semvec/pypi-packages huggingface dataset:
futhark-ffi 0.16.0
A Python library using the Futhark C backend via CFFI 

# futhark-ffi
[![Test](https://github.com/pepijndevos/futhark-pycffi/actions/workflows/ci.yml/badge.svg)](https://github.com/pepijndevos/futhark-pycffi/actions/workflows/ci.yml)

Python library using the Futhark C backend via CFFI

Futhark provides several compiler backends, for example `futhark
opencl` which is a C backend, and `futhark pyopencl` which is a Python
backend based on PyOpenCL. However, the host-side code of the Python
backend is quite slow, leading to a lot of overhead when small,
frequent kernels are used.

A solution to reduce this overhead is to use CFFI to used the C
backend from Python, greatly reducing the calling overhead. The OpenCL
code is the same, so this is not interesting for long-running kernels.

This library supports the following Futhark backends: `c`, `opencl`,
`multicore`, and `cuda`.

Futhark arrays are mapped to and from Numpy arrays. Multiple outputs
and multi-dimensional arrays are supported.

## Installation

[Install Futhark](https://futhark.readthedocs.io/en/latest/installation.html), then simply
```bash
pip install futhark-ffi
```

## Usage

Generate a C library, and build a Python binding for it

```bash
futhark opencl --library test.fut
build_futhark_ffi test
```

Use the Python wrapper

```python
import numpy as np
import _test
from futhark_ffi import Futhark

test = Futhark(_test)
res = test.test3(np.arange(10))
test.from_futhark(res)
```

## Example usage

* https://github.com/mortvest/hastl
ensembl-utils 0.8.0
Ensembl Python general-purpose utils
Requires Python: >=3.10
Keywords: ensembl, python, utils

# Ensembl Python general-purpose utils

[![License](https://img.shields.io/badge/license-Apache_2.0-blue.svg)](https://github.com/Ensembl/ensembl-utils/blob/main/LICENSE)
[![Coverage](https://ensembl.github.io/ensembl-utils/coverage/coverage-badge.svg)](https://ensembl.github.io/ensembl-utils/coverage)
[![CI](https://github.com/Ensembl/ensembl-utils/actions/workflows/ci.yml/badge.svg?branch=main)](https://github.com/Ensembl/ensembl-utils/actions/workflows/ci.yml)
[![Release](https://img.shields.io/pypi/v/ensembl-utils)](https://pypi.org/project/ensembl-utils)

Centralise generic Python utils used by other projects within Ensembl design to facilitate frequent tasks such as input file path checks, archive files IO manipulation or logging setup, among others.

For more information, please consult this repository's [GitHub pages](https://ensembl.github.io/ensembl-utils/).

## Getting Started

### Basic installation

This library is publicly available in [PyPI](https://pypi.org/project/ensembl-utils/) so it can be easily installed with your favourite Python dependency and packaging management tool, e.g.
```bash
pip install ensembl-utils
```

### Quick usage

Besides the standard `import ensembl.utils`, this library also provides some useful command line scripts:
- `extract_file` - to easily extract archive files in different formats

Furthermore, `ensembl-utils` also has a [`pytest`](https://docs.pytest.org/) plugin with some useful functionalities to ease your unit testing. You can enable it by adding it explicitly when running pytest:
```bash
pytest -p ensembl.utils.plugin ...
```

Or adding the following line to your `conftest.py`:
```python
pytest_plugins = ("ensembl.utils.plugin",)
```

## Dependencies

This repository has been developed to support [SQLAlchemy](https://www.sqlalchemy.org) version 1.4 (1.4.45 or later, to ensure "future-compatibility") as well as version 2.0+.
yonyou-mm-dev-mcp 0.1.10
制造云开发者平台
Requires Python: >=3.13

# MMDev - 制造云开发者平台

MMDev 是一个用于制造云开发的开发者平台，提供元数据服务和工具集成，帮助开发者快速获取代码元数据和异常编码。

## 功能特性

- **代码元数据服务**: 根据项目和代码路径获取业务模块、业务对象、代码类型等信息。
- **异常编码服务**: 根据项目、业务模块、业务对象和异常内容生成异常编码。
- **开发工具集成**: 提供代码生成规则和优化规则的工具支持。

## 环境要求

- Python 版本: 3.13 或更高
- 依赖库: `fastmcp`, `httpx`

## 安装步骤
1. 安装 Python 3.13 或更高版本。
2. 安装Python包管理器 uv
    ```bash
    pip install uv
    ```
    验证安装‌
    ```bash
    uv --version
    ```

3. 克隆项目到本地:
   ```bash
   git clone https://github.com/ggoop/mmdev.git
   cd mmdev
   ```
4. 安装依赖:
    ```bash
    # With uv (recommended)
    uv sync
    ```
5. 启动服务:
    ```bash
    uv run main.py
    ```
## mcp 配置
```json
    {
        "mcpServers": {
            "aaddoopp/mmdev": {
                "command": "uvx",
                "args": [
                    "yonyou-mm-dev-mcp@latest"
                ],
                "env": {
                    "server": "https://xxxx"
                }
            }
        }
    }
```
py-skyrc-charger 0.0.12
take control of a SkyRC charger via USB
Requires Python: >=3.9


# py skyrc charger

this package allows to interface with skyrc chargers using the usb-connection as a more flexible replacement for the *Charge Master* software.
This library was created by reverse engineering the protocol and does not support the complete set of functions yet.

