semvec/pypi-packages huggingface dataset:
sphinx-lookup 0.2.2
Lightweight CLI for querying the Sphinx Scanner LLM Attack Database
Requires Python: >=3.10
Keywords: ai-safety, attacks, cli, llm, red-team, security

# Sphinx Lookup

**Lightweight CLI for querying the Sphinx Scanner LLM Attack Database**

Query 1900+ documented LLM/AI attacks directly from your terminal. No Docker, Redis, or Elasticsearch required — just 4 dependencies.

## Installation

```bash
pip install sphinx-lookup
```

## Quick Start

```bash
# Configure your Supabase credentials (one-time setup)
sphinx-lookup configure

# Search for attacks
sphinx-lookup search "prompt injection"

# List attacks by category
sphinx-lookup list --category jailbreak

# Show full attack details
sphinx-lookup show <attack-id>

# View statistics
sphinx-lookup stats
```

## Commands

| Command | Description |
|---------|-------------|
| `configure` | Interactive setup for Supabase credentials |
| `search <query>` | Search attacks by name or description |
| `list` | List attacks with optional filters |
| `show <id>` | Display full attack card (Steckbrief) |
| `categories` | Show category breakdown with counts |
| `stats` | Database overview statistics |

## Attack Categories

- `prompt_injection` - Manipulating LLM behavior through crafted prompts
- `jailbreak` - Bypassing safety filters and restrictions
- `adversarial_input` - Inputs designed to cause unexpected behavior
- `data_extraction` - Extracting training data or sensitive information
- `model_manipulation` - Altering model weights or behavior
- `denial_of_service` - Causing service disruption
- `privacy_attack` - Violating user privacy
- `agent_manipulation` - Targeting AI agents and tool use

## Modalities

Filter attacks by input type: `text`, `reasoning`, `vision`, `audio`, `multimodal`, `code`

```bash
sphinx-lookup list --modality vision
```

## Configuration

Credentials are read in this order (later overrides earlier):
1. `.env` in current directory
2. `~/.sphinx-lookup.env`
3. Environment variables (`SUPABASE_URL`, `SUPABASE_KEY`)

## Requirements

- Python 3.10+
- Supabase project with Sphinx Scanner database

## Part of Sphinx Scanner

This is the lightweight lookup CLI from the [Sphinx Scanner](https://github.com/sphinx-scanner/sphinx-scanner) project. For the full crawling and extraction pipeline, see the main project.

## License

MIT
h2o-secure-store 1.3.3
Python client for H2O Secure Store
Requires Python: >=3.8

# `h2o-secure-store`

[![pypi](https://img.shields.io/pypi/v/h2o-secure-store?style=flat-square)](https://pypi.org/project/h2o-secure-store/)

Python client for [H2O Secure Store](https://docs.h2o.ai/secure-store/).

## Installation

```sh
pip install h2o-secure-store
```

## Usage

Visit the [documentation](https://docs.h2o.ai/secure-store/py/initialization) for more information.
pdf-text-image-xtract 0.2.0
A package for extracting images, texts and tables from pdfs
Requires Python: >=3.12

# PDF Text, Tables & Image Extractor

A robust Python-based utility designed to extract text, tables, and images from PDF documents. This tool supports both **local file systems** and **Amazon S3**, utilizing multi-threading to speed up the extraction process.

## 🚀 Features

* **Hybrid Storage Support:** Seamlessly process PDFs from local paths or S3 buckets (`s3://...`).
* **Comprehensive Extraction:**
    * **Text:** Full-page text extraction.
    * **Tables:** Structured table data.
    * **Images:** High-fidelity image extraction.
* **Multi-threaded Performance:** Extracts text and images in parallel to significantly reduce processing time.
* **Cloud Ready:** Built-in methods to save results directly back to S3 as JSON, Markdown and PNG files.
* **PDF type Detection** Can detect if a pdf is a scanned pdf with no text overlay
---

## 🛠 Installation

### 1. Install using pip
```bash
pip install pdf_text_image_xtract
```
## Basic Example Usage

```python

from pdf_text_image_xtract import PDFTextImageExtractor

# Initialize the extractor with a local path
extractor = PDFTextImageExtractor("documents/report.pdf")

# Process the PDF (runs text and image extraction in parallel)
extractor.process()

# Save results to local disk
extractor.save_text_data_to_json("output/data.json")
extractor.save_images_to_disk("output/extracted_images")

```

## Cloud Example (AWS S3)

```python
from pdf_text_image_xtract import PDFTextImageExtractor

# Initialize with an S3 path
s3_path = "s3://my-bucket/input/document.pdf"
extractor = PDFTextImageExtractor(s3_path)

# Extract data
extractor.process()

# Upload results directly back to S3
extractor.save_text_data_to_json_s3(
    bucket_name="my-bucket",
    object_key="results/text_data.json",
)

extractor.save_images_to_s3(
    bucket_name="my-bucket",
    folder="results/images",
)

```
dspp-reader 0.4.0
Software for reading SQMs and TESS-W4C devices, used for measuring sky brightness.
Requires Python: >=3.13
Keywords: sqm, sqm-le, tess-4c, sky, darkness, astronomy

# Dark Sky Protection Photometers Reader

[![Upload Python Package](https://github.com/dark-sky-protection/dspp_reader/actions/workflows/python-publish.yml/badge.svg)](https://github.com/dark-sky-protection/dspp_reader/actions/workflows/python-publish.yml)
[![Documentation Status](https://readthedocs.org/projects/dspp-reader/badge/?version=latest)](https://dspp-reader.readthedocs.io/en/latest/?badge=latest)
![PyPI - Version](https://img.shields.io/pypi/v/dspp-reader)



# Overview

This package implements two console scripts `read-sqmle` and `read-tessw4c`

Those scripts implement very similar arguments. use `--help` to get a list of usable arguments

The recommended way to make it work is through a configuration file, in order to create a configuration
file use `--config-file-example` and to use an existing configuration file use `--config-file <path-to-file.yaml>`

Examples are not included here because this is a very early state of development.

# Configuration file example

>[!NOTE]
>This is not updated automatically, so always rely on `--config-file-example`

For TESS-W4C devices
```yaml
site_id: ctio
site_name: Cerro Tololo
site_latitude: -30.169166
site_longitude: -70.804
site_elevation: 2174
site_timezone: America/Santiago
sun_altitude: -10
device_type: tess-w4c
device_id: stars1823
device_altitude: 45
device_azimuth: 0
device_ip: 0.0.0.0
device_port: 32
use_udp: false
udp_bind_ip: 0.0.0.0
udp_port: 2255
read_all_the_time: false
save_to_file: true
save_to_database: false
post_to_api: false
save_files_to: /path/to/where/to/save/the/data
api_endpoint: http://<hostname>/api/tess-w4c
api_token: <get-an-appropriate-api-token>
file_format: tsv
```

For SQM-LE devices
```yaml
site_id: ctio
site_name: Cerro Tololo
site_latitude: -30.169166
site_longitude: -70.804
site_elevation: 2174
site_timezone: America/Santiago
sun_altitude: -10
device_type: sqm-le
device_id: '1823'
device_altitude: 45
device_azimuth: 0
device_ip: 0.0.0.0
device_port: 10001
device_window_correction: -0.11
number_of_reads: 5
reads_frequency: 30
read_all_the_time: false
save_to_file: true
save_to_database: false
post_to_api: false
save_files_to:  /path/to/where/to/save/the/data
api_endpoint: http://<hostname>/api/sqm-le
api_token: <get-an-appropriate-api-token>
file_format: tsv
```
freeagent 0.2.1
Public class
Requires Python: >=3.8

# freeagent

`freeagent` is a python library for using the freeagent API.

