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)
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
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)
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)
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 "::::"