如何使用moviepy动画正确引用fig和ax [英] How to correctly refer to fig and ax with moviepy animation

问题描述

data_dict = {'x': {(0, 0): 3760.448435678077,(0, 12): 4851.68102541007,(0，2226):5297.61518907981，(0，2479):4812.134249142693，(0, 2724): 4756.5295525777465,(0，3724):3760.448435678077，(0, 4598): 4763.265306122449,(0, 4599): 5155.102040816327,(0, 4600): 5191.836734693878,(1, 0): 3822.238314568112,(1, 12): 4856.1910324326145,(1，2226):5304.678983022428，(1，2479):4815.435125468252，(1，2724):4761.889691080804，(1，3724):3768.2889580569245，(1，4598):4768.908833716798，(1, 4599): 5159.900248610219,(1, 4600): 5198.053973405109,(2，0):3880.710643551325，(2, 12): 4860.815600807341,(2，2226):5311.463202354179，(2，2479):4818.773880246848，(2, 2724): 4767.173347353905,(2，3724):3775.726731574509，(2, 4598): 4774.4490391107765,(2，4599):5164.871993247027，(2，4600):5203.992167369707，(3，0):3936.0658098882136，(3，12):4865.552525110625，(3, 2226): 5317.975399527148,(3, 2479): 4822.152977310737,(3，2724):4772.381182203056，(3, 3724): 3782.7732491325282,(3, 4598): 4779.888739700325,(3, 4599): 5170.010331730589,(3，4600):5209.661736027094，(4, 0): 3988.491290089178,(4，12):4870.399599918841，(4, 2226): 5324.223126993423,(4, 2479): 4825.574880492175,(4, 2724): 4777.513856434266,(4，3724):3789.4400036326792，(4，4598):4785.230752881375，(4，4599):5175.308321064745，(4，4600):5215.073098816687，(5, 0): 4038.1625164006414,(5, 12): 4875.354619808369,(5, 2226): 5330.2139372050915,(5，2479):4829.04205362342，(5, 2724): 4782.572030853543,(5, 3724): 3795.7384879766646,(5, 4598): 4790.477896049872,(5，4599):5180.7590182533295，(5，4600):5220.2366751779045，(6，0):4085.2436834766995，(6, 12): 4880.415379355583,(6, 2226): 5335.955382614236,(6, 2479): 4832.55696053673,(6，2724):4787.5563662668965，(6, 3724): 3801.6801950661807,(6，4598):4795.632986601749，(6，4599):5186.355480300186，(6，4600):5225.16288455017，(7, 0): 4129.888499451394,(7，12):4885.5796731368655，(7, 2226): 5341.4550156729465,(7, 2479): 4836.122065064363,(7, 2724): 4792.4675234803335,(7, 3724): 3807.2766178029274,(7，4598):4800.698841932945，(7, 4599): 5192.090764209151,(7, 4600): 5229.8621463729005,(8, 0): 4172.2408853249335,(8, 12): 4890.845295728588,(8, 2226): 5346.720388833307,(8, 2479): 4839.739831038576,(8，2724):4797.306163299865，(8, 3724): 3812.539249088603,(8，4598):4805.678279439399，(8, 4599): 5197.9579269840615,(8，4600):5234.344880085516，(9，0):4212.43562629731，(9，12):4896.210041707129，(9, 2226): 5351.759054547402,(9，2479):4843.412722291625，(9, 2724): 4802.072946531498,(9, 3724): 3817.479581824906,(9，4598):4810.574116517045，(9，4599):5203.950025628757，(9, 4600): 5238.621505127434,(10, 0): 4250.598978423163,(10，12):4901.671705648866，(10，2226):5356.578565267323，(10，2479):4847.1432026557695，(10, 2724): 4806.7685339812415,(10, 3724): 3822.1091089135375,(10, 4598): 4815.389170561825,(10，4599):5210.060117147079，(10, 4600): 5242.702440938076,(11, 0): 4286.849233720921,(11, 12): 4907.228082130176,(11, 2226): 5361.186473445152,(11，2479):4850.933735963267，(11, 2724): 4811.393586455103,(11，3724):3826.4393232561943，(11, 4598): 4820.126258969674,(11, 4599): 5216.281258542863,(11, 4600): 5246.5981069568625,(12, 0): 4321.297246645838,(12，12):4912.876965727434，(12, 2226): 5365.590331532978,(12，2479):4854.786786046375，(12, 2724): 4815.948764759092,(12, 3724): 3830.481717754576,(12，4598):4824.788199136532，(12, 4599): 5222.606506819949,(12，4600):5250.318922623211，(13，0):4354.046924629284，(13，12):4918.6161510170205，(13, 2226): 5369.797691982883,(13, 2479): 4858.70481673735,(13，2724):4820.434729699218，(13，3724):3834.247785310383，(13, 4598): 4829.377808458337,(13, 4599): 5229.028918982174,(13, 4600): 5253.875307376542,(14，0):4385.195685194348，(14，12):4924.443432575308，(14, 2226): 5373.816107246958,(14, 2479): 4862.690291868448,(14, 2724): 4824.852142081489,(14，3724):3837.7490188253105，(14, 4598): 4833.897904331024,(14, 4599): 5235.541552033379,(14, 4600): 5257.277680656276,(15, 0): 4414.834881979362,(15，12):4930.356604978678，(15，2226):5377.653129777288，(15，2479):4866.74567527193，(15, 2724): 4829.201662711913,(15，3724):3840.9969112010617，(15, 4598): 4838.351304150532,(15, 4599): 5242.137462977402,(15, 4600): 5260.53646190183,(16, 0): 4443.050201835423,(16, 12): 4936.353462803505,(16，2226):5381.316312025957，(16, 2479): 4870.873430780051,(16，2724):4833.483952396497，(16, 3724): 3844.002955339333,(16, 4598): 4842.740825312798,(16，4599):5248.809708818081，(16, 4600): 5263.662070552626,(17, 0): 4469.92203501027,(17，12):4942.4318006261665，(17，2226):5384.813206445053，(17, 2479): 4875.07602222507,(17, 2724): 4837.699671941253,(17, 3724): 3846.7786441418243,(17，4598):4847.069285213763，(17，4599):5255.551346559254，(17，4600):5266.664926048083，(18, 0): 4495.525820288381,(18, 12): 4948.589413023038,(18，2226):5388.151365486662，(18，2479):4879.3559134392435，(18，2724):4841.849482152186，(18, 3724): 3849.3354705102342,(18, 4598): 4851.339501249362,(18，4599):5262.355433204761，(18, 4600): 5269.555447827619,(19，0):4518.893324127626，(19, 12): 4954.824094570498,(19，2226):5391.338341602872，(19, 2479): 4883.71556825483,(19, 2724): 4845.934043835307,(19, 3724): 3851.6849273462612,(19，4598):4855.554290815534，(19, 4599): 5269.21502575844,(19, 4600): 5272.344055330656}}

