def batchRun():
# Create variable of type ordered dictionary (NetPyNE's customized version)
params = specs.ODict()
# fill in with parameters to explore and range of values (key has to coincide with a variable in simConfig)
#params[''] = []
#params[''] = []
#params['EEconn'] = [0.05, 0.1, 0.2]
#params['IEconn'] = [0.1, 0.2, 0.3]
#params['numCells'] = [10, 100, 500, 1000, 5000, 10000]
#params['ampIClamp2'] = [0.24, 0.25, 0.26]
#params['noisePV5'] = [True, False]
#params['PT5_noise_amp'] = [0.1, 0.25, 0.5, 0.75, 1.0]
#params['PT5_noise_std'] = [0.1, 0.25, 0.5, 0.75, 1.0]
#params['ampIClamp1'] = [0.3, 0.4, 0.5, 0.6]
#params['ampIClamp2'] = [0.3, 0.3125, 0.325, 0.375]
params['GABAAfastWeight'] = [0.01, 0.001, 0.0001, 0.00001]
params['noise'] = [False, True]
# create Batch object with paramaters to modify, and specifying files to use
b = Batch(params=params, cfgFile='cfg.py', netParamsFile='netParams.py')
# Set output folder, grid method (all param combinations), and run configuration
b.batchLabel = batchLabel
b.method = 'grid'
if runType == 'hpc_slurm':
b.saveFolder = saveFolder + b.batchLabel
b.runCfg = {
'type': 'hpc_slurm',
'allocation': allocation,
'walltime': '00:30:00',
'nodes': 4,
'coresPerNode': 48,
'email': '*****@*****.**',
'folder': runFolder,
'script': 'init.py',
'mpiCommand': 'ibrun',
'skip': True,
'custom': '#SBATCH -p skx-normal'
}
elif runType == 'mpi_bulletin':
if not os.path.isdir('data'):
os.mkdir('data')
b.saveFolder = 'data/' + b.batchLabel
b.runCfg = {'type': 'mpi_bulletin', 'script': 'init.py', 'skip': True}
else:
raise Exception(
"Problem in batch file: runType must be either 'hpc_slurm' or 'mpi_bulletin'."
)
# Run batch simulations
b.run()
def Synnmda():
# Create variable of type ordered dictionary (NetPyNE's customized version)
params = specs.ODict()
# fill in with parameters to explore and range of values (key has to coincide with a variable in simConfig)
params['gnmdad1'] = list(np.linspace(0.001, 0.05, 20))
params['gnmdad2'] = list(np.linspace(0.01, 0.02, 20))
# params['ampan01'] = [1,2,3,4,5]
# params['ampan02'] = [1,2,3,4,5]
# params['nmdan01'] = [1,2,3,4,5]
# params['nmdan02'] = [1,2,3,4,5]
# create Batch object with paramaters to modify, and specifying files to use
b = Batch(
params=params,
cfgFile='MSN_cfg.py',
netParamsFile='MSN_params.py',
)
# Set output folder, grid method (all param combinations), and run configuration
b.batchLabel = 'synNMDA'
b.saveFolder = 'MSN_data'
b.method = 'grid'
b.runCfg = {'type': 'mpi_bulletin', 'script': 'MSN_init.py', 'skip': True}
# Run batch simulations
b.run()
def custom():
params = specs.ODict()
params[('seeds', 'conn')] = [1]
# params[('EEGain')] = [0.5, 1, 2, 4]
# params[('IClamp1')] = [0.001]
b = Batch(params=params, netParamsFile='netParams.py', cfgFile='cfg.py')
return b
def custom():
params = specs.ODict()
# params[('seeds', 'conn')] = [1]
# params[('EEGain')] = [1, 2]
params[('wgkbar')] = [0.0001, 0.0002, 0.0004, 0.0008]
params[('IClamp1')] = [0.2, 0.4, 0.8, 1.6]
b = Batch(params=params, netParamsFile='netParams.py', cfgFile='cfg.py')
return b
def EEbalance(): params = specs.ODict() params["EEgain"] = [0.01, 0.1, 0.15, 0.2] params["glutAmp"] = [0.4,0.5,0.6] b = Batch(params=params, netParamsFile='netParams.py', cfgFile='cfg.py') grouped = [] # list of params that are grouped (vary together) for p in b.params: if p['label'] in grouped: p['group'] = True return b
def batchRun():
# Create variable of type ordered dictionary (NetPyNE's customized version)
params = specs.ODict()
# fill in with parameters to explore and range of values (key has to coincide with a variable in simConfig)
params['numCells'] = [10, 100, 500, 1000, 5000]
#params['EEconv'] = [1.5, 3.0, 6.0] # Default 3.0
#params['IEconv'] = [6.0, 12.0, 24.0] # Default 12.0
#params['ampIClamp1'] = [0.1, 0.5, 1.0]
#params['GABAAfastWeight'] = [0.0001, 0.01, 1.0]
#params['GABAAslowWeight'] = [0.0001, 0.001, 0.01]
# create Batch object with paramaters to modify, and specifying files to use
b = Batch(params=params, cfgFile='cfg.py', netParamsFile='netParams.py')
# Set output folder, grid method (all param combinations), and run configuration
b.batchLabel = batchLabel
b.method = 'grid'
if runType == 'hpc_slurm':
b.saveFolder = '/oasis/scratch/comet/jwgraham/temp_project/EEE_network/eee_net/' + b.batchLabel
b.runCfg = {
'type': 'hpc_slurm',
'allocation': 'csd403',
'walltime': '01:00:00',
'nodes': 4,
'coresPerNode': 24,
'email': '*****@*****.**',
'folder': '/home/jwgraham/EEE_network/eee_net/',
'script': 'init.py',
'mpiCommand': 'ibrun',
'skip': True
}
elif runType == 'mpi_bulletin':
if not os.path.isdir('batch_data'):
os.mkdir('batch_data')
b.saveFolder = 'batch_data/' + b.batchLabel
b.runCfg = {
'type': 'mpi_bulletin',
'script': 'init_norun.py',
'skip': True
}
else:
raise Exception(
"Problem in batch file: runType must be either 'hpc_slurm' or 'mpi_bulletin'."
