qpms/misc/dispersion-SVD.py

#!/usr/bin/env python3


import argparse, re, random, string
from scipy.constants import hbar, e as eV, pi, c

def make_action_sharedlist(opname, listname):
    class opAction(argparse.Action):
        def __call__(self, parser, args, values, option_string=None):
            if (not hasattr(args, listname)) or getattr(args, listname) is None:
                setattr(args, listname, list())
            getattr(args,listname).append((opname, values))
    return opAction

parser = argparse.ArgumentParser()
#TODO? použít type=argparse.FileType('r') ?
parser.add_argument('--TMatrix', action='store', required=True, help='Path to TMatrix file')
parser.add_argument('--griddir', action='store', required=True, help='Path to the directory with precalculated translation operators')
#sizepar = parser.add_mutually_exclusive_group(required=True)
parser.add_argument('--hexside', action='store', type=float, required=True, help='Lattice hexagon size length')
parser.add_argument('--output', action='store', help='Path to output PDF')
parser.add_argument('--nSV', action='store', metavar='N', type=int, default=1, help='Store and draw N minimun singular values')
parser.add_argument('--background_permittivity', action='store', type=float, default=1., help='Background medium relative permittivity (default 1)')
parser.add_argument('--sparse', action='store', type=int, help='Skip frequencies for preview')
parser.add_argument('--eVmax', action='store', type=float, help='Skip frequencies above this value')
parser.add_argument('--eVmin', action='store', type=float, help='Skip frequencies below this value')
parser.add_argument('--kdensity', action='store', type=int, default=66, help='Number of k-points per x-axis segment')
#TODO some more sophisticated x axis definitions
parser.add_argument('--gaussian', action='store', type=float, metavar='σ', help='Use a gaussian envelope for weighting the interaction matrix contributions (depending on the distance), measured in unit cell lengths (?) FIxME).')
popgrp=parser.add_argument_group(title='Operations')
popgrp.add_argument('--tr', dest='ops', action=make_action_sharedlist('tr', 'ops'), default=list()) # the default value for dest can be set once
popgrp.add_argument('--tr0', dest='ops', action=make_action_sharedlist('tr0', 'ops'))
popgrp.add_argument('--tr1', dest='ops', action=make_action_sharedlist('tr1', 'ops'))
popgrp.add_argument('--sym', dest='ops', action=make_action_sharedlist('sym', 'ops'))
popgrp.add_argument('--sym0', dest='ops', action=make_action_sharedlist('sym0', 'ops'))
popgrp.add_argument('--sym1', dest='ops', action=make_action_sharedlist('sym1', 'ops'))
#popgrp.add_argument('--mult', dest='ops', nargs=3, metavar=('INCSPEC', 'SCATSPEC', 'MULTIPLIER'), action=make_action_sharedlist('mult', 'ops'))
#popgrp.add_argument('--mult0', dest='ops', nargs=3, metavar=('INCSPEC', 'SCATSPEC', 'MULTIPLIER'), action=make_action_sharedlist('mult0', 'ops'))
#popgrp.add_argument('--mult1', dest='ops', nargs=3, metavar=('INCSPEC', 'SCATSPEC', 'MULTIPLIER'), action=make_action_sharedlist('mult1', 'ops'))
popgrp.add_argument('--multl', dest='ops', nargs=3, metavar=('INCL[,INCL,...]', 'SCATL[,SCATL,...]', 'MULTIPLIER'), action=make_action_sharedlist('multl', 'ops'))
popgrp.add_argument('--multl0', dest='ops', nargs=3, metavar=('INCL[,INCL,...]', 'SCATL[,SCATL,...]', 'MULTIPLIER'), action=make_action_sharedlist('multl0', 'ops'))
popgrp.add_argument('--multl1', dest='ops', nargs=3, metavar=('INCL[,INCL,...]', 'SCATL[,SCATL,...]', 'MULTIPLIER'), action=make_action_sharedlist('multl1', 'ops'))
parser.add_argument('--frequency_multiplier', action='store', type=float, default=1., help='Multiplies the frequencies in the TMatrix file by a given factor.')
# TODO enable more flexible per-sublattice specification
pargs=parser.parse_args()
print(pargs)


translations_dir = pargs.griddir 
TMatrix_file = pargs.TMatrix
pdfout = pargs.output if pargs.output else (''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(10)) + '.pdf')
print(pdfout)

hexside = pargs.hexside #375e-9
epsilon_b = pargs.background_permittivity #2.3104
gaussianSigma = pargs.gaussian if pargs.gaussian else None # hexside * 222 / 7
interpfreqfactor = pargs.frequency_multiplier
kdensity = pargs.kdensity
minfreq = pargs.eVmin*eV/hbar if pargs.eVmin else None
maxfreq = pargs.eVmax*eV/hbar if pargs.eVmax else None
skipfreq = pargs.sparse if pargs.sparse else None
svn = pargs.nSV

