The Equi7Grid is a spatial reference system designed to handle efficiently the archiving, processing, and displaying of high resolution raster image data. It supports geo-datacubes holding large volumes of satellite imagery, as it preserves geometric accuracy and minimises data oversampling over global land surfaces to a very low value of 3%.
This package contains:
- Geometries and projection-files defining the contentinal zones, coordinate system, projection parameters, base tilings, etc.
- A python class for working with Equi7Grid: how to convert to, how to use the tiling system, how to identify coordinates, etc.
A detailed documentation on the Equi7Grid definition is at
~/docs/doc_files/
and its scientific background is published in this journal article.
2024 May:
For the seven continental Equi7 coordinate systems, the newly available EPSG codes EPSG:27701 - EPSG:27707 are available via
- with
proj>=9.4.0from the generic coordinate transformation software proj (e.g. used within GDAL). - with
EPSG>=v11.002from the Geodetic Parameter Dataset of EPSG.
Several updates are in the pipeline of this python package:
- interface to the EPSG codes
- updates on the continental zone bordes - streamlining along political delimiters
- flexible tile extents and grid samplings, allowing also user-defined tile extents
- updated interfaces to reprojection methods (e.g. to and from UTM, or LonLat)
Easiest access to Equi7's seven continental coordinate reference systems (CRSs) is via the EPSG codes:
Africa EPSG:27701
Antarctica EPSG:27702
Asia EPSG:27703
Europe EPSG:27704
North America EPSG:27705
Oceania EPSG:27706
South America EPSG:27707
Shapefiles for the continental zone boundaries and tilings are here
~/src/equi7grid/grids/
... with files in PROJ projected to the Equi7Grid space (meters), and in GEOG corresponding files in common geographic Lon-Lat space (degrees):
Overlays for visualisation in Google Earth are here:
~/docs/doc_files/google_earth_overlays/
The 7 projections (or more precisely the Projected Coordinate Reference Systems, PROJCS) are completely defined by WKT-strings in the .prj-files at
~/wkt/
or simply by following proj4-strings:
AF: '+proj=aeqd +lat_0=8.5 +lon_0=21.5 +x_0=5621452.01998 +y_0=5990638.42298 +datum=WGS84 +units=m +no_defs'
AN: '+proj=aeqd +lat_0=-90 +lon_0=0 +x_0=3714266.97719 +y_0=3402016.50625 +datum=WGS84 +units=m +no_defs'
AS: '+proj=aeqd +lat_0=47 +lon_0=94 +x_0=4340913.84808 +y_0=4812712.92347 +datum=WGS84 +units=m +no_defs'
EU: '+proj=aeqd +lat_0=53 +lon_0=24 +x_0=5837287.81977 +y_0=2121415.69617 +datum=WGS84 +units=m +no_defs'
NA: '+proj=aeqd +lat_0=52 +lon_0=-97.5 +x_0=8264722.17686 +y_0=4867518.35323 +datum=WGS84 +units=m +no_defs'
OC: '+proj=aeqd +lat_0=-19.5 +lon_0=131.5 +x_0=6988408.5356 +y_0=7654884.53733 +datum=WGS84 +units=m +no_defs'
SA: '+proj=aeqd +lat_0=-14 +lon_0=-60.5 +x_0=7257179.23559 +y_0=5592024.44605 +datum=WGS84 +units=m +no_defs'
This package can be installed through pip:
pip install Equi7GridInstalls for scipy and gdal are required from conda or conda-forge (see below how to set up a fresh environment).
The Equi7Grid package provides python tools to interact with different projections, query information from the Equi7Grid geometries, and access the tiling system.
