semvec/pypi-packages huggingface dataset:
Solveit 0.1.2
A comprehensive toolkit for data processing and problem-solving
Requires Python: >=3.7

# Solveit

[![PyPI version](https://badge.fury.io/py/Solveit.svg)](https://badge.fury.io/py/Solveit)
[![Python 3.7+](https://img.shields.io/badge/python-3.7+-blue.svg)](https://www.python.org/downloads/release/python-370/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

A comprehensive Python toolkit for data processing and problem-solving.

## Features

- **Data Cleaning**: Advanced tools for handling missing values, outliers, and duplicates
- **Unit Conversion**: Comprehensive unit conversion system
- **Pathfinding**: Implementation of various pathfinding algorithms
- **Financial Calculations**: Tools for financial computations
- **Time Management**: Scheduling and timeline optimization
- **Visualization**: Easy-to-use plotting utilities

## Installation

```bash
pip install Solveit
```

## Usage Examples

### Data Cleaning
```python
from solveit.data.cleaner import DataCleaner
import pandas as pd

# Create sample data
data = {
    'Name': ['John', 'Jane', 'John', None, 'Bob'],
    'Age': [25, 30, 25, None, 100],
    'Salary': [50000, None, 50000, 60000, 1000000]
}
df = pd.DataFrame(data)

# Initialize cleaner
cleaner = DataCleaner()

# Clean the data
cleaned_df = cleaner.process_file(
    input_path='data.csv',
    output_path='cleaned_data.csv',
    operations=[
        ('remove_duplicates', {}),
        ('handle_missing_values', {'strategy': 'mean'}),
        ('remove_outliers', {'columns': ['Age', 'Salary']})
    ]
)
```

### Unit Conversion
```python
from solveit.units.converter import UnitConverter, UnitType

converter = UnitConverter()

# Convert kilometers to miles
miles = converter.convert(100, "km", "mile", UnitType.LENGTH)
print(f"100 km = {miles:.2f} miles")

# Convert Celsius to Fahrenheit
fahrenheit = converter.convert(0, "C", "F", UnitType.TEMPERATURE)
print(f"0°C = {fahrenheit}°F")
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## Author

- **Kashyapsinh Gohil** - [GitHub](https://github.com/KashyapSinh-Gohil)
- Email: k.agohil000@gmail.com

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
MuCOS 0.2
Multi-SuCOS: Score derivative compounds based how well they recapitulate the shape and colour of multiple (fragment) inspiration hits
Requires Python: >=3.10

# 🐄 MuCOS

> pronounced: "moo-cos"

Multi-SuCOS: Score derivative compounds based how well they recapitulate the shape and colour of multiple (fragment) inspiration hits

## Based on:

- [SuCOS](https://github.com/susanhleung/SuCOS)
- [OpenEye Tversky](https://docs.eyesopen.com/applications/rocs/theory/measure_similarity.html#tversky)
- [Malhotra and Karanicolas' COS measure](https://pubs.acs.org/doi/abs/10.1021/acs.jmedchem.6b00725)
event-sms-notifier 0.1.0
A library for event notifications with SMS capabilities using Africa's Talking
Requires Python: >=3.6
Keywords: event notification sms africa's talking period tracking reminder

# Event SMS Notifier

A Python library for event notifications with SMS capabilities using Africa's Talking API. Perfect for period tracking, appointment reminders, and general event notifications.

## Installation

```bash
pip install event-sms-notifier
```

## Quick Start

```python
from event_notifier import EventNotifier
from datetime import datetime, timedelta

# Initialize the notifier
notifier = EventNotifier(
    username="your_username",  # Your Africa's Talking username
    api_key="your_api_key",    # Your Africa's Talking API key
    sender_id="your_sender_id" # Your sender ID
)

# Add an event
notifier.add_event({
    "name": "Important Meeting",
    "datetime": datetime.now() + timedelta(minutes=30)
})

# Start monitoring
notifier.start_monitoring(
    recipients="+1234567890",  # Phone number to receive notifications
    interval_seconds=120       # Check every 2 minutes
)
```

## Features

- Event monitoring and SMS notifications
- Flexible datetime input (string or datetime object)
- Customizable message templates
- Support for multiple recipients
- Configurable check intervals
- Built-in period tracking capabilities

## Period Tracking Example

