Queries + coordinate systems

examples/dev-examples/README.md

@@ -0,0 +1,3 @@
+# dev-examples
+
+The examples are useful when developing parts of the codebase. They are not intended to be used as a reference for how to use the library. For that, please refer to the documentation, the example notebooks and the examples in the `example/` directory.

examples/dev-examples/spatial_query_and_rasterization.py

@@ -0,0 +1,115 @@
+import numpy as np
+from multiscale_spatial_image import MultiscaleSpatialImage
+from spatial_image import SpatialImage
+
+from spatialdata import Labels2DModel
+from spatialdata._core._spatial_query import bounding_box_query
+from spatialdata._core._spatialdata_ops import (
+ get_transformation,
+ remove_transformation,
+ set_transformation,
+)
+from spatialdata._core.transformations import Affine
+
+
+def _visualize_crop_affine_labels_2d() -> None:
+ """
+ This examples show how the bounding box spatial query works for data that has been rotated.
+
+ Notes
+ -----
+ The bounding box query gives the data, from the intrinsic coordinate system, that is inside the bounding box of
+ the inverse-transformed query bounding box.
+ In this example I show this data, and I also show how to obtain the data back inside the original bounding box.
+
+ To undertand the example I suggest to run it and then:
+ 1) select the "rotated" coordinate system from napari
+ 2) disable all the layers but "0 original"
+ 3) then enable "1 cropped global", this shows the data in the extrinsic coordinate system we care ("rotated"),
+ and the bounding box we want to query
+ 4) then enable "2 cropped rotated", this show the data that has been queries (this is a bounding box of the
+ requested crop, as exaplained above)
+ 5) then enable "3 cropped rotated processed", this shows the data that we wanted to query in the first place,
+ in the target coordinate system ("rotated"). This is probaly the data you care about if for instance you want to
+ use tiles for deep learning. Note that for obtaning this answer there is also a better function (not available at
+ the time of this writing): rasterize(), which is faster and more accurate, so it should be used instead. The
+ function rasterize() transforms all the coordinates of the data into the target coordinate system, and it returns
+ only SpatialImage objects. So it has different use cases than the bounding box query.
+ 6) finally switch to the "global" coordinate_system. This is, for how we constructed the example, showing the
+ original image as it would appear its intrinsic coordinate system (since the transformation that maps the
+ original image to "global" is an identity. It then shows how the data showed at the point 5), localizes in the
+ original image.
+ """
+ ##
+ # in this test let's try some affine transformations, we could do that also for the other tests
+ image = np.random.randint(low=10, high=100, size=(100, 100))
+ # y: [5, 9], x: [0, 4] has value 1
+ image[50:, :50] = 2
+ labels_element = Labels2DModel.parse(image)
+ set_transformation(
+ labels_element,
+ Affine(
+ np.array(
+ [
+ [np.cos(np.pi / 6), np.sin(-np.pi / 6), 20],
+ [np.sin(np.pi / 6), np.cos(np.pi / 6), 0],
+ [0, 0, 1],
+ ]
+ ),
+ input_axes=("x", "y"),
+ output_axes=("x", "y"),
+ ),
+ "rotated",
+ )
+
+ # bounding box: y: [5, 9], x: [0, 4]
+ labels_result_rotated = bounding_box_query(
+ labels_element,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="rotated",
+ )
+ labels_result_global = bounding_box_query(
+ labels_element,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="global",
+ )
+ from napari_spatialdata import Interactive
+
+ from spatialdata import SpatialData
+
+ old_transformation = get_transformation(labels_result_global, "global")
+ remove_transformation(labels_result_global, "global")
+ set_transformation(labels_result_global, old_transformation, "rotated")
+ d = {
+ "1 cropped_global": labels_result_global,
+ "0 original": labels_element,
+ }
+ if labels_result_rotated is not None:
+ d["2 cropped_rotated"] = labels_result_rotated
+
+ assert isinstance(labels_result_rotated, SpatialImage) or isinstance(
+ labels_result_rotated, MultiscaleSpatialImage
+ )
+ transform = labels_result_rotated.attrs["transform"]["rotated"]
+ transform_rotated_processed = transform.transform(labels_result_rotated, maintain_positioning=True)
+ transform_rotated_processed_recropped = bounding_box_query(
+ transform_rotated_processed,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="rotated",
+ )
+ d["3 cropped_rotated_processed_recropped"] = transform_rotated_processed_recropped
+ remove_transformation(labels_result_rotated, "global")
+
+ sdata = SpatialData(labels=d)
+ Interactive(sdata)
+ ##
+
+
+if __name__ == "__main__":
+ _visualize_crop_affine_labels_2d()

examples/dev-examples/README.md

@@ -0,0 +1,3 @@
+# dev-examples
+
+The examples are useful when developing parts of the codebase. They are not intended to be used as a reference for how to use the library. For that, please refer to the documentation, the example notebooks and the examples in the `example/` directory.

