✨ XarrayCanvasIterDataPipe for creating blank datashader canvas #34
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Quickly and accurately render even the largest data, and pandas extension arrays for spatial/geometric operations! Unfortunately, had to pin the maximum Python version to 3.11 because scipy (the version with Python 3.10 wheels) sets a hard upper pin for Python <3.12.
An iterable-style DataPipe for creating a blank canvas from raster images! The datashader.Canvas is created based on the input xarray object's spatial extent and coordinates, with the metadata obtained using rioxarray. Included a doctest for getting a datashader.Canvas from an xarray.DataArray. Added a new section in the API docs and intersphinx mappings for datashader.
Include the 'spatial' extras dependencies in the Continuous Integration tests, and install datashader for Readthedocs. Also mentioned installing the 'spatial' extras in CONTRIBUTING.md.
Ensure that xarray.Dataset objects can be turned into a datashader.Canvas too! Clarified that the `.rio.x_dim` and `.rio.y_dim` attributes are required, and made some minor stylistic changes to the docstring and code in datashader.py.
Fixes `AttributeError: 'NoneType' object has no attribute 'Canvas'` when the optional datashader dependency is not installed.
This was referenced Aug 13, 2022
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An iterable-style DataPipe for creating a blank canvas from raster images. This initiates the integration of
zen3geowithdatashader!Preview at https://zen3geo--34.org.readthedocs.build/en/34/api.html#zen3geo.datapipes.XarrayCanvas
Part 1 out of 2 of superseding #32. Decided that while datashader has heavier dependencies like scipy, it is a more scalable and structured, multi-step way of doing rasterization of vector data. 1st step being to define a canvas, and 2nd step is to burn the vector (points/lines/polygons) onto that canvas via some aggregation function.
TODO:
XarrayCanvasIterDataPipe