# TODO multiplier operation definitions and parsing
#factor13inc = 10
#factor13scat=10

ops = list()
opre = re.compile('(tr|sym|copy|multl|mult)(\d*)')
for oparg in pargs.ops:
    opm = opre.match(oparg[0])
    if opm:
        ops.append(((opm.group(2),) if opm.group(2) else (0,1), opm.group(1), oparg[1]))
    else:
        raise # should not happen
print(ops)

#ops = (
#    # co, typ operace (symetrizace / transformace / kopie), specifikace (operace nebo zdroj), 
#    # co: 0, 1, (0,1), (0,), (1,), #NI: 'all'
#    # typ operace: sym, tr, copy
#    # specifikace:
#    # sym, tr: 'σ_z', 'σ_y', 'C2'; sym: 'C3', 
#    # copy: 0, 1 (zdroj)
#    ((0,1), 'sym', 'σ_z'),
#    #((0,1), 'sym', 'σ_x'),
#    #((0,1), 'sym', 'σ_y'),
#    ((0,1), 'sym', 'C3'),
#    ((1), 'tr', 'C2'),
#
#)

# -----------------finished basic CLI parsing (except for op arguments) ------------------
import time
begtime=time.time()

from matplotlib.path import Path
import matplotlib.patches as patches
import matplotlib.pyplot as plt
import qpms
import numpy as np
import os, sys, warnings, math
from matplotlib import pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from scipy import interpolate
nx = None
s3 = math.sqrt(3)



pdf = PdfPages(pdfout)

# In[3]:

# specifikace T-matice zde
cdn = c/ math.sqrt(epsilon_b)
TMatrices_orig, freqs_orig, freqs_weirdunits_orig, lMax = qpms.loadScuffTMatrices(TMatrix_file)
my, ny = qpms.get_mn_y(lMax)
nelem = len(my)

ž = np.arange(2*nelem)
tž = ž // nelem
mž = my[ž%nelem]
nž = ny[ž%nelem]
TEž = ž[(mž+nž+tž) % 2 == 0]
TMž = ž[(mž+nž+tž) % 2 == 1]

č = np.arange(2*2*nelem)
žč = č % (2* nelem)
tč = tž[žč]
mč = mž[žč]
nč = nž[žč]
TEč = č[(mč+nč+tč) % 2 == 0]
TMč = č[(mč+nč+tč) % 2 == 1]

TMatrices = np.array(np.broadcast_to(TMatrices_orig[:,nx,:,:,:,:],(len(freqs_orig),2,2,nelem,2,nelem)) )

#TMatrices[:,:,:,:,:,ny==3] *= factor13inc
#TMatrices[:,:,:,ny==3,:,:] *= factor13scat
xfl = qpms.xflip_tyty(lMax)
yfl = qpms.yflip_tyty(lMax)
zfl = qpms.zflip_tyty(lMax)
c2rot = qpms.apply_matrix_left(qpms.yflip_yy(3),qpms.xflip_yy(3),-1)

reCN = re.compile('(\d*)C(\d+)')
#TODO C nekonečno

for op in ops:
    if op[0] == 'all':
        targets = (0,1)
    elif isinstance(op[0],int):
        targets = (op[0],)
    else:
        targets = op[0]
        
    if op[1] == 'sym':
        mCN = reCN.match(op[2]) # Fuck van Rossum for not having assignments inside expressions
        if op[2] == 'σ_z':
            for t in targets:
                TMatrices[:,t] = (TMatrices[:,t] + qpms.apply_ndmatrix_left(zfl,qpms.apply_ndmatrix_left(zfl, TMatrices[:,t], (-4,-3)),(-2,-1)))/2
        elif op[2] == 'σ_y':
            for t in targets:
                TMatrices[:,t] = (TMatrices[:,t] + qpms.apply_ndmatrix_left(yfl,qpms.apply_ndmatrix_left(yfl, TMatrices[:,t], (-4,-3)),(-2,-1)))/2
        elif op[2] == 'σ_x':
            for t in targets:
                TMatrices[:,t] = (TMatrices[:,t] + qpms.apply_ndmatrix_left(xfl,qpms.apply_ndmatrix_left(xfl, TMatrices[:,t], (-4,-3)),(-2,-1)))/2
        elif op[2] == 'C2': # special case of the latter
            for t in targets:
                TMatrices[:,t] = (TMatrices[:,t] + qpms.apply_matrix_left(c2rot,qpms.apply_matrix_left(c2rot, TMatrices[:,t], -3),-1))/2                
        elif mCN:
            rotN = int(mCN.group(2))
            TMatrix_contribs = np.empty((rotN,TMatrices.shape[0],2,nelem,2,nelem), dtype=np.complex_)
            for t in targets:
                for i in range(rotN):
                    rotangle = 2*np.pi*i / rotN
                    rot =  qpms.WignerD_yy_fromvector(lMax,np.array([0,0,rotangle]))
                    rotinv = qpms.WignerD_yy_fromvector(lMax,np.array([0,0,-rotangle]))
                    TMatrix_contribs[i] = qpms.apply_matrix_left(rot,qpms.apply_matrix_left(rotinv, TMatrices[:,t], -3),-1)
                TMatrices[:,t] = np.sum(TMatrix_contribs, axis=0) / rotN
        else:
            raise
    elif op[1] == 'tr':
        mCN = reCN.match(op[2]) # Fuck van Rossum for not having assignments inside expressions
        if op[2] == 'σ_z':
            for t in targets:
                TMatrices[:,t] = qpms.apply_ndmatrix_left(zfl,qpms.apply_ndmatrix_left(zfl, TMatrices[:,t], (-4,-3)),(-2,-1))
        elif op[2] == 'σ_y':
            for t in targets:
                TMatrices[:,t] = qpms.apply_ndmatrix_left(yfl,qpms.apply_ndmatrix_left(yfl, TMatrices[:,t], (-4,-3)),(-2,-1))
        elif op[2] == 'σ_x':
            for t in targets:
                TMatrices[:,t] = qpms.apply_ndmatrix_left(xfl,qpms.apply_ndmatrix_left(xfl, TMatrices[:,t], (-4,-3)),(-2,-1))
        elif op[2] == 'C2':
            for t in targets:
                TMatrices[:,t] = qpms.apply_matrix_left(c2rot,qpms.apply_matrix_left(c2rot, TMatrices[:,t], -3),-1)       
        elif mCN:
            rotN = int(mCN.group(2))
            power = int(mCN.group(1)) if mCN.group(1) else 1
            TMatrix_contribs = np.empty((rotN,TMatrices.shape[0],2,nelem,2,nelem), dtype=np.complex_)
            for t in targets:
                rotangle = 2*np.pi*power/rotN
                rot = qpms.WignerD_yy_fromvector(lMax, np.array([0,0,rotangle]))
                rotinv = qpms.WignerD_yy_fromvector(lMax, np.array([0,0,-rotangle]))
                TMatrices[:,t] = qpms.apply_matrix_left(rot, qpms.apply_matrix_left(rotinv, TMatrices[:,t], -3),-1)
        else:
            raise
    elif op[1] == 'copy':
        raise # not implemented
    elif op[1] == 'mult':
        raise # not implemented
    elif op[1] == 'multl':
        incy = np.full((nelem,), False, dtype=bool)
        for incl in op[2][0].split(','):
            l = int(incl)
            incy += (l == ny)
        scaty = np.full((nelem,), False, dtype=bool)
        for scatl in op[2][1].split(','):
            l = int(scatl)
            scaty += (l == ny)
        for t in targets:
            TMatrices[:,t,:,scaty,:,incy] *= float(op[2][2])
    else:
        raise #unknown operation; should not happen

TMatrices_interp = interpolate.interp1d(freqs_orig*interpfreqfactor, TMatrices, axis=0, kind='linear',fill_value="extrapolate")



# In[4]:

om = np.linspace(np.min(freqs_orig), np.max(freqs_orig),100)
TMatrix0ip = np.reshape(TMatrices_interp(om)[:,0], (len(om), 2*nelem*2*nelem))
f, axa = plt.subplots(2, 2, figsize=(15,15))

#print(TMatrices.shape)
#plt.plot(om, TMatrices[:,0,0,0,0].imag,'r',om, TMatrices[:,0,0,0,0].real,'r--',om, TMatrices[:,0,2,0,2].imag,'b',om, TMatrices[:,0,2,0,2].real,'b--'))
        
ax = axa[0,0]
ax2 = ax.twiny()
ax2.set_xlim([ax.get_xlim()[0]/eV*hbar,ax.get_xlim()[1]/eV*hbar])
ax.plot(
         om, TMatrix0ip[:,:].imag,'-',om, TMatrix0ip[:,:].real,'--',
)
ax = axa[0,1]
ax2 = ax.twiny()
ax2.set_xlim([ax.get_xlim()[0]/eV*hbar,ax.get_xlim()[1]/eV*hbar])
ax.plot(
         om, abs(TMatrix0ip[:,:]),'-'
)
ax.set_yscale('log')

ax = axa[1,1]
ax2 = ax.twiny()
ax2.set_xlim([ax.get_xlim()[0]/eV*hbar,ax.get_xlim()[1]/eV*hbar])
ax.plot(
         om, np.unwrap(np.angle(TMatrix0ip[:,:]),axis=0),'-'
)

ax = axa[1,0]
ax.text(0.5,0.5,str(pargs).replace(',',',\n'),horizontalalignment='center',verticalalignment='center',transform=ax.transAxes)
pdf.savefig(f)


# In[ ]:

#kdensity = 66 #defined from cl arguments
bz_0 = np.array((0,0,0.,))
bz_K1 = np.array((1.,0,0))*4*np.pi/3/hexside/s3
bz_K2 = np.array((1./2.,s3/2,0))*4*np.pi/3/hexside/s3
bz_M = np.array((3./4, s3/4,0))*4*np.pi/3/hexside/s3
k0Mlist = bz_0 + (bz_M-bz_0) * np.linspace(0,1,kdensity)[:,nx]
kMK1list = bz_M + (bz_K1-bz_M) * np.linspace(0,1,kdensity)[:,nx]
kK10list = bz_K1 + (bz_0-bz_K1) * np.linspace(0,1,kdensity)[:,nx]
k0K2list = bz_0 + (bz_K2-bz_0) * np.linspace(0,1,kdensity)[:,nx]
kK2Mlist = bz_K2 + (bz_M-bz_K2) * np.linspace(0,1,kdensity)[:,nx]
B1 = 2* bz_K1 - bz_K2
B2 = 2* bz_K2 - bz_K1
klist = np.concatenate((k0Mlist,kMK1list,kK10list,k0K2list,kK2Mlist), axis=0)
kxmaplist = np.concatenate((np.array([0]),np.cumsum(np.linalg.norm(np.diff(klist, axis=0), axis=-1))))


# In[ ]:

n2id = np.identity(2*nelem)
n2id.shape = (2,nelem,2,nelem)
extlistlist = list()
leftmatrixlistlist = list()
minsvTElistlist=list()
minsvTMlistlist=list()
nan = float('nan')
omegalist = list()
filecount = 0
for trfile in os.scandir(translations_dir):
    filecount += 1
    if (skipfreq and filecount % skipfreq):
        continue
    try:
        npz = np.load(trfile.path, mmap_mode='r')
        k_0 = npz['precalc_params'][()]['k_hexside'] / hexside
        omega = k_0 * c / math.sqrt(epsilon_b)
        if((minfreq and omega < minfreq) or (maxfreq and omega > maxfreq)):
            continue
    except:
        print ("Unexpected error, trying to continue with another file:", sys.exc_info()[0])
        continue   
    try:
        tdic = qpms.hexlattice_precalc_AB_loadunwrap(trfile.path, return_points=True)
    except:
        print ("Unexpected error, trying to continue with another file:", sys.exc_info()[0])
        continue
    k_0 = tdic['k_hexside'] / hexside
    omega = k_0 * c / math.sqrt(epsilon_b)
    omegalist.append(omega)
    print(filecount, omega/eV*hbar)
    sys.stdout.flush()
    a_self = tdic['a_self'][:,:nelem,:nelem]
    b_self = tdic['b_self'][:,:nelem,:nelem]
    a_u2d = tdic['a_u2d'][:,:nelem,:nelem]
    b_u2d = tdic['b_u2d'][:,:nelem,:nelem]
    a_d2u = tdic['a_d2u'][:,:nelem,:nelem]
    b_d2u = tdic['b_d2u'][:,:nelem,:nelem]
    unitcell_translations = tdic['self_tr']*hexside*s3
    u2d_translations = tdic['u2d_tr']*hexside*s3
    d2u_translations = tdic['d2u_tr']*hexside*s3
    if gaussianSigma:
        unitcell_envelope = np.exp(-np.sum(tdic['self_tr']**2,axis=-1)/(2*gaussianSigma**2))
        u2d_envelope = np.exp(-np.sum(tdic['u2d_tr']**2,axis=-1)/(2*gaussianSigma**2))
        d2u_envelope = np.exp(-np.sum(tdic['d2u_tr']**2,axis=-1)/(2*gaussianSigma**2))
    
