Welcome to NNGT’s documentation!#

Overview#

The Neural Network Growth and Topology (NNGT) module provides a unified interface to access, generate, and analyze networks via any of the well-known Python graph libraries: networkx, igraph, and graph-tool.

For people in neuroscience, the library also provides tools to grow and study detailed biological networks by interfacing efficient graph libraries with highly distributed activity simulators.

The library has two main targets:

people looking for a unifying interface for these three graph libraries, allowing to run and share a single code on different platforms
neuroscience people looking for an easy way to generate complex networks while keeping track of neuronal populations and their biological properties

Main classes#

NNGT provides four main classes, the two first being aimed at the graph-theoretical community, the third and fourth are more for the neuroscience community:

Graph: provides a simple implementation over graphs objects from graph libraries (namely the addition of a name, management of detailed nodes and connection properties, and simple access to basic graph measurements).
SpatialGraph: a Graph embedded in space (nodes have positions and connections are associated to a distance)
Network: provides more detailed characteristics to emulate biological neural networks, such as classes of inhibitory and excitatory neurons, synaptic properties…
SpatialNetwork: combines spatial embedding and biological properties

Generation of graphs#

Structured connectivity:: connectivity between the nodes can be chosen from various well-known graph models
Populations:: populations of neurons are distributed afterwards on the structured connectivity, and can be set to respect various constraints (for instance a given fraction of inhibitory neurons and synapses)
Synaptic properties:: synaptic weights and delays can be set from various distributions or correlated to edge properties

Interacting with NEST#

The generated graphs can be used to easily create complex networks using the NEST simulator, on which you can then simulate their activity.

The docs#

Installation#

Dependencies#

This package depends on several libraries (the number varies according to which modules you want to use).

Basic dependencies#

Regardless of your needs, the following libraries are required:

numpy (>= 1.11 required for full support)
scipy

Though NNGT implements a default (limited) backend, installing one of the following libraries is highly recommended to do some proper network analysis:

graph_tool (> 2.22)
or igraph
or networkx (>= 2.4)

Additionnal dependencies#

matplotlib (optional but will limit the functionalities if not present)
shapely for complex spatial embedding
peewee (> 3) for database features

Note

If they are not present on your computer, pip will directly try to install scipy and numpy. However, if you want advanced network analysis features, you will have to install the graph library yourself (only networkx can be installed directly using pip)

Simple install#

Linux#

Install the requirements (through apt on debian/ubuntu/mint, pacman and trizen on arch-based distributions, or yum on fedora/centos. Otherwise you can also install the latest versions via pip:

pip install --user numpy scipy matplotlib networkx

To install the last stable release, just use:

pip install --user nngt

Under most linux distributions, the simplest way to get the latest version of NNGT is to install to install both pip and git, then simply type into a terminal:

pip install --user git+https://github.com/Silmathoron/NNGT.git

Mac#

I recommend using Homebrew or Macports with which you can install all required features to use NEST and NNGT with graph-tool. The following command lines are used with python 3.7 but you can use any python >= 3.5 (just replace all 37/3.7 by the desired version).

Homebrew

brew tap homebrew/core
brew tap brewsci/science

brew install gcc-8 cmake gsl autoconf automake libtool
brew install python

if you want nest, add

brew install nest --with-python

(note that setting --with-python=3 might be necessary)

Macports

sudo port select gcc mp-gcc8 && sudo port install gsl +gcc8
sudo port install autoconf automake libtool
sudo port install python37 pip
sudo port select python python37
sudo port install py37-cython
sudo port select cython cython37
sudo port install py37-numpy py37-scipy py37-matplotlib py37-ipython
sudo port select ipython ipython-3.7
sudo port install py-graph-tool gtk3

Once the installation is done, you can just install:

export CC=gcc-8
export CXX=gcc-8
pip install --user nngt

Windows#

It’s the same as Linux for windows users once you’ve installed Python and pip, but NEST won’t work.

Note

igraph can be installed on windows if you need something faster than networkx.

Using the multithreaded algorithms

Install a compiler (the default msvc should already be present, otherwise you can install VisualStudio) before you make the installation.

In case of problems with msvc:

install MinGW or MinGW-W64
use it to install gcc with g++ support

open a terminal, add the compiler to your PATH and set it as default: e.g.

set PATH=%PATH%;C:\MinGW\bin
set CC=C:\MinGW\bin\mingw32-gcc.exe
set CXX=C:\MinGW\bin\mingw32-g++.exe

in that same terminal window, run pip install --user nngt

Local install#

If you want to modify the library more easily, you can also install it locally, then simply add it to your PYTHONPATH environment variable:

cd && mkdir .nngt-install
cd .nngt-install
git clone https://github.com/Silmathoron/NNGT.git .
git submodule init
git submodule update
nano .bash_profile

Then add:

export PYTHONPATH="/path/to/your/home/.nngt-install/src/:PYTHONPATH"

In order to update your local repository to keep it up to date, you will need to run the two following commands:

git pull origin master
git submodule update --remote --merge

Configuration#

The configuration file is created in ~/.nngt/nngt.conf after you first run import nngt in python. Here is the default file:

#-------------------------#
# NNGT configuration file #
#-------------------------#

version = {version}

#-------------------------
## default backend       -----------------------------------------------------
#-------------------------

# library that will be used in the background to handle graph generation
# (choose among "graph-tool", "igraph", "networkx", or "nngt"). Note that only
# the 3 first options will allow full graph analysis features while only the
# last one allows for fully distributed memory on clusters.

backend = graph-tool


#----------------------
## Matplotlib backend --------------------------------------------------------
#----------------------

# Uncomment and choose among your available backends.
# See http://matplotlib.org/faq/usage_faq.html#what-is-a-backend for details

#mpl_backend = Qt5Agg

# use TeX rendering for axis labels
use_tex = False

# color library either matplotlib or seaborn
color_lib = matplotlib

# palette to use
palette_continuous = magma
palette_discrete = Set1


#-----------------------------
## Settings for database    -------------------------------------------------
#-----------------------------

use_database = False

# use a database (by default, will be stored in SQLite database)
db_to_file = False
db_folder  = ~/.nngt/database
db_name    = main

# database url if you do not want to use a SQLite file
# example of real database url: db_url = mysql://user:password@host:port/my_db
# db_url = mysql:///nngt_db


#-----------------------------
## Settings for data logging -------------------------------------------------
#-----------------------------

# which messages are printed? (see logging module levels:
# https://docs.python.org/2/library/logging.html#levels)
# set to INFO or below to add the config messages on import
# set to WARNING or above to remove the messages on import
log_level = WARNING

# write log to file?
log_to_file = True
# if True, write to default folder '~/.nngt/log'
#log_folder = ~/.nngt/log


#----------------------------
## Multithreaded/MPI algorithms ----------------------------------------------
#----------------------------

# C++ algorithms using OpenMP are compiled and imported using Cython if True,
# otherwise regular numpy/scipy algorithms are used.
# Multithreaded algorithms should be prefered if available.

multithreading = True

# If using MPI, current MT or normal functions will be used except for the
# distance_rule algorithm, which will be overloaded by its MPI version.
# Note that the MPI version is not locally multithreaded.

mpi = False

It can be necessary to modify this file to use the desired graph library, but mostly to correct problems with GTK and matplotlib (if the plot module complains, try Gtk3Agg and Qt4Agg/Qt5Agg).

Using NEST#

If you want to simulate activities on your complex networks, NNGT can directly interact with the NEST simulator to implement the network inside PyNEST. For this, you will need to install NEST with Python bindings, which requires:

the python headers (python-dev package on debian-based distribs)
autoconf
automake
libtool
libltdl
libncurses
readlines
gsl (the GNU Scientific Library) for many neuronal models

Intro & user manual#

Yet another graph library?#

It is not ;)

This library is based on existing graph libraries (such as graph-tool, igraph, networkx, and possibly soon SNAP) and acts as a convenient interface to build various networks from efficient and verified algorithms. Most importantly, it provides a series of analysis functions that are guaranteed to provide the same results with all backends, enabling fully portable codes (see Consistent tools for graph analysis).

Moreover, it also acts as an interface between those graph libraries and the NEST and DeNSE simulators.

Documentation structure#

For users that are in a hurry, you can go directly to the Tutorial section. For more specific and detailed examples, several topics are then detailed separately in the following pages:

Graph generation#

This page gives example on how to generate increasingly complex network structures. The example files can be found at: docs/examples/simple_graphs.py, docs/examples/multi_groups_network.py, docs/examples/basic_nest_network.py, and docs/examples/nest_receptor_ports.py.

Content:

Principle
Modularity
Setting weights
Examples
Advanced examples
- Receptor ports in NEST

Principle #

In order to keep the code as generic and easy to maintain as possible, the generation of graphs or networks is divided in several steps:

Structured connectivity: a simple graph is generated as an assembly of nodes and edges, without any biological properties. This allows us to implement known graph-theoretical algorithms in a straightforward fashion.
Populations: detailed properties can be implemented, such as inhibitory synapses and separation of the neurons into inhibitory and excitatory populations – these can be done while respecting user-defined constraints.
Synaptic properties: eventually, synaptic properties such as weight/strength and delays can be added to the network.

Modularity #

The library as been designed so that these various operations can be realized in any order!

Juste to get work on a topological graph/network:

Create graph class
Connect
Set connection weights (optional)
Spatialize (optional)
Set types (optional: to use with NEST)

To work on a really spatially embedded graph/network:

Create spatial graph/network
Connect (can depend on positions)
Set connection weights (optional, can depend on positions)
Set types (optional)

Or to model a complex neural network in NEST:

Create spatial network (with space and neuron types)
Connect (can depend on types and positions)
Set connection weights and types (optional, can depend on types and positions)

Setting weights #

The weights can be either user-defined or generated by one of the available distributions (Attributes and distributions). User-defined weights are generated via:

a list of edges
a list of weights

Pre-defined distributions require the following variables:

a distribution name (“constant”, “gaussian”…)
a dictionary containing the distribution properties
an optional attribute for distributions that are correlated to another (e.g. the distances between neurons)
a optional value defining the variance of the Gaussian noise that should be applied on the weights

There are several ways of settings the weights of a graph which depend on the time at which you assign them.

At graph creation: You can define the weights by entering a weights argument to the constructor; this should be a dictionary containing at least the name of the weight distribution: {"distrib": "distribution_name"}. If entered, this will be stored as a graph property and used to assign the weights whenever new edges are created unless you specifically assign rules for those new edges’ weights.
At any given time: You can use the set_weights() function to set the weights of a graph explicitely by using:

graph.set_weights(elist=edges_to_weigh, distrib="distrib_of_choice", ...)

For more details on weights, other attributes, and available distributions, see Properties of graph components.

Examples #

import nngt
import nngt.generation as ng

Simple generation #

num_nodes  = 1000
avg_deg_er = 25
avg_deg_sf = 100

# random graphs
g1 = ng.erdos_renyi(nodes=num_nodes, avg_deg=avg_deg_er)

# the same graph but undirected
g2 = ng.erdos_renyi(nodes=num_nodes, avg_deg=avg_deg_er, directed=False)

# 2-step generation of a scale-free with Gaussian weight distribution
w = {
    "distribution": "gaussian",
    "avg": 60.,
    "std": 5.
}

g3 = nngt.Graph(num_nodes, weights=w)
ng.random_scale_free(2.2, 2.9, avg_deg=avg_deg_sf, from_graph=g3)

# same in 1 step
g4 = ng.random_scale_free(
    2.2, 2.9, avg_deg=avg_deg_sf, nodes=num_nodes, weights=w)

Networks composed of heterogeneous groups #

'''
Make the population
'''

# two groups of neurons
g1 = nngt.NeuralGroup(500, neuron_type=1)  # neurons 0 to 499
g2 = nngt.NeuralGroup(500, neuron_type=1)  # neurons 500 to 999

# make population (without NEST models)
pop = nngt.NeuralPop.from_groups(
    (g1, g2), ("left", "right"), with_models=False)

# create network from this population
net = nngt.Network(population=pop)


'''
Connect the groups
'''

# inter-groups (Erdos-Renyi)
prop_er1 = {"density": 0.005}
ng.connect_groups(net, "left", "right", "erdos_renyi", **prop_er1)

# intra-groups (Newman-Watts)
prop_nw = {
    "coord_nb": 20,
    "proba_shortcut": 0.1,
    "reciprocity_circular": 1.
}

ng.connect_groups(net, "left", "left", "newman_watts", **prop_nw)

Use with NEST #

Generating a network with excitatory and inhibitory neurons:

'''
Build a network with two populations:
* excitatory (80%)
* inhibitory (20%)
'''
num_nodes = 1000

# 800 excitatory neurons, 200 inhibitory
net = nngt.Network.exc_and_inhib(num_nodes, ei_ratio=0.2)

'''
Connect the populations.
'''
# exc -> inhib (Erdos-Renyi)
ng.connect_neural_types(net, 1, -1, "erdos_renyi", density=0.035)

# exc -> exc (Newmann-Watts)
prop_nw = {
    "coord_nb": 10,
    "proba_shortcut": 0.1,
    "reciprocity_circular": 1.
}
ng.connect_neural_types(net, 1, 1, "newman_watts", **prop_nw)

# inhib -> exc (Random scale-free)
prop_rsf = {
    "in_exp": 2.1,
    "out_exp": 2.6,
    "density": 0.2
}
ng.connect_neural_types(net, -1, 1, "random_scale_free", **prop_rsf)

# inhib -> inhib (Erdos-Renyi)

Send the network to NEST:

if nngt.get_config('with_nest'):
    import nest
    import nngt.simulation as ns

    '''
    Prepare the network and devices.
    '''
    # send to NEST
    gids = net.to_nest()
    # excite
    ns.set_poisson_input(gids, rate=100000.)
    # record
    groups = [key for key in net.population]
    recorder, record = ns.monitor_groups(groups, net)

    '''
    Simulate and plot.
    '''
    simtime = 100.
    nest.Simulate(simtime)

    if nngt.get_config('with_plot'):
        ns.plot_activity(
            recorder, record, network=net, show=True, limits=(0,simtime))

You can check that connections from neurons that are marked as inhibitory are automatically assigned a negative sign in NEST:

    # sign of NNGT versus NEST inhibitory connections
    igroup = net.population["inhibitory"]
    # in NNGT
    iedges = net.get_edges(source_node=igroup.ids)
    w_nngt = set(net.get_weights(edges=iedges))
    # in NEST
    iconn  = nest.GetConnections(
        source=list(net.population["inhibitory"].nest_gids),
        target=list(net.population.nest_gids))
    w_nest = set(nest.GetStatus(iconn, "weight"))
    # in NNGT, inhibitory weights are positive to work with graph analysis
    # methods; they are automatically converted to negative weights in NEST

Returns: NNGT weights: {1.0} versus NEST weights {-1.0}.

Advanced examples #

Receptor ports in NEST #

Some models, such as multisynaptic neurons, or advanced models incorporating various neurotransmitters use an additional information, called "port" to identify the synapse that will be used by the nest.Connect method. These models can also be used with NNGT by telling the NeuralGroup which type of port the neuron should try to bind to.

NB: the port is specified in the source neuron and declares which synapse of the target neuron is concerned.

'''
Build a network with two populations:
* excitatory (80%)
* inhibitory (20%)
'''
num_neurons = 50   # number of neurons
avg_degree  = 20   # average number of neighbours
std_degree  = 3    # deviation for the Gaussian graph

# parameters
neuron_model = "ht_neuron"      # hill-tononi model
exc_syn = {'receptor_type': 1}  # 1 is 'AMPA' in this model
inh_syn = {'receptor_type': 3}  # 3 is 'GABA_A' in this model

synapses = {
    (1, 1):   exc_syn,
    (1, -1):  exc_syn,
    (-1, 1):  inh_syn,
    (-1, -1): inh_syn,
}

pop = nngt.NeuralPop.exc_and_inhib(
    num_neurons, en_model=neuron_model, in_model=neuron_model,
    syn_spec=synapses)

# create the network and send it to NEST
w_prop = {"distribution": "gaussian", "avg": 0.2, "std": .05}
net = nngt.generation.gaussian_degree(
    avg_degree, std_degree, population=pop, weights=w_prop)

'''
Send to NEST and set excitation and recorders
'''
if nngt.get_config('with_nest'):
    import nest
    import nngt.simulation as ns

    nest.ResetKernel()

    gids = net.to_nest()

    # add noise to the excitatory neurons
    excs = list(pop["excitatory"].nest_gids)
    inhs = list(pop["inhibitory"].nest_gids)
    ns.set_noise(excs, 10., 2.)
    ns.set_noise(inhs, 5., 1.)

    # record
    groups = [key for key in net.population]
    recorder, record = ns.monitor_groups(groups, net)

    '''
    Simulate and plot.
    '''
    simtime = 2000.
    nest.Simulate(simtime)

    if nngt.get_config('with_plot'):
        ns.plot_activity(
            recorder, record, network=net, show=True, histogram=False,
            limits=(0, simtime))

Go to other tutorials:

Properties of graph components#

This section details the different attributes and properties which can be associated to nodes/neurons and connections in graphs and networks.

Content:

Components of a graph
Node attributes
Edge attributes
- Weights and delays
- Custom edge attributes
Attributes and distributions

Components of a graph #

In the graph libraries used by NNGT, the main components of a graph are nodes (also called vertices in graph theory), which correspond to neurons in neural networks, and edges, which link nodes and correspond to synaptic connections between neurons in biology.

The library supposes for now that nodes/neurons and edges/synapses are always added and never removed. Because of this, we can attribute indices to the nodes and the edges which will be directly related to the order in which they have been created (the first node will have index 0, the second index 1, etc).

The source file for the examples given here can be found at doc/examples/attributes.py.

Node attributes #

If you are just working with basic graphs (for instance looking at the influence of topology with purely excitatory networks), then your nodes do not necessarily need to have attributes. This is the same if you consider only the average effect of inhibitory neurons by including inhibitory connections between the neurons but not a clear distinction between populations of purely excitatory and purely inhibitory neurons. However, if you want to include additional information regarding the nodes, to account for specific differences in their properties, then node attributes are what you need. They are stored in node_attributes. Furthermore, to model more realistic neuronal networks, you might also want to define different groups and types of neurons, then connect them in specific ways. This specific feature will be provides by NeuralGroup objects.

Three types of node attributes #

In the library, there is a difference between:

standard attributes, which are stored in any type of Graph and can be created, modified, and accessed via the new_node_attribute(), set_node_attribute(), and get_node_attributes() functions.
spatial properties (the positions of the neurons), which are stored in a specific positions numpy.ndarray and can be accessed using the get_positions() function,
biological/group properties, which define assemblies of nodes sharing common properties, and are stored inside a NeuralPop object.

Standard attributes #

Standard attributes can be any given label that might vary among the nodes in the network and will be attached to each node.

Users can define any attribute, through the new_node_attribute() function.

''' -------------- #
# Generate a graph #
# -------------- '''

num_nodes = 1000
avg_deg   = 25

graph = ng.erdos_renyi(nodes=num_nodes, avg_deg=avg_deg)


''' ----------------- #
# Add node attributes #
# ----------------- '''

# Let's make a network of animals where nodes represent either cats or dogs.
# (no discrimination against cats or dogs was intended, no animals were harmed
# while writing or running this code)
animals  = ["cat" for _ in range(600)]  # 600 cats
animals += ["dog" for _ in range(400)]  # and 400 dogs
np.random.shuffle(animals)              # which we assign randomly to the nodes

graph.new_node_attribute("animal", value_type="string", values=animals)

Attributes can have different types:

"double" for floating point numbers
"int” for integers
"string" for strings
"object" for any other python object

Here we create a second node attribute of type "double":

# Nodes can have attributes of multiple types, let's add a size to our animals
catsizes = np.random.normal(50, 5, 600)   # cats around 50 cm
dogsizes = np.random.normal(80, 10, 400)  # dogs around 80 cm

# We first create the attribute without values (for "double", default to NaN)
graph.new_node_attribute("size", value_type="double")

# We now have to attributes: one containing strings, the other numbers (double)
print(graph.node_attributes)

# get the cats and set their sizes
cats = graph.get_nodes(attribute="animal", value="cat")
graph.set_node_attribute("size", values=catsizes, nodes=cats)

# We set 600 values so there are 400 NaNs left
assert np.sum(np.isnan(graph.get_node_attributes(name="size"))) == 400, \
    "There were not 400 NaNs as predicted."

# None of the NaN values belongs to a cat
assert not np.any(np.isnan(graph.get_node_attributes(cats, name="size"))), \
    "Got some cats with NaN size! :'("

# get the dogs and set their sizes
dogs = graph.get_nodes(attribute="animal", value="dog")
graph.set_node_attribute("size", values=dogsizes, nodes=dogs)

Biological/group properties #

Note

All biological/group properties are stored in a NeuralPop object inside a Network instance; this attribute can be accessed through population. NeuralPop objects can also be created from a Graph or SpatialGraph but they will not be stored inside the object.

The NeuralPop class allows you to define specific groups of neurons (described by a NeuralGroup). Once these populations are defined, you can constrain the connections between those populations. If the connectivity already exists, you can use the GroupProperty class to create a population with groups that respect specific constraints.

For more details on biological properties, see Neural groups and populations.

Edge attributes #

Like nodes, edges can also be attributed specific values to characterize them. However, where nodes are directly numbered and can be indexed and accessed easily, accessing edges is more complicated, especially since, usually, not all possible edges are present in a graph.

To easily access the desired edges, it is thus recommended to use the get_edges() function.

Edge attributes can then be created and recovered using similar functions as node attributes, namely new_edge_attribute(), set_edge_attribute(), and get_edge_attributes().

Weights and delays #

By default, graphs in NNGT are weighted: each edge is associated a “weight” value (this behavior can be changed by setting weighted=False upon creation).

Similarly, Network objects always have a “delay” associated to their connections.

Both attributes can either be set upon graph creation, through the weights and delays keyword arguments, or any any time using set_weights() and set_delays().

Note

When working with NEST and using excitatory and inhibitory neurons via groups (see Neural groups and populations), the weight of all connections (including inhibitory connections) should be positive: the excitatory or inhibitory type of the synapses will be set automatically when the NEST network is created based on the type of the source neuron.

In general, it is also not a good idea to use negative weights directly since standard graph analysis methods cannot handle them. If you are not working with biologically realistic neurons and want to set some inhibitory connections that do not depend on a “neuronal type”, use the set_types() method.

Let us see how the get_edges() function can be used to facilitate the creation of various weight patterns:

# Same as for node attributes, one can give attributes to the edges
# Let's give weights to the edges depending on how often the animals interact!
# cat's interact a lot among themselves, so we'll give them high weights
cat_edges = graph.get_edges(source_node=cats, target_node=cats)

# check that these are indeed only between cats
cat_set  = set(cats)
node_set = set(np.unique(cat_edges))

assert cat_set == node_set, "Damned, something wrong happened to the cats!"

# uniform distribution of weights between 30 and 50
graph.set_weights(elist=cat_edges, distribution="uniform",
                  parameters={"lower": 30, "upper": 50})

# dogs have less occasions to interact except some which spend a lot of time
# together, so we use a lognormal distribution
dog_edges = graph.get_edges(source_node=dogs, target_node=dogs)
graph.set_weights(elist=dog_edges, distribution="lognormal",
                  parameters={"position": 2.2, "scale": 0.5})

# Cats do not like dogs, so we set their weights to -5
# Dogs like chasing cats but do not like them much either so we let the default
# value of 1
cd_edges = graph.get_edges(source_node=cats, target_node=dogs)
graph.set_weights(elist=cd_edges, distribution="constant",
                  parameters={"value": -5})

# Let's check the distribution (you should clearly see 4 separate shapes)
if nngt.get_config("with_plot"):
    nngt.plot.edge_attributes_distribution(graph, "weight")

Note that here, the weights were generated randomly from specific distributions; for more details on the available distributions and their parameters, see Attributes and distributions.

Custom edge attributes #

Non-default edge attributes (besides “weights” or “delays”) can also be created through smilar functions as node attributes:

class Human:
    def __init__(self, name):
        self.name = name
    def __repr__(self):
        return "Human<{}>".format(self.name)

# let's create a class for humans and store it when two animals have interacted
# with the same human (the default will be an empty list if they did not)

# Alice interacted with all animals between 8 and 48
Alice   = Human("Alice")
animals = [i for i in range(8, 49)]
edges   = graph.get_edges(source_node=animals, target_node=animals)

graph.new_edge_attribute("common_interaction", value_type="object", val=[])
graph.set_edge_attribute("common_interaction", val=[Alice], edges=edges)

# Now suppose another human, Bob, interacted with all animals between 0 and 40
Bob     = Human("Bob")
animals = [i for i in range(0, 41)]
edges2  = graph.get_edges(source_node=animals, target_node=animals)

# to update the values, we need to get them to add Bob to the list
ci = graph.get_edge_attributes(name="common_interaction", edges=edges2)

for interactions in ci:
    interactions.append(Bob)

graph.set_edge_attribute("common_interaction", values=ci, edges=edges2)

# now some of the initial `edges` should have had their attributes updated
new_ci = graph.get_edge_attributes(name="common_interaction", edges=edges)
print(np.sum([0 if len(interaction) < 2 else 1 for interaction in new_ci]),
      "interactions have been updated among the", len(edges), "from Alice.")

Attributes and distributions #

Node and edge attributes can be generated based on the following distributions:

uniform

a flat distribution with identical probability for all values,
parameters: "lower" and "upper" values.

delta

the Dirac delta “distribution”, where a single value can be drawn,
parameters: "value".

Gaussian

the normal distribution $P(x) = P_0 e^{(x - \mu)^2/(2\sigma^2)}$
parameters: "avg" ( $\mu$ ) and "std" ( $\sigma$ ).

lognormal

$P(x) = P_0 e^{(\log(x) - \mu)^2/(2\sigma^2)}$
parameters: "position" ( $\mu$ ) and "scale" ( $\sigma$ ).

linearly correlated

a distribution which evolves linearly between two values depending on the value of a reference variable
parameters: "correl_attribute" (the reference variable, usually another attribute), "lower" and "upper", the minimum and maximum values.

Go to other tutorials:

Consistent tools for graph analysis#

NNGT provides several functions for topological analysis that return consistent results for all backends (the results will always be the same regardless of which library is used under the hood). This section describes these functions and gives an overview of the currently supported methods.

Note

It is of course possible to use any function from the library on the graph attribute; however, not using one of the supported NNGT functions below will usually return results that are not consistent between libraries (and the code will obviously no longer be portable).

Supported functions#

The following table details which functions are supported for directed and undirected networks, and whether they also work with weighted edges.

The test file where these functions are checked can be found here: testing/library_compatibility.py.

For each type of graph, the table tells which libraries are supported for the given function (graph-tool is gt, networkx is nx and igraph is ig). Custom implementation of a function is denoted by nngt, meaning that the function can be used even if no graph library is installed. A library marked between parentheses denotes partial support and additional explanation is usually given in the footnotes. A cross means that no consistent implementation is currently provided and the function will raise an error if one tries to use it on such graphs. Methods that are not defined for weighted or directed graphs are marked by NA.

Method	Unweighted undirected	Unweighted directed	Weighted undirected	Weighted directed
`all_shortest_paths()`	gt, nx, ig	gt, nx, ig	gt, nx, ig	gt, nx, ig
`average_path_length()`	gt, nx, ig	gt, nx, ig	gt, nx, ig	gt, nx, ig
`assortativity()` [1]	gt, nx, ig	gt, nx, ig	gt, ig	gt, ig
`betweenness()`	gt, nx, ig	gt, nx, ig	gt, nx, ig	gt, nx, ig
`betweenness_distrib()`	gt, nx, ig	gt, nx, ig	gt, nx, ig	gt, nx, ig
`closeness()` [2]	gt, nx, (ig)	gt, nx, (ig)	gt, nx, (ig)	gt, nx, (ig)
`connected_components()`	gt, nx, ig	gt, nx, ig	gt, nx, ig	gt, nx, ig
`degree_distrib()`	gt, nx, ig, nngt	gt, nx, ig, nngt	gt, nx, ig, nngt	gt, nx, ig, nngt
`diameter()` [3]	gt, nx, ig	gt, nx, ig	gt, nx, ig	gt, nx, ig
`global_clustering()`	gt, nx, ig, nngt	nngt	nngt	nngt
`local_clustering()` [4]	gt, nx, ig, nngt	nngt	nngt	nngt
`reciprocity()`	gt, nx, ig, nngt	gt, nx, ig, nngt	NA	NA
`shortest_distance()`	gt, nx, ig	gt, nx, ig	gt, nx, ig	gt, nx, ig
`shortest_path()`	gt, nx, ig	gt, nx, ig	gt, nx, ig	gt, nx, ig
`spectral_radius()`	nngt	nngt	nngt	nngt
`subgraph_centrality()`	nngt	nngt	nngt	nngt
`transitivity()` [5]	gt, nx, ig, nngt	nngt	nngt	nngt

[1]	networkx could be used via a workaround but an issue has been raised to find out how to best deal with this.

[2]	since definitions of the maximum distances differ between libraries, igraph is currently not usable if the in- or out-degree of any of the nodes is zero; it also does not provide an implementation for the harmonic closeness.

[3]	the implementation of the diameter for graph-tool is approximmate so results may occasionaly be inexact with this backend.

[4]	for directed and weighted networks, definitions and implementations differ between graph libraries, so generic implementations are provided in NNGT. See “Clustering in weighted and directed networks” for details.

[5]	identical to `global_clustering`.

Clustering in weighted and directed networks#

For directed clustering, NNGT provides the total clustering porposed in [Fagiolo2007]

$C_i^d = \frac{\frac{1}{2} (A + A^T)^3}{d_i^{tot}(d_i^{tot} - 1) - d_i^{\leftrightarrow}}$

with $d_i^{\leftrightarrow} = A^2_{ii}$ is the reciprocal degree.

For undirected weighted clustering, NNGT provides the definition proposed in [Barrat2004], [Onnela2005] as well as a new continuous definition.

$C_{B,i}^u = \frac{(WA^2)_{ii}}{s_i (d_i - 1)}$

$C_{O,i}^u = \frac{(W^{\left[\frac{1}{3}\right]})^3_{ii}}{d_i (d_i - 1)}$

$C_{c,i}^u = \frac{\left(W^{\left[\frac{2}{3}\right]}\right)^3_{ii}}{\left(s^{\left[\frac{1}{2}\right]}_i\right)^2 - s_i}$

with $s^{\left[\frac{1}{2}\right]}$ the generalized strength associated to the matrix $W^{\left[\frac{1}{2}\right]} = \{\sqrt{w_{ij}}\}$ .

For directed weighted clustering, the generalization of Barrat from [Clemente2018] is provided, as well as a generalization of Onnela and of the continuous clustering:

$C_{B,i}^d = \frac{\frac{1}{2}((W + W^T)(A+A^T)^2)_{ii}}{s_i (d_i^{tot} - 1) - s_{c,i}^{\leftrightarrow}}$

with $s$ the total strength and $s_{c,i}^{\leftrightarrow} = \frac{1}{2} (WA + AW)_{ii}$ the arithmetic reciprocal strength,

$C_{O,i}^d = \frac{\frac{1}{2}(W^{\left[\frac{1}{3}\right]} + (W^{\left[\frac{1}{3}\right]})^T)^3_{ii}}{d_i^{tot}(d_i^{tot} - 1) - d_i^{\leftrightarrow}}$

$C_{c,i}^d = \frac{\frac{1}{2}\left(W^{\left[\frac{2}{3}\right]} + W^{\left[\frac{2}{3}\right],T}\right)^3_{ii}}{\left(s^{\left[\frac{1}{2}\right]}_i\right)^2 - 2s^{\leftrightarrow}_i - s_i}$

with $s^{\left[\frac{1}{2}\right]}$ the total generalized strength and $s_i^\leftrightarrow = \left( W^{\left[\frac{1}{2}\right]} \right)^2$ the geometric reciprocal strength.

Global clusterings are defined as the sum of all numerators divided by the sum of all denominators for all definitions.

References#

[Barrat2004]

Barrat, Barthelemy, Pastor-Satorras, Vespignani. The Architecture of Complex Weighted Networks. PNAS 2004, 101 (11). DOI: 10.1073/pnas.0400087101.

[Clemente2018]

Clemente, Grassi. Directed Clustering in Weighted Networks: A New Perspective. Chaos, Solitons & Fractals 2018, 107, 26–38. DOI: 10.1016/j.chaos.2017.12.007, arXiv: 1706.07322.

[Fagiolo2007]

Fagiolo. Clustering in Complex Directed Networks. Phys. Rev. E 2007, 76, (2), 026107. DOI: 10.1103/PhysRevE.76.026107, arXiv: physics/0612169.

[Onnela2005]

Onnela, Saramäki, Kertész, Kaski. Intensity and Coherence of Motifs in Weighted Complex Networks. Phys. Rev. E 2005, 71 (6), 065103. DOI: 10.1103/physreve.71.065103, arXiv: cond-mat/0408629.

[Saramaki2007]

Saramäki, Kivelä, Onnela, Kaski, Kertész. Generalizations of the Clustering Coefficient to Weighted Complex Networks. Phys. Rev. E 2007, 75 (2), 027105. DOI: 10.1103/PhysRevE.75.027105, arXiv: cond-mat/0608670.

[Zhang2005]

Zhang, Horvath. A General Framework for Weighted Gene Co-Expression Network Analysis. Statistical Applications in Genetics and Molecular Biology 2005, 4 (1). DOI: 10.2202/1544-6115.1128, PDF.

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Parallelism#

Principle
Parallelism and random numbers
Using OpenMP (shared-memory parallelism)
- Setting multithreading
- Graph-tool caveat
Using MPI (distributed-memory parallelism)
- Fully distributed setup
Parallelized generation algorithms

Principle #

The NNGT package provides the possibility to use multithreaded algorithms to generate networks. This feature means that the computation is distributed on several CPUs and can be useful for:

machines with several cores but low frequency
generation functions requiring large amounts of computation
very large graphs

However, the multithreading part concerns only the generation of the edges; if a graph library such as graph-tool, igraph, or networkx is used, the building process of the graph object will be taken care of by this library. Since this process is not multithreaded, obtaining the graph object can be much longer than the actual generation process.

NNGT provides two types of parallelism:

shared-memory parallelism, using OpenMP, which can be set using nngt.set_config() ("multithreading", True) or, setting the number of threads, with nngt.set_config("omp", 8) to use 8 threads.
distributed-memory parallelism using MPI, which is set through nngt.set_config("mpi", True). In that case, the python script must be run as mpirun -n 8 python name_of_the_script.py to be run in parallel.

These two ways of running code in parallel differ widely, both regarding the situations in which they can be useful, and in the way the user should interact with the resulting graph.

The easiest tool, because it does not significantly differ from the single-thread case on the user side, is OpenMP, which is why we will describe it first. Using MPI is a lot different and will require the user to adapt the code to use it and will depend on the backend used.

Parallelism and random numbers #

When using parallel algorithms, additional care is necessary when dealing with random number generation. Here again, the situation differs between the OpenMP and MPI cases.

Warning

Never use the standard random module, only use numpy.random!

When using OpenMP, the parallel algorithms will use the random seeds defined by the user through nngt.set_config("seeds", list_of_seeds). One seed per thread is necessary. These seeds are not used on the python level, so they are independent from whatever random generation could happen using numpy (e.g. to set node positions in space, or to generate attributes). To make a simulation fully reproducible, the user must set both the random seeds and the python level random number generators through the master seed. For instance, with 4 threads:

master_seed = 0
nngt.set_config({"msd": master_seed, "seeds": [1, 2, 3, 4]})

Note

If the seeds are not provided, then they are generated automatically, from the master seed for the first call to a graph-generation method (using $\{MSD + 1 + i\}_{i \in {0.. N}}$ , with N the number of threads), then using a random number generated through numpy. This means that all previous calls to numpy.random will affect the random seeds used for the second or later calls to graph-generation methods unless new seeds are manually set by the user befor each new call (this does not mean that the code will not be reproducible, only that changes in the random calls in the code that occur before calls to graph-generation methods would affect the random structure of the generated graphs).

Warning

This is also how you should initialize random numbers when using MPI!

This may surprise experienced MPI users, but NNGT is implemented in such a way that shared properties are generated on all threads through the initial python master seed, then generation algorithms save the current common state, then re-initialize the RNGs for parallel generation, and finally restore the previous, common random state once the parallel generation is done. Of course the parallel initialization differs every time, but it is changed in a reproducible way through the master seed.

Using OpenMP (shared-memory parallelism)#

Setting multithreading #

Multithreading in NNGT can be set via

>>> nngt.set_config({"multithreading": True, "omp": num_omp_threads})

and you can then switch it off using

>>> nngt.set_config("multithreading", False)

This will automatically switch between the standard and multithreaded algorithms for graph generation.

Graph-tool caveat #

The graph-tool library also provides some multithreading capabilities, using

>>> graph_tool.openmp_set_num_threads(num_omp_threads)

However, this sets the number of OpenMP threads session-wide, which means that it will interfere with the ``NEST`` setup! Hence, if you are working with both NEST and graph-tool, you have to use the same number of OpenMP threads in both libraries.

To prevent bad surprises as much as possible, NNGT will raise an error if a value of "omp" is provided, which differs from the current NEST configuration. Regardless of this precaution, keeping only one value for the number of threads and using it consistently throughout the code is strongly advised.

Using MPI (distributed-memory parallelism)#

Note

MPI algorithms are currently restricted to gaussian_degree() and distance_rule() only.

Handling MPI can be significantly more difficult than using OpenMP because it differs more strongly from the “standard” single-thread case.

NNGT provides two different ways of using MPI:

When using one of the three graph libraries (graph-tool, igraph, or networkx), the connections are generated in parallel, but the final object is stored only on the master process. This means that in this case, the memory load will weigh only on this process, leading to a strong load imbalance. This feature is aimed at people who would require parallelism to speed up their graph generation but, for some reason, cannot use the OpenMP parallelism.
For “real” memory distribution, e.g. for people working on clusters, who require a balanced memory-load, NNGT provides a custom backend, that can be set using nngt.set_config('backend', 'nngt'). In this case, each process stores only a fraction of all the edges. However, nodes and graph properties are fully available on all processes.

Warning

When using MPI with graph-tool, igraph, or networkx, all operations on the graph that has been generated must be limited to the root process. To that end, NNGT provides the on_master_process() function that returns True only on the root MPI process. Using the ‘nngt’ backend, the edge_nb() method, as well as all other edge-related methods will return information on the local edges only!

Fully distributed setup #

The python file should include (before any graph generation):

import nngt

msd   = 0             # choose a master seed
seeds = [1, 2, 3, 4]  # choose initial seeds, one per MPI process

nngt.set_config({
    "mpi": True,
    "backend": "nngt",
    "msd": msd,
    "seeds": seeds,
})

The file should then be executed using:

>>> mpirun -n 4 python name_of_the_script.py

Note

Graph saving is available in parallel in the fully distributed setup through the to_file() and save_to_file() functions as in any other configuration.

Parallelized generation algorithms #

Generation of some directed graphs are available with parallel implementations (see table below). No undirected graph generation mechanisms are currently implemented.

