[WIP] torch DataSet + utils

[kevinyamauchi]

This PR adds a torch DataSet with some addition utils. Initially, this implements a spot ROI dataset for replicating this squidpy example. The DataSet is implemented such that it is compatible with monai and pytorch lightning, which gives us access to a ton of tooling (e.g., multi-GPU training, tensorboard logging, learning rate schedulers).

This PR requires #132, #143, and image bounding box query with transforms to be merged.

[codecov]

Codecov Report

Merging #145 (41d818a) into main (9975a5c) will decrease coverage by 2.64%.
The diff coverage is 34.61%.

Additional details and impacted files

@@ Coverage Diff @@## main #145 +/- ##
==========================================
- Coverage 89.59% 86.95% -2.64% 
==========================================
Files 24 27 +3 Lines 3854 3995 +141 ==========================================
+ Hits 3453 3474 +21 - Misses 401 521 +120 

Impacted Files	Coverage Δ
spatialdata/_core/data_extent.py	0.00% <0.00%> (ø)
spatialdata/_dataloader/transforms.py	0.00% <0.00%> (ø)
spatialdata/_dataloader/datasets.py	6.84% <6.84%> (ø)
spatialdata/utils.py	84.52% <12.50%> (-3.61%)	⬇️
spatialdata/__init__.py	93.33% <83.33%> (-6.67%)	⬇️
spatialdata/_core/_spatialdata.py	91.20% <86.36%> (-0.27%)	⬇️
spatialdata/_core/_rasterize.py	83.00% <100.00%> (+0.34%)	⬆️
spatialdata/_core/_spatial_query.py	93.82% <100.00%> (-0.42%)	⬇️
spatialdata/_core/core_utils.py	91.91% <100.00%> (-0.27%)	⬇️
spatialdata/_core/models.py	86.14% <100.00%> (ø)

[kevinyamauchi]

I have created an example notebook showing how the Dataset and transforms work for the SpotCropDataset. We can now generate tiles such that they are compatible with monai, torchvision, and pytorch lightning, so we can add tons of augmentations, dataloader cacheing, multiGPU, etc.. Once #132 lands, I can update the spot centroid fetching to use the polygons item.

https://gist.github.com/kevinyamauchi/3a1d1c375b084732c5f60b19afabf461

[kevinyamauchi]

I've updated it to now use the Shapes element to get the spot locations. See the example notebook below:

https://gist.github.com/kevinyamauchi/77f986889b7626db4ab3c1075a3a3e5e

[LucaMarconato]

minor features of this PR:

• overloading of __get_item__() and __set_item__() for SpatialData.

[LucaMarconato]

I have pushed a code that has still open todos and bugs to fix, but it is usable. An example of usage is in this script from the sandbox (which run like this shows a bug.

But if you use it

• from the coordinate system global
• only with visium data (not querying the xenium image with the visium cirlces)
• not cropping the data first but making the tiles from the full data

it will work. So it should be good enough for the deep learning example.

[LucaMarconato]

Current todos:

• wrong tiling result (wrong queried data and wrong content) when tiling a cropped multiscale image (probably due to this: 2 bugs with spatial cropping (with multiscale rasters and when missing the table) #178)
• make tests

Initial plan, postponed to a new PR (see #184):

• extend functionality to get tiles from the raw space (not just from the target space)
  • when querying tiles from the raw space, allow to specify either units, either pixels. Not both
  • when querying tiles from the target space, allow to specify also only units (and infer pixels), or only pixels. This is possible only when the data is not a multiscale, otherwise the pixels could not be determined

[giovp]

@kevinyamauchi@LucaMarconato minor (maybe nitpick) comments.

one major one regarding examples. They have to be in spatialdata-notebooks, not here. Everything that is not API should stay there. Ok to have python files and not notebooks (although better notebooks) but would still move.

[giovp]

why this is not in the bounding box related module? I would put it there.

[LucaMarconato]

this and other functions that could populate this file are not used for spatial queries so I would not put them in _spatial_query.py. The complexity of that file increase when implementing non-bounding box queries, so I would keep this code in another place.

[giovp]

I would make torch as optional depedency, therefore I think in Init of this module or where the ImageTilesDataset is import, something like this would be needed

try:
fromspatialdata._dl.datasetsimportImageTilesDatasetexceptImportErrorase:
_error: str|None=str(e)
else:
_error=None

[LucaMarconato]

would you add torch somewhere in pyproject.toml or it would be responsibility of the user to install it properly? I would go for the second.

[giovp]

Current todos:

• wrong tiling result (wrong queried data and wrong content) when tiling a cropped multiscale image (probably due to this: 2 bugs with spatial cropping (with multiscale rasters and when missing the table) #178)

• extend functionality to get tiles from the raw space (not just from the target space)

  • when querying tiles from the raw space, allow to specify either units, either pixels. Not both
  • when querying tiles from the target space, allow to specify also only units (and infer pixels), or only pixels. This is possible only when the data is not a multiscale, otherwise the pixels could not be determined

• make tests

@LucaMarconato I would not extend functionality in this PR. Think priority wise is to add some minimal test and merge right away. We need to do the module conversion of the repo to start testing intersphinx for notebooks and documentation (and also this change will impact io/napari/plot so there will be lot of work to be done there as well.

[LucaMarconato]

Ok, I created an issue to keep track of that. I will make the tests and ask for review (btw I am working on the Xenium + Visium data atm, but I am going to work on this PR right after).

[kevinyamauchi]

This looks good to me! I can't approve because I opened the PR. For me, the main things to do before merging:

• make torch import optional
• make sure functions have docstrings (at least a description of what the function does)

[kevinyamauchi]

please add a docstring. I think the input parameters aren't clear (e.g., tile_dim_in_units vs. tile_dim_in_pixels)

[LucaMarconato]

one major one regarding examples. They have to be in spatialdata-notebooks, not here. Everything that is not API should stay there. Ok to have python files and not notebooks (although better notebooks) but would still move.

ok I deleted the folder examples and moved this to the sandbox. I'll made these example not to show things to other users but to debug/visually test the spatial query, rasterization and tiler. I am not using notebooks because I use these for debugging, setting breakpoint etc.

[giovp]

@LucaMarconato could you also quickly re add mypy in CI, seems like it's skipped atm, wasn't aware of that
https://github.com/scverse/spatialdata/blob/main/.pre-commit-config.yaml

[LucaMarconato]

@giovp restored mypy in ci, we have some problems with the installation

[LucaMarconato]

Gonna merge and increase the coverage in a next pr.

