/
initialize_simulations.py
571 lines (431 loc) · 18.4 KB
/
initialize_simulations.py
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#!/usr/bin/env python
'''
Initialize simulations, create jobscripts, submit to the cluster queue
(using Slurm) from the login node. Adapt to Your cluster accordingly
Usage:
python initialize_simulations.py
Copyright (C) 2016 Espen Hagen
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
'''
import pylab as pl
from NeuroTools.parameters import ParameterSpace, ParameterRange
import os
from hashlib import md5
import pickle
import pprint
import time
from glob import glob
#pl.seed(1234)
##### FUNCTION DECLARATIONS ########
def check_all_crit(param, search_keys, search_values):
if (len(search_keys)>0):
if (param[search_keys[0]] == search_values[0]):
check = check_all_crit(param,\
search_keys[1::],\
search_values[1::])
return check
else:
check = False
return check
elif (len(search_keys) == 0):
check = True
return check
def search_param_list(param_list, search_dict):
search_keys = search_dict.keys()
search_values = search_dict.values()
idx_list = []
uuid_list = []
for i_param in range(len(param_list)):
if check_all_crit(param_list[i_param], search_keys, search_values):
idx_list.append(i_param)
uuid_list.append(param_list[i_param].uuid)
print "Found", len(idx_list), "simulation(s) matching your criteria"
return idx_list, uuid_list
def which(program):
def is_exe(fpath):
return os.path.isfile(fpath) and os.access(fpath, os.X_OK)
fpath, fname = os.path.split(program)
if fpath:
if is_exe(program):
return program
else:
for path in os.environ["PATH"].split(os.pathsep):
path = path.strip('"')
exe_file = os.path.join(path, program)
if is_exe(exe_file):
return exe_file
################################################################################
# Main simulation parameterspaces
################################################################################
PS = {}
PS['simres_RS'] = ParameterSpace({
'morphology' : os.path.join('morphologies', 'C120398A-P1.CNG_sansAxon.hoc'), #L4 Stellate Cell, rat
'rm' : ParameterRange([11250]),
'Ra' : ParameterRange([150]),
'cm' : ParameterRange([0.9]),
'custom_code' : ParameterRange([['add_spines.hoc']]),
'active' : ParameterRange([False]),
'v_init' : ParameterRange([-66.]), #Beierlein
'pop_params_n' : ParameterRange([4000]),
'pop_geom' : ParameterRange([[500, -250, 250]]),
'tau1' : ParameterRange([0.2]),
'tau2' : ParameterRange([2.0]), #was 2, [1.75]),
'weight' : ParameterRange([0.0004]),
'e' : ParameterRange([0]),
'spiketime' : ParameterRange([2.4]),
'disttype' : ParameterRange(['hard_sphere']), #type of synaptic distribution probability
'sigma' : ParameterRange([[165]]), #spread of distribution
'my' : ParameterRange([35]), #offset
'mean_n_syn' : ParameterRange([7]),
'section_syn' : ParameterRange([['apic', 'dend']]),
'icsd_diam' : ParameterRange([400E-6]),
'electrode_r' : ParameterRange([15]),
'simulated' : False,
'randomseed' : ParameterRange([474472279, 3495581941, 2563836960, 400995787, 3077891106, 123456789]),
})
PS['simres_P4'] = ParameterSpace({
'morphology' : ParameterRange([
os.path.join('morphologies', 'C060998B-P4.CNG_sansAxon.hoc'), # PC
