Source code for bmtk.simulator.pointnet.modules.spikes_inputs

# Copyright 2023. Allen Institute. All rights reserved
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import os
from bmtk.simulator.pointnet.gids import GidPool
from bmtk.simulator.pointnet.modules.sim_module import SimulatorMod
from bmtk.simulator.pointnet.io_tools import io
from bmtk.utils.reports.spike_trains import SpikeTrains
from bmtk.simulator.pointnet.pyfunction_cache import py_modules
import nest




class SpikesInputsMod(SimulatorMod):
    def __init__(self, name, input_type, module, **kwargs):
        self._name = name
        self._input_type = input_type
        self._module = module
        self._params = kwargs
        self._spike_trains = None
        self._run_counter = 0
        self._warned = False



    def initialize(self, sim):
        io.log_info('Build virtual cell stimulations for {}'.format(self._name))
        
        # if input_file is a list, then we'll load each file in the list
        if isinstance(self._params['input_file'], list):
            # if run_counter is greater than the length of the input_file list, then 
            # raise an error
            if self._run_counter >= len(self._params['input_file']):
                # raise Exception('Number of input_files is less than number of runs')
                # just warn instead of raising an exception
                if not self._warned:
                    io.log_warning('Number of input_files is less than number of runs')
                    self._warned = True
                return
            input_path = self._params['input_file'][self._run_counter]
            t_offset = nest.GetKernelStatus('biological_time')
            # reset the virtual spike map to redifine the spikes
            sim.net._virtual_ids_map = {}
            sim.net._virtual_gids = GidPool()
        else:
            input_path = self._params['input_file']
            t_offset = 0.0
        self._run_counter += 1

            
        
        node_set = sim.net.get_node_set(self._params['node_set'])
       
        if self._module == 'function':
            if 'spikes_function' not in self._params:
                io.log_exception('missing parameter "spikes_function" for input {self._name}, module {self._module}')
            spikes_generator = self._params['spikes_function']
            if spikes_generator not in py_modules.spikes_generators:
                io.log_exception(f'Could not find @spikes_generator function "{spikes_generator}" required for {self._name} inputs.')
            spikes_func = py_modules.spikes_generator(name=spikes_generator)
            
            self._spike_trains = SpikeTrains(cache_to_disk=False)
            for node in node_set.fetch_nodes():
                timestamps = spikes_func(node, sim)
                self._spike_trains.add_spikes(
                    node_ids=node.node_id, 
                    timestamps=timestamps, 
                    population=node.population_name
                )
        else:
            self._spike_trains = SpikeTrains.load(
                # path=self._params['input_file'], 
                path=input_path,
                file_type=self._module, 
                **self._params
            )

        sim.net.add_spike_trains(self._spike_trains, node_set, sim.get_spike_generator_params(), t_offset=t_offset)