Merge pull request #7 from jessica-mitchell/conn_mgn

Adjust the images for top of page and update links

doc/htmldoc/installation/cmake_options.rst

@@ -262,7 +262,7 @@ follwing switch for the invocation of ``cmake``. It expects either
     -Dwith-libneurosim=[OFF|ON|</path/to/libneurosim>]
 
 For details on how to use the Connection Generator Interface, see the
-:ref:`guide on connection management <conn_builder_conngen>`.
+:ref:`guide on connection generation <connection_generation>`.
 
 .. _compile_with_python:
 

doc/htmldoc/nest_behavior/running_simulations.rst

@@ -129,7 +129,7 @@ NEST also has some models that determine the precise time of the
 threshold crossing during the interval. Please see the documentation on
 :ref:`precise spike time neurons <sim_precise_spike_times>`
 for details about neuron update in continuous time and the
-:ref:`documentation on connection management <connection_management>`
+:ref:`documentation on the connectivty concept  <connectivity_concept>`
 for how to set the delay when creating synapses.
 
 .. _stepped_simulations:

doc/htmldoc/networks/spatially_structured_networks.rst

@@ -1317,7 +1317,7 @@ The following table presents some query functions provided by NEST.
 +---------------------------------+-----------------------------------------------------+
 | ``nest.GetConnections()``       | Retrieve connections (all or for a given            |
 |                                 | source or target); see also                         |
-|                                 | http://www.nest-simulator.org/connection_management.|
+|                                 | :ref:`connectivity_concept`.                        |
 +---------------------------------+-----------------------------------------------------+
 | ``nest.GetNodes()``             | Returns a NodeCollection of all elements with given |
 |                                 | properties.                                         |

doc/htmldoc/synapses/connectivity_concept.rst

@@ -3,56 +3,48 @@
 Connectivity concept
 =====================
 
-.. grid:: 8
+.. grid:: 2 2 7 7
     :gutter: 0
 
-    .. grid-item-card:: Autapse
-		:link: autapse_multapse
-		:link-type: ref
+    .. grid-item-card:: Autapse and multapse
+		  :link: autapse_multapse
+		  :link-type: ref
+		  :img-top: ../static/img/Autapse_multapse_v.png
 
-		.. image:: ../static/img/Autapse.png
-
-    .. grid-item-card:: Multapse
-		:link: autapse_multapse
-		:link-type: ref
-
-		.. image:: ../static/img/Multapse.png
 
     .. grid-item-card:: One to one
-		:link: one_to_one
-		:link-type: ref
+		  :link: one_to_one
+		  :link-type: ref
+		  :img-top: ../static/img/One_to_one.png
 
-		.. image:: ../static/img/One_to_one.png
 
     .. grid-item-card:: All to all
-		:link: all_to_all
-		:link-type: ref
-
-		.. image:: ../static/img/All_to_all.png
+		  :link: all_to_all
+		  :link-type: ref
+		  :img-top: ../static/img/All_to_all.png
 
     .. grid-item-card:: Pairwise Bernoulli
-		:link: pairwise_bernoulli
-		:link-type: ref
+		  :link: pairwise_bernoulli
+		  :link-type: ref
+		  :img-top: ../static/img/Pairwise_bernoulli.png
 
-		.. image:: ../static/img/Pairwise_bernoulli.png
 
     .. grid-item-card:: Fixed total number
-		:link: fixed_total_number
-		:link-type: ref
+		  :link: fixed_total_number
+		  :link-type: ref
+		  :img-top: ../static/img/Fixed_total_number.png
 
-		.. image:: ../static/img/Fixed_total_number.png
 
     .. grid-item-card:: Fixed in-degree
-		:link: fixed_indegree
-		:link-type: ref
+		  :link: fixed_indegree
+		  :link-type: ref
+		  :img-top: ../static/img/Fixed_indegree.png
 
-		.. image:: ../static/img/Fixed_indegree.png
 
     .. grid-item-card:: Fixed out-degree
-		:link: fixed_outdegree
-		:link-type: ref
-
-		.. image:: ../static/img/Fixed_outdegree.png
+		  :link: fixed_outdegree
+		  :link-type: ref
+		  :img-top: ../static/img/Fixed_outdegree.png
 
