Several improvements to docs

README.md

@@ -37,7 +37,7 @@ The full code can be [viewed here](https://github.com/pints-team/pints/blob/main
 
 Beyond time-series models, PINTS can be used on any error function or log-likelihood that takes real-valued, continuous parameters.
 
-A graphical overview of the methods included in PINTS can be [viewed here](https://pints-team.github.io/pints-methods-overview/).
+An overview of the methods provided by PINTS can be [viewed here](https://pints.readthedocs.io/en/stable/#provided-methods).
 
 ### Examples and documentation
 

docs/source/abc_samplers/index.rst

@@ -11,7 +11,8 @@ given a :class:`LogPrior` and a :class:`ErrorMeasure`.
 
 
 .. toctree::
+    :maxdepth: 1
 
     base_classes
     abc_smc
-    rejection_abc
+    rejection_abc

docs/source/boundaries.rst

@@ -9,14 +9,6 @@ Simple boundaries for an optimisation can be created using
 More complex types can be made using :class:`LogPDFBoundaries` or a custom
 implementation of the :class:`Boundaries` interface.
 
-Overview:
-
-- :class:`Boundaries`
-- :class:`ComposedBoundaries`
-- :class:`LogPDFBoundaries`
-- :class:`RectangularBoundaries`
-
-
 .. autoclass:: Boundaries
 
 .. autoclass:: ComposedBoundaries

docs/source/conf.py

@@ -39,6 +39,7 @@
 autodoc_default_options = {
     'members': None,
     'inherited-members': None,
+    'special-members': '__call__',
 }
 
 # Add any paths that contain templates here, relative to this directory.
@@ -55,7 +56,7 @@
 
 # General information about the project.
 project = u'Pints'
-copyright = u'2022, Pints Authors'
+copyright = u'2017-2026, Pints Authors'
 author = u'Pints Authors'
 
 # The version info for the project you're documenting, acts as replacement for

docs/source/core_classes_and_methods.rst

@@ -9,19 +9,6 @@ Pints provides the :class:`SingleOutputProblem` and
 inverse problems based on time series data and
 :class:`ForwardModel`.
 
-Overview:
-
-- :class:`ForwardModel`
-- :class:`ForwardModelS1`
-- :class:`MultiOutputProblem`
-- :class:`SingleOutputProblem`
-- :class:`TunableMethod`
-- :func:`version`
-
-.. autofunction:: version
-
-.. autoclass:: TunableMethod
-
 
 Forward model
 *************
@@ -40,3 +27,12 @@ Problems
 
 .. autoclass:: MultiOutputProblem
 
+Hyperparameters
+***************
+
+.. autoclass:: TunableMethod
+
+PINTS version
+*************
+
+.. autofunction:: version

docs/source/diagnostic_plots.rst

@@ -7,31 +7,31 @@ Diagnostic plots
 For users who have Matplotlib installed, Pints offers a number of diagnostic
 plots that can be used to quickly check obtained results.
 
-Plotting functions:
+Plots of functions:
 
 - :func:`function`
 - :func:`function_between_points`
 - :func:`surface`
 
-Diagnosing MCMC results:
+MCMC Diagnostic plots:
 
 - :func:`autocorrelation`
 - :func:`histogram`
 - :func:`pairwise`
 - :func:`series`
 - :func:`trace`
 
-Functions
----------
+Plots of functions
+------------------
 
 .. autofunction:: function
 
 .. autofunction:: function_between_points
 
 .. autofunction:: surface
 
-MCMC Diagnostics
-----------------
+MCMC Diagnostic plots
+---------------------
 
 .. autofunction:: autocorrelation
 

docs/source/error_measures.rst

@@ -13,17 +13,6 @@ Example::
     x = [1,2,3]
     fx = error(x)
 
-Overview:
-
-- :class:`ErrorMeasure`
-- :class:`MeanSquaredError`
-- :class:`NormalisedRootMeanSquaredError`
-- :class:`ProbabilityBasedError`
-- :class:`ProblemErrorMeasure`
-- :class:`RootMeanSquaredError`
-- :class:`SumOfErrors`
-- :class:`SumOfSquaresError`
-
 
 .. autoclass:: ErrorMeasure
 

docs/source/function_evaluation.rst

@@ -18,14 +18,6 @@ Example::
         ]
     fx = e.evaluate(x)
 
-Overview:
-
-- :func:`evaluate`
-- :class:`Evaluator`
-- :class:`ParallelEvaluator`
-- :class:`SequentialEvaluator`
-- :class:`MultiSequentialEvaluator`
-
 
 .. autofunction:: evaluate