NUMERICAL PYTHON - Cineca

NUMERICAL PYTHON

Python for computational science

19 - 21 Nov 2012 CINECA

m.cestari@cineca.it

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Introduction (1)

Numerical Python: first implemented as 2 different libraries Numeric, Numarray NumPy comprehends all the features of the two libraries It is the de facto standard

NumPy offers efficient array computation; feat highly required by scientific users

most of the scientific and mathematical Python-based packages use internally NumPy arrays

Introduction (2)

Sub-Packages

Purpose

Comments

core lib linalg fft

basic objects

all names exported to numpy

Addintional utilities

all names exported to numpy

Basic linear algebra

LinearAlgebra derived from Numeric

Discrete Fourier transforms FFT derived from Numeric

random distutils testing

f2py

Random number generators

Enhanced build and distribution unit-testing

Automatic wrapping of Fortran code

RandomArray derived from Numeric

improvements built on standard distutils

utility functions useful for testing

a useful utility needed by SciPy

Introduction (3)

As usual, to import the NumPy module type:

>>> import numpy >>> from numpy import * >>> import numpy as np

# default for this presentation # and for standard documentation

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