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.
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