def band_v(vval, pinfos):
'''带内的相互作用'''
npat = len(pinfos)
ret = numpy.zeros((npat, npat, npat))
ndit = numpy.nditer(ret, flags=['multi_index'])
while not ndit.finished:
k1idx, k2idx, k3idx = ndit.multi_index
#p2这个带
k1v = pinfos[k1idx]
k2v = pinfos[k2idx]
k3v = pinfos[k3idx]
k4v = shift_kv(k1v, k2v)
k4v = shift_kv(k4v, Point(-k3v.coord[0], -k3v.coord[1], 1))
#公式中的K
ckv = shift_kv(k3v, Point(-k2v.coord[0], -k2v.coord[1], 1))
#p这个带只需要第二个
_, k1nu, _ = get_nu_numpy(k1v.coord[0], k1v.coord[1])
_, k2nu, _ = get_nu_numpy(k2v.coord[0], k2v.coord[1])
_, k3nu, _ = get_nu_numpy(k3v.coord[0], k3v.coord[1])
_, k4nu, _ = get_nu_numpy(k4v.coord[0], k4v.coord[1])
#计算数值
s3_4 = 0.25*numpy.sqrt(3)
vab = k1nu[0]*k2nu[1]*k3nu[1]*k4nu[0] + k1nu[1]*k2nu[0]*k3nu[0]*k4nu[1]
ret[k1idx, k2idx, k3idx] += 2 * vval * \
numpy.cos(0.25*ckv.coord[0] + s3_4*ckv.coord[1]) * vab
vac = k1nu[0]*k2nu[2]*k3nu[2]*k4nu[0] + k1nu[2]*k2nu[0]*k3nu[0]*k4nu[2]
ret[k1idx, k2idx, k3idx] += 2 * vval * \
numpy.cos(0.5*ckv.coord[0]) * vac
vbc = k1nu[1]*k2nu[2]*k3nu[2]*k4nu[1] + k1nu[2]*k2nu[1]*k3nu[1]*k4nu[2]
ret[k1idx, k2idx, k3idx] += 2 * vval * \
numpy.cos(-0.25*ckv.coord[0] + s3_4*ckv.coord[1]) * vbc
ndit.iternext()
return ret
def load_eqtriangle(lines, hexa, nps, ltris, ladjs):
'''读取正三角形'''
if nps == -1 or hexa is None:
raise ValueError('没有初始化Square')
idx_to_tri = {}
for line in lines:
lstr = line.split(':')
idx = int(lstr[1])
v0str = lstr[3].split(',')
v1str = lstr[5].split(',')
v2str = lstr[7].split(',')
v0pt = Point(float(v0str[0]), float(v0str[1]), 1)
v1pt = Point(float(v1str[0]), float(v1str[1]), 1)
v2pt = Point(float(v2str[0]), float(v2str[1]), 1)
rtri = Eqtriangle([v0pt, v1pt, v2pt])
idx_to_tri[idx] = rtri
#最上面是2*nps+1，最下面是4*nps-1，一共nps个
num = (2*nps+1) + (4*nps-1)
num = num * nps
ltris = numpy.ndarray(num, dtype=Eqtriangle)
#print(len(idx_to_tri), num)
if len(idx_to_tri) != num:
raise ValueError('nps和Rtriangle大小不一致')
for idx in range(num):
ltris[idx] = idx_to_tri[idx]
return hexa, nps, ltris, ladjs
def load_triangle(lines, rtg, nps, ltris, ladjs):
'''加载三角形\n
加载之后会按照idx的顺序组成一个列表，这样加载adj的时候会按照列表的顺序找到
相应的Triangle
'''
if nps == -1 or rtg is None:
raise ValueError('没有初始化Square')
#
if rtg.width >= rtg.height: #如果比较宽
nshape = (nps * numpy.floor_divide(rtg.width, rtg.height), nps)
else:
nshape = (nps, nps * numpy.floor_divide(rtg.height, rtg.width))
nshape = (numpy.int(nshape[0]), numpy.int(nshape[1]))
#
ltris = numpy.ndarray(nshape[0] * nshape[1] * 4, dtype=Triangle)
if len(lines) != nshape[0] * nshape[1] * 4:
raise ValueError('ltris读取时长度对应不上')
for line in lines:
lstr = line.split(':')
idx = int(lstr[1])
v0str = lstr[3].split(',')
v1str = lstr[5].split(',')
v2str = lstr[7].split(',')
v0pt = Point(float(v0str[0]), float(v0str[1]), 1)
v1pt = Point(float(v1str[0]), float(v1str[1]), 1)
v2pt = Point(float(v2str[0]), float(v2str[1]), 1)
tri = Triangle([v0pt, v1pt, v2pt])
ltris[idx] = tri
return rtg, nps, ltris, ladjs
def pi_ab_minus_ec(posia, negaa, lamb, qval, dispb, ksft, area):
'''使用能量cutoff作为flow parameter的bubble\n
posi是dispa为+LAMBDA的边，nega是dispa为-LAMBDA的边, lamb是LAMBDA\n
这两个边应该是限制在dispa这个带的第n个patch中的，这两个边也就暗含了n\n
dispa和dispb是两个带的色散关系\n
qval是需要平移的大小，应该用一个Point来包装，\n
kshf是动量相加的函数, 这个函数应该能处理好到第一布里渊区的映射\n
```(10.112)本身已经处理好了动量守恒，k, k-q是需要满足动量守恒的关系的，而处理好```
```k-q到第一布里渊区的映射就处理好了Umklapp```
'''
'''
10.112中的 PI^-(n, q) = -LAMBDA (2pi)^-2 beta^-1 Int_{k in k_n} G'(k)G(- k + Q)
= -LAMBDA (2pi)^-2 Int_{k in k_n} CITA() -DELTA()
{ beta^-1 sum_{omega} [(i*omega-disp(k))(-i*omega-disp(-k + q))]^-1 }
在零温下这个频率积分等于，注意-k那里把频率也给反过来了
+CITA(+disp(k)disp(-k+q)) / (abs(disp(k)) + abs(disp(-k+q)))
原式就等于
= LAMBDA (2pi)^-2 Int_{k in k_n} {
DELTA(abs(disp(k))-LAMBDA) CITA(abs(disp(-k+q)-LAMBDA))
CITA(disp(k)disp(-k+q)) / (abs(disp(k)) + abs(disp(-k+q))) }
第二个CITA限制了disp(k)和disp(-k+q)同号，积分积掉DELTA，分类讨论正负
= LAMBDA (2pi)^-2 {
Int_{disp(k) = +LAMBDA} CITA(disp(-k+q) - LAMBDA) / (LAMBDA + disp(-k+q)) +
Int_{disp(k) = -LAMBDA} CITA(-disp(-k+q) -LAMBDA) / (LAMBDA - disp(-k+q))
}
'''
#积分正LAMBDA的线
intposi = 0.
