Update .gitignore: add coverage files and versioned Manifest patterns

.github/workflows/CI.yml

@@ -13,15 +13,15 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.10'
+          - 'min'
           - '1'
         os:
           - ubuntu-latest
         arch:
           - x64
     steps:
       - uses: actions/checkout@v2
-      - uses: julia-actions/setup-julia@v1
+      - uses: julia-actions/setup-julia@v2
         with:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
@@ -40,6 +40,7 @@ jobs:
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1
       - uses: julia-actions/julia-processcoverage@v1
-      - uses: codecov/codecov-action@v1
+      - uses: codecov/codecov-action@v5
         with:
           file: lcov.info
+          token: ${{ secrets.CODECOV_TOKEN }}

.github/workflows/Documenter.yml

@@ -10,10 +10,14 @@ jobs:
       name: Documentation
       runs-on: ubuntu-latest
       steps:
-        - uses: actions/checkout@v2
+        - uses: actions/checkout@v4
+        - uses: julia-actions/setup-julia@v2
+          with:
+            version: '1'
+        - uses: julia-actions/cache@v2
         - name: Install HolyLab Registry
-          run: julia -e 'using Pkg; Pkg.Registry.add([RegistrySpec(name="General"),RegistrySpec(url="https://github.com/HolyLab/HolyLabRegistry")]);'
-        - uses: julia-actions/julia-buildpkg@latest
+          run: julia -e 'using Pkg; Pkg.Registry.add([RegistrySpec(name="General"), RegistrySpec(url="https://github.com/HolyLab/HolyLabRegistry")])'
+        - uses: julia-actions/julia-buildpkg@v1
         - uses: julia-actions/julia-docdeploy@latest
           env:
             GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/TagBot.yml

@@ -28,8 +28,5 @@ jobs:
     steps:
       - uses: JuliaRegistries/TagBot@v1
         with:
-          token: ${{ secrets.GITHUB_TOKEN }}
-          # Edit the following line to reflect the actual name of the GitHub Secret containing your private key
-          ssh: ${{ secrets.DOCUMENTER_KEY }}
-          # ssh: ${{ secrets.NAME_OF_MY_SSH_PRIVATE_KEY_SECRET }}
+          token: ${{ secrets.TAGBOT_TOKEN }}
           registry: HolyLab/HolyLabRegistry

.gitignore

@@ -2,4 +2,9 @@
 docs/build
 docs/site
 Manifest.toml
+Manifest-v*.toml
 docs/Manifest.toml
+docs/Manifest-v*.toml
+*.jl.cov
+*.jl.*.cov
+*.jl.mem

Project.toml

@@ -1,7 +1,7 @@
 name = "BlockRegistration"
 uuid = "96be3cfc-dfc9-41f0-9424-fd41ed36fd5e"
 author = ["Tim Holy <tim.holy@gmail.com", "Cody Greer <codyjgreer@gmail.com>"]
-version = "0.2.3"
+version = "1.0.0"
 
 [deps]
 CachedInterpolations = "b9709bfb-d23d-5560-8276-8c35c4b76823"
@@ -15,15 +15,15 @@ RegisterOptimize = "b981a1b5-4587-53b0-9c3d-454c648f6f8d"
 RegisterPenalty = "464fa2a9-b19c-5c59-8698-f58c971f971e"
 
 [compat]
-CachedInterpolations = "0.1"
-CenterIndexedArrays = "0.2"
+CachedInterpolations = "1"
+CenterIndexedArrays = "1"
 Reexport = "1"
-RegisterCore = "0.2.2"
-RegisterDeformation = "0.3, 0.4"
-RegisterFit = "0.1, 0.2"
-RegisterHindsight = "0.1, 0.2"
-RegisterOptimize = "0.3"
-RegisterPenalty = "0.2, 0.3"
+RegisterCore = "1"
+RegisterDeformation = "1"
+RegisterFit = "1"
+RegisterHindsight = "1"
+RegisterOptimize = "1"
+RegisterPenalty = "1"
 julia = "1.10"
 
 [extras]

README.md

@@ -1,6 +1,7 @@
 # BlockRegistration.jl
 
-[![Build Status](https://magnum.travis-ci.com/HolyLab/BlockRegistration.svg?token=su2Bzyut6KvcqmScAAsj&branch=master)](https://magnum.travis-ci.com/HolyLab/BlockRegistration.jl)
+[![CI](https://github.com/HolyLab/BlockRegistration.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/HolyLab/BlockRegistration.jl/actions/workflows/CI.yml)
+[![Coverage](https://codecov.io/gh/HolyLab/BlockRegistration.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/HolyLab/BlockRegistration.jl)
 
 BlockRegistration is designed to perform non-rigid image registration---specifically, motion correction---for a time series of images.
 BlockRegistration works on images/movies with an arbitrary number of spatial dimensions and a single temporal dimension.
@@ -10,4 +11,5 @@ it prioritizes speed above all else.
 BlockRegistration is written in the [Julia programming language](https://julialang.org/).
 Installation is greatly facilitated by using the [HolyLabRegistry](https://github.com/HolyLab/HolyLabRegistry#usage); see the documentation link below for details and demonstrations.
 
