Introducing python and the ipython notebook
Even if many tools of TRIQS are coded in C++, they usually have a user interface written in python. This means that everything is controlled by python scripts, pre- and post-processing of the data, choice of DMFT scheme, plotting, etc. We chose to use python because it is a very simple and intuitive language. Being an interpreted language, there is never a need to recompile code if you decide to change variables, add some extra pieces of data analysis, etc.
In the following we will provide a brief overview of some core aspects of the language. For a thorough introduction to Python for scientific data analysis we recommend these resources:
Python can be used either in interactive or in script mode.
In script mode, you edit a file (with an extension .py), say my_script.py, and run it with python. You do this from a shell:
>>> python my_script.py
Interactive shell mode
From a shell, you can also directly type
This will bring you in interactive mode. You can type commands one after the other and they are executed on the fly.
The ipython notebook
This is what you are looking at right now. It is also an interactive mode, with all your commands appearing in a “mathematica”-like notebook. The advantage, as we will see, is that the result of commands, even plots, directly appears and stays in the notebook.
Let us start with a first simple code that we will execute in the notebook. To do so, click on the input line below and execute it with Shift+Enter
As you can see the result of the command appears above.
As an exercise, run this same command
- using the script mode (edit a my_script.py file, add the print line above and execute the file from a shell)
- in interactive shell mode (you quit the interactive mode with Ctrl+d)
A very short introduction to python
The quickest way to learn python is certainly to follow the tutorial at the following link:
We will quickly show some examples that should be self-explanatory.
# Comments start with a # # Setting variables and doing calculations x = 3 y = 6.7 print('first result: ', (x+y)/4.2) # Using complex numbers i = 1j print('i^2: ', i*i) print('complex calculation: ', (2+4j)*(4-2j))
first result: 2.3095238095238093 i^2: (-1+0j) complex calculation: (16+12j)
Simple loops / indentation
# Look how indentation is used in python to define code blocks # Also note that range(5) produces numbers from 0 to 4 x = 1 for i in range(5): x = x + i print("i = ", i) print("x = ", x) print("That's it!")
i = 0 x = 1 i = 1 x = 2 i = 2 x = 4 i = 3 x = 7 i = 4 x = 11 That's it!
# Comparing symbols are == (equal), != (not equal), >, <, <=, >=, etc. for i in range(0, 10, 2): if i == 4: print("i is 4") elif i == 6: print("i is 6") else: print("i is different")
i is different i is different i is 4 i is 6 i is different
Defining a function
# Define a new function def fnct(x): y = x**2 - 5.0 return y print(fnct(3.))
# In order to have access to new functions, you # import them from a library. Here we import mathematical functions # from the math library. After the import, python knows about # cos and pi # to import everything in module math: # from math import * from math import cos,pi print("cos(pi/2) = %.3f"%(cos(pi/2.0))) print("cos(pi) = %.3f"%(cos(pi)))
cos(pi/2) = 0.000 cos(pi) = -1.000
# Lists are defined with  # Note that indices start at 0 (not 1 like in Fortran) l = [1,2,3,4] print("The second element of l is ", l) # Lists are not vectors, adding lists appends them l2 = [5,6] l3 = l+l2 print("l3 is ", l3)
The second element of l is 2 l3 is [1, 2, 3, 4, 5, 6]
The numpy library
numpy is a very important library in python. It mainly allows to manipulate arrays (matrices and vectors) and do linear algebra with them.
from numpy import array,dot # vectors v = array([1,1,2,3]) w = array([1,1,1,1]) print("Adding term by term: ", v+w) print("dot product: ", dot(v,w)) # matrices A = array([[1,0],[0,1]]) B = array([[1,2],[3,4]]) print("A x B = ") print(dot(A,B))
Adding term by term: [2 2 3 4] dot product: 7 A x B = [[1 2] [3 4]]
Defining a new class
This is for those that already know about object-oriented languages. In python, just like C++ for example, you can define your own classes. Here’s an example:
# A new class # Note that all member functions must have "self" as a first argument class MyObject: # The constructor is called __init__ def __init__(self, x): self.x = x def what_is_x(self): print("x is ", self.x) def change_x(self, x): self.x = x A = MyObject(10) A.what_is_x() A.change_x(12) A.what_is_x()
x is 10 x is 12
When you put a question mark after a command and type Ctrl-Enter it gives the help. If you type the parenthesis and then press Tab it will tell you what arguments are expected.