some misc scripts
Former-commit-id: a8644b3f9afb67a7fa770911a217086193018bf1
This commit is contained in:
Marek Nečada
parent
042197f978
commit
3e7af7d25e
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@ -0,0 +1,35 @@
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import qpms
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import numpy as np
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from numpy import newaxis as nx
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import math
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import cmath
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import os
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from scipy.constants import c, e as eV, hbar
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s3 = math.sqrt(3)
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("omega")
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#parser.add_argument("maxlayer")
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args = parser.parse_args()
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omega_eV = float(args.omega)
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print(omega_eV)
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epsilon_b = 2.3104
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hexside = 375e-9
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lMax = 3
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maxlayer = 222
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my, ny = qpms.get_mn_y(lMax)
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nelem = len(my)
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omega = omega_eV * eV / hbar
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k_0 = omega * math.sqrt(epsilon_b) / c
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output_prefix = './diracpoints-newdata/%d/' % maxlayer
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os.makedirs(output_prefix, exist_ok=True)
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qpms.hexlattice_precalc_AB_save(file=output_prefix+str(omega_eV), lMax=lMax, k_hexside=k_0*hexside,
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maxlayer=maxlayer, savepointinfo=True)
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@ -0,0 +1,403 @@
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# coding: utf-8
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# In[1]:
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translations_dir = '/l/necadam1/translations-precalc/diracpoints-newdata/222'
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TMatrix_file ='/m/home/home4/46/necadam1/unix/tmatrix-experiments/twisted_triangle/silver/twisted_triangle.TMatrix.nonan'
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pdfout = '/m/home/home4/46/necadam1/unix/tmp/pdf_out/inv-2-mag10-10.pdf'
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hexside = 375e-9
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epsilon_b = 2.3104
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gaussianSigma = None # hexside * 222 / 7
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factor13inc = 10
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factor13scat=10
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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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interpfreqfactor = 0.5
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import qpms
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import numpy as np
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import os, sys
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import warnings
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import 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.constants import hbar, e as eV, pi, c
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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[2]:
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#TODO později
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#import argparse
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#parser = argparse.ArgumentParser()
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#parser.add_argument('--sym', 'mz', 'my', 'mx', 'C3', 'C2' type=str, help='symmetrize both particles')
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#args = parser.parse_args()
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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, lMax = qpms.loadScuffTMatrices(TMatrix_file)
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my, ny = qpms.get_mn_y(lMax)
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nelem = len(my)
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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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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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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] == 'C3': # FIXME fuj fuj fuj, použij regex!!!
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rotN = 3
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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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elif op[2] == 'C2':
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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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else:
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raise
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elif op[1] == 'tr':
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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] == 'C3': # TODO use regex and generalize
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rotN = 3
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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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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 op[1] == 'copy':
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raise
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else:
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raise
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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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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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pdf.savefig(f)
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# In[ ]:
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kdensity = 66
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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/5)[:,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/5)[:,nx]
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kK2Mlist = bz_K2 + (bz_M-bz_K2) * np.linspace(0,1,kdensity/5)[:,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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# In[ ]:
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n2id = np.identity(2*nelem)
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n2id.shape = (2,nelem,2,nelem)
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extlistlist = list()
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leftmatrixlistlist = list()
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minsvTElistlist=list()
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minsvTMlistlist=list()
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nan = float('nan')
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omegalist = list()
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filecount = 0
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for trfile in os.scandir(translations_dir):
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filecount += 1
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try:
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npz = np.load(trfile.path, mmap_mode='r')
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k_0 = npz['precalc_params'][()]['k_hexside'] / hexside
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omega = k_0 * c / math.sqrt(epsilon_b)
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if(omega < 2.4e15 or omega > 2.7e15 ):
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continue
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except:
