collection
JSON Machine
| collection | JSON Machine | |
|---|---|---|
| 4 | 4 | |
| 1,361 | 1,019 | |
| 0.8% | - | |
| 6.5 | 5.9 | |
| 10 days ago | 6 days ago | |
| PHP | ||
| Creative Commons Zero v1.0 Universal | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
collection
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Show HN: I Made a Visualization of MoMA Artists of 20 Century
I took the official dataset () from MoMA and visualized the artists who have at least one drawing in the museum's collection and lived in 20 century.
The X axis represents the year of birth, Y — lifespan, size — count of works, color — gender.
() https://github.com/MuseumofModernArt/collection/tree/master
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Demo video for our WIP 'Virtual Gallery (VR)' Technology, 'Portico'
Portico takes published museum collection data and turns it into a virtual gallery/viewing room dynamically. This example is the first few items from the published MOMA collection ((https://github.com/MuseumofModernArt/collection).
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5 Best Public Datasets to Practice Your Data Analysis Skills
The collection includes two datasets - ‘Artist’ and ‘Artwork’ available in both CSV and JSON formats. The data can either be forked or downloaded directly from the GitHub page. However, the dataset has incomplete information and should only be used for research purposes. That is why it’s the perfect candidate as it resembles a real-world scenario where data is often missing.
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Google Charts Dashboard: a Tutorial with an Artistic Touch of MoMA 🖼
On GitHub, MoMA publishes and periodically updates a public dataset which contains ~140,000 records, representing all of the works that have been accessioned into MoMA’s collection and cataloged in our database. It includes basic metadata for each work (e.g., title, artist, date made, medium, dimensions, and date of acquisition). This dataset is placed in the public domain using a CC0 License (so we're free to use it in this tutorial) and available in CSV and JSON formats.
JSON Machine
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Handling big API request (2.5gb) without timeout
To handle this problem i've used "https://github.com/halaxa/json-machine" to decrease the memory usage and "https://github.com/spatie/async" to decrease the take taken for the "update process" to complete
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Lazy JSON
Under the hood, it uses JSON Machine as a lexer and parser.
Seems like the issue is still open ! https://github.com/halaxa/json-machine/issues/36
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Is any one actually using PHP generators in their projects?
Yes. We used it heavily when processing large amounts of data from huge API responses. I like how the whole processing chain can be built on top of it, for example using nikic/iter. Generators are also the heart of a low-memory JSON parsing library halaxa/json-machine, which I maintain, and many other similar projects.
What are some alternatives?
map - PHP arrays and collections made easy
msgpack.php - A pure PHP implementation of the MessagePack serialization format / msgpack.org[PHP]
lazy-json - 🐼 Framework-agnostic package to load JSON of any dimension and from any source into Laravel lazy collections recursively.
Laminas Serializer - Serialize and deserialize PHP structures to a variety of representations
dflydev-dot-access-data - Given a deep data structure representing a configuration, access configuration by dot notation.
etl - PHP - ETL (Extract Transform Load) data processing library
php-caching-generator - A rewindable PHP Generator class that caches its generated values.
cube.js - 📊 Cube — The Semantic Layer for Building Data Applications
collection - A (memory) friendly, easy, lazy and modular collection class.
Fregata - Fregata - a PHP database migrator