## Initial setup

Create an API app entry at the [Freeagent Dev Portal](https://dev.freeagent.com)

## Exmple

```python
from os import environ
import json

from freeagent import FreeAgent

def _load_token():
    with open("token.json", "r") as f:
        token = json.load(f)
    except (FileNotFoundError, json.JSONDecodeError):
        token = None
    return token

def _save_token(token_data):
    # save the token
    with open("token.json", "w") as f:
        json.dump(token_data, f)

client_id = environ["FREEAGENT_ID"]
client_secret = environ["FREEAGENT_SECRET"]

token = _load_token()

freeagent_client = FreeAgent()
freeagent_client.authenticate(client_id, client_secret ,_save_token, token)

main_response = freeagent_client.get_api("users/me")
print(
f"✅ Authenticated! User info: {main_response['user']['first_name']} {main_response['user']['last_name']}"
)

paypal_id = freeagent_client.bank.get_first_paypal_id()
paypal_data = freeagent_client.bank.get_unexplained_transactions(paypal_id)
```

## Documentation

Full documentation is available at  
👉 [https://a16bitsysop.github.io/freeagentPY/](https://a16bitsysop.github.io/freeagentPY/)

---

## Running Tests

Run tests:

```bash
pytest
```

## Contributing

Contributions are welcome! Please:

1. Fork the repo
2. Create your feature branch `git checkout -b my-feature`
3. Edit the source code to add and test your changes
4. Commit your changes `git commit -m 'Add some feature'`
5. Push to your branch `git push origin my-feature`
6. Open a Pull Request

Please follow the existing code style and write tests for new features.

---

## License

This project is licensed under the MIT [MIT License](https://github.com/a16bitsysop/freeagentPY/blob/main/LICENSE).

---

## Contact

Created and maintained by Duncan Bellamy.
Feel free to open issues or reach out on GitHub.

---
hdfstream 0.0.24
Python client for the hdfstream service.
Requires Python: >=3.10

# Python client module for the hdfstream HDF5 streaming service

This module provides facilities to access HDF5 files stored on a
remote server which streams their contents in messagepack format. It
attempts to replicate the [h5py](https://www.h5py.org/) high level
interface to some extent.

The source code and issue tracker are hosted on github:
https://github.com/jchelly/hdfstream-python

Releases are hosted on pypi: https://pypi.org/project/hdfstream/

For documentation see: https://hdfstream-python.readthedocs.io/en/latest

## Installation

The module can be installed using pip:
```
pip install hdfstream
```

## Quick start

### Connecting to the server

You can connect to the server as follows:
```
import hdfstream
root = hdfstream.open("https://localhost:8443/hdfstream", "/")
```
Here, the first parameter is the server URL and the second is the name
of the directory to open. This returns a RemoteDirectory object.

### Opening a file

The RemoteDirectory behaves like a python dictionary where the keys
are the names of files and subdirectories within the directory. A file
or subdirectory can be opened by like this:
```
# Open a HDF5 file
snap_file = root["EAGLE/Fiducial_models/RefL0012N0188/snapshot_028_z000p000/snap_028_z000p000.0.hdf5"]
```
which opens the specified file and returns a RemoteFile object.

### Reading datasets

The file object acts like a dictionary containing HDF5 groups and
datasets, so we can read a dataset as follows:
```
# Read all dark matter particle positions in the file
dm_pos = snap_file["PartType1/Coordinates"][...]
```
or if we only want to download part of the dataset:
```
# Read the first 100 dark matter particle positions
dm_pos = snap_file["PartType1/Coordinates"][:100,:]
```
HDF5 attributes can be accessed using the attrs field of group and dataset objects:
```
print(snap_file["Header"].attrs)
```

## Building the documentation

To make a local copy of the documentation in html format:
```
pip install sphinx sphinx-rtd-theme sphinx-autodoc-typehints
cd docs
make html
```

## Testing

There are some basic unit tests which can be run without access to a
server. The repository includes a few pre-recorded responses from the
server and a small amount of simulation data to check that the module
can decode responses correctly. The tests can be run by running
`pytest` in the source directory.

To regenerate the stored responses, assuming the server is available:
```
rm -r ./tests/cassettes/
pytest --record-mode=rewrite
```
Other pytest command line flags which might be useful:
  * `--disable-recording`: run a "live" test ignoring the stored responses and generating real http requests
  * `--server`: specify the server URL to use in tests
  * `--no-verify-cert`: don't verify certificates (e.g. when testing against a local development server)
stwcs 1.7.6
Computes the WCS of HST images.

