Cleanup the guess algorithms for curve fitting #896
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nkanazawa1989
commented
Aug 23, 2022
| # due to limitation of the experimental resource, e.g. limited job circuits. | ||
| freq = float(fit_out[2]) | ||
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| if freq < 1.5 / (dt * len(x)): |
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Maybe we can handle this outside the function, i.e. composite one, to get two individual guesses.
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Summary
This PR simplifies complicated oscillation signal initial guess, in which we usually must take special care for low frequency signal (e.g. FFT is not robust to bias, thus we should precisely eliminate base offset, but this is hardly estimated for low frequency signal). This PR adds/modifies several initial guess functions, and these functions are integrated into a meta function
composite_sinusoidal_estimatethat internally assembles the best algorithm.Details and comments
New algorithm
sinusoidal_freq_offsetis also introduced by the PR. This function simultaneously estimates base offset and frequency and thus this is robust when both base and frequency are not known. This gist shows some robustness analysis I did, and the result indicates the algorithm provides reasonable guess when data contains >T/2 samples.(something still missing)
exp_decaywhen it is used for damped oscillation signal. Any thoughts for improvement?