As part of our short course on Python for Physics and Astronomy we consider how users interact with their computing environment. A programming language such as Python provides tools to build code that computes scientific models, captures data, sorts it and analyzes it largely without operator action. In effect, once you have written the program, you point it at the data or task it is to do, and wait for it to return new science to you. This is the command line, or batch, model of computing and is at the core of large data science today. Indeed, from your handheld devices to supercomputers, the work that is done is for the most part autonomous. We have seen how Python has built-in components to accept input from the command line, the operating system, the computer that is hosting the program, and the Internet or cloud. What about the other side, the user's perspective on computing?
As an end user, would you prefer to move a mouse or tap a screen in order to select a file, or to type in the path and file name? What if you had to make operational decisions based on graphical output, or changing real world environments as data are collected? In modern computing, most of us interact with the machine and software through a graphical user interface or GUI.
In a unix-like enviroment (Linux or MacOS), the command line is an accessible and often preferred way to instruct a program on what to do. A typical program, as we've seen, might start like this example to interpolate a data file and plot the result:
import sys import numpy as np from scipy.interpolate import UnivariateSpline import matplotlib.pyplot as plt
sfactorflag = True
if len(sys.argv) == 1: print " " print "Usage: interpolate_data.py indata.dat outdata.dat nout [sfactor]" print " " sys.exit("Interpolate data with a univariate spline\n") elif len(sys.argv) == 4: infile = sys.argv outfile = sys.argv nout = int(sys.argv) sfactorflag = False elif len(sys.argv) == 5: infile = sys.argv outfile = sys.argv nout = int(sys.argv) sfactor = float(sys.argv) else: print " " print "Usage: interpolate_data.py indata.dat outdata.dat nout [sfactor]" print " " sys.exit("Interpolate data with a univariate spline\n")
It uses "sys" to parse the command line arguments into text and numbers that control what the program will do. Because its first line directs the system to use the python interpreter, if the program is marked as executable to the user it will run as a single command followed by arguments. In this case it would be something like
interpolate_data.py indata.dat outdata.dat nout sfactor
where indata.dat is a text-based data file of x,y pairs, one pair per line, outdata.dat is the interpolated file, nout is the number of points to be interpolated, and sfactor is an optional floating point smoothing factor. When you run this it will read the files, do the interpolation without further interaction, and (as written) plot a result as well as write out a data file. The rest of the code is
# Take x,y coordinates from a plain text file # Open the file with data infp = open(infile, 'r') # Read all the lines into a list intext = infp.readlines() # Split data text and parse into x,y values # Create empty lists xdata =  ydata =  i = 0 for line in intext: try: # Treat the case of a plain text comma separated entry entry = line.strip().split(",") # Get the x,y values for these fields xval = float(entry) yval = float(entry) xdata.append(xval) ydata.append(yval) i = i + 1 except: try: # Treat the case of a plane text blank space separated entry entry = line.strip().split() xval = float(entry) yval = float(entry) xdata.append(xval) ydata.append(yval) i = i + 1 except: pass # How many points found? nin = i if nin < 1: sys.exit('No objects found in %s' % (infile,))
# Import data into a np arrays x = np.array(xdata) y = np.array(ydata)
# Function to interpolate the data with a univariate cubic spline if sfactorflag: f_interpolated = UnivariateSpline(x, y, k=3, s=sfactor) else: f_interpolated = UnivariateSpline(x, y, k=3)
# Values of x for sampling inside the boundaries of the original data x_interpolated = np.linspace(x.min(),x.max(), nout) # New values of y for these sample points y_interpolated = f_interpolated(x_interpolated)
# Create an plot with labeled axes plt.figure().canvas.set_window_title(infile) plt.xlabel('X') plt.ylabel('Y') plt.title('Interpolation') plt.plot(x, y, color='red', linestyle='None', marker='.', markersize=10., label='Data') plt.plot(x_interpolated, y_interpolated, color='blue', linestyle='-', marker='None', label='Interpolated', linewidth=1.5) plt.legend() plt.minorticks_on() plt.show()
# Open the output file outfp = open(outfile, 'w') # Write the interpolated data for i in range(nout): outline = "%f %f\n" % (x[i],y[i]) outfp.write(outline) # Close the output file outfp.close() # Exit gracefully exit()
Aftet the fitting is done the program runs pyplot to display the results. The interactive window it opens and manages is a GUI, but it has been set up by the command line code. Of course there are many variations on command line interfacing, and the one shown here with coded argument parsing is perhaps the simplest and would serve as a template for most applications. Python offers other ways to manage the command line too. The os module is useful to have access to the operating system from within a Python routine. Some examples are
