Lecture Supplementary Material 2022

This is a collection of codes and Jupyter notebooks to supplement the 2022 IS Radar Summer School lectures.

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Using in the Cloud

You can interact with the Jupyter notebooks in this repository using the free server, Binder.

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Using Locally

If you want to use this locally, here are the basics steps:

1. clone this repository
2. set up a python environment
3. run a Jupyter Lab server

Below, we have some basic guiding instructions for installing and using this repository on the Windows 10, macOS, and Linux operating systems.

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Windows

Recommended Python and Git Installation

We recommend using a package manager to obtain several common developer tools, including git and miniconda. We'll use git to clone git repositories and miniconda to provide a Python installation. Miniconda is a free minimal installation of conda. Many Python packages depend on other system libraries and conda provides both the Python packages and the system libraries in a platform independent way. Basically this helps make using Python on Windows and macOS much easier.

1. Download and install chocolatey. It’s a package manager for helping you install and manage other things: https://chocolatey.org/
2. In a powershell terminal with administrative privledges, install git, bash, miniconda, and the mingw32 compiler: 1. choco install git 2. choco install miniconda3 --params="'/AddToPath:1'" --params="'/RegisterPython:1'"

For more information, see the documentation on chocolatey for git, miniconda3, and anaconda3

Using Anaconda instead of miniconda

If you would prefer to install the full Anaconda instead of miniconda, replace step 2.ii. with:

choco install anaconda3 --params="'/AddToPath:1'"

Installing Python and Git Without Chocolatey

If you would prefer to install git and miniconda without using chocolatey, you can do so by following these instructions:

1. Download and install git-bash from here
2. Download and install miniconda or anaconda

Environment Setup and Usage

All of the following commands should be run either from the "Anaconda Powershell" or "Git Bash":

1) Set up a Python environment:

$ conda create -n isrschool anaconda   #(or use a different name than isrschool)
$ conda activate isrschool

2) Clone this repository:

$ git clone https://github.com/isrsummerschool/lecture_supplements_2022.git

3) Install the python packages that the lecture supplement notebooks need:

$ cd lecture_supplements_2022
$ pip install -r requirements.txt

4) Now you can start up a Jupyter Lab server and work with the notebooks:

$ jupyter lab

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macOS

1) Install Anaconda for mac from https://www.anaconda.com/products/individual

2) Set up a Python environment

Open a terminal, then:

$ conda create -n isrschool anaconda   #(or use a different name than isrschool)
$ conda activate isrschool

3) Install git

$ conda install git 

4) Clone this repository:

$ git clone https://github.com/isrsummerschool/lecture_supplements_2022.git

5) Install the python packages that the lecture supplement notebooks need:

$ cd lecture_supplements_2022
$ pip install -r requirements.txt

6) Now you can start up a Jupyter Lab server and work with the notebooks:

$ jupyter lab

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Linux

Here we provide example commands for Ubuntu. The process is identical in other Linux distributions, but the package names and package manager may be different.

1) Install git with your package manager and clone the repository:

$ sudo apt-get install git-all

2) Clone this repository:

$ git clone https://github.com/isrsummerschool/lecture_supplements_2022.git

3) Set up a python environment (for more details read this: https://virtualenv.pypa.io/en/latest/user_guide.html)

Install the Python pip and virtualenv packages:

$ sudo apt-get install python3-pip
$ pip install virtualenv

and then create a virtual environment:

$ virtualenv isrschool
$ source isrschool/bin/activate

and finally, install the python packages that the lecture supplement notebooks need:

$ cd lecture_supplements_2022
$ pip install -r requirements.txt

4) Now you can start up a Jupyter Lab server and work with the notebooks:

$ jupyter lab