baudekin / mypubnotebooks

UNDER CONSTRUCTION

MyPubNotebooks

This is the site of my public notebooks for Julia usage. I was trained in applied mathematics along time go but used that training mainly as software developer not mathematician. The purpose of this site to provide examples of using Julia to preform statistical analysis based on the Jayne's statistical interpretation. Note there will be notebooks on using using Julia with linear algebra because I'm rusty and to do almost anything math related on computer you need to understand how to apply linear algebra. The resource links for putting this site together is at bottom of the page.

Install Juliaup and utilize it

I use Juliaup to mange my different versions Julia. Below is how install it on the Mac:

brew install juliaup
juliaup list
juliaup add 1.9.4
juliaup status

To utilize version 1.9.4 run:

julia +1.9.4 

or set the default version to 1.9.4:

julia default 1.9.4
julia

to update your installed version run:

juliaup update

Install and run Juypterlab.

Setup a project from juila project file under this github project.

cd ./MyLinearAlgerbra/

]
activate .
instantiate
status
update
<delete>

Install IJulia and Juypterlab for the first time.

If Juypterlab has not been installed for you system you will be prompted to install it using miniconda. You will want to do that. Mini Python will installed in a sub directory under ~/.julia/conda Note it will take a while to Jupyterlab.

]
activate .
add IJulia
<delete>
jupyterlab()
install JupyterLab via Conda, y/n? [y]: y

Define Kernel to use your project file.

To create juypterlab kernel for my Linear Algebra notes:

]
activate .
<delete>
using IJulia
IJulia.installkernel("MyLinearAlgerbra", "--project=.")

The kernals on MacOS are stored under /Users//Library/Jupyter/kernels. When you run installkernal command it creates a directy under kernels directory. For example:

├── kernels
│   └── mylinearalgerbra-1.9
│       ├── kernel.json
│       ├── logo-32x32.png
│       ├── logo-64x64.png
│       └── logo-svg.svg

To remove kernel just remove the directory aka mylinearalgerbra-1.9. If you can't find the kernel directory fire up python env with juypter installed and run: /Users//.julia/conda/3/aarch64/bin/jupyter --runtime-dir. Note you can also use the following juypter commands to manage your kernels:

jupyter kernelspec list
jupyter kernelspec remove <kernel-name>

Launch juypterlab in current directory.

Start juypterlab based on the current Tom's Obvious Minimal Language (TOML) project file. If you cd to ./MyLinearAlgerbra you will use the Project.toml located there. Note the first time you run jupyterlab from julia it will prompt you to install it using Conda it will take a while. Below is example of activating my MyLinearAlgerbra project.

cd ./MyLinearAlgerbra

]<enter>
activate .
<delete>

Below is how to start JupyterLab so it points to the notebooks located under ./MyLinearAlgerbra

using IJulia
jupyterlab(dir=pwd(), detached=true)

Select Kernerl and notebook to run.

Diff Notebooks using git diff

pip install --upgrade pip
nbdime config-git --enable --global

Doing git diff will give you something like this:

Resource Links

• Julia Setup and Packages
  • Julia - https://github.com/JuliaLang
  • Juliaup Muliplexer for julia - https://github.com/JuliaLang/juliaup
  • IJulia Julia-Language backend for Julia - https://github.com/JuliaLang/IJulia.jl
  • Linear Algebra Julia Package - https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/
  • Turing Julia Package - https://juliapackages.com/u/turinglang and https://github.com/TuringLang
  • Project file syntaz: Tom's Obvious Minimal Language - https://toml.io/en/
• Juypter Setup and Usage
  • I can't work effecently without vi mode so I use jypterlab-vim: https://github.com/jupyterlab-contrib/jupyterlab-vim
  • I perfer having a darkmode so I use: https://github.com/jupyterlab/jupyterlab_pygments
  • Juypter Markdown and LaTex - https://saturncloud.io/blog/how-to-use-latex-in-jupyter-notebook/
  • LaTex Wiki - https://en.wikibooks.org/wiki/LaTeX
  • Notebook Diff Instructions - https://nbdime.readthedocs.io/en/latest/
• Bayesain Links:
  • Statistical Rethinking A Bayesian Course with Examples in R and STAN By Richard McElreath
  • Probability Theory: The Logic of Science By E. T. Jaynes
  • Bayesian Inference Videos
  • How to use Julia to perform Bayesian Analysis