.. Objectives .. ---------- .. By the end of this tutorial, you will be able to .. 1. Defining a list of numbers .. 2. Squaring a list of numbers .. 3. Plotting data points. .. 4. Plotting errorbars. .. Prerequisites .. ------------- .. 1. getting started with plotting .. Author : Amit Internal Reviewer : Anoop Jacob Thomas External Reviewer : Checklist OK? : [2010-10-05] .. #[[Anoop: Add quickref]] .. #[[Anoop: Slides are incomplete, add summary slide, thank you slide etc.]] =============================== Plotting Experimental Data =============================== {{{ Show the slide containing title }}} Hello and welcome , this tutorial on Plotting Experimental data is presented by the fossee team. {{{ Show the Outline Slide }}} .. #[[Anoop: outline slide is missing]] Here we will discuss plotting Experimental data. 1. We will see how we can represent a sequence of numbers in Python. 2. We will also become familiar with elementwise squaring of such a sequence. 3. We will also see how we can use our graph to indicate Error. One needs to be familiar with the concepts of plotting mathematical functions in Python. We will use data from a Simple Pendulum Experiment to illustrate our points. .. #[[Anoop: what do you mean by points here? if you mean the points/numbered list in outline slide, then remove the usage point from here.]] {{{ Simple Pendulum data Slide }}} .. #[[Anoop: slides are incomplete, work on slides and context switches]] As we know for a simple pendulum length,L is directly proportional to the square of time,T. We shall be plotting L and T^2 values. First we will have to initiate L and T values. We initiate them as sequence of values. To tell ipython a sequence of values we write the sequence in comma seperated values inside two square brackets. This is also called List so to create two sequences .. #[[Anoop: instead of saying "to tell ipython a sequence of values" and make it complicated, we can tell, we define a sequence as]] L,t type in ipython shell. .. #[[Anoop: sentence is incomplete, can be removed]] :: In []: L = [0.1, 0.2, 0.3, 0.4, 0.5,0.6, 0.7, 0.8, 0.9] In []: t= [0.69, 0.90, 1.19,1.30, 1.47, 1.58, 1.77, 1.83, 1.94] To obtain the square of sequence t we will use the function square with argument t.This is saved into the variable tsquare.:: In []: tsquare=square(t) array([ 0.4761, 0.81 , 1.4161, 1.69 , 2.1609, 2.4964, 3.1329, 3.3489, 3.7636]) .. #[[Anoop: how do you get the array([ 0.4761 ....]) output?]] Now to plot L vs T^2 we will simply type :: In []: plot(L,t,'.') .. #[[Anoop: be consistent with the spacing and all.]] '.' here represents to plot use small dots for the point. :: In []: clf() You can also specify 'o' for big dots.:: In []: plot(L,t,'o') In []: clf() .. #[[Anoop: Make sure code is correct, corrected plot(L,t,o) to plot(L,t,'o')]] {{{ Slide with Error data included }}} .. #[[Anoop: again slides are incomplete.]] Now we shall try and take into account error into our plots . The Error values for L and T are on your screen.We shall again intialize the sequence values in the same manner as we did for L and t .. #[[Anoop: give introduction to error and say what we are going to do]] :: In []: delta_L= [0.08,0.09,0.07,0.05,0.06,0.00,0.06,0.06,0.01] In []: delta_T= [0.04,0.08,0.11,0.05,0.03,0.03,0.01,0.07,0.01] Now to plot L vs T^2 with an error bar we use the function errorbar() The syntax of the command is as given on the screen. :: In []: errorbar(L,tsquare,xerr=delta_L, yerr=delta_T, fmt='b.') This gives a plot with error bar for x and y axis. The dots are of blue color. The parameters xerr and yerr are error on x and y axis and fmt is the format of the plot. similarly we can draw the same error bar with big red dots just change the parameters to fmt to 'ro'. :: In []: clf() In []: errorbar(L,tsquare,xerr=delta_L, yerr=delta_T, fmt='ro') thats it. you can explore other options to errorbar using the documentation of errorbar.:: In []: errorbar? {{{ Summary Slides }}} In this tutorial we have learnt : 1. How to declare a sequence of number , specifically the kind of sequence we learned was a list. 2. Plotting experimental data extending our knowledge from mathematical functions. 3. The various options available for plotting dots instead of lines. 4. Plotting experimental data such that we can also represent error. We did this using the errorbar() function. {{{ Show the "sponsored by FOSSEE" slide }}} .. #[[Anoop: again slides are incomplete]] This tutorial was created as a part of FOSSEE project. Hope you have enjoyed and found it useful. Thankyou