[kevinyamauchi]

This PR adds a torch DataSet with some addition utils. Initially, this implements a spot ROI dataset for replicating this squidpy example. The DataSet is implemented such that it is compatible with monai and pytorch lightning, which gives us access to a ton of tooling (e.g., multi-GPU training, tensorboard logging, learning rate schedulers).

This PR requires #132, #143, and image bounding box query with transforms to be merged.

[codecov]

Codecov Report

Merging #145 (41d818a) into main (9975a5c) will decrease coverage by 2.64%.
The diff coverage is 34.61%.

Additional details and impacted files

@@ Coverage Diff @@## main #145 +/- ##
==========================================
- Coverage 89.59% 86.95% -2.64% 
==========================================
Files 24 27 +3 Lines 3854 3995 +141 ==========================================
+ Hits 3453 3474 +21 - Misses 401 521 +120 

Impacted Files	Coverage Δ
spatialdata/_core/data_extent.py	0.00% <0.00%> (ø)
spatialdata/_dataloader/transforms.py	0.00% <0.00%> (ø)
spatialdata/_dataloader/datasets.py	6.84% <6.84%> (ø)
spatialdata/utils.py	84.52% <12.50%> (-3.61%)	⬇️
spatialdata/__init__.py	93.33% <83.33%> (-6.67%)	⬇️
spatialdata/_core/_spatialdata.py	91.20% <86.36%> (-0.27%)	⬇️
spatialdata/_core/_rasterize.py	83.00% <100.00%> (+0.34%)	⬆️
spatialdata/_core/_spatial_query.py	93.82% <100.00%> (-0.42%)	⬇️
spatialdata/_core/core_utils.py	91.91% <100.00%> (-0.27%)	⬇️
spatialdata/_core/models.py	86.14% <100.00%> (ø)

[kevinyamauchi]

I have created an example notebook showing how the Dataset and transforms work for the SpotCropDataset. We can now generate tiles such that they are compatible with monai, torchvision, and pytorch lightning, so we can add tons of augmentations, dataloader cacheing, multiGPU, etc.. Once #132 lands, I can update the spot centroid fetching to use the polygons item.

https://gist.github.com/kevinyamauchi/3a1d1c375b084732c5f60b19afabf461

[kevinyamauchi]

I've updated it to now use the Shapes element to get the spot locations. See the example notebook below:

https://gist.github.com/kevinyamauchi/77f986889b7626db4ab3c1075a3a3e5e

[LucaMarconato]

minor features of this PR:

• overloading of __get_item__() and __set_item__() for SpatialData.

[LucaMarconato]

I have pushed a code that has still open todos and bugs to fix, but it is usable. An example of usage is in this script from the sandbox (which run like this shows a bug.

But if you use it

• from the coordinate system global
• only with visium data (not querying the xenium image with the visium cirlces)
• not cropping the data first but making the tiles from the full data

it will work. So it should be good enough for the deep learning example.

[LucaMarconato]

Current todos:

• wrong tiling result (wrong queried data and wrong content) when tiling a cropped multiscale image (probably due to this: 2 bugs with spatial cropping (with multiscale rasters and when missing the table) #178)
• make tests

Initial plan, postponed to a new PR (see #184):

• extend functionality to get tiles from the raw space (not just from the target space)
  • when querying tiles from the raw space, allow to specify either units, either pixels. Not both
  • when querying tiles from the target space, allow to specify also only units (and infer pixels), or only pixels. This is possible only when the data is not a multiscale, otherwise the pixels could not be determined

[giovp]

@kevinyamauchi@LucaMarconato minor (maybe nitpick) comments.

one major one regarding examples. They have to be in spatialdata-notebooks, not here. Everything that is not API should stay there. Ok to have python files and not notebooks (although better notebooks) but would still move.

[giovp]

why this is not in the bounding box related module? I would put it there.

[LucaMarconato]

this and other functions that could populate this file are not used for spatial queries so I would not put them in _spatial_query.py. The complexity of that file increase when implementing non-bounding box queries, so I would keep this code in another place.

[giovp]

I would make torch as optional depedency, therefore I think in Init of this module or where the ImageTilesDataset is import, something like this would be needed

try:
fromspatialdata._dl.datasetsimportImageTilesDatasetexceptImportErrorase:
_error: str|None=str(e)
else:
_error=None

[LucaMarconato]

would you add torch somewhere in pyproject.toml or it would be responsibility of the user to install it properly? I would go for the second.

[giovp]

Current todos:

• wrong tiling result (wrong queried data and wrong content) when tiling a cropped multiscale image (probably due to this: 2 bugs with spatial cropping (with multiscale rasters and when missing the table) #178)

• extend functionality to get tiles from the raw space (not just from the target space)

  • when querying tiles from the raw space, allow to specify either units, either pixels. Not both
  • when querying tiles from the target space, allow to specify also only units (and infer pixels), or only pixels. This is possible only when the data is not a multiscale, otherwise the pixels could not be determined

• make tests

@LucaMarconato I would not extend functionality in this PR. Think priority wise is to add some minimal test and merge right away. We need to do the module conversion of the repo to start testing intersphinx for notebooks and documentation (and also this change will impact io/napari/plot so there will be lot of work to be done there as well.

[LucaMarconato]

Ok, I created an issue to keep track of that. I will make the tests and ask for review (btw I am working on the Xenium + Visium data atm, but I am going to work on this PR right after).

[kevinyamauchi]

This looks good to me! I can't approve because I opened the PR. For me, the main things to do before merging:

• make torch import optional
• make sure functions have docstrings (at least a description of what the function does)

[kevinyamauchi]

please add a docstring. I think the input parameters aren't clear (e.g., tile_dim_in_units vs. tile_dim_in_pixels)

[LucaMarconato]

one major one regarding examples. They have to be in spatialdata-notebooks, not here. Everything that is not API should stay there. Ok to have python files and not notebooks (although better notebooks) but would still move.

ok I deleted the folder examples and moved this to the sandbox. I'll made these example not to show things to other users but to debug/visually test the spatial query, rasterization and tiler. I am not using notebooks because I use these for debugging, setting breakpoint etc.

[giovp]

@LucaMarconato could you also quickly re add mypy in CI, seems like it's skipped atm, wasn't aware of that
https://github.com/scverse/spatialdata/blob/main/.pre-commit-config.yaml

[LucaMarconato]

@giovp restored mypy in ci, we have some problems with the installation

[LucaMarconato]

Gonna merge and increase the coverage in a next pr.

