Ch.7: Introduction to classes

Oct 23, 2014

Basics of classes

Class = functions + data (variables) in one unit

• A class packs together data (a collection of variables) and functions as one single unit

• As a programmer you can create a new class and thereby a new object type (like float, list, file, ...)

• A class is much like a module: a collection of "global" variables and functions that belong together

• There is only one instance of a module while a class can have many instances (copies)

• Modern programming applies classes to a large extent

• It will take some time to master the class concept

• Let's learn by doing!

Representing a function by a class; background

Consider a function of $t$ with a parameter $v_0$:

$$y(t; v_0)=v_0t - {1\over2}gt^2$$

We need both $v_0$ and $t$ to evaluate $y$ (and $g=9.81$), but how should we implement this?

Having $t$ and $v_0$ as arguments:



In [1]:

def y(t, v0):
g = 9.81
return v0*t - 0.5*g*t**2



Having $t$ as argument and $v_0$ as global variable:



In [1]:

def y(t):
g = 9.81
return v0*t - 0.5*g*t**2



Motivation: $y(t)$ is a function of $t$ only

Representing a function by a class; idea

• With a class, y(t) can be a function of t only, but still have

v0 and g as parameters with given values.

• The class packs together a function y(t) and data (v0, g)

Representing a function by a class; technical overview

• We make a class Y for $y(t;v_0)$ with variables v0 and g and a function value(t) for computing $y(t;v_0)$

• Any class should also have a function __init__ for initialization of the variables

Representing a function by a class; the code



In [1]:

class Y:
def __init__(self, v0):
self.v0 = v0
self.g = 9.81

def value(self, t):
return self.v0*t - 0.5*self.g*t**2



Usage:



In [1]:

y = Y(v0=3)            # create instance (object)
v = y.value(0.1)       # compute function value



Representing a function by a class; the constructor

When we write



In [1]:

y = Y(v0=3)



we create a new variable (instance) y of type Y. Y(3) is a call to the constructor:



In [1]:

def __init__(self, v0):
self.v0 = v0
self.g = 9.81



What is this self variable? Stay cool - it will be understood later as you get used to it

• Think of self as y, i.e., the new variable to be created. self.v0 = ... means that we attach a variable v0 to self (y).

• Y(3) means Y.__init__(y, 3), i.e., set self=y, v0=3

• Remember: self is always first parameter in a function, but never inserted in the call!

• After y = Y(3), y has two variables v0 and g



In [1]:

print y.v0
print y.g



In mathematics you don't understand things. You just get used to them. John von Neumann, mathematician, 1903-1957.

Representing a function by a class; the value method

• Functions in classes are called methods

• Variables in classes are called attributes

Here is the value method:



In [1]:

def value(self, t):
return self.v0*t - 0.5*self.g*t**2



Example on a call:



In [1]:

v = y.value(t=0.1)



self is left out in the call, but Python automatically inserts y as the self argument inside the value method. Think of the call as



In [1]:

Y.value(y, t=0.1)



Inside value things "appear" as



In [1]:

return y.v0*t - 0.5*y.g*t**2



self gives access to "global variables" in the class object.

Representing a function by a class; summary

• Class Y collects the attributes v0 and g and the method value as one unit

• value(t) is function of t only, but has automatically access to the parameters v0 and g as self.v0 and self.g

• The great advantage: we can send y.value as an ordinary function of t to any other function that expects a function f(t) of one variable



In [1]:

def make_table(f, tstop, n):
for t in linspace(0, tstop, n):
print t, f(t)

def g(t):
return sin(t)*exp(-t)

table(g, 2*pi, 101)         # send ordinary function

y = Y(6.5)
table(y.value, 2*pi, 101)   # send class method



Representing a function by a class; the general case

Given a function with $n+1$ parameters and one independent variable,

$$f(x; p_0,\ldots,p_n)$$

it is wise to represent f by a class where $p_0,\ldots,p_n$ are attributes and where there is a method, say value(self, x), for computing $f(x)$



In [1]:

class MyFunc:
def __init__(self, p0, p1, p2, ..., pn):
self.p0 = p0
self.p1 = p1
...
self.pn = pn

def value(self, x):
return ...



Class for a function with four parameters

$$v(r; \beta, \mu_0, n, R) = \left({\beta\over 2\mu_0}\right)^{{1\over n}} {n \over n+1}\left( R^{1 + {1\over n}} - r^{1 + {1\over n}}\right)$$


In [1]:

class VelocityProfile:
def __init__(self, beta, mu0, n, R):
self.beta, self.mu0, self.n, self.R = \
beta, mu0, n, R

def value(self, r):
beta, mu0, n, R = \
self.beta, self.mu0, self.n, self.R
n = float(n)  # ensure float divisions
v = (beta/(2.0*mu0))**(1/n)*(n/(n+1))*\
(R**(1+1/n) - r**(1+1/n))
return v

v = VelocityProfile(R=1, beta=0.06, mu0=0.02, n=0.1)
print v.value(r=0.1)



Rough sketch of a Python class



In [1]:

class MyClass:
def __init__(self, p1, p2):
self.attr1 = p1
self.attr2 = p2

def method1(self, arg):
# can init new attribute outside constructor:
self.attr3 = arg
return self.attr1 + self.attr2 + self.attr3

def method2(self):
print 'Hello!'

m = MyClass(4, 10)
print m.method1(-2)
m.method2()



It is common to have a constructor where attributes are initialized, but this is not a requirement - attributes can be defined whenever desired

You can learn about other versions and views of class Y in the course book

• The book features a section on a different version of class Y where there is no constructor (which is possible)

• The book also features a section on how to implement classes without using classes

• These sections may be clarifying - or confusing

But what is this self variable? I want to know now!

Warning.

You have two choices:

1. follow the detailed explanations of what self really is

2. postpone understanding self until you have much more experience with class programming (suddenly self becomes clear!)

The syntax



In [1]:

y = Y(3)



can be thought of as



In [1]:

Y.__init__(y, 3)   # class prefix Y. is like a module prefix



Then



In [1]:

self.v0 = v0



is actually



In [1]:

y.v0 = 3



How self works in the value method



In [1]:

v = y.value(2)



can alternatively be written as



In [1]:

v = Y.value(y, 2)



So, when we do instance.method(arg1, arg2), self becomes instance inside method.

Working with multiple instances may help explain self

id(obj): print unique Python identifier of an object



In [1]:

class SelfExplorer:
"""Class for computing a*x."""
def __init__(self, a):
self.a = a
print 'init: a=%g, id(self)=%d' % (self.a, id(self))

def value(self, x):
print 'value: a=%g, id(self)=%d' % (self.a, id(self))
return self.a*x




In [1]:

s1 = SelfExplorer(1)
id(s1)
s2 = SelfExplorer(2)
id(s2)
s1.value(4)
SelfExplorer.value(s1, 4)
s2.value(5)
SelfExplorer.value(s2, 5)



Another class example: a bank account

• Attributes: name of owner, account number, balance

• Methods: deposit, withdraw, pretty print



In [1]:

class Account:
def __init__(self, name, account_number, initial_amount):
self.name = name
self.no = account_number
self.balance = initial_amount

def deposit(self, amount):
self.balance += amount

def withdraw(self, amount):
self.balance -= amount

def dump(self):
s = '%s, %s, balance: %s' % \
(self.name, self.no, self.balance)
print s



Example on using class Account



In [1]:

a1 = Account('John Olsson', '19371554951', 20000)
a2 = Account('Liz Olsson',  '19371564761', 20000)
a1.deposit(1000)
a1.withdraw(4000)
a2.withdraw(10500)
a1.withdraw(3500)
print "a1's balance:", a1.balance
a1.dump()
a2.dump()



Use underscore in attribute names to avoid misuse

Possible, but not intended use:



In [1]:

a1.name = 'Some other name'
a1.balance = 100000
a1.no = '19371564768'



The assumptions on correct usage:

• The attributes should not be changed!

• The balance attribute can be viewed

• Changing balance is done through withdraw or deposit

Remedy:

Attributes and methods not intended for use outside the class can be marked as protected by prefixing the name with an underscore (e.g., _name). This is just a convention - and no technical way of avoiding attributes and methods to be accessed.

Improved class with attribute protection (underscore)



In [1]:

class AccountP:
def __init__(self, name, account_number, initial_amount):
self._name = name
self._no = account_number
self._balance = initial_amount

def deposit(self, amount):
self._balance += amount

def withdraw(self, amount):
self._balance -= amount

def get_balance(self):    # NEW - read balance value
return self._balance

def dump(self):
s = '%s, %s, balance: %s' % \
(self._name, self._no, self._balance)
print s



Usage of improved class AccountP



In [1]:

a1 = AccountP('John Olsson', '19371554951', 20000)
a1.withdraw(4000)

print a1._balance      # it works, but a convention is broken

print a1.get_balance() # correct way of viewing the balance

a1._no = '19371554955' # this is a "serious crime"!



Another example: a phone book

• A phone book is a list of data about persons

• Data about a person: name, mobile phone, office phone, private phone, email

• Let us create a class for data about a person!

• Methods:

• Constructor for initializing name, plus one or more other data

• Write out person data

Basic code of class Person



In [1]:

class Person:
def __init__(self, name,
mobile_phone=None, office_phone=None,
private_phone=None, email=None):
self.name = name
self.mobile = mobile_phone
self.office = office_phone
self.private = private_phone
self.email = email

self.mobile = number

self.office = number

self.private = number



Code of a dump method for printing all class contents



In [1]:

class Person:
...
def dump(self):
s = self.name + '\n'
if self.mobile is not None:
s += 'mobile phone:   %s\n' % self.mobile
if self.office is not None:
s += 'office phone:   %s\n' % self.office
if self.private is not None:
s += 'private phone:  %s\n' % self.private
if self.email is not None:
s += 'email address:  %s\n' % self.email
print s



Usage:



In [1]:

p1 = Person('Hans Petter Langtangen', email='hpl@simula.no')
p2 = Person('Aslak Tveito', office_phone='67828282')
phone_book = [p1, p2]                           # list
phone_book = {'Langtangen': p1, 'Tveito': p2}   # better
for p in phone_book:
p.dump()



Another example: a class for a circle

• A circle is defined by its center point $x_0$, $y_0$ and its radius $R$

• These data can be attributes in a class

• Possible methods in the class: area, circumference

• The constructor initializes $x_0$, $y_0$ and $R$



In [1]:

class Circle:
def __init__(self, x0, y0, R):
self.x0, self.y0, self.R = x0, y0, R

def area(self):
return pi*self.R**2

def circumference(self):
return 2*pi*self.R




In [1]:

c = Circle(2, -1, 5)
print 'A circle with radius %g at (%g, %g) has area %g' % \
(c.R, c.x0, c.y0, c.area())



Test function for class Circle



In [1]:

def test_Circle():
R = 2.5
c = Circle(7.4, -8.1, R)

from math import pi
exact_area = pi*R**2
computed_area = c.area()
diff = abs(exact_area - computed_area)
tol = 1E-14
assert diff < tol, 'bug in Circle.area, diff=%s' % diff

exact_circumference = 2*pi*R
computed_circumference = c.circumference()
diff = abs(exact_circumference - computed_circumference)
assert diff < tol, 'bug in Circle.circumference, diff=%s' % diff



Special methods



In [1]:

class MyClass:
def __init__(self, a, b):
...

p1 = MyClass(2, 5)
p2 = MyClass(-1, 10)

p3 = p1 + p2
p4 = p1 - p2
p5 = p1*p2
p6 = p1**7 + 4*p3



Special methods allow nice syntax and are recognized by double leading and trailing underscores



In [1]:

def __init__(self, ...)
def __call__(self, ...)

# Python syntax
y = Y(4)
print y(2)
z = Y(6)
print y + z

# What's actually going on
Y.__init__(y, 4)
print Y.__call__(y, 2)
Y.__init__(z, 6)



We shall learn about many more such special methods

Example on a call special method

Replace the value method by a call special method:



In [1]:

class Y:
def __init__(self, v0):
self.v0 = v0
self.g = 9.81

def __call__(self, t):
return self.v0*t - 0.5*self.g*t**2



Now we can write



In [1]:

y = Y(3)
v = y(0.1) # same as v = y.__call__(0.1) or Y.__call__(y, 0.1)



Note:

• The instance y behaves and looks as a function!

• The value(t) method does the same, but __call__ allows nicer syntax for computing function values

Representing a function by a class revisited

Given a function with $n+1$ parameters and one independent variable,

$$f(x; p_0,\ldots,p_n)$$

it is wise to represent f by a class where $p_0,\ldots,p_n$ are attributes and __call__(x) computes $f(x)$



In [1]:

class MyFunc:
def __init__(self, p0, p1, p2, ..., pn):
self.p0 = p0
self.p1 = p1
...
self.pn = pn

def __call__(self, x):
return ...



Can we automatically differentiate a function?

Given some mathematical function in Python, say



In [1]:

def f(x):
return x**3



can we make a class Derivative and write



In [1]:

dfdx = Derivative(f)



so that dfdx behaves as a function that computes the derivative of f(x)?



In [1]:

print dfdx(2)   # computes 3*x**2 for x=2



Automagic differentiation; solution

Method.

We use numerical differentiation "behind the curtain":

$$f'(x) \approx {f(x+h)-f(x)\over h}$$

for a small (yet moderate) $h$, say $h=10^{-5}$

Implementation.



