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  • shaprann · 2 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
744463486 https://github.com/pydata/xarray/issues/1887#issuecomment-744463486 https://api.github.com/repos/pydata/xarray/issues/1887 MDEyOklzc3VlQ29tbWVudDc0NDQ2MzQ4Ng== shaprann 43274047 2020-12-14T14:07:32Z 2020-12-14T15:47:18Z NONE

Just wanted to confirm, that boolean indexing is indeed highly relevant, especially for assigning values instead of just selecting them. Here is a use case which I encounter very often:

I'm working with very sparse data (e.g a satellite image of some islands surrounded by water), and I want to modify it using some_vectorized_function(). Of course I could use some_vectorized_function() to process the whole image, but boolean masking allows me to save a lot of computations.

Here is how I would achieve this in numpy:

``` import numpy as np import some_vectorized_function

image = np.array( # image.shape == (3, 7, 7) [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 454, 454, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 565, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 343, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]],

 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
  [0.0, 454, 565, 0.0, 0.0, 0.0, 0.0],
  [0.0, 0.0, 667, 0.0, 0.0, 0.0, 0.0],
  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
  [0.0, 0.0, 0.0, 0.0, 0.0, 878, 0.0],
  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]],

 [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
  [0.0, 565, 676, 0.0, 0.0, 0.0, 0.0],
  [0.0, 0.0, 323, 0.0, 0.0, 0.0, 0.0],
  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
  [0.0, 0.0, 0.0, 0.0, 0.0, 545, 0.0],
  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]]

) image = np.moveaxis(image, 0, -1) # image.shape == (7, 7, 3)

"image" is a standard RGB image

with shape == (height, width, channel)

but only 4 pixels contain relevant data!

mask = np.all(image > 0, axis=-1) # mask.shape == (7, 7) # mask.dtype == bool # mask.sum() == 4

image[mask] = some_vectorized_function(image[mask]) # len(image[mask]) == 4 # image[mask].shape == (4, 3) ```

The most important fact here is that image[mask] is just a list of 4 pixels, which I can process and then assign them back into their original place. And as you see, this boolean masking also plays very nice with broadcasting, which allows me to mask a 3D array with a 2D mask.

Unfortunately, nothing like this is currently possible with XArray. If implemented, it would enable some crazy speedups for operations like spatial interpolation, where we don't want to interpolate the whole image, but only some pixels that we care about. 

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   Boolean indexing with multi-dimensional key arrays 294241734
665976915 https://github.com/pydata/xarray/issues/2314#issuecomment-665976915 https://api.github.com/repos/pydata/xarray/issues/2314 MDEyOklzc3VlQ29tbWVudDY2NTk3NjkxNQ== shaprann 43274047 2020-07-29T23:12:37Z 2020-07-29T23:12:37Z NONE

This particular use case is extremely common when working with spatio-temporal data. Can anyone suggest a good workaround for this?

 
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   Chunked processing across multiple raster (geoTIF) files 344621749

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