Warning...this requires scripting and iteration, and is not theoretical -- it is a real problem I haven't been able to solve, but I'm sure someone here can... :-)(adsbygoogle = window.adsbygoogle || []).push({});

Data: each .csv file is a test recorded at a time interval of 7.5Hz and each file has 3 columns. The first column is time in seconds, the second column is a multiplier (see formula below), and third column is the measured value (to be "transformed"). There is also a corresponding value for each log in the "W.csv" file.

Formula (to produce a value for each file): R = [W_log_Val] / [#.log_val] -->

Goal: A function/transform "F" that can be applied on the variable recorded in the third column ("col3_val") such that the coefficient of variation of all the "R" values is minimized. The CV should definitely be less than 3%, but I expect a good solution could easily make it less than 1%.

- W_log_Val is the corresponding value for that file located in the "W.csv" file.

- the #.log_val = ∑(col2_val)*(F(col3_val)) (a summation over the rows in the file)
- F(col3_val) is the function/transformation of the measured value to be found

Additional Constraint: A plot of the R values against VF values should show no trend/pattern, where -->

See an example of the trend when no transform is applied in attached jpg.

- VF = [#.log_val]/[Ts]
- "Ts" = the total time for each log (i.e., the value in the last row of the first column for each log)

Example:

applying a SQRT transformation to the measured value "col3_val" helps significantly, but the CV is still around 8-9% and does not satisfy the constraint.

See example of trend after applying sqrt in attached PDF

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# Find Function/Transform for signal that minimizes CV of data

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