simulate.Masses(nsamp, distribution='powerlaw') always returns the same distribution

[daniellabardalezgagliuffi]

Any alpha value for the powerlaw distribution of masses returns the exact same distribution.

nsamp = 100000

mass = simulate.simulateMasses(nsamp,distribution='power-law', parameters={'alpha':0.6},mass_range=[0.01,0.08])
mass2 = simulate.simulateMasses(nsamp,distribution='power-law', parameters={'alpha':-0.6},mass_range=[0.01,0.08])
mass3 = simulate.simulateMasses(nsamp,distribution='power-law', parameters={'alpha':100},mass_range=[0.01,0.08])
mass4 = simulate.simulateMasses(nsamp,distribution='power-law', parameters={'alpha':-100},mass_range=[0.01,0.08])

I don't know where the problem is exactly, but I think the default of alpha=0.5 is plotted regardless of the user input alpha. If I try to interpolate the distribution on my own, it doesn't work unless I include fill_value='extrapolate' on interp1d:

x = np.linspace(0.01,0.08,num=int(nsamp))
y = x**(1.-0.6)
y2 = x**(1.-100)
f = interp1d(y,x, fill_value='extrapolate')
f2 = interp1d(y2,x, fill_value='extrapolate')
masses = f(np.random.uniform(size=nsamp))
masses2 = f2(np.random.uniform(size=nsamp))

Now, those are definitely different distributions!

[daniellabardalezgagliuffi]

Add:

np.random.seed()

At the beginning of simulate.anything.