-
Notifications
You must be signed in to change notification settings - Fork 8
/
draw_phononTB_HAN.py
1751 lines (1480 loc) · 77.6 KB
/
draw_phononTB_HAN.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
import os.path
import time
import sys
from math import *
from math import sqrt
from numpy.linalg import *
import numpy as np
import matplotlib.pyplot as plt
import cmath
import datetime as dt
#from numeric import *
class Read_FC_from_other_calculators:
def __init__(self, calculator):
self.fc_information = []
self.hopping = []
self.calculator = calculator # phononpy and DFPT in QE
if self.calculator == "PHONOPY" or self.calculator == "phonopy" or self.calculator == "Phonopy":
self.version = '1.12.6.53' # latest version, FORCE_CONSTANTS file depends on the version so please check
if self.calculator == "QE" or self.calculator == "Quantum Espresso" or self.calculator == "Quantum":
self.version = 'Not yet'
print "Not implemented yet"
return 0
def read_FC(self, FC_symmetry, supercell, mass, symprec, cutoff_distance):
if self.calculator == "PHONOPY" or self.calculator == "phonopy" or self.calculator == "Phonopy":
self.FC_symmetry = FC_symmetry ### True or False
self.supercell = supercell ### [2, 2, 2]
self.supercell_index = []
for i in range(self.supercell[2]):
for j in range(self.supercell[1]):
for k in range(self.supercell[0]):
self.supercell_index.append([k,j,i])
self.mass = mass
self.symprec = symprec ### default = 1e-4
self.cutoff_distance = cutoff_distance
self.read_FC_from_phonopy()
else:
# To be implemented
return 0
def read_POSCAR(self, POSCAR_file):
f = open(POSCAR_file, 'r')
tempf = f.readlines()
f.close()
univ_prim = float(tempf[1].split()[0])
a_prim = np.array([float(tempf[2].split()[i]) for i in range(3)]) * univ_prim
b_prim = np.array([float(tempf[3].split()[i]) for i in range(3)]) * univ_prim
c_prim = np.array([float(tempf[4].split()[i]) for i in range(3)]) * univ_prim
atom_type = np.array([str(tempf[5].split()[i]) for i in range(len(tempf[5].split()))])
num_type = np.array([int(tempf[6].split()[i]) for i in range(len(tempf[6].split()))])
total_num_atom = np.sum(num_type)
#print total_num_atom
if tempf[7][0] == 'D' or tempf[7][0] == 'd':
# Direct coordinates
mode = 'direct'
elif tempf[7][0] == 'C' or tempf[7][0] == 'c':
# Cartesian coordinates
mode = 'cart'
else:
# Starnge input?
print "POSCAR ERROR: please check direct or cartesian coordintes"
return 0
direct_coord = []
if mode == 'direct':
for i in range(total_num_atom):
tempcoord = np.array([float(tempf[8+i].split()[j]) for j in range(3)])
direct_coord.append(tempcoord)
elif mode == 'cart':
for i in range(total_num_atom):
tempcoord = np.array([float(tempf[8+i].split()[j]) for j in range(3)])
temp_direct_coord = from_cartesin_to_direct([a_prim, b_prim, c_prim], tempcoord)
direct_coord.append(temp_direct_coord)
else:
pass
self.latt_vec_prim = np.array([a_prim, b_prim, c_prim])
self.atom_type_prim = atom_type
self.num_type_prim = num_type
self.total_num_atom_prim = total_num_atom
self.direct_coord_prim = direct_coord
def read_SPOSCAR(self, SPOSCAR_file):
f = open(SPOSCAR_file, 'r')
tempf = f.readlines()
f.close()
univ_super = float(tempf[1].split()[0])
a_super = np.array([float(tempf[2].split()[i]) for i in range(3)]) * univ_super
b_super = np.array([float(tempf[3].split()[i]) for i in range(3)]) * univ_super
c_super = np.array([float(tempf[4].split()[i]) for i in range(3)]) * univ_super
num_type = np.array([int(tempf[5].split()[i]) for i in range(len(tempf[5].split()))])
total_num_atom = np.sum(num_type)
#print total_num_atom
if tempf[6][0] == 'D' or tempf[6][0] == 'd':
# Direct coordinates
mode = 'direct'
elif tempf[6][0] == 'C' or tempf[6][0] == 'c':
# Cartesian coordinates
mode = 'cart'
else:
# Starnge input?
print "POSCAR ERROR: please check direct or cartesian coordintes"
return 0
direct_coord = []
if mode == 'direct':
for i in range(total_num_atom):
tempcoord = np.array([float(tempf[7+i].split()[j]) for j in range(3)])
direct_coord.append(tempcoord)
elif mode == 'cart':
for i in range(total_num_atom):
tempcoord = np.array([float(tempf[7+i].split()[j]) for j in range(3)])
temp_direct_coord = from_cartesin_to_direct([a_super, b_super, c_super], tempcoord)
direct_coord.append(temp_direct_coord)
else:
pass
self.latt_vec_super = np.array([a_super, b_super, c_super])
self.total_num_atom_super = total_num_atom
self.direct_coord_super = direct_coord
def read_FC_from_phonopy(self, POSCAR='POSCAR', SPOSCAR='SPOSCAR', FORCE_CONSTANTS='FORCE_CONSTANTS'):
self.read_POSCAR(POSCAR)
self.read_SPOSCAR(SPOSCAR)
if len(self.mass) != self.total_num_atom_prim:
print "ERROR: size error of mass or POSCAR error "
return 0
f = open(FORCE_CONSTANTS, 'r')
tempf = f.readlines()
f.close()
total_line = 1 + self.total_num_atom_prim * (4 * self.total_num_atom_super)
if total_line != len(tempf):
print "ERROR: Something wrong, please check your phonopy version or FC, POSCAR, and SPOSCAR files"
print "Version of phonopy we support is " + self.version
return 0
for i in range(self.total_num_atom_prim):
for j in range(self.total_num_atom_super):
templine = 1 + i*(4*self.total_num_atom_super) + (4*j)
#print tempf[templine].split()
a_atom, b_atom = int(tempf[templine].split()[0]) -1 , int(tempf[templine].split()[1]) -1 # 0, 1, 2, 3, 4, 5, ... atoms
a_prim_index, a_super_pos = self.find_supercell_position(a_atom)
b_prim_index, b_super_pos = self.find_supercell_position(b_atom)
distance, b_min_super_pos, multi = self.find_nearest_supercell(a_prim_index, a_super_pos, b_prim_index, b_super_pos)
#print a_prim_index, a_super_pos, b_prim_index, b_super_pos, distance, multi, b_min_super_pos
if multi != 1:
#print distance, multi
FC_temp = np.zeros((3, 3))
FC_temp[0][0] = float(tempf[templine+1].split()[0]) ; FC_temp[0][1] = float(tempf[templine+1].split()[1]) ; FC_temp[0][2] = float(tempf[templine+1].split()[2])
FC_temp[1][0] = float(tempf[templine+2].split()[0]) ; FC_temp[1][1] = float(tempf[templine+2].split()[1]) ; FC_temp[1][2] = float(tempf[templine+2].split()[2])
FC_temp[2][0] = float(tempf[templine+3].split()[0]) ; FC_temp[2][1] = float(tempf[templine+3].split()[1]) ; FC_temp[2][2] = float(tempf[templine+3].split()[2])
#print a_prim_index, np.array(a_super_pos), a_prim_index, np.array(a_super_pos), distance, FC_temp
for k in range(multi): # This case: multi != 1
dup_check = self.check_duplicate(distance, a_prim_index, b_prim_index, b_min_super_pos[k])
if dup_check and distance <= self.cutoff_distance:
self.fc_information.append([a_prim_index, np.array(a_super_pos), b_prim_index, np.array(b_min_super_pos[k]), distance, FC_temp/multi])
ji_vector = np.dot(self.latt_vec_prim.transpose(), np.array(self.direct_coord_prim[b_prim_index]) + b_min_super_pos[k] - np.array(self.direct_coord_prim[a_prim_index]) - a_super_pos)
V_pps, V_ppp = self.find_sigma_pi_from_fc(FC_temp, ji_vector)
self.hopping.append([a_prim_index, b_prim_index, np.array(b_min_super_pos[k]), [V_pps, V_ppp]])
else:
for k in range(multi): # This case: multi = 1
dup_check = self.check_duplicate(distance, a_prim_index, b_prim_index, b_min_super_pos[k])
if dup_check and distance <= self.cutoff_distance:
FC_temp = np.zeros((3, 3))
FC_temp[0][0] = float(tempf[templine+1].split()[0]) ; FC_temp[0][1] = float(tempf[templine+1].split()[1]) ; FC_temp[0][2] = float(tempf[templine+1].split()[2])
FC_temp[1][0] = float(tempf[templine+2].split()[0]) ; FC_temp[1][1] = float(tempf[templine+2].split()[1]) ; FC_temp[1][2] = float(tempf[templine+2].split()[2])
FC_temp[2][0] = float(tempf[templine+3].split()[0]) ; FC_temp[2][1] = float(tempf[templine+3].split()[1]) ; FC_temp[2][2] = float(tempf[templine+3].split()[2])
self.fc_information.append([a_prim_index, np.array(a_super_pos), b_prim_index, np.array(b_min_super_pos[k]), distance, FC_temp/multi])
ji_vector = np.dot(self.latt_vec_prim.transpose(), np.array(self.direct_coord_prim[b_prim_index]) + b_min_super_pos[k] - np.array(self.direct_coord_prim[a_prim_index]) - a_super_pos)
V_pps, V_ppp = self.find_sigma_pi_from_fc(FC_temp, ji_vector)
self.hopping.append([a_prim_index, b_prim_index, np.array(b_min_super_pos[k]), [V_pps, V_ppp]])
#print self.fc_information
#self.fc_information = np.array(self.fc_information)
#np.save('inoformation_file', self.fc_information)
return 0
def check_duplicate(self, distance, a_atom_index, b_atom_index, b_atom_super_pos):
### This function checks whether hopping paramters are duplicated or not
dup_check = True
for i in range(len(self.fc_information)):
if distance == self.fc_information[i][4]:
if a_atom_index == b_atom_index:
if a_atom_index == self.fc_information[i][0] and b_atom_index == self.fc_information[i][2]:
if np.all(np.array(b_atom_super_pos) == self.fc_information[i][3]):
if np.all(np.array(b_atom_super_pos) == np.array([0,0,0])):
dup_check = False
if np.all(np.array(b_atom_super_pos) == -1 * self.fc_information[i][3]):
dup_check = False
else:
if a_atom_index == self.fc_information[i][0] and b_atom_index == self.fc_information[i][2]:
if np.all(np.array(b_atom_super_pos) == self.fc_information[i][3]):
dup_check = False
if a_atom_index == self.fc_information[i][2] and b_atom_index == self.fc_information[i][0]:
if np.all(np.array(b_atom_super_pos) == self.fc_information[i][3]):
dup_check = False
if np.all(np.array(b_atom_super_pos) == -1 * self.fc_information[i][3]):
dup_check = False
return dup_check
def find_supercell_position(self, atom_index):
prim_index = int(atom_index) / (self.supercell[0] * self.supercell[1] * self.supercell[2])