    
    TMatrices_om = TMatrices_interp(omega)

    minsvTElist = np.full((klist.shape[0], svn),np.nan)
    minsvTMlist = np.full((klist.shape[0], svn),np.nan)
    leftmatrixlist = np.full((klist.shape[0],2,2,nelem,2,2,nelem),np.nan,dtype=complex)
    isNaNlist = np.zeros((klist.shape[0]), dtype=bool)
    
    # sem nějaká rozumná smyčka
    for ki in range(klist.shape[0]):
        k = klist[ki]
        if (k_0*k_0 - k[0]*k[0] - k[1]*k[1] < 0):
            isNaNlist[ki] = True
            continue

        phases_self = np.exp(1j*np.tensordot(k,unitcell_translations,axes=(0,-1)))
        phases_u2d = np.exp(1j*np.tensordot(k,u2d_translations,axes=(0,-1)))
        phases_d2u = np.exp(1j*np.tensordot(k,d2u_translations,axes=(0,-1)))
        if gaussianSigma:
            phases_self *= unitcell_envelope
            phases_u2d *= u2d_envelope
            phases_d2u *= d2u_envelope
        leftmatrix = np.zeros((2,2,nelem, 2,2,nelem), dtype=complex)

        leftmatrix[0,0,:,0,0,:] = np.tensordot(a_self,phases_self, axes=(0,-1)) # u2u, E2E
        leftmatrix[1,0,:,1,0,:] = leftmatrix[0,0,:,0,0,:] # d2d, E2E
        leftmatrix[0,1,:,0,1,:] = leftmatrix[0,0,:,0,0,:] # u2u, M2M
        leftmatrix[1,1,:,1,1,:] = leftmatrix[0,0,:,0,0,:] # d2d, M2M
        leftmatrix[0,0,:,0,1,:] = np.tensordot(b_self,phases_self, axes=(0,-1)) # u2u, M2E
        leftmatrix[0,1,:,0,0,:] = leftmatrix[0,0,:,0,1,:] # u2u, E2M
        leftmatrix[1,1,:,1,0,:] = leftmatrix[0,0,:,0,1,:] # d2d, E2M
        leftmatrix[1,0,:,1,1,:] = leftmatrix[0,0,:,0,1,:] # d2d, M2E
        leftmatrix[0,0,:,1,0,:] = np.tensordot(a_d2u, phases_d2u,axes=(0,-1)) #d2u,E2E
        leftmatrix[0,1,:,1,1,:] = leftmatrix[0,0,:,1,0,:] #d2u, M2M
        leftmatrix[0,0,:,1,1,:] = np.tensordot(b_d2u, phases_d2u,axes=(0,-1)) #d2u,M2E
        leftmatrix[0,1,:,1,0,:] = leftmatrix[0,0,:,1,1,:] #d2u, E2M
        leftmatrix[1,0,:,0,1,:] = np.tensordot(b_u2d, phases_u2d,axes=(0,-1)) #u2d,M2E
        leftmatrix[1,1,:,0,0,:] = leftmatrix[1,0,:,0,1,:] #u2d, E2M
        #leftmatrix is now the translation matrix T
        for j in range(2):
            leftmatrix[j] = -np.tensordot(TMatrices_om[j], leftmatrix[j], axes=([-2,-1],[0,1]))
            # at this point, jth row of leftmatrix is that of -MT
            leftmatrix[j,:,:,j,:,:] += n2id
        #now we are done, 1-M

