def __init__(self, network, parentRegion = None, ontologyTerm = None, *args, **keywords):
"""
Regions represent a physical subset of a nervous system. They can also be hierarchical with regions nested within other regions. Regions can also be associated with an entry in one of the :class:`ontologies <Library.Ontology.Ontology>` in the library.
You create a region by messaging a network:
>>> region1 = network.createRegion(...)
"""
if ontologyTerm is not None:
if 'name' not in keywords:
keywords['name'] = ontologyTerm.name
if 'abbreviation' not in keywords and ontologyTerm.abbreviation is not None:
keywords['abbreviation'] = ontologyTerm.abbreviation
NeuroObject.__init__(self, network, *args, **keywords)
self.parentRegion = None
self.subRegions = []
self.ontologyTerm = ontologyTerm
self.arborizations = []
self.pathways = []
self.neurons = []
if parentRegion is not None:
parentRegion._addSubRegion(self)
def __init__(self, network, neurite1, neurite2, *args, **keywords):
"""
GapJunction objects represent a gap junction between two :class:`neurites <Network.Neurite.Neurite>` in a :class:`network <Network.Network.Network>`.
Instances of this class are created by using the gapJunctionWith method of :meth:`Neuron <Network.Neuron.Neuron.gapJunctionWith>` and :meth:`Neurite <Network.Neurite.Neurite.gapJunctionWith>` objects.
>>> neuron1.gapJunctionWith(neuron2)
"""
NeuroObject.__init__(self, network, *args, **keywords)
self._neurites = set([neurite1, neurite2])
def __init__(self, network, *args, **keywords):
"""
Muscle objects represent muscles in the :class:`network <Network.Network.Network>` and can be :class:`innervated <Network.Innervation.Innervation>` by :class:`neurites <Network.Neurite.Neurite>`.
Create a muscle by messaging the network:
>>> muscle1 = network.createMuscle()
>>> neuron1.innervate(muscle1)
"""
NeuroObject.__init__(self, network, *args, **keywords)
self._innervations = []
def __init__(self, network, *args, **keywords):
"""
Muscle objects represent muscles in the :class:`network <Network.Network.Network>` and can be :class:`innervated <Network.Innervation.Innervation>` by :class:`neurites <Network.Neurite.Neurite>`.
Create a muscle by messaging the network:
>>> muscle1 = network.createMuscle()
>>> neuron1.innervate(muscle1)
"""
NeuroObject.__init__(self, network, *args, **keywords)
self._innervations = []
def __init__(self, network, neurite, muscle, *args, **keywords):
"""
Innervations represent a :class:`neurite's <Network.Neurite.Neurite>` connection to a :class:`muscle <Network.Muscle.Muscle>`.
Create an innervation by messaging a :meth:`neuron <Network.Neuron.Neuron.innervate>` or :meth:`neurite <Network.Neurite.Neurite.innervate>`:
>>> neuron1 = network.createNeuron()
>>> muscle1 = network.createMuscle()
>>> innervation_1_1 = neuron1.innervate(muscle1)
"""
NeuroObject.__init__(self, network, *args, **keywords)
self.neurite = neurite
self.muscle = muscle
def __init__(self, network, neurite, muscle, *args, **keywords):
"""
Innervations represent a :class:`neurite's <Network.Neurite.Neurite>` connection to a :class:`muscle <Network.Muscle.Muscle>`.
Create an innervation by messaging a :meth:`neuron <Network.Neuron.Neuron.innervate>` or :meth:`neurite <Network.Neurite.Neurite.innervate>`:
>>> neuron1 = network.createNeuron()
>>> muscle1 = network.createMuscle()
>>> innervation_1_1 = neuron1.innervate(muscle1)
"""
NeuroObject.__init__(self, network, *args, **keywords)
self.neurite = neurite
self.muscle = muscle
def __init__(self, network, preSynapticNeurite = None, postSynapticPartners = [], activation = None, *args, **keywords):
"""
A Synapse object represents a chemical synapse between a single pre-synaptic neurite and one or more post-synaptic neurites.
Instances of this class are created by using the synapseOn method of :meth:`Neuron <Network.Neuron.Neuron.synapseOn>` and :meth:`Neurite <Network.Neurite.Neurite.synapseOn>` objects.
A synapse's activation attribute should be one of None (meaning unknown), 'excitatory' or 'inhibitory'.
>>> neuron1.synapseOn(neuron2, activation = 'excitatory')
"""
NeuroObject.__init__(self, network, *args, **keywords)
self.preSynapticNeurite = preSynapticNeurite
self.postSynapticPartners = postSynapticPartners
self.activation = activation
def __init__(self, neurite, region, sendsOutput=None, receivesInput=None, *args, **keywords):
"""
Arborizations represent a neurite's arborization within a region.
