J.C. Kantor ([email protected])

In [1]:

```
from IPython.core.display import HTML
HTML(open("styles/custom.css", "r").read())
```

Out[1]:

The first step is to obtain a working IPython environment. The easiest approach is to sign up for a free account on a cloud-based service such as Wakari.io. You'll need internet connectivity to access your work, but there's nothing to install, no software to maintain, and you can work with nothing more than a browser on a Chromebook or tablet.

Alternatively, you can install an IPython environment on your laptop. There are at least two excellent and free packages available:

- Anaconda available from Continuum Analytics.
- Enthought Canopy available from Enthought, Inc.

There are differences between these packages, particularly in the methods used to download and maintain additional Python libraries. In both cases the process for downloading and installing the software are well documented and easy to follow. You should allow about 10-30 minutes for the installation depending on your experience and 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
```

IPython notebooks (like this one) are simply files in a directory with a `.ipynb`

suffix. They can be stored in any directory, including a Dropbox or Google Drive directory.

To start a notebook session, open a terminal window and navigate to the directory where you will be keeping your notebooks. Then execute the following statement at the command line:

```
> ipython notebook --pylab=inline
```

The terminal window will show information indicating start up of an ipython session, then browser window will open listing notebooks in your current directory. At this point your options are

- 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 overview of IPython notebooks prepared by the team that created the software.

In [2]:

```
from IPython.display import YouTubeVideo
YouTubeVideo("H6dLGQw9yFQ",560,315)
```

Out[2]:

Basic arithmetic functions

In [1]:

```
a = 12
b = 2
print "a + b = ", a + b
print "a**b = ", a**b
print "a/b = ", a/b
```

Calling a builtin function

In [2]:

```
sin(2*pi)
```

Out[2]:

In [3]:

```
xList = [1, 2, 3, 4]
print xList
```

In [4]:

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

Out[4]:

In [5]:

```
# Two ways to sum a list of numbers
print sum(x)
print reduce(add,x)
```

In [6]:

```
# Two ways to add a two lists of numbers
print add(x,y)
print map(add,x,y)
```

In [7]:

```
for x in xList:
print "x =",x, " sin(x) = ", sin(x)
```

In [8]:

```
mw = {'CH4': 16.04, 'H2O': 18.02, 'O2':32.00, 'CO2': 44.01}
print mw
```

We can a value to an existing dictionary.

In [9]:

```
mw['C8H18'] = 114.23
print mw
```

We can retrieve a value from a dictionary.

In [10]:

```
mw['CH4']
```

Out[10]:

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

In [11]:

```
for species in mw.keys():
print "The molar mass of {:<s} is {:<7.2f}".format(species, mw[species])
```

Dictionaries can be sorted by key or by value

In [12]:

```
for species in sorted(mw):
print " {:<8s} {:>7.2f}".format(species, mw[species])
```

In [13]:

```
for species in sorted(mw, key = mw.get):
print " {:<8s} {:>7.2f}".format(species, mw[species])
```

IPython notebooks include plotting functionality very similar to Matlab's. Here are some examples.

In [14]:

```
x = linspace(0,10)
y = sin(x)
z = cos(x)
plot(x,y,'b',x,z,'r')
xlabel('Radians');
ylabel('Value');
title('Plotting Demonstration')
legend(['Sin','Cos'])
grid()
```

In [15]:

```
plot(y,z)
axis('equal')
```

Out[15]:

In [16]:

```
subplot(2,1,1)
plot(x,y)
title('Sin(x)')
subplot(2,1,2)
plot(x,z)
title('Cos(x)')
```

Out[16]:

`Sympy`

for routine problem solving.

In [17]:

```
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 and select the first solution
f = sym.solve(eqn,P)[0]
# Use the sympy plot function to plot
sym.plot(f,(V,1,10),xlabel='Volume m**3',ylabel='Pressure Pa')
```

Out[17]:

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

Interative learning

On-line tutorials, books, etc.

- Khan Academy Videos on Python Programming
- Python Tutorial
- Think Python: How to Think Like a Computer Scientist

Books

Official documentation, examples, and galleries.

Engineering applications

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```