In [1]:

class Derivative:
def __init__(self, f, h=1E-5):
self.f = f
self.h = float(h)

def __call__(self, x):
f, h = self.f, self.h      # make short forms
return (f(x+h) - f(x))/h



Automagic differentiation; demo



In [1]:

from math import *
df = Derivative(sin)
x = pi
df(x)
cos(x)  # exact
def g(t):
return t**3
dg = Derivative(g)
t = 1
dg(t)  # compare with 3 (exact)



Automagic differentiation; useful in Newton's method

Newton's method solves nonlinear equations $f(x)=0$, but the method requires $f'(x)$



In [1]:

def Newton(f, xstart, dfdx, epsilon=1E-6):
...
return x, no_of_iterations, f(x)



Suppose $f'(x)$ requires boring/lengthy derivation, then class Derivative is handy:



In [1]:

def f(x):
return 100000*(x - 0.9)**2 * (x - 1.1)**3
df = Derivative(f)
xstart = 1.01
Newton(f, xstart, df, epsilon=1E-5)



Automagic differentiation; test function

• How can we test class Derivative?

• Method 1: compute $(f(x+h)-f(x))/h$ by hand for some $f$ and $h$

• Method 2: utilize that linear functions are differentiated exactly by our numerical formula, regardless of $h$

Test function based on method 2:



In [1]:

def test_Derivative():
# The formula is exact for linear functions, regardless of h
f = lambda x: a*x + b
a = 3.5; b = 8
dfdx = Derivative(f, h=0.5)
diff = abs(dfdx(4.5) - a)
assert diff < 1E-14, 'bug in class Derivative, diff=%s' % diff



Automagic differentiation; explanation of the test function

Use of lambda functions:



In [1]:

f = lambda x: a*x + b



is equivalent to



In [1]:

def f(x):
return a*x + b



Lambda functions are convenient for producing quick, short code

Use of closure:



In [1]:

f = lambda x: a*x + b
a = 3.5; b = 8
dfdx = Derivative(f, h=0.5)
dfdx(4.5)



Looks straightforward...but

• How can Derivative.__call__ know a and b when it calls our f(x) function?

• Local functions inside functions remember (have access to) all local variables in the function they are defined (!)

• f can access a and b in test_Derivative even when called from __call__ in class Derivative

• f is known as a closure in computer science

Automagic differentiation detour; sympy solution (exact differentiation via symbolic expressions)

SymPy can perform exact, symbolic differentiation:



In [1]:

>>> from sympy import *
>>> def g(t):
...     return t**3
...
>>> t = Symbol('t')
>>> dgdt = diff(g(t), t)           # compute g'(t)
>>> dgdt
3*t**2

>>> # Turn sympy expression dgdt into Python function dg(t)
>>> dg = lambdify([t], dgdt)
>>> dg(1)
3



Automagic differentiation detour; class based on sympy



In [1]:

import sympy as sp

class Derivative_sympy:
def __init__(self, f):
# f: Python f(x)
x = sp.Symbol('x')
sympy_f = f(x)
sympy_dfdx = sp.diff(sympy_f, x)
self.__call__ = sp.lambdify([x], sympy_dfdx)




In [1]:

def g(t):
return t**3
def h(y):
return sp.sin(y)
dg = Derivative_sympy(g)
dh = Derivative_sympy(h)
dg(1)   # 3*1**2 = 3
from math import pi
dh(pi)  # cos(pi) = -1



Automagic integration; problem setting

Given a function $f(x)$, we want to compute

$$F(x; a) = \int_a^x f(t)dt$$

Technique: Trapezoidal rule

$$\int_a^x f(t)dt = h\left({1\over2}f(a) + \sum_{i=1}^{n-1} f(a+ih) + {1\over2}f(x)\right)$$

Desired application code:



In [1]:

def f(x):
return exp(-x**2)*sin(10*x)

a = 0; n = 200
F = Integral(f, a, n)
x = 1.2
print F(x)



Automagic integration; implementation



In [1]:

def trapezoidal(f, a, x, n):
h = (x-a)/float(n)
I = 0.5*f(a)
for i in range(1, n):
I += f(a + i*h)
I += 0.5*f(x)
I *= h
return I



Class Integral holds f, a and n as attributes and has a call special method for computing the integral:



In [1]:

class Integral:
def __init__(self, f, a, n=100):
self.f, self.a, self.n = f, a, n

def __call__(self, x):
return trapezoidal(self.f, self.a, x, self.n)



Automagic integration; test function

• How can we test class Integral?

• Method 1: compute by hand for some $f$ and small $n$

• Method 2: utilize that linear functions are integrated exactly by our numerical formula, regardless of $n$

Test function based on method 2:



In [1]:

def test_Integral():
f = lambda x: 2*x + 5
F = lambda x: x**2 + 5*x - (a**2 + 5*a)
a = 2
dfdx = Integralf, a, n=4)
x = 6
diff = abs(I(x) - (F(x) - F(a)))
assert diff < 1E-15, 'bug in class Integral, diff=%s' % diff



Special method for printing

• In Python, we can usually print an object a by print a, works for built-in types (strings, lists, floats, ...)

• Python does not know how to print objects of a user-defined class, but if the class defines a method __str__, Python will use this method to convert an object to a string

Example:



In [1]:

class Y:
...
def __call__(self, t):
return self.v0*t - 0.5*self.g*t**2

def __str__(self):
return 'v0*t - 0.5*g*t**2; v0=%g' % self.v0



Demo:



In [1]:

y = Y(1.5)
y(0.2)
print y



Class for polynomials; functionality

A polynomial can be specified by a list of its coefficients. For example, $1 - x^2 + 2x^3$ is

$$1 + 0\cdot x - 1\cdot x^2 + 2\cdot x^3$$

and the coefficients can be stored as [1, 0, -1, 2]

Desired application code:



In [1]:

p1 = Polynomial([1, -1])
print p1
p2 = Polynomial([0, 1, 0, 0, -6, -1])
p3 = p1 + p2
print p3.coeff
print p3
p2.differentiate()
print p2



How can we make class Polynomial?

Class Polynomial; basic code



In [1]:

class Polynomial:
def __init__(self, coefficients):
self.coeff = coefficients

def __call__(self, x):
s = 0
for i in range(len(self.coeff)):
s += self.coeff[i]*x**i
return s





In [1]:

class Polynomial:
...

# return self + other

if len(self.coeff) > len(other.coeff):
coeffsum = self.coeff[:]  # copy!
for i in range(len(other.coeff)):
coeffsum[i] += other.coeff[i]
else:
coeffsum = other.coeff[:] # copy!
for i in range(len(self.coeff)):
coeffsum[i] += self.coeff[i]
return Polynomial(coeffsum)



Class Polynomial; multiplication

Mathematics:

Multiplication of two general polynomials:

$$\left(\sum_{i=0}^Mc_ix^i\right)\left(\sum_{j=0}^N d_jx^j\right) = \sum_{i=0}^M \sum_{j=0}^N c_id_j x^{i+j}$$

The coeff. corresponding to power $i+j$ is $c_i\cdot d_j$. The list r of coefficients of the result: r[i+j] = c[i]*d[j] (i and j running from 0 to $M$ and $N$, resp.)

Implementation:



In [1]:

class Polynomial:
...
def __mul__(self, other):
M = len(self.coeff) - 1
N = len(other.coeff) - 1
coeff = [0]*(M+N+1)  # or zeros(M+N+1)
for i in range(0, M+1):
for j in range(0, N+1):
coeff[i+j] += self.coeff[i]*other.coeff[j]
return Polynomial(coeff)



Class Polynomial; differentation

Mathematics:

Rule for differentiating a general polynomial:

$${d\over dx}\sum_{i=0}^n c_ix^i = \sum_{i=1}^n ic_ix^{i-1}$$

If c is the list of coefficients, the derivative has a list of coefficients, dc, where dc[i-1] = i*c[i] for i running from 1 to the largest index in c. Note that dc has one element less than c.

Implementation:



In [1]:

class Polynomial:
...
def differentiate(self):    # change self
for i in range(1, len(self.coeff)):
self.coeff[i-1] = i*self.coeff[i]
del self.coeff[-1]

def derivative(self):       # return new polynomial
dpdx = Polynomial(self.coeff[:])  # copy
dpdx.differentiate()
return dpdx



Class Polynomial; pretty print



In [1]:

class Polynomial:
...
def __str__(self):
s = ''
for i in range(0, len(self.coeff)):
if self.coeff[i] != 0:
s += ' + %g*x^%d' % (self.coeff[i], i)
# fix layout (lots of special cases):
s = s.replace('+ -', '- ')
s = s.replace(' 1*', ' ')
s = s.replace('x^0', '1')
s = s.replace('x^1 ', 'x ')
s = s.replace('x^1', 'x')
if s[0:3] == ' + ':  # remove initial +
s = s[3:]
if s[0:3] == ' - ':  # fix spaces for initial -
s = '-' + s[3:]
return s



Class for polynomials; usage

Consider

$$p_1(x)= 1-x,\quad p_2(x)=x - 6x^4 - x^5$$

and their sum

$$p_3(x) = p_1(x) + p_2(x) = 1 -6x^4 - x^5$$


In [1]:

p1 = Polynomial([1, -1])
print p1
p2 = Polynomial([0, 1, 0, 0, -6, -1])
p3 = p1 + p2
print p3.coeff
p2.differentiate()
print p2



The programmer is in charge of defining special methods!

