def show_plot(axs,
name,
d,
models=['M1', 'M2', 'FS', 'GA', 'GI', 'ST', 'SN'],
**k):
dd = {}
by_sets = k.pop('by_sets', False)
for keys, val in misc.dict_iter(d):
if keys[-1] != name:
continue
if by_sets and keys[0][0:3] != 'set':
continue
first_keys = keys[:-2]
if type(first_keys) == str:
first_keys = [first_keys]
new_keys = ['_'.join(first_keys)] + keys[-2:]
dd = misc.dict_recursive_add(dd, new_keys, val)
d = dd
# if k.get('fig_and_axes', False):
# else:
# fig, axs=ps.get_figure(n_rows=len(models), n_cols=1, w=1000.0, h=800.0,
# fontsize=k.get('fontsize',10))
labels = k.pop('labels', sorted(d.keys()))
# colors=misc.make_N_colors('Paired', max(len(labels), 6))
colors = k.pop('colors', misc.make_N_colors('jet', max(len(labels), 1)))
linestyles = ['-'] * len(labels)
j = 0
nets = k.get('nets', sorted(d.keys()))
for key in nets:
v = d[key]
# axs[0].set_title(k)
for i, model in enumerate(models):
kk = {
'label': model + ' ' + labels[j],
'linestyle': linestyles[j],
'color': colors[j]
}
if 'win' in k.keys():
kk['win'] = k['win']
if 't_start' in k.keys():
kk['t_start'] = k['t_start']
if 't_stop' in k.keys():
kk['t_stop'] = k['t_stop']
print model
if model in v.keys():
v[model][name].plot(ax=axs[i], **kk)
j += 1
for ax in axs:
ax.legend()
def show_hist(name, d, models=['M1', 'M2', 'FS', 'GA', 'GI', 'ST', 'SN'], **k):
fig, axs = ps.get_figure(n_rows=len(models),
n_cols=1,
w=1000.0,
h=800.0,
fontsize=10)
labels = k.pop('labels', sorted(d.keys()))
colors = misc.make_N_colors('jet', len(labels))
linestyles = ['solid'] * len(labels)
linewidth = [2.0] * len(labels)
j = 0
for key in sorted(d.keys()):
v = d[key]
# axs[0].serunt_title(k)
for i, model in enumerate(models):
if 'spike_stastistic' in v[model]:
st = v[model]['spike_statistic']
st.rates = {
'mean': round(st.rates['mean'], 2),
'std': round(st.rates['std'], 2),
'CV': round(st.rates['CV'], 2)
}
s = str(st.rates)
else:
s = ''
# print s
# print labels[j]
k.update({
'label': (model + ' ' + labels[j] + ' ' + s),
'histtype': 'step',
'linestyle': linestyles[j],
'color': colors[j],
'linewidth': linewidth[j]
})
# print k
h = v[model][name].hist(ax=axs[i], **k)
ylim = list(axs[i].get_ylim())
ylim[0] = 0.0
axs[i].set_ylim(ylim)
axs[i].legend_box_to_line()
j += 1
# import pylab
# pylab.show()
return fig, axs
def show_opt_hist(d, axs, name):
colors = misc.make_N_colors('jet', len(d['opt_rate'].keys()))
i = 0
for key in sorted(d['opt_rate'].keys()):
data = d['opt_rate'][key]
data.plot(axs[0], name, **{'color': colors[i]})
i += 1
for key in sorted(d['hist'].keys()):
data = d['hist'][key]
data.hist(axs[0])
linestyles = ['solid'] * len(colors)
linewidth = [2] * len(colors)
beautify_hist(axs[0], colors, linestyles, linewidth)
def show_neuron_numbers(d, models, **k):
attr='firing_rate'
linestyle=['-','--']
labels=k.pop('labels', models)
colors=misc.make_N_colors('jet', max(len(labels), 1))
fig, axs=ps.get_figure(n_rows=1, n_cols=1, w=500.0, h=500.0, fontsize=10)
l_ids=[]
ax=axs[0]
for k, model in enumerate(models):
max_set=0
for j, name in enumerate(sorted([d.keys()[0]])):
v=d[name]
sets=[s for s in sorted(v.keys()) if s[0:3]=='set']
ids=0
for i, _set in enumerate(sets):
if not model in v[_set].keys():
break
obj=v[_set][model][attr]
ids+=len(obj.ids)
if max_set<=i:
max_set+=1
l_ids.append(ids)
obj=Data_bar(**{'y':numpy.array(l_ids)})
obj.bar(ax)
ax.set_xticklabels(labels)
return fig
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)):
d['Net_0{0}'.format(i)]['gi']['firing_rate'].plot(
axs[i], **{'win': 1.})
d['Net_0{0}'.format(i)]['st']['firing_rate'].plot(
axs[i], **{'win': 1.})
axs[i].set_xlim(0.0, 1500.0)
colors = misc.make_N_colors('copper', len(kw_list))
# for net, c in zip(nets, colors):
# kw={ 'all':True,
# 'color':c,
# 'p_95':False,}
# d[net]['gi_st']['phases_diff_with_cohere'].hist(axs[-1], **kw)
# axs[-1].set_xlim(-numpy.pi, numpy.pi)
pylab.show()
