#!/usr/bin/env python
#-*- coding:utf-8 -*-
#
# This file is part of the NNGT project to generate and analyze
# neuronal networks and their activity.
# Copyright (C) 2015-2017 Tanguy Fardet
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
""" Graph data strctures in NNGT """
from collections import OrderedDict, defaultdict
import logging
import weakref
from copy import deepcopy
import numpy as np
from numpy.random import randint, uniform
import scipy.sparse as ssp
import scipy.spatial as sptl
import nngt
from nngt.lib import (InvalidArgument, nonstring_container, is_integer,
default_neuron, default_synapse, POS, WEIGHT, DELAY,
DIST, TYPE, BWEIGHT)
from nngt.lib._frozendict import _frozendict
from nngt.lib.rng_tools import _eprop_distribution
from nngt.lib.logger import _log_message
__all__ = [
'GroupProperty',
'NeuralPop',
]
logger = logging.getLogger(__name__)
#-----------------------------------------------------------------------------#
# NeuralPop
#------------------------
#
[docs]class NeuralPop(OrderedDict):
"""
The basic class that contains groups of neurons and their properties.
:ivar has_models: :obj:`bool`,
``True`` if every group has a ``model`` attribute.
:ivar size: :obj:`int`,
Returns the number of neurons in the population.
:ivar syn_spec: :obj:`dict`,
Dictionary containing informations about the synapses between the
different groups in the population.
:ivar is_valid: :obj:`bool`,
Whether this population can be used to create a network in NEST.
"""
# number of created populations
__num_created = 0
# store weakrefs to created populations
__pops = weakref.WeakValueDictionary()
#-------------------------------------------------------------------------#
# Class attributes and methods
@classmethod
def _nest_reset(cls):
'''
Reset the _to_nest bool and potential parent networks.
'''
for pop in cls.__pops.valuerefs():
if pop() is not None:
pop()._to_nest = False
for g in pop().values():
g._to_nest = False
if pop().parent is not None:
pop().parent._nest_gids = None
[docs] @classmethod
def from_network(cls, graph, *args):
'''
Make a NeuralPop object from a network. The groups of neurons are
determined using instructions from an arbitrary number of
:class:`~nngt.properties.GroupProperties`.
'''
return cls(parent=graph, graph=graph, group_prop=args)
[docs] @classmethod
def from_groups(cls, groups, names=None, syn_spec=None, parent=None,
with_models=True):
'''
Make a NeuralPop object from a (list of) :class:`~nngt.NeuralGroup`
object(s).
.. versionchanged:: 0.8
Added `syn_spec` parameter.
Parameters
----------
groups : list of :class:`~nngt.NeuralGroup` objects
Groups that will be used to form the population.
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 position of the group in `groups`,
stored as a string. In this case, the first group in a population
named `pop` will be retreived by either `pop[0]` or `pop['0']`.
parent : :class:`~nngt.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.
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.
.. code-block:: python
# 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
:class:`~nngt.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.
'''
if not nonstring_container(groups):
groups = [groups]
for i, g in enumerate(groups):
assert g.is_valid(), "Group number " + str(i) + " is invalid."
gsize = len(groups)
neurons = []
names = [str(i) for i in range(gsize)] if names is None else names
assert len(names) == gsize, "`names` and `groups` must have " +\
"the same size."
if syn_spec is not None:
_check_syn_spec(syn_spec, names, groups)
current_size = 0
for g in groups:
# generate the neuron ids if necessary
ids = g.ids
if len(ids) == 0:
ids = list(range(current_size, current_size + g.size))
g.ids = ids
current_size += len(ids)
neurons.extend(ids)
neurons = list(set(neurons))
pop = cls(current_size, parent=parent, with_models=with_models)
for name, g in zip(names, groups):
pop[name] = g
g._pop = weakref.ref(pop)
g._net = weakref.ref(parent) if parent is not None else None
# take care of synaptic connections
pop._syn_spec = deepcopy(syn_spec if syn_spec is not None else {})
return pop
[docs] @classmethod
def exc_and_inhib(cls, size, iratio=0.2, en_model=default_neuron,
en_param=None, in_model=default_neuron, in_param=None,
syn_spec=None, parent=None):
'''
Make a NeuralPop with a given ratio of inhibitory and excitatory
neurons.
