-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathms_network_inspect.py
395 lines (327 loc) · 17.1 KB
/
ms_network_inspect.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
#THIS IS THE PART OF THE NEURAL NETWORK. IT TAKES THE VALUES TO CALCULATE THE MEMBRANE POTENTIALS OF THE INPUT NEURONS AND THEN CALCULATES THE NETWORK OUTPUT
#the unit in space is 1arcmin!
import pylab as pyl
import numpy as np
import cv2
import sys
import os
import matplotlib.pyplot as plt
import nest
import datetime
import nest.raster_plot
import nest.topology as tp
from microsaccades_functions import *
pgf_with_rc_fonts = {"font.family": "serif","font.serif": [],"font.sans-serif": []}
plt.rcParams.update(pgf_with_rc_fonts)
nest.ResetKernel() # since we run the script multiple times
#to get different poisson outputs
msd = int(np.random.normal(5000,1000,1)[0]) #master seed
nest.SetKernelStatus({'local_num_threads' : 4})
n_vp = nest.GetKernelStatus('total_num_virtual_procs')
msdrange1 = range(msd, msd+n_vp)
pyrngs = [np.random.RandomState(s) for s in msdrange1]
msdrange2 = range(msd+n_vp+1, msd+1+2*n_vp)
nest.SetKernelStatus({'grng_seed': msd+n_vp,
'rng_seeds': msdrange2})
#necessary paramater definitions
#frames = 100 #replaced by motion detectors only
#----------------------------------------------------------------------------INPUT-RATES-FROM-MS_INPUT
extent = 121.
delay = 30. # speed of point in poletti 2010 -> maybe increase a bit for more reacton later on
t_start = 0
t_end = 1000
weight = 40.
weight_std = 1.5
#I_E = 410.
if len(sys.argv)==10:
sim_title = sys.argv[1]
sim_title_2 = sys.argv[2]
sim_nr = sys.argv[3]
handle_name = sys.argv[4]
#extent = float(sys.argv[3])
extent_x = float(sys.argv[5])/2.
extent_y = float(sys.argv[6])/2.-1.
exp_nr = sys.argv[7]
cond_nr = sys.argv[8]
mdw = float(sys.argv[9])
sar=True
else:
sim_title = sys.argv[1]
sim_nr = sys.argv[2]
handle_name = sys.argv[3]
#extent = float(sys.argv[3])
extent_x = float(sys.argv[4])/2.
extent_y = float(sys.argv[5])/2.-1.
exp_nr = sys.argv[6]
cond_nr = sys.argv[7]
mdw = float(sys.argv[8])
sim_title_2 = ''
sar=False
#delay = float(sys.argv[5])
vel = 0. #float(sys.argv[6])
#weight = float(sys.argv[7])
#extent_x = extent
#extent_y = extent
#center = extent/2.
#extent_x = extent_x/2.
#extent_y = extent_y/2.
center_x = extent_x/2.
center_y = extent_y/2.
#extent_x = extent_x+1.
#extent_y = extent_y+1.
print weight,delay
#weight = 0.
#p_file = open('data/parasolic_values.data','r+')
#p_file = open('data/'+str(sim_title)+str(sim_title_2)+'/'+str(sim_nr)+'/parasolic_rates_'+str(handle_name)+'_on.data','r+')
#p_data = np.load(p_file)
#p_file.close()
#parasolic_rates=poissonRateParasols(p_data)
#print maxs
#print 'midget max: ' + str(max(maxs)) + ' ' + str(mmr)
#paxs = []
#for i in range(len(parasolic_rates)):
# for j in range(len(parasolic_rates[0])):
# paxs.append(max(parasolic_rates[i][j][t_start:t_end]))
pmr = 480000.
#print 'parsolic max: ' + str(max(paxs)) + ' ' + str(pmr)
#ps_file = open('data/mo_det_cal/input_max_spikes.txt','a+')
#ps_file.write(str(handle_name)+' m: '+ str(max(maxs))+' p: '+ str(max(paxs))+'\n')
#ps_file.close()
#for rates
mrs = []
prs=[]
#print midget_rates
#print parasolic_rates
#-----------------------------------------------------------------------------------------NETWORK-PART
#---------------------------------------------------------------------------INITIALIZE-POISSON-NEURONS
#storage = open('data/'+sim_title+'/network/'+handle_name+'.data','w+')
#storage.close()
#nest.SetKernelStatus({'resolution': 0.001,
# 'overwrite_files': True,
# 'data_path': 'data/'+sim_title+'/network/',
# 'data_prefix': ''})
#set the initial Poisson rate
rate = 100.
