def precompute(args):
'''计算'''
#avsb一个共有两个能带
#第一个是t=1的d能带，第二个是t=0.5的p能带
brlu = brillouin()
ltris, ladjs = hexagon_split(brlu, args.mesh)
if numpy.mod(args.patches, 6) != 0:
raise ValueError("patch数量需要是6的倍数")
pinfos = numpy.ndarray((2, args.patches), dtype=object)
pinfos[0, :] = get_von_hove_patches(args.patches, d1_disp)
pinfos[1, :] = get_von_hove_patches(args.patches, p2_disp)
#lpats = [find_patch(tri.center, pinfos, None, None, None, mode=3) for tri in ltris]
#画一下费米面
d1pats = [
find_patch(tri.center, pinfos[0, :], None, None, None, mode=3)
for tri in ltris
]
edges = const_energy_line(ltris, ladjs, 0.0, d1_disp)
dedgs = brlu.edges + edges
draw_components(pinfos[0], dedgs, ltris, sgcc=[1]*len(dedgs), rtcc=d1pats, save=\
'{0}d1sur.svg'.format(args.prefix))
#p带的费米面
p2pats = [
find_patch(tri.center, pinfos[1, :], None, None, None, mode=3)
for tri in ltris
]
edges = const_energy_line(ltris, ladjs, 0.0, p2_disp)
dedgs = brlu.edges + edges
draw_components(pinfos[1], dedgs, ltris, sgcc=[1]*len(dedgs), rtcc=p2pats, save=\
'{0}p2sur.svg'.format(args.prefix))
#两个费米面在一起
d1sur = const_energy_line(ltris, ladjs, 0.0, d1_disp)
p2sur = const_energy_line(ltris, ladjs, 0.0, p2_disp)
draw_components(numpy.hstack([pinfos[0], pinfos[1]]),\
d1sur + p2sur + brlu.edges, [],\
sgcc=[1]*len(d1sur) + [2]*len(p2sur) + [3]*len(brlu.edges),\
save='{0}duosur.svg'.format(args.prefix))
#保存切分的信息
tri_save_to('{0}tris.txt'.format(args.prefix), brlu, args.mesh, ltris,
ladjs)
#保存区域的信息
dis_save_to('{0}d1pats.txt'.format(args.prefix), d1pats)
dis_save_to('{0}p2pats.txt'.format(args.prefix), p2pats)
#
edges, pidxs = const_energy_line_in_patches(ltris, ladjs, d1pats, 0.5,
d1_disp)
draw_components([], edges, [], sgcc=pidxs)
edges, pidxs = const_energy_line_in_patches(ltris, ladjs, p2pats, 0.3,
p2_disp)
draw_components([], edges, [], sgcc=pidxs)
def precompute(args):
'''计算patches'''
disp = {'square': dispersion, 'hole': hole_disp}[args.disp]
dispgd = {
'square': dispersion_gradient,
'hole': dispersion_gradient
}[args.disp]
#正方格子的布里渊区
brlu = brillouin()
#找到patches
pats = get_patches(brlu, args.patches, disp)
#切分布里渊区，切分的三角形的数目是mesh*mesh*4
ltris, ladjs = square_split(brlu, args.mesh)
tri_save_to('{0}_triangle_{1}.txt'.format(args.prefix, args.disp), brlu,
args.mesh, ltris, ladjs)
#patches和费米面的图片
lsur = const_energy_line(ltris, ladjs, 0., disp)
patches_visualize(pats, lsur,
'{0}_surface_{1}.svg'.format(args.prefix, args.disp))
#求出每个Rtriangle所在的patch
#这种投影法把交点设置在Umklapp surface
step = 3.1415927 / args.mesh / 2
lpats = [find_patch(tri.center, pats, disp, dispgd, step) for tri in ltris]
dis_save_to('{0}_district_{1}.txt'.format(args.prefix, args.disp), lpats)
district_visualize(ltris, lpats,
'{0}_patches_{1}.svg'.format(args.prefix, args.disp))
def precompute(args):
'''提前计算'''
brlu = brillouin()
#切分布里渊区
ltris, ladjs = rectangle_split(brlu, args.mesh)
tri_save_to(
'{0}_tris.txt'.format(args.prefix),
brlu, args.mesh, ltris, ladjs
)
#找到patches
pinfos = get_rect_patches(args.patches, args.disp)
#画一下费米面
surs = const_energy_line(ltris, ladjs, 0., dispersion)
#
pyplot.figure()
#绘制patches对应的点
xvals = []
yvals = []
for idx, pnt in enumerate(pinfos, 0):
xvals.append(pnt.coord[0])
yvals.append(pnt.coord[1])
pyplot.text(pnt.coord[0]+0.1, pnt.coord[1], 's%s' % idx)
pyplot.scatter(xvals, yvals, c='g', lw=4)
#绘制费米面的线
