semvec/pypi-packages huggingface dataset:
stela-toolkit 0.4.3
A Python package for interpolating astrophysical light curves using Gaussian Processes (more ML models to come!) in order to compute frequency-domain and standard time domain data products.
Requires Python: >=3.8

![Python](https://img.shields.io/pypi/pyversions/stela-toolkit?cacheSeconds=3600)
[![PyPI](https://img.shields.io/pypi/v/stela-toolkit)](https://pypi.org/project/stela-toolkit/)
![IMG_7099 (1)](https://github.com/user-attachments/assets/55d66b16-e91c-4d8a-a3b9-047002945dc1)

<p align="right" style="font-size: 0.9em;">
  <em>Logo design by Elizabeth Jarboe</em>
</p>


**STELA (Sampling Time for Even Lightcurve Analysis) Toolkit** is a Python package for interpolating astrophysical light curves using Gaussian Processes (more ML models to come!) in order to compute frequency-domain and standard time domain data products.

---

## Documentation

Full documentation is available at:  
[https://collinlewin.github.io/stela-toolkit/](https://collinlewin.github.io/STELA-Toolkit/)

The documentation includes:
- **Overview** of all features
- **Tutorial notebook** with GP modeling and lag analysis
- **Gaussian Process guide** with detailed Bayesian background
- **Module reference** for all functions and classes

---

## Features

- Gaussian Process regression for all ML skill levels! Built-in normality testing and transformations to help reach normality, kernel selection, hyperparameter training, and much more.
- Frequency-domain products: power and cross spectra, coherence, lag-frequency and lag-energy spectra
- Time-domain lag analysis using ICCF and GP-based cross-correlation
- Simulation of synthetic light curves with custom underlying power spectra and injected lags
- Preprocessing: outlier removal methods, polynomial detrending, trimming, etc.
- Convenient plotting in every class using .plot()

---

## Requirements

STELA Toolkit requires Python ≥ 3.8 and the following core packages:

- numpy ≥ 1.20, <2.0
- scipy ≥ 1.7
- matplotlib ≥ 3.5
- astropy ≥ 5.0
- torch ≥ 1.10
- gpytorch ≥ 1.9
- statsmodels ≥ 0.13

You can install all dependencies automatically when installing the package (see below).

---

## Installation

You can install the package using pip:

```bash
pip install stela-toolkit
```

If you are installing directly from the GitHub repository:

```bash
git clone https://github.com/collinlewin/STELA-Toolkit.git
cd STELA-Toolkit
pip install .
```

You can verify the installation by running:

```python
import stela_toolkit
print(stela_toolkit.__version__)
```

If this runs without an error, you're good to go!
somacore 1.0.29
Python-language API specification and base utilities for implementation of the SOMA system.
Requires Python: >=3.9

# `somacore`: the Python version of the SOMA specification

`somacore` is the Python interpretation of the [abstract SOMA specification](https://github.com/single-cell-data/SOMA/blob/main/abstract_specification.md).
If you’re using SOMA to store or retrive data, you probably want to **install a SOMA implementation instead**, and not this package directly:

- [tiledbsoma](https://pypi.org/project/tiledbsoma/) ([source code](https://github.com/single-cell-data/TileDB-SOMA/))

This core pacakge contains base interfaces, shared types, and other cross-implementation code.
It is intended for use primarily by SOMA implementors or libraries that handle SOMA data, rather than end users.
odbctools 0.7.1
A set of tools to simplify connecting to and working with an ODBC data source
Requires Python: >=3.12
Keywords: odbc, database, data, sql, informix

odbctools
=========

Overview
--------
odbc tools is a set of tools to simplify connecting to and working with an ODBC data source. It includes a context manager for handling connections to the data source and the ability to return a result set in an easy to use format or write the results to a file.

Install It
----------
From PyPI ::

    $ pip install odbctools


Use It
------
Odbctools by default uses a file called config.ini, or you can override this for different scenarios, including passing the dsn directly.

Create config.ini ::

    [odbc]
    DSN=SourceName

Code Example ::

    from odbctools import OdbcManager

    query_string = "Your query here"
    params = [p1, p2]

    with OdbcManager() as datasource:
        result_list = datasource.get_dictionaries(query_string, params)
        for result_dict in result_list:
            for col_name, col_data in result_dict.items():
                print("{0}{1}".format(str(col_name).ljust(25), col_data))

Another Example ::

    from odbctools import OdbcManager

    query_string = "Your query here"
    params = [p1, p2]

    with OdbcManager(dsn='ODBC Name Here') as datasource:
        result_list = datasource.get_dictionaries_in_queue(query_string, params)
        for result_dict in result_list:
            for col_name, col_data in result_dict.items():
                print("{0}{1}".format(str(col_name).ljust(25), col_data))

Encapsulating the connection in your classes is also possible ::

        class Foo:
            def __init__(self):
                self.connection = OdbcManager(dsn='unity64')

            def __enter__(self):
                self.connection.open_connection()
                return self

            def __exit__(self, exc_type, exc_val, exc_tb):
                self.connection.close_connection()

        with Foo() as f:
            data = f.connection.get_dictionaries('select top 1 * from people', list())

*Note: You can now create a OdbcManager object with a connection string, passing in the connection_string argument.*

Returning data from a query
---------------------------
**query** - Returns list that contains [0]A list containing the query columns [1:] A list of the resulting data (rows)

**get_dictionaries** - Queries data source, returns a list of dictionaries.

**get_dictionaries_in_queue** - Queries data source, returns a deque of dictionaries.

**write_to_csv** - Queries data source and writes results to a CSV file

Updating data
-------------
*Note: by default commit is turned off, if you want to enable auto commit, you can set it when creating the OdbcManager object or when calling this function.*

**query_no_resultset** - For running DDL/DML queries for creating, deleting, updating records.


Dependencies
------------
* pypyodbc

License
--------
MIT
agtos 0.4.0
The Agent Operating System - Orchestrate AI agents and tools through natural conversation
Requires Python: <4.0,>=3.10

# agtOS

> The Agent Operating System - Currently in Private Beta 🔒

## What is agtOS?

A revolutionary platform that transforms how AI agents interact with your tools and systems. 

Imagine if your AI assistant could:
- 🤖 Orchestrate multiple AI agents seamlessly
- 🔧 Create new tools through natural conversation
- 🌐 Connect to all MCP, CLI, and REST APIs automatically
- 🔐 Handle credentials securely without exposing them

**agtOS makes this possible.**

## Private Beta Access

agtOS is currently in private beta. An invite code is required for activation.

```bash
pip install agtos
agtos  # You'll need an invite code
```

## Learn More

- **Documentation**: Coming soon
- **Website**: [agtos.ai](https://agtos.ai)
- **Beta Access**: Contact us for an invite code

---

*Building the future of AI agent collaboration.*