This lesson is being piloted (Beta version)

# Writing classes

## Overview

Teaching: 20 min
Exercises: 25 min
Questions
• How are classes written in Python?

• What do methods look like?

• How can a class customise how its instances are constructed?

Objectives
• Write classes from scratch

• Write methods for classes

• Write custom __init__ methods

In the previous section, we’ve seen how objects can have different behaviour, provided by methods, which in turn are provided by the class of an object.

But what if we want to make our own classes and objects?

If we wanted to plot a variety of quadratic functions, with a consistent set of styles, we could define a class that does this:

from matplotlib.pyplot import subplots
from numpy import linspace

color = 'red'
linewidth = 1

def plot(self, a, b, c):
'''Plot the line a * x ** 2 + b * x + c and output to the screen.
x runs between -10 and 10, with 1000 intermediary points.
The line is plotted in the colour specified by color, and with width
linewidth.'''

fig, ax = subplots()
x = linspace(-10, 10, 1000)
ax.plot(x, a * x ** 2 + b * x + c,
color=self.color, linewidth=self.linewidth)


Similarly to how def is used to define a function, the class keyword is used to define a new class. Both functions and variables can be created inside the class block, and these will be accessible on any objects of the class that are created.

When functions are defined within a class, they will become methods of instances of the class. In order for the function to be aware of the object that they need to refer to, methods are always given the instance as their first argument. By convention, the first argument of methods is always called self, so that the object can be referred to consistently whenever it is needed.

Note that variables within methods are local to that method. For example, fig and ax will be deleted once the method finishes running. To access variables attached to the object, their names must be prefixed by self..

## Other names than self

While it is possible to use any variable name for the first argument of a method, and Python will not complain, other programmers will. Since one aim when programming is to be as clear as possible to others who may read the program later, we strongly recommend following the convention of calling the first argument to methods self.

## Naming classes

Another convention in Python is that class names start with a capital letter, and instead of underscores, initial letters of subsequent words are also capitalised. This makes it easier to distinguish classes from objects and other variables at a glance.

So far this code hasn’t visibly done anything; while we have defined a class, we have yet to use it. Let’s do that now.

plotter = QuadraticPlotter()
plotter.plot(1, 2, 3)
plotter.plot(1, 0, -1)


Notice that we only supply the arguments a, b, and c to plotter.plot()— Python automatically adds the object to become the self parameter.

So far, this hasn’t done anything that we couldn’t have done with a function to perform the setup and then do the plot—perhaps something like:

def quadratic_plot(a, b, c, color='red', linewidth=1):
'''Plot the line a * x ** 2 + b * x + c and output to the screen.
x runs between -10 and 10, with 1000 intermediary points.
The line is plotted in the colour specified by color, and with width
linewidth.'''

fig, ax = subplots()
x = linspace(-10, 10, 1000)
ax.plot(x, a * x ** 2 + b * x + c, color=color, linewidth=linewidth)


However, what if we wanted to plot some of the curves in a thick blue line? With this function, we could set the color and linewidth on every call, but that would create a lot of repetition, and hence opportunities for the code to become inconsistent.

We could also use a dict to hold the common options:

thick_blue = {'color': 'blue', 'linewidth': 5}



## **

The ** syntax here tells Python to take the thick_blue dict, and use its keys and values as keywords and keyword arguments. We’ll look at this operator later in the lesson where we talk about decorators.

Using objects on the other hand gives a neat alternative way of achieving this result:

blue_plotter = QuadraticPlotter()
blue_plotter.color = 'blue'
blue_plotter.linewidth = 5

plotter.plot(3, -5, 5)
blue_plotter.plot(-3, 1, 0)
plotter.plot(2, 10, 2)
blue_plotter.plot(-2, 13, 4)


The two objects plotter and blue_plotter can store the different states needed to set up the two styles of plot, whilst keeping the plotting functionlity common, so it doesn’t need to be written separately for red and blue versions. We no longer have to specify the colour every time we want to plot with a non-default colour—instead, we can use the QuadraticPlotter instance that has the colour we want set.