examples/dev-examples/spatial_query_and_rasterization.py

@@ -0,0 +1,115 @@
+import numpy as np
+from multiscale_spatial_image import MultiscaleSpatialImage
+from spatial_image import SpatialImage
+
+from spatialdata import Labels2DModel
+from spatialdata._core._spatial_query import bounding_box_query
+from spatialdata._core._spatialdata_ops import (
+ get_transformation,
+ remove_transformation,
+ set_transformation,
+)
+from spatialdata._core.transformations import Affine
+
+
+def _visualize_crop_affine_labels_2d() -> None:
+ """
+ This examples show how the bounding box spatial query works for data that has been rotated.
+
+ Notes
+ -----
+ The bounding box query gives the data, from the intrinsic coordinate system, that is inside the bounding box of
+ the inverse-transformed query bounding box.
+ In this example I show this data, and I also show how to obtain the data back inside the original bounding box.
+
+ To undertand the example I suggest to run it and then:
+ 1) select the "rotated" coordinate system from napari
+ 2) disable all the layers but "0 original"
+ 3) then enable "1 cropped global", this shows the data in the extrinsic coordinate system we care ("rotated"),
+ and the bounding box we want to query
+ 4) then enable "2 cropped rotated", this show the data that has been queries (this is a bounding box of the
+ requested crop, as exaplained above)
+ 5) then enable "3 cropped rotated processed", this shows the data that we wanted to query in the first place,
+ in the target coordinate system ("rotated"). This is probaly the data you care about if for instance you want to
+ use tiles for deep learning. Note that for obtaning this answer there is also a better function (not available at
+ the time of this writing): rasterize(), which is faster and more accurate, so it should be used instead. The
+ function rasterize() transforms all the coordinates of the data into the target coordinate system, and it returns
+ only SpatialImage objects. So it has different use cases than the bounding box query.
+ 6) finally switch to the "global" coordinate_system. This is, for how we constructed the example, showing the
+ original image as it would appear its intrinsic coordinate system (since the transformation that maps the
+ original image to "global" is an identity. It then shows how the data showed at the point 5), localizes in the
+ original image.
+ """
+ ##
+ # in this test let's try some affine transformations, we could do that also for the other tests
+ image = np.random.randint(low=10, high=100, size=(100, 100))
+ # y: [5, 9], x: [0, 4] has value 1
+ image[50:, :50] = 2
+ labels_element = Labels2DModel.parse(image)
+ set_transformation(
+ labels_element,
+ Affine(
+ np.array(
+ [
+ [np.cos(np.pi / 6), np.sin(-np.pi / 6), 20],
+ [np.sin(np.pi / 6), np.cos(np.pi / 6), 0],
+ [0, 0, 1],
+ ]
+ ),
+ input_axes=("x", "y"),
+ output_axes=("x", "y"),
+ ),
+ "rotated",
+ )
+
+ # bounding box: y: [5, 9], x: [0, 4]
+ labels_result_rotated = bounding_box_query(
+ labels_element,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="rotated",
+ )
+ labels_result_global = bounding_box_query(
+ labels_element,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="global",
+ )
+ from napari_spatialdata import Interactive
+
+ from spatialdata import SpatialData
+
+ old_transformation = get_transformation(labels_result_global, "global")
+ remove_transformation(labels_result_global, "global")
+ set_transformation(labels_result_global, old_transformation, "rotated")
+ d = {
+ "1 cropped_global": labels_result_global,
+ "0 original": labels_element,
+ }
+ if labels_result_rotated is not None:
+ d["2 cropped_rotated"] = labels_result_rotated
+
+ assert isinstance(labels_result_rotated, SpatialImage) or isinstance(
+ labels_result_rotated, MultiscaleSpatialImage
+ )
+ transform = labels_result_rotated.attrs["transform"]["rotated"]
+ transform_rotated_processed = transform.transform(labels_result_rotated, maintain_positioning=True)
+ transform_rotated_processed_recropped = bounding_box_query(
+ transform_rotated_processed,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="rotated",
+ )
+ d["3 cropped_rotated_processed_recropped"] = transform_rotated_processed_recropped
+ remove_transformation(labels_result_rotated, "global")
+
+ sdata = SpatialData(labels=d)
+ Interactive(sdata)
+ ##
+
+
+if __name__ == "__main__":
+ _visualize_crop_affine_labels_2d()

examples/dev-examples/README.md

@@ -0,0 +1,3 @@
+# dev-examples
+
+The examples are useful when developing parts of the codebase. They are not intended to be used as a reference for how to use the library. For that, please refer to the documentation, the example notebooks and the examples in the `example/` directory.

examples/dev-examples/spatial_query_and_rasterization.py

@@ -0,0 +1,115 @@
+import numpy as np
+from multiscale_spatial_image import MultiscaleSpatialImage
+from spatial_image import SpatialImage
+
+from spatialdata import Labels2DModel
+from spatialdata._core._spatial_query import bounding_box_query
+from spatialdata._core._spatialdata_ops import (
+ get_transformation,
+ remove_transformation,
+ set_transformation,
+)
+from spatialdata._core.transformations import Affine
+
+
+def _visualize_crop_affine_labels_2d() -> None:
+ """
+ This examples show how the bounding box spatial query works for data that has been rotated.
+
+ Notes
+ -----
+ The bounding box query gives the data, from the intrinsic coordinate system, that is inside the bounding box of
+ the inverse-transformed query bounding box.
+ In this example I show this data, and I also show how to obtain the data back inside the original bounding box.
+
+ To undertand the example I suggest to run it and then:
+ 1) select the "rotated" coordinate system from napari
+ 2) disable all the layers but "0 original"
+ 3) then enable "1 cropped global", this shows the data in the extrinsic coordinate system we care ("rotated"),
+ and the bounding box we want to query
+ 4) then enable "2 cropped rotated", this show the data that has been queries (this is a bounding box of the
+ requested crop, as exaplained above)
+ 5) then enable "3 cropped rotated processed", this shows the data that we wanted to query in the first place,
+ in the target coordinate system ("rotated"). This is probaly the data you care about if for instance you want to
+ use tiles for deep learning. Note that for obtaning this answer there is also a better function (not available at
+ the time of this writing): rasterize(), which is faster and more accurate, so it should be used instead. The
+ function rasterize() transforms all the coordinates of the data into the target coordinate system, and it returns
+ only SpatialImage objects. So it has different use cases than the bounding box query.
+ 6) finally switch to the "global" coordinate_system. This is, for how we constructed the example, showing the
+ original image as it would appear its intrinsic coordinate system (since the transformation that maps the
+ original image to "global" is an identity. It then shows how the data showed at the point 5), localizes in the
+ original image.
+ """
+ ##
+ # in this test let's try some affine transformations, we could do that also for the other tests
+ image = np.random.randint(low=10, high=100, size=(100, 100))
+ # y: [5, 9], x: [0, 4] has value 1
+ image[50:, :50] = 2
+ labels_element = Labels2DModel.parse(image)
+ set_transformation(
+ labels_element,
+ Affine(
+ np.array(
+ [
+ [np.cos(np.pi / 6), np.sin(-np.pi / 6), 20],
+ [np.sin(np.pi / 6), np.cos(np.pi / 6), 0],
+ [0, 0, 1],
+ ]
+ ),
+ input_axes=("x", "y"),
+ output_axes=("x", "y"),
+ ),
+ "rotated",
+ )
+
+ # bounding box: y: [5, 9], x: [0, 4]
+ labels_result_rotated = bounding_box_query(
+ labels_element,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="rotated",
+ )
+ labels_result_global = bounding_box_query(
+ labels_element,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="global",
+ )
+ from napari_spatialdata import Interactive
+
+ from spatialdata import SpatialData
+
+ old_transformation = get_transformation(labels_result_global, "global")
+ remove_transformation(labels_result_global, "global")
+ set_transformation(labels_result_global, old_transformation, "rotated")
+ d = {
+ "1 cropped_global": labels_result_global,
+ "0 original": labels_element,
+ }
+ if labels_result_rotated is not None:
+ d["2 cropped_rotated"] = labels_result_rotated
+
+ assert isinstance(labels_result_rotated, SpatialImage) or isinstance(
+ labels_result_rotated, MultiscaleSpatialImage
+ )
+ transform = labels_result_rotated.attrs["transform"]["rotated"]
+ transform_rotated_processed = transform.transform(labels_result_rotated, maintain_positioning=True)
+ transform_rotated_processed_recropped = bounding_box_query(
+ transform_rotated_processed,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="rotated",
+ )
+ d["3 cropped_rotated_processed_recropped"] = transform_rotated_processed_recropped
+ remove_transformation(labels_result_rotated, "global")
+
+ sdata = SpatialData(labels=d)
+ Interactive(sdata)
+ ##
+
+
+if __name__ == "__main__":
+ _visualize_crop_affine_labels_2d()

examples/dev-examples/README.md

@@ -0,0 +1,3 @@
+# dev-examples
+
+The examples are useful when developing parts of the codebase. They are not intended to be used as a reference for how to use the library. For that, please refer to the documentation, the example notebooks and the examples in the `example/` directory.

