Read files from scientific_python folder in alphabetical order and try to understand how all prints and asserts work.

This repository has a structure of a Python package with some additional files.

• scientific_python folder represents top level package of the same name. This package contains several sub-packages and modules. You can start with a_intro sub-package and read its modules one by one in alphabetical order. All modules can be used as separate modules and scripts without package installation
• bin folder contains scripts that can be used after package installation. Now they are used for testing
• doc folder is used for documentation. Now it contains only MS Word file with course annotation (in Russian)
• misc contains some additional course materials, e.g. sample Python project and staff related to Juyter notebooks used in class:
  • sample_project is an example of basic Python project with setup.py, a package and a subpackage, tests and a script. Students can use it as a base for their course projects
  • jupyter_notebooks contains notebooks and other files used in class sorted by date
  • share_jupyter contains little Docker project that runs my class Jupyter server and exposes notebook HTML copies to the world on sai.homb.it
• setup.py is used to install this package
• requirements.txt file contains Python dependencies of the project
• virtualenv_activation.sh is a sample shell script (for *nix systems only) that can be used to activate virtualenv and install the package. Use it by typing . virtualenv_activation.sh or source virtualenv_activation.sh. For exit virtualenv type deactivate
• Dockerfile and docker-compose.yml files can be used to run the project inside Docker container
• .gitignore and .gitattributes are git files
• .dockerignore is just a link to .gitignore, it used to prevent load garbage into Docker container
• .travis.yml is a Travis configuration file. Travis is a continuous integration (CI) system used to test this project with various Python versions, 2.7 and 3.5+ are supported

Remember to use Python 3, 3.6 and later is good enough in 2018. You can check python version typing in console python3 --version or import sys; print(sys.version) in Python itself

• Anaconda Python distribution is a good choice for scientific Python programming on every platform. It includes a lot of pre-compiled numerical and scientific packages and conda package manager where you can find even more packages, like astropy or scikit-learn
• Official Python distribution: good on Windows or macOS when you'd like to build your environment from scratch

Instead of official Python distribution I recommend to use Homebrew package manager, install it and type brew install python

Probably you already have Python 3, check its version before start. If you haven't use your package manager

iOS doesn't have any application to use for scientific programming. The best choice is Pythonista paid application that can run and edit Python 3.6 code and supports numpy package, but doesn't support scipy and other useful packages

PyDroid 3 looks good

• Spyder: the scientific Python development environment, if you have Anaconda, you probably have Spyder
• Visual Studio Code (do not be confused with Visual Studio, they are two different products): a powerful source code editor. GitHub has integrated web-based Visual Studio Code called GitHub Codespaces
• IDLE: a simple Python source code editor. It is a part of Python standard library, so if you have Python, you probably have IDLE
• PyCharm: a powerful Python IDE (integrated development environment). PyCharm is closed source product, but Community edition is free to use and every student or professor can ask for a free professional version
• Jupyter Notebook: not an editor in the usual sense, but powerful web-based tool for running Python (among with other language) code for data analysis. You can run Jupyter server on your own hardware or try it on Azure Notebooks or Google Colab
• Almost all popular interlanguage code editors supports Python

• Learn Python in Y minutes: short and deep language tutorial. This tutorial has Russian version
• Official Python tutorial
• A Byte of Python: a free handbook with initial language tutorial, there is an unofficial but still good Russian translation
• Practical Python for astronomers
• Python 3 in one picture: print it and enjoy
• Comprehensive Python Cheatsheet
• Use of Python programming language in astronomy and science
• WTF Python: non-obvious language features

• Zen of Python or just run import this in Python
• Official documentation
• Python code style guide

Use some version control system. Git is a good choice, see some tutorials:

• GitHowTo — good course on command line Git, Russian version
• Git Book — official online Git handbook, Russian version
• GitHub Tutorials — official tutorial of https://github.com
• Bitbucket tutorials — official tutorial of https://bitbucket.com, another public Git hoster
• Version Control with Git — Coursera course by Atlassian (Bitbucket owner)
• A Quick Introduction to Version Control with Git and GitHub — paper in Computational Biology

Classes had place in classroom 48 of Sternberg Astronomical Institute MSU at 9:00 on Wednesdays from September to December 2018. Records of on-line translations of the seminars are hosted on YouTube. Development of the course in 2018 is supported by BASIS foundation.

Seminars had place in classroom 17 of Sternberg Astronomical Institute MSU at 13:30 on Fridays from September to December 2017. Records of on-line translations of the seminars are hosted on YouTube.

Copyright (c) 2017-2018, Konstantin L. Malanchev.

All program code in this repository is distributed under the terms of the MIT license. All data files are properties of their authors, see COPYRIGHT_NOTE files in folders with data files