Matplotlibを使用してOriginのウォーターフォールプロットを模倣する

Update: as you've now updated your question to make clearer what you're after, let me demonstrate three different ways to plot such data, which all have lots of pros and cons. The general gist (at least for me!) is that matplotlib is bad in 3D, especially when it comes to creating publishable figures (again, my personal opinion, your mileage may vary.)

What I did: I've used the original data behind the second image you've posted. In all cases, I used zorder and added polygon data (in 2D: fill_between(), in 3D: PolyCollection) to enhance the "3D effect", i.e. to enable "plotting in front of each other". The code below shows:

• plot_2D_a() uses color to indicate angle, hence keeping the original y-axis; though this technically can now only be used to read out the foremost line plot, it still gives the reader a "feeling" for the y scale.

• plot_2D_b() removes unnecessary spines/ticks and rather adds the angle as text labels; this comes closest to the second image you've posted

• plot_3D() uses mplot3d to make a "3D" plot; while this can now be rotated to analyze the data, it breaks (at least for me) when trying to zoom, yielding cut-off data and/or hidden axes.

結局、で滝のプロットを達成するための多くの方法がありmatplotlib、あなたはあなたが何を求めているかを自分で決める必要があります。個人的に、私はおそらく私たちがしたいplot_2D_a()ことはで簡単に再スケーリングすることができますので、ほとんどの時間、多かれ少なかれ「すべての3次元」も適切な軸を維持しながら（+カラーバー）読者ができるようにすること、関連するすべての情報を取得するあなたはどこかにそれを公開したら、静止画像として。

コード：

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()