Order API reference (#67)

* order API reference

* add some links to API reference

* fix name in ref

* add docstring for module

* list files instead of all names

* fix filenames

README.md

@@ -11,7 +11,7 @@
 
 **KernelInterpolation.jl** is a [Julia](https://julialang.org/) package that
 implements methods for multivariate interpolation in arbitrary dimension based on symmetric (conditionally) positive-definite kernels
-with a focus on radial-basis functions. It can be used for classical interpolation of scattered data, as well as for generalized
+with a focus on radial basis functions. It can be used for classical interpolation of scattered data, as well as for generalized
 (Hermite-Birkhoff) interpolation by using a meshfree collocation approach. This can be used to solve partial differential equations both
 stationary ones and time-dependent ones by using some time integration method from [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl).
 

docs/src/index.md

@@ -11,7 +11,7 @@
 
 [**KernelInterpolation.jl**](https://github.com/JoshuaLampert/KernelInterpolation.jl) is a [Julia](https://julialang.org/) package that
 implements methods for multivariate interpolation in arbitrary dimension based on symmetric (conditionally) positive-definite kernels
-with a focus on radial-basis functions. It can be used for classical interpolation of scattered data, as well as for generalized
+with a focus on radial basis functions. It can be used for classical interpolation of scattered data, as well as for generalized
 (Hermite-Birkhoff) interpolation by using a meshfree collocation approach. This can be used to solve partial differential equations both
 stationary ones and time-dependent ones by using some time integration method from [OrdinaryDiffEq.jl](https://github.com/SciML/OrdinaryDiffEq.jl).
 

docs/src/interpolation.md

@@ -233,8 +233,9 @@ to visualize the interpolant are the `Delaunay2D` filter to create a surface plo
 
 ## Overview of kernels and adding a custom kernel
 
-In the previous example, we used the [`ThinPlateSplineKernel`](@ref), which is a predefined kernel in KernelInterpolation.jl. There are a number of different
-kernels already defined, which can be used in an analogous way. For an overview of the existing radial-symmetric kernels, see the following table.
+In the previous example, we used the [`ThinPlateSplineKernel`](@ref), which is a predefined kernel in KernelInterpolation.jl.
+There is a number of different [kernels already defined](@ref api-kernels), which can be used in an analogous way. For an
+overview of the existing radial-symmetric kernels, see the following table.
 
 | Kernel name | Formula | Order | Smoothness
 | --- | --- | --- | ---

docs/src/ref.md

@@ -6,4 +6,75 @@ CurrentModule = KernelInterpolation
 
 ```@autodocs
 Modules = [KernelInterpolation]
+Pages = ["KernelInterpolation.jl"]
+```
+
+## [Kernel functions](@id api-kernels)
+
+```@autodocs
+Modules = [KernelInterpolation]
+Pages = [joinpath("kernels", "kernels.jl"), joinpath("kernels", "radialsymmetric_kernel.jl"), joinpath("kernels", "special_kernel.jl")]
+```
+
+## Node sets
+
+```@autodocs
+Modules = [KernelInterpolation]
+Pages = ["nodes.jl"]
+```
+
+## Interpolation
+
+```@autodocs
+Modules = [KernelInterpolation]
+Pages = ["interpolation.jl"]
+```
+
+## [Differential Operators](@id api-diffops)
+
+```@autodocs
+Modules = [KernelInterpolation]
+Pages = ["differential_operators.jl"]
+```
+
+## [Partial differential equations](@id api-equations)
+
+```@autodocs
+Modules = [KernelInterpolation]
+Pages = ["equations.jl"]
+```
+
+## Discretization
+
+```@autodocs
+Modules = [KernelInterpolation]
+Pages = ["discretization.jl"]
+```
+
+## Kernel matrices
+
+```@autodocs
+Modules = [KernelInterpolation]
+Pages = ["kernel_matrices.jl"]
+```
+
+## [Callbacks](@id api-callbacks)
+
+```@autodocs
+Modules = [KernelInterpolation]
+Pages = [joinpath("callbacks_step", "alive.jl"), joinpath("callbacks_step", "summary.jl"), joinpath("callbacks_step", "save_solution.jl")]
+```
+
+## Input/Output
+
+```@autodocs
+Modules = [KernelInterpolation]
+Pages = ["io.jl"]
+```
+
+## Utilities
+
+```@autodocs
+Modules = [KernelInterpolation]
+Pages = ["util.jl"]
 ```

docs/src/tutorial_differentiating_interpolation.md

@@ -112,8 +112,9 @@ derivative in the ``i``-th direction, ``i\in\{1,\ldots,d\}``, of the interpolati
     differentiation (AD) by using [ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl). This allows for flexibility,
     simplicity, and easier extension, but it might be slower than computing the derivatives analytically.
 
-KernelInterpolation.jl already provides some common differential operators. For example, we can compute the first derivative
-of the interpolation `itp` at a specific point `x` by using the [`PartialDerivative`](@ref) operator.
+KernelInterpolation.jl already provides some [common differential operators](@ref api-diffops). For example,
+we can compute the first derivative of the interpolation `itp` at a specific point `x` by using the
+[`PartialDerivative`](@ref) operator.
 
 ```@example diff-itp
 d1 = PartialDerivative(1)

src/KernelInterpolation.jl

@@ -1,3 +1,14 @@
+"""
+    KernelInterpolation
+
+**KernelInterpolation.jl** is a Julia package that implements methods for multivariate interpolation in arbitrary
+dimension based on symmetric (conditionally) positive-definite kernels with a focus on radial basis functions.
+It can be used for classical interpolation of scattered data, as well as for generalized (Hermite-Birkhoff)
+interpolation by using a meshfree collocation approach. This can be used to solve partial differential equations
+both stationary ones and time-dependent ones by using some time integration method from OrdinaryDiffEq.jl.
+
+See also: [KernelInterpolation.jl](https://github.com/JoshuaLampert/KernelInterpolation.jl)
+"""
 module KernelInterpolation
 
 using DiffEqCallbacks: PeriodicCallback, PeriodicCallbackAffect