Install and open a lesson notebook

You need Julia 1.12 or a compatible newer Julia 1.x release. If necessary, follow the official Julia installation instructions. The commands below labelled julia belong in Julia; the sh command belongs in a terminal.

Install TamerOp by name from Julia's General registry. You do not need to clone its repository or install its dependencies individually. Plotting and notebooks are optional additions.

Create an environment

A Julia environment records the packages used by your work. In Julia, create one in a folder of your choice. This example uses tamerop-work under your home directory:

import Pkg
workdir = joinpath(homedir(), "tamerop-work")
mkpath(workdir)
cd(workdir)
Pkg.activate(workdir)
Pkg.add("TamerOp")

Pkg selects a compatible registered release and records the environment in Project.toml and Manifest.toml. Keep those files with your work. Installation and initial precompilation can take several minutes; you do not need to repeat Pkg.add each time you start Julia.

Check the installation with a small computation:

import TamerOp as OP
diagram = OP.cubical_persistence([0 0 0; 0 5 0; 0 0 0])
OP.finite_intervals(diagram; dim=1)

The result is [(0, 5)]: the outer squares surround a hole at zero, and the central square fills it at five. The ring lesson develops the interpretation with figures.

Open and run a notebook

Download the executed ring notebook, the square inspection notebook, or the input-routes guide. Each already contains its static figures, which you can read without running the code.

To rerun these website notebooks, use the development version: the registered 0.1.0 release does not include their VisualStyle and module-inspector APIs. Keep that choice in a separate lesson environment, leaving the registered package in your work environment:

import Pkg
lessondir = joinpath(homedir(), "tamerop-lessons")
mkpath(lessondir)
cd(lessondir)
Pkg.activate(lessondir)
Pkg.add(name="TamerOp", rev="main")
Pkg.add(["CairoMakie", "IJulia"])

This explicitly tracks the repository's development branch rather than a registered release. Restart Julia if TamerOp was already loaded, then activate the lesson environment and start Jupyter:

import Pkg
lessondir = joinpath(homedir(), "tamerop-lessons")
Pkg.activate(lessondir)
import IJulia
IJulia.notebook(dir=lessondir)

Save the downloaded notebook in tamerop-lessons. IJulia can offer to install Jupyter if it is missing. In the browser, open the downloaded notebook, select a Julia kernel, and run the cells from the top. The first package load and figure may take longer while Julia compiles them. The notebook uses CairoMakie for static figures. The square lesson's optional live inspector explains how to add WGLMakie and run its examples in a live Julia session; the saved figures and main computation need only the packages installed above.

If the kernel cannot find a package, activate your lesson folder in a cell before the lesson's imports:

import Pkg
Pkg.activate("/absolute/path/to/tamerop-lessons")

Use your actual folder path. Keep Project.toml and Manifest.toml with the notebook so you can reopen the environment. From a terminal in that folder, julia --project=. also starts Julia with those packages selected.

Update or return to a registered release

With the intended environment active, Pkg.status("TamerOp") shows which version or source branch it uses. Pkg.update("TamerOp") updates registered packages to compatible releases; an environment tracking main instead follows that branch. Restart Julia before loading updated code.

If an existing environment tracks a GitHub URL or development checkout and you want to return it to registered releases, run:

import Pkg
Pkg.free("TamerOp")
Pkg.update("TamerOp")

If name-only installation cannot find TamerOp, check the capitalization and run Pkg.Registry.update() before retrying. If General is missing from Pkg.Registry.status(), add it with Pkg.Registry.add("General").

Read before running

The ring lesson and downloaded notebook both show the computed figures without Julia. Begin by predicting which squares are present and when their hole fills. Running the code then checks your prediction.