Reduce list indents to give better results with the new Sphinx theme

Author: apdavison

doc/connections.txt

@@ -349,10 +349,10 @@ User-defined connection algorithms
 If you wish to use a specific connection/wiring algorithm not covered by the
 PyNN built-in ones, the options include:
 
-    * constructing a list or array of connections and using the
+* constructing a list or array of connections and using the
       :class:`FromListConnector` or :class:`ArrayConnector` class;
-    * using the Connection Set Algebra and the :class:`CSAConnector` class;
-    * writing your own :class:`Connector` class - see the
+* using the Connection Set Algebra and the :class:`CSAConnector` class;
+* writing your own :class:`Connector` class - see the
       :doc:`developers_guide` for guidance on this.
 
 
@@ -362,25 +362,25 @@ Projections
 A :class:`Projection` is a container for a set of connections between two
 populations of neurons, where by population we mean one of:
 
-    * a :class:`Population` object - a group of neurons all of the same type;
-    * a :class:`PopulationView` object - part of a :class:`Population`;
-    * a :class:`Assembly` - a heterogeneous group of neurons, which may be of different types.
+* a :class:`Population` object - a group of neurons all of the same type;
+* a :class:`PopulationView` object - part of a :class:`Population`;
+* a :class:`Assembly` - a heterogeneous group of neurons, which may be of different types.
 
 Creating a :class:`Projection` in PyNN also creates the connections at the
 level of the simulator. To create a :class:`Projection` we must specify:
 
-    * the pre-synaptic population;
-    * the post-synaptic population;
-    * a connection/wiring method;
-    * a synapse type
+* the pre-synaptic population;
+* the post-synaptic population;
+* a connection/wiring method;
+* a synapse type
 
 Optionally, we can also specify:
 
-    * the name of the post-synaptic mechanism (e.g. ‘excitatory’, ‘NMDA’)
+* the name of the post-synaptic mechanism (e.g. ‘excitatory’, ‘NMDA’)
       (if not specified, PyNN picks a default depending on the weight
       parameter of the synapse type);
-    * a label (autogenerated if not specified);
-    * a :class:`Space` object, which determines how distances should be
+* a label (autogenerated if not specified);
+* a :class:`Space` object, which determines how distances should be
       calculated for distance-dependent wiring schemes or parameter values.
 
 Here is a minimal example:
@@ -536,13 +536,13 @@ attributes after creation, using the :meth:`set` method.
 :meth:`set` accepts any number of keyword arguments, where the key is the
 attribute name, and the value is either:
 
-    * a numeric value (all connections will be set to the same value);
-    * a :class:`~pyNN.random.RandomDistribution` object (each connection will be
+* a numeric value (all connections will be set to the same value);
+* a :class:`~pyNN.random.RandomDistribution` object (each connection will be
         set to a different value, drawn from the distribution);
-    * a list or NumPy array of the same length as the number of connections in
+* a list or NumPy array of the same length as the number of connections in
       the :class:`Projection`;
-    * a generator;
-    * a string expressing a function of the distance between pre- and post-synaptic
+* a generator;
+* a string expressing a function of the distance between pre- and post-synaptic
       neurons.
 
 .. todo:: clarify whether this is the number of *local* connections or the total

doc/developers/contributing.txt

@@ -19,13 +19,13 @@ Requirements
 In addition to the requirements listed in :doc:`../installation`, you will need to
 install:
 
-    * pytest_
-    * coverage_
+* pytest_
+* coverage_
 
 to run tests, and:
 
-    * Sphinx_
-    * matplotlib
+* Sphinx_
+* matplotlib
 
 to build the documentation.
 
@@ -54,8 +54,8 @@ and then you can pull in any upstream changes::
 
 To get PyNN onto your :envvar:`PYTHONPATH` there are many options, such as:
 
-    * pip editable mode (`pip install -e /path/to/PyNN`)
-    * creating a symbolic link named :file:`pyNN` from somewhere that is already
+* pip editable mode (`pip install -e /path/to/PyNN`)
+* creating a symbolic link named :file:`pyNN` from somewhere that is already
       on your :envvar:`PYTHONPATH`, such as the :file:`site-packages` directory,
       to the :file:`pyNN_dev/pyNN` directory.
 
