Python ParameterRange Examples

Example #1

File: set_up_parameters_distributed_taumem.py Project: CINPLA/Skaar_et_al_2020_PLoS_Comput_Biol

'''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)

Example #2

'''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, 37))
    PSET['g'] = ps.ParameterRange(np.linspace(3.5, 8.0, 37))
    PSET['J'] = ps.ParameterRange(np.linspace(0.05, 0.4, 37))

    # set up directory structure
    savefolder = os.path.join('./lfp_simulations_grid/')
    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)
    if not os.path.isdir(parameterset_dest):
        os.mkdir(parameterset_dest)
    if not os.path.isdir(log_dir):
        os.mkdir(log_dir)
    if not os.path.isdir(nest_output):
        os.mkdir(nest_output)

    print('Start parameter iteration')

Example #3

'''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['delay'] = ps.ParameterRange([1.25, 1.35, 1.45, 1.55, 1.65, 1.75])
    PSET['simtime'] = 3000.

    # set up directory structure
    savefolder = os.path.join('./lfp_simulations_varying_delays/')
    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)
    if not os.path.isdir(parameterset_dest):
        os.mkdir(parameterset_dest)
    if not os.path.isdir(log_dir):
        os.mkdir(log_dir)
    if not os.path.isdir(nest_output):
        os.mkdir(nest_output)

Example #4

'''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(
        [0.8, 0.9, 1.0, 1.1, 1.2, 1.6, 2.0, 2.4, 2.8, 3.2, 3.6, 4.0])
    PSET['g'] = ps.ParameterRange(
        [3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0])
    PSET['J'] = ps.ParameterRange([0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4])
    PSET['simtime'] = 30500.

    # set up directory structure
    savefolder = os.path.join('./heat_plot_simulations/')
    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)
    if not os.path.isdir(parameterset_dest):
        os.mkdir(parameterset_dest)
    if not os.path.isdir(log_dir):
        os.mkdir(log_dir)
    if not os.path.isdir(nest_output):

Example #5

File: example_parallel_network_parameterspace.py Project: solveignaess/LFPy

    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
        # populationParams['POP_SIZE']):
        POPSCALING=ps.ParameterRange([1.]),

        # preserve expected synapse in-degree or total number of connections
        PRESERVE=ps.ParameterRange(['indegree'])))

PSPACES['MPI5'] = ps.ParameterSpace(dict())

Example #6

File: set_up_parameters_varying_taumem.py Project: CINPLA/Skaar_et_al_2020_PLoS_Comput_Biol

'''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['tauMem'] = ps.ParameterRange(
        [5., 6., 7., 8., 9., 10., 11., 12., 13., 14.])
    PSET['simtime'] = 3000.

    # set up directory structure
    savefolder = os.path.join('./lfp_simulations_varying_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)
    if not os.path.isdir(parameterset_dest):
        os.mkdir(parameterset_dest)
    if not os.path.isdir(log_dir):
        os.mkdir(log_dir)
    if not os.path.isdir(nest_output):

Example #7

File: set_up_parameters_distributed_t_ref.py Project: CINPLA/Skaar_et_al_2020_PLoS_Comput_Biol

'''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(
        [0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0])
    PSET['simtime'] = 3000.

    PSET['tauMem_gaussian'] = False
    PSET['delay_gaussian'] = False
    PSET['J_gaussian'] = False
    PSET['t_ref_gaussian'] = True
    PSET['theta_gaussian'] = False

    # set up directory structure
    savefolder = os.path.join('./lfp_simulations_gaussian_t_ref/')
    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):

Example #8

'''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['theta'] = ps.ParameterRange(np.linspace(12, 28, 9))
    PSET['simtime'] = 3000.

    # set up directory structure
    savefolder = os.path.join('./lfp_simulations_varying_theta/')
    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)
    if not os.path.isdir(parameterset_dest):
        os.mkdir(parameterset_dest)
    if not os.path.isdir(log_dir):
        os.mkdir(log_dir)
    if not os.path.isdir(nest_output):
        os.mkdir(nest_output)