.. versionchanged:: 0.8
Added `syn_spec` 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 : :class:`~nngt.Network`, optional (default: None)
Network associated to this population.
See also
--------
:func:`nest.Connect` for a description of the dict that can be passed
as values for the `syn_spec` parameter.
'''
num_exc_neurons = int(size*(1-iratio))
pop = cls(size, parent)
gExc = pop.create_group(
"excitatory", range(num_exc_neurons), 1, en_model, en_param)
gInh = pop.create_group(
"inhibitory", range(num_exc_neurons, size), -1, in_model, in_param)
if syn_spec is not None:
_check_syn_spec(
syn_spec, ["excitatory", "inhibitory"], pop.values())
pop._syn_spec = deepcopy(syn_spec)
else:
pop._syn_spec = {}
return pop
[docs] @classmethod
def copy(cls, pop):
''' Copy an existing NeuralPop '''
new_pop = cls.__init__(parent=pop.parent, with_models=pop.has_models)
for name, group in pop.items():
new_pop.create_group(
name, group.ids, group.model, group.neuron_param)
new_pop._syn_spec = pop.syn_spec
return new_pop
#-------------------------------------------------------------------------#
# Contructor and instance attributes
def __init__(self, size=None, parent=None, with_models=True, *args,
**kwargs):
'''
Initialize NeuralPop instance
Parameters
----------
size : int, optional (default: 0)
Number of neurons that the population will contain.
parent : :class:`~nngt.Network`, optional (default: None)
Network associated to this population.
with_models : :class:`bool`
whether the population's groups contain models to use in NEST
*args : items for OrderedDict parent
**kwargs : :obj:`dict`
Returns
-------
pop : :class:`~nngt.NeuralPop` object.
'''
self._is_valid = False
self._desired_size = size if parent is None else parent.node_nb()
self._size = 0
self._parent = None if parent is None else weakref.ref(parent)
# array of strings containing the name of the group where each neuron
# belongs
if self._desired_size is None:
self._neuron_group = None
self._max_id = 0
else:
self._neuron_group = np.repeat(-1, self._desired_size)
self._max_id = len(self._neuron_group) - 1
if parent is not None and 'group_prop' in kwargs:
dic = _make_groups(parent, kwargs["group_prop"])
self._is_valid = True
self.update(dic)
self._syn_spec = {}
self._has_models = with_models
# whether the network this population represents was sent to NEST
self._to_nest = False
# init the OrderedDict
super(NeuralPop, self).__init__(*args)
# update class properties
self.__id = self.__class__.__num_created
self.__class__.__num_created += 1
self.__class__.__pops[self.__id] = self
def __reduce__(self):
'''
Overwrite this function to make NeuralPop pickable.
OrderedDict.__reduce__ returns a 3 to 5 tuple:
- the first is the class
- the second is the init args in Py2, empty sequence in Py3
- the third can be used to store attributes
- the fourth is None and needs to stay None
- the last must be kept unchanged: odict_iterator in Py3
'''
state = super(NeuralPop, self).__reduce__()
last = state[4] if len(state) == 5 else None
dic = state[2]
od_args = state[1][0] if state[1] else state[1]
args = (dic.get("_size", None), dic.get("_parent", None),
dic.get("_has_models", True), od_args)
newstate = (NeuralPop, args, dic, None, last)
return newstate
def __getitem__(self, key):
if isinstance(key, (int, np.integer)):
assert key >= 0, "Index must be positive, not {}.".format(key)
new_key = tuple(self.keys())[key]
return OrderedDict.__getitem__(self, new_key)
else:
return OrderedDict.__getitem__(self, key)
def __setitem__(self, key, value):
if self._to_nest:
raise RuntimeError("Populations items can no longer be modified "
"once the network has been sent to NEST!")
