Numerical and Scientific Computing in Python

[Pages:49]Numerical and Scientific Computing in Python

v0.1.2 Summer 2019

Research Computing Services IS & T

RCS Team and Expertise

Our Team

Scientific Programmers Systems Administrators Graphics/Visualization Specialists Account/Project Managers Special Initiatives (Grants) Maintains and administers the Shared Computing Cluster

Located in Holyoke, MA ~19,000 CPUs running Linux

Consulting Focus:

Bioinformatics Data Analysis / Statistics Molecular modeling Geographic Information Systems Scientific / Engineering Simulation Visualization

CONTACT US: help@scv.bu.edu

Running Python for the Tutorial

If you have an SCC account, log on and use Python there.

Run:

module load python/3.6.2 spyder &

Note that the spyder program takes a while to load!

Run Spyder

Click on the Start Menu in the bottom left corner and type: spyder

After a second or two it will be found. Click to run it.

Be patient...it takes a while to start.

Download from the RCS website

Open a browser and go to:



Click on the numerical_sci_python.zip file and download it.

After it downloads extract it to a folder:

This presentation is there Several example files can be found there as well.

SCC Python Tutorials

Introduction to Python, Part one Introduction to Python, Part two Numerical and Scientific Computing in Python Python for Data Analysis Data Visualization in Python Introduction to Python Scikit-learn

Outline

Python lists The numpy library Speeding up numpy: numba and numexpr Libraries: scipy and opencv Alternatives to Python

Python's strengths

Python is a general purpose language.

In contrast with R or Matlab which started out as specialized languages

Python lends itself to implementing complex or specialized algorithms for solving computational problems.

It is a highly productive language to work with that's been applied to hundreds of subject areas.

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