Updating the package, now AD and CUDA capable, still zero heap allocations

.github/workflows/ci.yml

@@ -0,0 +1,97 @@
+name: CI
+
+on:
+  pull_request:
+    branches:
+      - main
+      - develop
+  push:
+    branches:
+      - main
+      - develop
+    tags: '*'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: ${{ startsWith(github.ref, 'refs/pull/') }}
+
+jobs:
+  test:
+    name: Julia ${{ matrix.version }} - ${{ matrix.os }} - ${{ matrix.arch }} - ${{ github.event_name }}
+    runs-on: ${{ matrix.os }}
+
+    strategy:
+      fail-fast: false
+      matrix:
+        version:
+          - '1.10'
+        os:
+          - ubuntu-latest
+        arch:
+          - x64
+
+    steps:
+      - uses: actions/checkout@v6
+
+      - uses: julia-actions/setup-julia@v3
+        with:
+          version: ${{ matrix.version }}
+          arch: ${{ matrix.arch }}
+
+      - uses: julia-actions/cache@v2
+
+      - name: Add unregistered dependencies
+        run: |
+          julia --project=. -e '
+            using Pkg
+            Pkg.develop(PackageSpec(
+                url="https://github.com/rainerheintzmann/EvalMultiPoly.jl"
+            ))
+            Pkg.instantiate()
+          '
+
+      - uses: julia-actions/julia-buildpkg@v1
+
+      - uses: julia-actions/julia-runtest@v1
+
+      - uses: julia-actions/julia-processcoverage@v1
+
+      - uses: codecov/codecov-action@v4
+        env:
+          CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+        with:
+          file: lcov.info
+
+  docs:
+    name: Documentation
+    runs-on: ubuntu-latest
+
+    permissions:
+      contents: write
+
+    steps:
+      - uses: actions/checkout@v6
+
+      - uses: julia-actions/setup-julia@v3
+        with:
+          version: '1.10'
+
+      - uses: julia-actions/cache@v2
+
+      - name: Install documentation dependencies
+        run: |
+          julia --project=docs -e '
+            using Pkg
+            Pkg.develop([
+                PackageSpec(
+                    url="https://github.com/rainerheintzmann/EvalMultiPoly.jl"
+                ),
+                PackageSpec(path=pwd())
+            ])
+            Pkg.instantiate()
+          '
+
+      - name: Build and deploy documentation
+        uses: julia-actions/julia-docdeploy@releases/v1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.gitignore

@@ -22,3 +22,19 @@ docs/site/
 # committed for packages, but should be committed for applications that require a static
 # environment.
 Manifest.toml
+examples/Manifest.toml
+examples/*.tiff
+examples/image_registration_2dpolim.jl
+
+*.mp4
+*.png
+examples/figures/*
+examples/*.tif
+examples/*.ipynb
+*.jpeg
+*.gif
+*.jpg
+*.txt
+*.txt
+.*tiff
+examples/6-4-Rigis.jl

Project.toml

@@ -1,8 +1,38 @@
 name = "DataToFunctions"
 uuid = "64cfdffa-4d02-49ee-ae8b-a805370874f5"
-authors = ["RainerHeintzmann <heintzmann@gmail.com>"]
 version = "0.1.0"
+authors = ["RainerHeintzmann <heintzmann@gmail.com>"]
 
 [deps]
+Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
+ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+EvalMultiPoly = "c78649ec-f9ed-405e-be6d-0472f43586aa"
 FourierTools = "b18b359b-aebc-45ac-a139-9c0ccbb2871e"
 Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
+
+[compat]
+Adapt = "4.7.0"
+Aqua = "0.8"
+BenchmarkTools = "1"
+CUDA = "6"
+ChainRulesCore = "1.26.1"
+EvalMultiPoly = "0.1"
+ForwardDiff = "1"
+FourierTools = "0.4, 0.5"
+Interpolations = "0.15.1, 0.16"
+StaticArrays = "1.9.6, 1.10"
+Test = "1.9.3"
+Zygote = "0.6, 0.7"
+julia = "1"
+
+[extras]
+Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
+BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
+ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
+
+[targets]
+test = ["Aqua", "Test", "Zygote", "BenchmarkTools", "ForwardDiff", "CUDA"]