STWCS
-----

.. image:: https://github.com/spacetelescope/stwcs/actions/workflows/weekly_cron.yml/badge.svg
    :target: https://github.com/spacetelescope/stwcs/actions
    :alt: CI Status
    
.. image:: https://readthedocs.org/projects/stwcs/badge/?version=latest
    :target: http://stwcs.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status

`STWCS <https://stwcs.readthedocs.io>`__ provides support for WCS distortion models and coordinate
transformation for the imaging instruments on the Hubble Space Telescope (HST).

Note
----
Starting with version 1.4, STWCS requires Python 3.5 and above.

Installation
------------

To clone the project from github::

    git clone https://github.com/spacetelescope/stwcs.git

To install::

    python setup.py install
Topsis-Hardik-102303945 1.0.0
A Python package to implement TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)
Requires Python: >=3.6

# Topsis-Hardik-102303945

A Python package to implement TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution).

## Installation

```bash
pip install Topsis-Hardik-102303945
```

## Usage

This package can be used via the command line.

```bash
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
```

### Arguments

1.  **InputDataFile**: Path to the input CSV file containing data.
    - First column should be the name/ID of the alternative.
    - Other columns must be numeric.
2.  **Weights**: Comma-separated numbers representing weights for each criterion (e.g., "1,1,1,2").
3.  **Impacts**: Comma-separated signs ('+' or '-') representing the impact of each criterion (e.g., "+,+,-,+").
4.  **ResultFileName**: Path where the result CSV will be saved.

### Example

```bash
topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
```

## Output

The output file will contain the original data with two additional columns:
- **Topsis Score**: The calculated performance score.
- **Rank**: The ranking of the alternative.
Topsis-Paridhi-102303715 1.0.3
TOPSIS implementation as a Python package
Requires Python: >=3.7

# TOPSIS Python Package

**Topsis-Paridhi-102303715** is a Python package for solving **Multiple Criteria Decision Making (MCDM)** problems using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)**.

This package allows users to rank alternatives based on multiple criteria by providing:
- a CSV input file
- a weight vector
- an impact vector

The package computes the **TOPSIS score** and **rank** for each alternative.

---

## Installation

Use the package manager `pip` to install the package:

```bash
pip install Topsis-Paridhi-102303715
```

## Input Parameters
1. Weights Vector
A comma-separated list of numeric values representing the importance of each criterion.
Example:
0.25,0.25,0.25,0.25

2. Impacts Vector
A comma-separated list of impacts where:
- "+" indicates a beneficial criterion
- "-" indicates a non-beneficial criterion

Example:
+,+,-,+

## Usage: 

```bash
topsis <input_file.csv> "<weights>" "<impacts>" <output_file.csv>
```
Example
topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+" output.csv

## Output
The output file is a CSV file containing:
- original input columns
- Topsis Score
- Rank

## Sample Output
TOPSIS RESULTS
| Item | Similarity Score | Rank |
|------|------------------|------|
| A    | 1.000000         | 1    |
| C    | 0.976532         | 2    |
| B    | 0.845210         | 3    |

Higher TOPSIS score indicates a better alternative.

## Input File Format:
Input file must be a CSV file:
- First column should contain alternative names
- Remaining columns must contain numeric values only
- File must contain at least 3 columns

Example sample.csv
| Item | Feature1 | Feature2 | Feature3 |
|------|----------|----------|----------|
| A    | 10       | 7        | 9        |
| B    | 8        | 6        | 5        |
| C    | 9        | 9        | 8        |

## Validation Rules:
The program performs the following validations:
- Input file existence check
- Minimum column count check
- Numeric value validation
- Equal number of weights, impacts, and criteria
- Impacts must be either + or -
- Weights and impacts must be comma-separated
- Appropriate error messages are displayed if invalid input is detected.

## License
This project is licensed under the MIT License.