What u're trying to do is called weighted moving average smoothing.
However that should be done on 2-dimensions not 1 (like smoothing over all adjacent drops by coordinates not just by consequent drops) since your initial data are at least 2D.
Problem is you cannot apply that type of calculations at all to data which have finite number of factors.
There's measuring theory that differentiates types of scales. Claim/nrf is a categorial scale with only 2 attributes possible. Measuring theory forbids any arithmetic operation on categorial scaled data allowing only 2 operations on them (equal to, not equal to)
Your method won't work because it have obvious theoretic flaws in its core. Also prone to curve-fitting issues because u have finite amount of data but infinite amount of options of choosing smoothing weights each giving different overall result.
However that should be done on 2-dimensions not 1 (like smoothing over all adjacent drops by coordinates not just by consequent drops) since your initial data are at least 2D.
Problem is you cannot apply that type of calculations at all to data which have finite number of factors.
There's measuring theory that differentiates types of scales. Claim/nrf is a categorial scale with only 2 attributes possible. Measuring theory forbids any arithmetic operation on categorial scaled data allowing only 2 operations on them (equal to, not equal to)
Your method won't work because it have obvious theoretic flaws in its core. Also prone to curve-fitting issues because u have finite amount of data but infinite amount of options of choosing smoothing weights each giving different overall result.