pysheds
Dask
| pysheds | Dask | |
|---|---|---|
| 1 | 32 | |
| 685 | 12,022 | |
| - | 0.8% | |
| 6.5 | 9.6 | |
| 21 days ago | 2 days ago | |
| Python | Python | |
| GNU General Public License v3.0 only | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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pysheds
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Large Scale Hydrology: Geocomputational tools that you use
For dem processing, taudem5 has a cli and arcgis toolbox and supposedly supports clusters for large problems. The gdal tools do not have a watershed tool afaik, but handle large datasets well and and can be called from a number of different languages. As others have mentioned, there are many python geoprocessing packages and some work well. I'd second rasterio, the python gdal bindings, and xarray. Pysheds looks interesting but haven't tried.
Dask
- The Distributed Tensor Algebra Compiler (2022)
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A peek into Location Data Science at Ola
Data scientists work on phenomenally large datasets, and Dask is a handy tool for exploration within the confines of a single cloud VM or their local PCs. Location data visualization is an essential part of deciding further algorithm development and roadmap for projects. This lays the foundation for data engineering and science to work at scale, with petabytes of data.
- File format for large data with many columns
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What is the best way to save a csv.file in number only ? PC hangs when my file is more than 2GB
Dask
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Large Scale Hydrology: Geocomputational tools that you use
We're using a lot of Python. In addition to these, gridMET, Dask, HoloViz, and kerchunk.
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msgspec - a fast & friendly JSON/MessagePack library
I wrote this for speeding up the RPC messaging in dask, but figured it might be useful for others as well. The source is available on github here: https://github.com/jcrist/msgspec.
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What does it mean to scale your python powered pipeline?
Dask: Distributed data frames, machine learning and more
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Data pipelines with Luigi
To do that, we are efficiently using Dask, simply creating on-demand local (or remote) clusters on task run() method:
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Is Numpy always more efficient than Pandas? And how much should we rely on Python anyway?
Look into Dask, see: https://dask.org/
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Ask HN: Is PySPark a Dead-End?
[1] https://dask.org/
What are some alternatives?
whitebox-python - WhiteboxTools Python Frontend
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
dsm2dtm - Generates DTM (Digital Terrain Model) from DSM (Digital Surface Model).
Numba - NumPy aware dynamic Python compiler using LLVM
WhiteboxTools-ArcGIS - ArcGIS Python Toolbox for WhiteboxTools
Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.
Arda - Maps of J. R. R. Tolkien's Middle Earth using DEM (Digital Elevation Model) and place vectors
NetworkX - Network Analysis in Python
gdal - GDAL is an open source MIT licensed translator library for raster and vector geospatial data formats.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Interactive Parallel Computing with IPython - IPython Parallel: Interactive Parallel Computing in Python
statsmodels - Statsmodels: statistical modeling and econometrics in Python