C++ / Python reader for SONATA circuit files: SONATA guide
pip install libsonatapip install git+https://github.com/BlueBrain/libsonatagit clone [email protected]:BlueBrain/libsonata.git --recursive
cd libsonata
mkdir build && cd build
cmake -DCMAKE_BUILD_TYPE=Release -DEXTLIB_FROM_SUBMODULES=ON ..
make -j>>> import libsonata
>>> nodes = libsonata.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, say 42
>>> population.get_attribute('mtype', 42)
# ...or Selection of nodes (see below) => returns NumPy array with corresponding values
>>> selection = libsonata.Selection(values=[1, 5, 9, 42]) # nodes 1, 5, 9, 42
>>> mtypes = population.get_attribute('mtype', selection)
>>> list(zip(selection.flatten(), mtypes))
[(1, u'mtype_of_1'), (5, u'mtype_of_5'), (9, u'mtype_of_9'), (42, u'mtype_of_42')]List of element IDs (either node_id, or edge_id) 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.
>>> selection = libsonata.Selection([1, 2, 3, 5])
>>> selection.ranges
[(1, 4), (5, 6)]>>> selection = libsonata.Selection([(1, 4), (5, 6)])
>>> selection.flatten()
[1, 2, 3, 5]
>>> selection.flat_size
4
>>> bool(selection)
TruePopulation handling for EdgeStorage is analogous to NodeStorage:
>>> edges = libsonata.EdgeStorage('path/to/H5/file')
# list populations
>>> edges.population_names
# open population
>>> population = edges.open_population(<name>)# total number of edges in the population
>>> population.size
# attribute names
>>> population.attribute_names
# get attribute value for single edge, say 123
>>> population.get_attribute('delay', 123)
# ...or Selection of edges => returns NumPy array with corresponding values
>>> selection = libsonata.Selection([1, 5, 9])
>>> population.get_attribute('delay', selection) # returns delays for edges 1, 5, 9...with additional methods for querying connectivity, where the results are selections that can be applied like above
# get source / target node ID for the 42nd edge:
>>> population.source_node(42)
>>> population.target_node(42)
# query connectivity (result is Selection object)
>>> selection_to_1 = population.afferent_edges(1) # all edges with target node_id 1
>>> population.target_nodes(selection_to_1) # since selection only contains edges
# targeting node_id 1 the result will be a
# numpy array of all 1's
>>> selection_from_2 = population.efferent_edges(2) # all edges sourced from node_id 2
>>> selection = population.connecting_edges(2, 1) # this selection is all edges from
# node_id 2 to node_id 1
# ...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])>>> import libsonata
>>> spikes = libsonata.SpikeReader('path/to/H5/file')
# list populations
>>> spikes.get_populations_names()
# open population
>>> population = spikes['<name>']# get all spikes [(node_id, timestep)]
>>> population.get()
[(5, 0.1), (2, 0.2), (3, 0.3), (2, 0.7), (3, 1.3)]
# get all spikes betwen tstart and tstop
>>> population.get(tstart=0.2, tstop=1.0)
[(2, 0.2), (3, 0.3), (2, 0.7)]
# get spikes attribute sorting (by_time, by_id, none)
>>> population.sorting
'by_time'
Pandas can be used to create a dataframe and get a better representation of the data>>> import pandas
data = population.get()
df = pandas.DataFrame(data=data, columns=['ids', 'times']).set_index('times')
print(df)
ids
times
0.1 5
0.2 2
0.3 3
0.7 2
1.3 3>>> somas = libsonata.SomaReportReader('path/to/H5/file')
# list populations
>>> somas.get_populations_names()
# open population
>>> population_somas = somas['<name>']# get times (tstart, tstop, dt)
>>> population_somas.times
(0.0, 1.0, 0.1)
# get unit attributes
>>> population_somas.time_units
'ms'
>>> population_somas.data_units
'mV'
# node_ids sorted?
>>> population_somas.sorted
True
# get a list of all node ids in the selected population
>>> population_somas.get_node_ids()
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
# get the DataFrame of the node_id values for the timesteps between tstart and tstop
>>> data_frame = population_somas.get(node_ids=[13, 14], tstart=0.8, tstop=1.0)
# get the data values
>>> data_frame.data
[[13.8, 14.8], [13.9, 14.9]]
# get the list of timesteps
>>> data_frame.times
[0.8, 0.9]
# get the list of node ids
>>> data_frame.ids
[13, 14]Once again, pandas can be used to create a dataframe using the data, ids and times lists
>>> import pandas
df = pandas.DataFrame(data_frame.data, columns=data_frame.ids, index=data_frame.times)
print(df)
13 14
0.8 13.8 14.8
0.9 13.9 14.9>>> elements = libsonata.ElementReportReader('path/to/H5/file')
# list populations
>>> elements.get_populations_names()
# open population
>>> population_elements = elements['<name>']# get times (tstart, tstop, dt)
>>> population_elements.times
(0.0, 4.0, 0.2)
>>> population_elements.get_node_ids()
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
# get the DataFrame of the node_id values for the timesteps between tstart and tstop
>>> data_frame = population_elements.get(node_ids=[13, 14], tstart=0.8, tstop=1.0)
# get the data values (list of list of floats with data[time_index][element_index])
>>> data_frame.data
[[46.0, 46.1, 46.2, 46.3, 46.4, 46.5, 46.6, 46.7, 46.8, 46.9], [56.0, 56.1, 56.2, 56.3, 56.4, 56.5, 56.6, 56.7, 56.8, 56.9]]
# get the list of timesteps
>>> data_frame.times
[0.8, 1.0]
# get the list of (node id, element_id)
>>> data_frame.ids
[(13, 30), (13, 30), (13, 31), (13, 31), (13, 32), (14, 32), (14, 33), (14, 33), (14, 34), (14, 34)]The same way than with spikes and soma reports, pandas can be used to get a better representation of the data
>>> import pandas
df = pandas.DataFrame(data_frame.data, columns=pandas.MultiIndex.from_tuples(data_frame.ids), index=data_frame.times)
print(df)
13 14
30 30 31 31 32 32 33 33 34 34
0.8 46.0 46.1 46.2 46.3 46.4 46.5 46.6 46.7 46.8 46.9
1.0 56.0 56.1 56.2 56.3 56.4 56.5 56.6 56.7 56.8 56.9For big datasets, using numpy arrays could greatly improve the performance
>>> import numpy
np_data = numpy.asarray(data_frame.data)
np_ids = numpy.asarray(data_frame.ids).T
np_times = numpy.asarray(data_frame.times)
df = pandas.DataFrame(np_data, columns=pandas.MultiIndex.from_arrays(np_ids), index=np_times)This project/research has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2).
libsonata is distributed under the terms of the GNU Lesser General Public License version 3, unless noted otherwise, for example, for external dependencies. Refer to COPYING.LESSER and COPYING files for details.
Copyright (C) 2018-2020, Blue Brain Project/EPFL and contributors.
libsonata is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License version 3 as published by the Free Software Foundation.
libsonata is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details.
You should have received a copy of the GNU Lesser General Public License along with libsonata. If not, see <https://www.gnu.org/licenses/>.