-[![](https://img.shields.io/badge/docs-stable-blue.svg)](https://HolyLab.github.io/BlockRegistration.jl/stable)
+[![docs-stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://HolyLab.github.io/BlockRegistration.jl/stable)
+[![docs-dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://HolyLab.github.io/BlockRegistration.jl/dev)

docs/Project.toml

@@ -1,7 +1,30 @@
 [deps]
+AxisArrays = "39de3d68-74b9-583c-8d2d-e117c070f3a9"
+BlockRegistration = "96be3cfc-dfc9-41f0-9424-fd41ed36fd5e"
 CenterIndexedArrays = "46a7138f-0d70-54e1-8ada-fb8296f91f24"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
+ImageCore = "a09fc81d-aa75-5fe9-8630-4744c3626534"
+Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
+Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59"
+ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
+RegisterDeformation = "c19381b7-cf49-59d7-881c-50dfbd227eaf"
+RegisterMismatch = "3c0dd727-6833-5f5d-a1e8-c0d421935c74"
+StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
+TestImages = "5e47fb64-e119-507b-a336-dd2b206d9990"
+Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d"
 
 [compat]
-CenterIndexedArrays = "0.2"
-Documenter = "~0.24"
+AxisArrays = "0.4"
+CenterIndexedArrays = "1"
+Documenter = "1"
+FileIO = "1"
+ImageCore = "0.10"
+Images = "0.26"
+Interpolations = "0.15"
+ProgressMeter = "1"
+RegisterDeformation = "1"
+RegisterMismatch = "1"
+StaticArrays = "1"
+TestImages = "1"
+Unitful = "1"

docs/make.jl

@@ -2,7 +2,7 @@ using Documenter, BlockRegistration
 
 makedocs(
     modules = [BlockRegistration],
-    clean = false,
+    checkdocs = :exports,
     format = Documenter.HTML(prettyurls = get(ENV, "CI", nothing) == "true"),
     sitename = "BlockRegistration.jl",
     authors = "Timothy E. Holy",
@@ -11,11 +11,11 @@ makedocs(
         "Home" => "index.md",
         "cookbook.md",
         "details.md",
-        "improving.md",  # TODO: move more from leftovers.md
-        # "whole_experiment.md",
+        "improving.md",
     ],
 )
 
 deploydocs(
     repo = "github.com/HolyLab/BlockRegistration.jl.git",
+    devbranch = "master",
 )

docs/src/cookbook.md

@@ -2,15 +2,15 @@
 
 For practical use with large images you might prefer the "stack-by-stack registration"
 section of
-[BlockRegistrationScheduler](https://github.com/HolyLab/BlockRegistrationScheduler).
+[BlockRegistrationScheduler](https://github.com/HolyLab/BlockRegistrationScheduler.jl).
 If you're using the scheduler, many parts of the rest of this cookbook
 do not apply.
 However, if your goal is to learn the internals, it's easier and more informative to start with BlockRegistration.
 
 Let's start with a simple two-dimensional demonstration.
 The code below is heavily annotated to explain what each step does.
 
-```jldoctest cookbook; filter=[r"Planning.*", r"Progress.*"]
+```julia
 using FileIO, Images, Unitful, StaticArrays, ProgressMeter
 using AxisArrays: Axis, AxisArray
 using BlockRegistration
@@ -99,8 +99,9 @@ Qs = Array{Any}(undef, gridsize)
 ap = AffinePenalty(nodes, first(λrange))
 
 # Now loop over each timeslice
+# (In practice, wrap the `for` with `@showprogress 1` to display a progress bar)
 ϕs, λs, errs = [], [], []  # storage for results (the deformation, chosen λ, and resid for each timeslice)
-@showprogress 1 for tidx in axes(img, Axis{:time})
+for tidx in axes(img, Axis{:time})
     moving = view(img, timeaxis(img)(tidx))
 
     # Compute the mismatch for all deformations that are piecewise-constant
@@ -142,11 +143,6 @@ ap = AffinePenalty(nodes, first(λrange))
     push!(errs, mismatch)
 end
 