README.md

@@ -1,2 +1,90 @@
 # DataToFunctions.jl
-Represents (measured) data as a function, which supports scaling and shifting. It is intended to be used as a tool for fitting data, where the fitting function is given by measured data.
+
+[![CI](https://github.com/RainerHeintzmann/DataToFunctions.jl/actions/workflows/ci.yml/badge.svg)](https://github.com/RainerHeintzmann/DataToFunctions.jl/actions/workflows/ci.yml)
+[![Development Documentation](https://img.shields.io/badge/docs-dev-blue.svg)](https://rainerheintzmann.github.io/DataToFunctions.jl/dev/)
+
+`DataToFunctions.jl` represents data as continuously evaluated, parameterized functions.
+
+The package is intended for fitting, image registration, and inverse problems
+where measured data itself forms part of the model. It provides efficient
+interpolation-based coordinate transformations with support for affine,
+polynomial, and user-defined transformations.
+
+## Features
+
+- Affine transformations in arbitrary dimensions using homogeneous coordinates;
+- A convenient seven-parameter affine model for 2-D data;
+- Polynomial coordinate transformations based on
+  [`EvalMultiPoly.jl`](https://github.com/RainerHeintzmann/EvalMultiPoly.jl);
+- User-defined Cartesian and homogeneous-coordinate transformations;
+- Allocation-free in-place CPU evaluation for performance-critical workflows;
+- Automatic differentiation of affine and polynomial transformation parameters;
+- Custom `ChainRulesCore` reverse rules for efficient Zygote gradients;
+- Compatibility with gradient-based optimization such as `Optim.jl` with
+  `LBFGS`;
+- Backend-generic execution, including CUDA arrays for supported interpolation
+  methods.
+
+## Quick example
+
+```julia
+using DataToFunctions
+
+data = rand(Float32, 128, 128)
+
+f = get_function_affine(data)
+
+p = (
+    0.2f0,    # x shift
+   -0.1f0,    # y shift
+    1.0f0,    # x scale
+    1.0f0,    # y scale
+    0.0f0,    # xy shear
+    0.0f0,    # yx shear
+    0.01f0,   # rotation [rad]
+)
+
+warped = f(p)
+```
+
+For repeated forward evaluations, an in-place version is available:
+
+```julia
+f! = get_function_affine_inplace(data)
+
+out = similar(data)
+f!(out, p)
+```
+
+On CPU, the in-place transformation path is designed to run without heap
+allocations after compilation when using concrete tuple parameters and a
+preallocated output array.
+
+## Automatic differentiation
+
+The affine and polynomial transformation interfaces provide custom reverse-mode
+rules for differentiation with respect to transformation parameters.
+
+For example:
+
+```julia
+using Zygote
+
+target = f(p)
+
+loss(q) = sum(abs2, f(q) .- target)
+
+gradient = Zygote.gradient(loss, p)[1]
+```
+
+These gradients can be used directly in optimization workflows.
+
+## Documentation
+
+- [Development documentation](https://rainerheintzmann.github.io/DataToFunctions.jl/dev/)
+
+The development documentation follows the `develop` branch.
+
+<!-- Stable documentation can be added after the first tagged release at:
+
+- `https://rainerheintzmann.github.io/DataToFunctions.jl/stable/` -->

docs/Project.toml

@@ -0,0 +1,11 @@
+[deps]
+DataToFunctions = "64cfdffa-4d02-49ee-ae8b-a805370874f5"
+Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+DocumenterTools = "35a29f4d-8980-5a13-9543-d66fff28ecb8"
+EvalMultiPoly = "c78649ec-f9ed-405e-be6d-0472f43586aa"
+GR = "28b8d3ca-fb5f-59d9-8090-bfdbd6d07a71"
+Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
+SyntheticObjects = "e7028c27-0967-45e9-8fdb-dbc10ccb2b0a"

docs/make.jl

@@ -0,0 +1,23 @@
+import Pkg
+Pkg.activate(@__DIR__)
+
+cd(@__DIR__) # go into `docs` folder
+
+using Documenter, Literate, DataToFunctions
+
+# convert tutorial/examples to markdown
+# Literate.markdown("./src/tutorial.jl", "./src")
+# Which markdown files to compile to HTML
+# (which is also the sidebar and the table
+# of contents for your documentation)
+
+pages = Any[
+        "Introduction" => "index.md",
+        "Tutorial" => "tutorial.md",
+        "API" => "api.md",
+        ]
+
+# compile to HTML:
+makedocs(; sitename="DataToFunctions.jl", pages, modules = [DataToFunctions], checkdocs = :exports)
+
+deploydocs(repo = "github.com/RainerHeintzmann/DataToFunctions.jl.git", devbranch = "develop")

docs/src/api.md

@@ -0,0 +1,42 @@
+# API Reference
+
+This page lists the public transformation interface provided by
+`DataToFunctions.jl`.
+
+## Affine transformations
+
+```@docs
+DataToFunctions.get_function_affine
+DataToFunctions.get_function_affine_inplace
+```
+
+## Polynomial transformations
+
+```@docs
+DataToFunctions.get_function_poly
+DataToFunctions.get_function_poly_inplace
+```
+
+## User-defined transformations
+
+```@docs
+DataToFunctions.get_function_tuple
+DataToFunctions.get_function_tuple_inplace
+DataToFunctions.get_function_homogen
+DataToFunctions.get_function_homogen_inplace
+```
+
+## Mode-based interface
+
+```@docs
+DataToFunctions.get_interpolated_function
+```
+
+## Transformation modes
+
+If `AffineMode` and `PolynomialMode` have docstrings, they can be included here:
+
+```@docs
+DataToFunctions.AffineMode
+DataToFunctions.PolynomialMode
+```