Tutorial Guide
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Control System I ETHZ
Python tutorial
September 2018
1
What is Python?
Python is a high level, object oriented, interpreted language:
• high level: the programmer does not have to care about low-level implementation details such as registers, memory addresses but handles variables, arrays, objects, logic constructors, loops.
• object oriented : programming is based on the concept of objects which
can contain data in form of fields known as attributes and functions known
as methods.
• interpreted : the original program is translated in something (bytecode)
that a virtual machine, called interpreter can understand and execute.
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Why Python?
Python is:
• Easy to learn : few basic features and self-explanatory syntax. Would
you guess what does the following code return?
if 3 in [1 ,2 , 3 ] :
print ( " Hello ! " )
• Support: the majority of libraries are open source with a big development
and support community. If you are in trouble google your problem, in the
99 % of the cases the answer is out there.
• Object oriented: in Python everything is an object. Thinking in a
object oriented way helps to better organize the code and facilitate the
transition to more complex programming languages as C/C++.
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3
What Python is used for?
• Data Science and Machine Learning: Python is one of the most used
programming language as high-level interface for machine learning and
numerical algorithms libraries. During this course, we will make use of
some of them: numpy, scipy, signal just to name a few.
• Glue code: it means that Python can interface and interoperate multiple
pieces of code together.
• Web browsers and applications’ GUI.
3.1
Which version?
Python 3.x is a newer version and as such supports newer features. On the
other hand, python 2.7 will only receive updates until 2020 [1], and is used for
facing compatibility issues. The tutorials and the course programming exercises
will use python 3.x. If your PC already has another version, follow the provided
installation instructions.
4
IDEs
How do we write code? Theoretically the minimum setup is given by any text
editor and a Python interpreter. This solution would work but is not the best for
fast development and troubleshooting. IDEs are programs designed for making
our lives easier.
IDE stands for Integrated Development Environment and is a program dedicated to software development. IDEs generally supports useful features such
as:
• save and reload code files
• run code within the IDE
• built-in debugger
• syntax highlighting and auto-completion
• automatic code formatting: this is an important features since python is
a indention based programming language, meaning that
for i in range ( 2 ) :
print ( 1 )
print ( 2 )
and
for i in range ( 2 ) :
print ( 1 )
print ( 2 )
2
give different results, respectively:
>>
>>
>>
>>
1
1
2
2
and
>> 1
>> 1
>> 2
A good editor will highlight code blocks and indentation errors, making
debugging easier.
5
What is a Jupyter Notebook?
The Jupyter Notebook is an open-source web-based application that allow you to
create and show documents that contain live code, equations, visualizations and
narrative text. It is used for numerical simulations, modeling, data visualization,
etc [2].
The Jupyter Notebook App can be executed on a local desktop and in addition to displaying/editing/running notebooks documents, has a ”dashboard”,
a ”control panel” showing local files and able to run and close active kernels.
In the following sections, we will guide you through the installation instructions of PyCharm, one of the most used python IDE and the Jupyter Notebook. The first will be used for development and main tool for coding. The
second will be a useful tool for reports, data visualization and the coming python
tutorials.
6
Installation instructions (Windows/Linux)
6.1
6.1.1
Setup Python
Using Anaconda
A convenient way to install python 3.x together with the Jupyter application
is to use the Anaconda distribution. The advantage is that you have access to
over 720 scientific packages. The system requirement is around 3 Gb of free
space on disk.
Linux
1. Go to https://www.anaconda.com/download/, select and save Python
3.6 version
3
2. Open a terminal, go to the download folder and change the script mode
to executable. Then execute the script:
$ cd {installation folder path}
installation-folder-path$ chmod +x Ananconda3-5.2.0-linux-x86 64.sh
{installation-folder-path}$ ./Ananconda3-5.2.0-linux-x86 64.sh
3. Follow the installations instructions in the terminal and when asked to
install Visual Studio Code, prompt no.
4. Add Anaconda path to the system PATH. Open with your favourite editor
the bash file .bashrc. E.g, using gedit and append the line
export PATH={anaconda3-install-directory}/bin:$PATH
Save and exit. In the terminal type source ./bashrc. You can verify
the installation typing:
$ conda
The conda command manual will show in the terminal. conda is a tool for
managing and deploying applications, environments and packages. For installing a new package you can simply type conda install
in a terminal console.
Windows
1. Go to https://www.anaconda.com/download/, select and save Python
3.6 version.
2. Click on the downloaded executable and follow the default installation
instructions. Do not install Visual Studio.
