Merge pull request #3 from una-auxme/documentation

Fixed broken references

src/feature_graph.jl

@@ -6,7 +6,7 @@
 """
     FeatureGraph(nf, ef, senders, receivers)
 
-Data structure that is used as an input for the [GraphNetCore.GraphNetwork](@ref).
+Data structure that is used as an input for the [`GraphNetwork`](@ref).
 
 # Arguments
 - `nf`: Node features of the graph.

src/graph_network.jl

@@ -19,7 +19,7 @@ include("graph_net_blocks.jl")
 The central data structure that contains the neural network and the normalisers corresponding to the components of the GNN (edge features, node features and output).
 
 # Arguments
-- `model`: The Enocde-Process-Decode model as a [Lux.Chain](@ref).
+- `model`: The Enocde-Process-Decode model as a [Lux](https://github.com/LuxDL/Lux.jl) Chain.
 - `ps`: Parameters of the model.
 - `st`: State of the model.
 - `e_norm`: Normaliser for the edge features of the GNN.
@@ -57,7 +57,7 @@ end
 """
     build_model(quantities_size::Integer, dims, output_size::Integer, mps::Integer, layer_size::Integer, hidden_layers::Integer, device::Function)
 
-Constructs the Encode-Process-Decode model as a [Lux.Chain](@ref) with the given arguments.
+Constructs the Encode-Process-Decode model as a [Lux](https://github.com/LuxDL/Lux.jl) Chain with the given arguments.
 
 # Arguments
 - `quantities_size`: Sum of dimensions of each node feature.
@@ -66,10 +66,10 @@ Constructs the Encode-Process-Decode model as a [Lux.Chain](@ref) with the given
 - `mps`: Number of message passing steps.
 - `layer_size`: Size of hidden layers.
 - `hidden_layers`: Number of hidden layers.
-- `device`: Device where the model should be loaded (see [Lux.gpu_device()](@ref) and [Lux.cpu_device()](@ref)).
+- `device`: Device where the model should be loaded (see [Lux GPU Management](https://lux.csail.mit.edu/dev/manual/gpu_management#gpu-management)).
 
 # Returns
-- `model`: The Encode-Process-Decode model as a [Lux.Chain](@ref).
+- `model`: The Encode-Process-Decode model as a [Lux](https://github.com/LuxDL/Lux.jl) Chain.
 """
 function build_model(quantities_size::Integer, dims, output_size::Integer, mps::Integer, layer_size::Integer, hidden_layers::Integer, device::Function)
     encoder = Encoder(build_mlp(quantities_size, layer_size, layer_size, hidden_layers, dev=device), build_mlp(dims + 1, layer_size, layer_size, hidden_layers, dev=device))
@@ -103,8 +103,8 @@ end
 
 
 # Arguments
-- `gn`: The used [GraphNetCore.GraphNetwork](@ref).
-- `graph`: Input data stored in a [GraphNetCore.FeatureGraph](@ref).
+- `gn`: The used [`GraphNetwork`](@ref).
+- `graph`: Input data stored in a [`FeatureGraph`](@ref).
 - `target_quantities_change`: Derivatives of quantities of interest (e.g. via finite differences from data).
 - `mask`: Mask for excluding node types that should not be updated.
 - `loss_function`: Loss function that is used to calculate the error.
@@ -124,13 +124,13 @@ end
 """
     save!(gn, opt_state, df_train::DataFrame, df_valid::DataFrame, step::Integer, train_loss::Float32, path::String; is_training = true)
 
-Creates a checkpoint of the [GraphNetCore.GraphNetwork](@ref) at the given training step.
+Creates a checkpoint of the [`GraphNetwork`](@ref) at the given training step.
 
 # Arguments
-- `gn`: The [GraphNetCore.GraphNetwork](@ref) that a checkpoint is created of.
+- `gn`: The [`GraphNetwork`](@ref) that a checkpoint is created of.
 - `opt_state`: State of the optimiser.
-- `df_train`: [DataFrames.DataFram](@ref) that stores the train losses at the checkpoints.
-- `df_valid`: [DataFrames.DataFram](@ref) that stores the validation losses at the checkpoints (only improvements are saved).
+- `df_train`: [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl) DataFrame that stores the train losses at the checkpoints.
+- `df_valid`: [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl) DataFrame that stores the validation losses at the checkpoints (only improvements are saved).
 - `step`: Current training step where the checkpoint is created.
 - `train_loss`: Current training loss.
 - `path`: Path to the folder where checkpoints are saved.
@@ -178,7 +178,7 @@ end
 """
     load(quantities, dims, norms, output, message_steps, ls, hl, opt, device::Function, path::String)
 
-Loads the [GraphNetCore.GraphNetwork](@ref) from the latest checkpoint at the given path. 
+Loads the [`GraphNetwork`](@ref) from the latest checkpoint at the given path. 
 
 # Arguments
 - `quantities`: Sum of dimensions of each node feature.
@@ -189,14 +189,14 @@ Loads the [GraphNetCore.GraphNetwork](@ref) from the latest checkpoint at the gi
 - `ls`: Size of hidden layers.
 - `hl`: Number of hidden layers.
 - `opt`: Optimiser that is used for training. Set this to `nothing` if you want to use the optimiser from the checkpoint.
-- `device`: Device where the model should be loaded (see [Lux.gpu_device()](@ref) and [Lux.cpu_device()](@ref)).
+- `device`: Device where the model should be loaded (see [Lux GPU Management](https://lux.csail.mit.edu/dev/manual/gpu_management#gpu-management)).
 - `path`: Path to the folder where the checkpoint is.
 
 # Returns
-- `gn`: The loaded [GraphNetCore.GraphNetwork](@ref) from the checkpoint.
+- `gn`: The loaded [`GraphNetwork`](@ref) from the checkpoint.
 - `opt_state`: The loaded optimiser state. Is nothing if no checkpoint was found or an optimiser was passed as an argument.
-- `df_train`: [DataFrames.DataFram](@ref) containing the train losses at the checkpoints.
-- `df_valid`: [DataFrames.DataFram](@ref) containing the validation losses at the checkpoints (only improvements are saved).
+- `df_train`: [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl) DataFrame containing the train losses at the checkpoints.
+- `df_valid`: [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl) DataFrame containing the validation losses at the checkpoints (only improvements are saved).
 """
 function load(quantities, dims, norms, output, message_steps, ls, hl, opt, device::Function, path::String)
     if isfile(joinpath(path, "checkpoints"))

src/normaliser.jl

@@ -34,7 +34,7 @@ end
 Inverses the normalised data.
 
 # Arguments
-- `n`: The used [GraphNetCore.NormaliserOffline](@ref).
+- `n`: The used [`NormaliserOffline`](@ref).
 - `data`: Data to be converted back.
 
 # Returns
@@ -75,7 +75,7 @@ It is recommended to use offline normalization since the minimum and maximum do
 
 # Arguments
 - `dims`: Dimension of the quantity to normalize.
-- `device`: Device where the Normaliser should be loaded (see [Lux.gpu_device()](@ref) and [Lux.cpu_device()](@ref)).
+- `device`: Device where the Normaliser should be loaded (see [Lux GPU Management](https://lux.csail.mit.edu/dev/manual/gpu_management#gpu-management)).
 
 # Keyword Arguments
 - `max_acc = 10f6`: Maximum number of accumulation steps.
@@ -92,8 +92,8 @@ Online normalization if the minimum and maximum of the quantity is not known.
 It is recommended to use offline normalization since the minimum and maximum do not need to be inferred from data.
 
 # Arguments
-- `d`: Dictionary containing the fields of the struct [GraphNetCore.NormaliserOnline](@ref).
-- `device`: Device where the Normaliser should be loaded (see [Lux.gpu_device()](@ref) and [Lux.cpu_device()](@ref)).
+- `d`: Dictionary containing the fields of the struct [`NormaliserOnline`](@ref).
+- `device`: Device where the Normaliser should be loaded (see [Lux GPU Management](https://lux.csail.mit.edu/dev/manual/gpu_management#gpu-management)).
 """
 function NormaliserOnline(d::Dict{String, Any}, device::Function)
     NormaliserOnline(d["max_accumulations"], d["std_epsilon"], d["acc_count"], d["num_accumulations"], device(d["acc_sum"]), device(d["acc_sum_squared"]))
@@ -114,7 +114,7 @@ end
 Inverses the normalised data.
 
 # Arguments
-- `n`: The used [GraphNetCore.NormaliserOnline](@ref).
+- `n`: The used [`NormaliserOnline`](@ref).
 - `data`: Data to be converted back.
 
 # Returns

src/utils.jl

@@ -14,7 +14,7 @@ Converts the given faces of a mesh to edges.
 - `faces`: Two-dimensional array with the node indices in the first dimension.
 
 # Returns
-- A tuple containing the edge pairs. (See [parse_edges](@ref))
+- A tuple containing the edge pairs. (See [`parse_edges`](@ref))
 """
 function triangles_to_edges(faces::AbstractArray{T, 2} where T <: Integer)
     edges = hcat(faces[1:2, :], faces[2:3, :], permutedims(hcat(faces[3, :], faces[1, :])))
@@ -94,7 +94,7 @@ end
 """
     mse_reduce(target, output)
 
-Calculates the mean squared error of the given arguments with [Tullio](@ref) for GPU compatibility.
+Calculates the mean squared error of the given arguments with [Tullio](https://github.com/mcabbott/Tullio.jl) for GPU compatibility.
 
 # Arguments
 - `target`: Ground truth from the data.
@@ -111,7 +111,7 @@ end
 """
     tullio_reducesum(a, dims)
 
-Implementation of the function [reducesum](@ref) with [Tullio](@ref) for GPU compatibility.
+Implementation of the function [`reducesum`](@ref) with [Tullio](https://github.com/mcabbott/Tullio.jl) for GPU compatibility.
 
 # Arguments
 - `a`: Array as input for reducesum.