kreślenie pojedyncze punkty na wykresie

Question

mam działkę skrzypce, które wygląda tak:

Chcę wykreślić kilka pojedynczych kropek (lub linie, krzyże, punkty w zależności co jest najłatwiejsze) na każdy x -wartość na szczycie skrzypiec, na przykład:

Jak mogę to zabrać?

Jest to kod do tworzenia działek skrzypcowe (patrz Violin Plot with Matplotlib)

from matplotlib.pyplot import figure, show 
from scipy.stats import gaussian_kde 
from numpy.random import normal 
from numpy import arange 

def violin_plot(ax, data, pos, bp=False): 
    '''                                               
    create violin plots on an axis                                        
    ''' 
    dist = max(pos)-min(pos) 
    w = min(0.15*max(dist,1.0),0.5) 
    for d,p in zip(data,pos): 
     k = gaussian_kde(d) #calculates the kernel density                                  
     m = k.dataset.min() #lower bound of violin                                    
     M = k.dataset.max() #upper bound of violin                                    
     x = arange(m,M,(M-m)/100.) # support for violin                                   
     v = k.evaluate(x) #violin profile (density curve)                                   
     v = v/v.max()*w #scaling the violin to the available space                                
     ax.fill_betweenx(x,p,v+p,facecolor='y',alpha=0.3) 
     ax.fill_betweenx(x,p,-v+p,facecolor='y',alpha=0.3) 
    if bp: 
     ax.boxplot(data,notch=1,positions=pos,vert=1) 

if __name__=="__main__": 
    pos = range(5) 
    data = [normal(size=100) for i in pos] 
    fig=figure() 
    ax = fig.add_subplot(111) 
    violin_plot(ax,data,pos,bp=1) 
    fig.savefig("violins.gif") 

Answer 1

Wystarczy wykreślić dodatkowe informacje tuż po drugiej działki:

from matplotlib.pyplot import figure, show 
from scipy.stats import gaussian_kde 
from numpy.random import normal 
from numpy import arange 

def violin_plot(ax, data, pos, bp=False): 
    ''' 
    create violin plots on an axis 
    ''' 
    dist = max(pos)-min(pos) 
    w = min(0.15*max(dist,1.0),0.5) 
    for d,p in zip(data,pos): 
     k = gaussian_kde(d) #calculates the kernel density 
     m = k.dataset.min() #lower bound of violin 
     M = k.dataset.max() #upper bound of violin 
     x = arange(m,M,(M-m)/100.) # support for violin 
     v = k.evaluate(x) #violin profile (density curve) 
     v = v/v.max()*w #scaling the violin to the available space 
     ax.fill_betweenx(x,p,v+p,facecolor='y',alpha=0.3) 
     ax.fill_betweenx(x,p,-v+p,facecolor='y',alpha=0.3) 
    if bp: 
     ax.boxplot(data,notch=1,positions=pos,vert=1) 

if __name__=="__main__": 
    pos = range(5) 
    data = [normal(size=100) for i in pos] 
    fig=figure() 
    ax = fig.add_subplot(111) 
    violin_plot(ax,data,pos,bp=1) 

    data_x = [0, 1, 2, 2, 3, 4] 
    data_y = [1.5, 1., 0.7, 2.5, 1, 1.5] 
    ax.plot(data_x, data_y, 'or') 
    fig.savefig("violins.gif") 

co daje