2D dispersion calculations with the fixed frequency.
Former-commit-id: 9a3298c0fa8124c6171a1b9df78d39c3c5adc270
This commit is contained in:
Marek Nečada
parent
02e34d525b
commit
f05a010a14
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@ -404,7 +404,7 @@ for trfile in os.scandir(translations_dir):
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leftmatrix[j] = -np.tensordot(TMatrices_om[j], leftmatrix[j], axes=([-2,-1],[0,1]))
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# at this point, jth row of leftmatrix is that of -MT
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leftmatrix[j,:,:,j,:,:] += n2id
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#now we are done, 1-M
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#now we are done, 1-MT
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leftmatrixlist[ki] = leftmatrix
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@ -0,0 +1,512 @@
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#!/usr/bin/env python3
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import argparse, re, random, string
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import subprocess
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from scipy.constants import hbar, e as eV, pi, c
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def make_action_sharedlist(opname, listname):
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class opAction(argparse.Action):
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def __call__(self, parser, args, values, option_string=None):
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if (not hasattr(args, listname)) or getattr(args, listname) is None:
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setattr(args, listname, list())
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getattr(args,listname).append((opname, values))
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return opAction
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parser = argparse.ArgumentParser()
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#TODO? použít type=argparse.FileType('r') ?
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parser.add_argument('--TMatrix', action='store', required=True, help='Path to TMatrix file')
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#parser.add_argument('--griddir', action='store', required=True, help='Path to the directory with precalculated translation operators')
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parser.add_argument('--output_prefix', action='store', required=True, help='Prefix to the pdf and/or npz output (will be appended frequency and hexside)')
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#sizepar = parser.add_mutually_exclusive_group(required=True)
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parser.add_argument('--hexside', action='store', type=float, required=True, help='Lattice hexagon size length')
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parser.add_argument('--output', action='store', help='Path to output PDF')
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parser.add_argument('--store_SVD', action='store_false', help='If specified without --SVD_output, it will save the data in a file named as the PDF output, but with .npz extension instead')
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parser.add_argument('--plot_TMatrix', action='store_true', help='Visualise TMatrix on the first page of the output')
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#parser.add_argument('--SVD_output', action='store', help='Path to output singular value decomposition result')
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parser.add_argument('--nSV', action='store', metavar='N', type=int, default=1, help='Store and draw N minimun singular values')
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parser.add_argument('--maxlayer', action='store', type=int, default=100, help='How far to sum the lattice points to obtain the dispersion')
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parser.add_argument('--scp_to', action='store', metavar='N', type=str, help='SCP the output files to a given destination')
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parser.add_argument('--background_permittivity', action='store', type=float, default=1., help='Background medium relative permittivity (default 1)')
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parser.add_argument('--eVfreq', action='store', required=True, type=float, help='Frequency in eV')
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parser.add_argument('--kdensity', action='store', type=int, default=33, help='Number of k-points per x-axis segment')
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parser.add_argument('--lMax', action='store', type=int, help='Override lMax from the TMatrix file')
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#TODO some more sophisticated x axis definitions
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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).')
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popgrp=parser.add_argument_group(title='Operations')
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popgrp.add_argument('--tr', dest='ops', action=make_action_sharedlist('tr', 'ops'), default=list()) # the default value for dest can be set once
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popgrp.add_argument('--tr0', dest='ops', action=make_action_sharedlist('tr0', 'ops'))
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popgrp.add_argument('--tr1', dest='ops', action=make_action_sharedlist('tr1', 'ops'))
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popgrp.add_argument('--sym', dest='ops', action=make_action_sharedlist('sym', 'ops'))
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popgrp.add_argument('--sym0', dest='ops', action=make_action_sharedlist('sym0', 'ops'))
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popgrp.add_argument('--sym1', dest='ops', action=make_action_sharedlist('sym1', 'ops'))
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#popgrp.add_argument('--mult', dest='ops', nargs=3, metavar=('INCSPEC', 'SCATSPEC', 'MULTIPLIER'), action=make_action_sharedlist('mult', 'ops'))
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#popgrp.add_argument('--mult0', dest='ops', nargs=3, metavar=('INCSPEC', 'SCATSPEC', 'MULTIPLIER'), action=make_action_sharedlist('mult0', 'ops'))
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#popgrp.add_argument('--mult1', dest='ops', nargs=3, metavar=('INCSPEC', 'SCATSPEC', 'MULTIPLIER'), action=make_action_sharedlist('mult1', 'ops'))
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popgrp.add_argument('--multl', dest='ops', nargs=3, metavar=('INCL[,INCL,...]', 'SCATL[,SCATL,...]', 'MULTIPLIER'), action=make_action_sharedlist('multl', 'ops'))
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popgrp.add_argument('--multl0', dest='ops', nargs=3, metavar=('INCL[,INCL,...]', 'SCATL[,SCATL,...]', 'MULTIPLIER'), action=make_action_sharedlist('multl0', 'ops'))
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popgrp.add_argument('--multl1', dest='ops', nargs=3, metavar=('INCL[,INCL,...]', 'SCATL[,SCATL,...]', 'MULTIPLIER'), action=make_action_sharedlist('multl1', 'ops'))
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parser.add_argument('--frequency_multiplier', action='store', type=float, default=1., help='Multiplies the frequencies in the TMatrix file by a given factor.')
