Plotting with an introduction to functions

Transcription

1 Plotting with an introduction to functions N. Shearer a, T. Whyntie b, c a Durham University, b Langton Star Centre, c Queen Mary University of London Coding with CERN@school Summer 2014

2 Acknowledgments This work was supported by the Royal Commission of the Exhibition of 1851 and the IOP Top50 Work Placements scheme. Forfurther details, see: Copyright 2014 This work is licensed under the Creative Commons Attribution License. See for more information. N. Shearer Coding with functions 2

3 Introduction what are we going to do? Start using Python with Linux. Write some code to produce a plot. - The number of Timepix counts will be plotted against energy. Save and view the plot. Notes: - Code to type into the console is in green. - Key terms are bold. - The full script can be found here if you have problems. N. Shearer Coding with CERN@school: functions 3

4 Starting Linux Go to the VM VirtualBox Manager. Click on the Xubuntu VM and then click on the Start arrow. See the guide to running and installing Virtual Machines (VMs) for more information about this. Once the VM has opened, log in. To bring up a terminal press: N. Shearer Coding with CERN@school: functions 4

5 Initial coding commands A directory is a system of files (often called a folder) To find out which directory you are currently in: - pwd To see what s in the directory: - ls To move up a directory: - cd../ To create a new directory: - mkdir CERNatschool - cd CERNatschool Type these commands, then press Return to execute them. Typing cd takes you into the directory. N. Shearer Coding with CERN@school: functions 5

6 Creating a new file You should have the text editor, Vim, installed. Text editors allow you to write and edit python scripts. To create a new file, ensure that you are in the appropriate directory, then type: - vim filename.py - We suggest that you make the file name something like plotting.py. Once Vim has opened, press the i key to enter insert mode. This lets you start typing text into the file. N. Shearer Coding with CERN@school: functions 6

7 A bit of practice Type into Vim: - print I love CERN@school Press the escape button on your keyboard. To save, type :w, then press the enter button on your keyboard. To exit, type :q, then press enter again to return to the command window. In the command terminal type: - python filename.py The output appears in the terminal. N. Shearer Coding with CERN@school: functions 7

8 Importing modules Re-open the script and clear what you just wrote. The first step to writing our code is to import some necessary modules. - import os, inspect - import time - import matplotlib.pyplot as plt - from matplotlib import rc (handles files) (handles time) (plots things) (handles font) Modules are files containing definitions and statements that will be useful for us. Importing is a bit like getting a piece of lab equipment out and setting it up; these modules are the equipment that we use in programming. N. Shearer Coding with CERN@school: functions 8

9 Importing modules and commenting Here is an example of the code you just wrote. Putting # before a statement means it is not read by Python, so acts as a comment, or a note to yourself and others explaining why you ve written certain things very useful when code starts becoming long and complicated! N. Shearer Coding with CERN@school: functions 9

10 Changing font settings Changing the font is useful for making information in a plot clearer. Firstly specify what you want the font to look like: - font={'family':'serif',\ 'weight':'regular',\ 'size':8 } Make this the default font setting using: - rc('font', **font) Continue to add #comments in your script to remind yourself later of what you did! Lines can be carried over to the next line with a slash \ character. rc parameters are properties within matplotlib that can be customised. N. Shearer Coding with CERN@school: functions 10

11 Some more importing of modules To set global plot constants: - from matplotlib import rcparams Various properties can be set using rcparams within the matplotlib module, for example: - rcparams['axes.linewidth'] = 0.7 The numpy module does maths for us: - import numpy as np To find the path of the current directory: - path = os.path.dirname(os.path.abspath(\ inspect.getfile(inspect.currentframe()))) Importing numpy as np means we can just call it np throughout the rest of the script. N. Shearer Coding with CERN@school: functions 11

12 Printing a heading for the output Note the indent of four spaces after an if statement. Anything within the brackets after the word print will be printed to the command terminal. if name == " main : print("#######################################") print(" CERN@school: Making Plots with Python ") print("#######################################") print("*") print("* Time and date : %s" \ % (time.strftime("%h:%m, %A %d %B %Y (%Z)"))) print("* Working directory : '%s'" % (path)) print("*") print("#######################################") print("") Ensures that this section of code is only executed if this is the main program running (don t worry too much about this). N. Shearer Coding with CERN@school: functions 12

13 Producing the plot It can be helpful to close all unnecessary figures before a new figure is created: - plt.close('all') To make the figure that displays the plot: - plot = plt.figure(101, \ figsize = (3.0, 3.0), \ dpi=150, \ facecolor="white", \ edgecolor="white" \ ) Tells Python to produce a figure. Specifies size of figure. Specifies colour of figure. N. Shearer Coding with CERN@school: functions 13

14 Showing the plot To show the plot when the program runs: - plot.show() To ensure the figure only closes when a user responds by pressing any key: - raw_input('press any key to continue...') These lines have to be at the end of your program, so make sure that any code you write from now on goes before them. N. Shearer Coding with CERN@school: functions 14

