Learning line

See bottom for the functions to-be-developed for The Big Project

Tuesday part 1: Paradigms, Introduction to formalised development

Tuesday part 2: TDD

Goal: students have done multiple TDD cycles individually

Why is TDD important?
What is TDD?
What is the TDD cycle?
How the git history of TDD cycle looks like
Why TDD gives better software architecture
Why helpful error messages are important
Different types of tests: unit tests, etc.
Test for documentation
Why tests are valuable: they are what you discuss with your boss
Encourage to get sloppier in the end: there will be unexpected errors :-)
There is at least 1 easy function description that makes no sense when writing tests. Change the description
Can miss code to be tested, will measure codecov in TDD2
Can test for style, will lint codecov in TDD2

Other activities:

Collect GitHub usernames

Evaluation: git history on students's repositories

Wednesday part 1: social coding, pair programming

Wednesday part 2: Testing

Goal: students have done multiple TDD cycles within a testing framework socially

Add CI to TDD development
Add build badge
Add linter CI script
Add Markdown spellcheck
Add broken links checker
Measure codecov
Do PR to demonstrate a bug
Hamcrest notation
Writing functions for tests
Testing frameworks
Beyoncé principle
Recursive tests

Other activities:

Rotate pairs
Big project: shared repo with simple tests

Evaluation: Code on multiple GitHub tested thoroughly by GHA, developed by groups

Thursday part 1: algorithms

Goal: students have created multiple different types of algorithms

Design by contract
The role of assert, https://docs.python.org/3/reference/simple_stmts.html#assert
What is a good algorithm
DRY
Dimensions of a algorithm: speed, memory, disc, network
Type of algorithms overview
Iterative versus recursive
Exact or approximate
Deterministic or non-deterministic
Digital or quantum
Single-thread, multi-thread, distributed
Divide and conquer: qsort
Measuring speed to make rooftop plot
Caching results/LUTs
Do the bare minimum, e.g. nth_element
Using data structures to simplify algorithms, e.g. use an ordered set to sort
Big O
Data structure: dict, for NW parameters

Evaluation: Code on multiple GitHub tested thoroughly by GHA, developed by groups

Thursday part 2: data structures

Goal: students have develop tested code together

Essential Python classes
YAGNI
Winnebago class
Design a simple class

Evaluation: Code on multiple GitHub tested by GHA, 100% codecov, developed by groups

Friday part 1: Modular programming

Friday part 2: Optimisation

Goal: students have attempted to optimize code together

Problem: code is slow. How to make it fast?
Use code from here
Profiling
Need to have small functions
Only useful in release mode on big problems!
Bottlenecks: CPU versus file IO versus web IO
Techniques: sorting, storing
Other language: only if able
Multiple threads: not yet
Myths: experienced devs know where the bottleneck is
Myths: for loops are always evil, always use assembly/C/C++/Rust

Evaluation: Code on one GitHub tested and profiled by GHA, developed by all

Functions to be developed for The Small Project

These are barely (sometimes purposefully vague!) suggestions:

if you do not need all, that is OK
if you know better, that is OK

Easy

At day 'TDD'

Name	Purpose
`is_even(x)`	Returns `True` if `x` is even
`is_odd(x)`	Returns `True` if `x` is odd
`is_probability(p)`	Returns `True` if `p` is a probability (i.e. a chance of something happening)
`is_zero(x)`	Returns `True` if `x` is zero

Extra

Name	Purpose
`are_numbers(x)`	Returns `True` if `x` is zero, one or more numbers
`are_strings(x)`	Returns `True` if `x` is zero, one or more strings
`is_dividable_by_three(x)`	Returns `True` if `x` is dividable by 3
`is_number(x)`	Returns `True` if `x` is a number
`is_string(x)`	Returns `True` if `s` is a string

roman_str_to_number
is_perfect
calc_n_ways_to_climb_stairs

Prime, easy

At day 'Testing', medium_questions.py

Name	Purpose
`are_primes(x)`	Returns `True`/`False` for each element in `x` being prime yes/no
`get_digits(n)`	Returns all the digits of number `n`
`get_proper_divisors(n)`	Returns all proper divisors of number `n`
`is_palindrome(n)`	Returns `True` if the number `n` is a palindrome
`is_palindrome(s)`	Returns `True` if the string `s` is a palindrome
`is_perfect(x)`	Returns `True` if `x` is a perfect number
`is_prime(x)`	Returns `True` if `x` is prime
`is_roman_number(s)`	Returns `True` if `s` is a string that is a roman number
`roman_number_to_int(s)`	Returns an integer equal to the roman number that is string `s`
`sum_digits(x)`	Returns the sum of the digits of number `x`
`to_roman_number(i)`	Returns a Roman number as a string equivalent to the integer that is `i`

