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Open Source Science

https://github.com/durandtibo/arctix

https://github.com/durandtibo/arctix

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
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    Found codemeta.json file
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    Scientific vocabulary similarity
    Low similarity (10.6%) to scientific vocabulary

Keywords

asynchronous polars-dataframe python3 time-series
Last synced: 6 months ago · JSON representation

Repository

Basic Info
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 0
  • Open Issues: 1
  • Releases: 7
Topics
asynchronous polars-dataframe python3 time-series
Created over 2 years ago · Last pushed 6 months ago
Metadata Files
Readme License Code of conduct Codeowners

README.md

arctix

CI Nightly Tests Nightly Package Tests Codecov
Documentation Documentation
Code style: black Doc style: google Ruff Doc style: google
PYPI version Python BSD-3-Clause
Downloads Monthly downloads

Overview

The arctix package consists of functionalities to prepare dataset of asynchronous time series. It is design to make dataset preparation reusable and reproducible. For each dataset, arctix provides 3 main functions:

  • fetch_data to load the raw data are loaded in a polars.DataFrame. When possible, it downloads automatically the data.
  • prepare_data to prepare the data. It outputs the prepared data in polars.DataFrame, and the metadata.
  • to_array to convert the prepared data to a dictionary of numpy arrays.

For example, it is possible to use the following lines to download and prepare the MultiTHUMOS data.

```pycon

from pathlib import Path from arctix.dataset.multithumos import fetchdata, preparedata, toarray datasetpath = Path("/path/to/dataset/multithumos") dataraw = fetchdata(datasetpath) # doctest: +SKIP data, metadata = preparedata(dataraw) # doctest: +SKIP arrays = toarray(data) # doctest: +SKIP

```

Documentation

  • latest (stable): documentation from the latest stable release.
  • main (unstable): documentation associated to the main branch of the repo. This documentation may contain a lot of work-in-progress/outdated/missing parts.

Installation

We highly recommend installing a virtual environment. arctix can be installed from pip using the following command:

shell pip install arctix

To make the package as slim as possible, only the minimal packages required to use arctix are installed. To include all the packages, you can use the following command:

shell pip install arctix[all]

Please check the get started page to see how to install only some specific packages or other alternatives to install the library. The following is the corresponding karbonn versions and dependencies.

| batcharray | batcharray | coola | iden | numpy | polars | python | |--------------|----------------|----------------|------------------|---------------|-----------------|---------------| | main | >=0.1,<1.0 | >=0.8.4,<1.0 | ">=0.1,<1.0" | >=1.22,<3.0 | >=1.0,<2.0 | >=3.9,<3.14 | | 0.0.8 | >=0.1,<1.0 | >=0.8.4,<1.0 | ">=0.1,<1.0" | >=1.22,<3.0 | >=1.0,<2.0 | >=3.9,<3.14 | | 0.0.7 | >=0.0.2,<1.0 | >=0.3,<1.0 | ">=0.0.3,<1.0" | >=1.22,<3.0 | >=1.0,<2.0 | >=3.9,<3.13 | | 0.0.6 | >=0.0.2,<0.1 | >=0.3,<1.0 | ">=0.0.3,<1.0" | >=1.22,<2.0 | >=0.20.0,<1.0 | >=3.9,<3.13 | | 0.0.5 | >=0.0.2,<0.1 | >=0.3,<1.0 | ">=0.0.3,<1.0" | >=1.22,<2.0 | >=0.20.0,<1.0 | >=3.9,<3.13 | | 0.0.4 | >=0.0.2,<0.1 | >=0.3,<1.0 | ">=0.0.3,<1.0" | >=1.22,<2.0 | >=0.20.0,<1.0 | >=3.9,<3.13 | | 0.0.3 | >=0.0.2,<0.1 | >=0.3,<1.0 | ">=0.0.3,<1.0" | >=1.22,<2.0 | >=0.20.0,<1.0 | >=3.9,<3.13 |

Contributing

Please check the instructions in CONTRIBUTING.md.

API stability

:warning: While arctix is in development stage, no API is guaranteed to be stable from one release to the next. In fact, it is very likely that the API will change multiple times before a stable 1.0.0 release. In practice, this means that upgrading arctix to a new version will possibly break any code that was using the old version of arctix.

License

arctix is licensed under BSD 3-Clause "New" or "Revised" license available in LICENSE file.

Owner

  • Name: Thibaut Durand
  • Login: durandtibo
  • Kind: user
  • Location: Vancouver, Canada
  • Company: Borealis AI

Researcher in Computer Vision and Machine Learning. This profile is used for personal projects/contributions

GitHub Events

Total
  • Create event: 170
  • Issues event: 1
  • Release event: 1
  • Delete event: 160
  • Issue comment event: 182
  • Push event: 373
  • Pull request review event: 118
  • Pull request event: 336
Last Year
  • Create event: 170
  • Issues event: 1
  • Release event: 1
  • Delete event: 160
  • Issue comment event: 182
  • Push event: 373
  • Pull request review event: 118
  • Pull request event: 336

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 2
  • Total pull requests: 684
  • Average time to close issues: 6 days
  • Average time to close pull requests: 7 days
  • Total issue authors: 2
  • Total pull request authors: 3
  • Average comments per issue: 1.0
  • Average comments per pull request: 0.94
  • Merged pull requests: 542
  • Bot issues: 1
  • Bot pull requests: 452
Past Year
  • Issues: 2
  • Pull requests: 354
  • Average time to close issues: 6 days
  • Average time to close pull requests: 7 days
  • Issue authors: 2
  • Pull request authors: 3
  • Average comments per issue: 1.0
  • Average comments per pull request: 0.98
  • Merged pull requests: 264
  • Bot issues: 1
  • Bot pull requests: 334
View more stats
Top Authors
Issue Authors
  • durandtibo (1)
  • dependabot[bot] (1)
Pull Request Authors
  • dependabot[bot] (411)
  • durandtibo (232)
  • github-actions[bot] (41)
Top Labels
Issue Labels
dependencies (1) python (1)
Pull Request Labels
dependencies (411) python (367) github_actions (13)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 16,700 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 14
  • Total maintainers: 1
pypi.org: arctix
  • Versions: 14
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 16,700 Last month
Rankings
Dependent packages count: 7.4%
Forks count: 30.4%
Average: 31.1%
Stargazers count: 39.3%
Dependent repos count: 47.6%
Maintainers (1)
tdurand
Last synced: 6 months ago