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"""

ydata=[float(_) for _ in ydata.strip().splitlines()]


from scipy.optimize import curve_fit as cf
import numpy as np
from matplotlib import pyplot as plt
from scipy.optimize import basinhopping as bh
xdata=np.arange(0.05,500.01,0.05)

def damped_sin_fun(x,a,b,c,d,e):
    return a*(np.exp(-x/e))*np.sin(b*x+c)+d

fft = np.fft.fft(ydata - np.array(ydata).mean())
timestep = xdata[1] - xdata[0]
freq = np.fft.fftfreq(len(fft), d=timestep)
largest_component = np.abs(fft).argmax()
phase_guess = np.angle(fft[largest_component]) * freq[largest_component]
initial_frequency_guess = freq[largest_component] * 2 * np.pi

p_opt,p_cov=cf(damped_sin_fun,xdata,ydata, p0=[0.05, initial_frequency_guess, np.pi/2 ,np.mean(ydata),2.3])

print(initial_frequency_guess)
print(p_opt)
#print(((damped_sin_fun(xdata,*p_opt)-ydata)**2).mean())
optimize=bh(lambda args: ((damped_sin_fun(xdata,*args)-ydata)**2).mean(), [0.05, initial_frequency_guess, np.pi/2 ,np.mean(ydata),2.3]).x


plt.plot(xdata,damped_sin_fun(xdata,*p_opt))
plt.xlim((0,10))
#plt.plot(xdata,damped_sin_fun(xdata,*optimize))
#plt.plot(xdata,ydata, 'r.-', ms=1)
plt.show()