semvec/pypi-packages huggingface dataset:
village-data-analysis 1.5
Generates report in xlsx and csv format about the inputed geometric shape file within the provided time range.

# Village data analysis package

## Description

Generates report in xlsx and csv format about the inputed geometric shape file within the provided time range.

## Requirements

To be able to test this code shape file data is required.

## Installation

For installation please use the standard command as follows:
```
pip install village_data_analysis
```

### Sample code:

Please note that the package expects WSG84 formatted geometric shapes. Note that "year n" below supposed to be integers.

```
import village_data_analysis.analyzer as ap

shapefile_paths = [
    ("year 1", "shapeile_1.shp"),
    ("year 2", "shapeile_2.shp"),
    ("year 3", "shapeile_3.shp"),
    ("year n", "shapeile_n.shp"),
]

ap.create_report(shapefile_paths, 2000, 5)
```

Developed by [Lexunit.ai](https://Lexunit.ai) for The Nature Conservancy.
tokencal 0.1.2
A powerful package for estimating token prices in large language models.
Requires Python: >=3.6

# Tokencal: Token Price Estimation for LLMs
<p align="center">
  <img src="tokencal.png" height="500" alt="Tokencal" />
</p>

<p align="center">
    <a href="https://pypi.org/project/tokencal/" target="_blank">
        <img alt="Version" src="https://img.shields.io/pypi/v/tokencal?style=for-the-badge&color=3670A0">
    </a>
</p>

## Overview
**Tokencal** is a sophisticated tool designed to estimate token prices for large language models (LLMs). Whether you're a developer, researcher, or business, Tokencal helps you optimize token usage and manage costs effectively. 

## Features
- **Accurate Estimations**: Get precise token price estimates for various LLMs.
- **Efficient Performance**: Fast calculations to save your time.
- **Extensive Compatibility**: Works with multiple LLMs.

## Installation
To install Tokencal, simply run:
```bash
pip install tokencal
```
## Usage
Using Tokencal is straightforward. Here’s a quick example to get you started:
```
from tokencal import TokenEstimator

# Initialize the estimator with your LLM model details
estimator = TokenEstimator(model_name="Your_LLM_Model")

# Estimate token price
price = estimator.estimate_price(text="Sample text to estimate token price.")
print(f"Estimated token price: {price}")
```

## Keywords
Token price estimation, LLM, Large Language Models, cost optimization, token usage