semvec/pypi-packages huggingface dataset:
github-events-monitor 1.0.1
GitHub Events Monitor with FastAPI and MCP server
License: MIT
Requires Python: >=3.11
Keywords: events, fastapi, github, mcp, monitoring

# GitHub Events Monitor

A Python-based service that monitors GitHub Events (WatchEvent, PullRequestEvent, IssuesEvent), stores them in SQLite, exposes data through an MCP server (each tool = API endpoint), and publishes interactive visualizations to a GitHub Pages site from the docs/ folder.

## Architecture (C4 L1)

[Person: Dashboard User] → [Container: GitHub Pages (docs/)] ← [Container: Data Exporter] ← [Container: Events DB (SQLite)] ← [Container: Event Monitor Service] ← [External System: GitHub API /events]
[Person: API Consumer] → [Container: MCP Server] → [Container: Events DB (SQLite)]

## Components

- Event Monitor: Polls https://api.github.com/events, filters target types, stores to SQLite, computes PR metrics.
- MCP Server: Tools to retrieve metrics/data directly from the DB.
- Data Exporter: Builds docs/data.json and Plotly HTML charts in docs/.
- GitHub Pages: Static dashboard served from docs/.

## Setup

1) Python env
- python -m venv venv
- source venv/bin/activate
- pip install -r requirements.txt

2) Initialize DB
- sqlite3 database/events.db < database/schema.sql

3) Run monitor
- python service/github_monitor.py            # continuous
- python service/github_monitor.py --once     # single cycle (useful in CI)
Optional: export GITHUB_TOKEN for higher limits.

4) Export dashboard data
- python service/data_exporter.py

5) MCP server
- python mcp/server.py

## GitHub Pages
- Push to main branch.
- Enable Pages from Settings → Pages → Deploy from a branch → main → /docs.

## Notes
- created_at_ts (epoch) enables correct time filtering and SQLite date ops.
- For production, consider PostgreSQL, containerization, and a retention policy for events.
gReLU 1.0.10
gReLU is a python library to train, interpret, and apply deep learning models to DNA sequences
License: MIT
Requires Python: >=3.8

[![DOI](https://zenodo.org/badge/788591787.svg)](https://doi.org/10.5281/zenodo.15627611)

# gReLU

gReLU is a Python library to train, interpret, and apply deep learning models to DNA sequences. Code documentation is available [here](https://genentech.github.io/gReLU/).

![Flowchart](media/flowchart.jpg)

## Installation

To install from source:

```shell
git clone https://github.com/Genentech/gReLU.git
cd gReLU
pip install .
```

To install using pip:

```shell
pip install gReLU
```
Typical installation time including all dependencies is under 10 minutes.

To train or use transformer models containing flash attention layers, [flash-attn](https://github.com/Dao-AILab/flash-attention) needs to be installed first:
```shell
conda install -c conda-forge cudatoolkit-dev -y
pip install torch ninja
pip install flash-attn --no-build-isolation
pip install gReLU
```

## Contributing

See our [contribution guide](https://genentech.github.io/gReLU/contributing.html).

## Additional requirements

If you want to use genome annotation features through the function `grelu.io.genome.read_gtf`, you will need to install the following UCSC utilities: `genePredToBed`, `genePredToGtf`, `bedToGenePred`, `gtfToGenePred`, `gff3ToGenePred`.

If you want to create bigWig files through the function `grelu.data.preprocess.make_insertion_bigwig`, you will need to install the following UCSC utilities: `bedGraphToBigWig`.

UCSC utilities can be installed from `http://hgdownload.cse.ucsc.edu/admin/exe/`, for example using the following commands:

```shell
rsync -aP rsync://hgdownload.soe.ucsc.edu/genome/admin/exe/linux.x86_64/bedGraphToBigWig /usr/bin/
rsync -aP rsync://hgdownload.soe.ucsc.edu/genome/admin/exe/linux.x86_64/genePredToBed /usr/bin/
rsync -aP rsync://hgdownload.soe.ucsc.edu/genome/admin/exe/linux.x86_64/genePredToGtf /usr/bin/
rsync -aP rsync://hgdownload.soe.ucsc.edu/genome/admin/exe/linux.x86_64/bedToGenePred /usr/bin/
rsync -aP rsync://hgdownload.soe.ucsc.edu/genome/admin/exe/linux.x86_64/gtfToGenePred /usr/bin/
rsync -aP rsync://hgdownload.soe.ucsc.edu/genome/admin/exe/linux.x86_64/gff3ToGenePred /usr/bin/
```

or via bioconda:

```shell
conda install -y \
bioconda::ucsc-bedgraphtobigwig \
bioconda::ucsc-genepredtobed    \
bioconda::ucsc-genepredtogtf    \
bioconda::ucsc-bedtogenepred    \
bioconda::ucsc-gtftogenepred    \
bioconda::ucsc-gff3togenepred
```

If you want to create ATAC-seq coverage bigWig files using `grelu.data.preprocess.make_insertion_bigwig`, you will need to install bedtools. See https://bedtools.readthedocs.io/en/latest/content/installation.html for instructions.

## Citation

Please cite our preprint: https://www.biorxiv.org/content/10.1101/2024.09.18.613778v1
dash-deckgl 0.9.1.post1
Deck.gl component for Dash
License: MIT

Package: dashDeckgl
Title: Deck.gl component for Dash
Version: 0.9.1
Description: Deck.gl component for Dash
Depends: R (>= 3.0.2)
Imports: 
Suggests: 
License: MIT + file LICENSE
URL: https://github.com/oceanum-io/dash-deckgl
BugReports: https://github.com/oceanum-io/dash-deckgl/issues
Encoding: UTF-8
LazyData: true
KeepSource: true
EASLT 0.0.5
Enhanced Automatic Shifted Log Transformer with Monte Carlo optimization
License: MIT
Requires Python: >=3.7
Keywords: data-transformation, log-transformation, normality, monte-carlo-optimization, statistical-preprocessing, data-science, machine-learning, numba-acceleration, feng-transformation, adaptive-algorithms

# Enhanced Automatic Shifted Log Transformer (EASLT)

Automatically transform skewed data into more normal distributions using a Monte Carlo–optimized shifted log transformation.

---

## 🚀 Features

- **Works with negative, zero, and positive values**
- **Reduces skewness and kurtosis**
- **Adaptive, data-driven transformation**
- **Multi-test normality assessment**
- **Scikit-learn compatible**
- **Numba-accelerated for speed**

---

## 📦 Installation

```bash
pip install EASLT
```

---

## ⚡ Quick Start

```python
from EASLT import AutomaticShiftedLogTransformer
import numpy as np

# Example skewed data
np.random.seed(42)
data = np.random.exponential(2, (1000, 3))

# Transform
transformer = AutomaticShiftedLogTransformer(mc_iterations=1000, random_state=42)
data_transformed = transformer.fit_transform(data)
```

---

## 📚 How It Works

EASLT:
1. **Detects data complexity** (normal, mild issues, needs transformation)
2. **Applies robust shifting** for negative/zero values
3. **Detects outliers** and optionally winsorizes
4. **Optimizes parameters** via Monte Carlo with Dirichlet sampling
5. **Applies Feng's shifted log transformation**

---

## 📝 License

MIT License © 2025 Muhammad Akmal Husain

## 🔗 Links

- **[GitHub Repository](https://github.com/AkmalHusain2003/enhanced-automatic-shifted-log)**
- **[Documentation](https://github.com/AkmalHusain2003/enhanced-automatic-shifted-log#readme)**
seqspec 0.4.0
A tool for working with sequencing specifications
License: MIT
Requires Python: >=3.12
Keywords: bioinformatics, sequencing, specifications

# seqspec

![github version](https://img.shields.io/badge/Version-0.4.0-informational)
[![pypi version](https://img.shields.io/pypi/v/seqspec)](https://pypi.org/project/seqspec/0.4.0/)
![python versions](https://img.shields.io/pypi/pyversions/seqspec)
[![license](https://img.shields.io/pypi/l/seqspec)](LICENSE)

`seqspec`, short for "sequence specification" (pronounced "seek-speck"), is a file format that describes data generated from genomics experiments. Both the file format and `seqspec` tool [enable uniform processing](./docs/UNIFORM.md) of genomics data.

![alt text](docs/images/simple_file_structure.png)
**Figure 1**: Anatomy of a `seqspec` file.

We have multiple tutorials to get you up and running with `seqspec`:

1. Learn how to use `seqspec` to [standardize your genomics data preprocessing](docs/UNIFORM.ipynb).

2. Understand how to [manipulate `seqspec` files](docs/USING_SEQSPEC.ipynb) using the `seqspec` command-line tool.

## Citation

The `seqspec` format and tool are described in this [publication](https://doi.org/10.1093/bioinformatics/btae168). If you use `seqspec` please cite

```
Ali Sina Booeshaghi, Xi Chen, Lior Pachter, A machine-readable specification for genomics assays, Bioinformatics, Volume 40, Issue 4, April 2024, btae168.
```

`seqspec` was inspired by and builds off of the Teichmann Lab [Single Cell Genomics Library Structure](https://github.com/Teichlab/scg_lib_structs) by [Xi Chen](https://github.com/dbrg77).

## Documentation

- [Install `seqspec`: `docs/INSTALLATION.md`](docs/INSTALLATION.md)
- [Learn about the `seqspec` file format: `docs/DOCUMENTATION.md`](docs/SEQSPEC_FILE.md)
- [Learn about the `seqspec` tool: `docs/DOCUMENTATION.md`](docs/SEQSPEC_TOOL.md)
- [Learn about the `seqspec` specification : `docs/SPECIFICATION.md`](docs/SPECIFICATION.md)
- [Write a `seqspec`: `docs/TUTORIAL.md`](docs/TUTORIAL.md)
- [View example `seqspec` files: `https://www.sina.bio/seqspec-builder/assays.html`](https://www.sina.bio/seqspec-builder/assays.html)
- [Contribute a `seqspec` : `docs/CONTRIBUTING.md`](docs/CONTRIBUTING.md)
- [Watch a YouTube video about `seqspec`](https://youtu.be/NSj6Vpzy8tU)
- [Read the manuscript that describes `seqspec`](https://doi.org/10.1093/bioinformatics/btae168)