geom_sina()¶import numpy as np
import pandas as pd
from lets_plot import *
LetsPlot.setup_html()
df = pd.read_csv("https://raw.githubusercontent.com/JetBrains/lets-plot-docs/refs/heads/master/data/mpg.csv")
print(df.shape)
df.head()
xaes, yaes = "drv", "cty"
vparams = dict(size=0, fill="#dddddd")
sparams = dict(seed=42)
g = ggplot(df, aes(xaes, yaes))
v = g + geom_violin(**vparams)
s = v + geom_sina(**sparams)
s
shape/stroke¶gggrid([
v + geom_sina(aes(shape=xaes), **sparams) + ggtitle("shape aesthetic"),
v + geom_sina(shape=22, **sparams) + ggtitle("shape parameter"),
v + geom_sina(aes(stroke=xaes), shape=21, **sparams) + scale_stroke(range=[.5, 2]) + ggtitle("stroke aesthetic"),
v + geom_sina(shape=21, stroke=2, **sparams) + ggtitle("stroke parameter"),
], ncol=2)
seed¶gggrid([
v + geom_sina(seed=0) + ggtitle("seed=0"),
v + geom_sina(seed=0) + ggtitle("seed=0"),
v + geom_sina(seed=1) + ggtitle("seed=1"),
])
def violin_sina_plot(**params):
seed = 42
return g + \
geom_violin(**{**vparams, **params}) + \
geom_sina(seed=seed, **params) + \
ggtitle("\n".join(["{0}: {1}".format(k, v) for k, v in dict(params).items()]))
weight aesthetic¶def weight_plot():
size = 1.5
seed = 42
return g + \
geom_violin(size=0, fill="red", alpha=.1) + \
geom_violin(aes(weight="displ"), size=0, fill="blue", alpha=.1) + \
geom_sina(color="red", size=size, **sparams) + \
geom_sina(aes(weight="displ"), color="blue", size=size, **sparams) + \
ggsize(1000, 500)
weight_plot()
orientation¶ggplot(df, aes(yaes, xaes, fill=xaes)) + geom_violin(alpha=.25) + geom_sina(shape=21, **sparams)
quantiles¶def quantiles_plot(quantiles):
seed = 42
return g + \
geom_violin(aes(fill="..quantile.."), alpha=1/3, quantile_lines=True, quantiles=quantiles) + \
geom_sina(aes(color="..quantile.."), shape=1, size=2, quantiles=quantiles, **sparams) + \
scale_continuous(["color", "fill"], low="yellow", high="blue") + \
ggtitle("quantiles={0}".format(quantiles))
gggrid([
quantiles_plot(None),
quantiles_plot([.1, .5, .9]),
quantiles_plot([.5]),
], ncol=1) + ggsize(600, 1200)
show_half¶g + \
geom_violin(show_half=-1, **vparams) + \
geom_sina(show_half=1, **sparams)
scale¶gggrid([
violin_sina_plot(scale='area'),
violin_sina_plot(scale='width'),
violin_sina_plot(scale='count'),
], ncol=1) + ggsize(600, 900)
kernel¶gggrid([
violin_sina_plot(kernel=None),
violin_sina_plot(kernel='gaussian'),
violin_sina_plot(kernel='epanechikov'),
violin_sina_plot(kernel='triangular'),
], ncol=2)
bw¶gggrid([
violin_sina_plot(bw=None),
violin_sina_plot(bw='nrd0'),
violin_sina_plot(bw='nrd'),
violin_sina_plot(bw=2),
], ncol=2)
adjust¶gggrid([
violin_sina_plot(adjust=None),
violin_sina_plot(adjust=2),
], ncol=2)
alpha/color/fill/size aesthetics¶gggrid([
v + geom_sina(aes(alpha=xaes), **sparams) + ggtitle("alpha aesthetic"),
v + geom_sina(alpha=.5, **sparams) + ggtitle("alpha parameter"),
v + geom_sina(aes(color=xaes), **sparams) + ggtitle("color aesthetic"),
v + geom_sina(color="red", **sparams) + ggtitle("color parameter"),
v + geom_sina(aes(fill=xaes), shape=21, **sparams) + ggtitle("fill aesthetic"),
v + geom_sina(fill="red", shape=21, **sparams) + ggtitle("fill parameter"),
v + geom_sina(aes(size=xaes), **sparams) + ggtitle("size aesthetic"),
v + geom_sina(size=1.5, **sparams) + ggtitle("size parameter"),
], ncol=2)
width aesthetic¶gggrid([
g + geom_violin() + geom_sina(**sparams) + ggtitle("Default plot"),
g + geom_violin(aes(width=xaes)) + geom_sina(aes(width=xaes), **sparams) + ggtitle("width aesthetic"),
g + geom_violin(width=1/3) + geom_sina(width=1/3, **sparams) + ggtitle("width parameter"),
], ncol=1) + ggsize(600, 900)
position¶def position_plot(position):
data = {
'x': ['a'] * 6,
'y': [1, 1, 2, 1, 2, 2],
'g': ['x'] * 3 + ['y'] * 3,
}
return ggplot(data, aes('x', 'y', color='g', fill='g')) + \
geom_violin(alpha=.25, position=position) + \
geom_sina(shape=21, color="black", position=position, **sparams) + \
ggtitle("position={0}".format(position))
gggrid([
position_plot(None),
position_plot('dodge'),
position_plot('identity'),
position_plot(position_jitter(seed=42)),
position_plot(position_jitterdodge(seed=42)),
position_plot(position_nudge(x=.2)),
], ncol=2)
show_legend¶gggrid([
v + geom_sina(aes(color=xaes), **sparams) + ggtitle("Default"),
v + geom_sina(aes(color=xaes), show_legend=False, **sparams) + ggtitle("show_legend=False"),
])
inherit_aes¶gggrid([
ggplot(df, aes(color=xaes)) + \
geom_violin(aes(xaes, yaes)) + \
geom_sina(aes(xaes, yaes), **sparams) + \
ggtitle("Default"),
ggplot(df, aes(color=xaes)) + \
geom_violin(aes(xaes, yaes)) + \
geom_sina(aes(xaes, yaes), inherit_aes=False, **sparams) + \
ggtitle("inherit_aes=False"),
])
manual_key¶v + geom_sina(manual_key=layer_key("Sina"), **sparams)
sampling¶gggrid([
v + geom_sina(**sparams) + ggtitle("Default"),
v + geom_sina(sampling=sampling_random(20, seed=42), **sparams) + ggtitle("sampling_random"),
])
tooltips¶def get_tooltips(title):
return layer_tooltips().title(title).line("(^x, ^y)")\
.line("@|@..violinwidth..")\
.line("@|@..density..")\
.line("@|@..count..")\
.line("@|@..scaled..")\
.line("@|@..quantile..")
g + geom_violin(tooltips=get_tooltips("Violin"), **vparams) + \
geom_sina(tooltips=get_tooltips("Sina"), **sparams)
color_by/fill_by¶v + geom_sina(aes(paint_a=xaes, paint_b=xaes), shape=21, color_by='paint_a', fill_by='paint_b', **sparams) + \
scale_brewer('paint_a', name="Color: paint_a='Dark2'", palette='Dark2') + \
scale_brewer('paint_b', name="Fill: paint_b='Set2'", palette='Set2')
stat='identity'¶def identity_data():
return {
'x': (['a'] * 3 + ['b'] * 3) * 2,
'y': [1, 2, 3, 1.1, 2.1, 3.1] * 2,
'violinwidth': [.2, .4, .3] * 2 + [.5, .6, .4] * 2,
'quantile': [.3, .6, 1] * 4,
'group': ['s'] * 6 + ['t'] * 6,
}
def get_identity_tooltips(title):
return layer_tooltips().title(title).line("(@x, @y)")\
.line("@|@violinwidth")\
.line("@|@quantile")
ggplot(identity_data()) + \
geom_violin(aes('x', 'y', violinwidth='violinwidth', quantile='quantile', fill='group'),
alpha=.25, stat='identity', tooltips=get_identity_tooltips("Violin")) + \
geom_sina(aes('x', 'y', violinwidth='violinwidth', quantile='quantile', color='group'),
stat='identity', tooltips=get_identity_tooltips("Sina"), **sparams)
'sina' stat¶g + geom_violin(color="red", fill="red", alpha=.1, manual_key=layer_key("stat='ydensity'")) + \
geom_violin(stat='sina', color="blue", alpha=0, manual_key=layer_key("stat='sina'")) + \
geom_errorbar(aes(ymin='..y..', ymax='..y..', width='..violinwidth..'), stat='sina', color="blue") + \
geom_point(color="blue", size=2) + \
ggsize(1000, 500)
ggtb()¶s + ggtb()
s + facet_wrap(facets=xaes, ncol=3, scales='free')
s + scale_y_continuous(name="City miles per gallon", limits=[0, 50], trans='sqrt')
gggrid([
s + ggtitle("Default"),
s + coord_flip() + ggtitle("coord_flip()"),
s + coord_polar() + ggtitle("coord_polar()"),
], ncol=1) + ggsize(800, 1600)
s + theme_bw() + theme(panel_background=element_rect(fill="#ffffcc")) + flavor_solarized_light()
tests = [
{
'title': "Empty data",
'data': {
'x': [],
'y': [],
}
},
{
'title': "One element",
'data': {
'x': ['a'],
'y': [0],
}
},
{
'title': "Continuous x",
'data': {
'x': [0, 1],
'y': [0, 0],
}
},
{
'title': "NaN's in data",
'data': {
'x': ['a', 'b', np.nan, None, 'b', 'b'],
'y': [0, 1, 1, 1, np.nan, None],
}
},
{
'title': "Empty data, identity stat",
'data': {
'x': [],
'y': [],
},
'stat': 'identity'
},
{
'title': "One element, identity stat",
'data': {
'x': ['a'],
'y': [0],
},
'stat': 'identity'
},
{
'title': "Continuous x, identity stat",
'data': {
'x': [0, 1],
'y': [0, 0],
},
'stat': 'identity'
},
{
'title': "NaN's in data, identity stat",
'data': {
'x': ['a', 'b', np.nan, None, 'b', 'b'],
'y': [0, 1, 1, 1, np.nan, None],
},
'stat': 'identity'
},
]
gggrid([
ggplot(t['data'], aes('x', 'y')) + \
geom_sina(stat=t['stat'] if 'stat' in t else None, **sparams) + \
ggtitle(t['title'])
for t in tests
], ncol=2)
def get_mapping(var):
if var is None:
return aes('x', 'y')
else:
return aes('x', 'y', color=var, fill=var)
p_working = lambda var: ggplot({'x': [0], 'y': [0]}, get_mapping(var))
p_no_data = lambda var: ggplot(mapping=aes('x', 'y', color=var, fill=var)) if var is not None else ggplot(mapping=aes('x', 'y'))
p_empty = lambda var: ggplot({'x': [], 'y': []}, get_mapping(var))
p_facet_nan = lambda var: ggplot({'x': [0, np.nan], 'y': [0, 0], 'g': ["A", "B"]}, get_mapping(var)) + facet_grid(x='g')
p_facet_cross = lambda var: ggplot({'x': [0, 0], 'y': [0, 0], 'g1': ["A", "B"], 'g2': ["C", "D"]}, get_mapping(var)) + facet_grid(x='g1', y='g2')
p_group = ggplot({'x': [0, np.nan], 'y': [0, 0], 'g': ["A", "B"]}, aes('x', 'y', color='g', fill='g'))
p_nan = lambda var: ggplot({'x': [np.nan], 'y': [np.nan]}, get_mapping(var))
p_lim = lambda var: ggplot({'x': [0], 'y': [0]}, get_mapping(var)) + xlim(1, 2)
gggrid([
p_working("..violinwidth..") + geom_sina(**sparams),
p_no_data("..violinwidth..") + geom_sina(**sparams),
p_empty("..violinwidth..") + geom_sina(**sparams),
p_facet_nan("..violinwidth..") + geom_sina(**sparams),
p_facet_cross("..violinwidth..") + geom_sina(**sparams),
p_group + geom_sina(**sparams),
p_nan("..violinwidth..") + geom_sina(**sparams),
p_lim("..violinwidth..") + geom_sina(**sparams),
], ncol=2)
def regression_data1():
np.random.seed(42)
return {
"val": np.random.randint(5, size=18),
}
ggplot(regression_data1(), aes(y="val")) + \
geom_violin(**vparams) + \
geom_sina(tooltips=layer_tooltips(["..y..", "..violinwidth.."]),
**sparams) + \
ggtitle("All points should be inside the violin")
ggplot(regression_data1(), aes(y="val")) + \
geom_violin(**vparams) + \
geom_sina(seed=0) + \
ggtitle("All points should be inside the plot panel")