def lp():
for perc in np.linspace(0.0, 0.8, 9): # now is percent block
h.gnablock_ina2005 = perc # h.perc_ina2005
sim.runSim()
sim.gatherData()
sim.saveData(include=['simData'],
filename='data/19dec06blkA_%02d' % (100 * perc))
def generate_and_run(simulation,
simulator,
network=None,
return_results=False,
base_dir=None,
target_dir=None,
num_processors=1):
"""
Generates the network in the specified simulator and runs, if appropriate
"""
if network == None:
network = load_network_json(simulation.network)
print_v("Generating network %s and running in simulator: %s..." %
(network.id, simulator))
if simulator == 'NEURON':
_generate_neuron_files_from_neuroml(network,
dir_for_mod_files=target_dir)
from neuromllite.NeuronHandler import NeuronHandler
nrn_handler = NeuronHandler()
for c in network.cells:
if c.neuroml2_source_file:
src_dir = os.path.dirname(
os.path.abspath(c.neuroml2_source_file))
nrn_handler.executeHoc('load_file("%s/%s.hoc")' %
(src_dir, c.id))
generate_network(network, nrn_handler, generate_network, base_dir)
if return_results:
raise NotImplementedError(
"Reloading results not supported in Neuron yet...")
elif simulator.lower() == 'sonata': # Will not "run" obviously...
from neuromllite.SonataHandler import SonataHandler
sonata_handler = SonataHandler()
generate_network(network,
sonata_handler,
always_include_props=True,
base_dir=base_dir)
print_v("Done with Sonata...")
elif simulator.lower().startswith('graph'): # Will not "run" obviously...
from neuromllite.GraphVizHandler import GraphVizHandler, engines
try:
if simulator[-1].isalpha():
engine = engines[simulator[-1]]
level = int(simulator[5:-1])
else:
engine = 'dot'
level = int(simulator[5:])
except Exception as e:
print_v("Error parsing: %s: %s" % (simulator, e))
print_v(
"Graphs of the network structure can be generated at many levels of detail (1-6, required) and laid out using GraphViz engines (d - dot (default); c - circo; n - neato; f - fdp), so use: -graph3c, -graph2, -graph4f etc."
)
return
handler = GraphVizHandler(level, engine=engine, nl_network=network)
generate_network(network,
handler,
always_include_props=True,
base_dir=base_dir)
print_v("Done with GraphViz...")
elif simulator.lower().startswith('matrix'): # Will not "run" obviously...
from neuromllite.MatrixHandler import MatrixHandler
try:
level = int(simulator[6:])
except:
print_v("Error parsing: %s" % simulator)
print_v(
"Matrices of the network structure can be generated at many levels of detail (1-n, required), so use: -matrix1, -matrix2, etc."
)
return
handler = MatrixHandler(level, nl_network=network)
generate_network(network,
handler,
always_include_props=True,
base_dir=base_dir)
print_v("Done with MatrixHandler...")
elif simulator.startswith('PyNN'):
#_generate_neuron_files_from_neuroml(network)
simulator_name = simulator.split('_')[1].lower()
from neuromllite.PyNNHandler import PyNNHandler
pynn_handler = PyNNHandler(simulator_name, simulation.dt, network.id)
syn_cell_params = {}
for proj in network.projections:
synapse = network.get_child(proj.synapse, 'synapses')
post_pop = network.get_child(proj.postsynaptic, 'populations')
if not post_pop.component in syn_cell_params:
syn_cell_params[post_pop.component] = {}
for p in synapse.parameters:
post = ''
if synapse.pynn_receptor_type == "excitatory":
post = '_E'
elif synapse.pynn_receptor_type == "inhibitory":
post = '_I'
syn_cell_params[post_pop.component][
'%s%s' % (p, post)] = synapse.parameters[p]
cells = {}
for c in network.cells:
if c.pynn_cell:
cell_params = {}
if c.parameters:
for p in c.parameters:
cell_params[p] = evaluate(c.parameters[p],
network.parameters)
dont_set_here = [
'tau_syn_E', 'e_rev_E', 'tau_syn_I', 'e_rev_I'
]
for d in dont_set_here:
if d in c.parameters:
raise Exception(
'Synaptic parameters like %s should be set ' +
'in individual synapses, not in the list of parameters associated with the cell'
% d)
if c.id in syn_cell_params:
cell_params.update(syn_cell_params[c.id])
print_v("Creating cell with params: %s" % cell_params)
exec('cells["%s"] = pynn_handler.sim.%s(**cell_params)' %
(c.id, c.pynn_cell))
