In [3]:

    

%matplotlib inline
from pylab import *
from astropy.modeling import models, fitting

d_June17th = array([#[32950,25],
            #[32651,11],
            #[32550,7.32],
            [32350,4.21],
            #[32150,10],
            [32450,5.99],
                   [32301,7.61]])
d_june19th=array([[32860,3.89],
                 [32960,9.7],
                 [32763,3.34],
                 [32664,4.81]])

#define vectors to plot
x=d_june19th[:,0]
y=d_june19th[:,1]


# Find the best approximation of the data using a Parabola
g_init = models.Polynomial1D(2)
fit_g = fitting.LinearLSQFitter()
g = fit_g(g_init, x, y)

# Plot the data with the best-fit model
plt.figure(figsize=(8,5))
plt.plot(x, y, 'ko',label='data')
x_precise=np.arange(x.min()-200,x.max()+200)
plt.plot(x_precise, g(x_precise), label='parabola')
plt.xlabel('Position')
plt.ylabel('width')
plt.legend(loc=2)


    

    
Out[3]:





<matplotlib.legend.Legend at 0x7fa4ca28d278>






    

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