Function	OMP	MPI
`all_to_all()`	no	no
`circular()`	no	no
`distance_rule()`	yes	yes
`erdos_renyi()`	no	no
`fixed_degree()`	yes	yes
`from_degree_list()`	yes	yes
`gaussian_degree()`	yes	yes
`newman_watts()`	no	no
`random_scale_free()`	no	no

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Neural groups and populations#

One of the key features of NNGT is to enable users to group nodes (neurons) into groups sharing common properties in order to facilitate the generation of network, the analysis of its properties, or complex simulations with NEST.

The complete example file containing the code discussed here, as well as additional information on how to access NeuralGroup and NeuralPop properties can be found there: docs/examples/introduction_to_groups.py.

Contents

Neuronal groups
- Creating simple groups
- More realistic groups
Populations
- Simple populations
- NEST-enabled populations
Complex populations and metagroups

Neuronal groups #

Neuronal groups are entities containing neurons which share common properties. Inside a population, a single neuron belongs to a single NeuralGroup object. Conversely the union of all groups contains all neurons in the network once and only once.

When creating a group, it is therefore important to make sure that it forms a coherent set of neurons, as this will make network handling easier.

For more versatile grouping, where neurons can belong to multiple ensembles, see the section about meta-groups below: Complex populations and metagroups.

Creating simple groups #

Neural groups can be created easily through calls to nngt.NeuralGroup.

>>> group = nngt.NeuralGroup()

creates a single empty group (nothing very interesting).

Minimally, any useful group requires at least neuron ids and a type (excitatory or inhibitory) to be useful.

To create a useful group, one can therefore either just tell how many neurons it should contain:

group1 = NeuralGroup(500)  # a group with 500 neurons

or directly pass it a list of ids (to avoid typing nngt. all the time, we do from nngt import NeuralGroup at the beginning)

group2 = NeuralGroup(range(10, 20))  # 10 neurons with ids from 10 to 19

More realistic groups #

When designing neuronal networks, one usually cares about their type (excitatory or inhibitory for instance), their properties, etc.

By default, neural groups are created excitatory and the following lines are therefore equivalent:

exc   = NeuralGroup(800, neuron_type=1)   # excitatory group
exc2  = NeuralGroup(800, neuron_type=1)   # also excitatory

To create an inhibitory group, the neural type must be set to -1:

inhib = NeuralGroup(200, neuron_type=-1)  # inhibitory group

Moving towards really realistic groups to run simulation on NEST afterwards, the last step is to associate a neuronal model and set the properties of these neurons (and optionally give them names):

pyr = NeuralGroup(800, neuron_type=1, neuron_model="iaf_psc_alpha",
                  neuron_param={"tau_m": 50.}, name="pyramidal_cells")

fsi = NeuralGroup(200, neuron_type=-1, neuron_model="iaf_psc_alpha",
                  neuron_param={"tau_m": 20.},
                  name="fast_spiking_interneurons")

Populations #

Populations are ensembles of neuronal groups which describe all neurons in a corresponding network. They are usually created before the network and then used to generate connections, but the can also be generated after the network creation, then associated to it.

Simple populations #

To create a population, you can start from scratch by creating an empty population, then adding groups to it:

pop = nngt.NeuralPop(with_models=False)              # empty population
pop.create_group(200, "first_group")                 # create excitatory group
pop.create_group(5, "second_group", neuron_type=-1)  # create inhibitory group

NNGT also provides a two default routine to create simple populations:

uniform(), to generate a single population where all neurons belong to the same group,
exc_and_inhib(), to generate a mixed excitatory and inhibitory population.

As before, we do from nngt import NeuralPop to avoid typing nngt. all the time.

To create such populations, just use:

unif_pop = NeuralPop.uniform(1000)                     # only excitatory
ei_pop   = NeuralPop.exc_and_inhib(1000, iratio=0.25)  # 25% inhibitory

Eventually, a population can be created from exiting groups using from_groups():

print(exc.neuron_type, exc2.neuron_type, inhib.neuron_type)
ei_pop2 = NeuralPop.from_groups([exc, exc2, inhib], ["e1", "e2", "i"],

Note that, here, we pass with_models=False to the population because these groups were created without the information necessary to create a network in NEST (a valid neuron model).

NEST-enabled populations #

To create a NEST-enabled population, one can use one of the standard classmethods (uniform() and exc_and_inhib()) and pass it valid parameters for the neuronal models (optionally also a synaptic model and neuronal/synaptic parameters).

Otherwise, one can build the population from groups that already contain these properties, e.g. the previous pyr and fsi groups:

# optional synaptic properties
syn_spec = {
    'default': {"model": "tsodyks2_synapse"},           # default connections
    ("pyramidal_cells", "pyramidal_cells"): {"U": 0.6}  # change a parameter
}

nest_pop = NeuralPop.from_groups([pyr, fsi], syn_spec=syn_spec)

Warning

syn_spec can contain any synaptic model and parameters associated to the NEST model; however, neither the synaptic weight nor the synaptic delay can be set there. For details on how to set synaptic weight and delays between groups, see connect_groups().

To see how to use a population to create a Network and send it to NEST, see Use with NEST.

Complex populations and metagroups #

When building complex neuronal networks, it may be useful to have neurons belong to multiple groups at the same time. Because standard groups can contain a neuron only once, meta-groups were introduced to provide this additional functionality.

Contrary to normal groups, a neuron can belong to any number of metagroups, which allow to make various sub- or super-groups. For instance, when modeling a part of cortex, neurons will belong to a layer, and to a given cell class whithin that layer. In that case, you may want to create specific groups for cell classes, like L3Py, L5Py, L3I, L5I for layer 4 and 5 pyramidal cells as well as interneurons, but you can then also group neurons in a same layer together, and same with pyramidal neurons or interneurons.

First create the normal groups:

nmod = "iaf_psc_exp"

idsL2gc = range(100)
idsL3py, idsL3i = range(100, 200), range(200, 300)
idsL4gc = range(300, 400)
idsL5py, idsL5i = range(400, 500), range(500, 600)
idsL6 = range(600, 700)

L2GC = NeuralGroup(idsL2gc, neuron_model=nmod, name="L2GC", neuron_type=1)
L3Py = NeuralGroup(idsL3py, neuron_model=nmod, name="L3Py", neuron_type=1)
L3I  = NeuralGroup(idsL3i,  neuron_model=nmod, name="L3I",  neuron_type=-1)
L4GC = NeuralGroup(idsL4gc, neuron_model=nmod, name="L4GC", neuron_type=1)
L5Py = NeuralGroup(idsL5py, neuron_model=nmod, name="L5Py", neuron_type=1)
L5I  = NeuralGroup(idsL5i,  neuron_model=nmod, name="L5I",  neuron_type=-1)
L6c  = NeuralGroup(idsL6,   neuron_model=nmod, name="L6c",  neuron_type=1)

Then make the metagroups for the layers:

L2 = MetaGroup(idsL2gc, name="L2")
L3 = MetaGroup(L3Py.ids + L3I.ids, name="L3")
L4 = MetaGroup(idsL4gc, name="L4")
L5 = MetaGroup(L5Py.ids + L5I.ids, name="L5")
L6 = MetaGroup(idsL6, name="L6")

Create the population:

pop_column = NeuralPop.from_groups(
    [L2GC, L3Py, L3I, L4GC, L5Py, L5I, L6c], meta_groups=[L2, L3, L4, L5, L6])

Then add additional metagroups for cell types:

pyr = MetaGroup(L3Py.ids + L5Py.ids, name="pyramidal")
pop_column.add_meta_group(pyr)  # add from existing meta-group

pop_column.create_meta_group(L3I.ids + L5I.ids, "interneurons")  # single line

pop_column.create_meta_group(L2GC.ids + L4GC.ids, "granule")

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Interacting with the NEST simulator#

This section details how to create detailed neuronal networks, then run simulations on them using the NEST simulator.

Readers are supposed to have a good grap of the way NEST handles neurons and models, and how to create and setup NEST nodes. If this is not the case, please see the NEST user doc and the PyNEST tutorials first.

NNGT tools with regard to NEST can be separated into

the structural tools (Network, NeuralPop …) that are used to prepare the neuronal network and setup its properties and connectivity; these tools should be used before
the make_nest_network() and the associated, to_nest() functions that are used to send the previously prepared network to NEST;
then, after using one of the previous functions, all the other functions contained in the nngt.simulation module can be used to add stimulations to the neurons or monitor them.

Note

Calls to nest.ResetKernel will also reset all networks and populations, which means that after such a call, populations, parameters, etc, can again be changed until the next invocation of make_nest_network() or to_nest().

Example files associated to the interactions between NEST and NNGT can be found here: docs/examples/nest_network.py / docs/examples/nest_receptor_ports.py.

Content:

Creating detailed neuronal networks
- NeuralPop and NeuralGroup
- The Network class
Changing the parameters of neurons
- Before sending the network to NEST
- After sending the network to NEST, randomizing

Creating detailed neuronal networks #

NeuralPop and NeuralGroup#

These two classes are the basic blocks to design neuronal networks: a NeuralGroup is a set of neurons sharing common properties while the NeuralPop is the main container that represents the whole network as an ensemble of groups.

Depending on your perspective, you can either create the groups first, then build the population from them, or create the population first, then split it into various groups.

For more details on groups and populations, see Neural groups and populations.

Neuronal groups before the population

Neural groups can be created as follow:

# 100 inhibitory neurons
basic_group = nngt.NeuralGroup(100, neuron_type=-1)
# 10 excitatory (default) aeif neurons
aeif_group  = nngt.NeuralGroup(10, neuron_model="aeif_psc_alpha")
# an unspecified number of aeif neurons with specific parameters
p = {"E_L": -58., "V_th": -54.}
aeif_g2 = nngt.NeuralGroup(neuron_model="aeif_psc_alpha", neuron_param=p)

In the case where the number of neurons is specified upon creation, NNGT can check that the number of neurons matches in the network and the associated population and raise a warning if they don’t. However, it is just a security check and it does not prevent the network for being created if the numbers don’t match.

Once the groups are created, you can simply generate the population using

pop = nngt.NeuralPop.from_groups([basic_group, aeif_group], ["b", "a"])

This created a population separated into “a” and “b” from the previously created groups.

Population before the groups

A population with excitatory and inhibitory neurons

pop = nngt.NeuralPop(1000)
pop.create_group(800, "first")
pop.create_group(200, "second", neuron_type=-1)

or, more compact

pop = nngt.NeuralPop.exc_and_inhib(1000, iratio=0.2)

The Network class#

Besides connectivity, the main interest of the NeuralGroup is that you can pass it the biological properties that the neurons belonging to this group will share.

Since we are using NEST, these properties are:

the model’s name
its non-default properties
the synapses that the neurons have and their properties
the type of the neurons (1 for excitatory or -1 for inhibitory)

''' Create groups with different parameters '''
# adaptive spiking neurons
base_params = {
    'E_L': -60., 'V_th': -58., 'b': 20., 'tau_w': 100.,
    'V_reset': -65., 't_ref': 2., 'g_L': 10., 'C_m': 250.
}
# oscillators
params1, params2 = base_params.copy(), base_params.copy()
params1.update(
    {'E_L': -65., 'b': 40., 'I_e': 200., 'tau_w': 400., "V_th": -57.})
# bursters
params2.update({'b': 25., 'V_reset': -55., 'tau_w': 300.})

oscill = nngt.NeuralGroup(
    nodes=400, neuron_model='aeif_psc_alpha', neuron_type=1,
    neuron_param=params1)

burst = nngt.NeuralGroup(
    nodes=200, neuron_model='aeif_psc_alpha', neuron_type=1,
    neuron_param=params2)

adapt = nngt.NeuralGroup(
    nodes=200, neuron_model='aeif_psc_alpha', neuron_type=1,
    neuron_param=base_params)

synapses = {
    'default': {'model': 'tsodyks2_synapse'},
    ('oscillators', 'bursters'): {'model': 'tsodyks2_synapse', 'U': 0.6},
    ('oscillators', 'oscillators'): {'model': 'tsodyks2_synapse', 'U': 0.7},
    ('oscillators', 'adaptive'): {'model': 'tsodyks2_synapse', 'U': 0.5}
}

'''
Create the population that will represent the neuronal
network from these groups
'''
pop = nngt.NeuralPop.from_groups(
    [oscill, burst, adapt],
    names=['oscillators', 'bursters', 'adaptive'], syn_spec=synapses)

'''
Create the network from this population,
using a Gaussian in-degree
'''
net = ng.gaussian_degree(

Once this network is created, it can simply be sent to nest through the command: gids = net.to_nest(), and the NEST gids are returned.

In order to access the gids from each group, you can do:

oscill_gids = net.nest_gids[oscill.ids]

or directly:

oscill_gids = oscill.nest_gids

As shown in “Use with NEST”, synaptic strength from inhibitory neurons in NNGT are positive (for compatibility with graph analysis tools) but they are automatically converted to negative values when the network is created in NEST.

Changing the parameters of neurons #

Before sending the network to NEST #

Once the NeuralPop has been created, you can change the parameters of the neuron groups before you send the network to NEST.

To do this, you can use the set_param() function, to which you pass the parameter dict and the name of the NeuralGroup you want to modify.

If you are dealing directly with NeuralGroup objects, you can access and modify their neuron_param attribute as long as the network has not been sent to nest. Once sent, these parameters become unsettable and any wourkaround to circumvent this will not change the values inside NEST anyway.

After sending the network to NEST, randomizing #

Once the network has been sent to NEST, neuronal parameters can still be changed, but only for randomization purposes. It is possible to randomize the neuronal parameters through the randomize_neural_states() function. This sets the parameters using a specified distribution and stores their values inside the network nodes’ attributes.

Go to other tutorials:

Principle #

The interesting fact about having a link between the graph and the simulation is that you can easily analyze the activity be taking into account what you know from the graph structure.

Sorted rasters #

Rater plots can be sorted depending on some specific node property, e.g. the degree or the betweenness:

import nest

import nngt
from nngt.simulation import monitor_nodes, plot_activity

pop = nngt.NeuralPop.uniform(1000, neuron_model="aeif_psc_alpha")
net = nngt.generation.gaussian_degree(100, 20, population=pop)

nodes = net.to_nest()
recorders, recordables = monitor_nodes(nodes)
simtime = 1000.
nest.Simulate(simtime)

fignums = plot_activity(
    recorders, recordables, network=net, show=True, hist=False,
    limits=(0.,simtime), sort="in-degree")

Activity properties #

NNGT can also be used to analyze the general properties of a raster.

Either from a .gdf file containing the raster data

import nngt
from nngt.simulation import analyze_raster

a = analyze_raster("path/to/raster.gdf")
print(a.phases)
print(a.properties)

Or from a spike detector gid sd:

a = analyze_raster(sd)

Additional information:

Simulation module#

Module to interact easily with the NEST simulator. It allows to:

build a NEST network from Network or SpatialNetwork objects,
monitor the activity of the network (taking neural groups into account)
plot the activity while separating the behaviours of predefined neural groups

Content#

`nngt.simulation.ActivityRecord`(spike_data, …)	Class to record the properties of the simulated activity.
`nngt.simulation.activity_types`(…[, …])	Analyze the spiking pattern of a neural network.
`nngt.simulation.analyze_raster`([raster, …])	Return the activity types for a given raster.
`nngt.simulation.get_nest_adjacency`([…])	Get the adjacency matrix describing a NEST network.
`nngt.simulation.get_recording`(network, record)	Return the evolution of some recorded values for each neuron.
`nngt.simulation.make_nest_network`(network[, …])	Create a new network which will be filled with neurons and connector objects to reproduce the topology from the initial network.
`nngt.simulation.monitor_groups`(group_names, …)	Monitoring the activity of nodes in the network.
`nngt.simulation.monitor_nodes`(gids[, …])	Monitoring the activity of nodes in the network.
`nngt.simulation.plot_activity`([…])	Plot the monitored activity.
`nngt.simulation.randomize_neural_states`(…)	Randomize the neural states according to the instructions.
`nngt.simulation.raster_plot`(times, senders)	Plotting routine that constructs a raster plot along with an optional histogram.
`nngt.simulation.reproducible_weights`(…[, …])	Find the values of the connection weights that will give PSP responses of min_weight and max_weight in mV.
`nngt.simulation.save_spikes`(filename[, …])	Plot the monitored activity.
`nngt.simulation.set_minis`(network, …[, …])	Mimick spontaneous release of neurotransmitters, called miniature PSCs or “minis” that can occur at excitatory (mEPSCs) or inhibitory (mIPSCs) synapses.
`nngt.simulation.set_noise`(gids, mean, std)	Submit neurons to a current white noise.
`nngt.simulation.set_poisson_input`(gids, rate)	Submit neurons to a Poissonian rate of spikes.
`nngt.simulation.set_step_currents`(gids, …)	Set step-current excitations

Details#

class nngt.simulation.ActivityRecord(spike_data, phases, properties, parameters=None)[source]#

Class to record the properties of the simulated activity.

Initialize the instance using spike_data (store proxy to an optional network) and compute the properties of provided data.

Parameters:

Parameters:	spike_data (2D array) – Array of shape (num_spikes, 2), containing the senders on the 1st row and the times on the 2nd row. phases (dict) – Limits of the different phases in the simulated period. properties (dict) – Values of the different properties of the activity (e.g. “firing_rate”, “IBI”…). parameters (dict, optional (default: None)) – Parameters used to compute the phases.

spike_data (2D array) – Array of shape (num_spikes, 2), containing the senders on the 1st row and the times on the 2nd row.
phases (dict) – Limits of the different phases in the simulated period.
properties (dict) – Values of the different properties of the activity (e.g. “firing_rate”, “IBI”…).
parameters (dict, optional (default: None)) – Parameters used to compute the phases.

Note

The firing rate is computed as num_spikes / total simulation time, the period is the sum of an IBI and a bursting period.

data#: Returns the (N, 2) array of (senders, spike times).

phases#

“bursting” for periods of high activity where a large fraction of the network is recruited.
“quiescent” for periods of low activity
“mixed” for firing rate in between “quiescent” and “bursting”.
“localized” for periods of high activity but where only a small fraction of the network is recruited.

Note

See parameters for details on the conditions used to differenciate these phases.

Type:	Return the phases detected

properties#

Returns the properties of the activity. Contains the following entries:

“firing_rate”: average value in Hz for 1 neuron in the network.
“bursting”: True if there were bursts of activity detected.
“burst_duration”, “IBI”, “ISI”, and “period” in ms, if “bursting” is True.
“SpB” (Spikes per Burst): average number of spikes per neuron during a burst.

simplify()[source]#

nngt.simulation.activity_types(spike_detector, limits, network=None, phase_coeff=(0.5, 10.0), mbis=0.5, mfb=0.2, mflb=0.05, skip_bursts=0, simplify=False, fignums=[], show=False)[source]#

Analyze the spiking pattern of a neural network.

@todo:: think about inserting t=0. and t=simtime at the beginning and at the end of times.

Parameters:

Parameters:	spike_detector (NEST node(s) (tuple or list of tuples)) – The recording device that monitored the network’s spikes. limits (tuple of floats) – Time limits of the simulation region which should be studied (in ms). network (`Network`, optional (default: None)) – Neural network that was analyzed phase_coeff (tuple of floats, optional (default: (0.2, 5.))) – A phase is considered ‘bursting’ when the interspike between all spikes that compose it is smaller than `phase_coeff[0] / avg_rate` (where `avg_rate` is the average firing rate), ‘quiescent’ when it is greater that `phase_coeff[1] / avg_rate`, ‘mixed’ otherwise. mbis (float, optional (default: 0.5)) – Maximum interspike interval allowed for two spikes to be considered in the same burst (in ms). mfb (float, optional (default: 0.2)) – Minimal fraction of the neurons that should participate for a burst to be validated (i.e. if the interspike is smaller that the limit BUT the number of participating neurons is too small, the phase will be considered as ‘localized’). mflb (float, optional (default: 0.05)) – Minimal fraction of the neurons that should participate for a local burst to be validated (i.e. if the interspike is smaller that the limit BUT the number of participating neurons is too small, the phase will be considered as ‘mixed’). skip_bursts (int, optional (default: 0)) – Skip the skip_bursts first bursts to consider only the permanent regime. simplify (bool, optional (default: False)) – If `True`, ‘mixed’ phases that are contiguous to a burst are incorporated to it. return_steps (bool, optional (default: False)) – If `True`, a second dictionary, phases_steps will also be returned. @todo: not implemented yet fignums (list, optional (default: [])) – Indices of figures on which the periods can be drawn. show (bool, optional (default: False)) – Whether the figures should be displayed.

spike_detector (NEST node(s) (tuple or list of tuples)) – The recording device that monitored the network’s spikes.
limits (tuple of floats) – Time limits of the simulation region which should be studied (in ms).
network (Network, optional (default: None)) – Neural network that was analyzed
phase_coeff (tuple of floats, optional (default: (0.2, 5.))) – A phase is considered ‘bursting’ when the interspike between all spikes that compose it is smaller than phase_coeff[0] / avg_rate (where avg_rate is the average firing rate), ‘quiescent’ when it is greater that phase_coeff[1] / avg_rate, ‘mixed’ otherwise.
mbis (float, optional (default: 0.5)) – Maximum interspike interval allowed for two spikes to be considered in the same burst (in ms).
mfb (float, optional (default: 0.2)) – Minimal fraction of the neurons that should participate for a burst to be validated (i.e. if the interspike is smaller that the limit BUT the number of participating neurons is too small, the phase will be considered as ‘localized’).
mflb (float, optional (default: 0.05)) – Minimal fraction of the neurons that should participate for a local burst to be validated (i.e. if the interspike is smaller that the limit BUT the number of participating neurons is too small, the phase will be considered as ‘mixed’).
skip_bursts (int, optional (default: 0)) – Skip the skip_bursts first bursts to consider only the permanent regime.
simplify (bool, optional (default: False)) – If True, ‘mixed’ phases that are contiguous to a burst are incorporated to it.
return_steps (bool, optional (default: False)) – If True, a second dictionary, phases_steps will also be returned. @todo: not implemented yet
fignums (list, optional (default: [])) – Indices of figures on which the periods can be drawn.
show (bool, optional (default: False)) – Whether the figures should be displayed.

Note

Effects of skip_bursts and limits[0] are cumulative: the limits[0] first milliseconds are ignored, then the skip_bursts first bursts of the remaining activity are ignored.

Returns:	phases (dict) – Dictionary containing the time intervals (in ms) for all four phases (bursting’, `quiescent’, `mixed’, and `localized) as lists. E.g: `phases["bursting"]` could give `[[123.5,334.2], [857.1,1000.6]]`.

nngt.simulation.analyze_raster(raster=None, limits=None, network=None, phase_coeff=(0.5, 10.0), mbis=0.5, mfb=0.2, mflb=0.05, skip_bursts=0, skip_ms=0.0, simplify=False, fignums=[], show=False)[source]#

Return the activity types for a given raster.

Parameters:

Parameters:	raster (array-like (N, 2) or str) – Either an array containing the ids of the spiking neurons on the first column, then the corresponding times on the second column, or the path to a NEST .gdf recording. limits (tuple of floats) – Time limits of the simulation regrion which should be studied (in ms). network (`Network`, optional (default: None)) – Network on which the recorded activity was simulated. phase_coeff (tuple of floats, optional (default: (0.2, 5.))) – A phase is considered ‘bursting’ when the interspike between all spikes that compose it is smaller than `phase_coeff[0] / avg_rate` (where `avg_rate` is the average firing rate), ‘quiescent’ when it is greater that `phase_coeff[1] / avg_rate`, ‘mixed’ otherwise. mbis (float, optional (default: 0.5)) – Maximum interspike interval allowed for two spikes to be considered in the same burst (in ms). mfb (float, optional (default: 0.2)) – Minimal fraction of the neurons that should participate for a burst to be validated (i.e. if the interspike is smaller that the limit BUT the number of participating neurons is too small, the phase will be considered as ‘localized’). mflb (float, optional (default: 0.05)) – Minimal fraction of the neurons that should participate for a local burst to be validated (i.e. if the interspike is smaller that the limit BUT the number of participating neurons is too small, the phase will be considered as ‘mixed’). skip_bursts (int, optional (default: 0)) – Skip the skip_bursts first bursts to consider only the permanent regime. simplify (bool, optional (default: False)) – If `True`, ‘mixed’ phases that are contiguous to a burst are incorporated to it. fignums (list, optional (default: [])) – Indices of figures on which the periods can be drawn. show (bool, optional (default: False)) – Whether the figures should be displayed.

raster (array-like (N, 2) or str) – Either an array containing the ids of the spiking neurons on the first column, then the corresponding times on the second column, or the path to a NEST .gdf recording.
limits (tuple of floats) – Time limits of the simulation regrion which should be studied (in ms).
network (Network, optional (default: None)) – Network on which the recorded activity was simulated.
phase_coeff (tuple of floats, optional (default: (0.2, 5.))) – A phase is considered ‘bursting’ when the interspike between all spikes that compose it is smaller than phase_coeff[0] / avg_rate (where avg_rate is the average firing rate), ‘quiescent’ when it is greater that phase_coeff[1] / avg_rate, ‘mixed’ otherwise.
mbis (float, optional (default: 0.5)) – Maximum interspike interval allowed for two spikes to be considered in the same burst (in ms).
mfb (float, optional (default: 0.2)) – Minimal fraction of the neurons that should participate for a burst to be validated (i.e. if the interspike is smaller that the limit BUT the number of participating neurons is too small, the phase will be considered as ‘localized’).
mflb (float, optional (default: 0.05)) – Minimal fraction of the neurons that should participate for a local burst to be validated (i.e. if the interspike is smaller that the limit BUT the number of participating neurons is too small, the phase will be considered as ‘mixed’).
skip_bursts (int, optional (default: 0)) – Skip the skip_bursts first bursts to consider only the permanent regime.
simplify (bool, optional (default: False)) – If True, ‘mixed’ phases that are contiguous to a burst are incorporated to it.
fignums (list, optional (default: [])) – Indices of figures on which the periods can be drawn.
show (bool, optional (default: False)) – Whether the figures should be displayed.

Note

Effects of skip_bursts and limits[0] are cumulative: the limits[0] first milliseconds are ignored, then the skip_bursts first bursts of the remaining activity are ignored.

Returns:	activity (ActivityRecord) – Object containing the phases and the properties of the activity from these phases.

nngt.simulation.get_nest_adjacency(id_converter=None)[source]#

Get the adjacency matrix describing a NEST network.

Parameters:	id_converter (dict, optional (default: None)) – A dictionary which maps NEST gids to the desired neurons ids.
Returns:	mat_adj (`lil_matrix`) – Adjacency matrix of the network.

nngt.simulation.get_recording(network, record, recorder=None, nodes=None)[source]#

Return the evolution of some recorded values for each neuron.

Parameters:

Parameters:	network (`nngt.Network`) – Network for which the activity was simulated. record (str or list) – Name of the record(s) to obtain. recorder (tuple of ints, optional (default: all multimeters)) – GID of the “spike_detector” objects recording the network activity. nodes (array-like, optional (default: all nodes)) – NNGT ids of the nodes for which the recording should be returned.
Returns:	values (dict of dict of arrays) – Dictionary containing, for each record, an M array with the recorded values for n-th neuron is stored under entry n (integer). A times entry is also added; it should be the same size for all records, otherwise an error will be raised.

network (nngt.Network) – Network for which the activity was simulated.
record (str or list) – Name of the record(s) to obtain.
recorder (tuple of ints, optional (default: all multimeters)) – GID of the “spike_detector” objects recording the network activity.
nodes (array-like, optional (default: all nodes)) – NNGT ids of the nodes for which the recording should be returned.

Returns:

values (dict of dict of arrays) – Dictionary containing, for each record, an M array with the recorded values for n-th neuron is stored under entry n (integer). A times entry is also added; it should be the same size for all records, otherwise an error will be raised.

Examples

After the creation of a Network called net, use the following code:

import nest

rec, _ = monitor_nodes(
    net.nest_gids, "multimeter", {"record_from": ["V_m"]}, net)
nest.Simulate(100.)
recording = nngt.simulation.get_recording(net, "V_m")

# access the membrane potential of first neuron + the times
V_m   = recording["V_m"][0]
times = recording["times"]

nngt.simulation.make_nest_network(network, send_only=None, weights=True)[source]#

Create a new network which will be filled with neurons and connector objects to reproduce the topology from the initial network.

Changed in version 0.8: Added send_only parameter.

Parameters:

Parameters:	network (`nngt.Network` or `nngt.SpatialNetwork`) – the network we want to reproduce in NEST. send_only (int, str, or list of str, optional (default: None)) – Restrict the nodes that are created in NEST to either inhibitory or excitatory neurons send_only $\in \{ 1, -1\}$ to a group or a list of groups. weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if `None` or `False` then use binary edges; if `True`, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.
Returns:	gids (tuple (nodes in NEST)) – GIDs of the neurons in the network.

network (nngt.Network or nngt.SpatialNetwork) – the network we want to reproduce in NEST.
send_only (int, str, or list of str, optional (default: None)) – Restrict the nodes that are created in NEST to either inhibitory or excitatory neurons send_only $\in \{ 1, -1\}$ to a group or a list of groups.
weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if None or False then use binary edges; if True, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.

Returns:

gids (tuple (nodes in NEST)) – GIDs of the neurons in the network.

nngt.simulation.monitor_groups(group_names, network, nest_recorder=None, params=None)[source]#

Monitoring the activity of nodes in the network.

Parameters:

Parameters:	group_name (list of strings) – Names of the groups that should be recorded. network (`Network` or subclass) – Network which population will be used to differentiate groups. nest_recorder (strings or list, optional (default: “spike_detector”0)) – Device(s) to monitor the network. params (dict or list of, optional (default: {})) – Dictionarie(s) containing the parameters for each recorder (see NEST documentation for details).
Returns:	recorders (list or NodeCollection of the recorders’ gids) recordables (list of the recordables’ names.)

group_name (list of strings) – Names of the groups that should be recorded.
network (Network or subclass) – Network which population will be used to differentiate groups.
nest_recorder (strings or list, optional (default: “spike_detector”0)) – Device(s) to monitor the network.
params (dict or list of, optional (default: {})) – Dictionarie(s) containing the parameters for each recorder (see NEST documentation for details).

Returns:

recorders (list or NodeCollection of the recorders’ gids)
recordables (list of the recordables’ names.)

nngt.simulation.monitor_nodes(gids, nest_recorder=None, params=None, network=None)[source]#

Monitoring the activity of nodes in the network.

Parameters:

Parameters:	gids (tuple of ints or list of tuples) – GIDs of the neurons in the NEST subnetwork; either one list per recorder if they should monitor different neurons or a unique list which will be monitored by all devices. nest_recorder (strings or list, optional (default: “spike_detector”)) – Device(s) to monitor the network. params (dict or list of, optional (default: {})) – Dictionarie(s) containing the parameters for each recorder (see NEST documentation for details). network (`Network` or subclass, optional (default: None)) – Network which population will be used to differentiate groups.
Returns:	recorders (list or NodeCollection containing the recorders’ gids) recordables (list of the recordables’ names.)

gids (tuple of ints or list of tuples) – GIDs of the neurons in the NEST subnetwork; either one list per recorder if they should monitor different neurons or a unique list which will be monitored by all devices.
nest_recorder (strings or list, optional (default: “spike_detector”)) – Device(s) to monitor the network.
params (dict or list of, optional (default: {})) – Dictionarie(s) containing the parameters for each recorder (see NEST documentation for details).
network (Network or subclass, optional (default: None)) – Network which population will be used to differentiate groups.

Returns:

recorders (list or NodeCollection containing the recorders’ gids)
recordables (list of the recordables’ names.)

nngt.simulation.plot_activity(gid_recorder=None, record=None, network=None, gids=None, axis=None, show=False, limits=None, histogram=False, title=None, fignum=None, label=None, sort=None, average=False, normalize=1.0, decimate=None, transparent=True, kernel_center=0.0, kernel_std=None, resolution=None, cut_gaussian=5.0)[source]#

Plot the monitored activity.

Changed in version 1.2: Switched hist to histogram and default value to False.

Changed in version 1.0.1: Added axis parameter, restored missing fignum parameter.

Parameters:

Parameters:	gid_recorder (tuple or list of tuples, optional (default: None)) – The gids of the recording devices. If None, then all existing “spike_detector”s are used. record (tuple or list, optional (default: None)) – List of the monitored variables for each device. If gid_recorder is None, record can also be None and only spikes are considered. network (`Network` or subclass, optional (default: None)) – Network which activity will be monitored. gids (tuple, optional (default: None)) – NEST gids of the neurons which should be monitored. axis (matplotlib axis object, optional (default: new one)) – Axis that should be use to plot the activity. This takes precedence over fignum. show (bool, optional (default: False)) – Whether to show the plot right away or to wait for the next plt.show(). histogram (bool, optional (default: False)) – Whether to display the histogram when plotting spikes rasters. limits (tuple, optional (default: None)) – Time limits of the plot (if not specified, times of first and last spike for raster plots). title (str, optional (default: None)) – Title of the plot. fignum (int, or dict, optional (default: None)) – Plot the activity on an existing figure (from `figure.number`). This parameter is ignored if axis is provided. label (str or list, optional (default: None)) – Add labels to the plot (one per recorder). sort (str or list, optional (default: None)) – Sort neurons using a topological property (“in-degree”, “out-degree”, “total-degree” or “betweenness”), an activity-related property (“firing_rate” or neuronal property) or a user-defined list of sorted neuron ids. Sorting is performed by increasing value of the sort property from bottom to top inside each group. normalize (float or list, optional (default: None)) – Normalize the recorded results by a given float. If a list is provided, there should be one entry per voltmeter or multimeter in the recorders. If the recording was done through monitor_groups, the population can be passed to normalize the data by the nuber of nodes in each group. decimate (int or list of ints, optional (default: None)) – Represent only a fraction of the spiking neurons; only one neuron in decimate will be represented (e.g. setting decimate to 5 will lead to only 20% of the neurons being represented). If a list is provided, it must have one entry per NeuralGroup in the population. kernel_center (float, optional (default: 0.)) – Temporal shift of the Gaussian kernel, in ms (for the histogram). kernel_std (float, optional (default: 0.5% of simulation time)) – Characteristic width of the Gaussian kernel (standard deviation) in ms (for the histogram). resolution (float or array, optional (default: 0.1kernel_std)) – The resolution at which the firing rate values will be computed. Choosing a value smaller than kernel_std is strongly advised. If resolution is an array, it will be considered as the times were the firing rate should be computed (for the histogram). cut_gaussian* (float, optional (default: 5.)) – Range over which the Gaussian will be computed (for the histogram). By default, we consider the 5-sigma range. Decreasing this value will increase speed at the cost of lower fidelity; increasing it with increase the fidelity at the cost of speed.

gid_recorder (tuple or list of tuples, optional (default: None)) – The gids of the recording devices. If None, then all existing “spike_detector”s are used.
record (tuple or list, optional (default: None)) – List of the monitored variables for each device. If gid_recorder is None, record can also be None and only spikes are considered.
network (Network or subclass, optional (default: None)) – Network which activity will be monitored.
gids (tuple, optional (default: None)) – NEST gids of the neurons which should be monitored.
axis (matplotlib axis object, optional (default: new one)) – Axis that should be use to plot the activity. This takes precedence over fignum.
show (bool, optional (default: False)) – Whether to show the plot right away or to wait for the next plt.show().
histogram (bool, optional (default: False)) – Whether to display the histogram when plotting spikes rasters.
limits (tuple, optional (default: None)) – Time limits of the plot (if not specified, times of first and last spike for raster plots).
title (str, optional (default: None)) – Title of the plot.
fignum (int, or dict, optional (default: None)) – Plot the activity on an existing figure (from figure.number). This parameter is ignored if axis is provided.
label (str or list, optional (default: None)) – Add labels to the plot (one per recorder).
sort (str or list, optional (default: None)) – Sort neurons using a topological property (“in-degree”, “out-degree”, “total-degree” or “betweenness”), an activity-related property (“firing_rate” or neuronal property) or a user-defined list of sorted neuron ids. Sorting is performed by increasing value of the sort property from bottom to top inside each group.
normalize (float or list, optional (default: None)) – Normalize the recorded results by a given float. If a list is provided, there should be one entry per voltmeter or multimeter in the recorders. If the recording was done through monitor_groups, the population can be passed to normalize the data by the nuber of nodes in each group.
decimate (int or list of ints, optional (default: None)) – Represent only a fraction of the spiking neurons; only one neuron in decimate will be represented (e.g. setting decimate to 5 will lead to only 20% of the neurons being represented). If a list is provided, it must have one entry per NeuralGroup in the population.
kernel_center (float, optional (default: 0.)) – Temporal shift of the Gaussian kernel, in ms (for the histogram).
kernel_std (float, optional (default: 0.5% of simulation time)) – Characteristic width of the Gaussian kernel (standard deviation) in ms (for the histogram).
resolution (float or array, optional (default: 0.1*kernel_std)) – The resolution at which the firing rate values will be computed. Choosing a value smaller than kernel_std is strongly advised. If resolution is an array, it will be considered as the times were the firing rate should be computed (for the histogram).
cut_gaussian (float, optional (default: 5.)) – Range over which the Gaussian will be computed (for the histogram). By default, we consider the 5-sigma range. Decreasing this value will increase speed at the cost of lower fidelity; increasing it with increase the fidelity at the cost of speed.

Warning

Sorting with “firing_rate” only works if NEST gids form a continuous integer range.

Returns:	lines (list of lists of `matplotlib.lines.Line2D`) – Lines containing the data that was plotted, grouped by figure.

nngt.simulation.randomize_neural_states(network, instructions, groups=None, nodes=None, make_nest=False)[source]#

Randomize the neural states according to the instructions.

Changed in version 0.8: Changed ids to nodes argument.

Parameters:

Parameters:	network (`Network` subclass instance) – Network that will be simulated. instructions (dict) – Variables to initialize. Allowed keys are “V_m” and “w”. Values are 3-tuples of type `("distrib_name", double, double)`. groups (list of `NeuralGroup`, optional (default: None)) – If provided, only the neurons belonging to these groups will have their properties randomized. nodes (array-like, optional (default: all neurons)) – NNGT ids of the neurons that will have their status randomized. make_nest (bool, optional (default: False)) – If `True` and network has not been converted to NEST, automatically generate the network, else raises an exception.

network (Network subclass instance) – Network that will be simulated.
instructions (dict) – Variables to initialize. Allowed keys are “V_m” and “w”. Values are 3-tuples of type ("distrib_name", double, double).
groups (list of NeuralGroup, optional (default: None)) – If provided, only the neurons belonging to these groups will have their properties randomized.
nodes (array-like, optional (default: all neurons)) – NNGT ids of the neurons that will have their status randomized.
make_nest (bool, optional (default: False)) – If True and network has not been converted to NEST, automatically generate the network, else raises an exception.

Example

instructions = {
    "V_m": ("uniform", -80., -60.),
    "w": ("normal", 50., 5.)
}

nngt.simulation.raster_plot(times, senders, limits=None, title='Spike raster', histogram=False, num_bins=1000, color='b', decimate=None, axis=None, fignum=None, label=None, show=True, sort=None, sort_attribute=None, network=None, transparent=True, kernel_center=0.0, kernel_std=30.0, resolution=None, cut_gaussian=5.0, **kwargs)[source]#

Plotting routine that constructs a raster plot along with an optional histogram.

Changed in version 1.2: Switched hist to histogram.

Changed in version 1.0.1: Added axis parameter.

Parameters:

Parameters:	times (list or `numpy.ndarray`) – Spike times. senders (list or `numpy.ndarray`) – Index for the spiking neuron for each time in times. limits (tuple, optional (default: None)) – Time limits of the plot (if not specified, times of first and last spike). title (string, optional (default: ‘Spike raster’)) – Title of the raster plot. histogram (bool, optional (default: True)) – Whether to plot the raster’s histogram. num_bins (int, optional (default: 1000)) – Number of bins for the histogram. color (string or float, optional (default: ‘b’)) – Color of the plot lines and markers. decimate (int, optional (default: None)) – Represent only a fraction of the spiking neurons; only one neuron in decimate will be represented (e.g. setting decimate to 10 will lead to only 10% of the neurons being represented). axis (matplotlib axis object, optional (default: new one)) – Axis that should be use to plot the activity. fignum (int, optional (default: None)) – Id of another raster plot to which the new data should be added. label (str, optional (default: None)) – Label the current data. show (bool, optional (default: True)) – Whether to show the plot right away or to wait for the next plt.show(). kernel_center (float, optional (default: 0.)) – Temporal shift of the Gaussian kernel, in ms. kernel_std (float, optional (default: 30.)) – Characteristic width of the Gaussian kernel (standard deviation) in ms. resolution (float or array, optional (default: 0.1kernel_std)) – The resolution at which the firing rate values will be computed. Choosing a value smaller than kernel_std is strongly advised. If resolution is an array, it will be considered as the times were the firing rate should be computed. cut_gaussian* (float, optional (default: 5.)) – Range over which the Gaussian will be computed (for the histogram). By default, we consider the 5-sigma range. Decreasing this value will increase speed at the cost of lower fidelity; increasing it with increase the fidelity at the cost of speed.
Returns:	lines (list of `matplotlib.lines.Line2D`) – Lines containing the data that was plotted.

times (list or numpy.ndarray) – Spike times.
senders (list or numpy.ndarray) – Index for the spiking neuron for each time in times.
limits (tuple, optional (default: None)) – Time limits of the plot (if not specified, times of first and last spike).
title (string, optional (default: ‘Spike raster’)) – Title of the raster plot.
histogram (bool, optional (default: True)) – Whether to plot the raster’s histogram.
num_bins (int, optional (default: 1000)) – Number of bins for the histogram.
color (string or float, optional (default: ‘b’)) – Color of the plot lines and markers.
decimate (int, optional (default: None)) – Represent only a fraction of the spiking neurons; only one neuron in decimate will be represented (e.g. setting decimate to 10 will lead to only 10% of the neurons being represented).
axis (matplotlib axis object, optional (default: new one)) – Axis that should be use to plot the activity.
fignum (int, optional (default: None)) – Id of another raster plot to which the new data should be added.
label (str, optional (default: None)) – Label the current data.
show (bool, optional (default: True)) – Whether to show the plot right away or to wait for the next plt.show().
kernel_center (float, optional (default: 0.)) – Temporal shift of the Gaussian kernel, in ms.
kernel_std (float, optional (default: 30.)) – Characteristic width of the Gaussian kernel (standard deviation) in ms.
resolution (float or array, optional (default: 0.1*kernel_std)) – The resolution at which the firing rate values will be computed. Choosing a value smaller than kernel_std is strongly advised. If resolution is an array, it will be considered as the times were the firing rate should be computed.
cut_gaussian (float, optional (default: 5.)) – Range over which the Gaussian will be computed (for the histogram). By default, we consider the 5-sigma range. Decreasing this value will increase speed at the cost of lower fidelity; increasing it with increase the fidelity at the cost of speed.

Returns:

lines (list of matplotlib.lines.Line2D) – Lines containing the data that was plotted.

nngt.simulation.reproducible_weights(weights, neuron_model, di_param={}, timestep=0.05, simtime=50.0, num_bins=1000, log=False)[source]#

Find the values of the connection weights that will give PSP responses of min_weight and max_weight in mV.

Parameters:

Parameters:	weights (list of floats) – Exact desired synaptic weights. neuron_model (string) – Name of the model used. di_param (dict, optional (default: {})) – Parameters of the model, default parameters if not supplied. timestep (float, optional (default: 0.01)) – Timestep of the simulation in ms. simtime (float, optional (default: 10.)) – Simulation time in ms (default: 10). num_bins (int, optional (default: 10000)) – Number of bins used to discretize the exact synaptic weights. log (bool, optional (default: False)) – Whether bins should use a logarithmic scale.

weights (list of floats) – Exact desired synaptic weights.
neuron_model (string) – Name of the model used.
di_param (dict, optional (default: {})) – Parameters of the model, default parameters if not supplied.
timestep (float, optional (default: 0.01)) – Timestep of the simulation in ms.
simtime (float, optional (default: 10.)) – Simulation time in ms (default: 10).
num_bins (int, optional (default: 10000)) – Number of bins used to discretize the exact synaptic weights.
log (bool, optional (default: False)) – Whether bins should use a logarithmic scale.

Note

If the parameters used are not the default ones, they MUST be provided, otherwise the resulting weights will likely be WRONG.

nngt.simulation.save_spikes(filename, recorder=None, network=None, save_positions=True, **kwargs)[source]#

Plot the monitored activity.

New in version 0.7.

Parameters:

Parameters:	filename (str) – Path to the file where the activity should be saved. recorder (tuple or list of tuples, optional (default: None)) – The NEST gids of the recording devices. If None, then all existing “spike_detector”s are used. network (`Network` or subclass, optional (default: None)) – Network which activity will be monitored. save_positions (bool, optional (default: True)) – Whether to include the position of the neurons in the file; this requires network to be provided. **kwargs (see `numpy.savetxt()`)

filename (str) – Path to the file where the activity should be saved.
recorder (tuple or list of tuples, optional (default: None)) – The NEST gids of the recording devices. If None, then all existing “spike_detector”s are used.
network (Network or subclass, optional (default: None)) – Network which activity will be monitored.
save_positions (bool, optional (default: True)) – Whether to include the position of the neurons in the file; this requires network to be provided.
**kwargs (see numpy.savetxt())

nngt.simulation.set_minis(network, base_rate, weight, syn_type=1, nodes=None, gids=None)[source]#

Mimick spontaneous release of neurotransmitters, called miniature PSCs or “minis” that can occur at excitatory (mEPSCs) or inhibitory (mIPSCs) synapses. These minis consists in only a fraction of the usual strength of a spike- triggered PSC and can be modeled by a Poisson process. This Poisson process occurs independently at every synapse of a neuron, so a neuron receiving $k$ inputs will be subjected to these events with a rate $k*\lambda$ , where $\lambda$ is the base rate.

Parameters:

Parameters:	network (`Network` object) – Network on which the minis should be simulated. base_rate (float) – Rate for the Poisson process on one synapse ( $\lambda$ ), in Hz. weight (float or array of size N) – Amplitude of a minitature post-synaptic event. syn_type (int, optional (default: 1)) – Synaptic type of the noisy connections. By default, mEPSCs are generated, by taking into account only the excitatory degrees and synaptic weights. To setup mIPSCs, used syn_type=-1. nodes (array-like (size N), optional (default: all nodes)) – NNGT ids of the neurons that should be subjected to minis. gids (array-like (size N), optional (default: all neurons)) – NEST gids of the neurons that should be subjected to minis.

network (Network object) – Network on which the minis should be simulated.
base_rate (float) – Rate for the Poisson process on one synapse ( $\lambda$ ), in Hz.
weight (float or array of size N) – Amplitude of a minitature post-synaptic event.
syn_type (int, optional (default: 1)) – Synaptic type of the noisy connections. By default, mEPSCs are generated, by taking into account only the excitatory degrees and synaptic weights. To setup mIPSCs, used syn_type=-1.
nodes (array-like (size N), optional (default: all nodes)) – NNGT ids of the neurons that should be subjected to minis.
gids (array-like (size N), optional (default: all neurons)) – NEST gids of the neurons that should be subjected to minis.

Note

nodes and gids are not compatible, only one one the two arguments can be used in any given call to set_minis.

nngt.simulation.set_noise(gids, mean, std)[source]#

Submit neurons to a current white noise.

Parameters:	gids (tuple) – NEST gids of the target neurons. mean (float) – Mean current value. std (float) – Standard deviation of the current
Returns:	noise (tuple) – The NEST gid of the noise_generator.

nngt.simulation.set_poisson_input(gids, rate, syn_spec=None, **kwargs)[source]#

Submit neurons to a Poissonian rate of spikes.

Changed in version 2.0: Added kwargs.

Parameters:	gids (tuple) – NEST gids of the target neurons. rate (float) – Rate of the spike train (in Hz). syn_spec (dict, optional (default: static synapse with weight 1)) – Properties of the connection between the `poisson_generator` object and the target neurons. *kwargs (dict*) – Other optional parameters for the poisson_generator.
Returns:	poisson_input (tuple) – The NEST gid of the `poisson_generator`.

nngt.simulation.set_step_currents(gids, times, currents)[source]#

Set step-current excitations

Parameters:	gids (tuple) – NEST gids of the target neurons. times (list or `numpy.ndarray`) – List of the times where the current will change (by default the current generator is initiated at I=0. for t=0.) currents (list or `numpy.ndarray`) – List of the new current value after the associated time value in times.
Returns:	noise (tuple) – The NEST gid of the noise_generator.

Go to other tutorials:

Note

This library provides many tools which will (or not) be loaded on startup depending on the python packages available on your computer. The default behaviour of those tools is set in the ~/.nngt/nngt.conf file (see Configuration). Moreover, to see all potential messages related to the import of those tools, you can use the logging function of NNGT, either by setting the log_level value to INFO, or by setting log_to_file to True, and having a look at the log file in ~/.nngt/log/.

Description#

The graph objects#

Neural networks are described by four graph classes which inherit from the main class of the chosen graph library (gt.Graph, igraph.Graph or networkx.DiGraph):

Graph: base for simple topological graphs with no spatial structure, nor biological properties
SpatialGraph: subclass for spatial graphs without biological properties
Network: subclass for topological graphs with biological properties (to interact with NEST)
SpatialNetwork: subclass with spatial and biological properties (to interact with NEST)

Using these objects, the user can access to the topological structure of the network (for neuroscience, this includes the connections’ type – inhibitory or excitatory – and its synaptic weight, which is always positive)

Additional properties#

Nodes/neurons are defined by a unique index which can be used to access their properties and those of the connections between them.

The graph objects can have other attributes, such as:

shape for SpatialGraph and SpatialNetwork, which describes the spatial delimitations of the neurons’ environment (e.g. many in vitro culture are contained in circular dishes),
population, for Network, which contains informations on the various groups of neurons that exist in the network (for instance inhibitory and excitatory neurons can be grouped together),
connections which stores the informations about the synaptic connections between the neurons.

Graph-theoretical models#

Several classical graphs are efficiently implemented and the generation procedures are detailed in the documentation.

Main module#

Overview

NNGT
- Available modules
- Units
Main classes and functions
Details

For more details regarding the main classes, see:

Graph classes#

`nngt.Graph`([nodes, name, weighted, …])	The basic graph class, which inherits from a library class such as `graph_tool.Graph`, `networkx.DiGraph`, or `igraph.Graph`.
`nngt.SpatialGraph`([nodes, name, weighted, …])	The detailed class that inherits from `Graph` and implements additional properties to describe spatial graphs (i.e.
`nngt.Network`([name, weighted, directed, …])	The detailed class that inherits from `Graph` and implements additional properties to describe various biological functions and interact with the NEST simulator.
`nngt.SpatialNetwork`(population[, name, …])	Class that inherits from `Network` and `SpatialGraph` to provide a detailed description of a real neural network in space, i.e.

Details#

class nngt.Graph(nodes=0, name='Graph', weighted=True, directed=True, copy_graph=None, **kwargs)[source]#

The basic graph class, which inherits from a library class such as graph_tool.Graph, networkx.DiGraph, or igraph.Graph.

The objects provides several functions to easily access some basic properties.

Initialize Graph instance

Changed in version 2.0: Renamed from_graph to copy_graph.

Parameters:

Parameters:	nodes (int, optional (default: 0)) – Number of nodes in the graph. name (string, optional (default: “Graph”)) – The name of this `Graph` instance. weighted (bool, optional (default: True)) – Whether the graph edges have weight properties. directed (bool, optional (default: True)) – Whether the graph is directed or undirected. copy_graph (`Graph`, optional) – An optional `Graph` that will be copied. kwargs (optional keywords arguments) – Optional arguments that can be passed to the graph, e.g. a dict containing information on the synaptic weights (`weights={"distribution": "constant", "value": 2.3}` which is equivalent to `weights=2.3`), the synaptic delays, or a `type` information.

nodes (int, optional (default: 0)) – Number of nodes in the graph.
name (string, optional (default: “Graph”)) – The name of this Graph instance.
weighted (bool, optional (default: True)) – Whether the graph edges have weight properties.
directed (bool, optional (default: True)) – Whether the graph is directed or undirected.
copy_graph (Graph, optional) – An optional Graph that will be copied.
kwargs (optional keywords arguments) – Optional arguments that can be passed to the graph, e.g. a dict containing information on the synaptic weights (weights={"distribution": "constant", "value": 2.3} which is equivalent to weights=2.3), the synaptic delays, or a type information.

Note

When using copy_graph, only the topological properties are copied (nodes, edges, and attributes), spatial and biological properties are ignored. To copy a graph exactly, use copy().

Returns:	self (`Graph`)

adjacency_matrix(types=False, weights=False, mformat='csr')[source]#

Return the graph adjacency matrix.

Note

Source nodes are represented by the rows, targets by the corresponding columns.

Parameters:

Parameters:	types (bool, optional (default: False)) – Wether the edge types should be taken into account (negative values for inhibitory connections). weights (bool or string, optional (default: False)) – Whether the adjacecy matrix should be weighted. If True, all connections are multiply bythe associated synaptic strength; if weight is a string, the connections are scaled bythe corresponding edge attribute. mformat (str, optional (default: “csr”)) – Type of `scipy.sparse` matrix that will be returned, by default `scipy.sparse.csr_matrix`.
Returns:	mat (`scipy.sparse` matrix) – The adjacency matrix of the graph.

types (bool, optional (default: False)) – Wether the edge types should be taken into account (negative values for inhibitory connections).
weights (bool or string, optional (default: False)) – Whether the adjacecy matrix should be weighted. If True, all connections are multiply bythe associated synaptic strength; if weight is a string, the connections are scaled bythe corresponding edge attribute.
mformat (str, optional (default: “csr”)) – Type of scipy.sparse matrix that will be returned, by default scipy.sparse.csr_matrix.

Returns:

mat (scipy.sparse matrix) – The adjacency matrix of the graph.

copy()[source]#: Returns a deepcopy of the current Graph instance

edge_attributes#: Access edge attributes.

See also

node_attributes, get_edge_attributes, new_edge_attribute, set_edge_attribute.

edge_id(edge)#

Return the ID a given edge or a list of edges in the graph. Raises an error if the edge is not in the graph or if one of the vertices in the edge is nonexistent.

Parameters:	edge (2-tuple or array of edges) – Edge descriptor (source, target).
Returns:	index (int or array of ints) – Index of the given edge.

edge_nb()#: Returns the number of edges.

Warning

When using MPI, returns only the local number of edges.

edges_array#: Edges of the graph, sorted by order of creation, as an array of 2-tuple.

edges_attributes#: Access edge attributes.

See also

node_attributes, get_edge_attributes, new_edge_attribute, set_edge_attribute.

static from_file(filename, fmt='auto', separator=' ', secondary=';', attributes=None, attributes_types=None, notifier='@', ignore='#', from_string=False, name='LoadedGraph', directed=True, cleanup=False)[source]#

Import a saved graph from a file.

Changed in version 2.0: Added optional attributes_types and cleanup arguments.

Parameters:

Parameters:	filename (str) – The path to the file. fmt (str, optional (default: deduced from filename)) – The format used to save the graph. Supported formats are: “neighbour” (neighbour list), “ssp” (scipy.sparse), “edge_list” (list of all the edges in the graph, one edge per line, represented by a `source target`-pair), “gml” (gml format, default if filename ends with ‘.gml’), “graphml” (graphml format, default if filename ends with ‘.graphml’ or ‘.xml’), “dot” (dot format, default if filename ends with ‘.dot’), “gt” (only when using graph_tool as library, detected if filename ends with ‘.gt’). separator (str, optional (default ” “)) – separator used to separate inputs in the case of custom formats (namely “neighbour” and “edge_list”) secondary (str, optional (default: “;”)) – Secondary separator used to separate attributes in the case of custom formats. attributes (list, optional (default: [])) – List of names for the attributes present in the file. If a notifier is present in the file, names will be deduced from it; otherwise the attributes will be numbered. For “edge_list”, attributes may also be present as additional columns after the source and the target. attributes_types (dict, optional (default: str)) – Backup information if the type of the attributes is not specified in the file. Values must be callables (types or functions) that will take the argument value as a string input and convert it to the proper type. notifier (str, optional (default: “@”)) – Symbol specifying the following as meaningfull information. Relevant information are formatted `@info_name=info_value`, where `info_name` is in (“attributes”, “directed”, “name”, “size”) and associated `info_value` are of type (`list`, `bool`, `str`, `int`). Additional notifiers are `@type=SpatialGraph/Network/SpatialNetwork`, which must be followed by the relevant notifiers among `@shape`, `@population`, and `@graph`. from_string (bool, optional (default: False)) – Load from a string instead of a file. ignore (str, optional (default: “#”)) – Ignore lines starting with the ignore string. name (str, optional (default: from file information or ‘LoadedGraph’)) – The name of the graph. directed (bool, optional (default: from file information or True)) – Whether the graph is directed or not. cleanup (bool, optional (default: False)) – If true, removes nodes before the first one that appears in the edges and after the last one and renumber the nodes from 0.
Returns:	graph (`Graph` or subclass) – Loaded graph.

filename (str) – The path to the file.
fmt (str, optional (default: deduced from filename)) – The format used to save the graph. Supported formats are: “neighbour” (neighbour list), “ssp” (scipy.sparse), “edge_list” (list of all the edges in the graph, one edge per line, represented by a source target-pair), “gml” (gml format, default if filename ends with ‘.gml’), “graphml” (graphml format, default if filename ends with ‘.graphml’ or ‘.xml’), “dot” (dot format, default if filename ends with ‘.dot’), “gt” (only when using graph_tool as library, detected if filename ends with ‘.gt’).
separator (str, optional (default ” “)) – separator used to separate inputs in the case of custom formats (namely “neighbour” and “edge_list”)
secondary (str, optional (default: “;”)) – Secondary separator used to separate attributes in the case of custom formats.
attributes (list, optional (default: [])) – List of names for the attributes present in the file. If a notifier is present in the file, names will be deduced from it; otherwise the attributes will be numbered. For “edge_list”, attributes may also be present as additional columns after the source and the target.
attributes_types (dict, optional (default: str)) – Backup information if the type of the attributes is not specified in the file. Values must be callables (types or functions) that will take the argument value as a string input and convert it to the proper type.
notifier (str, optional (default: “@”)) – Symbol specifying the following as meaningfull information. Relevant information are formatted @info_name=info_value, where info_name is in (“attributes”, “directed”, “name”, “size”) and associated info_value are of type (list, bool, str, int). Additional notifiers are @type=SpatialGraph/Network/SpatialNetwork, which must be followed by the relevant notifiers among @shape, @population, and @graph.
from_string (bool, optional (default: False)) – Load from a string instead of a file.
ignore (str, optional (default: “#”)) – Ignore lines starting with the ignore string.
name (str, optional (default: from file information or ‘LoadedGraph’)) – The name of the graph.
directed (bool, optional (default: from file information or True)) – Whether the graph is directed or not.
cleanup (bool, optional (default: False)) – If true, removes nodes before the first one that appears in the edges and after the last one and renumber the nodes from 0.

Returns:

graph (Graph or subclass) – Loaded graph.

classmethod from_library(library_graph, name='ImportedGraph', weighted=True, directed=True, **kwargs)[source]#

Create a Graph by wrapping a graph object from one of the supported libraries.

Parameters:	library_graph (object) – Graph object from one of the supported libraries (graph-tool, igraph, networkx). name (str, optional (default: “ImportedGraph”)) **kwargs – Other standard arguments (see `__init__()`)

classmethod from_matrix(matrix, weighted=True, directed=True, population=None, shape=None, positions=None, name=None)[source]#

Creates a Graph from a scipy.sparse matrix or a dense matrix.

Parameters:

Parameters:	matrix (`scipy.sparse` matrix or `numpy.ndarray`) – Adjacency matrix. weighted (bool, optional (default: True)) – Whether the graph edges have weight properties. directed (bool, optional (default: True)) – Whether the graph is directed or undirected. population (`NeuralPop`) – Population to associate to the new `Network`. shape (`Shape`, optional (default: None)) – Shape to associate to the new `SpatialGraph`. positions ((N, 2) array) – Positions, in a 2D space, of the N neurons. name (str, optional) – Graph name.
Returns:	`Graph`

matrix (scipy.sparse matrix or numpy.ndarray) – Adjacency matrix.
weighted (bool, optional (default: True)) – Whether the graph edges have weight properties.
directed (bool, optional (default: True)) – Whether the graph is directed or undirected.
population (NeuralPop) – Population to associate to the new Network.
shape (Shape, optional (default: None)) – Shape to associate to the new SpatialGraph.
positions ((N, 2) array) – Positions, in a 2D space, of the N neurons.
name (str, optional) – Graph name.

Returns:

Graph

get_attribute_type(attribute_name, attribute_class=None)[source]#

Return the type of an attribute (e.g. string, double, int).

Parameters:	attribute_name (str) – Name of the attribute. attribute_class (str, optional (default: both)) – Whether attribute_name is a “node” or an “edge” attribute.
Returns:	type (str) – Type of the attribute.

get_betweenness(btype='both', weights=None)[source]#

Returns the normalized betweenness centrality of the nodes and edges.

Parameters:

Parameters:	g (`Graph`) – Graph to analyze. btype (str, optional (default ‘both’)) – The centrality that should be returned (either ‘node’, ‘edge’, or ‘both’). By default, both betweenness centralities are computed. weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if `None` or `False` then use binary edges; if `True`, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.
Returns:	nb (`numpy.ndarray`) – The nodes’ betweenness if btype is ‘node’ or ‘both’ eb (`numpy.ndarray`) – The edges’ betweenness if btype is ‘edge’ or ‘both’

g (Graph) – Graph to analyze.
btype (str, optional (default ‘both’)) – The centrality that should be returned (either ‘node’, ‘edge’, or ‘both’). By default, both betweenness centralities are computed.
weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if None or False then use binary edges; if True, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.

Returns:

nb (numpy.ndarray) – The nodes’ betweenness if btype is ‘node’ or ‘both’
eb (numpy.ndarray) – The edges’ betweenness if btype is ‘edge’ or ‘both’

See also

betweenness()

get_degrees(mode='total', nodes=None, weights=None, edge_type='all')[source]#

Degree sequence of all the nodes.

Changed in version 2.0: Changed deg_type to mode, node_list to nodes, use_weights to weights, and edge_type to edge_type.

Parameters:

Parameters:	mode (string, optional (default: “total”)) – Degree type (among ‘in’, ‘out’ or ‘total’). nodes (list, optional (default: None)) – List of the nodes which degree should be returned weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if `None` or `False` then use binary edges; if `True`, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required. edge_type (int or str, optional (default: all)) – Restrict to a given synaptic type (“excitatory”, 1, or “inhibitory”, -1), using either the “type” edge attribute for non-`Network` or the `inhibitory` nodes.
Returns:	degrees (`numpy.array`) .. warning :: – When using MPI with “nngt” (distributed) backend, returns only the degrees associated to local edges. “Complete” degrees are obtained by taking the sum of the results on all MPI processes.

mode (string, optional (default: “total”)) – Degree type (among ‘in’, ‘out’ or ‘total’).
nodes (list, optional (default: None)) – List of the nodes which degree should be returned
weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if None or False then use binary edges; if True, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.
edge_type (int or str, optional (default: all)) – Restrict to a given synaptic type (“excitatory”, 1, or “inhibitory”, -1), using either the “type” edge attribute for non-Network or the inhibitory nodes.

Returns:

degrees (numpy.array)
.. warning :: – When using MPI with “nngt” (distributed) backend, returns only the degrees associated to local edges. “Complete” degrees are obtained by taking the sum of the results on all MPI processes.

get_delays(edges=None)[source]#

Returns the delays of all or a subset of the edges.

Changed in version 1.0.1: Added the possibility to ask for a subset of edges.

Parameters:	edges ((E, 2) array, optional (default: all edges)) – Edges for which the type should be returned.
Returns:	the list of delays

get_density()[source]#: Density of the graph: $\frac{E}{N^2}$ , where E is the number of edges and N the number of nodes.

get_edge_attributes(edges=None, name=None)[source]#

Attributes of the graph’s edges.

Changed in version 1.0: Returns the full dict of edges attributes if called without arguments.

New in version 0.8.

Parameters:

Parameters:	edge (tuple or list of tuples, optional (default: `None`)) – Edge whose attribute should be displayed. name (str, optional (default: `None`)) – Name of the desired attribute.
Returns:	Dict containing all graph’s attributes (synaptic weights, delays…) by default. If edge is specified, returns only the values for these edges. If name is specified, returns value of the attribute for each edge.

edge (tuple or list of tuples, optional (default: None)) – Edge whose attribute should be displayed.
name (str, optional (default: None)) – Name of the desired attribute.

Returns:

Dict containing all graph’s attributes (synaptic weights, delays…)
by default. If edge is specified, returns only the values for these
edges. If name is specified, returns value of the attribute for each
edge.

Note

The attributes values are ordered as the edges in edges_array() if edges is None.

get_edge_types(edges=None)[source]#

Return the type of all or a subset of the edges.

Changed in version 1.0.1: Added the possibility to ask for a subset of edges.

Parameters:	edges ((E, 2) array, optional (default: all edges)) – Edges for which the type should be returned.
Returns:	the list of types (1 for excitatory, -1 for inhibitory)

get_edges(attribute=None, value=None, source_node=None, target_node=None)[source]#

Return the edges in the network fulfilling a given condition.

Parameters:

Parameters:	attribute (str, optional (default: all nodes)) – Whether the attribute of the returned edges should have a specific value. value (object, optional (default : None)) – If an attribute name is passed, then only edges with attribute being equal to value will be returned. source_node (int or list of ints, optional (default: all nodes)) – Retrict the edges to those stemming from source_node. target_node (int or list of ints, optional (default: all nodes)) – Retrict the edges to those arriving at target_node.

attribute (str, optional (default: all nodes)) – Whether the attribute of the returned edges should have a specific value.
value (object, optional (default : None)) – If an attribute name is passed, then only edges with attribute being equal to value will be returned.
source_node (int or list of ints, optional (default: all nodes)) – Retrict the edges to those stemming from source_node.
target_node (int or list of ints, optional (default: all nodes)) – Retrict the edges to those arriving at target_node.

See also

get_nodes(), edge_attributes

get_node_attributes(nodes=None, name=None)[source]#

Attributes of the graph’s edges.

Changed in version 1.0.1: Corrected default behavior and made it the same as get_edge_attributes().

New in version 0.9.

Parameters:

nodes (list of ints, optional (default: None)) – Nodes whose attribute should be displayed.
name (str, optional (default: None)) – Name of the desired attribute.

Returns:

Dict containing all nodes attributes by default. If nodes is
specified, returns a dict containing only the attributes of these
nodes. If name is specified, returns a list containing the values of
the specific attribute for the required nodes (or all nodes if
unspecified).

get_nodes(attribute=None, value=None)[source]#

Return the nodes in the network fulfilling a given condition.

Parameters:	attribute (str, optional (default: all nodes)) – Whether the attribute of the returned nodes should have a specific value. value (object, optional (default : None)) – If an attribute name is passed, then only nodes with attribute being equal to value will be returned.

See also

get_edges(), node_attributes

get_weights(edges=None)[source]#

Returns the weights of all or a subset of the edges.

Changed in version 1.0.1: Added the possibility to ask for a subset of edges.

Parameters:	edges ((E, 2) array, optional (default: all edges)) – Edges for which the type should be returned.
Returns:	the list of weights

graph#: Returns the underlying library object.

Warning

Do not add or remove edges directly through this object.

See also

Underlying graph objects and libraries, Consistent tools for graph analysis

graph_id#: Unique int identifying the instance.

is_connected(mode='strong')[source]#

Return whether the graph is connected.

Parameters:	mode (str, optional (default: “strong”)) – Whether to test connectedness with directed (“strong”) or undirected (“weak”) connections.

References

[ig-connected]

igraph - is_connected

is_directed()[source]#: Whether the graph is directed or not

is_network()[source]#: Whether the graph is a subclass of Network (i.e. if it has a NeuralPop attribute).

is_spatial()[source]#: Whether the graph is embedded in space (i.e. is a subclass of SpatialGraph).

is_weighted()[source]#: Whether the edges have weights

static make_network(graph, neural_pop, copy=False, **kwargs)[source]#

Turn a Graph object into a Network, or a SpatialGraph into a SpatialNetwork.

Parameters:	graph (`Graph` or `SpatialGraph`) – Graph to convert neural_pop (`NeuralPop`) – Population to associate to the new `Network` copy (bool, optional (default: `False`)) – Whether the operation should be made in-place on the object or if a new object should be returned.

Notes

In-place operation that directly converts the original graph if copy is False, else returns the copied Graph turned into a Network.

static make_spatial(graph, shape=None, positions=None, copy=False)[source]#

Turn a Graph object into a SpatialGraph, or a Network into a SpatialNetwork.

Parameters:	graph (`Graph` or `SpatialGraph`) – Graph to convert. shape (`Shape`, optional (default: None)) – Shape to associate to the new `SpatialGraph`. positions ((N, 2) array) – Positions, in a 2D space, of the N neurons. copy (bool, optional (default: `False`)) – Whether the operation should be made in-place on the object or if a new object should be returned.

Notes

In-place operation that directly converts the original graph if copy is False, else returns the copied Graph turned into a SpatialGraph. The shape argument can be skipped if positions are given; in that case, the neurons will be embedded in a rectangle that contains them all.

name#: Name of the graph.

neighbours(node, mode='all')[source]#

Return the neighbours of node.

Parameters:	node (int) – Index of the node of interest. mode (string, optional (default: “all”)) – Type of neighbours that will be returned: “all” returns all the neighbours regardless of directionality, “in” returns the in-neighbours (also called predecessors) and “out” retruns the out-neighbours (or successors).
Returns:	neighbours (set) – The neighbours of node.

new_edge(source, target, attributes=None, ignore=False, self_loop=False)#

Adding a connection to the graph, with optional properties.

Changed in version 2.0: Added self_loop argument to enable adding self-loops.

Parameters:

source (int/node) – Source node.
target (int/node) – Target node.
attributes (dict, optional (default: {})) – Dictionary containing optional edge properties. If the graph is weighted, defaults to {"weight": 1.}, the unit weight for the connection (synaptic strength in NEST).
ignore (bool, optional (default: False)) – If set to True, ignore attempts to add an existing edge and accept self-loops; otherwise an error is raised.
self_loop (bool, optional (default: False)) – Whether to allow self-loops or not.

Returns:

The new connection or None if nothing was added.

new_edge_attribute(name, value_type, values=None, val=None)[source]#

Create a new attribute for the edges.

Parameters:

name (str) – The name of the new attribute.
value_type (str) – Type of the attribute, among ‘int’, ‘double’, ‘string’, or ‘object’
values (array, optional (default: None)) – Values with which the edge attribute should be initialized. (must have one entry per node in the graph)
val (int, float or str , optional (default: None)) – Identical value for all edges.

new_edges(edge_list, attributes=None, check_duplicates=False, check_self_loops=True, check_existing=True, ignore_invalid=False)#

Add a list of edges to the graph.

Changed in version 2.0: Can perform all possible checks before adding new edges via the check_duplicates check_self_loops, and check_existing arguments.

Parameters:

edge_list (list of 2-tuples or np.array of shape (edge_nb, 2)) – List of the edges that should be added as tuples (source, target)
attributes (dict, optional (default: {})) – Dictionary containing optional edge properties. If the graph is weighted, defaults to {"weight": ones}, where ones is an array the same length as the edge_list containing a unit weight for each connection (synaptic strength in NEST).
check_duplicates (bool, optional (default: False)) – Check for duplicate edges within edge_list.
check_self_loops (bool, optional (default: True)) – Check for self-loops.
check_existing (bool, optional (default: True)) – Check whether some of the edges in edge_list already exist in the graph or exist multiple times in edge_list (also performs check_duplicates).
ignore_invalid (bool, optional (default: False)) – Ignore invalid edges: they are not added to the graph and are silently dropped. Unless this is set to true, an error is raised whenever one of the three checks fails.
.. warning:: – Setting check_existing to False will lead to undefined behavior if existing edges are provided! Only use it (for speedup) if you are sure that you are indeed only adding new edges.

Returns:

Returns new edges only.

new_node(n=1, neuron_type=1, attributes=None, value_types=None, positions=None, groups=None)#

Adding a node to the graph, with optional properties.

Parameters:

n (int, optional (default: 1)) – Number of nodes to add.
neuron_type (int, optional (default: 1)) – Type of neuron (1 for excitatory, -1 for inhibitory)
attributes (dict, optional (default: None)) – Dictionary containing the attributes of the nodes.
value_types (dict, optional (default: None)) – Dict of the attributes types, necessary only if the attributes do not exist yet.
positions (array of shape (n, 2), optional (default: None)) – Positions of the neurons. Valid only for SpatialGraph or SpatialNetwork.
groups (str, int, or list, optional (default: None)) – NeuralGroup to which the neurons belong. Valid only for Network or SpatialNetwork.

Returns:

The node or a tuple of the nodes created.

new_node_attribute(name, value_type, values=None, val=None)[source]#

Create a new attribute for the nodes.

Parameters:

name (str) – The name of the new attribute.
value_type (str) – Type of the attribute, among ‘int’, ‘double’, ‘string’, or ‘object’
values (array, optional (default: None)) – Values with which the node attribute should be initialized. (must have one entry per node in the graph)
val (int, float or str , optional (default: None)) – Identical value for all nodes.

node_attributes#: Access node attributes.

See also

edge_attributes, get_node_attributes, new_node_attribute, set_node_attribute.

node_nb()#: Returns the number of nodes.

Warning

When using MPI, returns only the local number of nodes.

nodes_attributes#: Access node attributes.

See also

edge_attributes, get_node_attributes, new_node_attribute, set_node_attribute.

classmethod num_graphs()[source]#: Returns the number of alive instances.

set_delays(delay=None, elist=None, distribution=None, parameters=None, noise_scale=None)[source]#

Set the delay for spike propagation between neurons.

Parameters:

delay (float or class:numpy.array, optional (default: None)) – Value or list of delays (for user defined delays).
elist (class:numpy.array, optional (default: None)) – List of the edges (for user defined delays).
distribution (class:string, optional (default: None)) – Type of distribution (choose among “constant”, “uniform”, “gaussian”, “lognormal”, “lin_corr”, “log_corr”).
parameters (dict, optional (default: {})) – Dictionary containing the properties of the delay distribution.
noise_scale (class:int, optional (default: None)) – Scale of the multiplicative Gaussian noise that should be applied on the delays.

set_edge_attribute(attribute, values=None, val=None, value_type=None, edges=None)[source]#

Set attributes to the connections between neurons.

Warning

The special “type” attribute cannot be modified when using graphs that inherit from the Network class. This is because for biological networks, neurons make only one kind of synapse, which is determined by the nngt.NeuralGroup they belong to.

Parameters:

attribute (str) – The name of the attribute.
value_type (str) – Type of the attribute, among ‘int’, ‘double’, ‘string’
values (array, optional (default: None)) – Values with which the edge attribute should be initialized. (must have one entry per node in the graph)
val (int, float or str , optional (default: None)) – Identical value for all edges.
value_type (str, optional (default: None)) – Type of the attribute, among ‘int’, ‘double’, ‘string’. Only used if the attribute does not exist and must be created.
edges (list of edges or array of shape (E, 2), optional (default: all)) – Edges whose attributes should be set. Others will remain unchanged.

set_name(name='')[source]#: set graph name

set_node_attribute(attribute, values=None, val=None, value_type=None, nodes=None)[source]#

Set attributes to the connections between neurons.

Parameters:

attribute (str) – The name of the attribute.
value_type (str) – Type of the attribute, among ‘int’, ‘double’, ‘string’
values (array, optional (default: None)) – Values with which the edge attribute should be initialized. (must have one entry per node in the graph)
val (int, float or str , optional (default: None)) – Identical value for all edges.
value_type (str, optional (default: None)) – Type of the attribute, among ‘int’, ‘double’, ‘string’. Only used if the attribute does not exist and must be created.
nodes (list of nodes, optional (default: all)) – Nodes whose attributes should be set. Others will remain unchanged.

set_types(edge_type, nodes=None, fraction=None)[source]#

Set the synaptic/connection types.

Changed in version 2.0: Changed syn_type to edge_type.

Warning

Parameters:

edge_type (int, string, or array of ints) – Type of the connection among ‘excitatory’ (also 1) or ‘inhibitory’ (also -1).
nodes (int, float or list, optional (default: None)) – If nodes is an int, number of nodes of the required type that will be created in the graph (all connections from inhibitory nodes are inhibitory); if it is a float, ratio of edge_type nodes in the graph; if it is a list, ids of the edge_type nodes.
fraction (float, optional (default: None)) – Fraction of the selected edges that will be set as edge_type (if nodes is not None, it is the fraction of the specified nodes’ edges, otherwise it is the fraction of all edges in the graph).

Returns:

t_list (numpy.ndarray) – List of the types in an order that matches the edges attribute of the graph.

set_weights(weight=None, elist=None, distribution=None, parameters=None, noise_scale=None)[source]#

Set the synaptic weights.

Parameters:

weight (float or class:numpy.array, optional (default: None)) – Value or list of the weights (for user defined weights).
elist (class:numpy.array, optional (default: None)) – List of the edges (for user defined weights).
distribution (class:string, optional (default: None)) – Type of distribution (choose among “constant”, “uniform”, “gaussian”, “lognormal”, “lin_corr”, “log_corr”).
parameters (dict, optional (default: {})) – Dictionary containing the properties of the weight distribution. Properties are as follow for the distributions
- ‘constant’: ‘value’
- ‘uniform’: ‘lower’, ‘upper’
- ‘gaussian’: ‘avg’, ‘std’
- ‘lognormal’: ‘position’, ‘scale’
noise_scale (class:int, optional (default: None)) – Scale of the multiplicative Gaussian noise that should be applied on the weights.

Note

If distribution and parameters are provided and the weights are set for the whole graph (elist is None), then the distribution properties will be kept as the new default for subsequent edges. That is, if new edges are created without specifying their weights, then these new weights will automatically be drawn from this previous distribution.

to_file(filename, fmt='auto', separator=' ', secondary=';', attributes=None, notifier='@')[source]#: Save graph to file; options detailed below.

See also

nngt.lib.save_to_file() function for options.

type#: Type of the graph.

class nngt.SpatialGraph(nodes=0, name='SpatialGraph', weighted=True, directed=True, from_graph=None, shape=None, positions=None, **kwargs)[source]#

The detailed class that inherits from Graph and implements additional properties to describe spatial graphs (i.e. graph where the structure is embedded in space.

Initialize SpatialClass instance.

Parameters:

nodes (int, optional (default: 0)) – Number of nodes in the graph.
name (string, optional (default: “Graph”)) – The name of this Graph instance.
weighted (bool, optional (default: True)) – Whether the graph edges have weight properties.
directed (bool, optional (default: True)) – Whether the graph is directed or undirected.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment (None leads to a square of side 1 cm)
positions (numpy.array (N, 2), optional (default: None)) – Positions of the neurons; if not specified and nodes is not 0, then neurons will be reparted at random inside the Shape object of the instance.
**kwargs (keyword arguments for Graph or) – Shape if no shape was given.

Returns:

self (SpatialGraph)

get_positions(neurons=None)[source]#

Returns a copy of the neurons’ positions as a (N, 2) array.

Parameters:	neurons (int or array-like, optional (default: all neurons)) – List of the neurons for which the position should be returned.

shape#: The environment’s spatial structure.

class nngt.Network(name='Network', weighted=True, directed=True, from_graph=None, population=None, inh_weight_factor=1.0, **kwargs)[source]#

The detailed class that inherits from Graph and implements additional properties to describe various biological functions and interact with the NEST simulator.

Initializes Network instance.

Parameters:

nodes (int, optional (default: 0)) – Number of nodes in the graph.
name (string, optional (default: “Graph”)) – The name of this Graph instance.
weighted (bool, optional (default: True)) – Whether the graph edges have weight properties.
directed (bool, optional (default: True)) – Whether the graph is directed or undirected.
from_graph (GraphObject, optional (default: None)) – An optional GraphObject to serve as base.
population (nngt.NeuralPop, (default: None)) – An object containing the neural groups and their properties: model(s) to use in NEST to simulate the neurons as well as their parameters.
inh_weight_factor (float, optional (default: 1.)) – Factor to apply to inhibitory synapses, to compensate for example the strength difference due to timescales between excitatory and inhibitory synapses.

Returns:

self (Network)

classmethod exc_and_inhib(size, iratio=0.2, en_model='aeif_cond_alpha', en_param=None, in_model='aeif_cond_alpha', in_param=None, syn_spec=None, **kwargs)[source]#

Generate a network containing a population of two neural groups: inhibitory and excitatory neurons.

Parameters:

size (int) – Number of neurons in the network.
i_ratio (double, optional (default: 0.2)) – Ratio of inhibitory neurons: $\frac{N_i}{N_e+N_i}$ .
en_model (string, optional (default: ‘aeif_cond_alpha’)) – Nest model for the excitatory neuron.
en_param (dict, optional (default: {})) – Dictionary of parameters for the the excitatory neuron.
in_model (string, optional (default: ‘aeif_cond_alpha’)) – Nest model for the inhibitory neuron.
in_param (dict, optional (default: {})) – Dictionary of parameters for the the inhibitory neuron.
syn_spec (dict, optional (default: static synapse)) – Dictionary containg a directed edge between groups as key and the associated synaptic parameters for the post-synaptic neurons (i.e. those of the second group) as value. If provided, all connections between groups will be set according to the values contained in syn_spec. Valid keys are:
- (‘excitatory’, ‘excitatory’)
- (‘excitatory’, ‘inhibitory’)
- (‘inhibitory’, ‘excitatory’)
- (‘inhibitory’, ‘inhibitory’)

Returns:

net (Network or subclass) – Network of disconnected excitatory and inhibitory neurons.

See also

exc_and_inhib()

classmethod from_gids(gids, get_connections=True, get_params=False, neuron_model='aeif_cond_alpha', neuron_param=None, syn_model='static_synapse', syn_param=None, **kwargs)[source]#

Generate a network from gids.

Warning

Unless get_connections and get_params is True, or if your population is homogeneous and you provide the required information, the information contained by the network and its population attribute will be erroneous! To prevent conflicts the to_nest() function is not available. If you know what you are doing, you should be able to find a workaround…

Parameters:

gids (array-like) – Ids of the neurons in NEST or simply user specified ids.
get_params (bool, optional (default: True)) – Whether the parameters should be obtained from NEST (can be very slow).
neuron_model (string, optional (default: None)) – Name of the NEST neural model to use when simulating the activity.
neuron_param (dict, optional (default: {})) – Dictionary containing the neural parameters; the default value will make NEST use the default parameters of the model.
syn_model (string, optional (default: ‘static_synapse’)) – NEST synaptic model to use when simulating the activity.
syn_param (dict, optional (default: {})) – Dictionary containing the synaptic parameters; the default value will make NEST use the default parameters of the model.

Returns:

net (Network or subclass) – Uniform network of disconnected neurons.

get_edge_types()[source]#

Return the type of all or a subset of the edges.

Changed in version 1.0.1: Added the possibility to ask for a subset of edges.

Parameters:	edges ((E, 2) array, optional (default: all edges)) – Edges for which the type should be returned.
Returns:	the list of types (1 for excitatory, -1 for inhibitory)

get_neuron_type(neuron_ids)[source]#

Return the type of the neurons (+1 for excitatory, -1 for inhibitory).

Parameters:	neuron_ids (int or tuple) – NEST gids.
Returns:	ids (int or tuple) – Ids in the network. Same type as the requested gids type.

id_from_nest_gid(gids)[source]#

Return the ids of the nodes in the nngt.Network instance from the corresponding NEST gids.

Parameters:	gids (int or tuple) – NEST gids.
Returns:	ids (int or tuple) – Ids in the network. Same type as the requested gids type.

nest_gids#

neuron_properties(idx_neuron)[source]#

Properties of a neuron in the graph.

Parameters:	idx_neuron (int) – Index of a neuron in the graph.
Returns:	dict of the neuron’s properties.

classmethod num_networks()[source]#: Returns the number of alive instances.

population#: NeuralPop that divides the neurons into groups with specific properties.

set_types(edge_type, nodes=None, fraction=None)[source]#

Set the synaptic/connection types.

Changed in version 2.0: Changed syn_type to edge_type.

Warning

Parameters:

edge_type (int, string, or array of ints) – Type of the connection among ‘excitatory’ (also 1) or ‘inhibitory’ (also -1).
nodes (int, float or list, optional (default: None)) – If nodes is an int, number of nodes of the required type that will be created in the graph (all connections from inhibitory nodes are inhibitory); if it is a float, ratio of edge_type nodes in the graph; if it is a list, ids of the edge_type nodes.
fraction (float, optional (default: None)) – Fraction of the selected edges that will be set as edge_type (if nodes is not None, it is the fraction of the specified nodes’ edges, otherwise it is the fraction of all edges in the graph).

Returns:

t_list (numpy.ndarray) – List of the types in an order that matches the edges attribute of the graph.

to_nest(send_only=None, weights=True)[source]#: Send the network to NEST.

See also

make_nest_network() for parameters

classmethod uniform(size, neuron_model='aeif_cond_alpha', neuron_param=None, syn_model='static_synapse', syn_param=None, **kwargs)[source]#

Generate a network containing only one type of neurons.

Parameters:

size (int) – Number of neurons in the network.
neuron_model (string, optional (default: ‘aief_cond_alpha’)) – Name of the NEST neural model to use when simulating the activity.
neuron_param (dict, optional (default: {})) – Dictionary containing the neural parameters; the default value will make NEST use the default parameters of the model.
syn_model (string, optional (default: ‘static_synapse’)) – NEST synaptic model to use when simulating the activity.
syn_param (dict, optional (default: {})) – Dictionary containing the synaptic parameters; the default value will make NEST use the default parameters of the model.

Returns:

net (Network or subclass) – Uniform network of disconnected neurons.

class nngt.SpatialNetwork(population, name='SpatialNetwork', weighted=True, directed=True, shape=None, from_graph=None, positions=None, **kwargs)[source]#

Class that inherits from Network and SpatialGraph to provide a detailed description of a real neural network in space, i.e. with positions and biological properties to interact with NEST.

Initialize SpatialNetwork instance

Parameters:

name (string, optional (default: “Graph”)) – The name of this Graph instance.
weighted (bool, optional (default: True)) – Whether the graph edges have weight properties.
directed (bool, optional (default: True)) – Whether the graph is directed or undirected.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment (None leads to a square of side 1 cm)
positions (numpy.array, optional (default: None)) – Positions of the neurons; if not specified and nodes != 0, then neurons will be reparted at random inside the Shape object of the instance.
population (class:~nngt.NeuralPop, optional (default: None)) – Population from which the network will be built.

Returns:

self (SpatialNetwork)

set_types(syn_type, nodes=None, fraction=None)[source]#

Set the synaptic/connection types.

Changed in version 2.0: Changed syn_type to edge_type.

Warning

Parameters:

edge_type (int, string, or array of ints) – Type of the connection among ‘excitatory’ (also 1) or ‘inhibitory’ (also -1).
nodes (int, float or list, optional (default: None)) – If nodes is an int, number of nodes of the required type that will be created in the graph (all connections from inhibitory nodes are inhibitory); if it is a float, ratio of edge_type nodes in the graph; if it is a list, ids of the edge_type nodes.
fraction (float, optional (default: None)) – Fraction of the selected edges that will be set as edge_type (if nodes is not None, it is the fraction of the specified nodes’ edges, otherwise it is the fraction of all edges in the graph).

Returns:

t_list (numpy.ndarray) – List of the types in an order that matches the edges attribute of the graph.

Main functions#

`nngt.generate`(di_instructions, **kwargs)	Generate a `Graph` or one of its subclasses from a `dict` containing all the relevant informations.
`nngt.get_config`([key, detailed])	Get the NNGT configuration as a dictionary.
`nngt.load_from_file`(filename[, fmt, …])	Load a Graph from a file.
`nngt.num_mpi_processes`()	Returns the number of MPI processes (1 if MPI is not used)
`nngt.on_master_process`()	Check whether the current code is executing on the master process (rank 0) if MPI is used.
`nngt.save_to_file`(graph, filename[, fmt, …])	Save a graph to file.
`nngt.seed`([msd, seeds])	Seed the random generator used by NNGT (i.e.
`nngt.set_config`(config[, value, silent])	Set NNGT’s configuration.
`nngt.use_backend`(backend[, reloading, silent])	Allows the user to switch to a specific graph library as backend.

Details#

nngt.generate(di_instructions, **kwargs)[source]#

Generate a Graph or one of its subclasses from a dict containing all the relevant informations.

Parameters:	di_instructions (`dict`) – Dictionary containing the instructions to generate the graph. It must have at least `"graph_type"` in its keys, with a value among `"distance_rule", "erdos_renyi", "fixed_degree", "newman_watts", "price_scale_free", "random_scale_free"`. Depending on the type, di_instructions should also contain at least all non-optional arguments of the generator function.

See also

generation

nngt.get_config(key=None, detailed=False)[source]#: Get the NNGT configuration as a dictionary.

Note

This function has no MPI barrier on it.

nngt.load_from_file(filename, fmt='auto', separator=' ', secondary=';', attributes=None, attributes_types=None, notifier='@', ignore='#', name='LoadedGraph', directed=True, cleanup=False)[source]#

Load a Graph from a file.

Changed in version 2.0: Added optional attributes_types and cleanup arguments.

Warning

Support for GraphML and DOT formats are currently limited and require one of the non-default backends (DOT requires graph-tool).

Parameters:

filename (str) – The path to the file.
fmt (str, optional (default: “neighbour”)) – The format used to save the graph. Supported formats are: “neighbour” (neighbour list, default if format cannot be deduced automatically), “ssp” (scipy.sparse), “edge_list” (list of all the edges in the graph, one edge per line, represented by a source target-pair), “gml” (gml format, default if filename ends with ‘.gml’), “graphml” (graphml format, default if filename ends with ‘.graphml’ or ‘.xml’), “dot” (dot format, default if filename ends with ‘.dot’), “gt” (only when using graph_tool`<http://graph-tool.skewed.de/>_ as library, detected if `filename ends with ‘.gt’).
separator (str, optional (default ” “)) – separator used to separate inputs in the case of custom formats (namely “neighbour” and “edge_list”)
secondary (str, optional (default: “;”)) – Secondary separator used to separate attributes in the case of custom formats.
attributes (list, optional (default: [])) – List of names for the attributes present in the file. If a notifier is present in the file, names will be deduced from it; otherwise the attributes will be numbered. For “edge_list”, attributes may also be present as additional columns after the source and the target.
attributes_types (dict, optional (default: str)) – Backup information if the type of the attributes is not specified in the file. Values must be callables (types or functions) that will take the argument value as a string input and convert it to the proper type.
notifier (str, optional (default: “@”)) – Symbol specifying the following as meaningfull information. Relevant information are formatted @info_name=info_value, where info_name is in (“attributes”, “directed”, “name”, “size”) and associated info_value are of type (list, bool, str, int). Additional notifiers are @type=SpatialGraph/Network/SpatialNetwork, which must be followed by the relevant notifiers among @shape, @population, and @graph.
ignore (str, optional (default: “#”)) – Ignore lines starting with the ignore string.
name (str, optional (default: from file information or ‘LoadedGraph’)) – The name of the graph.
directed (bool, optional (default: from file information or True)) – Whether the graph is directed or not.
cleanup (bool, optional (default: False)) – If true, removes nodes before the first one that appears in the edges and after the last one and renumber the nodes from 0.

Returns:

graph (Graph or subclass) – Loaded graph.

nngt.num_mpi_processes()[source]#: Returns the number of MPI processes (1 if MPI is not used)

nngt.on_master_process()[source]#

Check whether the current code is executing on the master process (rank 0) if MPI is used.

Returns:	True if rank is 0, if mpi4py is not present or if MPI is not used, otherwise False.

nngt.save_to_file(graph, filename, fmt='auto', separator=' ', secondary=';', attributes=None, notifier='@')[source]#

Save a graph to file.

@todo: implement dot, xml/graphml, and gt formats

Parameters:

graph (Graph or subclass) – Graph to save.
filename (str) – The path to the file.
fmt (str, optional (default: “auto”)) – The format used to save the graph. Supported formats are: “neighbour” (neighbour list, default if format cannot be deduced automatically), “ssp” (scipy.sparse), “edge_list” (list of all the edges in the graph, one edge per line, represented by a source target-pair), “gml” (gml format, default if filename ends with ‘.gml’), “graphml” (graphml format, default if filename ends with ‘.graphml’ or ‘.xml’), “dot” (dot format, default if filename ends with ‘.dot’), “gt” (only when using graph_tool as library, detected if filename ends with ‘.gt’).
separator (str, optional (default ” “)) – separator used to separate inputs in the case of custom formats (namely “neighbour” and “edge_list”)
secondary (str, optional (default: “;”)) – Secondary separator used to separate attributes in the case of custom formats.
attributes (list, optional (default: None)) – List of names for the edge attributes present in the graph that will be saved to disk; by default (None), all attributes will be saved.
notifier (str, optional (default: “@”)) – Symbol specifying the following as meaningfull information. Relevant information are formatted @info_name=info_value, with info_name in (“attributes”, “attr_types”, “directed”, “name”, “size”). Additional notifiers are @type=SpatialGraph/Network/SpatialNetwork, which are followed by the relevant notifiers among @shape, @population, and @graph to separate the sections.

Note

Positions are saved as bytes by numpy.nparray.tostring()

nngt.seed(msd=None, seeds=None)[source]#

Seed the random generator used by NNGT (i.e. the numpy RandomState: for details, see numpy.random.RandomState).

Parameters:	msd (int, optional) – Master seed for numpy RandomState. Must be convertible to 32-bit unsigned integers. seeds (list of ints, optional) – Seeds for RandomState (when using MPI). Must be convertible to 32-bit unsigned integers, one entry per MPI process.

nngt.set_config(config, value=None, silent=False)[source]#

Set NNGT’s configuration.

Parameters:	config (dict or str) – Either a full configuration dictionary or one key to be set together with its associated value. value (object, optional (default: None)) – Value associated to config if config is a key.

Examples

>>> nngt.set_config({'multithreading': True, 'omp': 4})
>>> nngt.set_config('multithreading', False)

Notes

See the config file nngt/nngt.conf.default or ~/.nngt/nngt.conf for details about your configuration.

This function has an MPI barrier on it, so it must always be called on all processes.

See also

get_config()

nngt.use_backend(backend, reloading=True, silent=False)[source]#

Allows the user to switch to a specific graph library as backend.

Warning

If Graph objects have already been created, they will no longer be compatible with NNGT methods.

Parameters:

backend (string) – Name of a graph library among ‘graph_tool’, ‘igraph’, ‘networkx’, or ‘nngt’.
reloading (bool, optional (default: True)) – Whether the graph objects should be reloaded through reload (this should always be set to True except when NNGT is first initiated!)
silent (bool, optional (default: False)) – Whether the changes made to the configuration should be logged at the DEBUG (True) or INFO (False) level.

Side classes#

`nngt.GroupProperty`(size[, constraints, …])	Class defining the properties needed to create groups of neurons from an existing `Graph` or one of its subclasses.
`nngt.MetaGroup`([nodes, name])	Class defining a meta-group of neurons.
`nngt.NeuralGroup`([nodes, neuron_type, …])	Class defining groups of neurons.
`nngt.NeuralPop`([size, parent, meta_groups, …])	The basic class that contains groups of neurons and their properties.

Details#

class nngt.GroupProperty(size, constraints={}, neuron_model=None, neuron_param={}, syn_model=None, syn_param={})[source]#

Class defining the properties needed to create groups of neurons from an existing Graph or one of its subclasses.

Variables:	size – `int` Size of the group. constraints – `dict`, optional (default: {}) Constraints to respect when building the `NeuralGroup` . neuron_model – str, optional (default: None) name of the model to use when simulating the activity of this group. neuron_param – dict, optional (default: {}) the parameters to use (if they differ from the model’s defaults)

Create a new instance of GroupProperties.

Notes

The constraints can be chosen among:

“avg_deg”, “min_deg”, “max_deg” (int) to constrain the total degree of the nodes
“avg/min/max_in_deg”, “avg/min/max_out_deg”, to work with the in/out-degrees
“avg/min/max_betw” (double) to constrain the betweenness centrality
“in_shape” (nngt.geometry.Shape) to chose neurons inside a given spatial region

Examples

>>> di_constrain = { "avg_deg": 10, "min_betw": 0.001 }
>>> group_prop = GroupProperties(200, constraints=di_constrain)

class nngt.MetaGroup(nodes=None, name=None, **kwargs)[source]#

Class defining a meta-group of neurons.

Its main variables are:

Variables:	ids – `list` of `int` the ids of the neurons in this group. is_metagroup – `bool` whether the group is a meta-group or not (neuron_type is `None` for meta-groups)

Calling the class creates a group of neurons. The default is an empty group but it is not a valid object for most use cases.

Parameters:	nodes (int or array-like, optional (default: None)) – Desired size of the group or, a posteriori, NNGT indices of the neurons in an existing graph. name (str, optional (default: “Group N”)) – Name of the meta-group.
Returns:	A new `MetaGroup` object.

excitatory#: Return the ids of all excitatory nodes inside the meta-group.

inhibitory#: Return the ids of all inhibitory nodes inside the meta-group.

class nngt.NeuralGroup(nodes=None, neuron_type=1, neuron_model=None, neuron_param=None, name=None)[source]#

Class defining groups of neurons.

Its main variables are:

Variables:

ids – list of int the ids of the neurons in this group.
neuron_type – int the default is 1 for excitatory neurons; -1 is for inhibitory neurons; meta-groups must have neuron_type set to None
neuron_model – str, optional (default: None) the name of the model to use when simulating the activity of this group
neuron_param – dict, optional (default: {}) the parameters to use (if they differ from the model’s defaults)
is_metagroup – bool whether the group is a meta-group or not (neuron_type is None for meta-groups)

Warning

Equality between NeuralGroup`s only compares the size and neuronal type, ``model` and param attributes. This means that groups differing only by their ids will register as equal.

Calling the class creates a group of neurons. The default is an empty group but it is not a valid object for most use cases.

Changed in version 0.8: Removed syn_model and syn_param.

Parameters:

nodes (int or array-like, optional (default: None)) – Desired size of the group or, a posteriori, NNGT indices of the neurons in an existing graph.
neuron_type (int, optional (default: 1)) – Type of the neurons (1 for excitatory, -1 for inhibitory) or None if not relevant (only allowed for metag roups).
neuron_model (str, optional (default: None)) – NEST model for the neuron.
neuron_param (dict, optional (default: model defaults)) – Dictionary containing the parameters associated to the NEST model.

Returns:

A new NeuralGroup instance.

copy()[source]#: Return a deep copy of the group.

has_model#

ids#

is_metagroup#

is_valid#

i.e. if it has either a size or some ids associated to it.

New in version 1.0.

Type:	Whether the group can be used in a population

name#

nest_gids#

neuron_model#

neuron_param#

neuron_type#

parent#: Return the parent NeuralPop of the group

properties#

size#

class nngt.NeuralPop(size=None, parent=None, meta_groups=None, with_models=True, *args, **kwargs)[source]#

The basic class that contains groups of neurons and their properties.

Variables:	has_models – `bool`, `True` if every group has a `model` attribute. size – `int`, Returns the number of neurons in the population. syn_spec – `dict`, Dictionary containing informations about the synapses between the different groups in the population. is_valid – `bool`, Whether this population can be used to create a network in NEST.

Initialize NeuralPop instance.

Parameters:

size (int, optional (default: 0)) – Number of neurons that the population will contain.
parent (Network, optional (default: None)) – Network associated to this population.
meta_groups (dict of str/NeuralGroup items) – Optional set of groups. Contrary to the primary groups which define the population and must be disjoint, meta groups can overlap: a neuron can belong to several different meta groups.
with_models (bool) – whether the population’s groups contain models to use in NEST
*args (items for OrderedDict parent)
**kwargs (dict)

Returns:

pop (NeuralPop object.)

add_meta_group(group, name=None, replace=False)[source]#

Add an existing meta group to the population.

Parameters:	group (`NeuralGroup`) – Meta group. name (str, optional (default: group name)) – Name of the meta group. replace (bool, optional (default: False)) – Whether to override previous exiting meta group with same name.

Note

The name of the group is automatically updated to match the name argument.

add_to_group(group_name, ids)[source]#

Add neurons to a specific group.

Parameters:	group_name (str or int) – Name or index of the group. ids (list or 1D-array) – Neuron ids.

copy()[source]#: Return a deep copy of the population.

create_group(neurons, name, neuron_type=1, neuron_model=None, neuron_param=None, replace=False)[source]#

Create a new group in the population.

Parameters:

neurons (int or array-like) – Desired number of neurons or list of the neurons indices.
name (str) – Name of the group.
neuron_type (int, optional (default: 1)) – Type of the neurons : 1 for excitatory, -1 for inhibitory.
neuron_model (str, optional (default: None)) – Name of a neuron model in NEST.
neuron_param (dict, optional (default: None)) – Parameters for neuron_model in the NEST simulator. If None, default parameters will be used.
replace (bool, optional (default: False)) – Whether to override previous exiting meta group with same name.

create_meta_group(neurons, name, neuron_param=None, replace=False)[source]#

Create a new meta group and add it to the population.

Parameters:

neurons (int or array-like) – Desired number of neurons or list of the neurons indices.
name (str) – Name of the group.
neuron_type (int, optional (default: 1)) – Type of the neurons : 1 for excitatory, -1 for inhibitory.
neuron_model (str, optional (default: None)) – Name of a neuron model in NEST.
neuron_param (dict, optional (default: None)) – Parameters for neuron_model in the NEST simulator. If None, default parameters will be used.
replace (bool, optional (default: False)) – Whether to override previous exiting meta group with same name.

classmethod exc_and_inhib(size, iratio=0.2, en_model='aeif_cond_alpha', en_param=None, in_model='aeif_cond_alpha', in_param=None, syn_spec=None, parent=None, meta_groups=None)[source]#

Make a NeuralPop with a given ratio of inhibitory and excitatory neurons.

Changed in version 0.8: Added syn_spec parameter.

Changed in version 1.2: Added meta_groups parameter

Parameters:

size (int) – Number of neurons contained by the population.
iratio (float, optional (default: 0.2)) – Fraction of the neurons that will be inhibitory.
en_model (str, optional (default: default_neuron)) – Name of the NEST model that will be used to describe excitatory neurons.
en_param (dict, optional (default: default NEST parameters)) – Parameters of the excitatory neuron model.
in_model (str, optional (default: default_neuron)) – Name of the NEST model that will be used to describe inhibitory neurons.
in_param (dict, optional (default: default NEST parameters)) – Parameters of the inhibitory neuron model.
syn_spec (dict, optional (default: static synapse)) – Dictionary containg a directed edge between groups as key and the associated synaptic parameters for the post-synaptic neurons (i.e. those of the second group) as value. If provided, all connections between groups will be set according to the values contained in syn_spec. Valid keys are:
- (‘excitatory’, ‘excitatory’)
- (‘excitatory’, ‘inhibitory’)
- (‘inhibitory’, ‘excitatory’)
- (‘inhibitory’, ‘inhibitory’)
parent (Network, optional (default: None)) – Network associated to this population.
meta_groups (list dict of str/NeuralGroup items) – Additional set of groups which can overlap: a neuron can belong to several different meta groups. Contrary to the primary ‘excitatory’ and ‘inhibitory’ groups, meta groups are therefore no necessarily disjoint. If all meta-groups have a name, they can be passed directly through a list; otherwise a dict is necessary.

See also

nest.Connect(), as()

excitatory#: Return the ids of all excitatory nodes inside the population.

New in version 1.3.

classmethod from_groups(groups, names=None, syn_spec=None, parent=None, meta_groups=None, with_models=True)[source]#

Make a NeuralPop object from a (list of) NeuralGroup object(s).

Parameters:

groups (list of NeuralGroup objects) – Groups that will be used to form the population. Note that a given neuron can only belong to a single group, so the groups should form pairwise disjoints complementary sets.
names (list of str, optional (default: None)) – Names that can be used as keys to retreive a specific group. If not provided, keys will be the group name (if not empty) or the position of the group in groups, stored as a string. In the latter case, the first group in a population named pop will be retreived by either pop[0] or pop[‘0’].
parent (Graph, optional (default: None)) – Parent if the population is created from an exiting graph.
syn_spec (dict, optional (default: static synapse)) – Dictionary containg a directed edge between groups as key and the associated synaptic parameters for the post-synaptic neurons (i.e. those of the second group) as value. If a ‘default’ entry is provided, all unspecified connections will be set to its value.
meta_groups (list or dict of str/NeuralGroup items) – Additional set of groups which can overlap: a neuron can belong to several different meta groups. Contrary to the primary groups, meta groups do therefore no need to be disjoint. If all meta-groups have a name, they can be passed directly through a list; otherwise a dict is necessary.
with_model (bool, optional (default: True)) – Whether the groups require models (set to False to use populations for graph theoretical purposes, without NEST interaction)

Example

For synaptic properties, if provided in syn_spec, all connections between groups will be set according to the values. Keys can be either group names or types (1 for excitatory, -1 for inhibitory). Because of this, several combination can be available for the connections between two groups. Because of this, priority is given to source (presynaptic properties), i.e. NNGT will look for the entry matching the first group name as source before looking for entries matching the second group name as target.

# we created groups `g1`, `g2`, and `g3`
prop = {
    ('g1', 'g2'): {'model': 'tsodyks2_synapse', 'tau_fac': 50.},
    ('g1', g3'): {'weight': 100.},
    ...
}
pop = NeuronalPop.from_groups(
    [g1, g2, g3], names=['g1', 'g2', 'g3'], syn_spec=prop)

Note

If the population is not generated from an existing Graph and the groups do not contain explicit ids, then the ids will be generated upon population creation: the first group, of size N0, will be associated the indices 0 to N0 - 1, the second group (size N1), will get N0 to N0 + N1 - 1, etc.

classmethod from_network(graph, *args)[source]#: Make a NeuralPop object from a network. The groups of neurons are determined using instructions from an arbitrary number of GroupProperties.

get_group(neurons, numbers=False)[source]#

Return the group of the neurons.

Parameters:	neurons (int or array-like) – IDs of the neurons for which the group should be returned. numbers (bool, optional (default: False)) – Whether the group identifier should be returned as a number; if `False`, the group names are returned.

get_param(groups=None, neurons=None, element='neuron')[source]#

Return the element (neuron or synapse) parameters for neurons or groups of neurons in the population.

Parameters:

groups (str, int or array-like, optional (default: None)) – Names or numbers of the groups for which the neural properties should be returned.
neurons (int or array-like, optional (default: None)) – IDs of the neurons for which parameters should be returned.
element (list of str, optional (default: "neuron")) – Element for which the parameters should be returned (either "neuron" or "synapse").

Returns:

param (list) – List of all dictionaries with the elements’ parameters.

has_models#

ids#: Return all the ids of the nodes inside the population.

New in version 1.2.

inhibitory#: Return the ids of all inhibitory nodes inside the population.

New in version 1.3.

is_valid#: Whether the population can be used to create a NEST network.

meta_groups#

nest_gids#: Return the NEST gids of the nodes inside the population.

New in version 1.3.

parent#: Parent Network, if it exists, otherwise None.

set_model(model, group=None)[source]#

Set the groups’ models.

Parameters:	model (dict) – Dictionary containing the model type as key (“neuron” or “synapse”) and the model name as value (e.g. {“neuron”: “iaf_neuron”}). group (list of strings, optional (default: None)) – List of strings containing the names of the groups which models should be updated.

Note

By default, synapses are registered as “static_synapse”s in NEST; because of this, only the neuron_model attribute is checked by the has_models function: it will answer True if all groups have a ‘non-None’ neuron_model attribute.

Warning

No check is performed on the validity of the models, which means that errors will only be detected when building the graph in NEST.

set_neuron_param(params, neurons=None, group=None)[source]#

Set the parameters of specific neurons or of a whole group.

New in version 1.0.

Parameters:

params (dict) – Dictionary containing parameters for the neurons. Entries can be either a single number (same for all neurons) or a list (one entry per neuron).
neurons (list of ints, optional (default: None)) – Ids of the neurons whose parameters should be modified.
group (list of strings, optional (default: None)) – List of strings containing the names of the groups whose parameters should be updated. When modifying neurons from a single group, it is still usefull to specify the group name to speed up the pace.

Note

If both neurons and group are None, all neurons will be modified.

Warning

No check is performed on the validity of the parameters, which means that errors will only be detected when building the graph in NEST.

size#: Number of neurons in this population.

syn_spec#

The properties of the synaptic connections between groups. Returns a dict containing tuples as keys and dicts of parameters as values.

The keys are tuples containing the names of the groups in the population, with the projecting group first (presynaptic neurons) and the receiving group last (post-synaptic neurons).

Example

For a population of excitatory (“exc”) and inhibitory (“inh”) neurons.

syn_spec = {
    ("exc", "exc"): {'model': 'stdp_synapse', 'weight': 2.5},
    ("exc", "inh"): {'model': 'static_synapse'},
    ("exc", "inh"): {'model': 'stdp_synapse', 'delay': 5.},
    ("inh", "inh"): {
        'model': 'stdp_synapse', 'weight': 5.,
        'delay': ('normal', 5., 2.)}
    }
}

New in version 0.8.

classmethod uniform(size, neuron_type=1, neuron_model='aeif_cond_alpha', neuron_param=None, syn_model='static_synapse', syn_param=None, parent=None, meta_groups=None)[source]#

Make a NeuralPop of identical neurons belonging to a single “default” group.

Changed in version 1.2: Added neuron_type and meta_groups parameters

Parameters:

size (int) – Number of neurons in the population.
neuron_type (int, optional (default: 1)) – Type of the neurons in the population: 1 for excitatory or -1 for inhibitory.
neuron_model (str, optional (default: default neuron model)) – Neuronal model for the simulator.
neuron_param (dict, optional (default: default neuron parameters)) – Parameters associated to neuron_model.
syn_model (str, optional (default: default static synapse)) – Synapse model for the simulator.
syn_param (dict, optional (default: default synaptic parameters)) – Parameters associated to syn_model.
parent (Graph object, optional (default: None)) – Parent graph described by the population.
meta_groups (list or dict of str/NeuralGroup items) – Set of groups which can overlap: a neuron can belong to several different meta groups, i.e. they do no need to be disjoint. If all meta-groups have a name, they can be passed directly through a list; otherwise a dict is necessary.

NNGT #

Package aimed at facilitating the analysis of Neural Networks Growth and Topology.

The library mainly provides algorithms for

generating networks
analyzing their activity
studying the graph theoretical properties of those networks

Available modules #

analysis: Tools to study graph topology and neuronal activity.
core: Where the main classes are coded; however, most useful classes and methods for users are loaded at the main level (nngt) when the library is imported, so nngt.core should generally not be used.
generation: Functions to generate specific networks.
geometry: Tools to work on metric graphs (see PyNCulture).
io: Tools for input/output operations.
lib: Basic functions used by several most other modules.
simulation: Tools to provide complex network generation with NEST and help analyze the influence of the network structure on neuronal activity.
plot: plot data or graphs using matplotlib and graph_tool.

Units #

Functions related to spatial embedding of networks are using micrometers (um) as default unit; other units from the metric system can also be provided:

mm for milimeters
cm centimeters
dm for decimeters
m for meters

Main classes and functions #

`nngt.Graph`([nodes, name, weighted, …])	The basic graph class, which inherits from a library class such as `graph_tool.Graph`, `networkx.DiGraph`, or `igraph.Graph`.
`nngt.GroupProperty`(size[, constraints, …])	Class defining the properties needed to create groups of neurons from an existing `Graph` or one of its subclasses.
`nngt.MetaGroup`([nodes, name])	Class defining a meta-group of neurons.
`nngt.Network`([name, weighted, directed, …])	The detailed class that inherits from `Graph` and implements additional properties to describe various biological functions and interact with the NEST simulator.
`nngt.NeuralGroup`([nodes, neuron_type, …])	Class defining groups of neurons.
`nngt.NeuralPop`([size, parent, meta_groups, …])	The basic class that contains groups of neurons and their properties.
`nngt.SpatialGraph`([nodes, name, weighted, …])	The detailed class that inherits from `Graph` and implements additional properties to describe spatial graphs (i.e.
`nngt.SpatialNetwork`(population[, name, …])	Class that inherits from `Network` and `SpatialGraph` to provide a detailed description of a real neural network in space, i.e.
`nngt.generate`(di_instructions, **kwargs)	Generate a `Graph` or one of its subclasses from a `dict` containing all the relevant informations.
`nngt.get_config`([key, detailed])	Get the NNGT configuration as a dictionary.
`nngt.load_from_file`(filename[, fmt, …])	Load a Graph from a file.
`nngt.num_mpi_processes`()	Returns the number of MPI processes (1 if MPI is not used)
`nngt.on_master_process`()	Check whether the current code is executing on the master process (rank 0) if MPI is used.
`nngt.save_to_file`(graph, filename[, fmt, …])	Save a graph to file.
`nngt.seed`([msd, seeds])	Seed the random generator used by NNGT (i.e.
`nngt.set_config`(config[, value, silent])	Set NNGT’s configuration.
`nngt.use_backend`(backend[, reloading, silent])	Allows the user to switch to a specific graph library as backend.

Details #

class nngt.Graph(nodes=0, name='Graph', weighted=True, directed=True, copy_graph=None, **kwargs)[source]#

The basic graph class, which inherits from a library class such as graph_tool.Graph, networkx.DiGraph, or igraph.Graph.

The objects provides several functions to easily access some basic properties.

Initialize Graph instance

Changed in version 2.0: Renamed from_graph to copy_graph.

Parameters:

nodes (int, optional (default: 0)) – Number of nodes in the graph.
name (string, optional (default: “Graph”)) – The name of this Graph instance.
weighted (bool, optional (default: True)) – Whether the graph edges have weight properties.
directed (bool, optional (default: True)) – Whether the graph is directed or undirected.
copy_graph (Graph, optional) – An optional Graph that will be copied.
kwargs (optional keywords arguments) – Optional arguments that can be passed to the graph, e.g. a dict containing information on the synaptic weights (weights={"distribution": "constant", "value": 2.3} which is equivalent to weights=2.3), the synaptic delays, or a type information.

Note

When using copy_graph, only the topological properties are copied (nodes, edges, and attributes), spatial and biological properties are ignored. To copy a graph exactly, use copy().

Returns:	self (`Graph`)

adjacency_matrix(types=False, weights=False, mformat='csr')[source]#

Return the graph adjacency matrix.

Note

Source nodes are represented by the rows, targets by the corresponding columns.

Parameters:

types (bool, optional (default: False)) – Wether the edge types should be taken into account (negative values for inhibitory connections).
weights (bool or string, optional (default: False)) – Whether the adjacecy matrix should be weighted. If True, all connections are multiply bythe associated synaptic strength; if weight is a string, the connections are scaled bythe corresponding edge attribute.
mformat (str, optional (default: “csr”)) – Type of scipy.sparse matrix that will be returned, by default scipy.sparse.csr_matrix.

Returns:

mat (scipy.sparse matrix) – The adjacency matrix of the graph.

copy()[source]#: Returns a deepcopy of the current Graph instance

edge_attributes#: Access edge attributes.

See also

node_attributes, get_edge_attributes, new_edge_attribute, set_edge_attribute.

edges_attributes#: Access edge attributes.

See also

node_attributes, get_edge_attributes, new_edge_attribute, set_edge_attribute.

Import a saved graph from a file.

Changed in version 2.0: Added optional attributes_types and cleanup arguments.

Parameters:

filename (str) – The path to the file.
fmt (str, optional (default: deduced from filename)) – The format used to save the graph. Supported formats are: “neighbour” (neighbour list), “ssp” (scipy.sparse), “edge_list” (list of all the edges in the graph, one edge per line, represented by a source target-pair), “gml” (gml format, default if filename ends with ‘.gml’), “graphml” (graphml format, default if filename ends with ‘.graphml’ or ‘.xml’), “dot” (dot format, default if filename ends with ‘.dot’), “gt” (only when using graph_tool as library, detected if filename ends with ‘.gt’).
separator (str, optional (default ” “)) – separator used to separate inputs in the case of custom formats (namely “neighbour” and “edge_list”)
secondary (str, optional (default: “;”)) – Secondary separator used to separate attributes in the case of custom formats.
attributes (list, optional (default: [])) – List of names for the attributes present in the file. If a notifier is present in the file, names will be deduced from it; otherwise the attributes will be numbered. For “edge_list”, attributes may also be present as additional columns after the source and the target.
attributes_types (dict, optional (default: str)) – Backup information if the type of the attributes is not specified in the file. Values must be callables (types or functions) that will take the argument value as a string input and convert it to the proper type.
notifier (str, optional (default: “@”)) – Symbol specifying the following as meaningfull information. Relevant information are formatted @info_name=info_value, where info_name is in (“attributes”, “directed”, “name”, “size”) and associated info_value are of type (list, bool, str, int). Additional notifiers are @type=SpatialGraph/Network/SpatialNetwork, which must be followed by the relevant notifiers among @shape, @population, and @graph.
from_string (bool, optional (default: False)) – Load from a string instead of a file.
ignore (str, optional (default: “#”)) – Ignore lines starting with the ignore string.
name (str, optional (default: from file information or ‘LoadedGraph’)) – The name of the graph.
directed (bool, optional (default: from file information or True)) – Whether the graph is directed or not.
cleanup (bool, optional (default: False)) – If true, removes nodes before the first one that appears in the edges and after the last one and renumber the nodes from 0.

Returns:

graph (Graph or subclass) – Loaded graph.

classmethod from_library(library_graph, name='ImportedGraph', weighted=True, directed=True, **kwargs)[source]#

Create a Graph by wrapping a graph object from one of the supported libraries.