Extra

Name	Purpose
`are_primes(x, m)`	Returns `True`/`False` for each element in `x` being prime yes/no using method `m`
`can_use_prime_method(x, m)`	Returns `True` if `m` is a prime finding method that can be used on `x`
`get_all_prime_methods()`	Returns all prime finding methods
`is_prime(x, m)`	Returns `True` if `x` is prime using method `m`
`is_prime_td(x)`	Returns `True` if `x` is prime using the Trial Division method
`is_prime_ss(x)`	Returns `True` if `x` is likely to be prime using the Solovay-Strassen method
`is_prime_ss(x, p)`	Returns `True` if `x` is likelier than probability `p` to be prime using the Solovay-Strassen method
`is_prime_method(m)`	Returns `True` if `m` is a prime finding method

Prime, advanced

At day 'Testing' and 'Optimisation'

Name	Purpose
`calc_p_is_prime(x, m)`	Returns the change that number `x` is prime using method `m`
`calc_p_is_prime_bpsw(x)`	Returns the chance that number `x` is prime using the Baillie-PSW primality test
`calc_p_is_prime_mr(x)`	Returns the chance that number `x` is prime using the Miller-Rabin primality test
`calc_p_is_prime_ss(x)`	Returns the chance that number `x` is prime using the Solovay-Strassen primality method
`is_coprime(a, b)`	Returns `True` is `a` is coprime to `b`
`is_factorial_prime(x)`	Returns `True` if `x` is a factorial prime
`is_mersenne_prime(x)`	Returns `True` if `x` is a Mersenne prime
`is_proth_prime(x)`	Returns `True` if `x` is a Proth prime
`is_perfect_power(x)`	Returns `True` if `x` is a perfect power
`is_prime_aks(x)`	Returns `True` if `x` is prime using the Agrawal-Kayal-Saxena primality test
`is_prime_bpsw(x)`	Returns `True` if `x` is likely to be prime using the Baillie-PSW primality test
`is_prime_ec(x)`	Returns `True` if `x` is prime using an elliptic curve primality test
`is_prime_ec(x, m)`	Returns `True` if `x` is prime using the elliptic curve primality test `m`
`is_prime_eccm(x)`	Returns `True` if `x` is prime using the complex multiplication elliptic curve primality test
`is_prime_ecpp(x)`	Returns `True` if `x` is prime using the Atkin-Morain elliptic curve primality test
`is_prime_ecgk(x)`	Returns `True` if `x` is prime using the Goldwasser-Kilian elliptic curve primality test
`is_prime_mr(x)`	Returns `True` if `x` is likely to be prime using the Miller-Rabin primality test
`is_primorial_prime(x)`	Returns `True` if `x` is a primorial prime
`is_twin_prime(x)`	Returns `True` if `x` is a twin prime

Functions to be developed for The Medium Project

At day 'Algorithms' and 'Optimisation'

These are barely (sometimes purposefully vague!) suggestions:

if you do not need all, that is OK
if you know better, that is OK

Name	Purpose
`bubble_sort(x)`	Returns the sorted elements of `x` using bubble sort
`block_sort(x)`	Returns the sorted elements of `x` using block sort
`cube_sort(x)`	Returns the sorted elements of `x` using cube sort
`cocktail_shaker_sort(x)`	Returns the sorted elements of `x` using cocktail shaker sort
`comb_sort(x)`	Returns the sorted elements of `x` using comb sort
`cycle_sort(x)`	Returns the sorted elements of `x` using cycle sort
`exchange_sort(x)`	Returns the sorted elements of `x` using exchange sort
`gnome_sort(x)`	Returns the sorted elements of `x` using gnome sort
`heap_sort(x)`	Returns the sorted elements of `x` using heap sort
`intro_sort(x)`	Returns the sorted elements of `x` using intro sort
`insertion_sort(x)`	Returns the sorted elements of `x` using insertion sort
`is_sorted(x)`	Returns `True` if the elements of `x` are in ascending order
`is_sorting_method(m)`	Returns `True` if `m` is a sorting method
`library_sort(x)`	Returns the sorted elements of `x` using library sort
`merge_sort(x)`	Returns the sorted elements of `x` using merge sort
`odd_even_sort(x)`	Returns the sorted elements of `x` using odd-even sort
`quick_sort(x)`	Returns the sorted elements of `x` using quick sort
`selection_sort(x)`	Returns the sorted elements of `x` using selection sort
`shell_sort(x)`	Returns the sorted elements of `x` using shell sort
`tim_sort(x)`	Returns the sorted elements of `x` using tim sort
`tree_sort(x)`	Returns the sorted elements of `x` using tree sort
`patience_sort(x)`	Returns the sorted elements of `x` using patience sort
`smooth_sort(x)`	Returns the sorted elements of `x` using smooth sort
`sort(x)`	Returns the sorted elements of `x`
`sort(x, m)`	Returns the sorted elements of `x` using method `m`
`strand_sort(x)`	Returns the sorted elements of `x` using strand sort
`tournament_sort(x)`	Returns the sorted elements of `x` using tournament sort