[kevinyamauchi]

This PR adds a torch DataSet with some addition utils. Initially, this implements a spot ROI dataset for replicating this squidpy example. The DataSet is implemented such that it is compatible with monai and pytorch lightning, which gives us access to a ton of tooling (e.g., multi-GPU training, tensorboard logging, learning rate schedulers).

This PR requires #132, #143, and image bounding box query with transforms to be merged.

[codecov]

Codecov Report

Merging #145 (41d818a) into main (9975a5c) will decrease coverage by 2.64%.
The diff coverage is 34.61%.

Additional details and impacted files

@@ Coverage Diff @@## main #145 +/- ##
==========================================
- Coverage 89.59% 86.95% -2.64% 
==========================================
Files 24 27 +3 Lines 3854 3995 +141 ==========================================
+ Hits 3453 3474 +21 - Misses 401 521 +120 

Impacted Files	Coverage Δ
spatialdata/_core/data_extent.py	0.00% <0.00%> (ø)
spatialdata/_dataloader/transforms.py	0.00% <0.00%> (ø)
spatialdata/_dataloader/datasets.py	6.84% <6.84%> (ø)
spatialdata/utils.py	84.52% <12.50%> (-3.61%)	⬇️
spatialdata/__init__.py	93.33% <83.33%> (-6.67%)	⬇️
spatialdata/_core/_spatialdata.py	91.20% <86.36%> (-0.27%)	⬇️
spatialdata/_core/_rasterize.py	83.00% <100.00%> (+0.34%)	⬆️
spatialdata/_core/_spatial_query.py	93.82% <100.00%> (-0.42%)	⬇️
spatialdata/_core/core_utils.py	91.91% <100.00%> (-0.27%)	⬇️
spatialdata/_core/models.py	86.14% <100.00%> (ø)

[kevinyamauchi]

I have created an example notebook showing how the Dataset and transforms work for the SpotCropDataset. We can now generate tiles such that they are compatible with monai, torchvision, and pytorch lightning, so we can add tons of augmentations, dataloader cacheing, multiGPU, etc.. Once #132 lands, I can update the spot centroid fetching to use the polygons item.

https://gist.github.com/kevinyamauchi/3a1d1c375b084732c5f60b19afabf461

[kevinyamauchi]

I've updated it to now use the Shapes element to get the spot locations. See the example notebook below:

https://gist.github.com/kevinyamauchi/77f986889b7626db4ab3c1075a3a3e5e

[LucaMarconato]

minor features of this PR:

• overloading of __get_item__() and __set_item__() for SpatialData.

[LucaMarconato]

I have pushed a code that has still open todos and bugs to fix, but it is usable. An example of usage is in this script from the sandbox (which run like this shows a bug.

But if you use it

• from the coordinate system global
• only with visium data (not querying the xenium image with the visium cirlces)
• not cropping the data first but making the tiles from the full data

it will work. So it should be good enough for the deep learning example.

[LucaMarconato]

Current todos:

• wrong tiling result (wrong queried data and wrong content) when tiling a cropped multiscale image (probably due to this: 2 bugs with spatial cropping (with multiscale rasters and when missing the table) #178)
• make tests

Initial plan, postponed to a new PR (see #184):

• extend functionality to get tiles from the raw space (not just from the target space)
  • when querying tiles from the raw space, allow to specify either units, either pixels. Not both
  • when querying tiles from the target space, allow to specify also only units (and infer pixels), or only pixels. This is possible only when the data is not a multiscale, otherwise the pixels could not be determined

[giovp]

@kevinyamauchi@LucaMarconato minor (maybe nitpick) comments.

one major one regarding examples. They have to be in spatialdata-notebooks, not here. Everything that is not API should stay there. Ok to have python files and not notebooks (although better notebooks) but would still move.

[giovp]

why this is not in the bounding box related module? I would put it there.

[LucaMarconato]

this and other functions that could populate this file are not used for spatial queries so I would not put them in _spatial_query.py. The complexity of that file increase when implementing non-bounding box queries, so I would keep this code in another place.

[giovp]

I would make torch as optional depedency, therefore I think in Init of this module or where the ImageTilesDataset is import, something like this would be needed

try:
fromspatialdata._dl.datasetsimportImageTilesDatasetexceptImportErrorase:
_error: str|None=str(e)
else:
_error=None

[LucaMarconato]

would you add torch somewhere in pyproject.toml or it would be responsibility of the user to install it properly? I would go for the second.

[giovp]

Current todos:

• wrong tiling result (wrong queried data and wrong content) when tiling a cropped multiscale image (probably due to this: 2 bugs with spatial cropping (with multiscale rasters and when missing the table) #178)

• extend functionality to get tiles from the raw space (not just from the target space)

  • when querying tiles from the raw space, allow to specify either units, either pixels. Not both
  • when querying tiles from the target space, allow to specify also only units (and infer pixels), or only pixels. This is possible only when the data is not a multiscale, otherwise the pixels could not be determined

• make tests

@LucaMarconato I would not extend functionality in this PR. Think priority wise is to add some minimal test and merge right away. We need to do the module conversion of the repo to start testing intersphinx for notebooks and documentation (and also this change will impact io/napari/plot so there will be lot of work to be done there as well.

[LucaMarconato]

Ok, I created an issue to keep track of that. I will make the tests and ask for review (btw I am working on the Xenium + Visium data atm, but I am going to work on this PR right after).

[kevinyamauchi]

This looks good to me! I can't approve because I opened the PR. For me, the main things to do before merging:

• make torch import optional
• make sure functions have docstrings (at least a description of what the function does)

[kevinyamauchi]

please add a docstring. I think the input parameters aren't clear (e.g., tile_dim_in_units vs. tile_dim_in_pixels)

[LucaMarconato]

one major one regarding examples. They have to be in spatialdata-notebooks, not here. Everything that is not API should stay there. Ok to have python files and not notebooks (although better notebooks) but would still move.

ok I deleted the folder examples and moved this to the sandbox. I'll made these example not to show things to other users but to debug/visually test the spatial query, rasterization and tiler. I am not using notebooks because I use these for debugging, setting breakpoint etc.

[giovp]

@LucaMarconato could you also quickly re add mypy in CI, seems like it's skipped atm, wasn't aware of that
https://github.com/scverse/spatialdata/blob/main/.pre-commit-config.yaml

[LucaMarconato]

@giovp restored mypy in ci, we have some problems with the installation

[LucaMarconato]

Gonna merge and increase the coverage in a next pr.

