def sum_up_fig(args):
fig, ax = plt.subplots(1, figsize=(2, 2))
plt.subplots_adjust(left=.4, bottom=.2)
## SAS
SAS = []
for i in range(1, 6):
args.filename = 'sas' + str(i) + '.zip'
args2, DATA = get_scan(args)
mean, std = get_spike_train_similarity(DATA,\
args.t0, args.time_span, args.N_target)
SAS.append(100 * mean)
ax.bar([0], [np.array(SAS).mean()], yerr=[np.array(SAS).std()])
## BS
BS = []
for i in range(1, 6):
args.filename = 'bs' + str(i) + '.zip'
args2, DATA = get_scan(args)
mean, std = get_spike_train_similarity(DATA,\
args.t0, args.time_span, args.N_target)
BS.append(100 * mean)
ax.bar([1], [np.array(BS).mean()], yerr=[np.array(BS).std()])
set_plot(ax,
xticks=[0, 1.],
xticks_labels=['SAS', 'BS'],
ylabel='spike pattern \n similarity (%)')
fig.savefig('fig.svg')
ntwk.show()
def plot_sim(filename, ge,
omf_data=None,
Umodel_data=None,
mf_data=None):
## load file
data = ntwk.load_dict_from_hdf5(filename)
# ## plot
fig, AX = ntwk.activity_plots(data,
smooth_population_activity=10,
COLORS=COLORS,
pop_act_log_scale=True)
if Umodel_data is not None:
AX[2].plot(Umodel_data['t'],
Umodel_data['desired_Vm'], '-', lw=2, color='dimgrey', label='mean-field')
if mf_data is None:
mf = ntwk.FastMeanField(data, tstop=6., dt=2.5e-3, tau=20e-3)
mf.build_TF_func(tf_sim_file='neural_network_dynamics/theory/tf_sim_points.npz')
X, mVm, sVm = mf.run_single_connectivity_sim(mf.ecMatrix, verbose=True)
t = np.linspace(0, data['tstop'], X.shape[1])
for i, label in enumerate(data['REC_POPS']):
AX[-1].plot(t, 1e-2+X[i,:], '--', lw=1, color=COLORS[i])
ge.plot(t, 1e3*mVm[i,:], sy=1e3*sVm[i,:], color='k', lw=1, label='mean-field', ax=AX[2+i])
AX[2+i].plot(t, 1e3*(mVm[i,:]-sVm[i,:]), 'k-', lw=0.5)
AX[2+i].plot(t, 1e3*(mVm[i,:]+sVm[i,:]), 'k-', lw=0.5)
# AX[2].plot(mf_data['t'], mf_data['desired_Vm'], 'k--', lw=2, label='U-model')
# AX[2].legend(frameon=False, loc='best')
if omf_data is None:
try:
omf_data = load_dict(filename.replace('ntwk.h5', 'mf.npz'))
except FileNotFoundError:
pass
if omf_data is not None:
t = np.linspace(0, data['tstop'], len(omf_data['pyrExc']))
for i, label in enumerate(data['REC_POPS']):
AX[-1].plot(t, 1e-2+omf_data[label], '-', lw=4, color=COLORS[i], alpha=.5)
# AX[-1].plot(t, omf_data[label], 'k--')
figM, _, _ = plot_matrix(data)
ntwk.show()
parser.add_argument('-df',
'--data_folder',
help='Folder for data',
default='sparse_vs_balanced/data/')
parser.add_argument("--filename",
'-f',
help="filename",
type=str,
default='data.zip')
parser.add_argument("-a",
"--analyze",
help="perform analysis of params space",
action="store_true")
parser.add_argument("-c",
"--compare_regimes",
help="perform analysis of params space",
action="store_true")
args = parser.parse_args()
Model = vars(args)
