The latest version of this notebook is available at http://github.com/jckantor/CBE20255 for noncommercial use under terms of the Creative Commons Attribution Noncommericial ShareAlike License.

J.C. Kantor ([email protected])

Getting Started with Python and Jupyter Notebooks

The purpose of this Jupyter Notebook is to get you started using Python and Jupyter Notebooks for routine chemical engineering calculations. This introduction assumes this is your first exposure to Python or Jupyter notebooks.

Step 0: Gain Executable Access to Jupyter Notebooks

Jupyter notebooks are documents that can be viewed and executed inside any modern web browser. Since you're reading this notebook, you already know how to view a Jupyter notebook. The next step is to learn how to execute computations that may be embedded in a Jupyter notebook.

To execute Python code in a notebook you will need access to a Python kernal. A kernal is simply a program that runs in the background, maintains workspace memory for variables and functions, and executes Python code. The kernal can be located on the same laptop as your web browser or located in an on-line cloud service.

Important Note Regarding Versions There are two versions of Python in widespread use. Version 2.7, released in 2010, and was the last release of the 2.x series. Version 3.5 is the most recent release of the 3.x series which represents the future direction of language. It has taken years for the major scientific libraries to complete the transition from 2.x to 3.x, but it is now safe to recommend Python 3.x for widespread use. So for this course be sure to use latest verstion, currently 3.5, of the Python language.

Using Jupyter/Python in the Cloud

The easiest way to use Jupyter notebooks is to sign up for a free account on a cloud-based service such as Wakari.io or SageMathCloud. You will need continuous internet connectivity to access your work, but the advantages are there is no software to install or maintain. All you need is a modern web browser on your laptop, Chromebook, tablet or other device.

There are also demonstration sites in the cloud, such as tmpnb.org. These start an interactive session where you can upload an existing notebook or create a new one from scratch. Though convenient, these sites are intended mainly for demonstration and generally quite overloaded. More significantly, there is no way to retain your work between sessions, and some python functionality is removed for security reasons.

Vocareum

This course will use a cloud-based service Vocareum to provide on-line access to Jupyter notebooks and Python through the Sakai learn management system. Once you log into Sakai, you can access Vocareum directly and use it to complete and submit course assignments.

Installing Jupyter/Python on your Laptop

For regular off-line use you should consider installing a Jupyter Notebook/Python environment directly on your laptop. This will provide you with reliable off-line access to a computational environment. This will also allow you to install additional code libraries to meet particular needs.

Choosing this option will require an initial software installation and routine updates. For this course the recommended package is Anaconda available from Continuum Analytics. Downloading and installing the software is well documented and easy to follow. Allow about 10-30 minutes for the installation depending on your connection speed.

After installing be sure to check for updates before proceeding further. With the Anaconda package this is done by executing the following two commands in a terminal window:

> conda update conda
> conda update anaconda

Anaconda includes an 'Anaconda Navigator' application that simplifies startup of the notebook environment and manage the update process.

Step 1: Start a Jupyter Notebook Session

If you are using a cloud-based service a Jupyter session will be started when you log on.

If you have installed a Jupyter/Python distribution on your laptop then you can open a Jupyter session in one of two different ways:

  • Use the Anaconda Navigator App, or
  • open a terminal window on your laptop and execute the following statement at the command line:

      > jupyter notebook

Either way, once you have opened a session you should see a browser window like this:

Screen Shot Jupyter Session

At this point the browser displays a list of directories and files. You can navigate amoung the directories in the usual way by clicking on directory names or on the 'breadcrumbs' located just about the listing.

Jupyter notebooks are simply files in a directory with a .ipynb suffix. They can be stored in any directory including Dropbox or Google Drive. Upload and create new Jupyter notebooks in the displayed directory using the appropriate buttons. Use the checkboxes to select items for other actions, such as to duplicate, to rename, or to delete notebooks and directories.

  • select one of your existing notebooks to work on,
  • start a new notebook by clicking on the New Notebook button, or
  • import a notebook from another directory by dragging it onto the list of notebooks.

An IPython notebook consists of cells that hold headings, text, or python code. The user interface is relatively self-explanatory. Take a few minutes now to open, rename, and save a new notebook.

Here's a quick video overview of Jupyter notebooks.

In [1]:
from IPython.display import YouTubeVideo
YouTubeVideo("HW29067qVWk",560,315,rel=0)
Out[1]:

Step 2: Simple Calculations with Python

Python is an elegant and modern language for programming and problem solving that has found increasing use by engineers and scientists. In the next few cells we'll demonstrate some basic Python functionality.

Basic Arithmetic Operations

Basic arithmetic operations are built into the Python langauge. Here are some examples. In particular, note that exponentiation is done with the ** operator.

In [2]:
a = 12
b = 2

print(a + b)
print(a**b)
print(a/b)
14
144
6.0

Python Libraries

The Python language has only very basic operations. Most math functions are in various math libraries. The numpy library is convenient library. This next cell shows how to import numpy with the prefix np, then use it to call a common mathematical functions.

In [3]:
import numpy as np

# mathematical constants
print(np.pi)
print(np.e)

# trignometric functions
angle = np.pi/4
print(np.sin(angle))
print(np.cos(angle))
print(np.tan(angle))
3.141592653589793
2.718281828459045
0.707106781187
0.707106781187
1.0

Working with Lists

Lists are a versatile way of organizing your data in Python. Here are some examples, more can be found on this Khan Academy video.

