'100-noss':[paths[0]+s1.format(2), nets1],
# '20-ss':[paths[2]+s3.format(2,5.0), nets1],
# '50-ss':[paths[2]+s3.format(1,2.5), nets1],
# '100-ss':[paths[2]+s3.format(0,1.0), nets1],
# '20-ST-ss':[paths[1]+s2.format(1,5.0), nets2],
# '100-ST-ss':[paths[1]+s2.format(0,1.0), nets2]
}
d={}
for key, val in files.items():
nets=val[1]
d_tmp=effect_conns.gather('', nets, models, attrs,
dic_keys=[key],
file_names=[val[0]])
misc.dict_update(d, d_tmp)
print d.keys()
pp(d)
from Go_NoGo_compete import show_heat_map, show_variability_several
builder=[['10-noss', nets1],
# ['25-noss', nets1],
['50-noss', nets1],
# ['75-noss', nets1],
['100-noss', nets1]
]
dd={}
titles=[]
i=0
for name, nets in builder:
fileName, fileOut, data_path =sys.argv[1:]
with Barrier():
if comm.rank()==0:
out=pickle_load(fileName, all_mpi=True)
else:
out=None
out=comm.bcast(out, root=0)
sim_time, args, kwargs=out
d={'sd':{'active':True,
'params':{'to_memory':False,
'to_file':True}}}
kwargs=misc.dict_update(kwargs, d)
mkdir(data_path+'nest/')
my_nest.ResetKernel(display=False,
data_path=data_path+'nest/',
**{'threads_local': np_local})
import pprint
pp=pprint.pprint
d=my_nest.GetKernelStatus()
if comm.rank()==0:
print comm.size()
pp(d)
import threading
print threading.active_count()
# comm.obj.
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(oscillation_common.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, 0.25]
kw['coherence_xcut'] = [0, 50]
kw.update(k)
figs.append(oscillation_common.show_summed_STN(d, **kw))
return figs
# '50-ss':[paths[2]+s3.format(1,2.5), nets1],
# '100-ss':[paths[2]+s3.format(0,1.0), nets1],
# '20-ST-ss':[paths[1]+s2.format(1,5.0), nets2],
# '100-ST-ss':[paths[1]+s2.format(0,1.0), nets2]
}
d = {}
for key, val in files.items():
nets = val[1]
d_tmp = effect_conns.gather('',
nets,
models,
attrs,
dic_keys=[key],
file_names=[val[0]])
misc.dict_update(d, d_tmp)
print d.keys()
pp(d)
from Go_NoGo_compete import show_heat_map
builder = [
['10-noss', nets1],
# ['25-noss', nets1],
['50-noss', nets1],
# ['75-noss', nets1],
['100-noss', nets1]
]
dd = {}
titles = []
i = 0
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(oscillation_common.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, 0.25]
kw['coherence_xcut']=[0,50]
kw.update(k)
figs.append(oscillation_common.show_summed_STN(d, **kw))
return figs