--- a/other_type_of_plots/script.rst Wed Dec 01 16:51:35 2010 +0530
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,264 +0,0 @@
-.. 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!