Spent most of today searching for a method of interpolation using Scipy. Finally found one:

Still need to determine how many samples I need for a strong Fourier Series. Look over the Nyquist-Shannon sampling theorem also.

Since I’m using Scipy, here’s a good introduction to Scipy: http://scipy-lectures.github.com/intro/scipy.html

This numerical recipes blog looks promising: http://numericalrecipes.wordpress.com/2009/04/30/the-discrete-fourier-transform/

The dspGuru looks like a good site to become familiar with: http://www.dspguru.com/dsp/faqs/multirate/interpolation

Loading a csv file with the elbow angle values would be useful: http://stackoverflow.com/questions/3518778/how-to-read-csv-into-record-array-in-numpy

KEEP WORKING TOWARDS:

1. Interpolate the 1D data and obtain [LARGER AMOUNT] of sampled data points.

2. Determine how many the [LARGER AMOUNT] of sampled points should be.

3. Run FFT on the discrete points.

4. Take the resulting frequencies to represent the elbows rotational function.

5. Keep in mind how to connect and streamline this process so that others can replicate.

6. Make sure it’s visually communicative.

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