Fitting data with optimize.curve_fit
lab1.py <http://scipy-user.10969.n7.nabble.com/file/n18692/lab1.py> I have a couple questions. The data show as counts is the total number of counts in 60 seconds. When using the count rate instead of the total counts as the y data, curve_fit does not want to give a meaningful answer. It gives the co-variance as infinity and the cosine that is fit does not match the data. Using total counts y*60, the co-variance is reasonable and the cosine fits the data. Why does increasing the counts by 60 allow curve_fit to give a reasonable answer? A similar problem happens when trying to fit the first harmonic to this data, A11*cos(3x/pi) + A31*cos(3x/pi) but I must increase the counts artificially by at least 10 times for curve_fit to give me a curve that resembles the data being fit. Is there a better way to fit this data? Is what I am doing here legitimate artificially increase y to get a fit then just dividing by that amount to get the data back to count rate? Sorry for the noob questions and thanks. -- View this message in context: http://scipy-user.10969.n7.nabble.com/Fitting-data-with-optimize-curve-fit-t... Sent from the Scipy-User mailing list archive at Nabble.com.
Have you tried dividing by 60.0 (instead of 60) to get count rate from counts? Annoying integer arithmetic things can cause issues like this. Kevin Gullikson On Tue, Sep 24, 2013 at 7:10 PM, TFSM <tesla.bamf@gmail.com> wrote:
lab1.py <http://scipy-user.10969.n7.nabble.com/file/n18692/lab1.py> I have a couple questions. The data show as counts is the total number of counts in 60 seconds. When using the count rate instead of the total counts as the y data, curve_fit does not want to give a meaningful answer. It gives the co-variance as infinity and the cosine that is fit does not match the data. Using total counts y*60, the co-variance is reasonable and the cosine fits the data.
Why does increasing the counts by 60 allow curve_fit to give a reasonable answer?
A similar problem happens when trying to fit the first harmonic to this data, A11*cos(3x/pi) + A31*cos(3x/pi) but I must increase the counts artificially by at least 10 times for curve_fit to give me a curve that resembles the data being fit.
Is there a better way to fit this data? Is what I am doing here legitimate artificially increase y to get a fit then just dividing by that amount to get the data back to count rate? Sorry for the noob questions and thanks.
-- View this message in context: http://scipy-user.10969.n7.nabble.com/Fitting-data-with-optimize-curve-fit-t... Sent from the Scipy-User mailing list archive at Nabble.com. _______________________________________________ SciPy-User mailing list SciPy-User@scipy.org http://mail.scipy.org/mailman/listinfo/scipy-user
On Tue, Sep 24, 2013 at 8:10 PM, TFSM <tesla.bamf@gmail.com> wrote:
lab1.py <http://scipy-user.10969.n7.nabble.com/file/n18692/lab1.py> I have a couple questions. The data show as counts is the total number of counts in 60 seconds. When using the count rate instead of the total counts as the y data, curve_fit does not want to give a meaningful answer. It gives the co-variance as infinity and the cosine that is fit does not match the data. Using total counts y*60, the co-variance is reasonable and the cosine fits the data.
Why does increasing the counts by 60 allow curve_fit to give a reasonable answer?
A similar problem happens when trying to fit the first harmonic to this data, A11*cos(3x/pi) + A31*cos(3x/pi) but I must increase the counts artificially by at least 10 times for curve_fit to give me a curve that resembles the data being fit.
Is there a better way to fit this data? Is what I am doing here legitimate artificially increase y to get a fit then just dividing by that amount to get the data back to count rate? Sorry for the noob questions and thanks.
The mysteries of numerical optimization Many or most optimizers are not scale invariant, at least not under default options for example default starting values are ones, which might not be good in a rescaled version forward derivative: it could also be that epsfcn is not appropriate in the rescaled version http://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.leastsq.h... ftol looks scale invariant but not xtol which should, however, only affect the precision of the final estimate. Josef
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participants (3)
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josef.pktd＠gmail.com -
Kevin Gullikson -
TFSM