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Spelling and capitalization in scientific.rst
Changed python to Python and corrected some spelling
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docs/scenarios/scientific.rst

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@@ -9,7 +9,7 @@ Python is frequently used for high-performance scientific applications. Python
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is widely used in academia and scientific projects because it is easy to write,
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and it performs really well.
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Due to its high performance nature, scientific computing in python often refers
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Due to its high performance nature, scientific computing in Python often refers
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to external libraries, typically written in faster languages (like C, or
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FORTRAN for matrix operations). The main libraries used are `NumPy`_, `SciPy`_
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and `Matplotlib`_. Going into detail about these libraries is beyond the scope
@@ -24,11 +24,11 @@ Tools
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IPython
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-------
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`IPytthon <http://ipython.org/>`_ is an enhanced version of Python interpreter.
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`IPython <http://ipython.org/>`_ is an enhanced version of Python interpreter.
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The features it provides are of great interest for the scientists. The `inline mode`
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allow graphics and plots to be displayed in the terminal (Qt based version).
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Moreover the `notebook` mode supports literate programming and reproducible science
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generating a web-based python notebook. This notebook allowing to store chunk of
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generating a web-based Python notebook. This notebook allowing to store chunk of
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Python code along side to the results and additional comments (HTML, LaTeX, Markdown).
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The notebook could be shared and exported in various file formats.
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@@ -64,7 +64,7 @@ SciPy
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`SciPy <http://scipy.org/>`_ is a library that uses Numpy for more mathematical
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functions. SciPy uses NumPy arrays as the basic data structure. SciPy comes
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with modules for various commonly used tasks in scientific programing, for
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with modules for various commonly used tasks in scientific programming, for
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example: linear algebra, integration (calculus), ordinary differential equation
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solvers and signal processing.
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@@ -86,7 +86,7 @@ based on Numpy and which provides many useful functions for accessing,
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indexing, merging and grouping data easily. The main data structure (DataFrame)
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is close to what could be found in the R statistical package, that is
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an heterogeneous data tables with name indexing, time series operations
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and auto-alignement of data.
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and auto-alignment of data.
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Rpy2
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----
@@ -120,7 +120,7 @@ Many people who do scientific computing are on Windows. And yet many of the
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scientific computing packages are notoriously difficult to build and install.
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`Christoph Gohlke <http://www.lfd.uci.edu/~gohlke/pythonlibs/>`_ however, has
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compiled a list of Windows binaries for many useful Python packages. The list
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of packages has grown from a mainly scientific python resource to a more
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of packages has grown from a mainly scientific Python resource to a more
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general list. It might be a good idea to check it out if you're on Windows.
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Enthought Python Distribution (EPD)

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