025-squirrel/docs/milestones/mil-002-census-analysis.md

MIL-002 Squirrel census analysis

Metadata

Key	Value
ID	MIL-002
CrossReference	BC-001, US-001
Language	en
Domain	it

Version History

Date	Status	Author	Reviewer	Change	Commit
2026-10-13	Deprecated	Jens Tirsvad Nielsen	AI as stand-in for S01	Initial version	f1b3c03
2026-10-13	Accepted	Jens Tirsvad Nielsen	AI as stand-in for S01	CrossReference: added US-001	259d017

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Purpose

Decide whether the squirrel census analysis is modelled, implemented and tested well enough to produce the assignment result, squirrel_count.csv, from the 2018 census file.

Deliverable

A branch merged to main containing the user story, use case, system sequence diagram and domain model, ADR-0001, the squirrel package with its command line entry point, the pytest tests, the census file under data/ and the generated squirrel_count.csv.

Go / No-Go Criteria

#	Criterion (objectively checkable)	Go	No-Go
1	Running the program on the 2018 census file writes squirrel_count.csv with Gray 2473, Cinnamon 392, Black 103	Matches	Differs
2	python -m pytest exits 0 and covers reading, counting, summary and export	Exit 0	Failure or a function untested
3	The package imports no third-party module (checked by pyproject.toml and a test)	None	Any import
4	Source review against qc-programming-python has verdict Go	Go	No-Go
5	Model artifacts (user story (US), use case (UC), system sequence diagram (SSD), domain model (DM)) and ADR-0001 have review verdict Go	All Go	Any No-Go

Dependencies

Depends on	Reason
MIL-001	Tooling, constants and test infrastructure are needed first

Traceability

Business Case objective / KPI / user story	Reference
Objectives 1 and 2, Success Criteria 1, 2 and 3	BC-001

Ownership

Role	Stakeholder ID (SA)
Owner	S01
Approving reviewer	S01

Target Date

2026-10-15 — one day after MIL-001, inside the single-day effort

Tasks

#	Task	Summary	Needs its own Use Case/User Story?	Reference
1	Write user story and use case	Write US-001 and brief UC-001 "Count squirrels by fur colour": the participant supplies the census file and receives squirrel_count.csv. Needed because it is a goal an actor pursues.	No
2	Model the interaction and concepts	Create ssd.md and dm.md in docs/uc-001/, then reconcile docs/domain-model.md. Concepts: Census file, Sighting, Fur colour, Colour count, Summary.	No	UC-001
3	Record ADR-0001 standard library instead of Pandas	Decide to use the csv module and collections.Counter so the repository has no runtime dependencies (S03), while the function names follow the lecture steps (S02).	No
4	Read the census file	Implement read_csv(path) returning the sightings with the primary fur colour column, using only the standard library. The file is read as UTF-8.	Yes	UC-001
5	Count squirrels by fur colour	Implement count_by_fur_color(rows, colors) returning the count for gray, cinnamon and black, using the values defined in constants.py.	Yes	UC-001
6	Build the summary and export it	Implement build_summary(counts) and write_csv(summary, path) producing squirrel_count.csv with the columns Fur Color and Count, mirroring the lecture's DataFrame-to-comma-separated-values (CSV) step.	Yes	UC-001
7	Add the command line entry point	Add python -m squirrel [input] [output] with the census file path defaulting under data/ and the output defaulting to squirrel_count.csv. Exit non-zero with a message when the input is missing.	Yes	UC-001
8	Add pytest tests with fixture rows	Test each function with a small fixture CSV file so the tests run without the 730 kilobytes (KB) census file, plus one test that checks the real census file counts when the file is present.	No
9	Add the census file and generate the result	Copy the 2018 census file into data/ and run the program once to produce squirrel_count.csv, so others can run the project without downloading the file.	No

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