for label in ['A', 'B', 'C']:
axes[label].yaxis.set_ticks_position('none')
"""
Load data
"""
LOAD_ORIGINAL_DATA = True
if LOAD_ORIGINAL_DATA:
label = '33fb5955558ba8bb15a3fdce49dfd914682ef3ea'
data_path = original_data_path
else:
from network_simulations import init_models
from config import data_path
models = init_models('Fig3')
label = models[0].simulation.label
"""
Create MultiAreaModel instance to have access to data structures
"""
M = MultiAreaModel({})
# spike data
spike_data = {}
for area in areas:
spike_data[area] = {}
for pop in M.structure[area]:
spike_data[area][pop] = np.load(os.path.join(data_path,
label,
axes['B2'] = panel_factory.new_empty_panel(1, 2, r'', label_position=-0.25)
axes['C'] = panel_factory.new_panel(2, 1, r'C', label_position=-0.25)
axes['C2'] = panel_factory.new_empty_panel(2, 2, r'', label_position=-0.25)
# Simulation
if LOAD_ORIGINAL_DATA:
data = {}
data_labels = [('533d73357fbe99f6178029e6054b571b485f40f6'),
('0adda4a542c3d5d43aebf7c30d876b6c5fd1d63e'),
('33fb5955558ba8bb15a3fdce49dfd914682ef3ea')]
data_path = original_data_path
else:
from network_simulations import init_models
from config import data_path
models = init_models('Fig2')
data_labels = [M.simulation.label for M in models]
keys = ['LA', 'HA', 'LA_post']
for key, label in zip(keys, data_labels):
fn = os.path.join(data_path, label, 'Analysis/pop_rates.json')
with open(fn, 'r') as f:
data[key] = json.load(f)
"""
Create MultiAreaModel instance to have access to data structures
"""
M = MultiAreaModel({})
labels = ['A', 'B', 'C']
axes[label].yaxis.set_ticks_position('none')
"""
Load data
"""
LOAD_ORIGINAL_DATA = True
if LOAD_ORIGINAL_DATA:
# use T=10500 simulation for spike raster plots
label_spikes = '3afaec94d650c637ef8419611c3f80b3cb3ff539'
# and T=100500 simulation for all other panels
label = '99c0024eacc275d13f719afd59357f7d12f02b77'
data_path = original_data_path
else:
from network_simulations import init_models
from config import data_path
models = init_models('Fig5')
label_spikes = models[0].simulation.label
label = models[1].simulation.label
"""
Create MultiAreaModel instance to have access to data structures
"""
M = MultiAreaModel({})
# spike data
spike_data = {}
for area in areas:
spike_data[area] = {}
for pop in M.structure[area]:
spike_data[area][pop] = np.load(
os.path.join(data_path, label_spikes, 'recordings',
'{}-spikes-{}-{}.npy'.format(label_spikes, area,
'380856f3b32f49c124345c08f5991090860bf9a3',
'5a7c6c2d6d48a8b687b8c6853fb4d98048681045',
'c1876856b1b2cf1346430cf14e8d6b0509914ca1',
'a30f6fba65bad6d9062e8cc51f5483baf84a46b7',
'1474e1884422b5b2096d3b7a20fd4bdf388af7e0',
'f18158895a5d682db5002489d12d27d7a974146f',
'08a3a1a88c19193b0af9d9d8f7a52344d1b17498',
'5bdd72887b191ec22a5abcc04ca4a488ea216e32',
'99c0024eacc275d13f719afd59357f7d12f02b77'
]
data_path = original_data_path
label_plot = labels[-1] # chi=1.9
else:
from network_simulations import init_models
from config import data_path
models = init_models('Fig8')
labels = [M.simulation.label for M in models]
sim_FC = {}
for label in labels:
fn = os.path.join(data_path, label, 'Analysis',
'functional_connectivity_synaptic_input.npy')
sim_FC[label] = np.load(fn)
sim_FC_bold = {}
for label in [label_plot]:
fn = os.path.join(data_path, label, 'Analysis',
'functional_connectivity_bold_signal.npy')
sim_FC_bold[label] = np.load(fn)
label = label_plot
}
LOAD_ORIGINAL_DATA = True
if LOAD_ORIGINAL_DATA:
labels = [
'33fb5955558ba8bb15a3fdce49dfd914682ef3ea',
'5bdd72887b191ec22a5abcc04ca4a488ea216e32',
'3afaec94d650c637ef8419611c3f80b3cb3ff539',
'99c0024eacc275d13f719afd59357f7d12f02b77'
]
data_path = original_data_path
else:
from network_simulations import init_models
from config import data_path
models = init_models('Fig6')
labels = [M.simulation.label for M in models]
area = 'V1'
power_spectra = {chi: {} for chi in chi_list}
for chi, label in zip(chi_list, labels):
fp = os.path.join(data_path, label, 'Analysis', 'power_spectrum_subsample')
power_spectra[chi] = {
'f': np.load(os.path.join(fp, 'power_spectrum_subsample_freq.npy')),
'power': np.load(os.path.join(fp, 'power_spectrum_subsample_V1.npy'))
}
rate_histograms = {chi: {} for chi in chi_list}
for chi, label in zip(chi_list, labels):
fp = os.path.join(data_path, label, 'Analysis', 'rate_histogram')
if LOAD_ORIGINAL_DATA:
labels = [
'33fb5955558ba8bb15a3fdce49dfd914682ef3ea',
'1474e1884422b5b2096d3b7a20fd4bdf388af7e0',
'99c0024eacc275d13f719afd59357f7d12f02b77',
'f18158895a5d682db5002489d12d27d7a974146f',
'08a3a1a88c19193b0af9d9d8f7a52344d1b17498',
'5bdd72887b191ec22a5abcc04ca4a488ea216e32'
]
label_stat_rate = '99c0024eacc275d13f719afd59357f7d12f02b77'
data_path = original_data_path
else:
from network_simulations import init_models
from config import data_path
models = init_models('Fig4')
labels = [M.simulation.label for M in models]
label_stat_rate = labels[2] # chi=1.9
rate_time_series = {label: {} for label in labels}
rate_time_series_pops = {label: {} for label in labels}
for label in labels:
for area in M.area_list:
fn = os.path.join(data_path, label, 'Analysis',
'rate_time_series_full',
'rate_time_series_full_{}.npy'.format(area))
rate_time_series[label][area] = np.load(fn)
rate_time_series_pops[label][area] = {}
for pop in M.structure[area]:
fn = os.path.join(
data_path, label, 'Analysis', 'rate_time_series_full',
"""
Load data
"""
"""
Create MultiAreaModel instance to have access to data structures
"""
M = MultiAreaModel({})
LOAD_ORIGINAL_DATA = True
if LOAD_ORIGINAL_DATA:
label = '99c0024eacc275d13f719afd59357f7d12f02b77'
data_path = original_data_path
else:
from network_simulations import init_models
from config import data_path
models = init_models('Fig7')
label = models[0].simulation.label
rate_time_series = {}
for area in M.area_list:
fn = os.path.join(data_path, label, 'Analysis', 'rate_time_series_full',
'rate_time_series_full_{}.npy'.format(area))
rate_time_series[area] = np.load(fn)
fn = os.path.join(data_path, label, 'Analysis', 'rate_time_series_full',
'rate_time_series_full_Parameters.json')
with open(fn, 'r') as f:
rate_time_series['Parameters'] = json.load(f)
cross_correlation = {}
for area in M.area_list: