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Schelling’s Model of Housing Segregation
Getting started
Introduction
Our Model
Data representation
Code structure
Testing
Running your program
Grading
Cleaning up
Submission
Schelling’s Model of Housing Segregation
Due: Friday, October 23, 3pm CDT
The goal of this assignment is to give you practice using nested loops, lists of lists, and functions.
You may work alone or in a pair on this assignment. The algorithms you need to implement for this assignment
are more challenging than the rst
assignment. We recommend starting early.
Getting started
If you are going to work alone, before you start working on the assignment’s tasks, please take a moment to
follow the steps described in Coursework Basics (../../resources/coursework-basics.html#coursework-basics) page
to get the les
for this assignment (these steps will be the same as the ones you followed to get the les
for
Short Assignment #1). Please note that you will not be able to start working on the assignment until you fetch
the assignment les
(which will appear in a pa2 directory in your repository)
If you are going to work in a pair, please follow the “Working in a Pair” instructions in Coursework Basics
(../../resources/coursework-basics.html#coursework-basics) to get started.
Students working in pairs should NOT use their individual repositories
Please note that this means you will not be able to start working on the assignment until you request and setup
your pair repository.
Introduction
The New York Times has an interesting tool (http://projects.nytimes.com/census/2010/explorer?
ref=censusbureau) for viewing the distribution of racial and ethnic groups in cities and states around the country.
If you look at the map for the Greater Chicago area, you will see that many areas are heavily segregated, but that
some, such as the suburb of Glendale Heights, are not. The forces that led to this arrangement of people are
complex and it is unlikely that any simple model can truly answer the questions of how segregation arises and
why it persists. Keeping that in mind, one can still ask simple questions that may shed some light on the situation.
In his 1978 book Micromotive and Macrobehavior, Thomas Schelling considered the following question: is it
possible that a group of individuals would prefer to live in integrated neighborhoods and yet, as the result of
their collective choices end up in segregated neighborhoods?
Schelling examined this question using a very simple model: A city has two kinds of people, Cubs fans and White
Sox fans, for example, and each person in the city requires a certain number of their neighbors to be like them to
feel comfortable in their location. Schelling used this model to study the patterns that emerge if, over time,
people who are unsatised
with their initial locations or become unsatised
over time relocate to new locations
that satisfy their criteria. What happens depends on the ratio of the two populations and on the satisfaction
criteria used by the individuals, but segregation is a frequent outcome.
Schelling’s model of housing segregation has led to myriad follow-up work examining dierent
facets of the
model’s outcomes in dierent
circumstances, as well as applying the model to longitudinal datasets
(https://www.americanscientist.org/article/the-math-of-segregation) of urban environments. Data about Chicago
(https://arxiv.org/pdf/1406.5215.pdf#page=4) features in a number of these analyses.
Our Model
Your task in this assignment will be to implement a variant of Schelling’s model. Before you can do so, we need to
specify the details of the abstract model we will be using and then describe the concrete data structures we will
be using to implement the model.
For the model, we need to specify, the:
shape and composition of a city,
boundaries of a home’s neighborhood,
denition
for a similarity score,
denition
for satisfaction,
rule for relocating homeowners,
composition of a relocation wave in the simulation,
composition of a step in the simulation, and
stopping conditions for the simulation.
Cities: A city is modelled as an grid (where the value of can be dierent
for each city). Each grid
location (or cell) represents a home, which can be for sale, occupied by a maroon homeowner, or occupied by a
blue homeowner. You may assume that a city has at least one home for sale and homes that are for sale are not
occupied.
Here is a sample city that we will use throughout this write-up:
Sample City
N × N N
Sample City
(../../_images/a20-sample-writeup.png)
In this and in nearly all subsequent city depictions, a white cell indicates a home that is for sale, a maroon (dark
red) cell indicates that a home is occupied by a maroon homeowner, and a blue cell indicates a home that is
occupied by a blue homeowner. This gure
also includes the location (in parentheses) of each home (cell) to help
orient you in the city.
Neighborhood
The neighborhood of a home will be dened
by a parameter R. The R-neighborhood of the home at Location (i,j)
in an city contains all Locations (k,l) such that:
If you look carefully at the denition,
you will note that Location (i,j) itself is considered part of its own
neighborhood.
N × N
0 ≤ k < N
0 ≤ l < N
0 ≤ |i − k| + |j − l| ≤ R
We will refer to a neighborhood with parameter R=x as an “R-x neighborhood”.
The following gures
show the neighborhoods around Locations (2,2) and (3,0) for dierent
values of R. In this
set of gures
(and this set only), we use yellow to indicate that a location is included in the specied
neighborhood and white to indicate that a location is not included in the neighborhood.
Neighborhood around (2,2) Neighborhood around (3,0)
R = 0 R = 1 R = 1 R = 2
(../../_images/sampleneighborhood-2-2-0.png)
(../../_images/sampleneighborhood-2-2-1.png)
(../../_images/sampleneighborhood-3-0-1.png)
(../../_images/sampleneighborhood-3-0-2.png)
Notice that Location (3,0), which is closer to the boundaries of the city, has fewer neighboring homes for the
same value of R than Location (2,2), which is the middle of the city. We use the term boundary neighborhood to
refer to a neighborhood that is near the boundary of the city and thus does not have a full set of neighbors.
Similarity score
We dene
the similarity score of a homeowner at a given location to be S/H where S is the number of homes in
the neighborhood centered on that location with occupants of the same color as the homeowner and H is the
number of occupied homes in the neighborhood.
Since a homeowner is included in their own neighborhood, S and H will each be at least one, and so, a
homeowner’s similarity score will always be dened
and will be greater than zero.
The gures
below illustrate this concept using a the sample city shown above with R-1 neighborhoods (left) and
R-2 neighborhoods (right). The similarity scores are shown beneath the location in each grid cell. Notice the gure
does not include similarity scores for the homes that are for sale (that is, those that are shown in white), because
they are unoccupied and it does not make sense to compute a similarity score for a home without a homeowner.
Similarity scores
City parameters:
R: 1
City parameters:
R:2
(../../_images/a20-sample-writeup-1-40-70-3-1-init-satscores.png)
(../../_images/a20-sample-writeup-2-40-70-1-1-init-satscores.png)
Satisfaction
A homeowner is satised
with their location if their similarity score falls within a specied
range, inclusive of the
end points of the range.
Ideally, we would allow dierent
homeowners to use dierent
ranges, but to simplify your task, we will use the
same range for everyone in a city. The range will be a parameter to the simulation.
Why use a range? Because it allows us to simulate dierent
levels of demand for integration (or segregation)
across dierent
runs of the simulation. To model a population that prefers integration, we might use a range of
[0.45, 0.55], whereas to simulate a more ambivalent population we could use [0.20, 0.80].
Let’s apply this denition
of satisfaction to our sample R-1 neighborhood. The gure
on the left uses satisfaction
range of , while the gure
on the right uses . Throughout this assignment, we indicate
unsatised
homeowners with a yellow border.
Satisfaction Examples
City parameters:
R: 1
Satisfaction range: [0.45, 0.55]
City Parameters:
R: 1
Satisfaction range: [0.4, 0.7]
(../../_images/a20-sample-writeup-1-45-55-1-1-initannotated-sat-scores.png)
(../../_images/a20-sample-writeup-1-40-70-3-1-initannotated-sat-scores.png)
Notice that some of the people who were unsatised
when the range was very narrow ( ) are no
longer unsatised
once the range is expanded a bit ( ).
Homes for sale
We will use an ordered list to keep track of the homes that are for sale. The initial ordering of the list the
homes that are for sale is a parameter to the simulation. As our homeowners move around the city, homes
that were for sale will come o
the list and newly empty homes will be added to the list. Our homeowners prefer
[0.45, 0.55] [0.4, 0.7]
[0.45, 0.55]
[0.4, 0.7]
to consider homes that have recently come on the market rst
and so, newly empty homes will be added to the
beginning of the list.
Relocation rule
Our homeowners want to live in satisfactory locations and will relocate when they are unsatised.
We’ll describe
how to relocate a specic
homeowner and then talk about how to navigate through the city nding
homeowners
to relocate.
Homeowners will only move to homes in locations that are satisfactory. How do we decide whether a new
location is satisfactory: we’ll temporarily swap the homeowner to the new location, apply the satisfaction rule to
the new location, and then swap the homeowner back to their original location.
A city will always have at least one home available for sale at any given time and typically, will have many more
than one for sale. How does the homeowner choose among multiple satisfactory homes? In real life, homeowners
have lots of dierent
criteria. In our simulation, everyone will use the same mechanism for picking a new home:
they start out eager to check out new homes, but over time they will lose patience and just choose the next one
that is satisfactory. We will model patience with a parameter that will be the same for everyone in the city.
Homeowners will evaluate homes that are for sale in the order they appear in the list of homes for sale. The
homeowner’s patience level starts out at the specied
level and decreases every time they nd
a home that is
satisfactory. As soon as their patience level hits zero, they stop looking and choose that home. If a homeowner
evaluates all of the homes that are for sale and never gets to a patience level of zero, they will choose not to
move.
The gure
below shows a city and the order of homes that are for sale before and after we relocate the
homeowner in Location (0, 0).
Relocating the homeowner in Location (0, 0)
City parameters
R: 1
Satisfaction range: [0.4, 0.7]
Patience level: 3
Relocating the homeowner in Location (0, 0)
City parameters
R: 1
Satisfaction range: [0.4, 0.7]
Patience level: 3
City before relocation
(../../_images/a20-sample-writeup-1-40-70-3-1-initannotated-sat-scores.png)
City after relocation
(../../_images/a20-sample-writeup-1-40-70-3-1-reloc-
1M-1-annotated-sat-scores-arrow.png)
Homes for sale before relocation
(../../_images/a20-sample-writeup-1-40-70-3-1-initannotated-sat-scores-opens.png)
Homes for sale after relocation
(../../_images/a20-sample-writeup-1-40-70-3-1-reloc-
1M-1-annotated-sat-scores-arrow-opens.png)
Let’s walk through the details of this relocation given the specied
parameters for the city. Following the
relocation rule, we will try relocating the homeowner to each of the homes that are for sale in turn until they run
out of patience or we run out of homes.
Attempted relocations of the homeowner at Location (0, 0)
City parameters
R: 1
Satisfaction range: [0.4, 0.7]
Patience Level: 3
For Sale Temporary City Explanation
(../../_images/a20-sample-writeup-
1-40-70-3-1-init-annotated-satscores.png)
Original grid with the (unsatised)
homeowner in Location (0, 0).
Attempted relocations of the homeowner at Location (0, 0)
City parameters
R: 1
Satisfaction range: [0.4, 0.7]
Patience Level: 3
For Sale Temporary City Explanation
Location (0, 2)
(../../_images/by_hand_0_2-1-40-70-
3-1-init-annotated-sat-scores.png)
Homeowner is not satised
in the
home at Location (0, 2)
Attempted relocations of the homeowner at Location (0, 0)
City parameters
R: 1
Satisfaction range: [0.4, 0.7]
Patience Level: 3
For Sale Temporary City Explanation
Location (1,0)
(../../_images/by_hand_1_0-1-40-70-
3-1-init-annotated-sat-scores.png)
Homeowner is satised
in the
home at Location (1, 0), but wants
to look at two more homes.
Attempted relocations of the homeowner at Location (0, 0)
City parameters
R: 1
Satisfaction range: [0.4, 0.7]
Patience Level: 3
For Sale Temporary City Explanation
Location (1,4)
(../../_images/by_hand_1_4-1-40-70-
3-1-init-annotated-sat-scores.png)
Homeowner is satised
in the
home at Location (1, 4), but wants
to look at one more home.
Attempted relocations of the homeowner at Location (0, 0)
City parameters
R: 1
Satisfaction range: [0.4, 0.7]
Patience Level: 3
For Sale Temporary City Explanation
Location (3,3)
(../../_images/by_hand_3_3-1-40-70-
3-1-init-annotated-sat-scores.png)
Homeowner is satised
in the
home at Location (3, 3) and has
run out of patience and so, will
relocate to this home.