[kevinyamauchi]

This PR adds a torch DataSet with some addition utils. Initially, this implements a spot ROI dataset for replicating this squidpy example. The DataSet is implemented such that it is compatible with monai and pytorch lightning, which gives us access to a ton of tooling (e.g., multi-GPU training, tensorboard logging, learning rate schedulers).

This PR requires #132, #143, and image bounding box query with transforms to be merged.

[codecov]

Codecov Report

Merging #145 (41d818a) into main (9975a5c) will decrease coverage by 2.64%.
The diff coverage is 34.61%.

Additional details and impacted files

@@ Coverage Diff @@## main #145 +/- ##
==========================================
- Coverage 89.59% 86.95% -2.64% 
==========================================
Files 24 27 +3 Lines 3854 3995 +141 ==========================================
+ Hits 3453 3474 +21 - Misses 401 521 +120 

Impacted Files	Coverage Δ
spatialdata/_core/data_extent.py	0.00% <0.00%> (ø)
spatialdata/_dataloader/transforms.py	0.00% <0.00%> (ø)
spatialdata/_dataloader/datasets.py	6.84% <6.84%> (ø)
spatialdata/utils.py	84.52% <12.50%> (-3.61%)	⬇️
spatialdata/__init__.py	93.33% <83.33%> (-6.67%)	⬇️
spatialdata/_core/_spatialdata.py	91.20% <86.36%> (-0.27%)	⬇️
spatialdata/_core/_rasterize.py	83.00% <100.00%> (+0.34%)	⬆️
spatialdata/_core/_spatial_query.py	93.82% <100.00%> (-0.42%)	⬇️
spatialdata/_core/core_utils.py	91.91% <100.00%> (-0.27%)	⬇️
spatialdata/_core/models.py	86.14% <100.00%> (ø)

[kevinyamauchi]

I have created an example notebook showing how the Dataset and transforms work for the SpotCropDataset. We can now generate tiles such that they are compatible with monai, torchvision, and pytorch lightning, so we can add tons of augmentations, dataloader cacheing, multiGPU, etc.. Once #132 lands, I can update the spot centroid fetching to use the polygons item.

https://gist.github.com/kevinyamauchi/3a1d1c375b084732c5f60b19afabf461

[kevinyamauchi]

I've updated it to now use the Shapes element to get the spot locations. See the example notebook below:

https://gist.github.com/kevinyamauchi/77f986889b7626db4ab3c1075a3a3e5e

[LucaMarconato]

minor features of this PR:

• overloading of __get_item__() and __set_item__() for SpatialData.

[LucaMarconato]

I have pushed a code that has still open todos and bugs to fix, but it is usable. An example of usage is in this script from the sandbox (which run like this shows a bug.

But if you use it

• from the coordinate system global
• only with visium data (not querying the xenium image with the visium cirlces)
• not cropping the data first but making the tiles from the full data

it will work. So it should be good enough for the deep learning example.

[LucaMarconato]

Current todos:

• wrong tiling result (wrong queried data and wrong content) when tiling a cropped multiscale image (probably due to this: 2 bugs with spatial cropping (with multiscale rasters and when missing the table) #178)
• make tests

Initial plan, postponed to a new PR (see #184):

• extend functionality to get tiles from the raw space (not just from the target space)
  • when querying tiles from the raw space, allow to specify either units, either pixels. Not both
  • when querying tiles from the target space, allow to specify also only units (and infer pixels), or only pixels. This is possible only when the data is not a multiscale, otherwise the pixels could not be determined

[giovp]

@kevinyamauchi@LucaMarconato minor (maybe nitpick) comments.

one major one regarding examples. They have to be in spatialdata-notebooks, not here. Everything that is not API should stay there. Ok to have python files and not notebooks (although better notebooks) but would still move.

[giovp]

why this is not in the bounding box related module? I would put it there.

[LucaMarconato]

this and other functions that could populate this file are not used for spatial queries so I would not put them in _spatial_query.py. The complexity of that file increase when implementing non-bounding box queries, so I would keep this code in another place.

[giovp]

I would make torch as optional depedency, therefore I think in Init of this module or where the ImageTilesDataset is import, something like this would be needed

try:
fromspatialdata._dl.datasetsimportImageTilesDatasetexceptImportErrorase:
_error: str|None=str(e)
else:
_error=None

[LucaMarconato]

would you add torch somewhere in pyproject.toml or it would be responsibility of the user to install it properly? I would go for the second.

[giovp]

Current todos:

• wrong tiling result (wrong queried data and wrong content) when tiling a cropped multiscale image (probably due to this: 2 bugs with spatial cropping (with multiscale rasters and when missing the table) #178)

• extend functionality to get tiles from the raw space (not just from the target space)

  • when querying tiles from the raw space, allow to specify either units, either pixels. Not both
  • when querying tiles from the target space, allow to specify also only units (and infer pixels), or only pixels. This is possible only when the data is not a multiscale, otherwise the pixels could not be determined

• make tests

@LucaMarconato I would not extend functionality in this PR. Think priority wise is to add some minimal test and merge right away. We need to do the module conversion of the repo to start testing intersphinx for notebooks and documentation (and also this change will impact io/napari/plot so there will be lot of work to be done there as well.

[LucaMarconato]

Ok, I created an issue to keep track of that. I will make the tests and ask for review (btw I am working on the Xenium + Visium data atm, but I am going to work on this PR right after).

[kevinyamauchi]

This looks good to me! I can't approve because I opened the PR. For me, the main things to do before merging:

• make torch import optional
• make sure functions have docstrings (at least a description of what the function does)

[kevinyamauchi]

please add a docstring. I think the input parameters aren't clear (e.g., tile_dim_in_units vs. tile_dim_in_pixels)

[LucaMarconato]

one major one regarding examples. They have to be in spatialdata-notebooks, not here. Everything that is not API should stay there. Ok to have python files and not notebooks (although better notebooks) but would still move.

ok I deleted the folder examples and moved this to the sandbox. I'll made these example not to show things to other users but to debug/visually test the spatial query, rasterization and tiler. I am not using notebooks because I use these for debugging, setting breakpoint etc.

[giovp]

@LucaMarconato could you also quickly re add mypy in CI, seems like it's skipped atm, wasn't aware of that
https://github.com/scverse/spatialdata/blob/main/.pre-commit-config.yaml

[LucaMarconato]

@giovp restored mypy in ci, we have some problems with the installation

[LucaMarconato]

Gonna merge and increase the coverage in a next pr.

