Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://github.com/pyOpenSci/software-submission/issues/18 below:

Software Submission for Review · Issue #18 · pyOpenSci/software-submission · GitHub

Submitting Author: Anita Graser (@anitagraser)
All current maintainers: Anita Graser (@anitagraser)
Package Name: MovingPandas
One-Line Description of Package: Trajectory classes and functions built on top of GeoPandas
Repository Link: https://github.com/movingpandas/movingpandas
Version submitted: 0.2
Editor: Jenny Palomino (@jlpalomino)
Reviewer 1: Ivan Ogasawara (@xmnlab)
Reviewer 2: Martin Fleischmann (@martinfleis)
Archive:
JOSS DOI: N/A
Version accepted: v 0.3.rc1
Date accepted (month/day/year): 03/19/2020

• Include a brief paragraph describing what your package does:

MovingPandas is a package for dealing with movement data. MovingPandas implements a Trajectory class and corresponding methods based on GeoPandas. A trajectory has a time-ordered series of point geometries. These points and associated attributes are stored in a GeoDataFrame. MovingPandas implements spatial and temporal data access and analysis functions (covered in the open access publication [0]) as well as plotting functions.
A usage example is available at http://exploration.movingpandas.org,

[0] Graser, A. (2019). MovingPandas: Efficient Structures for Movement Data in Python. GI_Forum ‒ Journal of Geographic Information Science 2019, 1-2019, 54-68. doi:10.1553/giscience2019_01_s54. URL: https://www.austriaca.at/rootcollection?arp=0x003aba2b

• 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 this section of our guidebook.

• Explain how the and why the package falls under these categories (briefly, 1-2 sentences):

Geospatial (primary): The MovingPandas Trajectory class implements is a spatio-temporal data model for movement data.

Data visualization (secondary): The implemented plot functions enable straight-forward movement data exploration that goes beyond plotting the individual point locations by ensuring that trajectories are represented by linear segments between consecutive points.

• Who is the target audience and what are scientific applications of this package?

Movement data / trajectories appear in many different scientific domains, including physics, biology, ecology, chemistry, transport and logistics, astrophysics, remote sensing, and more.
For example, the provided tutorials cover the analysis of migrating birds as well as the analysis of ship movement within a port.

• Are there other Python packages that accomplish the same thing? If so, how does yours differ?

scikit-mobility is a similar package which is also in an early development stage and also deals with movement data. They implement TrajectoryDataFrames and FlowDataFrames on top of Pandas instead of GeoPandas. There is little overlap in the covered use cases and implemented functionality (comparing MovingPandas tutorials and scikit-mobility tutorials). MovingPandas focuses on spatio-temporal data exploration with corresponding functions for data manipulation and analysis. scikit-mobility on the other hand focuses on computing human mobility metrics, generating synthetic trajectories and assessing privacy risks.

• 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:

#14

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.
• has an OSI approved license
• contains a README with instructions for installing the development version.
• includes documentation with examples for all functions.
• contains a vignette (notebook) with examples of its essential functions and uses.
• has a test suite.
• has continuous integration, such as Travis CI, AppVeyor, CircleCI, and/or others.

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

• 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: Do not submit your package separately to JOSS

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.

• I agree to abide by pyOpenSci's Code of Conduct 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

Editor and review templates can be found here

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4