update docs

Author: MikaelSlevinsky

README.md

@@ -19,7 +19,7 @@ julia> using FastTransforms, LinearAlgebra
 
 ## Fast orthogonal polynomial transforms
 
-The 34 orthogonal polynomial transforms are listed in `FastTransforms.kind2string.(0:33)`. Univariate transforms may be planned with the standard normalization or with orthonormalization. For multivariate transforms, the standard normalization may be too severe for floating-point computations, so it is omitted. Here are two examples:
+The orthogonal polynomial transforms are listed in `FastTransforms.Transforms` or `FastTransforms.kind2string.(instances(FastTransforms.Transforms))`. Univariate transforms may be planned with the standard normalization or with orthonormalization. For multivariate transforms, the standard normalization may be too severe for floating-point computations, so it is omitted. Here are two examples:
 
 ### The Chebyshev--Legendre transform
 

docs/src/dev.md

@@ -10,10 +10,7 @@ The C library generates assembly for vectorized operations such as single instru
 
 C libraries are easier to call from any other language, partly explaining why the Python package manager Spack [already supports the C library](https://spack.readthedocs.io/en/latest/package_list.html#fasttransforms) through third-party efforts.
 
-In Julia, a parametric composite type with unrestricted type parameters is just about as big as `Any`. Such a type allows the Julia API to far exceed the C API in its ability to unify all of the orthogonal polynomial transforms and present them as linear operators. The `mutable struct FTPlan{T, N, K}`, together with `AdjointFTPlan` and `TransposeFTPlan`, are the core Julia types in this repository. Whereas `T` is understood to represent element type of the plan and `N` represents the number of leading dimensions of the array on which it operates, `K` is a mere integer which serves to distinguish the orthogonal polynomials at play. For example, `FTPlan{Float64, 1, LEG2CHEB}` represents the necessary pre-computations to convert 64-bit Legendre series to Chebyshev series (of the first kind). `N == 1` because Chebyshev and Legendre series are naturally represented with vectors of coefficients. However, this particular plan may operate not only on vectors but also on matrices, column-by-column.
-
-!!! note
-    When working with specialized `FTPlan`s, it is prudent to use the named constants for `K`, such as `FastTransforms.LEG2CHEB`, rather than their literal integer values as these may change when future plans become operational.
+In Julia, a parametric composite type with unrestricted type parameters is just about as big as `Any`. Such a type allows the Julia API to far exceed the C API in its ability to unify all of the orthogonal polynomial transforms and present them as linear operators. The `mutable struct FTPlan{T, N, K}`, together with `AdjointFTPlan` and `TransposeFTPlan`, are the core Julia types in this repository. Whereas `T` is understood to represent element type of the plan and `N` represents the number of leading dimensions of the array on which it operates, `K` is a mere enumeration which serves to distinguish the orthogonal polynomials at play. For example, `FTPlan{Float64, 1, LEG2CHEB}` represents the necessary pre-computations to convert 64-bit Legendre series to Chebyshev series (of the first kind). `N == 1` because Chebyshev and Legendre series are naturally represented with vectors of coefficients. However, this particular plan may operate not only on vectors but also on matrices, column-by-column.
 
 ## The developer's right to build from source