Python利用matplotlib实现动态可视化详解

from collections import deque
# Create a deque with a maximum length of 3
data_points = deque(maxlen=3)
# Add new data points to the deque
data_points.append(40)
data_points.append(60)
data_points.append(30)
# display the data points in the deque
print(data_points)  # deque([40, 60, 30], maxlen=3)
# Add more data points to the deque
data_points.append(13)
data_points.append(99)
# The oldest data point is/are automatically 
# removed from front of queue.
# deque([30, 13, 99], maxlen=3)
print(data_points)  

from collections import deque
import random
# Create a deque with fixed length 5
data_points = deque(maxlen=5)
# Generate and append data points to the deque
# we iterate 2 extra times to demonstrate how 
# queue removes values from front
for i in range(7):
     # generate random numbers between 0 
    # to 100 (both inclusive)
    new_data_point = random.randint(0, 100)
    data_points.append(new_data_point)
    print(data_points)

from collections import deque
import random
# Create a deque with fixed length
data_points = deque(maxlen=5)
# Append 5 data points to the deque at once using extend method.
data_points.extend([1, 2, 3, 4, 5])
# Print the deque before removing the first element
print("Deque before removing the first element:", data_points)
# Remove the first element from the deque
data_points.popleft()
# Print the deque after removing the first element
print("Deque after removing the first element:", data_points)

import matplotlib.pyplot as plt
from collections import deque
import random
# Create a fixed-length deque of size 50 to store the data points
data_points = deque(maxlen=50)
# Create an empty plot
fig, ax = plt.subplots()
line = ax.plot([])
# Set the x-axis and y-axis limits to 100
ax.set_xlim(0, 100)
ax.set_ylim(0, 100)
# Iterate through 50 data points and update the plot
for i in range(50):
    # Generate and add data points to the deque
    new_x = i
    # generate a random number between 0 to 100 for y axis
    new_y = random.randint(0, 100)
    data_points.append((new_x, new_y))
    # Update the plot with the new data points
    x_values = [x for x, y in data_points]
    y_values = [y for x, y in data_points]
    line.set_data(x_values, y_values)
    # pause the plot for 0.01s before next point is shown
    plt.pause(0.01)
# Show the plot
plt.show()

import matplotlib.pyplot as plt
from collections import deque
import random
# Create a fixed-length deque to store the data points
data_points = deque(maxlen=50)
# Create an empty plot
fig, ax = plt.subplots()
line, = ax.plot([])
# Set the x-axis and y-axis limits
ax.set_xlim(0, 100)
ax.set_ylim(0, 100)
# Iterate through the data points and update the plot
for i in range(100):
	# Generate and add data points to the deque
	new_x = i
	new_y = random.randint(0, 100)
	data_points.append((new_x, new_y))
	# Update the plot with the new data points
	x_values = [x for x, y in data_points]
	y_values = [y for x, y in data_points]
	line.set_data(x_values, y_values)
	plt.pause(0.01)
	# Clear the plot for the next set of values
	line.set_data([], [])
# Show the plot
plt.show()

import matplotlib.pyplot as plt
from collections import deque
import random
# Create a fixed-length deque of length 50 to store the data points
data_points = deque(maxlen=50)
# Create an empty plot
fig, ax = plt.subplots()
# Set the x-axis and y-axis limits to 100
ax.set_xlim(0, 100)
ax.set_ylim(0, 100)
# Create a scatter plot to visualize the data points
scatter = ax.scatter([], [])
# Iterate through the data points and update the scatter plot
for i in range(100):
    # Generate and add data points to the deque
    new_x = i
    new_y = random.randint(0, 100)
    data_points.append((new_x, new_y))
    # Update the scatter plot with the new data points
    x_values = [x for x, y in data_points]
    y_values = [y for x, y in data_points]
    scatter.set_offsets(list(zip(x_values, y_values)))
    plt.pause(0.01)
# Show the plot
plt.show()

import matplotlib.pyplot as plt
from collections import deque
import random
from matplotlib.animation import FuncAnimation
# Create a fixed-length deque of length 50 to store the data points
data_points = deque(maxlen=50)
# Create an empty plot
fig, ax = plt.subplots()
line, = ax.plot([])
# Set the x-axis and y-axis limits to 100
ax.set_xlim(0, 100)
ax.set_ylim(0, 100)
# Create a scatter plot to visualize the data points
scatter = ax.scatter([], [])
# Iterate through the data points and update the scatter plot
for i in range(100):
	# Generate and add data points to the deque
	new_x = i
	new_y = random.randint(0, 100)
	data_points.append((new_x, new_y))
	# Update the scatter plot with the new data points
	x_values = [x for x, y in data_points]
	y_values = [y for x, y in data_points]
	scatter.set_offsets(list(zip(x_values, y_values)))
	line.set_data(x_values, y_values)
	plt.pause(0.01)
# Save the animation as an animated GIF
plt.show()