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print ("Unexpected error, trying to continue with another file:", sys.exc_info()[0])
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continue
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try:
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tdic = qpms.hexlattice_precalc_AB_loadunwrap(trfile.path, return_points=True)
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except:
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print ("Unexpected error, trying to continue with another file:", sys.exc_info()[0])
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continue
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k_0 = tdic['k_hexside'] / hexside
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omega = k_0 * c / math.sqrt(epsilon_b)
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omegalist.append(omega)
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print(filecount, omega/eV*hbar)
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a_self = tdic['a_self'][:,:nelem,:nelem]
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b_self = tdic['b_self'][:,:nelem,:nelem]
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a_u2d = tdic['a_u2d'][:,:nelem,:nelem]
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b_u2d = tdic['b_u2d'][:,:nelem,:nelem]
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a_d2u = tdic['a_d2u'][:,:nelem,:nelem]
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b_d2u = tdic['b_d2u'][:,:nelem,:nelem]
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unitcell_translations = tdic['self_tr']*hexside*s3
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u2d_translations = tdic['u2d_tr']*hexside*s3
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d2u_translations = tdic['d2u_tr']*hexside*s3
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if gaussianSigma:
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unitcell_envelope = np.exp(-np.sum(unitcell_translations**2,axis=-1)/(2*gaussianSigma**2))
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u2d_envelope = np.exp(-np.sum(u2d_translations**2,axis=-1)/(2*gaussianSigma**2))
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d2u_envelope = np.exp(-np.sum(d2u_translations**2,axis=-1)/(2*gaussianSigma**2))
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TMatrices_om = TMatrices_interp(omega)
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minsvTElist = np.full((klist.shape[0]),np.nan)
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minsvTMlist = np.full((klist.shape[0]),np.nan)
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leftmatrixlist = np.full((klist.shape[0],2,2,nelem,2,2,nelem),np.nan,dtype=complex)
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isNaNlist = np.zeros((klist.shape[0]), dtype=bool)
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# sem nějaká rozumná smyčka
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for ki in range(klist.shape[0]):
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k = klist[ki]
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if (k_0*k_0 - k[0]*k[0] - k[1]*k[1] < 0):
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isNaNlist[ki] = True
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continue
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phases_self = np.exp(1j*np.tensordot(k,unitcell_translations,axes=(0,-1)))
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phases_u2d = np.exp(1j*np.tensordot(k,u2d_translations,axes=(0,-1)))
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phases_d2u = np.exp(1j*np.tensordot(k,d2u_translations,axes=(0,-1)))
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if gaussianSigma:
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phases_self *= unitcell_envelope
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phases_u2d *= u2d_envelope
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phases_d2u *= d2u_envelope
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leftmatrix = np.zeros((2,2,nelem, 2,2,nelem), dtype=complex)
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leftmatrix[0,0,:,0,0,:] = np.tensordot(a_self,phases_self, axes=(0,-1)) # u2u, E2E
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leftmatrix[1,0,:,1,0,:] = leftmatrix[0,0,:,0,0,:] # d2d, E2E
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leftmatrix[0,1,:,0,1,:] = leftmatrix[0,0,:,0,0,:] # u2u, M2M
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leftmatrix[1,1,:,1,1,:] = leftmatrix[0,0,:,0,0,:] # d2d, M2M
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leftmatrix[0,0,:,0,1,:] = np.tensordot(b_self,phases_self, axes=(0,-1)) # u2u, M2E
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leftmatrix[0,1,:,0,0,:] = leftmatrix[0,0,:,0,1,:] # u2u, E2M
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leftmatrix[1,1,:,1,0,:] = leftmatrix[0,0,:,0,1,:] # d2d, E2M
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leftmatrix[1,0,:,1,1,:] = leftmatrix[0,0,:,0,1,:] # d2d, M2E
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leftmatrix[0,0,:,1,0,:] = np.tensordot(a_d2u, phases_d2u,axes=(0,-1)) #d2u,E2E
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leftmatrix[0,1,:,1,1,:] = leftmatrix[0,0,:,1,0,:] #d2u, M2M
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leftmatrix[0,0,:,1,1,:] = np.tensordot(b_d2u, phases_d2u,axes=(0,-1)) #d2u,M2E
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leftmatrix[0,1,:,1,0,:] = leftmatrix[0,0,:,1,1,:] #d2u, E2M
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leftmatrix[1,0,:,0,1,:] = np.tensordot(b_u2d, phases_u2d,axes=(0,-1)) #u2d,M2E
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leftmatrix[1,1,:,0,0,:] = leftmatrix[1,0,:,0,1,:] #u2d, E2M
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#leftmatrix is now the translation matrix T
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for j in range(2):
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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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leftmatrixlist[ki] = leftmatrix
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nnlist = np.logical_not(isNaNlist)
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leftmatrixlist_s = np.reshape(leftmatrixlist,(klist.shape[0], 2*2*nelem,2*2*nelem))[nnlist]
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leftmatrixlist_TE = leftmatrixlist_s[np.ix_(np.arange(leftmatrixlist_s.shape[0]),TEč,TEč)]
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leftmatrixlist_TM = leftmatrixlist_s[np.ix_(np.arange(leftmatrixlist_s.shape[0]),TMč,TMč)]
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minsvTElist[nnlist] = np.amin(np.linalg.svd(leftmatrixlist_TE, compute_uv=False), axis=-1)
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minsvTMlist[nnlist] = np.amin(np.linalg.svd(leftmatrixlist_TM, compute_uv=False), axis=-1)
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minsvTMlistlist.append(minsvTMlist)
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minsvTElistlist.append(minsvTElist)
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|
||||
|
||||
# In[ ]:
|
||||
|
||||
minsvTElistarr = np.array(minsvTElistlist)
|
||||
minsvTMlistarr = np.array(minsvTMlistlist)
|
||||
omegalist = np.array(omegalist)
|
||||
omlist = np.broadcast_to(omegalist[:,nx], minsvTElistarr.shape)
|
||||
kxmlarr = np.broadcast_to(kxmaplist[nx,:], minsvTElistarr.shape)
|
||||
klist = np.concatenate((k0Mlist,kMK1list,kK10list,k0K2list,kK2Mlist), axis=0)
|
||||
|
||||
|
||||
# In[ ]:
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.path import Path
|
||||
import matplotlib.patches as patches
|
||||
f, ax = plt.subplots(1, figsize=(20,15))
|
||||
sc = ax.scatter(kxmlarr, omlist/eV*hbar, c = np.sqrt(minsvTMlistarr), 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[ ]:
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.path import Path
|
||||
import matplotlib.patches as patches
|
||||
f, ax = plt.subplots(1, figsize=(20,15))
|
||||
sc = ax.scatter(kxmlarr, omlist/eV*hbar, c = np.sqrt(minsvTElistarr), 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[ ]:
|
||||
|
||||
pdf.close()
|
||||
|
||||
|
||||
# In[ ]:
|
||||
|
||||
unitcell_translations
|
||||
|
||||
|
||||
# In[ ]:
|
||||
|
||||
|
||||
|
||||
Loading…
Reference in New Issue