Dolphot-LC Software Submission

[whit5224]

Submitting Author: Ramona White (@whit5224)
All current maintainers: (@whit5224, @patkel)
Package Name: Dolphot-LC
One-Line Description of Package: Dolphot-LC is an automated Hubble Space Telescope (HST) data pipeline based on the popular Dolphot analysis package; this package allows for the creation of lightcurves and difference images from HST data.
Repository Link: https://github.com/patkel/dolphot_lc
Version submitted: 0.0.2
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

---

Code of Conduct & Commitment to Maintain Package

• I agree to abide by pyOpenSci's Code of Conduct during the review process and in maintaining my package after should it be accepted.
• I have read and will commit to package maintenance after the review as per the pyOpenSci Policies Guidelines.

Description

Dolphot-LC is an automated Hubble Space Telescope (HST) data pipeline based on the popular Dolphot analysis package. This package allows for the creation of lightcurves and difference images from HST data. Dolphot-LC allows the user to run their own fits images through the pipeline and generate results and requires a coadded template image and science images that are already aligned to template. To make package use easier, Dolphot-LC has been documented extensively in a Read the Docs: https://dolphot-lc.readthedocs.io/en/latest/index.html and Jupyter Notebook: https://nbviewer.org/gist/whit5224/287af111f44bf83a23eaaf19a5121c75.

Scope

• Please indicate which category or categories.
  Check out our package scope page to learn more about our
  scope. (If you are unsure of which category you fit, we suggest you make a pre-submission inquiry):

  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization¹
  • Workflow automation
  • Citation management and bibliometrics
  • Scientific software wrappers
  • Database interoperability

Domain Specific

• Geospatial
• Education

Community Partnerships

If your package is associated with an
existing community please check below:

• Astropy:My package adheres to Astropy community standards
• Pangeo: My package adheres to the Pangeo standards listed in the pyOpenSci peer review 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):

  • Who is the target audience and what are scientific applications of this package?
    The target audience are scientist and astrophysicists interested in analyzing HST and JWST data with the goal of creating light curves and difference images.

  • Are there other Python packages that accomplish the same thing? If so, how does yours differ?
    Dolphot-LC implements and expands upon the DOLPHOT stellar photometry package. Our software makes it possible apply DOLPHOT to difference images in order to measure changes in brightness due to astrophysical transients. Dolphot-LC improves quality and usability by allowing the creating of light curves and difference images.

  • 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:

• does not violate the Terms of Service of any service it interacts with.
• uses an OSI approved license.
• contains a README with instructions for installing the development version.
• includes documentation with examples for all functions.
• contains a tutorial with examples of its essential functions and uses.
• has a test suite.
• has continuous integration setup, such as GitHub Actions CircleCI, and/or others.

Publication Options

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

JOSS Checks

• The package has an obvious research application according to JOSS's definition in their submission requirements. 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: "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 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: JOSS accepts our review as theirs. You will NOT need to go through another full review. JOSS will only review your paper.md file. Be sure to link to this pyOpenSci issue when a JOSS issue is opened for your package. Also be sure to tell the JOSS editor that this is a pyOpenSci reviewed package once you reach this step.

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.

• 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.

Confirm each of the following by checking the box.

• I have read the author guide.
• I expect to maintain this package for at least 2 years and can help find a replacement for the maintainer (team) if needed.

Please fill out our survey

• Last but not least please fill out our pre-review survey. This helps us track submission and improve our peer review process. We will also ask our reviewers
  and editors to fill this out.

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

Editor and Review Templates

The editor template can be found here.

The review template can be found here.

Footnotes

1. Please fill out a pre-submission inquiry before submitting a data visualization package. ↩

[Batalex]

Hey @whit5224, thank you for submitting Dolphot-LC! I am Alex, pyOpenSci current Editor-in-Chief. I went through the docs and the code, and I think that Dolphot-LC is in scope for us.
Over the next few days, I'll go over the E-i-C checks so that we can address a few things before moving on to the actual review.

[whit5224]

Thanks for the initial check! Look forward to hearing back from you soon.

[Batalex]

Editor in Chief checks

Hi there! Thank you for submitting your package for pyOpenSci review. Below are the basic checks that your package needs to pass to begin our review. If some of these are missing, we will ask you to work on them before the review process begins.

Please check our Python packaging guide for more information on the elements below.

• Installation The package can be installed from a community repository such as PyPI (preferred), and/or a community channel on conda (e.g. conda-forge, bioconda).
  • The package imports properly into a standard Python environment import package.
• Fit The package meets criteria for fit and overlap.
• Documentation The package has sufficient online documentation to allow us to evaluate package function and scope without installing the package. This includes:
  • User-facing documentation that overviews how to install and start using the package.
  • Short tutorials that help a user understand how to use the package and what it can do for them.
  • API documentation (documentation for your code's functions, classes, methods and attributes): this includes clearly written docstrings with variables defined using a standard docstring format.
• Core GitHub repository Files
  • README The package has a README.md file with clear explanation of what the package does, instructions on how to install it, and a link to development instructions.
  • Contributing File The package has a CONTRIBUTING.md file that details how to install and contribute to the package.
  • Code of Conduct The package has a CODE_OF_CONDUCT.md file.
  • License The package has an OSI approved license.
    NOTE: We prefer that you have development instructions in your documentation too.
• Issue Submission Documentation All of the information is filled out in the YAML header of the issue (located at the top of the issue template).
• Automated tests Package has a testing suite and is tested via a Continuous Integration service.
• Repository The repository link resolves correctly.
• Package overlap The package doesn't entirely overlap with the functionality of other packages that have already been submitted to pyOpenSci.
• Archive (JOSS only, may be post-review): The repository DOI resolves correctly.
• Version (JOSS only, may be post-review): Does the release version given match the GitHub release (v1.0.0)?

---

• Initial onboarding survey was filled out
  We appreciate each maintainer of the package filling out this survey individually. 🙌
  Thank you authors in advance for setting aside five to ten minutes to do this. It truly helps our organization. 🙌

---

---

Editor comments

Hey there,
Sorry it took me so long to get back to you.

I think that dolphot_lc needs some more work before we proceed with the review. I made the call to not check some of the items, here is why.

The package imports properly into a standard Python environment import package.

The package installation does not install its dependencies. This is problematic because the user has to go to the repository to find the requirements.txt file. I encourage you to take a look at our packaging guide to properly set up that. On a side note, I am curious as to how you built the wheel hosted on pypi since it's not clear from the CI and the source code.

Documentation

I'd like to see more content to get a better grip of what dolphot_lc does (arguably, I'm not familiar with the field, so this might be a ME problem). Namely, think of your documentation as something that should fill the two following axes:

• expertise level: tutorial, how-to guide, technical reference
• content structure: what are we going to do, how are we going to do it using your package, (optionally) why is it done that way?

Readme

Think of the readme as the front page for your package. Like a resume, you only have a few moments of attention span from a reader before they lose interest and look for something else.
Here is an example you can take inspiration from

Expand

# dolphot_lc

A Python package for <stuff>.


[![MIT License](https://img.shields.io/badge/License-MIT-green.svg)](https://choosealicense.com/licenses/mit/)


A brief description of what this project does and who it's for. 


## Installation

Install `dolphot_lc` with pip

```shell
pip install dolphot_lc
```
    
## Getting started

The 30 seconds tutorial.

```py
import antigravity
```

## Documentation

[Documentation](https://linktodocumentation)

## Contributing

Contributions are always welcome!

See `contributing.md` for ways to get started.

Please adhere to this project's `code of conduct`.

Using our package guide, the readme will also be present on the pypi front page

Contributing

The file is present, but the content is lacking. For instance, you set the expectations that users can contribute by fixing bugs or proposing new features, but there is no explanation as to how to do that (how to build the package, how to test it, etc.)

Tests

Same as before, I encourage you to take a look at our guide. Using a proven test framework will increase the quality of your tests, as well as aligning dolphot_lc on the community-established practices.

[cmarmo]

Hi @whit5224 , I'm Chiara and I'm following up your submission as editor in chief.
Do you mind giving an update about the initial checks on your code?
No pressure, just let us know how do you plan to proceed so we can fairly share the packages among editors and reviewers.
Thank you for submitting your package to pyOpenSci!