Multiple minimum singular value support
Former-commit-id: c8351b2b509d5887bcde06fadcfb21c3f79a0054
This commit is contained in:
Marek Nečada
parent
a3dec16ee3
commit
2ff17ba650
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@ -19,6 +19,7 @@ parser.add_argument('--griddir', action='store', required=True, help='Path to th
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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('--nSV', action='store', metavar='N', type=int, default=1, help='Store and draw N minimun singular values')
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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('--sparse', action='store', type=int, help='Skip frequencies for preview')
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parser.add_argument('--eVmax', action='store', help='Skip frequencies above this value')
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@ -58,6 +59,7 @@ kdensity = pargs.kdensity
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minfreq = pargs.eVmin*eV/hbar if pargs.eVmin else None
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maxfreq = pargs.eVmax*eV/hbar if pargs.eVmax else None
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skipfreq = pargs.sparse if pargs.sparse else None
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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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@ -92,6 +94,9 @@ print(ops)
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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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@ -324,8 +329,8 @@ for trfile in os.scandir(translations_dir):
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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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minsvTElist = np.full((klist.shape[0], svn),np.nan)
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minsvTMlist = np.full((klist.shape[0], svn),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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@ -373,8 +378,14 @@ for trfile in os.scandir(translations_dir):
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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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svarr = np.linalg.svd(leftmatrixlist_TE, compute_uv=False)
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argsortlist = np.argsort(svarr, axis=-1)[...,:svn]
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minsvTElist[nnlist] = svarr[argsortlist]
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#minsvTElist[nnlist] = np.amin(np.linalg.svd(leftmatrixlist_TE, compute_uv=False), axis=-1)
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svarr = np.linalg.svd(leftmatrixlist_TM, compute_uv=False)
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argsortlist = np.argsort(svarr, axis=-1)[...,:svn]
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minsvTMlist[nnlist] = svarr[argsortlist]
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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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@ -393,18 +404,16 @@ omegalist = omegalist[omegaorder]
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minsvTElistarr = minsvTElistarr[omegaorder]
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minsvTMlistarr = minsvTMlistarr[omegaorder]
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omlist = np.broadcast_to(omegalist[:,nx], minsvTElistarr.shape)
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kxmlarr = np.broadcast_to(kxmaplist[nx,:], minsvTElistarr.shape)
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omlist = np.broadcast_to(omegalist[:,nx], minsvTElistarr[...,0].shape)
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kxmlarr = np.broadcast_to(kxmaplist[nx,:], minsvTElistarr[...,0].shape)
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klist = np.concatenate((k0Mlist,kMK1list,kK10list,k0K2list,kK2Mlist), axis=0)
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# In[ ]:
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from matplotlib.path import Path
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import matplotlib.patches as patches
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f, ax = plt.subplots(1, figsize=(20,15))
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sc = ax.scatter(kxmlarr, omlist/eV*hbar, c = np.sqrt(minsvTMlistarr), s =40, lw=0)
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ax.plot(kxmaplist, np.linalg.norm(klist,axis=-1)*cdn/eV*hbar, '-',
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for minN in range(svn):
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f, ax = plt.subplots(1, figsize=(20,15))
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sc = ax.scatter(kxmlarr, omlist/eV*hbar, c = np.sqrt(minsvTMlistarr[...,minN]), s =40, lw=0)
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ax.plot(kxmaplist, np.linalg.norm(klist,axis=-1)*cdn/eV*hbar, '-',
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kxmaplist, np.linalg.norm(klist+B1, axis=-1)*cdn/eV*hbar, '-',
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kxmaplist, np.linalg.norm(klist+B2, axis=-1)*cdn/eV*hbar, '-',
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kxmaplist, np.linalg.norm(klist-B2, axis=-1)*cdn/eV*hbar, '-',
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@ -418,27 +427,24 @@ ax.plot(kxmaplist, np.linalg.norm(klist,axis=-1)*cdn/eV*hbar, '-',
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kxmaplist, np.linalg.norm(klist-2*B2-B1, axis=-1)*cdn/eV*hbar, '-',
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kxmaplist, np.linalg.norm(klist-2*B1-B2, axis=-1)*cdn/eV*hbar, '-',
