semvec/pypi-packages huggingface dataset:
extract-load-s3 1.0.10
Unified data ingestion framework to move datasets from SFTP/Oracle/Postgres/Snowflake to S3.
Requires Python: >=3.9

# 📦 extract-load-s3

A simple, extensible Python utility to **extract data from different sources and load the original files into Amazon S3** It can be used in different data pipeline orchestration tool like airflow.

**Pypi repo link**

```
https://pypi.org/project/extract-load-s3/
```

Currently supported:

- **SFTP → S3** (fully functional)

More data sources (Postgres, Oracle, Snowflake, etc.) will be added soon.

---

## 🚀 Installation

```bash
pip install extract-load-s3
```

once installed run

```bash
extract-load-s3 \
    --flow sftp_to_s3 \
    --file_name "/path/to/file.zip" \
    --s3_bucket raw \
    --ssh_host 192.168.1.15 \
    --ssh_user your_username \
    --ssh_password your_password
```

You do not need to pass AWS credentials or endpoint URL unless:
you want to override environment/IAM role credentials
you’re using LocalStack or MinIO

| Argument                  | Required?         | Description                                                  |
| ------------------------- | ----------------- | ------------------------------------------------------------ |
| `--flow`                  | **Yes**           | Which ingestion flow to run (`sftp_to_s3`, more coming soon) |
| `--file_name`             | **Yes** for SFTP  | Remote SFTP file path                                        |
| `--s3_bucket`             | **Yes**           | Destination S3 bucket                                        |
| `--s3_key`                | No                | Custom S3 key / prefix; if omitted, timestamp is appended    |
| `--ssh_host`              | Required for SFTP | SFTP server host                                             |
| `--ssh_user`              | Required for SFTP | SFTP username                                                |
| `--ssh_password`          | Required for SFTP | SFTP password                                                |
| `--aws_access_key_id`     | No                | AWS key; if omitted, boto3 uses IAM role / env vars          |
| `--aws_secret_access_key` | No                | AWS secret                                                   |
| `--aws_endpoint_url`      | No                | Custom S3 endpoint (LocalStack, MinIO, custom S3 gateways)   |
| `--db_conn_str`           | No                | Future DB connection string                                  |

## 1. SFTP → S3

This flow:

Connects to an SFTP server <br>
Streams the remote file <br>
Uploads the file to S3 using multipart upload <br>
Validates file integrity via SHA256 checksum <br>

```bash
--flow sftp_to_s3
```

to use with localstacl

```
--aws_endpoint_url http://localhost:4566 \
--aws_access_key_id test \
--aws_secret_access_key test

```
peircean-abduction 1.2.3
A Logic Harness for abductive inference. Anomaly in → Hypothesis out.
Requires Python: >=3.10
Keywords: abduction, ai-agents, anomaly-detection, explanation, inference, llm, mcp, peirce, reasoning

# Peircean Abduction

**A Logic Harness for Abductive Inference.**

> "Abduction is the process of forming an explanatory hypothesis. It is the only logical operation which introduces any new idea." — Charles Sanders Peirce

Peircean Abduction is a **Model Context Protocol (MCP)** server that gives LLMs the ability to perform rigorous abductive reasoning. It forces models to generate multiple competing hypotheses for an observation and evaluate them using **Inference to the Best Explanation (IBE)**.

## ✨ Features

*   **MCP-First Design:** Built to be used directly within Claude Desktop, Cursor, or any MCP-compliant client.
*   **Provider Agnostic:** Works with **Anthropic**, **OpenAI**, **Gemini**, and **Ollama**.
*   **No API Key Required (Default):** By default, it generates *prompts* for you to run. You only need an API key if you want the tool to execute the reasoning loop autonomously.
*   **Council of Critics:** Simulates a debate between a Logician, Empiricist, and Scientist to refine hypotheses.

## 🚀 Quick Start

### 1. Install

```bash
pip install peircean-abduction
```

### 2. Connect to Claude Desktop

Run the installer to automatically configure Claude Desktop:

```bash
peircean --install
```

### 3. Use it!

Open Claude and ask:

> "Use the `peircean_observe_anomaly` tool to analyze why my server latency spiked but CPU usage is flat."

## 🛠️ Configuration

You can configure the provider and other settings via the interactive wizard:

```bash
peircean config wizard
```

Or by setting environment variables in a `.env` file:

```bash
# Provider Selection
PEIRCEAN_PROVIDER=anthropic  # anthropic, openai, gemini, ollama
PEIRCEAN_MODEL=claude-3-5-sonnet-20241022

# API Keys (Only needed for interactive/autonomous mode)
ANTHROPIC_API_KEY=sk-...
OPENAI_API_KEY=sk-...

# Feature Toggles
PEIRCEAN_ENABLE_COUNCIL=true
PEIRCEAN_INTERACTIVE_MODE=false # Set to true to let the tool call the LLM directly
```

## 📦 Supported Models

*   **Anthropic:** Claude 3.5 Sonnet (Recommended), Claude 3 Opus, Claude 3 Haiku
*   **OpenAI:** GPT-4o, GPT-4 Turbo
*   **Gemini:** Gemini 1.5 Pro
*   **Ollama:** Llama 3, Mistral

## 📚 Documentation

*   [Installation Guide](docs/getting-started/installation.md)
*   [Configuration Guide](docs/guides/configuration.md)
*   [API Reference](docs/reference/api.md)

## 📄 License

MIT
toper 1.0.3
A Python package for graph processing and feature extraction
Requires Python: >=3.7
Keywords: graph, feature-extraction, network-analysis, TopER, GraphRepresentationLearning

# TopER: Topological Embeddings in Graph Representation Learning

This repository contains the implementation of TopER, a novel graph embedding method introduced in the paper titled "TopER: Topological Embeddings in Graph Representation Learning." Graph embeddings are crucial for graph representation learning, enabling the exploration of graphs using machine learning methods. However, existing deep learning techniques often rely on black-box, high-dimensional graph embeddings. There is a growing need for interpretable, low-dimensional embeddings to facilitate efficient data visualization for graph datasets, thus providing practical tools for effective dataset analysis.

## Overview
TopER simplifies a fundamental concept of topological data analysis, specifically filtration, to compute the evolution rate of graph structures induced by a user-defined function on nodes or edges. This approach results in a low-dimensional, interpretable graph embedding that offers advantages in data visualization and graph classification tasks.

## Repository Structure
- **Datasets**:
  - Contains the benchmark datasets considered in the paper.
- **functions_to_calculate_[a,b]**:
  - Contains the main algorithms to calculate vectors [a,b] for each graph in an entered benchmark dataset.
  - **Main.py**:
    - Python script to calculate the vectors X and Y for each filtration function and save them in .csv files.
  - **features_main.py**:
    - Python script to calculate [a,b] vectors and save them in a .csv file using the .csv files calculated previously.
- **classification_codes**:
  - Contains codes for performing graph classification using the final .csv file generated by `features_main.py`.

## Abstract of the Paper
Graph embeddings play a pivotal role in graph representation learning, allowing machine learning methods to explore and understand graph data. In our paper, "TopER: Topological Embeddings in Graph Representation Learning," we introduce TopER as a novel graph embedding method. TopER leverages the concept of filtration from topological data analysis to compute the evolution rate of graph structures induced by user-defined functions on nodes or edges.

Our experiments demonstrate that TopER outperforms or matches the performance of state-of-the-art deep learning models in graph classification tasks. Additionally, we provide theoretical stability guarantees for TopER. Notably, with just two parameters, TopER serves as a low-dimensional topological graph embedding, providing one of the first effective methods for visualizing graph datasets.

