Submission Group 14: airpyllution

Submitting Authors:

Christopher Alexander (@christopheralex)
Daniel King (@dankfe)
Mel Liow (@mel-liow)

Package Name: airpyllution
One-Line Description of Package: A Python package for visualizing or obtaining future, historic and current air pollution data from the OpenWeatherMap API. Repository Link: airpyllution
Version submitted: 2.0.0 Editor: Florencia D'Andrea (@flor14)

Reviewers:

Siqi Tao ()
Qingqing Song ()
Joyce Wang ()

Description

airpyllution is a Python package for visualizing or obtaining future, historic and current air pollution data using the OpenWeather API. Our goal is to enable users the ability to explore air pollution levels in locations around the world by providing visual charts and graphs. We make the data accessible and easy to comprehend in just a few lines of code.

get_air_pollution(): Fetches the air pollution levels based on a location. Based on the values of the polluting gases, this package uses the Air Quality Index to determine the level of pollution for the location and produces a coloured map of the area displaying the varying regions of air quality.
get_pollution_history(): Requires a start and end date and fetches historic air pollution data for a specific location. The function returns a data frame with the values of the polluting gases over the specified date range.
get_pollution_forecast(): Fetches air pollution data for the next 5 days for a specific location. The function returns a time series plot of the predicted pollution levels.

Scope

Please indicate which category or categories this package falls under:
[X] Data retrieval
[ ] Data extraction
[ ] Data munging
[ ] Data deposition
[ ] Reproducibility
[ ] Geospatial
[ ] Education
[ ] Data visualization*

* Please fill out a pre-submission inquiry before submitting a data visualization package. For more info, see [notes on categories][NotesOnCategories] of our guidebook.

Explain how the and why the package falls under these categories (briefly, 1-2 sentences): Our package contains wrappers for accessing the OpenWeatherMap Air pollution API, a free data source for weather data.
Who is the target audience and what are scientific applications of this package? The target audience are for people who are interested in visualising local air pollution for e.g. forecasting or building an air pollution application. Additionally, this package can be useful for researchers or scientists who would like to analyse data in a more suitable format.
Are there other Python packages that accomplish the same thing? If so, how does yours differ? Although there is an abundance of python weather packages and APIs in the Python ecosystem (e.g. python-weather, weather-forecast), this particular package looks at specifically air pollution data and uses the Air Pollution API from OpenWeather. This is a unique package which provides simple and easy to use functions and allows users to quickly access and visualise air pollution data.
If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:

Technical checks

For details about the pyOpenSci packaging requirements, see our packaging guide. Confirm each of the following by checking the box. This package:

[x] does not violate the Terms of Service of any service it interacts with.
[x] has an OSI approved license.
[x] contains a README with instructions for installing the development version.
[x] includes documentation with examples for all functions.
[ ] contains a vignette with examples of its essential functions and uses.
[x] has a test suite.
[x] has continuous integration, such as Travis CI, AppVeyor, CircleCI, and/or others.

Publication options

[ ] Do you wish to automatically submit to the [Journal of Open Source Software][JournalOfOpenSourceSoftware]? If so:

JOSS Checks

- [ ] The package has an **obvious research application** according to JOSS's definition in their [submission requirements][JossSubmissionRequirements]. Be aware that completing the pyOpenSci review process **does not** guarantee acceptance to JOSS. Be sure to read their submission requirements (linked above) if you are interested in submitting to JOSS. - [ ] The package is not a "minor utility" as defined by JOSS's [submission requirements][JossSubmissionRequirements]: "Minor ‘utility’ packages, including ‘thin’ API clients, are not acceptable." pyOpenSci welcomes these packages under "Data Retrieval", but JOSS has slightly different criteria. - [ ] The package contains a `paper.md` matching [JOSS's requirements][JossPaperRequirements] with a high-level description in the package root or in `inst/`. - [ ] The package is deposited in a long-term repository with the DOI: *Note: Do not submit your package separately to JOSS*

Are you OK with Reviewers Submitting Issues and/or pull requests to your Repo Directly?

This option will allow reviewers to open smaller issues that can then be linked to PR's rather than submitting a more dense text based review. It will also allow you to demonstrate addressing the issue via PR links.

[x] Yes I am OK with reviewers submitting requested changes as issues to my repo. Reviewers will then link to the issues in their submitted review.

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[x] I agree to abide by [pyOpenSci's Code of Conduct][PyOpenSciCodeOfConduct] during the review process and in maintaining my package should it be accepted.

P.S. *Have feedback/comments about our review process? Leave a comment [here][Comments]

Editor and Review Templates

[Editor and review templates can be found here][Templates]

Package Review

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

[x] As the reviewer I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).