        leftmatrixlist[ki] = leftmatrix


    nnlist = np.logical_not(isNaNlist)
    leftmatrixlist_s = np.reshape(leftmatrixlist,(klist.shape[0], 2*2*nelem,2*2*nelem))[nnlist]
    leftmatrixlist_TE = leftmatrixlist_s[np.ix_(np.arange(leftmatrixlist_s.shape[0]),TEč,TEč)]
    leftmatrixlist_TM = leftmatrixlist_s[np.ix_(np.arange(leftmatrixlist_s.shape[0]),TMč,TMč)]
    #svarr = np.linalg.svd(leftmatrixlist_TE, compute_uv=False)
    #argsortlist = np.argsort(svarr, axis=-1)[...,:svn]
    #minsvTElist[nnlist] = svarr[...,argsortlist]
    #minsvTElist[nnlist] = np.amin(np.linalg.svd(leftmatrixlist_TE, compute_uv=False), axis=-1)
    minsvTElist[nnlist] = np.linalg.svd(leftmatrixlist_TE, compute_uv=False)[...,-svn:]
    #svarr = np.linalg.svd(leftmatrixlist_TM, compute_uv=False)
    #argsortlist = np.argsort(svarr, axis=-1)[...,:svn]
    #minsvTMlist[nnlist] = svarr[...,argsortlist]
    #minsvTMlist[nnlist] = np.amin(np.linalg.svd(leftmatrixlist_TM, compute_uv=False), axis=-1)
    minsvTMlist[nnlist] = np.linalg.svd(leftmatrixlist_TM, compute_uv=False)[...,-svn:]
    minsvTMlistlist.append(minsvTMlist)
    minsvTElistlist.append(minsvTElist) 

    
        


# In[ ]:

minsvTElistarr = np.array(minsvTElistlist)
minsvTMlistarr = np.array(minsvTMlistlist)
omegalist = np.array(omegalist)
# order to make the scatter plots "nice"
omegaorder = np.argsort(omegalist)
omegalist = omegalist[omegaorder]
minsvTElistarr = minsvTElistarr[omegaorder]
minsvTMlistarr = minsvTMlistarr[omegaorder]

omlist = np.broadcast_to(omegalist[:,nx], minsvTElistarr[...,0].shape)
kxmlarr = np.broadcast_to(kxmaplist[nx,:], minsvTElistarr[...,0].shape)
klist = np.concatenate((k0Mlist,kMK1list,kK10list,k0K2list,kK2Mlist), axis=0)


# In[ ]:
for minN in reversed(range(svn)):
    f, ax = plt.subplots(1, figsize=(20,15))
    sc = ax.scatter(kxmlarr, omlist/eV*hbar, c = np.sqrt(minsvTMlistarr[...,minN]), s =40, lw=0)
    ax.plot(kxmaplist, np.linalg.norm(klist,axis=-1)*cdn/eV*hbar, '-',
           kxmaplist, np.linalg.norm(klist+B1, axis=-1)*cdn/eV*hbar, '-',
           kxmaplist, np.linalg.norm(klist+B2, axis=-1)*cdn/eV*hbar, '-',
           kxmaplist, np.linalg.norm(klist-B2, axis=-1)*cdn/eV*hbar, '-',
           kxmaplist, np.linalg.norm(klist-B1, axis=-1)*cdn/eV*hbar, '-',
           kxmaplist, np.linalg.norm(klist+B2-B1, axis=-1)*cdn/eV*hbar, '-',
            kxmaplist, np.linalg.norm(klist-B2+B1, axis=-1)*cdn/eV*hbar, '-',
            kxmaplist, np.linalg.norm(klist-B2-B1, axis=-1)*cdn/eV*hbar, '-',
            kxmaplist, np.linalg.norm(klist+B2+B1, axis=-1)*cdn/eV*hbar, '-',
            kxmaplist, np.linalg.norm(klist-2*B1, axis=-1)*cdn/eV*hbar, '-',
            kxmaplist, np.linalg.norm(klist-2*B2, axis=-1)*cdn/eV*hbar, '-',
            kxmaplist, np.linalg.norm(klist-2*B2-B1, axis=-1)*cdn/eV*hbar, '-',
            kxmaplist, np.linalg.norm(klist-2*B1-B2, axis=-1)*cdn/eV*hbar, '-',
            kxmaplist, np.linalg.norm(klist-2*B1-2*B2, axis=-1)*cdn/eV*hbar, '-',
    #        kxmaplist, np.linalg.norm(klist+2*B2-B1, axis=-1)*cdn, '-',
    #        kxmaplist, np.linalg.norm(klist+2*B1-B2, axis=-1)*cdn, '-',
           )
    ax.set_xlim([np.min(kxmlarr),np.max(kxmlarr)])
    #ax.set_ylim([2.15,2.30])
    ax.set_ylim([np.min(omlist/eV*hbar),np.max(omlist/eV*hbar)])
    ax.set_xticks([0, kxmaplist[len(k0Mlist)-1], kxmaplist[len(k0Mlist)+len(kMK1list)-1], kxmaplist[len(k0Mlist)+len(kMK1list)+len(kK10list)-1], kxmaplist[len(k0Mlist)+len(kMK1list)+len(kK10list)+len(k0K2list)-1], kxmaplist[len(k0Mlist)+len(kMK1list)+len(kK10list)+len(k0K2list)+len(kK2Mlist)-1]])
    ax.set_xticklabels(['Γ', 'M', 'K', 'Γ', 'K\'','M'])
    f.colorbar(sc)
    
