forked from jonescompneurolab/hnn
-
Notifications
You must be signed in to change notification settings - Fork 0
/
pmanu_gamma.py
1713 lines (1343 loc) · 55.3 KB
/
pmanu_gamma.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
# pmanu_gamma.py - plot functions for gamma manuscript
#
# v 1.10.0-py35
# rev 2016-05-01 (SL: return_data_dir() and it.izip)
# last major: (SL: plot updates)
import numpy as np
import os
import fileio as fio
import currentfn
import dipolefn
import specfn
import spikefn
import paramrw
import ac_manu_gamma as acg
import axes_create as ac
def spec_fig():
f = acg.FigSimpleSpec()
dproj = fio.return_data_dir()
d = os.path.join(dproj, '2013-12-04/ftremor-003')
ddata = fio.SimulationPaths()
ddata.read_sim(dproj, d)
expmt_group = ddata.expmt_groups[0]
f_dpl = ddata.file_match(expmt_group, 'rawdpl')[0]
fspec = ddata.file_match(expmt_group, 'rawspec')[0]
fparam = ddata.file_match(expmt_group, 'param')[0]
dpl = dipolefn.Dipole(f_dpl)
dpl.baseline_renormalize(fparam)
dpl.convert_fAm_to_nAm()
xlim = (200., 700.)
dpl.plot(f.ax['dipole'], xlim, layer='L5')
pc = {
'spec': specfn.pspec_ax(f.ax['spec'], fspec, (50, 1050), layer='L5'),
}
cb = f.f.colorbar(pc['spec'], ax=f.ax['spec'], format='%.1e')
f.ax['spec'].set_xlim(xlim)
f.ax['spec'].set_ylabel('Frequency (Hz)')
f.ax['spec'].set_xlabel('Time (ms)')
f.set_fontsize(14)
# fname = os.path.join(d, 'testing.eps')
f.saveeps(d, 'testing')
f.close()
# all the data comes from one sim
def hf_epochs(ddata):
# runtype = 'debug'
runtype = 'pub'
# create the figure from the ac template
f = acg.FigHFEpochs(runtype)
# hard coded for now
n_sim = 0
n_trial = 0
# and assume just the first expmt for now
expmt = ddata.expmt_groups[0]
# hard code the 50 ms epochs that will be used here.
# centers for the data in tuples (t, f)
tf_specmax = [
(79.525, 115.),
(136.925, 114.),
(324.350, 109.),
(418.400, 106.),
]
# these are approximate centers on which to draw sines
tf_centers = [
[83.],
[137.],
[330.],
[411., 429.],
]
# these all come from one filename
f_spec = ddata.return_specific_filename(expmt, 'rawspec', n_sim, n_trial)
f_dpl = ddata.return_specific_filename(expmt, 'rawdpl', n_sim, n_trial)
f_spk = ddata.return_specific_filename(expmt, 'rawspk', n_sim, n_trial)
f_param = ddata.return_specific_filename(expmt, 'param', n_sim, n_trial)
f_current = ddata.return_specific_filename(expmt, 'rawcurrent', n_sim, n_trial)
# p_dict is needed for the spike thing.
_, p_dict = paramrw.read(f_param)
# figure out the tstop and xlim
tstop = paramrw.find_param(f_param, 'tstop')
dt = paramrw.find_param(f_param, 'dt')
xlim = (50., tstop)
# grab the dipole data
dpl = dipolefn.Dipole(f_dpl)
dpl.baseline_renormalize(f_param)
dpl.convert_fAm_to_nAm()
# current data
I_soma = currentfn.SynapticCurrent(f_current)
I_soma.convert_nA_to_uA()
# grab the spike data for histogram
s = spikefn.spikes_from_file(f_param, f_spk)
n_bins = 500
s_list = np.concatenate(s['L5_pyramidal'].spike_list)
# spikes
spikes = {
'L5': spikefn.filter_spike_dict(s, 'L5_'),
}
# xrange is just the length of the window, dx is the distance from the center
xrange = 50.
dx = xrange / 2.
# pc will be a list of the colorbar props for each spec (length len(f.gspec))
pc = []
# plot the aggregate data too
specfn.pspec_ax(f.ax['L_spec'], f_spec, xlim, layer='L5')
dpl.plot(f.ax['L_dpl'], xlim, layer='L5')
spikefn.spike_png(f.ax['L_spk'], spikes['L5'])
# change the color
color_dpl_L5 = '#1e90ff'
f.set_linecolor('L_dpl', color_dpl_L5)
# grab a list of the ax handles for the leftmost plot
ax_L_keys = [ax for ax in f.ax.keys() if ax.startswith('L_')]
# set all these xlim correctly
for ax_h in ax_L_keys:
f.ax[ax_h].set_xlim(xlim)
list_ylim_dpl = []
# now plot the individual epochs
for i in range(len(f.gspec_ex)):
# for each (t, f) pair, find the xlim_window
t_center = tf_specmax[i][0]
f_center = tf_specmax[i][1]
xlim_window = (t_center - dx, t_center + dx)
print xlim_window
# crude setting of vertical lines to denote roi
for ax_h in ax_L_keys:
f.ax[ax_h].axvline(x=xlim_window[0], color='b')
f.ax[ax_h].axvline(x=xlim_window[1], color='k')
# this is the highlight portion, one fixed
dx_hl = 0.5 * (1000. / f_center)
dx_hl_fixed = 10.