for edg in posia:
kval = middle_point(edg.ends[0], edg.ends[1])
nega_k = Point(-kval.coord[0], -kval.coord[1], 1)
kprim = ksft(nega_k, qval)
#CITA
disp_kprim = dispb(kprim.coord[0], kprim.coord[1])
if disp_kprim < lamb:
continue
#要计算线元的长度
intposi += edg.length / (lamb + disp_kprim)
#积分负LAMBDA的线
intnega = 0.
for edg in negaa:
kval = middle_point(edg.ends[0], edg.ends[1])
nega_k = Point(-kval.coord[0], -kval.coord[1], 1)
kprim = ksft(nega_k, qval)
#CITA
disp_kprim = dispb(kprim.coord[0], kprim.coord[1])
if -disp_kprim < lamb:
continue
intnega += edg.length / (lamb - disp_kprim)
#乘上系数
result = lamb * (intposi + intnega) / area#numpy.square(numpy.pi*2)
return result
def __init__(self, brlu, ltris, ladjs, mpinfo, mlpats, \
mdisp, mdispgd, ksft, lamb0, find_mode):
self._brlu = brlu
self._ltris = ltris
self._nps = numpy.sqrt(len(ltris)) / 2
self._bandnum = len(mpinfo)
self._ladjs = ladjs
self._mpinfo = mpinfo
self._mlpats = mlpats
self._mdisp = mdisp
self._mdispgd = mdispgd
self._ksft = ksft
self._lamb0 = lamb0
self._patchnum = len(self._mpinfo[0])
#找到每个对应的n4
#现在每个带都有自己的idx4
if find_mode == 1:
data_list = []
idxit = numpy.nditer(U, flags=['multi_index'])
step = numpy.minimum(brlu.width, brlu.height) / 10 / self._nps
while not idxit.finished:
bd1, bd2, bd3, bd4, idx1, idx2, idx3 = idxit.multi_index
kv1, kv2, kv3 = mpinfo[bd1, idx1], mpinfo[bd2,
idx2], mpinfo[bd3,
idx3]
kv4 = ksft(ksft(kv1, kv2),
Point(-kv3.coord[0], -kv3.coord[1], 1))
data_list.append(
(kv4, mpinfo[bd4, :], mdisp[bd4], mdispgd[bd4], step))
#idx4 = find_patch(kv4, mpinfo[bd4, :], mdisp[bd4],\
# mdispgd[bd4], numpy.pi / 2 / self._nps)
#self._mk4tab[idxit.multi_index] = idx4
idxit.iternext()
with multiprocessing.Pool(get_procs_num()) as pool:
self._mk4tab = pool.starmap(find_patch, data_list)
self._mk4tab = numpy.reshape(self._mk4tab, U.shape)
return
data_list = []
idxit = numpy.nditer(U, flags=['multi_index'])
while not idxit.finished:
bd1, bd2, bd3, bd4, idx1, idx2, idx3 = idxit.multi_index
kv1, kv2, kv3 = mpinfo[bd1, idx1], mpinfo[bd2, idx2], mpinfo[bd3,
idx3]
kv4 = ksft(ksft(kv1, kv2), Point(-kv3.coord[0], -kv3.coord[1], 1))
data_list.append((kv4, mpinfo[bd4, :]))
idxit.iternext()
find_func = {2: find_patch_mode2, 3: find_patch_mode3}[find_mode]
with multiprocessing.Pool(get_procs_num()) as pool:
self._mk4tab = pool.starmap(find_func, data_list)
self._mk4tab = numpy.reshape(self._mk4tab, U.shape)
def get_sublattice_u(umat, pinfos):
'''将能带表示变回到子格子表示'''
npat = len(pinfos)
ret = numpy.zeros((3, 3, 3, 3, npat, npat, npat))
place_holder = numpy.zeros((npat, npat, npat))
ndit = numpy.nditer(place_holder, flags=['multi_index'])
while not ndit.finished:
k1idx, k2idx, k3idx = ndit.multi_index
#p2这个带
k1v = pinfos[k1idx]
k2v = pinfos[k2idx]
k3v = pinfos[k3idx]
k4v = shift_kv(k1v, k2v)
k4v = shift_kv(k4v, Point(-k3v.coord[0], -k3v.coord[1], 1))
_, k1nu, _ = get_nu_numpy(k1v.coord[0], k1v.coord[1])
_, k2nu, _ = get_nu_numpy(k2v.coord[0], k2v.coord[1])
_, k3nu, _ = get_nu_numpy(k3v.coord[0], k3v.coord[1])
_, k4nu, _ = get_nu_numpy(k4v.coord[0], k4v.coord[1])
place_holder2 = numpy.zeros((3, 3, 3, 3))
ndit2 = numpy.nditer(place_holder2, flags=['multi_index'])
while not ndit2.finished:
si1, si2, si3, si4 = ndit2.multi_index
ret[si1, si2, si3, si4, k1idx, k2idx, k3idx] =\
umat[k1idx, k2idx, k3idx] * k1nu[si1] * k2nu[si2] * k3nu[si3] * k4nu[si4]
ndit2.iternext()
ndit.iternext()
return ret
def get_patches(brlu: Square, npatch, dispfun):
'''获得费米面上面的patch\n
dispfun是色散关系\n
'''
gap = numpy.pi * 2 / npatch
angles = [gap * (idx + 0.5) for idx in range(npatch)]
#解出每个角度下和费米面的交点
patches = []
#半径最大是这么多
maxv = brlu.width * 1.414 / 2.
