User Interfaces

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.

Command Line Interfacing and Access to the Operating System

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:

 #!/usr/bin/python

 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[1]
   outfile = sys.argv[2]
   nout = int(sys.argv[3])
   sfactorflag = False
 elif len(sys.argv) == 5:
   infile = sys.argv[1]
   outfile = sys.argv[2]
   nout = int(sys.argv[3])
   sfactor = float(sys.argv[4]) 
 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[0])
     yval = float(entry[1])
     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[0])
       yval = float(entry[1])
       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

 import os

 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:

 #!/usr/bin/python

 # Process images in a directory tree

 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))[0]+'_d.fits'
     subprocess.call(["/usr/local/bin/fits_dark.py", infilename, darkfile, outfilename]) 

 exit()

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.

Graphical User Interfaces and Plotting

First, read the comprehensive section on Tkinter to see how that code works. This section is an update of an older description of how to use Tk with matplotlib.

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[0] < 3:

 import Tkinter as Tk
 from tkFileDialog import askopenfilename

else:

 import tkinter as Tk
 from tkFileDialog import askopenfilename

Define a function that will load a new file into the dataspace once its name is known.

 def load_file(newfile):
 
   # Take x,y coordinates from a plain text file
   # Open the file with data

   global x
   global y
   global nin
 
   try:
     infp = open(newfile, 'r')
    
   except:
     return(0)
   
   # 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[0])
       yval = float(entry[1])
       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[0])
         yval = float(entry[1])
         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,))

 # Import  data into a np arrays

 x = np.array(xdata)
 y = np.array(ydata)

 return(nin)

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[1]
 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 window so it will not clutter the desktop or cause errors later.

 npts = load_file(infile)

 if npts <= 0:
   print "Could not find data in  %s \n" % (infile,)
   exit()
 

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)
 p, = 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)

 canvas.mpl_connect('key_press_event', on_key_event)

Add a Quit button as an example of what you might include on this canvas.

 def mpl_quit():

   # 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.RIGHT)

Add a File button to change the file once the plot is displayed

 def mpl_file():
   infile = askopenfilename()
   npts = load_file(infile)
   if npts > 0:
     p.set_xdata(x)
     p.set_ydata(y)
     canvas.show()
   return

 button  = Tk.Button(master=root, text='File', command=mpl_file)
 button.pack(side=Tk.LEFT)

Now we are ready to run the loop interact with the graphics.

 Tk.mainloop()

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. The File button will allow selection of a new file to plot. When you click your Exit button the root window will be removed and the program will exit.

Running a Server for Javascript in a Browser Engine

Python includes packages that enable a simple webserver which may be used to run advanced graphics operations through javascript within a browser's javascript engine. We will cover use of javascript, and Three.js in particular, as a supplement or replacement for 3D visualization in Python. In order to do this without the burden of managing a full Apache installation, we turn to Python. This shell script in Linux will start a web server in the directory that the script is running in:

 python -m CGIHTTPServer 8000 1>/dev/null 2>/dev/null &
 echo "Use localhost:8000"
 echo

By using port 8000 the server is distinct from the one on port 80 used for web applications. The site would appear by putting

 http://localhost:8000

in a Google Chrome or Mozilla Firefox browser window running on the same user account on the same machine. Note the redirects for stdio and stderr to /dev/null keeps output from appearing in the console. The server may be killed by identifying its process ID in Linux with the command

 ps -e | grep python

followed by

 kill -s 9  pid

where "pid" is the ID number found in the first line. Alternatively, if it is the only python process running you may kill it with

 killall python

Any file in the directory tree below the starting directory is now accessible in the browser, and html files will be parsed to run the included javascript. If here is a cgi-bin directory at the top level, the server will see it and use it. One use of this low level server is to create a virtual instrument that is accessible from the web, but not exposed to it directly. A remote web server on the same network that can access port 8000 on the instrument machine can run code and get response from the instrument by calling cgi-bin operations.

For programmers, however, this utility allows development and debugging of web software without the need for a large server.