In [4]:
xList = [1, 2, 3, 4]
xList
Out[4]:
[1, 2, 3, 4]

Concatentation is the operation of joining one list to another.

In [5]:
# Concatenation
x = [1, 2, 3, 4];
y = [5, 6, 7, 8];

x + y
Out[5]:
[1, 2, 3, 4, 5, 6, 7, 8]

Sum a list of numbers

In [6]:
np.sum(x)
Out[6]:
10

An element-by-element operation between two lists may be performed with

In [7]:
print(np.add(x,y))
print(np.dot(x,y))
[ 6  8 10 12]
70

A for loop is a means for iterating over the elements of a list. The colon marks the start of code that will be executed for each element of a list. Indenting has meaning in Python. In this case, everything in the indented block will be executed on each iteration of the for loop. This example also demonstrates string formatting.

In [8]:
for x in xList:
    print("sin({0}) = {1:8.5f}".format(x,np.sin(x)))
sin(1) =  0.84147
sin(2) =  0.90930
sin(3) =  0.14112
sin(4) = -0.75680

Working with Dictionaries

Dictionaries are useful for storing and retrieving data as key-value pairs. For example, here is a short dictionary of molar masses. The keys are molecular formulas, and the values are the corresponding molar masses.

In [9]:
mw = {'CH4': 16.04, 'H2O': 18.02, 'O2':32.00, 'CO2': 44.01}
mw
Out[9]:
{'CH4': 16.04, 'CO2': 44.01, 'H2O': 18.02, 'O2': 32.0}

We can a value to an existing dictionary.

In [10]:
mw['C8H18'] = 114.23
mw
Out[10]:
{'C8H18': 114.23, 'CH4': 16.04, 'CO2': 44.01, 'H2O': 18.02, 'O2': 32.0}

We can retrieve a value from a dictionary.

In [11]:
mw['CH4']
Out[11]:
16.04

A for loop is a useful means of interating over all key-value pairs of a dictionary.

In [12]:
for species in mw.keys():
    print("The molar mass of {:<s} is {:<7.2f}".format(species, mw[species]))
The molar mass of O2 is 32.00  
The molar mass of CO2 is 44.01  
The molar mass of H2O is 18.02  
The molar mass of CH4 is 16.04  
The molar mass of C8H18 is 114.23 

Dictionaries can be sorted by key or by value

In [13]:
for species in sorted(mw):
    print(" {:<8s}  {:>7.2f}".format(species, mw[species]))
 C8H18      114.23
 CH4         16.04
 CO2         44.01
 H2O         18.02
 O2          32.00
In [14]:
for species in sorted(mw, key = mw.get):
    print(" {:<8s}  {:>7.2f}".format(species, mw[species]))
 CH4         16.04
 H2O         18.02
 O2          32.00
 CO2         44.01
 C8H18      114.23

Plotting with Matplotlib

Importing the matplotlib.pyplot library gives IPython notebooks plotting functionality very similar to Matlab's. Here are some examples using functions from the

In [15]:
%matplotlib inline

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0,10)
y = np.sin(x)
z = np.cos(x)

plt.plot(x,y,'b',x,z,'r')
plt.xlabel('Radians');
plt.ylabel('Value');
plt.title('Plotting Demonstration')
plt.legend(['Sin','Cos'])
plt.grid()
In [16]:
plt.plot(y,z)
plt.axis('equal')
Out[16]:
(-1.0, 1.0, -1.0, 1.0)
In [17]:
plt.subplot(2,1,1)
plt.plot(x,y)
plt.title('Sin(x)')

plt.subplot(2,1,2)
plt.plot(x,z)
plt.title('Cos(x)')
Out[17]:
<matplotlib.text.Text at 0x1127628d0>

Solve Equations using Sympy Library

One of the best features of Python is the ability to extend it's functionality by importing special purpose libraries of functions. Here we demonstrate the use of a symbolic algebra package Sympy for routine problem solving.

In [18]:
import sympy as sym

sym.var('P V n R T');

# Gas constant
R = 8.314        # J/K/gmol
R = R * 1000     # J/K/kgmol

# Moles of air
mAir = 1         # kg
mwAir = 28.97    # kg/kg-mol
n = mAir/mwAir   # kg-mol

# Temperature
T = 298

# Equation
eqn = sym.Eq(P*V,n*R*T)

# Solve for P 
f = sym.solve(eqn,P)
print(f[0])

# Use the sympy plot function to plot
sym.plot(f[0],(V,1,10),xlabel='Volume m**3',ylabel='Pressure Pa')
85521.9882637211/V
Out[18]:
<sympy.plotting.plot.Plot at 0x11217bd30>

Step 3: Where to Learn More

Python offers a full range of programming language features, and there is a seemingly endless range of packages for scientific and engineering computations. Here are some suggestions on places you can go for more information on programming for engineering applications in Python.

Introduction to Python for Science

This excellent introduction to python is aimed at undergraduates in science with no programming experience. It is free and available at the following link.

Tutorial Introduction to Python for Science and Engineering.

The following text is licensed by the Hesburgh Library for use by Notre Dame students and faculty only. Please refer to the library's acceptable use policy. Others can find it at Springer or Amazon. Resources for this book are available on github.

pycse is a package of python functions, examples, and document prepared by John Kitchin at Carnegie Mellon University. It is a recommended for its coverage of topics relevant to chemical engineers, including a chapter on typical chemical engineering computations.

Interative learning and on-line tutorials

Official documentation, examples, and galleries.

In [ ]: