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+.. 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   : Puneeth
+   External Reviewer   :
+   Language Reviewer   : Bhanukiran
+   Checklist OK?       : <10-11-2010, Anand, OK> [2010-10-05]
+
+.. #[Puneeth: Quickref missing]
+
+===================
+Other type of plots
+===================
+
+{{{ show the first slide }}}
+
+Hello and welcome to the tutorial ``The other kinds of plots``.
+
+.. #[Puneeth: this sentence doesn't read well]
+
+{{{ show the outline slide }}}
+
+.. #[Puneeth: motivate looking at other plots. Why are we looking at
+.. them? Tell that we have only looked at one type of plot all the
+.. while, etc.]
+
+Till now we have seen only one kind of plotting, and in this tutorial we
+are going to see more kinds of plots such as the scatter plot, the pie chart, the bar chart and
+the log-log plot. This tutorial covers the making of other kinds of
+plots and also gives an introduction to matplotlib help.
+
+.. #[Puneeth: cover, see and introduce you. be consistent. does, the
+.. "We" include the viewer or not?]
+
+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, a scatter diagram or a scatter graph.
+
+Before we proceed further, start your IPython interpreter
+::
+
+    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
+a 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 produce the scatter plot, we first 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()))
+
+By default loadtxt converts the value to float. The
+``dtype=type(int())`` argument in loadtxt converts the value to
+integer as we require the data as integer further in the tutorial.
+
+.. #[Puneeth: make a remark about dtype, that has not been covered in
+.. the loadtxt tutorial.]
+
+{{{ 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 see another kind of plot, the pie chart, for the same data.
+
+.. #[Puneeth: instead of just saying that, say that let's plot a pie
+.. chart for the same data. continuity, will be good.]
+
+{{{ 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.
+
+.. #[Puneeth, this part can be move above.]
+
+{{{ 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 the bar charts. 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 the log-log plot. A log-log graph or a 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. 
+::
+
+    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!