semvec/pypi-packages huggingface dataset:
password-generator-v1 0.1.0
A secure password generator Python package
Requires Python: >=3.6

# Scientific Calculator

A Python package for performing scientific calculations.

## Features

- Basic arithmetic operations: addition, subtraction, multiplication, division
- Scientific functions: sine, cosine, tangent, logarithm, exponential, square root, power
- Error handling for invalid inputs
- Command-line interface for easy use

## Installation

Install from PyPI:
```
pip install scientific-calculator
```

Or install from source:
```
pip install .
```

## Usage

### As a Python module

```python
from scientific_calculator import ScientificCalculator

calc = ScientificCalculator()
result = calc.add(2, 3)
print(result)  # 5.0

result = calc.sin(90)
print(result)  # 1.0
```

### Command-line interface

```
python -m scientific_calculator.cli add 2 3
# Result: 5.0

python -m scientific_calculator.cli sin 90
# Result: 1.0
```

## Available Operations

- `add a b`: a + b
- `subtract a b`: a - b
- `multiply a b`: a * b
- `divide a b`: a / b
- `sin x`: sin(x) in degrees
- `cos x`: cos(x) in degrees
- `tan x`: tan(x) in degrees
- `log x [base]`: log(x, base) default base 10
- `exp x`: e^x
- `sqrt x`: square root of x
- `power base exponent`: base^exponent

## License

MIT License
rebrandly-mcp 1.0.0
MCP Server for rebrandly.com
Requires Python: >=3.13
Keywords: mcp, rebrandly, url shortener

## Rebrandly MCP Server

This MCP server provides tools to interact with [rebrandly.com](https://rebrandly.com) to manage short URLs.

### Prerequisites

- For Option 1:
  - [uv](https://docs.astral.sh/uv/) from Astral
  - [Python 3.13](https://www.python.org/downloads/) or higher
- For Option 2:
  - [Docker](https://www.docker.com/)


### Configuration

Use either of the below option to configure your MCP client. To understand the configuration options, please refer to the [documentation](https://gofastmcp.com/integrations/mcp-json-configuration).

**Option 1: Via uvx**
```json
{
  "mcpServers": {
    "rebrandly-mcp": {
      "command": "uvx",
      "args": [
        "rebrandly-mcp"
      ],
      "env": {
        "REBRANDLY_API_KEY": "__API_KEY__"
      },
      "disabled": false,
      "autoApprove": []
    }
  }
}
```


**Option 2: Via docker**

```json
{
  "mcpServers": {
    "rebrandly-mcp": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "--env",
        "REBRANDLY_API_KEY=__API_KEY__",
        "vimalpaliwal/rebrandly-mcp:latest"
      ],
      "env": {},
      "disabled": false,
      "autoApprove": []
    }
  }
}
```

**Note:** Replace `__API_KEY__` with your actual API key.


### Available Tools

- `generate_short_url`: Generate a new short URL
- `delete_short_url`: Delete an existing short URL
- `get_or_list_short_url`: Get or list existing short URL(s)

### Basic Usage Examples

- Shorten linkedin.com/in/xxxxx for me
- Get me a short URL for youtube.com/xxxxx having yyyyy as the slug/slashtag
- Delete the short URL for rebrandly.com/xxxx for me please
- List all the short links
ml4gw-buoy 0.4.1
Deploy Aframe and AMPLFI models over open data
Requires Python: <3.13,>=3.10

# Installation

This library is `pip` installable with

```bash
pip install ml4gw-buoy
```

It is recommended that you install `buoy` in a virtual environment such as `conda`.

# Usage

The function of this library is to run trained [Aframe](https://github.com/ML4GW/aframe) and [AMPLFI](https://github.com/ML4GW/amplfi) models over a gravitaional wave event reported by the LIGO-Virgo-KAGRA collaboration during their third observing run, O3.

Note: the trained models will be downloaded from [HuggingFace](https://huggingface.co/ML4GW) and require about 320 MB of space in total. 

To produce model outputs, first identify an event of interest. This can either be a catalog event, e.g., from [GWTC-3](https://arxiv.org/pdf/2111.03606), formatted like GW190521, or it can be a G event or superevent from [GraceDB](https://gracedb.ligo.org), formatted like G363842 or S200213t. Note that LIGO credentials are required to use the latter option. To analyze events from data that is not yet released, a container with frame-discovery dependencies can be pulled with `apptainer pull /home/aframe/images/aframe/buoy.sif docker://ghcr.io/ml4gw/buoy/buoy:v0.4.0`.

Once an event has been identified, run:

```bash
buoy --events <EVENT_NAME> --outdir <OUTPUT_DIRECTORY>
```

The output directory is structured as follows will contain a directory matching the name of the event.
Inside, there will be a `data` directory containing data created during the analysis, and a `plots`
directory containing Aframe's response to the event as well as a skymap and corner plot from AMPLFI.

Multiple events can be specified at once, e.g.:

```bash
buoy --events '["GW190828_063405", "GW190521", "S200213t"]' --outdir <OUTPUT_DIRECTORY>
```

About 10 MB of space is required for each event.