for ang in angles:
xcoff = numpy.cos(ang)
ycoff = numpy.sin(ang)
def __raddisp(rad):
kxv = rad * xcoff
if numpy.abs(kxv) > numpy.pi:
kxv = numpy.sign(kxv) * numpy.pi
kyv = rad * ycoff
if numpy.abs(kyv) > numpy.pi:
kyv = numpy.sign(kyv) * numpy.pi
return dispfun(kxv, kyv)
rrad = optimize.bisect(__raddisp, 0., maxv)
patches.append(Point(rrad * xcoff, rrad * ycoff, 1))
return patches
def pi_plus_ec(posi, nega, lamb, qval, disp, ksft, area):
'''使用能量cutoff作为flow parameter的bubble\n
posi是能量为+LAMBDA的边，nega是能量为-LAMBDA的边, lamb是LAMBDA\n
disp是色散关系，qval是需要平移的大小，应该用一个Point来包装，\n
kshf是动量相加的函数, 这个函数应该能处理好到第一布里渊区的映射\n
area是第一布里渊区的面积\n
```(10.112)本身已经处理好了动量守恒，k, k-q是需要满足动量守恒的关系的，而处理好```
```k-q到第一布里渊区的映射就处理好了Umklapp```
'''
'''
10.112中的 PI^+(n, q) = +LAMBDA (2pi)^-2 beta^-1 Int_{k in k_n} G'(k)G(k - Q)
其中有一个beta是频率积分带来的，2pi^2是动量积分带来的
G(k)=CITA(LAMBDA < abs(disp(k))) / i*omega - disp(k)
G'(k)=-DELTA(abs(disp(k))-LAMBDA) / i*omege - disp(k)
在零温的情况下10.112中的频率部分可以积分出来，此后的k都是不包含频率的
= +LAMBDA (2pi)^-2 Int_{k in k_n} CITA() -DELTA()
{ beta^-1 sum_{omega} [(i*omega-disp(k))(i*omega-disp(k - q))]^-1 }
花括号中的内容求和完之后等于 - CITA(-disp(k)disp(k-q)) / (abs(disp(k)) + abs(disp(k-p)))
积分会变成
= +LAMBDA (2pi)^-2 Int_{k in k_n} DELTA(abs(disp(k))-LAMBDA) CITA(LAMBDA<abs(disp(k-q)))
CITA(-disp(k)disp(k-q)) / (abs(disp(k)) + abs(disp(k-p)))
因为采用的能量cutoff中有一个 DELTA(abs(disp(k))-LAMBDA)，disp(k)等于正的或者负的LAMBDA
而CITA(-disp(k)disp(k-q))限制了disp(k)和disp(k-q)符号相反
所以上式变成
(第一项disp(k)=LAMBDA>0，于是disp(k-q)<0，而且abs(disp(k))=-disp(k)>LAMBDA)
(第二项类似，分子中的abs(disp(k))都可以直接换成LAMBDA，abs(disp(k-q))也都知道符号)
= +LAMBDA (2pi)^-2 Int_{k in kn} {
DELTA(disp(k)-LAMBDA)CITA(-disp(k-q)-LAMBDA) / (LAMBDA - disp(k - q))
DELTA(disp(k)+LAMBDA)CITA(disp(k-q)-LAMBDA) / (LAMBDA + disp(k - q)) }
还可以从积分里面把DELTA给积分掉，这样对于二维平面的积分也会变成对
disp(k) = LAMBDA 或者 -LAMBDA的线的积分
= +LAMBDA (2pi)^-2 *
[Int_{disp(k) = +LAMBDA} CITA(-disp(k-q)-LAMBDA) / (LAMBDA - disp(k - q))]
+[Int_{disp(k) = -LAMBDA} CITA(disp(k-q)-LAMBDA) / (LAMBDA + disp(k - q)) ]
'''
nega_q = Point(-qval.coord[0], -qval.coord[1], 1)
#积分正LAMBDA的线
intposi = 0.
for edg in posi:
kval = middle_point(edg.ends[0], edg.ends[1])
kprim = ksft(kval, nega_q)
#CITA
disp_kprim = disp(kprim.coord[0], kprim.coord[1])
if -disp_kprim < lamb:
continue
#线积分，计算线元的长度
intposi += edg.length / (lamb - disp_kprim)
#积分负LAMBDA的线
intnega = 0.