 .. rst-class:: center
 
@@ -109,7 +101,7 @@ Have a look at the section :ref:`handling_connections` to get more tips on how t
 Connection rules
 ----------------
 
-Here we elaborate on the connectivity concepts with details on :ref:`autapse_multapse`, :ref:`deterministic_rules`, :ref:`probabilistic_rules`, and the :ref:`conn_builder_conngen` (a method to create connections via CSA, the Connection Set Algebra [2]_).
+Here we elaborate on the connectivity concepts with details on :ref:`autapse_multapse`, :ref:`deterministic_rules`, :ref:`probabilistic_rules`, and the :ref:`connection_generator` (a method to create connections via CSA, the Connection Set Algebra [2]_).
 Finally, we introduce the rule :ref:`tripartite_connectivity` for third-party connections in addition to primary connections between ``pre`` and ``post``.
 Each primary rule is described with an illustration, a NEST code example, and mathematical details.
 The mathematical details are extracted from the study on connectivity concepts [1]_ and contain a symbol which we recommend to use for describing this type of connectivity, the corresponding expression from CSA, and a formal definition with an algorithmic construction rule and the resulting connectivity distribution.

doc/htmldoc/synapses/synapse_specification.rst

@@ -1,7 +1,7 @@
 .. _synapse_spec:
 
 Synapse specification
-========================
+=====================
 
 This page is about parameterizing synapses. See :ref:`connectivity_concept` for specifying connections, :ref:`connection_generator` for generation of connectivity and :ref:`handling_connections` for inspecting and modifying ``SynapseCollections``.
 
@@ -41,7 +41,7 @@ as long as they are supported by the chosen connection rule.
 .. _scalar_params:
 
 Scalar parameters
-~~~~~~~~~~~~~~~~~
+-----------------
 
 Scalar parameters must be given with the correct type. The ``weight``
 for instance must be a float, while the ``receptor_type`` has to be of
@@ -61,7 +61,7 @@ will be applied to all connections created in the current
 .. _array_params:
 
 Array parameters
-~~~~~~~~~~~~~~~~
+----------------
 
 Array parameters can be used with the rules ``one_to_one``, ``all_to_all``,
 ``fixed_total_number``, ``fixed_indegree``, and ``fixed_outdegree``.
@@ -71,7 +71,7 @@ lists. As with the scalar parameters, all parameters have to be
 specified as arrays of the correct type.
 
 One-to-one
-^^^^^^^^^^
+~~~~~~~~~~
 
 For rule :ref:`one_to_one` the array must have the same length as there
 are nodes in ``A`` and ``B``.
@@ -85,7 +85,7 @@ are nodes in ``A`` and ``B``.
     nest.Connect(A, B, conn_spec_dict, syn_spec_dict)
 
 All-to-all
-^^^^^^^^^^
+~~~~~~~~~~
 
 When connecting with rule :ref:`all_to_all`, the array parameter must
 have dimension `len(B) x len(A)`.
@@ -98,7 +98,7 @@ have dimension `len(B) x len(A)`.
     nest.Connect(A, B, syn_spec=syn_spec_dict)
 
 Random, fixed total number
-^^^^^^^^^^^^^^^^^^^^^^^^^^
+~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 For rule :ref:`fixed_total_number`, the array has to be same the length as the
 number of connections ``N``.
@@ -112,7 +112,7 @@ number of connections ``N``.
     nest.Connect(A, B, conn_spec_dict, syn_spec_dict)
 
 Random, fixed in-degree
-^^^^^^^^^^^^^^^^^^^^^^^
+~~~~~~~~~~~~~~~~~~~~~~~
 
 For rule :ref:`fixed_indegree` the array has to be a two-dimensional
 NumPy array or Python list with shape ``(len(B), indegree)``, where
@@ -130,7 +130,7 @@ of the identity of the source neurons.
     nest.Connect(A, B, conn_spec_dict, syn_spec_dict)
 
 Random, fixed out-degree
-^^^^^^^^^^^^^^^^^^^^^^^^
+~~~~~~~~~~~~~~~~~~~~~~~~
 
 For rule :ref:`fixed_outdegree` the array has to be a two-dimensional
 NumPy array or Python list with shape ``(len(A), outdegree)``, where
@@ -150,7 +150,7 @@ regardless of the identity of the target neuron.
 .. _dist_params:
 
 Expressions as parameters
-~~~~~~~~~~~~~~~~~~~~~~~~~
+-------------------------
 
 ``nest.Parameter`` objects support a flexible specification of synapse
 parameters through expressions.  This includes parameters drawn from random
@@ -215,7 +215,7 @@ For further information on the available distributions see
 .. _collocated_synapses:
 
 Collocated synapses
-~~~~~~~~~~~~~~~~~~~
+-------------------
 
 Some modeling applications require multiple connections between the
 same pairs of nodes. An example of this could be a network, where each
@@ -716,4 +716,3 @@ using the dot-notation:
 Note that some parameters like ``source`` and ``target`` are read-only and
 cannot be set. The documentation of a specific synapse model will
 point out which parameters can be set and which are read-only.
-

doc/htmldoc/tutorials/pynest_tutorial/part_1_neurons_and_simple_neural_networks.rst

@@ -510,7 +510,7 @@ Connections
 
 This is an abbreviated version of the documentation for the :py:func:`.Connect`
 function, please see NEST's online help for the full version and
-:ref:`Connection Management <connection_management>` for an introduction
+:ref:`Connectivity concept <connectivity_concept>` for an introduction
 and examples.
 
 -  ``Connect(pre, post, conn_spec=None, syn_spec=None, return_synapsecollection=False)``

doc/htmldoc/tutorials/pynest_tutorial/part_2_populations_of_neurons.rst

@@ -202,7 +202,7 @@ Here we have just used very simple connection schemes. Connectivity
 patterns requiring the specification of further parameters, such as
 in-degree or connection probabilities, must be defined in a dictionary
 containing the key ``rule`` and the key for parameters associated to the
-rule. Please see :ref:`Connection management <connection_management>`
+rule. Please see :ref:`Connectivity concept <connectivity_concept>`
 for an illustrated guide to the usage of :py:func:`.Connect`, as well as the example below.
 
 Connecting populations with random connections
@@ -268,7 +268,7 @@ the same population simultaneously in the role of ``pre`` and ``post``.
 
 For more information on connecting neurons, please read the
 documentation of the :py:func:`.Connect` function and consult the guide at
-:ref:`Connection management <connection_management>`.
+:ref:`connectivity concept <connectivity_concept>`.
 
 Specifying the behaviour of devices
 -----------------------------------

doc/htmldoc/tutorials/pynest_tutorial/part_3_connecting_networks_with_synapses.rst

@@ -119,7 +119,7 @@ parameters associated with the distribution can be set (for example
     nest.Connect(epop1, neuron, "all_to_all", syn_dict)
 
 Available distributions and associated parameters are described in
-:ref:`Connection Management <connection_management>`, the most common
+:ref:`parametrization <param_ex>`, the most common
 ones are:
 
 +-------------------+------------------------+

doc/htmldoc/whats_new/v3.7/index.rst

@@ -43,7 +43,8 @@ See connectivity documentation:
 
 Connectivity concepts
 ---------------------
-The documentation on :ref:`connection_management` now serves as a living reference for the connection rules defined in the article "Connectivity concepts in neuronal network modeling" [1]_.
+
+The documentation on :ref:`connectivity_concept` now serves as a living reference for the connection rules defined in the article "Connectivity concepts in neuronal network modeling" [1]_.
 
 References
 ----------

models/multimeter.h

@@ -96,8 +96,8 @@ it should record from by using the standard ``Connect`` routine.
     nest.Connect(mm, neurons)
 
 To learn more about possible connection patterns and additional
-options when using ``Connect``, see the guide on :ref:`connection
-management <connection_management>`.
+options when using ``Connect``, see the guide on :ref:`connectivity
+concept <connectivity_concept>`.
 
 The above call to ``Connect`` would fail if the neurons would not
 support the sampling of the values ``V_m`` and ``g_ex``. It would also