Parameters:	library_graph (object) – Graph object from one of the supported libraries (graph-tool, igraph, networkx). name (str, optional (default: “ImportedGraph”)) **kwargs – Other standard arguments (see `__init__()`)

classmethod from_matrix(matrix, weighted=True, directed=True, population=None, shape=None, positions=None, name=None)[source]#

Creates a Graph from a scipy.sparse matrix or a dense matrix.

Parameters:

matrix (scipy.sparse matrix or numpy.ndarray) – Adjacency matrix.
weighted (bool, optional (default: True)) – Whether the graph edges have weight properties.
directed (bool, optional (default: True)) – Whether the graph is directed or undirected.
population (NeuralPop) – Population to associate to the new Network.
shape (Shape, optional (default: None)) – Shape to associate to the new SpatialGraph.
positions ((N, 2) array) – Positions, in a 2D space, of the N neurons.
name (str, optional) – Graph name.

Returns:

Graph

get_attribute_type(attribute_name, attribute_class=None)[source]#

Return the type of an attribute (e.g. string, double, int).

Parameters:	attribute_name (str) – Name of the attribute. attribute_class (str, optional (default: both)) – Whether attribute_name is a “node” or an “edge” attribute.
Returns:	type (str) – Type of the attribute.

get_betweenness(btype='both', weights=None)[source]#

Returns the normalized betweenness centrality of the nodes and edges.

Parameters:

g (Graph) – Graph to analyze.
btype (str, optional (default ‘both’)) – The centrality that should be returned (either ‘node’, ‘edge’, or ‘both’). By default, both betweenness centralities are computed.
weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if None or False then use binary edges; if True, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.

Returns:

nb (numpy.ndarray) – The nodes’ betweenness if btype is ‘node’ or ‘both’
eb (numpy.ndarray) – The edges’ betweenness if btype is ‘edge’ or ‘both’

See also

betweenness()

get_degrees(mode='total', nodes=None, weights=None, edge_type='all')[source]#

Degree sequence of all the nodes.

Changed in version 2.0: Changed deg_type to mode, node_list to nodes, use_weights to weights, and edge_type to edge_type.

Parameters:

mode (string, optional (default: “total”)) – Degree type (among ‘in’, ‘out’ or ‘total’).
nodes (list, optional (default: None)) – List of the nodes which degree should be returned
weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if None or False then use binary edges; if True, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.
edge_type (int or str, optional (default: all)) – Restrict to a given synaptic type (“excitatory”, 1, or “inhibitory”, -1), using either the “type” edge attribute for non-Network or the inhibitory nodes.

Returns:

degrees (numpy.array)
.. warning :: – When using MPI with “nngt” (distributed) backend, returns only the degrees associated to local edges. “Complete” degrees are obtained by taking the sum of the results on all MPI processes.

get_delays(edges=None)[source]#

Returns the delays of all or a subset of the edges.

Changed in version 1.0.1: Added the possibility to ask for a subset of edges.

Parameters:	edges ((E, 2) array, optional (default: all edges)) – Edges for which the type should be returned.
Returns:	the list of delays

get_density()[source]#: Density of the graph: $\frac{E}{N^2}$ , where E is the number of edges and N the number of nodes.

get_edge_attributes(edges=None, name=None)[source]#

Attributes of the graph’s edges.

Changed in version 1.0: Returns the full dict of edges attributes if called without arguments.

New in version 0.8.

Parameters:

edge (tuple or list of tuples, optional (default: None)) – Edge whose attribute should be displayed.
name (str, optional (default: None)) – Name of the desired attribute.

Returns:

Dict containing all graph’s attributes (synaptic weights, delays…)
by default. If edge is specified, returns only the values for these
edges. If name is specified, returns value of the attribute for each
edge.

Note

The attributes values are ordered as the edges in edges_array() if edges is None.

get_edge_types(edges=None)[source]#

Return the type of all or a subset of the edges.

Changed in version 1.0.1: Added the possibility to ask for a subset of edges.

Parameters:	edges ((E, 2) array, optional (default: all edges)) – Edges for which the type should be returned.
Returns:	the list of types (1 for excitatory, -1 for inhibitory)

get_edges(attribute=None, value=None, source_node=None, target_node=None)[source]#

Return the edges in the network fulfilling a given condition.

Parameters:

attribute (str, optional (default: all nodes)) – Whether the attribute of the returned edges should have a specific value.
value (object, optional (default : None)) – If an attribute name is passed, then only edges with attribute being equal to value will be returned.
source_node (int or list of ints, optional (default: all nodes)) – Retrict the edges to those stemming from source_node.
target_node (int or list of ints, optional (default: all nodes)) – Retrict the edges to those arriving at target_node.

See also

get_nodes(), edge_attributes

get_node_attributes(nodes=None, name=None)[source]#

Attributes of the graph’s edges.

Changed in version 1.0.1: Corrected default behavior and made it the same as get_edge_attributes().

New in version 0.9.

Parameters:

nodes (list of ints, optional (default: None)) – Nodes whose attribute should be displayed.
name (str, optional (default: None)) – Name of the desired attribute.

Returns:

Dict containing all nodes attributes by default. If nodes is
specified, returns a dict containing only the attributes of these
nodes. If name is specified, returns a list containing the values of
the specific attribute for the required nodes (or all nodes if
unspecified).

get_nodes(attribute=None, value=None)[source]#

Return the nodes in the network fulfilling a given condition.

Parameters:	attribute (str, optional (default: all nodes)) – Whether the attribute of the returned nodes should have a specific value. value (object, optional (default : None)) – If an attribute name is passed, then only nodes with attribute being equal to value will be returned.

See also

get_edges(), node_attributes

get_weights(edges=None)[source]#

Returns the weights of all or a subset of the edges.

Changed in version 1.0.1: Added the possibility to ask for a subset of edges.

Parameters:	edges ((E, 2) array, optional (default: all edges)) – Edges for which the type should be returned.
Returns:	the list of weights

graph#: Returns the underlying library object.

Warning

Do not add or remove edges directly through this object.

See also

Underlying graph objects and libraries, Consistent tools for graph analysis

graph_id#: Unique int identifying the instance.

is_connected(mode='strong')[source]#

Return whether the graph is connected.

Parameters:	mode (str, optional (default: “strong”)) – Whether to test connectedness with directed (“strong”) or undirected (“weak”) connections.

References

[ig-connected]

igraph - is_connected

is_directed()[source]#: Whether the graph is directed or not

is_network()[source]#: Whether the graph is a subclass of Network (i.e. if it has a NeuralPop attribute).

is_spatial()[source]#: Whether the graph is embedded in space (i.e. is a subclass of SpatialGraph).

is_weighted()[source]#: Whether the edges have weights

static make_network(graph, neural_pop, copy=False, **kwargs)[source]#

Turn a Graph object into a Network, or a SpatialGraph into a SpatialNetwork.

Parameters:	graph (`Graph` or `SpatialGraph`) – Graph to convert neural_pop (`NeuralPop`) – Population to associate to the new `Network` copy (bool, optional (default: `False`)) – Whether the operation should be made in-place on the object or if a new object should be returned.

Notes

In-place operation that directly converts the original graph if copy is False, else returns the copied Graph turned into a Network.

static make_spatial(graph, shape=None, positions=None, copy=False)[source]#

Turn a Graph object into a SpatialGraph, or a Network into a SpatialNetwork.

Parameters:	graph (`Graph` or `SpatialGraph`) – Graph to convert. shape (`Shape`, optional (default: None)) – Shape to associate to the new `SpatialGraph`. positions ((N, 2) array) – Positions, in a 2D space, of the N neurons. copy (bool, optional (default: `False`)) – Whether the operation should be made in-place on the object or if a new object should be returned.

Notes

name#: Name of the graph.

neighbours(node, mode='all')[source]#

Return the neighbours of node.

Parameters:	node (int) – Index of the node of interest. mode (string, optional (default: “all”)) – Type of neighbours that will be returned: “all” returns all the neighbours regardless of directionality, “in” returns the in-neighbours (also called predecessors) and “out” retruns the out-neighbours (or successors).
Returns:	neighbours (set) – The neighbours of node.

new_edge_attribute(name, value_type, values=None, val=None)[source]#

Create a new attribute for the edges.

Parameters:

name (str) – The name of the new attribute.
value_type (str) – Type of the attribute, among ‘int’, ‘double’, ‘string’, or ‘object’
values (array, optional (default: None)) – Values with which the edge attribute should be initialized. (must have one entry per node in the graph)
val (int, float or str , optional (default: None)) – Identical value for all edges.

new_node_attribute(name, value_type, values=None, val=None)[source]#

Create a new attribute for the nodes.

Parameters:

name (str) – The name of the new attribute.
value_type (str) – Type of the attribute, among ‘int’, ‘double’, ‘string’, or ‘object’
values (array, optional (default: None)) – Values with which the node attribute should be initialized. (must have one entry per node in the graph)
val (int, float or str , optional (default: None)) – Identical value for all nodes.

node_attributes#: Access node attributes.

See also

edge_attributes, get_node_attributes, new_node_attribute, set_node_attribute.

nodes_attributes#: Access node attributes.

See also

edge_attributes, get_node_attributes, new_node_attribute, set_node_attribute.

classmethod num_graphs()[source]#: Returns the number of alive instances.

set_delays(delay=None, elist=None, distribution=None, parameters=None, noise_scale=None)[source]#

Set the delay for spike propagation between neurons.

Parameters:

delay (float or class:numpy.array, optional (default: None)) – Value or list of delays (for user defined delays).
elist (class:numpy.array, optional (default: None)) – List of the edges (for user defined delays).
distribution (class:string, optional (default: None)) – Type of distribution (choose among “constant”, “uniform”, “gaussian”, “lognormal”, “lin_corr”, “log_corr”).
parameters (dict, optional (default: {})) – Dictionary containing the properties of the delay distribution.
noise_scale (class:int, optional (default: None)) – Scale of the multiplicative Gaussian noise that should be applied on the delays.

set_edge_attribute(attribute, values=None, val=None, value_type=None, edges=None)[source]#

Set attributes to the connections between neurons.

Warning

Parameters:

attribute (str) – The name of the attribute.
value_type (str) – Type of the attribute, among ‘int’, ‘double’, ‘string’
values (array, optional (default: None)) – Values with which the edge attribute should be initialized. (must have one entry per node in the graph)
val (int, float or str , optional (default: None)) – Identical value for all edges.
value_type (str, optional (default: None)) – Type of the attribute, among ‘int’, ‘double’, ‘string’. Only used if the attribute does not exist and must be created.
edges (list of edges or array of shape (E, 2), optional (default: all)) – Edges whose attributes should be set. Others will remain unchanged.

set_name(name='')[source]#: set graph name

set_node_attribute(attribute, values=None, val=None, value_type=None, nodes=None)[source]#

Set attributes to the connections between neurons.

Parameters:

attribute (str) – The name of the attribute.
value_type (str) – Type of the attribute, among ‘int’, ‘double’, ‘string’
values (array, optional (default: None)) – Values with which the edge attribute should be initialized. (must have one entry per node in the graph)
val (int, float or str , optional (default: None)) – Identical value for all edges.
value_type (str, optional (default: None)) – Type of the attribute, among ‘int’, ‘double’, ‘string’. Only used if the attribute does not exist and must be created.
nodes (list of nodes, optional (default: all)) – Nodes whose attributes should be set. Others will remain unchanged.

set_types(edge_type, nodes=None, fraction=None)[source]#

Set the synaptic/connection types.

Changed in version 2.0: Changed syn_type to edge_type.

Warning

Parameters:

edge_type (int, string, or array of ints) – Type of the connection among ‘excitatory’ (also 1) or ‘inhibitory’ (also -1).
nodes (int, float or list, optional (default: None)) – If nodes is an int, number of nodes of the required type that will be created in the graph (all connections from inhibitory nodes are inhibitory); if it is a float, ratio of edge_type nodes in the graph; if it is a list, ids of the edge_type nodes.
fraction (float, optional (default: None)) – Fraction of the selected edges that will be set as edge_type (if nodes is not None, it is the fraction of the specified nodes’ edges, otherwise it is the fraction of all edges in the graph).

Returns:

t_list (numpy.ndarray) – List of the types in an order that matches the edges attribute of the graph.

set_weights(weight=None, elist=None, distribution=None, parameters=None, noise_scale=None)[source]#

Set the synaptic weights.

Parameters:

weight (float or class:numpy.array, optional (default: None)) – Value or list of the weights (for user defined weights).
elist (class:numpy.array, optional (default: None)) – List of the edges (for user defined weights).
distribution (class:string, optional (default: None)) – Type of distribution (choose among “constant”, “uniform”, “gaussian”, “lognormal”, “lin_corr”, “log_corr”).
parameters (dict, optional (default: {})) – Dictionary containing the properties of the weight distribution. Properties are as follow for the distributions
- ‘constant’: ‘value’
- ‘uniform’: ‘lower’, ‘upper’
- ‘gaussian’: ‘avg’, ‘std’
- ‘lognormal’: ‘position’, ‘scale’
noise_scale (class:int, optional (default: None)) – Scale of the multiplicative Gaussian noise that should be applied on the weights.

Note

to_file(filename, fmt='auto', separator=' ', secondary=';', attributes=None, notifier='@')[source]#: Save graph to file; options detailed below.

See also

nngt.lib.save_to_file() function for options.

type#: Type of the graph.

class nngt.GroupProperty(size, constraints={}, neuron_model=None, neuron_param={}, syn_model=None, syn_param={})[source]#

Class defining the properties needed to create groups of neurons from an existing Graph or one of its subclasses.

Variables:	size – `int` Size of the group. constraints – `dict`, optional (default: {}) Constraints to respect when building the `NeuralGroup` . neuron_model – str, optional (default: None) name of the model to use when simulating the activity of this group. neuron_param – dict, optional (default: {}) the parameters to use (if they differ from the model’s defaults)

Create a new instance of GroupProperties.

Notes

The constraints can be chosen among:

“avg_deg”, “min_deg”, “max_deg” (int) to constrain the total degree of the nodes
“avg/min/max_in_deg”, “avg/min/max_out_deg”, to work with the in/out-degrees
“avg/min/max_betw” (double) to constrain the betweenness centrality
“in_shape” (nngt.geometry.Shape) to chose neurons inside a given spatial region

Examples

>>> di_constrain = { "avg_deg": 10, "min_betw": 0.001 }
>>> group_prop = GroupProperties(200, constraints=di_constrain)

class nngt.MetaGroup(nodes=None, name=None, **kwargs)[source]#

Class defining a meta-group of neurons.

Its main variables are:

Variables:	ids – `list` of `int` the ids of the neurons in this group. is_metagroup – `bool` whether the group is a meta-group or not (neuron_type is `None` for meta-groups)

Calling the class creates a group of neurons. The default is an empty group but it is not a valid object for most use cases.

Parameters:	nodes (int or array-like, optional (default: None)) – Desired size of the group or, a posteriori, NNGT indices of the neurons in an existing graph. name (str, optional (default: “Group N”)) – Name of the meta-group.
Returns:	A new `MetaGroup` object.

excitatory#: Return the ids of all excitatory nodes inside the meta-group.

inhibitory#: Return the ids of all inhibitory nodes inside the meta-group.

class nngt.Network(name='Network', weighted=True, directed=True, from_graph=None, population=None, inh_weight_factor=1.0, **kwargs)[source]#

The detailed class that inherits from Graph and implements additional properties to describe various biological functions and interact with the NEST simulator.

Initializes Network instance.

Parameters:

nodes (int, optional (default: 0)) – Number of nodes in the graph.
name (string, optional (default: “Graph”)) – The name of this Graph instance.
weighted (bool, optional (default: True)) – Whether the graph edges have weight properties.
directed (bool, optional (default: True)) – Whether the graph is directed or undirected.
from_graph (GraphObject, optional (default: None)) – An optional GraphObject to serve as base.
population (nngt.NeuralPop, (default: None)) – An object containing the neural groups and their properties: model(s) to use in NEST to simulate the neurons as well as their parameters.
inh_weight_factor (float, optional (default: 1.)) – Factor to apply to inhibitory synapses, to compensate for example the strength difference due to timescales between excitatory and inhibitory synapses.

Returns:

self (Network)

classmethod exc_and_inhib(size, iratio=0.2, en_model='aeif_cond_alpha', en_param=None, in_model='aeif_cond_alpha', in_param=None, syn_spec=None, **kwargs)[source]#

Generate a network containing a population of two neural groups: inhibitory and excitatory neurons.

Parameters:

size (int) – Number of neurons in the network.
i_ratio (double, optional (default: 0.2)) – Ratio of inhibitory neurons: $\frac{N_i}{N_e+N_i}$ .
en_model (string, optional (default: ‘aeif_cond_alpha’)) – Nest model for the excitatory neuron.
en_param (dict, optional (default: {})) – Dictionary of parameters for the the excitatory neuron.
in_model (string, optional (default: ‘aeif_cond_alpha’)) – Nest model for the inhibitory neuron.
in_param (dict, optional (default: {})) – Dictionary of parameters for the the inhibitory neuron.
syn_spec (dict, optional (default: static synapse)) – Dictionary containg a directed edge between groups as key and the associated synaptic parameters for the post-synaptic neurons (i.e. those of the second group) as value. If provided, all connections between groups will be set according to the values contained in syn_spec. Valid keys are:
- (‘excitatory’, ‘excitatory’)
- (‘excitatory’, ‘inhibitory’)
- (‘inhibitory’, ‘excitatory’)
- (‘inhibitory’, ‘inhibitory’)

Returns:

net (Network or subclass) – Network of disconnected excitatory and inhibitory neurons.

See also

exc_and_inhib()

classmethod from_gids(gids, get_connections=True, get_params=False, neuron_model='aeif_cond_alpha', neuron_param=None, syn_model='static_synapse', syn_param=None, **kwargs)[source]#

Generate a network from gids.

Warning

Parameters:

gids (array-like) – Ids of the neurons in NEST or simply user specified ids.
get_params (bool, optional (default: True)) – Whether the parameters should be obtained from NEST (can be very slow).
neuron_model (string, optional (default: None)) – Name of the NEST neural model to use when simulating the activity.
neuron_param (dict, optional (default: {})) – Dictionary containing the neural parameters; the default value will make NEST use the default parameters of the model.
syn_model (string, optional (default: ‘static_synapse’)) – NEST synaptic model to use when simulating the activity.
syn_param (dict, optional (default: {})) – Dictionary containing the synaptic parameters; the default value will make NEST use the default parameters of the model.

Returns:

net (Network or subclass) – Uniform network of disconnected neurons.

get_edge_types()[source]#

Return the type of all or a subset of the edges.

Changed in version 1.0.1: Added the possibility to ask for a subset of edges.

Parameters:	edges ((E, 2) array, optional (default: all edges)) – Edges for which the type should be returned.
Returns:	the list of types (1 for excitatory, -1 for inhibitory)

get_neuron_type(neuron_ids)[source]#

Return the type of the neurons (+1 for excitatory, -1 for inhibitory).

Parameters:	neuron_ids (int or tuple) – NEST gids.
Returns:	ids (int or tuple) – Ids in the network. Same type as the requested gids type.

id_from_nest_gid(gids)[source]#

Return the ids of the nodes in the nngt.Network instance from the corresponding NEST gids.

Parameters:	gids (int or tuple) – NEST gids.
Returns:	ids (int or tuple) – Ids in the network. Same type as the requested gids type.

nest_gids#

neuron_properties(idx_neuron)[source]#

Properties of a neuron in the graph.

Parameters:	idx_neuron (int) – Index of a neuron in the graph.
Returns:	dict of the neuron’s properties.

classmethod num_networks()[source]#: Returns the number of alive instances.

population#: NeuralPop that divides the neurons into groups with specific properties.

set_types(edge_type, nodes=None, fraction=None)[source]#

Set the synaptic/connection types.

Changed in version 2.0: Changed syn_type to edge_type.

Warning

Parameters:

edge_type (int, string, or array of ints) – Type of the connection among ‘excitatory’ (also 1) or ‘inhibitory’ (also -1).
nodes (int, float or list, optional (default: None)) – If nodes is an int, number of nodes of the required type that will be created in the graph (all connections from inhibitory nodes are inhibitory); if it is a float, ratio of edge_type nodes in the graph; if it is a list, ids of the edge_type nodes.
fraction (float, optional (default: None)) – Fraction of the selected edges that will be set as edge_type (if nodes is not None, it is the fraction of the specified nodes’ edges, otherwise it is the fraction of all edges in the graph).

Returns:

t_list (numpy.ndarray) – List of the types in an order that matches the edges attribute of the graph.

to_nest(send_only=None, weights=True)[source]#: Send the network to NEST.

See also

make_nest_network() for parameters

classmethod uniform(size, neuron_model='aeif_cond_alpha', neuron_param=None, syn_model='static_synapse', syn_param=None, **kwargs)[source]#

Generate a network containing only one type of neurons.

Parameters:

size (int) – Number of neurons in the network.
neuron_model (string, optional (default: ‘aief_cond_alpha’)) – Name of the NEST neural model to use when simulating the activity.
neuron_param (dict, optional (default: {})) – Dictionary containing the neural parameters; the default value will make NEST use the default parameters of the model.
syn_model (string, optional (default: ‘static_synapse’)) – NEST synaptic model to use when simulating the activity.
syn_param (dict, optional (default: {})) – Dictionary containing the synaptic parameters; the default value will make NEST use the default parameters of the model.

Returns:

net (Network or subclass) – Uniform network of disconnected neurons.

class nngt.NeuralGroup(nodes=None, neuron_type=1, neuron_model=None, neuron_param=None, name=None)[source]#

Class defining groups of neurons.

Its main variables are:

Variables:

ids – list of int the ids of the neurons in this group.
neuron_type – int the default is 1 for excitatory neurons; -1 is for inhibitory neurons; meta-groups must have neuron_type set to None
neuron_model – str, optional (default: None) the name of the model to use when simulating the activity of this group
neuron_param – dict, optional (default: {}) the parameters to use (if they differ from the model’s defaults)
is_metagroup – bool whether the group is a meta-group or not (neuron_type is None for meta-groups)

Warning

Equality between NeuralGroup`s only compares the size and neuronal type, ``model` and param attributes. This means that groups differing only by their ids will register as equal.

Calling the class creates a group of neurons. The default is an empty group but it is not a valid object for most use cases.

Changed in version 0.8: Removed syn_model and syn_param.

Parameters:

nodes (int or array-like, optional (default: None)) – Desired size of the group or, a posteriori, NNGT indices of the neurons in an existing graph.
neuron_type (int, optional (default: 1)) – Type of the neurons (1 for excitatory, -1 for inhibitory) or None if not relevant (only allowed for metag roups).
neuron_model (str, optional (default: None)) – NEST model for the neuron.
neuron_param (dict, optional (default: model defaults)) – Dictionary containing the parameters associated to the NEST model.

Returns:

A new NeuralGroup instance.

copy()[source]#: Return a deep copy of the group.

has_model#

ids#

is_metagroup#

is_valid#

i.e. if it has either a size or some ids associated to it.

New in version 1.0.

Type:	Whether the group can be used in a population

name#

nest_gids#

neuron_model#

neuron_param#

neuron_type#

parent#: Return the parent NeuralPop of the group

properties#

size#

class nngt.NeuralPop(size=None, parent=None, meta_groups=None, with_models=True, *args, **kwargs)[source]#

The basic class that contains groups of neurons and their properties.

Variables:	has_models – `bool`, `True` if every group has a `model` attribute. size – `int`, Returns the number of neurons in the population. syn_spec – `dict`, Dictionary containing informations about the synapses between the different groups in the population. is_valid – `bool`, Whether this population can be used to create a network in NEST.

Initialize NeuralPop instance.

Parameters:

size (int, optional (default: 0)) – Number of neurons that the population will contain.
parent (Network, optional (default: None)) – Network associated to this population.
meta_groups (dict of str/NeuralGroup items) – Optional set of groups. Contrary to the primary groups which define the population and must be disjoint, meta groups can overlap: a neuron can belong to several different meta groups.
with_models (bool) – whether the population’s groups contain models to use in NEST
*args (items for OrderedDict parent)
**kwargs (dict)

Returns:

pop (NeuralPop object.)

add_meta_group(group, name=None, replace=False)[source]#

Add an existing meta group to the population.

Parameters:	group (`NeuralGroup`) – Meta group. name (str, optional (default: group name)) – Name of the meta group. replace (bool, optional (default: False)) – Whether to override previous exiting meta group with same name.

Note

The name of the group is automatically updated to match the name argument.

add_to_group(group_name, ids)[source]#

Add neurons to a specific group.

Parameters:	group_name (str or int) – Name or index of the group. ids (list or 1D-array) – Neuron ids.

copy()[source]#: Return a deep copy of the population.

create_group(neurons, name, neuron_type=1, neuron_model=None, neuron_param=None, replace=False)[source]#

Create a new group in the population.

Parameters:

neurons (int or array-like) – Desired number of neurons or list of the neurons indices.
name (str) – Name of the group.
neuron_type (int, optional (default: 1)) – Type of the neurons : 1 for excitatory, -1 for inhibitory.
neuron_model (str, optional (default: None)) – Name of a neuron model in NEST.
neuron_param (dict, optional (default: None)) – Parameters for neuron_model in the NEST simulator. If None, default parameters will be used.
replace (bool, optional (default: False)) – Whether to override previous exiting meta group with same name.

create_meta_group(neurons, name, neuron_param=None, replace=False)[source]#

Create a new meta group and add it to the population.

Parameters:

neurons (int or array-like) – Desired number of neurons or list of the neurons indices.
name (str) – Name of the group.
neuron_type (int, optional (default: 1)) – Type of the neurons : 1 for excitatory, -1 for inhibitory.
neuron_model (str, optional (default: None)) – Name of a neuron model in NEST.
neuron_param (dict, optional (default: None)) – Parameters for neuron_model in the NEST simulator. If None, default parameters will be used.
replace (bool, optional (default: False)) – Whether to override previous exiting meta group with same name.

Make a NeuralPop with a given ratio of inhibitory and excitatory neurons.

Changed in version 0.8: Added syn_spec parameter.

Changed in version 1.2: Added meta_groups parameter

Parameters:

size (int) – Number of neurons contained by the population.
iratio (float, optional (default: 0.2)) – Fraction of the neurons that will be inhibitory.
en_model (str, optional (default: default_neuron)) – Name of the NEST model that will be used to describe excitatory neurons.
en_param (dict, optional (default: default NEST parameters)) – Parameters of the excitatory neuron model.
in_model (str, optional (default: default_neuron)) – Name of the NEST model that will be used to describe inhibitory neurons.
in_param (dict, optional (default: default NEST parameters)) – Parameters of the inhibitory neuron model.
syn_spec (dict, optional (default: static synapse)) – Dictionary containg a directed edge between groups as key and the associated synaptic parameters for the post-synaptic neurons (i.e. those of the second group) as value. If provided, all connections between groups will be set according to the values contained in syn_spec. Valid keys are:
- (‘excitatory’, ‘excitatory’)
- (‘excitatory’, ‘inhibitory’)
- (‘inhibitory’, ‘excitatory’)
- (‘inhibitory’, ‘inhibitory’)
parent (Network, optional (default: None)) – Network associated to this population.
meta_groups (list dict of str/NeuralGroup items) – Additional set of groups which can overlap: a neuron can belong to several different meta groups. Contrary to the primary ‘excitatory’ and ‘inhibitory’ groups, meta groups are therefore no necessarily disjoint. If all meta-groups have a name, they can be passed directly through a list; otherwise a dict is necessary.

See also

nest.Connect(), as()

excitatory#: Return the ids of all excitatory nodes inside the population.

New in version 1.3.

classmethod from_groups(groups, names=None, syn_spec=None, parent=None, meta_groups=None, with_models=True)[source]#

Make a NeuralPop object from a (list of) NeuralGroup object(s).

Parameters:

groups (list of NeuralGroup objects) – Groups that will be used to form the population. Note that a given neuron can only belong to a single group, so the groups should form pairwise disjoints complementary sets.
names (list of str, optional (default: None)) – Names that can be used as keys to retreive a specific group. If not provided, keys will be the group name (if not empty) or the position of the group in groups, stored as a string. In the latter case, the first group in a population named pop will be retreived by either pop[0] or pop[‘0’].
parent (Graph, optional (default: None)) – Parent if the population is created from an exiting graph.
syn_spec (dict, optional (default: static synapse)) – Dictionary containg a directed edge between groups as key and the associated synaptic parameters for the post-synaptic neurons (i.e. those of the second group) as value. If a ‘default’ entry is provided, all unspecified connections will be set to its value.
meta_groups (list or dict of str/NeuralGroup items) – Additional set of groups which can overlap: a neuron can belong to several different meta groups. Contrary to the primary groups, meta groups do therefore no need to be disjoint. If all meta-groups have a name, they can be passed directly through a list; otherwise a dict is necessary.
with_model (bool, optional (default: True)) – Whether the groups require models (set to False to use populations for graph theoretical purposes, without NEST interaction)

Example

# we created groups `g1`, `g2`, and `g3`
prop = {
    ('g1', 'g2'): {'model': 'tsodyks2_synapse', 'tau_fac': 50.},
    ('g1', g3'): {'weight': 100.},
    ...
}
pop = NeuronalPop.from_groups(
    [g1, g2, g3], names=['g1', 'g2', 'g3'], syn_spec=prop)

Note

classmethod from_network(graph, *args)[source]#: Make a NeuralPop object from a network. The groups of neurons are determined using instructions from an arbitrary number of GroupProperties.

get_group(neurons, numbers=False)[source]#

Return the group of the neurons.

Parameters:	neurons (int or array-like) – IDs of the neurons for which the group should be returned. numbers (bool, optional (default: False)) – Whether the group identifier should be returned as a number; if `False`, the group names are returned.

get_param(groups=None, neurons=None, element='neuron')[source]#

Return the element (neuron or synapse) parameters for neurons or groups of neurons in the population.

Parameters:

groups (str, int or array-like, optional (default: None)) – Names or numbers of the groups for which the neural properties should be returned.
neurons (int or array-like, optional (default: None)) – IDs of the neurons for which parameters should be returned.
element (list of str, optional (default: "neuron")) – Element for which the parameters should be returned (either "neuron" or "synapse").

Returns:

param (list) – List of all dictionaries with the elements’ parameters.

has_models#

ids#: Return all the ids of the nodes inside the population.

New in version 1.2.

inhibitory#: Return the ids of all inhibitory nodes inside the population.

New in version 1.3.

is_valid#: Whether the population can be used to create a NEST network.

meta_groups#

nest_gids#: Return the NEST gids of the nodes inside the population.

New in version 1.3.

parent#: Parent Network, if it exists, otherwise None.

set_model(model, group=None)[source]#

Set the groups’ models.

Parameters:	model (dict) – Dictionary containing the model type as key (“neuron” or “synapse”) and the model name as value (e.g. {“neuron”: “iaf_neuron”}). group (list of strings, optional (default: None)) – List of strings containing the names of the groups which models should be updated.

Note

Warning

No check is performed on the validity of the models, which means that errors will only be detected when building the graph in NEST.

set_neuron_param(params, neurons=None, group=None)[source]#

Set the parameters of specific neurons or of a whole group.

New in version 1.0.

Parameters:

params (dict) – Dictionary containing parameters for the neurons. Entries can be either a single number (same for all neurons) or a list (one entry per neuron).
neurons (list of ints, optional (default: None)) – Ids of the neurons whose parameters should be modified.
group (list of strings, optional (default: None)) – List of strings containing the names of the groups whose parameters should be updated. When modifying neurons from a single group, it is still usefull to specify the group name to speed up the pace.

Note

If both neurons and group are None, all neurons will be modified.

Warning

No check is performed on the validity of the parameters, which means that errors will only be detected when building the graph in NEST.

size#: Number of neurons in this population.

syn_spec#

The properties of the synaptic connections between groups. Returns a dict containing tuples as keys and dicts of parameters as values.

The keys are tuples containing the names of the groups in the population, with the projecting group first (presynaptic neurons) and the receiving group last (post-synaptic neurons).

Example

For a population of excitatory (“exc”) and inhibitory (“inh”) neurons.

syn_spec = {
    ("exc", "exc"): {'model': 'stdp_synapse', 'weight': 2.5},
    ("exc", "inh"): {'model': 'static_synapse'},
    ("exc", "inh"): {'model': 'stdp_synapse', 'delay': 5.},
    ("inh", "inh"): {
        'model': 'stdp_synapse', 'weight': 5.,
        'delay': ('normal', 5., 2.)}
    }
}

New in version 0.8.

classmethod uniform(size, neuron_type=1, neuron_model='aeif_cond_alpha', neuron_param=None, syn_model='static_synapse', syn_param=None, parent=None, meta_groups=None)[source]#

Make a NeuralPop of identical neurons belonging to a single “default” group.

Changed in version 1.2: Added neuron_type and meta_groups parameters

Parameters:

size (int) – Number of neurons in the population.
neuron_type (int, optional (default: 1)) – Type of the neurons in the population: 1 for excitatory or -1 for inhibitory.
neuron_model (str, optional (default: default neuron model)) – Neuronal model for the simulator.
neuron_param (dict, optional (default: default neuron parameters)) – Parameters associated to neuron_model.
syn_model (str, optional (default: default static synapse)) – Synapse model for the simulator.
syn_param (dict, optional (default: default synaptic parameters)) – Parameters associated to syn_model.
parent (Graph object, optional (default: None)) – Parent graph described by the population.
meta_groups (list or dict of str/NeuralGroup items) – Set of groups which can overlap: a neuron can belong to several different meta groups, i.e. they do no need to be disjoint. If all meta-groups have a name, they can be passed directly through a list; otherwise a dict is necessary.

class nngt.SpatialGraph(nodes=0, name='SpatialGraph', weighted=True, directed=True, from_graph=None, shape=None, positions=None, **kwargs)[source]#

The detailed class that inherits from Graph and implements additional properties to describe spatial graphs (i.e. graph where the structure is embedded in space.

Initialize SpatialClass instance.

Parameters:

nodes (int, optional (default: 0)) – Number of nodes in the graph.
name (string, optional (default: “Graph”)) – The name of this Graph instance.
weighted (bool, optional (default: True)) – Whether the graph edges have weight properties.
directed (bool, optional (default: True)) – Whether the graph is directed or undirected.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment (None leads to a square of side 1 cm)
positions (numpy.array (N, 2), optional (default: None)) – Positions of the neurons; if not specified and nodes is not 0, then neurons will be reparted at random inside the Shape object of the instance.
**kwargs (keyword arguments for Graph or) – Shape if no shape was given.

Returns:

self (SpatialGraph)

get_positions(neurons=None)[source]#

Returns a copy of the neurons’ positions as a (N, 2) array.

Parameters:	neurons (int or array-like, optional (default: all neurons)) – List of the neurons for which the position should be returned.

shape#: The environment’s spatial structure.

class nngt.SpatialNetwork(population, name='SpatialNetwork', weighted=True, directed=True, shape=None, from_graph=None, positions=None, **kwargs)[source]#

Class that inherits from Network and SpatialGraph to provide a detailed description of a real neural network in space, i.e. with positions and biological properties to interact with NEST.

Initialize SpatialNetwork instance

Parameters:

name (string, optional (default: “Graph”)) – The name of this Graph instance.
weighted (bool, optional (default: True)) – Whether the graph edges have weight properties.
directed (bool, optional (default: True)) – Whether the graph is directed or undirected.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment (None leads to a square of side 1 cm)
positions (numpy.array, optional (default: None)) – Positions of the neurons; if not specified and nodes != 0, then neurons will be reparted at random inside the Shape object of the instance.
population (class:~nngt.NeuralPop, optional (default: None)) – Population from which the network will be built.

Returns:

self (SpatialNetwork)

set_types(syn_type, nodes=None, fraction=None)[source]#

Set the synaptic/connection types.

Changed in version 2.0: Changed syn_type to edge_type.

Warning

Parameters:

edge_type (int, string, or array of ints) – Type of the connection among ‘excitatory’ (also 1) or ‘inhibitory’ (also -1).
nodes (int, float or list, optional (default: None)) – If nodes is an int, number of nodes of the required type that will be created in the graph (all connections from inhibitory nodes are inhibitory); if it is a float, ratio of edge_type nodes in the graph; if it is a list, ids of the edge_type nodes.
fraction (float, optional (default: None)) – Fraction of the selected edges that will be set as edge_type (if nodes is not None, it is the fraction of the specified nodes’ edges, otherwise it is the fraction of all edges in the graph).

Returns:

t_list (numpy.ndarray) – List of the types in an order that matches the edges attribute of the graph.

nngt.generate(di_instructions, **kwargs)[source]#

Generate a Graph or one of its subclasses from a dict containing all the relevant informations.

Parameters:	di_instructions (`dict`) – Dictionary containing the instructions to generate the graph. It must have at least `"graph_type"` in its keys, with a value among `"distance_rule", "erdos_renyi", "fixed_degree", "newman_watts", "price_scale_free", "random_scale_free"`. Depending on the type, di_instructions should also contain at least all non-optional arguments of the generator function.

See also

generation

nngt.get_config(key=None, detailed=False)[source]#: Get the NNGT configuration as a dictionary.

Note

This function has no MPI barrier on it.

Load a Graph from a file.

Changed in version 2.0: Added optional attributes_types and cleanup arguments.

Warning

Support for GraphML and DOT formats are currently limited and require one of the non-default backends (DOT requires graph-tool).

Parameters:

filename (str) – The path to the file.
fmt (str, optional (default: “neighbour”)) – The format used to save the graph. Supported formats are: “neighbour” (neighbour list, default if format cannot be deduced automatically), “ssp” (scipy.sparse), “edge_list” (list of all the edges in the graph, one edge per line, represented by a source target-pair), “gml” (gml format, default if filename ends with ‘.gml’), “graphml” (graphml format, default if filename ends with ‘.graphml’ or ‘.xml’), “dot” (dot format, default if filename ends with ‘.dot’), “gt” (only when using graph_tool`<http://graph-tool.skewed.de/>_ as library, detected if `filename ends with ‘.gt’).
separator (str, optional (default ” “)) – separator used to separate inputs in the case of custom formats (namely “neighbour” and “edge_list”)
secondary (str, optional (default: “;”)) – Secondary separator used to separate attributes in the case of custom formats.
attributes (list, optional (default: [])) – List of names for the attributes present in the file. If a notifier is present in the file, names will be deduced from it; otherwise the attributes will be numbered. For “edge_list”, attributes may also be present as additional columns after the source and the target.
attributes_types (dict, optional (default: str)) – Backup information if the type of the attributes is not specified in the file. Values must be callables (types or functions) that will take the argument value as a string input and convert it to the proper type.
notifier (str, optional (default: “@”)) – Symbol specifying the following as meaningfull information. Relevant information are formatted @info_name=info_value, where info_name is in (“attributes”, “directed”, “name”, “size”) and associated info_value are of type (list, bool, str, int). Additional notifiers are @type=SpatialGraph/Network/SpatialNetwork, which must be followed by the relevant notifiers among @shape, @population, and @graph.
ignore (str, optional (default: “#”)) – Ignore lines starting with the ignore string.
name (str, optional (default: from file information or ‘LoadedGraph’)) – The name of the graph.
directed (bool, optional (default: from file information or True)) – Whether the graph is directed or not.
cleanup (bool, optional (default: False)) – If true, removes nodes before the first one that appears in the edges and after the last one and renumber the nodes from 0.