for edg in nega:
kval = middle_point(edg.ends[0], edg.ends[1])
kprim = ksft(kval, nega_q)
#CITA
disp_kprim = disp(kprim.coord[0], kprim.coord[1])
if disp_kprim < lamb:
continue
intnega += edg.length / (lamb + disp_kprim)
#乘上系数
result = lamb * (intposi + intnega) / area #numpy.square(numpy.pi*2)
return result
def get_von_hove_patches(npat):
'''获取平分von Hove的patches'''
#pylint: disable=cell-var-from-loop
#一共有6个M点
mpts = [
Point(3.1415926, 3.1415926 / 1.7320508, 1),
Point(0, 2*3.1415926 / 1.7320508, 1),
Point(-3.1415926, 3.1415926 / 1.7320508, 1),
Point(-3.1415926, -3.1415926 / 1.7320508, 1),
Point(0, -2*3.1415926 / 1.7320508, 1),
Point(3.1415926, -3.1415926 / 1.7320508, 1)
]
angs = []
pat_per_k = npat // 6
gap = 1 / pat_per_k
for idx in range(6):
ridx = idx + 1 if idx + 1 < 6 else 0
for pidx in range(pat_per_k):
omega = (pidx + 0.5) * gap
vpt = middle_point(mpts[idx], mpts[ridx], sc1=1-omega, sc2=omega)
angs.append(get_absolute_angle(vpt.coord[0], vpt.coord[1]))
pats = []
for ang in angs:
rrad = optimize.bisect(
lambda rad: dispersion(rad*numpy.cos(ang), rad*numpy.sin(ang)),
0, 2*numpy.pi/numpy.sqrt(3)
)
pats.append(Point(rrad*numpy.cos(ang), rrad*numpy.sin(ang), 1))
return pats
def shift_kv(kpt: Point, sft: Point) -> Point:
'''将一个k点平移，然后重新对应到第一布里渊区'''
dest = [kpt.coord[idx] + sft.coord[idx] for idx in range(2)]
while numpy.abs(dest[0]) > numpy.pi:
#如果大于第一布里渊区就向零靠拢2pi
dest[0] -= numpy.sign(dest[0]) * numpy.pi * 2
while numpy.abs(dest[1]) > numpy.pi:
dest[1] -= numpy.sign(dest[1]) * numpy.pi * 2
return Point(dest[0], dest[1], 1)
def get_rect_patches(npatch, dispstr):
'''获取长方形的patches'''
from .patches import get_patches
from .square import dispersion as squdisp
from .square import hole_disp as squhole_disp
from .square import brillouin as sbr
disp = squdisp if dispstr == 'square' else squhole_disp
spats = get_patches(sbr(), npatch, disp)
patches = [Point(pat.coord[0] / 2., pat.coord[1], 1) for pat in spats]
return patches
def intra_band_u(uval, mpinfos):
'''带内的相互作用'''
size = numpy.shape(mpinfos)
ret = numpy.zeros((2, 2, 2, 2, size[1], size[1], size[1]))
place_holder = numpy.zeros((size[1], size[1], size[1]))
ndit = numpy.nditer(place_holder, flags=['multi_index'])
while not ndit.finished:
k1idx, k2idx, k3idx = ndit.multi_index
#d1这个带
k1v = mpinfos[0, k1idx]
k2v = mpinfos[0, k2idx]
k3v = mpinfos[0, k3idx]
k4v = shift_kv(k1v, k2v)
k4v = shift_kv(k4v, Point(-k3v.coord[0], -k3v.coord[1], 1))
#print(k1v, k2v, k3v, k4v)
#d这个带只需要第三个
_, _, k1nu = get_nu_numpy(k1v.coord[0], k1v.coord[1])
_, _, k2nu = get_nu_numpy(k2v.coord[0], k2v.coord[1])
_, _, k3nu = get_nu_numpy(k3v.coord[0], k3v.coord[1])
_, _, k4nu = get_nu_numpy(k4v.coord[0], k4v.coord[1])
#计算数值
for idx in range(3):
ret[0, 0, 0, 0, k1idx, k2idx, k3idx] +=\
uval * k1nu[idx] * k2nu[idx] * k3nu[idx] * k4nu[idx]
#p2这个带
k1v = mpinfos[1, k1idx]
k2v = mpinfos[1, k2idx]
k3v = mpinfos[1, k3idx]
k4v = shift_kv(k1v, k2v)
k4v = shift_kv(k4v, Point(-k3v.coord[0], -k3v.coord[1], 1))
#p这个带只需要第二个
_, k1nu, _ = get_nu_numpy(k1v.coord[0], k1v.coord[1])
_, k2nu, _ = get_nu_numpy(k2v.coord[0], k2v.coord[1])
_, k3nu, _ = get_nu_numpy(k3v.coord[0], k3v.coord[1])
_, k4nu, _ = get_nu_numpy(k4v.coord[0], k4v.coord[1])
#计算数值
for idx in range(3):
ret[1, 1, 1, 1, k1idx, k2idx, k3idx] +=\
uval * k1nu[idx] * k2nu[idx] * k3nu[idx] * k4nu[idx]
ndit.iternext()
return ret
def square_split(squ: Square, nps):
'''先将一个正方形切分成很多个小正方形\n
然后把小正方形切分成三角
'''
lsqus = numpy.ndarray((nps, nps), dtype=Square)