self._validity_check(key, value)
int_key = None
if is_integer(key):
new_key = tuple(self.keys())[key]
int_key = key
OrderedDict.__setitem__(self, new_key, value)
else:
OrderedDict.__setitem__(self, key, value)
int_key = list(super(NeuralPop, self).keys()).index(key)
# set name and parents
value._name = key
value._pop = weakref.ref(self)
value._net = self._parent
# update pop size/max_id
group_size = len(value.ids)
max_id = np.max(value.ids) if group_size != 0 else 0
_update_max_id_and_size(self, max_id)
self._neuron_group[value.ids] = int_key
if -1 in list(self._neuron_group):
self._is_valid = False
else:
if self._desired_size is not None:
self._is_valid = (self._desired_size == self._size)
else:
self._is_valid = True
def _sent_to_nest(self):
'''
Signify to the population and its groups that the network was sent
to NEST and that therefore properties and groups should no longer
be modified.
'''
self._to_nest = True
for g in self.values():
g._to_nest = True
@property
def size(self):
'''
Number of neurons in this population.
'''
return self._size
@property
def parent(self):
'''
Parent :class:`~nngt.Network`, if it exists, otherwise ``None``.
'''
return None if self._parent is None else self._parent()
@property
def syn_spec(self):
'''
The properties of the synaptic connections between groups.
Returns a :obj:`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.
.. code-block:: python
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.)}
}
}
.. versionadded:: 0.8
'''
return deepcopy(self._syn_spec)
@syn_spec.setter
def syn_spec(self, syn_spec):
raise NotImplementedError('`syn_spec` is not settable yet.')
@property
def has_models(self):
return self._has_models
@property
def is_valid(self):
'''
Whether the population can be used to create a NEST network.
'''
return self._is_valid
#-------------------------------------------------------------------------#
# Methods
[docs] def create_group(self, name, neurons, ntype=1, neuron_model=None,
neuron_param=None):
'''
Create a new groupe from given properties.
.. versionchanged:: 0.8
Removed `syn_model` and `syn_param`.
.. versionchanged:: 1.0
`neurons` can be an int to signify a desired size for the group
without actually setting the indices.
Parameters
----------
name : str
Name of the group.
neurons : int or array-like
Desired number of neurons or list of the neurons indices.
ntype : 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.
'''
if self._to_nest:
raise RuntimeError("Groups can no longer be created once the "
"network has been sent to NEST!")
neuron_param = {} if neuron_param is None else neuron_param.copy()
group = NeuralGroup(neurons, ntype=ntype,
neuron_model=neuron_model,
neuron_param=neuron_param, name=name)
group._pop = weakref.ref(self)
group._net = self._parent
self[name] = group
[docs] def set_model(self, model, group=None):
'''
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.
'''
if self._to_nest:
raise RuntimeError("Models cannot be changed after the network "
"has been sent to NEST!")
if group is None:
group = self.keys()
try:
for key, val in model.items():
for name in group:
if key == "neuron":
self[name].neuron_model = val
elif key == "synapse":
self[name].syn_model = val
else:
raise ValueError(
"Model type {} is not valid; choose among 'neuron'"
" or 'synapse'.".format(key))
except:
if model is not None:
raise InvalidArgument(
"Invalid model dict or group; see docstring.")
b_has_models = True
if model is None:
b_has_models = False
for group in iter(self.values()):
b_has_models *= group.has_model
self._has_models = b_has_models
[docs] def set_neuron_param(self, params, neurons=None, group=None):
'''
Set the parameters of specific neurons or of a whole group.
.. versionadded:: 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.
'''
if self._to_nest:
raise RuntimeError("Parameters cannot be changed after the "
"network has been sent to NEST!")
