#Python matlab interp1 code

#Python matlab interp1 code

If you don't need to change the code of the functions itself this could save you some time. This overhead can be bypassed using the much faster jsystem function created by avivrosenberg based on Java.

It also seems that the original MatLab program that I am converting should have used linear interpolation, so the question is probably moot. Approach 1: There is a python library that let's you use MATLAB functions through wrappers: mlabwrap. System Calls with jsystem In case your Matlab code makes a lot of calls to system functions, the performance drop might be noticeable because of the large overhead that comes with MATLAB’s system function. (2048 data points)Īny thoughts as to how I can get results that are consistent with MatLab?Įdit: I understand that there is some latitude in implementation for cubic interpolation algorithms which probably accounts for the differences I'm seeing. Python: interp1d(,kind='cubic')()īut for a real-world example they are not the same: x = 0.0000e+000 2.1333e+001 3.2000e+001 1.6000e+004 2.1333e+004 2.3994e+004 Approach 1: There is a python library that lets you use MATLAB functions through wrappers: mlabwrap.

In MatLab the usage is slightly different: yi = interp1(x,Y,xi,'cubic')įor a trivial example the results are the same: Short answer Use InterpolatedUnivariateSpline instead: f InterpolatedUnivariateSpline (row1, row2) return f (interp) Long answer UnivariateSpline is a 'one-dimensional smoothing spline fit to a given set of data points' whereas InterpolatedUnivariateSpline is a 'one-dimensional interpolating spline for a given set of data points'. I'm converting a MatLab program to Python, and I'm having problems understanding why 1d is giving different results than MatLab interp1.