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Update compat; standardize yml; use Type not DataType; to v0.18 (#133)
* Update compat * Standardize yml * Standardize make.jl * Use Type not DataType * Use Type not DataType * Use Type not DataType * Fix typos
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‎.github/workflows/CompatHelper.yml

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@@ -2,20 +2,47 @@ name: CompatHelper
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on:
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schedule:
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- cron: '00 00 * * *'
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- cron: 0 0 * * 0 # weekly
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workflow_dispatch:
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permissions:
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contents: write
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pull-requests: write
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jobs:
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CompatHelper:
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runs-on: ubuntu-latest
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steps:
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- name: Pkg.add("CompatHelper")
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run: julia -e 'using Pkg; Pkg.add("CompatHelper")'
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- name: CompatHelper.main()
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- name: Check if Julia is already available in the PATH
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id: julia_in_path
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run: which julia
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continue-on-error: true
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- name: Install Julia, but only if it is not already available in the PATH
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uses: julia-actions/setup-julia@latest
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with:
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version: '1'
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# arch: ${{ runner.arch }}
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if: steps.julia_in_path.outcome != 'success'
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- name: "Add the General registry via Git"
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run: |
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import Pkg
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ENV["JULIA_PKG_SERVER"] = ""
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Pkg.Registry.add("General")
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shell: julia --color=yes {0}
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- name: "Install CompatHelper"
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run: |
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import Pkg
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name = "CompatHelper"
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uuid = "aa819f21-2bde-4658-8897-bab36330d9b7"
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version = "3"
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Pkg.add(; name, uuid, version)
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shell: julia --color=yes {0}
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- name: "Run CompatHelper"
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run: |
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import CompatHelper
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CompatHelper.main()
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shell: julia --color=yes {0}
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env:
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GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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COMPATHELPER_PRIV: ${{ secrets.COMPATHELPER_PRIV }} # optional
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run: julia -e 'using CompatHelper; CompatHelper.main()'
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COMPATHELPER_PRIV: ${{ secrets.DOCUMENTER_KEY }}
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# based on:
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# https://github.com/JuliaRegistries/CompatHelper.jl

‎.github/workflows/Documentation.yml

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Pkg.instantiate()'
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- name: BuildAndDeploy
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env:
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# https://juliadocs.github.io/Documenter.jl/stable/man/hosting/#Authentication:-GITHUB_TOKEN
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# GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
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ssh: ${{ secrets.DOCUMENTER_KEY }}
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DOCUMENTER_KEY: ${{ secrets.DOCUMENTER_KEY }}

‎.github/workflows/SpellCheck.yml

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name: Spell Check
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on: [pull_request]
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jobs:
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typos-check:
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name: Spell Check with Typos
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runs-on: ubuntu-latest
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steps:
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- name: Checkout Actions Repository
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uses: actions/checkout@v4
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- name: Check spelling
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uses: crate-ci/typos@master

‎.github/workflows/TagBot.yml

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name: TagBot
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on:
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issue_comment:
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types:
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- created
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workflow_dispatch:
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jobs:
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TagBot:
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if: github.event_name == 'workflow_dispatch' || github.actor == 'JuliaTagBot'

‎.github/workflows/clean.yml

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# https://juliadocs.github.io/Documenter.jl/stable/man/hosting/#gh-pages-Branch
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name: DocPreviewCleanup
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on:
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pull_request:
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types: [closed]
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jobs:
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doc-preview-cleanup:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout gh-pages branch
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uses: actions/checkout@v4
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with:
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ref: gh-pages
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- name: Delete preview and history + push changes
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run: |
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if [ -d "previews/PR$PRNUM" ]; then
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git config user.name "Documenter.jl"
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git config user.email "documenter@juliadocs.github.io"
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git rm -rf "previews/PR$PRNUM"
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git commit -m "delete preview"
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git branch gh-pages-new $(echo "delete history" | git commit-tree HEAD^{tree})
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git push --force origin gh-pages-new:gh-pages
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fi
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env:
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PRNUM: ${{ github.event.number }}

‎Project.toml

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name = "MIRT"
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uuid = "7035ae7a-3787-11e9-139a-5545ed3dc201"
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authors = ["fessler <fessler@umich.edu>"]
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version = "0.17.0"
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version = "0.18"
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[deps]
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AVSfldIO = "b6189060-daf9-4c28-845a-cc0984b81781"
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[compat]
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AVSfldIO = "0.2"
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FFTW = "1"
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FileIO = "1.9, 1.14"
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FillArrays = "0.12, 0.13"
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ImageFiltering = "0.6, 0.7"
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ImageTransformations = "0.8, 0.9"
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Interpolations = "0.12, 0.13, 0.14"
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LazyGrids = "0.5"
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LinearMapsAA = "0.11"
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FileIO = "1"
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FillArrays = "1"
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ImageFiltering = "0.7"
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ImageTransformations = "0.10"
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Interpolations = "0.15"
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LazyGrids = "1"
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LinearMapsAA = "0.12"
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NFFT = "0.13"
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SpecialFunctions = "1, 2"
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Wavelets = "0.9"
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julia = "1.8"
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SpecialFunctions = "2"
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Wavelets = "0.10"
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julia = "1.10"

‎_typos.toml

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[default]
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extend-ignore-identifiers-re = [
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"allk",
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"als",
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"Nd",
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"iy",
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"pn",
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"MIRTio",
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]

‎doc/start-juno.md

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* This test should produce a grayscale image of the famous
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[Shepp-Logan phantom](https://en.wikipedia.org/wiki/Shepp%E2%80%93Logan_phantom) in the Plots tab of Atom.
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* To learn about the jiffy image display function `jim`, type `?jim` at the REPL.
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* Juno and Atom have lots of online documention.
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* Juno and Atom have lots of online documentation.
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I use the `vim-mode-plus` key bindings, installed using Atom preferences.
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* To start a Jupyter notebook for Julia, type at the REPL:

