semvec/pypi-packages huggingface dataset:
strainpycon-inferencemethods 1.0.0.dev2
Strain disambiguation methods for mixed DNA samples. Built upon strainpycon package

# Introduction

The Strain Inference Methods (SIM)(strainpycon_inferencemethods) are a fork of the 
Python 3 strainpycon package used to disambiguate multiple strains in mixed samples of DNA. 
SIM builds upon strainpycon with methods to:
-disambiguate samples with many genetic markers (tested up to 2461 markers)
-fix a particular strain within a sample reconstruction
-compare samples for shared strains
-detect the presence or absence of particular strains in samples


# Contacts

Please direct questions to:
Gary Vestal, mojihaka@protonmail.com

# Installation

$pip install strainpycon_inferencemethods

# Test usage

import numpy as np
from strainpycon_inferencemethods import StrainRecon, StrainMatchB, StrainMatchT

S = StrainRecon()
m, n = 24, 3  # SNP sites, number of strains

(meas1, strains1, freq1), (meas2, strains2, freq2) = S.random_data_with_shared_strain(
	m, n, shared_index=0, gamma=0.00
)

strainmat, freqvec = S.compute_fixed(meas1, 4, strain_of_interest)
strainmat, freqvec = S.compute_fixed(meas2, 4, strain_of_interest)



strain_of_interest = strains1[0]
smb = StrainMatchB(S)

smb.is_strain_in_sample(
        meas1, strain_of_interest, k=5, gamma=0.01, 
        bayes_k_threshold=0.1, fixed_proportion_threshold=0.05
)



smt = StrainMatchT(S)

smt.determine_similarity_of_samples(
	meas1, meas2, maxK=3, gamma=0.01, STIMThresh=1.8e-7, 
	alpha=0.05, neutral_is_positive=True
)

# Test cases...

...are in the /test/ folder

# Strainpycon

This library was forked from strainpycon 
by Ymir Vigfusson, Lars Ruthotto, Rebecca M. Mitchell, Lauri Mustonen, and Xiangxi Gao, 
under the MIT License.

Please refer to the full documentation of StrainPycon at:
https://www.ymsir.com/strainpycon/
tokenpal 0.1.0
Your friendly token counting pal for OpenAI, Anthropic, and Google LLM models. Simple, accurate, fast.
Requires Python: >=3.9
Keywords: tokens, tokenizer, openai, anthropic, google, llm, chatgpt, gpt, claude, gemini, ai, machine-learning, nlp

# token-count

A simple, practical Python package for counting tokens across OpenAI, Anthropic, and Google (Gemini) models using their official tokenizers and APIs.

## Features

- **OpenAI**: Local, offline token counting via `tiktoken` (official tokenizer)
- **Anthropic**: Official `messages.count_tokens` API
- **Google (Gemini)**: Official `models.countTokens` via `google-genai` SDK
- Provider-agnostic message format
- Robust model name handling with fallbacks
- Optional dependencies for each provider

## Installation

```bash
# Basic installation (OpenAI support only)
pip install token-count

# With Anthropic support
pip install 'token-count[anthropic]'

# With Google support  
pip install 'token-count[google]'

# With all providers
pip install 'token-count[anthropic,google]'
merch-scripts-package 99.0.1
This package name is reserved to prevent dependency confusion attacks. Do not install.
Requires Python: >=3.7

## merch_scripts_package

### ⚠️ Security Placeholder ⚠️
This package has been reserved by our organization to prevent dependency confusion attacks.

You should not install this package.

This is a dummy package and contains no functional code. It exists purely to occupy the name on PyPI, ensuring that a malicious actor cannot upload a package with the same name and trick our systems into installing it.
iflow-mcp_netdisk-mcp-server 0.1.0
百度网盘MCP Server
Requires Python: >=3.12

## 简介
fileupload_tool.py为Python语言的开发者提供，可以帮助您快速通过stdio模式调用网盘开放平台提供的文件上传MCP Server。

## 准备开发环境

我们推荐通过`uv`构建虚拟环境来运行MCP Server，关于`uv`你可以在 [这里](https://docs.astral.sh/uv/getting-started/features/) 找到一些说明。
按照 [官方流程](https://modelcontextprotocol.io/quickstart/server) ，你会安装`Python`包管理工具`uv`。除此之外，你也可以尝试其他方法（如`Anaconda`）来创建你的`Python`虚拟环境。

**`Windows 建议使用 pip 或者 anaconda 来准备运行环境，将 uv run 命令替换为 python。`**

### 1. UV安装
安装之后可通过 **`uv help`** 命令检查是否安装成功
```
1. On macOS and Linux.
   curl -LsSf https://astral.sh/uv/install.sh | sh
2. On Windows.
   powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
3. With pip.
   pip install uv
```

> <img src="https://bce.bdstatic.com/doc/xpan-open-platform/doc/586ceddb7e10c32c6c7cb84fdae58344_586cedd.jpg" width="500" height="300" alt="图片描述">


### 2. 通过uv添加mcp依赖
执行以下命令安装MCP
```
uv add "mcp[cli]"
```
执行如下命令，验证mcp依赖是否安装成功
```
uv run mcp
```
当出现下图时代表安装成功


> <img src="https://bce.bdstatic.com/doc/xpan-open-platform/doc/9e1ea997c959383324ac51ea41e2bf9c_9e1ea99.png" width="500" height="240" alt="图片描述">

### 3. 运行环境
```
执行uv run ./netdisk.py命令会读取当前目录下的 pyproject.toml。
执行后会自动创建虚拟环境和安装依赖，同时在这个环境中运行代码。
你可以验证netdisk.py是否可运行，若无报错则环境依赖均配置完成。
```
* 推荐运行环境：Python 3.12
* 运行环境要求：python >= 3.12
* 需要依赖的库：见pyproject.toml

**运行示例：**

* 依赖环境安装中：

> ![67c7480d78ab7b2c3dd98fd3a2741f3a.png](https://bce.bdstatic.com/doc/xpan-open-platform/doc/67c7480d78ab7b2c3dd98fd3a2741f3a_67c7480.png)

* 依赖环境安装完成

> ![0dc446ae37cbb06846fb80d71de82dcf.png](https://bce.bdstatic.com/doc/xpan-open-platform/doc/0dc446ae37cbb06846fb80d71de82dcf_0dc446a.png)

## 使用上传工具
在支持mcp的host中配置如下内容，然后发起上传交互即可。
```
{
  "mcpServers": {
    "netdisk-fileupload": {
        "command": "<您的uv绝对路径>",
        "args": [
            "--directory",
            "<您的fileupload_tool.py所在父目录绝对路径>",
            "run",
            "fileupload_tool.py"
        ],
        "env": {
          "BAIDU_NETDISK_ACCESS_TOKEN": "<您的AccessToken>"
        }
    }
  }
}
```
cf-dns-xethhung12 0.1.0
A small package targeting to help on python project packaging process
Requires Python: >=3.11


# cf_dns_xethhung12

## Execution
The program requires two environment variables, 
* `py_cf_dns_zone` Zone ID can be found in cloudflare domain over view page (right bottom corner) 
* `py_cf_dns_token` generated in cloudflare profile > API Token page, the token require Zone:ZOne:read and Zone:DNS:read permission to the selected zone
Run through python interpreter:

### Help command
```shell
python -m cf_dns_xethhung12 -h
```

### Load cloudflare DNS records to tf file and then import the resource into tf state

```shell
# make sure the two environment variable is set
cf-dns-xethhung12 cf-dns as-tf > dns.tf
cf-dns-xethhung12 cf-dns import-tf -f dns.tf # check the script generated and execute it carefully

## result should be similar to below
# .
# .
# .
# tofu import cloudflare_record.{resource name} {zone id}/{resource id in cloudflare}
# tofu import cloudflare_record.{resource name} {zone id}/{resource id in cloudflare}
# tofu import cloudflare_record.{resource name} {zone id}/{resource id in cloudflare}
# .
# .
# .
```

Run through python project script
```shell
cf-dns-xethhung12 -h
```
## Development
The project requires `python` (3+ version) installed and `pip` ready for use on adding manage dependencies

#### Tools
|Name|Platform|Type|Description|
|---|---|---|---|
|install-dependencies.sh|shell|script| The scripts for installing depencies required|
|build.sh|shell|script| The scripts for build the package|
|build-and-deploy.sh|shell|script| The scripts for build and deploy the package|

* install-dependencies.sh
The script will install dependencies listed in `dev-requirements.txt` and `requirements.txt`. The first requirement file contains the dependencies for development like build and deploy tools. The second requirement file defined all required dependencies for the making the package works (**actual dependencies**).

## Useful Scripts
### Project Versioning
For version update in `pyproject.toml`.
This project use package [`xh-py-project-versioning`](https://github.com/xh-dev/xh-py-project-versioning) to manipulate the project version.

Simple usage includes:\
Base on current version, update the patch number with dev id
`python -m xh_py_project_versioning --patch` \
In case current version is `0.0.1`, the updated version will be `0.0.2-dev+000` 

To prompt the dev version to official version use command.
`python -m xh_py_project_versioning -r`.
Through the command, version `0.0.2-dev+000` will be prompt to `0.0.2` official versioning.

Base on current version, update the patch number directly
`python -m xh_py_project_versioning --patch -d` \
In case current version is `0.0.1`, the updated version will be `0.0.2` 

Base on current version, update the minor number directly
`python -m xh_py_project_versioning --minor -d` \
In case current version is `0.0.1`, the updated version will be `0.1.0` 

Base on current version, update the minor number directly
`python -m xh_py_project_versioning --minor -d` \
In case current version is `0.0.1`, the updated version will be `1.0.0`
nadi-py 0.7.2
Python bindings to the Network Analysis and Data Integration (NADI) System
Requires Python: >=3.8
Keywords: river, hydrology, network analysis, environmental modeling

This can be installed from `pypi` with `pip install nadi-py` command.

Then you can simply import and use it:

```python
import nadi

net = nadi.Network.from_str("a -> b")
print([n.NAME for n in net.nodes])
```

The functions are available inside the `nadi.functions` submodule.

