else:
S = None
return chain(H,xyz,S=S)
def square_ladder(N,gamma,gamma_perp=None,do_cache=True):
if gamma_perp == None:
gamma_perp = gamma
H = [ matrix(zeros((N,N))) for i in range(2) ]
for n in range(1,N):
H[0][n-1,n] = -gamma_perp
H[0][n,n-1] = -gamma_perp
for n in range(N):
H[1][n,n] = -gamma
return chain(H,do_cache=do_cache)
def linchain(gamma,do_cache=True):
return square_ladder(N=1,gamma=gamma,do_cache=do_cache)
if __name__ == "__main__":
import cnt
import tightbinding
x = cnt.armchair(20)
ch = tightbinding.tight_binding_1stNN_graphene(x)
a = ch.Gs_L(energy=0.5)
b = ch.Gs_L(energy=1.0)
if self.period is None:
return self.integrate() / (self.x[-1] - self.x[0])
else:
return self.integrate() / (self.period)
if __name__ == '__main__':
import cnt,chain
import units
set_printoptions(precision=2,linewidth=200)
N = 50
B = 200
x = cnt.armchair(N)
ch = chain.tight_binding_1stNN_graphene(x)
ch.set_bfield([B * units.Tesla,0,0])
def do_scan():
scan = scan_adaptive(
lambda E: ch.band_energies(E)[2*N-10:2*N+10],
linspace(2*pi/3*0.9,2*pi/3*1.1,8),
periodic = False,
# periodic = True,
)
for i in range(10):
scan.refine_bends(maxquot=2.0)
print "Now at: %i points"%len(scan.x)