最近又用到了matplotlib 中畫圖的函數。總結幾個常用的函數的作用于差別。
from matplotlib import pyplot as plt
1.figure()
函數定義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()建立一個畫布。
主要講一個參數num：相當于給畫布定義一個id，如果給出了num，之前沒有使用，則建立一個新的畫布；如果之前使用了這個num，那麼傳回那個畫布的引用，在之前的畫布上繼續作圖。如果沒有給出num, 則每次建立一塊新的畫布。
import numpy as np
from matplotlib import pyplot as plt
from scipy.interpolate import interp1d
x=np.linspace(0,10*np.pi,num=20)
y=np.sin(x)
yn=np.cos(x)
f1=interp1d(x,y,kind='linear')#線性插值
f2=interp1d(x,y,kind='cubic')#三次樣條插值
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 interp1d
x=np.linspace(0,10*np.pi,num=20)
y=np.sin(x)
yn=np.cos(x)
f1=interp1d(x,y,kind='linear')#線性插值
f2=interp1d(x,y,kind='cubic')#三次樣條插值
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)
向圖中加入子圖的軸。傳回子圖的坐标軸axes
import numpy as np
from matplotlib import pyplot as plt
from scipy.interpolate import interp1d
x=np.linspace(0,10*np.pi,num=20)
y=np.sin(x)
yn=np.cos(x)
f1=interp1d(x,y,kind='linear')#線性插值
f2=interp1d(x,y,kind='cubic')#三次樣條插值
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()
通過ax設定各種圖的參數。
import matplotlib.pyplot as plt
fig = 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):目前圖中加子圖
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) :建立一個圖形和一組子圖。
nrows, ncols : int, optional, default: 1 子圖網絡的行列數。.
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
傳回： fig, ax
import matplotlib.pyplot as plt
data = {'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')
個人喜好用1畫一個圖，4畫多個圖。
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.