semvec/pypi-packages huggingface dataset:
xfilepy 0.3.1
Cross-platform, file handler using native pickers and security models.
Requires Python: >=3.11
Keywords: kivy, android, ios, desktop, files, document picker

# xfilepy

---

Cross-platform, file handler using native pickers and security models.

Provides overloads for creating file handlers via native file pickers and
saving/loading persistent access grants. Supported platforms are Android, iOS,
and Desktop (Windows, macOS, Linux). On all platforms you can choose files
outside your app's sandbox using the native file picker without asking the user
for broad filesystem permissions. Access to the chosen files is mediated by
platform-specific security models (e.g., SAF on Android, security-scoped
bookmarks on iOS). This allows your app to write to the files later without
re-prompting the user, even across app restarts.

## Usage
See [API Documentation](https://xfile-fb4acf.gitlab.io/) for full
details. Here is a brief example:

```python
import asyncio

from xfilepy import FileHandler, UserCancelledError

# Open via native picker
try:  # The user may cancel the picker, so we catch that exception
    fh = asyncio.run(FileHandler.create_via_picker())
    with fh.open() as f:
        data = f.read()
        print(data)
except UserCancelledError:
    print("User cancelled the file picker.")

# Save via native "Save as…"
fh = asyncio.run(FileHandler.create_via_save_dialog())
with fh.open(mode="w", encoding="utf-8") as f:
    f.write("Hello, world!")

# Serialize back to store in your settings
persist = fh.to_uri_string()

# Rehydrate a previously granted URI/bookmark
fh = FileHandler.from_uri_string(persist, require_write=True)
with fh.open(mode="a") as f:
    f.write("Appended line.")
```
flatdict 4.1.0
Python module for interacting with nested dicts as a single level dict with delimited keys.
Requires Python: >=3.10

# FlatDict

[![Version](https://img.shields.io/pypi/v/flatdict.svg?)](https://pypi.python.org/pypi/flatdict)
[![Status](https://github.com/gmr/flatdict/workflows/Testing/badge.svg)](https://github.com/gmr/flatdict/actions)
[![Coverage](https://img.shields.io/codecov/c/github/gmr/flatdict.svg?)](https://codecov.io/github/gmr/flatdict?branch=master)
[![License](https://img.shields.io/pypi/l/flatdict.svg?)](https://flatdict.readthedocs.org)

`FlatDict` and `FlatterDict` are dict classes that allow for single level,
delimited key/value pair mapping of nested dictionaries. You can interact with
`FlatDict` and `FlatterDict` like a normal dictionary and access child
dictionaries as you normally would or with the composite key.

*For example:*

```python
value = flatdict.FlatDict({'foo': {'bar': 'baz', 'qux': 'corge'}})
```

*would be the same as:*

```python
value == {'foo:bar': 'baz', 'foo:qux': 'corge'}
```

*values can be accessed as:*

```python
print(foo['foo:bar'])

# or

print(foo['foo']['bar'])
```

Additionally, lists and tuples are also converted into dicts using `enumerate()`,
using the `FlatterDict` class.

*For example:*

```python
value = flatdict.FlatterDict({'list': ['a', 'b', 'c']})
```

*will be the same as:*

```python
value == {'list:0': 'a', 'list:1': 'b', 'list:2': 'c'}
```

## API

Documentation is available at https://flatdict.readthedocs.io

## Versioning

This package attempts to use semantic versioning. API changes are indicated
by the major version, non-breaking improvements by the minor, and bug fixes
in the revision.

It is recommended that you pin your targets to greater or equal to the current
version and less than the next major version.

## Installation

```bash
pip install flatdict
```
worktree-mux 0.4.0
A tmux-based viewer for git worktrees
Requires Python: >=3.12
Keywords: cli, git, tmux, worktree

# worktree-mux

A tmux control panel for git worktrees.

When you run multiple AI agents or parallel feature branches, each git worktree becomes its own dev environment. worktree-mux maps **1 worktree → 1 tmux window**, so you can instantly jump between contexts to run commands, inspect logs, or debug — without losing your place.
- **What it does**: fast terminal access to every worktree
- **What it doesn't do**: create or manage worktrees — git still does that

_Think_: a dashboard for parallel coding environments.

See [PLAN.md](PLAN.md) for assumptions and design considerations.