)
# Run batch simulations
b.run()
def run_batch(experiment):
# Map params to netpyne format
params = specs.ODict()
grouped_params = []
for param in experiment["params"]:
params[param["mapsTo"]] = param["values"]
if param["inGroup"]:
grouped_params.append(param["mapsTo"])
with open("netParams.json", "r") as f:
net_params = json.load(f)
net_params = specs.NetParams(net_params["net"]["params"])
with open("simConfig.json", "r") as f:
sim_config = json.load(f)
sim_config = specs.SimConfig(sim_config["simConfig"])
batch = Batch(
cfg=sim_config,
netParams=net_params,
params=params,
groupedParams=grouped_params,
seed=experiment.get("seed", None),
)
# Label will be subfolder of saveFolder
batch.batchLabel = experiment.get("name", "batch_template_run")
# Have to overwrite the saveFolder, default goes to root folder which is not always allowed by OS
batch.saveFolder = os.getcwd()
# For now, we only support grid|list
batch.method = experiment.get("method", "grid")
# for now, we only support mpi_direct or bulletinboard
# * mpi_direct can be started by running batch.py
# * mpi_bulletin requires to run "mpiexec -n 4 nrniv -mpi batch.py", otherwise runs in single core
run_cfg = experiment.get("runCfg", None)
cores = run_cfg.get("cores", None)
cores = int(cores) if cores else None
if run_cfg:
batch.runCfg = {
"type": run_cfg.get("type", "mpi_bulletin"),
"script": run_cfg.get("script", "run.py"),
"skip": run_cfg.get("skip", True),
"cores": cores,
}
batch.run()
return batch
def batchTauWeight():
# Create an ordered dictionary to hold params (NetPyNE's customized version)
params = specs.ODict()
# Parameters and values to explore (corresponds to variable in simConfig)
params['synMechTau2'] = [3.0, 5.0, 7.0]
params['connWeight'] = [0.005, 0.01, 0.15]
run_batch('tauWeight',
params,
'tut8_cfg.py',
'tut8_netParams.py',
'tut8_init.py',
grouped=None)
def multi_params(): params = specs.ODict() #params["EEgain"] = [0.0008, 0.001, 0.0012] params["IEgain"] = [0.035, 0.04, 0.045]#[0.04, 0.05, 0.06] params["EIgain"] = [0.8, 0.9, 1.0, 1.1, 1.2]#[0.15, 0.2, 0.25] #params["IIgain"] = [0.00005, 0.0001, 0.0002]#[0.00008, 0.0001, 0.00012] #params["exc_noise_amp"] = [0.18, 0.2, 0.22] #params["inh_noise_amp"] = [0.18, 0.2, 0.22] b = Batch(params=params, netParamsFile='netParams.py', cfgFile='cfg.py')#, groupedParams=grouped) grouped = [] # list of params that are grouped (vary together) for p in b.params: if p['label'] in grouped: p['group'] = True return b
def stim_batch(): params = specs.ODict() params["glutAmp"] = [1.0, 1.5, 2.0]#, 2.0, 3.0]#, 4.0, 5.0] params["glutAmpExSynScale"] = [1.0, 1.5, 2.0]#, 2.0, 3.0]#, 4.0, 5.0] #params["synLocMiddle"] = [0.2,0.5,0.8] #params["allNaScale"] = [1.0, 1.05, 1.1, 1.15, 1.2] #params["RaScale"] = [0.6, 0.65, 0.7] params['synTime2'] = [200, 250, 300, 350, 400, 500, 600] params['glutAmp2'] = [0.0, 1.0, 3.0, 5.0, 7.0] b = Batch(params=params, netParamsFile='netParams.py', cfgFile='cfg.py')#, groupedParams=grouped) grouped = ["glutAmp","glutAmpExSynScale"] # list of params that are grouped (vary together) for p in b.params: if p['label'] in grouped: p['group'] = True return b
def batchRun():
# Create variable of type ordered dictionary (NetPyNE's customized version)
params = specs.ODict()
# fill in with parameters to explore and range of values (key has to coincide with a variable in simConfig)
#params['stimScale'] = [45.0, 90.0, 180.0]
params['noise'] = [True, False]
#params['numSynsPyrInh'] = [2, 4, 6, 8] # Default: 2
params['pyrInjectAmp'] = [0.8, 1.0, 1.2, 1.4]
# create Batch object with paramaters to modify, and specifying files to use
b = Batch(params=params, cfgFile='cfg.py', netParamsFile='netParams.py')
# Set output folder, grid method (all param combinations), and run configuration
b.batchLabel = batchLabel
b.method = 'grid'
b.saveFolder = batchSaveFolder
if runType == 'hpc_slurm':
b.runCfg = {
'type': 'hpc_slurm',
'allocation': allocation,
'walltime': '00:30:00',
'nodes': 4,
'coresPerNode': 48,
'email': '*****@*****.**',
'folder': runFolder,
'script': 'init.py',
'mpiCommand': 'ibrun',
'skip': True,
'custom': '#SBATCH -p skx-normal'
}
elif runType == 'mpi_bulletin':
b.runCfg = {'type': 'mpi_bulletin', 'script': 'init.py', 'skip': True}
else:
raise Exception(
"Problem in batch file: runType must be either 'hpc_slurm' or 'mpi_bulletin'."
)
# Run batch simulations
b.run()
def batchTauWeight():
# Create variable of type ordered dictionary (NetPyNE's customized version)
params = specs.ODict()
# fill in with parameters to explore and range of values (key has to coincide with a variable in simConfig)
params['synMechTau2'] = [3.0, 5.0, 7.0]
params['connWeight'] = [0.005, 0.01, 0.15]
# create Batch object with paramaters to modify, and specifying files to use
b = Batch(params=params, cfgFile='cfg.py', netParamsFile='netParams.py')
# Set output folder, grid method (all param combinations), and run configuration
b.batchLabel = 'tauWeight'
b.saveFolder = 'data'
b.method = 'grid'
b.runCfg = {'type': 'mpi_bulletin', 'script': 'init.py', 'skip': True}
# Run batch simulations
b.run()
def Synnmda():
# Create variable of type ordered dictionary (NetPyNE's customized version)
params = specs.ODict()
desc = np.dtype([('gnmdad1', float), ('gnmdad2', float), ('freq1', float),
('freq2', float), ('conn1', int), ('conn2', int)])
data = np.genfromtxt('curr_population.dat', dtype=desc)
# fill in with parameters to explore and range of values (key has to coincide with a variable in simConfig)
params['gnmdad1'] = list(data['gnmdad1'])
params['gnmdad2'] = list(data['gnmdad2'])
params['fred1'] = list(data['freq1'])
params['fred2'] = list(data['freq2'])
params['nmdan01'] = list(data['conn1'])
params['nmdan02'] = list(data['conn2'])
groupedParams = [
'gnmdad1', 'gnmdad2', 'fred1', 'fred2', 'nmdan01', 'nmdan02'
]
# create Batch object with paramaters to modify, and specifying files to use
b = Batch(params=params,
cfgFile='MSN_cfg.py',
netParamsFile='MSN_params.py',
groupedParams=groupedParams)
# Set output folder, grid method (all param combinations), and run configuration
b.batchLabel = 'synNMDA'
b.saveFolder = 'MSN_data'
b.method = 'grid'
b.runCfg = {
'type': 'hpc_slurm',
'allocation': 'msm110',
'script': 'MSN_init.py',
'walltime': '1:00:00',
'nodes': 1,
'coresPerNode': 24,
'mpiCommand': 'srun --mpi=pmi2',
'folder': '/home/hsong1/MSN_temp/',
'skip': True
}
# Run batch simulations
b.run()
def batchTauWeight():
# Create variable of type ordered dictionary (NetPyNE's customized version)
params = specs.ODict()
# Parameters and values to explore (corresponds to variable in simConfig)
params['synMechTau2'] = [3.0, 5.0, 7.0]
params['connWeight'] = [0.005, 0.01, 0.15]
# create Batch object with paramaters to modify, and model files to use
b = Batch(params=params,
cfgFile='tut8_cfg.py',
netParamsFile='tut8_netParams.py')
# Set output folder, optimization method, and run configuration
b.batchLabel = 'tauWeight'
b.saveFolder = 'tut8_data'
b.method = 'grid'
b.runCfg = {'type': 'mpi_bulletin', 'script': 'tut8_init.py', 'skip': True}
# Run batch simulations
b.run()
def batchTest():
# Create variable of type ordered dictionary (NetPyNE's customized version)
params = specs.ODict()
# fill in with parameters to explore and range of values (key has to coincide with a variable in simConfig)
params['glutAmp'] = [0.1, 0.2, 0.3, 0.4, 0.5, 1.0]
#params['glutAmp'] = [0.02, 0.2, 2.0]
#params['ampIClamp1'] = [0.1, 0.5, 1.0]
#params['GABAAfastWeight'] = [0.0001, 0.01, 1.0]
#params['GABAAslowWeight'] = [0.0001, 0.001, 0.01]
# create Batch object with paramaters to modify, and specifying files to use
b = Batch(params=params, cfgFile='cfg.py', netParamsFile='netParams.py')
# Set output folder, grid method (all param combinations), and run configuration
b.batchLabel = 'batch_test'
b.saveFolder = 'batch_data'
b.method = 'grid'
b.runCfg = {'type': 'mpi_bulletin', 'script': 'init.py', 'skip': True}
# Run batch simulations
b.run()
def batchRun():
# Create variable of type ordered dictionary (NetPyNE's customized version)
params = specs.ODict()
# fill in with parameters to explore and range of values (key has to coincide with a variable in simConfig)
params['PT5_exc_noise_amp'] = [0.0, 0.5, 1.0, 2.0] # Default 1.0
params['PT5_inh_noise_amp'] = [0.0, 0.5, 1.0, 2.0] # Default 1.0
#params['ampIClamp1'] = [0.1, 0.5, 1.0]
#params['GABAAfastWeight'] = [0.0001, 0.01, 1.0]
#params['GABAAslowWeight'] = [0.0001, 0.001, 0.01]
# create Batch object with paramaters to modify, and specifying files to use
b = Batch(params=params, cfgFile='cfg.py', netParamsFile='netParams.py')
# Set output folder, grid method (all param combinations), and run configuration
b.batchLabel = 'v01_batch04'
b.saveFolder = '/oasis/scratch/comet/jwgraham/temp_project/EEE_network/eee_net/' + b.batchLabel
b.method = 'grid'
b.runCfg = {
'type': 'hpc_slurm',
'allocation': 'shs100',
'walltime': '00:30:00',
'nodes': 1,
'coresPerNode': 24,
'email': '*****@*****.**',
'folder': '/home/jwgraham/EEE_network/eee_net/',
'script': 'init.py',
'mpiCommand': 'ibrun',
'skip': True
}
#b.runCfg = {'type': 'mpi_bulletin',
# 'script': 'init.py',
# 'skip': True}
# Run batch simulations
b.run()
def batchTauWeight():
# Create variable of type ordered dictionary (NetPyNE's customized version)
params = specs.ODict()
# fill in with parameters to explore and range of values (key has to coincide with a variable in simConfig)
params['synMechTau2'] = [3.0, 5.0, 7.0]
params['connWeight'] = [0.005, 0.01, 0.15]
# create Batch object with paramaters to modify, and specifying files to use
b = Batch(params=params,
cfgFile='tut8_cfg.py',
netParamsFile='tut8_netParams.py')
# Set output folder, grid method (all param combinations), and run configuration
b.batchLabel = 'tauWeight'
b.saveFolder = 'tut8_data'
b.method = 'grid'
b.runCfg = {
'type': 'hpc_slurm',
'allocation': 'shs100',
'walltime': '0:30:00',
'nodes': 1,
'coresPerNode': 24,
'email': '*****@*****.**',
'folder': '/home/jwgraham/EEE_network/batch_tut',
'script': 'tut8_init.py',
'mpiCommand': 'ibrun',
'skip': True
}
# b.runCfg = {'type': 'mpi_bulletin',
# 'script': 'tut8_init.py',
# 'skip': True}
# Run batch simulations
b.run()
def batchOptuna():
# parameters space to explore
## simple net
params = specs.ODict()
params['prob'] = [0.01, 0.5] # can add 3rd value for starting value (0)
params['weight'] = [0.001, 0.1]
params['delay'] = [1, 20]
pops = {}
pops['S'] = {'target': 5, 'width': 2, 'min': 2}
pops['M'] = {'target': 15, 'width': 2, 'min': 0.2}
# fitness function
fitnessFuncArgs = {}
fitnessFuncArgs['pops'] = pops
fitnessFuncArgs['maxFitness'] = 1000
def fitnessFunc(simData, **kwargs):
import numpy as np
pops = kwargs['pops']
maxFitness = kwargs['maxFitness']
popFitness = [None for i in pops.items()]
popFitness = [
min(np.exp(abs(v['target'] - simData['popRates'][k]) /
v['width']), maxFitness)
if simData["popRates"][k] > v['min'] else maxFitness
for k, v in pops.items()
]
fitness = np.mean(popFitness)
popInfo = '; '.join([
'%s rate=%.1f fit=%1.f' % (p, r, f)
for p, r, f in zip(list(simData['popRates'].keys()),
list(simData['popRates'].values()), popFitness)
])
print(' ' + popInfo)
return fitness
# create Batch object with paramaters to modify, and specifying files to use
b = Batch(params=params)
# Set output folder, grid method (all param combinations), and run configuration
b.batchLabel = 'simple'
b.saveFolder = './' + b.batchLabel
b.method = 'optuna'
b.runCfg = {
'type': 'mpi_direct', #'hpc_slurm',
'script': 'init.py',
# options required only for mpi_direct or hpc
'mpiCommand': 'mpiexec',
'nodes': 1,
'coresPerNode': 2,
# 'allocation': 'default',
# 'email': '*****@*****.**',
# 'reservation': None,
# 'folder': '/home/salvadord/evol'
#'custom': 'export LD_LIBRARY_PATH="$HOME/.openmpi/lib"' # only for conda users
}
b.optimCfg = {
'fitnessFunc': fitnessFunc, # fitness expression (should read simData)
'fitnessFuncArgs': fitnessFuncArgs,
'maxFitness': fitnessFuncArgs['maxFitness'],
'maxiters':
20, # Maximum number of iterations (1 iteration = 1 function evaluation)
'maxtime': 3600, # Maximum time allowed, in seconds
'maxiter_wait': 10,
'time_sleep': 5,
'popsize': 1 # unused - run with mpi
}
# Run batch simulations
b.run()
def batchASD():
# parameters space to explore
## simple net
params = specs.ODict()
params['prob'] = [0.01, 0.5,
[0.4, 0.3]] # can add 3rd value for starting value (0)
params['weight'] = [0.001, 0.1, [0.1, 0.2]]
params['delay'] = [1, 20, [5, 3]]
pops = {}
pops['S'] = {'target': 5, 'width': 2, 'min': 2}
pops['M'] = {'target': 15, 'width': 2, 'min': 0.2}
# fitness function
fitnessFuncArgs = {}
fitnessFuncArgs['pops'] = pops
fitnessFuncArgs['maxFitness'] = 1000
def fitnessFunc(simData, **kwargs):
import numpy as np
pops = kwargs['pops']