使用上述数据，我想使用 matplotlib 和 moviepy 制作动画群图.但是，在每帧下面的代码中，我得到了额外的积分，但保留了旧的积分:

 将numpy导入为np将熊猫作为pd导入从 scipy.stats 导入 gaussian_kde从 matplotlib 导入 pyplot 作为 plt从 moviepy.editor 导入 VideoClip从moviepy.video.io.bindings导入mplfig_to_npimage每秒帧数 = 10df = pd.DataFrame(data_dict)无花果，ax = plt.subplots(1，1)def swarm_plot(x):kde = gaussian_kde(x)density = kde(x)#估计每个数据点的局部密度# ax.clear()抖动 = np.random.rand(*x.shape) - .5# 通过 KDE 估计缩放抖动并将其添加到中心 x 坐标y = 1 +(密度*抖动* 1000 * 2)ax.scatter(x, y, s = 30, c = 'g')# plt.axis('off')返回无花果def draw_swarmplot(t):f = int(t * fps)无花果，ax = plt.subplots(1，1)dff = df.loc[f]返回 mplfig_to_npimage(swarm_plot(dff['x']))动画= VideoClip(lambda x:draw_swarmplot(x)，持续时间= 2)anim.to_videofile('swarmplot.mp4'，fps = fps)

因此，所有点都在动画中累积.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 以免出现关于 ax 对象的错误.因此，我的问题是应该同时引用 fig 和 ax ，又缺少什么使我的代码无法正常工作?

解决方案

fig 和 ax 变量的作用域受

如果您没有在每次迭代中覆盖 figure 和 axis 实例，而是使用以下清除功能之一，则当然可以避免所有这些情况:

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

在这种情况下，

ax.clear() 或 plt.cla() 可能是最合适的，可以按如下方式使用

  fig，ax = plt.subplots(1，1)def swarm_plot(x):kde =高斯_kde(x)density = kde(x)#估计每个数据点的局部密度抖动 = np.random.rand(*x.shape) - .5y = 1 +(密度 * 抖动 * 1000 * 2)ax.clear()ax.scatter(x, y, s = 30, c = 'g')返回无花果def draw_swarmplot(t):f = int(t * fps)dff = df.loc[f]返回mplfig_to_npimage(swarm_plot(dff ['x']))

这也将产生如上所示的输出.

data_dict = {'x': {(0, 0): 3760.448435678077,
  (0, 12): 4851.68102541007,
  (0, 2226): 5297.61518907981,
  (0, 2479): 4812.134249142693,
  (0, 2724): 4756.5295525777465,
  (0, 3724): 3760.448435678077,
  (0, 4598): 4763.265306122449,
  (0, 4599): 5155.102040816327,
  (0, 4600): 5191.836734693878,
  (1, 0): 3822.238314568112,
  (1, 12): 4856.1910324326145,
  (1, 2226): 5304.678983022428,
  (1, 2479): 4815.435125468252,
  (1, 2724): 4761.889691080804,
  (1, 3724): 3768.2889580569245,
  (1, 4598): 4768.908833716798,
  (1, 4599): 5159.900248610219,
  (1, 4600): 5198.053973405109,
  (2, 0): 3880.710643551325,
  (2, 12): 4860.815600807341,
  (2, 2226): 5311.463202354179,
  (2, 2479): 4818.773880246848,
  (2, 2724): 4767.173347353905,
  (2, 3724): 3775.726731574509,
  (2, 4598): 4774.4490391107765,
  (2, 4599): 5164.871993247027,
  (2, 4600): 5203.992167369707,
  (3, 0): 3936.0658098882136,
  (3, 12): 4865.552525110625,
  (3, 2226): 5317.975399527148,
  (3, 2479): 4822.152977310737,
  (3, 2724): 4772.381182203056,
  (3, 3724): 3782.7732491325282,
  (3, 4598): 4779.888739700325,
  (3, 4599): 5170.010331730589,
  (3, 4600): 5209.661736027094,
  (4, 0): 3988.491290089178,
  (4, 12): 4870.399599918841,
  (4, 2226): 5324.223126993423,
  (4, 2479): 4825.574880492175,
  (4, 2724): 4777.513856434266,
  (4, 3724): 3789.4400036326792,
  (4, 4598): 4785.230752881375,
  (4, 4599): 5175.308321064745,
  (4, 4600): 5215.073098816687,
  (5, 0): 4038.1625164006414,
  (5, 12): 4875.354619808369,
  (5, 2226): 5330.2139372050915,
  (5, 2479): 4829.04205362342,
  (5, 2724): 4782.572030853543,
  (5, 3724): 3795.7384879766646,
  (5, 4598): 4790.477896049872,
  (5, 4599): 5180.7590182533295,
  (5, 4600): 5220.2366751779045,
  (6, 0): 4085.2436834766995,
  (6, 12): 4880.415379355583,
  (6, 2226): 5335.955382614236,
  (6, 2479): 4832.55696053673,
  (6, 2724): 4787.5563662668965,
  (6, 3724): 3801.6801950661807,
  (6, 4598): 4795.632986601749,
  (6, 4599): 5186.355480300186,
  (6, 4600): 5225.16288455017,
  (7, 0): 4129.888499451394,
  (7, 12): 4885.5796731368655,
  (7, 2226): 5341.4550156729465,
  (7, 2479): 4836.122065064363,
  (7, 2724): 4792.4675234803335,
  (7, 3724): 3807.2766178029274,
  (7, 4598): 4800.698841932945,
  (7, 4599): 5192.090764209151,
  (7, 4600): 5229.8621463729005,
  (8, 0): 4172.2408853249335,
  (8, 12): 4890.845295728588,
  (8, 2226): 5346.720388833307,
  (8, 2479): 4839.739831038576,
  (8, 2724): 4797.306163299865,
  (8, 3724): 3812.539249088603,
  (8, 4598): 4805.678279439399,
  (8, 4599): 5197.9579269840615,
  (8, 4600): 5234.344880085516,
  (9, 0): 4212.43562629731,
  (9, 12): 4896.210041707129,
  (9, 2226): 5351.759054547402,
  (9, 2479): 4843.412722291625,
  (9, 2724): 4802.072946531498,
  (9, 3724): 3817.479581824906,
  (9, 4598): 4810.574116517045,
  (9, 4599): 5203.950025628757,
  (9, 4600): 5238.621505127434,
  (10, 0): 4250.598978423163,
  (10, 12): 4901.671705648866,
  (10, 2226): 5356.578565267323,
  (10, 2479): 4847.1432026557695,
  (10, 2724): 4806.7685339812415,
  (10, 3724): 3822.1091089135375,
  (10, 4598): 4815.389170561825,
  (10, 4599): 5210.060117147079,
  (10, 4600): 5242.702440938076,
  (11, 0): 4286.849233720921,
  (11, 12): 4907.228082130176,
  (11, 2226): 5361.186473445152,
  (11, 2479): 4850.933735963267,
  (11, 2724): 4811.393586455103,
  (11, 3724): 3826.4393232561943,
  (11, 4598): 4820.126258969674,
  (11, 4599): 5216.281258542863,
  (11, 4600): 5246.5981069568625,
  (12, 0): 4321.297246645838,
  (12, 12): 4912.876965727434,
  (12, 2226): 5365.590331532978,
  (12, 2479): 4854.786786046375,
  (12, 2724): 4815.948764759092,
  (12, 3724): 3830.481717754576,
  (12, 4598): 4824.788199136532,
  (12, 4599): 5222.606506819949,
  (12, 4600): 5250.318922623211,
  (13, 0): 4354.046924629284,
  (13, 12): 4918.6161510170205,
  (13, 2226): 5369.797691982883,
  (13, 2479): 4858.70481673735,
  (13, 2724): 4820.434729699218,
  (13, 3724): 3834.247785310383,
  (13, 4598): 4829.377808458337,
  (13, 4599): 5229.028918982174,
  (13, 4600): 5253.875307376542,
  (14, 0): 4385.195685194348,
  (14, 12): 4924.443432575308,
  (14, 2226): 5373.816107246958,
  (14, 2479): 4862.690291868448,
  (14, 2724): 4824.852142081489,
  (14, 3724): 3837.7490188253105,
  (14, 4598): 4833.897904331024,
  (14, 4599): 5235.541552033379,
  (14, 4600): 5257.277680656276,
  (15, 0): 4414.834881979362,
  (15, 12): 4930.356604978678,
  (15, 2226): 5377.653129777288,
  (15, 2479): 4866.74567527193,
  (15, 2724): 4829.201662711913,
  (15, 3724): 3840.9969112010617,
  (15, 4598): 4838.351304150532,
  (15, 4599): 5242.137462977402,
  (15, 4600): 5260.53646190183,
  (16, 0): 4443.050201835423,
  (16, 12): 4936.353462803505,
  (16, 2226): 5381.316312025957,
  (16, 2479): 4870.873430780051,
  (16, 2724): 4833.483952396497,
  (16, 3724): 3844.002955339333,
  (16, 4598): 4842.740825312798,
  (16, 4599): 5248.809708818081,
  (16, 4600): 5263.662070552626,
  (17, 0): 4469.92203501027,
  (17, 12): 4942.4318006261665,
  (17, 2226): 5384.813206445053,
  (17, 2479): 4875.07602222507,
  (17, 2724): 4837.699671941253,
  (17, 3724): 3846.7786441418243,
  (17, 4598): 4847.069285213763,
  (17, 4599): 5255.551346559254,
  (17, 4600): 5266.664926048083,
  (18, 0): 4495.525820288381,
  (18, 12): 4948.589413023038,
  (18, 2226): 5388.151365486662,
  (18, 2479): 4879.3559134392435,
  (18, 2724): 4841.849482152186,
  (18, 3724): 3849.3354705102342,
  (18, 4598): 4851.339501249362,
  (18, 4599): 5262.355433204761,
  (18, 4600): 5269.555447827619,
  (19, 0): 4518.893324127626,
  (19, 12): 4954.824094570498,
  (19, 2226): 5391.338341602872,
  (19, 2479): 4883.71556825483,
  (19, 2724): 4845.934043835307,
  (19, 3724): 3851.6849273462612,
  (19, 4598): 4855.554290815534,
  (19, 4599): 5269.21502575844,
  (19, 4600): 5272.344055330656}}