if neurons is not None: # specific neuron ids
groups = []
# get the groups they could belong to
if group is not None:
if nonstring_container(group):
groups.extend((self[g] for g in group))
else:
groups.append(self[group])
else:
groups.extend(self.values())
# update the groups parameters
for g in groups:
idx = np.where(np.in1d(g.ids, neurons, assume_unique=True))[0]
# set the properties of the nodes for each entry in params
for k, v in params.items():
default = np.NaN
if k in g.neuron_param:
default = g.neuron_param[k]
elif nngt.get_config('with_nest'):
try:
import nest
try:
default = nest.GetDefaults(g.neuron_model, k)
except nest.NESTError:
pass
except ImportError:
pass
vv = np.repeat(default, g.size)
vv[idx] = v
# update
g.neuron_param[k] = vv
else: # all neurons in one or several groups
group = self.keys() if group is None else group
if not nonstring_container(group):
group = [group]
start = 0
for name in group:
g = self[name]
for k, v in params.items():
if nonstring_container(v):
g.neuron_param[k] = v[start:start+g.size]
else:
g.neuron_param[k] = v
start += g.size
[docs] def get_param(self, groups=None, neurons=None, element="neuron"):
'''
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.
'''
if neurons is not None:
groups = self._neuron_group[neurons]
elif groups is None:
groups = tuple(self.keys())
key = "neuron_param" if element == "neuron" else "syn_param"
if isinstance(groups, (str, int, np.integer)):
return self[groups].properties[key]
else:
param = []
for group in groups:
param.append(self[group].properties[key])
return param
[docs] def get_group(self, neurons, numbers=False):
'''
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.
'''
names = np.array(tuple(self.keys()), dtype=object)
if numbers:
return self._neuron_group[neurons]
else:
if self._is_valid:
return names[self._neuron_group[neurons]]
else:
groups = []
for i in self._neuron_group[neurons]:
if i >= 0:
groups.append(names[i])
else:
groups.append(None)
return groups
[docs] def add_to_group(self, group_name, ids):
'''
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.
'''
if self._to_nest:
raise RuntimeError("Groups cannot be changed after the "
"network has been sent to NEST!")
idx = None
if is_integer(group_name):
assert 0 <= group_name < len(self), "Group index does not exist."
idx = group_name
else:
idx = list(self.keys()).index(group_name)
if ids:
self[group_name].ids += list(ids)
# update number of neurons
max_id = np.max(ids)
_update_max_id_and_size(self, max_id)
self._neuron_group[np.array(ids)] = idx
if -1 in list(self._neuron_group):
self._is_valid = False
else:
self._is_valid = True
def _validity_check(self, name, group):
if self._has_models and not group.has_model:
raise AttributeError(
"This NeuralPop requires group to have a model attribute that "
"is not `None`; to disable this, use `set_model(None)` "
"method on this NeuralPop instance.")
elif group.has_model and not self._has_models:
_log_message(logger, "WARNING",
"This NeuralPop is not set to take models into "
"account; use the `set_model` method to change its "
"behaviour.")
# ----------------------------- #
# NeuralGroup and GroupProperty #
# ----------------------------- #
[docs]class NeuralGroup(object):
"""
Class defining groups of neurons.
:ivar ids: :obj:`list` of :obj:`int`
the ids of the neurons in this group.
:ivar neuron_type: :class:`int`
the default is ``1`` for excitatory neurons; ``-1`` is for interneurons
:ivar model: :class:`string`, optional (default: None)
the name of the model to use when simulating the activity of this group
:ivar neuron_param: :class:`dict`, optional (default: {})
the parameters to use (if they differ from the model's defaults)
Note
----
By default, synapses are registered as ``"static_synapse"`` in NEST;
because of this, only the ``neuron_model`` attribute is checked by the
``has_model`` function.
Warning
-------
Equality between :class:`~nngt.properties.NeuralGroup`s only compares
the size and neuronal ``model`` and ``param`` attributes. This means
that groups differing only by their ``ids`` will register as equal.
"""
def __init__ (self, nodes=None, ntype=1, neuron_model=None, neuron_param=None,
name=None):
'''
Create a group of neurons (empty group is default, but it is not a
valid object for most use cases).
.. versionchanged:: 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.
ntype : int, optional (default: 1)
Type of the neurons (1 for excitatory, -1 for inhibitory).
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 :class:`~nngt.core.NeuralGroup` instance.
'''
assert ntype in (1, -1), "`ntype` can either be 1 or -1."
neuron_param = {} if neuron_param is None else neuron_param.copy()
self._has_model = False if neuron_model is None else True
self._neuron_model = neuron_model
if nodes is None:
self._desired_size = None
self._ids = []
elif nonstring_container(nodes):
self._desired_size = None
self._ids = list(nodes)
elif is_integer(nodes):
self._desired_size = nodes
self._ids = []
else:
raise InvalidArgument('`nodes` must be either array-like or int.')
self._name = "" if name is None else name
self._nest_gids = None
self._neuron_param = neuron_param if self._has_model else {}
self.neuron_type = ntype
# whether the network this group belongs to was sent to NEST
self._to_nest = False
# parents
self._pop = None
self._net = None
def __eq__ (self, other):
if isinstance(other, NeuralGroup):
same_size = self.size == other.size
same_nmodel = ((self.neuron_model == other.neuron_model)
* (self.neuron_param == other.neuron_param))
return same_size*same_nmodel
else:
return False
def __len__(self):
return self.size
@property
def name(self):
return self._name
@property
def neuron_model(self):
return self._neuron_model
@neuron_model.setter
def neuron_model(self, value):
if self._to_nest:
raise RuntimeError("Models cannot be changed after the "
"network has been sent to NEST!")
self._neuron_model = value
self._has_model = False if value is None else self._has_model
@property
def neuron_param(self):
if self._to_nest:
return _frozendict(self._neuron_param, message="Cannot set " +
"neuron params after the network has been " +
"sent to NEST!")
else:
return self._neuron_param
@neuron_param.setter
def neuron_param(self, value):
if self._to_nest:
raise RuntimeError("Parameters cannot be changed after the "
"network has been sent to NEST!")
self._neuron_param = value
@property
def size(self):
if self._desired_size is not None:
return self._desired_size
return len(self._ids)
@property
def ids(self):
return self._ids
@ids.setter
def ids(self, value):
if self._to_nest:
raise RuntimeError("Ids cannot be changed after the "
"network has been sent to NEST!")
if self._desired_size != len(value):
_log_message(logger, "WARNING",
'The length of the `ids` passed is not the same as '
'the initial size that was declared: {} before '
'vs {} now. Setting `ids` anyway, but check your '
'code!'.format(self._desired_size, len(value)))
self._ids = value
self._desired_size = None
@property
def nest_gids(self):
return self._nest_gids
@property
def has_model(self):
return self._has_model
@property
def properties(self):
dic = {
"neuron_type": self.neuron_type,
"neuron_model": self._neuron_model,
"neuron_param": deepcopy(self._neuron_param)
}
return dic
[docs] def is_valid(self):
'''
Whether the group can be used in a population: i.e. if it has either
a size or some ids associated to it.
.. versionadded:: 1.0
'''
return (self._desired_size is not None) or self._ids
[docs]class GroupProperty:
"""
Class defining the properties needed to create groups of neurons from an
existing :class:`~nngt.GraphClass` or one of its subclasses.
:ivar size: :class:`int`
Size of the group.
:ivar constraints: :class:`dict`, optional (default: {})
Constraints to respect when building the
:class:`~nngt.properties.NeuralGroup` .
:ivar neuron_model: :class:`string`, optional (default: None)
name of the model to use when simulating the activity of this group.
:ivar neuron_param: :class:`dict`, optional (default: {})
the parameters to use (if they differ from the model's defaults)
"""
def __init__ (self, size, constraints={}, neuron_model=None,
neuron_param={}, syn_model=None, syn_param={}):
'''
Create a new instance of GroupProperties.
Notes
-----
The constraints can be chosen among:
- "avg_deg", "min_deg", "max_deg" (:class:`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" (:class:`double`) to constrain the betweenness
centrality
- "in_shape" (:class:`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)
'''
self.size = size
self.constraints = constraints
self.neuron_model = neuron_model
self.neuron_param = neuron_param
self.syn_model = syn_model
self.syn_param = syn_param
def _make_groups(graph, group_prop):
'''
Divide `graph` into groups using `group_prop`, a list of group properties
@todo
'''
pass
# ----------- #
# Connections #
# ----------- #
[docs]class Connections:
"""
The basic class that computes the properties of the connections between
neurons for graphs.
