diff -r 88a01948450d -r d33698326409 other-type-of-plots/script.rst --- a/other-type-of-plots/script.rst Wed Nov 17 23:24:57 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 - 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!