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SciPy (pronounced “Sigh Pie”) is an open source Python library used by scientists, analysts, and engineers doing scientific computing and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering. SciPy builds on the NumPy array object and is part of the NumPy stack which includes tools like Matplotlib, pandas and SymPy. There is an expanding set of scientific computing libraries that are being added to the NumPy stack every day. This NumPy stack has similar users to other applications such as MATLAB, GNU Octave, and Scilab. The NumPy stack is also sometimes referred to as the SciPy stack.〔(【引用サイトリンク】title=Scientific Computing Tools for Python )〕 SciPy is also a family of conferences for users and developers of these tools: SciPy (in the United States), EuroSciPy (in Europe) and SciPy.in (in India). Enthought originated the SciPy conference in the United States and continues to sponsor many of the international conferences as well as host the (SciPy ) website. The SciPy library is currently distributed under the BSD license, and its development is sponsored and supported by an open community of developers. It is also supported by (Numfocus ) which is a community foundation for supporting reproducible and accessible science. ==Python Scientific Computing Environment== A typical Python Scientific Computing Environment includes many (dedicated software tools ). For example, * Plotting. The currently recommended 2-D plotting package is Matplotlib, however, there are many other plotting packages such as HippoDraw, Chaco, Biggles, and (Bokeh ). Other popular graphics tools include Python Imaging Library and MayaVi (for 3D visualization). * Optimization. While SciPy has its own optimization package, OpenOpt has access to more optimization solvers and can involve Automatic differentiation. CVXOpt is another popular optimization library. * Advanced data analysis. Via RPy, Python can interface to the R statistical package for advanced data analysis. * Database. NumPy can interface with (PyTables ), a hierarchical database package designed to efficiently manage large amounts of data using HDF5. * Interactive shell. IPython is an interactive environment that offers debugging and coding features similar to that which MATLAB offers. * Symbolic mathematics. There are several Python libraries—such as (PyDSTool ) Symbolic and SymPy—that offer symbolic mathematics. * Specialized extensions. The ("scikits" ) provide special-purpose add-ons to NumPy and Python. Of these, scikit-image, scikit-learn and statsmodels are mature packages. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「SciPy」の詳細全文を読む スポンサード リンク
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