xlim_hl = (t_center - dx_hl, t_center + dx_hl)
xlim_fixed = (t_center - dx_hl_fixed, t_center + dx_hl_fixed)
# fix xlim_window in case
if xlim_window[0] < xlim[0]:
xlim_window[0] = xlim[0]
if xlim_window[1] == -1:
xlim_window[1] = tstop
I_soma.plot_to_axis(f.ax_twinx['dpl'][i], 'L5')
# truncate and then plot the dpl
# dpl_short must be a dict of all the different dipoles
# so only need here the L5 key
t_short, dpl_short = dpl.truncate_ext(xlim_hl[0], xlim_hl[1])
t_fixed, dpl_fixed = dpl.truncate_ext(xlim_fixed[0], xlim_fixed[1])
# create a sine waveform for this interval
f_max = tf_specmax[i][1]
t_half = 0.5 * (1000. / f_max)
# plot the dipole for the xlim window
# plot the dipole and the current, either over the appropriate range or the whole window for now
dpl.plot(f.ax['dpl'][i], xlim_window, layer='L5')
f.ax['dpl'][i].hold(True)
for j in range(len(tf_centers[i])):
t0 = tf_centers[i][j] - t_half
T = tf_centers[i][j] + t_half
ylim_dpl = dpl.lim('L5', (t0, T))
props_dict = {
't': (t0, T),
'dt': dt,
'f': f_max,
'A': ylim_dpl[1],
}
f.add_sine(f.ax['dpl'][i], props_dict)
f.ax['dpl'][i].set_ylim((-0.025, 0.025))
# f.ax['dpl'][i].plot(t_fixed, dpl_fixed['L5'], 'g')
# f.ax['dpl'][i].plot(t_short, dpl_short['L5'], 'r')
# spec - i think must be plotted as xlim first and then truncated?
pc.append(specfn.pspec_ax(f.ax['spec'][i], f_spec, xlim, layer='L5'))
# plot the data
spikefn.pinput_hist_onesided(f.ax['hist'][i], s_list, n_bins)
spikefn.spike_png(f.ax['spk'][i], spikes['L5'])
# set xlim_windows accordingly
f.ax['hist'][i].set_xlim(xlim_window)
f.ax['spk'][i].set_xlim(xlim_window)
f.ax_twinx['dpl'][i].set_xlim(xlim_window)
f.ax['spec'][i].set_xlim(xlim_window)
# hist
f.ax['hist'][i].yaxis.set_ticks(np.arange(0, 9, 4))
# set the color of the I_soma line
f.ax['dpl'][i].lines[0].set_color(color_dpl_L5)
f.ax_twinx['dpl'][i].lines[0].set_color('k')
f.ysymmetry(f.ax_twinx['dpl'][i])
# no need for outputs
if runtype == 'debug':
f.f.colorbar(pc[i], ax=f.ax['spec'][i], format='%.3e')
if runtype == 'pub':
p_ticks = np.arange(0, 9e-5, 2e-5)
pctest = f.f.colorbar(pc[-1], ax=f.ax['spec'][-1], format='%.2e', ticks=p_ticks)
pctest.ax.set_yticklabels(p_ticks)
# pctest.ax.set_ytick(np.arange(0, 8e-5, 2e-5))
# pctest.ax.locator_params(axis='y', nbins=5)
# some fig naming stuff
dfig = os.path.join(ddata.dsim, expmt)
trial_prefix = ddata.trial_prefix_str % (n_sim, n_trial)
fprefix_short = trial_prefix + '-hf_epochs'
# use methods to save figs
f.savepng_new(dfig, fprefix_short)
f.saveeps(dfig, fprefix_short)
f.close()
def hf(ddata, xlim_window, n_sim, n_trial):
# data directories (made up for now)
# the resultant figure is saved in d0
# d = os.path.join(dproj, 'pub', '2013-06-28_gamma_weak_L5-000')
# for now grab the first experiment
# ddata = fio.SimulationPaths()
# ddata.read_sim(dproj, d)
expmt = ddata.expmt_groups[0]
runtype = 'debug'
# runtype = 'pub'
# prints the fig in ddata0
f = acg.FigHF(runtype)
# grab the relevant files
f_spec = ddata.return_specific_filename(expmt, 'rawspec', n_sim, n_trial)
f_dpl = ddata.return_specific_filename(expmt, 'rawdpl', n_sim, n_trial)
f_spk = ddata.return_specific_filename(expmt, 'rawspk', n_sim, n_trial)
f_param = ddata.return_specific_filename(expmt, 'param', n_sim, n_trial)
f_current = ddata.return_specific_filename(expmt, 'rawcurrent', n_sim, n_trial)
