Understanding Rebinning: A Data Resampling Technique

Rebinning is a data manipulation technique similar to smoothing, where N points are replaced by 1 point using a functional weighting. This process involves resampling data, linear interpolation, boxcar averaging, and convolution with a kernel function. It is essential to consider boundary effects and always plot the resampled data alongside the original for validation.

• Data Manipulation
• Resampling Technique
• Rebinning
• Data Analysis
• Convolution

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1. REBINNING Very straight forward but beware the black box

2. Rebinding of data is quite similar to smoothing You run a filter over N points, and replace those N points by 1 point using some functional weighting of the N-points

3. Resample data for rebin

4. RESAMPLING TO REBINNING Linear interpolation (replace N points by 1 point = 5 make a line from N-2 to N+2 and plug and plug the midpoint value in) Simple boxcar averaging of N points in a time series (boxcars do not overlap hence resampling and not smoothing). Kernal rebinning convolve N points with some function (gaussian) You need to worry about boundary effects sometimes always plot the resampled data on the original