semvec/pypi-packages huggingface dataset:
azcam 26.0.3
Acquisition and analysis package for scientific imaging
Requires Python: >=3.10
Keywords: ccd, imaging, astronomy, sensors

# AzCam

AzCam is a software framework for the acquisition and analysis of image data from scientific imaging systems as well as the control of instrumentation. It is intended to be customized for specific hardware, observational, and data reduction requirements.

Operation is based on the concept of *tools* which are the interfaces to both hardware and software code.  Examples of tools are *instrument* which controls instrument hardware, *telescope* which interfaces to a telescope, *linearity* which acquires and analyzes images to determine sensor linearity, and *exposure* which controls a scientific observation by interfacing with a variety of other tools. As an example, the *exposure* tool may move a telescope and multiple filter wheels, control a camera shutter, operate the camera by taking an exposure, display the resultant image, and begin data reduction of that image.

The *azcam* python package currently supports Astronomical Research Cameras, Inc. Gen3, Gen2, and Gen1 CCD controllers, Magellan Guider controllers, STA Archon controllers, and CMOS cameras using ASCOM. It also supports a variety of temperature controollers, telecopes, and image displays.

AzCam is not appropriate for consumer-level cameras and is not intended to have a common API across all systems. It's primary design principle is to allow interfacing to a wide variety of custom instrumentation which is required to acquire and analyze scientific image data.

## Documentation

See https://azcam.readthedocs.io.

See https://github.com/mplesser/azcam-tool.git for the standard GUI used by most telescope observers.

See https://github.com/mplesser/azcam-console.git for a python package which supports a local or remote command line interface and sensor characterization tools.

## Installation Example

`pip install azcam`

or 

```shell
git clone https://github.com/mplesser/azcam
git clone https://github.com/mplesser/azcam-console
pip install -e azcam
pip install -e azcam-console
```
use-lightcurve 0.2.4
Observability and evaluation SDK for LLM Agents
Requires Python: >=3.7

# use-lightcurve

The official Python SDK for Lightcurve, the observability and evaluation platform for LLM Agents.

## Installation

```bash
pip install use-lightcurve
```

## Quick Start

### Magic Mode (Auto-Instrumentation)

The easiest way to use Lightcurve is to enable auto-instrumentation for your AI libraries.

```python
import lightcurve

# Automatically monitors OpenAI, Anthropic, Gemini, and LangChain
lightcurve.monitor(
    api_key="lc_live_...",
    integrations=['openai', 'gemini'] # Optional: specify integrations
)

# Optional: Verify connection
if lightcurve.is_connected():
    print("✅ Lightcurve is active")
```

### Self-Hosted

If you are running the Lightcurve platform on your own infrastructure, specify your `base_url`:

```python
lightcurve.monitor(
    api_key="...",
    base_url="https://your-lightcurve-instance.com",
    integrations=['openai']
)
```

## Troubleshooting

### verify connection failure
If you don't see data in your dashboard:
1. Check your API key.
2. Ensure you are using the correct `base_url` if self-hosting.
3. Use `lightcurve.is_connected()` to check initialization status.

### 405 Method Not Allowed
- Ensure your `base_url` does NOT end with `/v1/ingest`. The SDK appends this automatically.
- Correct: `https://api.uselightcurve.com`
- Incorrect: `https://api.uselightcurve.com/v1/ingest`
nucleofind 1.2.3
NucleoFind: A Deep-Learning Network for Interpreting Nucleic Acid Electron Density
Requires Python: >=3.10

# NucleoFind <img src="https://github.com/Dialpuri/NucleoFind/assets/44945647/a7c6c30c-a9fb-4f30-bdc0-3705ae9df36f" alt="logo" width="50"/> 

[![PyPI version](https://badge.fury.io/py/nucleofind.svg)](https://badge.fury.io/py/nucleofind)
![GitHub Issues](https://img.shields.io/github/issues-raw/Dialpuri/NucleoFind)
![PyPI - Downloads](https://img.shields.io/pypi/dm/NucleoFind)
![GitHub repo size](https://img.shields.io/github/repo-size/Dialpuri/NucleoFind)
[![Build documentation](https://github.com/Dialpuri/NucleoFind/actions/workflows/build-documentation.yml/badge.svg)](https://github.com/Dialpuri/NucleoFind/actions/workflows/build-documentation.yml)

Nucleic acid electron density interpretation remains a difficult problem for computer programs to deal with. Programs tend to rely on exhaustive searches to recognise characteristic features. NucleoFind is a deep-learning-based approach to interpreting and segmenting electron density. Using a crystallographic map, the positions of the phosphate group, sugar ring and nitrogenous base group are able to be predicted with high accuracy. 

## Documentation
[Link to Documentation](https://dialpuri.github.io/NucleoFind/about-nucleofind.html)

## Publications

If you find _NucleoFind_ useful, please cite: 

- Jordan S Dialpuri, Jon Agirre, Kathryn D Cowtan, Paul S Bond, NucleoFind: A Deep-Learning Network for Interpreting Nucleic Acid Electron Density, Nucleic Acid Research, 2024 [https://doi.org/10.1093/nar/gkae715](https://doi.org/10.1093/nar/gkae715)