semvec/pypi-packages huggingface dataset:
nshm-toshi-client 1.1.1
client for toshi API
Requires Python: <4.0,>=3.10

# nshm-toshi-client

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

A python3 client for the nshm-toshi-api.

* Documentation: <https://gns-science.github.io/nshm-toshi-client>
* GitHub: <https://github.com/gns-science/nshm-toshi-client>
* PyPI: <https://pypi.org/project/nshm-toshi-client/>
* Free software: MIT
kivy-garden-i18n 0.3.0
i18n for Kivy
Requires Python: <4.0,>=3.10
Keywords: kivy

# kivy_garden.i18n

Internationalization support for Kivy applications.

```python
from kivy_garden.i18n.localizer import MappingBasedTranslatorFactory, Localizer

translations = {
    "greeting": {
        "ja": "おはよう",
        "en": "morning",
    },
    "app title": {
        "ja": "初めてのKivyプログラム",
        "en": "My First Kivy App",
    },
}
localizer = Localizer(MappingBasedTranslatorFactory(translations))

l = localizer
l.lang = "en"  # Set the "current language" to "en"
assert l.font_name == "Roboto"
assert l._("greeting") == "morning"
assert l._("app title") == "My First Kivy App"

l.lang = "ja"
assert l.font_name == "<a pre-installed Japanese font>"
assert l._("greeting") == "おはよう"
assert l._("app title") == "初めてのKivyプログラム"
```

Bindings:

```python
from kivy.lang import Builder

localizer.install(name="l")
label = Builder.load_string("""
Label:
    font_name: l.font_name
    text: l._("greeting")
""")
localizer.lang = "en"
assert label.font_name == "Roboto"
assert label.text == "morning"
localizer.lang = "ja"
assert label.font_name == "<a pre-installed Japanese font>"
assert label.text == "おはよう"
```

# Installation

Pin the minor version.

```
pip install "kivy-garden-i18n>=0.3,<0.4"
```

# Tested on

- CPython 3.10 + Kivy 2.3
- CPython 3.11 + Kivy 2.3
- CPython 3.12 + Kivy 2.3
- CPython 3.13 + Kivy 2.3
dimpyu 0.1.2
A lightweight, flexible Python library for handling physical units
Requires Python: >=3.7

# DimPyU

A lightweight, flexible Python library for handling physical units, supporting arithmetic operations, dimensional analysis, and smart conversions.

## Features

-   **Intuitive Syntax**: `10 * reg.km` or `reg.parse("10 km")`
-   **Arithmetic**: Add, substract, multiply, and divide quantities (`m/s`, `kg * m/s^2`).
-   **Conversions**: Easily convert between compatible units (`val.to('km')`).
-   **SI Prefixes**: Automatically handles prefixes like `micro`, `giga`, `nano` (e.g. `micrometer`).
-   **Numpy Support**: Seamlessly works with Numpy arrays for high-performance calculations on vectors.
-   **Physical Constants**: Includes standard constants like Speed of Light ($c$), Gravity ($g_0$), etc.

## Installation

### From Source (Local)
```bash
pip install .
```

### From GitHub
You can install directly from your repository:
```bash
pip install git+https://github.com/YOUR_USERNAME/dimpyu.git
```

### From PyPI (Coming working)
```bash
pip install dimpyu
```

## Usage

```python
from dimpyu import UnitRegistry

# Initialize registry (loads default units)
reg = UnitRegistry()

# 1. Simple Quantity Creation
distance = 10 * reg.km
time = 0.5 * reg.hr
speed = distance / time
print(speed)                # 20.0 km / hr
print(speed.to('m/s'))      # 5.555... m / s

# 2. String Parsing
q = reg.parse("50 kg * m / s^2")
print(q)                    # 50.0 kg m/s^2
print(q.to('N'))            # 50.0 N

# 3. Array / Numpy Support
lengths = [1, 2, 3] * reg.meter
print(lengths.to('cm'))     # [100., 200., 300.] centimeter

# 4. Physical Constants
from dimpyu import constants
energy = constants.h * constants.c / (500 * reg.nm)
print(energy.to('J'))
```

## Project Structure

-   `dimpyu/`: Core package code.
-   `main.py`: Demo script.
-   `fluid_mechanics_example.py`: Example solution for a pump calculation.
-   `heat_transfer_example.py`: Example solution for a heat transfer problem.

## License

MIT
randomcropclassselective 0.0.1
A custom Albumentations transform allowing random crops while preserving specific classes.
Requires Python: >=3.9

# Random Crop Class Selective

This is a simple custom Albumentations transform which acts as a random crop
while ensuring that specific classes will be preserved in the crop
post-transformation. 

This may be useful if it is desired to create augmentations of rare classes, to
ensure that these rare classes will actually be present in the augmentation.
colacloud-mcp 0.1.0
MCP server for COLA Cloud - access US alcohol label data from AI assistants
Requires Python: >=3.10
Keywords: ai, alcohol, cola, labels, mcp, ttb

# colacloud-mcp

MCP server for [COLA Cloud](https://colacloud.us) - access US alcohol label data from AI assistants.

Query 2.5M+ Certificate of Label Approval (COLA) records from the TTB (Alcohol and Tobacco Tax and Trade Bureau) directly from Claude, ChatGPT, or other MCP-compatible AI tools.

## Installation

```bash
# Using uvx (recommended, no install required)
uvx colacloud-mcp

# Or install with pip
pip install colacloud-mcp
```

## Configuration

### Claude Desktop

Add to your Claude Desktop config (`~/Library/Application Support/Claude/claude_desktop_config.json` on macOS):

```json
{
  "mcpServers": {
    "colacloud": {
      "command": "uvx",
      "args": ["colacloud-mcp"],
      "env": {
        "COLA_API_KEY": "your-api-key-here"
      }
    }
  }
}
```

### Other MCP Clients

Set the `COLA_API_KEY` environment variable and run:

```bash
export COLA_API_KEY="your-api-key-here"
colacloud-mcp
```

## Getting an API Key

1. Create an account at [app.colacloud.us](https://app.colacloud.us)
2. Go to Dashboard > API Keys
3. Create a new API key

Free tier includes 500 requests/month.

## Available Tools

| Tool | Description |
|------|-------------|
| `search_colas` | Search and filter COLA records by brand, product type, origin, dates, ABV |
| `get_cola` | Get detailed info for a specific COLA by TTB ID |
| `search_permittees` | Search permit holders (alcohol producers/importers) |
| `get_permittee` | Get permit holder details with recent COLAs |
| `lookup_barcode` | Find COLAs by product barcode (UPC/EAN) |
| `get_api_usage` | Check your API usage and rate limits |

## Example Queries

Once configured, you can ask Claude things like:

- "Search for Tito's vodka labels"
- "Find wine labels from Napa Valley approved in 2024"
- "Look up the product with barcode 080686001010"
- "Show me recent labels from permit holder CA-I-123"
- "What craft beers have an IBU over 60?"

## Data Available

Each COLA record includes:

- **Basic info**: Brand name, product name, product type, origin
- **Dates**: Application, approval, expiration dates
- **Label images**: Front, back, neck, strip labels (1-2 per record)
- **Extracted data**: ABV, volume, barcodes (via OCR/computer vision)
- **AI enrichment**: Category, tasting notes, product descriptions

## Development

```bash
# Clone and install
git clone https://github.com/cola-cloud-us/colacloud-mcp
cd colacloud-mcp
uv sync --dev

# Run locally
export COLA_API_KEY="your-key"
uv run colacloud-mcp

# Run tests
uv run pytest

# Lint
uv run ruff check .
```

## License

MIT
Topsis-Raiza-102303068 1.0.0
TOPSIS implementation for multi-criteria decision making
Requires Python: >=3.7

# Topsis-Raiza-102303068

## Description
This Python package implements the **TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)** method used in Multi-Criteria Decision Making (MCDM).

It allows users to rank alternatives based on multiple criteria using command-line input.

---

## Installation

Install the package from PyPI using:

```bash
pip install Topsis-Raiza-102303068
```

---

## Command Line Usage

```bash
topsis <InputDataFile> <Weights> <Impacts> <OutputFile>
```

---

## Example

```bash
topsis data.csv "1,1,1,2,1" "+,+,-,+,-" output.csv
```

---

## Input File Format

- First column: Name of alternatives (e.g., Fund Name)
- Remaining columns: Numeric criteria values
- Minimum 3 columns required
- Weights and impacts must match number of criteria

---

## Output File

The output CSV file contains:
- All original columns
- `Topsis Score`
- `Rank` (higher score = better rank)

---

## Requirements

- Python 3.7+
- pandas
- numpy

---

## Author

Raiza Duggal  
Roll No: 102303068