# pylab.plot(d['n'])
# 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
dtmp['IF'].plot(axs[1],
part='first',
**{
'color': colors[i],
'linestyle': '-'
})
dtmp['IF'].plot(axs[1],
part='mean',
# v=[ah.y, ah_GP.y]
# v=d['data'][key]['Net_0']['GI_FS']['phases_diff_with_cohere']
l1.append(ah.y)
l2.append(ah_GP.y)
l=numpy.array(l1[organize['TATIFS']])
pp(l)
# FSN
#####
pp(l)
colors=misc.make_N_colors('copper', len(l))
ln=list_names[organize['TATIFS']]
pp(ln)
ln=[a[2]+','+a[4] for a in ln]
pp(ln)
for i, trace in enumerate(l):
x=numpy.linspace(0,1000,len(trace))
# norm=sum(trace)*(x[-1]-x[0])/len(x)
# norm=numpy.mean(x)
ax.plot(x, (numpy.array(trace)-trace[0]), color=colors[i])
ax.set_xlim([0,1000])
# 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
dtmp['IF'].plot(axs[1], part='first',**{'color':colors[i],'linestyle':'-'})
dtmp['IF'].plot(axs[1], part='mean', **{'color':colors[i],'linestyle':'--'})
dtmp=d['Net_0{0}'.format(i)]
if 'nullcline' not in dtmp.keys(): continue
dtmp['nullcline'].plot(axs[2],**{'color':colors[i]})
def show_bulk(d, models, attr, **k):
# attr='mean_rate_slices'
linestyle=['-','--']
labels=k.pop('labels', sorted(d.keys()))
colors=misc.make_N_colors('jet', max(len(labels), 1))
fig, axs=ps.get_figure(n_rows=7, n_cols=1, w=1200.0, h=800.0, fontsize=10)
res=k.pop('res')
i=0
xticks=[]
for j in range(res):
for k in range(res):
if j==k:
xticks.append(i)
i+=1
for k, model in enumerate(models):
ax=axs[k]
# max_set=0
for j, name in enumerate(sorted(d.keys())):
v=d[name]
sets=[s for s in v.keys() if s[0:3]=='set']
for i, _set in enumerate(sets):
if not model in v[_set].keys():
break
obj=v[_set][model][attr]
obj.plot(ax, **{'color':colors[j],
'linestyle':linestyle[i],
'label':labels[j]
})
# if max_set<=i:
# max_set+=1
max_set=len(sets)
ax.set_ylabel(model+' (spikes/s)')
ax.set_xticks(xticks)
#Get artists and labels for legend and chose which ones to display
if k!=0:
continue
handles, _labels = ax.get_legend_handles_labels()
display = range(0,len(d.keys())*max_set, max_set)
#Create custom artists
linetype_labels=[]
linetype_handles=[]
for i in range(max_set):
linetype_handles.append(pylab.Line2D((0,1),(0,0),
color='k',
linestyle=linestyle[i]))
linetype_labels.append('Action '+str(i))
#Create legend from custom artist/label lists
ax.legend([handle for i,handle in enumerate(handles)
if i in display]+linetype_handles,
[label for i,label in enumerate(_labels)
if i in display]+linetype_labels, bbox_to_anchor=(1.22, 1.))
return fig
#########################################
ax=axs[2]
l=[]
for key in sorted(d['data'].keys()):
v=d['data'][key]['Net_1']['GI_ST']['phases_diff_with_cohere']
l.append(v)
l=l[organize['CTXSTRdelay']][::-1]
ln0=list_names[organize['CTXSTRdelay']][::-1]
ln=[str(int(float(a[-1]))) for a in ln0]
# ln0=list_names[organize['CSdelay']][1::2]
# ln+=['2.5, ' + str(int(float(a[-1]))) for a in ln0]
colors=misc.make_N_colors('copper', len(l))
for i, trace in enumerate(l):
x=numpy.linspace(-numpy.pi*3,numpy.pi*3,len(trace))
norm=sum(trace)*(x[-1]-x[0])/len(x)
ax.plot(x, trace/norm, color=colors[i])
ax.set_xlim([-numpy.pi,numpy.pi])
sm = pylab.cm.ScalarMappable(cmap='copper',
norm=pylab.normalize(vmin=0, vmax=len(ln)-1))
sm._A = []
box = ax.get_position()
pos=[box.x0+1.03*box.width, box.y0+box.height*0.1,
0.01, box.height*0.8]
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)):
d['Net_0{0}'.format(i)]['gi']['firing_rate'].plot(axs[i], **{'win':1.})
d['Net_0{0}'.format(i)]['st']['firing_rate'].plot(axs[i], **{'win':1.})
axs[i].set_xlim(0.0,1500.0)
colors=misc.make_N_colors('copper', len(kw_list))
# for net, c in zip(nets, colors):
# kw={ 'all':True,
# 'color':c,
# 'p_95':False,}
# d[net]['gi_st']['phases_diff_with_cohere'].hist(axs[-1], **kw)
# axs[-1].set_xlim(-numpy.pi, numpy.pi)
pylab.show()
# pylab.plot(d['n'])
