Precompile1

.github/workflows/CI.yml

@@ -23,6 +23,7 @@ jobs:
       fail-fast: false
       matrix:
         version:
+          - '1.12'
           - '1.11'
           - 'lts'
         os:

LINUX.md

@@ -0,0 +1,55 @@
+# SciBmad Linux Installation
+
+1. **Install JupyterLab**
+
+We recommend using SciBmad through [Jupyter lab](https://jupyterlab.readthedocs.io/en/latest/), which is a widely-used computational notebook authoring and editing environment. If you don't have it set up already, installation instructions can be found [here](https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html). 
+
+After installing Jupyter lab, make sure you can open it in your browser. This may be done in the terminal with the command `jupyter lab`.
+
+2. **Install Julia**
+
+While SciBmad can be used from either Python or Julia, Julia must be installed because SciBmad is written in Julia. To install and configure the long-term support (LTS) version of Julia on your computer, in a terminal run the following command:
+
+```
+curl -fsSL https://install.julialang.org | sh -s -- --default-channel lts
+```
+
+
+3. **Language-Specific Installation**
+
+Depending on whether you plan to use SciBmad in Python or Julia, follow your language-specific setup instructions below.
+
+## Python
+
+In Python, SciBmad is currently called through the [`juliacall` package](https://juliapy.github.io/PythonCall.jl/stable/juliacall/). We'll first install this package, and modify some of its configuration files to ensure the long-term support (LTS) version of Julia is used:
+
+```
+pip install juliacall
+find "$HOME" -type f -path "*/juliacall/juliapkg.json" -exec sed -i -E 's/^\s*"julia":.*$/  "julia": "~1.10.3",/' {} +
+```
+
+Finally, we'll install SciBmad using the following command:
+
+```
+python -c 'from juliacall import Main as jl; jl.seval("import Pkg;"); jl.Pkg.add("SciBmad")'
+```
+
+This may take several minutes to install. After it's complete, you're ready to go! Download the [python.ipynb](https://github.com/bmad-sim/SciBmad.jl/blob/main/examples/python.ipynb) SciBmad Jupyter notebook as a simple first example to run.
+
+### Julia
+
+In a terminal, run the command to install the Julia Jupyter kernel:
+
+```
+julia -e 'import Pkg; Pkg.add("IJulia");'
+```
+
+To customize your Julia Jupyter kernel install, see the [IJulia documentation](https://julialang.github.io/IJulia.jl/stable/manual/installation/).
+
+Finally, SciBmad can be installed with:
+
+```
+julia -e 'import Pkg; Pkg.add("SciBmad");'
+```
+
+This may take several minutes to install. After it's complete, you're ready to go! Download the [julia.ipynb](https://github.com/bmad-sim/SciBmad.jl/blob/main/examples/julia.ipynb) SciBmad Jupyter notebook as a simple first example to run.

MAC.md

@@ -0,0 +1,55 @@
+# SciBmad Linux Installation
+
+1. **Install JupyterLab**
+
+We recommend using SciBmad through [Jupyter lab](https://jupyterlab.readthedocs.io/en/latest/), which is a widely-used computational notebook authoring and editing environment. If you don't have it set up already, installation instructions can be found [here](https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html). 
+
+After installing Jupyter lab, make sure you can open it in your browser. This may be done in the terminal with the command `jupyter lab`.
+
+2. **Install Julia**
+
+While SciBmad can be used from either Python or Julia, Julia must be installed because SciBmad is written in Julia. To install and configure the long-term support (LTS) version of Julia on your computer, in a terminal run the following command:
+
+```
+curl -fsSL https://install.julialang.org | sh -s -- --default-channel lts
+```
+
+
+3. **Language-Specific Installation**
+
+Depending on whether you plan to use SciBmad in Python or Julia, follow your language-specific setup instructions below.
+
+## Python
+
+In Python, SciBmad is currently called through the [`juliacall` package](https://juliapy.github.io/PythonCall.jl/stable/juliacall/). We'll first install this package, and modify some of its configuration files to ensure the long-term support (LTS) version of Julia is used:
+
+```
+pip install juliacall
+find "$HOME" -type f -path "*/juliacall/juliapkg.json" -exec sed -i '' -E 's/^[[:space:]]*"julia":.*$/  "julia": "~1.10.3",/' {} +
+```
+
+Finally, we'll install SciBmad using the following command:
+
+```
+python -c 'from juliacall import Main as jl; jl.seval("import Pkg;"); jl.Pkg.add("SciBmad")'
+```
+
+This may take several minutes to install. After it's complete, you're ready to go! Download the [python.ipynb](https://github.com/bmad-sim/SciBmad.jl/blob/main/examples/python.ipynb) SciBmad Jupyter notebook as a simple first example to run.
+
+### Julia
+
+In a terminal, run the command to install the Julia Jupyter kernel:
+
+```
+julia -e 'import Pkg; Pkg.add("IJulia");'
+```
+
+To customize your Julia Jupyter kernel install, see the [IJulia documentation](https://julialang.github.io/IJulia.jl/stable/manual/installation/).
+
+Finally, SciBmad can be installed with:
+
+```
+julia -e 'import Pkg; Pkg.add("SciBmad");'
+```
+
+This may take several minutes to install. After it's complete, you're ready to go! Download the [julia.ipynb](https://github.com/bmad-sim/SciBmad.jl/blob/main/examples/julia.ipynb) SciBmad Jupyter notebook as a simple first example to run.

