Linux e chimica computazionale: comandi utili

[myttex]

Ciao a tutti!

Condivido con voi qualche mio appunto (in italiano) per l'utilizzo di shell Linux e WSL (Windows Subsystem Linux), molto utili in chimica computazionale. Ovviamente sono scritti come se fossimo al bar, ed è per questo che li trovo molto utili

https://github.com/mtplr/appunti-shell-linux

Qui invece potete trovare una risorsa molto interessante (in inglese) su un'introduzione allo "Scientific Computing", di Éamonn D. Murray (Imperial College, London):

https://gitlab.com/eamonnmurray/IntroToS.../README.md

Di seguito riporto l'indice:

There are currently sections on the following topics:


Computer Systems

Binary, Bits and Bytes
Components
High-Performance Computer Systems
Internal Representation of Data



Linux - 1. The Command line

Using the Terminal
Tab Completion
Permissions
Reading Files
The Manual and man Command
Wildcards
IO Redirection
History
Other Useful Commands



Linux - 2. More Advanced Use

Editing Files
The Bash Shell
Remote Access



Linux - 3. Useful Tools

Version Control with Git
Plotting
Visualizing Atomic Structures
Document Preparation with Latex



Python - 1. The Basics

Installing Python
Using Python
Hello World - Output
Variables, Strings and IO
Input
Basic Types
Mathematical Operations
Compound Assignments
More Advanced Math - Python Packages
Branching - if statements
Python Data Structures - Strings and Lists
Loops - while and for statements
More Compound Types - sets, tuples and dicts
Functions
Modules
Coding Style
Working with Files



Python - 2. More Advanced Topics

List Comprehensions
Scope
Classes
Using Arguments in your Python Script
Installing Additional Packages
Virtual Environments
Catching Exceptions
Assert
Gotchas



Jupyter

Installation
Starting Jupyter
Shortcuts and Cell Types
Full List of Shortcuts
Magics



Numpy

Installation
Basic Usage
A Simple Example
Comparison with Built-In Python
Useful Numpy Operations



Matplotlib

Installation
A First Example with Pyplot
A More Complex Example
Further Reading



Numerical Methods

Root Finding
Systems of Equations
Interpolation
Integration
Matrix Diagonalization
Fitting
FFT
Differential Equations
Machine Learning