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kxmaplist, np.linalg.norm(klist-2*B1-2*B2, axis=-1)*cdn/eV*hbar, '-',
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# kxmaplist, np.linalg.norm(klist+2*B2-B1, axis=-1)*cdn, '-',
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# kxmaplist, np.linalg.norm(klist+2*B1-B2, axis=-1)*cdn, '-',
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# kxmaplist, np.linalg.norm(klist+2*B2-B1, axis=-1)*cdn, '-',
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# kxmaplist, np.linalg.norm(klist+2*B1-B2, axis=-1)*cdn, '-',
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)
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ax.set_xlim([np.min(kxmlarr),np.max(kxmlarr)])
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#ax.set_ylim([2.15,2.30])
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ax.set_ylim([np.min(omlist/eV*hbar),np.max(omlist/eV*hbar)])
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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]])
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ax.set_xticklabels(['Γ', 'M', 'K', 'Γ', 'K\'','M'])
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f.colorbar(sc)
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ax.set_xlim([np.min(kxmlarr),np.max(kxmlarr)])
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#ax.set_ylim([2.15,2.30])
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ax.set_ylim([np.min(omlist/eV*hbar),np.max(omlist/eV*hbar)])
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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]])
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ax.set_xticklabels(['Γ', 'M', 'K', 'Γ', 'K\'','M'])
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f.colorbar(sc)
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pdf.savefig(f)
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pdf.savefig(f)
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# In[ ]:
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# In[ ]:
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import matplotlib.pyplot as plt
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from matplotlib.path import Path
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import matplotlib.patches as patches
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f, ax = plt.subplots(1, figsize=(20,15))
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sc = ax.scatter(kxmlarr, omlist/eV*hbar, c = np.sqrt(minsvTElistarr), s =40, lw=0)
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ax.plot(kxmaplist, np.linalg.norm(klist,axis=-1)*cdn/eV*hbar, '-',
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f, ax = plt.subplots(1, figsize=(20,15))
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sc = ax.scatter(kxmlarr, omlist/eV*hbar, c = np.sqrt(minsvTElistarr[...,minN]), s =40, lw=0)
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ax.plot(kxmaplist, np.linalg.norm(klist,axis=-1)*cdn/eV*hbar, '-',
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kxmaplist, np.linalg.norm(klist+B1, axis=-1)*cdn/eV*hbar, '-',
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kxmaplist, np.linalg.norm(klist+B2, axis=-1)*cdn/eV*hbar, '-',
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kxmaplist, np.linalg.norm(klist-B2, axis=-1)*cdn/eV*hbar, '-',
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@ -452,17 +458,17 @@ ax.plot(kxmaplist, np.linalg.norm(klist,axis=-1)*cdn/eV*hbar, '-',
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kxmaplist, np.linalg.norm(klist-2*B2-B1, axis=-1)*cdn/eV*hbar, '-',
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kxmaplist, np.linalg.norm(klist-2*B1-B2, axis=-1)*cdn/eV*hbar, '-',
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kxmaplist, np.linalg.norm(klist-2*B1-2*B2, axis=-1)*cdn/eV*hbar, '-',
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# kxmaplist, np.linalg.norm(klist+2*B2-B1, axis=-1)*cdn, '-',
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# kxmaplist, np.linalg.norm(klist+2*B1-B2, axis=-1)*cdn, '-',
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# kxmaplist, np.linalg.norm(klist+2*B2-B1, axis=-1)*cdn, '-',
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# kxmaplist, np.linalg.norm(klist+2*B1-B2, axis=-1)*cdn, '-',
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)
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ax.set_xlim([np.min(kxmlarr),np.max(kxmlarr)])
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#ax.set_ylim([2.15,2.30])
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ax.set_ylim([np.min(omlist/eV*hbar),np.max(omlist/eV*hbar)])
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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]])
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ax.set_xticklabels(['Γ', 'M', 'K', 'Γ', 'K\'','M'])
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f.colorbar(sc)
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ax.set_xlim([np.min(kxmlarr),np.max(kxmlarr)])
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#ax.set_ylim([2.15,2.30])
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ax.set_ylim([np.min(omlist/eV*hbar),np.max(omlist/eV*hbar)])
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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]])
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ax.set_xticklabels(['Γ', 'M', 'K', 'Γ', 'K\'','M'])
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f.colorbar(sc)
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pdf.savefig(f)
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pdf.savefig(f)
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pdf.close()
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print(time.strftime("%H.%M:%S",time.gmtime(time.time()-begtime)))
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