ElectrsCash
explorer
ElectrsCash | explorer | |
---|---|---|
4 | 20 | |
20 | 976 | |
- | 1.1% | |
3.1 | 9.4 | |
over 1 year ago | 8 days ago | |
Rust | Elixir | |
MIT License | MIT License |
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.
ElectrsCash
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Bitcoin indexer recomendation
If the electrum API provides what you need, you could use a Bitcoin Cash electrum server
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Is there a BCH Node that Provides Querying of OP_RETURN contents?
I think only ElectrsCash exists for more powerful querying of BCH, but there are also these options for BTC that could maybe theoretically be ported to BCH:
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Is there an ElectrumX server for BCH?
Thank you! I have not run a comparison, but I think it’s safe to assume that fulcrum is more performant in CPU usage, ElectrsCash more in disk usage. Both servers support the full electrum API, but have different API extensions that will be useful to developers. ElectrsCash is derived from electrs which is very popular in the BTC camp. Both server are written in efficient languages (ElectrsCash in Rust and fulcrum in C++), unlike ElectrumX which is written in Python.
explorer
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Polars
The Explorer library [0] in Elixir uses Polars underneath it.
[0] https://github.com/elixir-explorer/explorer
- Unpacking Elixir: Concurrency
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Elixir Livebook is a secret weapon for documentation
To ensure you do not miss this: LiveBook comes with a Vega Lite integration (https://livebook.dev/integrations -> https://livebook.dev/integrations/vega-lite/), which means you get access to a lot of visualisations out of the box, should you need that (https://vega.github.io/vega-lite/).
In the same "standing on giant's shoulders" stance, you can use Explorer (see example LiveBook at https://github.com/elixir-explorer/explorer/blob/main/notebo...), which leverages Polars (https://www.pola.rs), a very fast DataFrame library and now a company (https://www.pola.rs/posts/company-announcement/) with 4M$ seed.
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Does anyone else hate Pandas?
Already exists. Check out https://github.com/elixir-nx/explorer which provides a tidyverse-like API in Elixir using polars as the back end.
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Data wrangling in Elixir with Explorer, the power of Rust, the elegance of R
José from the Livebook team. I don't think I can make a pitch because I have limited Python/R experience to use as reference.
My suggestion is for you to give it a try for a day or two and see what you think. I am pretty sure you will find weak spots and I would be very happy to hear any feedback you may have. You can find my email on my GitHub profile (same username).
In general we have grown a lot since the Numerical Elixir effort started two years ago. Here are the main building blocks:
* Nx (https://github.com/elixir-nx/nx/tree/main/nx#readme): equivalent to Numpy, deeply inspired by JAX. Runs on both CPU and GPU via Google XLA (also used by JAX/Tensorflow) and supports tensor serving out of the box
* Axon (https://github.com/elixir-nx/axon): Nx-powered neural networks
* Bumblebee (https://github.com/elixir-nx/bumblebee): Equivalent to HuggingFace Transformers. We have implemented several models and that's what powers the Machine Learning integration in Livebook (see the announcement for more info: https://news.livebook.dev/announcing-bumblebee-gpt2-stable-d...)
* Explorer (https://github.com/elixir-nx/explorer): Series and DataFrames, as per this thread.
* Scholar (https://github.com/elixir-nx/scholar): Nx-based traditional Machine Learning. This one is the most recent effort of them all. We are treading the same path as scikit-learn but quite early on. However, because we are built on Nx, everything is derivable, GPU-ready, distributable, etc.
Regarding visualization, we have "smart cells" for VegaLite and MapLibre, similar to how we did "Data Transformations" in the video above. They help you get started with your visualizations and you can jump deep into the code if necessary.
I hope this helps!
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Would you still choose Elixir/Phoenix/LiveView if scaling and performance weren’t an issue to solve for?
There's a package in the Nx ecosystem called Explorer (https://github.com/elixir-nx/explorer). It uses bindings for the rust library, polars, which is much more betterer than Pandas.
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Updated Erlport alternative ?
FWIW around April this year I started using erlport with python polars in a production ETL app because explorer didn't have the features I needed at the time.
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ElixirConf 2022 - That's a wrap!
Machine learning is rapidly expanding within the Elixir ecosystem, with tools such as Nx, Axon, and Explorer being used both by individuals and companies such as Amplified, as mentioned above.
- Dataframes but for Elixir
- Quick candlestick summaries with Elixir's Explorer
What are some alternatives?
electrs - An efficient re-implementation of Electrum Server in Rust
dplyr - dplyr: A grammar of data manipulation
explorer - An open source block explorer
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
electrumx - Alternative implementation of spesmilo/electrum-server
axon - Nx-powered Neural Networks
BCHUnlimited
db-benchmark - reproducible benchmark of database-like ops
electrumx - Alternative implementation of spesmilo/electrum-server
arrow2 - Transmute-free Rust library to work with the Arrow format
wasmex - Execute WebAssembly from Elixir
vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualization and exploration of big tabular data at a billion rows per second 🚀