# p_dict is needed for the spike thing.
_, p_dict = paramrw.read(f_param)
# figure out the tstop and xlim
tstop = paramrw.find_param(f_param, 'tstop')
dt = paramrw.find_param(f_param, 'dt')
xlim = (50., tstop)
# fix xlim_window
if xlim_window[0] < xlim[0]:
xlim_window[0] = xlim[0]
if xlim_window[1] == -1:
xlim_window[1] = tstop
# grab the dipole data
dpl = dipolefn.Dipole(f_dpl)
dpl.baseline_renormalize(f_param)
dpl.convert_fAm_to_nAm()
dpl.plot(f.ax['dpl_L'], xlim, layer='agg')
# plot currents
I_soma = currentfn.SynapticCurrent(f_current)
I_soma.plot_to_axis(f.ax_twinx['dpl_L'], 'L5')
# spec
pc = {
'L': specfn.pspec_ax(f.ax['spec_L'], f_spec, xlim, layer='L5'),
}
# no need for outputs
if runtype == 'debug':
f.f.colorbar(pc['L'], ax=f.ax['spec_L'], format='%.3e')
# grab the spike data for histogram
s = spikefn.spikes_from_file(f_param, f_spk)
n_bins = 500
s_list = np.concatenate(s['L5_pyramidal'].spike_list)
spikefn.pinput_hist_onesided(f.ax['hist_L'], s_list, n_bins)
# spikes
spikes = {
'L5': spikefn.filter_spike_dict(s, 'L5_'),
}
# plot the data
spikefn.spike_png(f.ax['spk'], spikes['L5'])
# xlim_window
# xlim_window = (400., 450.)
f.ax['hist_L'].set_xlim(xlim_window)
f.ax['dpl_L'].set_xlim(xlim_window)
f.ax['spec_L'].set_xlim(xlim_window)
f.ax_twinx['dpl_L'].set_xlim(xlim_window)
f.ax_twinx['dpl_L'].lines[0].set_color('k')
f.ysymmetry(f.ax_twinx['dpl_L'])
f.ax['spk'].set_xlim(xlim_window)
# # save the fig in ddata0 (arbitrary)
trial_prefix = ddata.trial_prefix_str % (n_sim, n_trial)
f_prefix = '%s_hf' % trial_prefix
dfig = os.path.join(ddata.dsim, expmt)
f.savepng_new(dfig, f_prefix)
f.saveeps(dfig, f_prefix)
f.close()
def laminar(ddata):
# for now grab the first experiment
expmt = ddata.expmt_groups[0]
# runtype = 'debug'
runtype = 'pub'
# for now hard code the simulation run
n_run = 0
# prints the fig in ddata0
f = acg.FigLaminarComparison(runtype)
# grab the relevant files
f_spec = ddata.file_match(expmt, 'rawspec')[n_run]
f_dpl = ddata.file_match(expmt, 'rawdpl')[n_run]
f_spk = ddata.file_match(expmt, 'rawspk')[n_run]
f_param = ddata.file_match(expmt, 'param')[n_run]
f_current = ddata.file_match(expmt, 'rawcurrent')[n_run]
# figure out the tstop and xlim
tstop = paramrw.find_param(f_param, 'tstop')
dt = paramrw.find_param(f_param, 'dt')
xlim = (50., tstop)
# grab the dipole data
dpl = dipolefn.Dipole(f_dpl)
dpl.baseline_renormalize(f_param)
dpl.convert_fAm_to_nAm()
# calculate the Welch periodogram
pgram = {
'agg': specfn.Welch(dpl.t, dpl.dpl['agg'], dt),
'L2': specfn.Welch(dpl.t, dpl.dpl['L2'], dt),
'L5': specfn.Welch(dpl.t, dpl.dpl['L5'], dt),
}
# plot periodograms
pgram['agg'].plot_to_ax(f.ax['pgram_L'])
pgram['L2'].plot_to_ax(f.ax['pgram_M'])
pgram['L5'].plot_to_ax(f.ax['pgram_R'])
# plot currents
I_soma = currentfn.SynapticCurrent(f_current)
I_soma.convert_nA_to_uA()
I_soma.plot_to_axis(f.ax['current_M'], 'L2')
I_soma.plot_to_axis(f.ax['current_R'], 'L5')
f.set_linecolor('current_M', 'k')
f.set_linecolor('current_R', 'k')
# f.set_axes_pingping()
# cols have same suffix
list_cols = ['L', 'M', 'R']
# create handles list
list_h_pgram = ['pgram_'+col for col in list_cols]
list_h_dpl = ['dpl_'+col for col in list_cols]
# spec
pc = {
'L': specfn.pspec_ax(f.ax['spec_L'], f_spec, xlim, layer='agg'),
'R': specfn.pspec_ax(f.ax['spec_R'], f_spec, xlim, layer='L5'),
}
pc2 = {
'M': specfn.pspec_ax(f.ax['spec_M'], f_spec, xlim, layer='L2'),
}
# create a list of spec color handles
# list_h_spec_cb = ['pc_'+col for col in list_cols]
# get the vmin, vmax and add them to the master list
# f.equalize_speclim(pc)
# list_lim_spec = []
# no need for outputs
if runtype == 'debug':
f.f.colorbar(pc['L'], ax=f.ax['spec_L'], format='%.1e')
f.f.colorbar(pc2['M'], ax=f.ax['spec_M'], format='%.1e')
f.f.colorbar(pc['R'], ax=f.ax['spec_R'], format='%.1e')
# list_spec_handles = [ax for ax in f.ax.keys() if ax.startswith('spec')]
list_spec_handles = ['spec_M', 'spec_R']
f.remove_tick_labels(list_spec_handles, ax_xy='y')
elif runtype == 'pub':
f.f.colorbar(pc['L'], ax=f.ax['spec_L'], format='%.1e')
f.f.colorbar(pc2['M'], ax=f.ax['spec_M'], format='%.1e')
f.f.colorbar(pc['R'], ax=f.ax['spec_R'], format='%.1e')
list_spec_handles = ['spec_L', 'spec_R']
f.remove_tick_labels(list_spec_handles, ax_xy='y')
# grab the spike data
s = spikefn.spikes_from_file(f_param, f_spk)
# dipoles
dpl.plot(f.ax['dpl_L'], xlim, layer='agg')
# f.set_linecolor('dpl_L', 'k')
f.ax['dpl_L'].hold(True)
dpl.plot(f.ax['dpl_L'], xlim, layer='L5')
dpl.plot(f.ax['dpl_L'], xlim, layer='L2')
color_dpl_L5 = '#1e90ff'
# these colors mirror below, should be vars
f.ax['dpl_L'].lines[0].set_color('k')
f.ax['dpl_L'].lines[1].set_color(color_dpl_L5)
f.ax['dpl_L'].lines[2].set_color('#b22222')
# plot and color
dpl.plot(f.ax['dpl_M'], xlim, layer='L2')
f.set_linecolor('dpl_M', '#b22222')
# plot and color
dpl.plot(f.ax['dpl_R'], xlim, layer='L5')
f.set_linecolor('dpl_R', color_dpl_L5)
# equalize the ylim
# f.equalize_ylim(list_h_pgram)
f.equalize_speclim(pc)
f.equalize_ylim(['dpl_L', 'dpl_R'])
ylim_dpl_M = dpl.lim('L2', xlim)
# f.ysymmetry(f.ax['dpl_M'])
# f.ax['dpl_M'].set_ylim(ylim_dpl_M)
f.ax['dpl_M'].set_ylim((-0.01, 0.01))
for ax in f.ax.keys():
if ax.startswith('dpl'):
f.ax[ax].locator_params(axis='y', nbins=7)
# spikes
spikes = {
'L2': spikefn.filter_spike_dict(s, 'L2_'),
'L5': spikefn.filter_spike_dict(s, 'L5_'),
}
# plot the data
spikefn.spike_png(f.ax['spk_M'], spikes['L2'])
spikefn.spike_png(f.ax['spk_R'], spikes['L5'])
f.ax['spk_M'].set_xlim(xlim)
f.ax['spk_R'].set_xlim(xlim)
# thin the yaxis
# function defined in FigBase()
# f.thin_yaxis(f.ax['current_M'], 5)
f.ax['current_M'].locator_params(axis='y', nbins=5)
f.ax['current_M'].set_ylim((-0.20, 0))
f.ax['current_R'].locator_params(axis='y', nbins=5)
f.ax['current_R'].set_ylim((-0.8, 0))
# Welch number of labels
f.ax['pgram_M'].locator_params(axis='y', nbins=5)
f.ax['pgram_R'].locator_params(axis='y', nbins=5)
f.ax['pgram_L'].locator_params(axis='y', nbins=5)
# set the colors
f.set_linecolor('pgram_L', 'k')
f.set_linecolor('pgram_R', 'k')
f.set_linecolor('pgram_M', 'k')
# save the fig in ddata0 (arbitrary)
f_prefix = '%s_laminar' % ddata.sim_prefix
dfig = os.path.join(ddata.dsim, expmt)
f.savepng_new(dfig, f_prefix)
f.saveeps(dfig, f_prefix)
f.close()
# compares PING regimes for two different trial runs
def compare_ping():
dproj = fio.return_data_dir()
runtype = 'pub2'
# runtype = 'debug'
# data directories (made up for now)
# the resultant figure is saved in d0
d0 = os.path.join(dproj, 'pub', '2013-06-28_gamma_ping_L5-000')
d1 = os.path.join(dproj, 'pub', '2013-07-31_gamma_weak_L5-000')
# d1 = os.path.join(dproj, 'pub', '2013-06-28_gamma_weak_L5-000')
# hard code the data for now
ddata0 = fio.SimulationPaths()
ddata1 = fio.SimulationPaths()
# use read_sim() to read the simulations
ddata0.read_sim(dproj, d0)
ddata1.read_sim(dproj, d1)
# for now grab the first experiment in each
expmt0 = ddata0.expmt_groups[0]
expmt1 = ddata1.expmt_groups[0]
# for now hard code the simulation run
run0 = 0
run1 = 0
# prints the fig in ddata0
f = acg.FigL5PingExample(runtype)
# first panel data
f_spec0 = ddata0.file_match(expmt0, 'rawspec')[run0]
f_dpl0 = ddata0.file_match(expmt0, 'rawdpl')[run0]
f_spk0 = ddata0.file_match(expmt0, 'rawspk')[run0]
f_param0 = ddata0.file_match(expmt0, 'param')[run0]
f_current0 = ddata0.file_match(expmt0, 'rawcurrent')[run0]
# figure out the tstop and xlim
tstop0 = paramrw.find_param(f_param0, 'tstop')
dt = paramrw.find_param(f_param0, 'dt')
xlim0 = (50., tstop0)
# grab the dipole data
dpl0 = dipolefn.Dipole(f_dpl0)
dpl0.baseline_renormalize(f_param0)
dpl0.convert_fAm_to_nAm()
# calculate the Welch periodogram
f_max = 150.
pgram0 = specfn.Welch(dpl0.t, dpl0.dpl['L5'], dt)
pgram0.plot_to_ax(f.ax['pgram_L'], f_max)
# grab the spike data
s0 = spikefn.spikes_from_file(f_param0, f_spk0)
s0_L5 = spikefn.filter_spike_dict(s0, 'L5_')
# plot the spike histogram data
icell0_spikes = np.concatenate(s0_L5['L5_basket'].spike_list)
ecell0_spikes = np.concatenate(s0_L5['L5_pyramidal'].spike_list)
# 1 ms bins
n_bins = int(tstop0)
f.ax['hist_L'].hist(icell0_spikes, n_bins, facecolor='r', histtype='stepfilled', alpha=0.75, edgecolor='none')
f.ax_twinx['hist_L'].hist(ecell0_spikes, n_bins, facecolor='k')
# based on number of cells
f.ax['hist_L'].set_ylim((0, 20))
f.ax_twinx['hist_L'].set_ylim((0, 100))
f.ax_twinx['hist_L'].set_xlim(xlim0)
f.ax['hist_L'].set_xlim(xlim0)
# hack
labels = f.ax['hist_L'].yaxis.get_ticklocs()
labels_text = [str(label) for label in labels[:-1]]
for i in range(len(labels_text)):
labels_text[i] = ''
labels_text.append('20')
f.ax['hist_L'].set_yticklabels(labels_text)
labels_twinx = f.ax_twinx['hist_L'].yaxis.get_ticklocs()
labels_text = [str(label) for label in labels_twinx[:-1]]
for i in range(len(labels_text)):
labels_text[i] = ''
labels_text.append('100')
f.ax_twinx['hist_L'].set_yticklabels(labels_text)
# grab the current data
I_soma0 = currentfn.SynapticCurrent(f_current0)
I_soma0.convert_nA_to_uA()
# plot the data
dpl0.plot(f.ax['dpl_L'], xlim0, layer='L5')
spikefn.spike_png(f.ax['raster_L'], s0_L5)
f.ax['raster_L'].set_xlim(xlim0)
# second panel data
f_spec1 = ddata1.file_match(expmt1, 'rawspec')[run1]
f_dpl1 = ddata1.file_match(expmt1, 'rawdpl')[run1]
f_spk1 = ddata1.file_match(expmt1, 'rawspk')[run1]
f_param1 = ddata1.file_match(expmt1, 'param')[run1]
f_current1 = ddata1.file_match(expmt1, 'rawcurrent')[run1]
# figure out the tstop and xlim
tstop1 = paramrw.find_param(f_param1, 'tstop')
xlim1 = (50., tstop1)
# grab the dipole data
dpl1 = dipolefn.Dipole(f_dpl1)
dpl1.baseline_renormalize(f_param1)
dpl1.convert_fAm_to_nAm()
# calculate the Welch periodogram
pgram1 = specfn.Welch(dpl1.t, dpl1.dpl['L5'], dt)
pgram1.plot_to_ax(f.ax['pgram_R'], f_max)
# grab the spike data
s1 = spikefn.spikes_from_file(f_param1, f_spk1)
s1_L5 = spikefn.filter_spike_dict(s1, 'L5_')
# s1_L2 = spikefn.filter_spike_dict(s1, 'L2_')
# plot the spike histogram data
icell1_spikes = np.concatenate(s1_L5['L5_basket'].spike_list)
ecell1_spikes = np.concatenate(s1_L5['L5_pyramidal'].spike_list)
# 1 ms bins
n_bins = int(tstop1)
f.ax['hist_R'].hist(icell1_spikes, n_bins, facecolor='r', histtype='stepfilled', alpha=0.75, edgecolor='none')
f.ax_twinx['hist_R'].hist(ecell1_spikes, n_bins, facecolor='k')
# based on number of cells
f.ax['hist_R'].set_ylim((0, 12))
f.ax_twinx['hist_R'].set_ylim((0, 12))
f.ax_twinx['hist_R'].set_xlim(xlim0)
f.ax['hist_R'].set_xlim(xlim0)
# hack
labels = f.ax['hist_R'].yaxis.get_ticklocs()
labels_text = [str(label) for label in labels[:-1]]
for i in range(len(labels_text)):
labels_text[i] = ''
labels_text.append('12')
f.ax['hist_R'].set_yticklabels(labels_text)
labels_twinx = f.ax_twinx['hist_R'].yaxis.get_ticklocs()
labels_text = [str(label) for label in labels_twinx[:-1]]
for i in range(len(labels_text)):
labels_text[i] = ''
labels_text.append('12')
f.ax_twinx['hist_R'].set_yticklabels(labels_text)
# grab the current data
I_soma1 = currentfn.SynapticCurrent(f_current1)
I_soma1.convert_nA_to_uA()
# plot the data
dpl1.plot(f.ax['dpl_R'], xlim1, layer='L5')
f.ysymmetry(f.ax['dpl_R'])
spikefn.spike_png(f.ax['raster_R'], s1_L5)
f.ax['raster_R'].set_xlim(xlim1)
# plot the spec data
pc = {
'L': specfn.pspec_ax(f.ax['spec_L'], f_spec0, xlim0, layer='L5'),
'R': specfn.pspec_ax(f.ax['spec_R'], f_spec1, xlim1, layer='L5'),
}
# f.equalize_speclim(pc)
# grab the dipole figure handles
list_h_dpl = [h for h in f.ax.keys() if h.startswith('dpl')]
for ax_h in list_h_dpl:
f.ax[ax_h].locator_params(axis='y', nbins=5)
