Implement `geom_equals` and binary predicates that depend only on it. #926

thomcom · 2023-02-14T15:31:02Z

Closes #838
Closes #832
Depends on http://github.com/rapidsai/cudf/pull/12749

CI failure in this PR is caused by the above bug in CUDF.

Description

This PR implements binary predicates that depend only on equality, which is implemented here using columnar comparison in python.

I'm playing with benchmarks of this feature now. On only Point geometries, we begin to outperform geopandas at 50k points, with 60x performance at 10m points.

Checklist

I am familiar with the Contributing Guidelines.
New or existing tests cover these changes.
The documentation is up to date with these changes.

… instead of __init__.

Co-authored-by: Michael Wang <isVoid@users.noreply.github.com>

…rations

Co-authored-by: Mark Harris <mharris@nvidia.com>

…spatial into feature/all_equals_operations

isVoid

I haven't looked into the implementation yet, but I have some question regarding test coverage. Geometry equivalence can be vertex order invariant. Just want to make sure these are tested too.

python/cuspatial/cuspatial/tests/binpreds/test_equals_only_binops.py

…emoved before sort/comparison.

harrism

I'm worried that unlike other features in cuSpatial, this one has very complex logic that is implemented only in Python, and not in our base C++. So supporting it in other languages, like Java, is not possible without rewriting a LOT of code.

It's also hard to reason about what and where computation is happening in CUDA.

python/cuspatial/cuspatial/core/binpreds/binpreds.py

harrism · 2023-03-14T00:42:44Z

python/cuspatial/cuspatial/core/binpreds/binpreds.py

+            # Group them by the original index, and sum the results. If the
+            # sum of points in the rhs feature is equal to the number of
+            # points found in the polygon, then the polygon contains the
+            # feature.
            df_result = (
                result.groupby("idx").sum().sort_index()
                == result.groupby("idx").count().sort_index()


Why do you need to count the number of points in a feature? Isn't it just the size of the feature, which is stored? (Constant time vs. linear time)

In this case code complexity is reduced by using the point_indices object that is an argument to postprocess. Instead of having to handle all types and combinations in each postprocess, the relevant indices for a given row of the GeoSeries, which vary across geometry types, are computed in the preprocess step and then carried through the BinaryPredicate operations.

Isn't it just the size of the feature, which is stored?

The size of each feature in the column isn't stored. But can probably be easily computed with offest[1:] - offset[:-1]

However, you meant to compute "is all points of the feature contained in the polygon". Isn't it just a logical_or to PiP result of the same feature, aka a groupby max?

Since the result of BOOL8 is actually uint8, if you do sum and there are more than 256 points in the multipoint, will it overflow?

In the preprocess step,

cuspatial/python/cuspatial/cuspatial/core/binpreds/binpreds.py

Lines 172 to 197 in b7d4d17

# RHS conditioning:

point_indices = None

# point in polygon

if contains_only_linestrings(rhs):

# condition for linestrings

geom = rhs.lines

elif contains_only_polygons(rhs) is True:

# polygon in polygon

geom = rhs.polygons

elif contains_only_multipoints(rhs) is True:

# mpoint in polygon

geom = rhs.multipoints

else:

# no conditioning is required

geom = rhs.points

xy_points = geom.xy

# Arrange into shape for calling point-in-polygon, intersection, or

# equals

point_indices = geom.point_indices()

from cuspatial.core.geoseries import GeoSeries

final_rhs = GeoSeries(

GeoColumn._from_points_xy(xy_points._column)

).points

return (lhs, final_rhs, point_indices)

, depending on rhs type the point values are extracted and flattened into a GeoSeries of points. At that time point_indices is also recorded. In order to not use point_indices here that logic would have to be reproduced in the postprocess as well.

python/cuspatial/cuspatial/core/binpreds/binpreds.py

harrism · 2023-03-14T00:59:55Z

python/cuspatial/cuspatial/core/binpreds/binpreds.py

+            {"idx": indices[: len(result)], "equals": result}
+        )
+        gb_idx = result_df.groupby("idx")
+        result = (gb_idx.sum().sort_index() == gb_idx.count().sort_index())[


What is this doing?

$a_i == b_i \leftrightarrow \forall j$ in len(a) $a_j == b_j$

…ing comparins.

isVoid

Nit picking more corner cases for geom_equal and want more tests for overlaps and crossings. But ok to punt for future if need to meet timing requirement.

python/cuspatial/cuspatial/core/binpreds/binpreds.py

isVoid · 2023-03-17T17:44:32Z

python/cuspatial/cuspatial/core/binpreds/binpreds.py

+            # Group them by the original index, and sum the results. If the
+            # sum of points in the rhs feature is equal to the number of
+            # points found in the polygon, then the polygon contains the
+            # feature.
            df_result = (
                result.groupby("idx").sum().sort_index()
                == result.groupby("idx").count().sort_index()


Isn't it just the size of the feature, which is stored?

The size of each feature in the column isn't stored. But can probably be easily computed with offest[1:] - offset[:-1]

isVoid · 2023-03-17T17:46:07Z

python/cuspatial/cuspatial/core/binpreds/binpreds.py

+            # Group them by the original index, and sum the results. If the
+            # sum of points in the rhs feature is equal to the number of
+            # points found in the polygon, then the polygon contains the
+            # feature.
            df_result = (
                result.groupby("idx").sum().sort_index()
                == result.groupby("idx").count().sort_index()


However, you meant to compute "is all points of the feature contained in the polygon". Isn't it just a logical_or to PiP result of the same feature, aka a groupby max?

isVoid · 2023-03-17T17:48:33Z

python/cuspatial/cuspatial/core/binpreds/binpreds.py

+            # Group them by the original index, and sum the results. If the
+            # sum of points in the rhs feature is equal to the number of
+            # points found in the polygon, then the polygon contains the
+            # feature.
            df_result = (
                result.groupby("idx").sum().sort_index()
                == result.groupby("idx").count().sort_index()


Since the result of BOOL8 is actually uint8, if you do sum and there are more than 256 points in the multipoint, will it overflow?

isVoid · 2023-03-17T18:01:21Z

python/cuspatial/cuspatial/core/binpreds/binpreds.py

+
+    def _sort_linestrings(self, lhs, rhs, initial):
+        """Swap first and last values of each linestring to ensure that
+        the first point is the lowest value. This is necessary to ensure


How do you define the lowest value of a point?

I can't seem to reply to all of the comments so I'll address them here.

It is possible with a pile of conditional logic to compute the group sizes via their offsets buffers, but using object.point_indices() and groupby allows me to avoid implementing those code paths. It is a "time of development vs. time of execution" problem, and the overall cost to a python user is not significant I think.

Not sure what you mean.

Summing a bool column with 255 values in cudf returns an int64.

It is lowest in the context of the endpoints, the first point needs to be lexicographically before the last point in order for comparison of all the points via sorting to work. I updated the docs.

Can you move replies to: #926 (comment) ?

And to your reply on this comment:

It is lowest in the context of the endpoints, the first point needs to be lexicographically before the last point in order for comparison of all the points via sorting to work. I updated the docs.

Can you elaborate? How does the endpoints lexicographic order affects the equality of two linestrings?

It doesn't! Thanks for calling out another mistake. I had built some test cases around strings of length 2 and ended up believing the data had to do with the start and end of the strings. What really happens is that a linestring is equal to the opposite linestring, which works with sort, but doesn't work with linestrings constructed of the same point in different orders.

The updated code now reverses each linestring within its "bucket" along the GeoArrow x/y buffer, then does a forward and a reverse comparison and ors them.

python/cuspatial/cuspatial/core/binpreds/binpreds.py

isVoid

I have a few more questions. It's definitely getting closer.

python/cuspatial/cuspatial/tests/binpreds/test_equals_only_binpreds.py

isVoid · 2023-03-17T21:53:30Z

python/cuspatial/cuspatial/core/binpreds/binpreds.py

+
+    def _sort_linestrings(self, lhs, rhs, initial):
+        """Swap first and last values of each linestring to ensure that
+        the first point is the lowest value. This is necessary to ensure


And to your reply on this comment:

It is lowest in the context of the endpoints, the first point needs to be lexicographically before the last point in order for comparison of all the points via sorting to work. I updated the docs.