examples/dev-examples/spatial_query_and_rasterization.py

@@ -0,0 +1,115 @@
+import numpy as np
+from multiscale_spatial_image import MultiscaleSpatialImage
+from spatial_image import SpatialImage
+
+from spatialdata import Labels2DModel
+from spatialdata._core._spatial_query import bounding_box_query
+from spatialdata._core._spatialdata_ops import (
+ get_transformation,
+ remove_transformation,
+ set_transformation,
+)
+from spatialdata._core.transformations import Affine
+
+
+def _visualize_crop_affine_labels_2d() -> None:
+ """
+ This examples show how the bounding box spatial query works for data that has been rotated.
+
+ Notes
+ -----
+ The bounding box query gives the data, from the intrinsic coordinate system, that is inside the bounding box of
+ the inverse-transformed query bounding box.
+ In this example I show this data, and I also show how to obtain the data back inside the original bounding box.
+
+ To undertand the example I suggest to run it and then:
+ 1) select the "rotated" coordinate system from napari
+ 2) disable all the layers but "0 original"
+ 3) then enable "1 cropped global", this shows the data in the extrinsic coordinate system we care ("rotated"),
+ and the bounding box we want to query
+ 4) then enable "2 cropped rotated", this show the data that has been queries (this is a bounding box of the
+ requested crop, as exaplained above)
+ 5) then enable "3 cropped rotated processed", this shows the data that we wanted to query in the first place,
+ in the target coordinate system ("rotated"). This is probaly the data you care about if for instance you want to
+ use tiles for deep learning. Note that for obtaning this answer there is also a better function (not available at
+ the time of this writing): rasterize(), which is faster and more accurate, so it should be used instead. The
+ function rasterize() transforms all the coordinates of the data into the target coordinate system, and it returns
+ only SpatialImage objects. So it has different use cases than the bounding box query.
+ 6) finally switch to the "global" coordinate_system. This is, for how we constructed the example, showing the
+ original image as it would appear its intrinsic coordinate system (since the transformation that maps the
+ original image to "global" is an identity. It then shows how the data showed at the point 5), localizes in the
+ original image.
+ """
+ ##
+ # in this test let's try some affine transformations, we could do that also for the other tests
+ image = np.random.randint(low=10, high=100, size=(100, 100))
+ # y: [5, 9], x: [0, 4] has value 1
+ image[50:, :50] = 2
+ labels_element = Labels2DModel.parse(image)
+ set_transformation(
+ labels_element,
+ Affine(
+ np.array(
+ [
+ [np.cos(np.pi / 6), np.sin(-np.pi / 6), 20],
+ [np.sin(np.pi / 6), np.cos(np.pi / 6), 0],
+ [0, 0, 1],
+ ]
+ ),
+ input_axes=("x", "y"),
+ output_axes=("x", "y"),
+ ),
+ "rotated",
+ )
+
+ # bounding box: y: [5, 9], x: [0, 4]
+ labels_result_rotated = bounding_box_query(
+ labels_element,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="rotated",
+ )
+ labels_result_global = bounding_box_query(
+ labels_element,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="global",
+ )
+ from napari_spatialdata import Interactive
+
+ from spatialdata import SpatialData
+
+ old_transformation = get_transformation(labels_result_global, "global")
+ remove_transformation(labels_result_global, "global")
+ set_transformation(labels_result_global, old_transformation, "rotated")
+ d = {
+ "1 cropped_global": labels_result_global,
+ "0 original": labels_element,
+ }
+ if labels_result_rotated is not None:
+ d["2 cropped_rotated"] = labels_result_rotated
+
+ assert isinstance(labels_result_rotated, SpatialImage) or isinstance(
+ labels_result_rotated, MultiscaleSpatialImage
+ )
+ transform = labels_result_rotated.attrs["transform"]["rotated"]
+ transform_rotated_processed = transform.transform(labels_result_rotated, maintain_positioning=True)
+ transform_rotated_processed_recropped = bounding_box_query(
+ transform_rotated_processed,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="rotated",
+ )
+ d["3 cropped_rotated_processed_recropped"] = transform_rotated_processed_recropped
+ remove_transformation(labels_result_rotated, "global")
+
+ sdata = SpatialData(labels=d)
+ Interactive(sdata)
+ ##
+
+
+if __name__ == "__main__":
+ _visualize_crop_affine_labels_2d()

examples/dev-examples/README.md

@@ -0,0 +1,3 @@
+# dev-examples
+
+The examples are useful when developing parts of the codebase. They are not intended to be used as a reference for how to use the library. For that, please refer to the documentation, the example notebooks and the examples in the `example/` directory.