## Install

```bash
# Install from PyPI
uv tool install worktree-mux

# Or install from source
git clone https://github.com/tt6746690/worktree-mux.git
uv tool install worktree-mux/

# (Optional) Short alias — add to ~/.zshrc
alias wm='worktree-mux'
```

## Usage


1. **Create worktrees with git**: worktree-mux just views them

```bash
git worktree add .worktrees/feature/auth -b feature/auth
git worktree add .worktrees/fix/parser-bug -b fix/parser-bug
git worktree add .worktrees/refactor-models -b refactor-models
```

2. **See all worktrees** and their status with `worktree-mux` (or `worktree-mux ls`)

```
worktree-mux — my-repo
────────────────────────────────────────────────────────────────

    Branch              tmux  Modified   vs main   Last Commit
    ──────────────────  ────  ────────   ───────   ─────────────────
  ▸ feature/auth        ●     2 files    ↑3        12 minutes ago
    fix/parser-bug      ○     clean      ↑1        2 hours ago
    refactor-models     ○     3 files    ↑5 ↓2     35 minutes ago

  ● = tmux window open    ○ = no tmux window    ▸ = current
  Session: my-repo (3 worktrees, 1 open)
```

3. **Jump into a worktree**: creates a tmux window and switches to it with `worktree-mux cd`

```bash
worktree-mux cd auth             # (fuzzy) leaf name match
worktree-mux cd parser           # (fuzzy) substring match
worktree-mux cd feature/auth     # exact path match
worktree-mux cd                  # go to the main window (repo root)
```

Return to your previous session: `prefix + s` and toggle

4. **Live dashboard** of worktrees — auto-refreshing version of the status table with `worktree-mux dash`

```
worktree-mux — my-repo (refreshes every 5s)
──────────────────────────────────────────────────────────────────────

    Branch              tmux  Modified   vs main   Last Commit
    ──────────────────  ────  ────────   ───────   ─────────────────
  ▸ feature/auth        ●     2 files    ↑3        12 minutes ago
    fix/parser-bug      ○     clean      ↑1        2 hours ago
    refactor-models     ○     3 files    ↑5 ↓2     35 minutes ago

  ● = tmux window open    ○ = no tmux window    ▸ = current
```

5. **Cleanup**: worktree-mux automatically cleans up orphaned tmux windows (from removed worktrees) on every command. No manual close needed — just delete the worktree with `git worktree remove` and the window goes away.
pocket-numpy 0.0.3
pocketpy with numpy
Requires Python: >=3.9

# How to install & test (for user)

```
python3 -m pip install pocket-numpy
python3 -m pocket_numpy --help
python3 -m pocket_numpy tests/test_numpy.py
```

# Try is online

<https://cubao.github.io/xtensor-numpy>

---

# How to build (for dev)

```
make build
```

# How to test (for dev)

```
make test
```

See all available commands in `Makefile`.
latdraw 0.3.1
Draw Accelerator lattices in Python.
Requires Python: >=3.12

# latdraw


[![pypi](https://img.shields.io/pypi/v/latdraw.svg)](https://pypi.org/project/latdraw/)
[![python](https://img.shields.io/pypi/pyversions/latdraw.svg)](https://pypi.org/project/latdraw/)
[![Build Status](https://github.com/st-walker/latdraw/actions/workflows/dev.yml/badge.svg)](https://github.com/st-walker/latdraw/actions/workflows/dev.yml)
[![codecov](https://codecov.io/gh/st-walker/latdraw/branch/main/graphs/badge.svg)](https://codecov.io/github/st-walker/latdraw)



Draw Accelerator lattices in Python


* Documentation: <https://st-walker.github.io/latdraw>
* GitHub: <https://github.com/st-walker/latdraw>
* PyPI: <https://pypi.org/project/latdraw/>
* Free software: MIT


## Features

* TODO

## Credits

This package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [waynerv/cookiecutter-pypackage](https://github.com/waynerv/cookiecutter-pypackage) project template.
trustmemory 0.2.0
TrustMemory Python SDK - The Trust & Collective Intelligence Layer for Multi-Agent Systems
Requires Python: >=3.10
Keywords: a2a, agents, ai, knowledge, mcp, multi-agent, trust

# TrustMemory Python SDK

The official Python SDK for [TrustMemory](https://trustmemory.ai) - The Trust & Collective Intelligence Layer for Multi-Agent Systems.

TrustMemory enables AI agents to share verified knowledge, validate each other's claims, and build reputation through accurate contributions.

## Installation

```bash
pip install trustmemory
```

## Quick Start

```python
from trustmemory import TrustMemoryClient

# Connect with your agent API key
client = TrustMemoryClient(api_key="tm_sk_your_key")

# Search verified knowledge across all pools
results = client.search("OpenAI GPT-4o rate limits", min_confidence=0.7)
for r in results["results"]:
    print(f"  [{r['contributor_confidence']:.0%}] {r['statement']}")

# Contribute a knowledge claim
claim = client.contribute(
    pool_id="pool-id",
    statement="Claude 3.5 Sonnet supports 200K context window",
    confidence=0.95,
    evidence=[{"type": "documentation", "url": "https://docs.anthropic.com/..."}],
    tags=["anthropic", "context-window"],
)

# Validate another agent's claim
client.validate(
    pool_id="pool-id",
    claim_id="claim-id",
    verdict="agree",
    confidence_in_verdict=0.9,
    evidence="Independently confirmed via API testing",
)
```

## Agent Registration

Registration requires a User API Key from the [TrustMemory dashboard](https://trustmemory.ai/dashboard):

```python
from trustmemory import TrustMemoryClient

result = TrustMemoryClient.register(
    name="MyResearchAgent",
    owner_id="your-owner-id-from-dashboard",
    user_api_key="tm_user_your_key",
    capabilities=["research", "coding"],
    model="gpt-4o",
)
print(f"Agent ID: {result['agent_id']}")
print(f"API Key: {result['api_key']}")  # Save this! Shown only once.
```

## Async Support

```python
from trustmemory import AsyncTrustMemoryClient

async with AsyncTrustMemoryClient(api_key="tm_sk_...") as client:
    results = await client.search("rate limits", min_confidence=0.7)
    trust = await client.get_trust("agent-id")
```

## Features

- **Knowledge Pools** - Create, list, and query themed collections of verified claims
- **Claims** - Contribute, validate, and dispute knowledge claims
- **Trust Scores** - Check agent reputation and export verifiable attestations
- **Batch Operations** - Submit up to 50 claims or 20 searches in one request
- **Webhooks** - Subscribe to real-time events (trust changes, validations)
- **Agent Discovery** - Find agents by capabilities, domain, or trust score
- **Sync + Async** - Both `TrustMemoryClient` and `AsyncTrustMemoryClient`

## Documentation

- [API Documentation](https://trustmemory.ai/docs)
- [Interactive API Explorer (Swagger)](https://trustmemory.ai/api-docs)

## License

MIT
amcrest-api 0.1.10
Python Wrapper for Amcrest API v3.26 or greater.
Requires Python: >=3.11

# Amcrest API

Python wrapper for [Amcrest API version 3.26](https://support.amcrest.com/hc/en-us/articles/17903073032973-Amcrest-HTTP-API-SDK).

[![Coverage Status](https://coveralls.io/repos/github/bcpearce/Amcrest-API/badge.svg?branch=main)](https://coveralls.io/github/bcpearce/Amcrest-API?branch=main)

Implements asynchronous operations for Amcrest cameras for use with [HomeAssistant Amcrest Custom](https://github.com/bcpearce/homeassistant-amcrest-custom)
tiskitpy 2.2.0.post1
TIme Series toolKIT
Requires Python: >=3.8
Keywords: oceanography, marine, OBS

# TiSKitPy

Routines for time series data processing

Uses the [obspy](https://docs.obspy.org) Stream (data) and Inventory (metadata)
classes


[Documentation](https://tiskitpy.readthedocs.io)


## Main Classes

- ``SpectralDensity``: Calculate and manipulate spectral density functions.
- ``Decimator``: Decimate time series and update metadata with the decimator's
  response
- ``CleanRotator``: rotate data to minimize noise on vertical channel
- ``DataCleaner``: Transfer_Function-based data cleaning
- ``ResponseFunctions``: Frequency response functions for a given input channel.
- ``Compliance``: Seafloor Compliance
- ``SeafloorSynthetic``: Generate synthetic seafloor data, including compliance signal
            
               
## Functions

- ``FIR_corr``: transform zero-phase data to minimum phase.  Based on
  [Scherbaum, Of Poles and Zeros](https://doi.org/10.1007/978-1-4020-6861-4).
  Only works for LCHEAPO loggers, need to update to calculate/work for any
  zero-phase filter)
- ``readMSEED``: read in MSEED data, including if the file is too big (> 2 GB)
               for obspy's read() function
- ``rptransient``: calculate and remove periodic transient (VERY manual!).  
 	Based on Matlab code by E Wielandt, used in
 	[Deen et al., 2017](https://doi.org/10.1002/2017GL074892) and
 	[Aminian et al., 2025](https://doi.org/10.1093/gji/ggaf253)
- ``PetersonNoiseModel``: return the Peterson High and Low Noise Models
  ([Peterson, 1993](https://doi.org/10.3133/ofr9332))
- ``plot_compliance_stack()``: plot, from top to bottom, Z PSD, P PSD,
  Z-P coherence and Z-P frequency response function.


## Installation

First, install `obspy` using the instructions on their webpage.
Then, in the pip/conda environment that contains obspy, type 
`pip install tiskitpy`
catfood 1.0.9
A collection of various commonly used functions. / 各种常用函数的集合。

# 猫粮 🐱

[![Pypi 上的版本](https://img.shields.io/pypi/v/catfood.svg)](https://pypi.org/project/catfood)  

各种常用函数的集合。

## 安装

我自己仅在 CPython 3.12 - 3.14 上测试过。  

### Pypi

https://pypi.org/project/catfood/

```bash
python -m pip install catfood
```

### Test Pypi

https://test.pypi.org/project/catfood/

```bash
python -m pip install -i https://test.pypi.org/simple/ catfood
```

### 从源安装
```bash
git clone https://github.com/DuckDuckStudio/catfood.git
python -m pip install ./catfood
```

#### Build whl
```bash
# Windows PowerShell
git clone https://github.com/DuckDuckStudio/catfood.git
cd catfood

python -m venv .venv
& ".venv/Scripts/Activate.ps1"
python.exe -m pip install pip --upgrade

pip install ".[build_and_publish]" # 包括构建和发布依赖 build 和 twine
python -m build
ls dist/

# 从 whl 安装
pip install dist/catfood-1.0.0-py3-none-any.whl
```
xwr 0.3.7
Linux-Compatible Real-Time Raw Data Capture for TI mmWave Radars.
Requires Python: >=3.10

# `xwr`: Linux-Compatible Real-Time Raw Data Capture for TI mmWave Radars

[![pypi version](https://img.shields.io/pypi/v/xwr.svg)](https://pypi.org/project/xwr/)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/xwr)
![License - MIT](https://img.shields.io/badge/license-MIT-green)
![PyPI - Types](https://img.shields.io/pypi/types/xwr)
[![bear-ified](https://raw.githubusercontent.com/beartype/beartype-assets/main/badge/bear-ified.svg)](https://beartype.readthedocs.io)
[![CI](https://github.com/RadarML/xwr/actions/workflows/ci.yml/badge.svg)](https://github.com/RadarML/xwr/actions/workflows/ci.yml)
![GitHub issues](https://img.shields.io/github/issues/RadarML/xwr)

`xwr` is a pure-python, linux-based real time raw data capture system for TI mmWave radars, and includes four key components:

- [`xwr`](https://radarml.github.io/xwr/system/): a high-level data capture interface
- [`xwr.rsp`](https://radarml.github.io/xwr/rsp/rsp/): a radar signal processing library with Numpy, Pytorch, and Jax support
- [`xwr.radar`](https://radarml.github.io/xwr/radar/api/): a parameterized python interface for the default radar firmware
- [`xwr.capture`](https://radarml.github.io/xwr/dca/api/): a pure-python, real-time interface for the DCA1000EVM

See our [documentation site](https://radarml.github.io/xwr/) for more details, setup guides, the included demo, and more!

## Requirements

`xwr` assumes a linux-based system and radar hardware which consists of the DCA1000EVM and a supported TI mmWave Radar (XWR) development board.

> [!IMPORTANT] 
> Supported Devices:
>    - AWR1843 Family: AWR1843Boost, AWR1843AOPEVM
>    - AWR1642
>
> WIP:
>    - AWR2544LOPEVM
>    - AWR2944

## Install

The `xwr` library can be installed from pypi or github:

```sh
pip install xwr
# or
pip install git+ssh://github.com/RadarML/xwr.git
```

> [!WARNING]
> `xwr` does not include a copy of `torch` or `jax` by default! You must specify your own dependency and/or use the `xwr[torch]` and `xwr[jax]` extras if you intend to use these backends for the radar signal processing (`xwr.rsp`) submodule.