5), y=np.arange(5))
assert p == 'arrays'
def test_scalars():
p = qplot(x=2, y=3)
assert p == 'scalars'
def test_arrays():
p = qplot(x=np.arange(5), y=np.arange(5))
assert p == 'arrays'
def test_sample():
p = qplot(sample='np.arange(5)')
assert p == 'sample'
def test_range():
p = qplot(x=range(5), y=range(5))
assert p == 'range'
def test_scalars():
p = qplot(x=2, y=3)
assert p == 'scalars'
def test_onlyx():
p = qplot(x='np.arange(5)')
assert p == 'onlyx'
def test_onlyy():
p = qplot(y=np.arange(5))
# Small displacement in x-label on travis
assert p == ('range', {'tol': 8})
def test_series_labelling():
df = pd.DataFrame({'x_axis_label': [1, 2, 3],
'y_axis_label': [1, 2, 3],
'color_label': ['a', 'b', 'c']})
p = qplot(df.x_axis_label, df.y_axis_label, color=df.color_label)
assert p + _theme == 'series_labelling'
def test_onlyx():
p = qplot(x='np.arange(5)')
assert p == 'onlyx'
def test_onlyy():
p = qplot(y=np.arange(5))
# Small displacement in x-label on travis
assert p == ('range', {'tol': 8})
def test_string_arrays():
p = qplot(x='np.arange(5)', y='np.arange(5)')
assert p == 'string-arrays'
def test_onlyx():
p = qplot(x='np.arange(5)')
with pytest.warns(PlotnineWarning):
assert p == 'onlyx'
def test_sample():
p = qplot(sample='np.arange(5)')
assert p == 'sample'