    pdf.savefig(f)
    
    
    # In[ ]:
    
    f, ax = plt.subplots(1, figsize=(20,15))
    sc = ax.scatter(kxmlarr, omlist/eV*hbar, c = np.sqrt(minsvTElistarr[...,minN]), s =40, lw=0)
    ax.plot(kxmaplist, np.linalg.norm(klist,axis=-1)*cdn/eV*hbar, '-',
           kxmaplist, np.linalg.norm(klist+B1, axis=-1)*cdn/eV*hbar, '-',
           kxmaplist, np.linalg.norm(klist+B2, axis=-1)*cdn/eV*hbar, '-',
           kxmaplist, np.linalg.norm(klist-B2, axis=-1)*cdn/eV*hbar, '-',
           kxmaplist, np.linalg.norm(klist-B1, axis=-1)*cdn/eV*hbar, '-',
           kxmaplist, np.linalg.norm(klist+B2-B1, axis=-1)*cdn/eV*hbar, '-',
            kxmaplist, np.linalg.norm(klist-B2+B1, axis=-1)*cdn/eV*hbar, '-',
            kxmaplist, np.linalg.norm(klist-B2-B1, axis=-1)*cdn/eV*hbar, '-',
            kxmaplist, np.linalg.norm(klist+B2+B1, axis=-1)*cdn/eV*hbar, '-',
            kxmaplist, np.linalg.norm(klist-2*B1, axis=-1)*cdn/eV*hbar, '-',
            kxmaplist, np.linalg.norm(klist-2*B2, axis=-1)*cdn/eV*hbar, '-',
            kxmaplist, np.linalg.norm(klist-2*B2-B1, axis=-1)*cdn/eV*hbar, '-',
            kxmaplist, np.linalg.norm(klist-2*B1-B2, axis=-1)*cdn/eV*hbar, '-',
            kxmaplist, np.linalg.norm(klist-2*B1-2*B2, axis=-1)*cdn/eV*hbar, '-',
    #        kxmaplist, np.linalg.norm(klist+2*B2-B1, axis=-1)*cdn, '-',
    #        kxmaplist, np.linalg.norm(klist+2*B1-B2, axis=-1)*cdn, '-',
           )
    ax.set_xlim([np.min(kxmlarr),np.max(kxmlarr)])
    #ax.set_ylim([2.15,2.30])
    ax.set_ylim([np.min(omlist/eV*hbar),np.max(omlist/eV*hbar)])
    ax.set_xticks([0, kxmaplist[len(k0Mlist)-1], kxmaplist[len(k0Mlist)+len(kMK1list)-1], kxmaplist[len(k0Mlist)+len(kMK1list)+len(kK10list)-1], kxmaplist[len(k0Mlist)+len(kMK1list)+len(kK10list)+len(k0K2list)-1], kxmaplist[len(k0Mlist)+len(kMK1list)+len(kK10list)+len(k0K2list)+len(kK2Mlist)-1]])
    ax.set_xticklabels(['Γ', 'M', 'K', 'Γ', 'K\'','M'])
    f.colorbar(sc)
    
    pdf.savefig(f)
pdf.close()

print(time.strftime("%H.%M:%S",time.gmtime(time.time()-begtime)))