在MatLab中,用法略有不同:yi = interp1(x,Y,xi,’cubic’) 剪刀:f = interp1d(x,Y,kind=’cubic’) yi = f(xi) 对于一个简单的例子,结果是相同的: MatLab软件:interp1([0 1 2 3 4], [0 1 2 3 4],[1.5 2.5 3.5],’cubic’) 1.5000 2.5000 3.5000 Python:interp1d([1,2,3,4],[1,...
参数 参数数据类型意义x(N,) array_like一维数据y(…,N,…) array_likeN维数据,其中插值维度的长度必须与x长度相同kindstr or int, optional给出插值的样条曲线的阶数 ‘zero’ 、’nearest’零阶 ‘slinear’ 、’linear’线性 ‘quadratic’ 、’cubic’二阶和三阶样条曲线,更高阶的曲线可以直接使用整数值...
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Development Platform: MatlabINTERP1.M:Code Contentfunction yi = interp1(varargin)% yi=interp1(x,y,xi)根据数据(x,y)给出在xi的线性插值结果yi.% yi=interp1(x,y,xi,'spline')使用三次样条插值.% yi=interp1(x,y,xi,'cubic')使用三次插值....
I’m trying to port some MatLab code over to Scipy, and I’ve tried two different functions from scipy.interpolate, interp1d and UnivariateSpline. The interp1d results match the interp1d MatLab function, but the UnivariateSpline numbers come out different – and in some cases very different....
python interp1d函数用法 1、enumerate():自动得到下标和值 seq = ['one', 'two', 'three'] for i, element in enumerate(seq): print i, element 0 one 1 two 2 three 1. 2. 3. 4. 5. 6. 7. 遍历删除 # 带条件按下标删除用pop
It works similarly to Matlab™ or scipy functions with thelinearinterpolation mode on, except that it parallelises over any number of desired interpolation problems and exploits CUDA on the GPU Parameters forinterp1d x: a (N, ) or (D, N) Pytorch Tensor: Either 1-D or 2-D. It contain...