semvec/pypi-packages huggingface dataset:
ncmdump-py 1.1.6
Dump ncm files to mp3 or flac files.
Requires Python: >=3.7

# ncmdump-py

A simple package used to dump ncm files to mp3 or flac files, it can:

- Decrypt and dump `.ncm` files.
- Auto add album and cover info into `.mp3` or `.flac` files.
- Auto try download cover image when there is no cover data in `.ncm` files.

## Install

```bat
pip install ncmdump-py
```

## Usage

### Command-line tool

```plain
python -m ncmdump [-h] [--in-folder IN_FOLDER] [--out-folder OUT_FOLDER] [--dump-metadata] [--dump-cover] [files ...]
```

```plain
usage: ncmdump [-h] [--in-folder IN_FOLDER] [--out-folder OUT_FOLDER] [--dump-metadata] [--dump-cover] [files ...]

Dump ncm files with progress bar and logging info, only process files with suffix '.ncm'

positional arguments:
  files                 Files to dump, can follow multiple files.

optional arguments:
  -h, --help            show this help message and exit
  --in-folder IN_FOLDER
                        Input folder of files to dump.
  --out-folder OUT_FOLDER
                        Output folder of files dumped.
  --dump-metadata       Whether dump metadata.
  --dump-cover          Whether dump album cover.
```

### Import in your code

```python
from ncmdump import NeteaseCloudMusicFile

ncmfile = NeteaseCloudMusicFile("filename.ncm")
ncmfile.decrypt()

print(ncmfile.music_metadata)  # show music metadata

ncmfile.dump_music("filename.mp3")  # auto detect correct suffix

# Maybe you also need dump metadata or cover image
# ncmfile.dump_metadata("filename.json")  
# ncmfile.dump_cover("filename.jpeg")
```

---

*If you think this project is helpful to you, :star: it and let more people see!*
django-upgrade 1.29.1
Automatically upgrade your Django project code.
Requires Python: >=3.9
Keywords: Django

==============
django-upgrade
==============

.. image:: https://img.shields.io/readthedocs/django-upgrade?style=for-the-badge
   :target: https://django-upgrade.readthedocs.io/en/latest/

.. image:: https://img.shields.io/github/actions/workflow/status/adamchainz/django-upgrade/main.yml.svg?branch=main&style=for-the-badge
   :target: https://github.com/adamchainz/django-upgrade/actions?workflow=CI

.. image:: https://img.shields.io/badge/Coverage-100%25-success?style=for-the-badge
  :target: https://github.com/adamchainz/django-upgrade/actions?workflow=CI

.. image:: https://img.shields.io/pypi/v/django-upgrade.svg?style=for-the-badge
   :target: https://pypi.org/project/django-upgrade/

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg?style=for-the-badge
   :target: https://github.com/psf/black

.. image:: https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white&style=for-the-badge
   :target: https://github.com/pre-commit/pre-commit
   :alt: pre-commit

----

.. figure:: https://raw.githubusercontent.com/adamchainz/django-upgrade/main/docs/_static/logo.svg
  :alt: django-upgrade logo
  :align: center

Automatically upgrade your Django project code.

Documentation
-------------

Please see https://django-upgrade.readthedocs.io/.
eurelis-llmatoolkit 2.0.0
Framework to build and manage IA based applications. Based on Langchain and LlamaIndex under the hood.
Requires Python: >=3.11.3

# Eurelis-LlmaToolkit

![Python : 11](https://img.shields.io/badge/Python-=3.11-green)
[![uv](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json)](https://github.com/astral-sh/uv)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
![Code style : black](https://img.shields.io/badge/Code_style-black-black)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)

[![Open Issues](https://img.shields.io/github/issues-raw/Eurelis/Eurelis-KB-Framework)](https://github.com/Eurelis/Eurelis-KB-Framework/issues)
[![GitHub star chart](https://img.shields.io/github/stars/langchain-ai/langchain?style=social)](https://star-history.com/#Eurelis/Eurelis-KB-Framework)
[![X (formerly Twitter) URL](https://img.shields.io/twitter/url?url=https%3A%2F%2Fx.com%2FAgence_Eurelis&label=Follow%20%40Eurelis)](https://x.com/Agence_Eurelis)

Framework to build and manage IA based applications.
Based on LlamaIndex under the hood.
Reboot de la librairie [Eurelis-KB-Framework](https://github.com/Eurelis/Eurelis-KB-Framework).