```python
import nadi
import nadi.functions as fn

net = nadi.Network.from_str("a -> b")
fn.network.svg_save(net, "test.svg")
```

Refer to examples in [Nadi Book](https://nadi-system.github.io/python.html) for more details.
types-workalendar 17.0.0.20250827
Typing stubs for workalendar
Requires Python: >=3.9

## Typing stubs for workalendar

This is a [type stub package](https://typing.python.org/en/latest/tutorials/external_libraries.html)
for the [`workalendar`](https://github.com/workalendar/workalendar) package. It can be used by type checkers
to check code that uses `workalendar`. This version of
`types-workalendar` aims to provide accurate annotations for
`workalendar==17.0.*`.

This package is part of the [typeshed project](https://github.com/python/typeshed).
All fixes for types and metadata should be contributed there.
See [the README](https://github.com/python/typeshed/blob/main/README.md)
for more details. The source for this package can be found in the
[`stubs/workalendar`](https://github.com/python/typeshed/tree/main/stubs/workalendar)
directory.

This package was tested with the following type checkers:
* [mypy](https://github.com/python/mypy/) 1.17.1
* [pyright](https://github.com/microsoft/pyright) 1.1.404

It was generated from typeshed commit
[`4fa56fbc236c99159c6132254d8f182598469df8`](https://github.com/python/typeshed/commit/4fa56fbc236c99159c6132254d8f182598469df8).
py4D-browser-transform 0.0.1
py4D-browser plugin that adds utility functions which transform the datacube
Requires Python: >=3.10

# py4D-browser-transform

`py4D-browser-transform` is a plugin for [py4D-browser](https://github.com/sezelt/py4D-browser) that provides utility functions for transforming the datacube, currently including flipping, transposing, permuting axes. Other functions like cropping, resampling, and adjusting intensity offsets or scaling might be added in the future.

## Installation 
You can install `py4D-browser-transform` directly from GitHub using pip and git (assuming you have git avaialble):
```bash
pip install git+https://github.com/chiahao3/py4D-browser-transform
```

> 💡 **Note:** 
> - If you install into a fresh Python environment, `py4D-browser` and `py4DSTEM` will be automatically installed as dependencies so you don't need to install them first.
> - If you already have `py4D-browser` installed, you can install this plugin into the same Python environment.

## Usage

After installation, you should see the "Transform" submenu appear under the **"Plugins"** menu.  
From here, you can:

- Flip or transpose the diffraction pattern.  
- Permute dataset axes if your 4D dataset is not in the expected order.
![Demo of py4D-browser-transform](assets/demo.gif)

These operations would direcly modify the loaded datacube, but will not affect the raw file stored on disk. You can export the transformed datacube to disk using **File > Export Datacube**.

> 💡 **Note:** 
> - The order of flipping and permutation matters so if you mix these two operations, you'll have to reverse the operaitons in the exact opposite order to get back to the original state. For example, if you permute and then flip/transpose, you'll have to undo the flip/transpose and then reset the permute.

## License

GNU GPLv3

**py4D-browser-transform** is open source software distributed under a GPLv3 license.
It is free to use, alter, or build on, provided that any work derived from **py4D-browser-transform** is also kept free and open.
ddpw 5.5.1
A lightweight wrapper that scaffolds PyTorch's Distributed (Data) Parallel.
Requires Python: >=3.13
Keywords: pytorch, distributed compute

<h1 align="center">DDPW</h1>

**Distributed Data Parallel Wrapper (DDPW)** is a lightweight Python wrapper
relevant to [PyTorch](https://pytorch.org/) users.

DDPW handles basic logistical tasks such as creating threads on GPUs/SLURM
nodes, setting up inter-process communication, _etc._, and provides simple,
default utility methods to move modules to devices and get dataset samplers,
allowing the user to focus on the main aspects of the task. It is written in
Python 3.13. The [documentation](https://ddpw.projects.sujal.tv) contains
details on how to use this package.

## Overview

### Installation

[![PyPI](https://img.shields.io/pypi/v/ddpw)](https://pypi.org/project/ddpw/)

```bash
# with uv

# to instal and add to pyroject.toml
uv add [--active] ddpw
# or to simply instal
uv pip install ddpw

# with pip
pip install ddpw
```

### Examples

#### With the decorator `wrapper`

```python
from ddpw import Platform, wrapper

platform = Platform(device="gpu", n_cpus=32, ram=64, n_gpus=4, verbose=True)

@wrapper(platform)
def run(*args, **kwargs):
    # global and local ranks, and the process group in:
    # kwargs['global_rank'], # kwargs['local_rank'], kwargs['group']
    pass

if __name__ == '__main__':
    run(*args, **kwargs)
```

#### As a callable

```python
from ddpw import Platform, Wrapper

# some task
def run(*args, **kwargs):
    # global and local ranks, and the process group in:
    # kwargs['global_rank'], # kwargs['local_rank'], kwargs['group']
    pass

if __name__ == '__main__':
    # platform (e.g., 4 GPUs)
    platform = Platform(device='gpu', n_gpus=4)

    # wrapper
    wrapper = Wrapper(platform=platform)

    # start
    wrapper.start(task, *args, **kwargs)
```
codinsight 0.3.0
Add your description here
Requires Python: >=3.12

Codinsight - 複数ファイル解析の利用方法

概要
- 複数のファイルやディレクトリを再帰的に走査し、既存の解析エンジン（Style/Struct/Readability/Redundancy/Algorithm/Complexity/Quality）で一括解析を実行
- 拡張子のフィルタや除外パターン（node_modules, .git など）に対応
- 結果をファイル単位と集約統計（平均など）で取得可能
- Pydantic BaseModel で JSON 化が容易

## API 使用例