#create all newly needed models
#nest.CopyModel("poisson_generator", "var_poisson_generator",{"rate": rate})
#for the output
#iaf_psc_alpha: ({u'vp': 0, u'synaptic_elements': {}, u'V_reset': -70.0, u'supports_precise_spikes': False, u'V_th': -55.0, u'tau_minus': 20.0, u'tau_m': 10.0, u'I_e': 0.0, u'element_type': <SLILiteral: neuron>, u't_spike': -1.0, u'V_m': -70.0, u'local': True, u'recordables': (<SLILiteral: I_syn_ex>, <SLILiteral: I_syn_in>, <SLILiteral: V_m>, <SLILiteral: weighted_spikes_ex>, <SLILiteral: weighted_spikes_in>), u'E_L': -70.0, u'node_uses_wfr': False, u'tau_syn_ex': 2.0, u'parent': 2913, u'tau_minus_triplet': 110.0, u'thread_local_id': 5582, u'beta_Ca': 0.001, u't_ref': 2.0, u'thread': 0, u'frozen': False, u'archiver_length': 0, u'Ca': 0.0, u'C_m': 250.0, u'global_id': 5583, u'local_id': 2670, u'tau_syn_in': 2.0, u'model': <SLILiteral: iaf_psc_alpha>, u'V_min': -inf, u'tau_Ca': 10000.0},)
nest.CopyModel("iaf_cond_alpha", "std_iaf_cond_alpha",{'tau_syn_ex': 1.0, 'V_reset': -70.0})
nest.CopyModel("iaf_cond_alpha", "tr8_iaf_cond_alpha",{'tau_syn_ex': 1.0, 'V_reset': -70.0, 't_ref': 8.})
nest.CopyModel("spike_detector", "my_spike_detector",{'withtime': True, 'withgid': True})
nest.CopyModel('static_synapse','ex') #,{'weight': {'distribution' : 'uniform', 'low': .1, 'high': .2}})
nest.CopyModel('static_synapse','inh_400_delay', {'weight': -400., 'delay': 15.,}) #-400.,})
nest.CopyModel('static_synapse','ex_50', {'weight': 50.,}) #50
#stimes=[]
#stimes+=[i*20.0+10. for i in range(10)]
nest.CopyModel('spike_generator', 'my_spike_generator')
#nest.SetDefaults('iaf_psc_alpha_r',{'I_e' : 374.0})
#nest.SetDefaults('iaf_psc_alpha',{'I_e' : 350.0})
#nest.CopyModel("static_synapse", "ex", {"weight" : 0.1})
#nest.CopyModel("static_synapse", "inh", {"weight" : -0.1})
#----------------------------------------------------------------------------------------CREATE-LAYERS
gp_file = open('data/'+str(sim_title)+str(sim_title_2)+'/'+str(sim_nr)+'/p_pos_'+handle_name+'.data','r+')
#gp_file = open('data/test_vid/p_pos_test_vid.data','r+')
gp_data = np.load(gp_file)
gp_file.close()
gp_data = gp_data.tolist()
gp_pos=[]
gpgm_pos=[]
gp_r_0_pos=[]
gp_r_60_pos=[]
#gm_r_120_pos=[]
for i in range(len(gp_data)):
for j in range(len(gp_data[0])):
gp_pos+=[[0.5*gp_data[i][j][0]+0.25,0.5*gp_data[i][j][1]+0.25]]
gp_r_0_pos+=[[0.5*gp_data[i][j][0]+2.25,0.5*gp_data[i][j][1]+0.25]]
gp_r_60_pos+=[[0.5*gp_data[i][j][0]+1.25,0.5*gp_data[i][j][1]+0.25+1.732]]
if i < len(gp_data)/5 and j < len(gp_data[0])/5:
gpgm_pos+=[[5*0.5*gp_data[i][j][0]+0.25,5*0.5*gp_data[i][j][1]+0.25]]
#gp_r_120_pos+=[[gp_data[i][j][0]-1.25,gp_data[i][j][1]+0.25+1.732]]
#gm_pos=[[np.random.uniform(-0.5,0.5), np.random.uniform(-0.5,0.5)] for j in range(50)]
#print gm_pos
#midgets_V_input = tp.CreateLayer({'extent' : [extent_x,extent_y], 'center' : [center_x,center_y], 'positions' : gm_pos, 'elements': 'iaf_psc_alpha_mp', 'edge_wrap': True})
#parasolic_V_input = tp.CreateLayer({'extent' : [extent_x,extent_y], 'center' : [center_x,center_y], 'positions' : gp_pos, 'elements': 'iaf_psc_alpha_mp', 'edge_wrap': True})
#Test, WHETHER THAT WORKS!
OGIDs = open('/home/schrader/Documents/microsaccades/data/'+str(sim_title)+'/network/'+str(sim_nr)+'/GID_info_glob_modet_gmn_40_30.txt','r+')
OGIDs_par = open('/home/schrader/Documents/microsaccades/data/'+str(sim_title)+'/network/'+str(sim_nr)+'/GID_info.txt','r+')
layers=[] #list of layers
gmc_layers=[] #list of global motion corrected layers
out_layers=[]
layer_names = ['parasols','p_0_left_corr_modet_gmn_40_30','p_0_right_corr_modet_gmn_40_30'] # ,'p_0_left_corr','p_0_right_corr'] #,'p_60_up','p_60_down','p_120_up','p_120_down']
parpos_x=[]
parpos_y=[]
fmdpos_x=[]
fmdpos_y=[]
par_pos_times_list = []
ml_pos_times_list = []
mr_pos_times_list = []
for ln in layer_names:
lr=tp.CreateLayer({'extent' : [extent_x,extent_y], 'center' : [center_x,center_y], 'positions' : gp_pos, 'elements': 'my_spike_generator', 'edge_wrap': True})
if ln == 'parasols':
if sar==True:
l_file = open('/home/schrader/Documents/microsaccades/data/'+str(sim_title)+'/network/'+str(sim_nr)+'/spikes_'+ln+'_'+str(handle_name)+'.data','r+')
else:
l_file = open('/home/schrader/Documents/microsaccades/data/'+str(sim_title)+'/network/'+str(sim_nr)+'/spikes_'+ln+'_'+str(handle_name)+'.data','r+')
else:
if sar==True:
#l_file = open('/home/schrader/Documents/microsaccades/data/'+str(sim_title)+'/network/'+str(sim_nr)+'/spikes_'+ln+'_'+str(handle_name)+'_mdw_'+str(mdw)+'.data','r+')
l_file = open('/home/schrader/Documents/microsaccades/data/'+str(sim_title)+'/network/'+str(sim_nr)+'/spikes_'+ln+'_'+str(handle_name)+'_modet_gmn_40_30.data','r+')
else:
#l_file = open('/home/schrader/Documents/microsaccades/data/'+str(sim_title)+'/network/'+str(sim_nr)+'/spikes_'+ln+'_'+str(handle_name)+'_mdw_'+str(mdw)+'.data','r+')
l_file = open('/home/schrader/Documents/microsaccades/data/'+str(sim_title)+'/network/'+str(sim_nr)+'/spikes_'+ln+'_'+str(handle_name)+'_modet_gmn_40_30.data','r+')
#print ln
#print '---'
LIDs = nest.GetNodes(lr)
deltaID = 0
#print '---NEW-FILE---'
if ln == 'parasols':
OGIDs_par.seek(0)
for ogid in OGIDs_par:
ogid=ogid.split('\t')
#print str(ogid[0].strip())
#print str(ln.strip())
#print '---'
if str(ogid[0].strip())==str(ln.strip()):
deltaID = int(ogid[1])
lq=0
else:
OGIDs.seek(0)
for ogid in OGIDs:
ogid=ogid.split('\t')
#print str(ogid[0].strip())
#print str(ln.strip())
#print '---'
if str(ogid[0].strip())==str(ln.strip()):
deltaID = int(ogid[1])
lq=0
for line in l_file:
nid = int(line.split('[')[0])
st = line.split('[')[1]
st = st.split(']')[0]
st = filter(None, st.split(' '))
sta=[]
for s in st:
sta+=[float(s.strip())]
#print ln
#print np.array(sta)
#print nid-deltaID
nest.SetStatus([LIDs[0][nid-deltaID]], {'spike_times': np.array(sta)})
if ln == 'p_0_left_corr_modet_gmn_40_30':
if len(sta) > 0:
fmdpos_x += [tp.GetPosition([LIDs[0][nid-deltaID]])[0][1]]
fmdpos_y += [tp.GetPosition([LIDs[0][nid-deltaID]])[0][0]]
for s in sta:
ml_pos_times_list += [(int(s),tp.GetPosition([LIDs[0][nid-deltaID]])[0][1],tp.GetPosition([LIDs[0][nid-deltaID]])[0][0])]
if ln == 'p_0_right_corr_modet_gmn_40_30':
if len(sta) > 0:
fmdpos_x += [tp.GetPosition([LIDs[0][nid-deltaID]])[0][1]]
fmdpos_y += [tp.GetPosition([LIDs[0][nid-deltaID]])[0][0]]
for s in sta:
mr_pos_times_list += [(int(s),tp.GetPosition([LIDs[0][nid-deltaID]])[0][1],tp.GetPosition([LIDs[0][nid-deltaID]])[0][0])]
if ln == 'parasols':
if len(sta) > 0:
parpos_x += [tp.GetPosition([LIDs[0][nid-deltaID]])[0][1]]
parpos_y += [tp.GetPosition([LIDs[0][nid-deltaID]])[0][0]]
for s in sta:
par_pos_times_list += [(int(s),tp.GetPosition([LIDs[0][nid-deltaID]])[0][1],tp.GetPosition([LIDs[0][nid-deltaID]])[0][0])]
lq+=1
l_file.close()
if ln != 'parasols':
layers+=[lr]
gmc_lr=tp.CreateLayer({'extent' : [extent_x,extent_y], 'center' : [center_x,center_y], 'positions' : gp_pos, 'elements': 'std_iaf_cond_alpha', 'edge_wrap': True})
gmc_layers+=[gmc_lr]
olr=tp.CreateLayer({'extent' : [extent_x,extent_y], 'center' : [center_x,center_y], 'positions' : gp_pos, 'elements': 'my_spike_detector', 'edge_wrap': True})
out_layers+=[olr]
else:
parasolic = lr
psd = tp.CreateLayer({'extent' : [extent_x,extent_y], 'center' : [center_x,center_y], 'positions' : gp_pos, 'elements': 'tr8_iaf_cond_alpha', 'edge_wrap': True})
gmdet=tp.CreateLayer({'extent' : [extent_x,extent_y], 'center' : [center_x,center_y], 'positions' : gpgm_pos, 'elements': 'std_iaf_cond_alpha', 'edge_wrap': True})
gmn=tp.CreateLayer({'extent' : [extent_x,extent_y], 'center' : [center_x,center_y], 'positions' : [[center_x, center_y]], 'elements': 'std_iaf_cond_alpha', 'edge_wrap': True})
out_gmn=tp.CreateLayer({'extent' : [extent_x,extent_y], 'center' : [center_x,center_y], 'positions' : [[center_x, center_y]], 'elements': 'my_spike_detector', 'edge_wrap': True})
out_gmdet=tp.CreateLayer({'extent' : [extent_x,extent_y], 'center' : [center_x,center_y], 'positions' : gpgm_pos, 'elements': 'my_spike_detector', 'edge_wrap': True})
#print lr
OGIDs.close()
ml_times_pos_file = open('/home/schrader/Documents/microsaccades/data/'+str(sim_title)+'/network/'+str(sim_nr)+'/ml_times_pos_corr_modet_gmn_40_30.txt','w+') #_'+str(mdw)+'
for t in ml_pos_times_list:
ml_times_pos_file.write(str(t)+'\n')
ml_times_pos_file.close()
mr_times_pos_file = open('/home/schrader/Documents/microsaccades/data/'+str(sim_title)+'/network/'+str(sim_nr)+'/mr_times_pos_corr_modet_gmn_40_30.txt','w+') #_'+str(mdw)+'
for t in mr_pos_times_list:
mr_times_pos_file.write(str(t)+'\n')
mr_times_pos_file.close()
par_times_pos_file = open('/home/schrader/Documents/microsaccades/data/'+str(sim_title)+'/network/'+str(sim_nr)+'/par_times_pos.txt','w+')
for t in par_pos_times_list:
par_times_pos_file.write(str(t)+'\n')
par_times_pos_file.close()
#---------------------------------------------------------------------------------------------------------------CREATE-CONNECTIONS
#connections to left/right half of Reichardt detector
'''
parasolic_to_psd_conndict = {'connection_type' : 'convergent', 'synapse_model': 'ex', 'weights': 40., 'mask' : {'rectangular' : {'lower_left' : [-0.2,-0.2], 'upper_right' : [0.2,0.2]}}}
psd_to_gmdet_conndict = {'connection_type' : 'convergent', 'synapse_model': 'ex', 'weights': 7., 'mask' : {'rectangular' : {'lower_left' : [-5.1,-5.1*0.866], 'upper_right' : [4.9,4.9*0.866]}}}
gmdet_to_gmc_conndict = {'connection_type' : 'divergent', 'synapse_model': 'inh_400_delay', 'mask' : {'rectangular' : {'lower_left' : [-4.9,-4.9*0.866], 'upper_right' : [5.1,5.1*0.866]}}} #{'lower_left' : [-1.2,-2.4*0.866], 'upper_right' : [1.3,2.6*0.866]}}}
modet_to_gmc_conndict = {'connection_type' : 'convergent', 'synapse_model': 'ex_50', 'mask' : {'rectangular' : {'lower_left' : [-0.1,-0.1], 'upper_right' : [0.1,0.1]}}}
psd_to_gmn_conndict = {'connection_type' : 'convergent', 'synapse_model': 'ex', 'weights': 7.}
gmn_to_gmc_conndict = {'connection_type' : 'divergent', 'synapse_model': 'inh_400_delay'} #{'lower_left' : [-1.2,-2.4*0.866], 'upper_right' : [1.3,2.6*0.866]}}}
out_conndict = {'connection_type' : 'convergent', 'mask' : {'rectangular' : {'lower_left' : [-0.2,-0.2], 'upper_right' : [0.2,0.2]}}}
#connect them
#tp.ConnectLayers(midgets_V_input,midgets,V_input_conndict)
#tp.ConnectLayers(parasolic_V_input,parasolic,V_input_conndict)
tp.ConnectLayers(parasolic,psd,parasolic_to_psd_conndict)
tp.ConnectLayers(psd,gmdet,psd_to_gmdet_conndict)
tp.ConnectLayers(gmdet,out_gmdet,out_conndict)
tp.ConnectLayers(psd,gmn,psd_to_gmn_conndict)
tp.ConnectLayers(gmn,out_gmn,out_conndict)
for i in range(len(layers)):
tp.ConnectLayers(layers[i],gmc_layers[i],modet_to_gmc_conndict)
tp.ConnectLayers(gmdet,gmc_layers[i],gmdet_to_gmc_conndict)
tp.ConnectLayers(gmn,gmc_layers[i],gmn_to_gmc_conndict)
tp.ConnectLayers(gmc_layers[i],out_layers[i],out_conndict)
'''
#ctr = tp.FindNearestElement(parasolic,[120.,120.])
#fig = tp.PlotLayer(parasolic,nodesize=80)
#tp.PlotTargets(ctr,psd,fig=fig,mask=out_conndict['mask'],src_size=250, tgt_color='red',tgt_size=20)
#fig = tp.PlotLayer(psd)
#ctr = tp.FindNearestElement(parasolic,[120.,120.])
#tp.PlotTargets(ctr, psd ,fig = fig, mask=out_conndict['mask'], src_size=200, src_color = 'red', tgt_color = 'green' , tgt_size=20 )
#plt.show()
'''
#plt.plot(parpos_y,parpos_x,'b+')
fig = plt.figure(figsize=(4,2))
ax1 = fig.add_subplot(1,1,1, adjustable='box', aspect=1)
ax1.plot(fmdpos_y,fmdpos_x,'g.')
#plt.plot(fmdpos,'rx')
ax1.set_xlabel('position x [arcmin]')
ax1.set_ylabel('position y\n[arcmin]')
plt.savefig('img/murakami/spike_pos_on105_off'+str(cond_nr)+'.pgf')
plt.savefig('img/murakami/spike_pos_on105_off'+str(cond_nr)+'.pdf')
#plt.show()
'''
#-------------------------------------------------------------------------------------------------------------------------------
#---------------------------------------------------------------------------------------------------------------------SIMULATION
#run simulation
#nest.Simulate(t_end-t_start)
'''
MIDs = nest.GetNodes(midgets)
for n in range(len(MIDs[0])):
qr = I_E+np.random.normal(0,10,1)[0]
nest.SetStatus([MIDs[0][n]], {'I_e': qr})
nest.Simulate(200)
'''
#ps_file = open('/home/schrader/Documents/microsaccades/data/'+str(sim_title)+'/network/'+str(sim_nr)+'/p_corr_max_spikes.txt','a+')
#ps_file.write(str(handle_name)+'\t'+str(exp_nr)+'\t'+str(cond_nr)+'\t'+str(gmn_sum_spikes)+'\t'+str(gmdet_sum_spikes)+'\t'+str(all_spikes)+'\t'+str(modet_sum_spikes[0])+'\t'+str(modet_sum_spikes[1])+'\t'+str(modet_sum_spikes[2])+'\t'+str(modet_sum_spikes[3])+'\t'+str(modet_sum_spikes[4])+'\t'+str(modet_sum_spikes[5])+'\n')
#ps_file.close()
print 'finished'
for lr in layers:
lr = 0
for lr in out_layers:
lr = 0
for lr in gmc_layers:
lr = 0
parasolic = 0
gmdet = 0
out_gmdet = 0
gmn = 0
out_gmn = 0
psd = 0
sys.exit()