With the data above I want to make an animated swarm plot with matplotlib and moviepy. However, with the following code with every frame I get additional points, but with preserved old ones:

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)

As a result, all points are cumulated in the animation. I believe it is because of matplotlib fig and ax objects used incorrectly. However, in draw_swarmplot function I reset fig and ax objects after each iteration. Nevertheless, I still need to initialise fig and ax outside both function to not get an error regarding ax object. Therefore, my question is how both fig and ax should be referenced and what am I missing that makes my code not working as intended?

解决方案

The scoping of your fig and ax variables is subject to the Variable Scope and Crossing Boundaries sections of the Variables and Scope documentation. Specifically relevant,

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.

Note that the caveat "unless a variable with the same name is already defined" is in fact limited to local variables. As is clarified further in the example,

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

my_function()
print(a)

which will output

3
0

This is because

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

If you want to modify a global variable from within a function, use the keyword global, as the answer from @iliar says.

However this is not advised -

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. [...]

Two alternatives would be

• Implement this as a class
• Pass fig and ax into draw_swarmplot().

The former

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()

The latter

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)

For a simple case such as this I might be partial to the latter, but in more complex cases the former might be preferable. Both appear to correctly generate the desired output:

Of course all this could be avoided if you didn't overwrite the figure and axis instances in each iteration by instead using one of the clearing functions:

• plt.cla() to clear the current axis
• plt.clf() to clear the current figure
• fig.clear() to clear the figure fig (equivalent to plt.clf() if fig is the current figure)
• ax.clear() to clear the axis ax (equivalent to plt.cla() if ax is the current axis)

ax.clear() or plt.cla() may be the most appropriate in this case and would be used as follows

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']))

Which will also produce the output shown above.

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