Location (4,4) This location is not evaluated
because the homeowner has
already chosen a new spot.
Given a dierent
patience parameter, the homeowner would make a dierent
choice. If the patience parameter
had been two, the homeowner would have chosen Location (1, 4). If it had been four, the homeowner would have
chosen Location (4, 4), which is also satisfactory given the city’s parameters. And nally,
if the patience parameter
had been ve,
the homeowner would not have relocated because only four homes on the list are satisfactory.
Looking back at the before and after picture, you will see two things. First, the homeowner from Location (0, 0)
has relocated to Location (3, 3). Second, the list of homes for sale has changed: Location (3, 3) has been removed
from the list of homes that are for sale and Location (0, 0) has been added to the beginning of the list.
Relocation Wave
Our homeowners will move in waves based on their color. Each wave will start in the upper left corner of the city
(Location (0, 0)) and move through the city row by row until it reaches the lower right corner (Location (4, 4)), in
our sample city). During a wave, the homes in a row will be visited one by one from left to right. If the home is
occupied by a homeowner of the wave color who is unsatised
at the time of the visit, then the homeowner will
try to relocate during the visit. If the home is empty, has a homeowner of the other color, or has a homeowner of
the wave color who is satised
then nothing changes during the visit.
One quirk of this approach is that a given homeowner can move multiple times during a wave. If their new home
happens to come later in the traversal and if their new neighborhood changes in unsatisfactory ways (say, it
becomes less integrated than they might like), then they might move a second time.
The gures
below show our sample city (along with the state of the for-sale list) before and after a maroon wave.
Sample Maroon Wave
City parameters
R: 1
Satisfaction range: [0.4, 0.7]
Patience Level: 3
Sample Maroon Wave
City parameters
R: 1
Satisfaction range: [0.4, 0.7]
Patience Level: 3
City before the wave
(../../_images/a20-sample-writeup-1-40-70-3-1-initannotated-sat-scores.png)
City after the wave
(../../_images/a20-sample-writeup-1-40-70-3-1-reloc-
1M-3-annotated-sat-scores.png)
Homes for sale before the wave
(../../_images/a20-sample-writeup-1-40-70-3-1-initannotated-sat-scores-opens.png)
Homes for sale after the wave
(../../_images/a20-sample-writeup-1-40-70-3-1-reloc-
1M-3-annotated-sat-scores-opens.png)
Carefully placed print statements can be very helpful when debugging complex code. To make it easier to debug
our wave code, we added print statements to show what happens at each home during the wave. Here’s the
output for our sample maroon wave:
Doing step... 1
Maroon Wave
visiting...Location (0, 0) unsatisfied Maroon homeowner goes shopping...
Trying... (0, 2) unsatisfactory 0.75
Trying... (1, 0) satisfactory 0.6666666666666666
Trying... (1, 4) satisfactory 0.5
Trying... (3, 3) satisfactory 0.6
and relocates to Location (3, 3)
visiting...Location (0, 1) satisfied Maroon homeowner does not move
visiting...Location (0, 2) for sale, do nothing.
visiting...Location (0, 3) satisfied Maroon homeowner does not move
visiting...Location (0, 4) unsatisfied Maroon homeowner goes shopping...
Trying... (0, 0) unsatisfactory 1.0
Trying... (0, 2) unsatisfactory 0.75
Trying... (1, 0) satisfactory 0.6666666666666666
Trying... (1, 4) unsatisfactory 0.3333333333333333
Trying... (4, 4) satisfactory 0.6666666666666666
and fails to find a new home
visiting...Location (1, 0) for sale, do nothing.
visiting...Location (1, 1) Blue homeowner, do nothing
visiting...Location (1, 2) Blue homeowner, do nothing
visiting...Location (1, 3) Blue homeowner, do nothing
visiting...Location (1, 4) for sale, do nothing.
visiting...Location (2, 0) satisfied Maroon homeowner does not move
visiting...Location (2, 1) satisfied Maroon homeowner does not move
visiting...Location (2, 2) satisfied Maroon homeowner does not move
visiting...Location (2, 3) satisfied Maroon homeowner does not move
visiting...Location (2, 4) Blue homeowner, do nothing
visiting...Location (3, 0) Blue homeowner, do nothing
visiting...Location (3, 1) Blue homeowner, do nothing
visiting...Location (3, 2) Blue homeowner, do nothing
visiting...Location (3, 3) satisfied Maroon homeowner does not move
visiting...Location (3, 4) Blue homeowner, do nothing
visiting...Location (4, 0) Blue homeowner, do nothing
visiting...Location (4, 1) satisfied Maroon homeowner does not move
visiting...Location (4, 2) unsatisfied Maroon homeowner goes shopping...
Trying... (0, 0) unsatisfactory 1.0
Trying... (0, 2) unsatisfactory 0.75
Trying... (1, 0) satisfactory 0.6666666666666666
Trying... (1, 4) satisfactory 0.5
Trying... (4, 4) satisfactory 0.6666666666666666
and relocates to Location (4, 4)
visiting...Location (4, 3) unsatisfied Maroon homeowner goes shopping...
Trying... (4, 2) satisfactory 0.6666666666666666
Trying... (0, 0) unsatisfactory 1.0
Trying... (0, 2) unsatisfactory 0.75
Trying... (1, 0) satisfactory 0.6666666666666666
Trying... (1, 4) satisfactory 0.5
and relocates to Location (1, 4)
visiting...Location (4, 4) satisfied Maroon homeowner does not move
As an aside, notice that we labelled all the values that are printed and added a bit of indentation to make it clear
which lines go together. The clarity that comes from labelling the values is worth the few extra minutes that it
took to include the labels the print statements.
During this wave, most of the homes are for sale, have a Blue homeowner, or have a satised
Maroon homeowner.
The rst
attempted relocation is of the maroon homeowner at Location (0, 0). We discussed the details of this
move earlier. The next attempt happens at Location (0, 4). This homeowner tries all ve
homes that are for sale,
but only nds
two that are satisfactory and so, does not move. (Recall that the patience level is three and so, the
homeowner needs to nd
three satisfactory homes to move.)
The next attempt happens at Location (4, 2). This homeowner nds
three satisfactory homes and so, moves to the
third one (Location (4, 4). How is it the homeowner at Location (4, 2) nds
three satisfactory homes, while the
homeowner at Location (0, 4) only nds
two? The presence of the homeowner at Location (0, 4) makes Location
(1, 4) satisfactory for the homeowner from Location (4, 2)! In contrast, Location (0, 4) is empty when that
homeowner tries out Location (1, 4) for themselves.
Similarly, removing the homeowner from Location (4, 3) makes the home at Location (4, 2) satisfactory. As a
result, the homeowner from Location (4, 3) nds
three satisfactory homes and moves to the third one (Location
(1, 4)).
Simulation Step
A step in the simulation consists of a maroon wave followed by a blue wave. The gure
below shows our sample
city at the start of the step, at the end of the maroon wave, and at the end of blue wave, which is also the end of
the step.
Sample Simulation Step
City parameters
R: 1
Satisfaction range: [0.4, 0.7]
Patience Level: 3
Sample Simulation Step
City parameters
R: 1
Satisfaction range: [0.4, 0.7]
Patience Level: 3
City before
the step
(../../_images/a20-sample-writeup-
1-40-70-3-1-init-annotated-satscores.png)
City after
the maroon wave
(../../_images/a20-sample-writeup-
1-40-70-3-1-reloc-1M-3-annotatedsat-scores.png)
City after the blue wave
(after the step)
(../../_images/a20-sample-writeup-
1-40-70-3-1-nal-annotated-satscores.png)
Homes for sale before
the step
(../../_images/a20-sample-writeup-
1-40-70-3-1-init-annotated-satscores-opens.png)
Homes for sale after the
maroon wave
(../../_images/a20-sample-writeup-
1-40-70-3-1-reloc-1M-3-annotatedsat-scores-opens.png)
Homes for sale after
blue wave (after the step)
(../../_images/a20-sample-writeup-
1-40-70-3-1-nal-annotated-satscores-opens.png)
Stopping conditions
We will simulate steps until we have
1. executed a specied
maximum number of steps or
2. executed a step that had zero relocations.
