semvec/pypi-packages huggingface dataset:
fink-science 7.4.0
User-defined science module for the Fink broker.

[![Rubin](https://github.com/astrolabsoftware/fink-science/actions/workflows/run_test_rubin.yml/badge.svg)](https://github.com/astrolabsoftware/fink-science/actions/workflows/run_test_rubin.yml)
[![ZTF](https://github.com/astrolabsoftware/fink-science/actions/workflows/run_test_ztf.yml/badge.svg)](https://github.com/astrolabsoftware/fink-science/actions/workflows/run_test_ztf.yml)
[![PEP8](https://github.com/astrolabsoftware/fink-science/workflows/PEP8/badge.svg)](https://github.com/astrolabsoftware/fink-science/actions?query=workflow%3APEP8)
[![codecov](https://codecov.io/gh/astrolabsoftware/fink-science/branch/master/graph/badge.svg)](https://codecov.io/gh/astrolabsoftware/fink-science)

# Fink Science

This repository contains science modules used to generate added values to alert collected by the [Fink broker](https://github.com/astrolabsoftware/fink-broker). More information on the [documentation](https://fink-broker.readthedocs.io/en/latest/broker/science_modules/).

## Installation

`fink-science` can be easily installed from sources directly using a release tag (see [here](https://github.com/astrolabsoftware/fink-science/releases)):

```
# e.g. installing version 5.7.2
export TMPDIR=somewhere_with_disk_space
pip install --no-dependencies --cache-dir=$TMPDIR https://github.com/astrolabsoftware/fink-science/archive/5.7.2.zip
```
adasplash 0.1.1b0
AdaSplash: Efficient Adaptive Sparse Attention in Triton
Requires Python: >=3.8

# AdaSplash: Adaptive Sparse Flash Attention

[![PyPI version](https://badge.fury.io/py/adasplash.svg)](https://badge.fury.io/py/adasplash)

AdaSplash, aka flash entmax attention, is an efficient adaptive sparse attention mechanism implemented in Triton.
Check out our paper: https://arxiv.org/abs/2502.12082.

## Installation

You can install AdaSplash via pip:

```bash
pip install adasplash
```

Alternatively, install the latest development version directly from GitHub:

```bash
pip install git+https://github.com/deep-spin/adasplash.git
```

## Usage

AdaSplash provides three main functions, all available via `from adasplash import ...`:

### **Triton Entmax** (Optimized Entmax Activation)
```python
from adasplash import triton_entmax
import torch

x = torch.randn(128, 256).cuda()
y = triton_entmax(x, alpha=1.5, n_iter=10, fast_math=True)
```
- Uses **Halley's method + bisection** instead of pure bisection.
- Faster and more efficient than traditional Entmax implementations.

### **AdaSplash with Block Masking**
```python
from adasplash import adasplash

q = torch.randn(1, 8, 128, 64, device="cuda")
k = torch.randn(1, 8, 128, 64, device="cuda")
v = torch.randn(1, 8, 128, 64, device="cuda")

output = adasplash(q, k, v, alpha=1.5, niter=10, is_causal=True, varlen=None)
```
- Leverages **adaptive sparsity** for efficiency in both forward and backward passes.
- Requires **O(Tr × Tc) bits** of extra memory for storing a binary mask per block.

### **AdaSplash without Block Masking**
```python
from adasplash import adasplash_no_block_mask

output = adasplash_no_block_mask(q, k, v, alpha=1.5, niter=10, is_causal=True, varlen=None)
```
- Does **not** use block masking but still benefits from **tiling and fused ops** for efficiency.
- Requires **less memory** than the block-masked version.

### Key Features

Variable Length Sequences:
```python
varlen = torch.tensor([34, 128], device='cuda')  # Actual sequence lengths
output = adasplash(q, k, v, varlen=varlen)
```

Adaptive Sparsity Control:
```python
# Control sparsity via alpha parameter
output = adasplash(q, k, v, alpha=1.333)  # More dense
output = adasplash(q, k, v, alpha=2.0)  # More sparse
```

Causal and Non-causal Masking:
```python
output = adasplash(q, k, v, is_causal=True)  # Causal masking
output = adasplash(q, k, v, is_causal=False)  # Non-causal masking
```

## Benchmark

![Benchmark](benchmark.png)



## Testing
To ensure the library works as expected, install the development dependencies and run tests:

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

## Citation
If you use AdaSplash in your research, please cite:

```
@article{goncalves2025adasplash,
  title={AdaSplash: Adaptive Sparse Flash Attention},
  author={Nuno Gonçalves and Marcos Treviso and André F. T. Martins},
  journal={arXiv preprint arXiv:2502.12082},
  url={https://arxiv.org/abs/2502.12082},
  year={2025}
}
```

## License
AdaSplash is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
wolf-comm 0.0.47
A package to communicate with Wolf SmartSet Cloud

## Library to handle Wolf SmartSet communication.

### Features:
- Built-in authentication
- Session creation
- Fetch all devices you have
- Fetch all parameters description
- Fetch value for specific parameter

### Parameter descriptions
Parameters verified only for FGB-28 system.
Other should work.
Keep in mind that core implementation of fetching parameters is removing duplications by value_id and name.
FOSC 0.1.1a1
Framework for Optimal Extraction of Clusters
Requires Python: >=3.6

# FOSC
A Python version of the Framework for optimal extraction of clusters (FOSC) (CAMPELLO et al., 2013) [1].

> **Note:**
> This is a test version and may contain bugs. If you notice anything wrong, please contact the developers.

## Quick Start Tutorial

This is a tutorial that teaches how to quickly start using the FOSC package.

Considering you have a dataset in a CSV file, start by loading its data.

```python
import numpy as np

mat = np.genfromtxt(file_path, dtype=float, delimiter=',', usecols=range(0, -1), missing_values=np.nan)
```

> **Note:**
>
> Replace `file_path` with your dataset's path.
>
> In this example, the last column represents the cluster to which an object is assigned, according to another algorithm. Since we are using our own method, we ignore the last column with the `usecols` parameter.

Choose the MClSize (minimum cluster size) parameter and the method of linkage:

```python
mclsize = 30
linkage_method = "single"
```

Calculate the distance matrix:

```python
from scipy.spatial import distance_matrix

dist_mat = distance_matrix(mat, mat, p=2)
```

Now, you can instantiate the FOSC object:

```python
from FOSC import FOSC

foscFramework = FOSC(dist_mat, linkage_method, mclsize)
```

And run the FOSC algorithm:

```python
infiniteStability = foscFramework.propagateTree()
partition, lastObjects = foscFramework.findProminentClusters(1, infiniteStability)
```

The partition returned from the `findProminentClusters` method is the result of running the FOSC algorithm, i.e., the cluster extraction.

Finally, you can visualize the results using the functions available in the Plot module:

```python
from FOSC import Plot

Plot.plotDendrogram(foscFramework)
Plot.plotSilhouette(foscFramework, partition)
Plot.plotReachability(foscFramework, partition)
```

## References

[1] CAMPELLO, R. J.; MOULAVI, D.; ZIMEK, A.; SANDER, J. A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies. Data Mining and Knowledge Discovery, Springer, v. 27, p. 344â€“371, 2013.
fastapi-casdoor-fixed 0.1.10
Integration Casdoor with FastAPI
Requires Python: >=3.10
Keywords: auth, casdoor, fastapi, dependency

# fastAPI Casdoor
Integration Casdoor with FastAPI

## Installation

```bash
pip install fastapi-casdoor
```

## Usage

```python
from typing import Annotated, Depends
from fastapi import FastAPI
from fastapi_casdoor.deps import get_current_user
from fastapi_casdoor.models import User


@app.get("/")
async def root(user: Annotated[User, Depends(get_current_user)]):
    return {
        "user": user,
    }
```
doc2txt-antiword 0.1.3
A Python package to convert .doc files to .txt using the Antiword binary.
Requires Python: >=3.6

# Doc2txt-antiword

## Overview

**Antiword Converter** is a Python package designed to convert Microsoft Word `.doc` files into plain text format using the [Antiword](https://www.softpedia.com/get/Office-tools/Other-Office-Tools/Antiword.shtml) binary. This package provides a simple interface for converting `.doc` files to `.txt` format, either returning the converted text or writing it to an output file.

### Features:

- Converts `.doc` files to plain `.txt` format.
- Uses the **Antiword** binary for high-accuracy extraction.
- Simple API for converting documents with just one function.
- Error handling for common issues like missing files or incorrect formats.

## Requirements

- Python 3.6+
- Antiword binary (included in the package)

## Installation

To install `doc2txt-antiword` python package using `pip:`

`pip install doc2txt-antiword`

[pypi.org](https://pypi.org/project/doc2txt-antiword/ "https://pypi.org/project/doc2txt-antiword/")

To install the **doc2txt-antiword** package, follow these steps:

1. Clone the repository:

   ```bash
   git clone https://github.com/Bhola-kumar/doc2txt-antiword.git
   ```
2. Navigate into the directory:

   ```bash
   cd doc2txt-antiword
   ```
3. Install the package using `pip`:

   ```bash
   pip install .
   ```

   This will install the `doc2txt-antiword` package and its dependencies.

