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The metabolic modelling we will be using, {{ScrumPy}}}, is written in {{{Python}}}.
{{{Python}}} is a high-level, object-oriented, interpreted programming language, it has a
lange standard library, and supports multiple programming paradigms. It is also syntactically clear and easy to learn. This is a very brief introduction to some of the basic features of the language, for a
more complete introduction to the topic, see Lutz & Ascher, "Learning Python" O'Reilly Media inc. (Edition 2 or greater).		 The metabolic modelling we will be using, {{{ScrumPy}}}, is written in {{{Python}}}. {{{Python}}} is a high-level, object-oriented, interpreted programming language, it has a large standard library, and supports multiple programming paradigms. It is also syntactically clear and easy to learn. This is a very brief introduction to some of the basic features of the language, for a more complete introduction to the topic, see Lutz & Ascher, "Learning Python" O'Reilly Media inc. (Edition 2 or greater). A good source of {{{Python}}} documentation can be found [[http://docs.python.org/ | here]].
Line 10: Line 7:
We will be using {{{Python}}} from the {{{ScrumPy}}} environment. We will be using {{{Python}}} from the {{{ScrumPy}}} environment. To start a new {{{ScrumPy}}} session open
a terminal and type "{{{ScrumPy}}}":

{{{
user@machine:~$ ScrumPy &
}}}

which will launch the {{{ScrumPy}}} window.


== Data types ==

=== Numbers ===

The numerical types we will be dealing with are integers, {{{int}}}, and floating-point numbers {{{float}}}. Integers are written as a sequence of digits. Floats are written as digits with a decimal point in the sequence, and an optional exponent ({{{e}}} or {{{E}}}).

{{{#!python
>>> n_int = 135
>>> n_int
135
>>> n_float = 10e-10
>>> n_float
1.0000e-9
}}}

The type of a given data object can be checked using the built-in function {{{type()}}}.
{{{#!
>>> type(n_int)
<type 'int'>
>>>type(n_float)
<type 'float'>
}}}

Floats and integers can be interconverted using the constructors {{{int()}}} or {{{float()}}}.
{{{#!
>>> n_int2float=float(n_int)
>>> n_int2float
135.0
>>> type(n_int2float)
<type 'float'> #n_int is still an integer
}}}

The common matematical operators ({{{+,-,/,*}}}) work as expected, note that {{{x**y}}} means ''x''^y^.



=== Boolean ===

Booleans are a subtype of integers. A boolean type is either {{{True}}} or {{{False}}}, and can be very useful when writing conditional statements, i.e. {{{if something is True, do something}}}. Also, the integer {{{0}}} is {{{False}}}.

{{{#! python
>>> val=True
>>> if val:
        print 'val is true'

val is true

>>> val=False
>>> if val:
        print 'val is true'

>>>
}}}

=== Strings ===

Strings are collections of characters. Characters in a string can be accessed by ''indexing'', and ''membership'' of a subset of characters in a string can be evaluated.

{{{#! python
>>> s_1 = 'another string' #create string
>>> s_2 = s_1[:7] #create new string of characters 0 to 6 in s_1
>>> s_2
'another'
>>> if s_2 in s_1: #check for membership of s_2 in s_1
        print 'true'

true
}}}



== Lists and tuples ==

== Dictionaries ==

== Modules ==

== Objects ==

== Loops and conditionals ==

Introduction to Python

The metabolic modelling we will be using, ScrumPy, is written in Python. Python is a high-level, object-oriented, interpreted programming language, it has a large standard library, and supports multiple programming paradigms. It is also syntactically clear and easy to learn. This is a very brief introduction to some of the basic features of the language, for a more complete introduction to the topic, see Lutz & Ascher, "Learning Python" O'Reilly Media inc. (Edition 2 or greater). A good source of Python documentation can be found here.

Getting started

We will be using Python from the ScrumPy environment. To start a new ScrumPy session open a terminal and type "ScrumPy": 

user@machine:~$ ScrumPy &

which will launch the ScrumPy window.

Data types

Numbers

The numerical types we will be dealing with are integers, int, and floating-point numbers float. Integers are written as a sequence of digits. Floats are written as digits with a decimal point in the sequence, and an optional exponent (e or E). 

   1 >>> n_int = 135
   2 >>> n_int
   3 135
   4 >>> n_float = 10e-10
   5 >>> n_float
   6 1.0000e-9

The type of a given data object can be checked using the built-in function type(). 

>>> type(n_int)
<type 'int'>
>>>type(n_float)
<type 'float'>

Floats and integers can be interconverted using the constructors int() or float(). 

>>> n_int2float=float(n_int)
>>> n_int2float
135.0
>>> type(n_int2float)
<type 'float'>   #n_int is still an integer

The common matematical operators (+,-,/,*) work as expected, note that x**y means xy.

Boolean

Booleans are a subtype of integers. A boolean type is either True or False, and can be very useful when writing conditional statements, i.e. if something is True, do something. Also, the integer 0 is False. 

>>> val=True
>>> if val:
        print 'val is true'

val is true

>>> val=False
>>> if val:
        print 'val is true'

>>>

Strings

Strings are collections of characters. Characters in a string can be accessed by indexing, and membership of a subset of characters in a string can be evaluated. 

>>> s_1 = 'another string'   #create string
>>> s_2 = s_1[:7]            #create new string of characters 0 to 6 in s_1
>>> s_2
'another'
>>> if s_2 in s_1:          #check for membership of s_2 in s_1
        print 'true'

true

Lists and tuples

Dictionaries

Modules

Objects

Loops and conditionals

None: Meetings/Delhi2012/Practicals/Practical_2/PyIntro (last edited 2012-10-12 13:53:26 by hassan)