maxFitness = kwargs['maxFitness']
popFitness = [None for i in pops.items()]
popFitness = [
min(np.exp(abs(v['target'] - simData['popRates'][k]) /
v['width']), maxFitness)
if simData["popRates"][k] > v['min'] else maxFitness
for k, v in pops.items()
]
fitness = np.mean(popFitness)
popInfo = '; '.join([
'%s rate=%.1f fit=%1.f' % (p, r, f)
for p, r, f in zip(list(simData['popRates'].keys()),
list(simData['popRates'].values()), popFitness)
])
print(' ' + popInfo)
return fitness
# create Batch object with paramaters to modify, and specifying files to use
b = Batch(params=params)
# Set output folder, grid method (all param combinations), and run configuration
b.batchLabel = 'simple'
b.saveFolder = './' + b.batchLabel
b.method = 'asd'
b.runCfg = {
'type': 'mpi_direct', #'hpc_slurm',
'script': 'init.py',
# options required only for mpi_direct or hpc
'mpiCommand': 'mpiexec',
'nodes': 1,
'coresPerNode': 2,
# 'allocation': 'default',
# 'email': '*****@*****.**',
# 'reservation': None,
# 'folder': '/home/salvadord/evol'
#'custom': 'export LD_LIBRARY_PATH="$HOME/.openmpi/lib"' # only for conda users
}
b.optimCfg = {
'fitnessFunc': fitnessFunc, # fitness expression (should read simData)
'fitnessFuncArgs': fitnessFuncArgs,
'maxFitness': fitnessFuncArgs['maxFitness'],
'stepsize': 0.1, # Initial step size as a fraction of each parameter
'sinc': 2, # Step size learning rate (increase)
'sdec': 2, # Step size learning rate (decrease)
'pinc': 2, # Parameter selection learning rate (increase)
'pdec': 2, # Parameter selection learning rate (decrease)
#'pinitial': None, # Set initial parameter selection probabilities
#'sinitial': None, # Set initial step sizes; if empty, calculated from stepsize instead
'maxiters':
2, # Maximum number of iterations (1 iteration = 1 function evaluation)
'maxtime': 3600, # Maximum time allowed, in seconds
'abstol': 1e-6, # Minimum absolute change in objective function
'reltol': 1e-3, # Minimum relative change in objective function
#'stalliters': 10*len(params)*len(params), # Number of iterations over which to calculate TolFun (n = number of parameters)
#'stoppingfunc': None, # External method that can be used to stop the calculation from the outside.
#'randseed': None, # The random seed to use
'verbose': 2, # How much information to print during the run
#'label': None # A label to use to annotate the output
'maxiter_wait': 10,
'time_sleep': 5,
'popsize': 1
}
# Run batch simulations
b.run()
def batchEvol():
# parameters space to explore
## simple net
params = specs.ODict()
params['na11a'] = [0.001, 0.2]
params['na12a'] = [0.001, 0.2]
params['na13a'] = [0.001, 0.2]
params['na16a'] = [0.001, 0.2]
# fitness function
fitnessFuncArgs = {}
def fitnessFunc(simData, **kwargs):
import numpy as np
import json
with open("./data/original/NaV_0.json", "r") as f:
obs_data = json.load(f)
targetMemb_ = obs_data["simData"]["V_soma"]["cell_0"]
# # calculate MEMBRANE VOLTAGE at interesting point for comparison
sum_vol = []
for i in range(len(simData["t"])):
diff_vol_ = abs(targetMemb_[i] - simData["V_soma"]["cell_0"][i])
diff_vol = np.array(diff_vol_)
sum_vol.append(diff_vol)
fitness = sum(sum_vol) / 600
return fitness
# create Batch object with paramaters to modify, and specifying files to use
b = Batch(params=params,
cfgFile='cfg.py',
netParamsFile='netParams_SGGA_markov.py')
# Set output folder, grid method (all param combinations), and run configuration
b.batchLabel = 'na_evol'
b.saveFolder = 'data/' + b.batchLabel
b.method = 'evol'
b.runCfg = {
'type': 'mpi_bulletin', #'hpc_slurm',
'script': 'init_SG.py',
## options required only for hpc
# 'mpiCommand': 'mpirun',
# 'nodes': 1,
# 'coresPerNode': 2,
# 'allocation': 'default',
# 'email': '*****@*****.**',
# 'reservation': None,
# 'folder': '/home/salvadord/evol'
#'custom': 'export LD_LIBRARY_PATH="$HOME/.openmpi/lib"' # only for conda users
}
b.evolCfg = {
'evolAlgorithm': 'custom',
'fitnessFunc': fitnessFunc, # fitness expression (should read simData)
'fitnessFuncArgs': fitnessFuncArgs,
'pop_size': 40,
'num_elites':
1, # keep this number of parents for next generation if they are fitter than children
'mutation_rate': 0.4,
'crossover': 0.5,
'maximize': False, # maximize fitness function?
