Matplotlib Tutorial -- 学习笔记

Python

原文：　http://reverland.org/python/2012/09/07/matplotlib-tutorial

简单绘图

from pylab import *
figure(figsize=(8,6), dpi=80)
subplot(1,1,1)
plot(X, C, color="blue", linewidth=1.0, linestyle="-")
plot(X, S, color="green", linewidth=1.0, linestyle="-")
xlim(-4.0,4.0)
xticks(np.linspace(-4,4,9,endpoint=True))
ylim(-1.0,1.0)
yticks(np.linspace(-1,1,5,endpoint=True))

线条变宽

figure(figsize=(10,6), dpi=80) # 图像更大
plot(X, C, color="blue", linewidth=2.5, linestyle="-")
plot(X, S, color="green", linewidth=2.5, linestyle="-") #像素点

设置边界

xlim(X.min()*1.1, X.max()*1.1)
ylim(C.min()*1.1, C.max()*1.1)

设置刻度

xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])
yticks([-1, 0, +1])

设置刻度标签

xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
       [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'])
yticks([-1, 0, +1],
       [r'$-1$', r'$0$', r'$+1$'])

移动轴线(spine)

轴线(spines)是连接刻度标志和标示数据区域边界的线。它们现在可以被放置在任意地方，它们在子图的边缘。我们将改变这点，因为我们想让它们位于中间。因为一共有四个轴线(上/下/左/右)。我们将通过将它们的颜色设置成None，舍弃位于顶部和右部轴线。然后我们把底部和左部的轴线移动到数据空间坐标中的零点。

ax = gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))

添加图例

plot(X, C, color="blue", linewidth=2.5, linestyle="-", label="cosine")
plot(X, S, color="red",  linewidth=2.5, linestyle="-", label="sine")
legend(loc='upper left')

注解某些点

t = 2*np.pi/3
plot([t,t],[0,np.cos(t)], color ='blue', linewidth=2.5, linestyle="--")
scatter([t,],[np.cos(t),], 50, color ='blue')
annotate(r'$sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$',
         xy=(t, np.sin(t)), xycoords='data',
         xytext=(+10, +30), textcoords='offset points', fontsize=16,
         arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))
plot([t,t],[0,np.sin(t)], color ='red', linewidth=2.5, linestyle="--")
scatter([t,],[np.sin(t),], 50, color ='red')
annotate(r'$cos(\frac{2\pi}{3})=-\frac{1}{2}$',
         xy=(t, np.cos(t)), xycoords='data',
         xytext=(-90, -50), textcoords='offset points', fontsize=16,
         arrowprops=dict(arrowstyle="->", connectionstyle="arc3,rad=.2"))

魔鬼在于细节

调整标签

for label in ax.get_xticklabels() + ax.get_yticklabels():
    label.set_fontsize(16)
    label.set_bbox(dict(facecolor='white', edgecolor='None', alpha=0.65 ))

图像、子区、子图、刻度

一个图像(figure)意味着用户界面的整个窗口。在一个图像中可以有些子区(subplot)。subplot将绘图放置在常规的网格位置上而axes允许更自由的放置。它们都非常有用，取决于你的意图。我们已经隐式地使用了figure和subplot。当我们调用plot时，matplotlib调用gca来获取当前axes反过来调用gcf()获取当前图像(figure)。如果没有当前图像(figure)，它调用figure()创建一个，严格地说，是创建一个subplot(111)。

图像(Figure)

子区(subplots)

subplot(x,y,1)

你可以通过subplot在正常网格中布置图像。你需要指定行数和列数和区域的编号。注意gridspec命令是个更强大的选择。

子图(axes)

子图和子区(subplot)非常相似，但是允许把图片放置到图像(figure)中的任何地方。所以如果我们想要在一个大图片中嵌套一个小点的图片，我们通过子图(axes)来完成它。

import matplotlib.gridspec as gridspec
G = gridspec.GridSpec(3, 3)
axes_1 = subplot(G[0, :])
xticks([]), yticks([])
text(0.5,0.5, 'Axes 1',ha='center',va='center',size=24,alpha=.5)
axes_2 = subplot(G[1,:-1])
xticks([]), yticks([])
text(0.5,0.5, 'Axes 2',ha='center',va='center',size=24,alpha=.5)
axes_3 = subplot(G[1:, -1])
xticks([]), yticks([])
text(0.5,0.5, 'Axes 3',ha='center',va='center',size=24,alpha=.5)
axes_4 = subplot(G[-1,0])
xticks([]), yticks([])
text(0.5,0.5, 'Axes 4',ha='center',va='center',size=24,alpha=.5)
axes_5 = subplot(G[-1,-2])
xticks([]), yticks([])
text(0.5,0.5, 'Axes 5',ha='center',va='center',size=24,alpha=.5)

axes([0.1,0.1,.8,.8])
xticks([]), yticks([])
text(0.6,0.6, 'axes([0.1,0.1,.8,.8])',ha='center',va='center',size=20,alpha=.5)
axes([0.2,0.2,.3,.3])
xticks([]), yticks([])
text(0.5,0.5, 'axes([0.2,0.2,.3,.3])',ha='center',va='center',size=16,alpha=.5)

axes([0.1,0.1,.5,.5])
xticks([]), yticks([])
text(0.1,0.1, 'axes([0.1,0.1,.8,.8])',ha='left',va='center',size=16,alpha=.5)
axes([0.2,0.2,.5,.5])
xticks([]), yticks([])
text(0.1,0.1, 'axes([0.2,0.2,.5,.5])',ha='left',va='center',size=16,alpha=.5)
axes([0.3,0.3,.5,.5])
xticks([]), yticks([])
text(0.1,0.1, 'axes([0.3,0.3,.5,.5])',ha='left',va='center',size=16,alpha=.5)
axes([0.4,0.4,.5,.5])
xticks([]), yticks([])
text(0.1,0.1, 'axes([0.4,0.4,.5,.5])',ha='left',va='center',size=16,alpha=.5)

　刻度(ticks)

良好格式化的刻度是准备发表的图片中的重要部分。Matplotlib为刻度提供完全可配置的系统。刻度定位器指定刻度出现的位置，刻度格式器让刻度看起来如你所愿。主刻度和次要刻度可以分别放置和格式化，每个默认主刻度并不显示，也就是，它们只有一个空列表，因为它们作为空定位器(NullLocator)(参见下面)

刻度定位器(Tick Locator)

其他种类绘图

常规绘图

n = 256
X = np.linspace(-np.pi,np.pi,n,endpoint=True)
Y = np.sin(2*X)
plt.axes([0.025,0.025,0.95,0.95])
plt.plot (X, Y+1, color='blue', alpha=1.00)
plt.fill_between(X, 1, Y+1, color='blue', alpha=.25)
plt.plot (X, Y-1, color='blue', alpha=1.00)
plt.fill_between(X, -1, Y-1, (Y-1) > -1, color='blue', alpha=.25)
plt.fill_between(X, -1, Y-1, (Y-1) < -1, color='red',  alpha=.25)
plt.xlim(-np.pi,np.pi), plt.xticks([])
plt.ylim(-2.5,2.5), plt.yticks([])

散点图(scatter plots)

n = 1024
X = np.random.normal(0,1,n)
Y = np.random.normal(0,1,n)
T = np.arctan2(Y,X)
plt.axes([0.025,0.025,0.95,0.95])
plt.scatter(X,Y, s=75, c=T, alpha=.5)
plt.xlim(-1.5,1.5), plt.xticks([])
plt.ylim(-1.5,1.5), plt.yticks([])

条形图(bar plots)

n = 12
X = np.arange(n)
Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)
Y2 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)
plt.axes([0.025,0.025,0.95,0.95])
plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')
plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')
for x,y in zip(X,Y1):
    plt.text(x+0.4, y+0.05, '%.2f' % y, ha='center', va= 'bottom')
for x,y in zip(X,Y2):
    plt.text(x+0.4, -y-0.05, '%.2f' % y, ha='center', va= 'top')
plt.xlim(-.5,n), plt.xticks([])
plt.ylim(-1.25,+1.25), plt.yticks([])

等高线图(contour plots)

n = 256
x = np.linspace(-3,3,n)
y = np.linspace(-3,3,n)
X,Y = np.meshgrid(x,y)
def f(x,y):
    return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)
plt.axes([0.025,0.025,0.95,0.95])
plt.contourf(X, Y, f(X,Y), 8, alpha=.75, cmap=plt.cm.hot)
C = plt.contour(X, Y, f(X,Y), 8, colors='black', linewidth=.5)
plt.clabel(C, inline=1, fontsize=10)
plt.xticks([]), plt.yticks([])

Imshow

def f(x,y):
    return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)
n = 10
x = np.linspace(-3,3,3.5*n)
y = np.linspace(-3,3,3.0*n)
X,Y = np.meshgrid(x,y)
Z = f(X,Y)
plt.axes([0.025,0.025,0.95,0.95])
plt.imshow(Z,interpolation='nearest', cmap='bone', origin='lower')
plt.colorbar(shrink=.92)
plt.xticks([]), plt.yticks([])