os.chdir(path) changes the current working directory (CWD) to a new one os.getcdw() returns the CWD os.getenv(varname) returns the value of the environment variable varname
and there are many more, providing within the Python program many of the command line operating system tools available on the system. Here's an example of how that might be used in a program that processes many files in a directory:
# Process images in a directory
import os import sys import fnmatch import string import subprocess import pyfits
if len(sys.argv) != 2: print " " sys.exit("Usage: process_fits.py directory\n")
toplevel = sys.argv[-1]
# Search for files with this extension pattern = '*.fits'
for dirname, dirnames, filenames in os.walk(toplevel): for filename in fnmatch.filter(filenames, pattern): fullfilename = os.path.join(dirname, filename) try: # Open a fits image file hdulist = pyfits.open(fullfilename) except IOError: print 'Error opening ', fullfilename break
# Do the work on the files here ... # You can call a separate system process outside of Python this way darkfile = 'dark.fits' infilename = filename outfilename = os.path.splitext(os.path.basename(infilename))+'_d.fits' subprocess.call(["/usr/local/bin/fits_dark.py", infilename, darkfile, outfilename])
Here we used the os module's routines to walk through a directory tree, parse filenames, and then perform another operation on those files that is a separate command line Python program. Command line tools used to leverage the operating system's built-in functions can be very powerful, and take hours out of actually running a program on a large database.
How do we get to "point-and-click" operations that take us beyond the packaged pyplot routines to code of our own? Python offers a built-in package that is easy to use (for simple applications) and adapts to the operating system so that the programs have the look and feel of the native OS. It also has add-on modules that may be installed on some systems to make GUI interfaces in the style of Gnome (GTK) or KDE (Qt) as well as others. We will focus on the built-in "Tk" library and its use because it is simple and effective, and it may satisfy most needs without adding complexity that we usually associate with other programming languages like C++, or native C.
Tkinter is Python's standard GUI that is built on a library called Tck/Tk that is available on all operating systems. There are layers of software here, starting at the low-level of the operating system, and finishing with the top level of the user's experience. Python connects these with Tkinter, or just Tk for short. If you Google Python Tk you will see many links to tutorials and reference documentation. Take care in following that journey. The path through them is difficult and and you may not return soon!
A simpler alternative is to follow a few examples here that may be enough to get you started, and then go to Google when you are stuck with your particular application. I have excerpted these from Programming Python by Mark Lutz (O'Reilly 2011) which is highly recommended for self-study. Programming Python has been updated to include Python 3 too. Here's a 4-line program:
from Tkinter import * widget = Label(None, text='Hello world!') widget.pack() widget.mainloop()
That first line that imports tkinter is written here for Python 2.7. If you use Python 3, it will be "tkinter" instead. Otherwise, it should work similarly in both systems. The import * brings in all the components and will let you use them without a prefix like "Tkinter.", which should not be a problem for most applications because tk is a well-established part of Python. The second line creates a "Label" widget and puts text into it. The third places widget in the window, and the fourth starts and runs the GUI. That's it. Once it is running you will see something that looks like this:
Tk handles the window and the usual operations with it. The Label widget has several options, and entering them when it is created in this way is the most direct. You could also do it like this:
from Tkinter import * widget = Label() widget['text'] = 'Hello world!' widget.pack(side=TOP) widget.mainloop()
where we have changed the widget's content with a mapping key, and in this example, told "pack" where to put it. We will see more examples of the packing operation later.
