def simulate(from_disk=0,
kw={},
net='Net_0',
script_name=__file__.split('/')[-1][0:-3],
setup=Setup(50,20) ):
setup=Setup(50,20)
file_name = kw.get('file_name', dr.HOME_DATA+'/'+script_name)
file_name_figs = kw.get('file_name_figs', dr.HOME_DATA+'/fig/'+script_name)
sd=get_storage_list([net], file_name, '')[0]
d={}
if from_disk==0:
print net
dd={net:simulate_network(**kw)}
dd=compute_attrs(dd, net, setup)
save(sd, dd)
if from_disk==1:
filt= [net]+['gi','st', 'gi_st']+['spike_signal']
dd = load(sd, *filt)
pp(dd)
dd=compute_attrs(dd, net, setup)
save(sd, dd)
elif from_disk==2:
filt = [net]+['gi','st', 'gi_st']+['firing_rate', 'phases_diff_with_cohere']
dd = load(sd, *filt)
d = misc.dict_update(d, dd)
return d, file_name_figs
def main():
k = get_kwargs_builder()
from os.path import expanduser
home = expanduser("~")
attr = ['firing_rate', 'mean_rates', 'spike_statistic']
kwargs_dic = {
'mean_rates': {
't_start': k['start_rec']
},
'spike_statistic': {
't_start': k['start_rec']
},
}
file_name = (home + '/results/papers/inhibition/network/' +
__file__.split('/')[-1][0:-3])
models = ['M1', 'M2', 'FS', 'GI', 'GA', 'ST', 'SN']
info, nets, _ = get_networks()
sd = Storage_dic.load(file_name)
sd.add_info(info)
sd.garbage_collect()
d = {}
for net, from_disk in zip(nets, [1] * 2):
if not from_disk:
dd = run(net)
dd = compute(dd, models, attr, **kwargs_dic)
save(sd, dd)
elif from_disk:
filt = [net.get_name()] + models + attr
dd = load(sd, *filt)
d = misc.dict_update(d, dd)
figs = []
figs.append(show_fr(d, models))
figs.append(show_hr(d, models))
sd.save_figs(figs)
if DISPLAY: pylab.show()
pylab.show()
def _run(storage_dic, d, net, from_disk, attr, **kwargs):
model = net.get_single_unit()
kwargs_dic = {attr: kwargs}
if not from_disk:
dd = {net.get_name(): net.simulation_loop()}
dd = compute(dd, [model], [attr], **kwargs_dic)
save(storage_dic, dd)
elif from_disk:
# filt = [net.get_name()] + [model] + ['spike_signal']
# dd = load(storage_dic, *filt)
# dd = compute(dd, [model], [attr], **kwargs_dic)
# save(storage_dic, dd)
filt = [net.get_name()] + [model] + [attr]
dd = load(storage_dic, *filt)
dd = reduce_levels(dd, [model] + [attr])
d = misc.dict_update(d, dd)
return d
def simulate(
from_disk=0,
kw={},
net='Net_0',
script_name=__file__.split('/')[-1][0:-3],
setup=Setup(50, 20),
):
file_name = kw.get('file_name', dr.HOME_DATA + '/' + script_name)
file_name_figs = kw.get('file_name_figs',
dr.HOME_DATA + '/fig/' + script_name)
sd = get_storage_list([net], file_name, '')[0]
d = {}
if from_disk == 0:
print net
dd = {}
for call in kw.get('calls', [
simulate_irregular_firing,
simulate_ahp,
simulate_IV,
simulate_IF,
get_nullcline,
simulate_rebound_spike,
]):
misc.dict_update(dd, {net: call(**kw)})
save(sd, dd)
elif from_disk == 1:
filt = kw.get('filter', [net] + ['IV', 'IF', 'ahp', 'nullcline'] +
