semvec/pypi-packages huggingface dataset:
exobengal 1.1.2
A python package to train Machine Learning models on Kepler Datasets with customizable hyperparameters to detect exoplanet

# ExoBengal

Standardized tools for ML-based exoplanet candidate classification on NASA data, plus a companion docs website.

This repo contains:

- Python package `exobengal`:
  - `DetectExoplanet` for training/inference (RandomForest, CNN, kNN)
  - `ExoParams` convenience container for feature inputs
- Pretrained model artifacts in `models/`
- Example dataset(s) in `data/`
- Next.js docs site in `website/`

Full documentation is in `docs/`:

- Installation and requirements: `docs/installation.md`
- API reference: `docs/api.md`
- Data reference and preprocessing: `docs/data.md`
- Models and artifacts: `docs/models.md`
- Notebook walkthrough: `docs/notebook.md`

## Quick Start

Install the package from PyPI (or your local environment):

```bash
pip install exobengal
```

Make a prediction with the bundled RandomForest model:

```python
from exobengal.exobengal import DetectExoplanet

detector = DetectExoplanet()
sample = [365.0, 1.0, 288.0, 1.0, 4.44, 5778, 0.1, 5.0, 100.0]
print(detector.random_forest(sample))
```

## Project Structure

```
exobengal/           # Python package
  exobengal.py      # DetectExoplanet class and helpers
models/              # Trained models (.pkl, .h5, scaler)
data/                # Dataset CSVs
website/             # Next.js static website + docs
```

## Python API (quick view)

Constructor:

```python
DetectExoplanet(
  rf_model_path="models/random_forest_classifier.pkl",
  cnn_model_path="models/cnn_model.h5",
  knn_model_path="models/knn_model.pkl",
  scaler_path="models/scaler.pkl",
  imputer_path="models/imputer.pkl",
)
```

Training:

```python
detector.train_random_forest(data_path="data/cumulative_2025.09.20_12.15.37.csv")
detector.train_cnn(data_path="data/cumulative_2025.09.20_12.15.37.csv")
detector.train_knn(data_path="data/cumulative_2025.09.20_12.15.37.csv")
```

Inference (all return the same schema):

```python
from exobengal.exobengal import ExoParams

sample = [koi_period, koi_prad, koi_teq, koi_srad, koi_slogg, koi_steff, koi_impact, koi_duration, koi_depth]
detector.random_forest(sample)
detector.cnn(sample)
detector.knn(sample)

# Or use ExoParams for clarity
params = ExoParams(period=365.0, prad=1.0, teq=288.0, srad=1.0, slog_g=4.44, steff=5778, impact=0.1, duration=5.0, depth=100.0)
detector.random_forest(params)
```

Utility:

```python
detector.calculate_esi(koi_prad=1.05, koi_teq=290)
```

For full API and feature details, see `docs/api.md`.

## Models

Artifacts live in `models/`. See `docs/models.md` for details and retraining notes.

## Requirements

Python 3.8+. See `docs/installation.md` or `requirements.txt`.

## Website

The Next.js site (in `website/`) includes a docs experience. See its `README.md` for running locally.

## Development

Website (Node 20):

```bash
cd website
npm ci
npm run dev
```

Static export is enabled via `output: 'export'` and deployed to GitHub Pages with Actions.

## License

MIT License – see `LICENSE`.
behind-api-client 1.0.668
Generated API client for Behind API
Requires Python: >=3.7

# Behind API Client - Python

Auto-generated API client for Behind API.

## Installation

```bash
pip install -r requirements.txt
```

Or install the package:

```bash
pip install -e .
```

## Usage

```python
from behind_api_client import BehindApiClient

# Initialize the client
api_client = BehindApiClient("https://api.behind.ai:6002", "your-access-token")

# Use the API
result = api_client.zecamp10.v10.message.send(
    email="user@example.com",
    recipient_name="John Doe",
    account_name="my_account",
    template_name="welcome",
    data={},
    time=""
)
```

## Structure

The API client follows this structure:
- `api_client.{app}.{version}.{endpoint}.{method}(params)`

For example:
- `api_client.zecamp10.v10.companies.get(...)`
- `api_client.raet.v10.cv.create(...)`

Note: Hyphenated app names are converted to snake_case (e.g., `web-harvester` becomes `web_harvester`).

## Event Handlers

```python
api_client.on_expired(lambda data: print("Token expired"))
api_client.on_reject(lambda data: print(f"Rejected: {data}"))
api_client.on_too_many_requests(lambda data: print(f"Rate limited: {data}"))
```
inzicht 0.1.22
A Python library that provides generic CRUD functionality streamlining database operations.
Requires Python: >=3.10

# INZICHT

## SQLAlchemy CRUD Extension

A Python library that provides generic Create, Read, Update, and Delete (CRUD) functionality for SQLAlchemy models, streamlining database operations and reducing boilerplate code.

---

## Features
- **Generic CRUD Operations:** Perform CRUD operations on [SQLAlchemy](https://www.sqlalchemy.org) and [SQLModel](https://sqlmodel.tiangolo.com) models with minimal code.
- **Flexible Querying:** Easily apply filters, sorting, and pagination.
- **Extensible:** Override and customize CRUD methods as needed.

---

## Installation

Install the package via pip:

```bash
pip install inzicht
```

Install the package via poetry:

```bash
poetry add inzicht
```

## License

inzicht is distributed under the [MIT license](https://www.opensource.org/licenses/mit-license.php).