'''Sets up simulation directories and parameters for NEST simulations
including LFP approximations'''
import os
import parameters as ps
import numpy as np
from nest_parameters import get_unique_id, NEST_PSET
if __name__ == '__main__':
## Add the random varying parameters
PSET = ps.ParameterSpace(NEST_PSET)
PSET['eta'] = ps.ParameterRange(np.linspace(0.8, 4.0, 9))
PSET['g'] = ps.ParameterRange(np.linspace(3.5, 8.0, 10))
PSET['J'] = ps.ParameterRange(np.linspace(0.05, 0.4, 8))
PSET['sigma_factor'] = ps.ParameterRange([1.0])
PSET['simtime'] = 3000.
PSET['tauMem_gaussian'] = True
PSET['delay_gaussian'] = False
PSET['J_gaussian'] = False
PSET['t_ref_gaussian'] = False
PSET['theta_gaussian'] = False
# set up directory structure
savefolder = os.path.join('./lfp_simulations_gaussian_taumem/')
parameterset_dest = os.path.join(savefolder, 'parameters')
log_dir = os.path.join(savefolder, 'logs')
nest_jobscript_dest = os.path.join(savefolder, 'nest_jobs')
nest_output = os.path.join(savefolder, 'nest_output')
if not os.path.isdir(savefolder):
os.mkdir(savefolder)
Returns
-------
key: str
hash key
'''
sorted_params = sort_deep_dict(paramset)
string = pickle.dumps(sorted_params)
key = hashlib.md5(string).hexdigest()
return key
PSPACES = dict()
# check scaling with MPI pool size
PSPACES['MPI'] = ps.ParameterSpace(dict())
PSPACES['MPI'].update(
dict(
# Population sizes
POP_SIZE_REF=[2400, 480],
# allow different seeds for different network iterations
GLOBALSEED=ps.ParameterRange([1234, 65135, 216579876]),
# MPI pool size
MPISIZE=ps.ParameterRange([120, 240, 480, 960, 1920, 2880]),
# bool flag switching LFP calculations on or off (faster)
COMPUTE_LFP=ps.ParameterRange([False, True]),
# population size scaling (multiplied with values in
