semvec/pypi-packages huggingface dataset:
package-teste-pacotes 0.0.1
Teste de Criação de Pacotes
Requires Python: >=3.8

# package_name

Description. 
The package package_name is used to:
	- Histogram matching
	- Structural similary

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install package_name

```bash
pip install package_name
```

## Usage

```python
from package_name import file1_name
file1_name.my_function()
```

## Author
FÃ¡bio Alves

## License
[MIT](https://choosealicense.com/licenses/mit/)
image-processing-rluispdev 0.1
Image Processing Package Using Skimage
Requires Python: >=3.6


The package `image-processing-rluispdev` is used for:

## Processing:
- Histogram matching
- Structural similarity
- Image resizing

## Utils:
- Head image
- Save image
- Plot image
- Plot result
- Plot histogram

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install `image-processing-rluispdev`.

```bash
pip install image-processing-rluispdev


## Author
rluispdev

## License
[MIT](https://choosealicense.com/licenses/mit/)
mylabs-mcp.projectall-experts 0.2.0
All Model Context Protocol (MCP) servers
Requires Python: >=3.13

# projectall-experts

All Model Context Protocol (MCP) servers

## Prerequisites

1. Install `uv` from [Astral](https://docs.astral.sh/uv/getting-started/installation/) or the [GitHub README](https://github.com/astral-sh/uv#installation)
2. Install Python using `uv python install 3.13`

## Installation

Install the MCP server:
```bash
uv tool install mylabs-mcp.projectall-experts --force 
```

Add the server to your MCP client config (e.g. `~/.cursor-server/data/User/globalStorage/saoudrizwan.claude-dev/settings/cline_mcp_settings.json`):
```json
{
  "mcpServers": {
    "mylabs_mcp.projectfour_expert": {
      "command": "uvx",
      "args": [
        "--from",
        "mylabs-mcp-projectall-experts",
        "projectfour-expert"
      ],
      "env": {
        "SHELL": "/usr/bin/zsh"
      },
      "disabled": false,
      "autoApprove": []
    },    
    "mylabs_mcp.projectsix_expert": {
      "command": "uvx",
      "args": [
        "--from",
        "mylabs-mcp-projectall-experts",
        "projectsix-expert"
      ],
      "env": {
        "SHELL": "/usr/bin/zsh"
      },
      "disabled": false,
      "autoApprove": []
    },
    "mylabs_mcp.projectten_expert": {
      "command": "uvx",
      "args": [
        "--from",
        "mylabs-mcp-projectall-experts",
        "projectten-expert"
      ],
      "env": {
        "SHELL": "/usr/bin/zsh"
      },
      "disabled": false,
      "autoApprove": []
    }

  }
}
```

## Development

1. Set up the development environment:
```bash
uv sync --all-groups
```

2. Install pre-commit hooks:
```bash
GIT_CONFIG=/dev/null pre-commit install
```
fasttrig 0.1.6
Fast sine/cosine/tangent approximation using fourth degree polynomials
Requires Python: >=3.6

# fasttrig

Requires Numba for optimal performance.

A fast and lightweight approximation of sin, cos, and tan using 4th-degree polynomials.

Unit: radian

Accuracy: Error < 0.001

Important: Numba is required for fasttrig to achieve high performance.
Without Numba, performance will be even slower than NumPy.
With Numba, the efficiency can be 1.5 to 3.0 times NumPy.




## Example

```python
import fasttrig
import numpy as np


x = np.linspace(-np.pi, np.pi, 1000000)

y = fasttrig.sin(x)
c = fasttrig.cos(x)
t = fasttrig.tan(x)
#Scalar
z = fasttrig.sin(1902.0808)
rcdb 2.2.10
RCDB - Run Conditions DataBase
Requires Python: >=3.9

## RCDB

![Python tests](https://github.com/JeffersonLab/rcdb/actions/workflows/python-tests.yml/badge.svg)
[![Python examples work](https://github.com/JeffersonLab/rcdb/actions/workflows/python-examples.yml/badge.svg)](https://github.com/JeffersonLab/rcdb/actions/workflows/python-examples.yml)

Run Configuration/Conditions Database (RCDB) stores run related information and conditions. It uses MySQL or SQLite databases to store information about runs and provides interfaces to search runs, manage data, automatic CODA integration, etc. 

The interfaces available are:  
- Web site
- Command line interface (CLI)
- Python API
- C++ API
- Possibly JAVA API

#### Documentation:

https://jeffersonlab.github.io/rcdb/

#### Demo website:
One can visit HallD RCDB Web site as demo:  
https://halldweb.jlab.org/rcdb/


#### SQLite db:
Daily updated SQLite database is available here:  
https://halldweb.jlab.org/dist/rcdb2.sqlite