> ltemp = [ydata[i] - ydata[0] for i in range(ll)]
> ytemp = [ltemp[i] * .001 for i in range(ll)]
> ltemp = [xdata[i] - xdata[0] for i in range(ll)]
> xtemp = [ltemp[i] * .001 for i in range(ll)]
Use the vectorization given by numpy:
ytemp = (ydata - ydata[0]) * 0.001
xtemp = (xdata - xdata[0]) * 0.001
....
fitted = popt[0] - popt[1] * np.exp(-popt[2] * xtemp)
or better
fitted = func2fit(xtemp, *popt)
> #
> # popt is a list of the three optimized fittine parameters [a, b, c]
> # we are interested in the value of a.
> # cov is the 3 x 3 covariance matrix, the standard deviation (error) of the
> fit is
> # the square root of the diagonal.
> #
> popt,cov = curve_fit(func2fit, xtemp, ytemp)
> #
> # Here is what the fitted line looks like for plotting
> #
> fitted = [popt[0] - popt[1] * np.exp(-popt[2] * xtemp[i]) for i in
> range(ll)]
> #
> # And now plot the results to check the fit
> #
> fig1, ax1 = plt.subplots()
> plt.title('Normalized Data ' + str(run_num))
> color_dic = {0: "red", 1: "green", 2: "blue", 3: "red", 4: "green", 5:
> "blue"}
> ax1.plot(xtemp, ytemp, marker = '.', linestyle = 'none', color =
> color_dic[run_num])
> ax1.plot(xtemp, fitted, linestyle = '-', color = color_dic[run_num])
> plt.savefig('Normalized ' + str(run_num))
> perr = np.sqrt(np.diag(cov))
> return popt, cov, xdata[0], ydata[0], fitted, perr[0]
--
https://mail.python.org/mailman/listinfo/python-list
