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The Atomic Simulation Environment (ASE) is accessed through Python scripts, so it is thus necessary to learn some basic Python in order to perform calculations. The advantage is that ASE modules can be seamlessly used along with regular Python code, which makes it extremely easy to write scripts and programs for efficiently setting up and running a large number of calculations. Refer to the Python documentation for more detailed information. The following tutorial only convers the basic Python knowledge necessary for writing simple scripts.
Python can be run in different ways. In the terminal, run python to access the interactive prompt. Each line of code in a Python script can be executed interactively using the prompt. You can type exit() or press ctrl + D to exit the prompt.
>>> print "Hello world!"
>>> exit()
The interactive prompt is useful for testing small pieces of code, but it quickly becomes impractical for non-trivial tasks. More typically, you will be writing all your code in a .py file and then using the python command to execute the script
python scriptname.py arg1 arg2
this will use the python command to execute scriptname.py, taking in arguments arg1, arg2, and etc. You can also execute a script and then run additional commands interactively using the -i flag.
python -i scriptname.py arg1 arg2
This will execute scriptname.py taking in arguments arg1 and arg2, and then open the interactive prompt. You can access all variables, modules, and etc. that are available in scriptname.py.
Here are some basic data types for numbers:
i = 1 # an integer
i = 3/2 # −−>1, also an integer
i = 3./2 # −−>1.5
i = 3/2. # −−>1.5
i = 2**0.5 #−−>sqrt(2)
i = 5.1 + 2.4j # a complex number
Convenient built-in functions exist for converting amongst the data types:
i = int(1.) # an integer
i = float(1) # a floating point number
Strings must be terminated with the same type of quotation, either ' or ".
a = 'a "string"' # using single quotations
b = "and another 'string'" # using double quotations
They can be concatenated by using the + operator
c = a + b
You can also convert numbers into strings using str()
string = str(c)
You can also convert strings into numbers, as long as the content of the strings are convertible
number = int('10') # an integer
number = float('10') # a floating point
Strings are containers whose elements can be accessed by their index using square brackets [ ]
print b[1:3] # prints 'nd'
print len(b) # --> 18
print ' square root of 2=%.8f ' % (0.5**0.5) # prints 8 first digits of 1/sqrt(2)
Here are some of the common operators for assigning and comparing values. The mathematical operators +, -, *, / are used for addition, subtraction, multiplication, and division. For exponents, the double asterick ** is used.
The % operator is used to determine the remainder, as in
4%3 # --> 1
As you have already seen, = is the assignment operator, as in
a = 1
A few other convenient assignment operators are +=, -=, *=, /=, %=, and **=, which perform the operation before the equals sign before assigning it to the variable. Some examples
c += a # c = c+a
c -= a # c = c-a
c *= a # c = c*a
c /= a # c = c/a
c **= a # c = c**a
The usual comparison operators ==, !=, >, <, >=, and <= are used for testing for equallity, inequality, greater than, less than, greater or equal than, and less than or equal than. These operators return True or False.
The logical operators and, or, and not are convenient for using compound comparisons
1 == 1 and not 1 == 2 # returns True
The membership operator in are convenient for testing if an element is inside a container.
1 in [1,2,3] # True
1 not in [1,2,3] # False
In Python, scope is defined by indentation. Either tabbed spaces or regular spaces will work. As long as you are consistent, it will avoid confusing error messages. Scope is used to define code blocks in control flow (if, else, elif, etc.), in loops (for loops and while loops), in function definitions, and in class definitions.
if a>b:
print 'a is greater than b'
if a>=c and a<=d:
print '...and a lies in [c,d]'
elif a==b:
print 'a equals b'
else :
print 'a is less than b'
For loops are written using the for <element> in <list>: syntax, where the <element> represents each element in the <list>.
for i in range(3):
print i /2.
