There is a great need for software in the
scientific community that can simplify and reduce the work required to solve
complex mathematical equations. Otherwise manually solving science related problems
would take forever and be error-prone. Scientific computing aims to
resolve complicated problems in a range of fields including the physical and
engineering sciences, finance and economics, medical, social and
biological sciences. It can enhance communication of information by
creating visual representations of scientific data. The major numerical computing
environment and programming-language that most have heard of is MATLAB.
Unfortunately MATLAB is proprietary software and thus has a high monetary cost.
Fortunately there are open source alternatives that have much, if not all, of
the capabilities required for scientific computations.
SciPy
is an open source computing environment built for the Python programming
language. The core elements of SciPy are the NumPy and SciPy libraries that include
all the algorithms and mathematical tools required for core scientific
computing. There are also additional libraries to expand the features of SciPy
such as the Matplotlib library which is used to show plots.
Here’s a list of some of SciPy’s features and their
packages:
• Special Functions (scipy.special)
• Signal Processing (scipy.signal)
• Fourier Transforms (scipy.fftpack)
• Optimization (scipy.optimize)
• General plotting (scipy.[plt, xplt, gplt])
• Numerical Integration (scipy.integrate)
• Linear Algebra (scipy.linalg)
• Input/Output (scipy.io)
• Genetic Algorithms (scipy.ga)
• Statistics (scipy.stats)
• Distributed Computing (scipy.cow)
• Fast Execution (weave)
• Clustering Algorithms (scipy.cluster)
• Sparse Matrices* (scipy.sparse)
These allow the creation of vast variety of functions required for use by the scientific community. If you are looking for a powerful open source computing environment for scientific computing visit their site at http://www.scipy.org/ and download the software.
Get started with Python and SciPy: Introduction to Scientific Computing
Good Post! I agree with you in the need of more quality open source software. I think it is important to not just rely in proprietary software. I know a little bit of python and I'm interested to become proficient with python. I didn't know about SciPy. It is always good to find good Open Source environments. Your post might be a little short, but good overall. Thanks for sharing.
ReplyDeleteI enjoy that you brought tangible deliciousness in the form of open source programs into the scope of the topic. Scientific computing is all about applying computing tools for visualization, compilation, and performing analysis on data. Modeling physics, biology, and other sciences including neurosciences requires programs tailored to the task. In providing specifics about the Python-based SciPy app, you do a great job providing a great learning tool. Thanks Sander, and Merry Christmas.
ReplyDeleteGood post. SciPy looks like a really cool and interesting application. I am glad that you found an Open Source program that has a good use and shared it with others. That is what is so cool about programs in general. They are all tools which can be used and shared with others.
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