'''
from toolbox.data_to_disk import pickle_save, pickle_load
# import cPickle as pickle
import sys
from toolbox.my_population import sim_group
# Necessary for pickle se
# http://stefaanlippens.net/pickleproblem
from toolbox.signal_processing import phases_diff
from toolbox.parallelization import comm, Barrier
fileName, fileOut =sys.argv[1:]
with Barrier():
if comm.rank()==0:
out=pickle_load(fileName, all_mpi=True)
else:
out=None
out=comm.bcast(out, root=0)
sim_time, args, kwargs=out
g=sim_group(sim_time, *args, **kwargs)
ss=g.get_spike_signal()
mr=ss.mean_rate()
fr=ss.firing_rate(1)
pickle_save([mr, fr], fileOut)
]
k={'fan_in':10.0}
l1=[]
l2=[]
for rule in rules:
k.update({'display':False,
'rule':rule,
'source':source.get_name(),
'target':target.get_name(),
'save':{'active':True,
'overwrite':False,
'path':path_conn+rule}})
c1=Conn('n1_n2', **k)
c1.set(surfs, display_print=False)
l1.append(c1.n)
c2=Conn('n1_n2', **k)
c2.set(surfs, display_print=False)
l2.append(c2.n)
data_to_disk.pickle_save( [l1, l2],
path+'data'+str(comm.rank()),
all_mpi=True)
Created on Sep 22, 2014
@author: mikael
'''
import numpy
import pickle
import sys
from toolbox.data_to_disk import mkdir
from toolbox.my_nest import collect_spikes_mpi
from toolbox.parallelization import comm
print sys.argv
fileName, = sys.argv[1:]
fileName += 'data'
s, e = numpy.ones(2) * comm.rank(), numpy.ones(2) * comm.rank() + 1
s, e = collect_spikes_mpi(s, e)
mkdir('/'.join(fileName.split('/')[0:-1]))
if comm.rank() == 0:
print 'File name'
print fileName
if 4 < len(fileName) and fileName[-4:] != '.pkl':
fileName = fileName + '.pkl'
f = open(fileName, 'wb') #open in binary mode
pickle.dump([s, e], f, -1)
if __name__ == '__main__':
t = 10000.0
currdir = os.getcwd()
home = default_params.HOME + '/results/unittest/test_milner/simulation'
script_name = __file__.split('/')[-1][0:-3]
if len(sys.argv) > 1:
datadir, datadir_sd = sys.argv[1:]
else:
datadir = home + '/nest'
datadir_sd = home + '/spike_dic'
data_path = '/'.join(datadir.split('/')[0:-1])
for path in [datadir, datadir_sd]:
if os.path.isdir(path):
if comm.rank() == 0:
shutil.rmtree(path)
for path in [datadir_sd + '.pkl', datadir_sd + '.svg']:
if os.path.isfile(path):
if comm.rank() == 0:
os.remove(path)
if comm.rank() == 0:
data_to_disk.mkdir(datadir)
comm.barrier()
print comm.obj
my_nest.SetKernelStatus({'data_path': datadir, 'overwrite_files': True})
'all_set-all_set',
]
k = {'fan_in': 10.0}
l1 = []
l2 = []
for rule in rules:
k.update({
'display': False,
'rule': rule,
'source': source.get_name(),
'target': target.get_name(),
'save': {
'active': True,
'overwrite': False,
'path': path_conn + rule
}
})
c1 = Conn('n1_n2', **k)
c1.set(surfs, display_print=False)
l1.append(c1.n)
c2 = Conn('n1_n2', **k)
c2.set(surfs, display_print=False)
l2.append(c2.n)
data_to_disk.pickle_save([l1, l2],
path + 'data' + str(comm.rank()),
all_mpi=True)
pp = pprint.pprint
path_out, = sys.argv[1:]
n = nest.Create('iaf_neuron', 10)
model = 'static_synapse'
delays = numpy.random.random(10 * 10) + 2
weights = numpy.random.random(10 * 10) + 1
post = []
for _id in n:
post += [_id] * 10
my_nest.Connect_DC(n * 10, post, weights, delays, model)
# nest.ConvergentConnect(n, n)
conn = nest.GetConnections(n)
stat = nest.GetStatus(conn)
