ALLEN INSTITUTE

# Source code for bmtk.simulator.bionet.modules.record_spikes

    # 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.
    #
    # 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,
    # SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
    # 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
    from bmtk.simulator.bionet.modules.sim_module import SimulatorMod
    from bmtk.utils.reports.spike_trains import SpikeTrains, sort_order, sort_order_lu

    from neuron import h

    pc = h.ParallelContext()
    MPI_RANK = int(pc.id())
    N_HOSTS = int(pc.nhost())

    class SpikesMod(SimulatorMod):
        """Module use for saving spikes

        """

        def __init__(self, tmp_dir, spikes_file_csv=None, spikes_file=None, spikes_file_nwb=None, cache_to_disk=True,
                     spikes_sort_order=None, mode='a', compression='gzip'):
            # TODO: Have option to turn off caching spikes to csv.
            def _file_path(file_name):
                if file_name is None:
                    return None

                if os.path.isabs(file_name):
                    return file_name

                else:
                    rel_tmp = os.path.realpath(tmp_dir)
                    rel_fname = os.path.realpath(file_name)
                    if not rel_fname.startswith(rel_tmp):
                        return os.path.join(tmp_dir, file_name)
                    else:
                        return file_name

                # return file_name if os.path.isabs(file_name) else os.path.join(tmp_dir, file_name)

            self._csv_fname = _file_path(spikes_file_csv)
            self._save_csv = spikes_file_csv is not None

            self._h5_fname = _file_path(spikes_file)
            self._save_h5 = spikes_file is not None
            self._mode = mode

            self._nwb_fname = _file_path(spikes_file_nwb)
            self._save_nwb = spikes_file_nwb is not None

            self._tmpdir = tmp_dir
            self._sort_order = sort_order_lu.get(spikes_sort_order, sort_order.none)

            cache_name = os.path.basename(self._h5_fname or self._csv_fname or self._nwb_fname)
            self._spike_writer = SpikeTrains(cache_dir=tmp_dir, cache_name=cache_name, cache_to_disk=cache_to_disk)
            self._spike_writer.compression = compression

            self._gid_map = None

        def initialize(self, sim):
            # TODO: since it's possible that other modules may need to access spikes, set_spikes_recordings() should
            # probably be called in the simulator itself.
            sim.set_spikes_recording()
            self._gid_map = sim.net.gid_pool

        def block(self, sim, block_interval):
            # take spikes from Simulator spikes vector and save to the tmp file
            for gid, tVec in sim.spikes_table.items():
                pop_id = self._gid_map.get_pool_id(gid)
                for t in tVec:
                    self._spike_writer.add_spike(node_id=pop_id.node_id, timestamp=t, population=pop_id.population)

            pc.barrier()  # wait until all ranks have been saved
            sim.set_spikes_recording()  # reset recording vector

        def finalize(self, sim):
            # TODO: Get ride of flush/barrier calls, spike_trains should take care of it
            self._spike_writer.flush()
            pc.barrier()

            if self._save_csv:
                self._spike_writer.to_csv(self._csv_fname, sort_order=self._sort_order)
                pc.barrier()

            if self._save_h5:
                self._spike_writer.to_sonata(self._h5_fname, sort_order=self._sort_order, mode=self._mode,
                                             compression=self._spike_writer.compression)
                pc.barrier()

            if self._save_nwb:
                self._spike_writer.to_nwb(self._nwb_fname, sort_order=self._sort_order)
                pc.barrier()

            self._spike_writer.close()