‎doc/start.md

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* Install Julia (1.5 or later recommended) from https://julialang.org/
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* Explore the extensive documentation at https://docs.julialang.org/
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* Install Julia (1.10 or later recommended) from https://julialang.org
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* Explore the extensive documentation at https://docs.julialang.org
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* Launch Julia and use the `]` key to enter its package manager.
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* Add any packages needed for these notebooks using the `add` command.
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* For example `add Plots` to add the `Plots` package.
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* Other crucial standard packages are `FFTW` `FFTViews`
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* You will also need the package `IJulia` to run any Jupyter demo notebooks.
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* Add MIRT by typing `add MIRT`
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(Automatically loads from https://github.com/JeffFessler/MIRT.jl because MIRT.jl is a registered package.)
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(Automatically loads from https://github.com/JeffFessler/MIRT.jl
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because MIRT.jl is a registered package.)
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* Type `precompile` to have Julia precompile the added packages.
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* After you are done adding packages, press the backspace key to return to the REPL prompt.
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* Later if you need to add more packages just type the `]` key again at the REPL prompt to enter the package manager.
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* Julia is under active development so code is updated frequently. It is a wise to type `up` (short for `update`) at the package manager prompt every week or so to get automatic updates of any packages you have intalled.
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* After you are done adding packages, press the backspace key
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to return to the REPL prompt.
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* Later if you need to add more packages just type the `]` key again
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at the REPL prompt to enter the package manager.
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* Julia is under active development so code is updated frequently.
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It is a wise to type `up` (short for `update`) at the package manager prompt
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every week or so to get automatic updates of any packages you have installed.
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* For some Julia tutorials see
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http://web.eecs.umich.edu/~fessler/course/551/julia/tutor/
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http://web.eecs.umich.edu/~fessler/course/551/julia/tutor
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* For some signal processing demos in Julia see
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http://web.eecs.umich.edu/~fessler/course/551/julia/demo/
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http://web.eecs.umich.edu/~fessler/course/551/julia/demo

‎docs/make.jl

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org, reps = :JeffFessler, :MIRT
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eval(:(using $reps))
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using Documenter
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using Literate
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import Documenter
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import Literate
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#using DemoCards
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# examples_templates, examples_theme = cardtheme("grid")
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repo = eval(:($reps))
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DocMeta.setdocmeta!(repo, :DocTestSetup, :(using $reps); recursive=true)
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Documenter.DocMeta.setdocmeta!(repo, :DocTestSetup, :(using $reps); recursive=true)
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# preprocessing
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inc1 = "include(\"../../../inc/reproduce.jl\")"
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assets = ["assets/custom.css"],
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)
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makedocs(;
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Documenter.makedocs(;
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modules = [repo],
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authors = "Jeff Fessler and contributors",
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sitename = "$repo.jl",
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# examples_cb()
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if isci
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deploydocs(;
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Documenter.deploydocs(;
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repo = "github.com/$base",
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devbranch = "main",
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devurl = "dev",

‎src/algorithm/general/dot-curv.jl

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"""
13-
make_dot_curvf(curv::Function, [x, Tf::DataType; w = similar(x)])
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make_dot_curvf(curv::Function, [x, Tf::Type; w = similar(x)])
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Make a function with arguments `(v, x)`
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that computes the dot product between
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- `x` an array whose `size` and `eltype` is used to allocate `w`
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# option
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- `Tf::DataType = typeof(one(eltype(x)))`
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- `Tf::Type = typeof(one(eltype(x)))`
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Specify `eltype` of function `f(x)`, defaulting to unitless.
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- `w = similar(x, typeof(oneunit(Tf) / oneunit(eltype(x))^2))`
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work space for gradient calculation, with appropriate units (if needed).

‎src/algorithm/general/dot-grad.jl

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"""
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make_dot_gradf(grad::Function, [x, Tf::DataType; w = similar(x)])
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make_dot_gradf(grad::Function, [x, Tf::Type; w = similar(x)])
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Make a function with arguments `(v, x)`
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that computes the dot product between
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# in
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- `grad::Function` see above
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- `x` an array whose `size` and `eltype` is used to allocate `w`
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- `Tf::DataType = typeof(one(eltype(x)))`
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- `Tf::Type = typeof(one(eltype(x)))`
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Specify `eltype` of function `f(x)`, defaulting to unitless.
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# option

‎src/algorithm/general/pogm_restart.jl

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- ``x_{k+1} = y_{k+1} + \\beta_k (y_{k+1} - y_k) + \\gamma_k (y_{k+1} - x_k)`` : momentum update
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Proximal versions of the above for ``g(x) \\neq 0`` are in the below references,
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and use the proximal operater
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and use the proximal operator
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``prox_g(z) = argmin_x {1/2\\|z-x\\|^2 + g(x)}``.
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- Proximal Gradient method (PGM or ISTA) - ``\\beta_k = \\gamma_k = 0``. [BT09]

‎src/mri/kspace-spiral.jl

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Make k-space spiral trajectory based on GE 3T scanner constraints
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Option:
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- `N` dimention of reconstructed image
20+
- `N` dimension of reconstructed image
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- `Nt` # of time points
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- `fov` field of view in cm
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- `dt` time sampling interval out; default `5e-6` sec
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Multi-shot spiral design
165165
uses Duyn's approximate slewrate limited design
166-
augmented with archimedian `gmax` limit
166+
augmented with archimedean `gmax` limit
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# in [args]
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* `D` = FOV; cm

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