3. Add Anaconda path to the system PATH. Open a windows terminal by
typing cmd in the search window. Click on Command Prompt. Type
set PATH=%PATH%;;
/Scripts
Here we provide an example:
C:\Users\giuseppe>> set PATH=%PATH%;C:\Users\giuseppe\Anaconda3;
C:\Users\giuseppe\Anaconda3\Scripts
Mac
1. Go to https://www.anaconda.com/download/, select and save Python
3.6 version.
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2. Click on the downloaded executable and follow the default installation
instructions. Do not install Visual Studio.
3. Open Terminal and run the command
$ python3 -V
R
4. Run the command
$ open ~/.bash profile
and a text editor will open. Replace the line ”export PATH=...” with
export PATH="$PATH:/Users/thomasshipley/anaconda/bin"
Save the file, quit your terminal and restart it
5. When you run python3 -V it should now return ”Python 3.6.0”
6.2
Using ‘apt-get‘ and ‘pip‘ (recommended for Linux)
Ubuntu 16.04, and other versions of Debian Linux ship with both Python 3 and
Python 2 pre-installed. To make sure that our versions are up-to-date, let’s
update and upgrade the system with apt-get:
$ sudo apt-get update
$ sudo apt-get -y upgrade
The -y flag will confirm that we are agreeing for all items to be installed,
but depending on your version of Linux, you may need to confirm additional
prompts as your system updates and upgrades. Once the process is complete,
we can check the version of Python 3 that is installed in the system by typing:
$ python3 -V
You’ll receive output in the terminal window that will let you know the version number. The version number may vary depending on whether you are on
Ubuntu 16.04, or another version of Linux, but it will look similar to this:
Python 3.5.2
To manage software packages for Python, let’s install pip:
$ sudo apt-get install -y python3-pip
5
A tool for use with Python, pip installs and manages programming packages we
may want to use in our development projects. You can install missing Python
packages typing:
$ pip3 install --user {package name}
Here, package name can refer to any Python package or library. So if you
would like to install NumPy, you can do so with the command pip3 --user
install numpy. The option user is to make sure that installation is local and
not system-wide. There are a few more packages and development tools to install to ensure that we have a robust set-up for our programming environment:
$ sudo apt-get install build-essential libssl-dev libffi-dev python3-dev
6.3
6.3.1
Installing Jupyter
With Anaconda
Anaconda already comes with Jupyter Notebook app.
Linux You need to add Anaconda’s bin folder to the PATH environment variable in order to find the Jupyter Notebook application (if you still did not do
it in the previous steps). Modify the .bashrc file. You can use any text editor,
in the example we use gedit: gedit .bashrc. Append then the following line:
$ export PATH={anaconda3-install-directory}/bin:$PATH
Save and exit. In the terminal type source ./bashrc. Now you can open
the Jupyter web server typing the command jupyter notebook
Windows In your Start menu, after Anaconda installation, you’ll have a
bunch of neat new tools, including an entry for Jupyter Notebook. Click to
start it up and it will launch in the background and open up your browser to
the notebook console.
Mac Open Anaconda-Navigator through Launchpad and launch jupyter notebook. It will open up your browser to the notebook console.
With pip3 (Linux) First, ensure that you have the latest pip: older versions
may have trouble with some dependencies:
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$ python3 -m pip install --user --upgrade pip
Then install the Jupyter Notebook using:
$ python3 -m pip install --user jupyter
You can now start using Jupyter Notebook typing jupyter notebook in a open
terminal.
7
PyCharm
PyCharm is the standard de facto IDE for professional development of Python
applications. As a student, you are eligible for the free professional version.
1. Go to https://www.jetbrains.com/shop/eform/students, fill the form
with your details and follow the registration instructions.
2. You can now login with your new credentials at http://www.jetbrains.
com and access to all tools available for downlads with student license.
3. Under the tab download select PyCharm 2018.2.2 and download the
Linux, Windows or Mac distribution.
Linux
• Extract the content of the tar archive in your favourite directory
• Open a console and cd into the PyCharm folder:
$ cd
/{pycharm-dir}/bin/{pycharm-dir}/bin$ ./pycharm.sh
• Activate the program using your Jetbrains credentials.
• Follow the installation instructions and immediately start PyCharm
creating the first untitled project. We require this step for creating a
icon from which we can easily start PyCharm. In the IDE window,
click Tools first and then select Create Desktop Entry, press Ok.
• You can now close the IDE and open it from the launcher icon.
Windows
• Follow the Graphical installer instructions.
7
• Check 32-bit launcher or 64-bit launcher according to your platform
and associate .py extension so that Windows will automatically
use Pycharm as default program for opening python scripts.
• Activate the program using your Jetbrains credentials.
Mac
• Open the package and drag/drop PyCharm into your application
folder
• Open PyCharm and activate the program using your Jetbrains credentials.
7.1
Using Jupyter Notebook in PyCharm
Any python script (program) belongs to a project. Create a new folder called
test, this will contain all projects files. You can create a project by opening
your brand new PyCharm and selecting New Project. This will open the Create
Project window. Specify as location the test folder by clicking on the three dots
and navigating through the dialog window. Do not click on Create yet.
Before starting with coding , we need to select the proper interpreter. Linux
comes with a preinstalled python 2.7 and Python 3. You can either choose
Python 3 interpreter shipped with native Ubuntu or in case you want to use
additional packages provided by Anaconda we need to change it to the Anaconda
interpreter. After we add this new entry, it will not be necessary to repeat these
steps for future projects.
1. In the Create Project window click on Project Interpreter: Python x.x.
2. After selecting Existing Interpreter specify the Anaconda interpreter if
you previously installed it. For Windows users, after clicking Existing
Interpreter, just select System interpreter on the left and PyCharm will
authomatically select the Anaconda interpreter. In case you did not install
Anaconda, just select the default python 3.x interpreter. Again, click on
the three dots and navigate to the Anaconda installation folder.
3. Finally, click on Create.The apparently scary PyCharm IDE window will
appear. Do not panic. We will not need all of its features and you will
learn more by practising this great tool. The created project is empty,
but not for long!
4. In the leftmost column (the Project Explorer ) right click onto the project
folder (test), then New → Python File. Call it hello.
5. Repeat the same procedure but now create a Jupyter Notebook file by
selecting the corresponding entry in the scroll-down menu. Now your
project will contain 2 files:
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• test
– hello.ipynb
– hello.py
8
My first Python program
Double click on hello.py. In the editor window, start writing your first Python
program by typing:
print ( " Hello world ! " )
In order to run the code you can go to Run → Run ’hello’. The output will
likely be similar to the following:
/home/username/anaconda3/bin/python /home/username/test/hello.py
Hello world!
Process finished with exit code 0
Congratulations! You have successfully run your first python program.
9
My first Jupyter Notebook
If you are a Linux user open a terminal and type jupyter notebook. If you
are a Windows user just open the Jupyter client from the Start Menu. Jupyter
dashboard will appear, showing the content of the local directory. Navigate to
the folder where you want your first notebook to be saved. Than click on New→
Python3. A new notebook named Untitled will open. Clik on Untitled and
give it a new name, like FirstNotebook. Alternatively you can navigate to the
new notebook created in the test project. Jupyter notebooks have 2 types of
line:
• code line: where you can write normal Python code
• markdown line: where you can write explanatory text in Markdown.
You can find many Markdown cheat sheet on the web [3].
Click on the first line and type print("Hello world!"). Click on the plus
sign, this will add a new line. Generally the newly added line will be a code
line. To change its mode to Markdown, select Markdown from the scroll-down
menu. Copy past the following text:
The command ‘print(string)‘ prints to screen **string**.
Now select the first code line and click on the play button. This will run the
code contained inside the cell. For any code cell, Jupyter associate a pair of
square brackets. An asterisks appears inside if the cell is running, after the cell
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is run they instead contain an execution number.
You can understand in which order the code cells where executed looking at
the cell numbers. Although a notebook should be designed to run all code cells
in a sequential order, you are free to run any cell at any time. Note that the
cell will access to the variables known to the interpreter up to the previously
executed cells only. Now the following Markdown cell will be automatically selected. You can run it and visualize the highlighted text.
As you see, Markdown uses underscores, asterisks, dashes and indentation to
provide test highlighting and nested lists. Title of different sizes can be obtained
using multiple hash-tags .
That’s it! Now you can open, run, edit all notebooks contained in the tutorial folder. Have fun!
10
What’s next?
We will use Jupyter notebooks for giving a basic Python introduction. You
can walk through the tutorials, running each line sequentially. We strongly
encourage you to interact with the notebook and try to change the code. In this
way you can get a better understanding of how your changes affect execution
and output.
PyCharm will be the main tool for coding once you became familiar with
Python. It is a good practice to write a clear documentation. The reader,
might it be a professor, a customer or even yourself after some time, will easily
understand it and follow the logic of the implemented algorithms.
Jupyter Notebooks can be extremely useful for explaining and visualizing
data. You might want to make use of it for either reporting or part of your
documentation.
References
[1] https://www.python.org/dev/peps/pep-0373/#id2.
[2] http://jupyter.org/.
[3] https://github.com/adam-p/markdown-here/wiki/
Markdown-Cheatsheet#code.
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