semvec/pypi-packages huggingface dataset:
datajunction-reflection 0.0.69
OSS Implementation of a DataJunction Reflection Service
License: MIT
Requires Python: <4.0,>=3.10

# DJ Reflection Service

The reflection service polls the DJ core service for all nodes with associated tables, whether source
tables or materialized tables. For each node, it refreshes the node's schema based on the associated
table's schema that it retrieves from the query service. It also retrieves the available partitions and
the valid through timestamp of these tables and reflects them accordingly to DJ core.

This service uses a celery beat scheduler, with a configurable polling interval that defaults to once per
hour and async tasks for each node's reflection.
datajunction-server 0.0.69
DataJunction server library for running to a DataJunction server
License: MIT
Requires Python: <3.12,>=3.10
Keywords: metrics, semanticlayer

# DataJunction

## Introduction

DataJunction (DJ) is an open source **metrics platform** that allows users to define
metrics and the data models behind them using **SQL**, serving as a **semantic layer**
on top of a physical data warehouse. By leveraging this metadata, DJ can enable efficient
retrieval of metrics data across different dimensions and filters.

![DataJunction](docs/static/datajunction-illustration.png)

## Getting Started

To launch the DataJunction UI with a minimal DataJunction backend, start the default docker compose environment.

```sh
docker compose up
```

If you'd like to launch the full suite of services, including open-source implementations of the DataJunction query service and
DataJunction reflection service specifications, use the `demo` profile.

```sh
docker compose --profile demo up
```

DJUI: [http://localhost:3000/](http://localhost:3000/)
DJ Swagger Docs: [http://localhost:8000/docs](http://localhost:8000/docs)
DJQS Swagger Docs: [http://localhost:8001/docs](http://localhost:8001/docs)
Jaeger UI: [http://localhost:16686/search](http://localhost:16686/search)
Jupyter Lab: [http://localhost:8888](http://localhost:8888)

## How does this work?

At its core, DJ stores metrics and their upstream abstractions as interconnected nodes.
These nodes can represent a variety of elements, such as tables in a data warehouse
(**source nodes**), SQL transformation logic (**transform nodes**), dimensions logic,
metrics logic, and even selections of metrics, dimensions, and filters (**cube nodes**).

By parsing each node's SQL into an AST and through dimensional links between columns,
DJ can infer a graph of dependencies between nodes, which allows it to find the
appropriate join paths between nodes to generate queries for metrics.

## AI Integration

DataJunction provides an MCP (Model Context Protocol) client that enables AI assistants like Claude to interact with your semantic layer.

The MCP client is part of the [DataJunction Python client package](../datajunction-clients/python/).

For installation and setup, see the [MCP documentation in the client package](../datajunction-clients/python/README_MCP.md).

## Documentation

For more detailed documentation, visit [datajunction.io](https://datajunction.io).
logs-py 4.0.27
A python interface for the LOGS public API
License: MIT
Requires Python: >=3.8

# LOGS-Py

LOGS is a scientific data management system (SDMS) software allowing for automated data collection, visualization, and organization. Within its internal organizational concepts it allows you to enrich you experimental data with metadata. LOGS allows you to adapt many of its organization structures which enables your data management to follow your internal workflows.

**LOGS-Py is a Python package** interacting with the LOGS web API to enable you to extract and push data to LOGS and generally operateinteract with the LOGS backend in a programatic way. The main motivation behind in the desing of library is to keep this interaction as pythonic as possible. The communication with the API remains mainly in the background while the user of the library handles native Python objects whereby theuser is still able to interact with nearly are LOGS functionalities and entities.

Thus _this library_ firstly _targets lab and data scientist_ allowing them to freely interact with experimental data and its meta-data without any pre-knowledge of Web technologies and communication. Secondly, it _facilitates power-users_ to implement highly specific workflows automations and 3rd-party software intergrations with LOGS and other lab software.

## Installation

The **LOGS-Py** package can be easily installed by using `pip`.

For this open the `terminal` and do:

```bash
pip install logs-py
```
acoustotreams 0.2.15
A Python package for acoustic wave scattering based on the T-matrix method
License: MIT
Requires Python: <3.14,>=3.10

[![PyPI](https://img.shields.io/pypi/v/acoustotreams)](https://pypi.org/project/acoustotreams)
![License](https://img.shields.io/github/license/NikUstimenko/acoustotreams)
[![Documentation Status](https://img.shields.io/badge/docs-online-brightgreen)](https://NikUstimenko.github.io/acoustotreams)

# acoustotreams

The package `acoustotreams` adopts the framework of the `treams` package for acoustic wave scattering in finite and periodic arrangements of particles, based on the T-matrix method.

## Installation

The version of Python should be 3.10 or 3.11.

### Installation using pip

To install the package with pip, use

```sh
pip install acoustotreams
```
Preliminarily, you have to also install original `treams>0.4` as well as `numpy` and `scipy<1.17`
```sh
pip install treams
```

## Documentation

The documentation can be found at https://NikUstimenko.github.io/acoustotreams.

## Publications

When using this code please cite:

The following publications document the developments and methods for different parts of the code:

* [N. Ustimenko, I. Fernandez-Corbaton, and C. Rockstuhl, Singular value decomposition to describe bound states in the continuum in periodic metasurfaces, arXiv 2602.15741 (2026).](https://arxiv.org/abs/2602.15741)
* [O. Demeulenaere, N. Ustimenko, A. G. Athanassiadis,  L. Gulati, C. Rockstuhl, and P. Fischer, Ultrasonic metamaterial at MHz frequencies using microstructured glass, arXiv 2512.20506 (2026).](https://arxiv.org/abs/2512.20506)
* [N. Ustimenko, A. B. Evlyukhin, V. Kyrimi, A. V. Kildishev, and C. Rockstuhl, Lattice-induced sound trapping in biperiodic metasurfaces of acoustic resonators, Phys. Rev. Res. 8, 013074 (2026).](https://doi.org/10.1103/wnmk-zhrb)
* [N. Ustimenko, C. Rockstuhl, and A. V. Kildishev, Optimal multipole center for subwavelength acoustic scatterers, Appl. Phys. Lett. 126, 142201 (2025).](https://doi.org/10.1063/5.0257760)

## Features

* [x] T-matrix calculations using a spherical or cylindrical wave basis set
* [x] Scattering from clusters of particles
* [x] Scattering from particles and clusters arranged in 3d-, 2d-, and 1d-lattices
* [x] Calculation of sound propagation in stratified media
* [x] Band calculation in crystal structures