ValueError:无法在Python中将序列复制到MatplotLib动画的数组轴

我正在读取一个不断更新的

data.txt

文件，并计算传入数据流的滚动标准偏差。我将其存储在

std

中。我有一个窗口大小为100的滚动标准差

我收到一个错误：

ValueError:无法将大小为78的序列复制到维度为1的阵列轴上

其中，大小对应于

std

数组中的项数。（当然，每次我跑步时，这个数字都会增加）

我想知道为什么我会收到这个ValueError，我正在寻找任何修复它的建议！当我刚刚对ax1.plot（xar，yar）进行分级时，动画效果很好。。但是，一旦我尝试用图表来绘制ax1.plot（xar，std），问题就出现了

data.txt

中的数据如下所示：

[0.0, 0.0078125, 0.015625][0.0, 0.0078125, 0.015625][0.0, 0.0078125, 0.015625][0.0, 0.0078125, 0.015625][0.0, 0.0078125, 0.015625][0.0, 0.0078125, 0.015625][0.0244140625, 0.0322265625, 0.9609375][0.0244140625, 0.0322265625, 0.9609375][0.0244140625, 0.0322265625, 0.9609375][0.0244140625, 0.0322265625, 0.9609375][0.0244140625, 0.0322265625, 0.9609375][0.0244140625, 0.0322265625, 0.9609375][0.0244140625, 0.0322265625, 0.9609375][0.0244140625, 0.0322265625, 0.9609375][0.0244140625, 0.0322265625, 0.9609375][0.0263671875, 0.0341796875, 1.0341796875][0.0263671875, 0.0341796875, 1.0341796875][0.0263671875, 0.0341796875, 1.0341796875][0.0263671875, 0.0341796875, 1.0341796875][0.0263671875, 0.0341796875, 1.0341796875][0.0263671875, 0.0341796875, 1.0341796875][0.0263671875, 0.0341796875, 1.0341796875][0.0263671875, 0.0341796875, 1.0341796875][0.0244140625, 0.0341796875, 1.0048828125][0.0244140625, 0.0341796875, 1.0048828125][0.0244140625, 0.0341796875, 1.0048828125][0.0244140625, 0.0341796875, 1.0048828125][0.0244140625, 0.0341796875, 1.0048828125][0.0244140625, 0.0341796875, 1.0048828125][0.0244140625, 0.0341796875, 1.0048828125][0.0244140625, 0.0341796875, 1.0048828125][0.0244140625, 0.0341796875, 1.0048828125][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.033203125, 1.009765625][0.025390625, 0.033203125, 1.009765625][0.025390625, 0.033203125, 1.009765625][0.025390625, 0.033203125, 1.009765625][0.025390625, 0.033203125, 1.009765625][0.025390625, 0.033203125, 1.009765625][0.025390625, 0.033203125, 1.009765625][0.025390625, 0.033203125, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625]

fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)

def animate(i):
    data = pd.read_csv("C:\\Users\\Desktop\\data.txt", sep="\[|\]\[|\]",engine = 'python', header = None)
    data = data.iloc[0]
    data = data.astype(str).apply(lambda x: x.split(',')[-1]).astype(float)
    data.pop(0)
    xar = []
    yar = []
    std = []
    for j in range(len(data)):
        xar.append(j)
    for k in range(len(data)):
        yar.append(data.iloc[k])
    yar = pd.DataFrame(yar)
    std.append(pd.rolling_std(yar, 100))
    ax1.clear()
    ax1.plot(xar,std)
ani = animation.FuncAnimation(fig, animate, interval=.01)
plt.show()

我目前的代码如下：

[0.0, 0.0078125, 0.015625][0.0, 0.0078125, 0.015625][0.0, 0.0078125, 0.015625][0.0, 0.0078125, 0.015625][0.0, 0.0078125, 0.015625][0.0, 0.0078125, 0.015625][0.0244140625, 0.0322265625, 0.9609375][0.0244140625, 0.0322265625, 0.9609375][0.0244140625, 0.0322265625, 0.9609375][0.0244140625, 0.0322265625, 0.9609375][0.0244140625, 0.0322265625, 0.9609375][0.0244140625, 0.0322265625, 0.9609375][0.0244140625, 0.0322265625, 0.9609375][0.0244140625, 0.0322265625, 0.9609375][0.0244140625, 0.0322265625, 0.9609375][0.0263671875, 0.0341796875, 1.0341796875][0.0263671875, 0.0341796875, 1.0341796875][0.0263671875, 0.0341796875, 1.0341796875][0.0263671875, 0.0341796875, 1.0341796875][0.0263671875, 0.0341796875, 1.0341796875][0.0263671875, 0.0341796875, 1.0341796875][0.0263671875, 0.0341796875, 1.0341796875][0.0263671875, 0.0341796875, 1.0341796875][0.0244140625, 0.0341796875, 1.0048828125][0.0244140625, 0.0341796875, 1.0048828125][0.0244140625, 0.0341796875, 1.0048828125][0.0244140625, 0.0341796875, 1.0048828125][0.0244140625, 0.0341796875, 1.0048828125][0.0244140625, 0.0341796875, 1.0048828125][0.0244140625, 0.0341796875, 1.0048828125][0.0244140625, 0.0341796875, 1.0048828125][0.0244140625, 0.0341796875, 1.0048828125][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.033203125, 1.009765625][0.025390625, 0.033203125, 1.009765625][0.025390625, 0.033203125, 1.009765625][0.025390625, 0.033203125, 1.009765625][0.025390625, 0.033203125, 1.009765625][0.025390625, 0.033203125, 1.009765625][0.025390625, 0.033203125, 1.009765625][0.025390625, 0.033203125, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.0263671875, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.0107421875][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625][0.025390625, 0.0341796875, 1.009765625]

fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)

def animate(i):
    data = pd.read_csv("C:\\Users\\Desktop\\data.txt", sep="\[|\]\[|\]",engine = 'python', header = None)
    data = data.iloc[0]
    data = data.astype(str).apply(lambda x: x.split(',')[-1]).astype(float)
    data.pop(0)
    xar = []
    yar = []
    std = []
    for j in range(len(data)):
        xar.append(j)
    for k in range(len(data)):
        yar.append(data.iloc[k])
    yar = pd.DataFrame(yar)
    std.append(pd.rolling_std(yar, 100))
    ax1.clear()
    ax1.plot(xar,std)
ani = animation.FuncAnimation(fig, animate, interval=.01)
plt.show()

---

出现错误的原因是

std

成为数据帧的列表（一个元素）

pd.rolling_std（）

已经为您提供了一个数据帧，然后您将其附加到列表中。如果您直接分配它，它会工作得更好：

std = pd.rolling_std(yar, 100)

但是，当运行

pd.rolling_std（）

时，会发出弃用警告。因此，这条线应该是：

std = yar.rolling(window=100,center=False).std()

此外，对于如何生成

xar

和

yar

，还可以进行一些简化

xar

只是一个范围，

yar

包含

数据的所有元素，允许您编写：

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.animation as animation

def animate(i):
    data = pd.read_csv("C:\\Users\\Desktop\\data.txt", sep="\[|\]\[|\]",engine = 'python', header = None)
    data = data.iloc[0]
    data = data.astype(str).apply(lambda x: x.split(',')[-1]).astype(float)
    data.pop(0)
    xar = range(len(data))
    yar = pd.DataFrame(data)
    std = yar.rolling(window=100,center=False).std()
    ax1.clear()
    ax1.plot(xar,std)

fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
ani = animation.FuncAnimation(fig, animate, interval=.01)
plt.show()

其中的
animate（）
已经稍微简化了。
太棒了！这项工作做得很出色。谢谢你的支持！像你这样的人让这个社区非常棒。在动画的一开始，有一个很大的尖峰。我怎样才能删除动画中的初始尖峰？我不知道您的确切用例，所以我不知道。但是，当使用滚动平均值时，在有足够多的数据点获得第一个平均值（在上面的代码中为100）之前，在开始时有一堆nan（不是数字）值。因此，当您碰到100个元素时，可能会有一个凹凸，从nan值变为实际值。你们看到的可能是这个吗？这意味着对它并没有什么可做的，因为它是数据本身的属性。可以在初始平均值中使用较少的值，从1增加到100，但我不知道这是否更好。这需要更多的逻辑。也许你可以在这里提出一个后续问题来支持我：