super_pos = self.supercell_index[int(atom_index) % (self.supercell[0] * self.supercell[1] * self.supercell[2])]
return prim_index , np.array(super_pos)
def find_nearest_supercell(self, a_atom_index, a_atom_super_pos, b_atom_index, b_atom_super_pos):
distance = []
supercell_pos = []
min_super_pos = []
s_pos = self.direct_coord_prim[a_atom_index] + np.array(a_atom_super_pos)
p_pos = self.direct_coord_prim[b_atom_index] + np.array(b_atom_super_pos)
for i in (-1, 0, 1):
for j in (-1, 0, 1):
for k in (-1, 0, 1):
diff = s_pos - p_pos - np.array([i,j,k])*self.supercell
vec = np.dot(self.latt_vec_prim.transpose(), diff)
distance.append(np.linalg.norm(vec))
supercell_pos.append(np.array([i,j,k])*self.supercell + np.array(b_atom_super_pos))
distance = np.array(distance)
minimum = np.min(distance)
for i in range(27):
if abs(minimum - distance[i]) < self.symprec:
min_super_pos.append(supercell_pos[i])
multi = len(min_super_pos)
return round(minimum,5), np.array(min_super_pos), multi
def find_sigma_pi_from_fc(self, fc, ji_vector):
if np.linalg.norm(ji_vector) == 0:
l = 0.0 ; m = 0.0 ; n = 0.0
final_results = [0, fc[0][0]]
else:
l = ji_vector[0] / np.linalg.norm(ji_vector)
m = ji_vector[1] / np.linalg.norm(ji_vector)
n = ji_vector[2] / np.linalg.norm(ji_vector)
# l*l + m*m + n*n = 1.0
if (l == 0.0 and m == 0.0): # n = 1.0
final_results = [fc[2][2], fc[0][0]]
elif (round(l*l,6) == round(m*m,6)): #
if (round(l*l,6) == round(n*n,6)):
fc_matrix = np.array([fc[0][0], fc[0][1]])
transform_matrix = np.array([[l*l, 1.0-l*l],[l*m, -1.0*l*m]])
#print l*l, m*m, n*n, transform_matrix
final_results = np.dot(np.linalg.inv(transform_matrix), fc_matrix)
else:
fc_matrix = np.array([fc[0][0], fc[2][2]])
transform_matrix = np.array([[l*l, 1.0-l*l],[n*n, 1.0-n*n]])
#print l*l, m*m, n*n, transform_matrix
final_results = np.dot(np.linalg.inv(transform_matrix), fc_matrix)
else:
fc_matrix = np.array([fc[0][0], fc[1][1]])
transform_matrix = np.array([[l*l, 1.0-l*l],[m*m, 1.0-m*m]])
final_results = np.dot(np.linalg.inv(transform_matrix), fc_matrix)
V_pps = final_results[0] ; V_ppp = final_results[1]
return V_pps, V_ppp
def print_all_information(self):
filename = 'information_file'
f = open(filename, 'w')
#initial_line =' < ' + '\t' + 'Aatom' + '\t' + ' | H | ' + '\t' + 'Batom' + '\t' + ' [ ' + '\t' + 'superx' + '\t' + 'supery' + '\t' + 'superz' + '\t' + ' ] = ' + '\t' + 'distance' + '\t' + '===>' + '\t' + 'V_pps' + '\t' + 'V_ppp' + '\n'
initial_line =' < ' + '\t' + 'Aatom' + '\t' + ' | H | ' + '\t' + 'Batom' + '\t' + ' [ ' + '\t' + 'superx' + '\t' + 'supery' + '\t' + 'superz' + '\t' + ' ] = ' + '\t' + 'distance' + '\t' + '===>' + '\t' + 'Frobenius norm' + '\n'
f.write(initial_line)
for i in range(len(self.fc_information)):
#templine = ' < ' + '\t' + str(self.fc_information[i][0]) + '\t' + ' | H | ' + '\t' + str(self.fc_information[i][2]) + '\t' + ' [ ' + '\t' + str(self.fc_information[i][3][0]) + '\t' + str(self.fc_information[i][3][1]) + '\t' + str(self.fc_information[i][3][2]) + '\t' + ' ] = ' + '\t' + str(self.fc_information[i][4]) + '\t' + '===>' + '\t' + str(self.hopping[i][3][0]) + '\t' + str(self.hopping[i][3][1])
templine = ' < ' + '\t' + str(self.fc_information[i][0]) + '\t' + ' | H | ' + '\t' + str(self.fc_information[i][2]) + '\t' + ' [ ' + '\t' + str(self.fc_information[i][3][0]) + '\t' + str(self.fc_information[i][3][1]) + '\t' + str(self.fc_information[i][3][2]) + '\t' + ' ] = ' + '\t' + str(self.fc_information[i][4]) + '\t' + '===>' + '\t' + str(np.linalg.norm(self.fc_information[i][5],ord='fro'))
for j in range(3):
for k in range(3):
templine += '\t' + str(self.fc_information[i][5][j][k])
templine += '\n'
f.write(templine)
f.close()
class ForceConstant:
def __init__(self, dimension, num_atom):
self.dimension = dimension
self.num_atom = num_atom
self.hopping_save = []
self.fc_info = []
self.edge_cal = False
def set_geometry(self, lattice_vector, atom_position, atom_mass):
self.latt_vec = np.array(lattice_vector)
self.atom_pos = np.array(atom_position)
self.atom_mas = np.array(atom_mass)
if len(self.latt_vec) != self.dimension:
print 'Please check either dimension or lattice vector; They are differnt to each other'
if len(self.atom_pos) != self.num_atom:
print 'Please check either num_atom or atom_position; They are differnt to each other'
if len(self.atom_mas) != self.num_atom:
print 'Please check either atom_mas or atom_position; They are differnt to each other'
####reciprocal lattice####
if self.dimension == 3:
recip_fac = (2*np.pi) / np.dot(self.latt_vec[0], np.cross(self.latt_vec[1], self.latt_vec[2]))
recip_vec = np.array([np.cross(self.latt_vec[1], self.latt_vec[2]), np.cross(self.latt_vec[2], self.latt_vec[0]), np.cross(self.latt_vec[0], self.latt_vec[1])]) * recip_fac
if self.dimension == 2:
#print np.cross(self.latt_vec[0], self.latt_vec[1])
recip_fac = (2*np.pi) / np.cross(self.latt_vec[0], self.latt_vec[1])
recip_vec = np.array([[self.latt_vec[1][1],-self.latt_vec[0][1]],[-self.latt_vec[1][0],self.latt_vec[0][0]]]) * recip_fac
recip_vec = recip_vec.transpose()
self.recip_vec = recip_vec
def set_fc_direct(self,iatom, jatom, supercell_of_jatom, fc):
fc_temp = [iatom, jatom, supercell_of_jatom, fc]
self.fc_info.append(fc_temp)
def set_hopping(self, iatom, jatom, supercell_of_jatom, V_info):
#if len(force_constant) != self.dimension or len(force_constant[0]) != self.dimension:
# print 'Please check either dimension or the size of force constant; They are different to each other'
# return 0
if len(supercell_of_jatom) != self.dimension:
print 'Please check either dimension or the size of supercell; They are different to each other'
return 0
if iatom == jatom:
print 'iatom == jatom; Do you really need it? or Do you intend to consider NNN ?'