You create an arborization by messaging a :meth:`neuron <Network.Neuron.Neuron.arborize>` or :meth:`neurite <Network.Neurite.Neurite.arborize>`:
>>> neuron1 = network.createNeuron()
>>> region1 = network.createRegion()
>>> arborization_1_1 = neuron1.arborize(region1)
"""
NeuroObject.__init__(self, neurite.network, *args, **keywords)
self.neurite = neurite
self.region = region
self.sendsOutput = sendsOutput # does the neurite send output to the arbor? None = unknown
self.receivesInput = receivesInput # does the neurite receive input from the arbor? None = unknown
def __init__(self, network, preSynapticNeurite = None, postSynapticPartners = [], activation = None, preSynapticRegion = None, postSynapticRegion = None, *args, **keywords):
"""
A Synapse object represents a chemical synapse between a single pre-synaptic neurite and one or more post-synaptic neurites.
Instances of this class are created by using the synapseOn method of :meth:`Neuron <Network.Neuron.Neuron.synapseOn>` and :meth:`Neurite <Network.Neurite.Neurite.synapseOn>` objects.
A synapse's activation attribute should be one of None (meaning unknown), 'excitatory' or 'inhibitory'.
>>> neuron1.synapseOn(neuron2, activation = 'excitatory')
"""
NeuroObject.__init__(self, network, *args, **keywords)
self.preSynapticNeurite = preSynapticNeurite
self.postSynapticPartners = postSynapticPartners
self.activation = activation
self.preSynapticRegion = preSynapticRegion
self.postSynapticRegion = postSynapticRegion
def __init__(self, network, target = None, modality = None, *args, **keywords):
"""
Stimulus objects represent external stimulation of objects in the network.
Stimulii are created by calling the :meth:`stimulate <Network.Object.Object.stimulate>` method on an object in the network. The modality argument must be a :class:`modality <Library.Modality.Modality>` from the library or None to indicate unknown modality.
>>> stimulus = neuron1.stimulate(modality = library.modality('light'))
"""
if target is None:
raise ValueError, gettext('A stimulus must have a target')
if not keywords.has_key('name') or keywords['name'] is None:
keywords['name'] = modality.name
NeuroObject.__init__(self, network, *args, **keywords)
self.target = target
self.modality = modality
def __init__(self, network, target=None, modality=None, *args, **keywords):
"""
Stimulus objects represent external stimulation of objects in the network.
Stimulii are created by calling the :meth:`stimulate <Network.Object.Object.stimulate>` method on an object in the network. The modality argument must be a :class:`modality <Library.Modality.Modality>` from the library or None to indicate unknown modality.
>>> stimulus = neuron1.stimulate(modality = library.modality('light'))
"""
if target is None:
raise ValueError, gettext('A stimulus must have a target')
if not keywords.has_key('name') or keywords['name'] is None:
keywords['name'] = modality.name
NeuroObject.__init__(self, network, *args, **keywords)
self.target = target
self.modality = modality
def __init__(self, region1 , region2, region1Projects = None, region1Activation = None, region2Projects = None, region2Activation = None, *args, **keywords):
"""
Pathways connect pairs of :class:`regions <Network.Region.Region>`. They consist of bundles of :class:`neurites <Network.Neurite.Neurite>` which can be optionally specified.
You create a pathway by :meth:`messaging <Network.Region.Region.projectToRegion>` one of the regions:
>>> pathway_1_2 = region1.projectToRegion(region2)
"""
NeuroObject.__init__(self, region1.network, *args, **keywords)
self._neurites = []
self.region1 = region1
self.region1Projects = region1Projects
self.region1Activation = region1Activation
self.region2 = region2
self.region2Projects = region2Projects
self.region2Activation = region2Activation
def __init__(self, network, root, pathway=None, *args, **keywords):
"""
Neurites represent projections from :class:`neurons <Network.Neuron.Neuron>` or other neurites.
You create a neurite by messaging a :meth:`neuron <Network.Neuron.Neuron.extendNeurite>` or :meth:`neurite <Network.Neurite.Neurite.extendNeurite>`:
>>> neurite1 = neuron.extendNeurite(...)
>>> neurite2 = neurite1.extendNeurite(...)
"""
NeuroObject.__init__(self, network, *args, **keywords)
self.root = root
self._neurites = []
self.arborization = None
self._synapses = []
self._gapJunctions = []
self._innervations = []
self._pathway = pathway
if pathway is not None:
pathway.addNeurite(self)
def __init__(self, network, root, pathway = None, *args, **keywords):
"""
Neurites represent projections from :class:`neurons <Network.Neuron.Neuron>` or other neurites.
You create a neurite by messaging a :meth:`neuron <Network.Neuron.Neuron.extendNeurite>` or :meth:`neurite <Network.Neurite.Neurite.extendNeurite>`:
>>> neurite1 = neuron.extendNeurite(...)
>>> neurite2 = neurite1.extendNeurite(...)
"""
NeuroObject.__init__(self, network, *args, **keywords)
self.root = root
self._neurites = []
self.arborization = None
self._synapses = []
self._gapJunctions = []
self._innervations = []
self._pathway = pathway
if pathway is not None:
pathway.addNeurite(self)
def __init__(self, network, neuronClass = None, *args, **keywordArgs):
"""
Neurons represent individual neural cells in the network.
You create a neuron by messaging the network:
>>> neuron1 = network.createNeuron(...)
"""
# Upconvert old 'function' singleton param to list expected by new 'functions' param.
if 'function' in keywordArgs:
functions = set([function])
del function
# Upconvert old 'neurotransmitter' singleton param to list expected by new 'neurotransmitters' param.
if 'neurotransmitter' in keywordArgs:
neurotransmitters = [neurotransmitter]
del neurotransmitter
# Pull out the keyword arguments specific to this class before we call super.
# We need to do this so we can know if the caller specified an argument or not.
# For example, the caller might specify a neuron class and one attribute to override. We need to know which attributes _not_ to set.
localAttrNames = ['activation', 'functions', 'neurotransmitters', 'polarity', 'region', 'neuronImage', 'links']
localKeywordArgs = {}
for attrName in localAttrNames:
if attrName in keywordArgs:
localKeywordArgs[attrName] = keywordArgs[attrName]
del keywordArgs[attrName]
NeuroObject.__init__(self, network, *args, **keywordArgs)
self._neurites = []
self.neuronClass = neuronClass
self.activation = None
self._functions = set()
self.neurotransmitters = []
self.polarity = None
self.region = None
self._synapses = []
self.neuronImage = []
self.links = []
for attrName in localAttrNames:
if attrName == 'functions':
attrValue = set()
elif attrName in ('neurotransmitters', 'links', 'neuronImage'):
attrValue = []
else:
attrValue = None
if attrName in localKeywordArgs:
# The user has explicitly set the attribute.
if attrName == 'functions':
attrValue = set(localKeywordArgs[attrName])
elif attrName == 'neuronImage':
for img in localKeywordArgs[attrName]:
if img['path']:
imageLabel = img['label']
imageLocation = img['path']
myImage = self.Img(imageLabel, imageLocation)
attrValue.append(myImage)
else:
attrValue = localKeywordArgs[attrName]
elif self.neuronClass:
attrValue = getattr(self.neuronClass, attrName) # Inherit the value from the class
if attrName == 'functions':
attrName = '_functions'
setattr(self, attrName, attrValue)
if self.region is not None:
self.region.neurons.append(self)
def __init__(self, network, neuronClass = None, *args, **keywordArgs):
"""
Neurons represent individual neural cells in the network.
You create a neuron by messaging the network:
>>> neuron1 = network.createNeuron(...)
"""
# Upconvert old 'function' singleton param to list expected by new 'functions' param.
if 'function' in keywordArgs:
functions = set([function])
del function
# Upconvert old 'neurotransmitter' singleton param to list expected by new 'neurotransmitters' param.
if 'neurotransmitter' in keywordArgs:
neurotransmitters = [neurotransmitter]
del neurotransmitter
# Pull out the keyword arguments specific to this class before we call super.
# We need to do this so we can know if the caller specified an argument or not.
# For example, the caller might specify a neuron class and one attribute to override. We need to know which attributes _not_ to set.
localAttrNames = ['activation', 'functions', 'neurotransmitters', 'polarity', 'region', 'neuronImage', 'links']
localKeywordArgs = {}
for attrName in localAttrNames:
if attrName in keywordArgs:
localKeywordArgs[attrName] = keywordArgs[attrName]
del keywordArgs[attrName]
NeuroObject.__init__(self, network, *args, **keywordArgs)
self._neurites = []
self.neuronClass = neuronClass
self.activation = None
self._functions = set()
self.neurotransmitters = []
self.polarity = None
self.region = None
self._synapses = []
self.neuronImage = []
self.links = []
for attrName in localAttrNames:
if attrName == 'functions':
attrValue = set()
elif attrName in ('neurotransmitters', 'links', 'neuronImage'):
attrValue = []
else:
attrValue = None
if attrName in localKeywordArgs:
# The user has explicitly set the attribute.
if attrName == 'functions':
attrValue = set(localKeywordArgs[attrName])
if attrName == 'neuronImage':
for img in localKeywordArgs[attrName]:
imageLabel = img['label']
imageLocation = img['path']
myImage = self.Img(imageLabel, imageLocation)
attrValue.append(myImage)
else:
attrValue = localKeywordArgs[attrName]
elif self.neuronClass:
attrValue = getattr(self.neuronClass, attrName) # Inherit the value from the class
if attrName == 'functions':
attrName = '_functions'
setattr(self, attrName, attrValue)
if self.region is not None:
self.region.neurons.append(self)