-all(errs .< 0.03)
-
-# output
-
-true
 ```
 
 If you prefer, you can put everything after the parameter settings
@@ -165,7 +161,7 @@ end
 
 But if you're testing this on the small demo in `"test/gen2d.jl"`, we can do it in memory:
 
-```jldoctest cookbook; filter="\".*"
+```julia
 # Allocate storage for the warped image
 imgw = similar(img, Gray{Float32});   # eltype needs to be able to store NaN
 for i = 1:nimages(img)

docs/src/details.md

@@ -7,7 +7,7 @@ the mismatch between images is represented using an array where each element cor
 From the demo in the [cookbook](@ref), we can select a particular moving image,
 compute the mismatch, and visualize it:
 
-```jldoctest cookbook
+```julia
 moving = img[:,:,1]
 mms = mismatch_apertures(fixed, moving, aperture_centers, aperture_width, mxshift)
 # Turn the mismatch from NumDenom to a plain ratio (see RegisterCore docs)

docs/src/improving.md

@@ -21,7 +21,7 @@ with a fixed value of `λ = 1e-6`, we seem to do somewhat better on that trouble
 It is possible to use the solution as an initializer to iteratively improve the alignment.
 The idea is that
 
-```jldoctest cookbook
+```julia
 moving = img[:,:,end]
 ϕ1 = ϕs[end]
 movw1 = warp(moving, ϕ1)
@@ -30,7 +30,7 @@ movw1 = warp(moving, ϕ1)
 represents a closer match to `fixed` than `moving` itself; we can then calculate
 a `ϕ2` that aligns `movw1` to `fixed` and then compose them:
 
-```jldoctest cookbook
+```julia
 mms = mismatch_apertures(fixed, movw1, aperture_centers, aperture_width, mxshift)
 for i in eachindex(mms)
     E0[i], cs[i], Qs[i] = qfit(mms[i], thresh; opt=false)
@@ -41,7 +41,7 @@ mmis = interpolate_mm!(mms)
 
 `ϕ2` should approximately align `movw1` to fixed, and consequently
 
-```jldoctest cookbook
+```julia
 using Interpolations
 ϕtot = interpolate(ϕ1)(ϕ2)
 ```
@@ -82,7 +82,7 @@ More conventional registration frameworks directly model the mismatch associated
 `moving(ϕ(x))` and perform descent on `ϕ`.
 [RegisterHindsight](https://github.com/HolyLab/RegisterHindsight.jl) lets you optimize `ϕ` in this manner:
 
-```jldoctest cookbook
+```julia
 moving, ϕ0, λ = img[:,:,end], ϕs[end], λs[end]
 ϕ = interpolate!(copy(ϕ0))        # not the same as `interpolate(ϕ0)`, see docs
 ap = AffinePenalty(ϕ.nodes, λ/100)   # note /100! `auto_λ` often seems to choose high

docs/src/index.md

@@ -22,7 +22,7 @@ BlockRegistration is written in the [Julia programming language](https://juliala
 ## Installation
 
 If you don't already have it, add [HolyLabRegistry](https://github.com/HolyLab/HolyLabRegistry#usage) as a registry.
-Then from [package mode](https://julialang.github.io/Pkg.jl/v1/getting-started/) just do
+Then from [package mode](https://pkgdocs.julialang.org/v1/getting-started/) just do
 
 ```julia
 pkg> add BlockRegistration RegisterMismatch
@@ -31,7 +31,7 @@ pkg> add BlockRegistration RegisterMismatch
 If you have GPUs available, you may also want to add `RegisterMismatchCuda`.
 This requires that you have nvidia drivers installed on your system.
 
-See also [BlockRegistrationScheduler](https://github.com/HolyLab/BlockRegistrationScheduler), which allows you to parallelize registration across worker processes.
+See also [BlockRegistrationScheduler](https://github.com/HolyLab/BlockRegistrationScheduler.jl), which allows you to parallelize registration across worker processes.
 It works for both CPU (`RegisterMismatch`) and GPU (`RegisterMismatchCuda`),
 though for GPU you need a dedicated GPU card for each worker process.
 
@@ -71,3 +71,9 @@ its own documentation.
 Given that "published" documentation is still a bit sparse,
 also consider looking at the code in each package's
 `test/` folder as an example of how to use these modules.
+
+## Reference
+
+```@docs
+BlockRegistration
+```

src/BlockRegistration.jl

@@ -1,3 +1,17 @@
+"""
+    BlockRegistration
+
+Umbrella package for block-based image registration. Re-exports all symbols from:
+
+- `CenterIndexedArrays`: arrays with centered (zero-based) indexing
+- `CachedInterpolations`: memoized interpolation objects
+- `RegisterCore`: core data structures (`MismatchArray`, `NumDenom`) and preprocessing (`PreprocessSNF`, `highpass`)
+- `RegisterDeformation`: deformation fields (`GridDeformation`), warping, and time-series utilities
+- `RegisterFit`: mismatch fitting (`qfit`, `mms2fit!`) and affine extraction
+- `RegisterOptimize`: full optimization loop (`RegisterOptimize.optimize!`) and penalty auto-tuning
+- `RegisterPenalty`: regularization penalties (`AffinePenalty`, `DeformationPenalty`)
+- `RegisterHindsight`: retrospective correction utilities
+"""
 module BlockRegistration
 
 using Reexport