Changing single to double quotes for styling.

docs/source/user_libs_antennas.rst

@@ -30,7 +30,7 @@ A description of how the models were created can be found at http://dx.doi.org/1
 Module overview
 ===============
 
-* `antennas.py` is a module containing the descriptions of the antennas.
+* ``antennas.py`` is a module containing the descriptions of the antennas.
 
 
 How to use the module

docs/source/user_libs_opt_taguchi.rst

@@ -49,9 +49,9 @@ Package overview
     optimisation_taguchi_fitness.py
     optimisation_taguchi_plot.py
 
-* `OA_9_4_3_2.npy` and `OA_18_7_3_2.npy` are NumPy archives containing pre-built OAs from http://neilsloane.com/oadir/
-* `optimisation_taguchi_fitness.py` is a module containing fitness functions. There are some pre-built ones but users should add their own here.
-* `optimisation_taguchi_plot.py` is a module for plotting the results, such as parameter values and convergence history, from an optimisation process when it has completed.
+* ``OA_9_4_3_2.npy`` and ``OA_18_7_3_2.npy`` are NumPy archives containing pre-built OAs from http://neilsloane.com/oadir/
+* ``optimisation_taguchi_fitness.py`` is a module containing fitness functions. There are some pre-built ones but users should add their own here.
+* ``optimisation_taguchi_plot.py`` is a module for plotting the results, such as parameter values and convergence history, from an optimisation process when it has completed.
 
 Implementation
 --------------
@@ -65,28 +65,28 @@ The process by which Taguchi's method optimises parameters is illustrated in the
 
 In stage 1a, one of the 2 pre-built OAs will automatically be chosen depending on the number of parameters to optimise. Currently, up to 7 independent parameters can be optimised, although a method to construct OAs of any size is under testing.
 
-In stage 1b, a fitness function is required to set a goal against which to compare results from the optimisation process. A number of pre-built fitness functions can be found in the `optimisation_taguchi_fitness.py` module, e.g. `minvalue`, `maxvalue` and `xcorr`. Users can also easily add their own fitness functions to this module. All fitness functions must take two arguments:
+In stage 1b, a fitness function is required to set a goal against which to compare results from the optimisation process. A number of pre-built fitness functions can be found in the ``optimisation_taguchi_fitness.py`` module, e.g. ``minvalue``, ``maxvalue`` and ``xcorr``. Users can also easily add their own fitness functions to this module. All fitness functions must take two arguments:
 
-* `filename` a string containing the full path and filename of the output file
-* `args` a dictionary which can contain any number of additional arguments for the function, e.g. names (IDs) of outputs (rxs) from input file
+* ``filename`` a string containing the full path and filename of the output file
+* ``args`` a dictionary which can contain any number of additional arguments for the function, e.g. names (IDs) of outputs (rxs) from input file
 
 Additionally all fitness functions must return a single fitness value which the optimsation process will aim to maximise.
 
 Stages 2-6 are iterated by the optimisation process.
 
-Parameters and settings for the optimisation process are specified within a special Python block defined by `#taguchi` and `#end_taguchi` in the input file. The parameters to optimise must be defined in a dictionary named `optparams` and their initial ranges specified as lists with lower and upper values. The fitness function, it's parameters, and a stopping value are defined in dictionary named `fitness` which has keys for:
+Parameters and settings for the optimisation process are specified within a special Python block defined by ``#taguchi`` and ``#end_taguchi`` in the input file. The parameters to optimise must be defined in a dictionary named `optparams` and their initial ranges specified as lists with lower and upper values. The fitness function, it's parameters, and a stopping value are defined in dictionary named ``fitness`` which has keys for:
 
-* `name`, a string that is the name of the fitness function to be used.
-* `args`, a dictionary containing arguments to be passed to the fitness function. Within `args` there must be a key called `outputs` which contains a string or list of the names of one or more outputs in the model.
-* `stop`, a value which when exceeded the optimisation should stop.
+* ``name``, a string that is the name of the fitness function to be used.
+* ``args``, a dictionary containing arguments to be passed to the fitness function. Within ``args`` there must be a key called ``outputs`` which contains a string or list of the names of one or more outputs in the model.
+* ``stop``, a value which when exceeded the optimisation should stop.
 
-Optionally a variable called `maxiterations` maybe specified within the `#taguchi` block which will set a maximum number of iterations after which the optimisation process will terminate irrespective of any other criteria.
+Optionally a variable called ``maxiterations`` maybe specified within the ``#taguchi`` block which will set a maximum number of iterations after which the optimisation process will terminate irrespective of any other criteria.
 
 
 How to use the package
 ======================
 
-The package requires `#python` and `#end_python` to be used in the input file, as well as `#taguchi` and `#end_taguchi` for specifying parameters and setting for the optimisation process. A Taguchi optimisation is run using the command line option `--optimisation-taguchi`.
+The package requires ``#python`` and ``#end_python`` to be used in the input file, as well as ``#taguchi`` and ``#end_taguchi`` for specifying parameters and setting for the optimisation process. A Taguchi optimisation is run using the command line option ``--optimisation-taguchi``.
 
 Example
 -------