Best Numerical Analysis Software for Physics

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Discussion Overview

The discussion revolves around the selection of numerical analysis software suitable for physics applications, focusing on criteria such as availability and support for scientific libraries. Participants explore various software options and their functionalities, addressing different needs in numerical analysis.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant emphasizes the importance of software being freely available and supporting additional scientific libraries.
  • Another participant questions the vague nature of 'numerical analysis software' and asks for clarification on the intended use, suggesting that different goals may lead to different software recommendations.
  • A participant shares their experience with Python and its libraries (numpy, scipy, matplotlib, pyQt), expressing a preference for it due to its versatility and ease of use for non-programmers.
  • Speculative opinions are provided on various software: Sage is described as complex and requiring knowledge of multiple programs; Scilab and Freemat are likened to MATLAB; Euler is noted for its plotting capabilities; Julia is mentioned as a promising language but with uncertain library support.
  • R is suggested for statistical analysis, while Perl Data Language (PDL) is mentioned as another option, with links provided for further exploration.
  • A participant introduces specific applications of numerical analysis, mentioning the Einstein Field Equations and Schrödinger Equations as areas of interest.

Areas of Agreement / Disagreement

Participants express a range of opinions on the best software, indicating that multiple competing views remain without a consensus on a single recommendation.

Contextual Notes

The discussion highlights the varying needs and preferences for numerical analysis software, with limitations in defining specific requirements and the potential for different software to meet different goals.

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'Numerical analysis software' is a vague term.

What do you want to do with the software?

Are you looking for symbolic math?
Do you want to generate an harmonic analysis?
Are you interested in a statistics package to help analyze experimental data?
Do you need a tool to plot data?

Depending on your ultimate goal, there may be several packages which fit your requirements.
 
hhhmmm...at one time or another, a few years ago, I visited some of the webpages for the software listed in the link...at the end, I simply chose Python (numpy, scipy, matplotlib, pyQt, etc).

As a non-user of the software, a brief homepage visit and maybe a trial usage, I have the following speculative opinions...

Sage is huge, a beast, a conglomerate of many other programs (dozens) unified under a common python interface; needless to say, one would need to really know how to use those other individual programs in order to use them through Sage.

Scilab and Freemat are a-la-matlab kind of programs. Scilab seems rather popular with literature, conferences, etc.

Euler seems for mathematicians, fancy plots, reminds me of Mathematica.

Julia is picking up good, seems designed for good performance, very friendly language but I am not sure what kind of shape is in regards to libraries...I would like Julia to be my programming language of the future...we shall see...keeping an eye on it.

...for now, I am sticking with Python...many libraries not only for data analysis but also for plotting, and even for quick application prototyping, I am talking enabling some of us non-programmer engineers/scientists fairly easily and quickly build basic GUI-based applications (pyqwt, enthought, pyQt, pySide). Besides, once you learn Python, you will also be able to script many other applications that have chosen Python as their scripting language.

Anyway, that's just my non-educated opinion, no need to sue me.

Lastly, do you know anybody in the field that you are looking to get into? Have you asked anyone there what they use?
 
Numerical Analysis of

- Einstein Field Equations
- Schrödinger Equations

As a starter and graphs of solutions
 

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