当前位置:   article > 正文

scipy插值interpolation

scipy 插值
>>> from scipy.interpolate import interp1d
#
interp1d表示1维插值
>>>
>>> x = np.linspace(0, 10, num=11, endpoint=True) >>> y = np.cos(-x**2/9.0) >>> f = interp1d(x, y) >>> f2 = interp1d(x, y, kind='cubic')
#
kind='cubic' 省去kind参数 默认为线性插值
>>>
>>> xnew = np.linspace(0, 10, num=41, endpoint=True)
#xnew表示插值之后,画图,选取的画图点数,再用线将这些点连起来 >>> import matplotlib.pyplot as plt >>> plt.plot(x, y, 'o', xnew, f(xnew), '-', xnew, f2(xnew), '--') >>> plt.legend(['data', 'linear', 'cubic'], loc='best') >>> plt.show()
../_images/interpolate-1.png

插值的方法 通过kind 导入;

f2 = interp1d(x, y, kind='cubic') 返回一个插值后的函数

>>> from scipy.interpolate import interp1d
>>>
>>> x = np.linspace(0, 10, num=11, endpoint=True) >>> y = np.cos(-x**2/9.0) >>> f1 = interp1d(x, y, kind='nearest') >>> f2 = interp1d(x, y, kind='previous') >>> f3 = interp1d(x, y, kind='next') 
>>>
>>> xnew = np.linspace(0, 10, num=1001, endpoint=True) >>> import matplotlib.pyplot as plt >>> plt.plot(x, y, 'o') >>> plt.plot(xnew, f1(xnew), '-', xnew, f2(xnew), '--', xnew, f3(xnew), ':') >>> plt.legend(['data', 'nearest', 'previous', 'next'], loc='best') >>> plt.show() 
../_images/interpolate-2.png
 

转载于:https://www.cnblogs.com/ggzhangxiaochao/p/9083343.html

声明:本文内容由网友自发贡献,不代表【wpsshop博客】立场,版权归原作者所有,本站不承担相应法律责任。如您发现有侵权的内容,请联系我们。转载请注明出处:https://www.wpsshop.cn/w/码创造者/article/detail/803766
推荐阅读
相关标签
  

闽ICP备14008679号