
Laboratory 8: Various applications, plotting
Prerequisites: pylab/matplotlib: plot, legend, axislabelling, writing png/pdf files, root finding
To monitor the coding style we turn on the PEP8 style guide monitoring in Spyder by:
 Going to preferences in Spyder menu
 Clicking on Editor in the selection list on the left
 Clicking on Code Introspection/Analysis (top right corner)
 Ticking the box Realtime code style analysis
 Clicking Apply and OK (bottom right corner)
Create a file lab8.py with the following functions:
Write a function f1(x) which accepts an number x as input and computes and returns
Write a function f2(x) which accepts the number x as input and computes and returns
where log refers to the natural logarithm (the Python function name is math.log).
A function positive_places(f,xs) that takes as arguments some function f and a list of numbers xs and returns a list of thoseandonlythose elements x of xs for which f(x) is strictly greater than zero. This task was given already as a training exercise in lab 6 where we also provide some input and output examples.
In this lab, we ask that you write the same function without
usage of a for or while loop to practice list comprehension
or the use of filter.
Write a function create_plot_data(f, xmin, xmax, n) which returns
a tuple (xs, ys) where xs and ys are two sequences, each
containing n numbers:
The function is expected to work for any n >= 2.
Examples (here the tuple of returned sequences is a tuple of lists):
In [ ]: def f(x):
...: return x * 10
...:
In [ ]: create_plot_data(f, 1, 1, 2)
Out[ ]: ([1.0, 1.0], [10.0, 10.0])
In [ ]: create_plot_data(f, 0, 2, 5)
Out[ ]: ([0.0, 0.5, 1.0, 1.5, 2.0], [0.0, 5.0, 10.0, 15.0, 20.0])
In [ ]: def f(x):
...: return 0
...:
In [ ]: create_plot_data(f, 0, 1.5, 4)
Out[ ]: ([0.0, 0.5, 1.0, 1.5], [0, 0, 0, 0])
Using pylab or matplotlib, write a function myplot() that computes f1(x) [by
calling the function f1 of course, or  even better  calling
create_plot_data] and plots f1(x) using 1001 points for x
ranging from 2 to +2. The function should return None.
Then extend this function myplot to also plot f2(x) in the same graph.
To plot two or more curves on the same figure, just use the plot() command twice.
Label xaxis and provide a legend showing the function name
(i.e. f1 and f2) for the two curves.
Extend the function myplot() so that it saves a png file of the
graph (with name plot.png) and a pdf file of the graph (with
name plot.pdf) through pylab/matplotlib commands.
(Note: Generally, it is better to use pdf files rather than
png files as pdf files are based on vector graphics and produce
higher print quality, and can be zoomed without appearing
pixelated. On the other hand, png files are good choice, for
example, to include in webpages.)
Use the pylabnavigation bar (at the bottom of the figure) to zoom
into the image. For x > 0, what is the value x of the functions where
f1(x) = f2(x)?
(If you are using the the IPython console and by default your
plots appear inline, i.e. in the IPYthon console, then you
cannot zoom into the figure. If so, you should use the command
%matplotlib qt in the console. After you have done this,
the next plot figure should appear in its own popup window,
and allow you to zoom in and out in the figure.
To switch back to figures showing in the IPython console, use
%matplotlib inline).
Using this graphical information, write a function find_cross() which
uses scipy.optimize.brentq to find the value x
(approximately) for which f1(x) = cos(2 * pi * x) * exp(x * x) and
f2(x) = log(x + 2.2) have the same value. We are only interested
in the solution where x > 0.
Your function find_cross() should return the approximation of
the root that scipy.optimize.brentq returns (the default
tolerance settings are okay).
Write a function reverse_dic(d) that takes a dictionary d
as the input argument and returns a dictionary r. If the
dictionary d has a key k and an associated value v,
then the dictionary r should have a key v and a value
k. (This is only expected to work for dictionaries that have a
unique set of values although you do not need to check for this.)
Submit lab8.py using lab8 as the subject line.
Last updated: 20190106 at 17:34