"""
#-------------------------------------------------------------------------#
# Class methods
[docs] @staticmethod
def distances(graph, elist=None, pos=None, dlist=None, overwrite=False):
'''
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.
'''
n = graph.node_nb()
elist = graph.edges_array if elist is None else elist
if dlist is not None:
assert isinstance(dlist, np.ndarray), "numpy.ndarray required in "\
"Connections.distances"
graph.set_edge_attribute(DIST, value_type="double", values=dlist)
return dlist
else:
pos = graph._pos if hasattr(graph, "_pos") else pos
# compute the new distances
if graph.edge_nb():
ra_x = pos[elist[:,0], 0] - pos[elist[:,1], 0]
ra_y = pos[elist[:,0], 1] - pos[elist[:,1], 1]
ra_dist = np.sqrt( np.square(ra_x) + np.square(ra_y) )
#~ ra_dist = np.tile( , 2)
# update graph distances
graph.set_edge_attribute(DIST, value_type="double",
values=ra_dist, edges=elist)
return ra_dist
else:
return []
[docs] @staticmethod
def delays(graph=None, dlist=None, elist=None, distribution="constant",
parameters=None, noise_scale=None):
'''
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.
'''
elist = np.array(elist) if elist is not None else elist
if dlist is not None:
assert isinstance(dlist, np.ndarray), "numpy.ndarray required in "\
"Connections.delays"
num_edges = graph.edge_nb() if elist is None else elist.shape[0]
if len(dlist) != num_edges:
raise InvalidArgument("`dlist` must have one entry per edge.")
else:
parameters["btype"] = parameters.get("btype", "edge")
parameters["use_weights"] = parameters.get("use_weights", False)
dlist = _eprop_distribution(graph, distribution, elist=elist,
**parameters)
# add to the graph container
if graph is not None:
graph.set_edge_attribute(
DELAY, value_type="double", values=dlist, edges=elist)
return dlist
[docs] @staticmethod
def weights(graph=None, elist=None, wlist=None, distribution="constant",
parameters={}, noise_scale=None):
'''
Compute the weights of the graph's edges.
@todo: take elist into account
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.
'''
parameters["btype"] = parameters.get("btype", "edge")
parameters["use_weights"] = parameters.get("use_weights", False)
elist = np.array(elist) if elist is not None else elist
if wlist is not None:
assert isinstance(wlist, np.ndarray), "numpy.ndarray required in "\
"Connections.weights"
num_edges = graph.edge_nb() if elist is None else elist.shape[0]
if len(wlist) != num_edges:
raise InvalidArgument("`wlist` must have one entry per edge.")
else:
wlist = _eprop_distribution(graph, distribution, elist=elist,
**parameters)
# for normalize by the inhibitory weight factor
if graph is not None and graph.is_network():
if not np.isclose(graph._iwf, 1.):
adj = graph.adjacency_matrix(types=True, weights=False)
keep = (adj[elist[:, 0], elist[:, 1]] < 0).A1
wlist[keep] *= graph._iwf
# add to the graph container
bwlist = (np.max(wlist) - wlist if np.any(wlist)
else np.repeat(0., len(wlist)))
if graph is not None:
graph.set_edge_attribute(
WEIGHT, value_type="double", values=wlist, edges=elist)
graph.set_edge_attribute(
BWEIGHT, value_type="double", values=bwlist, edges=elist)
return wlist
[docs] @staticmethod
def types(graph, inhib_nodes=None, inhib_frac=None):
'''
@todo
Define the type of a set of neurons.
If no arguments are given, all edges will be set as excitatory.
Parameters
----------
graph : :class:`~nngt.Graph` or subclass
Graph on which edge types will be created.
inhib_nodes : int, float 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 : :class:`~numpy.ndarray`
List of the edges' types.
