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Category:SalatQ:

How to best fit a sinusoidal function to a large data set

I have a large data set with many 2 dimensional vectors with values between -20 to +20 and I want to fit a sinusoidal function to them. I have a couple questions:

Which functions should I be looking at? Should I be looking at Scipy or something built in to Mathematica like Fit?
I have tried using numpy.polyfit, but the function that works seems to default to fitting sines and cosines (I’m using sin to have a function that starts off small and rises to a peak, and cos for a function that has multiple peaks). How do I control what functions it fits?

A:

The python package scipy.optimize.curve_fit does this and most scipy packages do this.

A:

I’m not sure if I understand you question correctly, but just trying to make sure I understand what you are trying to do.
What you want to do is fit a set of data to a specific function, using one or more of a bunch of different functions.
The data will look something like:
y=f(x)

where x is the independent variable and y is the dependent variable.
Now say you have n points of data x and y and want to fit f(x) to that data. You don’t want to just make up any f(x), and call that fit.
If that is the case, your best bet is to use scipy.optimize.curve_fit, which is included with scipy.
What this will do is fit a user-defined model. There are a bunch of different functions in scipy.optimize.curve_fit that you can use to fit your data.
I can’t give you the exact syntax on what to do. I’ll try to point you in the right direction. As far as the documentation and syntax I can only point you to:
Hope that helps you!

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Al Qunut By Sudais Pdf Download

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