def compute(self, rdf):
cut_beg = 13.0
cut_end = 15.0
# Removes values with a density of zero, they will get added back later.
rdf = rdf[nonzero(rdf[:,1])[0], :]
# Finds the first index where the density is greater than 0.25*rho.
i0 = nonzero(rdf[:,1] > 0.25)[0][0]
# Get distance (r) and energy (e).
r = rdf[:,0]
e = -kB*self.temperature*log(rdf[:,1])
dr_final = (cut_end-self.min_distance)/self.npts
# Figure out what range we need
# Compute derivative from splines.
rr = arange(cut_end, r[i0], -dr_final)[::-1]
interp = Interpolator(r,e)
ff = [-interp.derivative(ri) for ri in rr]
v0 = [5.0, 0.01]
lj96_err = lambda v: ff - lj96_force(v,rr)
v = optimize.leastsq(lj96_err, v0)[0]
# Add more values to short distance to make sure that
# LAMMPS run won't fail when pair distance < table min.
rpad = arange(self.min_distance, rr[0]-0.5*dr_final, dr_final)
fpad = lj96_force(v, rpad) + ff[0] - lj96_force(v, rr[0])
rr = concatenate((rpad, rr))
ff = concatenate((fpad, ff))
# Now we cut off forces smoothly at any point past max_attraction.
ff[rr>cut_beg] *= 0.5*(1.0+cos(pi*(rr[rr>cut_beg]-cut_beg)/(cut_end-cut_beg)))
ff[rr>cut_end] = 0.0
# Compute energy by integrating forces.
# Integrating backwards reduces noise.
ee = -simpson_integrate(rr[::-1], ff[::-1])[::-1]
ee -= ee[-1]
self.distance.append(rr)
self.force.append(ff)
self.energy.append(ee)
