Fix various typos in the documentation

doc/htmldoc/devices/record_from_simulations.rst

@@ -101,7 +101,7 @@ kernel attribute ``recording_backends``.
 
 If a recording backend has global properties (i.e., parameters shared
 by all enrolled recording devices), those can be inspected with
-:py:func`.GetDefaults`
+:py:func:`.GetDefaults`
 
 ::
 

doc/htmldoc/nest_behavior/running_simulations.rst

@@ -82,7 +82,7 @@ In linear simulation scripts that build a network, simulate it, carry
 out some post-processing and exit, the user does not have to worry about
 the delay extrema *dmin* and *dmax* as they are set automatically to the
 correct values. However, NEST also allows subsequent calls
-to\ :py:func:`.Simulate`, which only work correctly if the content of the spike
+to :py:func:`.Simulate`, which only work correctly if the content of the spike
 buffers is preserved over the simulations.
 
 As mentioned above, the size of that buffer depends on *dmin+dmax* and
@@ -125,7 +125,7 @@ also means that the membrane potential recording will never show values
 above the threshold. The time of the spike is always the time at *the
 end of the interval* during which the threshold was crossed.
 
-NEST also has a some models that determine the precise time of the
+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

doc/htmldoc/neurons/parametrization.rst

@@ -167,7 +167,7 @@ others can be used when connecting.
    spatial_nodes = nest.Create('iaf_psc_alpha', positions=positions)
 
    parameter = -60 + nest.spatial.pos.x + (0.4 * nest.spatial.pos.x * nest.random.normal())
-   spatial_nodes.set('V_m'=parameter)
+   spatial_nodes.set(V_m=parameter)
 
    node_pos = np.array(nest.GetPosition(spatial_nodes))
    node_pos[:,1]
@@ -252,7 +252,7 @@ parameter:
     fig, ax = pyplot.subplots(figsize=(12, 6))
     bars = ax.hist(targets, bins=N, edgecolor='black', linewidth=1.2)
 
-    pyplot.xticks(bars[1] + 0.5,np.arange(1, N+1))
+    pyplot.xticks(bars[1] + 0.5,np.arange(1, N+2))
     ax.set_title('Connections from node with NodeID {}'.format(spatial_nodes[middle_node].get('global_id')))
     ax.set_xlabel('Target NodeID')
     ax.set_ylabel('Num. connections');
@@ -468,9 +468,9 @@ Using parameters makes it easy to set node properties
 |                                               |                                                    |
 | ::                                            | ::                                                 |
 |                                               |                                                    |
-|     for gid in nrns:                          |     nrns.V_m=nest.random.uniform(-20., 20)         |
-|       v_m = numpy.random.uniform(-20., 20.)   |                                                    |
-|       nest.SetStatus([node_id], {'V_m': V_m}) |                                                    |
+|     for gid in nrns:                          |     nrns.V_m = nest.random.uniform(-20.0, 20.0)    |
+|       v_m = numpy.random.uniform(-20.0, 20.0) |                                                    |
+|       nest.SetStatus(gid, {"V_m": v_m})       |                                                    |
 |                                               |                                                    |
 |                                               |                                                    |
 +-----------------------------------------------+----------------------------------------------------+

doc/htmldoc/synapses/connection_management.rst

@@ -150,7 +150,7 @@ Generator Interface and randomly connects 10% of the neurons from
    import csa
    cg = csa.cset(csa.random(0.1), 10000.0, 1.0)
 
-   # Map weight and delay indices to vaules from cg
+   # Map weight and delay indices to values from cg
    params_map = {'weight': 0, 'delay': 1}
 
    conn_spec_dict = {'rule': 'conngen', 'cg': cg, 'params_map': params_map}
@@ -359,7 +359,7 @@ fixed outdegree
 ^^^^^^^^^^^^^^^
 
 For rule ``fixed_outdegree`` the array has to be a two-dimensional
-NumPy array or Python list with shape ``(len(pre), outdegree)``, where
+NumPy array or Python list with shape ``(len(A), outdegree)``, where
 :hxt_ref:`outdegree` is the number of outgoing connections per source
 neuron. This means that the rows describe the source, while the
 columns represent the connections starting from the source neuron
@@ -529,7 +529,7 @@ If you want to connect with different :ref:`receptor types
 You can see how many synapse parameters you have by calling ``len()`` on
 your ``CollocatedSynapses`` object:
 
-  >>> len(syn_spec)
+  >>> len(syn_spec_dict)
   2
 
 Spatially-structured networks
@@ -575,7 +575,7 @@ columns and :math:`m` rows.
 
 ::
 
-    W = np.array([[0.5,  0.0, 1.5],
+    W = numpy.array([[0.5,  0.0, 1.5],
                   [1.3,  0.2, 0.0],
                   [0.0, 1.25, 1.3]])
 
@@ -588,20 +588,20 @@ columns and :math:`m` rows.
 
     # To only connect pairs with a nonzero weight, we use array indexing
     # to extract the weights and post-synaptic neurons.
-          nonzero_indices = numpy.where(weights != 0)[0]
-          weights = weights[nonzero_indices]
-          post = B[nonzero_indices]
+        nonzero_indices = numpy.where(weights != 0)[0]
+        weights = weights[nonzero_indices]
+        post = B[nonzero_indices]
 
     # Generate an array of node IDs for the column of the weight
     # matrix, with length based on the number of nonzero
     # elements. The array's dtype must be an integer.
-          pre_array = numpy.ones(len(nonzero_indices), dtype=numpy.int64) * pre.get('global_id')
+        pre_array = numpy.ones(len(nonzero_indices), dtype=numpy.int64) * pre.get('global_id')
 
     # nest.Connect() automatically converts post to a NumPy array
     # because pre_array contains multiple identical node IDs. When
     # also specifying a one_to_one connection rule, the arrays of
     # node IDs can then be connected.
-          nest.Connect(pre_array, post, conn_spec='one_to_one', syn_spec={'weight': weights})
+        nest.Connect(pre_array, post, conn_spec='one_to_one', syn_spec={'weight': weights})
 
 .. _receptor-types:
 
@@ -754,7 +754,7 @@ given model.
 
 To further customize the process of creating synapses, it is often
 useful to have the same basic synapse model available with different
-parametizations. To this end, :py:func:`.CopyModel` can be used to
+parametrizations. To this end, :py:func:`.CopyModel` can be used to
 create custom synapse types from already existing synapse types. In
 the simplest case, it takes the names of the existing model and the
 copied type to be created. The optional argument ``params`` allows to

doc/htmldoc/synapses/handling_connections.rst

@@ -40,7 +40,7 @@ as NodeCollections.
 Printing
     Printing a SynapseCollection produces a table with source and target node IDs, synapse model, weight and delay.
     If your SynapseCollection has more than 36 elements, only the first and last 15 connections are displayed.
-    To print all, first set ``print_all = True`` on your SynapseCollection.
+    To print all, first set ``print_full = True`` on your SynapseCollection.
 
     >>>  nest.Connect(nodes[:2], nodes[:2])
     >>>  synapses = nest.GetConnections()
@@ -52,7 +52,7 @@ Printing
                2        1  static_synapse    1.000   1.000
                2        2  static_synapse    1.000   1.000
 
-    >>> synapses.print_all = True
+    >>> synapses.print_full = True
 
 .. _conn_indexing:
 

doc/htmldoc/templates/index.html

@@ -78,6 +78,7 @@ <h2 id="nest-sample"> Here is a sample NEST script. Click each section and disco
 <div class="accordion"> <img id="pulse" src="_static/img/pulse.svg" style="position:relative" />
     <pre><code class="language-python">
             import nest
+	    import matplotlib.pyplot as plt
     </code></pre>
     <div>
        <ul>

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 Managementi <connection_management>`, the most common
+:ref:`Connection Management <connection_management>`, the most common
 ones are:
 
 +-------------------+------------------------+

models/aeif_psc_alpha.h

@@ -111,7 +111,7 @@ The following parameters can be set in the status dictionary.
  I_in    pA      Inhibitory synaptic current
  dI_in   pA/ms   First derivative of I_in
  w       pA      Spike-adaptation current
- g       pa      Spike-adaptation current
+ g       pA      Spike-adaptation current
 ======== ======= =======================================
 
 ======== ======= =======================================
@@ -128,7 +128,7 @@ The following parameters can be set in the status dictionary.
 ======== ======= ==================================
 **Spike adaptation parameters**
 ---------------------------------------------------
- a       ns      Subthreshold adaptation
+ a       nS      Subthreshold adaptation
  b       pA      Spike-triggered adaptation
  Delta_T mV      Slope factor
  tau_w   ms      Adaptation time constant

models/glif_cond.h

@@ -103,8 +103,12 @@ parameter setting of voltage_reset_fraction and voltage_reset_add may lead to th
 situation that voltage is bigger than threshold after reset. In this case, the neuron
 will continue to spike until the end of the simulation regardless the stimulated inputs.
 We recommend the setting of the parameters of these three models to follow the
-condition of :math:`(E_L + voltage_reset_fraction * ( V_th - E_L ) + voltage_reset_add)
-< (V_th + th_spike_add)`.
+condition of 
+
+.. math::
+
+    E_L + \mathrm{voltage_reset_fraction} \cdot \left( V_\mathrm{th} - E_L \right)
+    + \mathrm{voltage_reset_add} < V_\mathrm{th} + \mathrm{th_spike_add}
 
 Parameters
 ++++++++++

models/noise_generator.h

@@ -89,7 +89,7 @@ changes must be a multiple of the time step.
 
    You can use a :doc:`multimeter <multimeter>` to record the average current sent to all targets for each time step
    if simulating on a single thread; multiple MPI processes with one thread each also work. In this case,
-   the recording interval of the multimeter should be the equal to the simulation resolution to avoid confusing effects
+   the recording interval of the multimeter should be equal to the simulation resolution to avoid confusing effects
    due to offset or drift between the recording times of the multimeter and the switching times of the
    noise generator. In multi-threaded mode, recording of noise currents is prohibited for technical reasons.