如何僅使用Matplotlib重繪更新的數據

Question

如何重新繪製更新的數據，而不是從頭開始清除整個圖形和繪圖？

（我不希望這樣的事情）

到目前爲止我的代碼是類似的東西：（儘管，在這個例子中，我用我在互聯網上找到，而不是使用實際數據的一些隨機函數我使用）

import matplotlib 
matplotlib.use("TkAgg") 
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg 
#from matplotlib.figure import Figure 
import matplotlib.animation as animation 
from matplotlib import style 
from matplotlib import pyplot as plt 
from matplotlib.lines import Line2D 

import numpy as np 


f = plt.Figure() 


class SubplotAnimation(animation.TimedAnimation): 
    def __init__(self): 

     fig= plt.figure() 

     ax1 = fig.add_subplot(2, 1, 1) 
     par = ax1.twinx() 

     ax2 = fig.add_subplot(2, 1, 2) 
     par2 = ax2.twinx() 

     #********************** FUNCTIONS **************************************# 
     dt = 1 
     self.t = np.arange(0, 40, dt) 
     self.nse1 = np.random.randn(len(self.t))     # white noise 1 
     self.nse2 = np.random.randn(len(self.t))     # white noise 2 
     self.nse3 = np.random.randn(len(self.t))     # white noise 2 
     self.r = np.exp(-self.t/0.05) 

     cnse1 = np.convolve(self.nse1, self.r, mode='same')*dt # colored noise 1 
     cnse2 = np.convolve(self.nse2, self.r, mode='same')*dt # colored noise 2 
     cnse3 = np.convolve(self.nse3, self.r, mode='same')*dt # colored noise 2 

     # two signals with a coherent part and a random part 
     self.x = 0.01*np.sin(2*np.pi*10*self.t) + cnse1 
     self.y = 0.01*np.sin(2*np.pi*10*self.t) + cnse2 
     self.k = 0.01*np.sin(2*np.pi*10*self.t) + cnse3 
     self.z = 10 * self.t 


     ax1.set_xlabel('x') 
     ax1.set_ylabel('y') 
     par.set_ylabel("y2") 
     ###################### LINES ############################ 
     self.line1 = Line2D([], [], color='red', linewidth=2) 
     self.line1a = Line2D([], [], color='black', linewidth=1) 
     ax1.add_line(self.line1) 
     par.add_line(self.line1a) 
     ax1.set_xlim(0, 35) 
     ax1.set_ylim(-10, 10) 
     par.set_ylim(-10, 10) 

     ax2.set_xlabel('x') 
     ax2.set_ylabel('y') 
     par2.set_ylabel("y2") 
     ###################### LINES ############################ 
     self.line2 = Line2D([], [], color='black') 
     self.line2a = Line2D([], [], color='red') 
     self.line2b = Line2D([], [], color='grey') 
     ax2.add_line(self.line2) 
     par2.add_line(self.line2a) 
     par2.add_line(self.line2b) 
     ax2.set_xlim(0, 35) 
     ax2.set_ylim(-0.5, 0.5) 
     par2.set_ylim(-0.3, 0.3) 

     animation.TimedAnimation.__init__(self, fig, interval=30, blit=True) 

    def _draw_frame(self, framedata): 
     i = framedata 

     self.line1.set_data(self.t[:i], self.x[:i]) 
     self.line1a.set_data(self.t[:i], self.y[:i]) 

     self.line2.set_data(self.t[:i], self.x[:i]) 
     self.line2a.set_data(self.t[:i], self.y[:i]) 
     self.line2b.set_data(self.t[:i], self.k[:i]) 

     self._drawn_artists = [self.line1, self.line1a, 
      self.line2, self.line2a, self.line2b] 

    def new_frame_seq(self): 
     return iter(range(self.t.size)) 

    def _init_draw(self): 
     lines = [self.line1, self.line1a, 
      self.line2, self.line2a, self.line2b] 
     for l in lines: 
      l.set_data([], []) 

ani = SubplotAnimation() 
plt.show() 