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# TODO enable more flexible per-sublattice specification
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pargs=parser.parse_args()
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print(pargs)
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maxlayer=pargs.maxlayer
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hexside=pargs.hexside
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eVfreq = pargs.eVfreq
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freq = eVfreq*eV/hbar
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TMatrix_file = pargs.TMatrix
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pdfout = pargs.output if pargs.output else (
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'%s_%dnm_%.4f.pdf' % (pargs.output_prefix,hexside/1e-9,eVfreq) if pargs.output_prefix else
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(''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(10)) + '.pdf'))
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print(pdfout)
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if(pargs.store_SVD):
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if re.search('.pdf$', pdfout):
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svdout = re.sub('.pdf$', r'.npz', pdfout)
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else:
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svdout = pdfout + '.npz'
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else:
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svdout = None
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epsilon_b = pargs.background_permittivity #2.3104
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gaussianSigma = pargs.gaussian if pargs.gaussian else None # hexside * 222 / 7
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interpfreqfactor = pargs.frequency_multiplier
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scp_dest = pargs.scp_to if pargs.scp_to else None
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kdensity = pargs.kdensity
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svn = pargs.nSV
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# TODO multiplier operation definitions and parsing
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#factor13inc = 10
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#factor13scat=10
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ops = list()
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opre = re.compile('(tr|sym|copy|multl|mult)(\d*)')
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for oparg in pargs.ops:
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opm = opre.match(oparg[0])
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if opm:
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ops.append(((opm.group(2),) if opm.group(2) else (0,1), opm.group(1), oparg[1]))
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else:
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raise # should not happen
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print(ops)
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#ops = (
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# # co, typ operace (symetrizace / transformace / kopie), specifikace (operace nebo zdroj),
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# # co: 0, 1, (0,1), (0,), (1,), #NI: 'all'
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# # typ operace: sym, tr, copy
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# # specifikace:
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# # sym, tr: 'σ_z', 'σ_y', 'C2'; sym: 'C3',
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# # copy: 0, 1 (zdroj)
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# ((0,1), 'sym', 'σ_z'),
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# #((0,1), 'sym', 'σ_x'),
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# #((0,1), 'sym', 'σ_y'),
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# ((0,1), 'sym', 'C3'),
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# ((1), 'tr', 'C2'),
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#
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#)
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# -----------------finished basic CLI parsing (except for op arguments) ------------------
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import time
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begtime=time.time()
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from matplotlib.path import Path
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import matplotlib.patches as patches
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import matplotlib.pyplot as plt
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import qpms
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import numpy as np
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import os, sys, warnings, math
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from matplotlib import pyplot as plt
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from matplotlib.backends.backend_pdf import PdfPages