[kevinyamauchi]

This PR adds a torch DataSet with some addition utils. Initially, this implements a spot ROI dataset for replicating this squidpy example. The DataSet is implemented such that it is compatible with monai and pytorch lightning, which gives us access to a ton of tooling (e.g., multi-GPU training, tensorboard logging, learning rate schedulers).

This PR requires #132, #143, and image bounding box query with transforms to be merged.

[codecov]

Codecov Report

Merging #145 (41d818a) into main (9975a5c) will decrease coverage by 2.64%.
The diff coverage is 34.61%.

Additional details and impacted files

@@ Coverage Diff @@## main #145 +/- ##
==========================================
- Coverage 89.59% 86.95% -2.64% 
==========================================
Files 24 27 +3 Lines 3854 3995 +141 ==========================================
+ Hits 3453 3474 +21 - Misses 401 521 +120 

Impacted Files	Coverage Δ
spatialdata/_core/data_extent.py	0.00% <0.00%> (ø)
spatialdata/_dataloader/transforms.py	0.00% <0.00%> (ø)
spatialdata/_dataloader/datasets.py	6.84% <6.84%> (ø)
spatialdata/utils.py	84.52% <12.50%> (-3.61%)	⬇️
spatialdata/__init__.py	93.33% <83.33%> (-6.67%)	⬇️
spatialdata/_core/_spatialdata.py	91.20% <86.36%> (-0.27%)	⬇️
spatialdata/_core/_rasterize.py	83.00% <100.00%> (+0.34%)	⬆️
spatialdata/_core/_spatial_query.py	93.82% <100.00%> (-0.42%)	⬇️
spatialdata/_core/core_utils.py	91.91% <100.00%> (-0.27%)	⬇️
spatialdata/_core/models.py	86.14% <100.00%> (ø)

[kevinyamauchi]

I have created an example notebook showing how the Dataset and transforms work for the SpotCropDataset. We can now generate tiles such that they are compatible with monai, torchvision, and pytorch lightning, so we can add tons of augmentations, dataloader cacheing, multiGPU, etc.. Once #132 lands, I can update the spot centroid fetching to use the polygons item.

https://gist.github.com/kevinyamauchi/3a1d1c375b084732c5f60b19afabf461

[kevinyamauchi]

I've updated it to now use the Shapes element to get the spot locations. See the example notebook below:

https://gist.github.com/kevinyamauchi/77f986889b7626db4ab3c1075a3a3e5e

[LucaMarconato]

minor features of this PR:

• overloading of __get_item__() and __set_item__() for SpatialData.

[LucaMarconato]

I have pushed a code that has still open todos and bugs to fix, but it is usable. An example of usage is in this script from the sandbox (which run like this shows a bug.

But if you use it

• from the coordinate system global
• only with visium data (not querying the xenium image with the visium cirlces)
• not cropping the data first but making the tiles from the full data

it will work. So it should be good enough for the deep learning example.

[LucaMarconato]

Current todos:

• wrong tiling result (wrong queried data and wrong content) when tiling a cropped multiscale image (probably due to this: 2 bugs with spatial cropping (with multiscale rasters and when missing the table) #178)
• make tests

Initial plan, postponed to a new PR (see #184):

• extend functionality to get tiles from the raw space (not just from the target space)
  • when querying tiles from the raw space, allow to specify either units, either pixels. Not both
  • when querying tiles from the target space, allow to specify also only units (and infer pixels), or only pixels. This is possible only when the data is not a multiscale, otherwise the pixels could not be determined

[giovp]

@kevinyamauchi@LucaMarconato minor (maybe nitpick) comments.

one major one regarding examples. They have to be in spatialdata-notebooks, not here. Everything that is not API should stay there. Ok to have python files and not notebooks (although better notebooks) but would still move.

[giovp]

why this is not in the bounding box related module? I would put it there.

[LucaMarconato]

this and other functions that could populate this file are not used for spatial queries so I would not put them in _spatial_query.py. The complexity of that file increase when implementing non-bounding box queries, so I would keep this code in another place.

[giovp]

I would make torch as optional depedency, therefore I think in Init of this module or where the ImageTilesDataset is import, something like this would be needed

try:
fromspatialdata._dl.datasetsimportImageTilesDatasetexceptImportErrorase:
_error: str|None=str(e)
else:
_error=None

[LucaMarconato]

would you add torch somewhere in pyproject.toml or it would be responsibility of the user to install it properly? I would go for the second.

[giovp]

Current todos:

• wrong tiling result (wrong queried data and wrong content) when tiling a cropped multiscale image (probably due to this: 2 bugs with spatial cropping (with multiscale rasters and when missing the table) #178)

• extend functionality to get tiles from the raw space (not just from the target space)

  • when querying tiles from the raw space, allow to specify either units, either pixels. Not both
  • when querying tiles from the target space, allow to specify also only units (and infer pixels), or only pixels. This is possible only when the data is not a multiscale, otherwise the pixels could not be determined

• make tests

@LucaMarconato I would not extend functionality in this PR. Think priority wise is to add some minimal test and merge right away. We need to do the module conversion of the repo to start testing intersphinx for notebooks and documentation (and also this change will impact io/napari/plot so there will be lot of work to be done there as well.

[LucaMarconato]

Ok, I created an issue to keep track of that. I will make the tests and ask for review (btw I am working on the Xenium + Visium data atm, but I am going to work on this PR right after).

[kevinyamauchi]

This looks good to me! I can't approve because I opened the PR. For me, the main things to do before merging:

• make torch import optional
• make sure functions have docstrings (at least a description of what the function does)

[kevinyamauchi]

please add a docstring. I think the input parameters aren't clear (e.g., tile_dim_in_units vs. tile_dim_in_pixels)

[LucaMarconato]

one major one regarding examples. They have to be in spatialdata-notebooks, not here. Everything that is not API should stay there. Ok to have python files and not notebooks (although better notebooks) but would still move.

ok I deleted the folder examples and moved this to the sandbox. I'll made these example not to show things to other users but to debug/visually test the spatial query, rasterization and tiler. I am not using notebooks because I use these for debugging, setting breakpoint etc.

[giovp]

@LucaMarconato could you also quickly re add mypy in CI, seems like it's skipped atm, wasn't aware of that
https://github.com/scverse/spatialdata/blob/main/.pre-commit-config.yaml

[LucaMarconato]

@giovp restored mypy in ci, we have some problems with the installation

[LucaMarconato]

Gonna merge and increase the coverage in a next pr.

[kevinyamauchi]

This PR adds a torch DataSet with some addition utils. Initially, this implements a spot ROI dataset for replicating this squidpy example. The DataSet is implemented such that it is compatible with monai and pytorch lightning, which gives us access to a ton of tooling (e.g., multi-GPU training, tensorboard logging, learning rate schedulers).

This PR requires #132, #143, and image bounding box query with transforms to be merged.

[codecov]

Codecov Report

Merging #145 (41d818a) into main (9975a5c) will decrease coverage by 2.64%.
The diff coverage is 34.61%.

Additional details and impacted files

@@ Coverage Diff @@## main #145 +/- ##
==========================================
- Coverage 89.59% 86.95% -2.64% 
==========================================
Files 24 27 +3 Lines 3854 3995 +141 ==========================================
+ Hits 3453 3474 +21 - Misses 401 521 +120 

Impacted Files	Coverage Δ
spatialdata/_core/data_extent.py	0.00% <0.00%> (ø)
spatialdata/_dataloader/transforms.py	0.00% <0.00%> (ø)
spatialdata/_dataloader/datasets.py	6.84% <6.84%> (ø)
spatialdata/utils.py	84.52% <12.50%> (-3.61%)	⬇️
spatialdata/__init__.py	93.33% <83.33%> (-6.67%)	⬇️
spatialdata/_core/_spatialdata.py	91.20% <86.36%> (-0.27%)	⬇️
spatialdata/_core/_rasterize.py	83.00% <100.00%> (+0.34%)	⬆️
spatialdata/_core/_spatial_query.py	93.82% <100.00%> (-0.42%)	⬇️
spatialdata/_core/core_utils.py	91.91% <100.00%> (-0.27%)	⬇️
spatialdata/_core/models.py	86.14% <100.00%> (ø)

[kevinyamauchi]

I have created an example notebook showing how the Dataset and transforms work for the SpotCropDataset. We can now generate tiles such that they are compatible with monai, torchvision, and pytorch lightning, so we can add tons of augmentations, dataloader cacheing, multiGPU, etc.. Once #132 lands, I can update the spot centroid fetching to use the polygons item.

https://gist.github.com/kevinyamauchi/3a1d1c375b084732c5f60b19afabf461

[kevinyamauchi]

I've updated it to now use the Shapes element to get the spot locations. See the example notebook below:

https://gist.github.com/kevinyamauchi/77f986889b7626db4ab3c1075a3a3e5e

[LucaMarconato]

minor features of this PR:

• overloading of __get_item__() and __set_item__() for SpatialData.

[LucaMarconato]

I have pushed a code that has still open todos and bugs to fix, but it is usable. An example of usage is in this script from the sandbox (which run like this shows a bug.

But if you use it

• from the coordinate system global
• only with visium data (not querying the xenium image with the visium cirlces)
• not cropping the data first but making the tiles from the full data

it will work. So it should be good enough for the deep learning example.

[LucaMarconato]

Current todos:

• wrong tiling result (wrong queried data and wrong content) when tiling a cropped multiscale image (probably due to this: 2 bugs with spatial cropping (with multiscale rasters and when missing the table) #178)
• make tests

Initial plan, postponed to a new PR (see #184):

• extend functionality to get tiles from the raw space (not just from the target space)
  • when querying tiles from the raw space, allow to specify either units, either pixels. Not both
  • when querying tiles from the target space, allow to specify also only units (and infer pixels), or only pixels. This is possible only when the data is not a multiscale, otherwise the pixels could not be determined

[giovp]

@kevinyamauchi@LucaMarconato minor (maybe nitpick) comments.

one major one regarding examples. They have to be in spatialdata-notebooks, not here. Everything that is not API should stay there. Ok to have python files and not notebooks (although better notebooks) but would still move.

[giovp]

why this is not in the bounding box related module? I would put it there.

[LucaMarconato]

this and other functions that could populate this file are not used for spatial queries so I would not put them in _spatial_query.py. The complexity of that file increase when implementing non-bounding box queries, so I would keep this code in another place.

[giovp]

I would make torch as optional depedency, therefore I think in Init of this module or where the ImageTilesDataset is import, something like this would be needed

try:
fromspatialdata._dl.datasetsimportImageTilesDatasetexceptImportErrorase:
_error: str|None=str(e)
else:
_error=None

[LucaMarconato]

would you add torch somewhere in pyproject.toml or it would be responsibility of the user to install it properly? I would go for the second.

[giovp]

Current todos:

• wrong tiling result (wrong queried data and wrong content) when tiling a cropped multiscale image (probably due to this: 2 bugs with spatial cropping (with multiscale rasters and when missing the table) #178)

• extend functionality to get tiles from the raw space (not just from the target space)

  • when querying tiles from the raw space, allow to specify either units, either pixels. Not both
  • when querying tiles from the target space, allow to specify also only units (and infer pixels), or only pixels. This is possible only when the data is not a multiscale, otherwise the pixels could not be determined

• make tests

@LucaMarconato I would not extend functionality in this PR. Think priority wise is to add some minimal test and merge right away. We need to do the module conversion of the repo to start testing intersphinx for notebooks and documentation (and also this change will impact io/napari/plot so there will be lot of work to be done there as well.

[LucaMarconato]

Ok, I created an issue to keep track of that. I will make the tests and ask for review (btw I am working on the Xenium + Visium data atm, but I am going to work on this PR right after).

[kevinyamauchi]

This looks good to me! I can't approve because I opened the PR. For me, the main things to do before merging:

• make torch import optional
• make sure functions have docstrings (at least a description of what the function does)

[kevinyamauchi]

please add a docstring. I think the input parameters aren't clear (e.g., tile_dim_in_units vs. tile_dim_in_pixels)

[LucaMarconato]

one major one regarding examples. They have to be in spatialdata-notebooks, not here. Everything that is not API should stay there. Ok to have python files and not notebooks (although better notebooks) but would still move.

ok I deleted the folder examples and moved this to the sandbox. I'll made these example not to show things to other users but to debug/visually test the spatial query, rasterization and tiler. I am not using notebooks because I use these for debugging, setting breakpoint etc.

[giovp]

@LucaMarconato could you also quickly re add mypy in CI, seems like it's skipped atm, wasn't aware of that
https://github.com/scverse/spatialdata/blob/main/.pre-commit-config.yaml

[LucaMarconato]

@giovp restored mypy in ci, we have some problems with the installation

[LucaMarconato]

Gonna merge and increase the coverage in a next pr.

