revert the package name back to ISOKANN

axsk · Jun 7, 2024 · 3df300c · 3df300c
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diff --git a/Project.toml b/Project.toml
@@ -1,4 +1,4 @@
-name = "Isokann"
+name = "ISOKANN"
 uuid = "512544ac-929d-4ba3-aa9d-55e9313a4b47"
 authors = ["Sikorski <[email protected]> and contributors"]
 version = "0.1.0"

diff --git a/README.md b/README.md
@@ -1,22 +1,22 @@
-# Isokann
+# ISOKANN
 
-[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://axsk.github.io/Isokann.jl/dev)
+[![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://axsk.github.io/ISOKANN.jl/dev)
 
-Implementation of the Isokann algorithm. For a reference see our paper https://doi.org/10.1063/5.0140764 .
+Implementation of the ISOKANN algorithm. For a reference see our paper https://doi.org/10.1063/5.0140764 .
 Currently things are still fluctuating, so we have different implementations
 
-- forced/isokann.jl - Isokann with adaptive sampling and optimal control for overdamped Langevin systems
-- isomolly.jl - Isokann with adaptive sampling for Molly.jl systems (e.g. proteins)
+- forced/isokann.jl - ISOKANN with adaptive sampling and optimal control for overdamped Langevin systems
+- isomolly.jl - ISOKANN with adaptive sampling for Molly.jl systems (e.g. proteins)
 - isosimple.jl - attempt at a cleaner version of isomolly.jl
-- iso2.jl - Isokann 2 with multivariate memberships
+- iso2.jl - ISOKANN 2 with multivariate memberships
 
-After installing the package with `Pkg.add("https://github.com/axsk/Isokann.jl")` run a basic alanine dipeptide run with
+After installing the package with `Pkg.add("https://github.com/axsk/ISOKANN.jl")` run a basic alanine dipeptide run with
 
 ```julia
 
-using Isokann
+using ISOKANN
 
-iso = IsoRun()  # create the Isokann system/configuration
+iso = IsoRun()  # create the ISOKANN system/configuration
 run!(iso)       # run it for iso.nd steps
 
 plot_training(iso) # plot the training overview

diff --git a/docs/Manifest.toml b/docs/Manifest.toml
@@ -972,7 +972,7 @@ git-tree-sha1 = "5d8c5713f38f7bc029e26627b687710ba406d0dd"
 uuid = "7869d1d1-7146-5819-86e3-90919afe41df"
 version = "0.4.12"
 
-[[deps.Isokann]]
+[[deps.ISOKANN]]
 deps = ["BioStructures", "CUDA", "ChainRulesCore", "Chemfiles", "ClusterManagers", "Combinatorics", "DataFrames", "Dictionaries", "Distances", "FLoops", "Flux", "ForwardDiff", "Graphs", "JLD2", "LinearAlgebra", "LsqFit", "Lux", "MLUtils", "Molly", "NNlib", "Optimisers", "OrdinaryDiffEq", "PCCAPlus", "ParameterSchedulers", "Parameters", "PlotlyBase", "PlotlyKaleido", "Plots", "ProgressMeter", "PyCall", "Random", "SlurmClusterManager", "SnoopPrecompile", "SpecialFunctions", "SplitApplyCombine", "StaticArrays", "StatsBase", "StochasticDiffEq", "Unitful", "Zygote", "cuDNN"]
 path = ".."
 uuid = "512544ac-929d-4ba3-aa9d-55e9313a4b47"

diff --git a/docs/Project.toml b/docs/Project.toml
@@ -1,3 +1,3 @@
 [deps]
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
-Isokann = "512544ac-929d-4ba3-aa9d-55e9313a4b47"
+ISOKANN = "512544ac-929d-4ba3-aa9d-55e9313a4b47"
diff --git a/docs/make.jl b/docs/make.jl
@@ -1,17 +1,17 @@
 using Documenter
 using InteractiveUtils: @time_imports
-@time @time_imports using Isokann
+@time @time_imports using ISOKANN
 
 makedocs(
-    sitename="Isokann",
+    sitename="ISOKANN",
     format=Documenter.HTML(),
-    modules=[Isokann],
+    modules=[ISOKANN],
     warnonly=true
 )
 
 # Documenter can also automatically deploy documentation to gh-pages.
 # See "Hosting Documentation" and deploydocs() in the Documenter manual
 # for more information.
 deploydocs(
-    repo="github.com/axsk/Isokann.jl.git"
+    repo="github.com/axsk/ISOKANN.jl.git"
 )
diff --git a/docs/src/index.md b/docs/src/index.md
@@ -1,30 +1,30 @@
-# Isokann.jl
+# ISOKANN.jl
 
-Documentation for Isokann.jl
+Documentation for ISOKANN.jl
 