Documentation

The package includes all the following forms of documentation:

[x] A statement of need clearly stating problems the software is designed to solve and its target audience in README
[x] Installation instructions: for the development version of package and any non-standard dependencies in README
[x] Vignette(s) demonstrating major functionality that runs successfully locally
[x] Function Documentation: for all user-facing functions
[x] Examples for all user-facing functions
[x] Community guidelines including contribution guidelines in the README or CONTRIBUTING.
[x] Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a setup.py file or elsewhere.

Readme requirements The package meets the readme requirements below:

[x] Package has a README.md file in the root directory.

The README should include, from top to bottom:

[x] The package name
[x] Badges for continuous integration and test coverage, a repostatus.org badge, and any other badges. 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 badge for pyOpenSci peer-review will be provided upon acceptance.)
[x] Short description of goals of package, with descriptive links to all vignettes (rendered, i.e. readable, cf the documentation website section) unless the package is small and there’s only one vignette repeating the README.
[x] Installation instructions
[x] Any additional setup required (authentication tokens, etc)
[x] Brief demonstration usage
[x] Direction to more detailed documentation (e.g. your documentation files or website).
[x] If applicable, how the package compares to other similar packages and/or how it relates to other packages
[x] Citation information

Usability

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:

[x] The documentation is easy to find and understand
[x] The need for the package is clear
[x] All functions have documentation and associated examples for use

Functionality

[x] Installation: Installation succeeds as documented.
[x] Functionality: Any functional claims of the software been confirmed.
[x] Performance: Any performance claims of the software been confirmed.
[x] Automated tests: Tests cover essential functions of the package and a reasonable range of inputs and conditions. All tests pass on the local machine.
[x] Continuous Integration: Has continuous integration, such as Travis CI, AppVeyor, CircleCI, and/or others.
[x] Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines.

For packages co-submitting to JOSS

[ ] The package has an obvious research application according to JOSS's definition in their submission requirements.

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:

[ ] A short summary describing the high-level functionality of the software
[ ] Authors: A list of authors with their affiliations
[ ] A statement of need clearly stating problems the software is designed to solve and its target audience.
[ ] References: with DOIs for all those that have one (e.g. papers, datasets, software).

Final approval (post-review)

[ ] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing:

Review Comments

General comments:

Great work on the project! The repo is very organized and documentation is easy to follow. The example plots on the README gives a good overview of what the package can do, and the plots look great. All the functions worked, and automated tests passed when I cloned the repo.

Some notes and suggestions:

In the ReadTheDocs documentation for get_pollution_history, the table in the return section within your docstring doesn't seem to render properly in the generated documentation.
There should be a check that the API key entered is a string in get_pollution_history, and output an error message like in the other two functions.
I noticed that for all the functions there are checks for whether the inputs are correct. Maybe these could be extracted into a separate so there is less repetition in your code.
The tests are well done and cover most of the code. However, it might be a good idea to split the tests into smaller test functions by what the tests are testing (eg. whether they are testing for error, or for correct output) for clarity.

Package Review

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

[x] As the reviewer I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).

Documentation

The package includes all the following forms of documentation:

[x] A statement of need clearly stating problems the software is designed to solve and its target audience in README
[x] Installation instructions: for the development version of package and any non-standard dependencies in README
[x] Vignette(s) demonstrating major functionality that runs successfully locally
[x] Function Documentation: for all user-facing functions
[x] Examples for all user-facing functions
[x] Community guidelines including contribution guidelines in the README or CONTRIBUTING.
[x] Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a setup.py file or elsewhere.

Readme requirements The package meets the readme requirements below:

[x] Package has a README.md file in the root directory.

The README should include, from top to bottom:

[x] The package name
[x] Badges for continuous integration and test coverage, a repostatus.org badge, and any other badges. 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 badge for pyOpenSci peer-review will be provided upon acceptance.)
[x] Short description of goals of package, with descriptive links to all vignettes (rendered, i.e. readable, cf the documentation website section) unless the package is small and there’s only one vignette repeating the README.
[x] Installation instructions
[x] Any additional setup required (authentication tokens, etc)
[x] Brief demonstration usage
[x] Direction to more detailed documentation (e.g. your documentation files or website).
[x] If applicable, how the package compares to other similar packages and/or how it relates to other packages
[x] Citation information

Usability

[x] The documentation is easy to find and understand
[x] The need for the package is clear
[x] All functions have documentation and associated examples for use

Functionality

[x] Installation: Installation succeeds as documented.
[x] Functionality: Any functional claims of the software been confirmed.
[x] Performance: Any performance claims of the software been confirmed.
[x] Automated tests: Tests cover essential functions of the package and a reasonable range of inputs and conditions. All tests pass on the local machine.
[x] Continuous Integration: Has continuous integration, such as Travis CI, AppVeyor, CircleCI, and/or others.
[x] Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines.

For packages co-submitting to JOSS

[ ] The package has an obvious research application according to JOSS's definition in their submission requirements.

