python – 如何检查图中是否存在colorbar

在这里,我们还要注意用户不希望从外部引用任何命名对象. (这没有多大意义,因为我们无论如何都需要使用plt,但是嘿..问题也是如此)

import matplotlib.pyplot as plt
import numpy as np

fig,ax = plt.subplots()
im = ax.pcolormesh(np.array(np.random.rand(2,2) ))
ax.plot(np.cos(np.linspace(0.2,1.8))+0.9,np.sin(np.linspace(0.2,c="k",lw=6)
ax.set_title("Title")
cbar = plt.colorbar(im)
cbar.ax.set_ylabel("Label")


for i in range(10):
    # inside this loop we should not access any variables defined outside
    #   why? no real reason,but questioner asked for it.
    #draw new colormesh
    im = plt.gcf().gca().pcolormesh(np.random.rand(2,2))
    #check if there is more than one axes
    if len(plt.gcf().axes) > 1: 
        # if so,then the last axes must be the colorbar.
        # we get its extent
        pts = plt.gcf().axes[-1].get_position().get_points()
        # and its label
        label = plt.gcf().axes[-1].get_ylabel()
        # and then remove the axes
        plt.gcf().axes[-1].remove()
        # then we draw a new axes a the extents of the old one
        cax= plt.gcf().add_axes([pts[0][0],pts[0][1],pts[1][0]-pts[0][0],pts[1][1]-pts[0][1]  ])
        # and add a colorbar to it
        cbar = plt.colorbar(im,cax=cax)
        cbar.ax.set_ylabel(label)
        # unfortunately the aspect is different between the initial call to colorbar 
        #   without cax argument. Try to reset it (but still it's somehow different)
        cbar.ax.set_aspect(20)
    else:
        plt.colorbar(im)

plt.show()

通常,更好的解决方案是对绘图中已存在的对象进行操作,并仅使用新数据更新它们.因此,我们无需移除和添加轴,并找到更清洁,更快速的解决方案.

import matplotlib.pyplot as plt
import numpy as np

fig,lw=6)
ax.set_title("Title")
cbar = plt.colorbar(im)
cbar.ax.set_ylabel("Label")


for i in range(10):
    data = np.array(np.random.rand(2,2) )
    im.set_array(data.flatten())
    cbar.set_clim(vmin=data.min(),vmax=data.max()) 
    cbar.draw_all() 
    plt.draw()

plt.show()

更新：

实际上,后一种从外部引用对象的方法甚至与提问者所希望的多进程方法一起工作.

所以,这是一个更新图形的代码,无需删除颜色条.

import matplotlib.pyplot as plt
import numpy as np
import multiprocessing
import time

fig,c="w",lw=6)
ax.set_title("Title")
cbar = plt.colorbar(im)
cbar.ax.set_ylabel("Label")
tx = ax.text(0.2,0.8,"",fontsize=30,color="w")
tx2 = ax.text(0.2,0.2,color="w")

def do(number):
    start = time.time()
    tx.set_text(str(number))
    data = np.array(np.random.rand(2,2)*(number+1) )
    im.set_array(data.flatten())
    cbar.set_clim(vmin=data.min(),vmax=data.max()) 
    tx2.set_text("{m:.2f} < {ma:.2f}".format(m=data.min(),ma= data.max() )) 
    cbar.draw_all() 
    plt.draw()
    plt.savefig("multiproc/{n}.png".format(n=number))
    stop = time.time()

    return np.array([number,start,stop])


if __name__ == "__main__":
    multiprocessing.freeze_support()

    some_list = range(0,50)
    num_proc = 5
    p = multiprocessing.Pool(num_proc)
    nu = p.map(do,some_list)
    nu = np.array(nu)

    plt.close("all")
    fig,ax = plt.subplots(figsize=(16,9))
    ax.barh(nu[:,0],nu[:,2]-nu[:,1],height=np.ones(len(some_list)),left=nu[:,align="center")
    plt.show()

(最后的代码显示了一个时间表,可以看到确实发生了多处理)