@@ -169,20 +169,20 @@ in becoming release manager for PyNN, please contact us via the `mailing list`_.
 When you think a release is ready, run through the following checklist one
 last time:
 
-    * do all the tests pass? This means running :command:`pytest` in
+* do all the tests pass? This means running :command:`pytest` in
       :file:`test/unittests` and :file:`test/system` and running :command:`make doctest` in
       :file:`doc`. You should do this on at least two Linux systems -- one a very
       recent version and one at least a year old, and on at least one version of
       Mac OS X. You should also do this with multiple Python versions (3.7+).
-    * do all the example scripts generate the correct output? Run the
+* do all the example scripts generate the correct output? Run the
       :file:`run_all_examples.py` script in :file:`examples/tools` and then visually
       check the :file:`.png` files generated in :file:`examples/tools/Results`. Again,
       you should do this on at least two Linux systems and one Mac OS X system.
-    * does the documentation build without errors? You should then at least skim
+* does the documentation build without errors? You should then at least skim
       the generated HTML pages to check for obvious problems.
-    * have you updated the version numbers in :file:`pyproject.toml`, :file:`pyNN/__init__.py`,
+* have you updated the version numbers in :file:`pyproject.toml`, :file:`pyNN/__init__.py`,
       :file:`doc/conf.py` and :file:`doc/installation.txt`?
-    * have you updated the changelog?
+* have you updated the changelog?
 
 Once you've confirmed all the above, create source and wheel packages using::
 
@@ -201,23 +201,23 @@ repository (in the :file:`docs/PyNN` directory).
 
 If this is a final release, there are a few more steps:
 
-    * if it is a major release (i.e. an ``x.y.0`` release), create a new bug-fix
+* if it is a major release (i.e. an ``x.y.0`` release), create a new bug-fix
       branch::
 
         $ git branch x.y
 
-    * upload the packages to PyPI (Do **not** upload development releases to PyPI)::
+* upload the packages to PyPI (Do **not** upload development releases to PyPI)::
 
         $ twine upload dist/PyNN-x.y.z*
 
-    * update the :file:`codemeta.json` file
+* update the :file:`codemeta.json` file
 
-    * make an announcement on the `mailing list`_
+* make an announcement on the `mailing list`_
 
-    * if it is a major release, write a blog post about it with a focus on the
+* if it is a major release, write a blog post about it with a focus on the
       new features and major changes
 
-    * go home, take a headache pill and lie down for a while in a darkened room (-;
+* go home, take a headache pill and lie down for a while in a darkened room (-;
 
 
 

doc/download.txt

@@ -14,12 +14,12 @@ the links below.
 
 Older versions:
 
-    * `0.10.1 <https://pypi.python.org/pypi/PyNN/0.10.1>`_
-    * `0.9.6 <https://pypi.python.org/pypi/PyNN/0.9.6>`_
-    * `0.8.0 <https://pypi.python.org/pypi/PyNN/0.8.0>`_
-    * `0.7.5 <https://pypi.python.org/pypi/PyNN/0.7.5>`_
-    * `0.6.0 <https://pypi.python.org/pypi/PyNN/0.6.0>`_
-    * `0.5.0 <https://pypi.python.org/pypi/PyNN/0.5.0>`_
+* `0.10.1 <https://pypi.python.org/pypi/PyNN/0.10.1>`_
+* `0.9.6 <https://pypi.python.org/pypi/PyNN/0.9.6>`_
+* `0.8.0 <https://pypi.python.org/pypi/PyNN/0.8.0>`_
+* `0.7.5 <https://pypi.python.org/pypi/PyNN/0.7.5>`_
+* `0.6.0 <https://pypi.python.org/pypi/PyNN/0.6.0>`_
+* `0.5.0 <https://pypi.python.org/pypi/PyNN/0.5.0>`_
 
 
 Latest source code from GitHub

doc/installation.txt

@@ -7,18 +7,18 @@ install and run PyNN on Windows, but this has not been tested.
 
 Installing PyNN requires:
 
-    * Python (version 3.8+)
-    * a recent version of the NumPy_ package
-    * the lazyarray_ package
-    * the Neo_ package (>= 0.11.0)
-    * at least one of the supported simulators: e.g. NEURON, NEST, Brian 2 or Arbor.
+* Python (version 3.8+)
+* a recent version of the NumPy_ package
+* the lazyarray_ package
+* the Neo_ package (>= 0.11.0)
+* at least one of the supported simulators: e.g. NEURON, NEST, Brian 2 or Arbor.
 