饼图(Pie charts)

n = 20
Z = np.ones(n)
Z[-1] *= 2
plt.axes([0.025,0.025,0.95,0.95])
plt.pie(Z, explode=Z*.05, colors = ['%f' % (i/float(n)) for i in range(n)])
plt.gca().set_aspect('equal')
plt.xticks([]), plt.yticks([])

矢量图(quiver plots)

n = 8
X,Y = np.mgrid[0:n,0:n]
T = np.arctan2(Y-n/2.0, X-n/2.0)
R = 10+np.sqrt((Y-n/2.0)**2+(X-n/2.0)**2)
U,V = R*np.cos(T), R*np.sin(T)
plt.axes([0.025,0.025,0.95,0.95])
plt.quiver(X,Y,U,V,R, alpha=.5)
plt.quiver(X,Y,U,V, edgecolor='k', facecolor='None', linewidth=.5)
plt.xlim(-1,n), plt.xticks([])
plt.ylim(-1,n), plt.yticks([])

网格(grids)

ax = plt.axes([0.025,0.025,0.95,0.95])
ax.set_xlim(0,4)
ax.set_ylim(0,3)
ax.xaxis.set_major_locator(plt.MultipleLocator(1.0))
ax.xaxis.set_minor_locator(plt.MultipleLocator(0.1))
ax.yaxis.set_major_locator(plt.MultipleLocator(1.0))
ax.yaxis.set_minor_locator(plt.MultipleLocator(0.1))
ax.grid(which='major', axis='x', linewidth=0.75, linestyle='-', color='0.75')
ax.grid(which='minor', axis='x', linewidth=0.25, linestyle='-', color='0.75')
ax.grid(which='major', axis='y', linewidth=0.75, linestyle='-', color='0.75')
ax.grid(which='minor', axis='y', linewidth=0.25, linestyle='-', color='0.75')
ax.set_xticklabels([])
ax.set_yticklabels([])

多图绘制

fig = plt.figure()
fig.subplots_adjust(bottom=0.025, left=0.025, top = 0.975, right=0.975)
plt.subplot(2,1,1)
plt.xticks([]), plt.yticks([])
plt.subplot(2,3,4)
plt.xticks([]), plt.yticks([])
plt.subplot(2,3,5)
plt.xticks([]), plt.yticks([])
plt.subplot(2,3,6)
plt.xticks([]), plt.yticks([])

极轴图

ax = plt.axes([0.025,0.025,0.95,0.95], polar=True)
N = 20
theta = np.arange(0.0, 2*np.pi, 2*np.pi/N)
radii = 10*np.random.rand(N)
width = np.pi/4*np.random.rand(N)
bars = plt.bar(theta, radii, width=width, bottom=0.0)
for r,bar in zip(radii, bars):
    bar.set_facecolor( plt.cm.jet(r/10.))
    bar.set_alpha(0.5)
ax.set_xticklabels([])
ax.set_yticklabels([])

polar=True

三维绘图

from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = Axes3D(fig)
X = np.arange(-4, 4, 0.25)
Y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.cm.hot)
ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap=plt.cm.hot)
ax.set_zlim(-2,2)

绘制文本

eqs = []
eqs.append((r"$W^{3\beta}_{\delta_1 \rho_1 \sigma_2} = U^{3\beta}_{\delta_1 \rho_1} + \frac{1}{8 \pi 2} \int^{\alpha_2}_{\alpha_2} d \alpha^\prime_2 \left[\frac{ U^{2\beta}_{\delta_1 \rho_1} - \alpha^\prime_2U^{1\beta}_{\rho_1 \sigma_2} }{U^{0\beta}_{\rho_1 \sigma_2}}\right]$"))
eqs.append((r"$\frac{d\rho}{d t} + \rho \vec{v}\cdot\nabla\vec{v} = -\nabla p + \mu\nabla^2 \vec{v} + \rho \vec{g}$"))
eqs.append((r"$\int_{-\infty}^\infty e^{-x^2}dx=\sqrt{\pi}$"))
eqs.append((r"$E = mc^2 = \sqrt{{m_0}^2c^4 + p^2c^2}$"))
eqs.append((r"$F_G = G\frac{m_1m_2}{r^2}$"))
plt.axes([0.025,0.025,0.95,0.95])
for i in range(24):
    index = np.random.randint(0,len(eqs))
    eq = eqs[index]
    size = np.random.uniform(12,32)
    x,y = np.random.uniform(0,1,2)
    alpha = np.random.uniform(0.25,.75)
    plt.text(x, y, eq, ha='center', va='center', color="#11557c", alpha=alpha,
             transform=plt.gca().transAxes, fontsize=size, clip_on=True)
plt.xticks([]), plt.yticks([])