Project.toml

@@ -20,7 +20,7 @@ TypedTables = "9d95f2ec-7b3d-5a63-8d20-e2491e220bb9"
 
 [compat]
 AtomicAndPhysicalConstants = "0.8.0"
-BeamTracking = "0.3.3"
+BeamTracking = "0.3.6"
 Beamlines = "0.6.5"
 DifferentiationInterface = "0.7.4"
 ForwardDiff = "0.10,1"

README.md

@@ -6,14 +6,19 @@
 [![Coverage](https://codecov.io/gh/bmad-sim/SciBmad.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/bmad-sim/SciBmad.jl)
 !-->
 
-SciBmad is a new open source, high-performance, polymorphic, and forwards-/backwards-/Taylor-differentiable accelerator physics simulation ecosystem, consisting of a set of modular packages:
+SciBmad is a new open source, high-performance, CPU/GPU compatible, polymorphic, and forwards-/backwards-/Taylor-differentiable accelerator physics simulation ecosystem, usable within either Python and Julia.
+
+SciBmad consists of a set of modular packages:
 
 - **[`BeamTracking.jl`](https://github.com/bmad-sim/BeamTracking.jl):** Universally polymorphic, differentiable, portable, and parallelized integrators for simulating charged particle beams on the CPU and various GPUs including NVIDIA CUDA, Apple Metal, Intel oneAPI, and AMD ROCm
 - **[`GTPSA.jl`](https://github.com/bmad-sim/GTPSA.jl):** Fast high-order (Taylor mode) automatic differentiation using the Generalised Truncated Power Series Algebra (GTPSA) library
 - **[`Beamlines.jl`](https://github.com/bmad-sim/Beamlines.jl):** Defines accelerator lattices in a fast, flexible, fully-polymorphic, and differentiable way, providing both eagerly- and lazily-evaluated deferred expressions for interdependent parameters
 - **[`NonlinearNormalForm.jl`](https://github.com/bmad-sim/NonlinearNormalForm.jl):** Map-based perturbation theory of differential-algebraic maps, which may include spin and large damping, using Lie algebraic methods
-- **[`AtomicAndPhysicalConstants.jl`](https://github.com/bmad-sim/AtomicAndPhysicalConstants.jl):** Library providing physical constants and properties for any atomic or subatomic particle, in units chosen by the user, for use in simulations
+- **[`AtomicAndPhysicalConstants.jl`](https://github.com/bmad-sim/AtomicAndPhysicalConstants.jl):** Library providing physical constants and properties for any atomic or subatomic particle for use in simulations
+
+## Installation instructions
 
+SciBmad is compatible with [Windows](https://github.com/bmad-sim/SciBmad.jl/blob/main/WINDOWS.md), [Mac](https://github.com/bmad-sim/SciBmad.jl/blob/main/MAC.md), or [Linux](https://github.com/bmad-sim/SciBmad.jl/blob/main/LINUX). Click on your corresponding system to be linked to detailed installation instructions.
 
 ## Examples
 

WINDOWS.md

@@ -0,0 +1,57 @@
+# SciBmad Windows Installation
+
+1. **Install JupyterLab**
+
+We recommend using SciBmad through [Jupyter lab](https://jupyterlab.readthedocs.io/en/latest/), which is a widely-used computational notebook authoring and editing environment. If you don't have it set up already, installation instructions can be found [here](https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html). 
+
+After installing Jupyter lab, make sure you can open it in your browser.
+
+2. **Install Julia**
+
+While SciBmad can be used from either Python or Julia, Julia must be installed because SciBmad is written in Julia. To install and configure the long-term support (LTS) version of Julia on your computer, in a terminal run the following commands in Powershell:
+
+```
+winget install --name Julia --id 9NJNWW8PVKMN -e -s msstore
+juliaup add lts
+juliaup default lts
+```
+
+3. **Language-Specific Installation**
+
+Depending on whether you plan to use SciBmad in Python or Julia, follow your language-specific setup instructions below.
+
+## Python
+
+In Python, SciBmad is currently called through the [`juliacall` package](https://juliapy.github.io/PythonCall.jl/stable/juliacall/). We'll first install this package, and set an environment variable to ensure that the long-term suppport (LTS) version of Julia is used. In a (Anaconda) Powershell:
+
+```
+pip install juliacall
+setx PYTHON_JULIAPKG_EXE julia
+set PYTHON_JULIAPKG_EXE=julia
+```
+
+Finally, we'll install SciBmad using the following command:
+
+```
+python -c "from juliacall import Main as jl; jl.seval('import Pkg;'); jl.Pkg.add('SciBmad')"
+```
+
+This may take several minutes to install. After it's complete, you're ready to go! Download the [`python.ipynb'](https://github.com/bmad-sim/SciBmad.jl/blob/main/examples/python.ipynb) SciBmad Jupyter notebook as a simple first example to run.
+
+### Julia
+
+In a Powershell, run the command to install the Julia Jupyter kernel:
+
+```
+julia -e 'import Pkg; Pkg.add("IJulia");'
+```
+
+To customize your Julia Jupyter kernel install, see the [IJulia documentation](https://julialang.github.io/IJulia.jl/stable/manual/installation/).
+
+Finally, SciBmad can be installed with:
+
+```
+julia -e 'import Pkg; Pkg.add("SciBmad");'
+```
+
+This may take several minutes to install. After it's complete, you're ready to go! Download the [julia.ipynb](https://github.com/bmad-sim/SciBmad.jl/blob/main/examples/julia.ipynb) SciBmad Jupyter notebook as a simple first example to run.

src/SciBmad.jl

@@ -20,7 +20,7 @@ end
 
 const BTBL = Base.get_extension(BeamTracking, :BeamTrackingBeamlinesExt)
 
-export twiss, find_closed_orbit, track!
+export twiss, find_closed_orbit, track!, track
 
 function fast_coast_check(bl; use_KA=false, use_explicit_SIMD=false)
   # Just check if dpz/dz == 0
@@ -294,8 +294,8 @@ include("newton.jl")
     # Bend too, but that is not implemented yet.
     qf = Quadrupole(Kn1=0.36, L=0.5); # Matrix kick, 1 multipole
     sf = Sextupole(Kn2=0.1, L=0.2);   # Drift kick, 1 multipole
-    d1  = Drift(L=0.3, Kn3=1e-4, Kn4=1e-5); # Drift kick, 2 multipoles
-    d2  = Drift(L=0.3, Ksol=1e-6); # Solenoid kick, 1 multipole
+    d1 = Drift(L=0.3, Kn3=1e-4, Kn4=1e-5); # Drift kick, 2 multipoles
+    d2 = Drift(L=0.3, Ksol=1e-6); # Solenoid kick, 1 multipole
     b  = SBend(L=6.0, angle=pi/132); # Bend
     qd = Quadrupole(Kn1=-0.36, Ks20=1e-3,L=0.5); # matrix kick, 2 multipoles
     sd = Sextupole(Kn2=-0.1, Ksol=1e-6, L=0.2); # solenoid-kick, 2 multipoles

src/track.jl

@@ -1,7 +1,8 @@
 function track(
   v0::Union{AbstractMatrix,AbstractVector},
   bl::Beamline;
-  N_turns=1,
+  n_turns=1,
+  save_every_n_turns=1,
 )
   if v0 isa AbstractVector
     length(v0) == 6 || error("Track accepts a N x 6 matrix of N particle coordinates,
@@ -12,11 +13,13 @@ function track(
   N_particles = size(v0, 1)
   b0 = Bunch(v0)
   BTBL.check_bl_bunch!(bl, b0, false) # Do not notify
-  res = Array{eltype(v0),3}(undef, N_particles, N_turns+1, 6)
+  res = similar(v0, N_particles, save_every_n_turns+1, 6)
   res[:,1,:] .= b0.coords.v
-  for i in 1:N_turns
+  for i in 1:n_turns
     track!(b0, bl)
-    res[:,i+1,:] .= b0.coords.v
+    if mod(i, save_every_n_turns) == 0
+      res[:,Int(i/save_every_n_turns)+1,:] .= b0.coords.v
+    end
   end
   return res
 end