--- a/other-type-of-plots/script.rst	Tue Oct 12 14:30:53 2010 +0530
+++ b/other-type-of-plots/script.rst	Tue Oct 12 16:26:36 2010 +0530
@@ -3,7 +3,7 @@
 .. * scatter 
 .. * pie chart 
 .. * bar chart 
-.. * log 
+.. * loglog
 .. * illustration of other plots, matplotlib help
 
 ===================
@@ -17,12 +17,12 @@
 {{{ show the outline slide }}}
 
 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
+loglog plot. We will also see few other plots and also introduce you to
 the matplotlib help.
 
 Let us start with scatter plot. 
 
-{{{ switch to the next slide }}}
+{{{ 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
@@ -55,12 +55,14 @@
 {{{ 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 in one of the previous sessions,
+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()`` 
 ::
@@ -75,9 +77,11 @@
 problem to be tried out }}}
 
 Now here is a question for you to try out, plot the same data with red
-diamonds. 
+diamonds markers. 
 
-**Clue** - *try scatter? in your ipython interpreter* 
+.. **Clue** - *try scatter? in your ipython interpreter* 
+
+Pause here and solve the question before moving on.
 
 .. scatter(year,profit,color='r',marker='d')
 
@@ -95,6 +99,8 @@
 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()``.
 ::
 
@@ -111,7 +117,9 @@
 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* 
+.. **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'))
 
@@ -121,7 +129,7 @@
 with rectangular bars with lengths proportional to the values that
 they represent.
 
-{{{ switch to the slide showing the problem statement of third
+{{{ switch to the slide showing the problem statement of fifth
 exercise question }}}
 
 Plot a bar chart representing the profit percentage of company A, with
@@ -129,6 +137,8 @@
 
 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()``.
 ::
 
@@ -143,13 +153,14 @@
 
 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.
+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* 
+.. **Clue** - *try bar? in your ipython interpreter* 
 
 .. bar(year,profit,fill=False,hatch='/')
 
-{{{ switch to the slide which says about bar chart }}}
+{{{ 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
@@ -170,6 +181,8 @@
     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,
 ::
 
@@ -200,7 +213,7 @@
 matplotlib.sourceforge.net/users/screenshots.html and also at
 matplotlib.sourceforge.net/gallery.html
 
-{{{ switch to recap slide }}}
+{{{ 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