Open ritisha2000 opened 1 year ago
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function runs for 692 iterations and this takes ~2 minutes to produce an outputpokemon.csv
filePlease check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide
The package includes all the following forms of documentation:
pyproject.toml
file or elsewhere.Readme file requirements The package meets the readme requirements below:
The README should include, from top to bottom:
NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)
Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole. Package structure should follow general community best-practices. In general please consider whether:
Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.
The package contains a paper.md
matching JOSS's requirements with:
Estimated hours spent reviewing: 1 hr
Please check off boxes as applicable, and elaborate in comments below. Your review is not limited to these topics, as described in the reviewer guide
The package includes all the following forms of documentation:
pyproject.toml
file or elsewhere.Readme file requirements The package meets the readme requirements below:
The README should include, from top to bottom:
NOTE: If the README has many more badges, you might want to consider using a table for badges: see this example. Such a table should be more wide than high. (Note that the a badge for pyOpenSci peer-review will be provided upon acceptance.)
Reviewers are encouraged to submit suggestions (or pull requests) that will improve the usability of the package as a whole. Package structure should follow general community best-practices. In general please consider whether:
Note: Be sure to check this carefully, as JOSS's submission requirements and scope differ from pyOpenSci's in terms of what types of packages are accepted.
The package contains a paper.md
matching JOSS's requirements with:
Estimated hours spent reviewing: 1.5
Submitting Author: Ritisha Sharma (@ritisha2000) All current maintainers: Jakob Thoms (@J99thoms), Raul Aguilar (@AguilarRaul), Sneha Sunil (@snesunil) Package Name: Pokehelpyer One-Line Description of Package: Pokemon team builder based on the resistances and weaknesses of existing team members. Repository Link: https://github.com/UBC-MDS/pokehelpyer Version submitted: v0.1.7 Editor: Jakob Thoms (@J99thoms), Raul Aguilar (@AguilarRaul), Sneha Sunil (@snesunil), Ritisha Sharma (@ritisha2000) Reviewer 1: Renee Kwon Reviewer 2: Ty Andrews Reviewer3: Sarah Abdelazim Archive: TBD
Version accepted: TBD Date accepted (month/day/year): TBD
Description
pokehelpyer is a Python package designed to assist Pokémon players in building teams of pokémon. Users can provide a list of pokémon currently on their team, and pokehelpyer will recommend a suitable pokémon to add to the team based on the current team's overall weaknesses and resistances.
Scope
For all submissions, explain how the and why the package falls under the categories you indicated above. In your explanation, please address the following points (briefly, 1-2 sentences for each):
Who is the target audience and what are scientific applications of this package?
The target audience is pokemon players of all ages who would like to create a more robust team.
Are there other Python packages that accomplish the same thing? If so, how does yours differ?
There are websites and applications that help build pokémon teams, such as the Mariland Team Builder. However these tools simply present the player with a visual representation of their current team's weaknesses and resistances. They don't make recommendations. In other words, the existing tools simply given visual representations of the dictionaries created by calc_weaknesses and calc_resistances. There doesn't seem to be any existing Python packages which will use the weaknesses/resistances data to make reccomendations for additional team members.
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