diff --git a/multiarea_model/__init__.py b/multiarea_model/__init__.py
index a5d3ddec2c868de2bc7b0a3ed282930f650981a2..ede7d376c9264ea248928b906ca07314ada7202c 100644
--- a/multiarea_model/__init__.py
+++ b/multiarea_model/__init__.py
@@ -1,4 +1,6 @@
from . import default_params
+from .default_params import get_default_parameters
+from .default_params import nested_update
from .analysis import Analysis
from .simulation import Simulation
from .theory import Theory
diff --git a/multiarea_model/data_multiarea/Model.py b/multiarea_model/data_multiarea/Model.py
index 6d68fba7906b192ba3631f498599ababfcf5f8b3..661d483ff17cebe0b3a86f19bf533464ef0f8c6b 100644
--- a/multiarea_model/data_multiarea/Model.py
+++ b/multiarea_model/data_multiarea/Model.py
@@ -20,19 +20,16 @@ Sacha van Albada
import numpy as np
import json
import re
-import sys
import os
import scipy
import scipy.integrate
-import pprint
from copy import deepcopy
from nested_dict import nested_dict
from itertools import product
-from multiarea_model.default_params import network_params, nested_update
from multiarea_model.data_multiarea.VisualCortex_Data import process_raw_data
-def compute_Model_params(out_label='', mode='default'):
+def compute_Model_params(net_params, out_label='', mode='default'):
"""
Compute the parameters of the network, in particular the size
of populations, external inputs to them, and number of synapses
@@ -119,24 +116,10 @@ def compute_Model_params(out_label='', mode='default'):
overwrite default by custom values for parameters specified
in the parameter file.
"""
- net_params = deepcopy(network_params)
if mode == 'default':
prefix = 'default'
elif mode == 'custom':
prefix = 'custom_data_files/custom'
- with open(os.path.join(basepath, '.'.join(('_'.join((prefix,
- out_label,
- 'parameter_dict')),
- 'json'))), 'r') as f:
- custom_params = json.load(f)
- nested_update(net_params, custom_params)
- # print information on overwritten parameters
- print("\n")
- print("========================================")
- print("Customized parameters")
- print("--------------------")
- pprint.pprint(custom_params)
- print("========================================")
"""
Define parameter values
diff --git a/multiarea_model/default_params.py b/multiarea_model/default_params.py
index bbe72df88b54484b920e5030468c77cd076ea747..cef39e5c6a8c17b33b0a53deb29b6f227ebd8162 100644
--- a/multiarea_model/default_params.py
+++ b/multiarea_model/default_params.py
@@ -310,3 +310,10 @@ def check_custom_params(d, def_d):
def_val = def_d[key]
except KeyError:
raise KeyError('Unused key in custom parameter dictionary: {}'.format(key))
+
+def get_default_parameters():
+ parameters = {
+ 'sim_dict': sim_params,
+ 'network_dict': network_params
+ }
+ return parameters
diff --git a/multiarea_model/multiarea_model.py b/multiarea_model/multiarea_model.py
index e6766d8060724b58ef63e1e16ecffec4917c14ef..27c5b33b6896e452e35ab49b290c46dd42de6bf1 100644
--- a/multiarea_model/multiarea_model.py
+++ b/multiarea_model/multiarea_model.py
@@ -30,10 +30,8 @@ import numpy as np
import os
import pprint
import shutil
-from .default_params import complete_area_list, nested_update, network_params
-from .default_params import check_custom_params
+from .default_params import complete_area_list
from collections import OrderedDict
-from copy import deepcopy
from .data_multiarea.Model import compute_Model_params
from .analysis import Analysis
from config import base_path
@@ -61,7 +59,7 @@ dicthash.FLOOR_SMALL_FLOATS = True
class Model:
- def __init__(self, network_spec, theory=False, simulation=False,
+ def __init__(self, network_spec, theory=False, simulation=True,
analysis=False, *args, **keywords):
"""
Multiarea model class.
@@ -83,27 +81,20 @@ class Model:
whether to create an instance of the analysis class as member.
"""
- self.params = deepcopy(network_params)
+ self.params = network_spec['network_dict']
+ self.sim_spec = network_spec['sim_dict']
if isinstance(network_spec, dict):
- print("Initializing network from dictionary.")
- check_custom_params(network_spec, self.params)
- self.custom_params = network_spec
p_ = 'multiarea_model/data_multiarea/custom_data_files'
# Draw random integer label for data script to avoid clashes with
# parallelly created class instances
rand_data_label = np.random.randint(10000)
print("RAND_DATA_LABEL", rand_data_label)
- tmp_parameter_fn = os.path.join(base_path,
- p_,
- 'custom_{}_parameter_dict.json'.format(rand_data_label))
tmp_data_fn = os.path.join(base_path,
p_,
'custom_Data_Model_{}.json'.format(rand_data_label))
- with open(tmp_parameter_fn, 'w') as f:
- json.dump(self.custom_params, f)
# Execute Data script
- compute_Model_params(out_label=str(rand_data_label),
+ compute_Model_params(self.params, out_label=str(rand_data_label),
mode='custom')
else:
print("Initializing network from label.")
@@ -115,7 +106,6 @@ class Model:
'custom_Data_Model_{}.json'.format(network_spec))
with open(parameter_fn, 'r') as f:
self.custom_params = json.load(f)
- nested_update(self.params, self.custom_params)
with open(tmp_data_fn, 'r') as f:
dat = json.load(f)
@@ -175,8 +165,6 @@ class Model:
'config_files',
'custom_Data_Model_{}.json'.format(self.label))
- shutil.move(tmp_parameter_fn,
- parameter_fn)
shutil.move(tmp_data_fn,
data_fn)
@@ -191,12 +179,7 @@ class Model:
theory_spec = keywords['theory_spec']
self.init_theory(theory_spec)
- if simulation:
- if 'sim_spec' not in keywords:
- sim_spec = {}
- else:
- sim_spec = keywords['sim_spec']
- self.init_simulation(sim_spec)
+ self.init_simulation()
if analysis:
assert(getattr(self, 'simulation'))
@@ -212,8 +195,8 @@ class Model:
def connect(self):
self.simulation.connect()
- def simulate(self):
- self.simulation.simulate()
+ def simulate(self, t_sim):
+ self.simulation.simulate(t_sim)
def __str__(self):
s = "Multi-area network {} with custom parameters: \n".format(self.label)
@@ -229,8 +212,8 @@ class Model:
def init_theory(self, theory_spec):
self.theory = Theory(self, theory_spec)
- def init_simulation(self, sim_spec):
- self.simulation = Simulation(self, sim_spec)
+ def init_simulation(self):
+ self.simulation = Simulation(self, self.sim_spec)
def init_analysis(self, ana_spec):
assert(hasattr(self, 'simulation'))
diff --git a/multiarea_model/simulation.py b/multiarea_model/simulation.py
index 66f719c9b374d5d55123a5eb302eac5c4ee9c653..a0f9d25071dea0c158cd03b9d0dceef546205ccc 100644
--- a/multiarea_model/simulation.py
+++ b/multiarea_model/simulation.py
@@ -82,7 +82,7 @@ class Simulation:
self.copy_files()
print("Copied files.")
d = {'sim_params': self.custom_params,
- 'network_params': self.network.custom_params,
+ 'network_params': self.network.params,
'network_label': self.network.label}
with open(os.path.join(self.data_dir,
'_'.join(('custom_params', self.label))), 'w') as f:
@@ -91,7 +91,6 @@ class Simulation:
self.areas_simulated = self.params['areas_simulated']
self.areas_recorded = self.params['recording_dict']['areas_recorded']
- self.T = self.params['t_sim']
self.prepare()
@@ -124,8 +123,6 @@ class Simulation:
'simulation.py'),
os.path.join('multiarea_model',
'default_params.py'),
- os.path.join('config_files',
- ''.join(('custom_Data_Model_', self.network.label, '.json'))),
os.path.join('config_files',
'_'.join((self.network.label, 'config')))]
if self.network.params['connection_params']['replace_cc_input_source'] is not None:
@@ -296,11 +293,11 @@ class Simulation:
self.cortico_cortical_input()
self.save_network_gids()
- def simulate(self):
+ def simulate(self, t_sim):
"""
Simulate the network.