 Optional dependencies are:
 
-    * mpi4py_ (if you wish to run distributed simulations using MPI)
-    * either Jinja2_ or Cheetah_ (templating engines)
-    * the CSA_ library
-    * h5py (to use the SONATA format)
+* mpi4py_ (if you wish to run distributed simulations using MPI)
+* either Jinja2_ or Cheetah_ (templating engines)
+* the CSA_ library
+* h5py (to use the SONATA format)
 
 Installing PyNN
 ===============

doc/mc_installation.txt

@@ -7,17 +7,17 @@ install and run PyNN on Windows, but this has not been tested.
 
 Installing PyNN requires:
 
-    * Python (version 2.7, 3.4-3.6)
-    * a recent version of the NumPy_ package
-    * the lazyarray_ package
-    * the Neo_ package (>= 0.5.0)
-    * the NEURON simulator
+* Python (version 2.7, 3.4-3.6)
+* a recent version of the NumPy_ package
+* the lazyarray_ package
+* the Neo_ package (>= 0.5.0)
+* the NEURON simulator
 
 Optional dependencies are:
 
-    * mpi4py_ (if you wish to run distributed simulations using MPI)
-    * either Jinja2_ or Cheetah_ (templating engines)
-    * the CSA_ library
+* mpi4py_ (if you wish to run distributed simulations using MPI)
+* either Jinja2_ or Cheetah_ (templating engines)
+* the CSA_ library
 
 Installing PyNN
 ===============

doc/neurons.txt

@@ -125,9 +125,9 @@ To create a :class:`Population`, we need to specify at minimum the number of
 neurons and the cell type. Three additional arguments may optionally be
 specified:
 
-    * the spatial structure of the population;
-    * initial values for the neuron state variables;
-    * a label.
+* the spatial structure of the population;
+* initial values for the neuron state variables;
+* a label.
 
 .. testcode::
    :hide:

doc/random_numbers.txt

@@ -5,20 +5,20 @@ Random numbers
 There are four considerations for random number generation and consumption in
 PyNN:
 
-    **Reproducibility**:
+**Reproducibility**:
         When comparing simulations with different backends, we may wish to
         ensure that all backends use the same sequence of random numbers so that
         the only differences between simulations arise from the numerics of the
         simulators.
-    **Performance**:
+**Performance**:
         All simulators have their own built-in facilities for random number
         generation, and it may be faster to use these than to use random numbers
         generated by PyNN.
-    **Distributed simulations**:
+**Distributed simulations**:
         When distributing simulations across multiple processors using MPI, we
         may wish to ensure that the sequence of random numbers is independent of
         the number of computation nodes.
-    **Quality**:
+**Quality**:
         Different models have different requirements for the quality of the
         (pseudo-)random number generator used. For models that are not strongly
         dependent on this, we may wish to use a generator that is faster but has
@@ -37,11 +37,11 @@ have an optional *rng* argument, which should be an instance of a subclass of
 :class:`pyNN.random.AbstractRNG`.
 PyNN provides three such sub-classes:
 
-    :class:`~pyNN.random.NumpyRNG`:
+:class:`~pyNN.random.NumpyRNG`:
         Uses the :class:`numpy.random.RandomState` class (Mersenne Twister).
-    :class:`~pyNN.random.GSLRNG`:
+:class:`~pyNN.random.GSLRNG`:
         Uses the `GNU Scientific Library random number generators`_.
-    :class:`~pyNN.random.NativeRNG`:
+:class:`~pyNN.random.NativeRNG`:
         Signals that the simulator's own built-in RNG should be used.
 
 If you wish to use your own random number generator, it is reasonably

doc/reference/neuronmodels.txt

@@ -45,12 +45,12 @@ Standard cell types
 
 * Composed models:
 
-   * :class:`AdExp`
-   * :class:`LIF`
-   * :class:`~pyNN.standardmodels.receptors.CurrExpPostSynapticResponse`
-   * :class:`~pyNN.standardmodels.receptors.CondExpPostSynapticResponse`
-   * :class:`~pyNN.standardmodels.receptors.CondAlphaPostSynapticResponse`
-   * :class:`~pyNN.standardmodels.receptors.CondBetaPostSynapticResponse`
+  * :class:`AdExp`
+  * :class:`LIF`
+  * :class:`~pyNN.standardmodels.receptors.CurrExpPostSynapticResponse`
+  * :class:`~pyNN.standardmodels.receptors.CondExpPostSynapticResponse`
+  * :class:`~pyNN.standardmodels.receptors.CondAlphaPostSynapticResponse`
+  * :class:`~pyNN.standardmodels.receptors.CondBetaPostSynapticResponse`
 
 
 Base class

doc/releases/0.8-alpha-1.txt

@@ -95,11 +95,11 @@ when constructing a cell type for use in the :class:`Population` constructor
 (see above) and in the :meth:`Population.set` method, parameter values can be
 any of the following:
 
-    * a single number - sets the same value for all cells in the :class:`Population`;
-    * a :class:`RandomDistribution` object - for each cell, sets a different
+* a single number - sets the same value for all cells in the :class:`Population`;
+* a :class:`RandomDistribution` object - for each cell, sets a different
       random value drawn from the distribution;
-    * a list or 1D NumPy array of the same size as the :class:`Population`;
-    * a function that takes a single integer argument; this function will be
+* a list or 1D NumPy array of the same size as the :class:`Population`;
+* a function that takes a single integer argument; this function will be
       called with the index of every cell in the :class:`Population` to return
       the parameter value for that cell.
 
@@ -197,11 +197,11 @@ package, which provides a common Python object model for neurophysiology data
 
 Using Neo provides several advantages:
 
-    * data objects contain essential metadata, such as units, sampling interval, etc.;
-    * data can be saved to any of the file formats supported by Neo, including HDF5 and Matlab files;
-    * it is easier to handle data when running multiple simulations with the same network (calling :meth:`reset` between each one);
-    * it is possible to save multiple signals to a single file;
-    * better interoperability with other Python packages using Neo (for data analysis, etc.).
+* data objects contain essential metadata, such as units, sampling interval, etc.;
+* data can be saved to any of the file formats supported by Neo, including HDF5 and Matlab files;
+* it is easier to handle data when running multiple simulations with the same network (calling :meth:`reset` between each one);
+* it is possible to save multiple signals to a single file;
+* better interoperability with other Python packages using Neo (for data analysis, etc.).
 
 Note that Neo is based on NumPy, and most Neo data objects sub-class the NumPy
 :class:`ndarray` class, so much of your data handling code should work exactly
@@ -230,7 +230,7 @@ arguments to a :class:`SynapseType` sub-class such as :class:`StaticSynapse` or
 :class:`TsodyksMarkramSynapse`. For example, instead of::
 
     prj = Projection(p1, p2, AllToAllConnector(weights=0.05, delays=0.5))  # PyNN 0.7
-    
+
 you should now write::
 
     prj = Projection(p1, p2, AllToAllConnector(), StaticSynapse(weight=0.05, delay=0.5))  # PyNN 0.8
@@ -268,13 +268,13 @@ Specifying heterogeneous synapse parameters
 As for neuron parameters, synapse parameter values can now be any of the
 following:
 
-    * a single number - sets the same value for all connections in the :class:`Projection`;
-    * a :class:`RandomDistribution` object - for each connection, sets a different
+* a single number - sets the same value for all connections in the :class:`Projection`;
+* a :class:`RandomDistribution` object - for each connection, sets a different
       random value drawn from the distribution;
-    * a list or 1D NumPy array of the same size as the :class:`Projection`
+* a list or 1D NumPy array of the same size as the :class:`Projection`
       (although this is not very useful for random networks, whose size may not
       be known in advance);
-    * a function that takes a single float argument; this function will be
+* a function that takes a single float argument; this function will be
       called with the *distance* between the pre- and post-synaptic cell to return
       the parameter value for that cell.
 
@@ -305,10 +305,10 @@ as follows::
 
     prj.printWeights('exc_weights.txt', format='array')    # PyNN 0.7
     prj.save('weight', 'exc_weights.txt', format='array')  # PyNN 0.8
-    
+
     prj.saveConnections('exc_conn.txt')                    # PyNN 0.7
     prj.save('all', 'exc_conn.txt', format='list')         # PyNN 0.8
-    
+
 Also note that all three new methods can operate on several parameters at a time::
 
     weights, delays = prj.getWeights('array'), prj.getDelays('array')  # PyNN 0.7