[kevinyamauchi]

This PR adds a torch DataSet with some addition utils. Initially, this implements a spot ROI dataset for replicating this squidpy example. The DataSet is implemented such that it is compatible with monai and pytorch lightning, which gives us access to a ton of tooling (e.g., multi-GPU training, tensorboard logging, learning rate schedulers).

This PR requires #132, #143, and image bounding box query with transforms to be merged.

[codecov]

Codecov Report

Merging #145 (41d818a) into main (9975a5c) will decrease coverage by 2.64%.
The diff coverage is 34.61%.

Additional details and impacted files

@@ Coverage Diff @@## main #145 +/- ##
==========================================
- Coverage 89.59% 86.95% -2.64% 
==========================================
Files 24 27 +3 Lines 3854 3995 +141 ==========================================
+ Hits 3453 3474 +21 - Misses 401 521 +120 

Impacted Files	Coverage Δ
spatialdata/_core/data_extent.py	0.00% <0.00%> (ø)
spatialdata/_dataloader/transforms.py	0.00% <0.00%> (ø)
spatialdata/_dataloader/datasets.py	6.84% <6.84%> (ø)
spatialdata/utils.py	84.52% <12.50%> (-3.61%)	⬇️
spatialdata/__init__.py	93.33% <83.33%> (-6.67%)	⬇️
spatialdata/_core/_spatialdata.py	91.20% <86.36%> (-0.27%)	⬇️
spatialdata/_core/_rasterize.py	83.00% <100.00%> (+0.34%)	⬆️
spatialdata/_core/_spatial_query.py	93.82% <100.00%> (-0.42%)	⬇️
spatialdata/_core/core_utils.py	91.91% <100.00%> (-0.27%)	⬇️
spatialdata/_core/models.py	86.14% <100.00%> (ø)

[kevinyamauchi]

I have created an example notebook showing how the Dataset and transforms work for the SpotCropDataset. We can now generate tiles such that they are compatible with monai, torchvision, and pytorch lightning, so we can add tons of augmentations, dataloader cacheing, multiGPU, etc.. Once #132 lands, I can update the spot centroid fetching to use the polygons item.

https://gist.github.com/kevinyamauchi/3a1d1c375b084732c5f60b19afabf461

[kevinyamauchi]

I've updated it to now use the Shapes element to get the spot locations. See the example notebook below:

https://gist.github.com/kevinyamauchi/77f986889b7626db4ab3c1075a3a3e5e

[LucaMarconato]

minor features of this PR:

• overloading of __get_item__() and __set_item__() for SpatialData.

[LucaMarconato]

I have pushed a code that has still open todos and bugs to fix, but it is usable. An example of usage is in this script from the sandbox (which run like this shows a bug.

But if you use it

• from the coordinate system global
• only with visium data (not querying the xenium image with the visium cirlces)
• not cropping the data first but making the tiles from the full data

it will work. So it should be good enough for the deep learning example.

[LucaMarconato]

Current todos:

• wrong tiling result (wrong queried data and wrong content) when tiling a cropped multiscale image (probably due to this: 2 bugs with spatial cropping (with multiscale rasters and when missing the table) #178)
• make tests

Initial plan, postponed to a new PR (see #184):

• extend functionality to get tiles from the raw space (not just from the target space)
  • when querying tiles from the raw space, allow to specify either units, either pixels. Not both
  • when querying tiles from the target space, allow to specify also only units (and infer pixels), or only pixels. This is possible only when the data is not a multiscale, otherwise the pixels could not be determined

[giovp]

@kevinyamauchi@LucaMarconato minor (maybe nitpick) comments.

one major one regarding examples. They have to be in spatialdata-notebooks, not here. Everything that is not API should stay there. Ok to have python files and not notebooks (although better notebooks) but would still move.

[giovp]

why this is not in the bounding box related module? I would put it there.

[LucaMarconato]

this and other functions that could populate this file are not used for spatial queries so I would not put them in _spatial_query.py. The complexity of that file increase when implementing non-bounding box queries, so I would keep this code in another place.

[giovp]

I would make torch as optional depedency, therefore I think in Init of this module or where the ImageTilesDataset is import, something like this would be needed

try:
fromspatialdata._dl.datasetsimportImageTilesDatasetexceptImportErrorase:
_error: str|None=str(e)
else:
_error=None

[LucaMarconato]

would you add torch somewhere in pyproject.toml or it would be responsibility of the user to install it properly? I would go for the second.

[giovp]

Current todos:

• wrong tiling result (wrong queried data and wrong content) when tiling a cropped multiscale image (probably due to this: 2 bugs with spatial cropping (with multiscale rasters and when missing the table) #178)

• extend functionality to get tiles from the raw space (not just from the target space)

  • when querying tiles from the raw space, allow to specify either units, either pixels. Not both
  • when querying tiles from the target space, allow to specify also only units (and infer pixels), or only pixels. This is possible only when the data is not a multiscale, otherwise the pixels could not be determined

• make tests

@LucaMarconato I would not extend functionality in this PR. Think priority wise is to add some minimal test and merge right away. We need to do the module conversion of the repo to start testing intersphinx for notebooks and documentation (and also this change will impact io/napari/plot so there will be lot of work to be done there as well.

[LucaMarconato]

Ok, I created an issue to keep track of that. I will make the tests and ask for review (btw I am working on the Xenium + Visium data atm, but I am going to work on this PR right after).

[kevinyamauchi]

This looks good to me! I can't approve because I opened the PR. For me, the main things to do before merging:

• make torch import optional
• make sure functions have docstrings (at least a description of what the function does)

[kevinyamauchi]

please add a docstring. I think the input parameters aren't clear (e.g., tile_dim_in_units vs. tile_dim_in_pixels)

[LucaMarconato]

one major one regarding examples. They have to be in spatialdata-notebooks, not here. Everything that is not API should stay there. Ok to have python files and not notebooks (although better notebooks) but would still move.

ok I deleted the folder examples and moved this to the sandbox. I'll made these example not to show things to other users but to debug/visually test the spatial query, rasterization and tiler. I am not using notebooks because I use these for debugging, setting breakpoint etc.

[giovp]

@LucaMarconato could you also quickly re add mypy in CI, seems like it's skipped atm, wasn't aware of that
https://github.com/scverse/spatialdata/blob/main/.pre-commit-config.yaml

[LucaMarconato]

@giovp restored mypy in ci, we have some problems with the installation

[LucaMarconato]

Gonna merge and increase the coverage in a next pr.