os.path.join('morphologies', 'C150897B-P2.CNG_sansAxon.hoc'), # PC
os.path.join('morphologies', 'C231296A-P4B2.CNG_sansAxon.hoc'), # PC
os.path.join('morphologies', 'FLUO9-RIGHT.CNG_sansAxon.hoc'), # PC
os.path.join('morphologies', 'VD100726B-IDB.CNG_sansAxon.hoc'), # PC
]), #L4 pyramidal Cell, rat
'rm' : ParameterRange([11250]),
'Ra' : ParameterRange([150]),
'cm' : ParameterRange([0.9]),
'custom_code' : ParameterRange([['add_spines.hoc']]),
'active' : ParameterRange([False]),
'v_init' : ParameterRange([-66.]), #Beierlein
'pop_params_n' : ParameterRange([4000]),
'pop_geom' : ParameterRange([[500, -250, 250]]),
'tau1' : ParameterRange([0.2]),
'tau2' : ParameterRange([2.0]), #was 2, [1.75]),
'weight' : ParameterRange([0.0004]),
'e' : ParameterRange([0]),
'spiketime' : ParameterRange([2.4]),
'disttype' : ParameterRange(['hard_sphere']), #type of synaptic distribution probability
'sigma' : ParameterRange([[165]]), #spread of distribution
'my' : ParameterRange([35]), #offset
'mean_n_syn' : ParameterRange([7]),
'section_syn' : ParameterRange([['apic', 'dend']]),
'icsd_diam' : ParameterRange([400E-6]),
'electrode_r' : ParameterRange([15]),
'simulated' : False,
'randomseed' : 123456789,
})
PS['simres_FS'] = ParameterSpace({
'morphology' : os.path.join('morphologies', 'C120398A-I4.CNG_sansAxon.hoc'), # L4 Basket cell, rat
'rm' : ParameterRange([5625]),
'Ra' : ParameterRange([150]),
'cm' : ParameterRange([0.9]),
'custom_code' : ParameterRange([[]]),
'active' : ParameterRange([False]),
'v_init' : ParameterRange([-64.0]), #beierlein
'pop_params_n' : ParameterRange([1000]),
'pop_geom' : ParameterRange([[500, -250, 250]]),
'tau1' : ParameterRange([0.05]), #was 0.09
'tau2' : ParameterRange([0.20]), #was 0.45
'weight' : ParameterRange([0.00175,]),
'e' : ParameterRange([0]),
'spiketime' : ParameterRange([2.4]),
'disttype' : ParameterRange(['hard_sphere']), #type of synaptic distribution probability
'sigma' : ParameterRange([[165]]), #spread of distribution
'my' : ParameterRange([0]), #offset
'mean_n_syn' : ParameterRange([15]),
'section_syn' : ParameterRange(['somaproximal']),
'icsd_diam' : ParameterRange([400E-6]),
'electrode_r' : ParameterRange([15]),
'simulated' : False,
'randomseed' : ParameterRange([1817008249, 1862380098, 4050593744, 885958766, 1612109401, 123456789]),
})
PS['simres_RXXX'] = ParameterSpace({
'morphology' : os.path.join('morphologies', 'C120398A-P1.CNG_sansAxon.hoc'), #L4 Stellate Cell
'rm' : ParameterRange([11250]),
'Ra' : ParameterRange([150]),
'cm' : ParameterRange([0.9]),
'custom_code' : ParameterRange([['add_spines.hoc']]),
'active' : ParameterRange([False]),
'v_init' : ParameterRange([-66.]),
'pop_params_n' : ParameterRange([4000]),
'pop_geom' : ParameterRange([[500, -250, 250]]),
'tau1' : ParameterRange([0.2]),
'tau2' : ParameterRange([2.0]), #was 2, [1.75]),
'weight' : ParameterRange([0.0004]),
'e' : ParameterRange([0]),
'spiketime' : ParameterRange([1]),
'disttype' : ParameterRange(['hard_cyl']), #type of synaptic distribution
'sigma' : ParameterRange([
[100, 50],
[100, 100],
[100, 200],
[100, 300],
[100, 400],
# [100, 500] #
]), #spread of distrb.
'my' : ParameterRange([0]), #offset
'mean_n_syn' : ParameterRange([7]),
'section_syn' : ParameterRange([['apic', 'dend']]),
'icsd_diam' : ParameterRange([400E-6]),
'electrode_r' : ParameterRange([15]),
'simulated' : False,
'randomseed' : 123456789,
})
PS['simres_Gaussian'] = ParameterSpace({
'morphology' : os.path.join('morphologies', 'C120398A-P1.CNG_sansAxon.hoc'), #L4 Stellate Cell, rat
'rm' : ParameterRange([11250]),
'Ra' : ParameterRange([150]),
'cm' : ParameterRange([0.9]),
'custom_code' : ParameterRange([['add_spines.hoc']]),
'active' : ParameterRange([False]),
'v_init' : ParameterRange([-66.]),
'pop_params_n' : ParameterRange([4000]),
'pop_geom' : ParameterRange([[500, -250, 250]]),
'tau1' : ParameterRange([0.2]),
'tau2' : ParameterRange([2.0]), #was 2, [1.75]),
'weight' : ParameterRange([0.0004]),
'e' : ParameterRange([0]),
'spiketime' : ParameterRange([1]),
'disttype' : ParameterRange(['anisotrop']), #type of synaptic distribution
'sigma' : ParameterRange([[100, 100, 100]]), #spread of distrb.
'my' : ParameterRange([0]), #offset
'mean_n_syn' : ParameterRange([7]),
'section_syn' : ParameterRange([['apic', 'dend']]),
'electrode_r' : ParameterRange([15]),
'icsd_diam' : ParameterRange([400E-6]),
'simulated' : False,
'randomseed' : 123456789,
})
PS['simres_Cylindrical'] = ParameterSpace({
'morphology' : os.path.join('morphologies', 'C120398A-P1.CNG_sansAxon.hoc'), #L4 Stellate Cell, rat
'rm' : ParameterRange([11250]),
'Ra' : ParameterRange([150]),
'cm' : ParameterRange([0.9]),
'custom_code' : ParameterRange([['add_spines.hoc']]),
'active' : ParameterRange([False]),
'v_init' : ParameterRange([-66.]),
'pop_params_n' : ParameterRange([4000]),
'pop_geom' : ParameterRange([[500, -250, 250]]),
'tau1' : ParameterRange([0.2]),
'tau2' : ParameterRange([2.0]), #was 2, [1.75]),
'weight' : ParameterRange([0.0004]),
'e' : ParameterRange([0]),
'spiketime' : ParameterRange([1]),
'disttype' : ParameterRange(['hard_cyl']), #type of synaptic distribution
'sigma' : ParameterRange([[100, 200]]), #spread of distrb.
'my' : ParameterRange([0]), #offset
'mean_n_syn' : ParameterRange([7]),
'section_syn' : ParameterRange([['apic', 'dend']]),
'electrode_r' : ParameterRange([15]),
'icsd_diam' : ParameterRange([400E-6]),
'simulated' : False,
'randomseed' : 123456789,
})
PS['simres_Spherical'] = ParameterSpace({
'morphology' : os.path.join('morphologies', 'C120398A-P1.CNG_sansAxon.hoc'), #L4 Stellate Cell, rat
'rm' : ParameterRange([11250]),
'Ra' : ParameterRange([150]),
'cm' : ParameterRange([0.9]),
'custom_code' : ParameterRange([['add_spines.hoc']]),
'active' : ParameterRange([False]),
'v_init' : ParameterRange([-66.]),
'pop_params_n' : ParameterRange([4000]),
'pop_geom' : ParameterRange([[500, -250, 250]]),
'tau1' : ParameterRange([0.2]),
'tau2' : ParameterRange([2.0]), #was 2, [1.75]),
'weight' : ParameterRange([0.0004]),
'e' : ParameterRange([0]),
'spiketime' : ParameterRange([1]),
'disttype' : ParameterRange(['hard_sphere']), #type of synaptic distribution
'sigma' : ParameterRange([200]), #spread of distrb.
'my' : ParameterRange([0]), #offset
'mean_n_syn' : ParameterRange([7]),
'section_syn' : ParameterRange([['apic', 'dend']]),
'electrode_r' : ParameterRange([15]),
'icsd_diam' : ParameterRange([400E-6]),
'simulated' : False,
'randomseed' : 123456789,
})
PS['simres_RS_seed'] = ParameterSpace({
'morphology' : os.path.join('morphologies', 'C120398A-P1.CNG_sansAxon.hoc'), #L4 Stellate Cell, rat
'rm' : ParameterRange([11250]),
'Ra' : ParameterRange([150]),
'cm' : ParameterRange([0.9]),
'custom_code' : ParameterRange([['add_spines.hoc']]),
'active' : ParameterRange([False]),
'v_init' : ParameterRange([-66.]), #Beierlein
'pop_params_n' : ParameterRange([4000]),
'pop_geom' : ParameterRange([[500, -250, 250]]),
'tau1' : ParameterRange([0.2]),
'tau2' : ParameterRange([2.0]), #was 2, [1.75]),
'weight' : ParameterRange([0.0004]),
'e' : ParameterRange([0]),
'spiketime' : ParameterRange([2.4]),
'disttype' : ParameterRange(['hard_sphere']), #type of synaptic distribution probability
'sigma' : ParameterRange([[165]]), #spread of distribution
'my' : ParameterRange([35]), #offset
'mean_n_syn' : ParameterRange([7]),
'section_syn' : ParameterRange([['apic', 'dend']]),
'icsd_diam' : ParameterRange([400E-6]),
'electrode_r' : ParameterRange([15]),
'simulated' : False,
'randomseed' : ParameterRange([3910340497, 1164141840, 2224105389,
3706981294, 2204067231,
2782494339, 1603452434, 2825923326,
1328781694, 2662348289]),
})
PS['simres_FS_seed'] = ParameterSpace({
'morphology' : os.path.join('morphologies', 'C120398A-I4.CNG_sansAxon.hoc'), # L4 Basket cell, rat
'rm' : ParameterRange([5625]),
'Ra' : ParameterRange([150]),
'cm' : ParameterRange([0.9]),
'custom_code' : ParameterRange([[]]),
'active' : ParameterRange([False]),
'v_init' : ParameterRange([-64.0]), #beierlein
'pop_params_n' : ParameterRange([1000]),
'pop_geom' : ParameterRange([[500, -250, 250]]),
'tau1' : ParameterRange([0.05]), #was 0.09
'tau2' : ParameterRange([0.20]), #was 0.45
'weight' : ParameterRange([0.00175,]),
'e' : ParameterRange([0]),
'spiketime' : ParameterRange([2.4]),
'disttype' : ParameterRange(['hard_sphere']), #type of synaptic distribution probability
'sigma' : ParameterRange([[165]]), #spread of distribution
'my' : ParameterRange([0]), #offset
'mean_n_syn' : ParameterRange([15]),
'section_syn' : ParameterRange(['somaproximal']),
'icsd_diam' : ParameterRange([400E-6]),
'electrode_r' : ParameterRange([15]),
'simulated' : False,
'randomseed' : ParameterRange([2091789212, 2727741963, 1863543399,
1560910521, 1221154565,
1688305177, 2906496959, 3316370564,
3178327809, 3233636196]),
})
PS['simres_FS_seed_50'] = ParameterSpace({
'morphology' : os.path.join('morphologies', 'C120398A-I4.CNG_sansAxon.hoc'), # L4 Basket cell, rat
'rm' : ParameterRange([5625]),
'Ra' : ParameterRange([150]),
'cm' : ParameterRange([0.9]),
'custom_code' : ParameterRange([[]]),
'active' : ParameterRange([False]),
'v_init' : ParameterRange([-64.0]), #beierlein
'pop_params_n' : ParameterRange([500]),
'pop_geom' : ParameterRange([[500, -250, 250]]),
'tau1' : ParameterRange([0.05]), #was 0.09
'tau2' : ParameterRange([0.20]), #was 0.45
'weight' : ParameterRange([0.00175,]),
'e' : ParameterRange([0]),
'spiketime' : ParameterRange([2.4]),
'disttype' : ParameterRange(['hard_sphere']), #type of synaptic distribution probability
'sigma' : ParameterRange([[165]]), #spread of distribution
'my' : ParameterRange([0]), #offset
'mean_n_syn' : ParameterRange([15]),
'section_syn' : ParameterRange(['somaproximal']),
'icsd_diam' : ParameterRange([400E-6]),
'electrode_r' : ParameterRange([15]),
'simulated' : False,
'randomseed' : ParameterRange([2091789212, 2727741963, 1863543399,
1560910521, 1221154565,
1688305177, 2906496959, 3316370564,
3178327809, 3233636196]),
})
def get_md5(PSet):
'''get md5 hash of parameterset'''
try:
assert 'uuid' not in PSet.keys()
except AssertionError as ae:
raise ae, 'uuid already set.'
