In [1]:

    

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [40]:

    

def imf1(x):
    m1 = 0.5
    m2 = 1.39
    m3 = 6
    a0 = -1.26
    a1 = -1.49
    a2 = -3.02
    a3 = -2.28
    #normalising factors
    c1 = m1**a0/m1**a1
    c2 = (c1*m2**a1)/m2**a2
    c3 = (c2*m3**a2)/m3**a3
    
    y = np.zeros_like(x)
    
    y[x<m1] = np.power(x[x<m1],a0)
    y[(x>=m1) & (x<m2)] = c1*np.power(x[(x>=m1) & (x<m2)],a1)
    y[(x>=m2) & (x<m3)] = c2*np.power(x[(x>=m2) & (x<m3)],a2)
    y[x>=m3] = c3*np.power(x[x>=m3],a3)
    return(y)


    

In [41]:

    

x = np.linspace(0.09,120,1000)


    

In [42]:

    

y = imf1(x)


    

In [43]:

    

plt.plot(x,y)
plt.yscale("log")
plt.xscale("log")


    

In [28]:

    

m1 = 0.5
m2 = 1.39
m3 = 6
a0 = -1.26
a1 = -1.49
a2 = -3.02
a3 = -2.28


    

In [31]:

    

m1**a0/m1**a1


    

    
Out[31]:





0.8526348917679566

In [30]:

    




    

    
Out[30]:





2.8088897514759945

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