Functions to be developed for The Big Project

At days 'Data structures' and 'Optimisation'

These are barely (sometimes purposefully vague!) suggestions:

if you do not need all, that is OK
if you know better, that is OK

Name	Purpose
`align_dna_seqs(a, b, m)`	Returns the aligned sequences of DNA sequences `a` and `b` with alignment method `m`
`align_dna_seqs_nw(a, b)`	Returns the aligned sequences of DNA sequences `a` and `b` with the Needleman-Wunch alignment method
`align_dna_seqs_nw(a, b, p)`	Returns the aligned sequences of DNA sequences `a` and `b` with the Needleman-Wunch alignment method with parameters `p`
`align_dna_seqs(a, b)`	Returns the aligned sequences of DNA sequences `a` and `b`
`align_dna_seqs(s)`	Returns the aligned sequences of the zero, one or more DNA sequences `s`
`align_dna_seqs(s, m)`	Returns the aligned sequences of the zero, one or more DNA sequences `s` with alignment method `m`
`are_dna_seqs(a)`	Returns `True` if `a` is zero, one or more DNA sequences
`are_dna_seqs(a, b)`	Returns `True` if both `a` and `b` are DNA sequences
`check_are_dna_seqs(a, b)`	Throws a helpful error if either `a` or `b` is not a DNA sequence
`check_are_dna_seqs(s)`	Throws a helpful error if `s` are not DNA sequences
`check_different(a, b)`	Throws a helpful error if `a` and `b` are not different
`check_equal(a, b)`	Throws a helpful error if `a` and `b` are different
`check_is_alignment_method(m)`	Throws a helpful error if `m` is not an alignment method
`check_is_dna_seq(a)`	Throws a helpful error if `a` is not a DNA sequence
`check_is_nw_params(p)`	Throws a helpful error if `p` is not a valid Needleman-Wunch parameter data structure
`get_all_alignment_methods()`	Returns the descriptions of all implemented alignment methods
`is_alignment_method(m)`	Returns `True` if `m` is an alignment method
`is_alignment_score_param(p)`	Returns `True` if `p` a parameter set to score how good an alignment is
`is_dna_seq(s)`	Returns `True` if `s` is a DNA sequence
`is_nw_params(p)`	Returns `True` if `p` is a valid Needleman-Wunch parameter data structure
`score_alignment(a)`	Returns a score for how good an alignment is
`score_alignment(a, p)`	Returns a score for how good an alignment is, based on parameter set `p`

Projects overview

Day	Description	Group size	Branch model	CI	Code review
1	Introduction		`master`	No	No
2	TDD	1	`master`	No	No
3	Testing	2	`master`, `develop`	Yes	No
4-1	Algorithms	4	`master`, `develop`, topic	Yes	No
4-2	Data structures	all	`master`, `develop`, topic	Yes	Pull Request
5	Optimisation	all	`master`, `develop`, topic	Yes	Pull Request

programming_formalisms_project_2023: GitHub repo of 2023

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

learning_line.md

learning_line.md

Learning line

Tuesday part 1: Paradigms, Introduction to formalised development

Tuesday part 2: TDD

Wednesday part 1: social coding, pair programming

Wednesday part 2: Testing

Thursday part 1: algorithms

Thursday part 2: data structures

Friday part 1: Modular programming

Friday part 2: Optimisation

Functions to be developed for The Small Project

Easy

Extra

Prime, easy

Extra

Prime, advanced

Functions to be developed for The Medium Project

Functions to be developed for The Big Project

Projects overview

Files

learning_line.md

Latest commit

History

learning_line.md

File metadata and controls

Learning line

Tuesday part 1: Paradigms, Introduction to formalised development

Tuesday part 2: TDD

Wednesday part 1: social coding, pair programming

Wednesday part 2: Testing

Thursday part 1: algorithms

Thursday part 2: data structures

Friday part 1: Modular programming

Friday part 2: Optimisation

Functions to be developed for The Small Project

Easy

Extra

Prime, easy

Extra

Prime, advanced

Functions to be developed for The Medium Project

Functions to be developed for The Big Project

Projects overview