python matplotlib:figure,add_subplot,subplot，subplots讲解实现

最近又用到了matplotlib 中画图的函数。总结几个常用的函数的作用于区别。

from matplotlib import pyplot as plt

1.figure()

函数定义matplotlib.pyplot.figure(num=None, figsize=None, dpi=None, facecolor=None, edgecolor=None, frameon=True, FigureClass=<class 'matplotlib.figure.Figure'>, clear=False, **kwargs)

plt.figure()创建一个画布。

主要讲一个参数num：相当于给画布定义一个id，如果给出了num，之前没有使用，则创建一个新的画布；如果之前使用了这个num，那么返回那个画布的引用，在之前的画布上继续作图。如果没有给出num, 则每次创建一块新的画布。

import numpy as np
from matplotlib import pyplot as plt
from scipy.interpolate import interp1dx=np.linspace(0,10*np.pi,num=20)
y=np.sin(x)
yn=np.cos(x)
f1=interp1d(x,y,kind='linear')#线性插值
f2=interp1d(x,y,kind='cubic')#三次样条插值
x_pred=np.linspace(0,10*np.pi,num=1000)
y1=f1(x_pred)
y2=f2(x_pred)
plt.figure(1)
plt.plot(x_pred,y1,'r',label='linear')
plt.plot(x_pred,y2,'b--',label='cubic')
plt.legend()
# plt.show()
plt.figure(2)
plt.plot(x,yn,label='new')
plt.legend()
plt.show()

import numpy as np
from matplotlib import pyplot as plt
from scipy.interpolate import interp1dx=np.linspace(0,10*np.pi,num=20)
y=np.sin(x)
yn=np.cos(x)
f1=interp1d(x,y,kind='linear')#线性插值
f2=interp1d(x,y,kind='cubic')#三次样条插值
x_pred=np.linspace(0,10*np.pi,num=1000)
y1=f1(x_pred)
y2=f2(x_pred)
plt.figure(1)
plt.plot(x_pred,y1,'r',label='linear')
plt.plot(x_pred,y2,'b--',label='cubic')
plt.legend()
# plt.show()
plt.figure(1)
plt.plot(x,yn,label='new')
plt.legend()
plt.show()

2 add_subplot()

add_subplot(*args, **kwargs)

向图中加入子图的轴。返回子图的坐标轴axes

import numpy as np
from matplotlib import pyplot as plt
from scipy.interpolate import interp1dx=np.linspace(0,10*np.pi,num=20)
y=np.sin(x)
yn=np.cos(x)
f1=interp1d(x,y,kind='linear')#线性插值
f2=interp1d(x,y,kind='cubic')#三次样条插值
x_pred=np.linspace(0,10*np.pi,num=1000)
y1=f1(x_pred)
y2=f2(x_pred)
fig = plt.figure()
fig.add_subplot(221)
plt.plot(x_pred,y1,'r',label='linear')
fig.add_subplot(222)
plt.plot(x_pred,y2,'b--',label='cubic')plt.show()

通过ax设置各种图的参数。

import matplotlib.pyplot as pltfig = plt.figure()
fig.suptitle('bold figure suptitle', fontsize=14, fontweight='bold')
ax = fig.add_subplot(111)
fig.subplots_adjust(top=0.85)
ax.set_title('axes title')
ax.set_xlabel('xlabel')
ax.set_ylabel('ylabel')
ax.text(3, 8, 'boxed italics text in data coords', style='italic',bbox={'facecolor':'red', 'alpha':0.5, 'pad':10})
ax.text(2, 6, r'an equation: $E=mc^2$', fontsize=15)
# ax.text(3, 2, unicode('unicode: Institut f\374r Festk\366rperphysik', 'latin-1'))
ax.text(0.95, 0.01, 'colored text in axes coords',verticalalignment='bottom', horizontalalignment='right',transform=ax.transAxes,color='green', fontsize=15)
ax.plot([2,3,4], [1,2,5], 'o')
ax.annotate('annotate', xy=(2, 1), xytext=(3, 4),arrowprops=dict(facecolor='black', shrink=0.05))
ax.axis([0, 10, 0, 11])
plt.show()

3.subplot()

matplotlib.pyplot.subplot(*args, **kwargs):当前图中加子图

plt.subplot(221)# equivalent but more general
ax1=plt.subplot(2, 2, 1)

import numpy as np
import matplotlib.pyplot as plt# Fixing random state for reproducibility
np.random.seed(19680801)x = np.random.rand(10)
y = np.random.rand(10)
z = np.sqrt(x**2 + y**2)plt.subplot(321)
plt.scatter(x, y, s=80, c=z, marker=">")plt.subplot(322)
plt.scatter(x, y, s=80, c=z, marker=(5, 0))verts = np.array([[-1, -1], [1, -1], [1, 1], [-1, -1]])
plt.subplot(323)
plt.scatter(x, y, s=80, c=z, marker=verts)plt.subplot(324)
plt.scatter(x, y, s=80, c=z, marker=(5, 1))plt.subplot(325)
plt.scatter(x, y, s=80, c=z, marker='+')plt.subplot(326)
plt.scatter(x, y, s=80, c=z, marker=(5, 2))plt.show()

4. subplots()

matplotlib.pyplot.subplots(nrows=1, ncols=1, sharex=False, sharey=False, squeeze=True, subplot_kw=None, gridspec_kw=None, **fig_kw) :创建一个图形和一组子图。

nrows, ncols : int, optional, default: 1 子图网络的行列数。.

examples:

#First create some toy data:
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)#Creates just a figure and only one subplot
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Simple plot')#Creates two subplots and unpacks the output array immediately
f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.plot(x, y)
ax1.set_title('Sharing Y axis')
ax2.scatter(x, y)#Creates four polar axes, and accesses them through the returned array
fig, axes = plt.subplots(2, 2, subplot_kw=dict(polar=True))
axes[0, 0].plot(x, y)
axes[1, 1].scatter(x, y

返回： fig, ax

import matplotlib.pyplot as pltdata = {'apples': 10, 'oranges': 15, 'lemons': 5, 'limes': 20}
names = list(data.keys())
values = list(data.values())fig, axs = plt.subplots(1, 3, figsize=(9, 3), sharey=True)
axs[0].bar(names, values)
axs[1].scatter(names, values)
axs[2].plot(names, values)
fig.suptitle('Categorical Plotting')

个人喜好用1画一个图，4画多个图。

class matplotlib.axes.Axes(fig, rect, facecolor=None, frameon=True, sharex=None, sharey=None, label='', xscale=None, yscale=None, **kwargs):

The Axes contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system.