examples/dev-examples/spatial_query_and_rasterization.py

@@ -0,0 +1,115 @@
+import numpy as np
+from multiscale_spatial_image import MultiscaleSpatialImage
+from spatial_image import SpatialImage
+
+from spatialdata import Labels2DModel
+from spatialdata._core._spatial_query import bounding_box_query
+from spatialdata._core._spatialdata_ops import (
+ get_transformation,
+ remove_transformation,
+ set_transformation,
+)
+from spatialdata._core.transformations import Affine
+
+
+def _visualize_crop_affine_labels_2d() -> None:
+ """
+ This examples show how the bounding box spatial query works for data that has been rotated.
+
+ Notes
+ -----
+ The bounding box query gives the data, from the intrinsic coordinate system, that is inside the bounding box of
+ the inverse-transformed query bounding box.
+ In this example I show this data, and I also show how to obtain the data back inside the original bounding box.
+
+ To undertand the example I suggest to run it and then:
+ 1) select the "rotated" coordinate system from napari
+ 2) disable all the layers but "0 original"
+ 3) then enable "1 cropped global", this shows the data in the extrinsic coordinate system we care ("rotated"),
+ and the bounding box we want to query
+ 4) then enable "2 cropped rotated", this show the data that has been queries (this is a bounding box of the
+ requested crop, as exaplained above)
+ 5) then enable "3 cropped rotated processed", this shows the data that we wanted to query in the first place,
+ in the target coordinate system ("rotated"). This is probaly the data you care about if for instance you want to
+ use tiles for deep learning. Note that for obtaning this answer there is also a better function (not available at
+ the time of this writing): rasterize(), which is faster and more accurate, so it should be used instead. The
+ function rasterize() transforms all the coordinates of the data into the target coordinate system, and it returns
+ only SpatialImage objects. So it has different use cases than the bounding box query.
+ 6) finally switch to the "global" coordinate_system. This is, for how we constructed the example, showing the
+ original image as it would appear its intrinsic coordinate system (since the transformation that maps the
+ original image to "global" is an identity. It then shows how the data showed at the point 5), localizes in the
+ original image.
+ """
+ ##
+ # in this test let's try some affine transformations, we could do that also for the other tests
+ image = np.random.randint(low=10, high=100, size=(100, 100))
+ # y: [5, 9], x: [0, 4] has value 1
+ image[50:, :50] = 2
+ labels_element = Labels2DModel.parse(image)
+ set_transformation(
+ labels_element,
+ Affine(
+ np.array(
+ [
+ [np.cos(np.pi / 6), np.sin(-np.pi / 6), 20],
+ [np.sin(np.pi / 6), np.cos(np.pi / 6), 0],
+ [0, 0, 1],
+ ]
+ ),
+ input_axes=("x", "y"),
+ output_axes=("x", "y"),
+ ),
+ "rotated",
+ )
+
+ # bounding box: y: [5, 9], x: [0, 4]
+ labels_result_rotated = bounding_box_query(
+ labels_element,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="rotated",
+ )
+ labels_result_global = bounding_box_query(
+ labels_element,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="global",
+ )
+ from napari_spatialdata import Interactive
+
+ from spatialdata import SpatialData
+
+ old_transformation = get_transformation(labels_result_global, "global")
+ remove_transformation(labels_result_global, "global")
+ set_transformation(labels_result_global, old_transformation, "rotated")
+ d = {
+ "1 cropped_global": labels_result_global,
+ "0 original": labels_element,
+ }
+ if labels_result_rotated is not None:
+ d["2 cropped_rotated"] = labels_result_rotated
+
+ assert isinstance(labels_result_rotated, SpatialImage) or isinstance(
+ labels_result_rotated, MultiscaleSpatialImage
+ )
+ transform = labels_result_rotated.attrs["transform"]["rotated"]
+ transform_rotated_processed = transform.transform(labels_result_rotated, maintain_positioning=True)
+ transform_rotated_processed_recropped = bounding_box_query(
+ transform_rotated_processed,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="rotated",
+ )
+ d["3 cropped_rotated_processed_recropped"] = transform_rotated_processed_recropped
+ remove_transformation(labels_result_rotated, "global")
+
+ sdata = SpatialData(labels=d)
+ Interactive(sdata)
+ ##
+
+
+if __name__ == "__main__":
+ _visualize_crop_affine_labels_2d()

examples/dev-examples/README.md

@@ -0,0 +1,3 @@
+# dev-examples
+
+The examples are useful when developing parts of the codebase. They are not intended to be used as a reference for how to use the library. For that, please refer to the documentation, the example notebooks and the examples in the `example/` directory.