## Usage
1. Clone the repository:
   ```bash
   git clone https://github.com/your_username/TopER.git
   cd TopER
steamleaderboards 1.1.5
Retrieve and parse Steam leaderboards
Requires Python: <4.0,>=3.8
Keywords: steam, leaderboards, csv, xml, valve

<div align="center">
<img alt="" src="https://forge.steffo.eu/steffo/steamleaderboards/raw/branch/main/.media/icon-512.png" height="128" style="border-radius: 100%;">
<hgroup>
<h1>Steam Leaderboards</h1>
<p>Retrieve and parse Steam leaderboards</p>
</hgroup>
</div>

## Links

### Tools

<a href="https://www.python.org/">
    <img alt="Written in Python" title="Written in Python" src="https://img.shields.io/badge/language-python-3775a9" height="30px">
</a>

### Packaging

<a href="https://pypi.org/project/steamleaderboards">
    <img alt="Available on PyPI" title="Available on PyPI" src="https://img.shields.io/pypi/v/steamleaderboards?label=pypi&color=ffd242" height="30px">
</a>
&hairsp;
<a href="https://forge.steffo.eu/steffo/-/packages/pypi/steamleaderboards">
    <img alt="Available on Forgejo Packages" title="Available on Forgejo Packages" src="https://img.shields.io/badge/forgejo%20packages-latest-ff6600" height="30px">
</a>

### Documentation

<a href="https://forge.steffo.eu/steffo/steamleaderboards/wiki">
    <img alt="Documentation available" title="Documentation available" src="https://img.shields.io/website?url=https%3A%2F%2Fforge.steffo.eu%2Fsteffo%2Fsteamleaderboards%2Fwiki&up_color=175d36&up_message=available&down_message=error&label=documentation" height="30px">
</a>
&hairsp;
<a href="https://opensource.org/license/MIT">
    <img alt="Licensed under MIT license" title="Licensed under MIT license" src="https://img.shields.io/badge/license-MIT-3da638" height="30px">
</a>

### Development

<a href="https://forge.steffo.eu/steffo/steamleaderboards">
    <img alt="Code repository" title="Code repository" src="https://img.shields.io/gitea/last-commit/steffo/steamleaderboards?gitea_url=https%3A%2F%2Fforge.steffo.eu&color=374351" height="30px">
</a>
&hairsp;
<a href="https://forge.steffo.eu/steffo/steamleaderboards/releases">
    <img alt="Releases" title="Releases" src="https://img.shields.io/gitea/v/release/steffo/steamleaderboards?gitea_url=https%3A%2F%2Fforge.steffo.eu&label=last+release&color=374351" height="30px">
</a>
gps-tracker 0.9.2
Unofficial client to retrieve location data from Invoxia™ GPS trackers.
Requires Python: >=3.11

.. image:: https://img.shields.io/badge/-PyScaffold-005CA0?logo=pyscaffold
    :alt: Project generated with PyScaffold
    :target: https://pyscaffold.org/
.. image:: https://gitlab.com/ezlo.picori/gps_tracker/badges/main/pipeline.svg
    :alt: Pipeline status
    :target: https://gitlab.com/ezlo.picori/gps_tracker/-/commits/main
.. image:: https://gitlab.com/ezlo.picori/gps_tracker/badges/main/coverage.svg
    :alt: Coverage
    :target: https://gitlab.com/ezlo.picori/gps_tracker/-/commits/main
.. image:: https://readthedocs.org/projects/gps_tracker/badge/?version=latest
    :alt: ReadTheDocs
    :target: https://gps-tracker.readthedocs.io/en/latest/
.. image:: https://img.shields.io/pypi/v/gps_tracker.svg
    :alt: PyPI-Server
    :target: https://pypi.org/project/gps_tracker/

===========
gps_tracker
===========

    Unofficial client to retrieve location data from Invoxia™ GPS trackers.

Invoxia_\™ is known for their GPS Trackers with long battery life which rely on the LoRa_ or SigFox_
networks. Unfortunately, trackers location can only be accessed through their Android_ and iOS_ applications.
This ``gps_tracker`` package connects to the same API than your smartphone app and gives you an easy access to:

- The list of devices connected to your account
- Their current state
- Their location history

Data is only in read-access, you will still need the smartphone app to change your devices configuration.

Note that even though direct access to Invoxia™ API is not strictly prohibited in their `terms of use`_, it is
not encouraged either: company representatives have already stated that they do not currently consider making the
API opened for all customers and this feature is limited to their `pro tracking offer`_.
Therefore, by using ``gps_tracker`` you:

1. Accept to use this direct API access in a reasonable manner by limiting the query rate to the bare minimum required
for your application.

2. Understand that the Invoxia company may take any action they see fit regarding your account if they consider your
usage of their API to be in violation with their terms of use.

.. _Invoxia: https://www.invoxia.com/
.. _LoRa: https://lora-alliance.org/
.. _SigFox: https://www.sigfox.com/
.. _Android: https://play.google.com/store/apps/details?id=com.invoxia.track
.. _iOS: https://apps.apple.com/fr/app/invoxia-gps/id1261314542
.. _`terms of use`: https://www.invoxia.com/fr/legal/site/terms
.. _pro tracking offer: https://tracking.invoxia.com
esnb 0.10.0
Earth Science Notebook Tools for Climate and Weather Model Analysis
Requires Python: >=3.11
Keywords: model, analysis

[![](https://github.com/jkrasting/notebook-template/actions/workflows/python-app.yml/badge.svg)](https://github.com/jkrasting/notebook-template/actions/workflows/python-app.yml)
[![PyPi](https://img.shields.io/pypi/v/esnb.svg)](https://pypi.python.org/pypi/esnb/)
[![Documentation Status](https://readthedocs.org/projects/esnb/badge/?version=latest)](https://esnb.readthedocs.io/en/latest/?badge=latest)

# ESNB: Earth System Notebook
#### <i>(formerly `notebook-template`)</i>
Repository for analysis notebook template development.

Link to work-in-progress [documentation](https://docs.google.com/document/d/1cY-yWoEOANqsDICZWNFNkxbwUHjEXBL63mL6aBbVyyM/edit?usp=sharing) on Google Docs. (You may need to request access.)


## Standards
The final converged notebook should make use of established standards and conventions as much as possible.

[Link to EMDS Standards](https://github.com/Earth-System-Diagnostics-Standards/EMDS/blob/main/standards.md)