P = PSet.items()
P.sort()
m = md5()
m.update(pickle.dumps(P))
return m.hexdigest()
def get_md5s(PS_prefix):
'''return list of md5 hash keys from parameterspace prefix'''
md5s = []
PS = ParameterSpace(os.path.join('parameters', PS_prefix+'.pspace'))
for i, PSet in enumerate(PS.iter_inner()):
id = get_md5(PSet)
md5s += [id]
return md5s
###### Executed code ###########################################################
if __name__ == '__main__':
############################################################################
# REMOVING OLD SIMULATION RESULTS AND JOBSCRIPT FILES, SET UP NEW FILES
# (adapt accordingly for the computing facilities in use)
############################################################################
# if not os.path.isdir('savedata'):
# os.mkdir('savedata')
# if not os.path.isdir('parameters'):
# os.mkdir('parameters')
# delete_files = ['savedata/*', 'parameters/*', 'results_tau.txt']
# for f in delete_files:
# os.system('rm -r {}'.format(f))
#
# # Delay execution
# time.sleep(2)
# Create new parameter files:
PSet_ids = []
PSet_list = []
keys = pl.sort(PS.keys())
for i in keys:
for PSet in PS[i].iter_inner():
# create unique hash from PSet, convert dict to sorted list
id = get_md5(PSet)
if id not in PSet_ids: # avoid duplicate jobs with identical params
PSet_ids.append(id)
PSet['uuid'] = id
PSet_list.append(PSet)
url = 'parameters/{}.pset'.format(id)
PSet.save(url)
url = 'parameters/PS_{}.pspace'.format(i)
PS[i].save(url)
lwalltime = []
for i in range(len(PSet_list)):
lwalltime.append('{:02d}:{:02d}:{:02d}'.format(2, 0, 0))
pyrunscript = 'lfp_simulation.py'
jobscript = '''#!/bin/bash
##################################################################
#SBATCH --job-name monosynapseLFP
#SBATCH --time {}
#SBATCH -o logs/{}.txt
#SBATCH -e logs/{}.txt
#SBATCH -N 1 # using all cores via multiprocessing
#SBATCH --mem-per-cpu=16GB
#SBATCH --ntasks=1
#SBATCH --exclusive
##################################################################
unset DISPLAY # DISPLAY somehow problematic with Slurm
python {} {}
'''
for directory, fending in zip(['jobs', 'logs'], ['job', 'txt']):
if not os.path.isdir(directory):
os.mkdir(directory)
else:
os.system('rm {}'.format(os.path.join(directory,
'*.{}'.format(fending))))
for id in PSet_ids:
#write job scripts
f = file(os.path.join('jobs', '{}.job'.format(id)), 'w')
f.write(jobscript.format(lwalltime[i], id, id, pyrunscript, os.path.join('parameters', id+'.pset')))
f.close()
f = file('results_tau.txt', 'w')
f.write('UUID\tmorphology\tEPSP\t\tEPSC\n')
f.close()
search_dict = {}
print('search criteria: %s' % search_dict)
[idx_list, uuid_list] = search_param_list(PSet_list,search_dict)
print idx_list, uuid_list
if which('sbatch') is not None:
for id in PSet_ids[::-1]:
os.system('sbatch {}'.format(os.path.join('jobs', '{}.job'.format(id))))