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from scipy import interpolate
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nx = None
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s3 = math.sqrt(3)
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pdf = PdfPages(pdfout)
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# In[3]:
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# specifikace T-matice zde
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cdn = c/ math.sqrt(epsilon_b)
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TMatrices_orig, freqs_orig, freqs_weirdunits_orig, lMaxTM = qpms.loadScuffTMatrices(TMatrix_file)
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lMax = lMaxTM
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if pargs.lMax:
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lMax = pargs.lMax if pargs.lMax else lMaxTM
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my, ny = qpms.get_mn_y(lMax)
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nelem = len(my)
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if pargs.lMax: #force commandline specified lMax
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TMatrices_orig = TMatrices_orig[...,0:nelem,:,0:nelem]
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ž = np.arange(2*nelem)
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tž = ž // nelem
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mž = my[ž%nelem]
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nž = ny[ž%nelem]
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TEž = ž[(mž+nž+tž) % 2 == 0]
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TMž = ž[(mž+nž+tž) % 2 == 1]
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č = np.arange(2*2*nelem)
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žč = č % (2* nelem)
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tč = tž[žč]
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mč = mž[žč]
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nč = nž[žč]
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TEč = č[(mč+nč+tč) % 2 == 0]
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TMč = č[(mč+nč+tč) % 2 == 1]
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TMatrices = np.array(np.broadcast_to(TMatrices_orig[:,nx,:,:,:,:],(len(freqs_orig),2,2,nelem,2,nelem)) )
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#TMatrices[:,:,:,:,:,ny==3] *= factor13inc
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#TMatrices[:,:,:,ny==3,:,:] *= factor13scat
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xfl = qpms.xflip_tyty(lMax)
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yfl = qpms.yflip_tyty(lMax)
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zfl = qpms.zflip_tyty(lMax)
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c2rot = qpms.apply_matrix_left(qpms.yflip_yy(3),qpms.xflip_yy(3),-1)
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reCN = re.compile('(\d*)C(\d+)')
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#TODO C nekonečno
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for op in ops:
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if op[0] == 'all':
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targets = (0,1)
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elif isinstance(op[0],int):
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targets = (op[0],)
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else:
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targets = op[0]
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if op[1] == 'sym':
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mCN = reCN.match(op[2]) # Fuck van Rossum for not having assignments inside expressions
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if op[2] == 'σ_z':
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for t in targets:
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TMatrices[:,t] = (TMatrices[:,t] + qpms.apply_ndmatrix_left(zfl,qpms.apply_ndmatrix_left(zfl, TMatrices[:,t], (-4,-3)),(-2,-1)))/2
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elif op[2] == 'σ_y':
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for t in targets:
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TMatrices[:,t] = (TMatrices[:,t] + qpms.apply_ndmatrix_left(yfl,qpms.apply_ndmatrix_left(yfl, TMatrices[:,t], (-4,-3)),(-2,-1)))/2
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elif op[2] == 'σ_x':
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for t in targets:
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TMatrices[:,t] = (TMatrices[:,t] + qpms.apply_ndmatrix_left(xfl,qpms.apply_ndmatrix_left(xfl, TMatrices[:,t], (-4,-3)),(-2,-1)))/2
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elif op[2] == 'C2': # special case of the latter
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for t in targets:
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TMatrices[:,t] = (TMatrices[:,t] + qpms.apply_matrix_left(c2rot,qpms.apply_matrix_left(c2rot, TMatrices[:,t], -3),-1))/2
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elif mCN:
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rotN = int(mCN.group(2))
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TMatrix_contribs = np.empty((rotN,TMatrices.shape[0],2,nelem,2,nelem), dtype=np.complex_)
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for t in targets:
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for i in range(rotN):
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rotangle = 2*np.pi*i / rotN
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rot = qpms.WignerD_yy_fromvector(lMax,np.array([0,0,rotangle]))
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rotinv = qpms.WignerD_yy_fromvector(lMax,np.array([0,0,-rotangle]))
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TMatrix_contribs[i] = qpms.apply_matrix_left(rot,qpms.apply_matrix_left(rotinv, TMatrices[:,t], -3),-1)
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TMatrices[:,t] = np.sum(TMatrix_contribs, axis=0) / rotN
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else:
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raise
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elif op[1] == 'tr':
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mCN = reCN.match(op[2]) # Fuck van Rossum for not having assignments inside expressions
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if op[2] == 'σ_z':
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for t in targets:
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TMatrices[:,t] = qpms.apply_ndmatrix_left(zfl,qpms.apply_ndmatrix_left(zfl, TMatrices[:,t], (-4,-3)),(-2,-1))
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elif op[2] == 'σ_y':
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for t in targets:
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TMatrices[:,t] = qpms.apply_ndmatrix_left(yfl,qpms.apply_ndmatrix_left(yfl, TMatrices[:,t], (-4,-3)),(-2,-1))
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elif op[2] == 'σ_x':
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for t in targets:
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TMatrices[:,t] = qpms.apply_ndmatrix_left(xfl,qpms.apply_ndmatrix_left(xfl, TMatrices[:,t], (-4,-3)),(-2,-1))
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elif op[2] == 'C2':
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for t in targets:
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TMatrices[:,t] = qpms.apply_matrix_left(c2rot,qpms.apply_matrix_left(c2rot, TMatrices[:,t], -3),-1)
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elif mCN:
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rotN = int(mCN.group(2))
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power = int(mCN.group(1)) if mCN.group(1) else 1
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TMatrix_contribs = np.empty((rotN,TMatrices.shape[0],2,nelem,2,nelem), dtype=np.complex_)
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for t in targets:
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rotangle = 2*np.pi*power/rotN
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rot = qpms.WignerD_yy_fromvector(lMax, np.array([0,0,rotangle]))
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rotinv = qpms.WignerD_yy_fromvector(lMax, np.array([0,0,-rotangle]))
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TMatrices[:,t] = qpms.apply_matrix_left(rot, qpms.apply_matrix_left(rotinv, TMatrices[:,t], -3),-1)
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else:
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raise
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elif op[1] == 'copy':
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raise # not implemented
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elif op[1] == 'mult':
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raise # not implemented
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elif op[1] == 'multl':
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incy = np.full((nelem,), False, dtype=bool)
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for incl in op[2][0].split(','):
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l = int(incl)
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incy += (l == ny)
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scaty = np.full((nelem,), False, dtype=bool)
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for scatl in op[2][1].split(','):
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l = int(scatl)
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scaty += (l == ny)
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for t in targets:
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TMatrices[np.ix_(np.arange(TMatrices.shape[0]),np.array([t]),np.array([0,1]),scaty,np.array([0,1]),incy)] *= float(op[2][2])
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else:
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raise #unknown operation; should not happen
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TMatrices_interp = interpolate.interp1d(freqs_orig*interpfreqfactor, TMatrices, axis=0, kind='linear',fill_value="extrapolate")
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# In[4]:
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if(pargs.plot_TMatrix):
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om = np.linspace(np.min(freqs_orig), np.max(freqs_orig),100)