# f.equalize_ylim(list_h_dpl)
# and the pgrams
# list_h_pgram = [h for h in f.ax.keys() if h.startswith('pgram')]
# test = f.equalize_ylim(list_h_pgram)
# plot current and do lims
I_soma0.plot_to_axis(f.ax['current_L'], 'L5')
I_soma1.plot_to_axis(f.ax['current_R'], 'L5')
list_h_current = [ax_h for ax_h in f.ax.keys() if ax_h.startswith('current')]
f.equalize_ylim(list_h_current)
# this is a hack
# now in uA instead of nA
for ax_handle in f.ax.keys():
if ax_handle.startswith('current_'):
f.ax[ax_handle].set_ylim((-2, 0.))
# testing something
# f.ax['pgram_L'].set_yscale('log')
# f.ax['pgram_R'].set_yscale('log')
# f.ax['pgram_L'].set_ylim((1e-12, 1e-3))
# f.ax['pgram_R'].set_ylim((1e-12, 1e-3))
# save the fig in ddata0 (arbitrary)
f_prefix = 'gamma_L5ping_L5weak'
dfig = os.path.join(ddata0.dsim, expmt0)
# create the colorbars
cb = dict.fromkeys(pc)
if runtype in ('debug', 'pub2'):
for key in pc.keys():
key_ax = 'spec_' + key
cb[key] = f.f.colorbar(pc[key], ax=f.ax[key_ax], format='%.1e')
elif runtype == 'pub':
cb['R'] = f.f.colorbar(pc['R'], ax=f.ax['spec_R'], format='%.1e')
f.savepng_new(dfig, f_prefix)
f.saveeps(dfig, f_prefix)
f.close()
def sub_dist_examples():
dproj = fio.return_data_dir()
# runtype = 'pub2'
runtype = 'debug'
# data directories (made up for now)
# the resultant figure is saved in d0
d0 = os.path.join(dproj, 'pub', '2013-07-01_gamma_sub_50Hz-002')
d1 = os.path.join(dproj, 'pub', '2013-07-18_gamma_sub_100Hz-000')
# hard code the data for now
ddata0 = fio.SimulationPaths()
ddata1 = fio.SimulationPaths()
# use read_sim() to read the simulations
ddata0.read_sim(dproj, d0)
ddata1.read_sim(dproj, d1)
# for now grab the first experiment in each
expmt0 = ddata0.expmt_groups[0]
expmt1 = ddata1.expmt_groups[0]
# for now hard code the simulation run
run0 = 0
run1 = 0
# number of bins for the spike histograms
n_bins = 500
# prints the fig in ddata0
f = acg.FigSubDistExample(runtype)
# first panel data
f_spec0 = ddata0.file_match(expmt0, 'rawspec')[run0]
f_dpl0 = ddata0.file_match(expmt0, 'rawdpl')[run0]
f_spk0 = ddata0.file_match(expmt0, 'rawspk')[run0]
f_param0 = ddata0.file_match(expmt0, 'param')[run0]
# f_current0 = ddata0.file_match(expmt0, 'rawcurrent')[run0]
# figure out the tstop and xlim
tstop0 = paramrw.find_param(f_param0, 'tstop')
dt = paramrw.find_param(f_param0, 'dt')
xlim0 = (50., tstop0)
# grab the dipole data
dpl0 = dipolefn.Dipole(f_dpl0)
dpl0.baseline_renormalize(f_param0)
dpl0.convert_fAm_to_nAm()
# grab the current data
# I_soma0 = currentfn.SynapticCurrent(f_current0)
# grab the spike data
_, p_dict0 = paramrw.read(f_param0)
s0 = spikefn.spikes_from_file(f_param0, f_spk0)
s0 = spikefn.alpha_feed_verify(s0, p_dict0)
sp_list = s0['alpha_feed_prox'].spike_list[0]
sd_list = s0['alpha_feed_dist'].spike_list[0]
spikefn.pinput_hist(f.ax['hist_L'], f.ax_twinx['hist_L'], sp_list, sd_list, n_bins, xlim0)
# plot the data
dpl0.plot(f.ax['dpl_L'], xlim0, layer='L5')
# second panel data
f_spec1 = ddata1.file_match(expmt1, 'rawspec')[run1]
f_dpl1 = ddata1.file_match(expmt1, 'rawdpl')[run1]
f_spk1 = ddata1.file_match(expmt1, 'rawspk')[run1]
f_param1 = ddata1.file_match(expmt1, 'param')[run1]
# f_current1 = ddata1.file_match(expmt1, 'rawcurrent')[run1]
# figure out the tstop and xlim
tstop1 = paramrw.find_param(f_param1, 'tstop')
xlim1 = (50., tstop1)
# grab the dipole data
dpl1 = dipolefn.Dipole(f_dpl1)
dpl1.baseline_renormalize(f_param1)
dpl1.convert_fAm_to_nAm()
# # calculate the Welch periodogram
# pgram1 = specfn.Welch(dpl1.t, dpl1.dpl['L5'], dt)
# pgram1.plot_to_ax(f.ax['pgram_R'], f_max)
# grab the spike data
_, p_dict1 = paramrw.read(f_param1)
s1 = spikefn.spikes_from_file(f_param1, f_spk1)
s1 = spikefn.alpha_feed_verify(s1, p_dict1)
sp_list = s1['alpha_feed_prox'].spike_list[0]
sd_list = s1['alpha_feed_dist'].spike_list[0]
spikefn.pinput_hist(f.ax['hist_R'], f.ax_twinx['hist_R'], sp_list, sd_list, n_bins, xlim1)
# grab the current data
# I_soma1 = currentfn.SynapticCurrent(f_current1)
# plot the data
dpl1.plot(f.ax['dpl_R'], xlim1, layer='L5')
# spikefn.spike_png(f.ax['raster_R'], s1_L5)
# f.ax['raster_R'].set_xlim(xlim1)
# plot the spec data
pc = {
'L': specfn.pspec_ax(f.ax['spec_L'], f_spec0, xlim0, layer='L5'),
'R': specfn.pspec_ax(f.ax['spec_R'], f_spec1, xlim1, layer='L5'),
}
# f.equalize_speclim(pc)
# # grab the dipole figure handles
# # list_h_dpl = [h for h in f.ax.keys() if h.startswith('dpl')]
# # f.equalize_ylim(list_h_dpl)
# # and the pgrams
# # list_h_pgram = [h for h in f.ax.keys() if h.startswith('pgram')]
# # test = f.equalize_ylim(list_h_pgram)
# # plot current and do lims
# I_soma0.plot_to_axis(f.ax['current_L'], 'L5')
# I_soma1.plot_to_axis(f.ax['current_R'], 'L5')
# for ax_handle in f.ax.keys():
# if ax_handle.startswith('current_'):
# f.ax[ax_handle].set_ylim((-2000, 0.))
# # testing something
# # f.ax['pgram_L'].set_yscale('log')
# # f.ax['pgram_R'].set_yscale('log')
# # f.ax['pgram_L'].set_ylim((1e-12, 1e-3))
# # f.ax['pgram_R'].set_ylim((1e-12, 1e-3))
# save the fig in ddata0 (arbitrary)
f_prefix = 'gamma_sub_examples'
dfig = os.path.join(ddata0.dsim, expmt0)
# create the colorbars
cb = dict.fromkeys(pc)
if runtype == 'debug':
for key in pc.keys():
key_ax = 'spec_' + key
cb[key] = f.f.colorbar(pc[key], ax=f.ax[key_ax])
elif runtype == 'pub':
cb['R'] = f.f.colorbar(pc['R'], ax=f.ax['spec_R'])
f.savepng_new(dfig, f_prefix)
f.saveeps(dfig, f_prefix)
f.close()
def sub_dist_example2():
dproj = fio.return_data_dir()
runtype = 'pub2'
# runtype = 'debug'
# data directories (made up for now)
# the resultant figure is saved in d0
d0 = os.path.join(dproj, 'pub', '2013-08-07_gamma_sub_50Hz-000')
# hard code the data for now
ddata0 = fio.SimulationPaths()
# use read_sim() to read the simulations
ddata0.read_sim(dproj, d0)
# for now grab the first experiment in each
expmt0 = ddata0.expmt_groups[0]
# for now hard code the simulation run
run0 = 0
run1 = 1
# number of bins for the spike histograms
n_bins = 500
# prints the fig in ddata0
f = acg.FigSubDistExample(runtype)
# first panel data
f_spec0 = ddata0.file_match(expmt0, 'rawspec')[run0]
f_dpl0 = ddata0.file_match(expmt0, 'rawdpl')[run0]
f_spk0 = ddata0.file_match(expmt0, 'rawspk')[run0]
f_param0 = ddata0.file_match(expmt0, 'param')[run0]
# f_current0 = ddata0.file_match(expmt0, 'rawcurrent')[run0]
# figure out the tstop and xlim
tstop0 = paramrw.find_param(f_param0, 'tstop')
dt = paramrw.find_param(f_param0, 'dt')
xlim0 = (50., tstop0)
# grab the dipole data
dpl0 = dipolefn.Dipole(f_dpl0)
dpl0.baseline_renormalize(f_param0)
