qkun 0.4.1

Geoinformation Tools & Data Access for Satellite Observations

🌍 QKUN

Geoinformation Tools & Data Access for Satellite Observations

Qkun, pronaunced as "kun", is a Python package for querying, downloading, caching, and processing Earth observation satellite data.

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🚀 Features

• ✅ Query satellite data (current & historical) by spatial and temporal bounds
• ✅ Download observational data by mission, instrument, and processing level
• ✅ Local LRU cache with automatic size limit and on-demand re-download
• ✅ Digest large data files into lightweight YAML summaries for quick inspection

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📦 Installation

🧪 System Requirements:

• Python 3.9+
• Linux or macOS
• hdf5, netCDF, and other data format libraries
• use python virtual environment for isolation

If you are using MacOS, you may need to install hdf5 and netCDF libraries:

brew install hdf5 netcdf

If you are using Ubuntu, you may need to install hdf5 and netCDF libraries:

sudo apt-get install libhdf5-dev libnetcdf-dev

If you are using RedHat, you may need to install hdf5 and netCDF libraries:

sudo yum install hdf5 netcdf

Please activate a python virtual environment before installing the package:

python3 -m venv venv
source venv/bin/activate

🔗 Install from PyPI:

pip install qkun 

🛠️ Install locally (dev mode)

git clone https://github.com/yourusername/qkun.git
cd qkun
pip install -e .

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📁 Command-line Usage

🌐 Query satellite granules

search-granule pace OCI-L1B --start 2025-03-26 --end 2025-03-27 --lat -10 10 --lon 30 50

📦 Download satellite granules

search-granule pace OCI-L1B --start 2025-03-26 --end 2025-03-27 --lat -10 10 --lon 30 50 --quiet > download_list.txt
download-granule download_list.txt --select ::2 --save-dir ${HOME}/.cache/qkun

📊 Summarize large data files

digest-granule download_list.txt --select ::2 --save-dir ${HOME}/.cache/qkun

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🛰️ Supported Missions, Instruments & Data Levels

Mission	Instrument(s)	Data Level(s)
pace	OCI	L1B, L2
aqua	AIRS,MODIS	L1B, L2
gpm	GMI	L1B, L2
sentinel-5p	TROPOMI	L1B, L2
(More coming)	...	...

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🧠 Example Python API Usage

Assuming that you have already downloaded and digested the data using command-line tools, you can use the following Python API to access the data.

import os
from qkun import CACHE_FOLDER_PATH
from qkun.pace import OceanColor

basename = "PACE_OCI.20250326T103301.L1B.V3"
digest_path = os.path.join(CACHE_FOLDER_PATH, f"{basename}.global.yaml")

# create an instance of OceanColor
obs = OceanColor(digest_path, verbose=True)

# create auxiliary files such as footprint and field of view
# also saved in the cache folder
obs.process()

# get a lat-lon bounding box
box = obs.get_bounding_box()

# get instrument field of view
lon, lat = obs.get_fov()

# get data keys
keys = obs.get_data().variables.keys()

# subsample and average over bands
blue = obs.get_data("rhot_blue")[:,::5,::5].mean(axis=0)

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🧹 Caching By default, all downloaded data are cached locally in:

~/.cache/qkun/

To change the cache directory and size limit, set these global variables:

import qkun
qkun.CACHE_FOLDER_PATH = '/path/to/cache'
qkun.CACHE_SIZE_LIMIT = 10.  # in GB

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🤝 Contributing

Contributions are welcome! Please open an issue or PR if you’d like to:

• Add new missions/instruments
• Improve download or parsing logic
• Enhance command line tools or add GUI
• Expand test coverage

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📬 Contact

Maintained by @chengcli — feel free to reach out with ideas, feedback, or collaboration proposals.