 ```@meta
-CurrentModule = Isokann
+CurrentModule = ISOKANN
 ```
 
 ## Main entry points
 
 ```@docs
 IsoRun
-Isokann.run!
+ISOKANN.run!
 MollyLangevin
 propagate
 ```
 
 ## Public API
 
 ```@autodocs
-Modules = [Isokann, Isokann.OpenMM]
+Modules = [ISOKANN, ISOKANN.OpenMM]
 Private = false
 ```
 
 ## Internal API
 
 ```@autodocs
-Modules = [Isokann, Isokann.OpenMM]
+Modules = [ISOKANN, ISOKANN.OpenMM]
 Public = false
 ```
diff --git a/docs/src/installation.md b/docs/src/installation.md
@@ -8,28 +8,28 @@ curl -fsSL https://install.julialang.org | sh
 
 After restarting your shell you should be able to start the Julia REPL via the command `julia`.
 
-In the REPL you can add `Isokann.jl` to your project by entering the package mode (type `]`) and typing
+In the REPL you can add `ISOKANN.jl` to your project by entering the package mode (type `]`) and typing
 
 ```julia
-pkg> add https://github.com/axsk/Isokann.jl
-pkg> test Isokann
+pkg> add https://github.com/axsk/ISOKANN.jl
+pkg> test ISOKANN
 ```
 
 Note that this can take a while on the first run as Julia downloads and precompiles all dependencies.
 
-We plan on installing OpenMM automatically with Isokann. Right now, if you want to use openmm with Isokann you will need to make it available to PyCall.jl.
+We plan on installing OpenMM automatically with ISOKANN. Right now, if you want to use openmm with ISOKANN you will need to make it available to PyCall.jl.
 This should work automatically, when using the Conda.jl Conda environment (i.e. starting julia with the env `PYTHON=""` and running `pkg> build PyCall.`). See also the [PyCall docs](https://github.com/JuliaPy/PyCall.jl?tab=readme-ov-file#specifying-the-python-version).
 
 ## Development
 
-If you want to make changes to Isokann you should clone it into a directory
+If you want to make changes to ISOKANN you should clone it into a directory
 
-`git clone [email protected]:axsk/Isokann.jl.git`
+`git clone [email protected]:axsk/ISOKANN.jl.git`
 
 Then start Julia in that directory, activate it with `]activate .`,
 instantiate the dependencies with `]instantiate`.
 
 You should then be able to run the tests with `]test`
-or start `using Isokann`.
+or start `using ISOKANN`.
 
-We strongly recommend `using Revise` before Isokann, so that your changes will automatically load in your current session.
+We strongly recommend `using Revise` before ISOKANN, so that your changes will automatically load in your current session.
diff --git a/docs/src/introduction.md b/docs/src/introduction.md
@@ -1,13 +1,13 @@
 # Introduction
 
-This package provides the core Isokann algorithm as well as some wrappers and convenience routines to work with different kind of simulations and data.
+This package provides the core ISOKANN algorithm as well as some wrappers and convenience routines to work with different kind of simulations and data.
 
-The core Isokann algorithm is accessed by the `Iso2` type,
-which holds the neural network, optimizer, Isokann parameters and training data.
+The core ISOKANN algorithm is accessed by the `Iso2` type,
+which holds the neural network, optimizer, ISOKANN parameters and training data.
 
 You can construct it by passing a tuple of `(xs, ys)` of arrays as input data. Here `xs` is a matrix where the columns are starting points of trajectories and `ys` is a 3 dimensional array where `ys[d,k,n]` is the `d`-th coordinate of the `k`-th Koopman-replica of the `n`-th trajectory.
 
-To start training the neural network simply call the `run!` function passing the `Iso2` object and the number of Isokann iterations.
+To start training the neural network simply call the `run!` function passing the `Iso2` object and the number of ISOKANN iterations.
 The resulting \chi values can be obtained via the `chis` method
 
