semvec/pypi-packages huggingface dataset:
openstream 0.1.0
A Python package for streaming data processing
License: MIT
Requires Python: >=3.8

# openstream

A Python package for streaming data processing.

## Installation

```bash
pip install openstream
```

## Usage

```python
from openstream import stream_data

# Example usage
data = [1, 2, 3, 4, 5]
result = stream_data(data)
```

## Development

Install the package in development mode:

```bash
pip install -e ".[dev]"
```

Run tests:

```bash
pytest
```

## License

MIT License
kalpy-kaldi 0.9.0
Pybind11 bindings for Kaldi for use with the Montreal Forced Aligner
License: MIT
Requires Python: >=3.7

# kalpy
Pybind11 bindings for Kaldi for use in [Montreal Forced Aligner](montreal-forced-aligner.readthedocs.io/).

## Installation

Kalpy depends on Kaldi being built as shared libraries, and the easiest way to install is via conda-forge:

```
conda install -c conda-forge kalpy
```

Kalpy is also available on pip via the `kalpy-kaldi` package, but as this is only a binding library, it relies on Kaldi shared libraries being available. The `KALDI_ROOT` environment variable must be set to locate the shared libraries and header files.  The easiest way to install the appropriately built kaldi libraries is via `conda install -c conda-forge kaldi`.

```
export KALDI_ROOT=/path/to/conda/enviornment
pip install kalpy-kaldi
```

## Usage

Two libraries are installed, `_kalpy` which contains low level bindings conforming to the original C++ style, and `kalpy` which is a more pythonic interface for higher level operations.  The `kalpy` package is under heavy development and expansion to expose more functionality.
seismic-linter 0.2.0
A static analysis tool for preventing temporal causality leaks in seismology ML.
License: MIT
Requires Python: >=3.10
Keywords: seismology, machine-learning, static-analysis, linter, temporal-leakage

# seismic-linter

[![PyPI](https://img.shields.io/pypi/v/seismic-linter)](https://pypi.org/project/seismic-linter/)
[![CI](https://github.com/AmanSinghNp/seismic-linter/actions/workflows/ci.yml/badge.svg)](https://github.com/AmanSinghNp/seismic-linter/actions/workflows/ci.yml)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

**Stop publishing 99% accurate models that fail in production.**

seismic-linter automatically detects temporal causality violations in earthquake forecasting and seismology machine learning pipelines. It catches the silent bugs that make your model "cheat" by using future data during training—leading to papers with impressive results that completely fail during real-time deployment.

## The Problem

Earthquake forecasting suffers from a unique ML pathology: **temporal data leakage**. When you normalize magnitudes using global statistics, split data with `shuffle=True`, or fit transformers before temporal splitting, your model implicitly "knows" about future earthquakes. This creates artificially high accuracy that evaporates in production.

## The Solution

seismic-linter provides:
- 🔍 **Static analysis** - Scan your Python code for leakage patterns before running
- ⚡ **Runtime validation** - Decorators (`@verify_monotonicity`) and integrity checks
- 🧪 **Pytest Integration** - Use `validate_split_integrity(train_df, test_df)` after splitting. See [docs/api.md](docs/api.md) for full API.
- 📋 **Pre-commit hooks** - Block leaky code from entering your repository

The GitHub Action runs in a Linux container; Windows runners are not supported.

## Detected Rules

| Rule ID | Description | Severity |
|---------|-------------|----------|
| **T001** | Global statistics (mean/std) computed without temporal context | ⚠️ Warning |
| **T002** | Model `.fit()` called on potentially leaky data (e.g., raw `df`) | ℹ️ Info |
| **T003** | `train_test_split` with `shuffle=True` (random split) | ❌ Error |

## Configuration
Configuration is loaded from the `pyproject.toml` of the first path specified in the CLI arguments (or current directory if none).

Inline suppressions are supported using `# seismic-linter: ignore rule_id` (applies to current line only):
```python
df['norm'] = (df['mag'] - df['mag'].mean()) / df['mag'].std()  # seismic-linter: ignore T001
```

> **Note**: When using `github` output format, paths are relative to the current working directory where possible.

## Quick Example

```python
# ❌ This will trigger a warning
df['normalized'] = (df['magnitude'] - df['magnitude'].mean()) / df['magnitude'].std()

# ✅ This passes validation  
df['normalized'] = df.groupby('station')['magnitude'].transform(
    lambda x: (x - x.rolling(window=100).mean())
)
resilion 0.0.1
Mission resilience modeling framework (space systems first; extensible to humans, infra, edge)
License: MIT
Requires Python: >=3.10

# RESILION

RESILION is an open-source Python framework for mission resilience modeling under extreme and variable environments,
initially focused on space systems (space weather, orbit, GNSS, radiation).

Planned extensions include human performance, edge deployment, and application domains like aviation and agriculture.

Developed by HexaF Innovations LLC.

Non-affiliation notice: This project is not affiliated with any other organizations or products that may share similar names.