width = squ.width / nps
statx, staty = squ.vertex[1].coord
#从左下角开始，先水平的顺序进行标号
# [idx2]
# nps
# ...
# 1 x x
# 0 x x
# 0 1 ... nps [idx1]
for idx1 in range(nps):
for idx2 in range(nps):
xct = (idx1 + 0.5) * width + statx
yct = (idx2 + 0.5) * width + staty
lsqus[idx1, idx2] = Square(Point(xct, yct, 1), width)
#将每个小正方切分
ltris = numpy.ndarray((nps, nps, 4), dtype=Rtriangle)
for idx1 in range(nps):
for idx2 in range(nps):
ltris[idx1, idx2, :] = single_square_split(lsqus[idx1, idx2])
#找到每个小三角形挨着的小三角形
#这个时候的顺序应该是和小三角形的边的顺序一致的
ladjs = numpy.ndarray((nps, nps, 4), dtype=numpy.object)
for idx1 in range(nps):
for idx2 in range(nps):
#上面的那个小三角，第一条边是右侧，第二条是上边
adjs = [ltris[idx1, idx2, 3], None, ltris[idx1, idx2, 1]]
if idx2 + 1 < nps:
adjs[1] = ltris[idx1, idx2 + 1, 2]
ladjs[idx1, idx2, 0] = adjs
#左面的小三角，第一条边是上边，第二条是左面
adjs = [ltris[idx1, idx2, 0], None, ltris[idx1, idx2, 2]]
if idx1 - 1 >= 0:
adjs[1] = ltris[idx1 -1, idx2, 3]
ladjs[idx1, idx2, 1] = adjs
#下面的小三角，第一条边是左面，第二条是下面
adjs = [ltris[idx1, idx2, 1], None, ltris[idx1, idx2, 3]]
if idx2 - 1 >= 0:
adjs[1] = ltris[idx1, idx2 - 1, 0]
ladjs[idx1, idx2, 2] = adjs
#右面的小三角，第一条边是下面，第二条是右边
adjs = [ltris[idx1, idx2, 2], None, ltris[idx1, idx2, 0]]
if idx1 + 1 < nps:
adjs[1] = ltris[idx1 + 1, idx2, 1]
ladjs[idx1, idx2, 3] = adjs
#将小三角和它对应的近邻都reshape
ltris = numpy.reshape(ltris, nps*nps*4)
ladjs = numpy.reshape(ladjs, nps*nps*4)
return ltris, ladjs
def get_exchange_gamma(jval, pinfos):
'''获取交换相互作用的有效大小'''
pnum = len(pinfos)
result = numpy.zeros((pnum, pnum, pnum))
nditer = numpy.nditer(result, flags=['multi_index'])
co1 = 0.5
co2 = numpy.sqrt(3) * 0.5
while not nditer.finished:
kidx1, kidx2, kidx3 = nditer.multi_index
kv1, kv2, kv3 = pinfos[kidx1], pinfos[kidx2], pinfos[kidx3]
#q1v = k_2 - k_4 = k_3 - k_1
q1v = shift_kv(kv3, Point(-kv1.coord[0], -kv1.coord[1], 1))
#q2v = k_1 - k_4 = k_3 - k_2
q2v = shift_kv(kv3, Point(-kv2.coord[0], -kv2.coord[1], 1))
phase = numpy.cos(-co1*q1v.coord[0] + co2*q1v.coord[1])
phase += numpy.cos(co1*q1v.coord[0] + co2*q1v.coord[1])
phase += numpy.cos(q1v.coord[0])
phase += 0.5*numpy.cos(-co1*q2v.coord[0] + co2*q2v.coord[1])
phase += 0.5*numpy.cos(co1*q2v.coord[0] + co2*q2v.coord[1])
phase += 0.5*numpy.cos(q2v.coord[0])
result[kidx1, kidx2, kidx3] = -jval * phase
nditer.iternext()
return result
def get_patches(npat):
'''获得三角格子的patches，最简单的版本，按角度切分'''
#pylint: disable=cell-var-from-loop
deltaa = 2*numpy.pi / npat
angs = [(idx+0.5) * deltaa for idx in range(npat)]
#print(angs)
pats = []
for ang in angs:
rrad = optimize.bisect(
lambda rad: dispersion(rad*numpy.cos(ang), rad*numpy.sin(ang)),
0, 2*numpy.pi/numpy.sqrt(3)
)
pats.append(Point(rrad*numpy.cos(ang), rrad*numpy.sin(ang), 1))
return pats
def get_s_band_patches(pnum):
'''获得s能带的patch'''
if (pnum % 4) != 0:
raise ValueError('pnum必须是4的倍数')
ppnum = pnum // 4
patches = []
#先算右边
gap = numpy.pi / 2 / ppnum
maxr = numpy.pi * 1.414
for idx in range(ppnum * 2):
angle = (idx + 0.5) * gap
ycoff = numpy.cos(angle)
xcoff = numpy.sin(angle)
def __raddisp(rad):
'''以半径为基准的色散'''
xcord = numpy.pi - rad * xcoff
ycord = ycoff * rad
return s_band_disp(xcord, ycord)
rrad = optimize.bisect(__raddisp, 0., maxr)
patches.append(Point(numpy.pi - rrad * xcoff, ycoff * rrad, 1))
#再算左边
for idx in range(ppnum * 2):
angle = (idx + 0.5) * gap
ycoff = -numpy.cos(angle)
xcoff = numpy.sin(angle)
def __raddisp(rad):
'''以半径为基准的色散'''
xcord = -numpy.pi + rad * xcoff
ycord = ycoff * rad
return s_band_disp(xcord, ycord)
rrad = optimize.bisect(__raddisp, 0., maxr)
patches.append(Point(-numpy.pi + rrad * xcoff, ycoff * rrad, 1))
return patches
def pi_ab_minus_tf(ltris, tarea, lamb, dispa, dispb, qval, ksft, area):
'''温度流的-
这里的lamb就是T，ltris中的所有三角都应该要在同一个patch中,
tarea是每个小三角形的面积，dispa是和k相关的那个能带，dispb是-k+q相关的
'''
result = 0.
for tri in ltris:
#这个小三角形的k值
kval = tri.center
nega_k = Point(-kval.coord[0], -kval.coord[1], 1)
#-k+q
kprim = ksft(nega_k, qval)
#epsilon_k
eps_k = dispa(kval.coord[0], kval.coord[1])
#-epsilon_{-k+q}
neps_kp = -dispb(kprim.coord[0], kprim.coord[1])
#这个时候，因为epsilon_{-k+q}前面已经有了负号，分母上还是负号
if numpy.abs(eps_k - neps_kp) < 1.e-10:
#如果两个数值比较接近, Pi^{-}和Pi^{+}的公式完全一样，就是第二个能量要加个负号
# lim (eps_k -> -eps_kp) Pi^{-} =
# 1/T (e^{eps/T} (-eps/T*e^{eps/T} + eps/T + e^{eps/T} + 1)) / (e^{eps/T} + 1)^3
bval = eps_k / lamb
if bval > 25:
warnings.warn("数值不稳定", RuntimeWarning)
return 0.
expb = numpy.exp(bval)
num = expb * (-bval * expb + bval + expb + 1)
den = numpy.power((1+expb), 3)
d_val = num / den / lamb
else:
if (eps_k / lamb) > 25:
warnings.warn("数值不稳定", RuntimeWarning)
num_left = 0.