 ```julia
@@ -17,7 +17,7 @@ chis(iso)
 ```
 
 We also supply some basic simulations which can generate the data, e.g. [`Doublewell`](@ref), [`MuellerBrown`](@ref), [`Diffusion`](@ref), [`MollySimulation`](@ref) and [`OpenMMSimulation`](@ref).
-You can use the [`isodata`](@ref) function to sample data for Isokann.
+You can use the [`isodata`](@ref) function to sample data for ISOKANN.
 
 ```julia
 sim = Doublewell()

diff --git a/docs/src/isomu.md b/docs/src/isomu.md
@@ -1,6 +1,6 @@
 # IsoMu
 
-Analysing trajectory data from mu opiod receptor with Isokann and reaction path subsampling.
+Analysing trajectory data from mu opiod receptor with ISOKANN and reaction path subsampling.
 
 contact: a sikorski, s chewle
 
@@ -9,16 +9,16 @@ contact: a sikorski, s chewle
 
 1. run julia (install via: google juliaup)
 2. activate the project `julia> ]activate .`
-3. update Isokann to their github master branches
-- `julia> ]add https://github.com/axsk/Isokann.jl`
-4. load the module via `julia> using Isokann, Isokann.IsoMu`
+3. update ISOKANN to their github master branches
+- `julia> ]add https://github.com/axsk/ISOKANN.jl`
+4. load the module via `julia> using ISOKANN, ISOKANN.IsoMu`
 
 # Running the clustering
 ```julia
 # create a DataLink to the trajectory's directory
 link = DataLink("path/to/traj")
 
-# create the Isokann environment
+# create the ISOKANN environment
 mu = isokann(link)
 
 # train the network
@@ -33,7 +33,7 @@ save_reactive_path(mu, sigma=0.1, out="out/path.pbd", method=IsoMu.MaxPath())
 # Starting on SLURM with gpu
 srun --gres=gpu --partition gpu --constraint "A40-RTX-48GB" --pty bash
 
-then `Isokann.gpu!(mu::IsoRun)`
+then `ISOKANN.gpu!(mu::IsoRun)`
 
 # A more advanced example
 ```julia

diff --git a/examples/Manifest.toml b/examples/Manifest.toml
@@ -931,7 +931,7 @@ git-tree-sha1 = "8aa91235360659ca7560db43a7d57541120aa31d"
 uuid = "7869d1d1-7146-5819-86e3-90919afe41df"
 version = "0.4.11"
 
-[[deps.Isokann]]
+[[deps.ISOKANN]]
 deps = ["CUDA", "ChainRulesCore", "ClusterManagers", "Combinatorics", "Dictionaries", "FLoops", "Flux", "ForwardDiff", "JLD2", "LinearAlgebra", "LsqFit", "Lux", "MLUtils", "Molly", "NNlib", "Optimisers", "OrdinaryDiffEq", "PCCAPlus", "Parameters", "PlotlyBase", "PlotlyKaleido", "Plots", "ProgressMeter", "Random", "SlurmClusterManager", "SnoopPrecompile", "SpecialFunctions", "SplitApplyCombine", "StaticArrays", "StatsBase", "StochasticDiffEq", "Unitful", "Zygote"]
 path = ".."
 uuid = "512544ac-929d-4ba3-aa9d-55e9313a4b47"

