数据可视化有助于更有效地讲述有关数据的故事并使其易于呈现。有时很难用静态图表来解释数据的变化，为此，我们将讨论matplotlib提供的名为“Animation”的动画库之一。以下是要涵盖的主题。
最流行的Python二维绘图库是Matplolib。大多数人从Matplotlib开始他们的探索性数据分析之旅。它可以轻松创建绘图、直方图、条形图、散点图等。与Pandas和Seaborn一样，它可以创建更复杂的视觉效果。
但是也有一些缺陷：
Matplotlib的命令式 API，通常过于冗长。
有时糟糕的风格默认值。
对网络和交互式图表的支持不佳。
对于大型和复杂的数据通常很慢。
参考代码如下
import matplotlib.animation as anime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
fig = plt.figure()
l, = plt.plot([], [], 'k-')
l2, = plt.plot([], [], 'm--')
p1, = plt.plot([], [], 'ko')
p2, = plt.plot([], [], 'mo')
plt.xlabel('xlabel')
plt.ylabel('ylabel')
plt.title('title')
plt.xlim(-5, 5)
plt.ylim(-5, 5)
def func(x):
return np.sin(x) * 3
def func2(x):
return np.cos(x) * 3
metadata = dict(title="Movie", artist="sourabh")
writer = anime.PillowWriter(fps=15, metadata=metadata)
xlist = []
ylist = []
ylist2 = []
xlist2 = []
with writer.saving(fig, "sin+cosinewave.gif", 100):
for xval in np.linspace(-5, 5, 100):
xlist.append(xval)
ylist.append(func(xval))
l.set_data(xlist, ylist)
l2.set_data(xlist2, ylist2)
p1.set_data(xval, func(xval))
writer.grab_frame()
for xval in np.linspace(-5, 5, 100):
xlist2.append(xval)
ylist2.append(func2(xval))
l.set_data(xlist, ylist)
l2.set_data(xlist2, ylist2)
p2.set_data(xval, func2(xval))
writer.grab_frame()动画效果图如下。
参考代码如下，这段代码会运行一段时间。
import matplotlib
from matplotlib import cm
import matplotlib.animation as anime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
np.random.seed(29680801)
fig, ax = plt.subplots(subplot_kw=dict(projection='3d'))
plt.xlim(-5, 5)
plt.ylim(-5, 5)
metadata = dict(title="Movie", artist="sourabh")
writer = anime.PillowWriter(fps=15, metadata=metadata)
def func(x, y, r, t):
return np.cos(r / 2 + t) * np.exp(-np.square(r) / 50)
xdata = np.linspace(-10, 10, 1000)
ydata = np.linspace(-10, 10, 1000)
x_list, y_list = np.meshgrid(xdata, ydata)
r_list = np.sqrt(np.square(x_list) + np.square(y_list))
with writer.saving(fig, "exp3d.gif", 100):
for t in np.linspace(0, 20, 160):
z = func(x_list, y_list, r_list, t)
ax.set_zlim(-1, 1)
ax.plot_surface(x_list, y_list, z, cmap=cm.viridis)
writer.grab_frame()
plt.cla()
动画效果如下
参考代码如下
import matplotlib
from matplotlib import cm
import matplotlib.animation as anime
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
np.random.seed(23680545)
metadata = dict(title="Movie", artist="sourabh")
writer = anime.PillowWriter(fps=15, metadata=metadata)
fig = plt.figure()
plt.xlim(-8, 8)
plt.ylim(-8, 8)
def func(x):
return x * 1.2 + 0.1 + np.random.normal(0, 2, x.shape)
x = np.random.uniform(-7, 7, 10)
x = np.sort(x)
y = func(x)
coeff = np.polyfit(x, y, 1)
print(coeff)
xline = np.linspace(-6, 6, 40)
yline = np.polyval(coeff, xline)
lPnt, = plt.plot(x, y, 'o')
l, = plt.plot(xline, yline, 'k-', linewidth=3)
plt.show()
fig = plt.figure()
plt.xlim(-10, 10)
plt.ylim(-10, 10)
lPnt, = plt.plot([], [], 'o')
l, = plt.plot([], [], 'k-', linewidth=3)
x_List = []
y_List = []
x_pnt = []
y_pnt = []
with writer.saving(fig, "fitPlot.gif", 100):
for xval, yval in zip(x, y):
x_pnt.append(xval)
y_pnt.append(yval)
lPnt.set_data(x_pnt, y_pnt)
l.set_data(x_List, y_List)
writer.grab_frame()
writer.grab_frame()
for x_val, y_val in zip(xline, xline):
x_List.append(x_val)
y_List.append(y_val)
lPnt.set_data(x_pnt, y_pnt)
l.set_data(x_List, y_List)
writer.grab_frame()
for i in range(10):
writer.grab_frame()效果图如下
到此这篇关于使用matplotlib创建Gif动图的实现的文章就介绍到这了,更多相关matplotlib创建Gif动图内容请搜索以前的文章或继续浏览下面的相关文章希望大家以后多多支持！
