Geoconda is a collection of python packages that facilitate the development of python scripts for geoinformatics applications. It includes following python packages:
- boto3 - for working files in S3 storage, for example Allas. Allas S3 example in CSC geocomputing Github.
- cartopy - for map plotting.
- cfgrib - map GRIB files to the NetCDF Common Data Model
- dask - provides advanced parallelism for analytics, enabling performance at scale, including dask-geopandas, Dask-ML and Dask JupyterLab extension.
- descartes - use Shapely or GeoJSON-like geometric objects as matplotlib paths and patches.
- Google Earth Engine API. NEW 2022
- fiona - reads and writes spatial data files.
- geoalchemy2 - provides extensions to SQLAlchemy for working with spatial databases, primarily PostGIS.
- geopandas - GeoPandas extends the datatypes used by pandas.
- igraph - for fast routing. Routing examples in CSC geocomputing Github
- geopy - client for several popular geocoding web services. NEW 2023
- jupyter - Jupyter Notebooks and JupyterLab, best to use with Puhti web interface and Jupyter
- landsatlinks - for creating download URLs for Landsat Collection 2 Level 1 product bundles using the USGS/EROS Machine-to-Machine API. NEW 2023
- laspy - for reading, modifying, and creating .LAS LIDAR files.
- lidar - for delineating the nested hierarchy of surface depressions in digital elevation models (DEMs).
- metpy - reading, visualizing, and performing calculations with weather data. NEW 2022
- movingpandas - for trajectory data
- networkx - for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Routing examples in CSC geocomputing Github
- pyproj - performs cartographic transformations and geodetic computations.
- pyogrio - vectorized spatial vector file format I/O using GDAL/OGR. NEW 2022
- osmnx - download spatial geometries and construct, project, visualize, and analyze street networks from OpenStreetMap's APIs. Routing examples in CSC geocomputing Github
- owslib - for retrieving data from Open Geospatial Consortium (OGC) web services
- python-pdal - PDAL Python extension for lidar data
- Py6S - Python interface to the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) atmospheric Radiative Transfer Model
- pysal - spatial analysis functions.
- pdal - for lidar data
- pyntcloud - for working with 3D point clouds. NEW 2022
- pystac-client - for working with STAC Catalogs and APIs. STAC example in CSC geocomputing Github. NEW 2022
- python-cdo - scripting interface to CDO (Climate Data Operators).
- rasterio - access to geospatial raster data.
- rasterstats - for summarizing geospatial raster datasets based on vector geometries. It includes functions for zonal statistics and interpolated point queries. rasterstats example in CSC geocomputing Github
- rtree - spatial indexing and search.
- sentinelsat - downloading Sentinel images, [sentinelsat example in CSC geocomputing Github] (https://github.com/csc-training/geocomputing/tree/master/python/sentinel)
- shapely - manipulation and analysis of geometric objects in the Cartesian plane.
- scipy - inc pandas, numpy, matplotlib etc
- scikit-learn - machine learning for Python. Spatial machine learning scikit-learn (shallow learning) exercises
- skimage - algorithms for image processing.
- stackstac - STAC data to xarray, STAC example in CSC geocomputing Github.
- swiftclient, keystoneclient - for working with SWIFT storage, for example Allas. Allas Swift example in CSC geocomputing Github.
- xarray - for multidimensional raster data, inc. rioxarray. STAC example in CSC geocomputing Github.
- xarray-spatial - efficient common raster analysis functions for xarray. NEW 2022
- And many more, for retrieving the full list in Puhti use:
Additionally geoconda includes:
- spyder - Scientific Python Development Environment with graphical interface (similar to RStudio for R).
- GDAL/OGR commandline tools
- GMT The Generic Mapping Tools
- PDAL Point Data Abstraction Library
- ncview for visualizing netcdf files
Python has multiple packages for parallel computing, for example multiprocessing, joblib and dask. In our Puhti Python examples there are examples how to utilize these different parallelisation libraries.
If you think that some important GIS package for Python is missing from here, you can ask for installation from firstname.lastname@example.org.
geoconda module is available in Puhti:
Version number is the same as the Python version.
For using Python packages and other tools listed above, you can initialize them with:
module load geoconda
By default the latest geoconda module is loaded. If you want a specific version you can specify the version number of geoconda:
module load geoconda/[VERSION]
To check the exact packages and versions included in the loaded module:
You can add more Python packages to
geoconda, see instructions from CSC Python page.
You can edit your Python code in Puhti with:
- Visual Studio Code in Puhti web interface,
- Visual Studio Code on your local laptop,
- Jupyter Notebook or Lab in Puhti web interface or
- Spyder in Puhti web interface with remote desktop.
To open Spyder in Puhti web interface with remote desktop:
- Log in to Puhti web interface.
- Open Remote desktop: Apps -> Desktop, choose Desktop:
- After launcing the remote desktop open
Host Terminal(Desktop icon) and start Spyder:
module geoconda spyder
Using Allas from Python
There are two Python libraries installed in Geoconda that can interact with Allas. Swiftclient uses the swift protocol and boto3 uses S3 protocol. You can find CSC examples how to use both here. With large quantities of data in Allas, virtual rasters should be considered. More information on how to create and use virtual rasters can be found here.
License and acknowledgement
All packages are licensed under various free and open source licenses (FOSS), see the linked pages above for exact details.
Please acknowledge CSC and Geoportti in your publications, it is important for project continuation and funding reports. As an example, you can write "The authors wish to thank CSC - IT Center for Science, Finland (urn:nbn:fi:research-infras-2016072531) and the Open Geospatial Information Infrastructure for Research (Geoportti, urn:nbn:fi:research-infras-2016072513) for computational resources and support".
- CSC Python parallelisation examples
- Python spatial libraries
- Geoprocessing with Python using Open Source GIS
- GeoExamples, a lot of examples of using Python for spatial analysis
- Automating GIS processes course materials, where most of the exercises are done using Python (University of Helsinki)
- Geohack Week materials
- Multiprocessing Basics
- Geographic Data Science with Python
- Aalto Spatial Analytics course material