如何绘制matplotlib中的多个X轴或Y轴？

Question

我目前正在使用matplotlib在x轴上绘制2或3个其他测量（有时是分类）的测量。目前，我将x轴上的数据分组为元组并在绘图之前对它们进行排序......结果看起来像下面的左图。我想要做的就是如右图所示，用多个x轴绘制数据。 “治疗”x轴标签的分组将会锦上添花。

Answer 1

首先，冷静的问题！ matplotlib> = 1.0.0绝对有可能。 （新刺功能允许）

它需要巫术的公平一点，但...我的例子远非完美，但希望它有一定的道理：

import numpy as np 
import matplotlib.pyplot as plt 
import matplotlib as mpl 

def main(): 
    #-- Generate some data ---------------------------------------------------- 
    nx = 10 
    x = np.linspace(0, 2*np.pi, 10) 
    y = 2 * np.sin(x) 

    groups = [('GroupA', (x[0], x[nx//3])), 
       ('GroupB', (x[-2*nx//3], x[2*nx//3])), 
       ('GroupC', (x[-nx//3], x[-1]))] 

    #-- Plot the results ------------------------------------------------------ 
    fig = plt.figure() 
    ax = fig.add_subplot(111) 

    # Give ourselves a bit more room at the bottom 
    plt.subplots_adjust(bottom=0.2) 

    ax.plot(x,y, 'k^') 

    # Drop the bottom spine by 40 pts 
    ax.spines['bottom'].set_position(('outward', 40)) 

    # Make a second bottom spine in the position of the original bottom spine 
    make_second_bottom_spine(label='Treatment') 

    # Annotate the groups 
    for name, xspan in groups: 
     annotate_group(name, xspan) 

    plt.xlabel('Dose') 
    plt.ylabel('Response') 
    plt.title('Experimental Data') 

    plt.show() 


def annotate_group(name, xspan, ax=None): 
    """Annotates a span of the x-axis""" 
    def annotate(ax, name, left, right, y, pad): 
     arrow = ax.annotate(name, 
       xy=(left, y), xycoords='data', 
       xytext=(right, y-pad), textcoords='data', 
       annotation_clip=False, verticalalignment='top', 
       horizontalalignment='center', linespacing=2.0, 
       arrowprops=dict(arrowstyle='-', shrinkA=0, shrinkB=0, 
         connectionstyle='angle,angleB=90,angleA=0,rad=5') 
       ) 
     return arrow 
    if ax is None: 
     ax = plt.gca() 
    ymin = ax.get_ylim()[0] 
    ypad = 0.01 * np.ptp(ax.get_ylim()) 
    xcenter = np.mean(xspan) 
    left_arrow = annotate(ax, name, xspan[0], xcenter, ymin, ypad) 
    right_arrow = annotate(ax, name, xspan[1], xcenter, ymin, ypad) 
    return left_arrow, right_arrow 

def make_second_bottom_spine(ax=None, label=None, offset=0, labeloffset=20): 
    """Makes a second bottom spine""" 
    if ax is None: 
     ax = plt.gca() 
    second_bottom = mpl.spines.Spine(ax, 'bottom', ax.spines['bottom']._path) 
    second_bottom.set_position(('outward', offset)) 
    ax.spines['second_bottom'] = second_bottom 

    if label is not None: 
     # Make a new xlabel 
     ax.annotate(label, 
       xy=(0.5, 0), xycoords='axes fraction', 
       xytext=(0, -labeloffset), textcoords='offset points', 
       verticalalignment='top', horizontalalignment='center') 

if __name__ == '__main__': 
    main() 

Answer 2

乔的例子很好。我也会投我的。几个小时前我正在研究它，但后来不得不跑开会议。它从here盗取。

import matplotlib.pyplot as plt 
import matplotlib.ticker as ticker 

## the following two functions override the default behavior or twiny() 
def make_patch_spines_invisible(ax): 
    ax.set_frame_on(True) 
    ax.patch.set_visible(False) 
    for sp in ax.spines.itervalues(): 
     sp.set_visible(False) 

def make_spine_invisible(ax, direction): 
    if direction in ["right", "left"]: 
     ax.yaxis.set_ticks_position(direction) 
     ax.yaxis.set_label_position(direction) 
    elif direction in ["top", "bottom"]: 
     ax.xaxis.set_ticks_position(direction) 
     ax.xaxis.set_label_position(direction) 
    else: 
     raise ValueError("Unknown Direction : %s" % (direction,)) 

    ax.spines[direction].set_visible(True) 

data = (('A',0.01),('A',0.02),('B',0.10),('B',0.20)) # fake data 

fig = plt.figure(1) 
sb = fig.add_subplot(111) 
sb.xaxis.set_major_locator(ticker.FixedLocator([0,1,2,3])) 

sb.plot([i[1] for i in data],"*",markersize=10) 
sb.set_xlabel("dose") 

plt.subplots_adjust(bottom=0.17) # make room on bottom 

par2 = sb.twiny() # create a second axes 
par2.spines["bottom"].set_position(("axes", -.1)) # move it down 

## override the default behavior for a twiny axis 
make_patch_spines_invisible(par2) 
make_spine_invisible(par2, "bottom") 
par2.set_xlabel("treatment") 

par2.plot([i[1] for i in data],"*",markersize=10) #redraw to put twiny on same scale 
par2.xaxis.set_major_locator(ticker.FixedLocator([0,1,2,3])) 
par2.xaxis.set_ticklabels([i[0] for i in data]) 

plt.show() 

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