更新：由于您已经更新了您的问题，以便更清楚地了解您的目标，让我演示三种不同的绘制此类数据的方法，它们都有很多优点和缺点。

总的要领(至少对我来说是)在3D中，{}是不好的，尤其是当涉及到创建可发布的数字时(，我个人认为，你的里程数可能会有所不同。)

我做了些什么：我使用了你发布的第二张图片后面的原始数据。在所有情况下，我使用zorder并添加多边形数据(2D:fill_between()，3D:PolyCollection)来增强“3D效果”，即启用“在彼此前面绘制”。下面的代码显示：plot_2D_a()使用颜色来表示角度，因此保留了原来的y轴；虽然这在技术上现在只能用于读出最前面的线图，但它仍然给读者一种对y刻度的“感觉”。在

plot_2D_b()删除不必要的刺/记号，而是将角度添加为文本标签；这与您发布的第二张图像最接近

plot_3D()使用mplot3d绘制一个“3D”图；虽然现在可以旋转它来分析数据，但在尝试缩放时它会中断(至少对我来说)，产生截止数据和/或隐藏轴。在

最后，有许多方法可以在matplotlib中实现瀑布图，你必须自己决定你要的是什么。就我个人而言，我可能大部分时间都是这样，因为它允许在“所有3维”中轻松地重新缩放，同时还保留了适当的轴(+颜色条)，一旦您将其作为静态图像发布到某个地方，读者就可以获得所有相关信息。在

代码：import pandas as pd

import matplotlib as mpl

import matplotlib.pyplot as plt

from mpl_toolkits.mplot3d import Axes3D

from matplotlib.collections import PolyCollection

import numpy as np

def offset(myFig,myAx,n=1,yOff=60):

dx, dy = 0., yOff/myFig.dpi

return myAx.transData + mpl.transforms.ScaledTranslation(dx,n*dy,myFig.dpi_scale_trans)

## taken from

## http://www.gnuplotting.org/data/head_related_impulse_responses.txt

df=pd.read_csv('head_related_impulse_responses.txt',delimiter="\t",skiprows=range(2),header=None)

df=df.transpose()

def plot_2D_a():

""" a 2D plot which uses color to indicate the angle"""

fig,ax=plt.subplots(figsize=(5,6))

sampling=2

thetas=range(0,360)[::sampling]

cmap = mpl.cm.get_cmap('viridis')

norm = mpl.colors.Normalize(vmin=0,vmax=360)

for idx,i in enumerate(thetas):

z_ind=360-idx ## to ensure each plot is "behind" the previous plot

trans=offset(fig,ax,idx,yOff=sampling)

xs=df.loc[0]

ys=df.loc[i+1]

## note that I am using both .plot() and .fill_between(.. edgecolor="None" ..)

# in order to circumvent showing the "edges" of the fill_between

ax.plot(xs,ys,color=cmap(norm(i)),linewidth=1, transform=trans,zorder=z_ind)

## try alpha=0.05 below for some "light shading"

ax.fill_between(xs,ys,-0.5,facecolor="w",alpha=1, edgecolor="None",transform=trans,zorder=z_ind)

cbax = fig.add_axes([0.9, 0.15, 0.02, 0.7]) # x-position, y-position, x-width, y-height

cb1 = mpl.colorbar.ColorbarBase(cbax, cmap=cmap, norm=norm, orientation='vertical')

cb1.set_label('Angle')

## use some sensible viewing limits

ax.set_xlim(-0.2,2.2)

ax.set_ylim(-0.5,5)

ax.set_xlabel('time [ms]')

def plot_2D_b():

""" a 2D plot which removes the y-axis and replaces it with text labels to indicate angles """

fig,ax=plt.subplots(figsize=(5,6))

sampling=2

thetas=range(0,360)[::sampling]

for idx,i in enumerate(thetas):

z_ind=360-idx ## to ensure each plot is "behind" the previous plot

trans=offset(fig,ax,idx,yOff=sampling)

xs=df.loc[0]

ys=df.loc[i+1]

## note that I am using both .plot() and .fill_between(.. edgecolor="None" ..)

# in order to circumvent showing the "edges" of the fill_between

ax.plot(xs,ys,color="k",linewidth=0.5, transform=trans,zorder=z_ind)

ax.fill_between(xs,ys,-0.5,facecolor="w", edgecolor="None",transform=trans,zorder=z_ind)

## for every 10th line plot, add a text denoting the angle.

# There is probably a better way to do this.

if idx%10==0:

textTrans=mpl.transforms.blended_transform_factory(ax.transAxes, trans)

ax.text(-0.05,0,u'{0}º'.format(i),ha="center",va="center",transform=textTrans,clip_on=False)

## use some sensible viewing limits

ax.set_xlim(df.loc[0].min(),df.loc[0].max())

ax.set_ylim(-0.5,5)

## turn off the spines

for side in ["top","right","left"]:

ax.spines[side].set_visible(False)

## and turn off the y axis

ax.set_yticks([])

ax.set_xlabel('time [ms]')

#

def plot_3D():

""" a 3D plot of the data, with differently scaled axes"""

fig=plt.figure(figsize=(5,6))

ax= fig.gca(projection='3d')

"""

adjust the axes3d scaling, taken from https://stackoverflow.com/a/30419243/565489

"""

# OUR ONE LINER ADDED HERE: to scale the x, y, z axes

ax.get_proj = lambda: np.dot(Axes3D.get_proj(ax), np.diag([1, 2, 1, 1]))

sampling=2

thetas=range(0,360)[::sampling]

verts = []

count = len(thetas)

for idx,i in enumerate(thetas):

z_ind=360-idx

xs=df.loc[0].values

ys=df.loc[i+1].values

## To have the polygons stretch to the bottom,

# you either have to change the outermost ydata here,

# or append one "x" pixel on each side and then run this.

ys[0] = -0.5

ys[-1]= -0.5

verts.append(list(zip(xs, ys)))

zs=thetas

poly = PolyCollection(verts, facecolors = "w", edgecolors="k",linewidth=0.5 )

ax.add_collection3d(poly, zs=zs, zdir='y')

ax.set_ylim(0,360)

ax.set_xlim(df.loc[0].min(),df.loc[0].max())

ax.set_zlim(-0.5,1)

ax.set_xlabel('time [ms]')

# plot_2D_a()

# plot_2D_b()

plot_3D()

plt.show()