Within the mainloop (the details of which we do not see) the sytem is waiting for the user to do something. It reacts to your action and then goes back to waiting. Most of the time it is not doing anything except waiting for you. If you want the system to be doing something else, and then checking on you too, you have to cautiously program the actions of the GUI so that it remains responsive, or else spawn another process to handle other those tasks while the GUI waits on you. In those cases, the programming problem is to establish communication with the processes which would be running asynchronously to the main loop. We'll leave that one for you to ponder on a rainy night.
Now the main loop is running and waiting, but you have not asked it to do anything. It is just waiting for one of the available system window operations like resize or kill. We can add buttons which respond when clicked, and then run a "callback" routine. That is, you click the button, Tk captures the click and tells your program to run a specific routine because you hit that button. Routines that respond in this way are called callbacks.
import sys from Tkinter import * lastwords='You can start this program again and I will come back.' def saygoodbye(): print('Well, if you are going to act that way I am leaving.') print lastwords sys.exit()
widget = Button(None, text='Goodbye world!', command=saygoodbye) widget.pack() widget.mainloop()
This one makes a button instead of a label. Click the button and the program exits, writing on the screen (not in the GUI) the print statement's text. The callback to exit here is simple and does not take an argument. In this example we have passed information to the callback with a global variable lastwords. How to manage arguments and global variables within callbacks is a broader problem and diverts us from the task at hand of seeing how to build a GUI. Just keep in mind, that if you have to send a value or parameters in a callback, there are other tricks to learn, including different schemes of specifying the callback that utilize the object oriented features of Python.
We will take this one more level and put five widgets at once by using the concept of a frame and a root window in this example adapted from Python tutorials:
import sys from Tkinter import *
lastwords='You can start this program again and I will come back.'
def saygoodbye(): print('Well, if you are going to act that way I am leaving.') print lastwords sys.exit()
root = Tk() topframe = Frame(root) frame.pack( side = TOP )
bottomframe = Frame(root) bottomframe.pack( side = BOTTOM )
redbutton = Button(topframe, text="Red", fg="red") redbutton.pack( side = LEFT)
greenbutton = Button(topframe, text="Brown", fg="brown") greenbutton.pack( side = LEFT )
bluebutton = Button(topframe, text="Blue", fg="blue") bluebutton.pack( side = LEFT )
blackbutton = Button(bottomframe, text="Black", fg="black") blackbutton.pack( side = LEFT)
byebutton = Button(bottomframe, text="Exit", fg="magenta", command=saygoodbye ) byebutton.pack( side = RIGHT)
Notice we had to have a root window in which to place the frames, and that we made two frames in this window. The buttons in usual operation would each have a callback that would do something other than simply exit. The placement of the buttons was handled by pack(), selecting in the program preferences that pack manages for us. If we need more control, then we can add an anchor keyword to the pack statement. Anchor has arguments "N,S,E,W,NE,NW,SE,SW,CENTER) which tells pack where to put the widget within its allowed space. There are other pack options too, such as expand and fill, which can be used to customize the appearance of the GUI.
Tkinter has a wide selection of widget classes that will handle most common GUI applications:
Opening a file is fundamental to many uses of Python. We have seen that matplotlib has useful interactive graphics in a GUI, so how to we incorporate that into a root window of our own, and add file selection?
Here is a data plotting program that illustrates many of the key elements of Tk GUI programming. The complete program is available here as pyplot_data.example. Let's look at how this works.
First, we tell the operating system to use Python, and we comment the code with a simple description of what it does.
#!/usr/bin/python """ Interactively plot data using matplotlib pyplot within a Tk root window """
We import sys and numpy modules and use numpy as "np" in the usual way.
import sys import numpy as np
We import matplotlib and call it mpl for a short name. We tell mpl to use Tk by default.
import matplotlib as mpl mpl.use('TkAgg')
We import some backend things that may (or may not) be needed to get plotting to work in our own window.
