Neurodamus.io Sub-Modules

neurodamus.io.cell_readers

Collection of Cell Readers from different sources (Pure HDF5, SynTool…)

Functions

load_sonata(circuit_conf, all_gids[, ...])

A reader supporting additional dynamic properties from Sonata files.

split_round_robin(all_gids[, stride, ...])

Splits a numpy ndarray[uint32] round-robin.

Exceptions

neurodamus.io.cell_readers.dry_run_distribution(gid_metype_bundle, stride=1, stride_offset=0)

Distribute gid in metype bundles for dry run.

The principle is the following: all gids with the same metype have to be assigned to the same rank. This function receives a list of list of gids, each sublist containing gids of the same metype. The gid_metype_bundle list of lists is generated by the retrieve_unique_metype function. This function performs a round robin distribution of the inner lists, i.e. it returns a list of gids that are sequentially in the same metype. The return is a flattened numpy array of gids that shall be instantiated on the same rank.

Example

gid_metype_bundle = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10]]

stride = 2 stride_offset = 0 return = [1, 2, 3, 7, 8, 9]

stride = 2 stride_offset = 1 return = [4, 5, 6, 10]

Parameters:

gid_metype_bundle – list of lists of gids to be distributed

Returns:

A numpy array of gids that are sequentially in the same metype

neurodamus.io.cell_readers.load_sonata(circuit_conf, all_gids, stride=1, stride_offset=0, *, node_population, load_dynamic_props=(), has_extra_data=False, dry_run_stats=None, load_mode=None)

A reader supporting additional dynamic properties from Sonata files.

neurodamus.io.cell_readers.split_round_robin(all_gids, stride=1, stride_offset=0, total_cells=None)

Splits a numpy ndarray[uint32] round-robin. Assumes gids are 0-based. If the array is None generates new arrays based on the nr of total cells

neurodamus.io.synapse_reader

Module implementing interfaces to the several synapse readers (eg.: synapsetool, Hdf5Reader)

Classes

SynapseParameters()

Synapse parameter names and dtypes for numpy recarrays following the SONATA specification.

SonataReader(edge_file[, population])

Reader for SONATA edge files.

Exceptions

class neurodamus.io.synapse_reader.SonataReader(edge_file, population=None, *_, **kw)

Reader for SONATA edge files.

Uses libsonata. It will read each attribute for multiple GIDs at once and cache read data in a columnar fashion.

FIXME Remove the caching at the np.recarray level.

EMPTY_DATA = {}
LOOKUP_BY_TARGET_IDS = True
Parameters

alias of SynapseParameters

SYNAPSE_INDEX_NAMES = ('synapse_index',)
configure_override(mod_override)
custom_parameters = {'ipt', 'isec', 'offset'}

Custom parameters are skipped from direct loading and trigger _load_params_custom()

get_conn_counts(tgids)

Count synapses per source→target connection for given target gids.

Parameters:

  • tgids (array-like of int) – One or more target neuron gids to query.

  • Returns

  • -------

  • dict[int – Mapping target gid -> dict mapping source gid -> number of synapses from that source to the target. If a requested gid has no synapses it will be present with value equal to EMPTY_DATA (typically an empty dict).

  • dict[int – Mapping target gid -> dict mapping source gid -> number of synapses from that source to the target. If a requested gid has no synapses it will be present with value equal to EMPTY_DATA (typically an empty dict).

  • int]] – Mapping target gid -> dict mapping source gid -> number of synapses from that source to the target. If a requested gid has no synapses it will be present with value equal to EMPTY_DATA (typically an empty dict).

  • Notes

  • -----

  • using (- The method queries the underlying libsonata population)

  • self._population.afferent_edges

  • and (self._population.target_nodes)

  • self._population.source_nodes

  • (target (then counts unique)

  • source)

  • pairs.

  • from (- Results are cached in self._counts; only missing tgids are read)

  • calls. (the file on subsequent)

  • Example

  • -------

  • synapses (>>> # Suppose target 10 has 3 incoming)

  • none. (>>> # one from source 2; target 20 has)

  • np.array([10 (>>> tgids =)

  • 20])

  • reader.get_conn_counts(tgids) (>>> conn_counts =)

  • Result (>>> #)

  • { (>>> #)

  • 10 (>>> #)

  • 20 (>>> #)

  • } (>>> #)

get_counts(tgids)

Count synapses for the given target neuron ids.

Parameters:

  • tgids (array-like of int) – One or more target neuron gids to query.

  • Returns

  • -------

  • dict[int – Mapping target gid -> number of synapse edges targeting that gid. Any requested gid that has no synapses present in the underlying population will be included with a value of 0.

  • int] – Mapping target gid -> number of synapse edges targeting that gid. Any requested gid that has no synapses present in the underlying population will be included with a value of 0.

  • Notes

  • -----

  • using (The implementation queries the underlying libsonata population)

  • to (self._population.afferent_edges and self._population.target_nodes)

  • occurrences. (determine the edge-to-target mapping and then counts)

  • Example

  • -------

  • np.array([10 (>>> tgids =)

  • 20

  • 30])

  • reader.get_counts(tgids) (>>> counts =)

  • result (>>> # Possible)

get_property(gid, field_name)

Retrieves a full pre-loaded property given a gid and the property name.

get_synapse_parameters(gid) → numpy.recarray

Return the synapse parameters record array for the given gid, loading and caching it if needed.

has_property(field_name)

Checks whether source data has the given additional field.

parameter_mapping = {'D': 'depression_time', 'DTC': 'decay_time', 'F': 'facilitation_time', 'U': 'u_syn', 'conductance_ratio': 'conductance_scale_factor', 'nrrp': 'n_rrp_vesicles', 'synType': 'syn_type_id', 'weight': 'conductance'}
preload_data(gids, minimal_mode=False)

Preload SONATA fields for the specified IDs. Set minimal_mode to True to read a single synapse per connection

class neurodamus.io.synapse_reader.SynapseParameters

Synapse parameter names and dtypes for numpy recarrays following the SONATA specification.

For detailed info on the parameters, see: https://sonata-extension.readthedocs.io/en/latest/sonata_tech.html#edge-file

classmethod all_fields()

Return all defined field names.

classmethod dtype(extra_fields=None)

Return dtype including optional extra fields (all float64).

classmethod fields(exclude: set = (), with_translation: dict | None = None)

Return list of fields with optional exclusion and translation.

Returns list of tuples (field_name, translated_name or is_optional).

classmethod load_fields()

Return all fields except reserved ones.

classmethod make_synapse_parameters_array(data: dict, extra_fields: list[str], extra_cellular_calcium: float | None, extra_scale_vars: list[str])

Create a recarray from data with optional extra fields and apply patches.

Source

api/subpackages/io.rst