## General information

Eurelis-LlmaToolkit is released under the **[MIT license](/LICENSE)**.

### Changes

See [CHANGELOG.md](CHANGELOG.md)

### Security policy

See [SECURITY.md](SECURITY.md)

### Citing Eurelis-LlmaToolkit in publications

See [CITATION.cff](CITATION.cff)
passgenpro 0.1.0
Simple yet powerful password generator library.
Requires Python: >=3.7

﻿# passgenpro 🔐

A simple but powerful Python password generator.

## Usage

```python
from passgenpro.generator import generate

print(generate())               # 12 simvolluq random parol
print(generate(16, symbols=False))  # simvollar olmadan
cvglue 1.1.0
Glue tools of computer vision
Requires Python: >=3.6
Keywords: computer vision

<div align="center">
<img src="docs/icon1.png" width="250"/>

[![PyPI Version](https://img.shields.io/pypi/v/cvglue)](https://pypi.org/project/cvglue/) 
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE)

**中文简体** | [**English**](./README_EN.md)

</div>

----
CVGlue 是一个计算机视觉工具包。集成了人脸检测、姿态估计、质量评估等任务，提供统一的 OpenCV/PyTorch 数据处理接口，支持数据集自动化标注。


## ✨ 核心功能

**开箱即用**：统一接口调用多算法  
**高效可视化**：Jupyter 单行代码多格式图像显示（OpenCV/PIL/Tensor/文件路径）  
**数据集支持**：衔接 [IAP 数据集](https://github.com/Lamply/IAPDataset) 标注流程

| 类别 | 能力 | 支持算法 |
|------|------|----------|
| **人脸分析** | 人脸检测/关键点/姿态估计 | RetinaFace, AdaptiveWingLoss, FSA-Net |
| **质量评估** | 人脸质量评分 | TFace FIQA |
| **数据工具** | 图像预处理/可视化/标注 | OpenCV & PyTorch 封装 |
| **扩展模块** | 图像修复/通用分割 | LaMa, SegmentAnything (开发中) |


## 🚀 快速示例

创建一个 jupyter-notebook

```python
import cv2
import cvglue
from cvglue import displayer as display

parser = cvglue.parser.get_parser('lamply-faceid')
img = cv2.imread('tests/data/images/single_face_img.jpg')

anno = parser.parse_img(img)
iap_data = (img, anno)
img_disp = display.render_lamply(iap_data)
display.show([img, img_disp])
```

得到

<div align="center">
<img src="docs/output.png" width="600"/>
</div>


## ⚙️ 安装