```python
from event_notifier import EventNotifier
from datetime import datetime, timedelta

notifier = EventNotifier(
    username="your_username",
    api_key="your_api_key",
    sender_id="your_sender_id"
)

# Add period events
events = [
    {
        "name": "Period Starts",
        "datetime": datetime.now() + timedelta(days=1)
    },
    {
        "name": "Ovulation Day",
        "datetime": datetime.now() + timedelta(days=14)
    }
]

for event in events:
    notifier.add_event(event)

# Custom message template
template = """
🔴 Period Alert: {name}
Time: {time}
Take care!
"""

# Start monitoring
notifier.start_monitoring(
    recipients="+1234567890",
    interval_seconds=300,
    message_template=template
)
```

## Requirements

- Python 3.6+
- Africa's Talking account
- Valid API credentials
- Registered phone number

## Documentation

For more examples and detailed documentation, visit our [GitHub repository](https://github.com/yourusername/event-notifier).

## License

MIT License - see LICENSE file for details.
teos10 0.1
Unofficial Python implementation of the TEOS-10 properties of water

# teos10

![Tests](https://github.com/mvdh7/teos10/workflows/Tests/badge.svg)
[![Coverage](https://raw.githubusercontent.com/mvdh7/teos10/181b47628f9e0d70f1675f6ea7a577ac6ce55389/coverage/coverage.svg)](https://github.com/mvdh7/teos10/blob/main/coverage/coverage.txt)
[![pypi badge](https://img.shields.io/pypi/v/teos10.svg?style=popout)](https://pypi.org/project/teos10/)
[![Conda Version](https://img.shields.io/conda/vn/conda-forge/teos10.svg?style-popout)](https://anaconda.org/conda-forge/teos10)
[![DOI](https://img.shields.io/badge/DOI-10.5281%2Fzenodo.3813343-informational)](https://doi.org/10.5281/zenodo.3813343)
[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

Unofficial Python implementation of the TEOS-10 properties of water.

> **Why not just use [GSW-Python](https://teos-10.github.io/GSW-Python/)?**
>
> Because the functions here in teos10 can be automatically differentiated with [JAX](https://jax.readthedocs.io/en/latest/).

## Installation

    pip install teos10
    conda install conda-forge::teos10

## Usage

See the module-level docstring.

```python
    import teos10
    teos10?
```
wtu-mlflow 0.1.9
W-Train Utils for MLflow
Requires Python: >=3.7

# wtrainclient

## 가상환경 설정

```sh
pyenv install 3.8.18
pyenv virtualenv 3.8.18 wtrainclient3.8
pyenv activate wtrainclient3.8
```

---

## mlflow, minio 실행

```sh
cd docker
docker-compose up -d --build
```

---

## 환경 변수 설정

프로젝트를 실행하기 전에 아래의 환경 변수들을 설정해야 합니다:

| 환경변수                  | 설명                                                               | 예시                                      |
| ------------------------- | ------------------------------------------------------------------ | ----------------------------------------- |
| PROFILE                   | 개발/운영 환경설정, 개발환경에서는 모델을 실제로 업르도하지 않는다 | 운영: "prod" or "production", 개발: 그 외 |
| MLFLOW_S3_ENDPOINT_URL    | MLflow가 저장소로 사용하고있는 MinIO 엔드포인트 URL                | http://localhost:9000                     |
| MLFLOW_TRACKING_URI       | MLflow 트래킹 서버의 URI                                           | http://localhost:5001                     |
| AWS_ACCESS_KEY_ID         | MinIO 서버 접근을 위한 AWS 호환 액세스 키                          | minio                                     |
| AWS_SECRET_ACCESS_KEY     | MinIO 서버 접근을 위한 AWS 호환 시크릿 액세스 키                   | miniostorage                              |
| RABBIT_ENDPOINT_URL       | MinIO 서버에 모델 업로드 후 path 를 발행할 RMQ 엔드포인트 URL      | amqp://guest:guest@localhost:5672/        |
| RABBIT_MODEL_UPLOAD_TOPIC | 모델 업로드 path 를 전달할 토픽                                    | train.model.uploaded                      |
| TRAIN_ID                  | train_id (학습 서버에서 넣어주는 값)                               | 1                                         |
| MODEL_NAME                | model_name (학습 서버에서 넣어주는 값)                             | my_model                                  |

---