extraneous_requisites returns vertex IDs

CurricularAnalytics.py

CurricularAnalytics.py is a toolbox for studying and analyzing academic program curricula. The toolbox represents curricula as graphs, allowing various graph-theoretic measures to be applied in order to quantify the complexity of curricula. In addition to analyzing curricular complexity, the toolbox supports the ability to visualize curricula, to compare and contrast curricula, to create optimal degree plans for completing curricula that satisfy particular constraints, and to simulate the impact of various events on student progression through a curriculum.

CurricularAnalytics.py is a Python port of the original Julia package CurricularAnalytics.jl.

Documentation

Full documentation is available at GitHub Pages. Documentation for functions in this toolbox is also available via the Julia REPL help system. Additional tutorials can be found at the Curricular Analytics Notebooks site.

Installation

Installation is straightforward. First, ensure you have Python 3.8 or higher installed. Then, run the following command.

# Linux/macOS
python3 -m pip install -U curricularanalytics

# Windows
py -3 -m pip install -U curricularanalytics

Contributing and Reporting Bugs

We welcome contributions and bug reports! Please see CONTRIBUTING.md for guidance on development and bug reporting.

Development

To build a distribution for upload to PyPI, run the following command.

rm -r dist
python -m build
# To Test PyPI
python3 -m twine upload --repository testpypi dist/*
# To real PyPI
twine upload dist/*

Learn more about build and setuptools.

	if letter_grade == "A➕"
	return convert(Grade, 13)
	elseif letter_grade == "A"
	return convert(Grade, 12)
	elseif letter_grade == "A➖"
	return convert(Grade, 11)
	elseif letter_grade == "B➕"
	return convert(Grade, 10)
	elseif letter_grade == "B"
	return convert(Grade, 9)
	elseif letter_grade == "B➖"
	return convert(Grade, 8)
	elseif letter_grade == "C➕"
	return convert(Grade, 7)
	elseif letter_grade == "C"
	return convert(Grade, 6)
	elseif letter_grade == "C➖"
	return convert(Grade, 5)
	elseif letter_grade == "D➕"
	return convert(Grade, 4)
	elseif letter_grade == "D"
	return convert(Grade, 3)
	elseif letter_grade == "D➖"
	return convert(Grade, 2)
	elseif letter_grade == "P"
	return convert(Grade, 0)
	elseif letter_grade == "F"
	return convert(Grade, 0)
	elseif letter_grade == "I"
	return convert(Grade, 0)
	elseif letter_grade == "WP"
	return convert(Grade, 0)
	elseif letter_grade == "W"
	return convert(Grade, 0)
	elseif letter_grade == "WF"
	return convert(Grade, 0)

	if int_grade == 13
	return "A➕"
	elseif int_grade == 12
	return "A"
	elseif int_grade == 11
	return "A➖"
	elseif int_grade == 10
	return "B➕"
	elseif int_grade == 9
	return "B"
	elseif int_grade == 8
	return "B➖"
	elseif int_grade == 7
	return "C➕"
	elseif int_grade == 6
	return "C"
	elseif int_grade == 5
	return "C➖"
	elseif int_grade == 4
	return "D➕"
	elseif int_grade == 3
	return "D"
	elseif int_grade == 2
	return "D➖"
	elseif int_grade == 0
	return "F"

	elseif str == "prefix"
	course1.prefix == course2.prefix ? is_matched = true : is_matched = false
	elseif str == "num"
	course1.num == course2.num ? is_matched = true : is_matched = false
	elseif str == "name"
	course1.name == course2.name ? is_matched = true : is_matched = false
	elseif str == "canonical name"
	course1.canonical_name == course2.canonical_name ? is_matched = true : is_matched = false
	elseif str == "credit hours"
	course1.credit_hours == course2.credit_hours ? is_matched = true : is_matched = false

	# The longest path from vertx s to any other vertex in a DAG G (not necessarily unique).
	# Note: in a DAG G, longest paths in G = shortest paths in -G
	"""
	longest_path(g, s)

	The longest path from vertx s to any other vertex in a acyclic graph `g`. The longest path
	is not necessarily unique, i.e., there can be more than one longest path between two vertices.

	# Arguments
	Required:
	- `g::AbstractGraph` : acylic graph.
	- `s::Int` : index of the source vertex in `g`.

	```julia-repl
	julia> path = longest_paths(g, s)
	```
	"""
	function longest_path(g::AbstractGraph{T}, s::Int) where T
	if is_cyclic(g)
	error("longest_path(): input graph has cycles")
	end
	lp = Array{Edge}[]
	max = 0
	# shortest path from s to all vertices in -G
	for path in enumerate_paths(dijkstra_shortest_paths(g, s, -weights(g)))
	if length(path) > max
	lp = path
	max = length(path)
	end
	end
	return lp
	end

	# The longest path from vertx s to any other vertex in a DAG G (not necessarily unique).
	# Note: in a DAG G, longest paths in G = shortest paths in -G
	def longest_path(g: "nx.Graph[T]", s: T) -> List[T]:
	"""
	The longest path from vertex `s` to any other vertex in a acyclic graph `g`. The longest path
	is not necessarily unique, i.e., there can be more than one longest path between two vertices.

	For performance reasons, `longest_path` doesn't check that `g` is acyclic, unlike the Julia version.

	Args:
	g: Acylic graph.
	s: Index of the source vertex in `g`.
	"""
	lp: List[T] = []
	max = 0
	# shortest path from s to all vertices in -G
	for path in nx.shortest_path(g, s).values():
	if len(path) > max:
	lp = path
	max = len(path)
	return lp

Metric	Time
Blocking factor	1.5215 s
Delay factor	1.7919 s
Complexity	4.27298 s
Centrality	64.6228 s
Extraneous requisites	64.5413 s

Metric	% of time
Curriculum construction	2.2
Longest paths	2.9
Extraneous requisites	6.2
Total complexity	2.2
Basic metrics	86.3

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