examples/dev-examples/spatial_query_and_rasterization.py

@@ -0,0 +1,115 @@
+import numpy as np
+from multiscale_spatial_image import MultiscaleSpatialImage
+from spatial_image import SpatialImage
+
+from spatialdata import Labels2DModel
+from spatialdata._core._spatial_query import bounding_box_query
+from spatialdata._core._spatialdata_ops import (
+ get_transformation,
+ remove_transformation,
+ set_transformation,
+)
+from spatialdata._core.transformations import Affine
+
+
+def _visualize_crop_affine_labels_2d() -> None:
+ """
+ This examples show how the bounding box spatial query works for data that has been rotated.
+
+ Notes
+ -----
+ The bounding box query gives the data, from the intrinsic coordinate system, that is inside the bounding box of
+ the inverse-transformed query bounding box.
+ In this example I show this data, and I also show how to obtain the data back inside the original bounding box.
+
+ To undertand the example I suggest to run it and then:
+ 1) select the "rotated" coordinate system from napari
+ 2) disable all the layers but "0 original"
+ 3) then enable "1 cropped global", this shows the data in the extrinsic coordinate system we care ("rotated"),
+ and the bounding box we want to query
+ 4) then enable "2 cropped rotated", this show the data that has been queries (this is a bounding box of the
+ requested crop, as exaplained above)
+ 5) then enable "3 cropped rotated processed", this shows the data that we wanted to query in the first place,
+ in the target coordinate system ("rotated"). This is probaly the data you care about if for instance you want to
+ use tiles for deep learning. Note that for obtaning this answer there is also a better function (not available at
+ the time of this writing): rasterize(), which is faster and more accurate, so it should be used instead. The
+ function rasterize() transforms all the coordinates of the data into the target coordinate system, and it returns
+ only SpatialImage objects. So it has different use cases than the bounding box query.
+ 6) finally switch to the "global" coordinate_system. This is, for how we constructed the example, showing the
+ original image as it would appear its intrinsic coordinate system (since the transformation that maps the
+ original image to "global" is an identity. It then shows how the data showed at the point 5), localizes in the
+ original image.
+ """
+ ##
+ # in this test let's try some affine transformations, we could do that also for the other tests
+ image = np.random.randint(low=10, high=100, size=(100, 100))
+ # y: [5, 9], x: [0, 4] has value 1
+ image[50:, :50] = 2
+ labels_element = Labels2DModel.parse(image)
+ set_transformation(
+ labels_element,
+ Affine(
+ np.array(
+ [
+ [np.cos(np.pi / 6), np.sin(-np.pi / 6), 20],
+ [np.sin(np.pi / 6), np.cos(np.pi / 6), 0],
+ [0, 0, 1],
+ ]
+ ),
+ input_axes=("x", "y"),
+ output_axes=("x", "y"),
+ ),
+ "rotated",
+ )
+
+ # bounding box: y: [5, 9], x: [0, 4]
+ labels_result_rotated = bounding_box_query(
+ labels_element,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="rotated",
+ )
+ labels_result_global = bounding_box_query(
+ labels_element,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="global",
+ )
+ from napari_spatialdata import Interactive
+
+ from spatialdata import SpatialData
+
+ old_transformation = get_transformation(labels_result_global, "global")
+ remove_transformation(labels_result_global, "global")
+ set_transformation(labels_result_global, old_transformation, "rotated")
+ d = {
+ "1 cropped_global": labels_result_global,
+ "0 original": labels_element,
+ }
+ if labels_result_rotated is not None:
+ d["2 cropped_rotated"] = labels_result_rotated
+
+ assert isinstance(labels_result_rotated, SpatialImage) or isinstance(
+ labels_result_rotated, MultiscaleSpatialImage
+ )
+ transform = labels_result_rotated.attrs["transform"]["rotated"]
+ transform_rotated_processed = transform.transform(labels_result_rotated, maintain_positioning=True)
+ transform_rotated_processed_recropped = bounding_box_query(
+ transform_rotated_processed,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="rotated",
+ )
+ d["3 cropped_rotated_processed_recropped"] = transform_rotated_processed_recropped
+ remove_transformation(labels_result_rotated, "global")
+
+ sdata = SpatialData(labels=d)
+ Interactive(sdata)
+ ##
+
+
+if __name__ == "__main__":
+ _visualize_crop_affine_labels_2d()

examples/dev-examples/README.md

@@ -0,0 +1,3 @@
+# dev-examples
+
+The examples are useful when developing parts of the codebase. They are not intended to be used as a reference for how to use the library. For that, please refer to the documentation, the example notebooks and the examples in the `example/` directory.

