Matlab usage - short (may be too short) introduction by EugeniyMikhailov %!includeconf: ../configs/config.t2t %!style(html): ../evmik.css = Why Matlab = At the very least, it is a great replacement of a hand held calculator. Once you need to process multiple data point it became tedious with a simple calculator. Once you master Matlab it will not matter if it is one data point or millions of them. It can do complex math, plotting, fitting, etc. It is also the complete programming language which can stand by itself. Also, Matlab has **excellent help documentation** and a lot of tutorials at the web. There are free alternatives. [Octave http://www.gnu.org/software/octave/] is one example. This what I personally use instead of Matlab all the time. But it is a bit difficult with installation and it might be scary to see no GUI and only a command prompt for a beginner. = Getting Matlab = Please visit W&M IT [software support page http://www.wm.edu/offices/it/a-z/software/] and download Matlab from appropriate "Licensed Software >> Math & Statistics Software" section. They have several available versions. Either one is fine. Since we are learning Matlab, we will not have time to go to fancy toolboxes which Matlab provides/removes with new releases. = Statistics functions = == Mean of a set == Often you will need to find the mean and standard deviation of a particular sample set. Let's say we estimated a resistor value (**R**) with different method and obtained the following experimental values. | **R** | 1.20 | 1.10 | 1.11 | 1.05 | 1.05 | 1.15 | First we transfer it to a Matlab vector variable ``` R=[ 1.20, 1.10, 1.11, 1.05, 1.05, 1.15 ] Notice that for a vector variable I use **,** as a separator. Now we ready to calculate mean of this set ``` mean(R) with answer 1.1100 == Standard deviation of a set == Standard deviation normalized with **N-1** samples (so called unbiased estimator) is done with ``` std(R) with answer 0.058310. If you need normalization with **N** samples do ``` std(R,1) with answer 0.053229 In both cases there is to many significant figures which is unjustified from physics point of view. So remember, Matlab just does the math for you, the analysis and presentation of results is on you. = How to make a simple plot = Suppose during the measurements we obtain the following data for each current value **I** we measured voltage **V** with some uncertainty in voltage measurement **dV**. Our experimental data is represented in the following table. || I | V | dV | | 0.1 | 1.0 | 0.2 | | 1.0 | 2.2 | 0.1 | | 1.8 | 3.2 | 0.2 | | 3.3 | 3.8 | 0.2 | | 4.0 | 4.9 | 0.3 | First of all, we need to transfer it to the Matlab column vector variables. Pay attention to the delimiter **;** between numbers ``` I = [ .1; 1.0; 1.8; 3.3; 4.0 ] V = [ 1.0; 2.2; 3.2; 3.8; 4.9 ] dV = [ 0.2; 0.1; 0.2; 0.2; 0.3 ] ``` Now we make a simple plot without error bars to get the basic, text after **%** is considered as a comment and Matlab will disregard it. ``` figure(1) % all plotting output will go to the particular window plot(I,V, 'x') ``` [simple_unlabeled_plot.png] Easy, is not it? Here were mark our data point with crosses and used **'''x'''** parameter to specify this. You can do other markers as well. But **every plot must be present with title, proper axes labels and units**. Note **'** for the string specifiers ``` title('Dependence of voltage on current') xlabel('Current (A)') ylabel('Voltage (V)') ``` [simple_labeled_plot.png] No our plot is nice and pretty. = Fixing the range of the axes = Have a look at above plot. We see that one of the axes (y) does not contain origin point (y=0). Such plots are very common in finances and design to fool a reader, but they are generally not acceptable in scientific reports and definitely not in my class unless you have a good reason to do otherwise. The fix is very easy. Note that we are actually sending a vector [0,5] to the **``ylim``** function (fir x-axis we need to use **``xlim``** function). ``` ylim([0,5]) Now our plot is good to present [simple_labeled_and_proper_limits_plot.png] = Plotting with error bars = Let's open a new window for the plot ``` figure(2) Now we make plot with error bars assuming that they are the same for up and down parts ``` errorbar(I,V,dV) ylim([0,6]) ``` Yet again we need proper labeling ``` title('Dependence of voltage on current') xlabel('Current (A)') ylabel('Voltage (V)') ``` [errorbar_plot.png] = Fixing plots font size = Honestly Matlab is not the best tool to do presentation quality plots, it requires a quite a bit of messing with their GUI. But at least for a quick font size fix do the following. This might look like a magic spell so do read documentation on **``set``** command. ``` fontSize=24; set(gca,'FontSize',fontSize ); errorbar(I,V,dV) title('Dependence of voltage on current') xlabel('Current (A)') ylabel('Voltage (V)') ``` [errorbar_with_large_font_plot.png] = Fitting and data reduction = Modern experiments generate a lot of data, while humans can keep in their mind only about 7 things. So there is no way to intelligently work with large data set. We need to extract essential information out of our data i.e. do data reduction. Often we have some idea for analytical representation/model of the experimental data but we are not sure about the model parameters values. The process of extracting them is called **fitting**. So let's look at above Voltage vs Current dependence. According to the Ohm's law such dependence must be linear i.e **``V = R*I``**. Where **``R``** is constant coefficient called resistance. So first we need to define the model for Matlab. We **must** specify an independent variable name unless it is called **``x``**, which is not our choice here. Also, notice that **``fittype``** function wants to know independent variable //name// thus **``I``** is surrounded by **``' '``** ``` f=fittype( @(R,I) R*I, 'independent', 'I' ) %______________^ independent variable must be last in the function arguments %____________^ even though R is supposed to be fixed it is still a variable % from fitting point of view ``` Now our job is simple, we need to specify a guessed value of the R as starting point for the fit function. ``` param_guessed = [ 4 ] You might have a model with multiple parameters than it is a bit more evolved. Please, do read help file about fitting. Finally, we fit. ``` [fitobject, goodness_of_fit] = fit (I, V, f, 'StartPoint', param_guessed) ``` You will see the following ``` fitobject = General model: fitobject(I) = R*I Coefficients (with 95% confidence bounds): R = 1.291 (0.8872, 1.695) goodness_of_fit = sse: 2.6340 rsquare: 0.7050 dfe: 4 adjrsquare: 0.7050 rmse: 0.8115 ``` The most interesting part is **``R = 1.291 (0.8872, 1.695)``**, where 1.291 is the mean for the extracted parameter ``**R**`` and **``(0.8872, 1.695)``** is the interval within one standard deviation. So we get our error bars as well: one sigma is equal ``1.695 - 1.291 = 0.4040`` So our conclusion is that **``R = 1.3 +/- 0.4``**. == Checking the fitting result == We should not trust computed fit parameters blindly. For our simple case it is probably OK, but for more evolved nonlinear fitting functions result might go way off if we specify starting point significantly far from optimal. One way to see check fit quality is to look at Root Mean Squared error (**``rmse``**) parameter from above output. It reflect what is the typical deviation of the model/fit from experimental points. It should be about the size of your experimental error bars. If it is much smaller then most likely you are over fitting (you model has to many free parameters). If it is much bigger your fit model is not good enough. Another way to check consistency of the fit it to look at plotted data and the fit simultaneously. Let's open yet one more window and plot our data first ``` figure(3) fontSize=24; set(gca,'FontSize',fontSize ); errorbar(I,V,dV, 'x') hold on ``` notice **``hold on``** statement, now next plotting command will draw over presented plot i.e. graphics window //holds on// to already present content. Now we the plot corresponding to our model/fit. There are multiple way to do it, the simplest is ``` plot( fitobject ) title('Dependence of voltage on current') xlabel('Current (A)') ylabel('Voltage (V)') ``` [fitted_data.png] = Saving you Matlab plots = Well it nice to have Matlab plots. But as soon as you close Matlab they will go away. How to save them for a future use? Actually, quite easy with **``print``** command. Unlike the name suggest it does not make a carbon copy but actually an electronic one. For figure saved in png format do ``` print('-dpng', 'V_vs_I.png') If you find that you figures are to large specify resolution in dots per inch with **``-r``** option. For example ``` print('-dpng', '-r50', 'V_vs_I.png') or if you need a pdf (Matlab usually make very bad pdfs without a lot of tweaking) ``` print('-dpdf', 'V_vs_I.pdf') The **``-d``** stands for output driver option, i.e png or pdf in above examples. Make sure that you read documentation for **``print``** command there are some useful parameters. = Where to learn more = At W&M Physics 256 class teaches how scientists use computers. Matlab was a language of choice for this class. Feel free to read and use [my Physics 256 materials http://physics.wm.edu/~evmik/classes/2012_fall_practical_computing_for_scientists/] make sure that you read at least Lecture 02 slides. Also lecture 15 slides discuss data reduction and fitting.