if not self.check_duplicate_TBmode(iatom, jatom, supercell_of_jatom):
print "There is duplication !!!"
print iatom, jatom, supercell_of_jatom
sys.exit(1)
self.hopping_save.append([iatom, jatom, supercell_of_jatom, V_info])
fc = self.generate_fc_constant_TB(iatom, jatom, supercell_of_jatom, V_info[0], V_info[1])
fc_temp = [iatom, jatom, supercell_of_jatom, fc]
#print fc_temp
self.fc_info.append(fc_temp)
def check_duplicate_TBmode(self, iatom, jatom, supercell_of_jatom):
for i in range(len(self.fc_info)):
if (iatom == self.fc_info[i][0] and jatom == self.fc_info[i][1]):
if np.all(np.array(supercell_of_jatom) - self.fc_info[i][2] == 0):
return False
if (jatom == self.fc_info[i][0] and iatom == self.fc_info[i][1]):
if np.all(np.array(supercell_of_jatom) + self.fc_info[i][2] == 0):
return False
return True
def set_acoustic_sum_rule(self):
'''
It determines whether on-site force constants statisfy constraints or not
'''
if self.dimension == 2:
for i in range(self.num_atom):
fc_from_asr = np.zeros((self.dimension,self.dimension))
for j in range(len(self.fc_info)):
if i == self.fc_info[j][0] or i == self.fc_info[j][1]:
if (i == self.fc_info[j][0] and i == self.fc_info[j][1]):
if np.linalg.norm(self.fc_info[j][2] - np.array([0.0,0.0])) != 0:
fc_from_asr -= np.array(2.0*self.fc_info[j][3]) ### This case for 0, 0 [1,0] like NNN hopping
else:
fc_from_asr -= np.array(self.fc_info[j][3])
else:
fc_from_asr -= np.array(self.fc_info[j][3])
#print fc_from_asr
fc_temp = [i, i, [0,0], fc_from_asr]
self.fc_info.append(fc_temp)
hopping_from_asr = [0, fc_from_asr[0][0]]
hopping_temp = [i, i, [0,0], hopping_from_asr]
self.hopping_save.append(hopping_temp)
if self.dimension == 3:
for i in range(self.num_atom):
fc_from_asr = np.zeros((self.dimension,self.dimension))
for j in range(len(self.fc_info)):
if i == self.fc_info[j][0] or i == self.fc_info[j][1]:
if (i == self.fc_info[j][0] and i == self.fc_info[j][1]):
if np.linalg.norm(self.fc_info[j][2] - np.array([0.0,0.0,0.0])) != 0:
fc_from_asr -= np.array(2.0*self.fc_info[j][3]) ### This case for 0, 0 [1,0] like NNN hopping why 2.0? 0, 0, [1,0] == 0, 0 [-1, 0]
else:
fc_from_asr -= np.array(self.fc_info[j][3]) ### This case for 0, 0 [0,0] like on-site energies
#print i, self.fc_info[j][0], self.fc_info[j][1], self.fc_info[j][2], np.array(self.fc_info[j][3])
else:
fc_from_asr -= np.array(self.fc_info[j][3])
fc_temp = [i, i, [0,0,0], fc_from_asr]
#print fc_temp
self.fc_info.append(fc_temp)
hopping_from_asr = [0, fc_from_asr[0][0]]
hopping_temp = [i, i, [0,0,0], hopping_from_asr]
#self.hopping_save.append(hopping_temp)
return 0
def generate_fc_constant_TB(self, iatom, jatom, supercell_of_jatom, V_pps, V_ppp):
'''
Detail hopping parameters : https://en.wikipedia.org/wiki/Tight_binding
E_xx = l^2 V_pps + (1-l^2) V_ppp
E_xy = lm (V_pps - V_ppp)
E_xz = ln (V_pps- V_ppp)
l, m, n = direction cosines
'''
if self.dimension == 2:
position_i = np.dot(np.array(self.latt_vec).transpose(), np.array(self.atom_pos[iatom]).transpose())
position_j = np.dot(np.array(self.latt_vec).transpose(), np.array(self.atom_pos[jatom] + np.array(supercell_of_jatom)).transpose())
ji_vector = position_j - position_i
if np.linalg.norm(ji_vector) == 0:
l, m = 0.0, 0.0
else:
l = ji_vector[0] / np.linalg.norm(ji_vector)
m = ji_vector[1] / np.linalg.norm(ji_vector)
fc_xx = l*l*V_pps + (1.0 - l*l)*V_ppp
fc_xy = l*m*(V_pps - V_ppp)
fc_yy = m*m*V_pps + (1.0 - m*m)*V_ppp
fc = np.array([[fc_xx, fc_xy], [fc_xy, fc_yy]])
if self.dimension == 3:
position_i = np.dot(np.array(self.latt_vec).transpose(), np.array(self.atom_pos[iatom]).transpose())
position_j = np.dot(np.array(self.latt_vec).transpose(), np.array(self.atom_pos[jatom] + np.array(supercell_of_jatom)).transpose())
ji_vector = position_j - position_i
if np.linalg.norm(ji_vector) == 0:
l, m, n = 0.0, 0.0, 0.0
else:
l = ji_vector[0] / np.linalg.norm(ji_vector)
m = ji_vector[1] / np.linalg.norm(ji_vector)
n = ji_vector[2] / np.linalg.norm(ji_vector)
fc_xx = l*l*V_pps + (1.0 - l*l)*V_ppp
fc_xy = l*m*(V_pps - V_ppp)
fc_yy = m*m*V_pps + (1.0 - m*m)*V_ppp
fc_zz = n*n*V_pps + (1.0 - n*n)*V_ppp
fc_xz = l*n*(V_pps - V_ppp)
fc_yz = m*n*(V_pps - V_ppp)
fc = np.array([[fc_xx, fc_xy, fc_xz], [fc_xy, fc_yy, fc_yz], [fc_xz, fc_yz, fc_zz]])
return fc
def get_fc_other_calculators(self, other_calculator, asr):
self.set_geometry(other_calculator.latt_vec_prim, other_calculator.direct_coord_prim, other_calculator.mass)
for i in range(len(other_calculator.fc_information)):
self.set_fc_direct(other_calculator.fc_information[i][0], other_calculator.fc_information[i][2], other_calculator.fc_information[i][3], other_calculator.fc_information[i][5])
#for i in range(len(other_calculator.hopping)):
# self.set_hopping(other_calculator.hopping[i][0], other_calculator.hopping[i][1], other_calculator.hopping[i][2], other_calculator.hopping[i][3])
if asr:
self.set_acoustic_sum_rule()
def manipulate_interlayer_fc(self, layer1, layer2, multiplication_factor):
for i in range(len(self.fc_info)):
if int(self.fc_info[i][0]) in layer1:
if int(self.fc_info[i][1]) in layer2:
self.fc_info[i][3] = self.fc_info[i][3] * multiplication_factor
if int(self.fc_info[i][0]) in layer2:
if int(self.fc_info[i][1]) in layer1:
self.fc_info[i][3] = self.fc_info[i][3] * multiplication_factor
return 0
def print_info(self):
print 'Dimension = ' + str(self.dimension)
print 'Number of atoms = ' + str(self.num_atom)
if self.dimension == 2:
print 'Lattice vector = ' + ' a = ' + str(self.latt_vec[0]) + ' b = ' + str(self.latt_vec[1])
if self.dimension ==3:
print 'Lattice vector = ' + ' a = ' + str(self.latt_vec[0]) + ' b = ' + str(self.latt_vec[1]) + ' c = ' + str(self.latt_vec[2])
print 'Atomic position'
for i in range(self.num_atom):
line = ''
for j in range(self.dimension):
line += str(self.atom_pos[i][j]) + '\t'
print line
print 'All fc_info'
for i in range(len(self.fc_info)):
print str(self.fc_info[i])
return 0
def make_edge(self, num_repeat, direction):
if direction == 0:
print 'Edge direction is x '
elif direction == 1:
print 'Edge direction is y '
elif direction == 2:
print 'Edge direction is z '
if direction + 1 > self.dimension:
print 'ERROR: direction does not match with diemsion'
return 0
self.edge_cal = True
self.edge_direction = direction
multiple = np.zeros(self.dimension, dtype=float)
for i in range(self.dimension):
if i == self.edge_direction:
multiple[i] = num_repeat
else:
multiple[i] = 1.0
new_lattice = np.array([self.latt_vec[i]*multiple[i] for i in range(self.dimension)])
new_atompos = []
new_atommas = []
atomspos_modified = self.atom_pos / multiple
add = np.zeros(self.dimension, dtype=float)
add[self.edge_direction] = 1.0/ num_repeat
for i in range(self.num_atom):
for j in range(num_repeat):
new_atompos.append(atomspos_modified[i] + add*j)
new_atommas.append(self.atom_mas[i])
##########
for i in range(len(new_lattice)):
if i == self.edge_direction:
new_lattice[i] = np.array(new_lattice[i]) * 1.0
for i in range(len(new_atompos)):
for j in range(self.dimension):
if j == self.edge_direction:
new_atompos[i][j] = new_atompos[i][j] / 1.0
##########
self.latt_vec = new_lattice
self.atom_pos = new_atompos
self.atom_mas = new_atommas
self.num_atom = self.num_atom * num_repeat
new_fc_info = []
#for i in range(num_repeat):
# upper_limit = num_repeat - (i+1)
# lower_limit = -i
# #print upper_limit, lower_limit
# for j in range(len(self.hopping_save)):
# if self.hopping_save[j][2][self.edge_direction] > upper_limit or self.hopping_save[j][2][self.edge_direction] < lower_limit:
# pass
# else:
# reference_atom = int(self.hopping_save[j][0]*num_repeat + i)
# target_atom = int(self.hopping_save[j][1]*num_repeat + i + self.hopping_save[j][2][self.edge_direction])
# new_direction = [self.hopping_save[j][2][k] for k in range(self.dimension)]
# new_direction[self.edge_direction] = 0.0
# new_fc = self.generate_fc_constant_TB(reference_atom, target_atom, new_direction, self.hopping_save[j][3][0], self.hopping_save[j][3][1])
# temp_new_fc_info = [reference_atom, target_atom, new_direction, new_fc]
# new_fc_info.append(temp_new_fc_info)
##print new_fc_info
for i in range(num_repeat):
upper_limit = num_repeat - (i+1)
lower_limit = -i
#print upper_limit, lower_limit
for j in range(len(self.fc_info)):
if self.fc_info[j][2][self.edge_direction] > upper_limit or self.fc_info[j][2][self.edge_direction] < lower_limit:
pass
else:
reference_atom = int(self.fc_info[j][0]*num_repeat + i)
target_atom = int(self.fc_info[j][1]*num_repeat + i + self.fc_info[j][2][self.edge_direction])
new_direction = [self.fc_info[j][2][k] for k in range(self.dimension)]
new_direction[self.edge_direction] = 0.0
new_fc = self.fc_info[j][3]
temp_new_fc_info = [reference_atom, target_atom, new_direction, new_fc]
new_fc_info.append(temp_new_fc_info)
self.fc_info = new_fc_info
self.num_repeat = num_repeat
#for i in range
return 0