examples/dev-examples/spatial_query_and_rasterization.py

@@ -0,0 +1,115 @@
+import numpy as np
+from multiscale_spatial_image import MultiscaleSpatialImage
+from spatial_image import SpatialImage
+
+from spatialdata import Labels2DModel
+from spatialdata._core._spatial_query import bounding_box_query
+from spatialdata._core._spatialdata_ops import (
+ get_transformation,
+ remove_transformation,
+ set_transformation,
+)
+from spatialdata._core.transformations import Affine
+
+
+def _visualize_crop_affine_labels_2d() -> None:
+ """
+ This examples show how the bounding box spatial query works for data that has been rotated.
+
+ Notes
+ -----
+ The bounding box query gives the data, from the intrinsic coordinate system, that is inside the bounding box of
+ the inverse-transformed query bounding box.
+ In this example I show this data, and I also show how to obtain the data back inside the original bounding box.
+
+ To undertand the example I suggest to run it and then:
+ 1) select the "rotated" coordinate system from napari
+ 2) disable all the layers but "0 original"
+ 3) then enable "1 cropped global", this shows the data in the extrinsic coordinate system we care ("rotated"),
+ and the bounding box we want to query
+ 4) then enable "2 cropped rotated", this show the data that has been queries (this is a bounding box of the
+ requested crop, as exaplained above)
+ 5) then enable "3 cropped rotated processed", this shows the data that we wanted to query in the first place,
+ in the target coordinate system ("rotated"). This is probaly the data you care about if for instance you want to
+ use tiles for deep learning. Note that for obtaning this answer there is also a better function (not available at
+ the time of this writing): rasterize(), which is faster and more accurate, so it should be used instead. The
+ function rasterize() transforms all the coordinates of the data into the target coordinate system, and it returns
+ only SpatialImage objects. So it has different use cases than the bounding box query.
+ 6) finally switch to the "global" coordinate_system. This is, for how we constructed the example, showing the
+ original image as it would appear its intrinsic coordinate system (since the transformation that maps the
+ original image to "global" is an identity. It then shows how the data showed at the point 5), localizes in the
+ original image.
+ """
+ ##
+ # in this test let's try some affine transformations, we could do that also for the other tests
+ image = np.random.randint(low=10, high=100, size=(100, 100))
+ # y: [5, 9], x: [0, 4] has value 1
+ image[50:, :50] = 2
+ labels_element = Labels2DModel.parse(image)
+ set_transformation(
+ labels_element,
+ Affine(
+ np.array(
+ [
+ [np.cos(np.pi / 6), np.sin(-np.pi / 6), 20],
+ [np.sin(np.pi / 6), np.cos(np.pi / 6), 0],
+ [0, 0, 1],
+ ]
+ ),
+ input_axes=("x", "y"),
+ output_axes=("x", "y"),
+ ),
+ "rotated",
+ )
+
+ # bounding box: y: [5, 9], x: [0, 4]
+ labels_result_rotated = bounding_box_query(
+ labels_element,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="rotated",
+ )
+ labels_result_global = bounding_box_query(
+ labels_element,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="global",
+ )
+ from napari_spatialdata import Interactive
+
+ from spatialdata import SpatialData
+
+ old_transformation = get_transformation(labels_result_global, "global")
+ remove_transformation(labels_result_global, "global")
+ set_transformation(labels_result_global, old_transformation, "rotated")
+ d = {
+ "1 cropped_global": labels_result_global,
+ "0 original": labels_element,
+ }
+ if labels_result_rotated is not None:
+ d["2 cropped_rotated"] = labels_result_rotated
+
+ assert isinstance(labels_result_rotated, SpatialImage) or isinstance(
+ labels_result_rotated, MultiscaleSpatialImage
+ )
+ transform = labels_result_rotated.attrs["transform"]["rotated"]
+ transform_rotated_processed = transform.transform(labels_result_rotated, maintain_positioning=True)
+ transform_rotated_processed_recropped = bounding_box_query(
+ transform_rotated_processed,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="rotated",
+ )
+ d["3 cropped_rotated_processed_recropped"] = transform_rotated_processed_recropped
+ remove_transformation(labels_result_rotated, "global")
+
+ sdata = SpatialData(labels=d)
+ Interactive(sdata)
+ ##
+
+
+if __name__ == "__main__":
+ _visualize_crop_affine_labels_2d()

examples/dev-examples/README.md

@@ -0,0 +1,3 @@
+# dev-examples
+
+The examples are useful when developing parts of the codebase. They are not intended to be used as a reference for how to use the library. For that, please refer to the documentation, the example notebooks and the examples in the `example/` directory.

