Introduction to Python for Metabolic Modellers

The metabolic modelling software 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

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Likewise, sequences of characters are associated with the string type, and they too, can be added together:

"Apple" + "Pie" = "ApplePie"

However a string and an integer, cannot be meaningfully added together, so:

"Apple" + 2 = !! ERROR !!

A given data type can be as simple as a single digit, or as complex as a suite of genome databases. Python comes with a number of relatively simple (but still extremely useful) data-types, and ScrumPy extends this by providing additional data-types particularly useful for metabolic modelling and related activities.

We will start here by examining some of the common built-in types.

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.

   1 >>> s_1 = 'another string'   #create string
   2 >>> s_2 = s_1[:7]            #create new string of characters 0 to 7 in s_1
   3 >>> s_2
   4 'another'
   5 >>> if s_2 in s_1:          #check for membership of s_2 in s_1
   6         print 'true'
   7 
   8 true
   9 >>> type(s_1)
  10 <type 'str'>

Numerical types

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().

   1 >>> type(n_int)
   2 <type 'int'>
   3 >>>type(n_float)
   4 <type 'float'>

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

   1 >>> n_int2float=float(n_int)
   2 >>> n_int2float
   3 135.0
   4 >>> type(n_int2float)
   5 <type 'float'>   #n_int is still an integer

The common mathematical 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.

   1 >>> val=True
   2 >>> if val:
   3         print 'val is true'
   4 
   5 'val is true'
   6 
   7 >>> val=False
   8 >>> if val:
   9         print 'val is true'
  10 
  11 >>>

Lists (and tuples)

>>> val=True >>> if val:

• print 'val is true'

'val is true'

>>> val=False >>> if val:

• print 'val is true'

>>> }}}

Lists (and tuples)

Lists and tuples are collections of items in which are stored in a specific order and each item is associated with (indexed by) an integer. The main difference between the two is that tuples are immutable - once a tuple is created it cannot be changed, whereas lists can. For these exercises we will mainly use lists. An empty list can be created by assigning a pair of closed square brackets to a variable.

   1 >>> empty_list=[]

Items can be appended to a list by using the append() method.

   1 >>> empty_list.append('string 1')
   2 >>> empty_list
   3 ['string 1']

A list can also be created and populated in one go.

   1 >>> empty_list=['string 1']
   2 >>> empty_list
   3 ['string 1']
   4 
   5 
   6 {{{#!python
   7 >>> empty_list = ['string 1','string 2','string 3']
   8 >>> empty_list[0]
   9 'string 1'
  10 >>> empty_list[1]
  11 'string 2'
  12 >>> empty_list[2]
  13 'string 3'
  14 >>> empty_list[-1]
  15 'string 3'

Items can be removed from a list using the remove() method.

   1 >>> empty_list.remove('string 1')
   2 >>> empty_list
   3 []

As with strings, indexing can be used to copy a subset of a list, keep in mind that the indices of items in lists (like characters in strings) are numbered from 0. Membership of an item in a list can be evaluated as described for strings.

Subsets of lists (and strings) can be accessed using slicing:

   1 >>> empty_list[1:3]
   2 'string 2', 'string 3']

The index of a known item can be retrieved using the index() method.

   1 >>> empty_list.index('string_1')
   2 0

Lists can contain any objects, including other lists. These lists are referred to as nested:

   1 >>> empty_list = ['string 1','string 2','string 3']
   2 >>> nested = [1,2,3]
   3 >>> empty_list.append(nested)
   4 >>> empty_list
   5 ['string 1', 'string 2', 'string 3', [1, 2, 3]]
   6 >>> empty_list[3]
   7 [1, 2, 3]
   8 >>> empty_list[3][0]
   9 1

Subsets of lists (and strings) can be accessed using slicing:

   1 >>> empty_list[1:3]
   2 'string 2', 'string 3']

The index of a known item can be retrieved using the index() method.

   1 >>> empty_list.index('string_1')
   2 0

Lists can contain any objects, including other lists. These lists are referred to as nested:

   1 >>> empty_list = ['string 1','string 2','string 3']
   2 >>> nested = [1,2,3]
   3 >>> empty_list.append(nested)
   4 >>> empty_list
   5 ['string 1', 'string 2', 'string 3', [1, 2, 3]]
   6 >>> empty_list[3]
   7 [1, 2, 3]
   8 >>> empty_list[3][0]
   9 1

Dictionaries

In other programming languages dictionaries are sometimes called "associative arrays". Unlike lists, dictionaries store collections of items that are ordered by keys, not indices. There is no specific order of the items in a dictionary. The keys of a dictionary must be unique (for a given dictionary) and be hashable, for now this means that any object that is not a list can be used as a key. Here are some examples of dictionaries in action:

   1 >>> dict_1 = {'alfa':1,'beta':2}  #create a dictionary
   2 >>> keys = ['alfa','beta']
   3 >>> vals = [1,2]
   4 >>> dict_1 = dict(zip(keys,vals)) #create a dictionary from two lists
   5 >>> dict_1
   6 {'alfa':1,'beta':2}
   7 >>> dict_1['alfa']                #access value '1' by key 'alfa'
   8 1
   9 >>> dict_1.has_key('beta')        #check that dict_1 has key 'beta'
  10 True
  11 >>> dict_1.keys()                 #print keys of dict_1
  12 ['alfa','beta']
  13 >>> dict_1.values()               #print values
  14 [1,2]
  15 >>> dict_1['gamma'] = 1          #add new key:value pair
  16 >>> dict_1['alfa'] = 'a'         #overwrite key:value pair

As with lists, dictionaries can contain nested dictionaries:

   1 >>> dict_1 = {'alfa':1,'beta':2} #define first dictionary
   2 >>> dict_2 = {'gamma':1}         #define second dictionary
   3 >>> dict_1['nested'] = dict_2    #add dict_2 as value to key 'nested' in dict_1
   4 >>> dict_1
   5 {'beta': 2, 'alfa': 1, 'nested': {'gamma': 1}}
   6 >>> dict_1['nested']['gamma']    #access key 'gamma' in nested dictionary
   7 1

Modules and functions

user@machine:~$ more some_py_func.py

#print user-specified string def print_string(string):

• print string

}}}

   1 >>> import some_py          #import some_py.py (note absence of extension!)

   1 >>> reload(some_py_func)   #if an imported module is modified the changes are updated by reloading the module
   2 >>> some_py_func.print_string('Hello world!')
   3 
   4 Hello world!

It is usually more convenient to structure the stored code into functions that can be executed from the imported module. Functions are defined using the key-word def.

In many applications functions return objects to the user.

user@machine:~$ more some_py_func.py

#return list of characters in user-specified string
def print_string(string):
    return list(string)

   1 >>> import some_py_func
   2 >>> some_py_func.print_hello()
   3 Hello world!
   4 >>> def print_hello():          #functions can also be defined in the shell
   5         print 'Hello world!'
   6 >>> print_hello()
   7 Hello world!

Functions often require arguments that the user is supposed to provide.

user@machine:~$ more some_py_func.py

#print user-specified string
def print_string(string):
    print string


{{{#!python
>>> reload(some_py_func)   
>>> hello_list = some_py_func.print_string('Hello world!')
>>> hello_list
['H', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd', '!']

Objects

As mentioned, Python supports multiple programming paradigms, one of those being object-orientation. Object-orientation allows collection of data into objects, or class instances. The data collected in objectes is referred to as fields or attributes, objects also store functions that usually performs actions on the attributes. Object-specific functions are called methods. Here is a small example of class definition, initialisation, and usage of a class that stores a list and a dictionary.

user@machine:~$ more StringDL.py
class StringDnLs:
    def __init__(self):
        self.str_list=[]
        self.str_dict={}

    def add2List(self,item):
        self.str_list.append(item)

    def add2Dict(self,key,val):
        self.str_dict[key]=val

   1 >>> reload(some_py_func)   #if an imported module is modified the changes are updated by reloading the module
   2 >>> some_py_func.print_string('Hello world!')
   3 Hello world!

In many applications functions return objects to the user.

user@machine:~$ more some_py_func.py

#return list of characters in user-specified string
def print_string(string):
    return list(string)

   1 >>> reload(some_py_func)
   2 >>> hello_list = some_py_func.print_string('Hello world!')
   3 >>> hello_list
   4 ['H', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd', '!']

Objects

As mentioned, Python supports multiple programming paradigms, one of those being object-orientation. Object-orientation allows collection of data into objects, or class instances. The data collected in objectes is referred to as fields or attributes, objects also store functions that usually performs actions on the attributes. Object-specific functions are called methods. Here is a small example of class definition, initialisation, and usage of a class that stores a list and a dictionary.

user@machine:~$ more StringDL.py
class StringDnLs:
    def __init__(self):
        self.str_list=[]
        self.str_dict={}

    def add2List(self,item):
        self.str_list.append(item)

    def add2Dict(self,key,val):
        self.str_dict[key]=val

   1 >>> import StringDL                  #import module
   2 >>> strdn = StringDL.StringDnLs()    #create instance of class StringDnLs
   3 >>> strdn.str_list                   #print StringDnLs field str_list
   4 []                                   # which is empty
   5 >>> strdn.add2List('a string')       # add a string to str_list using method addList(item)
   6 >>> strdn.str_list
   7 ['a string']

Built-in functions, loops, conditionals, assignment, and evaluation

Some of the conventions for Python syntax we have already seen. Useful built-in functions include len(), which returns the length of an object,

   1 >>> a_list = ['a','b','c']
   2 >>> len(a_list)
   3 3

and dir(), which returns a list of methods and attributes of an object.

   1 >>> dir(strdn)  #strdn - as defined under Objects
   2 ['__doc__', '__init__', '__module__', 'add2Dict', 'add2List', 'str_dict', 'str_list']

You can read more about the built-in functions here.

The for loop is used to iterate over an iterable object, e.g. a list. Depending on how the loop is formulated the loop variable will either be an item in the iterable object or an index.

   1 >>> a_list = ['a','b','c']
   2 >>> for item in a_list:       #iterating over the items
   3         print item
   4 
   5 >>> range(0,10)
   6 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
   7 >>> range(10)
   8 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

The step size is 1 by default, but can be specified as the third argument:

   1 >>> range(0,10,2)
   2 [0, 2, 4, 6, 8]

Note that the step size must be an integer. If floating point steps are needed, the arange() function from the numpy package can be used. It is very similar to the range() function but accepts floating point arguments and returns array objects, which can be converted to lists using the method tolist()

   1 >>> from numpy import arange
   2 >>> arange(10)
   3 array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
   4 >>> arange(0,5,0.5).tolist()
   5 [0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5]

while loops iterate until a condition is fulfilled.

   1 >>> a_list = ['a','b','c']
   2 >>> i = 0                  #assign value 0 to variable i
   3 >>> while i<len(a_list):   #as long as i is less than 3
   4         print a_list[i]    #print item at index i in a_list
   5         i += 1             #increment i by 1
   6 
   7 a
   8 b
   9 c
  10 
  11 
  12 
  13 >>> for i in range(len(a_list)):   #iterating over indices
  14         print a_list[i]
  15 
  16 a
  17 b
  18 c

Note that range(integer_1,integer_2) is a built-in function that returns a list of integers ranging from the one integer, integer_1, to (but excluding) the other, integer_2. If the first argument is left blank, Python assumes integer_1 is 0. For example:

   1 >>> range(0,10)
   2 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
   3 >>> range(10)
   4 [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

The step size is 1 by default, but can be specified as the third argument:

   1 >>> range(0,10,2)
   2 [0, 2, 4, 6, 8]

Note that the step size must be an integer. If floating point steps are needed, the arange() function from the numpy package can be used. It is very similar to the range() function but accepts floating point arguments and returns array objects, which can be converted to lists using the method tolist()

   1 >>> from numpy import arange
   2 >>> arange(10)
   3 array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
   4 >>> arange(0,5,0.5).tolist()
   5 [0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5]

while loops iterate until a condition is fulfilled.

   1 >>> a_list = ['a','b','c']
   2 >>> i = 0                  #assign value 0 to variable i
   3 >>> while i<len(a_list):   #as long as i is less than 3
   4         print a_list[i]    #print item at index i in a_list
   5         i += 1             #increment i by 1
   6 
   7 a
   8 b
   9 c

This implies that if the condition i<len(a_list) is never fulfilled the loop continues indefinitely, which it will.

Loops can be combined with conditional statements, where a block of code is executed if a statement is true, else another block is executed. The else block is optional, but must be the last option and no statement may follow on the same line.

   1 >>> a_list = ['a',1,'c',2]
   2 >>> for item in a_list:
   3         if type(item)==type(str()):  #check if item is of string type
   4             print item+' is string'
   5         else:                        #if not...
   6             print str(item)+' is not string'
   7 
   8 'a is string'
   9 '1 is not string'
  10 'c is string'
  11 '2 is not string'

If several options are possible the elif statement can be used.

   1 >>> for item in a_list:
   2 
   3         if type(item)==type(str()):
   4             print item+' is string'
   5         elif type(item)==type(int()):
   6             print str(item)+' is int'
   7         else:
   8             print str(item)+' is unkown'
   9 
  10 'a is string'
  11 '1 is int'
  12 'c is string'
  13 '2 is int'

You may have noticed it already, but it is necessary to point out the distinction between assignment and evaluation:

}}}

You may have noticed it already, but it is necessary to point out the distinction between assignment and evaluation:

   1 >>> a = 'string' #assignment
   2 >>> a=='string'  #evaluation, is a equal to 'string'?
   3 True
   4 >>> a!='string'  #evaluation, is a not equal to 'string'?
   5 False