Matplotlib
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plot
- import matplotlib.plot as plt
- 初始化 clear
plt.clf()
- Tweak spacing to prevent clipping of ylabel
plt.tight_layout()
- 简单的例子
import numpy as np import matplotlib.pyplot as plt x = np.arange(0, 5, 0.1); y = np.sin(x) plt.clf() #clear the current figure plt.subplot(211) plt.plot(x, y) plt.show() plt.xlim(0,3) #调整坐标范围
- options for the color characters are: 'r' , 'g' , 'b' = blue, 'c' = cyan, 'm' = magenta, 'y' = yellow, 'k' = black, 'w' = white
- Options for line styles are: '-' = solid, '--' = dashed, ':' = dotted, '-.' = dot-dashed, '.' = points, 'o' = filled circles, '^' = filled triangles
- marker style [3]
- 对数坐标
- semilogx #x轴对数
- semilogy #y轴对数
- set_xscale("log", nonposx='clip')
- set_yscale("log", nonposy='clip')
- 误差棒
- errorbar(x, y, xerr=0.1 * x, yerr=5.0 + 0.75 * y, ls='None', marker='s') #ls='None' 不连线
- grid
- ax.grid()
- 设置坐标轴的极限
- ax.set_ylim(ymin=0.1)
- 参见[4]
- 产生多个图形窗口
plt.figure(1) plt.figure(2)
- pmesh,pcolormesh: 画二维的平面分布的图
- colorbar
直方图
- hist 命令
- 关键词有 bins=None, range=None, density=None, weights=None, cumulative=False, bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None, log=False, color=None, label=None, stacked=False, normed=None
# the histogram of the data n, bins, patches = ax.hist(x, 50, normed=1)
图像
- matplotlib里面可以用axes.imshow()
delta = 0.025 x = y = np.arange(-3.0, 3.0, delta) X, Y = np.meshgrid(x, y) Z = np.exp(-X**2 - Y**2) fig, ax = plt.subplots() im = ax.imshow(Z, interpolation='bilinear', cmap=cm.RdYlGn, origin='lower', extent=[-3, 3, -3, 3], vmax=abs(Z).max(), vmin=-abs(Z).max()) #lower 就是把index[0,0]放在左下,extent是数轴上标志的范围
- pylot.imshow(Z)
保存图片文件
- plt.savefig("filename.png")
- plt.savefig('SFH_LMC_miles.pdf',format='pdf')
- 保存文件一片空白
- 在 plt.show() 后调用了 plt.savefig() ,在 plt.show() 后实际上已经创建了一个新的空白的图片(坐标轴),这时候你再 plt.savefig() 就会保存这个新生成的空白图片。
- plt.show() 放在最后,或者
# gcf: Get Current Figure fig = plt.gcf() plt.show() fig1.savefig('tessstttyyy.png', dpi=100)