====Objective:====

Get started with anaconda, python, ipython notebooks, and pandas.  Begin producing simple visualizations of data and images.

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====Deliverable:====

For this lab, you will submit an ipython notebook.  This notebook will have two parts:

**Part 1:**  Your notebook should generate a random image.  We will run this
notebook 5 times; it should generate 5 different, moderately complex
images.  Each image should be 512 x 288.  Have fun with it!

The resulting image could, for example, look like this:

{{:cs401r_w2016:lab1.png?nolink|}}

**Part 2:** Your notebook should use the pandas library to read in the Rossman store sales data (a CSV dataset) and plot the sales of store #1.  Your plot should look something like this:

{{:cs401r_w2016:lab1_storesales.png?direct&700|}}

Done correctly, this should only take a few lines of code.

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====Grading standards:====

Your notebook will be graded on the following:

  * 20% Successfully turned in a notebook with working code
  * 20% Random image with 50 random elements
  * 20% Correctly used pandas to load store sales data
  * 30% Some sort of plot of sales data (only for store #1!)
  * 10% Tidy and legible figures, including labeled axes where appropriate

----
====Description:====

Throughout this class, we will be using a combination of ipython
notebooks and the anaconda python distribution.  For this lab, you
must install anaconda, and write a simple python program (using
ipython notebooks).  As described above, the notebook should do two things:
1) generate simple random images, and 2) plot some data using pandas.

For part 1, you can generate any sort of random image that you want -- consider
random lines, random curves, random text, etc.  Each time the program
is run, it should generate a different random image.  Your image
should have at least 50 random elements (they can all be the same
type, such as random lines, and can be created in a loop).  We won't
count the number of elements; this is just to encourage you to create
random images with moderate complexity.

In preparation for future labs, we strongly encourage you to use the
[[http://cairographics.org/|cairo]] package as part of your image generator.

For part 2, the data you should use is downloadable here:

[[http://liftothers.org/courses/stat_ml/store_train.csv|Rossman store sales data]]

----
====Installing anaconda:====

http://docs.continuum.io/anaconda/install

To generate images, check out PIL and cairo:

''conda install cairo''

To generate random numbers, check out the [[http://docs.scipy.org/doc/numpy-1.10.0/reference/routines.random.html|numpy.random]] module.

To create a new notebook, run:

''jupyter-notebook''

This should start an ipython kernel in the background, set up a
webserver, and point your browser to the webserver.  In the
upper-right corner, you will see a "new" menu; under that menu you
should see "Notebook" and "Python 2".  This will create a new
notebook.

**Note:** When you turn in your notebook, you should turn in the ''.ipynb'' file.  Do not take a screen shot, or turn in an HTML page.

Here's some starter code to help you generate an image.  The ''nbimage'' function will display the image inline in the notebook:

<code python>
import cairo
import numpy

# A simple function to display an image in an ipython notebook
def nbimage( data ):
    from IPython.display import display, Image
    from PIL.Image import fromarray
    from StringIO import StringIO

    s = StringIO()
    fromarray( data ).save( s, 'png' )
    display( Image( s.getvalue() ) )

WIDTH = 512
HEIGHT = 288

# this is a numpy buffer to hold the image data
data = numpy.zeros( (HEIGHT,WIDTH,4), dtype=numpy.uint8 )

# this creates a cairo context based on the numpy buffer
ims = cairo.ImageSurface.create_for_data( data, cairo.FORMAT_ARGB32, WIDTH, HEIGHT )
cr = cairo.Context( ims )

# draw a blue line
cr.set_source_rgba( 1.0, 0.0, 0.0, 1.0 )
cr.set_line_width( 2.0 )
cr.move_to( 0.0, 0.0 )
cr.line_to( 100.0, 100.0 )
cr.stroke()

# display the image
nbimage( data )
</code>

----
====Using Pandas:====

For the second part of this lab, you will need to understand the ''pandas'' python package, just a little bit.  For this lab, you only need to know how to select some data from a CSV file.

You should read through this tutorial and play with it.

[[http://synesthesiam.com/posts/an-introduction-to-pandas.html|Tutorial on using Pandas]]

For this lab, you need select the data for store #1 and plot it.

An important part of generating visualizations is conveying information cleanly and accurately.  You should therefore label all axes, and in particular, the x-axis should be labeled using dates (See the example image).  This involves a bit of python trickery, but check out some helpful functions in the hints below.

----
====Hints:====

When using an ipython notebook, it's nice to make your plots show up inline.  To do this, add the following lines to the first cell of your notebook:

<code python>

# this tells seaborn and matplotlib to generate plots inline in the notebook
%matplotlib inline  

# these two lines allow you to control the figure size
%pylab inline
pylab.rcParams['figure.figsize'] = (16.0, 8.0)

</code>

The following python functions might be helpful:

<code python>

import matplotlib.pyplot as plt
plt.plot_date

pandas.to_datetime

plt.legend
plt.xlabel
plt.ylabel

plt.tight_layout

</code>