semvec/pypi-packages huggingface dataset:
altitude-analisis 6.6.6
Advanced aircraft altitude analysis
Requires Python: >=3.8

### Just works like a charm.

# Key Features
## Noise Detection Methods

### Simple Method (default):
 - **Statistical filtering**

 - **Faster processing**

 - **Use for cleaner datasets**

### PRO Method:

- **Cluster-based noise detection**

- **Better for noisy data**

- **Automatically optimizes parameters**

- **Enable with --noise-method dbscan**

## Usage:

- **pip install altitude_analisis**
~~~python
from altitude_analisis import AltitudeAnalyzer
analyzer = AltitudeAnalyzer(fid=0000)
results = analyzer.analyze()
for result in results:
    print(result)
~~~
- **Enjoy**

### Params:
~~~python
:param table: Data table identifier
:param fid: Flight ID or list of Flight IDs
:param minutes: Minimum duration for level flight (minutes)
:param altitude: Minimum altitude threshold (feet)
:param timezone: Timezone for output formatting
:param api_url: Base URL for data API
:param noise_method: Noise detection method ('simple' or 'dbscan')
:param dbscan_eps: DBSCAN epsilon parameter
:param simple_jump_threshold: Jump threshold for simple method
:param simple_window_size: Window size for simple method
~~~
- **Silent mode**
- *--silent, type=bool, default=False*


###  Build  for:

- **Python minor version (3.8-3.13)**

- **OS (Linux, Windows, macOS)**

- **Architecture (x86_64, arm64)**
pycodetags-issue-tracker 0.3.0
Plugin for pycodetags to track issues, e.g. bugs, TODO, etc in your code
Requires Python: >=3.7
Keywords: FIXME, TODO, code-tag, code-tags, codetag, codetags, pep-350, pep350, pycodetags

# Issue Tracker

This is a PEP350 inspired issue tracker. Store issues in source code.

```python
# TODO: Fix this code <matth 2025-07-05 category=math priority=high release=2.01>
```

Also supports advanced scenarios with TODOs as Objects, Decorators, etc.

## Installation

```bash
pip install pycodetags pycodetags-issue-tracker
```

```bash
pipa install pycodetags 
pipx inject pycodetags pycodetags-issue-tracker
```

## Usage

Basic usage is to generate a TODO report and website.

```makefile
.PHONY: issues
issues:install_plugins
	@echo "Checking issues"
	$(VENV)	pycodetags data --src pycodetags --src plugins --format json>issues_site/data.json
	@echo "Current issues:"
	$(VENV) pycodetags issues --src pycodetags --src plugins --format text
	@echo "For best results, fix these issues:"
	$(VENV) pycodetags issues --src pycodetags --src plugins --format validate
	@echo "Generating HTML report"
	$(VENV) pycodetags issues --src pycodetags --src plugins --format html>issues_site/index.html
```

## Prior Art

PEPs and Standard Library Prior Art

- [PEP 350 - Code Tags](https://peps.python.org/pep-0350/) Rejected proposal, now implemented, mostly by `pycodetags`

## Documentation

- [Readthedocs](https://pycodetags.readthedocs.io/en/latest/)
pypi-alias 2.0.0
A small utility to make alias distributions on PyPI.
Requires Python: >=3.9

========
Overview
========



A small utility to make alias distributions on PyPI.

It will create an "empty" package with a different name (the "alias") that depends on the package in your current working directory.

* Free software: BSD 2-Clause License

Installation
============

::

    pip install pypi-alias

You can also install the in-development version with::

    pip install https://github.com/ionelmc/pypi-alias/archive/master.zip

Usage
=====

You can use ``pypi-alias`` with a distribution file (a ``.whl`` file or a sdist) or with a path, as long as it has a ``pyproject.toml``
with build configuration in it.

Example, if say, you have a ``foobar`` package, and you want to make an alias with name ``python-foobar``, assuming your in the checkout
path::

    pypi-alias python-foobar

You can specify a version::

    pypi-alias python-foobar --alias-version=1.0

You can use a dist file::

    pypi-alias python-foobar --alias-version=1.0 dist/foobar-1.2.3.tar.gz

And you can also add some build options for the resulting alias package (example: only build sdist in a different path)::

    pypi-alias python-foobar --alias-version=1.0 dist/foobar-1.2.3.tar.gz -- --dist --outdir=aliased-dist

The resulting files are in ``dist\``, assuming you haven't used ``--outdir``, you can upload them with twine::

    twine upload dist\python-foobar*

Development
===========

To run all the tests run::

    tox

Note, to combine the coverage data from all the tox environments run:

.. list-table::
    :widths: 10 90
    :stub-columns: 1

    - - Windows
      - ::

            set PYTEST_ADDOPTS=--cov-append
            tox

    - - Other
      - ::

            PYTEST_ADDOPTS=--cov-append tox


Changelog
=========

2.0.0 (2025-07-13)
------------------

* Now build/pkginfo are used to get package metadata. This means ``pyproject.toml`` projects are supported.
* Dropped support for Python 3.8 or older.

1.0.0 (2021-04-08)
------------------

* Added support for Python 3.
  Contributed by Emil Stenström in `#5 <https://github.com/ionelmc/pypi-alias/pull/5>`_.
* Dropped support for Python 2.
* Switched to argparse and added the ``--version`` and ``--dirty`` arguments.
* Added some light CI/testing.

0.2.0 (2015-06-18)
------------------

* Fix regression in ``__main__``.

0.1.0 (2015-06-18)
------------------

* First release on PyPI.
brave-mcp-langchain 0.2.0
A tool that can give you freedom of using Brave search and content fetching using MCP and Langchan
Requires Python: >=3.12

# brave-mcp-langchain

## Create venv
```bash
uv sync
```

## Install package

```bash
uv pip install brave-mcp-langchain
```

## Run MCP server in STDIO mode

```bash
uvx brave-mcp-langchain
```

### To run MCP server in SSE mode
```bash
uvx brave-mcp-langchain sse 5003
```

## MCP Setting

```json
{
  "mcpServers": {
    "brave-mcp-langchain": {
      "disabled": false,
      "timeout": 60,
      "type": "stdio",
      "command": "uvx",
      "args": [
        "brave-mcp-langchain"
      ]
    }
  }
}
```

# Use as Langchain tool

It can also be used as Langchain tool. Below is how to validate tool.

```python
import httpx
import asyncio
from langchain.tools import Tool
from brave_mcp_langchain import brave_tool

async def test_search():
    result = await brave_tool.search_tool.ainvoke({"query": "LangGraph overview", "max_results": 10})
    print(result)

    result = await brave_tool.fetch_content_tool.ainvoke({
        "url": "https://iamatulsingh.github.io"
    })
    print(result)

asyncio.run(test_search())
```

## Use with langchain example
```python
import asyncio
from langchain.agents import initialize_agent
from langchain.agents.agent_types import AgentType
from langchain_ollama import ChatOllama
from brave_mcp_langchain import brave_tool

llm = ChatOllama(model="llama3.1:8b")

tools = [
    brave_tool.search_tool,
    brave_tool.fetch_content_tool
]

agent = initialize_agent(
    tools=[brave_tool.search_tool, brave_tool.fetch_content_tool],
    llm=llm,
    agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,
    verbose=True
)

async def run_agent_query():
    response = await agent.ainvoke(
        "Search for 'iamatulsingh' overview, then fetch content from https://iamatulsingh.github.io"
    )
    print("\nAgent Response:")
    print(response)

asyncio.run(run_agent_query())
```

## 🧠 Inspiration & Attribution

This project, `brave-mcp-langchain`, was inspired by and partially based on the excellent work in [`duckduckgo-mcp-server`](https://github.com/nickclyde/duckduckgo-mcp-server) by [@nickclyde](https://github.com/nickclyde). That project laid the groundwork for integrating DuckDuckGo search and content fetching into the MCP ecosystem.

While `brave-mcp-langchain` extends the concept to support Brave Search and LangChain workflows, several architectural ideas and implementation patterns were adapted from `duckduckgo-mcp-server`, which is licensed under the [MIT License](https://github.com/nickclyde/duckduckgo-mcp-server/blob/main/LICENSE).

I'm grateful for the open-source community and contributors who make projects like this possible. If you’re interested in DuckDuckGo-based search tools, definitely check out the original repository!
awesome-structure 0.1.1
Всего одна команда в терминале, отделяющая вас от наслаждения прекрасной структурой вашего проекта 😌
Requires Python: <4.0,>=3.10
Keywords: cli, directory, tree, visualization

# awesome-structure
### О пакете

---

>Всего одна команда в терминале, отделяющая вас от наслаждения прекрасной структурой вашего проекта 😌

**CLI** (_Command Line Interface_) приложение для построения красивой визуализации структуры произвольного каталога.  
Например, для этого проекта, оно генерирует следующую визуализацию:
```
📁 awesome-structure
├── 📁 src
│   ├── 📁 awesome_structure
│   │   ├── 📁 renderers
│   │   │   ├── 🐍 __init__.py
│   │   │   ├── 🐍 icons.py
│   │   │   ├── 🐍 markdown.py
│   │   │   └── 🐍 terminal.py
│   │   ├── 🐍 __init__.py
│   │   ├── 🐍 cli.py
│   │   └── 🐍 tree_builder.py
│   └── 🐍 __init__.py
├── 📁 tests
│   ├── 📁 test_renderers
│   │   ├── 🐍 test_markdown.py
│   │   └── 🐍 test_terminal.py
│   ├── 🐍 __init__.py
│   ├── 🐍 conftest.py
│   ├── 🐍 test_cli.py
│   └── 🐍 test_tree_builder.py
├── 📄 .gitignore
├── 📄 LICENSE
├── 📄 poetry.lock
├── ⚙️ pyproject.toml
└── 📖 README.md
```
### Установка

---

Пакет опубликован в PyPi: **[awesome-structure](https://pypi.org/project/awesome-structure/)**

Воспользуйтесь удобным для вас менеджером пакетов:  
**pip**
```shell
pip install awesome-structure
```

**poetry**
```shell
poetry add awesome-structure
```

### Использование  

---
Вызовите ту самую заветную команду ``admire`` с указанием параметра пути к каталогу для получения удовольствия, так:
```shell
awesome-structure admire "[путь к вашему каталогу]"
```
или с явным казанием параметра ``--path`` так:
```shell
awesome-structure admire --path "[путь к вашему каталогу]"
```

Пример:
```shell
awesome-structure admire "C:\Program Files"
```

**Для корректной работы, настоятельно рекомендуется заключать путь в кавычки "".** 

Результат будет выведен в терминал.   

Если вы желаете получить результат в виде файла, доступно сохранение **markdown** разметки в файл _**awesome-structure.md**_.  
Просто при вызове команды укажите соответствующее значение в параметре ``mode``  
```shell
awesome-structure admire "C:\Program Files" --mode markdown
```

Файл будет сохранён в том каталоге, для которого строится визуализация.

По умолчанию, все скрытые файлы и каталоги (те, чьё название начинается с "**.**") не попадают в вывод результата.  
К ним могут относиться ``.env``, ``.gitignore`` и т.д.
Если вы хотите включить в вывод скрытые папки и файлы, добавьте к команде флаг ``--hidden``.
```shell
awesome-structure admire "C:\Program Files" --hidden
```
Флаги можно комбинировать и использоваться совместно. Например:
```shell
awesome-structure admire "C:\Program Files" --mode markdown --hidden
```
Порядок следования флагов не важен
```shell
awesome-structure admire "C:\Program Files" --hidden --mode markdown 
```
multiHGtest 0.0.5
Testing for differences in survival using hypergeometric P-values
Requires Python: >=3.6

# multiHGtest -- Testing for differences in survival using multiple hypergeometric P-values

This package implements the test for survival data described in 
[1] Kipnis, Galili, and Yakhini. Detecting rare and weak deviations of non-proportional hazard in survival analysis. arXiv:2310.00554. 2025.

## Overview

The multiHGtest package provides methods for comparing survival between two populations with sensitivity to rare hazard departures. The method uses exact hypergeometric tests at each time interval and combines the P-values using higher criticism or Fisher's combination test.

## Methods

- `hypergeom_test` - Exact hypergeometric test for comparing proportions
- `hchg_test` / `HCHGtest` - Higher criticism test of hypergeometric P-values 
- `fisher_hg_test` / `FisherHGtest` - Fisher combination test of hypergeometric P-values
- `hg_test_dashboard` / `testHG_dashboard` - Comprehensive dashboard with all test statistics

## Installation

```bash
pip install multiHGtest
```

## Quick Example

```python
import numpy as np
from multiHGtest import hchg_test, fisher_hg_test

# Example survival data (at-risk counts over time)
Nt1 = np.array([100, 95, 90, 85, 80])  # Group 1 at-risk counts
Nt2 = np.array([100, 92, 88, 82, 75])  # Group 2 at-risk counts

# Calculate events (if not provided)
Ot1 = -np.diff(Nt1)  # [5, 5, 5, 5]
Ot2 = -np.diff(Nt2)  # [8, 4, 6, 7]

# Run tests
hc_stat = hchg_test(Nt1[:-1], Nt2[:-1], Ot1, Ot2)
fisher_stat, fisher_pval = fisher_hg_test(Nt1[:-1], Nt2[:-1], Ot1, Ot2)

print(f"Higher Criticism statistic: {hc_stat:.4f}")
print(f"Fisher statistic: {fisher_stat:.4f}, p-value: {fisher_pval:.4f}")
```