A simulation of our sample city with an R of one, satisfaction range of , and patience level of three (that
is, the parameters we have used thus far) will stop after two steps as long as the maximum number of steps is at
least two. Even though there are unsatised
homeowners at the end of the rst
step, no relocations occur in the
second step because the patience level is too high. Our sample homeowners often nd
two satisfactory homes,
but not a third.
If we lower the patience level to one (that is, homeowners grab the rst
home that is satisfactory), while keeping
the rest of the parameters the same, the homeowners move to dierent
homes, more relocations occur (a total
of 12 over the course of the simulation), and the simulation runs for four steps instead of two.
Sample Simulation
City parameters
R: 1
Satisfaction range: [0.4, 0.7]
Patience Level: 1
Maximum number of steps: 10
[0.4, 0.7]
Sample Simulation
City parameters
R: 1
Satisfaction range: [0.4, 0.7]
Patience Level: 1
Maximum number of steps: 10
Initial city
(../../_images/a20-sample-writeup-1-40-70-3-1-initannotated-sat-scores.png)
Final city
(../../_images/a20-sample-writeup-1-40-70-1-10-
nal-annotated-sat-scores.png)
Initial homes for sale
(../../_images/a20-sample-writeup-1-40-70-3-1-initannotated-sat-scores-opens.png)
Final homes for sale
(../../_images/a20-sample-writeup-1-40-70-1-10-
nal-annotated-sat-scores-opens.png)
You can nd
our debugging output for this sample run here (full-sim-trace.html).
This simulation had the maximum number of steps set to ten. As we show in the gure
below, if we limited the
maximum number of steps to two (while keeping the rest of the parameters the same), the city would be
dierent.
Specically,
the three moves that occur in step 3 would not be done and the total number of moves
would be nine instead of 12.
Sample Simulation
City parameters
R: 1
Satisfaction range: [0.4, 0.7]
Patience Level: 1
Maximum number of steps: 2
Initial city
(../../_images/a20-sample-writeup-1-40-70-3-1-initannotated-sat-scores.png)
Final city
(../../_images/a20-sample-writeup-1-40-70-1-2-nalannotated-sat-scores.png)
Sample Simulation
City parameters
R: 1
Satisfaction range: [0.4, 0.7]
Patience Level: 1
Maximum number of steps: 2
Initial homes for sale
(../../_images/a20-sample-writeup-1-40-70-3-1-initannotated-sat-scores-opens.png)
Final homes for sale
(../../_images/a20-sample-writeup-1-40-70-1-2-nalannotated-sat-scores-opens.png)
Data representation
In addition to having an abstract model of a city, we need concrete representations for homeowners, homes that
are for sale, cities, and locations as well.
Representing Homeowners and individual homes for sale
Homeowners will be represented by strings: "M" for maroon homeowners and "B" for blue homeowners.
Homes that are for sale will be represented using the string "F" .
Representing cities
We will represent a city using a list of rows, where each row is itself a list of strings. You may assume that these
strings will always be one of "M" , "B" , or "F" . We will refer to this data structure as a grid or a city grid.
Here is the list representation for our sample city:
[["M", "M", "F", "M", "M"],
["F", "B", "B", "B", "F"],
["M", "M", "M", "M", "B"],
["B", "B", "B", "F", "B"],
["B", "M", "M", "M", "F"]]
Representing Locations
We will use pairs (tuples) of integers to represent locations. example, (2, 4) represents the cell in row 2,
column 4.
Tracking the homes that are for sale
We will use a list of pairs (locations) to track the locations of homes that are for sale. The initial list of locations
with homes that are for sale will be passed as a parameter to your simulation. Your code will never compute this
list from scratch. Instead, your implementation will modify the list adding and removing locations from the list as
homeowners relocate.
When looking for a new location for a homeowner, your implementation will walk through the ordered list of
locations with homes for sale and consider them as possible new homes for the homeowner. If in the process of
evaluating new homes for a homeowner identies
a suitable location (based on the relocation rule described
above), you should remove their new location from the list (using del or remove ) and insert their old location
into the front of the list (using the list insert method).
Code structure
Your task is to implement in the le
schelling.py a function:
def do_simulation(grid, R, sim_sat_range, patience, max_steps, homes_for_sale)
that executes simulation steps on the specied
city grid until one of the stopping conditions is met. This function
must return a single value: the number of relocations done during the simulation. Besides returning that value,
your function can and should modify the grid argument. At the end of the simulation, the grid should reect
the relocations that took place during the simulation.
You should think carefully about how to split your tasks into multiple functions. That is, you will write a series of
auxiliary functions (with appropriate function headers) that each implements a given subtask. When designing
your function decomposition, keep in mind that you need to do the following subtasks:
1. determine whether a homeowner in a given location is satised;
(We have already included a function
header for the function is_satisfied for this purpose, though you will need to complete the function.)
2. swap the values at two locations;
3. nd
a new location for an unsatised
homeowner, if one exists,
4. simulate a wave,
5. simulate a step of the simulation, and
6. run steps until one of the stopping conditions is met.
Do not combine the last three or four tasks into one mega-task, because the resulting code would be hard to read
and debug. You will lose signicant
design points if your design combines too many subtasks into a single
function.
Testing
We strongly encourage you to test your functions as you write them! The surest way to guarantee that you will
need to spend hours and hours debugging is to write all your code and only then start testing.
We have provided some test code for you, but you will also need to design some tests of your own.
Utlities for working with cities while testing
The cities used in our test code are stored in les.
You can nd
example city grids to play with in the tests
subdirectory of your pa2 directory. See the le
tests/README.txt for descriptions of the le
format and for a
list of the city grid les
included in the tests sub-directory.
We have provided a library utility.py that contains functions for working with cities, a few of which will be
useful for you for testing and debugging purposes. Please note that you will not need to call any of these utility
functions in your implementation.
read_grid takes one argument—a string that is the name of a le
containing a city—and returns the
corresponding city grid (that is, a list of lists of strings).
find_homes_for_sale takes one argument–a city grid—and returns a list of the locations of the homes
that are for sale.
print_grid takes one argument—a city grid—and prints the grid (including N , the number of
rows/columns in the city.
To load the sample city that we have used throughout this writeup, you can run:
sample_city = utility.read_grid("tests/a20-sample-writeup.txt")
assuming you’ve already imported utility into ipython3 .
Testing by hand
Recall that you can set-up ipython3 to reload code automatically when it is modied
by running the following
commands after you re
up ipython3 :
%load_ext autoreload
%autoreload 2
and then importing the modules (that is, the les
containing the code) for the rst
time:
import schelling, utility
Do NOT add the .py extension when you import schelling and utility .
Once you have done these tasks, you can test your code by hand: simply read a grid from a le
and then call the
function with the appropriate arguments.
For example, if you wanted do a few quick tests of your is_satised
function, you could do something like the
following:
sample_city = utility.read_grid("tests/a20-sample-writeup.txt")
schelling.is_satisfied(sample_city, 0, (2, 2), (0.0, 1.0)) # expected result: True
schelling.is_satisfied(sample_city, 1, (2, 2), (0.7, 1.0)) # expected result: False
The function is_satisfied does not modify the city grid and so, it is not necessary to reload the grid between
tests. On the other hand, the city grid and the initial list of homes for sale do need to be reloaded when testing
do_simulation :
sample_city = utility.read_grid("tests/a20-sample-writeup.txt")
for_sale = utility.find_homes_for_sale(sample_city)
schelling.do_simulation(sample_city, 1, (0.4, 0.7), 1, 1, for_sale) # do one step
sample_city = utility.read_grid("tests/a20-sample-writeup.txt")
for_sale = utility.find_homes_for_sale(sample_city)
schelling.do_simulation(sample_city, 1, (0.4, 0.7), 1, 4, for_sale) # do all four steps.
The result of these calls are as follows: The rst
call to do_one_simulation will return 6 , because six
relocations happen in the rst
step. See the le
tests/a20-sample-writeup-1-40-70-1-1-final.txt for the
resulting grid. The second call will return 12 , because 12 relocations occur during the steps of the simulation.
See the le
tests/a20-sample-writeup-1-40-70-1-