## Usage

Once installed, you can use the `AntiwordConverter` class to convert `.doc` files into `.txt` format.

### Example: Converting a `.doc` File and Returning the Text

```python
 from doc2txt.converter import AntiwordConverter

# Initialize the converter
converter = AntiwordConverter()

# Path to the input .doc file
doc_path = '/path/to/your/document.doc'

# Convert the .doc file to text and print the result
converted_text = converter.convert_doc_to_txt(doc_path)

if converted_text:
    print("Converted Text:")
    print(converted_text)
else:  # converted_text is None
    print("Conversion failed.")
```
transform-h4nnah 0.0.1
Koordinaatide konvertor.
Requires Python: >=3.8

# Coordinate transforming package

This package converts coordinates from WGS84 to L-Est97 and the other way around.


# To install:

pip install -i https://test.pypi.org/simple/transform-h4nnah

Because transform_h4nnah also uses Transformer from pyProj, this should be installed aswell, just in case:
pip install pyproj
hogwarts-logger 1.0.0
用于测试框架的日志封装
Requires Python: >=3.9

# hogwarts-logger

## what

hogwarts-logger，一个专门方便pycharm查看的log库。

## why

为了方便霍格沃兹学社的测试开发教学，开发了一个方便打印日志的库

## where 特点

- 可以打印pycharm直接点击到源文件位置的路径提示
- 可以打印上下log之间的耗时
- 可以打印函数调用链 function call trace

## how

### 项目级别logger

默认使用包名作为日志名字，适合项目中统一使用。

```python
from hogwarts_logger import Logger

logger = Logger.get_instance()
```

### 实例级别logger

允许创建多种Logger

```python
from hogwarts_logger import Logger

logger1 = Logger()
logger2 = Logger()
```

### 便捷使用

```python
from hogwarts_logger import *

trace('trace message')
debug('debug message')
info('info message')
```

### 日志级别

```python
from hogwarts_logger import Logger

logger = Logger()
logger.set_level(logger.trace_level)
logger.trace('trace')
logger.log('trace')
logger.debug('debug')
logger.info('info')
logger.warn('warn')
```

#### default 默认级别

```log
info
warn
```

#### info级别

```log
test_path_log.py:199:test_info_level 2025-03-23 03:47:05,971 I info
test_path_log.py:200:test_info_level 2025-03-23 03:47:05,972 W warn

```

#### debug级别

```log
test_path_log.py:188:test_debug_level:0.00  2025-03-23 03:47:05,968 D debug
test_path_log.py:189:test_debug_level:0.00  2025-03-23 03:47:05,969 I info
test_path_log.py:190:test_debug_level:0.00  2025-03-23 03:47:05,969 W warn
```

#### trace级别

```log
test_path_log.py:177:pytest_pyfunc_call:test_trace_level:0.00 2025-03-23 03:47:05,965 L trace
test_path_log.py:178:pytest_pyfunc_call:test_trace_level:0.00 2025-03-23 03:47:05,966 D debug
test_path_log.py:179:pytest_pyfunc_call:test_trace_level:0.00 2025-03-23 03:47:05,966 I info
test_path_log.py:180:pytest_pyfunc_call:test_trace_level:0.00 2025-03-23 03:47:05,967 W warn
```

## who

- [霍格沃兹测试开发学社](https://testing-studio.com)
- [测吧（北京）科技有限公司](https://ceba.ceshiren.com)
- [测试人社区](https://ceshiren.com)
- 思寒 seveniruby
insideopt-gains 0.0.9
InsideOpt Seeker Linux 311 Distribution
Requires Python: >=3.11.0
Keywords: insideopt, seeker, optimization

===========
test-seeker
===========

InsideOpt Seeker GAINS Linux Distribution

Credits
-------

This package was created with Cookiecutter_.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
anywsgi 0.0.1
TODO

-- coding: utf-8 --

# anywsgi-py

quick and dirty use any available WSGI server for running from command line

  * https://pypi.org/project/anywsgi/
  * https://github.com/clach04/anywsgi-py

## Getting Started

### Regular install

    pip install anywsgi

### Without a source code checkout

Picking up the latest version

    pip uninstall anywsgi; python -m pip install --upgrade git+https://github.com/clach04/anywsgi-py.git

### From a source code checkout

    # pip uninstall anywsgi
    # python -m pip install -r requirements.txt
    # TODO requirements_optional.txt
    python -m pip install -e .