[kevinyamauchi]

This PR adds a torch DataSet with some addition utils. Initially, this implements a spot ROI dataset for replicating this squidpy example. The DataSet is implemented such that it is compatible with monai and pytorch lightning, which gives us access to a ton of tooling (e.g., multi-GPU training, tensorboard logging, learning rate schedulers).

This PR requires #132, #143, and image bounding box query with transforms to be merged.

[codecov]

Codecov Report

Merging #145 (41d818a) into main (9975a5c) will decrease coverage by 2.64%.
The diff coverage is 34.61%.

Additional details and impacted files

@@ Coverage Diff @@## main #145 +/- ##
==========================================
- Coverage 89.59% 86.95% -2.64% 
==========================================
Files 24 27 +3 Lines 3854 3995 +141 ==========================================
+ Hits 3453 3474 +21 - Misses 401 521 +120 

Impacted Files	Coverage Δ
spatialdata/_core/data_extent.py	0.00% <0.00%> (ø)
spatialdata/_dataloader/transforms.py	0.00% <0.00%> (ø)
spatialdata/_dataloader/datasets.py	6.84% <6.84%> (ø)
spatialdata/utils.py	84.52% <12.50%> (-3.61%)	⬇️
spatialdata/__init__.py	93.33% <83.33%> (-6.67%)	⬇️
spatialdata/_core/_spatialdata.py	91.20% <86.36%> (-0.27%)	⬇️
spatialdata/_core/_rasterize.py	83.00% <100.00%> (+0.34%)	⬆️
spatialdata/_core/_spatial_query.py	93.82% <100.00%> (-0.42%)	⬇️
spatialdata/_core/core_utils.py	91.91% <100.00%> (-0.27%)	⬇️
spatialdata/_core/models.py	86.14% <100.00%> (ø)

[kevinyamauchi]

I have created an example notebook showing how the Dataset and transforms work for the SpotCropDataset. We can now generate tiles such that they are compatible with monai, torchvision, and pytorch lightning, so we can add tons of augmentations, dataloader cacheing, multiGPU, etc.. Once #132 lands, I can update the spot centroid fetching to use the polygons item.

https://gist.github.com/kevinyamauchi/3a1d1c375b084732c5f60b19afabf461

[kevinyamauchi]

I've updated it to now use the Shapes element to get the spot locations. See the example notebook below:

https://gist.github.com/kevinyamauchi/77f986889b7626db4ab3c1075a3a3e5e

[LucaMarconato]

minor features of this PR:

• overloading of __get_item__() and __set_item__() for SpatialData.

[LucaMarconato]

I have pushed a code that has still open todos and bugs to fix, but it is usable. An example of usage is in this script from the sandbox (which run like this shows a bug.

But if you use it

• from the coordinate system global
• only with visium data (not querying the xenium image with the visium cirlces)
• not cropping the data first but making the tiles from the full data

it will work. So it should be good enough for the deep learning example.

[LucaMarconato]

Current todos:

• wrong tiling result (wrong queried data and wrong content) when tiling a cropped multiscale image (probably due to this: 2 bugs with spatial cropping (with multiscale rasters and when missing the table) #178)
• make tests

Initial plan, postponed to a new PR (see #184):

• extend functionality to get tiles from the raw space (not just from the target space)
  • when querying tiles from the raw space, allow to specify either units, either pixels. Not both
  • when querying tiles from the target space, allow to specify also only units (and infer pixels), or only pixels. This is possible only when the data is not a multiscale, otherwise the pixels could not be determined

[giovp]

@kevinyamauchi@LucaMarconato minor (maybe nitpick) comments.

one major one regarding examples. They have to be in spatialdata-notebooks, not here. Everything that is not API should stay there. Ok to have python files and not notebooks (although better notebooks) but would still move.

[giovp]

why this is not in the bounding box related module? I would put it there.

[LucaMarconato]

this and other functions that could populate this file are not used for spatial queries so I would not put them in _spatial_query.py. The complexity of that file increase when implementing non-bounding box queries, so I would keep this code in another place.

[giovp]

I would make torch as optional depedency, therefore I think in Init of this module or where the ImageTilesDataset is import, something like this would be needed

try:
fromspatialdata._dl.datasetsimportImageTilesDatasetexceptImportErrorase:
_error: str|None=str(e)
else:
_error=None

[LucaMarconato]

would you add torch somewhere in pyproject.toml or it would be responsibility of the user to install it properly? I would go for the second.

[giovp]

Current todos:

• wrong tiling result (wrong queried data and wrong content) when tiling a cropped multiscale image (probably due to this: 2 bugs with spatial cropping (with multiscale rasters and when missing the table) #178)

• extend functionality to get tiles from the raw space (not just from the target space)

  • when querying tiles from the raw space, allow to specify either units, either pixels. Not both
  • when querying tiles from the target space, allow to specify also only units (and infer pixels), or only pixels. This is possible only when the data is not a multiscale, otherwise the pixels could not be determined

• make tests

@LucaMarconato I would not extend functionality in this PR. Think priority wise is to add some minimal test and merge right away. We need to do the module conversion of the repo to start testing intersphinx for notebooks and documentation (and also this change will impact io/napari/plot so there will be lot of work to be done there as well.

[LucaMarconato]

Ok, I created an issue to keep track of that. I will make the tests and ask for review (btw I am working on the Xenium + Visium data atm, but I am going to work on this PR right after).

[kevinyamauchi]

This looks good to me! I can't approve because I opened the PR. For me, the main things to do before merging:

• make torch import optional
• make sure functions have docstrings (at least a description of what the function does)

[kevinyamauchi]

please add a docstring. I think the input parameters aren't clear (e.g., tile_dim_in_units vs. tile_dim_in_pixels)

[LucaMarconato]

one major one regarding examples. They have to be in spatialdata-notebooks, not here. Everything that is not API should stay there. Ok to have python files and not notebooks (although better notebooks) but would still move.

ok I deleted the folder examples and moved this to the sandbox. I'll made these example not to show things to other users but to debug/visually test the spatial query, rasterization and tiler. I am not using notebooks because I use these for debugging, setting breakpoint etc.

[giovp]

@LucaMarconato could you also quickly re add mypy in CI, seems like it's skipped atm, wasn't aware of that
https://github.com/scverse/spatialdata/blob/main/.pre-commit-config.yaml

[LucaMarconato]

@giovp restored mypy in ci, we have some problems with the installation

[LucaMarconato]

Gonna merge and increase the coverage in a next pr.