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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def selfconsistency(self, u_int, J_coup, mean_field_prev=None):
 """Iterates over the hamiltonian to get the stable selfcosistent one"""

        if mean_field_prev is None:
            mean_field_prev = np.array([self.param['ekin']]*2)
        hlog = [mean_field_prev]

        self.oper['Hint'] = self.inter_spin_hamiltonian(u_int, J_coup)
        converging = True
        half_fill = (self.param['populations'] == 0.5).all()
        while converging:
            if half_fill:
                self.update_H(hlog[-1], self.param['lambda'])
            else:
                res = root(self.restriction, self.param['lambda'], (hlog[-1]))#, method='lm')
                if not res.success:
                    res.x = res.x * 0.5 + 0.5*self.param['lambda']
                    self.update_H(self.mean_field()*0.5 + 0.5*hlog[-1], res.x)
                    print('fail', self.param['populations'][3:5])
                if (self.quasiparticle_weight() < 0.001).all():
                    return hlog
                self.param['lambda'] = res.x

            hlog.append(self.mean_field())
            converging = (abs(hlog[-1] - hlog[-2]) > self.param['tol']).all() \
                    or (abs(self.restriction(self.param['lambda'], hlog[-1])) > self.param['tol']).all()

        return hlog