chap07


Modeling and Simulation in Python

Chapter 7

Copyright 2017 Allen Downey

License: Creative Commons Attribution 4.0 International


In [1]:
# Configure Jupyter so figures appear in the notebook
%matplotlib inline

# Configure Jupyter to display the assigned value after an assignment
%config InteractiveShell.ast_node_interactivity='last_expr_or_assign'

# import functions from the modsim.py module
from modsim import *

from pandas import read_html

Code from the previous chapter


In [2]:
filename = 'data/World_population_estimates.html'
tables = read_html(filename, header=0, index_col=0, decimal='M')
table2 = tables[2]
table2.columns = ['census', 'prb', 'un', 'maddison', 
                  'hyde', 'tanton', 'biraben', 'mj', 
                  'thomlinson', 'durand', 'clark']

In [3]:
un = table2.un / 1e9
un.head()

In [4]:
census = table2.census / 1e9
census.head()

In [5]:
def plot_results(census, un, timeseries, title):
    """Plot the estimates and the model.
    
    census: TimeSeries of population estimates
    un: TimeSeries of population estimates
    timeseries: TimeSeries of simulation results
    title: string
    """
    plot(census, ':', label='US Census')
    plot(un, '--', label='UN DESA')
    plot(timeseries, color='gray', label='model')
    
    decorate(xlabel='Year', 
             ylabel='World population (billion)',
             title=title)

In [6]:
def run_simulation(system, update_func):
    """Simulate the system using any update function.
    
    system: System object
    update_func: function that computes the population next year
    
    returns: TimeSeries
    """
    results = TimeSeries()
    results[system.t_0] = system.p_0
    
    for t in linrange(system.t_0, system.t_end):
        results[t+1] = update_func(results[t], t, system)
        
    return results

Quadratic growth

Here's the implementation of the quadratic growth model.


In [7]:
def update_func_quad(pop, t, system):
    """Compute the population next year with a quadratic model.
    
    pop: current population
    t: current year
    system: system object containing parameters of the model
    
    returns: population next year
    """
    net_growth = system.alpha * pop + system.beta * pop**2
    return pop + net_growth

Here's a System object with the parameters alpha and beta:


In [8]:
t_0 = get_first_label(census)
t_end = get_last_label(census)
p_0 = census[t_0]

system = System(t_0=t_0, 
                t_end=t_end,
                p_0=p_0,
                alpha=0.025,
                beta=-0.0018)

And here are the results.


In [9]:
results = run_simulation(system, update_func_quad)
plot_results(census, un, results, 'Quadratic model')
savefig('figs/chap07-fig01.pdf')

Exercise: Can you find values for the parameters that make the model fit better?

Equilibrium

To understand the quadratic model better, let's plot net growth as a function of population.


In [10]:
pop_array = linspace(0, 15, 100)
net_growth_array = system.alpha * pop_array + system.beta * pop_array**2
None

Here's what it looks like.


In [11]:
sns.set_style('whitegrid')

plot(pop_array, net_growth_array)
decorate(xlabel='Population (billions)',
         ylabel='Net growth (billions)')

sns.set_style('white')

savefig('figs/chap07-fig02.pdf')

Here's what it looks like. Remember that the x axis is population now, not time.

It looks like the growth rate passes through 0 when the population is a little less than 14 billion.

In the book we found that the net growth is 0 when the population is $-\alpha/\beta$:


In [12]:
-system.alpha / system.beta

This is the equilibrium the population tends toward.

sns is a library called Seaborn which provides functions that control the appearance of plots. In this case I want a grid to make it easier to estimate the population where the growth rate crosses through 0.

Dysfunctions

When people first learn about functions, there are a few things they often find confusing. In this section I present and explain some common problems with functions.

As an example, suppose you want a function that takes a System object, with variables alpha and beta, as a parameter and computes the carrying capacity, -alpha/beta. Here's a good solution:


In [13]:
def carrying_capacity(system):
    K = -system.alpha / system.beta
    return K
    
sys1 = System(alpha=0.025, beta=-0.0018)
pop = carrying_capacity(sys1)
print(pop)

Now let's see all the ways that can go wrong.

Dysfunction #1: Not using parameters. In the following version, the function doesn't take any parameters; when sys1 appears inside the function, it refers to the object we created outside the function.


In [14]:
def carrying_capacity():
    K = -sys1.alpha / sys1.beta
    return K
    
sys1 = System(alpha=0.025, beta=-0.0018)
pop = carrying_capacity()
print(pop)

This version actually works, but it is not as versatile as it could be. If there are several System objects, this function can only work with one of them, and only if it is named system.

Dysfunction #2: Clobbering the parameters. When people first learn about parameters, they often write functions like this:


In [15]:
def carrying_capacity(system):
    system = System(alpha=0.025, beta=-0.0018)
    K = -system.alpha / system.beta
    return K
    
sys1 = System(alpha=0.025, beta=-0.0018)
pop = carrying_capacity(sys1)
print(pop)

In this example, we have a System object named sys1 that gets passed as an argument to carrying_capacity. But when the function runs, it ignores the argument and immediately replaces it with a new System object. As a result, this function always returns the same value, no matter what argument is passed.

When you write a function, you generally don't know what the values of the parameters will be. Your job is to write a function that works for any valid values. If you assign your own values to the parameters, you defeat the whole purpose of functions.

Dysfunction #3: No return value. Here's a version that computes the value of K but doesn't return it.


In [16]:
def carrying_capacity(system):
    K = -system.alpha / system.beta
    
sys1 = System(alpha=0.025, beta=-0.0018)
pop = carrying_capacity(sys1)
print(pop)

A function that doesn't have a return statement always returns a special value called None, so in this example the value of pop is None. If you are debugging a program and find that the value of a variable is None when it shouldn't be, a function without a return statement is a likely cause.

Dysfunction #4: Ignoring the return value. Finally, here's a version where the function is correct, but the way it's used is not.


In [17]:
def carrying_capacity(system):
    K = -system.alpha / system.beta
    return K
    
sys2 = System(alpha=0.025, beta=-0.0018)
carrying_capacity(sys2)

# print(K)     This line won't work because K only exists inside the function.

In this example, carrying_capacity runs and returns K, but the return value is dropped.

When you call a function that returns a value, you should do something with the result. Often you assign it to a variable, as in the previous examples, but you can also use it as part of an expression.

For example, you could eliminate the temporary variable pop like this:


In [18]:
print(carrying_capacity(sys1))

Or if you had more than one system, you could compute the total carrying capacity like this:


In [19]:
total = carrying_capacity(sys1) + carrying_capacity(sys2)
total

Exercises

Exercise: In the book, I present a different way to parameterize the quadratic model:

$ \Delta p = r p (1 - p / K) $

where $r=\alpha$ and $K=-\alpha/\beta$. Write a version of update_func that implements this version of the model. Test it by computing the values of r and K that correspond to alpha=0.025, beta=-0.0018, and confirm that you get the same results.


In [20]:
# Solution goes here

In [21]:
# Solution goes here

In [22]:
# Solution goes here

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