time.sleep(0.2 * comm.rank())
print 'hej', comm.rank(), len(conn), len(post)
# pp(stat)
# pp([[d['source'], d['target']] for d in stat ])
# print nest.GetKernelStatus().keys()
print nest.GetKernelStatus(['num_connections'])
comm.barrier()
# my_nest.Connect_DC(n,n,weights,delays, model)
# nest_path, np,=sys.argv[1:]
# sim_group(nest_path, **{'total_num_virtual_procs':int(np)})
''' Created on Sep 19, 2014 @author: mikael ''' from toolbox import data_to_disk from toolbox.parallelization import comm import sys # print sys.argv _, path = sys.argv # print path+'data.pkl' data = data_to_disk.pickle_load(path + 'data.pkl') data_to_disk.pickle_save(data, path + 'data' + str(comm.rank()), all_mpi=True)
'''
import numpy #just to not get segmentation fault
import sys
from toolbox.data_to_disk import pickle_save, pickle_load, mkdir
from toolbox.parallelization import (comm, Barrier, map_parallel,
mockup_fun, mockup_fun_large_return_2,
mockup_fun_large_return_1)
from toolbox import misc
np_local=2
fileName, fileOut, data_path =sys.argv[1:]
with Barrier():
if comm.rank()==0:
out, mockup=pickle_load(fileName, all_mpi=True)
else:
out=None
mockup=None
out=comm.bcast(out, root=0)
mockup=comm.bcast(mockup, root=0)
print comm.rank()
with misc.Stopwatch('mpi'):
a=map_parallel(mockup, out, out, **{'local_num_threads':np_local})
pickle_save(a, fileOut)
'''
import numpy #just to not get segmentation fault
import sys
from toolbox.data_to_disk import pickle_save, pickle_load, mkdir
from toolbox.parallelization import (comm, Barrier, map_parallel, mockup_fun,
mockup_fun_large_return_2,
mockup_fun_large_return_1)
from toolbox import misc
np_local = 2
fileName, fileOut, data_path = sys.argv[1:]
with Barrier():
if comm.rank() == 0:
out, mockup = pickle_load(fileName, all_mpi=True)
else:
out = None
mockup = None
out = comm.bcast(out, root=0)
mockup = comm.bcast(mockup, root=0)
print comm.rank()
with misc.Stopwatch('mpi'):
a = map_parallel(mockup, out, out, **{'local_num_threads': np_local})
pickle_save(a, fileOut)
Created on Sep 22, 2014
@author: mikael
'''
import numpy
import pickle
import sys
from toolbox.data_to_disk import mkdir
from toolbox.my_nest import collect_spikes_mpi
from toolbox.parallelization import comm
print sys.argv
fileName, =sys.argv[1:]
fileName+='data'
s,e=numpy.ones(2)*comm.rank(),numpy.ones(2)*comm.rank()+1
s, e= collect_spikes_mpi(s, e)
mkdir('/'.join(fileName.split('/')[0:-1]))
if comm.rank()==0:
print 'File name'
print fileName
if 4<len(fileName) and fileName[-4:]!='.pkl':
fileName=fileName+'.pkl'
f=open(fileName, 'wb') #open in binary mode
path_out,=sys.argv[1:]
n=nest.Create('iaf_neuron', 10)
model='static_synapse'
delays=numpy.random.random(10*10)+2
weights=numpy.random.random(10*10)+1
post=[]
for _id in n:
post+=[_id]*10
my_nest.Connect_DC(n*10, post,weights, delays, model)
# nest.ConvergentConnect(n, n)
conn=nest.GetConnections(n)
stat=nest.GetStatus(conn)
time.sleep(0.2*comm.rank())
print 'hej', comm.rank(), len(conn), len(post)
# pp(stat)
# pp([[d['source'], d['target']] for d in stat ])
# print nest.GetKernelStatus().keys()
print nest.GetKernelStatus(['num_connections'])
comm.barrier()
# my_nest.Connect_DC(n,n,weights,delays, model)
# nest_path, np,=sys.argv[1:]
# sim_group(nest_path, **{'total_num_virtual_procs':int(np)})