Can you elaborate? How does the endpoints lexicographic order affects the equality of two linestrings?

isVoid

Last couple questions.

python/cuspatial/cuspatial/core/geoseries.py

python/cuspatial/cuspatial/tests/binpreds/test_equals_only_binpreds.py

…ires comparing against the linestring and its reverse.

thomcom · 2023-03-21T20:57:07Z

Hope this is coming along to your liking @isVoid. As for more overlaps and crosses, point->point is the only geometry type that is supported yet, so there's only tests for that.
There's a passing test, a failing test, and then 10k randomly sampled coords tested. I've added a test that validates successful comparisons at index 0, 1, and 2 on separate calls as well.

isVoid

Thanks. There's a typo in the test and 1 question for doc. Approving.

python/cuspatial/cuspatial/tests/binpreds/test_equals_only_binpreds.py

python/cuspatial/cuspatial/core/binpreds/binpreds.py

thomcom · 2023-03-23T22:09:13Z

/merge

* Implement `geom_equals` and binary predicates that depend only on it. (#926) This PR implements binary predicates that depend only on equality, which is implemented here using columnar comparison in python. I'm playing with benchmarks of this feature now. On only Point geometries, we begin to outperform geopandas at 50k points, with 60x performance at 10m points. Authors: - H. Thomson Comer (https://github.com/thomcom) Approvers: - Michael Wang (https://github.com/isVoid) URL: #926 * Add python API `pairwise_point_polygon_distance` (#988) This PR closes #756 , add `pairwise_point_polygon_distance` for python. Depend on #984 #976 Authors: - Michael Wang (https://github.com/isVoid) Approvers: - H. Thomson Comer (https://github.com/thomcom) URL: #988 * Add `dependency-file-generator` as `pre-commit` hook (#1008) Similarly to these [cudf](rapidsai/cudf#12819) and [cuml](rapidsai/cuml#5246) PRs, this PR adds an entry to `.pre-commit-config.yaml` to run the [dependency-file-generator](https://github.com/rapidsai/dependency-file-generator). It also adds an argument to the `rapidsai/shared-action-workflows/.github/workflows/checks.yaml` shared workflow to disable the `dependency-file-generator` from running in that shared workflow. This avoids having the `dependency-file-generator` run in two places since pre-commit is run in CI [here](https://github.com/rapidsai/cuspatial/blob/branch-23.04/ci/check_style.sh#L23). Authors: - AJ Schmidt (https://github.com/ajschmidt8) Approvers: - Ray Douglass (https://github.com/raydouglass) URL: #1008 * Add ZipCode Counting Notebook (#919) This PR adds a notebook that demonstrate the use of quadtree PiP with a custom `QuadTree` structure and joins dataframe. We can use this PR to discuss the possibility of adding the custom structure to the codebase. This also updates the dependency list to include notebook environment in "all" targeted conda envs. Authors: - Michael Wang (https://github.com/isVoid) Approvers: - H. Thomson Comer (https://github.com/thomcom) - Mark Harris (https://github.com/harrism) - AJ Schmidt (https://github.com/ajschmidt8) URL: #919 * Header-only `quadtree_point_in_polygon` (#979) Closes #985 