These are similar expressions, not equivalents. Returns the one-dimensional piecewise linear interpolant to a function with given discrete data points ( xp, fp ), evaluated at x. Some key differences Rough equivalents The table below gives rough equivalents for some common MATLAB expressions. This guide will help MATLAB users get started with NumPy. It also seems that the original MatLab program that I am converting should have used linear interpolation, so the question is probably moot. MATLAB and NumPy have a lot in common, but NumPy was created to work with Python, not to be a MATLAB clone. (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 objnew rfinterp1 (objold,newfreq) interpolates the network parameter data in objold at the specified frequencies, newfreq, storing the results in objnew. In MatLab the usage is slightly different: yi = interp1(x,Y,xi,'cubic')įor a trivial example the results are the same: Here's the basic syntax: Vq interp1( X, V, Xq) In this equation, 'X' and 'V' are your known data points, 'Xq' are the points you want to interpolate, and 'Vq' are the. It's a one-dimensional interpolation function that can handle any curve you throw at it. Next message (by thread): SciPy-user Dot in greater than 2D. This function in MATLAB is like the chameleon of interpolation, adapting to your needs.

I'm converting a MatLab program to Python, and I'm having problems understanding why 1d is giving different results than MatLab interp1. Previous message (by thread): SciPy-user equivalent to Matlabs interp1.

Python matlab interp1