#output:
# 0.0
# 0.5
# 1.0
While loops:
i = 10.
while i <1000.:
print i
i *= 10.5
#output:
# 10.0
# 105.0
Lists in Python can contain mixed data types. This sets up an empty list a
a = []
The append() method can be used to add items into the list. It is accessed using the “dot notation,” such as in a.append() since the append() is a method of the list object.
a.append(1) # list now contains integer 1
a.append('cat') # list now contains string 'cat'
You can print lists and their contents
print b # prints the entire list
print b[0], b[−1] # prints first and last element of b
print b[1:4] # 2nd,3rd,4th element of b
Many methods are available for modifying lists
c = [3,1,2]
c.sort() #−> 1,2,3
c.reverse() #−> 3,2,1
c.pop(1) #−> 3,1
print len(c) #−> 2
You can also have lists of lists
d = [[1.,0.,3.], [0. ,4. ,0.] , [0. ,0. ,8.]]
print d[0][2] # prints 3.0
Python supports a concept called list comprehension where you can generate lists concisely instead of using loops. To create a list of squares
squares = []
for x in range(10):
squares.append(x**2)
This can instead be done in a single line using
squares = [x**2 for x in range(10)]
Here range(10) generates a list from 0 up to but not including 10 at intervals of 1, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9].
Tuples are similar to lists except they are initialized using round brackets ( ) and they are immutable, which means their contents cannot be modified. These are useful if the container contains values that don’t need to be modified.
b = (1,2)
If you try b[0] = 2 you will get TypeError: 'tuple' object does not support item assignment.
Dictionaries are one of the most convenient containers in Python. To set up an empty dictionary
c = {} # an empty dictionary
Dictionaries contain key-value pairs, where given key in the dictionary will return the corresponding value dict[key] = value. They can contain mixed data types (e.g. strings and integers at the same time). To add key-value pairs
c['fish'] = 'green'
c[4] = 'blue'
c['x'] = 1
You can retrieve all keys and all values, or just the value corresponding to the given key
print c.keys() #−−> ['x',4,'fish']
print c.values() #−−> [1,'blue','green']
print c[4] #−−> 'blue'
You can also initialize the dictionary with all key-value pairs
d = {'a':1, 'b':2, 'c':'d'}
print d['b'] #−−> 2
d['b'] = 4
print d['b'] #−−> 4
Dictionaries also include other convenient methods, such as items(), which returns a list of tuples containing the key-value pairs, and has_key() which returns either True or False depending on whether or not the dictionary contains the key.
print d.items() #−−> [('a', 1), ('c', 'd'), ('b', 4)]
print d.has_key('e') #−−> False
File input and output is important for reading in results and writing out results for the ASE calculations.
To open a file for reading or writing:
f = open('file1.txt', 'r') #open a file for reading 'r'
g = open('output.txt', 'w') #open a file for writing 'w'
for x in f.readlines():
y = x.split() #split up each line by white spaces
print >>g, float(y[0])/3., float(y[−1])/2.
#write third of first and half of last column
g.close()
f.close()
If the file doesn’t exist, it will be created.
To open a file and add a line
f = open('file1.txt', 'a') #open file in append 'a' mode
for i in range(10):
print >> f, i
f.close()
To process a file line by line:
f = open('biginput.txt').readlines()
for line in f:
line.split()[0] # split the lines by spaces and return the first element
f.close()
Functions are defined using def and the proper scope indentation. To write an addition function,
def add(a,b):
return a + 2*b
Then add(1,2) will return 5.
A lot of useful modules are installed system-wide. They provide useful extended functionality. In addition to the ase modules, you will probably be using the numpy module most frequently. numpy provides array containers for matrix and matrix manipulation, similar to MATLAB.
To use a module, run
import numpy as np
this imports the numpy module as np, which is just a shorthand. All methods and classes contained in the numpy moduled can then be accessed using the dot notation
a = np.array([[1.,2.],[3.,4.]]) #initialize an array
b = np.array([[2.,4.],[6.,8.]]) #initialize another array
c = np.dot(a,b) #dot product
d = a + b #sum
If you only need certain methods in the module, you can import them selectively using from. Then the methods can be used without the module name and the dot notation.
from numpy import array
a = array([[1.,2.],[3.,4.]])
Modules that are not loaded by default must be loaded using the import statement.
The Atomic Simulation Environment (ASE) is simply another Python module that can be loaded using
import ase
Continue onto the ASE Tutorials to learn more.