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
def make_param_dict_list():
"""
create a list of parameter dictionaries for the model network.
"""
# there is certainly a way to do this with NeuroTools.
import numpy
rates = numpy.linspace(start = 10., stop = 100., num = 5)
weights = numpy.linspace(start = 0.1, stop = 1.0, num = 5)
from NeuroTools.parameters import ParameterSet, ParameterSpace, ParameterRange
params = ParameterSpace(ParameterSet({'rate':ParameterRange(rates),
'weight': ParameterRange(weights)}))
dictlist = [p.as_dict() for p in params.iter_inner()]
return dictlist
def make_param_dict_list(N):
"""
create a list of parameter dictionaries for the model network.
"""
N_snr, N_seeds = 5, 10
from NeuroTools.parameters import ParameterSpace, ParameterRange
import numpy
params = ParameterSpace({
'N' : N,
'snr' : ParameterRange(list(numpy.linspace(0.1,2.0,N_snr))),
'kernelseed' : ParameterRange(list([12345+ k for k in range(N_seeds)]))})
dictlist = [p.as_dict() for p in params.iter_inner()]
return dictlist
def make_param_dict_list():
"""
create a list of parameter dictionaries for the model network.
"""
# there is certainly a way to do this with NeuroTools.
import numpy
rates = numpy.linspace(start=10., stop=100., num=5)
weights = numpy.linspace(start=0.1, stop=1.0, num=5)
from NeuroTools.parameters import ParameterSet, ParameterSpace, ParameterRange
params = ParameterSpace(
ParameterSet({
'rate': ParameterRange(rates),
'weight': ParameterRange(weights)
}))
dictlist = [p.as_dict() for p in params.iter_inner()]
return dictlist
def make_param_dict_list(N):
"""
create a list of parameter dictionaries for the model network.
"""
N_snr, N_seeds = 5, 10
from NeuroTools.parameters import ParameterSpace, ParameterRange
import numpy
params = ParameterSpace({
'N':
N,
'snr':
ParameterRange(list(numpy.linspace(0.1, 2.0, N_snr))),
'kernelseed':
ParameterRange(list([12345 + k for k in range(N_seeds)]))
})
dictlist = [p.as_dict() for p in params.iter_inner()]
return dictlist
def test_gridsearcher(self):
def func(param_dict):
x, y = param_dict['x'], param_dict['y']
return x * x + y * y
from NeuroTools.parameters import ParameterSpace, ParameterRange
grid = ParameterSpace({
'x': ParameterRange([-2., -1., 0., 1., 2.]),
'y': ParameterRange([-2., -1., 0., 1., 2.])
})
gs = optimizers.GridSearcher(grid, func)
retdict = gs.search()
self.failUnlessEqual(retdict['min_value'], 0.)
self.failUnlessEqual(retdict['min_params'], {'x': 0., 'y': 0.})
Having fixed the background noise we are just studying now how different
signal to noise ratios are integrated by the neurons.
Laurent Perrinet, INCM, CNRS
$ Id $
"""
import os, sys, numpy, pylab, shelve
N, N_exp = 1000, 6
t_smooth = 100. # width (in ms) of the integration window
from NeuroTools.parameters import ParameterSpace, ParameterRange
snr = 2.0 * numpy.linspace(0.1,2.0,N_exp)
p = ParameterSpace({'snr' : ParameterRange(list(snr))})
name = sys.argv[0].split('.')[0] # name of the current script withpout the '.py' part
results = shelve.open('results/mat-' + name)
try:
temporal_ON = results['temporal_ON']
temporal_OFF = results['temporal_OFF']
lower_edges = results['lower_edges']
params = results['params']
#if (params == retina.params): raise('Parameters have changed')
except:
from retina import *
retina = Retina(N)
parser.add_option("-f", "--hostfile", action="callback", callback=read_hostfile,
type=str, help="Provide a hostfile")
(options, args) = parser.parse_args()
if len(args) < 2:
parser.error("incorrect number of arguments")
test_script, url = args[:2]
script_args = args[2:]
trials = options.trials
if hasattr(options, "host_list"):
host_list = options.host_list
else:
host_list = [socket.gethostname()] # by default, run just on the current host
# iterate over the parameter space, creating a job each time
parameter_space = ParameterSpace(url)
tempfiles = []
job_manager = JobManager(host_list, delay=0, quiet=False)
for sub_parameter_space in parameter_space.iter_inner(copy=True):
for parameter_set in sub_parameter_space.realize_dists(n=trials, copy=True):
##print parameter_set.pretty()
fd, tmp_url = tempfile.mkstemp(dir=os.getcwd())
os.close(fd)
tempfiles.append(tmp_url)
parameter_set.save(tmp_url)
job_manager.run(test_script, parameter_set._url, *script_args)
# wait until all jobs have finished
job_manager.wait()
def default_parameters():
# receptive field parameters
p = ParameterSpace({})
p.Ac = 1.
p.As = 1. / 3.
p.K1 = 1.05
p.K2 = 0.7
p.c1 = 0.14
p.c2 = 0.12
p.n1 = 7.