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg from matplotlib.backend_bases import key_press_handler
We import Figure which does does the plotting for us
from matplotlib.figure import Figure
Importing Tk is handled differently in Python 2 and Python 3. We also get askopenfilename which we will use to get a file using a GUI.
if sys.version_info < 3:
import Tkinter as Tk from tkFileDialog import askopenfilename
import tkinter as Tk from tkFileDialog import askopenfilename
This program optionally will take a filename on the command line. We define a flag to tell us later if there was one provided when the program started.
fileflag = True
Parse the command line and set the fileflag according to the user's wishes.
if len(sys.argv) == 1: fileflag = False elif len(sys.argv) == 2: fileflag = True infile = sys.argv else: print " " print "Usage: pyplot_data.py [indata.dat]" print " " sys.exit("\nUse pyplot to display a data file\n")
If there was no file provided, bring up a GUI to allow the user to select a file.
if not fileflag: root = Tk.Tk() infile = askopenfilename() root.quit() root.destroy()
After the file selection has been done, we need to remove traces of the is window so it will not clutter the desktop or cause errors later.
Open the file with the data if we can, and if not exit gracefully.
try: infp = open(infile, 'r') except: sys.exit("File %s could not be opened\n" % (infile,))
Read all the lines from the data into a list of text lines.
intext = infp.readlines()
Split data text and parse into x,y values. Start by createing empty lists for the data. We need to use lists since numpy arrays are immutable and we do not know how much data we have yet.
xdata =  ydata =  i = 0
for line in intext:
try: # Treat the case of a plain text comma separated entry entry = line.strip().split(",")
# Get the x,y values for these fields xval = float(entry) yval = float(entry) xdata.append(xval) ydata.append(yval) i = i + 1
except: try: # Treat the case of a plane text blank space separated entry
entry = line.strip().split()
xval = float(entry) yval = float(entry) xdata.append(xval) ydata.append(yval) i = i + 1 except: pass # How many points found?
nin = i if nin < 1: sys.exit('No objects found in %s\n' % (infile,))
In scanning the text lines we look for x,y pairs separated by whitespace, or by a comma. They have to be parsed differently, so we try each format. When we are done we note how many lines were successfully read.
Now that the data are fixed, we import x and y into numpy arrays.
x = np.array(xdata) y = np.array(ydata)
The data are now loaded and ready to plot, so the rest is GUI content.
Create the root/toplevel window with title
root = Tk.Tk() root.wm_title("PyPlot")
Set up the figure for the plot. Here we use a 7x5 aspect ratio at 200 DPI. On the screen his is nominally a 7x5 inch display, but depends on the monitor. Try 16x9 with 100 DPI for a larger monitor. Add a title to the plot and label the axes.
f = Figure(figsize=(7,5), dpi=200) a = f.add_subplot(111) a.plot(x,y) a.set_title(infile) a.set_xlabel('X') a.set_ylabel('Y')
Create tk.DrawingArea with mpl figure
canvas = FigureCanvasTkAgg(f, master=root) canvas.show() canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
Optionally add the default mpl plotting toolbar (comment out to disable)
toolbar = NavigationToolbar2TkAgg( canvas, root ) toolbar.update()
Pack the canvas to make things fit nicely.
canvas._tkcanvas.pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
Add the keypress event handler that is built into mpl. This may not be necessary, but it seems wise.
def on_key_event(event): print('You pressed %s'%event.key) key_press_handler(event, canvas, toolbar)
Add a Quit button as an example of what you might include on this canvas.
# Stop mainloop root.quit() # Prevent error running on Windows OS root.destroy() button = Tk.Button(master=root, text='Quit', command=mpl_quit) button.pack(side=Tk.BOTTOM)
Now we are ready to run the loop interact with the graphics.
The program runs waiting for user input. When you move the mouse, those events are trapped by mpl and used to update the cursor readout, or to activate the buttons on the mpl toolbar. When you click your Exit button the root window will be removed and the program will exit.