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import os
import csv
import h5py
import numpy as np
from .core import SortOrder, csv_headers, col_population, find_conversion
from .core import MPI_rank, comm_barrier
from bmtk.utils.sonata.utils import add_hdf5_magic, add_hdf5_version
[docs]def write_sonata(path, spiketrain_reader, mode='w', sort_order=SortOrder.none, units='ms',
population_renames=None, **kwargs):
path_dir = os.path.dirname(path)
if MPI_rank == 0 and path_dir and not os.path.exists(path_dir):
os.makedirs(path_dir)
spiketrain_reader.flush()
comm_barrier()
populations = spiketrain_reader.populations
spikes_root = None
if MPI_rank == 0:
h5 = h5py.File(path, mode=mode)
add_hdf5_magic(h5)
add_hdf5_version(h5)
spikes_root = h5.create_group('/spikes') if '/spikes' not in h5 else h5['/spikes']
for pop_name in populations: # metrics.keys():
if MPI_rank == 0 and pop_name in spikes_root:
# Problem if file already contains /spikes/<pop_name> # TODO: append new data to old spikes?!?
raise ValueError('sonata file {} already contains a spikes group {}, '.format(path, pop_name) +
'skiping(use option mode="w" to overwrite)')
pop_df = spiketrain_reader.to_dataframe(populations=pop_name, with_population_col=False, sort_order=sort_order,
on_rank='root')
if MPI_rank == 0:
spikes_pop_grp = spikes_root.create_group(pop_name)
if sort_order != SortOrder.unknown:
spikes_pop_grp.attrs['sorting'] = sort_order.value
spikes_pop_grp.create_dataset('timestamps', data=pop_df['timestamps'])
spikes_pop_grp['timestamps'].attrs['units'] = spiketrain_reader.units()
spikes_pop_grp.create_dataset('node_ids', data=pop_df['node_ids'])
comm_barrier()
[docs]def write_sonata_itr(path, spiketrain_reader, mode='w', sort_order=SortOrder.none, units='ms', population_renames=None,
**kwargs):
path_dir = os.path.dirname(path)
if MPI_rank == 0 and path_dir and not os.path.exists(path_dir):
os.makedirs(path_dir)
spiketrain_reader.flush()
comm_barrier()
conv_factor = find_conversion(spiketrain_reader.units, units)
if MPI_rank == 0:
h5 = h5py.File(path, mode=mode)
add_hdf5_magic(h5)
add_hdf5_version(h5)
spikes_root = h5.create_group('/spikes') if '/spikes' not in h5 else h5['/spikes']
population_renames = population_renames or {}
for pop_name in spiketrain_reader.populations:
n_spikes = spiketrain_reader.n_spikes(pop_name)
if n_spikes <= 0:
continue
if MPI_rank == 0:
spikes_grp = spikes_root.create_group('{}'.format(population_renames.get(pop_name, pop_name)))
if sort_order != SortOrder.unknown:
spikes_grp.attrs['sorting'] = sort_order.value
timestamps_ds = spikes_grp.create_dataset('timestamps', shape=(n_spikes,), dtype=np.float64)
timestamps_ds.attrs['units'] = units
node_ids_ds = spikes_grp.create_dataset('node_ids', shape=(n_spikes,), dtype=np.uint64)
for i, spk in enumerate(spiketrain_reader.spikes(populations=pop_name, sort_order=sort_order)):
if MPI_rank == 0:
timestamps_ds[i] = spk[0]*conv_factor
node_ids_ds[i] = spk[2]
comm_barrier()
[docs]def write_csv(path, spiketrain_reader, mode='w', sort_order=SortOrder.none, include_header=True,
include_population=True, units='ms', **kwargs):
path_dir = os.path.dirname(path)
if MPI_rank == 0 and path_dir and not os.path.exists(path_dir):
os.makedirs(path_dir)
df = spiketrain_reader.to_dataframe(sort_order=sort_order, on_rank='root')
if MPI_rank == 0:
df[['timestamps', 'population', 'node_ids']].to_csv(path, header=include_header, index=False, sep=' ')
comm_barrier()
[docs]def write_csv_itr(path, spiketrain_reader, mode='w', sort_order=SortOrder.none, include_header=True,
include_population=True, units='ms', **kwargs):
path_dir = os.path.dirname(path)
if MPI_rank == 0 and path_dir and not os.path.exists(path_dir):
os.makedirs(path_dir)
conv_factor = find_conversion(spiketrain_reader.units, units)
cols_to_print = csv_headers if include_population else [c for c in csv_headers if c != col_population]
if MPI_rank == 0:
f = open(path, mode=mode)
csv_writer = csv.writer(f, delimiter=' ')
if include_header:
csv_writer.writerow(cols_to_print)
for spk in spiketrain_reader.spikes(sort_order=sort_order):
if MPI_rank == 0:
ts = spk[0]*conv_factor
c_data = [ts, spk[1], spk[2]] if include_population else [ts, spk[2]]
csv_writer.writerow(c_data)
comm_barrier()
