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Description:

geodataflow.dataframes 0.2.1

geodataflow.dataframes
GeodataFlow backend implementation with GeoPandas (GeoDataFrames).
GeodataFlow is a Geoprocessing framework for fetching, translating and manipulating Geospatial data (Raster, Vector, EO/STAC collections) by using a Pipeline or sequence of operations on input data. It is very much like the GDAL library which handles raster and vector data.
The project is split up into several namespace packages. geodataflow.dataframes implements a backend for GeodataFlow using the GeoPandas library and other common OSGeo python packages. This package includes the geodataflow.core and geodataflow.spatial subpackages.
Although geodataflow.dataframes provides a list of useful modules, since Backend implementations for GeodataFlow load them using the paradigm of plugins, developers can easily write new operations and the list may grow up in the future.
Workflow examples
Assuming you are using geodataflow.dataframes (GeoPandas) as active backend implementation, GeodataFlow can run workflows as the following:

Converting a Shapefile to GeoPackage:
# ==============================================================
# Pipeline sample to convert a Shapefile to GeoPackage.
# ==============================================================
{
"pipeline": [
{
"type": "FeatureReader",
"connectionString": "input.shp"
},
# Extract the Centroid of input geometries.
{
"type": "GeometryCentroid"
},
# Transform CRS of geometries.
{
"type": "GeometryTransform",
"sourceCrs": 4326,
"targetCrs": 32630
},
# Save features to Geopackage.
{
"type": "FeatureWriter",
"connectionString": "output.gpkg"
}
]
}

Fetching metadata of a S2L2A Product (STAC):
# ==============================================================
# Pipeline sample to fetch metadata of a S2L2A Product (STAC).
# ==============================================================
{
"pipeline": [
{
"type": "FeatureReader",

# Define the input AOI in an embedded GeoJson.
"connectionString": {
"type": "FeatureCollection",
"crs": {
"type": "name",
"properties": { "name": "EPSG:4326" }
},
"features": [
{
"type": "Feature",
"properties": { "id": 0, "name": "My AOI for testing" },
"geometry": {
"type": "Polygon",
"coordinates": [[
[-1.746826,42.773227],
[-1.746826,42.860866],
[-1.558685,42.860866],
[-1.558685,42.773227],
[-1.746826,42.773227]
]]
}
}
]
}
},
# Transform CRS of geometries.
{
"type": "GeometryTransform",
"sourceCrs": 4326,
"targetCrs": 32630
},
# Fetch metadata of EO Products that match one SpatioTemporial criteria.
{
"type": "EOProductCatalog",

"driver": "STAC",
"provider": "https://earth-search.aws.element84.com/v0/search",
"product": "sentinel-s2-l2a-cogs",

"startDate": "2021-09-25",
"endDate": "2021-10-05",
"closestToDate": "2021-09-30",
"filter": "",

"preserveInputCrs": true
},
# Save features to Geopackage.
{
"type": "FeatureWriter",
"connectionString": "output.gpkg"
}
]
}

Installation
In order to read and write Cloud Optimized Geotiffs (COG), GDAL version 3.1 or greater is required. If your system GDAL is older than version 3.1, consider using Docker or Conda to get a modern GDAL.
Using pypi
To install the latest stable version from pypi, write this in the command-line:
> pip install geodataflow.dataframes[eodag,gee]

Optional extras:

eodag
EODAG - Earth Observation Data Access Gateway is a Python package for searching and downloading remotely sensed images while offering an unified API for data access regardless of the data provider.
Installing this extra EODAG adds access to more EO Products from different providers to EOProductCatalog and EOProductDataset modules.

gee
GEE - Google Earth Engine API is a geospatial processing service. With Earth Engine, you can perform geospatial processing at scale, powered by Google Cloud Platform. GEE requires authentication, please, read available documentation here.
Installing this extra GEE makes possible the access to Google Cloud Platform to GEEProductCatalog and GEEProductDataset modules.

To view all available CLI tool commands and options:
> geodataflow --help

Listing all available modules:
> geodataflow --modules

Run a workflow in the command-line interface:
> geodataflow --pipeline_file "/geodataflow/dataframes/tests/data/test_eo_stac_catalog.json"

Using docker
Building the container with:
> docker build -f ./Dockerfile.dataframes -t geodataflow/cli:1.0.0 .

Getting start:
> docker run --rm --name gdf geodataflow/cli:1.0.0 --help
> docker run --rm --name gdf geodataflow/cli:1.0.0 --modules

Creating an interactive bash shell:
> docker run --rm -it --entrypoint "bash" geodataflow/cli:1.0.0

To run workflows in Linux:
> docker run \
--rm --name gdf -v "$PWD/geodataflow/dataframes/tests/data:/tests/data" geodataflow/cli:1.0.0 \
--pipeline_file "/tests/data/test_eo_stac_catalog.json"

To run workflows in Windows:
> docker run ^
--rm --name gdf -v "%cd%/geodataflow/dataframes/tests/data:/tests/data" geodataflow/cli:1.0.0 ^
--pipeline_file "/tests/data/test_eo_stac_catalog.json"

Contribute
Have you spotted a typo in our documentation? Have you observed a bug while running GeodataFlow? Do you have a suggestion for a new feature?
Don't hesitate and open an issue or submit a pull request, contributions are most welcome!
License
GeodataFlow is licensed under Apache License v2.0.
See LICENSE file for details.
Credits
GeodataFlow is built on top of amazingly useful open source projects. See NOTICE file for details about those projects
and their licenses.
Thank you to all the authors of these projects!