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* Allow real-time updating so that the plot can follow changing data
* Allow real-time updating so that the plot can follow changing data
To this end we will write a Python 3 program that uses tkinter, matplotlib, and object oriented programming methods and leaves us with a useful tool, as well as a template for programs of your own. Matplotlib does have an option to embed its canvas in a Tk frame, but at this time (April 2018)
To this end we will write a Python 3 program that uses tkinter, matplotlib, and object oriented programming methods and leaves us with a useful tool, as well as a template for programs of your own. Matplotlib does have an option to embed its canvas in a Tk frame, but at this time (April 2018) we need to also embed the in Tkagg is marked as deprecated. Therefore, we create two windows, one for the output , and another for the new Tk control panel. windows remain responsive the mouse structure is extensible to other graphics or visualization applications.
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 MacOSX), 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 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
# 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.
First, read the comprehensive section on Tkinter to see how that code works, and then the one on[ http://prancer.physics.louisville.edu/astrowiki/index.php/Graphics_with_Python graphics with matplotlib] to learn the basics of the plotting toolkit. In this section we combine the two, addin interactive graphics user interface that controls plotting with matplotlib. Our goals are to
To this end we will write a Python 3 program that uses tkinter, matplotlib, and object oriented programming methods and leaves us with a useful tool, as well as a template for programs of your own. Matplotlib does have an option to embed its canvas in a Tk frame, but at this time (April 2018) the method we need to also embed the pyplot toolbar in Tkagg is marked as deprecated. Therefore, we create two windows, one for the pyplot output including its toolbar, and another for the new Tk control panel. Both windows remain responsive to the mouse events, and this structure is extensible to other graphics or visualization applications.
We begin our code as usual by requiring these libraries
import tkinter as tk from tkinter import ttk
such that Tk functions require the "tk." and ttk functions use "ttk".
-- Work in Progress --
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
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
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
For programmers, however, this utility allows development and debugging of web software without the need for a large server.