How should, e.g., __add__(self, other) be defined? This is completely up to the programmer, depending on what is meaningful by object1 + object2.

An anthropologist was asking a primitive tribesman about arithmetic. When the anthropologist asked, What does two and two make? the tribesman replied, Five. Asked to explain, the tribesman said, If I have a rope with two knots, and another rope with two knots, and I join the ropes together, then I have five knots.

Special methods for arithmetic operations



In [1]:

c = a + b    #  c = a.__add__(b)

c = a - b    #  c = a.__sub__(b)

c = a*b      #  c = a.__mul__(b)

c = a/b      #  c = a.__div__(b)

c = a**e     #  c = a.__pow__(e)



Special methods for comparisons



In [1]:

a == b       #  a.__eq__(b)

a != b       #  a.__ne__(b)

a < b        #  a.__lt__(b)

a <= b       #  a.__le__(b)

a > b        #  a.__gt__(b)

a >= b       #  a.__ge__(b)



Class for vectors in the plane

Mathematical operations for vectors in the plane:

\begin{align*} (a,b) + (c,d) &= (a+c, b+d)\\ (a,b) - (c,d) &= (a-c, b-d)\\ (a,b)\cdot(c,d) &= ac + bd\\ (a,b) &= (c, d)\hbox{ if }a=c\hbox{ and }b=d \end{align*}

Desired application code:



In [1]:

u = Vec2D(0,1)
v = Vec2D(1,0)
print u + v
a = u + v
w = Vec2D(1,1)
a == w
print u - v
print u*v



Class for vectors; implementation



In [1]:

class Vec2D:
def __init__(self, x, y):
self.x = x;  self.y = y

return Vec2D(self.x+other.x, self.y+other.y)

def __sub__(self, other):
return Vec2D(self.x-other.x, self.y-other.y)

def __mul__(self, other):
return self.x*other.x + self.y*other.y

def __abs__(self):
return math.sqrt(self.x**2 + self.y**2)

def __eq__(self, other):
return self.x == other.x and self.y == other.y

def __str__(self):
return '(%g, %g)' % (self.x, self.y)

def __ne__(self, other):
return not self.__eq__(other)  # reuse __eq__



The repr special method: eval(repr(p)) creates p



In [1]:

class MyClass:
def __init__(self, a, b):
self.a, self.b = a, b

def __str__(self):
"""Return string with pretty print."""
return 'a=%s, b=%s' % (self.a, self.b)

def __repr__(self):
"""Return string such that eval(s) recreates self."""
return 'MyClass(%s, %s)' % (self.a, self.b)




In [1]:

m = MyClass(1, 5)
print m      # calls m.__str__()
str(m)       # calls m.__str__()
s = repr(m)  # calls m.__repr__()
s
m2 = eval(s) # same as m2 = MyClass(1, 5)
m2           # calls m.__repr__()



Class Y revisited with repr print method



In [1]:

class Y:
"""Class for function y(t; v0, g) = v0*t - 0.5*g*t**2."""

def __init__(self, v0):
"""Store parameters."""
self.v0 = v0
self.g = 9.81

def __call__(self, t):
"""Evaluate function."""
return self.v0*t - 0.5*self.g*t**2

def __str__(self):
"""Pretty print."""
return 'v0*t - 0.5*g*t**2; v0=%g' % self.v0

def __repr__(self):
"""Print code for regenerating this instance."""
return 'Y(%s)' % self.v0



Class for complex numbers; functionality

Python already has a class complex for complex numbers, but implementing such a class is a good pedagogical example on class programming (especially with special methods).

Usage:



In [1]:

u = Complex(2,-1)
v = Complex(1)     # zero imaginary part
w = u + v
print w
w != u
u*v
u < v
print w + 4
print 4 - w



Class for complex numbers; implementation (part 1)



In [1]:

class Complex:
def __init__(self, real, imag=0.0):
self.real = real
self.imag = imag

return Complex(self.real + other.real,
self.imag + other.imag)

def __sub__(self, other):
return Complex(self.real - other.real,
self.imag - other.imag)

def __mul__(self, other):
return Complex(self.real*other.real - self.imag*other.imag,
self.imag*other.real + self.real*other.imag)

def __div__(self, other):
ar, ai, br, bi = self.real, self.imag, \
other.real, other.imag # short forms
r = float(br**2 + bi**2)
return Complex((ar*br+ai*bi)/r, (ai*br-ar*bi)/r)



Class for complex numbers; implementation (part 2)



In [1]:

def __abs__(self):
return sqrt(self.real**2 + self.imag**2)

def __neg__(self):   # defines -c (c is Complex)
return Complex(-self.real, -self.imag)

def __eq__(self, other):
return self.real == other.real and \
self.imag == other.imag

def __ne__(self, other):
return not self.__eq__(other)

def __str__(self):
return '(%g, %g)' % (self.real, self.imag)

def __repr__(self):
return 'Complex' + str(self)

def __pow__(self, power):
raise NotImplementedError(
'self**power is not yet impl. for Complex')



Refining the special methods for arithmetics

Can we add a real number to a complex number?



In [1]:

u = Complex(1, 2)
w = u + 4.5



Problem: we have assumed that other is Complex. Remedy:



In [1]:

class Complex:
...
if isinstance(other, (float,int)):
other = Complex(other)
return Complex(self.real + other.real,
self.imag + other.imag)

# or

if isinstance(other, (float,int)):
return Complex(self.real + other, self.imag)
else:
return Complex(self.real + other.real,
self.imag + other.imag)



Special methods for "right" operands; addition

What if we try this:



In [1]:

u = Complex(1, 2)
w = 4.5 + u



Problem: Python's float objects cannot add a Complex.

Remedy: if a class has an __radd__(self, other) special method, Python applies this for other + self



In [1]:

class Complex:
...
"""Rturn other + self."""
# other + self = self + other:



Special methods for "right" operands; subtraction

Right operands for subtraction is a bit more complicated since $a-b \neq b-a$:



In [1]:

class Complex:
...
def __sub__(self, other):
if isinstance(other, (float,int)):
other = Complex(other)
return Complex(self.real - other.real,
self.imag - other.imag)

def __rsub__(self, other):
if isinstance(other, (float,int)):
other = Complex(other)
return other.__sub__(self)



What's in a class?



In [1]:

class A:
"""A class for demo purposes."""
def __init__(self, value):
self.v = value



Any instance holds its attributes in the self.__dict__ dictionary (Python automatically creates this dict)



In [1]:

a = A([1,2])
print a.__dict__  # all attributes
dir(a)            # what's in object a?
a.__doc__         # programmer's documentation of A



Ooops - we can add new attributes as we want!



In [1]:

a.myvar = 10            # add new attribute (!)
a.__dict__
dir(a)
b = A(-1)
b.__dict__              # b has no myvar attribute
dir(b)



Summary of defining a class

Example on a defining a class with attributes and methods:



In [1]:

%matplotlib inline

class Gravity:
"""Gravity force between two objects."""
def __init__(self, m, M):
self.m = m
self.M = M
self.G = 6.67428E-11 # gravity constant

def force(self, r):
G, m, M = self.G, self.m, self.M
return G*m*M/r**2

def visualize(self, r_start, r_stop, n=100):
from scitools.std import plot, linspace
r = linspace(r_start, r_stop, n)
g = self.force(r)
title='m=%g, M=%g' % (self.m, self.M)
plot(r, g, title=title)



Summary of using a class

Example on using the class:



In [1]:

mass_moon = 7.35E+22
mass_earth = 5.97E+24

# make instance of class Gravity:
gravity = Gravity(mass_moon, mass_earth)

r = 3.85E+8  # earth-moon distance in meters
Fg = gravity.force(r)   # call class method



Summary of special methods

• c = a + b implies c = a.__add__(b)

• There are special methods for a+b, a-b, a*b, a/b, a**b, -a, if a:, len(a), str(a) (pretty print), repr(a) (recreate a with eval), etc.

• With special methods we can create new mathematical objects like vectors, polynomials and complex numbers and write "mathematical code" (arithmetics)

• The call special method is particularly handy: v = c(5) means v = c.__call__(5)

• Functions with parameters should be represented by a class with the parameters as attributes and with a call special method for evaluating the function

Summarizing example: interval arithmetics for uncertainty quantification in formulas

Uncertainty quantification:

Consider measuring gravity $g$ by dropping a ball from $y=y_0$ to $y=0$ in time $T$:

$$g = 2y_0T^{-2}$$

What if $y_0$ and $T$ are uncertain? Say $y_0\in [0.99,1.01]$ m and $T\in [0.43, 0.47]$ s. What is the uncertainty in $g$?

The uncertainty can be computed by interval arithmetics

Interval arithmetics.