else:
#e^{epsilon_k / T}
exp_k_t = numpy.exp(eps_k / lamb)
num_left = eps_k / lamb * exp_k_t / numpy.square(1 + exp_k_t)
if (neps_kp / lamb) > 25:
warnings.warn("数值不稳定", RuntimeWarning)
num_righ = 0.
else:
#e^{-epsilon_{-k+q} / T}
exp_nkp_t = numpy.exp(neps_kp / lamb)
num_righ = neps_kp / lamb * exp_nkp_t\
/ numpy.square(1 + exp_nkp_t)
d_val = (num_left - num_righ) / (eps_k - neps_kp)
result += d_val * tarea
result = result / area
return result
def pi_ab_plus_tf(ltris, tarea, lamb, dispa, dispb, qval, ksft, area):
'''温度流的+
这里的lamb就是T，ltris中的所有三角都应该要在同一个patch中,
tarea是每个小三角形的面积，dispa是和k相关的那个能带，dispb是k-q相关的
'''
nega_q = Point(-qval.coord[0], -qval.coord[1], 1)
result = 0.
for tri in ltris:
#这个小三角形的k值
kval = tri.center
#k-q
kprim = ksft(kval, nega_q)
#epsilon_k
eps_k = dispa(kval.coord[0], kval.coord[1])
#epsilon_{k-q}
eps_kp = dispb(kprim.coord[0], kprim.coord[1])
if numpy.abs(eps_k - eps_kp) < 1.e-10:
#如果特别小，可以利用
# lim (eps_k -> eps_kp) Pi^{+} =
# 1/T (e^{eps/T} (-eps/T*e^{eps/T} + eps/T + e^{eps/T} + 1)) / (e^{eps/T} + 1)^3
bval = eps_kp / lamb
#如果本身就很大，分母会比较大导致接近0
if bval > 25:
warnings.warn("数值不稳定", RuntimeWarning)
return 0.
expb = numpy.exp(bval)
num = expb * (-bval * expb + bval + expb + 1)
den = numpy.power((1+expb), 3)
d_val = num / den / lamb
else:
if (eps_k / lamb) > 25:
warnings.warn("数值不稳定", RuntimeWarning)
num_left = 0.
else:
#exp^{epsilon_k / T}
exp_k_t = numpy.exp(eps_k / lamb)
num_left = eps_k / lamb * exp_k_t / numpy.square(1 + exp_k_t)
if (eps_kp / lamb) > 25:
warnings.warn("数值不稳定", RuntimeWarning)
num_righ = 0.
else:
#e^{epsilon_{k-q} / T}
exp_kp_t = numpy.exp(eps_kp / lamb)
num_righ = eps_kp / lamb * exp_kp_t\
/ numpy.square(1 + exp_kp_t)
d_val = (num_left - num_righ) / (eps_k - eps_kp)
result += d_val * tarea
result = result / area
return result
def load_hexagon(lines, hexa, nps, ltris, ladjs):
'''读取六边形的段落'''
cntcor = None
height = None
nps = None
for line in lines:
if line.startswith('center: '):
corstr = line[8:].split(',')
cntcor = (float(corstr[0]), float(corstr[1]))
if line.startswith('height: '):
height = float(line[8:])
if line.startswith('nps: '):
nps = int(line[4:])
hexa = Hexagon(Point(cntcor[0], cntcor[1], 1), height)
return hexa, nps, ltris, ladjs
def load_rtriangle(lines, squ, nps, ltris, ladjs):
'''解析处理三角切分的段落\n
这个处理完之后会是一个按照idx进行排序的list
'''
if nps == -1 or squ is None:
raise ValueError('没有初始化Square')
idx_to_tri = {}
for line in lines:
lstr = line.split(':')
idx = int(lstr[1])
rtvstr = lstr[3].split(',')
v1str = lstr[5].split(',')
v2str = lstr[7].split(',')
rvpt = Point(float(rtvstr[0]), float(rtvstr[1]), 1)
v1pt = Point(float(v1str[0]), float(v1str[1]), 1)
v2pt = Point(float(v2str[0]), float(v2str[1]), 1)
rtri = Rtriangle(rvpt, v1pt, v2pt)
idx_to_tri[idx] = rtri
ltris = numpy.ndarray(nps*nps*4, dtype=Rtriangle)
if len(idx_to_tri) != nps*nps*4:
raise ValueError('nps和Rtriangle大小不一致')
for idx in range(nps*nps*4):
ltris[idx] = idx_to_tri[idx]
return squ, nps, ltris, ladjs
def load_square(lines, squ, nps, ltris, ladjs):
'''解析处理正方形的段落'''
cntcor = None
width = None
nps = None
for line in lines:
if line.startswith('center: '):
corstr = line[8:].split(',')
cntcor = (float(corstr[0]), float(corstr[1]))
if line.startswith('width: '):
width = float(line[7:])
if line.startswith('nps: '):
nps = int(line[4:])
squ = Square(Point(cntcor[0], cntcor[1], 1), width)
return squ, nps, ltris, ladjs
def load_rectangle(lines, rtg, nps, ltris, ladjs):
'''加载长方形'''
cntcor = None
width = None
height = None
nps = None
for line in lines:
if line.startswith('center: '):
corstr = line[8:].split(',')
cntcor = (float(corstr[0]), float(corstr[1]))
if line.startswith('width: '):
width = float(line[7:])
if line.startswith('height: '):
height = float(line[8:])
if line.startswith('nps: '):
nps = int(line[4:])
rtg = Rectangle(Point(cntcor[0], cntcor[1], 1), width, height)
return rtg, nps, ltris, ladjs
def get_initu(spats, ppats, uval):
'''获取初始化的u值'''
pnum = len(spats)
if pnum != len(ppats):
raise ValueError('patch数量不一致')
place_holder = numpy.ndarray((2, 2, 2, 2, pnum, pnum, pnum))
#kv4直接通过前三个kv可以确定，而他属于的idx4这里是不需要的
phiter = numpy.nditer(place_holder, flags=['multi_index'])
while not phiter.finished:
bd1, bd2, bd3, bd4, idx1, idx2, idx3 = phiter.multi_index
