I try to write script ploting live data from real object, but I need more efficient way. I think the problem is to storage always the same number of samples to plot, and collect and plot it simultaneously. I got some code, but I'm not fully happy with it.
import time
from math import sin, cos
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
x_value = 0
x_list = []
y1_list = []
y2_list = []
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
plt.rcParams['animation.html'] = 'jshtml'
fig, (ax1, ax2) = plt.subplots(2,1)
ax1.set_title("Power supply parameters")
ax1.set(ylabel='Voltage [V]')
ax2.set(xlabel='sample [n]', ylabel='Current [A]')
ax1.set_ylim(-1.5, 1.5)
ax2.set_ylim(-1.5,1.5)
ax1.grid(1)
ax2.grid(1)
box1 = ax1.text(.87, 0.95, str(0), transform=ax1.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
box2 = ax2.text(0.87, 0.95, str(0), transform=ax2.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
def annotate(last_y1, last_y2):
box1.set_text(str(round(last_y1,3)))
box2.set_text(str(round(last_y2,3)))
def data_gen(x_value):
x_value += 0.2
x_value = round(x_value,3)
total_1 = round(sin(x_value),3)
total_2 = round(cos(x_value),3)
time.sleep(0.1)
return (x_value,total_1,total_2)
def run(i):
global x_value ,x_list,y1_list, y2_list
x, y1, y2 = data_gen(x_value)
x_value += 0.2
if len(x_list) <=20:
x_list.append(x)
y1_list.append(y1)
y2_list.append(y2)
else:
x_list.append(x)
x_list.pop(0)
y1_list.append(y1)
y1_list.pop(0)
y2_list.append(y2)
y2_list.pop(0)
ax1.plot(x_list, y1_list, color='b')
ax2.plot(x_list, y2_list, color='r')
ax1.set_xlim(left=max(0, x_list[-1] - 50), right=x_list[-1] + 10)
ax2.set_xlim(left=max(0, x_list[-1] - 50), right=x_list[-1] + 10)
annotate(y1_list[-1], y2_list[-1])
ani = FuncAnimation(fig, run, interval=10)
plt.show()
I was wondering about using memoryview() to create some kind of buffer for the samples, cause appending and poping from list over and over seems to be too slow, but I'm not sure how to use it.
I will be greatefull for advices :)
[EDIT] Below fixed version for the future :)
import time
from math import sin, cos
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation
from matplotlib.lines import Line2D
x_value = 0
x_list = []
y1_list = []
y2_list = []
props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
plt.rcParams['animation.html'] = 'jshtml'
fig, (ax1, ax2) = plt.subplots(2,1)
ax1.set_title("Power supply parameters")
ax1.set(ylabel='Voltage [V]')
ax2.set(xlabel='sample [n]', ylabel='Current [A]')
ax1.set_ylim(-1.5, 1.5)
ax2.set_ylim(-1.5,1.5)
ax1.grid(1)
ax2.grid(1)
line1 = Line2D([0],[0.0],color='blue')
ax1.add_line(line1)
line2 = Line2D([0],[0.0],color='red')
ax2.add_line(line2)
box1 = ax1.text(.87, 0.95, str(0), transform=ax1.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
box2 = ax2.text(0.87, 0.95, str(0), transform=ax2.transAxes, fontsize=14,
verticalalignment='top', bbox=props)
def annotate(last_y1, last_y2):
box1.set_text(str(round(last_y1,3)))
box2.set_text(str(round(last_y2,3)))
def data_gen(x_value):
x_value = round(x_value,3)
total_1 = round(sin(x_value),3)
total_2 = round(cos(x_value),3)
return (x_value,total_1,total_2)
def run(i):
global x_value ,x_list,y1_list, y2_list
x, y1, y2 = data_gen(x_value)
x_value += 0.2
if len(x_list) <=200:
x_list.append(x)
y1_list.append(y1)
y2_list.append(y2)
else:
x_list.append(x)
x_list.pop(0)
y1_list.append(y1)
y1_list.pop(0)
y2_list.append(y2)
y2_list.pop(0)
ax1.set_xlim(left=max(0, x_list[-1] - 20), right=x_list[-1] + 10)
ax2.set_xlim(left=max(0, x_list[-1] - 20), right=x_list[-1] + 10)
line1.set_data(x_list,y1_list)
line2.set_data(x_list,y2_list)
annotate(y1_list[-1], y2_list[-1])
ani = FuncAnimation(fig, run, interval=1)
plt.show()
I'm going to go on a limb and argue that the way you are storing your data is probably the least time consuming part of your code.
One major issue is that you are creating new lines at every iteration, while you should be creating 2 Line2D objects and update their data (using Line2D.set_data()) instead. See for example https://matplotlib.org/gallery/animation/unchained.html