```python
from code_insight.core import CodeAnalysisType
from code_insight.multi_analysis import MultiFileAnalyzer

analyzer = MultiFileAnalyzer(
    exts={".py"},
    excludes={"node_modules", "target", ".git", ".venv", "__pycache__"},
)
result = analyzer.analyze(
    ["src", "tests"],
    [CodeAnalysisType.STYLE, CodeAnalysisType.STRUCT],
)
print(result.model_dump_json())
```

## 設定のカスタマイズ

```python
from code_insight.core import CodeAnalysis, AnalysisConfigs, CodeAnalysisType
from code_insight.code_analysis.quality import QualityAnalysisConfig
from code_insight.code_analysis.redundancy import RedundancyAnalysisConfig
from code_insight.code_analysis.style import StyleAnalysisConfig

# カスタム設定の作成
configs = AnalysisConfigs(
    quality=QualityAnalysisConfig(
        long_param_threshold=3,  # デフォルト: 5
        enabled=True
    ),
    redundancy=RedundancyAnalysisConfig(
        long_function_lines_threshold=30,  # デフォルト: 50
        long_function_complexity_threshold=8,  # デフォルト: 10
        ignored_function_names={"main", "__init__", "setup"}
    ),
    style=StyleAnalysisConfig(
        function_name_pattern=r"^[a-z_][a-z0-9_]*$",  # snake_case
        class_name_pattern=r"^[A-Z][a-zA-Z0-9]*$"     # PascalCase
    )
)

# 設定を使用した解析
analysis = CodeAnalysis(source_code, configs)
result = analysis.analyze([CodeAnalysisType.QUALITY, CodeAnalysisType.REDUNDANCY])

# 複数ファイル解析での設定使用
analyzer = MultiFileAnalyzer(configs=configs)
result = analyzer.analyze(["src"], [CodeAnalysisType.STYLE])
```

### 設定可能な項目

#### Quality解析
- `long_param_threshold`: 長引数関数の閾値（デフォルト: 5）
- `enabled`: 解析の有効/無効（デフォルト: True）

#### Redundancy解析
- `long_function_lines_threshold`: 長大関数の行数閾値（デフォルト: 50）
- `long_function_complexity_threshold`: 長大関数の複雑度閾値（デフォルト: 10）
- `ignored_function_names`: 未使用コード検出で無視する関数名（デフォルト: {"main", "__init__", "__main__"}）

#### Style解析
- `function_name_pattern`: 関数名の正規表現パターン（デフォルト: snake_case）
- `class_name_pattern`: クラス名の正規表現パターン（デフォルト: PascalCase）

#### その他の解析エンジン
- Algorithm, Complexity, Readability, Structの各解析エンジンにも同様の設定項目があります

主なオプション
- exts: 対象拡張子のセット（デフォルト: {".py"}）
- excludes: 除外ディレクトリ名のセット（デフォルト: {"node_modules", "target", ".git", ".venv", "__pycache__"}）

返却データの構造
- files: 各ファイルの解析結果（解析タイプ名 → メトリクス辞書）
- aggregate:
  - total_files: 解析対象に収集されたファイル数
  - analyzed_files: 実際に解析に成功したファイル数
  - errors: 解析時にエラーとなったファイルパスの一覧
  - by_type_avg: 解析タイプごとの各数値メトリクスの平均値

注意
- 現時点では直列処理のみ対応。大規模データや並列化は将来的な拡張予定
kernl-sdk 1.0.0rc7
Kernl SDK Python bindings
Requires Python: >=3.8

# Kernl SDK - Python Bindings

High-performance, cross-platform Python bindings for the Kernl SDK.

## Installation

