• Charge class
• Integral class
• test_integral script
• Shapes class (with subclassing)
• test_shapes script

• fitting1.py - Python nonlinear fitting (Requires Scientific module)
• gnuplot_nlfitting.plt - a Gnuplot script to perform a nonlinear fit (see comments)
• fft.py - Python program to compute the FFT and Power spectrum of a time based data set.

• Scientific Python
• Gnuplot

• gendata.py - generates noisy data and dumps to a file.  I fixed the argument parsing.  Type 'python gendata.py -h' to see the options (or look at the code).
• linfit.py - my version of the program you wrote in class today
• fitting1.py - example of a non-linear fit of a damped SHM data using the fitting module provided  by Scientific Python (their website).  You'll need this data file.

1. Write a script comparing the timing (use the "time" module) and accuracy of the romberg method versus the regular trapeziod method.  Try it out for several functions of your choosing.
2. Use the Euler-Cromer method to compute the angular displacement as a function of time for a simple pendulum for a range of amplitudes (5 - 120 degrees). In Euler-Cromer, you simply update the position at each time step with the newly updated velocity rather than the old velocity (see the Euler script above).  Compare the results with the small angle approximation for a pendulum. You may want to normalize your displacements with respect to the amplitude when plotting curves for comparison.

• quiver plots (from HW02)
• derivatives of noisy data using polyfit()
• numerical integration (trapezoid and Simpson's 1/3)

1. # 5 - Use the trapezoid and Simp. 1/3 methods to solve the problem with the Force(displacement) data provided.  Also, use polyfit() to fit that data with a polynomial and compute the analytical integral.  Compare the results.
2. # 11 
3. # 13

1. If you did not get HW1 completely working, get all parts of that done. Add a timing profile using the 'time' module.
   
   

2. Write programs to:

• Plot 2 different functions in a 2x1 subplot. You choose the functions, but don't use the ones done in class. Make sure the plots each have titles and labeled axes and specify different colors and markers (or line types) for each plot.
  Add a second Figure which is a polar plot of two different ellipses with different eccentricities and include a legend (you will need to fiddle with this to get a good position).

• Plot an animation (over two full cycles) of a simple wave on a string fixed at both ends. Have an option to save each frame as a 'png' file for later playback.

• Plot a vector plot showing the electric field of a dipole. Use color coding to indicate the magnitude of the E field.

I have also posted (and will post) the code snippets I show you in class.  These files are NOT full programs, just snippets of code to copy and paste and run.

• Lecture 1: Data structures in Python [download .py file]
• Lecture 2: Loops, Conditionals, and Functions [download .py file]
• Lecture 3: Plotting with the pylab (matplotlib) module [download .py file]

email your working code to jgladden_at_phy.olemiss.edu

HW#1

Write functions to:

1. generate a list of N random numbers with a Gaussian distribution.
   (use the random module)

2. compute the standard deviation of those numbers.

3. generate a histogram of a list of numbers.
   (args-> list of numbers, bin size, optional -> low and high cutoffs)

Document all functions with doc strings!!

You MAY NOT use the built in histogram functions in pylab (or other packages).

Write the histogram data to a tab deliminated text file for 4 different lists with different standard deviations, but the same number of total points and same average value.
(This could be 4 different files or 1 file with multiple columns)

You MAY use the built in “save” and “load” functions in pylab.

Read the all the histogram data into Python arrays and plot the 4 histograms with different colors on one plot. Include x and y labels, a title, and a legend indicating the appropriate stdev.

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