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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: