semvec/pypi-packages huggingface dataset:
forager-forward 0.1.1
Hunter.io v2 api implementation
Requires Python: >=3.10,<4.0

# Forager clients and services

A python wrapper for the Hunter.io v2 api as a client.
Email validation service.

## Installation

### Requirements

    - Python 3.10
    - httpx

### To install

   pip install forager_forward==0.1.1

## Usage

Service supports next method from Hunter.io v2 api

    - domain_search (with async adomain_search)
    - email_finder (with async aemail_finder)
    - verify_email (with async averify_email)
    - email_count (with async aemail_count)
    
Additionally, service supports crud methods for locally storing data

## How to use service

### Import service and instantiate it once

    from forager_forward.client_initializer import ClientInitializer

    initializer = ClientInitializer()

    initializer.initialize_client("api_key_got_from_hunter")


    client = initializer.client


### Once initialized somewhere in the code you can get instances in different places without additional initialization

    client = ClientInitializer().client


### Search addresses for a given domain

    client.domain_search("www.brillion.com.ua")

### Or pass company name

    client.domain_search(company="Brillion", limit=20, seniority="junior")

### Find email address

    client.email_finder(compayny="pmr", full_name="Sergiy Petrov", raw=True)

### Check email deliverabelity

    client.email_verifier("a@a.com")

### All data can be stored in Storage class instance. It has its own crud methods, and it is Singleton.

    from forager_forward.common.storage import Storage

    storage = Storage()

    storage.create(some_key, some_value)

    storage.update(some_key, some_value)

    some_variable = storage.read(some_key)

    storage.delete(some_key)

### To validate emails and store validation result use email_validation_service.

    from forager_forward.app_services.email_validation_service import EmailValidationService

    email_validator = EmailValidationService()

    email_validator.create_email_record("some_email@company.com")

    email_validator.read("another@company.com")

## Tests

    To run test firstly you need to install test dependency, then run

        pytest -cov
perdiem 0.1.2
A simple per diem library
Requires Python: >=3.11,<4.0

# PerDiem

A library that allows a user to get the per diem rates for a location and calculate the per diem rates for a given period of time. It takes the data from GSA's per diem page and the computer handles the rest!

## Usage

```python
from perdiem import PerDiem

# Look up the rates for Washington, DC (accept the first result)
rates = PerDiem.get_rates("Washington, DC")[0]

# Alternatively, look up the rates by zip code!
# rates = PerDiem.get_rates("20002")[0]

start_date = "2024-01-01"
end_date = "2024-01-08"

# Calculate the total costs for a trip
trip = PerDiem.calculate_per_diem(rates, start_date, end_date)
print(trip["total"])
# 2136.5
```

## Install

```sh
pip install perdiem
```

## Features

- [x] Look up Per Diem rate by Zip Code
- [x] Compute the total (factoring first and last day travel reductions)

## Future?

- [] An API?
- [] A CLI?
- [] Export to Excel?

## License

Apache 2.0
BigONavigator 0.2.2
A Python package to track and document the computational complexity of functions.
Requires Python: >=3.8

# BigONavigator - Navigating Computational Complexity

**BigONavigator** is not just a Python package; it's a journey into the heart of algorithm efficiency. Originally developed as a university project, this tool has grown into a robust resource for developers, researchers, and students alike. It elegantly navigates the complexities of computational performance, providing insights into the Big O notation of algorithms with precision and clarity.

### 🎓 University Project Background
This project was conceived and developed as part of a university course in Computer Science, aiming to bridge theoretical concepts with practical application. It offers an educational insight into algorithm complexity, making it a perfect tool for academic projects and research.

### 🌟 Key Features
- **Dynamic Complexity Estimation**: Intuitively estimate the computational complexity of Python functions. (COMING SOON)
- **Decorator-Driven Analysis**: Utilize decorators to effortlessly mark and track function complexities.
- **Comprehensive Complexity Table**: View a summarised table of all tracked functions and their complexities, fostering a deeper understanding of algorithm performance.

## 🛠 Installation

```bash
pip install BigONavigator
```

## 📈 Usage

Kickstart your complexity analysis with BigONavigator:

```python
from bigonavigator import O
from bigonavigator import show_complexity_table

@O['n']
def example_linear_function(data):
    # Implement linear time complexity operations
    pass

# Review the complexity summary
show_complexity_table()
```

## 📚 Documentation

Delve deeper into BigONavigator with our comprehensive documentation, which covers everything from setup to advanced features. Perfect for academic purposes and hands-on learning. Check it out [here](./DOCS.md).

## 🤝 Contributing

Your contributions can help make BigONavigator an even more valuable tool for the academic and developer community:

1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/YourAmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/YourAmazingFeature`)
5. Open a Pull Request

## 📞 Support & Feedback

We welcome feedback and queries! Please file any issues or suggestions on our [Issues page](https://github.com/velocitatem/BigONavigator/issues) or engage with us via [Discussions](https://github.com/velocitatem/BigONavigator/discussions).

## 📃 License

This project is open-source and available under the MIT License. See `LICENSE` for more details.