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TMatrix0ip = np.reshape(TMatrices_interp(om)[:,0], (len(om), 2*nelem*2*nelem))
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f, axa = plt.subplots(2, 2, figsize=(15,15))
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#print(TMatrices.shape)
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#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--'))
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ax = axa[0,0]
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ax2 = ax.twiny()
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ax2.set_xlim([ax.get_xlim()[0]/eV*hbar,ax.get_xlim()[1]/eV*hbar])
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ax.plot(
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om, TMatrix0ip[:,:].imag,'-',om, TMatrix0ip[:,:].real,'--',
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)
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ax = axa[0,1]
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ax2 = ax.twiny()
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ax2.set_xlim([ax.get_xlim()[0]/eV*hbar,ax.get_xlim()[1]/eV*hbar])
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ax.plot(
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om, abs(TMatrix0ip[:,:]),'-'
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)
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ax.set_yscale('log')
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ax = axa[1,1]
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ax2 = ax.twiny()
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ax2.set_xlim([ax.get_xlim()[0]/eV*hbar,ax.get_xlim()[1]/eV*hbar])
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ax.plot(
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om, np.unwrap(np.angle(TMatrix0ip[:,:]),axis=0),'-'
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)
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ax = axa[1,0]
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ax.text(0.5,0.5,str(pargs).replace(',',',\n'),horizontalalignment='center',verticalalignment='center',transform=ax.transAxes)
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pdf.savefig(f)
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# In[ ]:
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'''
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#kdensity = 66 #defined from cl arguments
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bz_0 = np.array((0,0,0.,))
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bz_K1 = np.array((1.,0,0))*4*np.pi/3/hexside/s3
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bz_K2 = np.array((1./2.,s3/2,0))*4*np.pi/3/hexside/s3
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bz_M = np.array((3./4, s3/4,0))*4*np.pi/3/hexside/s3
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k0Mlist = bz_0 + (bz_M-bz_0) * np.linspace(0,1,kdensity)[:,nx]
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kMK1list = bz_M + (bz_K1-bz_M) * np.linspace(0,1,kdensity)[:,nx]
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kK10list = bz_K1 + (bz_0-bz_K1) * np.linspace(0,1,kdensity)[:,nx]
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k0K2list = bz_0 + (bz_K2-bz_0) * np.linspace(0,1,kdensity)[:,nx]
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kK2Mlist = bz_K2 + (bz_M-bz_K2) * np.linspace(0,1,kdensity)[:,nx]
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B1 = 2* bz_K1 - bz_K2
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B2 = 2* bz_K2 - bz_K1
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klist = np.concatenate((k0Mlist,kMK1list,kK10list,k0K2list,kK2Mlist), axis=0)
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kxmaplist = np.concatenate((np.array([0]),np.cumsum(np.linalg.norm(np.diff(klist, axis=0), axis=-1))))
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'''
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klist = qpms.generate_trianglepoints(kdensity, v3d=True, include_origin=True)*3*math.pi/(3*kdensity*hexside)
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# In[ ]:
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n2id = np.identity(2*nelem)
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n2id.shape = (2,nelem,2,nelem)
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nan = float('nan')
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filecount = 0
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for one in (1,):
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omega = freq # from args; k_0 * c / math.sqrt(epsilon_b)
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k_0 = omega * math.sqrt(epsilon_b) / c
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tdic = qpms.hexlattice_get_AB(lMax,k_0*hexside,maxlayer)
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#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)
|
||||
if svdout:
|
||||
svUfullTElist = np.full((klist.shape[0], 2*nelem, 2*nelem), np.nan, dtype=complex)
|
||||
svVfullTElist = np.full((klist.shape[0], 2*nelem, 2*nelem), np.nan, dtype=complex)
|
||||
svSfullTElist = np.full((klist.shape[0], 2*nelem), np.nan, dtype=complex)
|
||||