dpl0.convert_fAm_to_nAm()
# grab the current data
# I_soma0 = currentfn.SynapticCurrent(f_current0)
# grab the spike data
_, p_dict0 = paramrw.read(f_param0)
s0 = spikefn.spikes_from_file(f_param0, f_spk0)
s0 = spikefn.alpha_feed_verify(s0, p_dict0)
sp_list = s0['alpha_feed_prox'].spike_list[0]
sd_list = s0['alpha_feed_dist'].spike_list[0]
spikefn.pinput_hist(f.ax['hist_L'], f.ax_twinx['hist_L'], sp_list, sd_list, n_bins, xlim0)
# plot the data
dpl0.plot(f.ax['dpl_L'], xlim0, layer='L5')
# second panel data
f_spec1 = ddata0.file_match(expmt0, 'rawspec')[run1]
f_dpl1 = ddata0.file_match(expmt0, 'rawdpl')[run1]
f_spk1 = ddata0.file_match(expmt0, 'rawspk')[run1]
f_param1 = ddata0.file_match(expmt0, 'param')[run1]
# f_current1 = ddata1.file_match(expmt1, 'rawcurrent')[run1]
# figure out the tstop and xlim
tstop1 = paramrw.find_param(f_param1, 'tstop')
xlim1 = (50., tstop1)
# grab the dipole data
dpl1 = dipolefn.Dipole(f_dpl1)
dpl1.baseline_renormalize(f_param1)
dpl1.convert_fAm_to_nAm()
# # calculate the Welch periodogram
# pgram1 = specfn.Welch(dpl1.t, dpl1.dpl['L5'], dt)
# pgram1.plot_to_ax(f.ax['pgram_R'], f_max)
# grab the spike data
_, p_dict1 = paramrw.read(f_param1)
s1 = spikefn.spikes_from_file(f_param1, f_spk1)
s1 = spikefn.alpha_feed_verify(s1, p_dict1)
sp_list = s1['alpha_feed_prox'].spike_list[0]
sd_list = s1['alpha_feed_dist'].spike_list[0]
spikefn.pinput_hist(f.ax['hist_R'], f.ax_twinx['hist_R'], sp_list, sd_list, n_bins, xlim1)
f.ax['hist_R'].set_ylim((0., 20.))
f.ax_twinx['hist_R'].set_ylim((20., 0.))
# grab the current data
# I_soma1 = currentfn.SynapticCurrent(f_current1)
# plot the data
dpl1.plot(f.ax['dpl_R'], xlim1, layer='L5')
# spikefn.spike_png(f.ax['raster_R'], s1_L5)
# f.ax['raster_R'].set_xlim(xlim1)
# plot the spec data
pc = {
'L': specfn.pspec_ax(f.ax['spec_L'], f_spec0, xlim0, layer='L5'),
'R': specfn.pspec_ax(f.ax['spec_R'], f_spec1, xlim1, layer='L5'),
}
# change the xlim format
f.set_notation_scientific([ax for ax in f.ax.keys() if ax.startswith('dpl')], n=2)
# f.equalize_speclim(pc)
# # grab the dipole figure handles
list_h_dpl = [h for h in f.ax.keys() if h.startswith('dpl')]
f.equalize_ylim(list_h_dpl)
# hack.
f.ax['dpl_R'].set_yticklabels('')
# and the pgrams
# list_h_pgram = [h for h in f.ax.keys() if h.startswith('pgram')]
# test = f.equalize_ylim(list_h_pgram)
# # plot current and do lims
# I_soma0.plot_to_axis(f.ax['current_L'], 'L5')
# I_soma1.plot_to_axis(f.ax['current_R'], 'L5')