class DynamicalMatrix:
def __init__(self, name, force_constant, alpha):
self.out_tag = name
self.dimension = force_constant.dimension
self.num_atom = force_constant.num_atom
self.latt_vec = np.array(force_constant.latt_vec)
self.atom_pos = force_constant.atom_pos
self.atom_mas = force_constant.atom_mas
self.fc_info = force_constant.fc_info
self.TRS_broken = alpha
#self.interlayer_int = interlayer_int
self.edge_cal = force_constant.edge_cal
self.recip_vec = force_constant.recip_vec
if self.edge_cal:
self.num_repeat = force_constant.num_repeat
def obtain_qpath(self, q_path, q_spacing):
q_vec_list = []
q_distance_list = []
special_q_distance_list = [0.0]
sq_distance = 0.0
#print np.dot(recip_vec, self.latt_vec)
for i in range(len(q_path)-1):
initial = np.array(q_path[i])
final = np.array(q_path[i+1])
#print np.dot(recip_vec.transpose(), (final-initial).transpose())
temp_sq_distance = np.linalg.norm(np.dot(self.recip_vec.transpose(), (final-initial).transpose()))
sq_distance += temp_sq_distance
special_q_distance_list.append(sq_distance)
q_distance = special_q_distance_list[i]
for j in range(q_spacing):
delta = (final - initial) / float(q_spacing-1)
temp = initial + delta*j
q_vec_list.append(temp)
temp_q_distance = np.linalg.norm(np.dot(self.recip_vec.transpose(), np.array(delta*j).transpose()))
#print temp_q_distance
q_distance_list.append(q_distance+temp_q_distance)
return special_q_distance_list, q_distance_list, q_vec_list
def find_fc_for_pair(self, iatom, jatom):
proper_fc_info = [] # [r_j-r_i, fc]
if iatom == jatom:
for i in range(len(self.fc_info)):
if (iatom == self.fc_info[i][0] and iatom == self.fc_info[i][1]):
#diff_r = np.array(self.atom_pos[jatom]) + np.dot(np.array(self.latt_vec).transpose(), np.array(self.fc_info[i][2]).transpose()) - np.array(self.atom_pos[iatom])
diff_r = np.array(self.atom_pos[jatom]) + np.array(self.fc_info[i][2]).transpose() - np.array(self.atom_pos[iatom])
proper_fc_info.append([diff_r, self.fc_info[i][3]])
else:
for i in range(len(self.fc_info)):
if (iatom == self.fc_info[i][0] and jatom == self.fc_info[i][1]):
#diff_r = np.array(self.atom_pos[jatom]) + np.dot(np.array(self.latt_vec).transpose(), np.array(self.fc_info[i][2]).transpose()) - np.array(self.atom_pos[iatom])
diff_r = np.array(self.atom_pos[jatom]) + np.array(self.fc_info[i][2]).transpose() - np.array(self.atom_pos[iatom])
proper_fc_info.append([diff_r, self.fc_info[i][3]])
if (jatom == self.fc_info[i][0] and iatom == self.fc_info[i][1]):
#diff_r = np.array(self.atom_pos[jatom]) - np.array(self.atom_pos[iatom]) - np.dot(np.array(self.latt_vec).transpose(), np.array(self.fc_info[i][2]).transpose())
diff_r = np.array(self.atom_pos[jatom]) - np.array(self.atom_pos[iatom]) - np.array(self.fc_info[i][2]).transpose()
proper_fc_info.append([diff_r, self.fc_info[i][3]])
return proper_fc_info
def construct_dynamicalmatrix_q(self, q_vec):
#recip_vec = self.reciprocal()
q = np.dot(self.recip_vec.transpose(), np.array(q_vec).transpose())
#q = np.array(q_vec)
dm = np.zeros((self.dimension * self.num_atom, self.dimension * self.num_atom), dtype=complex)
for i in range(self.num_atom):
for j in range(self.num_atom):
if i < j:
mass_factor = np.sqrt(float(self.atom_mas[i]) * float(self.atom_mas[j]))
dm_local = np.zeros((self.dimension, self.dimension), dtype=complex)
proper_fc_info = self.find_fc_for_pair(i,j)
for k in range(len(proper_fc_info)):
#r = np.array(proper_fc_info[k][0])
r = np.dot(self.latt_vec.transpose(), np.array(proper_fc_info[k][0]).transpose())
#print r
phase_factor = np.exp(np.vdot(r, q) * 1j)
dm_local += proper_fc_info[k][1] * phase_factor / mass_factor
dm[(i*self.dimension):(i*self.dimension+self.dimension), (j*self.dimension):(j*self.dimension+self.dimension)] += dm_local
dm[(j*self.dimension):(j*self.dimension+self.dimension), (i*self.dimension):(i*self.dimension+self.dimension)] += dm_local.conj().transpose()
if i == j:
mass_factor = np.sqrt(float(self.atom_mas[i]) * float(self.atom_mas[j]))
dm_local = np.zeros((self.dimension, self.dimension), dtype=complex)
proper_fc_info = self.find_fc_for_pair(i,j)
for k in range(len(proper_fc_info)):
r = np.dot(self.latt_vec.transpose(), np.array(proper_fc_info[k][0]).transpose())
phase_factor = np.exp(np.vdot(r, q) * 1j)
if np.linalg.norm(r) == 0.0:
dm_local += proper_fc_info[k][1] * phase_factor / mass_factor
else:
dm_local += proper_fc_info[k][1] * phase_factor / mass_factor + (proper_fc_info[k][1] * phase_factor / mass_factor).conj().transpose()
dm[(i*self.dimension):(i*self.dimension+self.dimension), (j*self.dimension):(j*self.dimension+self.dimension)] += dm_local
dynamical_matrix = (dm + dm.conj().transpose()) / 2.0
#dynamical_matrix = dm
return dynamical_matrix
def DM_spectral_decomposition(self, initial_dm):
'''
If A = VDV-1 --> sqrt(A) = Vsqrt(D)V-1 where D is diagnoal matrix which consists of eigenvalues
V = eigenvectors
'''
eigval = np.linalg.eigh(initial_dm)[0]
eigval = np.abs(eigval) #due to small imaginary at gamma
sqrt_eigval = np.sqrt(eigval)
V = np.linalg.eigh(initial_dm)[1]
V_inv = np.linalg.inv(V)
size = len(sqrt_eigval)
sqrt_D = np.zeros((size, size), dtype=complex)
for i in range(size):
sqrt_D[i,i] = sqrt_eigval[i]
decomp_dm = np.dot(np.dot(V, sqrt_D), V_inv)
decomp_dm = (decomp_dm + decomp_dm.conj().transpose()) / 2
return decomp_dm
def make_phTB_H_ver2(self, dm):
dm_new = np.zeros((len(dm)*2,len(dm)*2), dtype=complex)
gamma = np.zeros((len(dm),len(dm)), dtype=complex)
if not self.edge_cal: ### This calculations for bulk calculations
if len(self.TRS_broken) == 1:
#print 'constant alpha'
alpha_set = []
for i in range(self.num_atom):
alpha_set.append(self.TRS_broken[0])
elif len(self.TRS_broken) == self.num_atom:
alpha_set = self.TRS_broken
else:
print 'alpha error'
else: ### This calculations for edge calculations
if len(self.TRS_broken) == 1:
#print 'constant alpha'
alpha_set = []
for i in range(self.num_atom):
alpha_set.append(self.TRS_broken[0])
#print alpha_set
elif len(self.TRS_broken) == int(self.num_atom) / int(self.num_repeat):
alpha_set = []
for i in range(len(self.TRS_broken)):
for j in range(self.num_repeat):
alpha_set.append(self.TRS_broken[i])
else:
print 'alpha error'
for i in range(self.num_atom):
alpha = alpha_set[i]
#print alpha
if self.dimension == 2:
gamma[i*self.dimension+0,i*self.dimension+1] += alpha[2]
gamma[i*self.dimension+1,i*self.dimension+0] += alpha[2] *-1
if self.dimension == 3:
gamma[i*self.dimension+0,i*self.dimension+1] += alpha[2] *-1
gamma[i*self.dimension+0,i*self.dimension+2] += alpha[1]
gamma[i*self.dimension+1,i*self.dimension+2] += alpha[0] *-1
gamma[i*self.dimension+1,i*self.dimension+0] += alpha[2]
gamma[i*self.dimension+2,i*self.dimension+0] += alpha[1] *-1
gamma[i*self.dimension+2,i*self.dimension+1] += alpha[0]
for i in range(2):
for j in range(2):
for k in range(len(dm)):
for l in range(len(dm)):
position_x = i*len(dm) + k
position_y = j*len(dm) + l
if i == 0 and j == 0:
dm_new[position_x, position_y] = 0.0
elif i == 0 and j ==1:
#dm_new[position_x, position_y] = dm.transpose()[k,l]
dm_new[position_x,position_y] = 1j * dm[k,l]
elif i ==1 and j == 0:
#dm_new[position_x, position_y] = dm[k,l]
dm_new[position_x,position_y] = -1j * dm[k,l]
elif i ==1 and j == 1:
dm_new[position_x, position_y] = -2j* gamma[k,l]
else:
pass
return dm_new
def add_interlayer_int_term(self):
'''
To be implemented
'''
return 0
def get_phonon_band(self, q_path, q_spacing):
vasp2THZ = 15.633302
'''
First, obtain q_path
'''
special_q_distance_list, q_distance_list, q_vec_list = self.obtain_qpath(q_path, q_spacing)
'''
Second, solve dynamical matrix at a q point
'''
band_structure = []
atom_projected = []
pos_expectation = []
pos_expectation_nk = []
if self.edge_cal:
position_operator = np.zeros((2*self.dimension*self.num_atom, 2*self.dimension*self.num_atom), dtype=complex)
for i in range(self.num_atom):
cell_number = i % self.num_repeat
for j in range(self.dimension):
position_operator[self.dimension*i + j, self.dimension*i + j] = cell_number
position_operator[self.dimension*i + j + self.dimension*self.num_atom, self.dimension*i + j + self.dimension*self.num_atom] = cell_number
for i in range(len(q_vec_list)):
print 'Process: ' + str(i+1) +'/' + str(len(q_vec_list))
q_vec = q_vec_list[i]
dyn = self.construct_dynamicalmatrix_q(q_vec)
modified_dyn = self.DM_spectral_decomposition(dyn)
modified_dyn = self.make_phTB_H_ver2(modified_dyn)
#modified_dyn = transform_H_for_u(modified_dyn,num_atom, latt_vec, atom_pos, recip_vec, q_vec, dimension)
#print modified_dyn[6][6]
#w1 = (np.linalg.eigvalsh(modified_dyn).real) *vasp2THZ
w1, v1 = np.linalg.eigh(modified_dyn)
band_num = len(w1)
band_structure.append(w1)
"""atom projection module"""
atom_projected_nk = []
for j in range(band_num):
wave_square = np.multiply(v1[:,j].transpose(), np.conjugate(v1[:,j].transpose()))
atom_temp = []
for k in range(self.num_atom):
