semvec/pypi-packages huggingface dataset:
bio-compose 0.8.7
Create, execute, and introspect reproducible composite simulations of dynamic biological systems.
Requires Python: <4.0,>=3.10

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1bVtTmbMLnfFBv44rPwmBFsHvpX7woDWn?usp=sharing)
![Deployment Pipeline](https://github.com/biosimulators/bio-compose/actions/workflows/pipeline.yml/badge.svg)
[![Documentation](https://readthedocs.org/projects/bio-compose/badge/?version=latest)](https://bio-compose.readthedocs.io/en/latest/)

# **BioCompose**: Create, execute, and introspect reproducible composite simulations of dynamic biological systems.
#### __This service utilizes separate containers for REST API management, job processing, and datastorage with MongoDB, ensuring scalable and robust performance.__

## **Documentation**: 

The complete `BioCompose` documentation can be found here: https://bio-compose.readthedocs.io/en/latest/

## **Getting Started**:

### _HIGH-LEVEL `bio_compose` API:_

The primary method of user-facing interaction for this service is done through the use of a high-level "notebook" api called `bio_check`. 
A convenient notebook demonstrating the functionality of this service is hosted on Google Colab and can be accessed by clicking the above "Open In Colab" badge.

Installation of this tooling can be performed using PyPI as such:

```bash
pip install bio-compose
```

#### Alternatively, **the REST API can be accessed via Swagger UI here: [https://biochecknet.biosimulations.org/docs](https://biochecknet.biosimulations.org/docs)**

## Smoldyn to Simularium conversion:

A convienient template notebook for converting the outputs of Smoldyn simulations to Simularium trajectories can be
[found here.](https://colab.research.google.com/drive/17uMMRq3L3KqRIXnezahM6TtOtJYK8Cu6#scrollTo=6n5Wf58hthFm)


### FOR DEVELOPERS:
Poetry is used as the environment manager. Poetry uses a globally referenced configuration whose cache setup may lead to permission errors when running `poetry install`. In the event that such errors exist, run the following:
```bash
poetry config cache-dir ~/poetry-cache
mkdir -p ~/poetry-cache
chmod -R u+w ~/poetry-cache

# then install the project:
poetry install
```
coreframe 1.0.3
COmmon REsearch FRAMEwork
Requires Python: >=3.6
Keywords: python

![test results](https://github.com/kimlab/cf/actions/workflows/test.yml/badge.svg)

# COREFRAME

**COmmon REsearch FRAMEwork** - A Python framework for Earth science research and data analysis.

## Overview

COREFRAME is a specialized Python framework designed for Earth science research, providing robust tools for handling complex multidimensional datasets in atmospheric, oceanic, and climate sciences.

## Features

### CoreArray
- Coordinate-aware array operations based on NumPy
- Intelligent dimension handling
- Seamless integration with NumPy's universal functions
- Support for labeled dimensions and metadata

### HDF5 Integration
- High-level interface for HDF5 files
- Automatic coordinate system management
- Advanced time dimension handling with calendar support
- Comprehensive metadata preservation

### Performance Optimization
- Result caching system for computationally intensive operations
- Parallel processing capabilities with configurable worker pools
- Compressed storage options
- Hash-based cache management

### Data Analysis Tools
- Efficient time-series operations
- Support for various calendar systems
- Area-based spatial calculations
- Geographical coordinate support
- Gridded data handling

## Installation

```bash
pip install coreframe
```

### Requirements
- Python 3.6 or higher
- NumPy
- h5py

## Quick Start

```python
import numpy as np
from coreframe import CoreArray

# Create a sample dataset
data = np.random.rand(500, 100, 100)
time = np.arange('2001-01-01', '2002-05-15', dtype='datetime64[D]')
lat = np.linspace(-90, 90, 100)
lon = np.linspace(-180, 180, 100)

# Create CoreArray with coordinates
coords = {
    'time': time,
    'lat': lat,
    'lon': lon
}
arr = CoreArray(data, coords)

# Perform operations
result = arr.apply_by_time("time", "1M", np.max, axis=0)
```

## Key Use Cases
- Climate data analysis
- Atmospheric science research
- Oceanographic studies
- Earth system modeling
- Geospatial data processing
- Environmental time series analysis

## Contributing

We welcome contributions! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Support

For support, please open an issue on the GitHub repository.

## Authors

Developed and maintained by the MetaEarth Lab.

---

For more information, please contact: hyungjun@gmail.com