Rules for computing with intervals, $p=[a,b]$ and $q=[c,d]$:

• $p+q = [a + c, b + d]$

• $p-q = [a - d, b - c]$

• $pq = [\min(ac, ad, bc, bd), \max(ac, ad, bc, bd)]$

• $p/q = [\min(a/c, a/d, b/c, b/d), \max(a/c, a/d, b/c, b/d)]$ ($[c,d]$ cannot contain zero)

Obvious idea: make a class for interval arithmetics!

Class for interval arithmetics



In [1]:

class IntervalMath:
def __init__(self, lower, upper):
self.lo = float(lower)
self.up = float(upper)

a, b, c, d = self.lo, self.up, other.lo, other.up
return IntervalMath(a + c, b + d)

def __sub__(self, other):
a, b, c, d = self.lo, self.up, other.lo, other.up
return IntervalMath(a - d, b - c)

def __mul__(self, other):
a, b, c, d = self.lo, self.up, other.lo, other.up
return IntervalMath(min(a*c, a*d, b*c, b*d),
max(a*c, a*d, b*c, b*d))

def __div__(self, other):
a, b, c, d = self.lo, self.up, other.lo, other.up
if c*d <= 0: return None
return IntervalMath(min(a/c, a/d, b/c, b/d),
max(a/c, a/d, b/c, b/d))
def __str__(self):
return '[%g, %g]' % (self.lo, self.up)



Demo of the new class for interval arithmetics

Code:



In [1]:

I = IntervalMath   # abbreviate
a = I(-3,-2)
b = I(4,5)

expr = 'a+b', 'a-b', 'a*b', 'a/b'   # test expressions
for e in expr:
print e, '=', eval(e)



Output:



In [1]:

a+b = [1, 3]
a-b = [-8, -6]
a*b = [-15, -8]
a/b = [-0.75, -0.4]



Shortcomings of the class

This code



In [1]:

a = I(4,5)
q = 2
b = a*q



      File "IntervalMath.py", line 15, in __mul__
a, b, c, d = self.lo, self.up, other.lo, other.up
AttributeError: 'float' object has no attribute 'lo'

Problem: IntervalMath times int is not defined.

Remedy: (cf. class Complex)



In [1]:

class IntervalArithmetics:
...
def __mul__(self, other):
if isinstance(other, (int, float)):      # NEW
other = IntervalMath(other, other)   # NEW
a, b, c, d = self.lo, self.up, other.lo, other.up
return IntervalMath(min(a*c, a*d, b*c, b*d),
max(a*c, a*d, b*c, b*d))



(with similar adjustments of other special methods)

More shortcomings of the class

Try to compute g = 2*y0*T**(-2): multiplication of int (2) and IntervalMath (y0), and power operation T**(-2) are not defined



In [1]:

class IntervalArithmetics:
...
def __rmul__(self, other):
if isinstance(other, (int, float)):
other = IntervalMath(other, other)
return other*self

def __pow__(self, exponent):
if isinstance(exponent, int):
p = 1
if exponent > 0:
for i in range(exponent):
p = p*self
elif exponent < 0:
for i in range(-exponent):
p = p*self
p = 1/p
else:   # exponent == 0
p = IntervalMath(1, 1)
return p
else:
raise TypeError('exponent must int')



Adding more functionality to the class: rounding

"Rounding" to the midpoint value:



In [1]:

a = IntervalMath(5,7)
float(a)



is achieved by



In [1]:

class IntervalArithmetics:
...
def __float__(self):
return 0.5*(self.lo + self.up)



Adding more functionality to the class: repr and str methods



In [1]:

class IntervalArithmetics:
...
def __str__(self):
return '[%g, %g]' % (self.lo, self.up)

def __repr__(self):
return '%s(%g, %g)' % \
(self.__class__.__name__, self.lo, self.up)



Demonstrating the class: $g=2y_0T^{-2}$



In [1]:

g = 9.81
y_0 = I(0.99, 1.01)
Tm = 0.45                 # mean T
T = I(Tm*0.95, Tm*1.05)   # 10% uncertainty
print T
g = 2*y_0*T**(-2)
g
# computing with mean values:
T = float(T)
y = 1
g = 2*y_0*T**(-2)
print '%.2f' % g



Demonstrating the class: volume of a sphere



In [1]:

R = I(6*0.9, 6*1.1)   # 20 % error
V = (4./3)*pi*R**3
V
print V
print float(V)
# compute with mean values:
R = float(R)
V = (4./3)*pi*R**3
print V

`

20% uncertainty in $R$ gives almost 60% uncertainty in $V$