If we need to, we can check the values of the variables we defined:

print("Line width of red plotter is", plotter.linewidth)
print("Line width of blue plotter is", blue_plotter.linewidth)

Line width of red plotter is 1
Line width of blue plotter is 5


## Mutation revisitied

Note that the classes we create ourselves in this way will produce mutable objects. This means that we can change the values in objects of plotter.linewidth, and Python allows us to do that. It doesn’t throw an error.

## Zoom in

Currently QuadraticPlotter is hardcoded to plot between -10 and 10. Try adjusting it so that it can be adjusted in the same way as the color and linewidth can, while keeping the current defaults.

Use the new class to plot the curve with a = 3, b = 2, c = 1 both between -10 and 10, and between -5 and 50. Do this without changing the arguments to the plot method.

## Solution

class QuadraticPlotter:
color = 'red'
linewidth = 1
x_min = -10
x_max = 10

def plot(self, a, b, c):
'''Plot the line a * x ** 2 + b * x + c and output to the screen.
x runs between -10 and 10, with 1000 intermediary points.
The line is plotted in the colour specified by color, and with width
linewidth.'''

fig, ax = subplots()
x = linspace(self.x_min, self.x_max, 1000)
ax.plot(x, a * x ** 2 + b * x + c,
color=self.color, linewidth=self.linewidth)

wide_plot.x_min = -5
wide_plot.x_max = 50

narrow_plot.plot(3, 2, 1)
wide_plot.plot(3, 2, 1)


## Plots of fits

The following function performs an Orthogonal Distance Regression fit of some data, and plots the resulting fit line along with the data.

from scipy.odr import ODR, Model, RealData
from matplotlib.pyplot import show

def linear(params, x):
return params[0] * x + params[1]

def odr_fit(f, x, y, xerr=None, yerr=None, p0=None, num_params=None):
if not p0 and not num_params:
raise ValueError("p0 or num_params must be specified")
if p0 and (num_params is not None):
assert len(p0) == num_params

data_to_fit = RealData(x, y, xerr, yerr)
model_to_fit_with = Model(f)
if not p0:
p0 = tuple(1 for _ in range(num_params))

odr_analysis = ODR(data_to_fit, model_to_fit_with, p0)
odr_analysis.set_job(fit_type=0)
return odr_analysis.run()

def plot_results(f, fitobj, x, y,
xmin=None, xmax=None, xerr=None, yerr=None, filename=None):
fig, ax = subplots()
if xmin is None:
xmin = min(x)
if xmax is None:
xmax = max(x)

x_range = linspace(xmin, xmax, 1000)
ax.plot(x_range, f(fitobj.beta, x_range), label='Fit')
ax.errorbar(x, y, xerr=xerr, yerr=yerr, fmt='.', label='Data')
ax.set_xlabel(r'$x$')
ax.set_ylabel(r'$y$')
fig.suptitle(f'Data: $A={fitobj.beta[0]:.02}' f'\\pm{fitobj.cov_beta[0][0]**0.5:.02}, ' f'B={fitobj.beta[1]:.02}\\pm{fitobj.cov_beta[1][1]**0.5:.02}$')
ax.legend(loc=0, frameon=False)

if filename is not None:
fig.savefig(filename)

x_data = [0, 1, 2, 3, 4, 5]
y_data = [1, 3, 2, 4, 5, 5]
x_err = [0.2, 0.1, 0.3, 0.2, 0.5, 0.3]
y_err = [0.4, 0.4, 0.1, 0.2, 0.1, 0.4]

result = odr_fit(linear, x_data, y_data, x_err, y_err, num_params=2)
plot_results(linear, result, x_data, y_data, xerr=x_err, yerr=y_err)
show()


This code has a lot of repeated terms, and would have even more if we wanted to set custom formatting each time.

Try rewriting this as a class, turning most function arguments into variables attached to the object, and functions into methods. Some of these you won’t be able to set in the class definition, but will need to be set before the functions will work.

## Solution

class FitterPlotter:
x_data = None
y_data = None
x_err = None
y_err = None

fit_result = None
fit_form = None
num_fit_params = None

xmin = None
xmax = None

def odr_fit(self, p0=None):
if None in (self.x_data, self.y_data, self.fit_form):
raise ValueError("x_data, y_data, and fit_form must be specified")
if not p0 and not self.num_fit_params:
raise ValueError("p0 or num_fit_params must be specified")
if p0 and (self.num_fit_params is not None):
assert len(p0) == self.num_fit_params

data_to_fit = RealData(self.x_data, self.y_data, self.x_err, self.y_err)
model_to_fit_with = Model(self.fit_form)
if not p0:
p0 = tuple(1 for _ in range(self.num_fit_params))

odr_analysis = ODR(data_to_fit, model_to_fit_with, p0)
odr_analysis.set_job(fit_type=0)
self.fit_result = odr_analysis.run()
return self.fit_result

def plot_results(self, filename=None):
if None in (self.x_data, self.y_data):
raise ValueError("x_data and y_data must be specified")
fig, ax = subplots()
xmin, xmax = self.xmin, self.xmax
if xmin is None:
xmin = min(self.x_data)
if xmax is None:
xmax = max(self.x_data)

if self.fit_result is not None:
x_range = linspace(xmin, xmax, 1000)
ax.plot(x_range, self.fit_form(self.fit_result.beta, x_range),
label='Fit')
fig.suptitle(f'Data: $A={self.fit_result.beta[0]:.02}' f'\\pm{self.fit_result.cov_beta[0][0]**0.5:.02}, ' f'B={self.fit_result.beta[1]:.02}' f'\\pm{self.fit_result.cov_beta[1][1]**0.5:.02}$')

ax.errorbar(self.x_data, self.y_data, xerr=self.x_err, yerr=self.y_err,
fmt='.', label='Data')
ax.set_xlabel(r'$x$')
ax.set_ylabel(r'$y$')
ax.legend(loc=0, frameon=False)

if filename is not None:
fig.savefig(filename)

fitterplotter = FitterPlotter()
fitterplotter.x_data = [0, 1, 2, 3, 4, 5]
fitterplotter.y_data = [1, 3, 2, 4, 5, 5]
fitterplotter.x_err = [0.2, 0.1, 0.3, 0.2, 0.5, 0.3]
fitterplotter.y_err = [0.4, 0.4, 0.1, 0.2, 0.1, 0.4]
fitterplotter.fit_form = linear
fitterplotter.num_fit_params = 2

fitterplotter.odr_fit()
fitterplotter.plot_results()
show()


## Initialising instances

So far we can create an object with the defaults that we set in the class definition, and then customise it afterwards. But wouldn’t it be nice to be able to create an object with the attributes that we want straight out of the box?

To do this, we can define an initialiser for the class. When Python creates an instance of a class, it looks for a method called __init__. If it finds one, then it calls it, giving it all the arguments passed to the class.

For example, for the QuadraticPlotter, the variables color and linewidth could be passed as arguments to __init__ and set on initialisation, rather than being defined as part of the class definition:

from matplotlib.colors import is_color_like

def __init__(self, color='red', linewidth=1):
'''Set the initial attributes of this plotter.'''
assert is_color_like(color)

self.color = color
self.linewidth = linewidth

def plot(self, a, b, c):
'''Plot the line a * x ** 2 + b * x + c and output to the screen.
x runs between x_min and x_max, with 1000 intermediary points.
The line is plotted in the colour specified by color, and with width
linewidth.'''

fig, ax = subplots()
x = linspace(-10, 10, 1000)
ax.plot(x, a * x ** 2 + b * x + c,
color=self.color, linewidth=self.linewidth)

pink_plotter.plot(0, 1, 0)


This also lets us do some validation that the values we are given are usable, rather than deferring these errors to a long way down the line.

## Pronouncing __init__

The method name __init__ is most often pronounced “dunder init”, where the “dunder” is short for “double underscore”, since the name starts and ends with two underscores.

We’ll encounter more methods with “dunder” in the name in a later episode.

## Zoom in again

Try rewriting the “Zoom in” example above to set the bounds of the plot, as well as the color and linewidth, using arguments to the constructor.

## Solution

class QuadraticPlotter:
def __init__(self, color='red', linewidth=1, x_min=-10, x_max=10):
'''Set the initial attributes of this plotter.'''
assert is_color_like(color)
self.color = color
self.linewidth = linewidth
self.x_min = x_min
self.x_max = x_max

def plot(self, a, b, c):
'''Plot the line a * x ** 2 + b * x + c and output to the screen.
x runs between x_min and x_max, with 1000 intermediary points.
The line is plotted in the colour specified by color, and with width
linewidth.'''

fig, ax = subplots()
x = linspace(self.x_min, self.x_max, 1000)
ax.plot(x, a * x ** 2 + b * x + c,
color=self.color, linewidth=self.linewidth)

narrow_plot.plot(3, 2, 1)
wide_plot.plot(3, 2, 1)


## Initialising fitting

Adjust your solution to the Plots of fits challenge above so that it has an initialiser which checks that the needed parameters are given before initialising the object.

## Solution

class FitterPlotter:
fit_result = None

def __init__(self, x_data, y_data, x_err=None, y_err=None,
fit_form=None, num_fit_params=None, xmin=None, xmax=None):
self.x_data = x_data
self.y_data = y_data
self.x_err = x_err
self.y_err = y_err
self.fit_form = fit_form
self.num_fit_params = num_fit_params
self.xmin = xmin
self.xmax = xmax

def odr_fit(self, p0=None):
if self.fit_form is None:
raise ValueError("fit_form must be specified")
if not p0 and not self.num_fit_params:
raise ValueError("p0 or num_fit_params must be specified")
if p0 and (self.num_fit_params is not None):
assert len(p0) == self.num_fit_params

data_to_fit = RealData(self.x_data, self.y_data, self.x_err, self.y_err)
model_to_fit_with = Model(self.fit_form)
if not p0:
p0 = tuple(1 for _ in range(self.num_fit_params))

odr_analysis = ODR(data_to_fit, model_to_fit_with, p0)
odr_analysis.set_job(fit_type=0)
self.fit_result = odr_analysis.run()
return self.fit_result

def plot_results(self, filename=None):
fig, ax = subplots()
xmin, xmax = self.xmin, self.xmax
if xmin is None:
xmin = min(self.x_data)
if xmax is None:
xmax = max(self.x_data)

if self.fit_result is not None:
x_range = linspace(xmin, xmax, 1000)
ax.plot(x_range, self.fit_form(self.fit_result.beta, x_range),
label='Fit')
fig.suptitle(f'Data: $A={self.fit_result.beta[0]:.02}' f'\\pm{self.fit_result.cov_beta[0][0]**0.5:.02}, ' f'B={self.fit_result.beta[1]:.02}' f'\\pm{self.fit_result.cov_beta[1][1]**0.5:.02}$')

ax.errorbar(self.x_data, self.y_data, xerr=self.x_err, yerr=self.y_err,
fmt='.', label='Data')
ax.set_xlabel(r'$x$')
ax.set_ylabel(r'$y$')
ax.legend(loc=0, frameon=False)

if filename is not None:
fig.savefig(filename)

fitterplotter = FitterPlotter(
x_data=[0, 1, 2, 3, 4, 5],
y_data=[1, 3, 2, 4, 5, 5],
x_err=[0.2, 0.1, 0.3, 0.2, 0.5, 0.3],
y_err=[0.4, 0.4, 0.1, 0.2, 0.1, 0.4],
fit_form=linear,
num_fit_params=2
)

fitterplotter.odr_fit()
fitterplotter.plot_results()
show()


## Key Points

• Classes in Python are blocks started with the class keyword

• Method definitions look like functions, but must take a self argument

• The __init__ method is called when instances are constructed