'max_generations': 100,
'time_sleep':
20, # wait this time before checking again if sim is completed (for each generation)
'maxiter_wait':
15, # 40 * 5 sec = 200sec; max number of times to check if sim is completed (for each generation)
'defaultFitness':
10000 # set fitness value in case simulation time is over
}
# Run batch simulations
b.run()
def evolCellITS4():
# parameters space to explore
params = specs.ODict()
params[('tune', 'soma', 'Ra')] = [100. * 0.5, 100 * 1.5]
params[('tune', 'soma', 'cm')] = [0.75 * 0.5, 0.75 * 1.5]
params[('tune', 'soma', 'kv', 'gbar')] = [1700.0 * 0.5, 1700.0 * 1.5]
params[('tune', 'soma', 'naz', 'gmax')] = [72000.0 * 0.5, 72000.0 * 1.5]
params[('tune', 'soma', 'pas', 'e')] = [-70 * 1.5, -70.0 * 0.5]
params[('tune', 'soma', 'pas', 'g')] = [
3.3333333333333335e-05 * 0.5, 3.3333333333333335e-05 * 1.5
]
params[('tune', 'dend',
'Ra')] = [0.02974858749381221 * 0.5, 0.02974858749381221 * 1.5]
params[('tune', 'dend', 'cm')] = [0.75 * 0.5, 0.75 * 1.5]
params[('tune', 'dend', 'Nca', 'gmax')] = [0.3 * 0.5, 0.3 * 1.5]
params[('tune', 'dend', 'kca', 'gbar')] = [3.0 * 0.5, 3.0 * 1.5]
params[('tune', 'dend', 'km', 'gbar')] = [0.1 * 0.5, 0.1 * 1.5]
params[('tune', 'dend', 'naz', 'gmax')] = [15.0 * 0.5, 15.0 * 1.5]
params[('tune', 'dend', 'pas', 'e')] = [-70 * 1.5, -70.0 * 0.5]
params[('tune', 'dend', 'pas', 'g')] = [
3.3333333333333335e-05 * 0.5, 3.3333333333333335e-05 * 1.5
]
params[('tune', 'dend1',
'Ra')] = [0.015915494309189534 * 0.5, 0.015915494309189534 * 1.5]
params[('tune', 'dend1', 'cm')] = [0.75 * 0.5, 0.75 * 1.5]
params[('tune', 'dend1', 'Nca', 'gmax')] = [0.3 * 0.5, 0.3 * 1.5]
params[('tune', 'dend1', 'kca', 'gbar')] = [3.0 * 0.5, 3.0 * 1.5]
params[('tune', 'dend1', 'km', 'gbar')] = [0.1 * 0.5, 0.1 * 1.5]
params[('tune', 'dend1', 'naz', 'gmax')] = [15.0 * 0.5, 15.0 * 1.5]
params[('tune', 'dend1', 'pas', 'e')] = [-70 * 1.5, -70.0 * 0.5]
params[('tune', 'dend1', 'pas', 'g')] = [
3.3333333333333335e-05 * 0.5, 3.3333333333333335e-05 * 1.5
]
# current injection params
amps = list(np.arange(0.0, 0.65, 0.05)) # amplitudes
times = list(np.arange(1000, 2000 * len(amps), 2000)) # start times
dur = 500 # ms
targetRates = [
0., 0., 19., 29., 37., 45., 51., 57., 63., 68., 73., 77., 81.
]
# initial cfg set up
initCfg = {} # specs.ODict()
initCfg['duration'] = 2000 * len(amps)
initCfg[('hParams', 'celsius')] = 37
initCfg['savePickle'] = True
initCfg['saveJson'] = False
initCfg['saveDataInclude'] = ['simConfig', 'netParams', 'net', 'simData']
initCfg[('IClamp1', 'pop')] = 'ITS4'
initCfg[('IClamp1', 'amp')] = amps
initCfg[('IClamp1', 'start')] = times
initCfg[('IClamp1', 'dur')] = 1000
initCfg[('analysis', 'plotfI', 'amps')] = amps
initCfg[('analysis', 'plotfI', 'times')] = times
initCfg[('analysis', 'plotfI', 'dur')] = dur
initCfg[('analysis', 'plotfI', 'targetRates')] = targetRates
for k, v in params.items():
initCfg[k] = v[0] # initialize params in cfg so they can be modified
# fitness function
fitnessFuncArgs = {}
fitnessFuncArgs['targetRates'] = targetRates
def fitnessFunc(simData, **kwargs):
targetRates = kwargs['targetRates']
diffRates = [abs(x - t) for x, t in zip(simData['fI'], targetRates)]
fitness = np.mean(diffRates)
print(' Candidate rates: ', simData['fI'])
print(' Target rates: ', targetRates)
print(' Difference: ', diffRates)
return fitness
# create Batch object with paramaters to modify, and specifying files to use
b = Batch(params=params, initCfg=initCfg)
# Set output folder, grid method (all param combinations), and run configuration
b.batchLabel = 'ITS4_evol'
b.saveFolder = 'data/' + b.batchLabel
b.method = 'evol'
b.runCfg = {
'type': 'mpi_bulletin', #'hpc_slurm',
'script': 'init.py',
# # options required only for hpc
# 'mpiCommand': 'mpirun',
# 'nodes': 1,
# 'coresPerNode': 2,
# 'allocation': 'default',
# 'email': '*****@*****.**',
# 'reservation': None,
# 'folder': '/home/salvadord/evol'
# #'custom': 'export LD_LIBRARY_PATH="$HOME/.openmpi/lib"' # only for conda users
}
b.evolCfg = {
'evolAlgorithm': 'custom',
'fitnessFunc': fitnessFunc, # fitness expression (should read simData)
'fitnessFuncArgs': fitnessFuncArgs,
'pop_size': 40,
'num_elites':
1, # keep this number of parents for next generation if they are fitter than children
'mutation_rate': 0.4,
'crossover': 0.5,
'maximize': False, # maximize fitness function?
'max_generations': 20,
'time_sleep':
5, # wait this time before checking again if sim is completed (for each generation)
'maxiter_wait':
20, # max number of times to check if sim is completed (for each generation)
'defaultFitness':
1000 # set fitness value in case simulation time is over
}
# Run batch simulations
b.run()
def evolCellNGF():
# parameters space to explore
params = specs.ODict()
params[('tune', 'soma', 'Ra')] = [14., 14.] #*0.5, 14.*1.5]
params[('tune', 'soma', 'cm')] = [1.5 * 0.5, 1.5 * 1.5]
params[('tune', 'soma', 'ch_CavL',
'gmax')] = [0.056108352 * 0.5, 0.056108352 * 1.5]
params[('tune', 'soma', 'ch_CavN',
'gmax')] = [0.00058169587 * 0.5, 0.00058169587 * 1.5]
params[('tune', 'soma', 'ch_KCaS', 'gmax')] = [0.006 * 0.5, 0.006 * 1.5]
params[('tune', 'soma', 'ch_Kdrfastngf',
'gmax')] = [0.09 * 0.5, 0.09 * 1.5]
params[('tune', 'soma', 'ch_KvAngf', 'gmax')] = [0.052 * 0.5, 0.052 * 1.5]
params[('tune', 'soma', 'ch_KvAngf', 'gml')] = [1.0 * 0.5, 1.0 * 1.5]
params[('tune', 'soma', 'ch_KvAngf', 'gmn')] = [0.6 * 0.5, 0.6 * 1.5]
params[('tune', 'soma', 'ch_KvCaB',
'gmax')] = [1.0235317e-06 * 0.5, 1.0235317e-06 * 1.5]
params[('tune', 'soma', 'ch_Navngf', 'gmax')] = [0.1 * 0.5, 0.1 * 1.5]
params[('tune', 'soma', 'hd', 'ehd')] = [-30 * 1.5, -30 * 0.5]
params[('tune', 'soma', 'hd', 'elk')] = [-70 * 1.5, -70 * 0.5]
params[('tune', 'soma', 'hd', 'gbar')] = [1e-05 * 0.5, 1e-05 * 1.5]
params[('tune', 'soma', 'hd', 'vhalfl')] = [-90 * 1.5, -90 * 0.5]
# params[('tune', 'soma', 'iconc_Ca', 'caiinf')] = [5e-06*0.5, 5e-06*1.5] # convergence issues
# params[('tune', 'soma', 'iconc_Ca', 'catau')] = [-10*1.5, -10*0.5]
params[('tune', 'soma', 'pas', 'e')] = [-85 * 1.5, -85 * 0.5]
params[('tune', 'soma', 'pas', 'g')] = [5e-7 * 0.5, 5e-7 * 1.5]
params[('tune', 'dend', 'Ra')] = [14 * 0.5, 14 * 1.5]
params[('tune', 'dend', 'cm')] = [1.5 * 0.5, 1.5 * 1.5]
params[('tune', 'dend', 'ch_Kdrfastngf',
'gmax')] = [0.03 * 0.5, 0.03 * 1.5]
params[('tune', 'dend', 'ch_Navngf',
'gmax')] = [3.7860265 * 0.5, 3.7860265 * 1.5]
params[('tune', 'dend', 'pas', 'e')] = [-67 * 1.5, -67 * 0.5]
params[('tune', 'dend', 'pas', 'g')] = [0.0003 * 0.5, 0.0003 * 1.5]
# current injection params
interval = 10000
dur = 500 # ms
durSteady = 200 # ms
amps = list(np.arange(0.04 + 0.075, 0.121 + 0.075, 0.01)) # amplitudes
times = list(np.arange(1000, (dur + interval) * len(amps),
dur + interval)) # start times
targetRatesOnset = [43., 52., 68., 80., 96., 110., 119., 131., 139.]
targetRatesSteady = [22., 24., 27., 30., 33., 35., 37., 39., 41.]
#targetRates = [(o + s) / 2 for o, s in zip(targetRatesOnset, targetRatesSteady)]
# initial cfg set up
initCfg = {} # specs.ODict()
initCfg['duration'] = (dur + interval) * len(amps)
initCfg[('hParams', 'celsius')] = 37
initCfg['savePickle'] = True
initCfg['saveJson'] = False
initCfg['saveDataInclude'] = ['simConfig', 'netParams', 'net', 'simData']
initCfg[('IClamp1', 'pop')] = 'NGF'
initCfg[('IClamp1', 'amp')] = amps
initCfg[('IClamp1', 'start')] = times
initCfg[('IClamp1', 'dur')] = dur
initCfg[('analysis', 'plotTraces', 'timeRange')] = [0, initCfg['duration']]
initCfg[('analysis', 'plotfI', 'amps')] = amps
initCfg[('analysis', 'plotfI', 'times')] = times
initCfg[('analysis', 'plotfI', 'calculateOnset')] = True
initCfg[('analysis', 'plotfI', 'dur')] = dur
initCfg[('analysis', 'plotfI', 'durSteady')] = durSteady
initCfg[('analysis', 'plotfI', 'targetRates')] = [] #
initCfg[('analysis', 'plotfI', 'targetRatesOnset')] = targetRatesOnset
initCfg[('analysis', 'plotfI', 'targetRatesSteady')] = targetRatesSteady
for k, v in params.items():
initCfg[k] = v[0] # initialize params in cfg so they can be modified
# fitness function
fitnessFuncArgs = {}
fitnessFuncArgs['targetRatesOnset'] = targetRatesOnset
fitnessFuncArgs['targetRatesSteady'] = targetRatesSteady
def fitnessFunc(simData, **kwargs):
targetRatesOnset = kwargs['targetRatesOnset']
targetRatesSteady = kwargs['targetRatesSteady']
diffRatesOnset = [
abs(x - t) for x, t in zip(simData['fI_onset'], targetRatesOnset)
]
diffRatesSteady = [
abs(x - t) for x, t in zip(simData['fI_steady'], targetRatesSteady)
]
fitness = np.mean(diffRatesOnset + diffRatesSteady)
print(' Candidate rates: ', simData['fI_onset'] + simData['fI_steady'])
print(' Target rates: ', targetRatesOnset + targetRatesSteady)
print(' Difference: ', diffRatesOnset + diffRatesSteady)
return fitness
# create Batch object with paramaters to modify, and specifying files to use
b = Batch(params=params, initCfg=initCfg)
# Set output folder, grid method (all param combinations), and run configuration
b.batchLabel = 'NGF_evol'
b.saveFolder = 'data/' + b.batchLabel
b.method = 'evol'
b.runCfg = {
'type': 'mpi_bulletin', #'hpc_slurm',
'script': 'init.py',
# # options required only for hpc
# 'mpiCommand': 'mpirun',
# 'nodes': 1,
# 'coresPerNode': 2,
# 'allocation': 'default',
# 'email': '*****@*****.**',
# 'reservation': None,
# 'folder': '/home/salvadord/evol'
# #'custom': 'export LD_LIBRARY_PATH="$HOME/.openmpi/lib"' # only for conda users
}
b.evolCfg = {
'evolAlgorithm': 'custom',
'fitnessFunc': fitnessFunc, # fitness expression (should read simData)
'fitnessFuncArgs': fitnessFuncArgs,
'pop_size': 9,
'num_elites':
1, # keep this number of parents for next generation if they are fitter than children
'mutation_rate': 0.4,
'crossover': 0.5,
'maximize': False, # maximize fitness function?
'max_generations': 10,
'time_sleep':
10, # wait this time before checking again if sim is completed (for each generation)
'maxiter_wait':
20, # max number of times to check if sim is completed (for each generation)
'defaultFitness':
1000 # set fitness value in case simulation time is over
}
# Run batch simulations
b.run()
def batchEvol(networkType):
# parameters space to explore
if networkType == 'simple':
## simple net
params = specs.ODict()
params['prob'] = [0.01, 0.5]
params['weight'] = [0.001, 0.1]
params['delay'] = [1, 20]
pops = {}
pops['S'] = {'target': 5, 'width': 2, 'min': 2}
pops['M'] = {'target': 15, 'width': 2, 'min': 0.2}
elif networkType == 'complex':
# complex net
params = specs.ODict()
params['probEall'] = [0.05, 0.2] # 0.1
params['weightEall'] = [0.0025, 0.0075] #5.0
params['probIE'] = [0.2, 0.6] #0.4
params['weightIE'] = [0.0005, 0.002]
params['probLengthConst'] = [100,200]
params['stimWeight'] = [0.05, 0.2]
pops = {}
pops['E2'] = {'target': 5, 'width': 2, 'min': 1}
pops['I2'] = {'target': 10, 'width': 5, 'min': 2}
pops['E4'] = {'target': 30, 'width': 10, 'min': 1}
pops['I4'] = {'target': 10, 'width': 3, 'min': 2}
pops['E5'] = {'target': 40, 'width': 4, 'min': 1}
pops['I5'] = {'target': 25, 'width': 5, 'min': 2}
# fitness function
fitnessFuncArgs = {}
fitnessFuncArgs['pops'] = pops
fitnessFuncArgs['maxFitness'] = 1000
def fitnessFunc(simData, **kwargs):
import numpy as np
pops = kwargs['pops']
maxFitness = kwargs['maxFitness']
popFitness = [None for i in pops.items()]
popFitness = [min(np.exp( abs(v['target'] - simData['popRates'][k]) / v['width']), maxFitness)
if simData["popRates"][k]>v['min'] else maxFitness for k,v in pops.items()]
fitness = np.mean(popFitness)
popInfo = '; '.join(['%s rate=%.1f fit=%1.f'%(p,r,f) for p,r,f in zip(list(simData['popRates'].keys()), list(simData['popRates'].values()), popFitness)])
print(' '+popInfo)
return fitness
# create Batch object with paramaters to modify, and specifying files to use
b = Batch(params=params)
# Set output folder, grid method (all param combinations), and run configuration
b.batchLabel = 'simple'
b.saveFolder = './'+b.batchLabel
b.method = 'evol'
b.runCfg = {
'type': 'mpi_bulletin',#'hpc_slurm',
'script': 'init.py',
# options required only for hpc
'mpiCommand': 'mpirun',
'nodes': 1,
'coresPerNode': 2,
'allocation': 'default',
'email': '*****@*****.**',
'reservation': None,
'folder': '/home/salvadord/evol'
#'custom': 'export LD_LIBRARY_PATH="$HOME/.openmpi/lib"' # only for conda users
}
b.evolCfg = {
'evolAlgorithm': 'custom',
'fitnessFunc': fitnessFunc, # fitness expression (should read simData)
'fitnessFuncArgs': fitnessFuncArgs,
'pop_size': 6,
'num_elites': 1, # keep this number of parents for next generation if they are fitter than children
'mutation_rate': 0.4,
'crossover': 0.5,
'maximize': False, # maximize fitness function?
'max_generations': 4,
'time_sleep': 5, # wait this time before checking again if sim is completed (for each generation)
'maxiter_wait': 40, # max number of times to check if sim is completed (for each generation)
'defaultFitness': 1000 # set fitness value in case simulation time is over
}
# Run batch simulations
b.run()
from netpyne import specs
from netpyne.batch import Batch
import numpy as np
params = specs.ODict()
params['freq'] = 1
params['npulses'] = 1
params['duration'] = 50
params['mttxs'] = [x for x in np.linspace(0.5, 1.0, 6)]
#params['mn1p8'] = [x for x in np.linspace(0.5, 1.0, 6)]
b = Batch(params=params, cfgFile='cfg.py', netParamsFile='netParams.py')
b.batchLabel = 'mttxs'
b.saveFolder = 'mttxs_batch'
b.method = 'grid'
b.runCfg = {'type': 'mpi_bulletin', 'script': 'init.py', 'skip': True}
b.run()
