.. 2.4 LO: other types of plots (3) [anoop] 

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

.. * scatter 

.. * pie chart 

.. * bar chart 

.. * log 

.. * illustration of other plots, matplotlib help

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

Other type of plots

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

{{{ show the first slide }}}

Hello and welcome to the tutorial other type of plots.

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

log plot. We will also see few other plots and also introduce you to

the matplotlib help.

Let us start with scatter plot. 

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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.

Before we proceed further get your IPython interpreter running with

the ``-pylab`` option. Start your IPython interpreter as

::

    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 }}}

Now, let us plot a scatter plot showing the percentage profit of company A

from the year 2000-2010. The data for the same is available in the

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

{{{ 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 product the scatter plot first we need to load the data from the

file using ``loadtxt``. We learned 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()))

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. 

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

.. 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.

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

::

   pie(profit,labels=year)

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

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* 

.. 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 third

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.

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.

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

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

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

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

that. And it could be done as,

::

    x = linspace(1,20,100)

    y = 5*x**3

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

.. #[[Anoop: I am not so sure how to do the rest of it, so I guess we

   can just browse through the side and tell them few. What is your

   opinion??]]

Now let us see few plots from

matplotlib.sourceforge.net/users/screenshots.html

{{{ browse through the site quickly }}}

{{{ switch to recap 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!

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

    Reviewer 1:

    Reviewer 2:

    External reviewer: