No one will drive us from the paradise which Equi7Grid created for us

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GitHub: https://github.com/csaybar/equi7grid-lite 🌐

PyPI: https://pypi.org/project/equi7grid-lite/ 🛠️

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Overview 📊

The equi7grid-lite package implements a user-friendly Python interface to interact with the Equi7Grid grid system. With this package, users can convert geographic coordinates to Equi7Grid tiles and vice versa.

equi7grid-lite is an unofficial Python implementation of Equi7Grid. It offers improvements and a simplified interface for grid interaction. This implementation differs from the official version in three key ways:

• Quad-Tree Grid Splitting: Users split the grid in a Quad-Tree fashion, with each grid level divided into four tiles.
• Revised Grid ID Encoding: The grid ID is encoded in meters, without the "T1", "T3", or "T6" tile system references.
• Upper Bound Level: The grid level is capped at a threshold of 2,500,000 meters to manage grid complexity.

Key Features ✨

• Coordinate Conversion: Convert between geographic coordinates and Equi7Grid tiles easily. 🌍
• Quad-Tree Grid Management: Supports splitting and managing grids in a Quad-Tree structure. 🗺️
• Cubo Integration: Use equi7grid-lite in combination with Cubo for Earth Observation (EO) data retrieval. 🛰️

Please refer to the Equi7Grid repository for more information on the official implementation.

Installation ⚙️

Install the latest version from PyPI:

pip install equi7grid-lite

How to use 🛠️

Basic Usage: Working with the Equi7Grid 🗺️

Convert geographic coordinates to Equi7Grid tiles

from equi7grid_lite import Equi7Grid

grid_system = Equi7Grid(min_grid_size=2560)
lon, lat = -79.5, -5.49
grid_system.lonlat2grid(lon=lon, lat=lat)
# Equi7Grid(min_grid_size=2560)
# ----------------
# levels: 0, 1, ... , 7, 8
# zones: AN, NA, OC, SA, AF, EU, AS
# min_grid_size: 2560 meters
# max_grid_size: 1310720 meters

Convert from grid ID to geographic coordinates

grid_system.grid2lonlat(grid_id="SA2560_E2009N2525")
#                  id        lon       lat          x          y zone level  land             geometry
#0  SA2560_E2009N2525 -79.507568 -5.485739  5144320.0  6465280.0   SA    Z0  True  POLYGON ((514560...

Grid Management: Creating Grids for a Specific Region

You can create a grid for a given bounding box or polygon.

import geopandas as gpd
from equi7grid_lite import Equi7Grid

# Load country geometry (e.g., Peru)
world_filepath = gpd.datasets.get_path('naturalearth_lowres')
world = gpd.read_file(world_filepath)
country = world[world.name == "Peru"].geometry

# Create a grid of Equi7Grid tiles that cover the bounding box of the country
grid = grid_system.create_grid(
    level=4,
    zone="SA",
    mask=country # Only include tiles that intersect the polygon
)

# Export the grid to a GeoDataFrame
grid.to_file("grid.shp")

Cubo Integration 🛰️

Use the equi7grid-lite package with cubo to retrieve EO data.

import cubo
import rioxarray
from equi7grid_lite import Equi7Grid
from rasterio.enums import Resampling

# Initialize Equi7Grid system
grid_system = Equi7Grid(min_grid_size=2560)

# Use CUBO to retrieve EO data
lon, lat = -122.4194, 37.7749
cubo_params = grid_system.cubo_utm_parameters(lon=lon, lat=lat)
da = cubo.create(
    lat=cubo_params["lat"],
    lon=cubo_params["lon"],
    collection="sentinel-2-l2a",
    bands=["B04", "B03", "B02"],
    start_date="2021-08-01",
    end_date="2021-10-30",
    edge_size=cubo_params["distance"] // 10,
    resolution=10,
    query={"eo:cloud_cover": {"lt": 50}}
)

# Reproject and resample the cube
image_reprojected = da.to_dataset("band").rio.reproject(
    cubo_params["crs"],
    resolution=2.5,
    resampling=Resampling.lanczos
)

License 📝

This package is released under the MIT License. For more information, see the LICENSE file.

Contributing 🤝

Contributions are welcome! For bug reports or feature requests, please open an issue on GitHub. For contributions, please submit a pull request with a detailed description of the changes.

Citation 📄

If you use this package in your research, please consider citing the original Equi7Grid package and paper.

Package:

@software{bernhard_bm_2023_8252376,
  author       = {Bernhard BM and
                  Sebastian Hahn and
                  actions-user and
                  cnavacch and
                  Manuel Schmitzer and
                  shochsto and
                  Senmao Cao},
  title        = {TUW-GEO/Equi7Grid: v0.2.4},
  month        = aug,
  year         = 2023,
  publisher    = {Zenodo},
  version      = {v0.2.4},
  doi          = {10.5281/zenodo.8252376},
  url          = {https://doi.org/10.5281/zenodo.8252376}
}

Paper:

@article{BAUERMARSCHALLINGER201484,
  title = {Optimisation of global grids for high-resolution remote sensing data},
  journal = {Computers & Geosciences},
  volume = {72},
  pages = {84-93},
  year = {2014},
  issn = {0098-3004},
  doi = {https://doi.org/10.1016/j.cageo.2014.07.005},
  url = {https://www.sciencedirect.com/science/article/pii/S0098300414001629},
  author = {Bernhard Bauer-Marschallinger and Daniel Sabel and Wolfgang Wagner},
  keywords = {Remote sensing, High resolution, Big data, Global grid, Projection, Sampling, Equi7 Grid}
}