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Merge pull request #16 from babsey/docathon
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Fix doc of connections
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@steffengraber
steffengraber committed Jun 28, 2018
2 parents 5d58d0f + 903dbfa commit e90f7f0
Showing 1 changed file with 90 additions and 84 deletions.
174 changes: 90 additions & 84 deletions pynest/nest/lib/hl_api_connections.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,37 +36,38 @@ def GetConnections(source=None, target=None, synapse_model=None,
synapse_label=None):
"""Return an array of connection identifiers.
Any combination of source, target, synapse_model and
synapse_label parameters is permitted.
Any combination of `source`, `target`, `synapse_model` and
`synapse_label` parameters is permitted.
Parameters
----------
source : list, optional
Source GIDs, only connections from these
pre-synaptic neurons are returned
presynaptic neurons are returned.
target : list, optional
Target GIDs, only connections to these
post-synaptic neurons are returned
postsynaptic neurons are returned.
synapse_model : str, optional
Only connections with this synapse type are returned
Only connections with this synapse type are returned.
synapse_label : int, optional
(non-negative) only connections with this synapse label are returned
(non-negative) only connections with this synapse label are returned.
Returns
-------
array:
array
Connections as 5-tuples with entries
(source-gid, target-gid, target-thread, synapse-id, port)
Raises
------
TypeError
Notes
-----
Only connections with targets on the MPI process executing
the command are returned.
Raises
------
TypeError
KEYWORDS:
"""

params = {}
Expand Down Expand Up @@ -95,12 +96,11 @@ def GetConnections(source=None, target=None, synapse_model=None,

@check_stack
def Connect(pre, post, conn_spec=None, syn_spec=None, model=None):
"""
Connect pre nodes to post nodes.
"""Create connections between presynaptic and postsynaptic nodes.
Nodes in pre and post are connected using the specified connectivity
(all-to-all by default) and synapse type (static_synapse by default).
Details depend on the connectivity rule.
Nodes in `pre` and `post` are connected using the specified connectivity
(``all-to-all`` by default) and synapse type (``static_synapse`` by
default). Details depend on the connectivity rule.
Parameters
----------
Expand All @@ -113,7 +113,7 @@ def Connect(pre, post, conn_spec=None, syn_spec=None, model=None):
syn_spec : str or dict, optional
Specifies synapse model, see below
model : str or dict, optional
alias for syn_spec for backward compatibility
Alias for `syn_spec` for backward compatibility
Raises
------
Expand All @@ -124,100 +124,104 @@ def Connect(pre, post, conn_spec=None, syn_spec=None, model=None):
Connect does not iterate over subnets, it only connects explicitly
specified nodes.
Connectivity specification (conn_spec)
--------------------------------------
**Connectivity specification (`conn_spec`)**
Connectivity is specified either as a string containing the name of a
connectivity rule (default: 'all_to_all') or as a dictionary specifying
the rule and any mandatory rule-specific parameters (e.g. 'indegree').
connectivity rule (default: ``'all_to_all'``) or as a dictionary specifying
the rule and any mandatory rule-specific parameters (e.g. ``'indegree'``).
In addition, switches setting permission for establishing
self-connections ('autapses', default: True) and multiple connections
between a pair of nodes ('multapses', default: True) can be contained
in the dictionary. Another switch enables the creation of symmetric
connections ('symmetric', default: False) by also creating connections
in the opposite direction.
Available rules and associated parameters
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- 'all_to_all' (default)
- 'one_to_one'
- 'fixed_indegree', 'indegree'
- 'fixed_outdegree', 'outdegree'
- 'fixed_total_number', 'N'
- 'pairwise_bernoulli', 'p'
self-connections (``'autapses'``, default: ``True``) and multiple
connections between a pair of nodes (``'multapses'``, default: ``True``)
can be contained in the dictionary. Another switch enables the creation of
symmetric connections (``'symmetric'``, default: ``False``) by also
creating connections in the opposite direction.
**Available rules and associated parameters**
- ``'all_to_all'`` (default)
- ``'one_to_one'``
- ``'fixed_indegree'``, ``indegree``
- ``'fixed_outdegree'``, ``outdegree``
- ``'fixed_total_number'``, ``N``
- ``'pairwise_bernoulli'``, ``p``
**Example conn-spec choices**
Example conn-spec choices
~~~~~~~~~~~~~~~~~~~~~~~~~
- 'one_to_one'
- {'rule': 'fixed_indegree', 'indegree': 2500, 'autapses': False}
- {'rule': 'pairwise_bernoulli', 'p': 0.1}
Synapse specification (syn_spec)
--------------------------------------
**Synapse specification (`syn_spec`)**
The synapse model and its properties can be given either as a string
identifying a specific synapse model (default: 'static_synapse') or
identifying a specific synapse model (default: ``'static_synapse'``) or
as a dictionary specifying the synapse model and its parameters.
Available keys in the synapse specification dictionary are:
- 'model'
- 'weight'
- 'delay'
- 'receptor_type'
- ``'model'``
- ``'weight'``
- ``'delay'``
- ``'receptor_type'``
- any parameters specific to the selected synapse model.
All parameters are optional and if not specified, the default values
of the synapse model will be used. The key 'model' identifies the
of the synapse model will be used. The key ``'model'`` identifies the
synapse model, this can be one of NEST's built-in synapse models
or a user-defined model created via CopyModel().
or a user-defined model created via :code:`CopyModel()`.
If 'model' is not specified the default model 'static_synapse'
If ``'model'`` is not specified the default model ``'static_synapse'``
will be used.
All other parameters can be scalars, arrays or distributions.
In the case of scalar parameters, all keys must be doubles
except for 'receptor_type' which must be initialised with an integer.
Parameter arrays are available for the rules 'one_to_one',
'all_to_all', 'fixed_indegree' and 'fixed_outdegree':
- For 'one_to_one' the array has to be a one-dimensional
NumPy array with length len(pre).
- For 'all_to_all' the array has to be a two-dimensional NumPy array
with shape (len(post), len(pre)), therefore the rows describe the
target and the columns the source neurons.
- For 'fixed_indegree' the array has to be a two-dimensional NumPy array
with shape (len(post), indegree), where indegree is the number of
incoming connections per target neuron, therefore the rows describe the
target and the columns the connections converging to the target neuron,
regardless of the identity of the source neurons.
- For 'fixed_outdegree' the array has to be a two-dimensional NumPy array
with shape (len(pre), outdegree), where outdegree is the number of
outgoing connections per source neuron, therefore the rows describe the
source and the columns the connections starting from the source neuron
regardless of the identity of the target neuron.
except for ``'receptor_type'`` which must be initialised with an integer.
Parameter arrays are available for the rules ``'one_to_one'``,
``'all_to_all'``, ``'fixed_indegree'`` and ``'fixed_outdegree'``:
- For ``'one_to_one'`` the array has to be a one-dimensional
NumPy array with length :code:`len(pre)`.
- For ``'all_to_all'`` the array has to be a two-dimensional NumPy array
with shape (:code:`len(post)`, :code:`len(pre)`), therefore the rows
describe the target and the columns the source neurons.
- For ``'fixed_indegree'`` the array has to be a two-dimensional NumPy
array with shape (:code:`len(post)`, ``indegree``), where indegree is
the number of incoming connections per target neuron, therefore the rows
describe the target and the columns the connections converging to the
target neuron, regardless of the identity of the source neurons.
- For ``'fixed_outdegree'`` the array has to be a two-dimensional NumPy
array with shape (:code:`len(pre)`, ``outdegree``), where ``outdegree``
is the number of outgoing connections per source neuron, therefore the
rows describe the source and the columns the connections starting from
the source neuron regardless of the identity of the target neuron.
Any distributed parameter must be initialised with a further dictionary
specifying the distribution type ('distribution', e.g. 'normal') and
any distribution-specific parameters (e.g. 'mu' and 'sigma').
specifying the distribution type (``'distribution'``, e.g. ``'normal'``)
and any distribution-specific parameters (e.g. ``'mu'`` and ``'sigma'``).
To see all available distributions, run:
nest.slirun('rdevdict info')
To get information on a particular distribution, e.g. 'binomial', run:
nest.help('rdevdict::binomial')
Most common available distributions and associated parameters
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- 'normal' with 'mu', 'sigma'
- 'normal_clipped' with 'mu', 'sigma', 'low', 'high'
- 'lognormal' with 'mu', 'sigma'
- 'lognormal_clipped' with 'mu', 'sigma', 'low', 'high'
- 'uniform' with 'low', 'high'
- 'uniform_int' with 'low', 'high'
Example syn-spec choices
~~~~~~~~~~~~~~~~~~~~~~~~~
.. code_block:: python
nest.slirun('rdevdict info')
To get information on a particular distribution, e.g. ``'binomial'``, run:
.. code_block:: python
nest.help('rdevdict::binomial')
**Most common available distributions and associated parameters**
- ``'normal'`` with ``'mu'``, ``'sigma'``
- ``'normal_clipped'`` with ``'mu'``, ``'sigma'``, ``'low'``, ``'high'``
- ``'lognormal'`` with 'mu', 'sigma'
- ``'lognormal_clipped'`` with ``'mu'``, ``'sigma'``, ``'low'``, ``'high'``
- ``'uniform'`` with ``'low'``, ``'high'``
- ``'uniform_int'`` with ``'low'``, ``'high'``
**Example `syn-spec` choices**
- 'stdp_synapse'
- {'weight': 2.4, 'receptor_type': 1}
- {'model': 'stdp_synapse',
Expand All @@ -227,6 +231,8 @@ def Connect(pre, post, conn_spec=None, syn_spec=None, model=None):
'distribution': 'normal_clipped', 'low': 0.5,
'mu': 5.0, 'sigma': 1.0}
}
KEYWORDS:
"""

if model is not None:
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