Replies: 4 comments 2 replies
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Dear @elafmusa, |
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Dear @swhite2401 , I see that the orbit dose not change a lot: rms orbit x: 9.401504462441926 mu m, rms orbit y: 37.641367341992726 mu m fit_tune(ring, get_refpts(ring, 'QF*'), Fitting Tune... rms orbit x: 9.38948243175231 mu m, rms orbit y: 37.587432828408915 mu m |
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Ok thanks, could you send me your script so I can take a look? |
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Dear @elafmusa , I have done such correction for FCC lattice with errors (the results are for example in A.Franchi et al. "Analytic derivative... " IPAC23-MOPL069). I am using a smaller set of quadrupoles: focussing_quadrupoles_for_tune = 'QF[0-9]' and the option to match integer part of the tunes as well at.fit_tune returns Fitting Tune... Probably my initial errors are smaller then yours as well. best regards |
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Hi everyone,
I am using the function fit_tune for the Fcc-ee z lattice through my orbit and optics corrections implemented in PyAT
I used fit_tune after appling shift errors to the arc quadrupoles of value sigma= 10 micro randomly distributed with cut off at 2.5sigma
I noticed that the vertical tune is fitted well while the horizontal one is not, an example:
fit_tune(ring, get_refpts(ring, 'QF*'),
get_refpts(ring, 'QD*'),[0.2602333 , 0.37990665] )
Fitting Tune...
Initial value [0.25203866 0.37591178]
iter# 0 Res. 0.0004360366681724843
iter# 1 Res. 0.002409322744671066
iter# 2 Res. 0.013324557952700237
Final value [0.14480133 0.37976749]
I tried to reduce the error values to 1 micron, and i see the same effect.
Thank you in advanced,
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