```bash
pip install cvglue
```

一些较大的模型需要自行下载放置到 `TORCH_HOME` 路径下：
- `Resnet50_Final.pth`：https://github.com/biubug6/Pytorch_Retinaface
- `WFLW_4HG.pth`：https://github.com/protossw512/AdaptiveWingLoss
- `SDD_FIQA_checkpoints_r50.pth`：https://github.com/Tencent/TFace
- `FBCNN`: https://github.com/jiaxi-jiang/FBCNN/releases/download/v1.0/fbcnn_color.pth


## 🔌 第三方集成表

| 第三方代码             | 状态  | 用处             | 原项目链接                                                      |
| ----------------- | --- | -------------- | ---------------------------------------------------------- |
| FaceDetector      | ✅   | 人脸检测           | https://github.com/biubug6/Face-Detector-1MB-with-landmark |
| AdaptiveWing      | ✅   | 人脸关键点检测        | https://github.com/protossw512/AdaptiveWingLoss            |
| HeadPoseDetector  | ✅   | 头部姿态检测         | https://github.com/shamangary/FSA-Net                      |
| TFace             | ✅   | 人脸质量评价         | https://github.com/Tencent/TFace                           |
| LaMa              | ⏳   | 图像修复           | https://github.com/advimman/lama                           |
| FBCNN             | ⏳   | JPEG压缩修复       | https://github.com/jiaxi-jiang/FBCNN                       |
| SegmentAnything   | ⏳   | 通用分割           | https://github.com/facebookresearch/segment-anything       |
| InsightFace       | ✅   | faceid 提取/人脸属性 | https://github.com/TreB1eN/InsightFace_Pytorch             |
| AttributeDetector | ✅   | 人脸属性           | https://github.com/ageitgey/face_recognition               |
s4-clarity 1.8.0
A general purpose library for interacting with Clarity LIMS
Requires Python: >=2.7


==================
S4-Clarity Library
==================


.. image:: https://github.com/SemaphoreSolutions/s4-clarity-lib/actions/workflows/ci.yml/badge.svg?branch=master
    :target: https://github.com/SemaphoreSolutions/s4-clarity-lib/actions


Used in numerous labs around the world, the S4-Clarity library provides an easy-to-use integration with the BaseSpace Clarity LIMS API. The package includes:
   - Classes representing familiar Clarity API entities that provide read-write access to most properties.
   - Helper methods that simplify common operations.
   - Base classes for scripts that integrate with Clarity: EPPs, DSAs, and shell scripts.
   - Utilities that help with Clarity-related tasks, such as managing config slices, or automating the completion of a Step.

The S4-Clarity library lets developers interact with the Clarity API in fewer lines of code. With HTTP and XML boilerplate out of the way, you'll have your integration built in no time at all.


.. code-block:: python

    from s4.clarity.scripts import TriggeredStepEPP

    LibraryVolume = 2.0
    MolWeightBasePair = 660 * 1e6  # micrograms / mol
    AssumedBasePairs = 400.0
    TargetMolarity = 4.0
    Overage = 4


    class Normalization (TriggeredStepEPP):
        def on_record_details_enter(self):
            self.prefetch(self.PREFETCH_INPUTS, self.PREFETCH_OUTPUTS)

            for iomap in self.step.details.iomaps:
                library_concentration = iomap.input["Concentration"]
                library_molarity = library_concentration / (AssumedBasePairs * MolWeightBasePair)
                iomap.output["Concentration"] = library_concentration
                iomap.output["Molarity (nM)"] = library_molarity
                iomap.output["Library Vol (uL)"] = LibraryVolume
                iomap.output["Tris HCl (uL)"] = LibraryVolume * (library_molarity / TargetMolarity - 1)

            self.lims.artifacts.batch_update(self.step.details.outputs)


    if __name__ == "__main__":
        Normalization.main()

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

To install s4-clarity, simply use pip::

    $ pip install s4-clarity
pyramid-wtforms 3.1.0
pyramid_wtforms provides bindings for the Pyramid web framework to the WTForms library.
Requires Python: >=3.9

The API of this extension is inspired by [Flask-WTF](https://flask-wtf.readthedocs.org/en/latest/).

## Features

* Consistent APIs, users do not need to be confused with different name spaces.
* Provide single file / multi files field validation and handling which meet
  request model of Pyramid web framework.
* Support csrf support using builtin pyramid.session facility.
nifti_dynamic 0.2.0
A package for dynamic NIFTI analysis
Requires Python: >=3.8

![Banner](banner.jpg)

# nifti_dynamic 🧠⚡

Efficient TAC extraction and Patlak analysis of HUGE PET arrays in NIfTI format. Works with gzipped 4D PET images (saving 90% disk space vs DICOM) 📦. All algorithms load partial chunks to prevent memory crashes when handling massive arrays (440×440×645×69×4 bytes = 32GB).

> Implementation based on Andersen TL, et al. *Diagnostics* 2024;14(15):1590. [doi:10.3390/diagnostics14151590](https://doi.org/10.3390/diagnostics14151590)

## Installation 💾

```bash
pip install nifti_dynamic
```

**Important**: Always ensure `indexed_gzip` is installed for significantly faster reading of gzipped NIfTI arrays (.nii.gz) ⚡

## Performance ⏱️

- Automatic extraction of 4 VOIs: 30 seconds
- TAC for single organs: 10 seconds  
- TACs for all 114 TotalSegmentator organs: 10 minutes
- Full voxel Patlak for 440×440×645×69 array: 30 seconds

## CLI Usage 🚀

### Extract Input Function from Aorta

```bash
extract_input_function \
  --pet dpet.nii.gz \
  --totalseg totalseg.nii.gz \
  --output ./results
```

### Extract TACs for All Organs

```bash
extract_tacs \
  --pet dpet.nii.gz \
  --segmentation totalseg.nii.gz \
  --output ./tacs
```

### Voxel-wise Patlak Analysis

```bash
voxel_patlak \
  --pet dpet.nii.gz \
  --input-function input_fun.csv \
  --output ./patlak
```

### Downsample Dynamic PET (2x)

```bash
resample_pet \
  --pet dpet.nii.gz \
  --output dpet_2x.nii.gz
```

## Python API Usage

For programmatic access, see [example.py](example.py) for detailed Python API usage.