if c.pynn_cell != 'SpikeSourcePoisson':
exec(
"cells['%s'].default_initial_values['v'] = cells['%s'].parameter_space['v_rest'].base_value"
% (c.id, c.id))
pynn_handler.set_cells(cells)
receptor_types = {}
for s in network.synapses:
if s.pynn_receptor_type:
receptor_types[s.id] = s.pynn_receptor_type
pynn_handler.set_receptor_types(receptor_types)
for input_source in network.input_sources:
if input_source.pynn_input:
pynn_handler.add_input_source(input_source)
generate_network(network,
pynn_handler,
always_include_props=True,
base_dir=base_dir)
for pid in pynn_handler.populations:
pop = pynn_handler.populations[pid]
if 'all' in simulation.recordTraces or pop.label in simulation.recordTraces:
if pop.can_record('v'):
pop.record('v')
pynn_handler.sim.run(simulation.duration)
pynn_handler.sim.end()
traces = {}
events = {}
if not 'NeuroML' in simulator:
from neo.io import PyNNTextIO
for pid in pynn_handler.populations:
pop = pynn_handler.populations[pid]
if 'all' in simulation.recordTraces or pop.label in simulation.recordTraces:
filename = "%s.%s.v.dat" % (simulation.id, pop.label)
all_columns = []
print_v("Writing data for %s to %s" %
(pop.label, filename))
for i in range(len(pop)):
if pop.can_record('v'):
ref = '%s[%i]' % (pop.label, i)
traces[ref] = []
data = pop.get_data('v', gather=False)
for segment in data.segments:
vm = segment.analogsignals[0].transpose()[i]
if len(all_columns) == 0:
tt = np.array([
t * simulation.dt / 1000.
for t in range(len(vm))
])
all_columns.append(tt)
vm_si = [float(v / 1000.) for v in vm]
traces[ref] = vm_si
all_columns.append(vm_si)
times_vm = np.array(all_columns).transpose()
np.savetxt(filename, times_vm, delimiter='\t', fmt='%s')
if return_results:
_print_result_info(traces, events)
return traces, events
elif simulator == 'NetPyNE':
if target_dir == None:
target_dir = './'
_generate_neuron_files_from_neuroml(network,
dir_for_mod_files=target_dir)
from netpyne import specs
from netpyne import sim
# Note NetPyNE from this branch is required: https://github.com/Neurosim-lab/netpyne/tree/neuroml_updates
from netpyne.conversion.neuromlFormat import NetPyNEBuilder
import pprint
pp = pprint.PrettyPrinter(depth=6)
netParams = specs.NetParams()
simConfig = specs.SimConfig()
netpyne_handler = NetPyNEBuilder(netParams,
simConfig=simConfig,
verbose=True)
generate_network(network, netpyne_handler, base_dir=base_dir)
netpyne_handler.finalise()
simConfig = specs.SimConfig()
simConfig.tstop = simulation.duration
simConfig.duration = simulation.duration
simConfig.dt = simulation.dt
simConfig.seed = simulation.seed
simConfig.recordStep = simulation.dt
simConfig.recordCells = ['all']
simConfig.recordTraces = {}
for pop in netpyne_handler.popParams.values():
if 'all' in simulation.recordTraces or pop.id in simulation.recordTraces:
for i in pop['cellsList']:
id = pop['pop']
index = i['cellLabel']
simConfig.recordTraces['v_%s_%s' % (id, index)] = {
'sec': 'soma',
'loc': 0.5,
'var': 'v',
'conds': {
'pop': id,
'cellLabel': index
}
}
simConfig.saveDat = True
print_v("NetPyNE netParams: ")
pp.pprint(netParams.todict())
#print_v("NetPyNE simConfig: ")
#pp.pprint(simConfig.todict())
sim.initialize(
netParams,
simConfig) # create network object and set cfg and net params
sim.net.createPops()
cells = sim.net.createCells(
) # instantiate network cells based on defined populations
for proj_id in netpyne_handler.projection_infos.keys():
projName, prePop, postPop, synapse, ptype = netpyne_handler.projection_infos[
proj_id]
print_v("Creating connections for %s (%s): %s->%s via %s" %
(projName, ptype, prePop, postPop, synapse))
preComp = netpyne_handler.pop_ids_vs_components[prePop]
for conn in netpyne_handler.connections[projName]:
pre_id, pre_seg, pre_fract, post_id, post_seg, post_fract, delay, weight = conn
#connParam = {'delay':delay,'weight':weight,'synsPerConn':1, 'sec':post_seg, 'loc':post_fract, 'threshold':threshold}
connParam = {
'delay': delay,
'weight': weight,
'synsPerConn': 1,
'sec': post_seg,
'loc': post_fract
}
if ptype == 'electricalProjection':
if weight != 1:
raise Exception(
'Cannot yet support inputs where weight !=1!')
connParam = {
'synsPerConn': 1,
'sec': post_seg,
'loc': post_fract,
'gapJunction': True,
'weight': weight
}
else:
connParam = {
'delay': delay,
'weight': weight,
'synsPerConn': 1,
'sec': post_seg,
'loc': post_fract
}
#'threshold': threshold}
connParam['synMech'] = synapse
if post_id in sim.net.gid2lid: # check if postsyn is in this node's list of gids
sim.net._addCellConn(connParam, pre_id, post_id)
stims = sim.net.addStims(
) # add external stimulation to cells (IClamps etc)
simData = sim.setupRecording(
) # setup variables to record for each cell (spikes, V traces, etc)
sim.runSim() # run parallel Neuron simulation
sim.gatherData() # gather spiking data and cell info from each node
sim.saveData(
) # save params, cell info and sim output to file (pickle,mat,txt,etc)
if return_results:
raise NotImplementedError(
"Reloading results not supported in NetPyNE yet...")
elif simulator == 'jNeuroML' or simulator == 'jNeuroML_NEURON' or simulator == 'jNeuroML_NetPyNE':
from pyneuroml.lems import generate_lems_file_for_neuroml
from pyneuroml import pynml
lems_file_name = 'LEMS_%s.xml' % simulation.id
nml_file_name, nml_doc = generate_neuroml2_from_network(
network, base_dir=base_dir, target_dir=target_dir)
included_files = ['PyNN.xml']
for c in network.cells:
if c.lems_source_file:
included_files.append(c.lems_source_file)
'''
if network.cells:
for c in network.cells:
included_files.append(c.neuroml2_source_file)
'''
if network.synapses:
for s in network.synapses:
if s.lems_source_file:
included_files.append(s.lems_source_file)
print_v("Generating LEMS file prior to running in %s" % simulator)
pops_plot_save = []
pops_spike_save = []
gen_plots_for_quantities = {}
gen_saves_for_quantities = {}
for p in network.populations:
if simulation.recordTraces and ('all' in simulation.recordTraces or
p.id in simulation.recordTraces):
pops_plot_save.append(p.id)
if simulation.recordSpikes and ('all' in simulation.recordSpikes or
p.id in simulation.recordSpikes):
pops_spike_save.append(p.id)
if simulation.recordRates and ('all' in simulation.recordRates
or p.id in simulation.recordRates):
size = evaluate(p.size, network.parameters)
for i in range(size):
quantity = '%s/%i/%s/r' % (p.id, i, p.component)
gen_plots_for_quantities['%s_%i_r' %
(p.id, i)] = [quantity]
gen_saves_for_quantities['%s_%i.r.dat' %
(p.id, i)] = [quantity]
if simulation.recordVariables:
for var in simulation.recordVariables:
to_rec = simulation.recordVariables[var]
if ('all' in to_rec or p.id in to_rec):
size = evaluate(p.size, network.parameters)
for i in range(size):
quantity = '%s/%i/%s/%s' % (p.id, i, p.component,
var)
gen_plots_for_quantities['%s_%i_%s' %
(p.id, i, var)] = [
quantity
]
gen_saves_for_quantities['%s_%i.%s.dat' %
(p.id, i, var)] = [
quantity
]
generate_lems_file_for_neuroml(
simulation.id,
nml_file_name,
network.id,
simulation.duration,
simulation.dt,
lems_file_name,
target_dir=target_dir if target_dir else '.',
nml_doc=
nml_doc, # Use this if the nml doc has already been loaded (to avoid delay in reload)
include_extra_files=included_files,
gen_plots_for_all_v=False,
plot_all_segments=False,
gen_plots_for_quantities=
gen_plots_for_quantities, # Dict with displays vs lists of quantity paths
gen_plots_for_only_populations=
pops_plot_save, # List of populations, all pops if = []
gen_saves_for_all_v=False,
save_all_segments=False,
gen_saves_for_only_populations=
pops_plot_save, # List of populations, all pops if = []
gen_saves_for_quantities=
gen_saves_for_quantities, # Dict with file names vs lists of quantity paths
gen_spike_saves_for_all_somas=False,
gen_spike_saves_for_only_populations=
pops_spike_save, # List of populations, all pops if = []
gen_spike_saves_for_cells=
{}, # Dict with file names vs lists of quantity paths
spike_time_format='ID_TIME',
copy_neuroml=True,
lems_file_generate_seed=12345,
report_file_name='report.%s.txt' % simulation.id,
simulation_seed=simulation.seed if simulation.seed else 12345,
verbose=True)
lems_file_name = _locate_file(lems_file_name, target_dir)
if simulator == 'jNeuroML':
results = pynml.run_lems_with_jneuroml(
lems_file_name,
nogui=True,
load_saved_data=return_results,
reload_events=return_results)
elif simulator == 'jNeuroML_NEURON':
results = pynml.run_lems_with_jneuroml_neuron(
lems_file_name,
nogui=True,
load_saved_data=return_results,
reload_events=return_results)
elif simulator == 'jNeuroML_NetPyNE':
results = pynml.run_lems_with_jneuroml_netpyne(
lems_file_name,
nogui=True,
verbose=True,
load_saved_data=return_results,
reload_events=return_results,
num_processors=num_processors)
print_v("Finished running LEMS file %s in %s (returning results: %s)" %
(lems_file_name, simulator, return_results))
if return_results:
traces, events = results
_print_result_info(traces, events)
return results # traces, events =
# create network object and set cfg and net params sim.initialize(simConfig = cfg, netParams = netParams) # instantiate network populations sim.net.createPops() # instantiate network cells based on defined populations sim.net.createCells() # create connections between cells based on params sim.net.connectCells() # add network stimulation sim.net.addStims() # setup variables to record for each cell (spikes, V traces, etc) sim.setupRecording() # run parallel Neuron simulation sim.runSim() # gather spiking data and cell info from each node sim.gatherData() # save params, cell info and sim output to file (pickle,mat,txt,etc) sim.saveData() # plot spike raster sim.analysis.plotData()
j = 0
k = 0
for x in range(0, len(sim.net.cells)):
if 'inInh' in sim.net.cells[x].tags['pop']:
sim.net.cells[x].params['interval'] = 1000 / frinh[i]
i += 1
if 'inL23exc' in sim.net.cells[x].tags['pop']:
sim.net.cells[x].params['interval'] = 1000 / frl23[j]
j += 1
if 'inL5exc' in sim.net.cells[x].tags['pop']:
sim.net.cells[x].params['interval'] = 1000 / frl5[k]
k += 1
# %%
sim.setupRecording(
) # setup variables to record for each cell (spikes, V traces, etc)
sim.runSim() # run parallel Neuron simulation
sim.gatherData() # gather spiking data and cell info from each node
sim.saveData(
) # save params, cell info and sim output to file (pickle,mat,txt,etc)
sim.analysis.plotData() # plot spike raster
#%% Save raster for each population
sim.analysis.plotRaster(include=[('L23exc'), ('inL23exc')],
spikeHist='overlay',
spikeHistBin=10,
saveData='raster_TMSall_single_L23.pkl')
sim.analysis.plotRaster(include=[('L5exc'), ('inL5exc')],
spikeHist='overlay',
spikeHistBin=10,
saveData='raster_TMSall_single_L5.pkl')
sim.analysis.plotRaster(include=[('Inh'), ('inInh')],
spikeHist='overlay',
###############################################################################
# EXECUTION CODE (via netpyne)
###############################################################################
from netpyne import sim
# Create network and run simulation
sim.initialize( # create network object and set cfg and net params
simConfig = simConfig, # pass simulation config and network params as arguments
netParams = netParams)
sim.net.createPops() # instantiate network populations
sim.net.createCells() # instantiate network cells based on defined populations
sim.net.connectCells() # create connections between cells based on params
sim.setupRecording() # setup variables to record for each cell (spikes, V traces, etc)
sim.runSim() # run parallel Neuron simulation
sim.gatherData() # gather spiking data and cell info from each node
sim.saveData() # save params, cell info and sim output to file (pickle,mat,txt,etc)
sim.analysis.plotData() # plot spike raster
# ###############################################################################
# # INTERACTING WITH INSTANTIATED NETWORK
# ###############################################################################
# modify conn weights
sim.net.modifyConns({'conds': {'label': 'hop->hop'}, 'weight': 0.5})
sim.runSim() # run parallel Neuron simulation
sim.gatherData() # gather spiking data and cell info from each node
sim.saveData() # save params, cell info and sim output to file (pickle,mat,txt,etc)
