# Copyright 2017. Allen Institute. All rights reserved
#
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the
# following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following
# disclaimer.
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# 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote
# products derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
import os
import glob
import nest
from six import string_types
from six import moves
from bmtk.simulator.core.simulator import Simulator
from bmtk.simulator.pointnet.config import Config
from bmtk.simulator.pointnet.io_tools import io
import bmtk.simulator.utils.simulation_reports as reports
import bmtk.simulator.utils.simulation_inputs as inputs
from bmtk.utils.reports.spike_trains import SpikeTrains
from . import modules as mods
from bmtk.simulator.core.node_sets import NodeSet
[docs]class PointSimulator(Simulator):
def __init__(self, graph, dt=0.001, overwrite=True, print_time=False):
self._tstop = 0.0 # simulation time
self._dt = dt # time step
self._output_dir = './output/' # directory where log and temporary output will be stored
self._overwrite = overwrite
self._block_run = False
self._block_size = -1
self._cells_built = False
self._internal_connections_built = False
self._graph = graph
self._external_cells = {} # dict-of-dict of external pointnet cells with keys [network_name][cell_id]
self._internal_cells = {} # dictionary of internal pointnet cells with cell_id as key
self._nest_id_map = {} # a map between NEST IDs and Node-IDs
self._spikedetector = None
self._spikes_file = None # File where all output spikes will be collected and saved
self._tmp_spikes_file = None # temporary gdf files of spike-trains
self._spike_trains_ds = {} # used to temporary store NWB datasets containing spike trains
self._spike_detector = None
self._mods = []
self._inputs = {} # Used to hold references to nest input objects (current_generators, etc)
# TODO: Make this a parameter in the config file
# TODO: Allow different inputs to have different options
self._sg_params = {'precise_times': True}
# Reset the NEST kernel for a new simualtion
# TODO: move this into it's own function and make sure it is called before network is built
nest.ResetKernel()
nest.SetKernelStatus({"resolution": self._dt, "overwrite_files": self._overwrite, "print_time": print_time})
@property
def tstart(self):
return 0.0
@property
def dt(self):
return self._dt
@property
def tstop(self):
return self._tstop
[docs] def simulation_time(self, units='ms'):
units_lc = units.lower()
time_ms = self.tstop - self.tstart
if units_lc == 'ms':
return time_ms
elif units_lc == 's':
return time_ms/1000.0
@tstop.setter
def tstop(self, val):
self._tstop = val
@property
def n_steps(self):
return int((self.tstop-self.tstart)/self.dt)
@property
def net(self):
return self._graph
@property
def gid_map(self):
return self._graph._nestid2gid
[docs] def set_spike_generator_params(self, **params):
self._sg_params = params
[docs] def get_spike_generator_params(self):
return self._sg_params
def _get_block_trial(self, duration):
"""
Compute necessary number of block trials, the length of block simulation and the simulation length of the last
block run, if necessary.
"""
if self._block_run:
data_res = self._block_size * self._dt
fn = duration / data_res
n = int(fn)
res = fn - n
else:
n = -1
res = -1
data_res = -1
return n, res, data_res
'''
def set_spikes_recordings(self):
# TODO: Pass in output-dir and file name to save to
# TODO: Allow for sorting - overwrite bionet module
self._spike_detector = nest.Create("spike_detector", 1, {'label': os.path.join(self.output_dir, 'tmp_spike_times'),
'withtime': True, 'withgid': True, 'to_file': True})
# print self._spike_detector
for pop_name, pop in self._graph._nestid2nodeid_map.items():
# print pop.keys()
nest.Connect(pop.keys(), self._spike_detector)
# exit()
'''
[docs] def run(self, tstop=None):
if tstop is None:
tstop = self._tstop
for mod in self._mods:
mod.initialize(self)
io.barrier()
io.log_info('Starting Simulation')
n, res, data_res = self._get_block_trial(tstop)
if n > 0:
for r in moves.range(n):
nest.Simulate(data_res)
if res > 0:
nest.Simulate(res * self.dt)
if n < 0:
nest.Simulate(tstop)
io.barrier()
io.log_info('Simulation finished, finalizing results.')
for mod in self._mods:
mod.finalize(self)
io.barrier()
io.log_info('Done.')
[docs] def add_mod(self, mod):
self._mods.append(mod)
[docs] @classmethod
def from_config(cls, configure, graph):
# load the json file or object
if isinstance(configure, string_types):
config = Config.from_json(configure, validate=True)
elif isinstance(configure, dict):
config = configure
else:
raise Exception('Could not convert {} (type "{}") to json.'.format(configure, type(configure)))
if 'run' not in config:
raise Exception('Json file is missing "run" entry. Unable to build Bionetwork.')
run_dict = config['run']
# Get network parameters
# step time (dt) is set in the kernel and should be passed
overwrite = run_dict['overwrite_output_dir'] if 'overwrite_output_dir' in run_dict else True
print_time = run_dict['print_time'] if 'print_time' in run_dict else False
dt = run_dict['dt'] # TODO: make sure dt exists
network = cls(graph, dt=dt, overwrite=overwrite)
if 'output_dir' in config['output']:
network.output_dir = config['output']['output_dir']
if 'block_run' in run_dict and run_dict['block_run']:
if 'block_size' not in run_dict:
raise Exception('"block_run" is set to True but "block_size" not found.')
network._block_size = run_dict['block_size']
if 'duration' in run_dict:
network.tstop = run_dict['duration']
elif 'tstop' in run_dict:
network.tstop = run_dict['tstop']
if 'precise_times' in run_dict:
network.set_spike_generator_params(precise_times=run_dict['precise_times'])
if run_dict.get('allow_offgrid_spikes', False):
network.set_spike_generator_params(allow_offgrid_spikes=True)
# Create the output-directory, or delete existing files if it already exists
graph.io.log_info('Setting up output directory')
if not os.path.exists(config['output']['output_dir']):
os.mkdir(config['output']['output_dir'])
elif overwrite:
for gfile in glob.glob(os.path.join(config['output']['output_dir'], '*.gdf')):
os.remove(gfile)
graph.io.log_info('Building cells.')
graph.build_nodes()
graph.io.log_info('Building recurrent connections')
graph.build_recurrent_edges()
for sim_input in inputs.from_config(config):
node_set = graph.get_node_set(sim_input.node_set)
if sim_input.input_type == 'spikes' and sim_input.module in ['nwb', 'csv', 'sonata']:
io.log_info('Build virtual cell stimulations for {}'.format(sim_input.name))
path = sim_input.params['input_file']
spikes = SpikeTrains.load(path=path, file_type=sim_input.module, **sim_input.params)
#spikes = spike_trains.SpikesInput.load(name=sim_input.name, module=sim_input.module,
# input_type=sim_input.input_type, params=sim_input.params)
graph.add_spike_trains(spikes, node_set, network.get_spike_generator_params())
elif sim_input.module == 'IClamp':
network.add_mod(mods.IClampMod(input_type=sim_input.input_type, **sim_input.params))
elif sim_input.module == 'ecephys_probe':
network.add_mod(mods.PointECEphysUnitsModule(name=sim_input.name, **sim_input.params))
else:
graph.io.log_warning('Unknown input type {}'.format(sim_input.input_type))
sim_reports = reports.from_config(config)
for report in sim_reports:
if report.module == 'spikes_report':
mod = mods.SpikesMod(**report.params)
elif isinstance(report, reports.MembraneReport):
# For convience and for compliance with SONATA format. "membrane_report" and "multimeter_report is the
# same in pointnet.
mod = mods.MultimeterMod(**report.params)
else:
graph.io.log_exception('Unknown report type {}'.format(report.module))
network.add_mod(mod)
io.log_info('Network created.')
return network