diff --git a/examples/Project.toml b/examples/Project.toml
@@ -1,4 +1,4 @@
 [deps]
-Isokann = "512544ac-929d-4ba3-aa9d-55e9313a4b47"
+ISOKANN = "512544ac-929d-4ba3-aa9d-55e9313a4b47"
 MKL = "33e6dc65-8f57-5167-99aa-e5a354878fb2"
 Molly = "aa0f7f06-fcc0-5ec4-a7f3-a573f33f9c4c"
diff --git a/examples/iso2diala.ipynb b/examples/iso2diala.ipynb
@@ -9,7 +9,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-    "\u001b[32m\u001b[1m  Activating\u001b[22m\u001b[39m project at `~/code/Isokann`\n"
+    "\u001b[32m\u001b[1m  Activating\u001b[22m\u001b[39m project at `~/code/ISOKANN`\n"
      ]
     }
    ],

diff --git a/examples/reactionpath dipep.ipynb b/examples/reactionpath dipep.ipynb
@@ -8,7 +8,7 @@
    "source": [
     "using Pkg, Revise\n",
     "Pkg.activate(\"..\")\n",
-    "using Isokann, Plots\n",
+    "using ISOKANN, Plots\n",
     "Plots.default(fmt=:png)"
    ]
   },
@@ -58,7 +58,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# create the actual Isokann simulation\n",
+    "# create the actual ISOKANN simulation\n",
     "iso = IsoRun(;sim, loggers=[])"
    ]
   },
@@ -87,7 +87,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "x0 = Isokann.getcoords(sim)\n",
+    "x0 = ISOKANN.getcoords(sim)\n",
     "chi = iso.model\n",
     "path = @time reactionpath(sim, x0, chi, dt=0.0001)\n",
     "scatter_ramachandran(path)"
@@ -99,7 +99,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "Isokann.savecoords(sys, path, \"reactionpath.pdb\")"
+    "ISOKANN.savecoords(sys, path, \"reactionpath.pdb\")"
    ]
   },
   {

diff --git a/ext/MollyExt.jl b/ext/MollyExt.jl
@@ -1,6 +1,6 @@
 module MollyExt
 
-using Isokann
+using ISOKANN
 
 import Molly
 
@@ -19,7 +19,7 @@ The `MollyLangevin` struct represents a Langevin dynamics simulation for the Mol
 - `n_threads::Int`: The number of threads for force computations. Default is 1.
 
 """
-Base.@kwdef mutable struct MollyLangevin{S} <: IsoSimulation  # TODO: move this to Isokann to be reachable again
+Base.@kwdef mutable struct MollyLangevin{S} <: IsoSimulation  # TODO: move this to ISOKANN to be reachable again
   sys::S
   temp::Float64 = 298.0 # 298 K = 25 °C
   gamma::Float64 = 1.0
@@ -74,7 +74,7 @@ Burst simulation of the MollyLangeving system `ms`.
 Propagates `ny` samples for each initial position provided in the columns of `x0`.
 
 `propagate` is the main interface facilitating sampling of a system.
-TODO: specify the actual interface required for a simulation to be runnable by Isokann.
+TODO: specify the actual interface required for a simulation to be runnable by ISOKANN.
 
 # Arguments
 - `ms::MollyLangevin`: The MollyLangevin solver object.
@@ -157,7 +157,7 @@ end
 ## loaders for the molecules in /data
 
 const molly_data_dir = joinpath(dirname(pathof(Molly)), "..", "data")
-const isokann_data_dir = joinpath(dirname(pathof(Isokann)), "..", "data")
+const isokann_data_dir = joinpath(dirname(pathof(ISOKANN)), "..", "data")
 
 molly_data(path) = joinpath(molly_data_dir, path)
 isokann_data(path) = joinpath(isokann_data_dir, path)