Group 17 - colourpycker (Python)

[shaunhutch]

Submitting Author: Shaun Hutchinson (@shaunhutch), Lauren Zung (@lzung), Alex Taciuk (@ataciuk), Arjun Radhakrishnan (@rkrishnan-arjun)
All current maintainers: (@shaunhutch, @lzung, @ataciuk, @rkrishnan-arjun)
Package Name: xolourpycker
One-Line Description of Package: A package to extract a colour palette from an image for data visualization.
Repository Link: https://github.com/UBC-MDS/colourpycker
Version submitted: v2.0.0
Editor: @flor14
Reviewers: Jakob Thoms, Suraporn Puangpanbut, Chester Wang, Ritisha Sharma
Archive: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

---

Description

This package allows users to integrate unique colour palettes into their graphs for exploratory data analysis. The colours are retrieved from image data (via URL) and are selected based on their overall prominence in a picture. While there are existing tools that are used to process images and create figures independently, we aim to combine both of their functionalities to help programmers easily design effective and creative visualizations.

Scope

• Please indicate which category or categories this package falls under:
  • 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 of our guidebook.

• 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):
  This package is extracting the colours from images (data extraction). This is also processing image files for data visualizations (data munging). Finally we are using the extracted data to create visualizations.

  • Who is the target audience and what are scientific applications of this package?
    The target audience is anyone who would like to make data visualizations more appealing and correspond to a theme of an image. This could be someone who would like their visualizations match to a company logo or some other image. These could be data scientists, data analysts or anyone using Python to make data visualizations.

  • Are there other Python packages that accomplish the same thing? If so, how does yours differ?
    Yes there are python packages that do similar things. However, they do not combine both colour extraction and data visualizations like our package does. One such package is: Pillow This package adds image processing capabilities into Python interpreters. It can perform various image transformations but does not allow for colours to be extracted directly for further use. Another package is extcolors This extracts RGB colour codes from images into text along with the occurrence rate (proportion of pixels). However, we would need to use additional packages to create plots using common colours in the image.

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[Suraporn]

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

• 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).

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The package includes all the following forms of documentation:

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

Readme file requirements
The package meets the readme requirements below:

• Package has a README.md file in the root directory.

The README should include, from top to bottom:

• The package name
• Badges for:
  • Continuous integration and test coverage,
  • Docs building (if you have a documentation website),
  • A repostatus.org badge,
  • Python versions supported,
  • Current package version (on PyPI / Conda).

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.)

• Short description of package goals.
• Package installation instructions
• Any additional setup required to use the package (authentication tokens, etc.)
• Descriptive links to all vignettes. If the package is small, there may only be a need for one vignette which could be placed in the README.md file.
  • Brief demonstration of package usage (as it makes sense - links to vignettes could also suffice here if package description is clear)
• Link to your documentation website.
• If applicable, how the package compares to other similar packages and/or how it relates to other packages in the scientific ecosystem.
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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:

• Package documentation is clear and easy to find and use.
• The need for the package is clear
• All functions have documentation and associated examples for use
• The package is easy to install

Functionality

• Installation: Installation succeeds as documented.
• Functionality: Any functional claims of the software been confirmed.
• Performance: Any performance claims of the software been confirmed.
• Automated tests: Tests cover essential functions of the package and a reasonable range of inputs and conditions. All tests pass on the local machine.
• Continuous Integration: Has continuous integration setup (We suggest using Github actions but any CI platform is acceptable for review)
• Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines.
  A few notable highlights to look at:
  • Package supports modern versions of Python and not End of life versions.
  • Code format is standard throughout package and follows PEP 8 guidelines (CI tests for linting pass)

For packages also 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 Hr.

---

Review Comments

First of all, congratulations on creating such an interesting package, I really like the idea of the package that solves color template of the presentation problem, I personally got this problem before, and will definitely use this wonderful package!

1. I did install your package using pip install colourpycker worked well and it worked well on my M1 laptop.
2. Your package passed all tests with pytest tests/ --cov=colourpycker. Now, the test coverage is 82%, you might try to check more with pytest --cov-report term-missing --cov=colourpycker tests/ and write more tests to have better test coverage.
3. All four functions, get_color_palette, donut, scatterplot, negative, are well-written and well-documented with docstring, and I was able to pull the docstring with ?function_name to see the document when I need it.
4. As follow the examples in the usage section on the README.md, I got an error running scatterplot function as below. I think it is because I do not have penguins data on my session yet. I recommend putting a line importing an example data before running this example, so users can run the example without any problem .

`---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[8], line 1
----> 1 scatterplot('https://i.imgur.com/s9egWBB.jpg', penguins, 'bill_length_mm', 'body_mass_g', 'species', 50)

NameError: name 'penguins' is not defined`

5. Even with scatterplot function that we can pass our template picture, and our data, this function then returns the plot of our data with a specific color template of the picture, yet, the type of plot in this function is still limited, it should be a good idea if we can have more flexibility choosing plot type based on types in our data. Or we can have another function that creates a color map from the picture and we can use this color map later with other visualization libraries such as Altair, Seaborn, Matplotlib, Pandas, etc ..

Finally, thank you again for creating this interesting package, I am glad to review your wonderful package.

[ritisha2000]

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

• 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:

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

Readme file requirements
The package meets the readme requirements below:

• Package has a README.md file in the root directory.

The README should include, from top to bottom:

• The package name
• Badges for:
  • Continuous integration and test coverage,
  • Docs building (if you have a documentation website),
  • A repostatus.org badge,
  • Python versions supported,
  • Current package version (on PyPI / Conda).

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.)

• Short description of package goals.
• Package installation instructions
• Any additional setup required to use the package (authentication tokens, etc.)
• Descriptive links to all vignettes. If the package is small, there may only be a need for one vignette which could be placed in the README.md file.
  • Brief demonstration of package usage (as it makes sense - links to vignettes could also suffice here if package description is clear)
• Link to your documentation website.
• If applicable, how the package compares to other similar packages and/or how it relates to other packages in the scientific ecosystem.
• 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 whether:

• Package documentation is clear and easy to find and use.
• The need for the package is clear
• All functions have documentation and associated examples for use
• The package is easy to install

Functionality

• Installation: Installation succeeds as documented.
• Functionality: Any functional claims of the software been confirmed.
• Performance: Any performance claims of the software been confirmed.
• Automated tests: Tests cover essential functions of the package and a reasonable range of inputs and conditions. All tests pass on the local machine.
• Continuous Integration: Has continuous integration setup (We suggest using Github actions but any CI platform is acceptable for review)
• Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines.
  A few notable highlights to look at:
  • Package supports modern versions of Python and not End of life versions.
  • Code format is standard throughout package and follows PEP 8 guidelines (CI tests for linting pass)

For packages also 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 hour

Review Comments

1. The project idea is great and it is executed well. There is a lot of use for this package and the documentation shows the other packages that are available and what they lack that is provided by colorpicker.
2. I was able to install the package by following the installation instructions. In the usage it might be nice to specify that the penguins dataset needs to be loaded otherwise it gives an error.
3. I did not see a style checker in the ci GitHub actions. It might nice to add the command with flake8 so you can make sure the source code follows the appropriate style guidelines.
4. The cd in Github actions seems to be skipped.
5. All the functions are documented well. You could consider adding examples to the docstring for the functions: check_param_validity and rgb_to_hex.

Overall, the project is really great. Great job!

[ChesterAiGo]

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

• 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:

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

Readme file requirements
The package meets the readme requirements below:

• Package has a README.md file in the root directory.

The README should include, from top to bottom:

• The package name
• Badges for:
  • Continuous integration and test coverage,
  • Docs building (if you have a documentation website),
  • A repostatus.org badge,
  • Python versions supported,
  • Current package version (on PyPI / Conda).

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.)

• Short description of package goals.
• Package installation instructions
• Any additional setup required to use the package (authentication tokens, etc.)
• Descriptive links to all vignettes. If the package is small, there may only be a need for one vignette which could be placed in the README.md file.
  • Brief demonstration of package usage (as it makes sense - links to vignettes could also suffice here if package description is clear)
• Link to your documentation website.
• If applicable, how the package compares to other similar packages and/or how it relates to other packages in the scientific ecosystem.
• 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 whether:

• Package documentation is clear and easy to find and use.
• The need for the package is clear
• All functions have documentation and associated examples for use
• The package is easy to install

Functionality

• Installation: Installation succeeds as documented.
• Functionality: Any functional claims of the software been confirmed.
• Performance: Any performance claims of the software been confirmed.
• Automated tests: Tests cover essential functions of the package and a reasonable range of inputs and conditions. All tests pass on the local machine.
• Continuous Integration: Has continuous integration setup (We suggest using Github actions but any CI platform is acceptable for review)
• Packaging guidelines: The package conforms to the pyOpenSci packaging guidelines.
  A few notable highlights to look at:
  • Package supports modern versions of Python and not End of life versions.
  • Code format is standard throughout package and follows PEP 8 guidelines (CI tests for linting pass)

For packages also 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 hour

---

Review Comments

1. I find the idea of this package to be interesting and practical. There are indeed scenarios where user might want to perform EDA on image data in specific, and this package would certainly be helpful in such a situation.
2. The package is installable and I was able to use part of the functions. Most of the codes are well-documented and reviewers and easily understand. Nice work!
3. Having that said, I did notice a few parts where the code was hard-coded. It would be nice if the developers can replace those parts with parameters for more unrestricted usage.
4. The functions are written in a defensive manner which is nice. It would be even better if the developers could consider write more private functions and call them in the public functions for better structure.
5. It seems like the test coverage is only 82% and it would be nice to either have more tests to cover all branches or explain why it is unnecessary to test those branches.

In general I enjoyed using the package and the reviewing process. Nice work!