我处理模拟数据，最近经常使用matplotlib，遇到了一些问题(bug？)真烦人。在

我一直允许matplotlib自动设置记号标签和它们的类型(科学的，等等)，在一些数据中我得到了奇怪的科学股票标签。在

在寻找解决方法的过程中，我发现可以调用set_powerlimits((n，m))来设置将使用科学符号显示的数据的限制。但是我遇到过这个问题(如果我没记错的话)，数据跨越了几个数量级，而且我的数据到处都是，所以我需要某种程序化的解决方案，而不是硬套解决方案。

参见：http://matplotlib.org/api/ticker_api.html

下面我包含了示例数据、代码和屏幕截图。在#! /usr/bin/env python

from matplotlib import pyplot as plt

data = [

[1.83186088e-08,0.03275],

[1.07139009e-07,0.03275],

[2.06376627e-07,0.03275],

[3.03918517e-07,0.03275],

[4.06032883e-07,0.03275],

[5.01194017e-07,0.03275],

[6.02195723e-07,0.03275],

[7.03536925e-07,0.03275],

[8.04625154e-07,0.03275],

[9.06401951e-07,0.03275],

[1.00041895e-06,0.03275],

[1.10230745e-06,0.03275],

[1.2042525e-06,0.03275],

[1.30647822e-06,0.03275],

[1.40109887e-06,0.03275],

[1.50380097e-06,0.03275],

[1.60683242e-06,0.03275],

[1.70208505e-06,0.03275],

[1.80545692e-06,0.03275],

[1.90090648e-06,0.03275],

[2.00453092e-06,0.03275],

[2.10018627e-06,0.03275],

[2.20401747e-06,0.03275],

[2.30009359e-06,0.03275],

[2.4043033e-06,0.03275],

[2.50066449e-06,0.03275],

[2.60513728e-06,0.03275],

[2.70165405e-06,0.03275],

[2.80635938e-06,0.03275],

[2.90331342e-06,0.03275],

[3.00021199e-06,0.03275],

[3.10546819e-06,0.03275],

[3.20257899e-06,0.03275],

[3.30032923e-06,0.0327499999],

[3.40612833e-06,0.0327499999],

[3.50401732e-06,0.0327499997],

[3.60153069e-06,0.0327499996],

[3.70700708e-06,0.0327499993],

[3.80456907e-06,0.0327499988],

[3.90259984e-06,0.0327499982],

[4.00084149e-06,0.0327499973],

[4.10700266e-06,0.0327499959],

[4.2047462e-06,0.0327499942],

[4.30209468e-06,0.0327499918],

[4.40018204e-06,0.0327499886],

[4.50712875e-06,0.032749984],

[4.60630591e-06,0.0327499785],

[4.70519881e-06,0.0327499715],

[4.80398305e-06,0.0327499628],

[4.90251297e-06,0.0327499521],

[5.00182752e-06,0.032749939],

[5.10157551e-06,0.0327499232],

[5.20157575e-06,0.0327499043],

[5.30145192e-06,0.0327498822],

[5.40127044e-06,0.0327498565],

[5.500537e-06,0.0327498272],

[5.60773155e-06,0.0327497911],

[5.70660709e-06,0.0327497534],

[5.80610521e-06,0.0327497112],

[5.90651786e-06,0.0327496642],

[6.00749437e-06,0.0327496124],

[6.10822094e-06,0.0327495566],

[6.20042255e-06,0.0327495018],

[6.30049028e-06,0.0327494386],

[6.40035803e-06,0.0327493715],

[6.50035477e-06,0.0327493004],

[6.60056805e-06,0.0327492251],

[6.70029936e-06,0.0327491461],

[6.80054193e-06,0.0327490625],

[6.90130872e-06,0.0327489743],

[7.00202598e-06,0.0327488818],

[7.10217348e-06,0.0327487855],

[7.20243015e-06,0.0327486847],

[7.30199609e-06,0.0327485801],

[7.40193254e-06,0.0327484707],

[7.50188319e-06,0.0327483567],

[7.60306205e-06,0.0327482367],

[7.70357184e-06,0.0327481129],

[7.80343389e-06,0.0327479853],

[7.90330165e-06,0.0327478532],

[8.00348513e-06,0.0327477162],

[8.10167039e-06,0.0327475777],

[8.206328e-06,0.0327474253],

[8.3020567e-06,0.0327472819],

[8.40527826e-06,0.0327471228],

[8.50095898e-06,0.0327469714],

[8.60536828e-06,0.0327468019],

[8.70106059e-06,0.0327466426],

[8.80396558e-06,0.032746467],

[8.90727378e-06,0.0327462865],

[9.00225164e-06,0.0327461166],

[9.10359892e-06,0.0327459311],

[9.20470894e-06,0.0327457418],

[9.30582982e-06,0.0327455481],

[9.40750123e-06,0.0327453488],

[9.50134495e-06,0.0327451608],

[9.60358199e-06,0.0327449513],

[9.70705637e-06,0.0327447344],

[9.80377546e-06,0.0327445269],

[9.90091941e-06,0.032744314],

]

times=[]

vals=[]

for elem in data:

times.append(elem[0])

vals.append(elem[1])

plt.plot(times,vals)

plt.show()