[SOLVED] 7MRI0020 Scientific programming – Year 2024/2025 Coursework 2 Python

7MRI0020 Scientific programming – Year 2024/2025

Coursework 2

This coursework mark contributes 60% of your final mark for this module

This coursework will cover topics covered in the second half of the module, in particular:

–    Testing (Task 1)

–    Algorithm and complexity (task 2)

–    Concurrency and native language (task 3)

The topic of this coursework will be the Development of a software tool to split an image from top to bottom based on lowest change of intensities

Your implementation will be assessed using the following considerations:

–    functionality of the code (does it work?),

–    your use of language features (idiomatic python),

–    the quality of your comments and your adherence to formatting standard,

–    the efficiency of the implementation, both in term of computation and memory usage.

Finally, it should be noted that this is an individual project, and your code will be automatically checked for plagiarism and collusion. In the event, plagiarism is detected, you will be reported for miss-conduct. You can find more information about the relevant processes on this page.

You are expected to submit a single zipped folder containing only three python files named main.py, squareimage.py and  test_squareimage.py. This should be submitted via the dedicated Keats submission section on the 7MRI0020 module page, where the deadline is specified. Your submission should be anonymised, so do not include any identifiable information in the filename of the notebook nor in your code.

The aim of this coursework is to create a software tool to detect the path which links the top of an image (any pixel from the top row) to the bottom (any pixel from the bottom row) while minimising the overall absolute intensity differential on the path.

Starting from a 2D grid, we aim to establish a path that links any pixel from the top row to any pixel from the bottom row while satisfying these conditions:

–     (Condition 1) A given pixel on a row (red) can only be connected to the pixel under it on the following row or the two voxels on either side of the central one (blue), as illustrated beside.

–    (Condition 2) The sum of absolute differential value between all pixels on the path should be minimal. For example, the total value associated with the path highlighted in blue (2-4-2-1-5) on the right-hand side figure would be computed as:

|2-4| + |4-2| + |2-1| + |1-5|

Note  that  the  path  shown  is  not  the  optimal  solution  to  the problem.

Task  1  [40  marks]: The provided code implements a naive approach that evaluates all possible  solutions  (brute  force)  for  a  square  image  and  returns  a  best  solution  for  the aforementioned task.

The provided `find_minimal_path_brute_force` method lacks modularity and is hard to validate as it implements all required functionalities to extract the best path. Rewrite the components of     this      method      into     multiple      methods      instead.      You     should      call      the `find_minimal_path_alternative` method instead of `find_minimal_path_brute_force`.

Ensure all methods you add, including `find_minimal_path_alternative`, are thoroughly tested. Create a file `test_squareimage.py` to implement your unit tests using the `unittest` module.

For example, the following function:

def large_function(): a = np.random.randint(10) b = a * 10 c = b ** 2 return c

should be changed into the following:

def routineA(): return np.random.randint(10) def routineB(v): return v * 10 def routineC(v): return v**2 def split_function(): a = routineA() b = routineB(a) c = routineC(b) return c

where the `routineA`, `routineB`, `routineC` and `split_function` functions should be tested.

Note:

–    The `find_minimal_path_brute_force` method is not to be altered,

–    You are not expected to write unit tests for the other provided methods,

–    You should write as many tests as you think are required,

–    You can write both positive and negative tests.

Marking scheme:

–    Overall code presentation (organisation, comments, …) [5]

–     Working solution [5]

–     Appropriate re-organisation of the original method [5]

–    Appropriate sets of unit tests [25]

Task 2 [30 marks]: Implement a non-brute force approach (recursive, dynamic, divide and conquer, greedy, randomised, other) to efficiently solve the problem. Your approach should be called by the `find_minimal_path_optimised` method.

Marking scheme:

– Overall code presentation (organisation, comments, …) [5]

– Significant speedup [15]

– Working alternative approach [10]

Task 3 [20 marks]: In the method find_minimal_path_accelerated`, accelerate further your non-brute force approach using multi-threading and/or native language (as per Week 9 content). Note that should you have encounter issues with Task 2, you can accelerate the provided brute force approach. Note as well that depending on your Task 2 implementation, the obtained additional speed-up could be limited. In less than 200 words, comment and explain the obtained acceleration. You answer should be added as comment in the squareimage.py file.

Marking scheme:

– Overall code presentation (organisation, comments, …) [5]

– Significant speedup and/or appropriate justification [10]

– Working approach [5]

Task 4 [10 marks]: Decorate all methods listed below to display information about the approach and time it takes to extract the path. The following methods should be decorated:

– `find_minimal_path_brute_force`

– `find_minimal_path_alternative`

– `find_minimal_path_optimised`

– `find_minimal_path_accelerated`

For example, the text displayed after running the `find_minimal_path_alternative` method should read: `The alternative approach ran in n second(s)`, where `n` is replaced by the time it took in second with two decimals to represent floating values.

Marking scheme:

– Working decorators and appropriate display [5]

– Single function to decorate all methods [5]