1 .. Objectives

     2 .. ----------

     3 

     4 .. At the end of this tutorial, you will be able to 

     5 

     6 .. 1. Create scatter plot

     7 .. #. Create pie charts

     8 .. #. Create bar charts

     9 .. #. Create log-log plots.

    10 

    11 .. Prerequisites

    12 .. -------------

    13 

    14 ..   1. should have ``ipython`` and ``pylab`` installed. 

    15 ..   #. getting started with ``ipython``.

    16 ..   #. loading data from files

    17 ..   #. plotting the data

    18 

    19      

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

    21    Internal Reviewer   : Puneeth

    22    External Reviewer   :

    23    Language Reviewer   : Bhanukiran

    24    Checklist OK?       : <10-11-2010, Anand, OK> [2010-10-05]

    25 

    26 .. #[Puneeth: Quickref missing]

    27 

    28 ===================

    29 Other type of plots

    30 ===================

    31 

    32 {{{ show the first slide }}}

    33 

    34 Hello and welcome to the tutorial ``The other kinds of plots``.

    35 

    36 .. #[Puneeth: this sentence doesn't read well]

    37 

    38 {{{ show the outline slide }}}

    39 

    40 .. #[Puneeth: motivate looking at other plots. Why are we looking at

    41 .. them? Tell that we have only looked at one type of plot all the

    42 .. while, etc.]

    43 

    44 Till now we have seen only one kind of plotting, and in this tutorial we

    45 are going to see more kinds of plots such as the scatter plot, the pie chart, the bar chart and

    46 the log-log plot. This tutorial covers the making of other kinds of

    47 plots and also gives an introduction to matplotlib help.

    48 

    49 .. #[Puneeth: cover, see and introduce you. be consistent. does, the

    50 .. "We" include the viewer or not?]

    51 

    52 Let us start with scatter plot. 

    53 

    54 {{{ switch to the next slide, scatter plot }}}

    55 

    56 In a scatter plot, the data is displayed as a collection of points,

    57 each having the value of one variable determining the position on the

    58 horizontal axis and the value of the other variable determining the

    59 position on the vertical axis. This kind of plot is also called a

    60 scatter chart, a scatter diagram or a scatter graph.

    61 

    62 Before we proceed further, start your IPython interpreter

    63 ::

    64 

    65     ipython -pylab

    66 

    67 {{{ open the ipython interpreter in the terminal using the command

    68 ipython -pylab }}}

    69 

    70 {{{ switch to the next slide having the problem statement of first

    71 exercise }}}

    72 

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

    74 a company A from the year 2000-2010. The data for the same is available

    75 in the file ``company-a-data.txt``.

    76 

    77 {{{ open the file company-a-data.txt and show the content }}}

    78 

    79 The data file has two lines with a set of values in each line, the

    80 first line representing years and the second line representing the

    81 profit percentages.

    82 

    83 {{{ close the file and switch to the terminal }}}

    84 

    85 To produce the scatter plot, we first need to load the data from the

    86 file using ``loadtxt``. We learned it in one of the previous sessions,

    87 and it can be done as ::

    88 

    89     year,profit =

    90     loadtxt('/home/fossee/other-plot/company-a-data.txt',dtype=type(int()))

    91 

    92 By default loadtxt converts the value to float. The

    93 ``dtype=type(int())`` argument in loadtxt converts the value to

    94 integer as we require the data as integer further in the tutorial.

    95 

    96 .. #[Puneeth: make a remark about dtype, that has not been covered in

    97 .. the loadtxt tutorial.]

    98 

    99 {{{ switch to next slide, ``scatter`` function }}}

   100 

   101 Now in-order to generate the scatter graph we will use the function 

   102 ``scatter()`` 

   103 ::

   104 

   105 	scatter(year,profit)

   106 

   107 Notice that we passed two arguments to ``scatter()`` function, first

   108 one the values in x-coordinate, year, and the other the values in

   109 y-coordinate, the profit percentage.

   110 

   111 {{{ switch to the next slide which has the problem statement of

   112 problem to be tried out }}}

   113 

   114 Now here is a question for you to try out, plot the same data with red

   115 diamonds markers. 

   116 

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

   118 

   119 Pause here and solve the question before moving on.

   120 

   121 .. scatter(year,profit,color='r',marker='d')

   122 

   123 Now let us see another kind of plot, the pie chart, for the same data.

   124 

   125 .. #[Puneeth: instead of just saying that, say that let's plot a pie

   126 .. chart for the same data. continuity, will be good.]

   127 

   128 {{{ switch to the slide which says about pie chart }}}

   129 

   130 A pie chart or a circle graph is a circular chart divided into

   131 sectors, illustrating proportion.

   132 

   133 {{{ switch to the slide showing the problem statement of second

   134 exercise question }}}

   135 

   136 Plot a pie chart representing the profit percentage of company A, with

   137 the same data from file ``company-a-data.txt``. So let us reuse the

   138 data we have loaded from the file previously.

   139 

   140 .. #[Puneeth, this part can be move above.]

   141 

   142 {{{ switch to next slide, ``pie()`` function }}}

   143 

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

   145 ::

   146 

   147    pie(profit,labels=year)

   148 

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

   150 one the values and the next one the set of labels to be used in the

   151 pie chart.

   152 

   153 {{{ switch to the next slide which has the problem statement of

   154 problem to be tried out }}}

   155 

   156 Now here is a question for you to try out, plot a pie chart with the

   157 same data with colors for each wedges as white, red, black, magenta,

   158 yellow, blue, green, cyan, yellow, magenta and blue respectively.

   159 

   160 .. **Clue** - *try pie? in your ipython interpreter* 

   161 

   162 Pause here and solve the question before moving on.

   163 

   164 .. pie(t,labels=s,colors=('w','r','k','m','y','b','g','c','y','m','b'))

   165 

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

   167 

   168 Now let us move on to the bar charts. A bar chart or bar graph is a chart

   169 with rectangular bars with lengths proportional to the values that

   170 they represent.

   171 

   172 {{{ switch to the slide showing the problem statement of fifth

   173 exercise question }}}

   174 

   175 Plot a bar chart representing the profit percentage of company A, with

   176 the same data from file ``company-a-data.txt``. 

   177 

   178 So let us reuse the data we have loaded from the file previously.

   179 

   180 {{{ switch to the next slide, ``bar()`` function }}}

   181 

   182 We can plot the bar chart using the function ``bar()``.

   183 ::

   184 

   185    bar(year,profit)

   186 

   187 Note that the function ``bar()`` needs at least two arguments one the

   188 values in x-coordinate and the other values in y-coordinate which is

   189 used to determine the height of the bars.

   190 

   191 {{{ switch to the next slide which has the problem statement of

   192 problem to be tried out }}}

   193 

   194 Now here is a question for you to try, plot a bar chart which is not

   195 filled and which is hatched with 45\ :sup:`o` slanting lines as shown

   196 in the image in the slide. The data for the chart may be obtained from

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

   198 

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

   200 

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

   202 

   203 {{{ switch to the slide which says about log-log graph }}}

   204 

   205 Now let us move on to the log-log plot. A log-log graph or a log-log plot is

   206 a two-dimensional graph of numerical data that uses logarithmic scales

   207 on both the horizontal and vertical axes. Because of the nonlinear

   208 scaling of the axes, a function of the form y = ax\ :sup:`b` will

   209 appear as a straight line on a log-log graph

   210 

   211 {{{ switch to the slide showing the problem statement of fourth

   212 exercise question }}}

   213 

   214 

   215 Plot a `log-log` chart of y=5*x\ :sup:`3` for x from 1-20.

   216 

   217 Before we actually plot let us calculate the points needed for

   218 that. 

   219 ::

   220 

   221     x = linspace(1,20,100)

   222     y = 5*x**3

   223 

   224 {{{ switch to next slide, ``loglog()`` function }}}

   225 

   226 Now we can plot the log-log chart using ``loglog()`` function,

   227 ::

   228 

   229     loglog(x,y)

   230 

   231 To understand the difference between a normal ``plot`` and a ``log-log

   232 plot`` let us create another plot using the function ``plot``.

   233 ::

   234 

   235     figure(2)

   236     plot(x,y)

   237 

   238 {{{ show both the plots side by side }}}

   239 

   240 So that was ``log-log() plot``.

   241 

   242 {{{ switch to the next slide which says: "How to get help on

   243 matplotlib online"}}}

   244 

   245 Now we will see few more plots and also see how to access help of

   246 matplotlib over the internet.

   247 

   248 Help about matplotlib can be obtained from

   249 matplotlib.sourceforge.net/contents.html

   250 

   251 

   252 More plots can be seen at

   253 matplotlib.sourceforge.net/users/screenshots.html and also at

   254 matplotlib.sourceforge.net/gallery.html

   255 

   256 {{{ switch to summary slide }}}

   257 

   258 Now we have come to the end of this tutorial. We have covered scatter

   259 plot, pie chart, bar chart, log-log plot and also saw few other plots

   260 and covered how to access the matplotlib online help.

   261 

   262 {{{ switch to the thank you slide }}}

   263 

   264 Thank you!