如何使用 matplotlib FuncAnimation 顯示 numpy 數組的滑動窗口

我正在開發一種用於檢測信號峰值的簡單算法。為了對我的算法進行故障排除（並展示它），我想在整個信號持續時間內（即=時間點的20分鐘）觀察信號和檢測到的峰值。100Hz20000

我認為最好的方法是創建一個動畫圖，matplotlib.animation.FuncAnimation它會連續顯示信號滑動 1 個時間點及其在5幾秒的時間窗口（即500時間點）內的疊加峰值。信號存儲在 1D 中，numpy.ndarray而峰值信息存儲在numpy.ndarray包含峰值x和y坐標的2D中。

這是情節如何的“靜止框架”。

現在的問題是我不能用 FuncAnimation 來解決這個問題。

If my understanding is correct I need three main pieces: the init_func parameter, a function that create the empty frame upon which the plot is drawn, the func parameter, that is the function that actually create the plot for each frame, and the parameter frames which is defined in the help as Source of data to pass func and each frame of the animation.

Looking at examples of plots with FuncAnimation, I can only find use-cases in which the data to plot are create on the go, like here, or here, where the data to plot are created on the basis of the frame.

What I do not understand is how to implement this with data that are already there, but that are sliced on the basis of the frame. I would thus need the frame to work as a sort of sliding window, in which the first window goes from 0 to 499, the second from 1to 500 and so on until the end of the time-points in the ndarray, and an associated func that will select the points to plot on the basis of those frames. I do not know how to implement this.

I add the code to create a realistic signal, to simply detect the peaks and to plot the 'static' version of the plot I would like to animate:

import neurokit2 as nk
from sklearn.preprocessing import MinMaxScaler
import matplotlib.pyplot as plt
from scipy.signal import find_peaks
#create realistic data
data = nk.ecg_simulate(duration = 50, sampling_rate = 100, noise = 0.05,\
                       random_state = 1)
#scale data
scaler = MinMaxScaler()
scaled_arr = scaler.fit_transform(data.reshape(-1,1))
#find peaks
peak = find_peaks(scaled_arr.squeeze(), height = .66,\
        distance = 60, prominence = .5)
#plot
plt.plot(scaled_arr[0:500])
plt.scatter(peak[0][peak[0] < 500],\
         peak[1]['peak_heights'][peak[0] < 500],\
             color = 'red')