## supported devices

this code was developed and tested using the SkyRC T1000: https://www.skyrc.com/t1000
however chargers working with the original Charge Master software are likely to work as well. A list of supported devices is on the download page of Charge Master: https://www.skyrc.com/downloads

**tested on linux only**, windows should also work, but it is more difficult to setup the usb driver

# setup (linux)

## fix usb permissions

this might be necessary to run the code without sudo.

create a file `/lib/udev/rules.d/50-skyrc-t1000.rules` with the following content:
```bash
ACTION=="add", SUBSYSTEMS=="usb", ATTRS{idVendor}=="0000", ATTRS{idProduct}=="0001", MODE="660", GROUP="plugdev"
```

run:
```bash
sudo adduser $USER plugdev
```

# how to use

```python
import time
from py_skyrc_charger import Charger, Config, Action, ChargerResponse


def rec_data_callback_sample(data: ChargerResponse):
    print("---")
    print(f"got cmd: {data.command}")
    if data.is_error:
        print(f"error: {data.error_str}")
    else:
        print(f"got data: {data.data}")


if __name__ == "__main__":
    charger = Charger(rec_data_callback_sample, device_index=0)
    charger.connect()

    time.sleep(1.0)

    print("read version...")
    charger.poll_version()
    time.sleep(0.2)

    print("read settings...")
    charger.poll_settings()
    time.sleep(0.2)

    # configure charge program
    conf = Config(port=1,
                  action=Action.BALANCE,
                  cells=3,
                  cur_in=0.1,
                  cur_out=0.5)

    # read values in idle
    start_time = time.time()
    while time.time() - start_time < 5:
        charger.poll_all_vals()
        time.sleep(1.0)

    print("START")
    charger.start_program(conf)

    # read values while charging
    start_time = time.time()
    while time.time() - start_time < 10:
        charger.poll_all_vals()
        time.sleep(1.0)

    print("STOP")
    charger.stop_program(conf.port)

    # read values in idle
    start_time = time.time()
    while time.time() - start_time < 10:
        charger.poll_all_vals()
        time.sleep(1.0)

```
actuallyslowarg 0.1.2
A really slow CLI tool for testing tab completion with only argcomplete (no fastargsnap)
Requires Python: >=3.8
Keywords: cli, argparse, argcomplete, tab-completion, performance, comparison

# ActuallySlowArg

A really slow CLI tool for testing tab completion with only argcomplete (no fastargsnap).

This project is designed to compare the performance of tab completion between:
- **actuallyslowarg**: Uses only argcomplete (this project)
- **slowarg**: Uses fastargsnap + argcomplete

## Installation

```bash
pip install actuallyslowarg
```

## Usage

```bash
actuallyslowarg <command> [options]
```

### Commands

- `foo` - Run foo command
  - `--bar` - Bar value (integer)
  - `--baz` - Baz option (choices: a, b, c)

- `data` - Run data command
  - `--file` - Input file (string)
  - `--mode` - Mode (choices: fast, slow)

## Performance Testing

This tool is designed to test whether argcomplete alone would provide the same fast completion performance as fastargsnap.

The tool includes:
- Heavy imports (matplotlib, numpy, pandas, sklearn, requests, click)
- 5-second sleep to simulate slow startup
- Early completion detection to avoid heavy imports during completion
- Only argcomplete (no fastargsnap)

## Performance Results

### Completion Speed (Tab Completion)

| Tool | Method | Time | Performance |
|------|--------|------|-------------|
| **slowarg** | fastargsnap + argcomplete | **0.081s** | ⚡ **4x faster** |
| **actuallyslowarg** | argcomplete only | **0.325s** | 🐌 **Baseline** |

### Command Execution Speed

| Tool | Method | Time | Performance |
|------|--------|------|-------------|
| **slowarg** | fastargsnap + argcomplete | **9.9s** | Same |
| **actuallyslowarg** | argcomplete only | **16.4s** | Same |

## Key Findings

- **fastargsnap provides 4x faster completions** than argcomplete alone
- **Parser creation has real overhead** - even for simple parsers
- **JSON parsing is much faster** than Python object creation
- **fastargsnap is worth the complexity** for performance-critical CLI tools

## Development

```bash
# Install in development mode
pip install -e .

# Run tests
pytest

# Format code
black .

# Lint code
flake8
```

## License

MIT License
PuggleSolvers 1.0.1
Numerical solvers
Requires Python: >=3.9

![Tests](https://github.com/PlatypusBytes/solvers/actions/workflows/workflow.yml/badge.svg)
[![codecov](https://codecov.io/gh/PlatypusBytes/solvers/graph/badge.svg?token=CRWV3A3WLR)](https://codecov.io/gh/PlatypusBytes/solvers)

# README #

```
     _______.  ______    __      ____    ____  _______ .______       _________.
    /       | /  __  \  |  |     \   \  /   / |   ____||   _  \     /         |
   |   (----`|  |  |  | |  |      \   \/   /  |  |__   |  |_)  |   |   (------`
    \   \    |  |  |  | |  |       \      /   |   __|  |      /     \   \
.----)   |   |  `--'  | |  `----.   \    /    |  |____ |  |\  \------)   |
|_______/     \______/  |_______|    \__/     |_______|| _| `.__________/


Solvers: Numerical solvers for numerically solving partial differential equations
```

Consists of:

* Static solver
* [Newmark solver](https://ascelibrary.org/doi/10.1061/JMCEA3.0000098) (explicit and implicit with consistent mass matrix)
* [Zhai solver](https://onlinelibrary.wiley.com/doi/abs/10.1002/%28SICI%291097-0207%2819961230%2939%3A24%3C4199%3A%3AAID-NME39%3E3.0.CO%3B2-Y) (explicit with consistent mass matrix)
* Central difference solver (explicit with possibility to lump mass matrix)
* [Noh-Bathe solver](https://www.sciencedirect.com/science/article/abs/pii/S0045794913001934) (explicit with possibility to lump mass matrix)