svUfullTMlist = np.full((klist.shape[0], 2*nelem, 2*nelem), np.nan, dtype=complex)
|
||||
svVfullTMlist = np.full((klist.shape[0], 2*nelem, 2*nelem), np.nan, dtype=complex)
|
||||
svSfullTMlist = np.full((klist.shape[0], 2*nelem), np.nan, dtype=complex)
|
||||
|
||||
|
||||
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-MT
|
||||
|
||||
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)
|
||||
if svdout:
|
||||
svUfullTElist[nnlist], svSfullTElist[nnlist], svVfullTElist[nnlist] = np.linalg.svd(leftmatrixlist_TE, compute_uv=True)
|
||||
svUfullTMlist[nnlist], svSfullTMlist[nnlist], svVfullTMlist[nnlist] = np.linalg.svd(leftmatrixlist_TM, compute_uv=True)
|
||||
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:]
|
||||
|
||||
|
||||
'''
|
||||
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)
|
||||
'''
|
||||
|
||||
''' The new pretty diffracted order drawing '''
|
||||
maxlayer_reciprocal=4
|
||||
cdn = c/ math.sqrt(epsilon_b)
|
||||
bz_0 = np.array((0,0,))
|
||||
bz_K1 = np.array((1.,0))*4*np.pi/3/hexside/s3
|
||||
bz_K2 = np.array((1./2.,s3/2))*4*np.pi/3/hexside/s3
|
||||
bz_M = np.array((3./4, s3/4))*4*np.pi/3/hexside/s3
|
||||
|
||||
# reciprocal lattice basis
|
||||
B1 = 2* bz_K1 - bz_K2
|
||||
B2 = 2* bz_K2 - bz_K1
|
||||
|
||||
if svdout:
|
||||
np.savez(svdout, omega = freq, klist = klist, bzpoints = np.array([bz_0, bz_K1, bz_K2, bz_M, B1, B2]),
|
||||
uTE = svUfullTElist,
|
||||
vTE = svVfullTElist,
|
||||
sTE = svSfullTElist,
|
||||
uTM = svUfullTMlist,
|
||||
vTM = svVfullTMlist,
|
||||
sTM = svSfullTMlist,
|
||||
)
|
||||
|
||||
k2density = 100
|
||||
k0Mlist = bz_0 + (bz_M-bz_0) * np.linspace(0,1,k2density)[:,nx]
|
||||
kMK1list = bz_M + (bz_K1-bz_M) * np.linspace(0,1,k2density)[:,nx]
|
||||
kK10list = bz_K1 + (bz_0-bz_K1) * np.linspace(0,1,k2density)[:,nx]
|
||||
k0K2list = bz_0 + (bz_K2-bz_0) * np.linspace(0,1,k2density)[:,nx]
|
||||
kK2Mlist = bz_K2 + (bz_M-bz_K2) * np.linspace(0,1,k2density)[:,nx]
|
||||
k2list = np.concatenate((k0Mlist,kMK1list,kK10list,k0K2list,kK2Mlist), axis=0)
|
||||
kxmaplist = np.concatenate((np.array([0]),np.cumsum(np.linalg.norm(np.diff(k2list, axis=0), axis=-1))))
|
||||
|
||||
centers2=qpms.generate_trianglepoints(maxlayer_reciprocal, v3d = False, include_origin= True)*4*np.pi/3/hexside
|
||||
rot90 = np.array([[0,-1],[1,0]])
|
||||
centers2=np.dot(centers2,rot90)
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib
|
||||
from matplotlib.path import Path
|
||||
import matplotlib.patches as patches
|
||||
cmap = matplotlib.cm.prism
|
||||
colormax = np.amax(np.linalg.norm(centers2,axis=0))
|
||||
|
||||
|
||||
# In[ ]:
|
||||
for minN in reversed(range(svn)):
|
||||
f, axes = plt.subplots(1,3, figsize=(20,4))
|
||||
ax = axes[0]
|
||||
sc = ax.scatter(klist[:,0], klist[:,1], c = np.clip(np.abs(minsvTElist[:,minN]),0,1), lw=0)
|
||||
for center in centers2:
|
||||
circle=plt.Circle((center[0],center[1]),omega/cdn, facecolor='none', edgecolor=cmap(np.linalg.norm(center)/colormax),lw=0.5)
|
||||
ax.add_artist(circle)
|
||||
verts = [(math.cos(math.pi*i/3)*4*np.pi/3/hexside/s3,math.sin(math.pi*i/3)*4*np.pi/3/hexside/s3) for i in range(6 +1)]
|
||||
codes = [Path.MOVETO,Path.LINETO,Path.LINETO,Path.LINETO,Path.LINETO,Path.LINETO,Path.CLOSEPOLY,]
|
||||
path = Path(verts, codes)
|
||||
patch = patches.PathPatch(path, facecolor='none', edgecolor='black', lw=1)
|
||||
ax.add_patch(patch)
|
||||
ax.set_xticks([])
|
||||
ax.set_yticks([])
|
||||
f.colorbar(sc,ax=ax)
|
||||
|
||||
|
||||
ax = axes[1]
|
||||
sc = ax.scatter(klist[:,0], klist[:,1], c = np.clip(np.abs(minsvTMlist[:,minN]),0,1), lw=0)
|
||||
for center in centers2:
|
||||
circle=plt.Circle((center[0],center[1]),omega/cdn, facecolor='none', edgecolor=cmap(np.linalg.norm(center)/colormax),lw=0.5)
|
||||
ax.add_artist(circle)
|
||||
verts = [(math.cos(math.pi*i/3)*4*np.pi/3/hexside/s3,math.sin(math.pi*i/3)*4*np.pi/3/hexside/s3) for i in range(6 +1)]
|
||||
codes = [Path.MOVETO,Path.LINETO,Path.LINETO,Path.LINETO,Path.LINETO,Path.LINETO,Path.CLOSEPOLY,]
|
||||
path = Path(verts, codes)
|
||||
patch = patches.PathPatch(path, facecolor='none', edgecolor='black', lw=1)
|
||||
ax.add_patch(patch)
|
||||
ax.set_xticks([])
|
||||
ax.set_yticks([])
|
||||
f.colorbar(sc,ax=ax)
|
||||
|
||||
ax = axes[2]
|
||||
for center in centers2:
|
||||
ax.plot(kxmaplist, np.linalg.norm(k2list-center,axis=-1)*cdn, '-', color=cmap(np.linalg.norm(center)/colormax))
|
||||
|
||||
#ax.set_xlim([np.min(kxmlarr),np.max(kxmlarr)])
|
||||
#ax.set_ylim([np.min(omegalist),np.max(omegalist)])
|
||||
xticklist = [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_xticks(xticklist)
|
||||
for xt in xticklist:
|
||||
ax.axvline(xt, ls='dotted', lw=0.3,c='k')
|
||||
ax.set_xticklabels(['Γ', 'M', 'K', 'Γ', 'K\'','M'])
|
||||
ax.axhline(omega, c='black')
|
||||
ax.set_ylim([0,5e15])
|
||||
ax2 = ax.twinx()
|
||||
ax2.set_ylim([ax.get_ylim()[0]/eV*hbar,ax.get_ylim()[1]/eV*hbar])
|
||||
|
||||
pdf.savefig(f)
|
||||
|
||||
pdf.close()
|
||||
|
||||
if scp_dest:
|
||||
subprocess.run(['scp', pdfout, scp_dest])
|
||||
if svdout:
|
||||
subprocess.run(['scp', svdout, scp_dest])
|
||||
|
||||
print(time.strftime("%H.%M:%S",time.gmtime(time.time()-begtime)))
|
||||
Loading…
Reference in New Issue