kv1 = spats[idx1] if bd1 == 0 else ppats[idx1]
kv2 = spats[idx2] if bd2 == 0 else ppats[idx2]
kv3 = spats[idx3] if bd3 == 0 else ppats[idx3]
kv4 = shift_kv(shift_kv(kv1, kv2),
Point(-kv3.coord[0], -kv3.coord[1], 1))
place_holder[bd1, bd2, bd3, bd4, idx1, idx2, idx3] =\
band_uval(uval, bd1, bd2, bd3, bd4, kv1, kv2, kv3, kv4)
phiter.iternext()
return place_holder
def shift_kv(kpt: Point, sft: Point):
'''将一个动量平移'''
dest = [kpt.coord[idx] + sft.coord[idx] for idx in range(2)]
#找到离目标最近的一个第一布里渊区的中心
#如果这个点距离中心是最近的，那么他就在第一布里渊区里面了，如果
#离其他的点近，就平移一次，再检查一遍
b1x = -6.283185307179586#-2*numpy.pi
b1y = 3.6275987284684357#2*numpy.pi/numpy.sqrt(3)
brlu_cents = [(0, 0), (b1x, b1y), (0, 2*b1y), (-b1x, b1y),\
(-b1x, -b1y), (0, -2*b1y), (b1x, -b1y)]
#计算距离
dis2cents = [numpy.square(dest[0]-cnt[0]) + numpy.square(dest[1]-cnt[1])\
for cnt in brlu_cents]
while numpy.argmin(dis2cents) != 0:
ncnt = numpy.argmin(dis2cents)
dest[0] = dest[0] - brlu_cents[ncnt][0]
dest[1] = dest[1] - brlu_cents[ncnt][1]
dis2cents = [numpy.square(dest[0]-cnt[0]) + numpy.square(dest[1]-cnt[1])\
for cnt in brlu_cents]
return Point(dest[0], dest[1], 1)
def precompute_qfs(lval):
'''计算q_fs实际上也不需要多余的变量
bd2, bd3, idx2, idx3
'''
global QUICKQFS
#fs的所有自由度
data_list = []
place_holder = numpy.ndarray(( #alpha, beta, bd2, bd3
CONFIG.bandnum,
CONFIG.bandnum,
CONFIG.bandnum,
CONFIG.bandnum,
#nidx(alpha), idx2(bd2), idx3(bd3)
CONFIG.patchnum,
CONFIG.patchnum,
CONFIG.patchnum))
nditer = numpy.nditer(place_holder, flags=['multi_index'])
#
area = CONFIG.brlu.width * CONFIG.brlu.height
ltris = CONFIG.ltris
tarea = area / len(ltris)
mpinfo = CONFIG.mpinfo
ksft = CONFIG.ksft
lamb = CONFIG.lamb0 * numpy.exp(-lval)
mdisp = CONFIG.mdisp
#
while not nditer.finished:
alpha, beta, bd2, bd3, nidx, idx2, idx3 = nditer.multi_index
kv2, kv3 = mpinfo[bd2, idx2], mpinfo[bd3, idx3]
q_fs = ksft(kv3, Point(-kv2.coord[0], -kv2.coord[1], 1))
data_list.append(
(PATTRIS[alpha, nidx], tarea, lamb,\
mdisp[alpha], mdisp[beta], q_fs, ksft, area)
)
nditer.iternext()
#
with multiprocessing.Pool(get_procs_num()) as pool:
result = pool.starmap(pi_ab_plus_tf, data_list)
QUICKQFS = (lval, numpy.reshape(result, place_holder.shape))
def inverse_uval(pinfos, spats, ppats, uval):
'''将band表示转换回basis表示，需要一系列的patches，
然后利用find_patch找到这些patches对应的值'''
from .patches import find_patch
pnum = len(pinfos)
place_holder = numpy.zeros((2, 2, 2, 2, pnum, pnum, pnum))
img_part = numpy.zeros_like(place_holder)
phiter = numpy.nditer(place_holder, flags=['multi_index'])
step = numpy.pi / 100
#找出这些patches对应的P_b(k)
allpatdic = numpy.ndarray((2, pnum), dtype=int)
#print(pinfos[0])
#print(find_patch(pinfos[0], spats, s_band_disp, s_band_gd, step))
#print(find_patch(pinfos[0], ppats, p_band_disp, p_band_gd, step))
#raise
for idx, pat in enumerate(pinfos, 0):
allpatdic[0, idx] = find_patch(pat, spats, s_band_disp, s_band_gd,
step)
allpatdic[1, idx] = find_patch(pat, ppats, p_band_disp, p_band_gd,
step)
while not phiter.finished:
st1, st2, st3, st4, idx1, idx2, idx3 = phiter.multi_index
kv1 = pinfos[idx1]
kv2 = pinfos[idx2]
kv3 = pinfos[idx3]
kv4 = shift_kv(shift_kv(kv1, kv2),
Point(-kv3.coord[0], -kv3.coord[1], 1))
#每个点在不同能带下对应的patch
patdic = numpy.ndarray((2, 3), dtype=int)
patdic[:, 0] = allpatdic[:, idx1]
patdic[:, 1] = allpatdic[:, idx2]
patdic[:, 2] = allpatdic[:, idx3]
#bval = basis_uval(uval, st1, st2, st3, st4, kv1, kv2, kv3, kv4, patdic)
bval = basis_uval_2(uval, st1, st2, st3, st4, patdic, spats, ppats)
place_holder[st1, st2, st3, st4, idx1, idx2, idx3] = numpy.real(bval)
img_part[st1, st2, st3, st4, idx1, idx2, idx3] = numpy.imag(bval)
phiter.iternext()
return place_holder, img_part
def band_u(uval, pinfos):
'''带内的相互作用'''
npat = len(pinfos)
ret = numpy.zeros((npat, npat, npat))
ndit = numpy.nditer(ret, flags=['multi_index'])
while not ndit.finished:
k1idx, k2idx, k3idx = ndit.multi_index
#p2这个带
k1v = pinfos[k1idx]
k2v = pinfos[k2idx]
k3v = pinfos[k3idx]
k4v = shift_kv(k1v, k2v)
k4v = shift_kv(k4v, Point(-k3v.coord[0], -k3v.coord[1], 1))
#p这个带只需要第二个
_, k1nu, _ = get_nu_numpy(k1v.coord[0], k1v.coord[1])
_, k2nu, _ = get_nu_numpy(k2v.coord[0], k2v.coord[1])
_, k3nu, _ = get_nu_numpy(k3v.coord[0], k3v.coord[1])
_, k4nu, _ = get_nu_numpy(k4v.coord[0], k4v.coord[1])
#计算数值
for idx in range(3):
ret[k1idx, k2idx, k3idx] +=\
uval * k1nu[idx] * k2nu[idx] * k3nu[idx] * k4nu[idx]
ndit.iternext()
return ret
def brillouin():
'''布里渊区'''
return Hexagon(Point(0, 0, 1), 2*numpy.pi/numpy.sqrt(3))
def main():
""" Main Function """
print("CG LAB 3")
print("Brihat Ratna Bajracharya\n19/075\n")
global INPUT_FILE
try:
input_file_arg = sys.argv[1]
if os.path.exists(input_file_arg):
INPUT_FILE = input_file_arg
print("Using file: " + INPUT_FILE + "\n")
else:
print("Input file not found")
INPUT_FILE = 'cg_lab_3_input_file_5'
print("Using file: " + INPUT_FILE + "\n")
except:
print("No input file given. Taking default")
print("Using file: " + INPUT_FILE + "\n")
try:
''' reads input file '''
in_file = open(INPUT_FILE, 'r')
''' get number of vertices for polygon '''
print("Enter number of vertex of polygon:"),
vertex_num = int(in_file.readline())
print(vertex_num)
''' reads coords of point '''
input_coords = in_file.readline()
input_coords_list = input_coords.split()
# print(input_coords_list)
''' initialize vertex list '''
points = [None] * vertex_num
''' get coordinates of each vertices '''
for index in range(vertex_num):
print(" Enter coordinates of vertex V{}:".format(index + 1)),
input_coords_point = input_coords_list[index].split(',')
points[index] = Point(int(input_coords_point[0]),
int(input_coords_point[1]))
print(points[index])
''' calculate centroid of polygon '''
centroid = Point(
sum([point.x for point in points]) / len(points),
sum([point.y for point in points]) / len(points))
# print("Centroid of all Points: " + str(centroid))
''' sort vertices of polygon in anti-clockwise order '''
sorted_p = copy.deepcopy(points)
sorted_p.sort(
key=lambda p: math.atan2(p.y - centroid.y, p.x - centroid.x))
''' show points in sorted order '''
# print("\nPoints in sorted order")
# for index in range(vertex_num):
# print(sorted_p[index]),
# print("\n")
polygon = CircularDoublyLinkedList()
for index in range(vertex_num):
polygon.append(sorted_p[index])
''' displays content of polygon and count of vertices '''
polygon.display("Vertices of Polygon (sorted)")
# polygon.show_count()
''' polygon convex test '''
convex_check = is_polygon_convex(polygon)
print("\nRESULT: Polygon is {}convex.".format(
'' if convex_check else 'not '))
if convex_check:
''' check point inclusion only if polygon is convex '''
print("\n\nPOINT INCLUSION BY TURN TEST")
print("\n Enter coordinates of Query Point (P):"),
''' read query point from file '''
qp = in_file.readline().split()[0].split(',')
# print(qp)
query_point = Point(int(qp[0]), int(qp[1]))
print(query_point)
''' point inclusion using turn test '''
is_point_in_polygon = is_point_inclusion(polygon, query_point)
print("\nRESULT: Query Point {}inside given polygon.".format(
'' if is_point_in_polygon else 'not '))
print("\n\nRAY CASTING")
print("\n Enter coordinates of ray point (R):"),
''' read ray point start from file '''
if not convex_check:
in_file.readline()
rp = in_file.readline().split()[0].split(',')
# print(rp)
ray_point = Point(int(rp[0]), int(rp[1]))
print(ray_point)
ray_on_edge = check_ray_on_polygon_boundary(polygon, ray_point)
if ray_on_edge:
print("\nRESULT: Ray origin on boundary of polygon.")
print(
"\tRay origin on edge joining points {}.".format(ray_on_edge))
else:
''' assuming ray infinity point to the right side of polygon '''
ray_xcoord_infinity = max([point.x for point in sorted_p])
# ray_ycoord_infinity = sum([point.y for point in sorted_p]) / vertex_num
ray_point_infinity = Point(ray_xcoord_infinity + 1, ray_point.y)
ray_line_infinity = LineSegment(ray_point, ray_point_infinity)
# print("\n Ray Point Infinity: " + str(ray_point_infinity))
# print(" Ray Line: " + str(ray_line_infinity))
''' ray intersection using line_segment_intersection test '''
ray_intersection_count = ray_casting(polygon, ray_line_infinity)
ray_casting_result = ray_intersection_count % 2
print("\nRESULT: Ray origin {} of polygon.".format(
'inside' if ray_casting_result else 'outside'))
in_file.close()
print("\nDONE.")
except Exception as e:
print("\n\nERROR OCCURED !!!\n Type of Error: " + type(e).__name__)
print(" Error message: " + str(e.message))
def brillouin():
'''布里渊区'''
return Square(Point(0., 0., 1), numpy.pi * 2.)