This chapter is taken from the book A Primer on Scientific Programming with Python by H. P. Langtangen, 5th edition, Springer, 2016.
Consider class Line from the section A class for straight lines and
a subclass Parabola0 defined as
class Parabola0(Line):
pass
That is, class Parabola0 does not have any own code, but it inherits
from class Line. Demonstrate in a program or interactive session,
using dir and looking at the __dict__ object,
that an instance of class Parabola0 contains everything
(i.e., all attributes)
that an instance of class Line contains.
Filename: dir_subclass.
The task in this exercise is to make
a class Cubic for cubic functions
$$
\begin{equation*} c_3x^3 + c_2x^2 + c_1x + c_0\end{equation*}
$$
with a call operator and a table method as in classes
Line and Parabola from the section Inheritance and class hierarchies.
Implement class Cubic by
inheriting from class Parabola, and
call up functionality in class Parabola in the same way as class
Parabola calls up functionality in class Line.
Make a similar class Poly4 for 4-th degree polynomials
$$
\begin{equation*} c_4x^4 + c_3x^3 + c_2x^2 + c_1x + c_0\end{equation*}
$$
by inheriting from class
Cubic.
Insert print statements in all the __call__ methods such that
you can easily watch the program flow and
see when __call__ in the different classes is called.
Evaluate cubic and a 4-th degree polynomial at a point, and observe
the printouts from all the superclasses.
Filename: Cubic_Poly4.
This exercise follows the idea from the section Inheritance and class hierarchies
where more complex polynomials are subclasses of simpler ones.
Conceptually, a cubic polynomial is not a parabola, so many
programmers
will not accept class Cubic as a subclass of Parabola;
it should be the other way around, and
Exercise 2: Make polynomial subclasses of parabolas follows that approach. Nevertheless, one
can use inheritance solely for sharing code and not for expressing that
a subclass is a kind of the superclass. For code sharing it
is natural to start with the simplest polynomial as superclass and
add terms to the inherited data structure as we make subclasses
for higher degree polynomials.
Implement a class for the function \( f(x)=A\sin (wx) + ax^2 + bx + c \).
The class should have a call operator for evaluating the function for
some argument \( x \), and a constructor that takes the function
parameters A, w, a, b, and c as arguments. Also a table
method as in classes Line and Parabola should be
present. Implement the class by deriving it from class Parabola and
call up functionality already implemented in class Parabola whenever
possible.
Filename: sin_plus_quadratic.
Let class Polynomial from the document Introduction to classes in
Python [2] be
a superclass and implement class Parabola as a subclass. The
constructor in class Parabola should take the three coefficients in
the parabola as separate arguments. Try to reuse as much code as
possible from the superclass in the subclass. Implement class Line
as a subclass specialization of class Parabola.
Which class design do you prefer, class Line as a subclass of
Parabola and Polynomial, or Line as a superclass with extensions
in subclasses? (See also remark in Exercise 2: Make polynomial subclasses of parabolas.)
Filename: Polynomial_hier.
The document Introduction to classes in
Python [2]
presents class Circle. Make a similar class Ellipse for
representing an ellipse. Then create a new class Circle that is a
subclass of Ellipse.
Filename: Ellipse_Circle.
A point \( (x,y) \) in the plane can be represented by a class:
class Point(object):
def __init__(self, x, y):
self.x, self.y = x, y
def __str__(self):
return '(%g, %g)' % (self.x, self.y)
We can extend the Point class to also contain the representation of
the point in polar coordinates. To this end, create a subclass
PolarPoint whose constructor takes the polar representation of a
point, \( (r,\theta) \), as arguments. Store \( r \) and \( \theta \) as
data attributes and call the superclass constructor with the corresponding
\( x \) and \( y \) values (recall the relations \( x=r\cos\theta \) and
\( y=r\sin\theta \) between Cartesian and polar coordinates). Add a
__str__ method in class PolarPoint which prints out \( r \), \( \theta \),
\( x \), and \( y \). Write a test function that creates two PolarPoint
instances and compares the four data attributes x, y, r, and theta
with the expected values.
Filename: PolarPoint.
Consider a class F implementing the function
\( f(t;a,b) = e^{-at}\sin (bt) \):
class F(object):
def __init__(self, a, b):
self.a, self.b = a, b
def __call__(self, t):
return exp(-self.a*t)*sin(self.b*t)
We now want to study how the function \( f(t;a,b) \) varies with the
parameter \( b \), given \( t \) and \( a \). Mathematically, this means that we
want to compute \( g(b;t,a)=f(t;a,b) \). Write a subclass Fb of F
with a new __call__ method for evaluating \( g(b;t,a) \). Do not
reimplement the formula, but call the __call__ method in the
superclass to evaluate \( f(t;a,b) \). The Fs should work as follows:
f = Fs(t=2, a=4.5)
print f(3) # b=3
Before calling __call__ in the superclass, the data attribute b
in the superclass must be set to the right value.
Filename: Fb.
The purpose of this exercise is to investigate the accuracy of the
Backward1, Forward1, Forward3, Central2, Central4,
Central6 methods for the function
$$
\begin{equation*} v(x)={1-e^{x/\mu}\over 1-e^{1/\mu}}\tp\end{equation*}
$$
Compute the errors in the approximations for
\( x=0, 0.9 \) and \( \mu =1, 0.01 \). Illustrate in a plot
how the \( v(x) \) function looks like for these two \( \mu \) values.
Modify the src/oo/Diff2_examples.py program which produces tables of errors of difference approximations as discussed at the end of the section Extensions.
Filename: boundary_layer_derivative.
Make a subclass Sine1 of class FuncWithDerivatives from the section Superclass for defining an interface for the \( \sin x \) function. Implement the function
only, and rely on the inherited df and ddf methods for computing
the derivatives. Make another subclass Sine2 for \( \sin x \) where you
also implement the df and ddf methods using analytical expressions
for the derivatives. Compare Sine1 and Sine2 for computing the
first- and second-order derivatives of \( \sin x \) at two \( x \) points.
Filename: Sine12.
Carry out
the exercise called Implement a class for numerical differentiation
in the document Introduction to classes in Python [2].
Find the common code in the classes Derivative, Backward, and
Central. Move this code to a superclass, and let the three
mentioned classes be subclasses of this superclass. Compare the
resulting code with the hierarchy shown in the section Classes for differentiation.
Filename: numdiff_classes.
A one-sided, three-point, second-order accurate formula for
differentiating a function \( f(x) \) has the form
$$
\begin{equation}
f'(x)\approx {f(x-2h) -4f(x-h) + 3f(x)\over 2h}\tp
\tag{17}
\end{equation}
$$
Implement this formula in a subclass Backward2 of class Diff from
the section Class hierarchy for numerical differentiation. Compare Backward2 with Backward1 for
\( g(t)=e^{-t} \) for \( t=0 \) and \( h=2^{-k} \) for \( k=0,1,\ldots, 14 \) (write
out the errors in \( g'(t) \)).
Filename: Backward2.
Suppose you want to compute \( f''(x) \) of some mathematical function \( f(x) \), and that you apply some class from the section Class hierarchy for numerical differentiation twice, e.g.,
ddf = Central2(Central2(f))
Will this work?
Follow the program flow, and find out what
the resulting formula will be. Then see if this formula coincides with
a formula you know for approximating \( f''(x) \) (actually, to recover
the well-known formula with an \( h \) parameter, you would use \( h/2 \)
in the nested calls to Central2).
Classes are often used to model objects in the real world. We may
represent the data about a person in a program by a class Person,
containing the person's name, address, phone number, date of birth,
and nationality. A method __str__ may print the person's data.
Implement such a class Person.
A worker is a person with a job. In a program, a worker is naturally
represented as class Worker derived from class Person, because a
worker is a person, i.e., we have an is-a relationship. Class
Worker extends class Person with additional data, say name of
company, company address, and job phone number. The print
functionality must be modified accordingly. Implement this Worker
class.
A scientist is a special kind of a worker. Class Scientist may
therefore be derived from class Worker. Add data about the
scientific discipline (physics, chemistry, mathematics, computer
science, ...). One may also add the type of scientist: theoretical,
experimental, or computational. The value of such a type attribute
should not be restricted to just one category, since a scientist may
be classified as, e.g., both experimental and computational (i.e., you
can represent the value as a list or tuple). Implement class
Scientist.
Researcher, postdoc, and professor are special cases of a scientist.
One can either create classes for these job positions, or one may add
an attribute (position) for this information in class
Scientist. We adopt the former strategy. When, e.g., a researcher
is represented by a class Researcher, no extra data or methods are
needed. In Python we can create such an empty class by writing pass
(the empty statement) as the class body:
class Researcher(Scientist):
pass
Finally, make a demo program where you create and print instances of
classes Person, Worker, Scientist, Researcher, Postdoc, and
Professor. Print out the attribute contents of each instance (use
the dir function).
An alternative design is to introduce a class Teacher as a special
case of Worker and let Professor be both a Teacher and
Scientist, which is natural. This implies that class Professor has
two superclasses, Teacher and Scientist, or equivalently, class
Professor inherits from two superclasses. This is known as
multiple inheritance and technically achieved as follows in Python:
class Professor(Teacher, Scientist):
pass
It is a continuous debate in computer science whether multiple
inheritance is a good idea or not. One obvious problem in the present
example is that class Professor inherits two names, one via
Teacher and one via Scientist (both these classes inherit from
Person).
Filename: Person.
a)
Add the Monte Carlo integration method from the
document Random numbers and simple games [6] as a
subclass MCint in the Integrator hierarchy explained in the section Class hierarchy for numerical integration. Import the superclass Integrator from the
integrate module in the file with the new integration class.
b)
Make a test function for class MCint where you fix the seed
of the random number generator, use three function evaluations
only, and compare the result of this Monte Carlo integration
with results calculated by hand using the same three random
numbers.
c) Run the Monte Carlo integration class in a case with known analytical solution and see how the error in the integral changes with \( n=10^k \) function evaluations, \( k=3,4,5,6 \).
Filename: MCint_class.
Numerical integration methods can compute "any" integral \( \int_a^b f(x)dx \), but the result is not exact. The methods have a parameter \( n \), closely related to the number of evaluations of the function \( f \), that can be increased to achieve more accurate results. In this exercise we want to explore the relation between the error \( E \) in the numerical approximation to the integral and \( n \). Different numerical methods have different relations.
The relations are of the form $$ \begin{equation*} E = Cn^r,\end{equation*} $$ where and \( C \) and \( r < 0 \) are constants to be determined. That is, \( r \) is the most important of these parameters, because if Simpson's method has a more negative \( r \) than the Trapezoidal method, it means that increasing \( n \) in Simpson's method reduces the error more effectively than increasing \( n \) in the Trapezoidal method.
One can estimate \( r \) from numerical experiments. For a chosen \( f(x) \), where the exact value of \( \int_a^bf(x)dx \) is available, one computes the numerical approximation for \( N+1 \) values of \( n \): \( n_0 < n_1 < \cdots < n_N \) and finds the corresponding errors \( E_0,E_1,\ldots,E_N \) (the difference between the exact value and the value produced by the numerical method).
One way to estimate \( r \) goes as follows. For two successive experiments we have $$ \begin{equation*} E_i = Cn_i^r\tp\end{equation*} $$ and $$ \begin{equation*} E_{i+1} = Cn_{i+1}^r\end{equation*} $$ Divide the first equation by the second to eliminate \( C \), and then take the logarithm to solve for \( r \): $$ \begin{equation*} r = {\ln (E_{i}/E_{i+1})\over\ln (n_{i}/n_{i+1})}\tp \end{equation*} $$ We can compute \( r \) for all pairs of two successive experiments. Say \( r_i \) is the \( r \) value found from experiment \( i \) and \( i+1 \), $$ \begin{equation*} r_i = {\ln (E_{i}/E_{i+1})\over\ln (n_{i}/n_{i+1})},\quad i=0,1,\ldots,N-1\tp \end{equation*} $$ Usually, the last value, \( r_{N-1} \), is the best approximation to the true \( r \) value. Knowing \( r \), we can compute \( C \) as \( E_in_i^{-r} \) for any \( i \).
Use the method above to estimate \( r \) and \( C \) for the Midpoint method,
the Trapezoidal method, and Simpson's method. Make your own choice of
integral problem: \( f(x) \), \( a \), and \( b \). Let the parameter \( n \) be the
number of function evaluations in each method, and run the experiments
with \( n=2^{k}+1 \) for \( k=2,\ldots,11 \). The Integrator hierarchy from
the section Class hierarchy for numerical integration has all the requested methods implemented.
Filename: integrators_convergence.
Suppose you want to use classes in the Integrator
hierarchy from
the section Class hierarchy for numerical integration.
to calculate integrals of the form
$$
\begin{equation*} F(x) = \int_a^x f(t)dt\tp\end{equation*}
$$
Such functions
\( F(x) \) can be efficiently computed by the method from
the
exercise "Speed up repeated integral calculations"
in the document Introduction to classes [2].
Implement this computation of
\( F(x) \) in an additional method in the superclass Integrator.
Test that the implementation is correct for \( f(x)=2x-3 \) for all
the implemented integration methods (the Midpoint, Trapezoidal and
Gauss-Legendre methods, as well as Simpson's rule, integrate a linear
function exactly).
Filename: integrate_efficient.
Given a general nonlinear equation \( f(x)=0 \), we want to implement classes for solving such an equation, and organize the classes in a class hierarchy. Make classes for three methods: Newton's method, the Bisection method, and the Secant method.
It is not obvious how such a hierarchy should be organized. One idea is to let the superclass store the \( f(x) \) function and its derivative \( f'(x) \) (if provided - if not, use a finite difference approximation for \( f'(x) \)). A method
def solve(start_values=[0], max_iter=100, tolerance=1E-6):
...
in the superclass can implement a general iteration loop. The
start_values argument is a list of starting values for the algorithm
in question: one point for Newton, two for Secant, and an interval
\( [a,b] \) containing a root for Bisection. Let solve define a list
self.x holding all the computed approximations. The initial value of
self.x is simply start_values. For the Bisection method, one can
use the convention \( a, b, c \) = self.x[-3:], where \( [a,b] \) represents
the most recently computed interval and \( c \) is its midpoint. The
solve method can return an approximate root \( x \), the corresponding
\( f(x) \) value, a boolean indicator that is True if \( |f(x)| \) is less
than the tolerance parameter, and a list of all the approximations
and their \( f \) values (i.e., a list of \( (x, f(x)) \) tuples).
Filename: Rootfinders.
Given a function \( f(x) \) defined on a domain \( [a,b] \), the purpose of
many mathematical exercises is to sketch the function curve \( y=f(x) \),
compute the derivative \( f'(x) \), find local and global extreme points,
and compute the integral \( \int_a^b f(x)dx \).
Make a class CalculusCalculator which can perform all these actions
for any function \( f(x) \) using numerical differentiation and integration,
and a simple search method over a large number of function values
for finding extrema.
Here is an interactive session with the class where we analyze \( f(x)=x^2e^{-0.2x}\sin (2\pi x) \) on \( [0,6] \) with a grid (set of \( x \) coordinates) of 700 points:
>>> from CalculusCalculator import *
>>> def f(x):
... return x**2*exp(-0.2*x)*sin(2*pi*x)
...
>>> c = CalculusCalculator(f, 0, 6, resolution=700)
>>> c.plot() # plot f
>>> c.plot_derivative() # plot f'
>>> c.extreme_points()
All minima: 0.8052, 1.7736, 2.7636, 3.7584, 4.7556, 5.754, 0
All maxima: 0.3624, 1.284, 2.2668, 3.2604, 4.2564, 5.2548, 6
Global minimum: 5.754
Global maximum: 5.2548
>>> c.integral
-1.7353776102348935
>>> c.df(2.51) # c.df(x) is the derivative of f
-24.056988888465636
>>> c.set_differentiation_method(Central4)
>>> c.df(2.51)
-24.056988832723189
>>> c.set_integration_method(Simpson) # more accurate integration
>>> c.integral
-1.7353857856973565
Design the class such that the above session can be carried out.
Use classes from the Diff and Integrator
hierarchies (the sections Class hierarchy for numerical differentiation and Class hierarchy for numerical integration)
for numerical differentiation and integration (with, e.g., Central2
and Trapezoidal as default methods for differentiation and integration).
The method set_differentiation_method
takes a subclass name in the Diff hierarchy as argument, and
makes a data attribute df that holds a subclass instance for computing
derivatives.
With set_integration_method we can similarly set
the integration method as a subclass name in the Integrator
hierarchy, and then compute the integral \( \int_a^bf(x)dx \) and store
the value in the attribute integral.
The extreme_points method performs a print on
a MinMax instance, which is stored as an attribute in the
calculator class.
Filename: CalculusCalculator.
Extend class CalculusCalculator from Exercise 18: Make a calculus calculator class
to offer computations of inverse functions.
A numerical way of computing inverse functions is explained in the document Sequences and difference equations [7]. Other, perhaps more attractive methods are described in the exercises on inverse functions in the document Programming of ordinary differential equations [4].
Filename: CalculusCalculator2.
A very simple sketch of a human being can be made of a circle for the
head, two lines for the arms, one vertical line, a triangle, or a rectangle
for the torso, and
two lines for the legs.
Make such a drawing in a program, utilizing appropriate classes
in the Shape hierarchy.
Filename: draw_person.
Use the code from Exercise 20: Make line drawing of a person; program to make a
subclass of Shape that draws a person. Supply the following
arguments to the constructor: the center point of the head and the
radius \( R \) of the head. Let the arms and the torso be of length \( 4R \),
and the legs of length \( 6R \). The angle between the legs can be fixed
(say 30 degrees), while the angle of the arms relative to the torso
can be an argument to the constructor with a suitable default value.
Filename: Person.
Make a subclass of the class from Exercise 21: Make line drawing of a person; class
where the constructor can
take an argument describing the angle between the arms and the torso.
Use this new class to animate a person who waves her/his hands.
Filename: waving_person.