Types module¶
Accessing and setting node and parameter types in NEST.
Classes defining the different PyNEST types
- class nest.lib.hl_api_types.CollocatedSynapses(*args)¶
Bases:
objectClass for collocated synapse specifications.
Wrapper around a list of specifications, used when calling
Connect().Example
nodes = nest.Create('iaf_psc_alpha', 3) syn_spec = nest.CollocatedSynapses({'weight': 4., 'delay': 1.5}, {'synapse_model': 'stdp_synapse'}, {'synapse_model': 'stdp_synapse', 'alpha': 3.}) nest.Connect(nodes, nodes, conn_spec='one_to_one', syn_spec=syn_spec) conns = nest.GetConnections() print(conns.alpha) print(len(syn_spec))
- class nest.lib.hl_api_types.Compartments(node_collection, elements)¶
Bases:
CmBase
- nest.lib.hl_api_types.CreateParameter(parametertype, specs)¶
Create a parameter.
- Parameters:
parametertype (string) – Parameter type with or without distance dependency. Can be one of the following: ‘constant’, ‘linear’, ‘exponential’, ‘gaussian’, ‘gaussian2D’, ‘uniform’, ‘normal’, ‘lognormal’, ‘distance’, ‘position’
specs (dict) – Dictionary specifying the parameters of the provided parametertype, see Parameter types.
- Returns:
Object representing the parameter
- Return type:
Parameter
Notes
Instead of using CreateParameter you can also use the various parametrizations embedded in NEST. See for instance
uniform().Parameter types
Examples of available parameter types (parametertype parameter), with their function and acceptable keys for their corresponding specification dictionaries:
Constant
'constant' : {'value' : float} # constant value
Randomization
# random parameter with uniform distribution in [min,max) 'uniform' : {'min' : float, # minimum value, default: 0.0 'max' : float} # maximum value, default: 1.0 # random parameter with normal distribution 'normal': {'mean' : float, # mean value, default: 0.0 'std' : float} # standard deviation, default: 1.0 # random parameter with lognormal distribution 'lognormal' : {'mean' : float, # mean value of logarithm, default: 0.0 'std' : float} # standard deviation of log, default: 1.0
- class nest.lib.hl_api_types.Mask(datum)¶
Bases:
objectClass for spatial masks.
Masks are used when creating connections when nodes have spatial extent. A mask describes the area of the pool population that shall be searched to find nodes to connect to for any given node in the driver population. Masks are created using the
CreateMask()command.
- class nest.lib.hl_api_types.NodeCollection(data=None)¶
Bases:
objectClass for NodeCollection.
NodeCollection represents the nodes of a network. The class supports iteration, concatenation, indexing, slicing, membership, length, conversion to and from lists, test for membership, and test for equality. By using the membership functions
get()andset(), you can get and set desired parameters.A NodeCollection is created by the
Create()function, or by converting a list of nodes to a NodeCollection withnest.NodeCollection(list).If your nodes have spatial extent, use the member parameter
spatialto get the spatial information.Slicing a NodeCollection follows standard Python slicing syntax: nc[start:stop:step], where start and stop gives the zero-indexed right-open range of nodes, and step gives the step length between nodes. The step must be strictly positive.
Example
import nest nest.ResetKernel() # Create NodeCollection representing nodes nc = nest.Create('iaf_psc_alpha', 10) # Convert from list node_ids_in = [2, 4, 6, 8] new_nc = nest.NodeCollection(node_ids_in) # Convert to list nc_list = nc.tolist() # Concatenation Enrns = nest.Create('aeif_cond_alpha', 600) Inrns = nest.Create('iaf_psc_alpha', 400) nrns = Enrns + Inrns # Slicing and membership print(new_nc[2]) print(new_nc[1:2]) 6 in new_nc
- get(*params, **kwargs)¶
Get parameters from nodes.
- Parameters:
params (str or list, optional) –
Parameters to get from the nodes. It must be one of the following:
A single string.
A list of strings.
One or more strings, followed by a string or list of strings. This is for hierarchical addressing.
output (str, ['pandas','json'], optional) – If the returned data should be in a Pandas DataFrame or in a JSON serializable format.
- Returns:
int or float – If there is a single node in the NodeCollection, and a single parameter in params.
array_like – If there are multiple nodes in the NodeCollection, and a single parameter in params.
dict – If there are multiple parameters in params. Or, if no parameters are specified, a dictionary containing aggregated parameter-values for all nodes is returned.
DataFrame – Pandas Data frame if output should be in pandas format.
- Raises:
See also
Examples
>>> nodes.get() {'archiver_length': (0, 0, 0), 'beta_Ca': (0.001, 0.001, 0.001), 'C_m': (250.0, 250.0, 250.0), ... 'V_th': (-55.0, -55.0, -55.0), 'vp': (0, 0, 0)}
>>> nodes.get('V_m') (-70.0, -70.0, -70.0)
>>> nodes[0].get('V_m') -70.0
>>> nodes.get('V_m', 'C_m') {'V_m': (-70.0, -70.0, -70.0), 'C_m': (250.0, 250.0, 250.0)}
>>> voltmeter.get('events', 'senders') array([...], dtype=int64)
- index(node_id)¶
Find the index of a node ID in the NodeCollection.
- Parameters:
node_id (int) – Global ID to be found.
- Raises:
ValueError – If the node ID is not in the NodeCollection.
- set(params=None, **kwargs)¶
Set the parameters of nodes to params.
NB! This is almost the same implementation as SetStatus.
If kwargs is given, it has to be names and values of an attribute as keyword argument pairs. The values can be single values or list of the same size as the NodeCollection.
- Parameters:
- Raises:
See also
- tolist()¶
Convert NodeCollection to list.
- class nest.lib.hl_api_types.Parameter(datum)¶
Bases:
objectClass for parameters
A parameter may be used as a probability kernel when creating connections and nodes or as synaptic parameters (such as weight and delay). Parameters are created using the
CreateParameter()command.- GetValue()¶
Compute value of parameter.
- Returns:
out – The value of the parameter
- Return type:
value
See also
Example
import nest # normal distribution parameter P = nest.CreateParameter('normal', {'mean': 0.0, 'std': 1.0}) # get out value P.GetValue()
- apply(spatial_nc, positions=None)¶
- is_spatial()¶
- class nest.lib.hl_api_types.Receptors(node_collection, elements)¶
Bases:
CmBase
- class nest.lib.hl_api_types.SynapseCollection(data)¶
Bases:
objectClass for Connections.
SynapseCollection represents the connections of a network. The class supports indexing, iteration, length and equality. You can get and set connection parameters by using the membership functions
get()andset(). By using the membership functionsources()you get an iterator over source nodes, whiletargets()returns an interator over the target nodes of the connections.A SynapseCollection is created by the
GetConnections()function.- disconnect()¶
Disconnect the connections in the SynapseCollection.
- get(keys=None, output='')¶
Return a parameter dictionary of the connections.
If keys is a string, a list of values is returned, unless we have a single connection, in which case the single value is returned. keys may also be a list, in which case a dictionary with a list of values is returned.
- Parameters:
keys (str or list, optional) – String or a list of strings naming model properties. get then returns a single value or a dictionary with lists of values belonging to the given keys.
output (str, ['pandas','json'], optional) – If the returned data should be in a Pandas DataFrame or in a JSON serializable format.
- Returns:
dict – All parameters, or, if keys is a list of strings, a dictionary with lists of corresponding parameters
type – If keys is a string, the corresponding parameter(s) is returned
- Raises:
See also
Examples
>>> conns.get() {'delay': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], ... 'weight': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}
>>> conns.get('weight') [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
>>> conns[0].get('weight') 1.0
>>> nodes.get(['source', 'weight']) {'source': [1, 1, 1, 2, 2, 2, 3, 3, 3], 'weight': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}
- set(params=None, **kwargs)¶
Set the parameters of the connections to params.
NB! This is almost the same implementation as SetStatus
If kwargs is given, it has to be names and values of an attribute as keyword argument pairs. The values can be single values or list of the same size as the SynapseCollection.
- Parameters:
- Raises:
See also
- sources()¶
Returns iterator containing the source node IDs of the SynapseCollection.
- targets()¶
Returns iterator containing the target node IDs of the SynapseCollection.
- nest.lib.hl_api_types.serializable(data)¶
Make data serializable for JSON.
- nest.lib.hl_api_types.to_json(data, **kwargs)¶
Serialize data to JSON.
- Parameters:
data (any) –
kwargs (keyword argument pairs) – Named arguments of parameters for json.dumps function.
- Returns:
data_json – JSON format of the data
- Return type:
Functions to get information on NEST.
- nest.lib.hl_api_info.GetStatus(nodes, keys=None, output='')¶
Return the parameter dictionaries of nodes or connections.
If keys is given, a list of values is returned instead. keys may also be a list, in which case the returned list contains lists of values.
- Parameters:
nodes (NodeCollection or SynapseCollection) – Either a NodeCollection representing nodes, or a SynapseCollection of connection handles as returned by
GetConnections().keys (str or list, optional) – string or a list of strings naming model properties. GetStatus then returns a single value or a list of values belonging to the keys given.
output (str, optional) – Whether the returned data should be in a selected format (
output='json').
- Returns:
list of dicts – All parameters in a dict for each node or connection.
list of values – If keys is a string, the value of the corresponding parameter for each node or connection is returned.
list of lists of values – If keys is a list of strings, a list of values of the corresponding parameters for each node or connection is returned.
str – If output is json, the above formats are converted to JSON format before they are returned.
- Raises:
TypeError – If nodes or keys are on the wrong form.
See also
Examples
For nodes:
>>> nest.GetStatus(nodes) ({'archiver_length': 0, 'beta_Ca': 0.001, ... 'global_id': 1, ... 'vp': 0}, ... {'archiver_length': 0, 'beta_Ca': 0.001, ... 'global_id': 3, ... 'vp': 0})
>>> nest.GetStatus(nodes, 'V_m') (-70.0, -70.0, -70.0)
>>> nest.GetStatus(nodes, ['V_m', 'C_m']) ((-70.0, 250.0), (-70.0, 250.0), (-70.0, 250.0))
>>> nest.GetStatus(nodes, ['V_m', 'C_m'], output='json') '[[-70.0, 250.0], [-70.0, 250.0], [-70.0, 250.0]]'
For connections:
>>> nest.GetStatus(conns) ({'delay': 1.0, ... 'source': 1, ... 'weight': 1.0}, ... {'delay': 1.0, ... 'source': 3, ... 'weight': 1.0})
>>> nest.GetStatus(conns, 'weight') (1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0)
>>> nest.GetStatus(conns, ['source', 'delay']) ((1, 1.0), ... (3, 1.0))
>>> nest.GetStatus(conns, ['source', 'delay'], output='json') '[[1, 1.0], [1, 1.0], [1, 1.0], [2, 1.0], [2, 1.0], [2, 1.0], [3, 1.0], [3, 1.0], [3, 1.0]]'
- nest.lib.hl_api_info.SetStatus(nodes, params, val=None)¶
Set parameters of nodes or connections.
Parameters of nodes or connections, given in nodes, is set as specified by params. If val is given, params has to be a string with the name of an attribute, which is set to val on the nodes/connections. val can be a single value or a list of the same size as nodes.
- Parameters:
nodes (NodeCollection or SynapseCollection) – Either a NodeCollection representing nodes, or a SynapseCollection of connection handles as returned by
GetConnections().params (str or dict or list) – Dictionary of parameters (either lists or single values) or list of dictionaries of parameters of same length as nodes. If val is given, this has to be a string giving the name of a model property.
val (int, list, optional) – If given, params has to be the name of a model property.
- Raises:
TypeError – If nodes is not a NodeCollection of nodes, a SynapseCollection of synapses, or if the number of parameters don’t match the number of nodes or synapses.
See also