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CptS355 – Assignment 3 – Python Warm-up

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CptS355 – Assignment 3 –
Python Warm-up

Weight: This assignment will count for 7% of your final grade.
This assignment is to be your own work. Refer to the course academic integrity statement in the syllabus.
Turning in your assignment
All the problem solutions should be placed in a single file named HW3.py. At the top of the file in a
comment, please include your name and the names of the students with whom you discussed any of the
problems in this homework.
In addition, you will write unit tests using unittest testing framework. You will write your tests in the file
HW3tests.py – the template of this file is available on the HW3 assignment page. You will edit this file
and provide additional tests (add at least one test per problem).
To submit your assignment, please upload both files (HW3.py and HW3tests.py) on the Assignment3
(Python) DROPBOX on Canvas (under Assignments).
Please don’t zip your code; directly attach the .py files to the dropbox. You may turn in your assignment up
to 3 times. Only the last one submitted will be graded. Implement your code for Python3.
Grading
The assignment will be marked for good programming style as well as thoroughness of testing and clean
and correct execution. 6% of the points will be reserved for the test functions. Turning in “final” code that
produces debugging output is bad form, and points will be deducted if you kept the debugging output in
your code. We suggest you the following:
o Near the top of your program write a debug function that can be turned on and off by changing a
single variable. For example,
debugging = True
def debug(*s):
if debugging:
print(*s)
o Where you want to produce debugging output use:
debug(”This is my debugging output”,x,y)
instead of print.
(How it works: Using * in front of the parameter of a function means that a variable number of arguments can be
passed to that parameter. Then using *s as print’s argument passes along those arguments to print.)
Problems:
1(a) aggregate_log – 5%
Assume you keep track of the number of hours you study for each course you are enrolled in daily. You
maintain the log of your hours in a Python dictionary as follows:
The keys of the dictionary are the course numbers and the values are the dictionaries which include the
number of hours you studied on a particular day of the week.
Define a function, aggregate_log which adds up the number of hours you studied on each day of the
week and returns the summed values as a dictionary. Note that the keys in the resulting dictionary should
be the abbreviations for the days of the week and the values should be the total number of hours you have
studied on that day. aggregate_log would return the following for the above dictionary:
Important Notes:
1. Your function should not change the input dictionary value.
2. You should not hardcode the keys and values (language and course names) in your solution.
3. You are not allowed to import additional Python libraries we haven’t covered in class.
1(b) combine_dict – 6%
Define a function combine_dict which combines two given study logs and returns the merged
dictionary. The values of the common keys should be summed in the resulting dictionary. For example:
will return:
Important Notes:
1. Your function should not change the input dictionary value.
2. You should not hardcode the keys and values (language and course names) in your solution.
3. You are not allowed to import additional Python libraries we haven’t covered in class.

log_input = {‘CptS355’:{‘Mon’:3,’Wed’:2,’Sat’:2},
‘CptS360’:{‘Mon’:3,’Tue’:2,’Wed’:2,’Fri’:10},
‘CptS321’:{‘Tue’:2,’Wed’:2,’Thu’:3},
‘CptS322’:{‘Tue’:1,’Thu’:5,’Sat’:2}}
{‘Fri’: 10, ‘Mon’: 6, ‘Sat’: 4, ‘Thu’: 8, ‘Tue’: 5, ‘Wed’: 6}
log1 = {‘Mon’:3,’Wed’:2,’Sat’:2}
log2 = {‘Mon’:3,’Tue’:2,’Wed’:2,’Fri’:10}
combine_dict(log1,log2)
{‘Mon’: 6, ‘Wed’: 4, ‘Sat’: 2, ‘Tue’: 2, ‘Fri’: 10}
1(c) merge_logs – 12%
Now assume that you kept the log of number of hours you studied for your courses throughout the semester and
stored that data as a list of dictionaries. This list includes a dictionary for each week you recorded your log.
Define a function merge_logs which takes a list of course log dictionaries and returns a dictionary which
includes the combined logs for each class, i.e., the logs of each class should be merged to a single dictionary. You
should use combine_dict (from part (b)) in your solution.
merge_logs(log_list)
will return:
Important Notes:
1. Your function should not change the input dictionary value.
2. You should not hardcode the keys and values (language and course names) in your solution.
3. You are not allowed to import additional Python libraries we haven’t covered in class.
2(a) most_hours – 15%
Consider the combined log output in problem 1(c). Assume you would like to find the course with the
maximum total study time. Write a function “most_hours” that takes the log_input data as input and
returns the course having the maximum total hours. For example,
Your function definition should not use loops or recursion but use the Python map, reduce, and/or
filter functions. You may define and call helper (or anonymous) functions, however your helper
functions should not use loops or recursion. You will not get any points if your solution (or helper functions)
uses a loop. If you are using reduce, make sure to import it from functools.
You are not allowed to use Python libraries we haven’t covered in class.
log_list = [{‘CptS355’:{‘Mon’:3,’Wed’:2,’Sat’:2},’CptS360′:{‘Mon’:3,’Tue’:2,’Wed’:2,’Fri’:10},
‘CptS321’:{‘Tue’:2,’Wed’:2,’Thu’:3},’CptS322′:{‘Tue’:1,’Thu’:5,’Sat’:2}},
{‘CptS322’:{‘Mon’:2},’CptS360′:{‘Thu’:2, ‘Fri’:5},’CptS321′:{‘Mon’:1,’Sat’:3}},
{‘CptS355’:{‘Sun’:8},’CptS360′:{‘Fri’:5},’CptS321′:{‘Mon’:4},’CptS322′:{‘Sat’:3}}]
{‘CptS355’: {‘Mon’: 3, ‘Wed’: 2, ‘Sat’: 2, ‘Sun’: 8},
‘CptS360’: {‘Mon’: 3, ‘Tue’: 2, ‘Wed’: 2, ‘Fri’: 20, ‘Thu’: 2},
‘CptS321’: {‘Tue’: 2, ‘Wed’: 2, ‘Thu’: 3, ‘Mon’: 5, ‘Sat’: 3},
‘CptS322’: {‘Tue’: 1, ‘Thu’: 5, ‘Sat’: 5, ‘Mon’: 2}}
log_input = {‘CptS355’: {‘Mon’: 3, ‘Wed’: 2, ‘Sat’: 2, ‘Sun’: 8},
‘CptS360’: {‘Mon’: 3, ‘Tue’: 2, ‘Wed’: 2, ‘Fri’: 20, ‘Thu’: 2},
‘CptS321’: {‘Tue’: 2, ‘Wed’: 2, ‘Thu’: 3, ‘Mon’: 5, ‘Sat’: 3},
‘CptS322’: {‘Tue’: 1, ‘Thu’: 5, ‘Sat’: 5, ‘Mon’: 2}}
most_hours(log_input)
returns
(‘CptS360’, 29)
2(b) filter_log – 15%
Consider the log_input data in problem 1(a). Assume you would like to find the courses that you studies
for on a particular day of the week for more than some number of hours. Write a function “filter_log”
that takes the log_input data and returns the courses that has the given day in its log with more than or
equal to the required number of hours. For example,
Your function definition should not use loops or recursion but use the Python map, reduce, and/or
filter functions. You may define and call helper (or anonymous) functions, however your helper
functions should not use loops or recursion. You will not get any points if your solution (or helper functions)
uses a loop. If you are using reduce, make sure to import it from functools.
You are not allowed to use Python libraries we haven’t covered in class.

