#!/usr/bin/python
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="white", rc={"axes.facecolor": (0, 0, 0, 0)})
# Create the data
rs = np.random.RandomState(1979)
x = rs.randn(500)
g = np.tile(list("ABCDEFGHIJ"), 50)
df = pd.DataFrame(dict(x=x, g=g))
m = df.g.map(ord)
df["x"] += m
# Initialize the FacetGrid object
pal = sns.cubehelix_palette(10, rot=-.25, light=.7)
g = sns.FacetGrid(df, row="g", hue="g", aspect=15, size=.5, palette=pal)
# Draw the densities in a few steps
g.map(sns.kdeplot, "x", clip_on=False, shade=True, alpha=1, lw=1.5, bw=.2)
g.map(sns.kdeplot, "x", clip_on=False, color="w", lw=2, bw=.2)
g.map(plt.axhline, y=0, lw=2, clip_on=False)
# Define and use a simple function to label the plot in axes coordinates
def label(x, color, label):
ax = plt.gca()
ax.text(0, .2, label, fontweight="bold", color=color,
ha="left", va="center", transform=ax.transAxes)
g.map(label, "x")
# Set the subplots to overlap
g.fig.subplots_adjust(hspace=-.25)
# Remove axes details that don't play will with overlap
g.set_titles("")
g.set(yticks=[])
g.despine(bottom=True, left=True)
import numpy as np
import pylab as plt
import itertools
k = 20
X = np.linspace(0, 20, 500)
Y = np.zeros((k,X.size))
# Add some fake data
MU = np.random.random(k)
for n in xrange(k):
Y[n] += np.exp(-(X-MU[n]*n)**2 / (1+n/3))
Y *= 50
# Add some outliers for show
Y += 2*np.random.random(Y.shape)
displace = Y.max()/4
# Add a cutoff
Y[Y<1.0] = None
face_colors = itertools.cycle(["#D3D820", "#C9CC54",
"#D7DA66", "#FDFE42"])
fig = plt.figure()
ax = fig.add_subplot(111, axisbg='black')
ax.xaxis.set_visible(False)
ax.yaxis.set_visible(False)
for n,y in enumerate(Y):
# Vertically displace each plot
y0 = np.ones(y.shape) * n * displace
y1 = y + n*displace
plt.fill_between(X, y0,y1,lw=1,
facecolor=face_colors.next(),
zorder=len(Y)-n)
plt.show()
Seaborn也提供了一个非常简洁的例子:
但他们称之为欢乐/山脊图:https://seaborn.pydata.org/examples/kde_ridgeplot.html
我会坚持使用平面二维绘图,并以设定的垂直量替换每个级别。您必须播放这些级别(在下面的代码中我称之为
displace
)才能正确地看到异常值,但这在复制目标映像方面做得非常好。我认为,关键是将“零”值设置为None
,这样pylab就不会绘制它们。在{1美元^
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