C++ / Python reader for SONATA circuit files: https://github.com/AllenInstitute/sonata/blob/master/docs/SONATA_DEVELOPER_GUIDE.md
>> from sonata import NodeStorage
>> nodes = NodeStorage(<path to H5 file>)
# list populations
>> nodes.population_names
# open population
>> population = nodes.open_population(<name>)
# total number of nodes in the population
>> population.size
# attribute names
>> population.attribute_names
# get attribute value for single node
>> population.get_attribute('mtype', 42)
# ...or Selection of nodes (see below) => returns NumPy array with corresponding values
>> population.get_attribute('mtype', selection)
List of element IDs where adjacent IDs are grouped for the sake of efficient HDF5 file access.
For instance, {1, 2, 3, 5} sequence becomes {[1, 4), [5, 6)}.
Selection can be instantiated from:
- a sequence of scalar values (works for NumPy arrays as well)
- a sequence of pairs (interpreted as ranges above, works for N x 2 NumPy arrays as well)
EdgePopulation connectivity queries (see below) return Selections as well.
>> import numpy as np
>> from sonata import Selection
>> selection = Selection(np.asarray([1, 2, 3, 5]))
>> selection.ranges
[(1, 4), (5, 6)]
>> selection = Selection([(1, 4), (5, 6)])
>> selection.flatten()
[1, 2, 3, 5]
>> selection.flat_size
4
>> bool(selection)
True
>> selection = Selection([])
>> selection.ranges
[]
>> selection.flatten()
[]
>> selection.flat_size
0
>> bool(selection)
False
Analogous to NodeStorage.
>> from sonata import EdgeStorage
>> edges = EdgeStorage(<path to H5 file>)
# list populations
>> edges.population_names
# open population
>> population = edges.open_population(<name>)
Analogous to NodePopulation...
# total number of edges in the population
>> population.size
# attribute names
>> population.attribute_names
# get attribute value for single edge
>> population.get_attribute('delay', 42)
# ...or Selection of edges => returns NumPy array with corresponding values
>> population.get_attribute('delay', selection)
...with additional methods for querying connectivity
# source / target node ID(s)
>> population.source_node(42)
>> population.target_node(42)
# ...or their vectorized analogues
>> population.source_nodes([0, 1])
>> population.target_nodes([0, 1])
# ...(works for NumPy arrays as well)
>> import numpy as np
>> population.source_nodes(np.asarray([0, 1]))
>> population.target_nodes(np.asarray([0, 1]))
# query connectivity (result is Selection object)
>> selection = population.afferent_edges(1)
>> selection = population.efferent_edges(1)
>> selection = population.connecting_edges(1, 2)
# ...or their vectorized analogues
>> selection = population.afferent_edges([1, 2, 3])
>> selection = population.efferent_edges([1, 2, 3])
>> selection = population.connecting_edges([1, 2, 3], [4, 5, 6])
# ...(works for NumPy arrays as well)
>> import numpy as np
>> selection = population.afferent_edges(np.asarray([1, 2, 3]))