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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