"""
- nest.Simulate(self.T)
+ nest.Simulate(t_sim)
def memory(self):
"""
diff --git a/run_example_downscaled.py b/run_example_downscaled.py
deleted file mode 100644
index c875439494d54634e88a82710595143f224cfacc..0000000000000000000000000000000000000000
--- a/run_example_downscaled.py
+++ /dev/null
@@ -1,46 +0,0 @@
-import numpy as np
-import os
-
-import multiarea_model as model
-from config import base_path
-
-"""
-Down-scaled model.
-Neurons and indegrees are both scaled down to 10 %.
-Can usually be simulated on a local machine.
-
-Warning: This will not yield reasonable dynamical results from the
-network and is only meant to demonstrate the simulation workflow.
-"""
-d = {}
-conn_params = {'replace_non_simulated_areas': 'het_poisson_stat',
- 'g': -11.,
- 'K_stable': 'K_stable.npy',
- 'fac_nu_ext_TH': 1.2,
- 'fac_nu_ext_5E': 1.125,
- 'fac_nu_ext_6E': 1.41666667,
- 'av_indegree_V1': 3950.}
-input_params = {'rate_ext': 10.}
-neuron_params = {'V0_mean': -150.,
- 'V0_sd': 50.}
-network_params = {'N_scaling': 0.01,
- 'K_scaling': 0.01,
- 'fullscale_rates': os.path.join(base_path, 'tests/fullscale_rates.json'),
- 'input_params': input_params,
- 'connection_params': conn_params,
- 'neuron_params': neuron_params}
-
-sim_params = {'t_sim': 2000.,
- 'num_processes': 1,
- 'local_num_threads': 1,
- 'recording_dict': {'record_vm': False}}
-
-theory_params = {'dt': 0.1}
-
-M = model.Model(network_params, simulation=True,
- sim_spec=sim_params,
- theory=True,
- theory_spec=theory_params)
-M.create()
-M.connect()
-M.simulate()
diff --git a/run_example_fullscale.py b/run_example_fullscale.py
deleted file mode 100644
index 2b70f9d03bcf6e29f8126465fbe93a5807a5b7bb..0000000000000000000000000000000000000000
--- a/run_example_fullscale.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import numpy as np
-import os
-
-from multiarea_model import Model
-from start_jobs import start_job
-from config import submit_cmd, jobscript_template
-from config import base_path
-
-"""
-Example script showing how to simulate the multi-area model
-on a cluster.
-
-We choose the same configuration as in
-Fig. 3 of Schmidt et al. (2018).
-
-"""
-
-"""
-Full model. Needs to be simulated with sufficient
-resources, for instance on a compute cluster.
-"""
-d = {}
-conn_params = {'g': -11.,
- 'K_stable': os.path.join(base_path, 'K_stable.npy'),
- 'fac_nu_ext_TH': 1.2,
- 'fac_nu_ext_5E': 1.125,
- 'fac_nu_ext_6E': 1.41666667,
- 'av_indegree_V1': 3950.}
-input_params = {'rate_ext': 10.}
-neuron_params = {'V0_mean': -150.,
- 'V0_sd': 50.}
-network_params = {'N_scaling': 1.,
- 'K_scaling': 1.,
- 'connection_params': conn_params,
- 'input_params': input_params,
- 'neuron_params': neuron_params}
-
-sim_params = {'t_sim': 2000.,
- 'num_processes': 720,
- 'local_num_threads': 1,
- 'recording_dict': {'record_vm': False}}
-
-theory_params = {'dt': 0.1}
-
-M = Model(network_params, simulation=True,
- sim_spec=sim_params,
- theory=True,
- theory_spec=theory_params)
-p, r = M.theory.integrate_siegert()
-print("Mean-field theory predicts an average "
- "rate of {0:.3f} spikes/s across all populations.".format(np.mean(r[:, -1])))
-start_job(M.simulation.label, submit_cmd, jobscript_template)
diff --git a/run_groundstate.py b/run_groundstate.py
new file mode 100644
index 0000000000000000000000000000000000000000..97037c219feacebe5cbe1b03e00cb758aea2f7cc
--- /dev/null
+++ b/run_groundstate.py
@@ -0,0 +1,76 @@
+import os
+
+import multiarea_model as model
+from config import base_path
+import time
+import nest
+
+
+time_start = time.time()
+
+conn_params = {'replace_non_simulated_areas': 'het_poisson_stat',
+ 'g': -11.,
+ 'K_stable': 'K_stable.npy',
+ 'fac_nu_ext_TH': 1.2,
+ 'fac_nu_ext_5E': 1.125,
+ 'fac_nu_ext_6E': 1.41666667,
+ 'av_indegree_V1': 3950.}
+input_params = {'rate_ext': 10.}
+neuron_params = {'V0_mean': -150.,
+ 'V0_sd': 50.}
+network_params = {'N_scaling': 0.01,
+ 'K_scaling': 0.01,
+ 'fullscale_rates': os.path.join(
+ base_path,
+ 'tests/fullscale_rates.json'
+ ),
+ 'input_params': input_params,
+ 'connection_params': conn_params,
+ 'neuron_params': neuron_params}
+
+sim_params = {'t_sim': 2000.,
+ 'num_processes': 1,
+ 'local_num_threads': 1,
+ 'recording_dict': {'record_vm': False}}
+
+parameters = model.get_default_parameters()
+new_params = {
+ 'sim_dict': sim_params,
+ 'network_dict': network_params
+ }
+model.nested_update(parameters, new_params)
+
+M = model.Model(parameters)
+
+time_model = time.time()
+
+M.create()
+
+time_create = time.time()
+
+M.connect()
+
+time_connect = time.time()
+
+M.simulate(parameters['sim_dict']['t_sim'])
+
+time_simulate = time.time()
+
+print(
+ '\nTimes of Rank {}:\n'.format(
+ nest.Rank()) +
+ ' Total time: {:.3f} s\n'.format(
+ time_simulate -
+ time_start) +
+ ' Time to initialize: {:.3f} s\n'.format(
+ time_model -
+ time_start) +
+ ' Time to create: {:.3f} s\n'.format(
+ time_create -
+ time_model) +
+ ' Time to connect: {:.3f} s\n'.format(
+ time_connect -
+ time_create) +
+ ' Time to simulate: {:.3f} s\n'.format(
+ time_simulate -
+ time_create))
diff --git a/run_metastable.py b/run_metastable.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f4e13579c95c57167fedda02c8778b9a342c16e
--- /dev/null
+++ b/run_metastable.py
@@ -0,0 +1,68 @@
+import multiarea_model as model
+import time
+import nest
+
+
+time_start = time.time()
+
+conn_params = {'g': -11.0,
+ 'fac_nu_ext_TH': 1.2,
+ 'fac_nu_ext_5E': 1.125,
+ 'fac_nu_ext_6E': 1.41666667,
+ 'av_indegree_V1': 3950.0,
+ 'K_stable': 'K_stable.npy',
+ 'cc_weights_factor': 1.9,
+ 'cc_weights_I_factor': 2.0}
+neuron_params = {'V0_mean': -150.,
+ 'V0_sd': 50.}
+network_params = {'N_scaling': .01,
+ 'K_scaling': .01,
+ 'connection_params': conn_params,
+ 'neuron_params': neuron_params}
+
+sim_params = {'t_sim': 2000.,
+ 'num_processes': 1,
+ 'local_num_threads': 1,
+ 'recording_dict': {'record_vm': False}}
+
+parameters = model.get_default_parameters()
+new_params = {
+ 'sim_dict': sim_params,
+ 'network_dict': network_params
+ }
+model.nested_update(parameters, new_params)
+
+M = model.Model(parameters)
+
+time_model = time.time()
+
+M.create()
+
+time_create = time.time()
+
+M.connect()
+
+time_connect = time.time()
+
+M.simulate(parameters['sim_dict']['t_sim'])
+
+time_simulate = time.time()
+
+print(
+ '\nTimes of Rank {}:\n'.format(
+ nest.Rank()) +
+ ' Total time: {:.3f} s\n'.format(
+ time_simulate -
+ time_start) +
+ ' Time to initialize: {:.3f} s\n'.format(
+ time_model -
+ time_start) +
+ ' Time to create: {:.3f} s\n'.format(
+ time_create -
+ time_model) +
+ ' Time to connect: {:.3f} s\n'.format(
+ time_connect -
+ time_create) +
+ ' Time to simulate: {:.3f} s\n'.format(
+ time_simulate -
+ time_create))