Example #1
0
import matplotlib.pyplot as plt


problem = He5
k_max = 3
order = 100
std = 26
contour = gauss_contour((0, k_max), order)
ks, _ = contour

QNums = namedtuple("qnums", "l j k")
Q = QNums(l=1, j=1.5, k=ks)

problem.V0 = -70
H = mom.hamiltonian(contour, problem, Q)
eigvals_1, eigvecs_1 = energies(H)

# problem.V0=-60
# H = mom.hamiltonian(contour, problem, Q)
# eigvals_2, eigvecs_2 = energies(H)

problem.V0 = -52
H = mom.hamiltonian(contour, problem, Q)
eigvals_3, eigvecs_3 = energies(H)

problem.V0 = -47
H = mom.hamiltonian(contour, problem, Q)
eigvals_4, eigvecs_4 = energies(H)

problem.V0 = -40
H = mom.hamiltonian(contour, problem, Q)
Example #2
0
plot = False

problem = He5
k_max = 3
order = 100
num_wfs = 10
QNums = namedtuple('qnums', 'l j')
Q = QNums(l=1, j=1.5)
problem.V0=-50

contour = gauss_contour((0, 0.2 -0.2j, k_max), [order/2,order/2])
points, weights = contour

H = mom.hamiltonian(contour, problem, Q)
eigvals_real, eigvecs_real = energies(H)

contour = gauss_contour((0, k_max), order)
points, weights = contour

H = mom.hamiltonian(contour, problem, Q)
eigvals_comp, eigvecs_comp = energies(H)

rmax = 100
r_order = 500
r = sp.linspace(1e-1, rmax, r_order)

def sqrd_wf(eigvec):
    wf = mom.gen_wavefunction(eigvec, Q, contour)
    return r_order / rmax * r**2 * absq(wf(r))*10/ norm(r*wf(r))**2 
Example #3
0
r0list=sp.hstack([sp.linspace(a,b,100),sp.linspace(b,c,100)])
omegalist=[]
for r in r0list:
    omegalist.append(1/(problem.mass*r**2))
basis_size=50
k_max=3

contour = gauss_contour([0, k_max], basis_size)
points, _ = contour
QNums = namedtuple('qnums', 'l j')
Q = QNums(l=1, j=1.5)

res_osc = []

truncation = 5

for i,omega in enumerate(omegalist):
    print (i+1),":",omega
    problem.HO_omega=omega
    H = osc.hamiltonian(basis_size, problem, Q)
    eigvals, _ = energies(H)
    res_osc.append(eigvals[0:20])

clipped_res = sp.clip(res_osc, -sp.inf, truncation)

# plt.plot(r0list,clipped_res,'.')
# plt.show()

script_dir = os.path.dirname(os.path.realpath(__file__)) + "/"
sp.savetxt(script_dir + "E(r0).data", 
            sp.array([r0list]+zip(*clipped_res)).T)
Example #4
0
    while rand == -1 or rand == res or rand in states:
        rand = random.sample(range(int(seg*basis_size/3),int((seg+1)*basis_size/3)),1)[0]
    return rand

n_rounds=100
resonance_i=18
n_p_segs=1
results=[]
for i in range(n_rounds):
    states = []
    for i in range(3*n_p_segs):
        states.append(sp_states[get_rand(i%3,resonance_i,states)])
    states.append(sp_states[resonance_i])
    for i,sp in enumerate(states):
        states[i] = SP(i,l=sp.l,j=sp.j,E=sp.E,eigvec=sp.eigvec,contour=sp.contour,basis=sp.basis)
    interaction = two_body.gen_interaction(states, J, problem=problem)
    mb_H = coupled.hamiltonian(states, interaction, J)
    mb_eigvals, mb_eigvecs = energies(mb_H)
    results.append(mb_eigvals)

ks=[]
for ev in results:
    for e in ev:
        ks.append(scipy.sqrt(2*problem.mass*e))
# plt.plot(scipy.real(ks),scipy.imag(ks),'*')
# plt.show()

kdata = scipy.array([scipy.real(ks), scipy.imag(ks)])
print scipy.shape(kdata)
script_dir = os.path.dirname(os.path.realpath(__file__)) + "/"
scipy.savetxt(script_dir + "he6mcarloJ2.data", kdata.T)
Example #5
0

sierpinski = gauss_contour([0, sierpa, sierpb,  sierpg, sierph, k_max], [  9,19,39,19,  200])

contours = [triangle_contour(peak_x, peak_y, k_max, order/3), gauss_contour([0, 0 -peak_y*1j, 2*peak_x -peak_y*1j, 2*peak_x, k_max], [order/4,order/4,order/4,order/4])]

V0 = -47
k_res = []

for m, contour in enumerate(contours):
    problem.V0=V0
    k_res.append(sp.empty(len(contour[0]), 'complex'))
    QNums = namedtuple('qnums', 'l j k')
    Q = QNums(l=1, j=1.5, k=len(contour[0]))
    H = mom.hamiltonian(contour, problem, Q)
    eigvals, eigvecs = energies(H)
    
    print res_index(eigvecs)
    
    
    for i in xrange(len(eigvals)):
        k_res[m][i] = sp.sqrt(2*problem.mass*eigvals[i])
        if sp.real(k_res[m][i])<10 ** -6:
            k_res[m][i] = abs(k_res[m][i]) * 1j
        if sp.imag(k_res[m][i]) > 0:
            k_res[m][i] = sp.conj(k_res[m][i])  
            
    print find_closest(k_res[m])
    
    print "::::"