.. Objectives

.. ----------

.. At the end of this tutorial, you will be able to 

.. 1. Create scatter plot

.. #. Create pie charts

.. #. Create bar charts

.. #. Create log-log plots.

.. Prerequisites

.. -------------

..   1. should have ``ipython`` and ``pylab`` installed. 

..   #. getting started with ``ipython``.

..   #. loading data from files

..   #. plotting the data

.. Author              : Anoop Jacob Thomas <anoop@fossee.in>

   Internal Reviewer   : 

   External Reviewer   :

   Checklist OK?       : <put date stamp here, if OK> [2010-10-05]

===================

Other type of plots

===================

{{{ show the first slide }}}

Hello and welcome to the tutorial other type of plots.

{{{ show the outline slide }}}

In this tutorial we will cover scatter plot, pie chart, bar chart and

the matplotlib help.

Let us start with scatter plot. 

{{{ switch to the next slide, scatter plot }}}

In a scatter plot, the data is displayed as a collection of points,

each having the value of one variable determining the position on the

horizontal axis and the value of the other variable determining the

position on the vertical axis. This kind of plot is also called a

scatter chart, scatter diagram and scatter graph.

::

    ipython -pylab

{{{ open the ipython interpreter in the terminal using the command

ipython -pylab }}}

{{{ switch to the next slide having the problem statement of first

exercise }}}

{{{ open the file company-a-data.txt and show the content }}}

The data file has two lines with a set of values in each line, the

first line representing years and the second line representing the

profit percentages.

{{{ close the file and switch to the terminal }}}

To produce the scatter plot first we need to load the data from the

file using ``loadtxt``. We learned it in one of the previous sessions,

and it can be done as ::

    year,profit =

    loadtxt('/home/fossee/other-plot/company-a-data.txt',dtype=type(int()))

{{{ switch to next slide, ``scatter`` function }}}

Now in-order to generate the scatter graph we will use the function 

``scatter()`` 

::

	scatter(year,profit)

Notice that we passed two arguments to ``scatter()`` function, first

one the values in x-coordinate, year, and the other the values in

y-coordinate, the profit percentage.

{{{ switch to the next slide which has the problem statement of

problem to be tried out }}}

Now here is a question for you to try out, plot the same data with red

diamonds markers. 

.. **Clue** - *try scatter? in your ipython interpreter* 

Pause here and solve the question before moving on.

.. scatter(year,profit,color='r',marker='d')

Now let us move on to pie chart.

{{{ switch to the slide which says about pie chart }}}

A pie chart or a circle graph is a circular chart divided into

sectors, illustrating proportion.

{{{ switch to the slide showing the problem statement of second

exercise question }}}

Plot a pie chart representing the profit percentage of company A, with

the same data from file ``company-a-data.txt``. So let us reuse the

data we have loaded from the file previously.

{{{ switch to next slide, ``pie()`` function }}}

We can plot the pie chart using the function ``pie()``.

::

   pie(profit,labels=year)

Notice that we passed two arguments to the function ``pie()``. First

one the values and the next one the set of labels to be used in the

pie chart.

{{{ switch to the next slide which has the problem statement of

problem to be tried out }}}

Now here is a question for you to try out, plot a pie chart with the

same data with colors for each wedges as white, red, black, magenta,

yellow, blue, green, cyan, yellow, magenta and blue respectively.

.. **Clue** - *try pie? in your ipython interpreter* 

Pause here and solve the question before moving on.

.. pie(t,labels=s,colors=('w','r','k','m','y','b','g','c','y','m','b'))

{{{ switch to the slide which says about bar chart }}}

Now let us move on to bar chart. A bar chart or bar graph is a chart

with rectangular bars with lengths proportional to the values that

they represent.

{{{ switch to the slide showing the problem statement of fifth

exercise question }}}

Plot a bar chart representing the profit percentage of company A, with

the same data from file ``company-a-data.txt``. 

So let us reuse the data we have loaded from the file previously.

{{{ switch to the next slide, ``bar()`` function }}}

We can plot the bar chart using the function ``bar()``.

::

   bar(year,profit)

Note that the function ``bar()`` needs at least two arguments one the

values in x-coordinate and the other values in y-coordinate which is

used to determine the height of the bars.

{{{ switch to the next slide which has the problem statement of

problem to be tried out }}}

Now here is a question for you to try, plot a bar chart which is not

filled and which is hatched with 45\ :sup:`o` slanting lines as shown

in the image in the slide. The data for the chart may be obtained from

the file ``company-a-data.txt``.

.. **Clue** - *try bar? in your ipython interpreter* 

.. bar(year,profit,fill=False,hatch='/')

{{{ switch to the slide which says about log-log graph }}}

Now let us move on to log-log plot. A log-log graph or log-log plot is

a two-dimensional graph of numerical data that uses logarithmic scales

on both the horizontal and vertical axes. Because of the nonlinear

scaling of the axes, a function of the form y = ax\ :sup:`b` will

appear as a straight line on a log-log graph

{{{ switch to the slide showing the problem statement of fourth

exercise question }}}

Plot a `log-log` chart of y=5*x\ :sup:`3` for x from 1-20.

Before we actually plot let us calculate the points needed for

::

    x = linspace(1,20,100)

    y = 5*x**3

{{{ switch to next slide, ``loglog()`` function }}}

Now we can plot the log-log chart using ``loglog()`` function,

::

    loglog(x,y)

To understand the difference between a normal ``plot`` and a ``log-log

plot`` let us create another plot using the function ``plot``.

::

    figure(2)

    plot(x,y)

{{{ show both the plots side by side }}}

So that was ``log-log() plot``.

{{{ switch to the next slide which says: "How to get help on

matplotlib online"}}}

Now we will see few more plots and also see how to access help of

matplotlib over the internet.

Help about matplotlib can be obtained from

matplotlib.sourceforge.net/contents.html

More plots can be seen at

matplotlib.sourceforge.net/users/screenshots.html and also at

matplotlib.sourceforge.net/gallery.html

{{{ switch to summary slide }}}

Now we have come to the end of this tutorial. We have covered scatter

plot, pie chart, bar chart, log-log plot and also saw few other plots

and covered how to access the matplotlib online help.

{{{ switch to the thank you slide }}}

Thank you!