15 Showing the plot Save the file and exit Vim to return to the terminal, and type: - python filename.py The (empty) figure on the right should display. Close the window and press any key to continue. Press escape, then type :wq. The figure is the size and colour we specified. N. Shearer Coding with CERN@school: functions 15

16 Producing the plot - axes A good graph is clear with labelled axes. - This makes it much easier to understand. To ensure there is enough room around the plot for the axes labels: - plot.subplots_adjust(bottom=0.19, \ - left=0.19) To add axes to the plot: - plotax = plot.add_subplot(111) Run the script again. The figure now has a set of axes to plot on. N. Shearer Coding with CERN@school: functions 16

17 Producing the plot - axes To set labels for the axes: - plotax.set_xlabel('the $x$ axis') - plotax.set_ylabel('the $y$ axis') We can change these to more appropriately named axes later on. Temporary labels for the axes. N. Shearer Coding with CERN@school: functions 17

18 Producing the plot - data Firstly, we need to create an array (a list of numbers) to use as the x values in our plot, which is a range of energies: - x_energy_kev = np.arange(0.0,\ 100.1, 0.1) Print out the values to check what you're doing (these will show up on the console when you run the script): - print("* The x values:") - print x_energy_kev - print("*") Asks Python to run the function arange that is part of the library numpy. This dotted notation is used all the time in Python to refer to the parts of things as whole.part. Here, arange creates a list of numbers from 0.0 to 100.0, increasing in steps of 0.1. N. Shearer Coding with CERN@school: functions 18

19 Producing the plot - data For the y axis, we create a surrofunction (gives the number of counts detected), for which some constants need to be defined. The calibration constants: - a = b = c = t = Type the surrofunction in as follows: - surrofunc = a * x_energy_kev + b \ -(c /(x_energy_kev - t)) These are the values for LUCID TPX0, pixel New variables are created using an = to assign values to them. Remember, the \ is there to allow us to carry lines of code onto a new line. N. Shearer Coding with CERN@school: functions 19

20 Producing the plot Tell Python to plot the surrofunction against the x value: - surroline, = plotax.plot \ - (x_energy_kev, surrofunc, 'g') x axis y axis For a green plot. Set the limits of the y axis so that no space on the figure is wasted: - plotax.set_ylim(ymin=0.0, \ - ymax = 350.0) The function has been plotted on more appropriately sized axes. N. Shearer Coding with CERN@school: functions 20

21 Labelling the axes Let s give the axes more descriptive labels. To set the x axis label: - plotax.set_xlabel('energy $E$ / - kev', labelpad=10) To set the y axis label: - plotax.set_ylabel('tot counts', / - labelpad=10) Axis label Number of points between axis and label. N. Shearer Coding with CERN@school: functions 21

22 Adding a legend A legend is a key on the graph to help explain what it is about. plotax.legend([surroline],\ ['TPX0, pixel 00000'],\ loc='upper left',\ borderpad=0.6,\ borderaxespad=1.0,\ frameon=none,\ fancybox=true,\ fontsize=8) Location of legend Space within legend border Space between axes and legend border No frame around legend Draws a frame with round fancybox Determines font size within legend N. Shearer Coding with CERN@school: functions 22

23 Adding a grid and completing the code Grid lines can be added to the figure axes: - plotax.grid() That should be just about everything! Make sure that the following lines are still at the end of your script: - plot.show() - raw_input('press any key to continue...') N. Shearer Coding with CERN@school: functions 23

24 Returning to the terminal Press the escape button on your keyboard. To save, type :w, then press the enter button on your keyboard. To exit, type :q, then press enter again to return to the command window. In the command window type: - python filename.py N. Shearer Coding with CERN@school: functions 24

25 Let s see what we get A graph showing the relationship between number of counts and energy. A clear legend. Nicely labelled axes. Laid out exactly how we wanted it to be. N. Shearer Coding with CERN@school: functions 25

26 Let s see what we get It seems like a lot of effort just for one graph, but: The same code can be used to produce similar graphs with different data, functions or parameter values. We can make things look much better than in Excel, and easily design them to look exactly how we want them to ready for showing to other scientists or publishing in a scientific journal. N. Shearer Coding with CERN@school: functions 26

27 Conclusion what have we achieved? We have: used Python with a new operating system, Linux. created a Python script for plotting a graph. - The number of Timepix detector counts was plotted against the energy of the ionising radiation measured by the detector; - A command-line text editor, Vim, was used to write the script. used commands in the terminal to run the script in order to plot the graph. N. Shearer Coding with CERN@school: functions 27

28 Thank you for following along! Ask questions on Facebook or Twitter. N. Shearer a, T. Whyntie b, c a Durham University, b Langton Star Centre, c Queen Mary University of London Coding with CERN@school Summer 2014