Docs update I #101
Docs update I #101
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## master #101 +/- ##
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+ Coverage 81.34% 81.37% +0.02%
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Lines 5549 5561 +12
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+ Hits 4514 4525 +11
- Misses 1035 1036 +1 ☔ View full report in Codecov by Sentry. |
| randuniform([::Type{T}=Float64], dims::Dims{N}) -> Array{T,N} | ||
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| Create an array of size `dims` with random entries uniformly distributed in the allowed | ||
| values of `T`. |
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uniform is typically understood to be the uniform distribution on [0,1]. It is definitely not uniform over all values of T (which would not even be well defined).
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Ah I got confused by not reading the docstring of rand far enough:
help?> rand
search: rand randn transcode macroexpand @macroexpand @macroexpand1
rand([rng=default_rng()], [S], [dims...])
Pick a random element or array of random elements from the set of values
specified by S; S can be
• an indexable collection (for example 1:9 or ('x', "y", :z)),
• an AbstractDict or AbstractSet object,
• a string (considered as a collection of characters), or
• a type: the set of values to pick from is then equivalent to
typemin(S):typemax(S) for integers (this is not applicable to
BigInt), to [0, 1) for floating point numbers and to [0, 1)+i[0,
1) for complex floating point numbers;I am wondering though if this method really makes sense, as it is just a copy of rand
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Maybe not, but this also applies to randnormal which is just equal to randn.
src/auxiliary/random.jl
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| """ | ||
| randnormal([::Type{T}=Float64], dims::Dims{N}) -> Array{T,N} | ||
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| Create an array of size `dims` with random entries normally distributed. |
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The normal distribution with mean zero and standard deviation one is typically called the standard normal distribution, so maybe we should try to get this in.
src/auxiliary/random.jl
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| randisometry([::Type{T}=Float64], dims::Dims{2}) -> Array{T,2} | ||
| randhaar([::Type{T}=Float64], dims::Dims{2}) -> Array{T,2} | ||
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| Create a random isometry of size `dims`. |
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I guess here you can mention the Haar measure or uniform measure over the compact manifold of isometric matrices.
src/fusiontrees/manipulations.jl
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| -> <:AbstractDict{FusionTree{I,N-2*N₃}, <:Number} | ||
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| Perform a planar trace of the uncoupled indices of the fusion tree `f` at `q1` with those at | ||
| `q2`, which are required to be pairwise neighbouring. The result is returned as a dictionary |
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pairwise neighbouring might be a bit confusing, as there are different ways in which traces of several indices can be planar:
It could for example be that q2[1] = q1[1]+1 and q2[2] = q1[2]+1, but also that q2[2] = q1[2]-3 , namely with q2[2] = q2[1] +1 = q1[1]+2 = q1[2] + 3
src/sectors/sectors.jl
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| """ | ||
| Trivial | ||
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| Singleton type to represent the trivial sector, i.e. the unit element of the group |
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This can get a bit confusing. The trivial group has only a unit element, but Trivial() typically (in our use case) refers to the only irrep of the trivial group, which is of course the trivial irrep and acts as the unit element in the category Rep[trivial group]. But I also don't immediately know the best way to phrase this.
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Maybe it could also be mentioned that this is the unit object of the category Vect of ordinary vector spaces (not as the first sentence).
src/tensors/abstracttensor.jl
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| """ | ||
| numin(::Union{T,Type{T}}) where {T<:AbstractTensorMap} -> Int | ||
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| Return the number of input spaces of a tensor. This should be equivalent to `length(domain(t))`. |
src/tensors/abstracttensor.jl
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| """ | ||
| numind(::Union{T,Type{T}}) where {T<:AbstractTensorMap} -> Int | ||
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| Return the total number of input and output spaces of a tensor. This should be equivalent to `numout(t) + numin(t)`. |
src/tensors/abstracttensor.jl
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| dim(t::AbstractTensorMap) -> Int | ||
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| The total number of free parameters of a tensor, discounting the entries that are | ||
| fixed by symmetry. |
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I think it could be mentioned that this is the dimension of the hom space on which the tensormap is defined.
src/tensors/abstracttensor.jl
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| """ | ||
| domainind(::Union{T,Type{T}}) where {T<:AbstractTensorMap} -> Tuple{Int} | ||
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| Provide all indices of the domain of a tensor. |
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I find provide a strange word here, in contrast to return. Not sure why, could be subjective. For one of the other reason, it feels like Provide requires the user to provide something.
src/tensors/abstracttensor.jl
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| @doc """ | ||
| fusiontrees(t::AbstractTensorMap) | ||
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| Return an iterator over all fusion trees of a tensor. |
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I don't recall if on other places I have called this "splitting-fusion-tree pairs".
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It seems like fusion-splitting tree pairs is mentioned in the documentation somewhere, as well as splitting-fusion tree pairs. I am completely fine with either, but I think the current naming is definitely confusing.
This is a first step in a rewrite of the documentation. The goal is to add docstrings to all exported methods and have a correctly structured lib and index part, before rethinking how to structure the manual part of the documentation.