examples/dev-examples/spatial_query_and_rasterization.py

@@ -0,0 +1,115 @@
+import numpy as np
+from multiscale_spatial_image import MultiscaleSpatialImage
+from spatial_image import SpatialImage
+
+from spatialdata import Labels2DModel
+from spatialdata._core._spatial_query import bounding_box_query
+from spatialdata._core._spatialdata_ops import (
+ get_transformation,
+ remove_transformation,
+ set_transformation,
+)
+from spatialdata._core.transformations import Affine
+
+
+def _visualize_crop_affine_labels_2d() -> None:
+ """
+ This examples show how the bounding box spatial query works for data that has been rotated.
+
+ Notes
+ -----
+ The bounding box query gives the data, from the intrinsic coordinate system, that is inside the bounding box of
+ the inverse-transformed query bounding box.
+ In this example I show this data, and I also show how to obtain the data back inside the original bounding box.
+
+ To undertand the example I suggest to run it and then:
+ 1) select the "rotated" coordinate system from napari
+ 2) disable all the layers but "0 original"
+ 3) then enable "1 cropped global", this shows the data in the extrinsic coordinate system we care ("rotated"),
+ and the bounding box we want to query
+ 4) then enable "2 cropped rotated", this show the data that has been queries (this is a bounding box of the
+ requested crop, as exaplained above)
+ 5) then enable "3 cropped rotated processed", this shows the data that we wanted to query in the first place,
+ in the target coordinate system ("rotated"). This is probaly the data you care about if for instance you want to
+ use tiles for deep learning. Note that for obtaning this answer there is also a better function (not available at
+ the time of this writing): rasterize(), which is faster and more accurate, so it should be used instead. The
+ function rasterize() transforms all the coordinates of the data into the target coordinate system, and it returns
+ only SpatialImage objects. So it has different use cases than the bounding box query.
+ 6) finally switch to the "global" coordinate_system. This is, for how we constructed the example, showing the
+ original image as it would appear its intrinsic coordinate system (since the transformation that maps the
+ original image to "global" is an identity. It then shows how the data showed at the point 5), localizes in the
+ original image.
+ """
+ ##
+ # in this test let's try some affine transformations, we could do that also for the other tests
+ image = np.random.randint(low=10, high=100, size=(100, 100))
+ # y: [5, 9], x: [0, 4] has value 1
+ image[50:, :50] = 2
+ labels_element = Labels2DModel.parse(image)
+ set_transformation(
+ labels_element,
+ Affine(
+ np.array(
+ [
+ [np.cos(np.pi / 6), np.sin(-np.pi / 6), 20],
+ [np.sin(np.pi / 6), np.cos(np.pi / 6), 0],
+ [0, 0, 1],
+ ]
+ ),
+ input_axes=("x", "y"),
+ output_axes=("x", "y"),
+ ),
+ "rotated",
+ )
+
+ # bounding box: y: [5, 9], x: [0, 4]
+ labels_result_rotated = bounding_box_query(
+ labels_element,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="rotated",
+ )
+ labels_result_global = bounding_box_query(
+ labels_element,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="global",
+ )
+ from napari_spatialdata import Interactive
+
+ from spatialdata import SpatialData
+
+ old_transformation = get_transformation(labels_result_global, "global")
+ remove_transformation(labels_result_global, "global")
+ set_transformation(labels_result_global, old_transformation, "rotated")
+ d = {
+ "1 cropped_global": labels_result_global,
+ "0 original": labels_element,
+ }
+ if labels_result_rotated is not None:
+ d["2 cropped_rotated"] = labels_result_rotated
+
+ assert isinstance(labels_result_rotated, SpatialImage) or isinstance(
+ labels_result_rotated, MultiscaleSpatialImage
+ )
+ transform = labels_result_rotated.attrs["transform"]["rotated"]
+ transform_rotated_processed = transform.transform(labels_result_rotated, maintain_positioning=True)
+ transform_rotated_processed_recropped = bounding_box_query(
+ transform_rotated_processed,
+ axes=("y", "x"),
+ min_coordinate=np.array([25, 25]),
+ max_coordinate=np.array([75, 100]),
+ target_coordinate_system="rotated",
+ )
+ d["3 cropped_rotated_processed_recropped"] = transform_rotated_processed_recropped
+ remove_transformation(labels_result_rotated, "global")
+
+ sdata = SpatialData(labels=d)
+ Interactive(sdata)
+ ##
+
+
+if __name__ == "__main__":
+ _visualize_crop_affine_labels_2d()