Answer 1

所以我還挺解決了這個問題，但我也改變了代碼，以便......看一看我的新的，讓我知道如果您有任何建議，使其更好看： （你可能會發現代碼中沒有使用的東西，但那是因爲我試圖簡化我的代碼並取出一些行等）

import matplotlib 
matplotlib.use("TkAgg") #the back-end of matplotlib 
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg 
import Tkinter as tk 

import numpy as np 
import matplotlib.pyplot as plt 
import matplotlib.animation as animation 
from matplotlib.lines import Line2D 
from matplotlib import style 

ROTOR_SPEED = 100 
PUMP_STATUS = False 


LARGE_FONT = ("Verdana", 12) 
NORM_FONT = ("Verdana", 10) 
SMALL_FONT = ("Verdana", 8) 

style.use("ggplot") 


fig = plt.figure() 
fig.set_size_inches(10, 7) 
top_graph = plt.subplot2grid((2, 2), (0, 0), colspan=2) 



line1 = Line2D([], [], color='black') 

line2 = Line2D([], [], color='red', linewidth=2) 

top_graph.add_line(line1) 

top_graph.set_xlim(0, 24) 
top_graph.set_ylim(-20, 20) 

top_graph.set_aspect('equal', 'datalim') 

line, = top_graph.plot([], [], lw=1) 
line2, = top_graph.plot([], [], lw=3) 
xdata, ydata = [], [] 
xline, yline = [], [] 


def data_gen(): 
    t = data_gen.t 
    cnt = 0 
    height = 15 
    while cnt < 1000: 


     yield t, np.sin(2 * np.pi * t/10.), 2*[t]*(height), range(-height, height) 
     cnt+=1 
     t += 0.05 
data_gen.t = 0 

def run(data): 
    # update the data 
    t, y, t2, rang = data 

    repeats = t//24 

    run.index = int(round(t%24.0/0.05)) 

    if len(xdata) <= run.index: 
     xdata.append(t) 
     ydata.append(y) 
     xline = t2 
     yline = rang 

    else: 
     xdata[run.index] = t - (24 * repeats) 
     ydata[run.index] = y 

     xline = map(lambda (a, b): a-b, zip(t2, [24*repeats]*len(t2))) 
     yline = rang 



    line.set_data(xdata, ydata) 
    line2.set_data(xline, yline) 
    line2.set_color('white') 
    return line, line2 
run.index = 1 




class MainPage(tk.Tk): 

    def __init__(self, *args, **kwargs): 

     tk.Tk.__init__(self, *args, **kwargs) 

     container = tk.Frame(self) 

     container.pack(side="top", fill="both", expand = True) 

     container.grid_rowconfigure(0, weight=1) 
     container.grid_columnconfigure(0, weight=1) 



     #********** FRAMES*******# 

     self.frames = {} #empty.. 

     frame = GraphPage(container, self) 

     self.frames[GraphPage] = frame 

     frame.grid(row=0, column=0, sticky="nsew") 

     self.show_frame(GraphPage) 

    def show_frame(self, cont): 

     frame = self.frames[cont] 
     frame.tkraise() 



class GraphPage(tk.Frame): 

    def __init__(self, parent, controller): 
     tk.Frame.__init__(self,parent) 
     label = tk.Label(self, text="Example", font=LARGE_FONT) 
     label.grid(row=0, column=0, sticky='N') 

     canvas = FigureCanvasTkAgg(fig, self) 

     canvas.show() 
     canvas.get_tk_widget().grid(row=1, column=0, rowspan=6, columnspan=3, sticky='NSEW') 

app = MainPage() 
app.geometry("980x640") 
ani = animation.FuncAnimation(fig, run, data_gen, blit=True, interval=20, 
    repeat=False) 
app.mainloop()