for seg in surs:
if seg is None:
continue
xvals = [_pt.coord[0] for _pt in seg.ends]
yvals = [_pt.coord[1] for _pt in seg.ends]
pyplot.plot(xvals, yvals, c='y', lw=1)
pyplot.savefig('{0}_surface.svg'.format(args.prefix))
pyplot.close()
#
step = 3.1415927 / args.mesh / 4
slpats = []
for tri in ltris:
pt_ = find_patch(tri.center, pinfos, dispersion, dispersion_gradient, step)
slpats.append(pt_)
dis_save_to('{0}_district.txt'\
.format(args.prefix), slpats)
district_visualize(ltris, slpats, '{0}_district.svg'\
.format(args.prefix))
def precompute(args):
'''计算'''
#需要用到的所有东西
brlu = brillouin()
ltris, ladjs = hexagon_split(brlu, args.mesh)
pinfos = get_patches(args.patches)
lpats = [
find_patch(tri.center, pinfos, None, None, None, mode=2)
for tri in ltris
]
#画一下费米面
edges = const_energy_line(ltris, ladjs, 0.0, dispersion)
dedgs = brlu.edges + edges
draw_components(pinfos, dedgs, ltris, sgcc=[1]*len(dedgs), rtcc=lpats, save=\
'{0}_sur.svg'.format(args.prefix))
#保存切分的信息
tri_save_to('{0}_tris.txt'.format(args.prefix), brlu, args.mesh, ltris,
ladjs)
#保存区域的信息
dis_save_to('{0}_pats.txt'.format(args.prefix), lpats)
edges, pidxs = const_energy_line_in_patches(ltris, ladjs, lpats, 0.0,
dispersion)
draw_components([], edges, [], sgcc=pidxs)
def precompute(args):
'''提前计算'''
brlu = brillouin()
set_stripe(args.stripe)
set_potential(args.nu)
#切分布里渊区
ltris, ladjs = square_split(brlu, args.mesh)
tri_save_to(
'{0}_{1:.2f}_{2:.2f}_tris.txt'.\
format(args.prefix, args.stripe, args.nu),
brlu, args.mesh, ltris, ladjs
)
#找到patches
spats = get_s_band_patches(args.patches)
ppats = get_p_band_patches(args.patches)
#画一下费米面
ssur = const_energy_line(ltris, ladjs, 0., s_band_disp)
psur = const_energy_line(ltris, ladjs, 0., p_band_disp)
#
pyplot.figure()
#绘制patches对应的点
xvals = []
yvals = []
for idx, pnt in enumerate(spats, 0):
xvals.append(pnt.coord[0])
yvals.append(pnt.coord[1])
pyplot.text(pnt.coord[0] + 0.1, pnt.coord[1], 's%s' % idx)
pyplot.scatter(xvals, yvals, c='g', lw=4)
#绘制费米面的线
for seg in ssur:
if seg is None:
continue
xvals = [_pt.coord[0] for _pt in seg.ends]
yvals = [_pt.coord[1] for _pt in seg.ends]
pyplot.plot(xvals, yvals, c='y', lw=1)
#
xvals = []
yvals = []
for idx, pnt in enumerate(ppats, 0):
xvals.append(pnt.coord[0])
yvals.append(pnt.coord[1])
pyplot.text(pnt.coord[0] + 0.1, pnt.coord[1], 'p%s' % idx)
pyplot.scatter(xvals, yvals, c='r', lw=4)
#
for seg in psur:
if seg is None:
continue
xvals = [_pt.coord[0] for _pt in seg.ends]
yvals = [_pt.coord[1] for _pt in seg.ends]
pyplot.plot(xvals, yvals, c='k', lw=1)
pyplot.savefig('{0}_{1:.2f}_{2:.2f}_sur.svg'.\
format(args.prefix, args.stripe, args.nu))
pyplot.close()
#
step = 3.1415927 / args.mesh / 2
slpats = []
for tri in ltris:
pt_ = find_patch(tri.center, spats, s_band_disp, s_band_gd, step)
slpats.append(pt_)
dis_save_to('{0}_{1:.2f}_{2:.2f}_spt.txt'\
.format(args.prefix, args.stripe, args.nu), slpats)
district_visualize(ltris, slpats, '{0}_{1:.2f}_{2:.2f}_spt.svg'\
.format(args.prefix, args.stripe, args.nu))
plpats = []
for tri in ltris:
pt_ = find_patch(tri.center, ppats, p_band_disp, p_band_gd, step)
plpats.append(pt_)
dis_save_to('{0}_{1:.2f}_{2:.2f}_ppt.txt'\
.format(args.prefix, args.stripe, args.nu), plpats)
district_visualize(ltris, plpats, '{0}_{1:.2f}_{2:.2f}_ppt.svg'\
.format(args.prefix, args.stripe, args.nu))