semvec/pypi-packages huggingface dataset:
annotell-query 2.5.0
Annotell Query API Client
License: MIT
Requires Python: ~=3.6
Keywords: API,Annotell

# Annotell Query API Client

Python 3 library providing access to the Annotell Query API. 

To install with pip run `pip install annotell-query`

Set env ANNOTELL_CREDENTIALS, see [annotell-auth](https://github.com/annotell/annotell-python/tree/master/annotell-auth). 

## Judgement Query Example
Stream all items matching a query
```python
from annotell.query.query_api_client import QueryApiClient
query_client = QueryApiClient()
resp = query_client.query_judgements(query_filter="requestId = X")
for item in resp.items():
    print(item)
```

## Change log

### 2.5.0
- A new base Query client that can be inherrited for other applications 

### 2.4.1
- Use `annotell-auth[requests]>=2.0.0`

### 2.3.0
- Use `annotell-auth>=1.6`
- Remove metadata queries

### 2.2.0
- Use `annotell-auth>=1.5` with fault tolerant auth request session

### 2.1.0
- Use server default query limits 

### 2.0.0
- Rename library to `annotell-query`
- Rename `QueryApi` to `QueryApiClient`
- Add KPI query method

### 1.3.0
- Change constructor for authentication to only accept `auth`.
cassa 2.1.1
Python package to perform unsupervised and semi-supervised machine learning (ML) classification algorithms on generic tensors of pre-processed data
License: MIT
Requires Python: >=3.8,<3.11

# Classification with Automated Semi-Supervised Algorithms (CASSA) package

![test-main](https://github.com/giorgiosavastano/cassa/actions/workflows/python-test-main.yml/badge.svg)
![coverage-main](https://img.shields.io/codecov/c/github/giorgiosavastano/cassa)
![license](https://img.shields.io/github/license/giorgiosavastano/cassa)

## Overview

`CASSA` is a Python package to perform unsupervised and semi-supervised machine learning (ML) classification algorithms on generic tensors of pre-processed data, such as time series, altitude profiles, images, DDMs and spectra. Mainly tested on Earth Observation (EO) satellites data, such as GNSS-RO sTEC profiles and GNSS-R DDMs. It produces a database of labeled clusters that can be used to classify new unlabeled data.
The documentation is available at <https://cassa.readthedocs.io/en/latest/>.

It includes the following blocks:

* Parallelized distance matrix computation using earth mover's distance (EMD, aka Wasserstein metric)
* Spetral clustering using precomputed distance matrix
* Self-tuned spectral clustering using precomputed distance matrix
* HDBSCAN clustering using precomputed distance matrix
* Classification of new data based on database of labeled clusters

## Installation

    pip install cassa


### Authors

- Giorgio Savastano (<giorgiosavastano@gmail.com>)
- Karl Nordstrom (<karl.am.nordstrom@gmail.com>)

## References

Savastano, G., K. Nordström, and M. J. Angling (2022), Semi-supervised Classification of Lower-Ionospheric Perturbations using GNSS Radio Occultation Observations from Spire’s Cubesat Constellation. Submitt. to JSWSC.