Python

Python is an interpreted, high-level and general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects. In the following, you can find some useful links to install Anaconda and interesting Python libraries as well as specific tutorials.

Installation

For Python installation I recommend to use Anaconda: a package manager, an environment manager, a Python/R data science distribution, and a collection of over 7,500+ open-source packages. See the instructions for the installation here: Anaconda

After the installation, typing in the terminal 

python --version
you should get the latest version of Python from Anaconda (3.8.3 at the moment of writing this). If you get an older version, then modify your PATH to include Anaconda. For Mac under zsh, modify the .zlogin file in your home directory as follows: 
export ANACONDA_DIR="/Users/username/opt/anaconda3/bin"
export PATH=$ANACONDA_DIR ":" $PATH

Libraries

Helita

Helita is a Python library for solar physics focused on interfacing with code and projects from the Institute of Theoretical Astrophysics (ITA) and the Rosseland Centre for Solar Physics (RoCS) at the University of Oslo. It contains routines to read SST observations and Bifrost simulations. You can install it through pip or cloning the directory from github, namely,

pip install git+https://github.com/ITA-Solar/helita.git@master

or

git clone https://github.com/ITA-solar/helita.git
cd helita
python setup.py install

For further details, check Helita.

IRISpy

Python library to analyze IRIS Level 2 data: IRISpy

It is probable that to run IRISSpy for the first ime, the only library is missing to install is pyqtgraph, so type in the terminal 

conda install pyqtgraph
If everything is properly installed, you should be able to open a python session and type

import iris_lmsalpy as iris
without any problem.

AIApy

AIApy is a Python package for analyzing data from the Atmospheric Imaging Assembly (AIA) instrument onboard the Solar Dynamics Observatory spacecraft. It includes software for converting AIA images from level 1 to level 1.5, point spread function deconvolution, and computing the wavelength and temperature response functions for the EUV channels: AIApy

SunPy

SunPy is an open-source Python library for Solar Physics data analysis and vis ualization: Sunpy

ChiantiPy

ChiantiPy is the Python interface to the CHIANTI atomic database for astrophysical spectroscopy. It provides the capability to calculate the emission line and continuum spectrum of an optically thin plasma based on the data in the CHIANTI database.

To install it, first you need to get the CHIANTI database. If you have installed SSWIDL, then you already have it, so you just need to define the following enviroment variable (which depends on the location of your SSW folder), e.g., 

export XUVTOP="/Users/username/ssw/packages/chianti/dbase"
If you do not have the database, then download it from: CHIANTI. Extract the folder in a derided location and define the XUVTOP enviroment variable in a similar way as shown in the following:

export XUVTOP="/Users/username/CHIANTI_10.0.1_database/"
After that, close the following GitHub repository:

git clone --recursive https://github.com/chianti-atomic/ChiantiPy.git
cd ChiantiPy
python setup.py install
It is recommended to install ipyparallel 
pip install ipyparallel
since some routines in ChiantiPy use it. For further details, ChiantiPy

Scikit-learn

Scikit-learn (formerly scikits.learn and also known as sklearn) is a free software machine learning library for the Python programming language.[2] It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy: scikit-learn

SciPy meeting

The annual SciPy Conferences allows participants from academic, commercial, and governmental organizations to: - showcase their latest Scientific Python projects, - learn from skilled users and developers, and - collaborate on code development.

The conferences generally consists of multiple days of tutorials followed by two-three days of presentations, and concludes with 1-2 days developer sprints on projects of interest to the attendees.

https://conference.scipy.org/

Tutorials