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1.1 Introduction
Contents
• Introduction
– SciPy Organization
– Finding Documentation
SciPy is a collection of mathematical algorithms and convenience functions built on the Numpy extension for Python.
It adds significant power to the interactive Python session by exposing the user to high-level commands and classes
for the manipulation and visualization of data. With SciPy, an interactive Python session becomes a data-processing
and system-prototyping environment rivaling sytems such as MATLAB, IDL, Octave, R-Lab, and SciLab.
The additional power of using SciPy within Python, however, is that a powerful programming language is also available
for use in developing sophisticated programs and specialized applications. Scientific applications written in SciPy
benefit from the development of additional modules in numerous niche’s of the software landscape by developers
across the world. Everything from parallel programming to web and data-base subroutines and classes have been
made available to the Python programmer. All of this power is available in addition to the mathematical libraries in
SciPy.
This document provides a tutorial for the first-time user of SciPy to help get started with some of the features available
in this powerful package. It is assumed that the user has already installed the package. Some general Python facility
is also assumed such as could be acquired by working through the Tutorial in the Python distribution. For further
introductory help the user is directed to the Numpy documentation.
For brevity and convenience, we will often assume that the main packages (numpy, scipy, and matplotlib) have been
imported as:
>>> import numpy as np
>>> import scipy as sp
>>> import matplotlib as mpl
>>> import matplotlib.pyplot as plt
These are the import conventions that our community has adopted after discussion on public mailing lists. You will
see these conventions used throughout NumPy and SciPy source code and documentation. While we obviously don’t
require you to follow these conventions in your own code, it is highly recommended.
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SciPy Reference Guide, Release 0.11.0.dev-600e81f
1.1.1 SciPy Organization
SciPy is organized into subpackages covering different scientific computing domains. These are summarized in the
following table:
Subpackage Description
cluster Clustering algorithms
constants Physical and mathematical constants
fftpack Fast Fourier Transform routines
integrate Integration and ordinary differential equation solvers
interpolate Interpolation and smoothing splines
io Input and Output
linalg Linear algebra
maxentropy Maximum entropy methods
ndimage N-dimensional image processing
odr Orthogonal distance regression
optimize Optimization and root-finding routines
signal Signal processing
sparse Sparse matrices and associated routines
spatial Spatial data structures and algorithms
special Special functions
stats Statistical distributions and functions
weave C/C++ integration
Scipy sub-packages need to be imported separately, for example:
>>> from scipy import linalg, optimize
Because of their ubiquitousness, some of the functions in these subpackages are also made available in the scipy
namespace to ease their use in interactive sessions and programs. In addition, many basic array functions from numpy
are also available at the top-level of the scipy package. Before looking at the sub-packages individually, we will first
look at some of these common functions.
1.1.2 Finding Documentation
Scipy and Numpy have HTML and PDF versions of their documentation available at http://docs.scipy.org/, which
currently details nearly all available functionality. However, this documentation is still work-in-progress, and some
parts may be incomplete or sparse. As we are a volunteer organization and depend on the community for growth,
your participation - everything from providing feedback to improving the documentation and code - is welcome and
actively encouraged.
Python also provides the facility of documentation strings. The functions and classes available in SciPy use this method
for on-line documentation. There are two methods for reading these messages and getting help. Python provides the
command help in the pydoc module. Entering this command with no arguments (i.e. >>> help ) launches an
interactive help session that allows searching through the keywords and modules available to all of Python. Running
the command help with an object as the argument displays the calling signature, and the documentation string of the
object.
The pydoc method of help is sophisticated but uses a pager to display the text. Sometimes this can interfere with
the terminal you are running the interactive session within. A scipy-specific help system is also available under the
command sp.info. The signature and documentation string for the object passed to the help command are printed
to standard output (or to a writeable object passed as the third argument). The second keyword argument of sp.info
defines the maximum width of the line for printing. If a module is passed as the argument to help than a list of the
functions and classes defined in that module is printed. For example:
4 Chapter 1. SciPy Tutorial
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