ALLEN INSTITUTE

# Source code for bmtk.simulator.pointnet.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
    import glob
    import csv
    import pandas as pd

    from bmtk.utils.reports.spike_trains import SpikeTrains, sort_order, sort_order_lu
    from bmtk.simulator.pointnet.io_tools import io
    from bmtk.simulator.pointnet.nest_utils import nest_version
    from bmtk.simulator.pointnet.modules.sim_module import SimulatorMod

    import nest

    try:
        MPI_RANK = nest.Rank()
        N_HOSTS = nest.NumProcesses()

    except Exception as e:
        MPI_RANK = 0
        N_HOSTS = 1

    def create_spike_detector_nest2(label):
        return nest.Create(
            "spike_detector", 1,
            {
                'label': label,
                'withtime': True,
                'withgid': True,
                'to_file': True
            }
        )

    def create_spike_detector_nest3(label):
        return nest.Create(
            "spike_recorder", 1,
            {
                'label': label,
                'record_to': 'ascii'
            }
        )

    def read_spikes_file_nest2(spike_trains_writer, gid_map, label):
        for gdf_path in glob.glob(label + '*.gdf'):
            with open(gdf_path, 'r') as csv_file:
                csv_reader = csv.reader(csv_file, delimiter='\t')
                for r in csv_reader:
                    p = gid_map.get_pool_id(int(r[0]))
                    spike_trains_writer.add_spike(node_id=p.node_id, timestamp=float(r[1]), population=p.population)

    def read_spikes_file_nest3(spike_trains_writer, gid_map, label):
        for dat_path in glob.glob(label + '*.dat'):
            report_df = pd.read_csv(dat_path, index_col=False, sep='\t', comment='#')
            for nest_id, spikes_grp in report_df.groupby('sender'):
                gid = gid_map.get_pool_id(nest_id)
                timestamps = spikes_grp['time_ms'].values
                spike_trains_writer.add_spikes(node_ids=gid.node_id, timestamps=timestamps, population=gid.population)

    if nest_version[0] >= 3:
        create_spike_detector = create_spike_detector_nest3
        read_spikes_file = read_spikes_file_nest3
        NEST_spikes_file_format = 'dat'
    else:
        create_spike_detector = create_spike_detector_nest2
        read_spikes_file = read_spikes_file_nest2
        NEST_spikes_file_format = 'gdf'

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

        """

        def __init__(self, tmp_dir, spikes_file_csv=None, spikes_file=None, spikes_file_nwb=None, spikes_sort_order=None,
                     cache_to_disk=True, compression='gzip'):
            def _get_path(file_name):
                # Unless file-name is an absolute path then it should be placed in the $OUTPUT_DIR
                if file_name is None:
                    return None

                if os.path.isabs(file_name):
                    return file_name
                else:
                    abs_tmp = os.path.abspath(tmp_dir)
                    abs_fname = os.path.abspath(file_name)
                    if not abs_fname.startswith(abs_tmp):
                        return os.path.join(tmp_dir, file_name)
                    else:
                        return file_name

            self._csv_fname = _get_path(spikes_file_csv)
            self._h5_fname = _get_path(spikes_file)
            self._nwb_fname = _get_path(spikes_file_nwb)

            self._tmp_dir = tmp_dir
            self._tmp_file_base = 'tmp_spike_times'
            self._spike_labels = os.path.join(self._tmp_dir, self._tmp_file_base)

            # saving them to initialize SpikeTrains in initialize()
            self._tmp_dir = tmp_dir
            self._cache_to_disk = cache_to_disk
            self._compression = compression

            self._sort_order = sort_order.none if not spikes_sort_order else sort_order_lu[spikes_sort_order]

            self._spike_detector = None

        def initialize(self, sim):
            # Now this function takes care of initializing the spike_writer.
            self._spike_writer = SpikeTrains(cache_dir=self._tmp_dir, cache_to_disk=self._cache_to_disk)
            self._spike_writer.delimiter = '\t'
            self._spike_writer.gid_col = 0
            self._spike_writer.time_col = 1
            self._spike_writer.compression = self._compression

            self._spike_detector = create_spike_detector(self._spike_labels)
            nest.Connect(sim.net.gid_map.gids, self._spike_detector)

        def finalize(self, sim):
            # convert NEST gdf files into SONATA spikes/ format
            # TODO: Create a gdf_adaptor in bmtk/utils/reports/spike_trains to improve conversion speed.
            if MPI_RANK == 0:
                gid_map = sim.net.gid_map
                read_spikes_file(spike_trains_writer=self._spike_writer, gid_map=gid_map, label=self._spike_labels)
            io.barrier()

            if self._csv_fname is not None:
                self._spike_writer.to_csv(self._csv_fname, sort_order=self._sort_order)
                # io.barrier()

            if self._h5_fname is not None:
                # TODO: reimplement with pandas
                self._spike_writer.to_sonata(self._h5_fname, sort_order=self._sort_order,
                                             compression=self._spike_writer.compression)
                # io.barrier()

            if self._nwb_fname is not None:
                self._spike_writer.to_nwb(self._nwb_fname, sort_order=self._sort_order)
                # io.barrier()

            self._spike_writer.close()
            self._clean_files()

        def _clean_files(self):
            if MPI_RANK == 0:
                for nest_file in glob.glob(self._spike_labels + '*.' + NEST_spikes_file_format):
                    os.remove(nest_file)