'''
t_list = np.repeat(1., graph.edge_nb())
edges = graph.edges_array
num_inhib = 0
idx_inhib = []
if inhib_nodes is None and inhib_frac is None:
graph.new_edge_attribute("type", "double", val=1.)
return t_list
else:
n = graph.node_nb()
if inhib_nodes is None:
# set inhib_frac*num_edges random inhibitory connections
num_edges = graph.edge_nb()
num_inhib = int(num_edges*inhib_frac)
num_current = 0
while num_current < num_inhib:
new = randint(0,num_edges,num_inhib-num_current)
idx_inhib = np.unique(np.concatenate((idx_inhib, new)))
num_current = len(idx_inhib)
t_list[idx_inhib.astype(int)] *= -1.
else:
# get the dict of inhibitory nodes
num_inhib_nodes = 0
idx_nodes = {}
if nonstring_container(inhib_nodes):
idx_nodes = {i: -1 for i in inhib_nodes}
num_inhib_nodes = len(idx_nodes)
if isinstance(inhib_nodes, np.float):
if inhib_nodes > 1:
raise InvalidArgument(
"Inhibitory ratio (float value for `inhib_nodes`) "
"must be smaller than 1.")
num_inhib_nodes = int(inhib_nodes*n)
if is_integer(inhib_nodes):
num_inhib_nodes = int(inhib_nodes)
while len(idx_nodes) != num_inhib_nodes:
indices = randint(0,n,num_inhib_nodes-len(idx_nodes))
di_tmp = { i:-1 for i in indices }
idx_nodes.update(di_tmp)
for v in edges[:,0]:
if v in idx_nodes:
idx_inhib.append(v)
idx_inhib = np.unique(idx_inhib)
# set the inhibitory edge indices
for v in idx_inhib:
idx_edges = np.argwhere(edges[:,0]==v)
n = len(idx_edges)
if inhib_frac is not None:
idx_inh = []
num_inh = n*inhib_frac
i = 0
while i != num_inh:
ids = randint(0,n,num_inh-i)
idx_inh = np.unique(np.concatenate((idx_inh,ids)))
i = len(idx_inh)
t_list[idx_inh] *= -1.
else:
t_list[idx_edges] *= -1.
graph.set_edge_attribute("type", value_type="double", values=t_list)
return t_list
# ----- #
# Tools #
# ----- #
def _check_syn_spec(syn_spec, group_names, groups):
gsize = len(groups)
# test if all types syn_spec are contained
alltypes = set(((1, 1), (1, -1), (-1, 1), (-1, -1))).issubset(
syn_spec.keys())
# is there more than 1 type?
types = list(set(g.neuron_type for g in groups))
mt_type = len(types) > 1
# check that only allowed entries are present
edge_keys = []
for k in syn_spec.keys():
if isinstance(k, tuple):
edge_keys.extend(k)
edge_keys = set(edge_keys)
allkeys = group_names + types
assert edge_keys.issubset(allkeys), \
'`syn_spec` edge entries can only be made from {}.'.format(allkeys)
# warn if connections might be missing
nspec = len(edge_keys)
has_default = len(syn_spec) > nspec
if mt_type and nspec < gsize**2 and not alltypes and not has_default:
_log_message(
logger, "WARNING",
'There is not one synaptic specifier per inter-group'
'connection in `syn_spec` and no default model was provided. '
'Therefore, {} or 4 entries were expected but only {} were '
'provided. It might be right, but make sure all cases are '
'covered. Missing connections will be set as "static_'
'synapse".'.format(gsize**2, nspec))
for val in syn_spec.values():
assert 'weight' not in val, '`weight` cannot be set here.'
assert 'delay' not in val, '`delay` cannot be set here.'
def _update_max_id_and_size(neural_pop, max_id):
'''
Update NeuralPop after modification of a NeuralGroup ids.
'''
old_max_id = neural_pop._max_id
neural_pop._max_id = max(neural_pop._max_id, max_id)
# update size
neural_pop._size = 0
for g in neural_pop.values():
neural_pop._size += g.size
# update the group node property
if neural_pop._neuron_group is None:
neural_pop._neuron_group = np.repeat(-1, neural_pop._max_id + 1)
elif neural_pop._max_id >= len(neural_pop._neuron_group):
ngroup_tmp = np.repeat(-1, neural_pop._max_id + 1)
ngroup_tmp[:old_max_id + 1] = neural_pop._neuron_group
neural_pop._neuron_group = ngroup_tmp