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:

[ ] A short summary describing the high-level functionality of the software
[ ] Authors: A list of authors with their affiliations
[ ] A statement of need clearly stating problems the software is designed to solve and its target audience.
[ ] References: with DOIs for all those that have one (e.g. papers, datasets, software).

Final approval (post-review)

[ ] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing: 1.5h

Review Comments

Congratulations on creating such useful and meaningfull package! We can see the package is well organized and the documentation was quiet clear to guide users to follow and made it easy to install as well as use your package. The comments below are some tips I feel could make this package, which is already excellent, slightly more perfect.

About documentation: In readthedocs.io, the output dataframe of get_pollution_history() is not displayed completely. This does not really affect the user's understanding of the output, but perhaps it would be better to add a note like in the example usage of get_air_pollution() to indicate that the output example only shows the head and tail of the dataframe.
About the scripts and the tests:
- I noticed that all the functions exist in the same script. test function is in the same way. Since the package works well and the code has very high test coverage, I think you have a good grasp of the situation. However, based on my experience developing our packages, it might be better to separate the functions and the corresponding tests in different scripts for debugging and less likely to have merge conflicts in workflow
- Apart from that, more comments in the scripts may be helpful for the users to understand your code.
About the visualization
- For PM2.5 in output of the get_pollution_forecast(), the lengend is still showing PM2_5. Maybe it will be better to to change it like what you did in get_air_pollution() function.
- I really like the output of get_air_pollution(), this visualization of the interactive function is very practical and very simple to see. Is it possible to add a similar country or region concentration heat map that shows the concentration of a single pollutant in the subsequent update?

Package Review

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

[x] As the reviewer I confirm that there are no conflicts of interest for me to review this work (If you are unsure whether you are in conflict, please speak to your editor before starting your review).

Documentation

The package includes all the following forms of documentation:

[x] A statement of need clearly stating problems the software is designed to solve and its target audience in README
[x] Installation instructions: for the development version of package and any non-standard dependencies in README
[x] Vignette(s) demonstrating major functionality that runs successfully locally
[x] Function Documentation: for all user-facing functions
[x] Examples for all user-facing functions
[x] Community guidelines including contribution guidelines in the README or CONTRIBUTING.
[x] Metadata including author(s), author e-mail(s), a url, and any other relevant metadata e.g., in a setup.py file or elsewhere.

Readme requirements The package meets the readme requirements below:

[x] Package has a README.md file in the root directory.

The README should include, from top to bottom:

[x] The package name
[x] Badges for continuous integration and test coverage, a repostatus.org badge, and any other badges. 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 badge for pyOpenSci peer-review will be provided upon acceptance.)
[x] Short description of goals of package, with descriptive links to all vignettes (rendered, i.e. readable, cf the documentation website section) unless the package is small and there’s only one vignette repeating the README.
[x] Installation instructions
[x] Any additional setup required (authentication tokens, etc)
[x] Brief demonstration usage
[x] Direction to more detailed documentation (e.g. your documentation files or website).
[x] If applicable, how the package compares to other similar packages and/or how it relates to other packages
[x] Citation information

Usability

[x] The documentation is easy to find and understand
[x] The need for the package is clear
[x] All functions have documentation and associated examples for use

Functionality

[x] Installation: Installation succeeds as documented.
[x] Functionality: Any functional claims of the software been confirmed.
[x] Performance: Any performance claims of the software been confirmed.
[x] Automated tests: Tests cover essential functions of the package and a reasonable range of inputs and conditions. All tests pass on the local machine.
[x] Continuous Integration: Has continuous integration, such as Travis CI, AppVeyor, CircleCI, and/or others.
[x] Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines.

For packages co-submitting to JOSS

[ ] The package has an obvious research application according to JOSS's definition in their submission requirements.

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:

[ ] A short summary describing the high-level functionality of the software
[ ] Authors: A list of authors with their affiliations
[ ] A statement of need clearly stating problems the software is designed to solve and its target audience.
[ ] References: with DOIs for all those that have one (e.g. papers, datasets, software).

Final approval (post-review)

[ ] The author has responded to my review and made changes to my satisfaction. I recommend approving this package.

Estimated hours spent reviewing: 1.5 hours

Review Comments

This package is very useful and the package name is creative hahaha!

The repository is well organized.
It might be better to spilt the airpyllution.py script into three separate scripts (like what you did in the R package!), and the same with the test script.
Helper functions are used in functions get_air_pollution() and get_pollution_forecast to check if input longitude/latitude values are within their valid ranges, but not in the function get_pollution_history().
Similar to the R package, more instructions could be provided on how to obtain and use an API key.
In the plots generated by get_pollution_forecast(), the positions of dates on the x-axis look a bit ambiguous, it might be clearer to list all 5 dates on the x-axis (with smaller font maybe?).

UBC-MDS / software-review-2022