if args.analyze:
analyze_scan(Model)
ntwk.show()
elif args.compare_regimes:
compare_two_regimes(Model)
ntwk.show()
else:
run_scan(Model)
def analyze_scan(args, smooth=10):
PATTERNS, seeds, Model, DATA = get_scan({}, filename=args.filename)
fig1, ax1 = plt.subplots(1, figsize=(4, 2.5))
plt.subplots_adjust(bottom=.3, right=.95)
fig2, ax2 = plt.subplots(1, figsize=(4, 2.5))
plt.subplots_adjust(bottom=.3, right=.95)
COLORS = [Brown, Cyan, Pink, Purple, Pink, Grey, Kaki]
# COLORS = [Cyan, Pink, Brown, Purple, Pink, Grey, Kaki]
MARKERS = ['o', 'd', 'x']
SIZES = [5, 3, 6]
nmax_pre = min([args.Nvis_pre, args.N_aff_stim])
ax1.fill_between([0, args.time_span], [0, 0], [nmax_pre, nmax_pre],
color='k',
alpha=.1)
nmax_post = min([args.Nvis_post, args.N_target])
ax2.fill_between([0, args.time_span], [0, 0], [nmax_post, nmax_post],
color='k',
alpha=.1)
for i, data in enumerate(DATA):
ii, j = 0, 0
## AFFERENT SPIKES
cond = (data['tRASTER_PRE_in_terms_of_Pre_Pop'][j]>args.t0-args.pre_window) &\
(data['tRASTER_PRE_in_terms_of_Pre_Pop'][j]<args.t0+args.time_span+args.pre_window) &\
(data['iRASTER_PRE_in_terms_of_Pre_Pop'][j]<nmax_pre)
if j == 0:
ax1.plot(data['tRASTER_PRE_in_terms_of_Pre_Pop'][j][cond] -
args.t0,
data['iRASTER_PRE_in_terms_of_Pre_Pop'][j][cond] + ii,
MARKERS[i],
ms=SIZES[i],
color=COLORS[i],
label='trial ' + str(i + 1))
else:
ax1.plot(data['tRASTER_PRE_in_terms_of_Pre_Pop'][j][cond] -
args.t0,
data['iRASTER_PRE_in_terms_of_Pre_Pop'][j][cond] + ii,
MARKERS[i],
ms=SIZES[i],
color=COLORS[i])
## EXCITATORY SPIKES
cond = (data['tRASTER_RecExc']>args.t0-args.pre_window) & (data['iRASTER_RecExc']<nmax_post) &\
(data['tRASTER_RecExc']<args.t0+args.time_span+args.pre_window)
if j == 0:
ax2.plot(data['tRASTER_RecExc'][cond] - args.t0,
data['iRASTER_RecExc'][cond] + ii,
MARKERS[i],
ms=SIZES[i],
color=COLORS[i])
else:
ax2.plot(data['tRASTER_RecExc'][cond] - args.t0,
data['iRASTER_RecExc'][cond] + ii,
MARKERS[i],
ms=SIZES[i],
color=COLORS[i])
ax1.plot((-args.pre_window) * np.ones(2), [0, int(nmax_pre / 4) - 1],
'k-',
lw=3)
fig1.text(0.05,
0.5,
str(int(nmax_pre / 4)) + ' neurons',
rotation=90,
fontsize=14)
ax2.plot((-args.pre_window) * np.ones(2), [0, int(nmax_post / 4) - 1],
'k-',
lw=3)
fig2.text(0.05,
0.5,
str(int(nmax_post / 4)) + ' neurons',
rotation=90,
fontsize=14)
ax1.legend()
set_plot(ax1, ['bottom'],
xlabel='time from stim. onset (ms)',
xlim=[ax1.get_xlim()[0], args.time_span + args.pre_window],
yticks=[])
ax1.xaxis.label.set_size(13)
set_plot(ax2, ['bottom'],
xlabel='time from stim. onset (ms)',
xlim=[ax2.get_xlim()[0], args.time_span + args.pre_window],
yticks=[])
ax2.xaxis.label.set_size(13)
put_list_of_figs_to_svg_fig([fig1, fig2],
fig_name=args.filename.replace('.zip', '.svg'))
ntwk.show()