['rs_scatter'] +
['rs_voltage_{0}'.format(i) for i in range(6)] +
['irf_voltage_{0}'.format(i) for i in range(3)])
dd = load(sd, *filt)
d = misc.dict_update(d, dd)
return d, file_name_figs
def _run_XX(flag, storage_dic, stim, d, net, from_disk):
model = net.get_single_unit()
inp = net.get_single_unit_input()
if flag == 'IV':
attr = 'IV_curve'
stim_name = 'node.' + model + '.nest_params.I_e'
call = getattr(net, 'sim_IV_curve')
if flag == 'IF':
attr = 'IF_curve'
stim_name = 'node.' + model + '.nest_params.I_e'
call = getattr(net, 'sim_IF_curve')
if flag == 'FF':
attr = 'FF_curve'
stim_name = 'node.' + inp + '.rate'
call = getattr(net, 'sim_FF_curve')
if not from_disk:
k = {
'stim': stim,
'stim_name': stim_name,
'stim_time': net.get_sim_time(),
'model': model
}
dd = {net.get_name(): call(**k)}
dd = compute(dd, [model], [attr])
save(storage_dic, dd)
elif from_disk:
filt = [net.get_name()] + [model] + [attr]
dd = load(storage_dic, *filt)
pp(dd)
dd = reduce_levels(dd, [model] + [attr])
d = misc.dict_update(d, dd)
return d
def _optimize(flag, net, storage_dic, d, from_disk, **kwargs):
model = net.get_single_unit()
inp = net.get_single_unit_input()
attr = 'fmin'
f = [model]
if flag == 'opt_rate':
x = ['node.' + inp + '.rate']
if flag == 'opt_curr':
x = ['node.' + model + '.nest_params.I_e']
x0 = kwargs.get('x0', 900.)
opt = {'netw': {'optimization': {'f': f, 'x': x, 'x0': x0}}}
net.par.update_dic_rep(opt)
kwargs_fmin = {
'model': net,
'call_get_x0': 'get_x0',
'call_get_error': 'sim_optimization',
'verbose': kwargs.get('verbose', True)
}
if not from_disk:
f = Fmin(net.name, **kwargs_fmin)
dd = {net.get_name(): {model: {attr: f.fmin()}}}
print dd
save(storage_dic, dd)
elif from_disk:
filt = [net.get_name()] + [model] + [attr]
dd = load(storage_dic, *filt)
dd = reduce_levels(dd, [model] + [attr])
d = misc.dict_update(d, dd)
return d
def simulate(from_disk=0,
kw={},
net='Net_0',
script_name=__file__.split('/')[-1][0:-3],
setup=Setup(50,20),
):
file_name = kw.get('file_name', dr.HOME_DATA+'/'+script_name)
file_name_figs = kw.get('file_name_figs', dr.HOME_DATA+'/fig/'+script_name)
sd=get_storage_list([net], file_name, '')[0]
d={}
if from_disk==0:
print net
dd={}
for call in kw.get('calls',[simulate_irregular_firing,
simulate_ahp,
simulate_IV,
simulate_IF,
get_nullcline,
simulate_rebound_spike,]):
misc.dict_update(dd,{net:call(**kw)})
save(sd, dd)
elif from_disk==1:
filt = kw.get('filter', [net]+['IV', 'IF', 'ahp', 'nullcline']
+['rs_scatter']+['rs_voltage_{0}'.format(i) for i in range(6)]
+['irf_voltage_{0}'.format(i) for i in range(3)])
dd = load(sd, *filt)
d = misc.dict_update(d, dd)
return d, file_name_figs
nets = ['Net_{0:0>2}'.format(i) for i in range(len(kw_list))]
sd_list = get_storage_list(nets, path, '')
d = {}
for net, sd, kw in zip(nets, sd_list, kw_list):
if from_disk == 0: # and net=='Net_05':
print net
dd = {net: simulate_network(**kw)}
dd = compute_attrs(dd, net)
save(sd, dd)
if from_disk == 1:
filt = [net] + ['gi', 'st', 'gi_st'] + ['spike_signal']
dd = load(sd, *filt)
pp(dd)
dd = compute_attrs(dd, net)
save(sd, dd)
elif from_disk == 2:
filt = [net] + ['gi', 'st', 'gi_st'
] + ['firing_rate', 'phases_diff_with_cohere']
dd = load(sd, *filt)
d = misc.dict_update(d, dd)
pp(d)
fig, axs = get_fig_axs()
for i in range(len(nets)):
simulate_rebound_spike,
]:
misc.dict_update(dd,{net:call(**kw)})
save(sd, dd)
# if from_disk==1:
# filt= [net]+['gi','st', 'gi_st']+['spike_signal']
# dd = load(sd, *filt)
# pp(dd)
# dd=compute_attrs(dd, net)
# save(sd, dd)
elif from_disk==1:
filt = [net]+['IV', 'IF', 'nullcline'] +['rs_scatter']
dd = load(sd, *filt)
d = misc.dict_update(d, dd)
pp(d)
fig, axs=get_fig_axs()
colors=misc.make_N_colors('jet', len(nets))
for i in range(len(nets)):
dtmp=d['Net_0{0}'.format(i)]
if 'IV' not in dtmp.keys(): continue
dtmp['IV'].plot(axs[0], **{'color':colors[i]})
dtmp=d['Net_0{0}'.format(i)]
if 'IV' not in dtmp.keys(): continue
def plot(file_name, figs, setup, flag, **k):
nets = ['Net_0', 'Net_1']
attr = ['firing_rate', 'mean_rates', 'spike_statistic']
attr2 = ['psd', 'activity_histogram', 'activity_histogram_stat']
attr_coher = ['phase_diff', 'phases_diff_with_cohere', 'mean_coherence']
models = ['M1', 'M2', 'FS', 'GI', 'GA', 'ST', 'SN', 'GP']
models_coher = [
'GI_GA',
'GI_GI',
'GA_GA',
'GA_ST',
'GI_ST',
'GP_GP',
'ST_ST',
'GP_ST',
]
# Adding nets no file name
sd_list = get_storage_list(nets, file_name, '')
d = {}
for sd, net in zip(sd_list, nets):
filt = ([net] + models + models_coher + attr + attr2 + attr_coher)
dd = load(sd, *filt)
# cmp_statistical_test(models, dd)
d = misc.dict_update(d, dd)
kw = setup.plot_summed2()
kw['alphas'] = [1., 1.]
kw['coherence_xcut'] = [0, 50]
kw['coherence_color'] = ['grey', 'k']
kw['coherence_p_conf95_linestyle'] = '--'
kw['hatchs_ti_ti'] = ['', '']
kw['label_model'] = 'Black=Model'
kw['label_exp'] = 'White=Exp., Mallet et al 2008'
kw['linewidth'] = 1.
kw['set_text_on_bars'] = False
kw['top_label'] = False
kw['phases_diff_with_cohere_colors'] = ['grey', 'k']
kw['phases_diff_with_cohere_xcut'] = [-numpy.pi * 0.97, numpy.pi**0.97]
kw['phases_diff_with_cohere_remove_peaks'] = True
kw['scale'] = 1
kw['spk_stats_color_axis'] = 1
kw['spk_stats_colors'] = ['k', 'w']
kw['spk_stats_colors_ti_ta'] = ['k', 'w']
kw['xlim_cohere'] = [-1, 51]
kw.update(k)
figs.append(base_oscillation.show_summed2(d, **kw))
kw = setup.plot_summed_STN()
kw['alphas'] = [1., 1.]
kw['coherence_color'] = ['grey', 'k']
kw['coherence_p_conf95_linestyle'] = '--'
kw['hatchs_ti_ti'] = ['', '']
kw['label_model'] = 'Black=Model'
kw['label_exp'] = 'White=Exp., Mallet et al 2008'
kw['linewidth'] = 1.
kw['phases_diff_with_cohere_xcut'] = [-numpy.pi * 0.97, numpy.pi**0.97]
kw['phases_diff_with_cohere_remove_peaks'] = True
kw['phases_diff_with_cohere_colors'] = ['grey', 'k']
kw['scale'] = 1
kw['set_text_on_bars'] = False
kw['spk_stats_colors'] = ['k', 'w']
kw['spk_stats_colors_ti_ta'] = ['k', 'w']
kw['spk_stats_color_axis'] = 1
kw['top_label'] = False
kw['xlim_cohere'] = [-1, 51]
if flag == 'slow_wave':
kw['ylim_cohere'] = [0, 1.0]
elif flag == 'beta':
kw['ylim_cohere'] = [0, 0.4]
kw['coherence_xcut'] = [0, 50]
kw.update(k)
figs.append(base_oscillation.show_summed_STN(d, **kw))
return figs
def plot(file_name, figs, setup, flag, **k):
nets=['Net_0', 'Net_1']
attr = [
'firing_rate',
'mean_rates',
'spike_statistic'
]
attr2=['psd',
'activity_histogram',
'activity_histogram_stat']
attr_coher = [
'phase_diff',
'phases_diff_with_cohere',
'mean_coherence'
]
models = ['M1', 'M2', 'FS', 'GI', 'GA', 'ST', 'SN', 'GP']
models_coher = ['GI_GA', 'GI_GI', 'GA_GA', 'GA_ST', 'GI_ST', 'GP_GP',
'ST_ST', 'GP_ST',]
# Adding nets no file name
sd_list=get_storage_list(nets, file_name, '')
d = {}
for sd, net in zip(sd_list, nets):
filt = ([net]
+ models + models_coher
+ attr + attr2 + attr_coher)
dd = load(sd, *filt)
# cmp_statistical_test(models, dd)
d = misc.dict_update(d, dd)
kw=setup.plot_summed2()
kw['alphas']=[1.,1.]
kw['coherence_xcut']=[0,50]
kw['coherence_color']=['grey', 'k']
kw['coherence_p_conf95_linestyle']='--'
kw['hatchs_ti_ti']=['','']
kw['label_model']='Black=Model'
kw['label_exp']='White=Exp., Mallet et al 2008'
kw['linewidth']=1.
kw['set_text_on_bars']=False
kw['top_label']=False
kw['phases_diff_with_cohere_colors']=['grey','k']
kw['phases_diff_with_cohere_xcut']=[-numpy.pi*0.97, numpy.pi**0.97]
kw['phases_diff_with_cohere_remove_peaks']=True
kw['scale']=1
kw['spk_stats_color_axis']=1
kw['spk_stats_colors']=['k', 'w']
kw['spk_stats_colors_ti_ta']=['k','w']
kw['xlim_cohere']=[-1, 51]
kw.update(k)
figs.append(base_oscillation.show_summed2(d, **kw))
kw=setup.plot_summed_STN()
kw['alphas']=[1.,1.]
kw['coherence_color']=['grey','k']
kw['coherence_p_conf95_linestyle']='--'
kw['hatchs_ti_ti']=['','']
kw['label_model']='Black=Model'
kw['label_exp']='White=Exp., Mallet et al 2008'
kw['linewidth']=1.
kw['phases_diff_with_cohere_xcut']=[-numpy.pi*0.97, numpy.pi**0.97]
kw['phases_diff_with_cohere_remove_peaks']=True
kw['phases_diff_with_cohere_colors']=['grey','k']
kw['scale']=1
kw['set_text_on_bars']=False
kw['spk_stats_colors']=['k', 'w']
kw['spk_stats_colors_ti_ta']=['k','w']
kw['spk_stats_color_axis']=1
kw['top_label']=False
kw['xlim_cohere']=[-1, 51]
if flag=='slow_wave':
kw['ylim_cohere']=[0, 1.0]
elif flag=='beta':
kw['ylim_cohere']=[0, 0.4]
kw['coherence_xcut']=[0,50]
kw.update(k)
figs.append(base_oscillation.show_summed_STN(d, **kw))
return figs
