semvec/pypi-packages huggingface dataset:
mcp-server-sparql 0.1.0
MCP server for querying SPARQL endpoint
License: MIT
Requires Python: >=3.10
Keywords: mcp, sparql

# MCP Server SPARQL

A Model Context Protocol (MCP) server that provides tools for querying SPARQL endpoints.

## Usage

Example usage for querying the Wikidata SPARQL endpoint.

### uvx

```json
"mcpServers": {
  "mcp-server-sparql": {
    "command": "uvx",
    "args": ["mcp-server-sparql", "--endpoint", "https://query.wikidata.org/sparql"],
  }
}
```

### Tool: `query`

Execute a SPARQL query against the configured endpoint.

**Parameters:**

- `query_string`: A valid SPARQL query string

**Returns:**

- The query results in JSON format
ai2soft-Desktop-UI-automation 0.0.1
用于完与一些自动化的操作
License: MIT

ai2soft.automation 用于对桌面系统的一些操作进行自动化处理

依赖包不会自动安装，需要手动安装，以防止安装过程中出错

## 依赖包
    opencv-python >= 4.5.5.64',
    pygetwindow >= 0.0.9',
    mss >= 10.0.0',
    numpy >= 1.25.1',
    pyautogui >= 0.9.54',
# 可选
    paddleocr >= 2.9.1'
numfi 0.5.0
a numpy.ndarray subclass that does fixed-point arithmetic
License: MIT
Requires Python: >=3.6

# [numfi](https://github.com/ZZZZzzzzac/numfi)

numfi is a numpy.ndarray subclass that does fixed-point arithmetic.

Feature:  

- Automatically perform fixed-point arithmetic through overloaded operators

- Maximum compatibility with numpy and other library, just like a normal numpy.ndarray  

- Optimized calculation speed, and keep bit precision as much as possible

- mimic the behavior of matlab's fixed-point toolbox

## Install

**Prerequisite**: python3 and numpy

```bash
pip install numfi
```

or you can just copy [numfi.py](https://github.com/ZZZZzzzzac/numfi/blob/master/numfi/numfi.py) and do whatever you want, after all it's only 300 lines of code

## Quick start

```python
from numfi import numfi as fi
import numpy as np

# numfi(array=[], s=1, w=16, f=None, RoundingMethod='Nearest', OverflowAction='Saturate')
x = fi([1,0,0.1234],1,16,8) 
# print(numfi) return brief description of numfi object: x => s16/8-N/S
# s for 'signed', u for 'unsigned', followed by word bits(16) and fraction bits(8), N/S for 'Nearest' and 'Saturate` for rounding/overflow method

# any arithmetic operation with numfi will return a numfi object with proper precision and value
# By overloading operators, numfi object can do fixed-point arithmetic easily:

# normal arithmetic operation work with float form of x
y = x + 1
y = [1] - x
y = x * [3,0,-3]
y = fi([1,0,0.1234],1,21,15) / x
y = -x
y = x ** 0.5
y = x % 3
# comparison return np.array of bool, just like normal np.array
y = x > 0.5
y = x >= fi([1,0,0.1234],1,21,15)
y = x == x
y = x <= np.ones(3)
y = x < [1,1,1]
# bitwise operation work with integer form of x
y = x & 0b101 
y = 0b100 | x   # order of operands doesn't matter
y = x ^ x       # two numfi object can also be used in bitwise operations
y = x << 4
y = x >> 2
...

# numfi object can be used just like normal numpy array, and return same numfi object back
y = np.sin(x)
y = x[x>1]
y = x.sum()
y = x.reshape(3,1)
np.convolve(x[0],np.ones(3))
np.fft.fft(x,n=512)
plt.plot(x)
pandas.DataFrame(x)
f, t, Sxx = scipy.signal.spectrogram(x,nperseg=256,noverlap=128)
plt.pcolormesh(t, f, Sxx, shading='gouraud')
for i in x:
    print(i)
...
```

## Document

Details can be found here: [https://numfi.readthedocs.io/en/latest/?](https://numfi.readthedocs.io/en/latest/?)

## License

The project is licensed under the MIT license.