如何用电影动画正确引用无花果和斧头
How to correctly refer to fig and ax with moviepy animation
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根据上面的数据，我想用 matplotlib 和 moviepy 制作一个动画群图。但是，通过以下代码，每一帧我都得到了额外的分数，但保留了旧的分数：
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，动画中所有的点都被累积起来。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，我仍然需要在这两个函数之外初始化 fig 和 ax，以免出现有关 ax 对象的错误。因此，我的问题是应该如何引用 fig 和 ax 以及我缺少什么使我的代码无法按预期工作？
def draw_swarmplot(t):
f = int(t * fps)
fig, ax = plt.subplots(1, 1)
dff = df.loc[f]
应该是
def draw_swarmplot(t):
global fig,ax
f = int(t * fps)
fig, ax = plt.subplots(1, 1)
dff = df.loc[f]
否则它会初始化 draw_swarmplot 函数本地的新对象 fig 和 ax。为了分配给全局变量，您需要将它们声明为 global.
fig 和 ax 变量的范围以 Variable Scope and Crossing Boundaries sections of the Variables and Scope 文档为准。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，警告“除非已定义具有相同名称的变量”实际上仅限于 local 变量。正如在 example、
中进一步阐明的那样
a = 0
def my_function():
a = 3
print(a)
my_function()
print(a)
这将输出
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 [...]
如果你想在函数中修改一个全局变量，使用关键字 global，正如 @iliar 的回答所说。
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. [...]
两种选择是
- 将其实现为
class
- 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，我可能偏向于后者，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，如果您不在每次迭代中使用以下清除函数之一覆盖 figure 和 axis 实例，那么所有这一切都可以避免：
plt.cla()清除当前坐标轴plt.clf()清除当前数字fig.clear()清除图fig（如果fig是当前图，相当于plt.clf()）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，将按如下方式使用
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']))
这也会产生如上所示的输出。
您的代码的问题是您在每一帧使用 fig, ax = plt.subplots(1, 1) 重新创建一个新图形，因为在创建每一帧时调用了 draw_swarmplot(t)。
要解决这个问题，您只需在函数外部创建图形一次。为避免所有点都累积，每次制作新帧时使用àx.clear()清除轴。
由于代码不是很长，我将所有内容组合到一个 make_frame(t) 函数中。我认为它使代码更易于理解，但您肯定可以将其分成两个函数。我还添加了几行以防你想要固定轴限制，而不是在每一帧都有不同的限制。完整代码：
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()
# if you want to have fixed axis limits, use these
x_min = float(df.min())
x_max = float(df.max())
# for y values, set the values by eye inspection of the video
# since y values are randomnly draw at the creation of each frame
y_min = 0
y_max = 10
def make_frame(t) :
# select series
i = int(t * fps)
x = df.loc[i]['x']
# prepare data to plot
kde = gaussian_kde(x)
density = kde(x) # estimate the local density at each datapoint
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)
# plot
ax.clear()
ax.scatter(x, y, s = 30, c = 'g')
# comment next two lines if you don't want fixed axis limits
ax.set_xlim(x_min, x_max)
ax.set_ylim(0, 2)
return mplfig_to_npimage(fig)
anim = VideoClip(make_frame, duration=2)
anim.to_videofile('swarmplot.mp4', fps=fps)
# uncomment to display in jupyter notebook
#anim.ipython_display(fps=fps, loop=True, autoplay=True)
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根据上面的数据，我想用 matplotlib 和 moviepy 制作一个动画群图。但是，通过以下代码，每一帧我都得到了额外的分数，但保留了旧的分数：
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，动画中所有的点都被累积起来。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，我仍然需要在这两个函数之外初始化 fig 和 ax，以免出现有关 ax 对象的错误。因此，我的问题是应该如何引用 fig 和 ax 以及我缺少什么使我的代码无法按预期工作？
def draw_swarmplot(t):
f = int(t * fps)
fig, ax = plt.subplots(1, 1)
dff = df.loc[f]
应该是
def draw_swarmplot(t):
global fig,ax
f = int(t * fps)
fig, ax = plt.subplots(1, 1)
dff = df.loc[f]
否则它会初始化 draw_swarmplot 函数本地的新对象 fig 和 ax。为了分配给全局变量，您需要将它们声明为 global.
fig 和 ax 变量的范围以 Variable Scope and Crossing Boundaries sections of the Variables and Scope 文档为准。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，警告“除非已定义具有相同名称的变量”实际上仅限于 local 变量。正如在 example、
中进一步阐明的那样a = 0
def my_function():
a = 3
print(a)
my_function()
print(a)
这将输出
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 [...]
如果你想在函数中修改一个全局变量，使用关键字 global，正如 @iliar 的回答所说。
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. [...]
两种选择是
- 将其实现为
class - 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，我可能偏向于后者，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，如果您不在每次迭代中使用以下清除函数之一覆盖 figure 和 axis 实例，那么所有这一切都可以避免：
plt.cla()清除当前坐标轴plt.clf()清除当前数字fig.clear()清除图fig（如果fig是当前图，相当于plt.clf()）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，将按如下方式使用
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']))
这也会产生如上所示的输出。
您的代码的问题是您在每一帧使用 fig, ax = plt.subplots(1, 1) 重新创建一个新图形，因为在创建每一帧时调用了 draw_swarmplot(t)。
要解决这个问题，您只需在函数外部创建图形一次。为避免所有点都累积，每次制作新帧时使用àx.clear()清除轴。
由于代码不是很长，我将所有内容组合到一个 make_frame(t) 函数中。我认为它使代码更易于理解，但您肯定可以将其分成两个函数。我还添加了几行以防你想要固定轴限制，而不是在每一帧都有不同的限制。完整代码：
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()
# if you want to have fixed axis limits, use these
x_min = float(df.min())
x_max = float(df.max())
# for y values, set the values by eye inspection of the video
# since y values are randomnly draw at the creation of each frame
y_min = 0
y_max = 10
def make_frame(t) :
# select series
i = int(t * fps)
x = df.loc[i]['x']
# prepare data to plot
kde = gaussian_kde(x)
density = kde(x) # estimate the local density at each datapoint
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)
# plot
ax.clear()
ax.scatter(x, y, s = 30, c = 'g')
# comment next two lines if you don't want fixed axis limits
ax.set_xlim(x_min, x_max)
ax.set_ylim(0, 2)
return mplfig_to_npimage(fig)
anim = VideoClip(make_frame, duration=2)
anim.to_videofile('swarmplot.mp4', fps=fps)
# uncomment to display in jupyter notebook
#anim.ipython_display(fps=fps, loop=True, autoplay=True)