Also contains cleanup of docs for other spatial join functions, correct ordering of stream and MR parameters, and adds missing C++17 property from tests cmake configuration. Authors: - Mark Harris (https://github.com/harrism) Approvers: - Michael Wang (https://github.com/isVoid) - Paul Taylor (https://github.com/trxcllnt) URL: #979 * Reduce gtest times (#1018) Fixes #1017. Reduces C++ gtest total time (on my PC) from 47.9 seconds to 20.08 seconds. Several tests were running large datasets and combinations of size parameters that would be better to run as benchmarks rather than gtests. Reducing these by a factor of 10-100 saves a lot of development time and still exercises the code. In the case of `HausdorffTest/1.10000Spaces10Points (4850 ms)`, reducing it to 1000 spaces, 10 points reduced the time by nearly 100x, likely because it's $O(N^2)$. I modified any test that used close to 1s or more total time, since most column-API tests use under that, and most header-only tests use under 0.2s. | Test | Time Before (s) | Time After (s) | Speedup | |---|---|---|---| | DERIVE_TRAJECTORIES_TEST_EXP | 14.49 | 0.27 | 53.7x | | HAUSDORFF_TEST_EXP | 9.21 | 0.26 | 35.4x | | UTILITY_TEST | 1.86 | 0.30 | 6.2x | | POINT_BOUNDING_BOXES_TEST_EXP | 1.35 | 0.15 | 9x | | TRAJECTORY_DISTANCES_AND_SPEEDS_TEST_EXP | 0.80 | 0.13 | 6.2x | | TOTAL | 47.9 | 20.08 | 2.4x | Before: ``` (rapids) coder ➜ ~/cuspatial/cpp/build/release $ ninja test [0/1] Running tests... Test project /home/coder/cuspatial/cpp/build/release Start 1: SINUSOIDAL_PROJECTION_TEST 1/45 Test #1: SINUSOIDAL_PROJECTION_TEST ................. Passed 0.81 sec Start 2: HAVERSINE_TEST 2/45 Test #2: HAVERSINE_TEST ............................. Passed 0.77 sec Start 3: HAUSDORFF_TEST 3/45 Test #3: HAUSDORFF_TEST ............................. Passed 0.75 sec Start 4: JOIN_POINT_TO_LINESTRING_SMALL_TEST 4/45 Test #4: JOIN_POINT_TO_LINESTRING_SMALL_TEST ........ Passed 0.73 sec Start 5: JOIN_POINT_IN_POLYGON_TEST 5/45 Test #5: JOIN_POINT_IN_POLYGON_TEST ................. Passed 0.79 sec Start 6: POINT_IN_POLYGON_TEST 6/45 Test #6: POINT_IN_POLYGON_TEST ...................... Passed 0.80 sec Start 7: PAIRWISE_POINT_IN_POLYGON_TEST 7/45 Test #7: PAIRWISE_POINT_IN_POLYGON_TEST ............. Passed 0.76 sec Start 8: POINT_QUADTREE_TEST 8/45 Test #8: POINT_QUADTREE_TEST ........................ Passed 0.76 sec Start 9: LINESTRING_BOUNDING_BOXES_TEST 9/45 Test #9: LINESTRING_BOUNDING_BOXES_TEST ............. Passed 0.76 sec Start 10: POLYGON_BOUNDING_BOXES_TEST 10/45 Test #10: POLYGON_BOUNDING_BOXES_TEST ................ Passed 0.80 sec Start 11: POINT_DISTANCE_TEST 11/45 Test #11: POINT_DISTANCE_TEST ........................ Passed 0.79 sec Start 12: POINT_LINESTRING_DISTANCE_TEST 12/45 Test #12: POINT_LINESTRING_DISTANCE_TEST ............. Passed 0.78 sec Start 13: LINESTRING_DISTANCE_TEST 13/45 Test #13: LINESTRING_DISTANCE_TEST ................... Passed 0.78 sec Start 14: POINT_POLYGON_DISTANCE_TEST 14/45 Test #14: POINT_POLYGON_DISTANCE_TEST ................ Passed 0.76 sec Start 15: LINESTRING_INTERSECTION_TEST 15/45 Test #15: LINESTRING_INTERSECTION_TEST ............... Passed 0.83 sec Start 16: POINT_LINESTRING_NEAREST_POINT_TEST 16/45 Test #16: POINT_LINESTRING_NEAREST_POINT_TEST ........ Passed 0.77 sec Start 17: QUADTREE_POLYGON_FILTERING_TEST 17/45 Test #17: QUADTREE_POLYGON_FILTERING_TEST ............ Passed 0.79 sec Start 18: QUADTREE_LINESTRING_FILTERING_TEST 18/45 Test #18: QUADTREE_LINESTRING_FILTERING_TEST ......... Passed 0.76 sec Start 19: TRAJECTORY_DISTANCES_AND_SPEEDS_TEST 19/45 Test #19: TRAJECTORY_DISTANCES_AND_SPEEDS_TEST ....... Passed 0.79 sec Start 20: DERIVE_TRAJECTORIES_TEST 20/45 Test #20: DERIVE_TRAJECTORIES_TEST ................... Passed 0.76 sec Start 21: TRAJECTORY_BOUNDING_BOXES_TEST 21/45 Test #21: TRAJECTORY_BOUNDING_BOXES_TEST ............. Passed 0.75 sec Start 22: SPATIAL_WINDOW_POINT_TEST 22/45 Test #22: SPATIAL_WINDOW_POINT_TEST .................. Passed 0.75 sec Start 23: UTILITY_TEST 23/45 Test #23: UTILITY_TEST ............................... Passed 1.86 sec Start 24: HAVERSINE_TEST_EXP 24/45 Test #24: HAVERSINE_TEST_EXP ......................... Passed 0.14 sec Start 25: POINT_DISTANCE_TEST_EXP 25/45 Test #25: POINT_DISTANCE_TEST_EXP .................... Passed 0.11 sec Start 26: POINT_LINESTRING_DISTANCE_TEST_EXP 26/45 Test #26: POINT_LINESTRING_DISTANCE_TEST_EXP ......... Passed 0.11 sec Start 27: POINT_POLYGON_DISTANCE_TEST_EXP 27/45 Test #27: POINT_POLYGON_DISTANCE_TEST_EXP ............ Passed 0.13 sec Start 28: HAUSDORFF_TEST_EXP 28/45 Test #28: HAUSDORFF_TEST_EXP ......................... Passed 9.21 sec Start 29: LINESTRING_DISTANCE_TEST_EXP 29/45 Test #29: LINESTRING_DISTANCE_TEST_EXP ............... Passed 0.17 sec Start 30: LINESTRING_INTERSECTION_TEST_EXP 30/45 Test #30: LINESTRING_INTERSECTION_TEST_EXP ........... Passed 0.19 sec Start 31: POINT_LINESTRING_NEAREST_POINT_TEST_EXP 31/45 Test #31: POINT_LINESTRING_NEAREST_POINT_TEST_EXP .... Passed 0.12 sec Start 32: SINUSOIDAL_PROJECTION_TEST_EXP 32/45 Test #32: SINUSOIDAL_PROJECTION_TEST_EXP ............. Passed 0.12 sec Start 33: POINTS_IN_RANGE_TEST_EXP 33/45 Test #33: POINTS_IN_RANGE_TEST_EXP ................... Passed 0.11 sec Start 34: POINT_IN_POLYGON_TEST_EXP 34/45 Test #34: POINT_IN_POLYGON_TEST_EXP .................. Passed 0.12 sec Start 35: PAIRWISE_POINT_IN_POLYGON_TEST_EXP 35/45 Test #35: PAIRWISE_POINT_IN_POLYGON_TEST_EXP ......... Passed 0.11 sec Start 36: DERIVE_TRAJECTORIES_TEST_EXP 36/45 Test #36: DERIVE_TRAJECTORIES_TEST_EXP ............... Passed 14.49 sec Start 37: POINT_BOUNDING_BOXES_TEST_EXP 37/45 Test #37: POINT_BOUNDING_BOXES_TEST_EXP .............. Passed 1.35 sec Start 38: POLYGON_BOUNDING_BOXES_TEST_EXP 38/45 Test #38: POLYGON_BOUNDING_BOXES_TEST_EXP ............ Passed 0.11 sec Start 39: LINESTRING_BOUNDING_BOXES_TEST_EXP 39/45 Test #39: LINESTRING_BOUNDING_BOXES_TEST_EXP ......... Passed 0.11 sec Start 40: TRAJECTORY_DISTANCES_AND_SPEEDS_TEST_EXP 40/45 Test #40: TRAJECTORY_DISTANCES_AND_SPEEDS_TEST_EXP ... Passed 0.80 sec Start 41: POINT_QUADTREE_TEST_EXP 41/45 Test #41: POINT_QUADTREE_TEST_EXP .................... Passed 0.12 sec Start 42: OPERATOR_TEST_EXP 42/45 Test #42: OPERATOR_TEST_EXP .......................... Passed 0.14 sec Start 43: FIND_TEST_EXP 43/45 Test #43: FIND_TEST_EXP .............................. Passed 0.13 sec Start 44: JOIN_POINT_IN_POLYGON_SMALL_TEST_EXP 44/45 Test #44: JOIN_POINT_IN_POLYGON_SMALL_TEST_EXP ....... Passed 0.11 sec Start 45: JOIN_POINT_IN_POLYGON_LARGE_TEST_EXP 45/45 Test #45: JOIN_POINT_IN_POLYGON_LARGE_TEST_EXP ....... Passed 0.13 sec 100% tests passed, 0 tests failed out of 45 Total Test time (real) = 47.07 sec ``` After: ``` (rapids) coder ➜ ~/cuspatial/cpp/build/release $ ninja test [0/1] Running tests... Test project /home/coder/cuspatial/cpp/build/release Start 1: SINUSOIDAL_PROJECTION_TEST 1/45 Test #1: SINUSOIDAL_PROJECTION_TEST ................. Passed 0.78 sec Start 2: HAVERSINE_TEST 2/45 Test #2: HAVERSINE_TEST ............................. Passed 0.75 sec Start 3: HAUSDORFF_TEST 3/45 Test #3: HAUSDORFF_TEST ............................. Passed 0.74 sec Start 4: JOIN_POINT_TO_LINESTRING_SMALL_TEST 4/45 Test #4: JOIN_POINT_TO_LINESTRING_SMALL_TEST ........ Passed 0.77 sec Start 5: JOIN_POINT_IN_POLYGON_TEST 5/45 Test #5: JOIN_POINT_IN_POLYGON_TEST ................. Passed 0.76 sec Start 6: POINT_IN_POLYGON_TEST 6/45 Test #6: POINT_IN_POLYGON_TEST ...................... Passed 0.78 sec Start 7: PAIRWISE_POINT_IN_POLYGON_TEST 7/45 Test #7: PAIRWISE_POINT_IN_POLYGON_TEST ............. Passed 0.74 sec Start 8: POINT_QUADTREE_TEST 8/45 Test #8: POINT_QUADTREE_TEST ........................ Passed 0.75 sec Start 9: LINESTRING_BOUNDING_BOXES_TEST 9/45 Test #9: LINESTRING_BOUNDING_BOXES_TEST ............. Passed 0.75 sec Start 10: POLYGON_BOUNDING_BOXES_TEST 10/45 Test #10: POLYGON_BOUNDING_BOXES_TEST ................ Passed 0.73 sec Start 11: POINT_DISTANCE_TEST 11/45 Test #11: POINT_DISTANCE_TEST ........................ Passed 0.73 sec Start 12: POINT_LINESTRING_DISTANCE_TEST 12/45 Test #12: POINT_LINESTRING_DISTANCE_TEST ............. Passed 0.74 sec Start 13: LINESTRING_DISTANCE_TEST 13/45 Test #13: LINESTRING_DISTANCE_TEST ................... Passed 0.76 sec Start 14: POINT_POLYGON_DISTANCE_TEST 14/45 Test #14: POINT_POLYGON_DISTANCE_TEST ................ Passed 0.76 sec Start 15: LINESTRING_INTERSECTION_TEST 15/45 Test #15: LINESTRING_INTERSECTION_TEST ............... Passed 0.78 sec Start 16: POINT_LINESTRING_NEAREST_POINT_TEST 16/45 Test #16: POINT_LINESTRING_NEAREST_POINT_TEST ........ Passed 0.77 sec Start 17: QUADTREE_POLYGON_FILTERING_TEST 17/45 Test #17: QUADTREE_POLYGON_FILTERING_TEST ............ Passed 0.75 sec Start 18: QUADTREE_LINESTRING_FILTERING_TEST 18/45 Test #18: QUADTREE_LINESTRING_FILTERING_TEST ......... Passed 0.77 sec Start 19: TRAJECTORY_DISTANCES_AND_SPEEDS_TEST 19/45 Test #19: TRAJECTORY_DISTANCES_AND_SPEEDS_TEST ....... Passed 0.74 sec Start 20: DERIVE_TRAJECTORIES_TEST 20/45 Test #20: DERIVE_TRAJECTORIES_TEST ................... Passed 0.75 sec Start 21: TRAJECTORY_BOUNDING_BOXES_TEST 21/45 Test #21: TRAJECTORY_BOUNDING_BOXES_TEST ............. Passed 0.74 sec Start 22: SPATIAL_WINDOW_POINT_TEST 22/45 Test #22: SPATIAL_WINDOW_POINT_TEST .................. Passed 0.75 sec Start 