Returns:

graph (Graph or subclass) – Loaded graph.

nngt.num_mpi_processes()[source]#: Returns the number of MPI processes (1 if MPI is not used)

nngt.on_master_process()[source]#

Check whether the current code is executing on the master process (rank 0) if MPI is used.

Returns:	True if rank is 0, if mpi4py is not present or if MPI is not used, otherwise False.

nngt.save_to_file(graph, filename, fmt='auto', separator=' ', secondary=';', attributes=None, notifier='@')[source]#

Save a graph to file.

@todo: implement dot, xml/graphml, and gt formats

Parameters:

graph (Graph or subclass) – Graph to save.
filename (str) – The path to the file.
fmt (str, optional (default: “auto”)) – The format used to save the graph. Supported formats are: “neighbour” (neighbour list, default if format cannot be deduced automatically), “ssp” (scipy.sparse), “edge_list” (list of all the edges in the graph, one edge per line, represented by a source target-pair), “gml” (gml format, default if filename ends with ‘.gml’), “graphml” (graphml format, default if filename ends with ‘.graphml’ or ‘.xml’), “dot” (dot format, default if filename ends with ‘.dot’), “gt” (only when using graph_tool as library, detected if filename ends with ‘.gt’).
separator (str, optional (default ” “)) – separator used to separate inputs in the case of custom formats (namely “neighbour” and “edge_list”)
secondary (str, optional (default: “;”)) – Secondary separator used to separate attributes in the case of custom formats.
attributes (list, optional (default: None)) – List of names for the edge attributes present in the graph that will be saved to disk; by default (None), all attributes will be saved.
notifier (str, optional (default: “@”)) – Symbol specifying the following as meaningfull information. Relevant information are formatted @info_name=info_value, with info_name in (“attributes”, “attr_types”, “directed”, “name”, “size”). Additional notifiers are @type=SpatialGraph/Network/SpatialNetwork, which are followed by the relevant notifiers among @shape, @population, and @graph to separate the sections.

Note

Positions are saved as bytes by numpy.nparray.tostring()

nngt.seed(msd=None, seeds=None)[source]#

Seed the random generator used by NNGT (i.e. the numpy RandomState: for details, see numpy.random.RandomState).

Parameters:	msd (int, optional) – Master seed for numpy RandomState. Must be convertible to 32-bit unsigned integers. seeds (list of ints, optional) – Seeds for RandomState (when using MPI). Must be convertible to 32-bit unsigned integers, one entry per MPI process.

nngt.set_config(config, value=None, silent=False)[source]#

Set NNGT’s configuration.

Parameters:	config (dict or str) – Either a full configuration dictionary or one key to be set together with its associated value. value (object, optional (default: None)) – Value associated to config if config is a key.

Examples

>>> nngt.set_config({'multithreading': True, 'omp': 4})
>>> nngt.set_config('multithreading', False)

Notes

See the config file nngt/nngt.conf.default or ~/.nngt/nngt.conf for details about your configuration.

This function has an MPI barrier on it, so it must always be called on all processes.

See also

get_config()

nngt.use_backend(backend, reloading=True, silent=False)[source]#

Allows the user to switch to a specific graph library as backend.

Warning

If Graph objects have already been created, they will no longer be compatible with NNGT methods.

Parameters:

backend (string) – Name of a graph library among ‘graph_tool’, ‘igraph’, ‘networkx’, or ‘nngt’.
reloading (bool, optional (default: True)) – Whether the graph objects should be reloaded through reload (this should always be set to True except when NNGT is first initiated!)
silent (bool, optional (default: False)) – Whether the changes made to the configuration should be logged at the DEBUG (True) or INFO (False) level.

Analysis module#

Tools to analyze neuronal networks, using either their topological properties, their activity, or more importantly, taking both into account.

Content#

`nngt.analysis.adjacency_matrix`(graph[, …])	Adjacency matrix of the graph.
`nngt.analysis.all_shortest_paths`(g, source, …)	Yields all shortest paths from source to target.
`nngt.analysis.assortativity`(g, degree[, weights])	Returns the assortativity of the graph.
`nngt.analysis.average_path_length`(g[, …])	Returns the average shortest path length between sources and targets.
`nngt.analysis.bayesian_blocks`(t[, x, sigma, …])	Bayesian Blocks Implementation
`nngt.analysis.betweenness`(g[, btype, weights])	Returns the normalized betweenness centrality of the nodes and edges.
`nngt.analysis.betweenness_distrib`(graph[, …])	Betweenness distribution of a graph.
`nngt.analysis.binning`(x[, bins, log])	Binning function providing automatic binning using Bayesian blocks in addition to standard linear and logarithmic uniform bins.
`nngt.analysis.closeness`(g[, weights, nodes, …])	Returns the closeness centrality of some nodes.
`nngt.analysis.connected_components`(g[, ctype])	Returns the connected component to which each node belongs.
`nngt.analysis.degree_distrib`(graph[, …])	Degree distribution of a graph.
`nngt.analysis.diameter`(g[, directed, …])	Returns the diameter of the graph.
`nngt.analysis.get_b2`([network, …])	Return the B2 coefficient for the neurons.
`nngt.analysis.get_firing_rate`([network, …])	Return the average firing rate for the neurons.
`nngt.analysis.get_spikes`([recorder, …])	Return a 2D sparse matrix, where:
`nngt.analysis.global_clustering`(g[, …])	Returns the global clustering coefficient.
`nngt.analysis.global_clustering_binary_undirected`(g)	Returns the undirected global clustering coefficient.
`nngt.analysis.local_clustering`(g[, nodes, …])	Local (weighted directed) clustering coefficient of the nodes.
`nngt.analysis.local_clustering_binary_undirected`(g)	Returns the undirected local clustering coefficient of some nodes.
`nngt.analysis.node_attributes`(network, …)	Return node attributes for a set of nodes.
`nngt.analysis.num_iedges`(graph)	Returns the number of inhibitory connections.
`nngt.analysis.reciprocity`(g)	Calculate the edge reciprocity of the graph.
`nngt.analysis.shortest_distance`(g[, …])	Returns the length of the shortest paths between sources`and `targets.
`nngt.analysis.shortest_path`(g, source, target)	Returns a shortest path between source`and `target.
`nngt.analysis.small_world_propensity`(g[, …])	Returns the small-world propensity of the graph as first defined in [Muldoon2016].
`nngt.analysis.spectral_radius`(graph[, …])	Spectral radius of the graph, defined as the eigenvalue of greatest module.
`nngt.analysis.subgraph_centrality`(graph[, …])	Returns the subgraph centrality for each node in the graph.
`nngt.analysis.total_firing_rate`([network, …])	Computes the total firing rate of the network from the spike times.
`nngt.analysis.transitivity`(g[, directed, …])	Same as `global_clustering()`.
`nngt.analysis.triangle_count`(g[, nodes, …])	Returns the number or the strength (also called intensity) of triangles for each node.
`nngt.analysis.triplet_count`(g[, nodes, …])	Returns the number or the strength (also called intensity) of triplets for each node.

Details#

nngt.analysis.adjacency_matrix(graph, types=False, weights=False)[source]#

Adjacency matrix of the graph.

Parameters:

graph (Graph or subclass) – Network to analyze.
types (bool, optional (default: False)) – Whether the excitatory/inhibitory type of the connnections should be considered (only if the weighing factor is the synaptic strength).
weights (bool or string, optional (default: False)) – Whether weights should be taken into account; if True, then connections are weighed by their synaptic strength, if False, then a binary matrix is returned, if weights is a string, then the ponderation is the correponding value of the edge attribute (e.g. “distance” will return an adjacency matrix where each connection is multiplied by its length).

Returns:

a csr_matrix.

References

[gt-adjacency]

graph-tool - spectral.adjacency

[nx-adjacency]

networkx - convert_matrix.to_scipy_sparse_matrix

nngt.analysis.all_shortest_paths(g, source, target, directed=True, weights=None)[source]#

Yields all shortest paths from source to target. The algorithms returns an empty generator if there is no path between the nodes.

New in version 2.0.

Parameters:

g (Graph) – Graph to analyze.
source (int) – Node from which the paths starts.
target (int, optional (default: all nodes)) – Node where the paths ends.
directed (bool, optional (default: True)) – Whether the edges should be considered as directed or not (automatically set to False if g is undirected).
weights (str or array, optional (default: binary)) – Whether to use weighted edges to compute the distances. By default, all edges are considered to have distance 1.

Returns:

all_paths (generator) – Generator yielding paths as lists of ints.

References

[gt-sd]

graph-tool - topology.all_shortest_paths

[ig-sp]

igraph - get_all_shortest_paths

[nx-sp]

networkx - algorithms.shortest_paths.generic.all_shortest_paths

nngt.analysis.assortativity(g, degree, weights=None)[source]#

Returns the assortativity of the graph. This tells whether nodes are preferentially connected together depending on their degree.

Parameters:	g (`Graph`) – Graph to analyze. degree (str) – The type of degree that should be considered. weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if `None` or `False` then use binary edges; if `True`, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.

References

[newman-mixing-2003]

M. E. J. Newman, “Mixing patterns in networks”, Phys. Rev. E 67, 026126 (2003), see graph-tool below for links.

[gt-assortativity]

graph-tool - correlations.scalar_assortativity

[ig-assortativity]

igraph - assortativity

[nx-assortativity]

networkx - algorithms.assortativity.degree_assortativity_coefficient

nngt.analysis.average_path_length(g, sources=None, targets=None, directed=True, weights=None, unconnected=False)[source]#

Returns the average shortest path length between sources and targets. The algorithms raises an error if all nodes are not connected unless unconnected is set to True.

New in version 2.0.

The average path length is defined as

$L = \frac{1}{N_p} \sum_{u,v} d(u, v),$

where $N_p$ is the number of paths between sources and targets, and $d(u, v)$ is the shortest path distance from u to v.

If sources and targets are both None, then the total number of paths is $N_p = N(N - 1)$ , with $N$ the number of nodes in the graph.

Parameters:

g (Graph) – Graph to analyze.
sources (list of nodes, optional (default: all)) – Nodes from which the paths must be computed.
targets (list of nodes, optional (default: all)) – Nodes to which the paths must be computed.
directed (bool, optional (default: True)) – Whether the edges should be considered as directed or not (automatically set to False if g is undirected).
weights (str, optional (default: binary)) – Whether to use weighted edges to compute the distances. By default, all edges are considered to have distance 1.
unconnected (bool, optional (default: False)) – If set to true, ignores unconnected nodes and returns the average path length of the existing paths.

References

[gt-sd]

graph-tool - topology.shortest_distance

[ig-sp]

igraph - shortest_paths

[nx-sp]

networkx - algorithms.shortest_paths.generic.average_shortest_path_length

See also

nngt.analysis.shortest_distance()

nngt.analysis.bayesian_blocks(t, x=None, sigma=None, fitness='events', **kwargs)[source]#

Bayesian Blocks Implementation

This is a flexible implementation of the Bayesian Blocks algorithm described in Scargle 2012 [1]

New in version 0.7.

Parameters:

t (array_like) – data times (one dimensional, length N)
x (array_like (optional)) – data values
sigma (array_like or float (optional)) – data errors
fitness (str or object) – the fitness function to use. If a string, the following options are supported:
- ‘events’ : binned or unbinned event data
  
  extra arguments are p0, which gives the false alarm probability to compute the prior, or gamma which gives the slope of the prior on the number of bins.
- ‘regular_events’ : non-overlapping events measured at multiples
  
  of a fundamental tick rate, dt, which must be specified as an additional argument. The prior can be specified through gamma, which gives the slope of the prior on the number of bins.
- ‘measures’ : fitness for a measured sequence with Gaussian errors
  
  The prior can be specified using gamma, which gives the slope of the prior on the number of bins. If gamma is not specified, then a simulation-derived prior will be used.
Alternatively, the fitness can be a user-specified object of type derived from the FitnessFunc class.

Returns:

edges (ndarray) – array containing the (N+1) bin edges

Examples

Event data:

>>> t = np.random.normal(size=100)
>>> bins = bayesian_blocks(t, fitness='events', p0=0.01)

Event data with repeats:

>>> t = np.random.normal(size=100)
>>> t[80:] = t[:20]
>>> bins = bayesian_blocks(t, fitness='events', p0=0.01)

Regular event data:

>>> dt = 0.01
>>> t = dt * np.arange(1000)
>>> x = np.zeros(len(t))
>>> x[np.random.randint(0, len(t), len(t) / 10)] = 1
>>> bins = bayesian_blocks(t, fitness='regular_events', dt=dt, gamma=0.9)

Measured point data with errors:

>>> t = 100 * np.random.random(100)
>>> x = np.exp(-0.5 * (t - 50) ** 2)
>>> sigma = 0.1
>>> x_obs = np.random.normal(x, sigma)
>>> bins = bayesian_blocks(t, fitness='measures')

References

[1]	Scargle, J et al. (2012) http://adsabs.harvard.edu/abs/2012arXiv1207.5578S

See also

astroML.plotting.hist(): histogram plotting function which can make use of bayesian blocks.

nngt.analysis.betweenness(g, btype='both', weights=None)[source]#

Returns the normalized betweenness centrality of the nodes and edges.

Parameters:

g (Graph) – Graph to analyze.
btype (str, optional (default ‘both’)) – The centrality that should be returned (either ‘node’, ‘edge’, or ‘both’). By default, both betweenness centralities are computed.
weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if None or False then use binary edges; if True, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.

Returns:

nb (numpy.ndarray) – The nodes’ betweenness if btype is ‘node’ or ‘both’
eb (numpy.ndarray) – The edges’ betweenness if btype is ‘edge’ or ‘both’

References

[wiki-betw]

http://en.wikipedia.org/wiki/Centrality#Betweenness_centrality

[gt-betw]

graph-tool - centrality.betweenness

[ig-ebetw]

igraph - edge_betweenness

[ig-nbetw]

igraph - betweenness

[nx-ebetw]

networkx - algorithms.centrality.edge_betweenness_centrality

[nx-nbetw]

networkx - networkx.algorithms.centrality.betweenness_centrality

nngt.analysis.betweenness_distrib(graph, weights=None, nodes=None, num_nbins='bayes', num_ebins='bayes', log=False)[source]#

Betweenness distribution of a graph.

Parameters:

graph (Graph or subclass) – the graph to analyze.
weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if None or False then use binary edges; if True, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.
nodes (list or numpy.array of ints, optional (default: all nodes)) – Restrict the distribution to a set of nodes (only impacts the node attribute).
log (bool, optional (default: False)) – use log-spaced bins.
num_bins (int, list or str, optional (default: ‘bayes’)) – Any of the automatic methodes from numpy.histogram(), or ‘bayes’ will provide automatic bin optimization. Otherwise, an int for the number of bins can be provided, or the direct bins list.

Returns:

ncounts (numpy.array) – number of nodes in each bin
nbetw (numpy.array) – bins for node betweenness
ecounts (numpy.array) – number of edges in each bin
ebetw (numpy.array) – bins for edge betweenness

nngt.analysis.binning(x, bins='bayes', log=False)[source]#

Binning function providing automatic binning using Bayesian blocks in addition to standard linear and logarithmic uniform bins.

New in version 0.7.

Parameters:	x (array-like) – Array of data to be histogrammed bins (int, list or ‘auto’, optional (default: ‘bayes’)) – If bins is ‘bayes’, in use bayesian blocks for dynamic bin widths; if it is an int, the interval will be separated into log (bool, optional (default: False)) – Whether the bins should be evenly spaced on a logarithmic scale.

nngt.analysis.closeness(g, weights=None, nodes=None, mode='out', harmonic=False, default=nan)[source]#

Returns the closeness centrality of some nodes.

New in version 2.0.

Closeness centrality of a node u is defined, for the harmonic version, as the sum of the reciprocal of the shortest path distance $d_{uv}$ from u to the N - 1 other nodes in the graph (if mode is “out”, reciprocally $d_{vu}$ , the distance to u from another node v, if mode is “in”):

$C(u) = \frac{1}{N - 1} \sum_{v \neq u} \frac{1}{d_{uv}},$

or, using the arithmetic definition, as the reciprocal of the average shortest path distance to/from u over to all other nodes:

$C(u) = \frac{n - 1}{\sum_{v \neq u} d_{uv}},$

where d_{uv} is the shortest-path distance from u to v, and n is the number of nodes in the component.

By definition, the distance is infinite when nodes are not connected by a path in the harmonic case (such that $\frac{1}{d(v, u)} = 0$ ), while the distance itself is taken as zero for unconnected nodes in the first equation.

Parameters:

g (Graph) – Graph to analyze.
weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if None or False then use binary edges; if True, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.
nodes (list, optional (default: all nodes)) – The list of nodes for which the clutering will be returned
mode (str, optional (default: “out”)) – For directed graphs, whether the distances are computed from (“out”) or to (“in”) each of the nodes.
harmonic (bool, optional (default: False)) – Whether the arithmetic (default) or the harmonic (recommended) version of the closeness should be used.

Returns:

c (numpy.ndarray) – The list of closeness centralities, on per node.
.. warning :: – For compatibility reasons (harmonic closeness is not implemented for igraph), the arithmetic version is used by default; however, it is recommended to use the harmonic version instead whenever possible.

References

[gt-closeness]

graph-tool - centrality.closeness

[ig-closeness]

igraph - closeness

[nx-harmonic]

networkx - algorithms.centrality.harmonic_centrality

[nx-closeness]

networkx - algorithms.centrality.closeness_centrality

nngt.analysis.connected_components(g, ctype=None)[source]#

Returns the connected component to which each node belongs.

New in version 2.0.

Parameters:	g (`Graph`) – Graph to analyze. ctype (str, optional (default ‘scc’)) – Type of component that will be searched: either strongly connected (‘scc’, by default) or weakly connected (‘wcc’).
Returns:	cc, hist (`numpy.ndarray`) – The component associated to each node (cc) and the number of nodes in each of the component (hist).

References

[gt-cc]

graph-tool - topology.label_components

[ig-cc]

igraph - clusters

[nx-ucc]

networkx - algorithms.components.connected_components

[nx-scc]

networkx - algorithms.components.strongly_connected_components

[nx-wcc]

networkx - algorithms.components.weakly_connected_components

nngt.analysis.degree_distrib(graph, deg_type='total', nodes=None, weights=None, log=False, num_bins='bayes')[source]#

Degree distribution of a graph.

Parameters:

graph (Graph or subclass) – the graph to analyze.
deg_type (string, optional (default: “total”)) – type of degree to consider (“in”, “out”, or “total”).
nodes (list of ints, optional (default: None)) – Restrict the distribution to a set of nodes (default: all nodes).
weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if None or False then use binary edges; if True, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.
log (bool, optional (default: False)) – use log-spaced bins.
num_bins (int, list or str, optional (default: ‘bayes’)) – Any of the automatic methodes from numpy.histogram(), or ‘bayes’ will provide automatic bin optimization. Otherwise, an int for the number of bins can be provided, or the direct bins list.

See also

numpy.histogram(), binning()

Returns:	counts (`numpy.array`) – number of nodes in each bin deg (`numpy.array`) – bins

nngt.analysis.diameter(g, directed=True, weights=False, is_connected=False)[source]#

Returns the diameter of the graph.

Changed in version 2.0: Added directed and is_connected arguments.

It returns infinity if the graph is not connected (strongly connected for directed graphs) unless is_connected is True, in which case it returns the longest existing shortest distance.

Parameters:

g (Graph) – Graph to analyze.
directed (bool, optional (default: True)) – Whether to compute the directed diameter if the graph is directed. If False, then the graph is treated as undirected. The option switches to False automatically if g is undirected.
weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if None or False then use binary edges; if True, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.
is_connected (bool, optional (default: False)) – If False, check whether the graph is connected or not and return infinite diameter if graph is unconnected. If True, the graph is assumed to be connected.

Warning

For graph-tool, the [pseudo-diameter] is returned, which may sometime lead to inexact results.

See also

nngt.analysis.shortest_distance()

References

[pseudo-diameter]

http://en.wikipedia.org/wiki/Distance_%28graph_theory%29

[dijkstra]

E. Dijkstra, “A note on two problems in connexion with graphs.” Numerische Mathematik, 1:269-271, 1959.

[gt-diameter]

graph-tool - topology.pseudo_diameter

[ig-diameter]

igraph - diameter

[nx-diameter]

networkx - algorithms.distance_measures.diameter

[nx-dijkstra]

networkx - algorithms.shortest_paths.weighted.all_pairs_dijkstra

nngt.analysis.get_b2(network=None, spike_detector=None, data=None, nodes=None)[source]#

Return the B2 coefficient for the neurons.

Parameters:

network (nngt.Network, optional (default: None)) – Network for which the activity was simulated.
spike_detector (tuple of ints, optional (default: spike detectors)) – GID of the “spike_detector” objects recording the network activity.
data (array-like of shape (N, 2), optionale (default: None)) – Array containing the spikes data (first line must contain the NEST GID of the neuron that fired, second line must contain the associated spike time).
nodes (array-like, optional (default: all neurons)) – NNGT ids of the nodes for which the B2 should be computed.

Returns:

b2 (array-like) – B2 coefficient for each neuron in nodes.

nngt.analysis.get_firing_rate(network=None, spike_detector=None, data=None, nodes=None)[source]#

Return the average firing rate for the neurons.

Parameters:

network (nngt.Network, optional (default: None)) – Network for which the activity was simulated.
spike_detector (tuple of ints, optional (default: spike detectors)) – GID of the “spike_detector” objects recording the network activity.
data (numpy.array of shape (N, 2), optionale (default: None)) – Array containing the spikes data (first line must contain the NEST GID of the neuron that fired, second line must contain the associated spike time).
nodes (array-like, optional (default: all nodes)) – NNGT ids of the nodes for which the B2 should be computed.

Returns:

fr (array-like) – Firing rate for each neuron in nodes.

nngt.analysis.get_spikes(recorder=None, spike_times=None, senders=None, astype='ssp')[source]#

Return a 2D sparse matrix, where:

each row i contains the spikes of neuron i (in NEST),
each column j contains the times of the jth spike for all neurons.

Changed in version 1.0: Neurons are now located in the row corresponding to their NEST GID.

Parameters:

recorder (tuple, optional (default: None)) – Tuple of NEST gids, where the first one should point to the spike_detector which recorded the spikes.
spike_times (array-like, optional (default: None)) – If recorder is not provided, the spikes’ data can be passed directly through their spike_times and the associated senders.
senders (array-like, optional (default: None)) – senders[i] corresponds to the neuron which fired at spike_times[i].
astype (str, optional (default: “ssp”)) – Format of the returned data. Default is sparse lil_matrix (“ssp”) with one row per neuron, otherwise “np” returns a (T, 2) array, with T the number of spikes (the first row being the NEST gid, the second the spike time).

Example

>>> get_spikes()

>>> get_spikes(recorder)

>>> times = [1.5, 2.68, 125.6]
>>> neuron_ids = [12, 0, 65]
>>> get_spikes(spike_times=times, senders=neuron_ids)

Note

If no arguments are passed to the function, the first spike_recorder available in NEST will be used. Neuron positions correspond to their GIDs in NEST.

Returns:	CSR matrix containing the spikes sorted by neuron GIDs (rows) and time (columns).

nngt.analysis.global_clustering(g, directed=True, weights=None, method='continuous', combine_weights='mean')[source]#

Returns the global clustering coefficient.

This corresponds to the ratio of triangles to the number of triplets. For directed and weighted cases, see definitions of generalized triangles and triplets in the associated functions below.

Parameters:

g (Graph) – Graph to analyze.
directed (bool, optional (default: True)) – Whether to compute the directed clustering if the graph is directed.
weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if None or False then use binary edges; if True, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.
method (str, optional (default: ‘continuous’)) – Method used to compute the weighted clustering, either ‘barrat’ [Barrat2004], ‘continuous’, or ‘onnela’ [Onnela2005].
combine_weights (str, optional (default: ‘mean’)) – How to combine the weights of reciprocal edges if the graph is directed but directed is set to False. It can be:
- “sum”: the sum of the edge attribute values will be used for the new edge.
- “mean”: the mean of the edge attribute values will be used for the new edge.
- “min”: the minimum of the edge attribute values will be used for the new edge.
- “max”: the maximum of the edge attribute values will be used for the new edge.

References

[gt-global-clustering]

graph-tool - clustering.global_clustering

[ig-global-clustering]

igraph - transitivity_undirected

[nx-global-clustering]

networkx - algorithms.cluster.transitivity

[Barrat2004]

Barrat, Barthelemy, Pastor-Satorras, Vespignani. The Architecture of Complex Weighted Networks. PNAS 2004, 101 (11). DOI: 10.1073/pnas.0400087101.

[Onnela2005]

Onnela, Saramäki, Kertész, Kaski. Intensity and Coherence of Motifs in Weighted Complex Networks. Phys. Rev. E 2005, 71 (6), 065103. DOI: 10.1103/physreve.71.065103, arxiv:cond-mat/0408629.

See also

triplet_count() triangle_count()

nngt.analysis.global_clustering_binary_undirected(g)[source]#

Returns the undirected global clustering coefficient.

This corresponds to the ratio of undirected triangles to the number of undirected triads.

Parameters:	g (`Graph`) – Graph to analyze.

nngt.analysis.local_clustering(g, nodes=None, directed=True, weights=None, method='continuous', combine_weights='mean')[source]#

Local (weighted directed) clustering coefficient of the nodes.

If no weights are requested and the graph is undirected, returns the undirected binary clustering.

For all weighted cases, the weights are assumed to be positive and they are normalized to dimensionless values between 0 and 1 through a division by the highest weight.

The default method for weighted networks is based on a modification of the proposal in [Zhang2005] with:

$C_i = \frac{\left(W^{\left[\frac{2}{3}\right]}\right)^3_{ii}} {\left(s^{\left[\frac{1}{2}\right]}_i\right)^2 - s_i}$

for undirected networks, with $\tilde{W}$ the adjacency matrix (normalized to have its highest weight set to 1 if weighted), $s_i$ the strength of node $i$ , and $s^{[\frac{1}{2}]}_i = \sum_k \sqrt{w_{ik}}$ the strength associated to the matrix $W^{[\frac{1}{2}]} = \{\sqrt{w_{ij}}\}$ .

For directed networks, we used the total clustering defined in [Fagiolo2007], hence the second equation becomes:

$C_i = \frac{\frac{1}{2}\left(W^{\left[\frac{2}{3}\right]} + W^{\left[\frac{2}{3}\right],T}\right)^3_{ii}} {\left(s^{\left[\frac{1}{2}\right]}_i\right)^2 - 2s^{\leftrightarrow}_i - s_i}$

with $s^{\leftrightarrow} = \sum_k \sqrt{w_{ik}w_{ki}}$ the reciprocal strength (associated to reciprocal connections).

Contrary to ‘barrat’ and ‘onnela’ [Saramaki2007], this method displays all following properties:

fully continuous (no jump in clustering when weights go to zero),
equivalent to binary clustering when all weights are 1,
equivalence between no-edge and zero-weight edge cases,
normalized (always between zero and 1).

Parameters:

g (Graph object) – Graph to analyze.
nodes (array-like container with node ids, optional (default = all nodes)) – Nodes for which the local clustering coefficient should be computed.
directed (bool, optional (default: True)) – Whether to compute the directed clustering if the graph is directed.
weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if None or False then use binary edges; if True, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.
method (str, optional (default: ‘continuous’)) – Method used to compute the weighted clustering, either ‘barrat’ [Barrat2004]/[Clemente2018], ‘continuous’, or ‘onnela’ [Onnela2005].
combine_weights (str, optional (default: ‘mean’)) – How to combine the weights of reciprocal edges if the graph is directed but directed is set to False. It can be:
- “sum”: the sum of the edge attribute values will be used for the new edge.
- “mean”: the mean of the edge attribute values will be used for the new edge.
- “min”: the minimum of the edge attribute values will be used for the new edge.
- “max”: the maximum of the edge attribute values will be used for the new edge.

Returns:

lc (numpy.ndarray) – The list of clustering coefficients, on per node.

References

[Barrat2004]

Barrat, Barthelemy, Pastor-Satorras, Vespignani. The Architecture of Complex Weighted Networks. PNAS 2004, 101 (11). DOI: 10.1073/pnas.0400087101.

[Clemente2018]

Clemente, Grassi. Directed Clustering in Weighted Networks: A New Perspective. Chaos, Solitons & Fractals 2018, 107, 26–38. DOI: 10.1016/j.chaos.2017.12.007, arXiv: 1706.07322.

[Fagiolo2007]

Fagiolo. Clustering in Complex Directed Networks. Phys. Rev. E 2007, 76, (2), 026107. DOI: 10.1103/PhysRevE.76.026107, arXiv: physics/0612169.

[Onnela2005]

Onnela, Saramäki, Kertész, Kaski. Intensity and Coherence of Motifs in Weighted Complex Networks. Phys. Rev. E 2005, 71 (6), 065103. DOI: 10.1103/physreve.71.065103, arXiv: cond-mat/0408629.

[Saramaki2007]

[Zhang2005]

Zhang, Horvath. A General Framework for Weighted Gene Co-Expression Network Analysis. Statistical Applications in Genetics and Molecular Biology 2005, 4 (1). DOI: 10.2202/1544-6115.1128, PDF.

See also

undirected_binary_clustering(), global_clustering()

nngt.analysis.local_clustering_binary_undirected(g, nodes=None)[source]#

Returns the undirected local clustering coefficient of some nodes.

$C_i = \frac{A^3_{ii}}{d_i(d_i - 1)} = \frac{\Delta_i}{T_i}$

with $A$ the adjacency matrix, $d_i$ the degree of node $i$ , $\Delta_i$ is the number of triangles, and $T_i$ is the number of triplets to which $i$ belongs.

If g is directed, then it is converted to a simple undirected graph (no parallel edges), both directed and reciprocal edges are merged into a single edge.

Parameters:	g (`Graph`) – Graph to analyze. nodes (list, optional (default: all nodes)) – The list of nodes for which the clustering will be returned
Returns:	lc (`numpy.ndarray`) – The list of clustering coefficients, on per node.

References

[gt-local-clustering]

graph-tool - clustering.local_clustering

[ig-local-clustering]

igraph - transitivity_local_undirected

[nx-local-clustering]

networkx - algorithms.cluster.clustering

nngt.analysis.node_attributes(network, attributes, nodes=None, data=None)[source]#

Return node attributes for a set of nodes.

Parameters:

network (Graph) – The graph where the nodes belong.
attributes (str or list) – Attributes which should be returned, among: * “betweenness” * “clustering” * “closeness” * “in-degree”, “out-degree”, “total-degree” * “subgraph_centrality”
nodes (list, optional (default: all nodes)) – Nodes for which the attributes should be returned.
data (numpy.array of shape (N, 2), optional (default: None)) – Potential data on the spike events; if not None, it must contain the sender ids on the first column and the spike times on the second.

Returns:

values (array-like or dict) – Returns the attributes, either as an array if only one attribute is required (attributes is a str) or as a dict of arrays.

nngt.analysis.num_iedges(graph)[source]#

Returns the number of inhibitory connections.

For Network objects, this corresponds to the number of edges stemming from inhibitory nodes (given by nngt.NeuralPop.inhibitory()). Otherwise, counts the edges where the type attribute is -1.

nngt.analysis.reciprocity(g)[source]#

Calculate the edge reciprocity of the graph.

The reciprocity is defined as the number of edges that have a reciprocal edge (an edge between the same nodes but in the opposite direction) divided by the total number of edges. This is also the probability for any given edge, that its reciprocal edge exists. By definition, the reciprocity of undirected graphs is 1.

Parameters:	g (`Graph`) – Graph to analyze.

References

[gt-reciprocity]

graph-tool - topology.edge_reciprocity

nngt.analysis.shortest_distance(g, sources=None, targets=None, directed=True, weights=None)[source]#

Returns the length of the shortest paths between sources`and `targets. The algorithms return infinity if there are no paths between nodes.

New in version 2.0.

Parameters:

g (Graph) – Graph to analyze.
sources (list of nodes, optional (default: all)) – Nodes from which the paths must be computed.
targets (list of nodes, optional (default: all)) – Nodes to which the paths must be computed.
directed (bool, optional (default: True)) – Whether the edges should be considered as directed or not (automatically set to False if g is undirected).
weights (str or array, optional (default: binary)) – Whether to use weighted edges to compute the distances. By default, all edges are considered to have distance 1.

Returns:

distance (float, or 1d/2d numpy array of floats) – Distance (if single source and single target) or distance array. For multiple sources and targets, the shape of the matrix is (S, T), with S the number of sources and T the number of targets; for a single source or target, return a 1d-array of length T or S.

References

[gt-sd]

graph-tool - topology.shortest_distance

[ig-sp]

igraph - shortest_paths

[nx-sp]

networkx - algorithms.shortest_paths.weighted.multi_source_dijkstra

nngt.analysis.shortest_path(g, source, target, directed=True, weights=None)[source]#

Returns a shortest path between source`and `target. The algorithms returns an empty list if there is no path between the nodes.

New in version 2.0.

Parameters:

g (Graph) – Graph to analyze.
source (int) – Node from which the path starts.
target (int) – Node where the path ends.
directed (bool, optional (default: True)) – Whether the edges should be considered as directed or not (automatically set to False if g is undirected).
weights (str or array, optional (default: binary)) – Whether to use weighted edges to compute the distances. By default, all edges are considered to have distance 1.

Returns:

path (array of ints) – Order of the nodes making up the path from source to target.

References

[gt-sd]

graph-tool - topology.shortest_distance

[ig-sp]

igraph - shortest_paths

[nx-sp]

networkx - algorithms.shortest_paths.generic.shortest_path

nngt.analysis.small_world_propensity(g, directed=True, use_global_clustering=True, use_diameter=False, weights=None, combine_weights='mean', clustering='continuous', lattice=None, random=None, return_deviations=False)[source]#

Returns the small-world propensity of the graph as first defined in [Muldoon2016].

$\phi = 1 - \sqrt{\frac{\Pi_{[0, 1]}(\Delta_C^2) + \Pi_{[0, 1]}(\Delta_L^2)}{2}}$

with $\Delta_C$ the clustering deviation, i.e. the relative global clustering of g compared to two reference graphs

$\Delta_C = \frac{C_{latt} - C_g}{C_{latt} - C_{rand}}$

and $Delta_L$ the deviation of the average path length, i.e. relative average path length of g compared to that of the reference graphs

$\Delta_L = \frac{L_g - L_{rand}}{L_{latt} - L_{rand}}.$

In both cases, latt and rand refer to the equivalent lattice and Erdos-Renyi (ER) graphs obtained by rewiring g to obtain respectively the highest and lowest combination of clustering and average path length.

The rectangle function $\Pi_{[0, 1]}$ is used to clip both deviations to the [0, 1] range in case some graphs have a higher clustering than the lattice or a lower average path length than the ER graph.

Parameters:

g (Graph object) – Graph to analyze.
directed (bool, optional (default: True)) – Whether to compute the directed clustering if the graph is directed. If False, then the graph is treated as undirected. The option switches to False automatically if g is undirected.
use_global_clustering (bool, optional (default: True)) – If False, then the average local clustering is used instead of the global clustering.
use_diameter (bool, optional (default: False)) – Use the diameter instead of the average path length to have more global information. Ccan also be much faster in some cases, especially using graph-tool as the backend.
weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if None or False then use binary edges; if True, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.
combine_weights (str, optional (default: ‘mean’)) – How to combine the weights of reciprocal edges if the graph is directed but directed is set to False. It can be:
- “sum”: the sum of the edge attribute values will be used for the new edge.
- “mean”: the mean of the edge attribute values will be used for the new edge.
- “min”: the minimum of the edge attribute values will be used for the new edge.
- “max”: the maximum of the edge attribute values will be used for the new edge.
clustering (str, optional (default: ‘continuous’)) – Method used to compute the weighted clustering coefficients, either ‘barrat’ [Barrat2004], ‘continuous’ (recommended), or ‘onnela’ [Onnela2005].
lattice (nngt.Graph, optional (default: generated from g)) – Lattice to use as reference (since its generation is deterministic, enables to avoid multiple generations when running the algorithm several times with the same graph)
random (nngt.Graph, optional (default: generated from g)) – Random graph to use as reference. Can be useful for reproducibility or for very sparse graphs where ER algorithm would statistically lead to a disconnected graph.
return_deviations (bool, optional (default: False)) – If True, the deviations are also returned, in addition to the small-world propensity.

Note

If weights are provided, the distance calculation uses the inverse of the weights. This implementation differs slightly from the original implementation as it uses the global instead of the average clustering coefficient and it is generalized to directed networks.

References

[Muldoon2016]

(1, 2) Muldoon, Bridgeford, Bassett. Small-World Propensity and Weighted Brain Networks. Sci Rep 2016, 6 (1), 22057. DOI: 10.1038/srep22057, arXiv: 1505.02194.

[Barrat2004]

Barrat, Barthelemy, Pastor-Satorras, Vespignani. The Architecture of Complex Weighted Networks. PNAS 2004, 101 (11). DOI: 10.1073/pnas.0400087101.

[Onnela2005]

Onnela, Saramäki, Kertész, Kaski. Intensity and Coherence of Motifs in Weighted Complex Networks. Phys. Rev. E 2005, 71 (6), 065103. DOI: 10.1103/physreve.71.065103, arxiv:cond-mat/0408629.

Returns:	phi (float in [0, 1]) – The small-world propensity. delta_l (float) – The average path-length deviation (if return_deviations is True). delta_c (float) – The clustering deviation (if return_deviations is True).

nngt.analysis.spectral_radius(graph, typed=True, weights=True)[source]#

Spectral radius of the graph, defined as the eigenvalue of greatest module.

Parameters:	graph (`Graph` or subclass) – Network to analyze. typed (bool, optional (default: True)) – Whether the excitatory/inhibitory type of the connnections should be considered. weights (bool, optional (default: True)) – Whether weights should be taken into account, defaults to the “weight” edge attribute if present.
Returns:	the spectral radius as a float.

nngt.analysis.subgraph_centrality(graph, weights=True, nodes=None, normalize='max_centrality')[source]#

Returns the subgraph centrality for each node in the graph.

Defined according to [Estrada2005] as:

$sc(i) = e^{W}_{ii}$

where $W$ is the (potentially weighted and normalized) adjacency matrix.

Parameters:

graph (Graph or subclass) – Network to analyze.
weights (bool or string, optional (default: True)) – Whether weights should be taken into account; if True, then connections are weighed by their synaptic strength, if False, then a binary matrix is returned, if weights is a string, then the ponderation is the correponding value of the edge attribute (e.g. “distance” will return an adjacency matrix where each connection is multiplied by its length).
nodes (array-like container with node ids, optional (default = all nodes)) – Nodes for which the subgraph centrality should be returned (all centralities are computed anyway in the algorithm).
normalize (str or False, optional (default: “max_centrality”)) – Whether the centrality should be normalized. Accepted normalizations are “max_eigenvalue” (the matrix is divided by its largest eigenvalue), “max_centrality” (the largest centrality is one), and False to get the non-normalized centralities.

Returns:

centralities (numpy.ndarray) – The subgraph centrality of each node.

References

[Estrada2005]

Ernesto Estrada and Juan A. Rodríguez-Velázquez, Subgraph centrality in complex networks, PHYSICAL REVIEW E 71, 056103 (2005), DOI: 10.1103/PhysRevE.71.056103, arXiv: cond-mat/0504730.

nngt.analysis.total_firing_rate(network=None, spike_detector=None, nodes=None, data=None, kernel_center=0.0, kernel_std=30.0, resolution=None, cut_gaussian=5.0)[source]#

Computes the total firing rate of the network from the spike times. Firing rate is obtained as the convolution of the spikes with a Gaussian kernel characterized by a standard deviation and a temporal shift.

New in version 0.7.

Parameters:

network (nngt.Network, optional (default: None)) – Network for which the activity was simulated.
spike_detector (tuple of ints, optional (default: spike detectors)) – GID of the “spike_detector” objects recording the network activity.
data (numpy.array of shape (N, 2), optionale (default: None)) – Array containing the spikes data (first line must contain the NEST GID of the neuron that fired, second line must contain the associated spike time).
kernel_center (float, optional (default: 0.)) – Temporal shift of the Gaussian kernel, in ms.
kernel_std (float, optional (default: 30.)) – Characteristic width of the Gaussian kernel (standard deviation) in ms.
resolution (float or array, optional (default: 0.1*kernel_std)) – The resolution at which the firing rate values will be computed. Choosing a value smaller than kernel_std is strongly advised. If resolution is an array, it will be considered as the times were the firing rate should be computed.
cut_gaussian (float, optional (default: 5.)) – Range over which the Gaussian will be computed. By default, we consider the 5-sigma range. Decreasing this value will increase speed at the cost of lower fidelity; increasing it with increase the fidelity at the cost of speed.

Returns:

fr (array-like) – The firing rate in Hz.
times (array-like) – The times associated to the firing rate values.

nngt.analysis.transitivity(g, directed=True, weights=None)[source]#: Same as global_clustering().

nngt.analysis.triangle_count(g, nodes=None, directed=True, weights=None, method='normal', combine_weights='mean')[source]#

Returns the number or the strength (also called intensity) of triangles for each node.

Parameters:

g (Graph object) – Graph to analyze.
nodes (array-like container with node ids, optional (default = all nodes)) – Nodes for which the local clustering coefficient should be computed.
directed (bool, optional (default: True)) – Whether to compute the directed clustering if the graph is directed.
weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if None or False then use binary edges; if True, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.
method (str, optional (default: ‘normal’)) – Method used to compute the weighted triangles, either ‘normal’, where the weights are directly used, or the definitions associated to the weighted clustering: ‘barrat’ [Barrat2004], ‘continuous’, or ‘onnela’ [Onnela2005].
combine_weights (str, optional (default: ‘mean’)) – How to combine the weights of reciprocal edges if the graph is directed but directed is set to False. It can be:
- “sum”: the sum of the edge attribute values will be used for the new edge.
- “mean”: the mean of the edge attribute values will be used for the new edge.
- “min”: the minimum of the edge attribute values will be used for the new edge.
- “max”: the maximum of the edge attribute values will be used for the new edge.

Returns:

tr (array) – Number or weight of triangles to which each node belongs.

nngt.analysis.triplet_count(g, nodes=None, directed=True, weights=None, method='normal', combine_weights='mean')[source]#

Returns the number or the strength (also called intensity) of triplets for each node.

For binary networks, the triplets of node $i$ are defined as:

$T_i = \sum_{j,k} a_{ij}a_{ik}$

Parameters:

g (Graph object) – Graph to analyze.
nodes (array-like container with node ids, optional (default = all nodes)) – Nodes for which the local clustering coefficient should be computed.
directed (bool, optional (default: True)) – Whether to compute the directed clustering if the graph is directed.
weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if None or False then use binary edges; if True, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.
method (str, optional (default: ‘continuous’)) – Method used to compute the weighted triplets, either ‘normal’, where the edge weights are directly used, or the definitions used for weighted clustering coefficients, ‘barrat’ [Barrat2004] or ‘continuous’.
combine_weights (str, optional (default: ‘mean’)) – How to combine the weights of reciprocal edges if the graph is directed but directed is set to False. It can be:
- “sum”: the sum of the edge attribute values will be used for the new edge.
- “mean”: the mean of the edge attribute values will be used for the new edge.
- “min”: the minimum of the edge attribute values will be used for the new edge.
- “max”: the maximum of the edge attribute values will be used for the new edge.

Returns:

tr (array) – Number or weight of triplets to which each node belongs.

Core module#

Core classes and functions. Most of them are not visible in the module as they are directly loaded at nngt level.

Content#

class nngt.core.Connections[source]#

Bases: object

The basic class that computes the properties of the connections between neurons for graphs.

static delays(graph=None, dlist=None, elist=None, distribution='constant', parameters=None, noise_scale=None)[source]#

Compute the delays of the neuronal connections.

Parameters:

graph (class:~nngt.Graph or subclass) – Graph the nodes belong to.
dlist (class:numpy.array, optional (default: None)) – List of user-defined delays).
elist (class:numpy.array, optional (default: None)) – List of the edges which value should be updated.
distribution (class:string, optional (default: “constant”)) – Type of distribution (choose among “constant”, “uniform”, “lognormal”, “gaussian”, “user_def”, “lin_corr”, “log_corr”).
parameters (class:dict, optional (default: {})) – Dictionary containing the distribution parameters.
noise_scale (class:int, optional (default: None)) – Scale of the multiplicative Gaussian noise that should be applied on the weights.

Returns:

new_delays (class:scipy.sparse.lil_matrix) – A sparse matrix containing ONLY the newly-computed weights.

static distances(graph, elist=None, pos=None, dlist=None, overwrite=False)[source]#

Compute the distances between connected nodes in the graph. Try to add only the new distances to the graph. If they overlap with previously computed distances, recomputes everything.

Parameters:

graph (class:~nngt.Graph or subclass) – Graph the nodes belong to.
elist (class:numpy.array, optional (default: None)) – List of the edges.
pos (class:numpy.array, optional (default: None)) – Positions of the nodes; note that if graph has a “position” attribute, pos will not be taken into account.
dlist (class:numpy.array, optional (default: None)) – List of distances (for user-defined distances)

Returns:

new_dist (class:numpy.array) – Array containing ONLY the newly-computed distances.

static types(graph, inhib_nodes=None, inhib_frac=None, values=None)[source]#

Define the type of a set of neurons. If no arguments are given, all edges will be set as excitatory.

Parameters:

graph (Graph or subclass) – Graph on which edge types will be created.
inhib_nodes (int or list, optional (default: None)) – If inhib_nodes is an int, number of inhibitory nodes in the graph (all connections from inhibitory nodes are inhibitory); if it is a float, ratio of inhibitory nodes in the graph; if it is a list, ids of the inhibitory nodes.
inhib_frac (float, optional (default: None)) – Fraction of the selected edges that will be set as refractory (if inhib_nodes is not None, it is the fraction of the nodes’ edges that will become inhibitory, otherwise it is the fraction of all the edges in the graph).

Returns:

t_list (ndarray) – List of the edges’ types.

static weights(graph=None, elist=None, wlist=None, distribution='constant', parameters={}, noise_scale=None)[source]#

Compute the weights of the graph’s edges.

Parameters:

graph (class:~nngt.Graph or subclass) – Graph the nodes belong to.
elist (class:numpy.array, optional (default: None)) – List of the edges (for user defined weights).
wlist (class:numpy.array, optional (default: None)) – List of the weights (for user defined weights).
distribution (class:string, optional (default: “constant”)) – Type of distribution (choose among “constant”, “uniform”, “lognormal”, “gaussian”, “user_def”, “lin_corr”, “log_corr”).
parameters (class:dict, optional (default: {})) – Dictionary containing the distribution parameters.
noise_scale (class:int, optional (default: None)) – Scale of the multiplicative Gaussian noise that should be applied on the weights.

Returns:

new_weights (class:scipy.sparse.lil_matrix) – A sparse matrix containing ONLY the newly-computed weights.

nngt.core.GraphObject#: alias of nngt.core.nngt_graph._NNGTGraph

Generation module#

Functions that generates the underlying connectivity of graphs, as well as the connection properties (weight/strength and delay).

Content#

Generation functions#

`nngt.generation.all_to_all`([nodes, …])	Generate a graph where all nodes are connected.
`nngt.generation.circular`(coord_nb[, …])	Generate a circular graph.
`nngt.generation.distance_rule`(scale[, rule, …])	Create a graph using a 2D distance rule to create the connection between neurons.
`nngt.generation.erdos_renyi`([density, …])	Generate a random graph as defined by Erdos and Renyi but with a reciprocity that can be chosen.
`nngt.generation.fixed_degree`(degree[, …])	Generate a random graph with constant in- or out-degree.
`nngt.generation.from_degree_list`(degrees[, …])	Generate a random graph from a given list of degrees.
`nngt.generation.gaussian_degree`(avg, std[, …])	Generate a random graph with constant in- or out-degree.
`nngt.generation.newman_watts`(coord_nb[, …])	Generate a (potentially small-world) graph using the Newman-Watts algorithm.
`nngt.generation.price_scale_free`(m[, c, …])	@todo make the algorithm.
`nngt.generation.random_scale_free`(in_exp, …)	Generate a free-scale graph of given reciprocity and otherwise devoid of correlations.
`nngt.generation.watts_strogatz`(coord_nb[, …])	Generate a (potentially small-world) graph using the Watts-Strogatz algorithm.

Connectors#

`nngt.generation.connect_nodes`(network, …)	Function to connect nodes with a given graph model.
`nngt.generation.connect_groups`(network, …)	Function to connect excitatory and inhibitory population with a given graph model.
`nngt.generation.connect_neural_types`(…[, …])	Function to connect excitatory and inhibitory population with a given graph model.

Rewiring functions#

`nngt.generation.random_rewire`(g[, …])	Generate a new rewired graph from g.
`nngt.generation.lattice_rewire`(g[, …])	Build a (generally irregular) lattice by rewiring the edges of a graph.

Details#

nngt.generation.all_to_all(nodes=0, weighted=True, directed=True, multigraph=False, name='AllToAll', shape=None, positions=None, population=None, **kwargs)[source]#

Generate a graph where all nodes are connected.

New in version 1.0.

Parameters:

nodes (int, optional (default: None)) – The number of nodes in the graph.
reciprocity (double, optional (default: -1 to let it free)) – Fraction of edges that are bidirectional (only for directed graphs – undirected graphs have a reciprocity of 1 by definition)
weighted (bool, optional (default: True)) – Whether the graph edges have weights.
directed (bool, optional (default: True)) – Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes.
name (string, optional (default: “ER”)) – Name of the created graph.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment.
positions (numpy.ndarray, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network).

Note

nodes is required unless population is provided.

Returns:	graph_all (`Graph`, or subclass) – A new generated graph.

nngt.generation.circular(coord_nb, reciprocity=1.0, reciprocity_choice='random', nodes=0, weighted=True, directed=True, multigraph=False, name='Circular', shape=None, positions=None, population=None, from_graph=None, **kwargs)[source]#

Generate a circular graph.

The nodes are placed on a circle and connected to their coord_nb closest neighbours. If the graph is directed, the number of connections depends on the value of reciprocity: if reciprocity == 0., then only half of all possible connections will be created, so that no bidirectional edges exist; on the other hand, for reciprocity == 1., all possible edges are created; for intermediate values of reciprocity, the number of edges increases linearly as 0.5*(1 + reciprocity)*nodes*coord_nb.

Parameters:

coord_nb (int) – The number of neighbours for each node on the initial topological lattice (must be even).
reciprocity (double, optional (default: 1.)) – Proportion of reciprocal edges in the graph.
reciprocity_choice (str, optional (default: “random”)) – How reciprocal edges should be chosen, which can be either “random” or “closest”. If the latter option is used, then connections between first neighbours are rendered reciprocal first, then between second neighbours, etc.
nodes (int, optional (default: None)) – The number of nodes in the graph.
density (double, optional (default: 0.1)) – Structural density given by edges / (nodes`*`nodes).
edges (int (optional)) – The number of edges between the nodes
avg_deg (double, optional) – Average degree of the neurons given by edges / nodes.
weighted (bool, optional (default: True)) – Whether the graph edges have weights.
directed (bool, optional (default: True)) – Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes.
name (string, optional (default: “ER”)) – Name of the created graph.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment
positions (numpy.ndarray, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network).
from_graph (Graph or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

Returns:

graph_circ (Graph or subclass)

nngt.generation.connect_groups(network, source_groups, target_groups, graph_model, density=None, edges=None, avg_deg=None, unit='um', weighted=True, directed=True, multigraph=False, check_existing=True, ignore_invalid=False, **kwargs)[source]#

Function to connect excitatory and inhibitory population with a given graph model.

Changed in version 2.0: Added check_existing and ignore_invalid arguments.

Changed in version 1.2.0: Allow to use NeuralGroup as source_groups and target_groups arguments.

Parameters:

network (Network or SpatialNetwork) – The network to connect.
source_groups (str, NeuralGroup, or iterable) – Names of the source groups (which contain the pre-synaptic neurons) or directly the group objects themselves.
target_groups (str, NeuralGroup, or iterable) – Names of the target groups (which contain the post-synaptic neurons) or directly the group objects themselves.
graph_model (string) – The name of the connectivity model (among “erdos_renyi”, “random_scale_free”, “price_scale_free”, and “newman_watts”).
check_existing (bool, optional (default: True)) – Check whether some of the edges that will be added already exist in the graph.
ignore_invalid (bool, optional (default: False)) – Ignore invalid edges: they are not added to the graph and are silently dropped. Unless this is set to true, an error is raised if an existing edge is re-generated.
kwargs (keyword arguments) – Specific model parameters. or edge attributes specifiers such as weights or delays.

Note

For graph generation methods which set the properties of a specific degree (e.g. gaussian_degree()), the groups which have their property sets are the source_groups.

nngt.generation.connect_neural_groups(*args, **kwargs)[source]#: Deprecatd alias of connect_groups().

nngt.generation.connect_neural_types(network, source_type, target_type, graph_model, density=None, edges=None, avg_deg=None, unit='um', weighted=True, directed=True, multigraph=False, check_existing=True, ignore_invalid=False, **kwargs)[source]#

Function to connect excitatory and inhibitory population with a given graph model.

Changed in version 2.0: Added check_existing and ignore_invalid arguments.

Parameters:

network (Network or SpatialNetwork) – The network to connect.
source_type (int or list) – The type of source neurons (1 for excitatory, -1 for inhibitory neurons).
target_type (int or list) – The type of target neurons.
graph_model (string) – The name of the connectivity model (among “erdos_renyi”, “random_scale_free”, “price_scale_free”, and “newman_watts”).
check_existing (bool, optional (default: True)) – Check whether some of the edges that will be added already exist in the graph.
ignore_invalid (bool, optional (default: False)) – Ignore invalid edges: they are not added to the graph and are silently dropped. Unless this is set to true, an error is raised if an existing edge is re-generated.
kwargs (keyword arguments) – Specific model parameters. or edge attributes specifiers such as weights or delays.

Note

For graph generation methods which set the properties of a specific degree (e.g. gaussian_degree()), the nodes which have their property sets are the source_type.

nngt.generation.connect_nodes(network, sources, targets, graph_model, density=None, edges=None, avg_deg=None, unit='um', weighted=True, directed=True, multigraph=False, check_existing=True, ignore_invalid=False, **kwargs)[source]#

Function to connect nodes with a given graph model.

Changed in version 2.0: Added check_existing and ignore_invalid arguments.

Parameters:

network (Network or SpatialNetwork) – The network to connect.
sources (list) – Ids of the source nodes.
targets (list) – Ids of the target nodes.
graph_model (string) – The name of the connectivity model (among “erdos_renyi”, “random_scale_free”, “price_scale_free”, and “newman_watts”).
check_existing (bool, optional (default: True)) – Check whether some of the edges that will be added already exist in the graph.
ignore_invalid (bool, optional (default: False)) – Ignore invalid edges: they are not added to the graph and are silently dropped. Unless this is set to true, an error is raised if an existing edge is re-generated.
**kwargs (keyword arguments) – Specific model parameters. or edge attributes specifiers such as weights or delays.

Note

For graph generation methods which set the properties of a specific degree (e.g. gaussian_degree()), the nodes which have their property sets are the sources.

nngt.generation.distance_rule(scale, rule='exp', shape=None, neuron_density=1000.0, max_proba=-1.0, nodes=0, density=None, edges=None, avg_deg=None, unit='um', weighted=True, directed=True, multigraph=False, name='DR', positions=None, population=None, from_graph=None, **kwargs)[source]#

Create a graph using a 2D distance rule to create the connection between neurons. Available rules are linear and exponential.

Parameters:

scale (float) – Characteristic scale for the distance rule. E.g for linear distance- rule, $P(i,j) \propto (1-d_{ij}/scale))$ , whereas for the exponential distance-rule, $P(i,j) \propto e^{-d_{ij}/scale}$ .
rule (string, optional (default: ‘exp’)) – Rule that will be apply to draw the connections between neurons. Choose among “exp” (exponential), “gaussian” (Gaussian), or “lin” (linear).
shape (Shape, optional (default: None)) – Shape of the neurons’ environment. If not specified, a square will be created with the appropriate dimensions for the number of neurons and the neuron spatial density.
neuron_density (float, optional (default: 1000.)) – Density of neurons in space ( $neurons \cdot mm^{-2}$ ).
nodes (int, optional (default: None)) – The number of nodes in the graph.
p (float, optional) – Normalization factor for the distance rule; it is equal to the probability of connection when testing a node at zero distance.
density (double, optional) – Structural density given by edges / (nodes * nodes).
edges (int, optional) – The number of edges between the nodes
avg_deg (double, optional) – Average degree of the neurons given by edges / nodes.
unit (string (default: ‘um’)) – Unit for the length scale among ‘um’ ( $\mu m$ ), ‘mm’, ‘cm’, ‘dm’, ‘m’.
weighted (bool, optional (default: True)) – Whether the graph edges have weights.
directed (bool, optional (default: True)) – Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes.
name (string, optional (default: “DR”)) – Name of the created graph.
positions (numpy.ndarray, optional (default: None)) – A 2D (N, 2) or 3D (N, 3) shaped array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network).
from_graph (Graph or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

nngt.generation.erdos_renyi(density=None, nodes=0, edges=None, avg_deg=None, reciprocity=-1.0, weighted=True, directed=True, multigraph=False, name='ER', shape=None, positions=None, population=None, from_graph=None, **kwargs)[source]#

Generate a random graph as defined by Erdos and Renyi but with a reciprocity that can be chosen.

Parameters:

density (double, optional (default: -1.)) – Structural density given by edges / nodes $^2$ . It is also the probability for each possible edge in the graph to exist.
nodes (int, optional (default: None)) – The number of nodes in the graph.
edges (int (optional)) – The number of edges between the nodes
avg_deg (double, optional) – Average degree of the neurons given by edges / nodes.
reciprocity (double, optional (default: -1 to let it free)) – Fraction of edges that are bidirectional (only for directed graphs – undirected graphs have a reciprocity of 1 by definition)
weighted (bool, optional (default: True)) – Whether the graph edges have weights.
directed (bool, optional (default: True)) – Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes.
name (string, optional (default: “ER”)) – Name of the created graph.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment.
positions (numpy.ndarray, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network).
from_graph (Graph or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

Returns:

graph_er (Graph, or subclass) – A new generated graph or the modified from_graph.

Note

nodes is required unless from_graph or population is provided. If an from_graph is provided, all preexistant edges in the object will be deleted before the new connectivity is implemented.

nngt.generation.fixed_degree(degree, degree_type='in', nodes=0, reciprocity=-1.0, weighted=True, directed=True, multigraph=False, name='FD', shape=None, positions=None, population=None, from_graph=None, **kwargs)[source]#

Generate a random graph with constant in- or out-degree.

Parameters:

degree (int) – The value of the constant degree.
degree_type (str, optional (default: ‘in’)) – The type of the fixed degree, among 'in', 'out' or 'total'.

@todo

‘total’ not implemented yet.
nodes (int, optional (default: None)) – The number of nodes in the graph.
reciprocity (double, optional (default: -1 to let it free)) – @todo: not implemented yet. Fraction of edges that are bidirectional (only for directed graphs – undirected graphs have a reciprocity of 1 by definition)
weighted (bool, optional (default: True)) – Whether the graph edges have weights.
directed (bool, optional (default: True)) – @todo: only for directed graphs for now. Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes.
name (string, optional (default: “ER”)) – Name of the created graph.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment.
positions (numpy.ndarray, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network).
from_graph (Graph or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

Note

nodes is required unless from_graph or population is provided. If an from_graph is provided, all preexistant edges in the object will be deleted before the new connectivity is implemented.

Returns:	graph_fd (`Graph`, or subclass) – A new generated graph or the modified from_graph.

nngt.generation.from_degree_list(degrees, degree_type='in', weighted=True, directed=True, multigraph=False, name='DL', shape=None, positions=None, population=None, from_graph=None, **kwargs)[source]#

Generate a random graph from a given list of degrees.

Parameters:

degrees (list) – The list of degrees for each node in the graph.
degree_type (str, optional (default: ‘in’)) – The type of the fixed degree, among 'in', 'out' or 'total'. @todo ‘total’ not implemented yet.
nodes (int, optional (default: None)) – The number of nodes in the graph.
weighted (bool, optional (default: True)) – Whether the graph edges have weights.
directed (bool, optional (default: True)) – @todo: only for directed graphs for now. Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes.
name (string, optional (default: “ER”)) – Name of the created graph.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment.
positions (numpy.ndarray, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network).
from_graph (Graph or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

Returns:

graph_dl (Graph, or subclass) – A new generated graph or the modified from_graph.

nngt.generation.gaussian_degree(avg, std, degree_type='in', nodes=0, reciprocity=-1.0, weighted=True, directed=True, multigraph=False, name='GD', shape=None, positions=None, population=None, from_graph=None, **kwargs)[source]#

Generate a random graph with constant in- or out-degree.

Parameters:

avg (float) – The value of the average degree.
std (float) – The standard deviation of the Gaussian distribution.
degree_type (str, optional (default: ‘in’)) – The type of the fixed degree, among ‘in’, ‘out’ or ‘total’ (or the full version: ‘in-degree’…) @todo: Implement ‘total’ degree
nodes (int, optional (default: None)) – The number of nodes in the graph.
reciprocity (double, optional (default: -1 to let it free)) – @todo: not implemented yet. Fraction of edges that are bidirectional (only for directed graphs – undirected graphs have a reciprocity of 1 by definition)
weighted (bool, optional (default: True)) – Whether the graph edges have weights.
directed (bool, optional (default: True)) – @todo: only for directed graphs for now. Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes.
name (string, optional (default: “ER”)) – Name of the created graph.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment.
positions (numpy.ndarray, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network).
from_graph (Graph or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

Returns:

graph_gd (Graph, or subclass) – A new generated graph or the modified from_graph.

Note

nodes is required unless from_graph or population is provided. If an from_graph is provided, all preexistant edges in the object will be deleted before the new connectivity is implemented.

nngt.generation.lattice_rewire(g, target_reciprocity=1.0, node_attr_constraints=None, edge_attr_constraints=None, weight=None, weight_constraint='distance', distance_sort='inverse')[source]#

Build a (generally irregular) lattice by rewiring the edges of a graph.

New in version 2.0.

The lattice is based on a circular graph, meaning that the nodes are placed on a circle and connected based on the topological distance between them, the distance being defined through the positive modulo:

$d_{ij} = (i - j) \% N$

with $N$ the number of nodes in the graph.

Parameters:

g (Graph) – Graph based on which the lattice will be generated.
target_reciprocity (float, optional (default: 1.)) – Value of reciprocity that should be aimed at. Depending on the number of edges, it may not be possible to reach this value exactly.
node_attr_constraints (str, optional (default: randomize all attributes)) – Whether attribute randomization is constrained: either “preserve”, where all nodes keep their attributes, or “together”, where attributes are randomized by groups (all attributes of a given node are sent to the same new node). By default, attributes are completely and separately randomized.
edge_attr_constraints (str, optional (default: randomize all but weight)) – Whether attribute randomization is constrained. If “distance” is used, then all number attributes (float or int) are sorted and are first associated to the shortest or longest edges depending on the value of distance_sort. Note that, for directed graphs, if a reciprocal edge exists, it is immediately assigned the next highest (respectively lowest) attribute after that of its directed couterpart. If “together” is used, edges attributes are randomized by groups (all attributes of a given edge are sent to the same new edge) either randomly if weight is None, or following the constrained weight attribute. By default, attributes are completely and separately randomized (except for weight if it has been provided).
weight (str, optional (default: None)) – Whether a specific edge attribute should play the role of weight and have special constraints.
weight_constraint (str, optional (default: “distance”)) – Same as edge_attr_constraints` but only applies to weight and can only be “distance” or None since “together” was related to weight.
distance_sort (str, optional (default: “inverse”)) – How attributes are sorted with edge distance: either “inverse”, with the shortest edges being assigned the largest weights, or with a “linear” sort, where shortest edges are assigned the lowest weights.

nngt.generation.newman_watts(coord_nb, proba_shortcut=None, reciprocity_circular=1.0, reciprocity_choice_circular='random', nodes=0, edges=None, weighted=True, directed=True, multigraph=False, name='NW', shape=None, positions=None, population=None, from_graph=None, **kwargs)[source]#

Generate a (potentially small-world) graph using the Newman-Watts algorithm.

For directed networks, the reciprocity of the initial circular network can be chosen.

Changed in version 2.0: Added the reciprocity_circular and reciprocity_choice_circular options.

Parameters:

coord_nb (int) – The number of neighbours for each node on the initial topological lattice (must be even).
proba_shortcut (double, optional) – Probability of adding a new random (shortcut) edge for each existing edge on the initial lattice. If edges is provided, then will be computed automatically as edges / (coord_nb * nodes * (1 + reciprocity_circular) / 2)
reciprocity_circular (double, optional (default: 1.)) – Proportion of reciprocal edges in the initial circular graph.
reciprocity_choice_circular (str, optional (default: “random”)) – How reciprocal edges should be chosen in the initial circular graph. This can be either “random” or “closest”. If the latter option is used, then connections between first neighbours are rendered reciprocal first, then between second neighbours, etc.
nodes (int, optional (default: None)) – The number of nodes in the graph.
edges (int (optional)) – The number of edges between the nodes.
weighted (bool, optional (default: True)) – Whether the graph edges have weights.
directed (bool, optional (default: True)) – Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes.
name (string, optional (default: “ER”)) – Name of the created graph.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment
positions (numpy.ndarray, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network).
from_graph (Graph or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

Returns:

graph_nw (Graph or subclass)

Note

nodes is required unless from_graph or population is provided.

nngt.generation.price_scale_free(m, c=None, gamma=1, nodes=0, weighted=True, directed=True, seed_graph=None, multigraph=False, name='PriceSF', shape=None, positions=None, population=None, from_graph=None, **kwargs)[source]#

@todo make the algorithm.

Generate a Price graph model (Barabasi-Albert if undirected).

Parameters:

m (int) – The number of edges each new node will make.
c (double) – Constant added to the probability of a vertex receiving an edge.
gamma (double) – Preferential attachment power.
nodes (int, optional (default: None)) – The number of nodes in the graph.
weighted (bool, optional (default: True)) – Whether the graph edges have weights.
directed (bool, optional (default: True)) – Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes.
name (string, optional (default: “ER”)) – Name of the created graph.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment
positions (numpy.ndarray, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network).
from_graph (Graph or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

Returns:

graph_price (Graph or subclass.)

Note

nodes is required unless from_graph or population is provided.

nngt.generation.random_rewire(g, constraints=None, node_attr_constraints=None, edge_attr_constraints=None)[source]#

Generate a new rewired graph from g.

New in version 2.0.

Parameters:

g (Graph) – Base graph based on which a new rewired graph will be generated.
constraints (str, optional (default: no constraints)) – Defines which properties of g will be maintained in the rewired graph. By default, the graph is completely rewired into an Erdos-Renyi model. Available constraints are “in-degree”, “out-degree”, “total-degree”, “all-degrees”, and “clustering”.
node_attr_constraints (str, optional (default: randomize all attributes)) – Whether attribute randomization is constrained: either “preserve”, where all nodes keep their attributes, or “together”, where attributes are randomized by groups (all attributes of a given node are sent to the same new node). By default, attributes are completely and separately randomized.
edge_attr_constraints (str, optional (default: randomize all attributes)) – Whether attribute randomization is constrained. If constraints is “in-degree” (respectively “out-degree”) or “degrees”, this can be “preserve_in” (respectively “preserve_out”), in which case all attributes of a given edge are moved together to a new incoming (respectively outgoing) edge of the same node. Regardless of constraints, “together” can be used so that edges attributes are randomized by groups (all attributes of a given edge are sent to the same new edge). By default, attributes are completely and separately randomized.

nngt.generation.random_scale_free(in_exp, out_exp, nodes=0, density=None, edges=None, avg_deg=None, reciprocity=0.0, weighted=True, directed=True, multigraph=False, name='RandomSF', shape=None, positions=None, population=None, from_graph=None, **kwargs)[source]#

Generate a free-scale graph of given reciprocity and otherwise devoid of correlations.

Parameters:

in_exp (float) – Absolute value of the in-degree exponent $\gamma_i$ , such that $p(k_i) \propto k_i^{-\gamma_i}$
out_exp (float) – Absolute value of the out-degree exponent $\gamma_o$ , such that $p(k_o) \propto k_o^{-\gamma_o}$
nodes (int, optional (default: 0)) – The number of nodes in the graph.
density (double, optional) – Structural density given by edges / (nodes*nodes).
edges (int optional) – The number of edges between the nodes
avg_deg (double, optional) – Average degree of the neurons given by edges / nodes.
weighted (bool, optional (default: True)) – Whether the graph edges have weights.
directed (bool, optional (default: True)) – Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes. can contain multiple edges between two
name (string, optional (default: “ER”)) – Name of the created graph.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment.
positions (numpy.ndarray, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network)
from_graph (Graph or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

Returns:

graph_fs (Graph)

Note

As reciprocity increases, requested values of in_exp and out_exp will be less and less respected as the distribution will converge to a common exponent $\gamma = (\gamma_i + \gamma_o) / 2$ . Parameter nodes is required unless from_graph or population is provided.

nngt.generation.watts_strogatz(coord_nb, proba_shortcut=None, reciprocity_circular=1.0, reciprocity_choice_circular='random', shuffle='random', nodes=0, weighted=True, directed=True, multigraph=False, name='WS', shape=None, positions=None, population=None, from_graph=None, **kwargs)[source]#

Generate a (potentially small-world) graph using the Watts-Strogatz algorithm.

For directed networks, the reciprocity of the initial circular network can be chosen.

New in version 2.0.

Parameters:

coord_nb (int) – The number of neighbours for each node on the initial topological lattice (must be even).
proba_shortcut (double, optional) – Probability of adding a new random (shortcut) edge for each existing edge on the initial lattice. If edges is provided, then will be computed automatically as edges / (coord_nb * nodes * (1 + reciprocity_circular) / 2)
reciprocity_circular (double, optional (default: 1.)) – Proportion of reciprocal edges in the initial circular graph.
reciprocity_choice_circular (str, optional (default: “random”)) – How reciprocal edges should be chosen in the initial circular graph. This can be either “random” or “closest”. If the latter option is used, then connections between first neighbours are rendered reciprocal first, then between second neighbours, etc.
shuffle (str, optional (default: ‘random’)) – Whether to shuffle only ‘targets’ (out-degree of all nodes remains constant), ‘sources’ (in-degree remains constant), or randomly the source or the target for each edge (‘random’) in the case of directed graphs.
nodes (int, optional (default: None)) – The number of nodes in the graph.
weighted (bool, optional (default: True)) – Whether the graph edges have weights.
directed (bool, optional (default: True)) – Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes.
name (string, optional (default: “ER”)) – Name of the created graph.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment
positions (numpy.ndarray, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network).
from_graph (Graph or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

Returns:

graph_nw (Graph or subclass)

Note

nodes is required unless from_graph or population is provided.

Geometry module#

This module is a direct copy of the SENeC package PyNCulture. Therefore, in the examples below, you will have to import nngt instead of PyNCulture and replace pnc by nngt.geometry.

Overview#

`nngt.geometry.Shape`	alias of `nngt.geometry.backup_shape.BackupShape`
`nngt.geometry.culture_from_file`(args, *kwargs)
`nngt.geometry.plot_shape`(shape[, axis, m, …])	Plot a shape (you should set the axis aspect to 1 to respect the proportions).
`nngt.geometry.pop_largest`(shapes)	Returns the largest shape, removing it from the list.
`nngt.geometry.shapes_from_file`(args, *kwargs)

Principle#

Module dedicated to the description of the spatial boundaries of neuronal cultures. This allows for the generation of neuronal networks that are embedded in space.

The shapely library is used to generate and deal with the spatial environment of the neurons.

Examples

Basic features#

The module provides a backup Shape object, which can be used with only the numpy and scipy libraries. It allows for the generation of simple rectangle, disk and ellipse shapes.

import matplotlib.pyplot as plt

import PyNCulture as nc


fig, ax = plt.subplots()

''' Choose a shape (uncomment the desired line) '''
# culture = nc.Shape.rectangle(15, 20, (5, 0))
culture = nc.Shape.disk(20, (5, 0))
# culture = nc.Shape.ellipse((20, 5), (5, 0))

''' Generate the neurons inside '''
pos = culture.seed_neurons(neurons=1000, xmax=0., ymax=0.)

''' Plot '''
nc.plot_shape(culture, ax, show=False)
ax.scatter(pos[:, 0], pos[:, 1], s=2, zorder=2)

plt.show()

All these features are of course still available with the more advanced Shape object which inherits from shapely.geometry.Polygon.

Complex shapes from files#

import matplotlib.pyplot as plt

import PyNCulture as nc


''' Choose a file '''
culture_file = "culture_from_filled_polygons.svg"
# culture_file = "culture_with_holes.svg"
# culture_file = "culture.dxf"

shapes = nc.shapes_from_file(culture_file, min_x=-5000., max_x=5000.)

''' Plot the shapes '''
fig, ax = plt.subplots()
fig.suptitle("shapes")

for p in shapes:
    nc.plot_shape(p, ax, show=False)

plt.show()

''' Make a culture '''
fig2, ax2 = plt.subplots()
plt.title("culture")

culture = nc.culture_from_file(culture_file, min_x=-5000., max_x=5000.)

nc.plot_shape(culture, ax2)

''' Add neurons '''
fig3, ax3 = plt.subplots()
plt.title("culture with neurons")

culture_bis = nc.culture_from_file(culture_file, min_x=-5000., max_x=5000.)
pos = culture_bis.seed_neurons(neurons=1000, xmax=0)

nc.plot_shape(culture_bis, ax3, show=False)
ax3.scatter(pos[:, 0], pos[:, 1], s=2, zorder=3)

plt.show()

Content#

nngt.geometry.Shape#: alias of nngt.geometry.backup_shape.BackupShape

nngt.geometry.culture_from_file(*args, **kwargs)[source]#

nngt.geometry.pop_largest(shapes)[source]#

Returns the largest shape, removing it from the list. If shapes is a shapely.geometry.MultiPolygon, returns the largest shapely.geometry.Polygon without modifying the object.

New in version 0.3.

Parameters:	shapes (list of `Shape` objects or MultiPolygon.)

nngt.geometry.shapes_from_file(*args, **kwargs)[source]#

nngt.geometry.plot_shape(shape, axis=None, m='', mc='#999999', fc='#8888ff', ec='#444444', alpha=0.5, brightness='height', show_contour=True, show=True, **kwargs)[source]#

Plot a shape (you should set the axis aspect to 1 to respect the proportions).

Parameters:

shape (Shape) – Shape to plot.
axis (matplotlib.axes.Axes instance, optional (default: None)) – Axis on which the shape should be plotted. By default, a new figure is created.
m (str, optional (default: invisible)) – Marker to plot the shape’s vertices, matplotlib syntax.
mc (str, optional (default: “#999999”)) – Color of the markers.
fc (str, optional (default: “#8888ff”)) – Color of the shape’s interior.
ec (str, optional (default: “#444444”)) – Color of the shape’s edges.
alpha (float, optional (default: 0.5)) – Opacity of the shape’s interior.
brightness (str, optional (default: height)) – Show how different other areas are from the ‘default_area’ (lower values are darker, higher values are lighter). Difference can concern the ‘height’, or any of the properties of the Area objects.
show_contour (bool, optional (default: True)) – Whether the shapes should be drawn with a contour.
show (bool, optional (default: True)) – Whether the plot should be displayed immediately.
**kwargs (keywords arguments for matplotlib.patches.PathPatch)

Lib module#

Tools for the other modules.

Warning

These tools have been designed primarily for internal use throughout the library and often work only in very specific situations (e.g. find_idx_nearest() works only on sorted arrays), so make sure you read their doc carefully before using them.

Content#

`nngt.lib.InvalidArgument`	Error raised when an argument is invalid.
`nngt.lib.delta_distrib`([graph, elist, num, …])	Delta distribution for edge attributes.
`nngt.lib.find_idx_nearest`(array, values)	Find the indices of the nearest elements of values in a sorted array.
`nngt.lib.gaussian_distrib`(graph[, elist, …])	Gaussian distribution for edge attributes.
`nngt.lib.is_integer`(obj)	Return whether the object is an integer
`nngt.lib.is_iterable`(obj)	Return whether the object is iterable
`nngt.lib.lin_correlated_distrib`(graph[, …])
`nngt.lib.log_correlated_distrib`(graph[, …])
`nngt.lib.lognormal_distrib`(graph[, elist, …])	Lognormal distribution for edge attributes.
`nngt.lib.nonstring_container`(obj)	Returns true for any iterable which is not a string or byte sequence.
`nngt.lib.uniform_distrib`(graph[, elist, …])	Uniform distribution for edge attributes.

Details#

class nngt.lib.InvalidArgument[source]#: Error raised when an argument is invalid.

nngt.lib.delta_distrib(graph=None, elist=None, num=None, value=1.0, **kwargs)[source]#

Delta distribution for edge attributes.

Parameters:	graph (`Graph` or subclass) – Graph for which an edge attribute will be generated. elist (list of edges, optional (default: all edges)) – Generate values for only a subset of edges. value (float, optional (default: 1.)) – Value of the delta distribution. Returns (`numpy.ndarray`) – Attribute value for each edge in graph.

nngt.lib.find_idx_nearest(array, values)[source]#

Find the indices of the nearest elements of values in a sorted array.

Warning

Both array and values should be numpy.array objects and array MUST be sorted in increasing order.

Parameters:	array (reference list or np.ndarray) values (double, list or array of values to find in array)
Returns:	idx (int or array representing the index of the closest value in array)

nngt.lib.gaussian_distrib(graph, elist=None, num=None, avg=None, std=None, **kwargs)[source]#

Gaussian distribution for edge attributes.

Parameters:

graph (Graph or subclass) – Graph for which an edge attribute will be generated.
elist (list of edges, optional (default: all edges)) – Generate values for only a subset of edges.
avg (float, optional (default: 0.)) – Average of the Gaussian distribution.
std (float, optional (default: 1.5)) – Standard deviation of the Gaussian distribution.
Returns (numpy.ndarray) – Attribute value for each edge in graph.

nngt.lib.is_integer(obj)[source]#: Return whether the object is an integer

nngt.lib.is_iterable(obj)[source]#: Return whether the object is iterable

nngt.lib.lin_correlated_distrib(graph, elist=None, correl_attribute='betweenness', noise_scale=None, lower=None, upper=None, slope=None, offset=0.0, last_edges=False, **kwargs)[source]#

nngt.lib.log_correlated_distrib(graph, elist=None, correl_attribute='betweenness', noise_scale=None, lower=0.0, upper=2.0, **kwargs)[source]#

nngt.lib.lognormal_distrib(graph, elist=None, num=None, position=None, scale=None, **kwargs)[source]#

Lognormal distribution for edge attributes.

Parameters:

graph (Graph or subclass) – Graph for which an edge attribute will be generated.
elist (list of edges, optional (default: all edges)) – Generate values for only a subset of edges.
position (float, optional (default: 0.)) – Average of the normal distribution (i.e. log of the actual mean of the lognormal distribution).
scale (float, optional (default: 1.5)) – Standard deviation of the normal distribution.
Returns (numpy.ndarray) – Attribute value for each edge in graph.

nngt.lib.nonstring_container(obj)[source]#: Returns true for any iterable which is not a string or byte sequence.

nngt.lib.uniform_distrib(graph, elist=None, num=None, lower=None, upper=None, **kwargs)[source]#

Uniform distribution for edge attributes.

Parameters:

graph (Graph or subclass) – Graph for which an edge attribute will be generated.
elist (list of edges, optional (default: all edges)) – Generate values for only a subset of edges.
lower (float, optional (default: 0.)) – Min value of the uniform distribution.
upper (float, optional (default: 1.5)) – Max value of the uniform distribution.
Returns (numpy.ndarray) – Attribute value for each edge in graph.

Plot module#

Functions for plotting graphs and graph properties.

The following features are provided:

basic graph plotting
plotting the distribution of some attribute over the graph
animation of some recorded activity

Content#

`nngt.plot.Animation2d`(source, multimeter[, …])	Class to plot the raster plot, firing-rate, and average trajectory in a 2D phase-space for a network activity.
`nngt.plot.AnimationNetwork`(source, network)	Class to plot the raster plot, firing-rate, and space-embedded spiking activity (neurons on the graph representation flash when spiking) in time.
`nngt.plot.betweenness_distribution`(network)	Plotting the betweenness distribution of a graph.
`nngt.plot.compare_population_attributes`(…)	Compare node attributes between two sets of nodes.
`nngt.plot.correlation_to_attribute`(network, …)	For each node plot the value of reference_attributes against each of the other_attributes to check for correlations.
`nngt.plot.degree_distribution`(network[, …])	Plotting the degree distribution of a graph.
`nngt.plot.draw_network`(network[, nsize, …])	Draw a given graph/network.
`nngt.plot.edge_attributes_distribution`(…)	Return node attributes for a set of nodes.
`nngt.plot.library_draw`(network[, nsize, …])	Draw a given `Graph` using the underlying library’s drawing functions.
`nngt.plot.node_attributes_distribution`(…)	Return node attributes for a set of nodes.
`nngt.plot.palette_continuous`([numbers])
`nngt.plot.palette_discrete`([numbers])

Details#

class nngt.plot.Animation2d(source, multimeter, start=0.0, timewindow=None, trace=5.0, x='time', y='V_m', sort_neurons=None, network=None, interval=50, vector_field=False, **kwargs)[source]#

Class to plot the raster plot, firing-rate, and average trajectory in a 2D phase-space for a network activity.

Generate a SubplotAnimation instance to plot a network activity.

Parameters:

source (tuple) – NEST gid of the ``spike_detector``(s) which recorded the network.
multimeter (tuple) – NEST gid of the ``multimeter``(s) which recorded the network.
timewindow (double, optional (default: None)) – Time window which will be shown for the spikes and self.second.
trace (double, optional (default: 5.)) – Interval of time (ms) over which the data is overlayed in red.
x (str, optional (default: “time”)) – Name of the x-axis variable (must be either “time” or the name of a NEST recordable in the multimeter).
y (str, optional (default: “V_m”)) – Name of the y-axis variable (must be either “time” or the name of a NEST recordable in the multimeter).
vector_field (bool, optional (default: False)) – Whether the $\dot{x}$ and $\dot{y}$ arrows should be added to phase space. Requires additional ‘dotx’ and ‘doty’ arguments which are user defined functions to compute the derivatives of x and x in time. These functions take 3 parameters, which are x, y, and time_dependent, where the last parameter is a list of doubles associated to recordables from the neuron model (see example for details). These recordables must be declared in a time_dependent parameter.
sort_neurons (str or list, optional (default: None)) – Sort neurons using a topological property (“in-degree”, “out-degree”, “total-degree” or “betweenness”), an activity-related property (“firing_rate”, ‘B2’) or a user-defined list of sorted neuron ids. Sorting is performed by increasing value of the sort_neurons property from bottom to top inside each group.
**kwargs (dict, optional (default: {})) – Optional arguments such as ‘make_rate’, ‘num_xarrows’, ‘num_yarrows’, ‘dotx’, ‘doty’, ‘time_dependent’, ‘recordables’, ‘arrow_scale’.

class nngt.plot.AnimationNetwork(source, network, resolution=1.0, start=0.0, timewindow=None, trace=5.0, show_spikes=False, sort_neurons=None, decimate_connections=False, interval=50, repeat=True, resting_size=None, active_size=None, **kwargs)[source]#

Class to plot the raster plot, firing-rate, and space-embedded spiking activity (neurons on the graph representation flash when spiking) in time.

Generate a SubplotAnimation instance to plot a network activity.

Parameters:

source (tuple) – NEST gid of the ``spike_detector``(s) which recorded the network.
network (SpatialNetwork) – Network embedded in space to plot the actvity of the neurons in space.
resolution (double, optional (default: None)) – Time resolution of the animation.
timewindow (double, optional (default: None)) – Time window which will be shown for the spikes and self.second.
trace (double, optional (default: 5.)) – Interval of time (ms) over which the data is overlayed in red.
show_spikes (bool, optional (default: True)) – Whether a spike trajectory should be displayed on the network.
sort_neurons (str or list, optional (default: None)) – Sort neurons using a topological property (“in-degree”, “out-degree”, “total-degree” or “betweenness”), an activity-related property (“firing_rate”, ‘B2’) or a user-defined list of sorted neuron ids. Sorting is performed by increasing value of the sort_neurons property from bottom to top inside each group.
**kwargs (dict, optional (default: {})) – Optional arguments such as ‘make_rate’, or all arguments for the nngt.plot.draw_network().

nngt.plot.betweenness_distribution(network, btype='both', weights=False, nodes=None, logx=False, logy=False, num_nbins='auto', num_ebins=None, axes=None, colors=None, norm=False, show=True, **kwargs)[source]#

Plotting the betweenness distribution of a graph.

Parameters:

graph (Graph or subclass) – the graph to analyze.
btype (string, optional (default: “both”)) – type of betweenness to display (“node”, “edge” or “both”)
weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if None or False then use binary edges; if True, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.
nodes (list or numpy.array of ints, optional (default: all nodes)) – Restrict the distribution to a set of nodes (taken into account only for the node attribute).
logx (bool, optional (default: False)) – use log-spaced bins.
logy (bool, optional (default: False)) – use logscale for the degree count.
num_nbins (int or ‘auto’, optional (default: ‘auto’):) – Number of bins used to sample the node distribution. Defaults to unsupervised Bayesian blocks method.
num_ebins (int or ‘auto’, optional (default: None):) – Number of bins used to sample the edge distribution. Defaults to max(num_edges / 500., 10) (‘auto’ method will be slow).
axes (list of matplotlib.axis.Axis, optional (default: new ones)) – Axes which should be used to plot the histogram, if None, new ones are created.
show (bool, optional (default: True)) – Show the Figure right away if True, else keep it warm for later use.

nngt.plot.compare_population_attributes(network, attributes, nodes=None, reference_nodes=None, num_bins='auto', reference_color='gray', title=None, logx=False, logy=False, show=True, **kwargs)[source]#

Compare node attributes between two sets of nodes. Since number of nodes can vary, normalized distributions are used.

Parameters:

network (Graph) – The graph where the nodes belong.
attributes (str or list) – Attributes which should be returned, among: * “betweenness” * “clustering” * “in-degree”, “out-degree”, “total-degree” * “subgraph_centrality” * “b2” (requires NEST) * “firing_rate” (requires NEST)
nodes (list, optional (default: all nodes)) – Nodes for which the attributes should be returned.
reference_nodes (list, optional (default: all nodes)) – Reference nodes for which the attributes should be returned in order to compare with nodes.
num_bins (int or list, optional (default: ‘auto’)) – Number of bins to plot the distributions. If only one int is provided, it is used for all attributes, otherwize a list containing one int per attribute in attributes is required. Defaults to unsupervised Bayesian blocks method.
logx (bool or list, optional (default: False)) – Use log-spaced bins.
logy (bool or list, optional (default: False)) – use logscale for the node count.

nngt.plot.correlation_to_attribute(network, reference_attribute, other_attributes, nodes=None, fig=None, title=None, show=True)[source]#

For each node plot the value of reference_attributes against each of the other_attributes to check for correlations.

Changed in version 2.0: Added fig argument.

Parameters:

network (Graph) – The graph where the nodes belong.
reference_attribute (str or array-like) – Attribute which should serve as reference, among:
- “betweenness”
- “clustering”
- “in-degree”, “out-degree”, “total-degree”
- “in-strength”, “out-strength”, “total-strength”
- “subgraph_centrality”
- “b2” (requires NEST)
- “firing_rate” (requires NEST)
- a custom array of values, in which case one entry per node in nodes is required.
other_attributes (str or list) – Attributes that will be compared to the reference.
nodes (list, optional (default: all nodes)) – Nodes for which the attributes should be returned.
fig (matplotlib.figure.Figure, optional (default: new Figure)) – Figure to which the plot should be added.
title (str, optional (default: automatic).) – Custom title, use “” to remove the automatic title.
show (bool, optional (default: True)) – Whether the plot should be displayed immediately.

nngt.plot.degree_distribution(network, deg_type='total', nodes=None, num_bins='doane', weights=False, logx=False, logy=False, axis=None, axis_num=None, colors=None, norm=False, show=True, title=None, **kwargs)[source]#

Plotting the degree distribution of a graph.

Parameters:

graph (Graph or subclass) – The graph to analyze.
deg_type (string or N-tuple, optional (default: “total”)) – Type of degree to consider (“in”, “out”, or “total”)
nodes (list or numpy.array of ints, optional (default: all nodes)) – Restrict the distribution to a set of nodes.
num_bins (int or N-tuple, optional (default: ‘auto’):) – Number of bins used to sample the distribution. Defaults to unsupervised Bayesian blocks method.
weights (bool or str, optional (default: binary edges)) – Whether edge weights should be considered; if None or False then use binary edges; if True, uses the ‘weight’ edge attribute, otherwise uses any valid edge attribute required.
logx (bool, optional (default: False)) – Use log-spaced bins.
logy (bool, optional (default: False)) – Use logscale for the degree count.
axis (matplotlib.axes.Axes instance, optional (default: new one)) – Axis which should be used to plot the histogram, if None, a new one is created.
show (bool, optional (default: True)) – Show the Figure right away if True, else keep it warm for later use.
**kwargs (keyword arguments for matplotlib.axes.Axes.bar().)

nngt.plot.draw_network(network, nsize='total-degree', ncolor='group', nshape='o', nborder_color='k', nborder_width=0.5, esize=1.0, ecolor='k', ealpha=0.5, max_nsize=None, max_esize=2.0, curved_edges=False, threshold=0.5, decimate_connections=None, spatial=True, restrict_sources=None, restrict_targets=None, restrict_nodes=None, show_environment=True, fast=False, size=(600, 600), xlims=None, ylims=None, dpi=75, axis=None, colorbar=False, layout=None, show=False, **kwargs)[source]#

Draw a given graph/network.

Parameters:

network (Graph or subclass) – The graph/network to plot.
nsize (float, array of float or string, optional (default: “total-degree”)) – Size of the nodes as a percentage of the canvas length. Otherwise, it can be a string that correlates the size to a node attribute among “in/out/total-degree”, “in/out/total-strength”, or “betweenness”.
ncolor (float, array of floats or string, optional (default: 0.5)) – Color of the nodes; if a float in [0, 1], position of the color in the current palette, otherwise a string that correlates the color to a node attribute among “in/out/total-degree”, “betweenness” or “group”.
nshape (char, array of chars, or groups, optional (default: “o”)) – Shape of the nodes (see Matplotlib markers). When using groups, they must be pairwise disjoint; markers will be selected iteratively from the matplotlib default markers.
nborder_color (char, float or array, optional (default: “k”)) – Color of the node’s border using predefined Matplotlib colors). or floats in [0, 1] defining the position in the palette.
nborder_width (float or array of floats, optional (default: 0.5)) – Width of the border in percent of canvas size.
esize (float, str, or array of floats, optional (default: 0.5)) – Width of the edges in percent of canvas length. Available string values are “betweenness” and “weight”.
ecolor (str, char, float or array, optional (default: “k”)) – Edge color. If ecolor=”groups”, edges color will depend on the source and target groups, i.e. only edges from and toward same groups will have the same color.
max_esize (float, optional (default: 5.)) – If a custom property is entered as esize, this normalizes the edge width between 0. and max_esize.
threshold (float, optional (default: 0.5)) – Size under which edges are not plotted.
decimate_connections (int, optional (default: keep all connections)) – Plot only one connection every decimate_connections. Use -1 to hide all edges.
spatial (bool, optional (default: True)) – If True, use the neurons’ positions to draw them.
restrict_sources (str, group, or list, optional (default: all)) – Only draw edges starting from a restricted set of source nodes.
restrict_targets (str, group, or list, optional (default: all)) – Only draw edges ending on a restricted set of target nodes.
restrict_nodes (str, group, or list, optional (default: plot all nodes)) – Only draw a subset of nodes.
show_environment (bool, optional (default: True)) – Plot the environment if the graph is spatial.
fast (bool, optional (default: False)) – Use a faster algorithm to plot the edges. This method leads to less pretty plots and zooming on the graph will make the edges start or ending in places that will differ more or less strongly from the actual node positions.
size (tuple of ints, optional (default: (600,600))) – (width, height) tuple for the canvas size (in px).
dpi (int, optional (default: 75)) – Resolution (dot per inch).
axis (matplotlib axis, optional (default: create new axis)) – Axis on which the network will be plotted.
colorbar (bool, optional (default: False)) – Whether to display a colorbar for the node colors or not.
layout (str, optional (default: random or spatial positions)) – Name of a standard layout to structure the network. Available layouts are: “circular” or “random”. If no layout is provided and the network is spatial, then node positions will be used by default.
show (bool, optional (default: True)) – Display the plot immediately.
**kwargs (dict) – Optional keyword arguments including node_cmap to set the nodes colormap (default is “magma” for continuous variables and “Set1” for groups) and the boolean simple_nodes to make node plotting faster.

nngt.plot.edge_attributes_distribution(network, attributes, edges=None, num_bins='auto', logx=False, logy=False, norm=False, title=None, colors=None, show=True, **kwargs)[source]#

Return node attributes for a set of nodes.

New in version 1.0.3.

Parameters:

network (Graph) – The graph where the nodes belong.
attributes (str or list) – Attributes which should be returned (e.g. “betweenness”, “delay”, “weight”).
edges (list, optional (default: all edges)) – Edges for which the attributes should be returned.
num_bins (int or list, optional (default: ‘auto’)) – Number of bins to plot the distributions. If only one int is provided, it is used for all attributes, otherwise a list containing one int per attribute in attributes is required. Defaults to unsupervised Bayesian blocks method.
logx (bool or list, optional (default: False)) – Use log-spaced bins.
logy (bool or list, optional (default: False)) – use logscale for the node count.

nngt.plot.library_draw(network, nsize='total-degree', ncolor='group', nshape='o', nborder_color='k', nborder_width=0.5, esize=1.0, ecolor='k', ealpha=0.5, max_nsize=5.0, max_esize=2.0, curved_edges=False, threshold=0.5, decimate_connections=None, spatial=True, restrict_sources=None, restrict_targets=None, restrict_nodes=None, show_environment=True, size=(600, 600), xlims=None, ylims=None, dpi=75, axis=None, colorbar=False, show_labels=False, layout=None, show=False, **kwargs)[source]#

Draw a given Graph using the underlying library’s drawing functions.

New in version 2.0.

Parameters:

network (Graph or subclass) – The graph/network to plot.
nsize (float, array of float or string, optional (default: “total-degree”)) – Size of the nodes as a percentage of the canvas length. Otherwise, it can be a string that correlates the size to a node attribute among “in/out/total-degree”, or “betweenness”.
ncolor (float, array of floats or string, optional (default: 0.5)) – Color of the nodes; if a float in [0, 1], position of the color in the current palette, otherwise a string that correlates the color to a node attribute among “in/out/total-degree”, “betweenness” or “group”.
nshape (char, array of chars, or groups, optional (default: “o”)) – Shape of the nodes (see Matplotlib markers). When using groups, they must be pairwise disjoint; markers will be selected iteratively from the matplotlib default markers.
nborder_color (char, float or array, optional (default: “k”)) – Color of the node’s border using predefined Matplotlib colors). or floats in [0, 1] defining the position in the palette.
nborder_width (float or array of floats, optional (default: 0.5)) – Width of the border in percent of canvas size.
esize (float, str, or array of floats, optional (default: 0.5)) – Width of the edges in percent of canvas length. Available string values are “betweenness” and “weight”.
ecolor (str, char, float or array, optional (default: “k”)) – Edge color. If ecolor=”groups”, edges color will depend on the source and target groups, i.e. only edges from and toward same groups will have the same color.
max_esize (float, optional (default: 5.)) – If a custom property is entered as esize, this normalizes the edge width between 0. and max_esize.
threshold (float, optional (default: 0.5)) – Size under which edges are not plotted.
decimate_connections (int, optional (default: keep all connections)) – Plot only one connection every decimate_connections. Use -1 to hide all edges.
spatial (bool, optional (default: True)) – If True, use the neurons’ positions to draw them.
restrict_sources (str, group, or list, optional (default: all)) – Only draw edges starting from a restricted set of source nodes.
restrict_targets (str, group, or list, optional (default: all)) – Only draw edges ending on a restricted set of target nodes.
restrict_nodes (str, group, or list, optional (default: plot all nodes)) – Only draw a subset of nodes.
show_environment (bool, optional (default: True)) – Plot the environment if the graph is spatial.
fast (bool, optional (default: False)) – Use a faster algorithm to plot the edges. This method leads to less pretty plots and zooming on the graph will make the edges start or ending in places that will differ more or less strongly from the actual node positions.
size (tuple of ints, optional (default: (600, 600))) – (width, height) tuple for the canvas size (in px).
dpi (int, optional (default: 75)) – Resolution (dot per inch).
colorbar (bool, optional (default: False)) – Whether to display a colorbar for the node colors or not.
axis (matplotlib axis, optional (default: create new axis)) – Axis on which the network will be plotted.
layout (str, optional (default: library-dependent or spatial positions)) – Name of a standard layout to structure the network. Available layouts are: “circular”, “spring-block”, “random”. If no layout is provided and the network is spatial, then node positions will be used by default.
show (bool, optional (default: True)) – Display the plot immediately.
**kwargs (dict) – Optional keyword arguments including node_cmap to set the nodes colormap (default is “magma” for continuous variables and “Set1” for groups) and the boolean simple_nodes to make node plotting faster.

nngt.plot.node_attributes_distribution(network, attributes, nodes=None, num_bins='auto', logx=False, logy=False, norm=False, title=None, colors=None, show=True, **kwargs)[source]#

Return node attributes for a set of nodes.

Parameters:

network (Graph) – The graph where the nodes belong.
attributes (str or list) – Attributes which should be returned, among: * “betweenness” * “clustering” * “closeness” * “in-degree”, “out-degree”, “total-degree” * “subgraph_centrality” * “b2” (requires NEST) * “firing_rate” (requires NEST)
nodes (list, optional (default: all nodes)) – Nodes for which the attributes should be returned.
num_bins (int or list, optional (default: ‘auto’)) – Number of bins to plot the distributions. If only one int is provided, it is used for all attributes, otherwise a list containing one int per attribute in attributes is required. Defaults to unsupervised Bayesian blocks method.
logx (bool or list, optional (default: False)) – Use log-spaced bins.
logy (bool or list, optional (default: False)) – use logscale for the node count.

nngt.plot.palette_continuous(numbers=None)[source]#

nngt.plot.palette_discrete(numbers=None)[source]#

Known bugs#

erratic key release issue with animation of spiking networks,
see the issue tracker for an updated list.

Tutorial#

This page provides a step-by-step walkthrough of the basic features of NNGT.

To run this tutorial, it is recommended to use either IPython or Jupyter, since they will provide automatic autocompletion of the various functions, as well as easy access to the docstring help.

First, import the NNGT package:

>>> import nngt

Then, you will be able to use the help from IPython by typing, for instance:

>>> nngt.Graph?

In Jupyter, the docstring can be viewed using Shift + Tab.

The source file for the tutorial can be found here: doc/examples/introductory_tutorial.py.

Note

For a list of example files, see the ‘examples’ directory on GitHub.

For specific tutorials see also:

Content:

NNGT properties and configuration
The Graph object
- Basic functions
- Node and edge attributes
Generating and analyzing more complex networks
Using random numbers
Complex populations: NeuralGroup and NeuralPop
Real neuronal culture and NEST interaction: the Network
Underlying graph objects and libraries

NNGT properties and configuration #

Upon loading, NNGT will display its current configuration, e.g.:

# ----------- #
# NNGT loaded #
# ----------- #
Graph library:  igraph 0.7.1
Multithreading: True (1 thread)
MPI:            False
Plotting:       True
NEST support:   NEST 2.14.0
Shapely:        1.6.1
SVG support:    True
DXF support:    False
Database:       False

Let’s walk through this configuration:

the backend used here is igraph, so all graph-theoretical tools will be derived from those of the igraph library and we’re using version 0.7.1.
Multithreaded algorithms will be used, currently running on only one thread (see Parallelism for more details)
MPI algorithms are not in use (you cannot use both MT and MPI at the same time)
Plotting is available because the matplotlib library is installed
NEST is installed on the machine (version 2.14), so NNGT automatically loaded it
Shapely is also available, which allows the creation of complex structures for space-embedded networks (see Geometry module for more details)
Importing SVG files to generate spatial structures is possible, meaning that the svg.path module is installed.
Importing DXF files to generate spatial structures is not possible because the dxfgrabber module is not installed.
Using the database is not possible because peewee is not installed.

In general, most of NNGT options can be found/set through the get_config()/set_config() functions, or made permanent by modifying the ~/.nngt/nngt.conf configuration file.

The `Graph` object #

Basic functions #

Let’s create an empty Graph:

g = nngt.Graph()

We can then add some nodes to it

g.new_node(10)            # create nodes 0, 1, ... to 9
print(g.node_nb(), '\n')  # returns 10

And create edges between these nodes:

g.new_edge(1, 4)          # create one connection going from 1 to 4
print(g.edge_nb())        # returns 1
g.new_edges([(0, 3), (5, 9), (9, 3)])
print(g.edge_nb(), '\n')  # returns 4

Node and edge attributes #

Adding a node with specific attributes:

g2 = nngt.Graph()

# add a new node with attributes
attributes = {
    'size': 2.,
    'color': 'blue',
    'a': 5,
    'blob': []
}

attribute_types = {
    'size': 'double',
    'color': 'string',
    'a': 'int',
    'blob': 'object'
}

g2.new_node(attributes=attributes, value_types=attribute_types)
print(g2.node_attributes, '\n')

By default, nodes that are added without specifying attribute values will get their attributes filled with default values which depend on the type:

NaN for “double”
0 for “int”
"" for “string”
None for “object”

g2.new_node(2)
# for a double attribute like 'size', default value is NaN
print(g2.get_node_attributes(name="size"))
# for a string attribute like 'color', default value is ""
print(g2.get_node_attributes(name="color"))
# for an int attribute like 'a', default value is 0
print(g2.get_node_attributes(name='a'))
# for an object attribute like 'blob', default value is None
print(g2.get_node_attributes(name='blob'), '\n')

Adding several nodes and attributes at the same time:

g2.new_node(3, attributes={'size': [4., 5., 1.], 'color': ['r', 'g', 'b']},
            value_types={'size': 'double', 'color': 'string'})
print(g2.node_attributes['size'])
print(g2.node_attributes['color'], '\n')

Attributes can also be created afterwards:

import numpy as np
g3 = nngt.Graph(nodes=100)
g3.new_node_attribute('size', 'double',
                      values=np.random.uniform(0, 20, 100))
print(g3.node_attributes['size'][:5], '\n')

All the previous techniques can also be used with new_edge() or new_edges(), and new_edge_attribute(). Note that attributes can also be set selectively:

edges = g3.new_edges(np.random.randint(0, 50, (10, 2)))
g3.new_edge_attribute('rank', 'int')
g3.set_edge_attribute('rank', val=2, edges=edges[:3, :])
print(g3.edge_attributes['rank'], '\n')

Generating and analyzing more complex networks #

NNGT provides a whole set of methods to connect nodes in specific fashions inside a graph. These methods are present in the nngt.generation module, and the network properties can then be plotted and analyzed via the tools present in the nngt.plot and nngt.analysis modules.

from nngt import generation as ng
from nngt import analysis as na
from nngt import plot as nplt

NNGT implements some fast generation tools to create several of the standard networks, such as Erdős-Rényi:

g = ng.erdos_renyi(nodes=1000, avg_deg=100)

if nngt.get_config("with_plot"):
    nplt.degree_distribution(g, ('in', 'total'), show=False)

More heterogeneous networks, with scale-free degree distribution (but no correlations like in Barabasi-Albert networks and user-defined exponents) are also implemented:

if nngt.get_config("with_plot"):
    nplt.degree_distribution(g, ('in', 'out'), num_bins=30, logx=True,
                             logy=True, show=True)

For more details, see the full page on Graph generation.

Using random numbers #

By default, NNGT uses the numpy random-number generators (RNGs) which are seeded automatically when numpy is loaded.

However, you can seed the RNGs manually using the following command:

nngt.set_config("msd", 0)

which will seed the master seed to 0 (or any other value you enter). Once seeded manually, a NNGT script will always give the same results provided the same number of thread is being used.

Indeed, when using multithreading, sub-RNGs are used (one per thread). By default, these RNGs are seeded from the master seed as msd + n + 1 where n is the thread number, starting from zero. If needed, these sub-RNGs can also be seeded manually using (for 4 threads)

nngt.set_config("seeds", [1, 2, 3, 4])

Warning

When using NEST, the simulator’s RNGs must be seeded separately using the NEST commands; see the NEST user manual for details.

Complex populations: `NeuralGroup` and `NeuralPop`#

The NeuralGroup allows the creation of nodes that belong together. You can then make a population from these groups and connect them with specific connectivities using the connect_groups() function.

'''
Make the population
'''

# two groups of neurons
g1 = nngt.NeuralGroup(500, neuron_type=1)  # neurons 0 to 499
g2 = nngt.NeuralGroup(500, neuron_type=1)  # neurons 500 to 999

# make population (without NEST models)
pop = nngt.NeuralPop.from_groups(
    (g1, g2), ("left", "right"), with_models=False)

# create network from this population
net = nngt.Network(population=pop)


'''
Connect the groups
'''

# inter-groups (Erdos-Renyi)
prop_er1 = {"density": 0.005}
ng.connect_groups(net, "left", "right", "erdos_renyi", **prop_er1)

# intra-groups (Newman-Watts)
prop_nw = {
    "coord_nb": 20,
    "proba_shortcut": 0.1,
    "reciprocity_circular": 1.
}

ng.connect_groups(net, "left", "left", "newman_watts", **prop_nw)

For more details, see the full page on Neural groups and populations.

Real neuronal culture and NEST interaction: the `Network`#

Besides connectivity, the main interest of the NeuralGroup is that you can pass it the biological properties that the neurons belonging to this group will share.

Since we are using NEST, these properties are:

the model’s name
its non-default properties
the synapses that the neurons have and their properties
the type of the neurons (1 for excitatory or -1 for inhibitory)

''' Create groups with different parameters '''
# adaptive spiking neurons
base_params = {
    'E_L': -60., 'V_th': -58., 'b': 20., 'tau_w': 100.,
    'V_reset': -65., 't_ref': 2., 'g_L': 10., 'C_m': 250.
}
# oscillators
params1, params2 = base_params.copy(), base_params.copy()
params1.update(
    {'E_L': -65., 'b': 40., 'I_e': 200., 'tau_w': 400., "V_th": -57.})
# bursters
params2.update({'b': 25., 'V_reset': -55., 'tau_w': 300.})

oscill = nngt.NeuralGroup(
    nodes=400, neuron_model='aeif_psc_alpha', neuron_type=1,
    neuron_param=params1)

burst = nngt.NeuralGroup(
    nodes=200, neuron_model='aeif_psc_alpha', neuron_type=1,
    neuron_param=params2)

adapt = nngt.NeuralGroup(
    nodes=200, neuron_model='aeif_psc_alpha', neuron_type=1,
    neuron_param=base_params)

synapses = {
    'default': {'model': 'tsodyks2_synapse'},
    ('oscillators', 'bursters'): {'model': 'tsodyks2_synapse', 'U': 0.6},
    ('oscillators', 'oscillators'): {'model': 'tsodyks2_synapse', 'U': 0.7},
    ('oscillators', 'adaptive'): {'model': 'tsodyks2_synapse', 'U': 0.5}
}

'''
Create the population that will represent the neuronal
network from these groups
'''
pop = nngt.NeuralPop.from_groups(
    [oscill, burst, adapt],
    names=['oscillators', 'bursters', 'adaptive'], syn_spec=synapses)

'''
Create the network from this population,
using a Gaussian in-degree
'''
net = ng.gaussian_degree(

Once this network is created, it can simply be sent to nest through the command: gids = net.to_nest(), and the NEST gids are returned.

In order to access the gids from each group, you can do:

oscill_gids = net.nest_gid[oscill.ids]

For more details to use NNGT with NEST, see Interacting with the NEST simulator.

Underlying graph objects and libraries #

Starting with version 2.0 of NNGT, the library no longer uses inheritance but composition to provide access to the underlying graph object, which is stored in the graph attribute of the Graph class.

It can simply be accessed via:

g = nngt.Graph()

library_graph = g.graph

Using graph attribute, on can directly use functions of the underlying graph library (networkx, igraph, or graph-tool) if their equivalent is not yet provided in NNGT – see Consistent tools for graph analysis for implemented functions.

Warning

One notable exception to this behaviour relates to the creation and deletion of nodes or edges, for which you have to use the functions provided by NNGT. As a general rule, any operation that might alter the graph structure should be done through NNGT and never directly by calling functions or methods on the graph attribute.

Apart from this, you can use any analysis or drawing tool from the graph library.

Example using graph-tool #

>>> import graph_tool as gt
>>> import matplotlib.pyplot as plt
>>> print(gt.centrality.closeness(g.graph))
>>> gt.draw.graph_draw(g.graph)
>>> nngt.plot.draw_network(g)
>>> plt.show()

Example using igraph #

>>> import igraph as ig
>>> import matplotlib.pyplot as plt
>>> print(g.graph.closeness(mode='out'))
>>> ig.plot(g.graph)
>>> nngt.plot.draw_network(g)
>>> plt.show()

Example using networkx #

>>> import networkx as nx
>>> import matplotlib.pyplot as plt
>>> print(nx.closeness_centrality(g.graph.reverse()))
>>> nx.draw(g.graph)
>>> nngt.plot.draw_network(g)
>>> plt.show()

Note

People testing these 3 codes will notice that all closeness results are different (though I made sure the functions of each libraries worked on the same outgoing edges)! This example is given voluntarily to remind you, when using these libraries, to check that they indeed compute what you think they do and what are the underlying hypotheses or definitions.

To avoid such issues and make sure that results are the same with all libraries, use the functions provided in Consistent tools for graph analysis.

Go to other tutorials:

Database module#

NNGT provides a database to store NEST simulations. This database requires peewee>3 to work and can be switched on using:

nngt.set_config("use_database", True)

The commands are then used by calling nngt.database to access the database tools.

Functions
Recording a simulation
Checking results in the database

Functions #

nngt.database.get_results(table, column=None, value=None)#

Return the entries where the attribute column satisfies the required equality.

Parameters:

table (str) – Name of the table where the search should be performed (among 'simulation', 'computer', 'neuralnetwork', 'activity', 'synapse', 'neuron', or 'connection').
column (str, optional (default: None)) – Name of the variable of interest (a column on the table). If None, the whole table is returned.
value (column corresponding type, optional (default: None)) – Specific value for the variable of interest. If None, the whole column is returned.

Returns:

peewee.SelectQuery with entries matching the request.

nngt.database.is_clear()#: Check that the logs are clear.

nngt.database.log_simulation_end(network=None, log_activity=True)#: Record the simulation completion and simulated times, save the data, then reset.

nngt.database.log_simulation_start(network, simulator, save_network=True)#

Record the simulation start time, all nodes, connections, network, and computer properties, as well as some of simulation’s.

Parameters:	network (`Network` or subclass) – Network used for the current simulation. simulator (str) – Name of the simulator. save_network (bool, optional (default: True)) – Whether to save the network or not.

nngt.database.reset()#: Reset log status.

Recording a simulation #

nngt.database.log_simulation_start(net, "nest-2.14")
nest.Simulate(1000.)
nngt.database.log_simulation_end()

Checking results in the database #

The database contains the following tables, associated to their respective fields:

‘activity’: Activity,
‘computer’: Computer,
‘connection’: Connection,
‘neuralnetwork’: NeuralNetwork,
‘neuron’: Neuron,
‘simulation’: Simulation,
‘synapse’: Synapse.

These tables are the first keyword passed to get_results(), you can find the existing columns for each of the tables in the following classes descriptions:

Store results into a database

class nngt.database.db_generation.Activity(*args, **kwargs)[source]#

Class detailing the network’s simulated activity.

DoesNotExist#: alias of ActivityDoesNotExist

id = <IntegerField: Activity.id>#

raster = <PickledField: Activity.raster>#: Raster of the simulated activity.

simulations#

class nngt.database.db_generation.Computer(*args, **kwargs)[source]#

Class containing informations about the conputer.

DoesNotExist#: alias of ComputerDoesNotExist

cores = <IntegerField: Computer.cores>#: Number of cores returned by psutil.cpu_count() or -1

id = <IntegerField: Computer.id>#

name = <TextField: Computer.name>#: Name from platform.node() or "unknown"

platform = <TextField: Computer.platform>#: System information from platform.platform()

python = <TextField: Computer.python>#: Python version given by platform.python_version()

ram = <IntegerField: Computer.ram>#: Total memory given by psutil.virtual_memory().total (long) or -1

simulations#

class nngt.database.db_generation.Connection(*args, **kwargs)[source]#

Class detailing the existing connections in the network: a couple of pre- and post-synaptic neurons and a synapse.

DoesNotExist#: alias of ConnectionDoesNotExist

id = <IntegerField: Connection.id>#

post = <ForeignKeyField: Connection.post>#

post_id = <ForeignKeyField: Connection.post>#

pre = <ForeignKeyField: Connection.pre>#

pre_id = <ForeignKeyField: Connection.pre>#

simulations#

synapse = <ForeignKeyField: Connection.synapse>#

synapse_id = <ForeignKeyField: Connection.synapse>#

nngt.database.db_generation.migrate(*operations, **kwargs)[source]#

class nngt.database.db_generation.NeuralNetwork(*args, **kwargs)[source]#

Class containing informations about the neural network.

DoesNotExist#: alias of NeuralNetworkDoesNotExist

compressed_file = <LongCompressedField: NeuralNetwork.compressed_file>#: Compressed (bz2) string of the graph from str(graph); once uncompressed, can be loaded using Graph.from_file(name, from_string=True).

directed = <IntegerField: NeuralNetwork.directed>#: Whether the graph is directed or not

edges = <IntegerField: NeuralNetwork.edges>#: Number of edges.

id = <IntegerField: NeuralNetwork.id>#

network_type = <TextField: NeuralNetwork.network_type>#: Type of the network from Graph.type

nodes = <IntegerField: NeuralNetwork.nodes>#: Number of nodes.

simulations#

weight_distribution = <TextField: NeuralNetwork.weight_distribution>#: Name of the weight_distribution used.

weighted = <IntegerField: NeuralNetwork.weighted>#: Whether the graph is weighted or not.

class nngt.database.db_generation.Neuron(*args, **kwargs)[source]#

Base class that will be modified to contain all the properties of the neurons used during a simulation.

DoesNotExist#: alias of NeuronDoesNotExist

id = <IntegerField: Neuron.id>#

int_connections#

out_connections#

class nngt.database.db_generation.Simulation(*args, **kwargs)[source]#

Class containing all informations about the simulation properties.

DoesNotExist#: alias of SimulationDoesNotExist

activity = <ForeignKeyField: Simulation.activity>#: Activity table entry where the simulated activity is described.

activity_id = <ForeignKeyField: Simulation.activity>#

completion_time = <DateTimeField: Simulation.completion_time>#: Date and time at which the simulation ended.

computer = <ForeignKeyField: Simulation.computer>#: Computer table entry where the computer used is defined.

computer_id = <ForeignKeyField: Simulation.computer>#

connections = <ForeignKeyField: Simulation.connections>#: Connection table entry where the connections are described.

connections_id = <ForeignKeyField: Simulation.connections>#

grnd_seed = <IntegerField: Simulation.grnd_seed>#: Master seed of the simulation.

id = <IntegerField: Simulation.id>#

local_seeds = <PickledField: Simulation.local_seeds>#: List of the local threads seeds.

network = <ForeignKeyField: Simulation.network>#: Network table entry where the simulated network is described.

network_id = <ForeignKeyField: Simulation.network>#

pop_sizes = <PickledField: Simulation.pop_sizes>#: Pickled list containing the group sizes.

population = <PickledField: Simulation.population>#: Pickled list containing the neural group names.

resolution = <FloatField: Simulation.resolution>#: Timestep used to simulate the components of the neural network

simulated_time = <FloatField: Simulation.simulated_time>#: Virtual time that was simulated for the neural network.

simulator = <TextField: Simulation.simulator>#: Name of the neural simulator used (NEST, Brian…)

start_time = <DateTimeField: Simulation.start_time>#: Date and time at which the simulation started.

class nngt.database.db_generation.Synapse(*args, **kwargs)[source]#

Base class that will be modified to contain all the properties of the synapses used during a simulation.

DoesNotExist#: alias of SynapseDoesNotExist

connections#

id = <IntegerField: Synapse.id>#

Welcome to NNGT’s documentation!#

Overview#

Main classes#

Generation of graphs#

Interacting with NEST#

The docs#

Installation#

Dependencies#

Basic dependencies#

Additionnal dependencies#

Simple install#

Linux#

Mac#

Windows#

Local install#

Configuration#

Using NEST#

Intro & user manual#

Yet another graph library?#

Documentation structure#

Graph generation#

Properties of graph components#

Consistent tools for graph analysis#

Parallelism#

Neural groups and populations#

Interacting with the NEST simulator#

Activity analysis#

Description#

The graph objects#

Additional properties#

Graph-theoretical models#

Main module#

Known bugs#

Tutorial#

Complex populations: NeuralGroup and NeuralPop#

Database module#

Indices and tables#

Complex populations: `NeuralGroup` and `NeuralPop`#