python - 如何使用电影动画正确引用无花果和斧头

Question

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根据上面的数据，我想使用 matplotlib 和 moviepy 制作动画群图。然而，通过每帧的以下代码，我得到了额外的点，但保留了旧的点:

import numpy as np
import pandas as pd
from scipy.stats import gaussian_kde
from matplotlib import pyplot as plt
from moviepy.editor import VideoClip
from moviepy.video.io.bindings import mplfig_to_npimage
 
fps = 10
   
df = pd.DataFrame(data_dict)
fig, ax = plt.subplots(1, 1)

def swarm_plot(x):
    kde = gaussian_kde(x)
    density = kde(x)  # estimate the local density at each datapoint
        
    # ax.clear()
    jitter = np.random.rand(*x.shape) - .5
    # scale the jitter by the KDE estimate and add it to the centre x-coordinate
    y = 1 + (density * jitter * 1000 * 2)
    ax.scatter(x, y, s = 30, c = 'g')
    # plt.axis('off')
    return fig
        
def draw_swarmplot(t):
    f = int(t * fps)
    fig, ax = plt.subplots(1, 1)
    dff = df.loc[f]
   
    return mplfig_to_npimage(swarm_plot(dff['x']))
        
anim = VideoClip(lambda x: draw_swarmplot(x), duration=2)
anim.to_videofile('swarmplot.mp4', fps=fps)

因此，所有点都会在动画中累积。我相信这是因为 matplotlib fig 和 ax 对象使用不正确。但是，在draw_swarmplot函数中，我在每次迭代后重置fig和ax对象。尽管如此，我仍然需要在两个函数之外初始化 fig 和 ax 以避免出现有关 ax 对象的错误。因此，我的问题是应如何引用 fig 和 ax 以及我缺少什么导致我的代码无法按预期工作？

Answer 1

fig 和 ax 变量的范围受 Variable Scope 的约束。和 Crossing Boundaries Variables and Scope 的部分文档。具体相关，

When we use the assignment operator (=) inside a function, its default behaviour is to create a new local variable – unless a variable with the same name is already defined in the local scope.

请注意，“除非已定义同名变量”实际上仅限于本地变量。正如 example 中进一步阐明的那样,

a = 0
def my_function():
    a = 3
    print(a)

my_function()
print(a)

将输出

3
0

这是因为

By default, the assignment statement creates variables in the local scope. So the assignment inside the function does not modify the global variable [...]

如果您想在函数内修改全局变量，请使用关键字global，如 @iliar 中的答案。说。

但是不建议这样做 -

Note that it is usually very bad practice to access global variables from inside functions, and even worse practice to modify them. This makes it difficult to arrange our program into logically encapsulated parts which do not affect each other in unexpected ways. If a function needs to access some external value, we should pass the value into the function as a parameter. [...]

有两种选择

• 将其实现为类
• 将 fig 和 ax 传递给 draw_swarmplot()。

前者

class SwarmPlot:
    def __init__(self):
        self.fig, self.ax = plt.subplots(1, 1)
        anim = VideoClip(lambda x: self.draw_swarmplot(x, self.fig, self.ax), duration=2)
        anim.to_videofile('swarmplot.mp4', fps=fps)

    def swarm_plot(self, x):
        kde = gaussian_kde(x)
        density = kde(x)  # estimate the local density at each datapoint

        jitter = np.random.rand(*x.shape) - .5
        y = 1 + (density * jitter * 1000 * 2)
        self.ax.scatter(x, y, s = 30, c = 'g')
        return self.fig

    def draw_swarmplot(self, t, fig, ax):
        self.fig, self.ax = plt.subplots(1, 1)
        f = int(t * fps)
        dff = df.loc[f]

        return mplfig_to_npimage(self.swarm_plot(dff['x']))

S = SwarmPlot()

后者

def draw_swarmplot(t, fig, ax):
    fig, ax = plt.subplots(1, 1)
    f = int(t * fps)
    dff = df.loc[f]

    return mplfig_to_npimage(swarm_plot(dff['x']))

anim = VideoClip(lambda x: draw_swarmplot(x, fig, ax), duration=2)

对于像这样的简单情况，我可能会偏向后者，但在更复杂的情况下，前者可能更可取。两者似乎都能正确生成所需的输出:

当然，如果您没有在每次迭代中使用清除函数之一覆盖 figure 和 axis 实例，那么所有这一切都可以避免:

• plt.cla()清除当前轴
• plt.clf()清除当前数字
• fig.clear() 清除图窗 fig (相当于 plt.clf() if fig > 是当前数字)
• ax.clear()清除轴 ax (相当于 plt.cla() 如果 ax 是当前轴)

ax.clear() 或 plt.cla() 在这种情况下可能是最合适的，用法如下

fig, ax = plt.subplots(1, 1)
def swarm_plot(x):
    kde = gaussian_kde(x)
    density = kde(x)  # estimate the local density at each datapoint

    jitter = np.random.rand(*x.shape) - .5
    y = 1 + (density * jitter * 1000 * 2)
    ax.clear()
    ax.scatter(x, y, s = 30, c = 'g')
    return fig

def draw_swarmplot(t):
    f = int(t * fps)
    dff = df.loc[f]

    return mplfig_to_npimage(swarm_plot(dff['x']))

这也会产生上面所示的输出。