Python init_models Examples

Example #1

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,

Example #2

File: Fig2_bistability.py Project: jasperalbers/nest-benchmarks

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']

Example #3

    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,

Example #4

File: Fig8_interactions.py Project: jasperalbers/nest-benchmarks

        '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

Example #5

}

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')

Example #6

File: Fig4_metastability.py Project: jasperalbers/nest-benchmarks

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',

Example #7

File: Fig7_temporal_hierarchy.py Project: jasperalbers/nest-benchmarks

"""
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: