Many fix for GitHub

.gitlab-ci.yml

@@ -9,7 +9,7 @@ before_script:
         RegistrySpec(url = "https://github.com/JuliaRegistries/General.git")
       )
       Pkg.Registry.add(
-        RegistrySpec(url = "https://gitlab.com/tensors4fields/tensors4fieldsregistry.git")
+        RegistrySpec(url = "https://github.com/tensor4all/tensors4fieldsregistry.git")
       )'
 
 variables:

README.md

@@ -1,10 +1,9 @@
 # QuanticsTCI
 
-[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://tensors4fields.gitlab.io/quanticstci.jl/dev/index.html)
-[![pipeline status](https://gitlab.com/tensors4fields/quanticstci.jl/badges/main/pipeline.svg)](https://gitlab.com/tensors4fields/quanticstci.jl/-/commits/main)
-[![coverage report](https://gitlab.com/tensors4fields/quanticstci.jl/badges/main/coverage.svg)](https://gitlab.com/tensors4fields/quanticstci.jl/-/commits/main)
+[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://tensor4all.github.io/QuanticsTCI.jl/dev)
+[![CI](https://github.com/tensor4all/QuanticsTCI.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/tensor4all/QuanticsTCI.jl/actions/workflows/CI.yml)
 
-This module contains utilities for interpolations of functions in the quantics TCI / quantics tensor train (QTT) format. It is a small wrapper around [TensorCrossInterpolation.jl](https://gitlab.com/tensors4fields/TensorCrossInterpolation.jl) and [QuanticsGrids.jl](https://gitlab.com/tensors4fields/QuanticsGrids.jl) with more convenient functionality intended to cover the most common use cases. For more advanced or unusual use cases, it is likely that you will need to rely on those two libraries directly.
+This module contains utilities for interpolations of functions in the quantics TCI / quantics tensor train (QTT) format. It is a small wrapper around [TensorCrossInterpolation.jl](https://github.com/tensor4all/TensorCrossInterpolation.jl) and [QuanticsGrids.jl](https://github.com/tensor4all/QuanticsGrids.jl) with more convenient functionality intended to cover the most common use cases. For more advanced or unusual use cases, it is likely that you will need to rely on those two libraries directly.
 
 ## Installation
 
@@ -14,8 +13,8 @@ using Pkg; Pkg.add("QuanticsTCI.jl")
 ```
 
 This module depends on:
-- [TensorCrossInterpolation.jl](https://gitlab.com/tensors4fields/TensorCrossInterpolation.jl)
-- [QuanticsGrids.jl](https://gitlab.com/tensors4fields/QuanticsGrids.jl)
+- [TensorCrossInterpolation.jl](https://github.com/tensor4all/TensorCrossInterpolation.jl)
+- [QuanticsGrids.jl](https://github.com/tensor4all/QuanticsGrids.jl)
 
 ## Usage
 
@@ -48,7 +47,7 @@ This example is continued in the [package documentation](https://tensors4fields.
 
 ## Related libraries
 - [TensorCrossInterpolation.jl](https://gitlab.com/quanticstci/tensorcrossinterpolation.jl) to calculate tensor cross interpolations.
-- [QuanticsGrids.jl](https://gitlab.com/tensors4fields/QuanticsGrids.jl) for conversion between quantics and direct representations. More advanced use cases can be implemented directly using this library.
+- [QuanticsGrids.jl](https://github.com/tensor4all/QuanticsGrids.jl) for conversion between quantics and direct representations. More advanced use cases can be implemented directly using this library.
 - [ITensors.jl](https://github.com/ITensor/ITensors.jl) for MPS / MPO algorithms.
 
 ## References

docs/make.jl

@@ -6,11 +6,11 @@ DocMeta.setdocmeta!(QuanticsTCI, :DocTestSetup, :(using QuanticsTCI); recursive=
 makedocs(;
     modules=[QuanticsTCI],
     authors="Ritter.Marc <[email protected]> and contributors",
-    repo="https://gitlab.com/tensors4fields/QuanticsTCI.jl/blob/{commit}{path}#{line}",
+    repo="https://github.com/tensor4all/QuanticsTCI.jl/blob/{commit}{path}#{line}",
     sitename="QuanticsTCI.jl",
     format=Documenter.HTML(;
         prettyurls=get(ENV, "CI", "false") == "true",
-        repolink="https://gitlab.com/tensors4fields/QuanticsTCI.jl",
+        repolink="https://github.com/tensor4all/QuanticsTCI.jl",
         edit_link="main",
         assets=String[]),
     pages=[

docs/src/index.md

@@ -58,4 +58,4 @@ The plot shows obvious noise due to the insufficient maximum bond dimension. Acc
 
 - See the API Reference for all variants of calling [`quanticscrossinterpolate`](@ref).
 - If you are having trouble with convergence / efficiency of the TCI, you might have to tweak some of its options. All keyword arguments are forwarded to `TensorCrossInterpolation.crossinterpolate2()` internally. See its [documentation](https://tensors4fields.gitlab.io/tensorcrossinterpolation.jl/dev/documentation/#TensorCrossInterpolation.crossinterpolate2-Union{Tuple{N},%20Tuple{ValueType},%20Tuple{Type{ValueType},%20Any,%20Union{Tuple{Vararg{Int64,%20N}},%20Vector{Int64}}},%20Tuple{Type{ValueType},%20Any,%20Union{Tuple{Vararg{Int64,%20N}},%20Vector{Int64}},%20Vector{Vector{Int64}}}}%20where%20{ValueType,%20N}) for further information.
-- If you intend to work directly with the quantics representation, [QuanticsGrids.jl](https://gitlab.com/tensors4fields/QuanticsGrids.jl) is useful for conversion between quantics and direct representations. More advanced use cases can be implemented directly using this library.
+- If you intend to work directly with the quantics representation, [QuanticsGrids.jl](https://github.com/tensor4all/QuanticsGrids.jl) is useful for conversion between quantics and direct representations. More advanced use cases can be implemented directly using this library.

src/tciinterface.jl

@@ -55,7 +55,7 @@ Interpolate a function ``f(\mathbf{x})`` as a quantics tensor train. The tensor
 Arguments:
 - `ValueType` is the return type of `f`. Automatic inference is too error-prone.
 - `f` is the function to be interpolated. `f` may take multiple arguments. The return type should be `ValueType`.
-- `grid` is a `Grid{n}` object from [`QuanticsGrids.jl`](https://gitlab.com/tensors4fields/QuanticsGrids.jl) that describes a d-dimensional grid of discrete points indexed by binary digits. To avoid constructing a grid explicitly, use one of the other overloads.
+- `grid` is a `Grid{n}` object from [`QuanticsGrids.jl`](https://github.com/tensor4all/QuanticsGrids.jl) that describes a d-dimensional grid of discrete points indexed by binary digits. To avoid constructing a grid explicitly, use one of the other overloads.
 - `initialpivots` is a vector of pivots to be used for initialization.
 - `nrandominitpivot` determines how many random pivots should be used for initialization if no initial pivot is given.