You can retrieve all tiles covering a region of interest defined using Lon-Lat coordinates using search_tiles_in_roi:
tiles = Equi7Grid(500).search_tiles_in_roi(bbox=[(0, 30), (10, 40)], coverland=True)
assert sorted(tiles) == sorted([
'EU500M_E036N006T6', 'EU500M_E042N000T6', 'EU500M_E042N006T6',
'AF500M_E030N084T6', 'AF500M_E030N090T6', 'AF500M_E036N084T6',
'AF500M_E036N090T6', 'AF500M_E042N084T6', 'AF500M_E042N090T6'])The package provides with the image2equi7grid() a convenient method to quickly convert existing raster data stored as GeoTIFFs to tiles in Equi7Grid projection(s):
input_file = input_dir / "lake_in_russia_lonlat.tif"
image2equi7grid(Equi7Grid(100), input_file.as_posix(), out_dir.as_posix())
assert (out_dir / "EQUI7_AS100M/E018N066T6/lake_in_russia_lonlat_AS100M_E018N066T6.tif").exists()
assert (out_dir / "EQUI7_EU100M/E072N030T6/lake_in_russia_lonlat_EU100M_E072N030T6.tif").exists()The tool uses gdal to efficiently warp the raster data to the Equi7 projection, and generate a folder structure for each Equi7 tile that covers the input raster.
Note 1: The input file of this (advanced) example lies between Asia and Europe, and by default the function writes output for all tiles that cover the input, in this example for the Equi7 tiles EU100M_E072N030T6 and AS100M_E018N066T6. Checkout the function image2equi7grid() for more options on output, format, encoding, etc.
Note 2: Windows users might need to manually specify the gdal_path as part of the function arguments, for example:
image2equi7grid(gdal_path=r"C:\...your_path...\envs\equi7grid\Library\bin")With equi7_to_lonlat(), a simple but convenient method is available to quickly convert files that are already gridded and tiled in the Equi7Grid. Please see the following usage example:
equi7_to_lonlat(roi=(9, 46, 18, 50),
pixelsize=20,
input_folder_path = r'C:\...your_main_path...\EQUI7_SUBGRID\TILE',
input_file_path = 'your_dataset_SUBGRID_TILE.tif',
full_output_path = r'C:\...your_main_path...\your_dataset_46-50LON_9-18LAT.tif',
gdal_path = r'C:\...your_GDAL_path...\Library\bin',
...
)checkout the tests at
~/tests/test_equi7grid.py
which exemplify many more functions.
For development, we recommend using the make tool to automatically create python environments and install more complex
dependencies i.e. gdal.
Instruction on how to set up an environment on systems without proper make support, such as Windows, can be found in a
subsequent section.
Make sure miniconda3 is installed by following
the official installation instructions.
To create a new development environment using conda make the conda rule:
make condaThis will create a new conda environment called equi7grid and install all necessary dependencies.
Make sure you have installed virtualenv and gdal on your system.
For instance under Ubuntu you can install gdal using apt install libgdal-dev, and virtualenv
using apt install python3-venv.
To set up a virtualenv environment simply make the venv rule:
make venvThis will create a virtualenv environment within a venv folder at the root of the project.
It will install the gdal dependency using pygdal which requires gdal to be
installed on the system.
To create a testing environment you can set the TEST_ENV=1 parameter:
make venv TEST_ENV=1After activating the environment you can make the test rule to run all unit tests:
make testFirst make sure miniconda3 is installed on your system by following the installation instructions.
Create the equi7grid conda environment from the environment.yml provided at the root of the repository.
conda env create -f environment_win.yml
See also the official anaconda documentation for detailed instructions on environments and environment files.
Now you should be able to activate the environment:
conda activate equi7grid
Once activate, you can install the Equi7Grid package in development mode using pip by running the following command in
the root directory of the repository:
pip install -e .
To install the test dependencies as well use:
pip install -e .[testing]
Now you should be able to run all unit tests:
pytest tests/
You can also have a look at the source of the Makefile for more detailed installation and testing options.
We are happy if you want to contribute. Please raise an issue explaining what is missing or if you find a bug. We will also gladly accept pull requests against our master branch for new features or bug fixes.
If you want to contribute please follow these steps:
- Fork the Equi7Grid repository to your account
- Clone the repository
- make a new feature branch from the Equi7Grid master branch
- Add your feature
- Please include tests for your contributions in one of the test directories. We use py.test so a simple function called test_my_feature is enough
- submit a pull request to our master branch
If you use the software in a publication then please cite it using the Zenodo DOI. Be aware that this badge links to the latest package version.
Please select your specific version at https://doi.org/10.5281/zenodo.1048530 to get the DOI of that version. You should normally always use the DOI for the specific version of your record in citations. This is to ensure that other researchers can access the exact research artefact you used for reproducibility.
You can find additional information regarding DOI versioning at http://help.zenodo.org/#versioning
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