p.n2 = 8.
p.t1 = -6. # ms
p.t2 = -6. # ms
p.td = 6.0 # time differece between ON-OFF
p.sigma_c = 0.3 #0.4 # Allen 2006 # sigma of center gauss degree
p.sigma_s = 1.5 #p.sigma_c*1.5+0.4 # Allen 2006 # sigma of surround gauss degree
# Kernel dims
# temporal
p.size = 10. # degree
p.degree_per_pixel = 0.1133
# spatial
p.dt = 1.0 # ms
p.duration = 200. # ms
return p
def default_parameters():
# receptive field parameters
p = ParameterSpace({})
p.Ac = 1.
p.As = 1./3.
p.K1 = 1.05
p.K2 = 0.7
p.c1 = 0.14
p.c2 = 0.12
p.n1 = 7.
p.n2 = 8.
p.t1 = -6. # ms
p.t2 = -6. # ms
p.td = 6.0 # time differece between ON-OFF
p.sigma_c = 0.3#0.4 # Allen 2006 # sigma of center gauss degree
p.sigma_s = 1.5#p.sigma_c*1.5+0.4 # Allen 2006 # sigma of surround gauss degree
# Kernel dims
# temporal
p.size = 10. # degree
p.degree_per_pixel = 0.1133
# spatial
p.dt = 1.0 # ms
p.duration = 200. # ms
return p
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]),
})
- all the cc's are plotted
Performed at the NeuroTools demo session, INCF booth,
SfN annual meeting 2008, Washington. DC.
"""
import numpy, pylab
import NeuroTools.stgen as stgen
sg = stgen.StGen()
from NeuroTools.parameters import ParameterSpace
from NeuroTools.parameters import ParameterRange
from NeuroTools.sandbox import make_name
# creating a ParameterSpace
p = ParameterSpace({})
# adding fixed parameters
p.nu = 20. # rate [Hz]
p.duration = 1000.
# adding ParameterRanges
p.c = ParameterRange([0.0, 0.01, 0.1, 0.5])
p.jitter = ParameterRange([
0.0,
1.0,
5.0,
])
# calculation of the ParameterSpace dimension and the labels of the parameters
# containing a range
help="Provide a hostfile")
(options, args) = parser.parse_args()
if len(args) < 2:
parser.error("incorrect number of arguments")
test_script, url = args[:2]
script_args = args[2:]
trials = options.trials
if hasattr(options, "host_list"):
host_list = options.host_list
else:
host_list = [socket.gethostname()
] # by default, run just on the current host
# iterate over the parameter space, creating a job each time
parameter_space = ParameterSpace(url)
tempfiles = []
job_manager = JobManager(host_list, delay=0, quiet=False)
for sub_parameter_space in parameter_space.iter_inner(copy=True):
for parameter_set in sub_parameter_space.realize_dists(n=trials,
copy=True):
##print parameter_set.pretty()
fd, tmp_url = tempfile.mkstemp(dir=os.getcwd())
os.close(fd)
tempfiles.append(tmp_url)
parameter_set.save(tmp_url)
job_manager.run(test_script, parameter_set._url, *script_args)
# wait until all jobs have finished
job_manager.wait()
- all the cc's are plotted
Performed at the NeuroTools demo session, INCF booth,
SfN annual meeting 2008, Washington. DC.
"""
import numpy, pylab
import NeuroTools.stgen as stgen
sg = stgen.StGen()
from NeuroTools.parameters import ParameterSpace
from NeuroTools.parameters import ParameterRange
from NeuroTools.sandbox import make_name
# creating a ParameterSpace
p = ParameterSpace({})
# adding fixed parameters
p.nu = 20. # rate [Hz]
p.duration = 1000.
# adding ParameterRanges
p.c = ParameterRange([0.0,0.01,0.1,0.5])
p.jitter = ParameterRange([0.0,1.0,5.0,])
# calculation of the ParameterSpace dimension and the labels of the parameters
# containing a range
dims, labels = p.parameter_space_dimension_labels()
print "dimensions: ", dims
print ' labels: ', labels
Having fixed the background noise we are just studying now how different
signal to noise ratios are integrated by the neurons.
Laurent Perrinet, INCM, CNRS
$ Id $
"""
import os, sys, numpy, pylab, shelve
N, N_exp = 1000, 6
t_smooth = 100. # width (in ms) of the integration window
from NeuroTools.parameters import ParameterSpace, ParameterRange
snr = 2.0 * numpy.linspace(0.1, 2.0, N_exp)
p = ParameterSpace({'snr': ParameterRange(list(snr))})
name = sys.argv[0].split('.')[
0] # name of the current script withpout the '.py' part
results = shelve.open('results/mat-' + name)
try:
temporal_ON = results['temporal_ON']
temporal_OFF = results['temporal_OFF']
lower_edges = results['lower_edges']
params = results['params']
#if (params == retina.params): raise('Parameters have changed')
except:
from retina import *
retina = Retina(N)