log_input = {‘CptS355’: {‘Mon’: 3, ‘Wed’: 2, ‘Sat’: 2, ‘Sun’: 8},
‘CptS360’: {‘Mon’: 3, ‘Tue’: 2, ‘Wed’: 2, ‘Fri’: 20, ‘Thu’: 2},
‘CptS321’: {‘Tue’: 2, ‘Wed’: 2, ‘Thu’: 3, ‘Mon’: 5, ‘Sat’: 3},
‘CptS322’: {‘Tue’: 1, ‘Thu’: 5, ‘Sat’: 5, ‘Mon’: 2}}
filter_log(self.log_input,”Mon”, 3)
returns
[‘CptS355’, ‘CptS360’, ‘CptS321’]
3. graph_cycle – 12%
Consider the following directed graph where each node has at most one outgoing edge and each edge
has a weight. Assume the graph nodes are assigned unique labels. The edges of this graph can be
represented as a Python dictionary where the keys are the starting nodes of the edges and the values
are pairs of ending nodes and weights of the edges.
graph = {‘A’:(‘B’,5),’B’:(‘D’,3),’C’:(‘G’,10),’D’:(‘E’,4),’E’:(‘C’,5),
‘F’:(‘I’,4),’G’:(‘B’,9),’H’:(‘G’,5),’I’:(‘H’,3)}
Write a recursive function, graph_cycle, which takes a graph dictionary and a starting node as input
and returns the sequence of nodes that form a cycle in the graph. It returns the first cycle that exists in
the path beginning at node “start”. The function returns the list of the cycle node labels where the
starting node of the cycle should be included both in the beginning and at the end. If the graph doesn’t
have any cycles, it returns None.
You may define helper functions to implement your solution. Either your graph_cycle function or
your helper should be recursive.
For example:
graph = {‘A’:(‘B’,5),’B’:(‘D’,3),’C’:(‘G’,10),’D’:(‘E’,4),’E’:(‘C’,5),
‘F’:(‘I’,4),’G’:(‘B’,9),’H’:(‘G’,5),’I’:(‘H’,3)}
graph_cycle(self.graph,’F’) returns [‘G’, ‘B’, ‘D’, ‘E’, ‘C’, ‘G’]
graph_cycle(self.graph,’A’) returns [‘B’, ‘D’, ‘E’, ‘C’, ‘G’, ‘B’]
graph_cycle(self.graph,’C’) returns [‘C’, ‘G’, ‘B’, ‘D’, ‘E’, ‘C’]
Important Note:
– You are not allowed to use Python libraries we haven’t covered in class.
– You should not hardcode the keys ( course names) in your solution.
A B
C
D
E
F
G
I H
5 3
10
4
5
4
9
5
3
4. Iterators
filter_iter – 15%
Create an iterator whose constructor takes a function (op) and an iterable value (it) as argument and applies op on
each value of the input iterator. At each call to the next function, the iterator will return the result of (op x)
where x is the next value from the input sequence.
Important Note: Your filter_iter implementation should calculate the next (op x) value as
needed. An implementation that precomputes all values for the given iterator input and dumps all values
to a list ahead of time will be worth only 5 points.
For example:
– it = iter([-1,-2,7,-3,-2,4,5,3,-2,0,3,6,2,-1])
filter_iter(it, lambda x: x>0)
will represent the sequence:
7,4,5,3,3,6,2
– filter_iter(Numbers(-20), lambda x: x>0 and x%5==1)
will represent the infinite sequence:
1,6,11,16,21,26,31,36,41,46,51,56,61,66,71,76,81,86,91,96,….
The Numbers iterator is defined below.
class Numbers():
def __init__(self,init):
self.current = init
def __next__(self):
result = self.current
self.current += 1
return result
def __iter__(self):
return self
5. merge – 10%
Define a function merge that takes 2 iterable values “it1” and “it2” (which are sorted sequences of
increasing numbers), and merges the two input sequences. merge returns the first N elements from the
merged sequence. The numbers in the merged sequence needs to be sorted as well. (Note that the
iterator will remember its current position among calls to the merge function. It retrieves the next element
in the sequence in the subsequent calls to merge.)
If one of the input iterators raise StopIteration exception (i.e., reaches to the end of the input iterator), then
it should stop merging. For example , in the example below, it1 is an iterator with finite number of elements.
it1 = iter([2,3,5,7,11,13,17,19])
it2 = filter_iter(Numbers(1), lambda x: x%3==0)
# first call to merge
merge(it1,it2,5)
# note that 7 and 9 will be skipped
merge(it1,it2, 2), [11,12])
#reached to the end of the first sequence, so merge will return the remaining merged
output.
merge(it1,it2, 5), [17,18,19]
#reached to the end of the first sequence, so merge will return [].
merge(it1,it2, 4 []
Assignment rules – 4%
Make sure that your assignment submission complies with the following. :
– Make sure that all your debugging print statements are removed or disabled. When we run the tests,
only the unittest output should be displayed.
– Make sure to include your own tests in HW3tests.py file.
– Make sure that you don’t import any third part libraries in your code. In the past semesters, some
submissions included several unrelated imports. TAs won’t be able to run your code with those
dependencies.
it1 = filter_iter(Numbers(-20), lambda x: x>0 and x%2==0)
it2 = filter_iter(Numbers(1), lambda x: x%3==0)
# first call to merge
merge(it1,it2, 10)
# second call to merge ; iterator will remember its position;
# note that 14 and 15 will be skipped since previous merge call retrieved them
# from input iterator by calling next, but didn’t include them in the output
merge(it1,it2, 5)
merge(it1,it2, 5)
returns [2,3,4,6,6,8,9,10,12,12]
returns [16,18,18,20,21]
returns [24,26,27,28,30]
returns [2,3,3,5,6]
returns [11,12]
returns [17,18,19]
returns []
Testing your functions (6%)
We will be using the unittest Python testing framework in this assignment. See
https://docs.python.org/3/library/unittest.html for additional documentation.
The file HW3SampleTests.zip file includes 6 .py files where each one includes the unittest tests for a
different HW problem. These files import the HW3 module (HW3.py file) which will include your
implementations of the given problems.
You should add your own tests in the HW3tests.py file – a template of this file is provided. You are
expected to add at least one more test case for each problem. Make sure to create your own input
dictionaries (or change the given dictionaries extensively) for problems 1,2, and 3.
In Python unittest framework, each test function has a “test_” prefix. To run all tests, execute the
following command on the command line.
python -m unittest P1_HW3tests.py
You can run tests with more detail (higher verbosity) by passing in the -v flag:
python -m unittest -v P1_HW3tests.py
If you don’t add new test cases you will be deduced at least 6% in this homework.

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