23: UTILITY_TEST 23/45 Test #23: UTILITY_TEST ............................... Passed 0.30 sec Start 24: HAVERSINE_TEST_EXP 24/45 Test #24: HAVERSINE_TEST_EXP ......................... Passed 0.12 sec Start 25: POINT_DISTANCE_TEST_EXP 25/45 Test #25: POINT_DISTANCE_TEST_EXP .................... Passed 0.12 sec Start 26: POINT_LINESTRING_DISTANCE_TEST_EXP 26/45 Test #26: POINT_LINESTRING_DISTANCE_TEST_EXP ......... Passed 0.12 sec Start 27: POINT_POLYGON_DISTANCE_TEST_EXP 27/45 Test #27: POINT_POLYGON_DISTANCE_TEST_EXP ............ Passed 0.13 sec Start 28: HAUSDORFF_TEST_EXP 28/45 Test #28: HAUSDORFF_TEST_EXP ......................... Passed 0.26 sec Start 29: LINESTRING_DISTANCE_TEST_EXP 29/45 Test #29: LINESTRING_DISTANCE_TEST_EXP ............... Passed 0.14 sec Start 30: LINESTRING_INTERSECTION_TEST_EXP 30/45 Test #30: LINESTRING_INTERSECTION_TEST_EXP ........... Passed 0.19 sec Start 31: POINT_LINESTRING_NEAREST_POINT_TEST_EXP 31/45 Test #31: POINT_LINESTRING_NEAREST_POINT_TEST_EXP .... Passed 0.11 sec Start 32: SINUSOIDAL_PROJECTION_TEST_EXP 32/45 Test #32: SINUSOIDAL_PROJECTION_TEST_EXP ............. Passed 0.11 sec Start 33: POINTS_IN_RANGE_TEST_EXP 33/45 Test #33: POINTS_IN_RANGE_TEST_EXP ................... Passed 0.13 sec Start 34: POINT_IN_POLYGON_TEST_EXP 34/45 Test #34: POINT_IN_POLYGON_TEST_EXP .................. Passed 0.11 sec Start 35: PAIRWISE_POINT_IN_POLYGON_TEST_EXP 35/45 Test #35: PAIRWISE_POINT_IN_POLYGON_TEST_EXP ......... Passed 0.14 sec Start 36: DERIVE_TRAJECTORIES_TEST_EXP 36/45 Test #36: DERIVE_TRAJECTORIES_TEST_EXP ............... Passed 0.27 sec Start 37: POINT_BOUNDING_BOXES_TEST_EXP 37/45 Test #37: POINT_BOUNDING_BOXES_TEST_EXP .............. Passed 0.15 sec Start 38: POLYGON_BOUNDING_BOXES_TEST_EXP 38/45 Test #38: POLYGON_BOUNDING_BOXES_TEST_EXP ............ Passed 0.13 sec Start 39: LINESTRING_BOUNDING_BOXES_TEST_EXP 39/45 Test #39: LINESTRING_BOUNDING_BOXES_TEST_EXP ......... Passed 0.12 sec Start 40: TRAJECTORY_DISTANCES_AND_SPEEDS_TEST_EXP 40/45 Test #40: TRAJECTORY_DISTANCES_AND_SPEEDS_TEST_EXP ... Passed 0.13 sec Start 41: POINT_QUADTREE_TEST_EXP 41/45 Test #41: POINT_QUADTREE_TEST_EXP .................... Passed 0.14 sec Start 42: OPERATOR_TEST_EXP 42/45 Test #42: OPERATOR_TEST_EXP .......................... Passed 0.14 sec Start 43: FIND_TEST_EXP 43/45 Test #43: FIND_TEST_EXP .............................. Passed 0.15 sec Start 44: JOIN_POINT_IN_POLYGON_SMALL_TEST_EXP 44/45 Test #44: JOIN_POINT_IN_POLYGON_SMALL_TEST_EXP ....... Passed 0.12 sec Start 45: JOIN_POINT_IN_POLYGON_LARGE_TEST_EXP 45/45 Test #45: JOIN_POINT_IN_POLYGON_LARGE_TEST_EXP ....... Passed 0.13 sec 100% tests passed, 0 tests failed out of 45 Total Test time (real) = 20.08 sec ``` Authors: - Mark Harris (https://github.com/harrism) Approvers: - Michael Wang (https://github.com/isVoid) - Paul Taylor (https://github.com/trxcllnt) URL: #1018 * Re-add enabled_check_generated_files:false --------- Co-authored-by: H. Thomson Comer <thomcom@gmail.com> Co-authored-by: Michael Wang <isVoid@users.noreply.github.com> Co-authored-by: AJ Schmidt <ajschmidt8@users.noreply.github.com>

thomcom and others added 30 commits December 1, 2022 14:23

Start refactoring binops to handle various ops and inputs.

67a2a6a

Abstraction working with binops that depend on pip.

3038253

Clean it up in GeoSeries.

d7a842b

Correct abstraction for pip_only tests.

3301b95

Support the seven extra pip tests.

f6688d7

Support all supportable pip calls.

77b6858

Remove unused abstraction.

c69d2e6

Clean up binops.intersects docs.

de923c5

Pass all equals tests except one.

67b0df6

Merge

b4ea5e5

Finish abstracting pip based ops.

305f937

Pass all multi tests.

707217b

Add equals only binops.

376833a

Rename binops to binpreds.

f0d0c46

Cleaning up based on review.

441a585

Cleaned up unneeded future material.

e02a669

Resolve has_same_dimension naming issue.

6444a4c

Fix postprocess docs.

29870d4

Factor out op string argument and move logic to the __call__ function…

c2cc722

… instead of __init__.

Fix bug with complete overlap

18631e3

Merge new pip only binpreds refactor into equals.

a1ab8a7

Pass all equals only tests with new factoring.

023247e

Fix contains issue.

3ddf7c4

Update python/cuspatial/cuspatial/core/binpreds/binpreds.py

82f40ec

Co-authored-by: Michael Wang <isVoid@users.noreply.github.com>

Handle review comments from @isVoid.

b71f34f

Remove more equals code.

5338a73

Merge branch 'feature/all_pip_operations' into feature/all_equals_ope…

af5ae69

…rations

Update python/cuspatial/cuspatial/core/geoseries.py

34b3fbf

Co-authored-by: Mark Harris <mharris@nvidia.com>

Handle review comments from @harrism

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Merge branch 'feature/all_equals_operations' of github.com:thomcom/cu…

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…spatial into feature/all_equals_operations

thomcom mentioned this pull request Feb 16, 2023

Fix a bug with num_keys in _scatter_by_slice rapidsai/cudf#12749

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thomcom added 4 commits March 8, 2023 15:55

Rename binops to binpreds.

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Merge branch 'branch-23.04' into feature/all_equals_operations

5416e90

Update copyright dates.

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Getting started with _ sort_polygons. Needs to have the closed edge r…

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…emoved before sort/comparison.

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Dropping first point.

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Merge branch 'branch-23.04' into feature/all_equals_operations

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Convert polygons to multipoints without their extra endpoint for sort…

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…ing comparins.

Refactor out messy _sort_linestrings.

dbcb617

cleaning up

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thomcom added 3 commits March 17, 2023 21:11

Remove polygon related equals operations.

ce3aa49

Handle a review comment about wording.

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Trying to resolve docs formatting error.

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thomcom added 2 commits March 19, 2023 06:25

I think I've fixed the docs ci issue.

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thomcom added 2 commits March 21, 2023 15:02

Refactor equals binpred to handle equality of linestrings, which requ…

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Rename dtypes.

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Extra overlaps and crosses tests

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thomcom added 2 commits March 22, 2023 14:05

Merge branch 'branch-23.04' into feature/all_equals_operations

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Implement `geom_equals` and binary predicates that depend only on it. #926

Implement `geom_equals` and binary predicates that depend only on it. #926

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thomcom commented Mar 23, 2023

	# RHS conditioning:
	point_indices = None
	# point in polygon
	if contains_only_linestrings(rhs):
	# condition for linestrings
	geom = rhs.lines
	elif contains_only_polygons(rhs) is True:
	# polygon in polygon
	geom = rhs.polygons
	elif contains_only_multipoints(rhs) is True:
	# mpoint in polygon
	geom = rhs.multipoints
	else:
	# no conditioning is required
	geom = rhs.points
	xy_points = geom.xy

	# Arrange into shape for calling point-in-polygon, intersection, or
	# equals
	point_indices = geom.point_indices()
	from cuspatial.core.geoseries import GeoSeries

	final_rhs = GeoSeries(
	GeoColumn._from_points_xy(xy_points._column)
	).points
	return (lhs, final_rhs, point_indices)

Implement geom_equals and binary predicates that depend only on it. #926

Implement geom_equals and binary predicates that depend only on it. #926

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thomcom commented Mar 23, 2023

Implement `geom_equals` and binary predicates that depend only on it. #926

Implement `geom_equals` and binary predicates that depend only on it. #926

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