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# 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.

## 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.

## 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:

    >>> sp.info(optimize.fmin)
     fmin(func, x0, args=(), xtol=0.0001, ftol=0.0001, maxiter=None, maxfun=None,
          full_output=0, disp=1, retall=0, callback=None)

    Minimize a function using the downhill simplex algorithm.

    :Parameters:

      func : callable func(x,*args)
          The objective function to be minimized.
      x0 : ndarray
          Initial guess.
      args : tuple
          Extra arguments passed to func, i.e. ``f(x,*args)``.
      callback : callable
          Called after each iteration, as callback(xk), where xk is the
          current parameter vector.

    :Returns: (xopt, {fopt, iter, funcalls, warnflag})

      xopt : ndarray
          Parameter that minimizes function.
      fopt : float
          Value of function at minimum: ``fopt = func(xopt)``.
      iter : int
          Number of iterations performed.
      funcalls : int
          Number of function calls made.
      warnflag : int
          1 : Maximum number of function evaluations made.
          2 : Maximum number of iterations reached.
      allvecs : list
          Solution at each iteration.

    *Other Parameters*:

      xtol : float
          Relative error in xopt acceptable for convergence.
      ftol : number
          Relative error in func(xopt) acceptable for convergence.
      maxiter : int
          Maximum number of iterations to perform.
      maxfun : number
          Maximum number of function evaluations to make.
      full_output : bool
          Set to True if fval and warnflag outputs are desired.
      disp : bool
          Set to True to print convergence messages.
      retall : bool
          Set to True to return list of solutions at each iteration.

    :Notes:

        Uses a Nelder-Mead simplex algorithm to find the minimum of
        function of one or more variables.

Another useful command is `source`. When given a function written in Python as an argument, it prints out a listing of the source code for that function. This can be helpful in learning about an algorithm or understanding exactly what a function is doing with its arguments. Also don’t forget about the Python command `dir` which can be used to look at the namespace of a module or package.

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