initial = (band_num/2) + self.dimension*(k)
end = (band_num/2) + self.dimension*(k+1)
#print initial, end
projection = np.sum(wave_square[initial:end]) + np.sum(wave_square[initial-(band_num/2):end-(band_num/2)])
atom_temp.append(np.real(projection))
#print np.sum(atom_temp)
atom_projected_nk.append(atom_temp)
atom_projected.append(atom_projected_nk)
"""atom projection module"""
"""position expectation module"""
if self.edge_cal:
pos_expectation_nk = []
for j in range(band_num):
expectation = np.dot(np.conjugate(v1[:,j]), np.dot(position_operator, v1[:,j].transpose()))
#expectation = np.matmul(np.conjugate(v1[j]), v1[j].transpose())
pos_expectation_nk.append(expectation)
pos_expectation.append(pos_expectation_nk)
print 'total number of band is ' + str(band_num)
'''
Third, write phonon frequency file
'''
out_name = 'ph_frequecny_'+self.out_tag+'.out'
g = open(out_name,'w')
out_name2 = 'ph_frequecny_'+self.out_tag+'_projected.out'
g2 = open(out_name2,'w')
if self.edge_cal:
print "Edge calculations: position_expectation calcualtion"
out_name3 = 'ph_frequecny_'+self.out_tag+'_pos_expectation.out'
g3 = open(out_name3, 'w')
templine = ''
for i in range(len(special_q_distance_list)):
templine += str(special_q_distance_list[i])+ ' '
templine += '\n'
g.write(templine)
g2.write(templine)
if self.edge_cal:
g3.write(templine)
for i in range(len(q_vec_list)):
templine = str(q_distance_list[i])
for j in range(band_num):
templine += ' '+ str(band_structure[i][j]*vasp2THZ)
templine += '\n'
g.write(templine)
g.close()
for i in range(len(q_vec_list)):
for j in range(band_num):
templine = str(q_distance_list[i]) + ' ' + str(band_structure[i][j])
for k in range(self.num_atom):
templine += ' ' + str(atom_projected[i][j][k])
templine += '\n'
g2.write(templine)
g2.write('\n')
g2.close()
if self.edge_cal:
for i in range(len(q_vec_list)):
for j in range(band_num):
templine = str(q_distance_list[i]) + ' ' + str(band_structure[i][j]) + ' ' + str(pos_expectation[i][j])
templine += '\n'
g3.write(templine)
g3.write('\n')
g3.close()
return 0
def draw_phonon_band(self):
'''
Fourth, draw phonon band along q path
'''
file_name = 'ph_frequecny_'+self.out_tag+'.out'
f = open(file_name,'r')
tempf = f.readlines()
f.close()
totalline = len(tempf)
#print totalline
band_num = len(tempf[1].split()) - 1
sqx = [float(tempf[0].split()[i]) for i in range(len(tempf[0].split()))]
#print sqx
fig = plt.figure()
plt.axhline(y=0, color='black', linewidth=2)
for i in range(len(sqx)):
plt.axvline(x=sqx[i], color='black', linewidth=2)
qx = []
eigenval = np.zeros((band_num, totalline-1))
for i in range(1, totalline):
temp_val = tempf[i].split()
qx.append(float(temp_val[0]))
for j in range(band_num):
eigenval[j][i-1] = float(temp_val[1+j])
for i in range(band_num):
plt.plot(qx, eigenval[i], color='black')
#plt.ylim(0, max(eigenval[:][:]))
plt.xlim(min(sqx)-0.1, max(sqx)+0.1)
fig.savefig('phband.png')
plt.show()
return 0
def draw_phonon_band_edge_projection(self):
if not self.edge_cal:
print "ERROR: You didn't perform edge calculations"
return 0
f = open('ph_frequecny_'+self.out_tag+'_pos_expectation.out' , 'r')
#f = open('ph_frequecny_'+self.out_tag+'_projected.out' , 'r')
tempf = f.readlines()
f.close()
band_num = 2* self.dimension * self.num_atom
kpoint_num = (len(tempf)-1) / (band_num+1)
repeat_info = np.array([float(i) % self.num_repeat for i in range(self.num_atom)])
print band_num, kpoint_num
sqx = [float(tempf[0].split()[i]) for i in range(len(tempf[0].split()))]
fig = plt.figure()
plt.axhline(y=0, color='black', linewidth=2)
for i in range(len(sqx)):
plt.axvline(x=sqx[i], color='black', linewidth=2)
qx = []
eigenval = np.zeros((band_num, kpoint_num))
projected = np.zeros((band_num, kpoint_num))
for i in range(kpoint_num):
line = (band_num+1) * i + 1
qx.append(float(tempf[line].split()[0]))
for j in range(band_num):
temp_val = tempf[line+j].split()
frequency = float(temp_val[1])
#atominfo = np.array([float(temp_val[k+2]) for k in range(self.num_atom)])
#edgeinfo = np.sum(atominfo * repeat_info)
#print np.real(complex(temp_val[2]))
edgeinfo = np.real(complex(temp_val[2]))
eigenval[j][i] = frequency
projected[j][i] = edgeinfo
bubble_size = 40
for i in range(band_num/2, band_num):
plt.plot(qx, eigenval[i], linewidth=0.3, color='black')
plt.scatter(qx, eigenval[i], bubble_size, c=projected[i], cmap='RdBu', vmin=0, vmax=self.num_repeat-1, edgecolors='face')
#### save np file
save_path = '.'