| Name | coreframe JSON |
| Version |
1.0.3
JSON |
| download |
| home_page | None |
| Summary | COmmon REsearch FRAMEwork |
| upload_time | 2024-12-19 16:23:54 |
| maintainer | None |
| docs_url | None |
| author | MetaEarth Lab |
| requires_python | >=3.6 |
| license | None |
| keywords |
python
|
| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# 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
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