# Main Page

### From Python Wiki

## Contents |

## Welcome to the Vibrationdata Python Wiki

'This is a privately-hosted, non-commercial Wiki operated in partnership with the NASA Engineering & Safety Center (NESC).* Public participation is encouraged. *

If you would like to edit or create pages, please contact Tom Irvine for the password. Email: tom@vibrationdata.com

The purpose of this site is to share knowledge and tips for using Python to solve engineering problems, with an emphasis on structural dynamics, signal processing, and vibroacoustics. Python is an interpreted, interactive, object-oriented, open-source programming language. Add-on packages such as NumPy, SciPy, and Matplotlib make Python an attractive alternative to Matlab, and there is an emerging trend for some Matlab users to switch to Python. My colleagues at the Aerospace Corporation and at NASA are among those learning Python.

Python works on almost all computer platforms including Windows, Mac OS and Linux, and there are a number of different installation options.

Due to its elegance and generality, Python is gaining traction as a language for teaching computer programming. MIT, among others have gone to Python for their introductory computer science classes.

I have posted a selection of my own scripts at my vibrationdata Python page.

Please contribute your experience to this site. Here are Directions for Contributing.

Thank you, Tom Irvine

## General Python Topics

Collection of Standard Python Functions for Shock and Vibration

## Python Scripts for Signal Processing

Autocorrelation & Cross-correlation

Digital Filtering - Bessel, Butterworth & Mean filters

Fourier Transform - includes FFT, Waterfall FFT, Cepstrum & Sound Pressure Level

Time History Descriptive Statistics

## Python Scripts for Structural Dynamics

Generalized Eigenvalue Problem

## Python Scripts for Modal Transient Analysis

Digital Recursive Filtering Relationship ODE Solver

## Python Scripts for Linear Algebra

## Other Python Scripts

General Data Processing Using Pandas