```bash
pip install kernl-sdk
```

## Quick Start

```python
from kernl import Kernl

# Create a Kernl instance
kernl = Kernl()

# Initialize with a message
kernl.initialize("Hello, World!")

# Print the message
kernl.print()

# Get the message
print(kernl.message)  # "Hello, World!"

# Check if initialized
print(kernl.is_initialized)  # True
```

## Platform Support

- **macOS**: Universal binary (x86_64 + arm64)
- **Windows**: x64 and ARM64
- **Linux**: x64 and ARM64 with manylinux compatibility

## Requirements

- Python 3.8 or later
- Supported operating systems: macOS 10.14+, Windows 10+, Linux (Ubuntu 18.04+)

## Documentation

For complete documentation, examples, and API reference, visit:
https://github.com/your-org/kernl-sdk

## License

MIT License - see LICENSE file for details.
datawrap 1.0.0
Wrap dictionaries of data for quick traversal and useful messages when data doesn't match expectations.
Requires Python: >=3.7

DataWrap
========

Wrap dictionaries of data for quick traversal and provide **informative error messages** when data doesn't match expectations.

Working with large JSON/YAML files often means drilling through deeply nested structures.
Navigating those structures in Python either leads to additional code to check the shapes and attributes
of the data:

    with file.open('rt') as fh:
        data = yaml.safe_load(fh)
        if "sources" in data and isinstance(data["sources"], list):
            for source in data["sources"]:
                if "tables" in source and isinstance(source["tables"], list) and source["tables"]:
                    for table in source["tables"]:
                        print(f"{source['schema']}.{table['name']}")

    KeyError: 'name'

Or leads to a best-effort Duck typing that generates non-descriptive KeyError and IndexError exceptions that
leave the developer digging through the source data.

    with file.open('rt') as fh:
        data = yaml.safe_load(fh)
        for source in data["sources"]:
            for table in source["tables"]:
                print(f"{source['schema']}.{table['name']}")

    TypeError: 'NoneType' object is not iterable


---

The DictWrapper class provides a simple wrapper around nested data structures which track path traversal to
generate targeted exceptions.

    with file.open('rt') as fh:
        sources = DictWrapper(yaml.safe_load(fh), filename=str(file))
        for source in sources['sources']:
            for table in source['tables']:
                print(f"{source['schema']}.{table['name']}")

    KeyError: '[schema.yml] sources[0].tables[2] is missing key .name'


Usage
-----

Install using pip

    pip install datawrap

Wrap any nested dict/list structure with DictWrapper to get clear, contextual error messages when keys or indices are missing.

    from dict_wrapper import DictWrapper
    import json
    
    # Load JSON or YAML
    with open("schema.yml", "rt") as fh:
        data = json.load(fh)
    
    # Wrap the root object
    data = DictWrapper(data, filename="schema.yml")
    
    # Traverse as normal dicts/lists
    for system in data["sources"]:
        for table in system["tables"]:
            print(table["name"])


Key Features
------------

 - Drop-in replacement for normal dict/list access.
 - Tracks path context across nested dicts/lists.
 - Raises KeyError/IndexError with full path info.
 - Use .get(key, default) for safe access without raising.
 - Iteration yields wrapped elements, so the path context is preserved inside loops.
ir-support 1.2.6
Python package including some classes & functions supporting the subject 41013 Industrial Robotics at UTS, along with the Robotics Toolbox for Python
Requires Python: <3.12,>=3.8

# 41013 Industrial Robotics Support

This project is part of the subject 41013 Industrial Robotics at University of Technology Sydney (UTS), for <a href="https://github.com/petercorke/robotics-toolbox-python">Robotics Toolbox for Python<sup>&reg;</sup></a>
It is developed to provide equivalent support for the Python option of the subject, along with the Modified Robotics Toolbox in MATLAB.

# Installation
Requirement: Robotics Toolbox for Python

```
pip install rvc3python
```
then

```
pip install ir-support
```

Currently, it gets tested on Python 3.8 - 3.11

# Contact
 - via 41013 subject teams site 
 - or subject email 41013@uts.edu.au