From 97178cae4950a9bb48ca77f833991dc289129a4c Mon Sep 17 00:00:00 2001
From: Huite Bootsma <huitebootsma@gmail.com>
Date: Thu, 11 Jul 2024 22:47:24 +0200
Subject: [PATCH 1/2] Adjustments for numpy 2.0, wip

---
 pixi.lock                            | 246 ++++++++++++++-------------
 pyproject.toml                       |   2 +-
 tests/test_regrid/test_structured.py | 229 +++++++++++++------------
 xugrid/conversion.py                 |   2 +
 xugrid/ugrid/connectivity.py         |   1 +
 xugrid/ugrid/partitioning.py         |   1 +
 xugrid/ugrid/ugrid2d.py              |   1 +
 7 files changed, 253 insertions(+), 229 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index 089381cb9..202504793 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -217,7 +217,7 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/osx-64/numpy-1.26.4-py312he3a82b2_0.conda
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@@ -20592,35 +20592,37 @@ packages:
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-  md5: cd6a2298387f558c9ea70ee73a189791
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+  url: https://conda.anaconda.org/conda-forge/win-64/numpy-2.0.0-py310h1ec8c79_0.conda
+  sha256: 89c1b388c037204555cfec9fe6977028645c19a6ad9fcca2b644eeb2b7e6eece
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   - python_abi 3.10.* *_cp310
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
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   license: BSD-3-Clause
   license_family: BSD
   purls:
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-  size: 6491938
-  timestamp: 1707226191321
+  size: 6322865
+  timestamp: 1718615949009
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+  version: 2.0.0
+  build: py310h515e003_0
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-  md5: 6593de64c935768b6bad3e19b3e978be
+  url: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.0.0-py310h515e003_0.conda
+  sha256: a6a0b48d9a2466d763290f525323561907d8702826833157738606fc8b96f5aa
+  md5: baab5ef1064a13d3567d90133b799a03
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@@ -20635,17 +20637,18 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=conda-forge-mapping
-  size: 7009070
-  timestamp: 1707225917496
+  size: 7777664
+  timestamp: 1718615587959
 - kind: conda
   name: numpy
-  version: 1.26.4
-  build: py310hd45542a_0
+  version: 2.0.0
+  build: py310h52bbd9b_0
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-  md5: 267ee89a3a0b8c8fa838a2353f9ea0c0
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.0.0-py310h52bbd9b_0.conda
+  sha256: 57901e323c451a947c6f97086a436a440f04ec70cbe30c0b62712f135f45831b
+  md5: c185aced815798d1cc0f74fc355cdfaa
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+  - __osx >=11.0
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
   - libcxx >=16
@@ -20659,91 +20662,91 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=conda-forge-mapping
-  size: 5475744
-  timestamp: 1707226187124
+  size: 5768181
+  timestamp: 1718616123469
 - kind: conda
   name: numpy
-  version: 1.26.4
-  build: py310hf667824_0
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/numpy-1.26.4-py310hf667824_0.conda
-  sha256: 20ca447a8f840c01961f2bdf0847fc7b7785a62968e867d7aa4ca8a66d70f9ad
-  md5: 93e881c391880df90e74e43a4b67c16d
+  version: 2.0.0
+  build: py310he367959_0
+  subdir: osx-64
+  url: https://conda.anaconda.org/conda-forge/osx-64/numpy-2.0.0-py310he367959_0.conda
+  sha256: 1fddb0f5111e081857600db24abce8aae2cd7706cffc10d1b7719c370f349976
+  md5: 945d173f9de02dbfe841303195e4d9f2
   depends:
+  - __osx >=10.13
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
+  - libcxx >=16
   - liblapack >=3.9.0,<4.0a0
   - python >=3.10,<3.11.0a0
   - python_abi 3.10.* *_cp310
-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
   constrains:
   - numpy-base <0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=conda-forge-mapping
-  size: 5977469
-  timestamp: 1707226445438
+  size: 6882915
+  timestamp: 1718615707085
 - kind: conda
   name: numpy
-  version: 1.26.4
-  build: py311h0b4df5a_0
-  subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/numpy-1.26.4-py311h0b4df5a_0.conda
-  sha256: 14116e72107de3089cc58119a5ce5905c22abf9a715c9fe41f8ac14db0992326
-  md5: 7b240edd44fd7a0991aa409b07cee776
+  version: 2.0.0
+  build: py311h1461c94_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.0.0-py311h1461c94_0.conda
+  sha256: f5c8070a623a216f999aec9b60b181f0624b7b074cc08189bdb4da6376c01a5d
+  md5: 4998996a22ef05d2f486216075a3037f
   depends:
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
+  - libgcc-ng >=12
   - liblapack >=3.9.0,<4.0a0
+  - libstdcxx-ng >=12
   - python >=3.11,<3.12.0a0
   - python_abi 3.11.* *_cp311
-  - ucrt >=10.0.20348.0
-  - vc >=14.2,<15
-  - vc14_runtime >=14.29.30139
   constrains:
   - numpy-base <0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=conda-forge-mapping
-  size: 7104093
-  timestamp: 1707226459646
+  size: 8955242
+  timestamp: 1718615418098
 - kind: conda
   name: numpy
-  version: 1.26.4
-  build: py311h64a7726_0
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py311h64a7726_0.conda
-  sha256: 3f4365e11b28e244c95ba8579942b0802761ba7bb31c026f50d1a9ea9c728149
-  md5: a502d7aad449a1206efb366d6a12c52d
+  version: 2.0.0
+  build: py311h35ffc71_0
+  subdir: win-64
+  url: https://conda.anaconda.org/conda-forge/win-64/numpy-2.0.0-py311h35ffc71_0.conda
+  sha256: 5906dfbddd7ce315ceaebf56140e00ec12f981e9ccebdfe52a31826a9adf12f2
+  md5: 21ec404be139a6b20fde4f73c6ec484c
   depends:
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
-  - libgcc-ng >=12
   - liblapack >=3.9.0,<4.0a0
-  - libstdcxx-ng >=12
   - python >=3.11,<3.12.0a0
   - python_abi 3.11.* *_cp311
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
   constrains:
   - numpy-base <0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=conda-forge-mapping
-  size: 8065890
-  timestamp: 1707225944355
+  size: 7584309
+  timestamp: 1718616050139
 - kind: conda
   name: numpy
-  version: 1.26.4
-  build: py311h7125741_0
+  version: 2.0.0
+  build: py311h4268184_0
   subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-1.26.4-py311h7125741_0.conda
-  sha256: 160a52a01fea44fe9753a2ed22cf13d7b55c8a89ea0b8738546fdbf4795d6514
-  md5: 3160b93669a0def35a7a8158ebb33816
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.0.0-py311h4268184_0.conda
+  sha256: 078b4b7acab19b7314f7dae436805bcf1c231faedde9c393153234a2bcabf9e4
+  md5: 5c316e8847d997ad1b271be52ee06189
   depends:
+  - __osx >=11.0
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
   - libcxx >=16
@@ -20757,17 +20760,18 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=conda-forge-mapping
-  size: 6652352
-  timestamp: 1707226297967
+  size: 6878925
+  timestamp: 1718615710207
 - kind: conda
   name: numpy
-  version: 1.26.4
-  build: py311hc43a94b_0
+  version: 2.0.0
+  build: py311hc11d9cb_0
   subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/numpy-1.26.4-py311hc43a94b_0.conda
-  sha256: dc9628197125ee1d02b2e7a859a769d26291d747ed79337309b8a9e67a8b8e00
-  md5: bb02b8801d17265160e466cf8bbf28da
+  url: https://conda.anaconda.org/conda-forge/osx-64/numpy-2.0.0-py311hc11d9cb_0.conda
+  sha256: 0e82d37aa474ce84302775f02d31fe0c762844ec472f5ed8f0db8190d5fd1db9
+  md5: f3e2a534964152fca3ba80bea594f673
   depends:
+  - __osx >=10.13
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
   - libcxx >=16
@@ -20780,23 +20784,23 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=conda-forge-mapping
-  size: 7504319
-  timestamp: 1707226235372
+  size: 8016515
+  timestamp: 1718615633162
 - kind: conda
   name: numpy
-  version: 1.26.4
-  build: py312h8442bc7_0
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-1.26.4-py312h8442bc7_0.conda
-  sha256: c8841d6d6f61fd70ca80682efbab6bdb8606dc77c68d8acabfbd7c222054f518
-  md5: d83fc83d589e2625a3451c9a7e21047c
+  version: 2.0.0
+  build: py312h22e1c76_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.0.0-py312h22e1c76_0.conda
+  sha256: e5fc4a1053c8f02db78d4a50733d6c84d04e3c781749ae7478876ecdcd8c87ca
+  md5: 7956c7d65f87aecaba720af6088e72c3
   depends:
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
-  - libcxx >=16
+  - libgcc-ng >=12
   - liblapack >=3.9.0,<4.0a0
+  - libstdcxx-ng >=12
   - python >=3.12,<3.13.0a0
-  - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
   constrains:
   - numpy-base <0a0
@@ -20804,16 +20808,16 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=conda-forge-mapping
-  size: 6073136
-  timestamp: 1707226249608
+  size: 8352992
+  timestamp: 1718615528478
 - kind: conda
   name: numpy
-  version: 1.26.4
-  build: py312h8753938_0
+  version: 2.0.0
+  build: py312h49bc9c5_0
   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/numpy-1.26.4-py312h8753938_0.conda
-  sha256: 73570817a5109d396b4ebbe5124a89525959269fd33fa33fd413700289fbe0ef
-  md5: f9ac74c3b07c396014434aca1e58d362
+  url: https://conda.anaconda.org/conda-forge/win-64/numpy-2.0.0-py312h49bc9c5_0.conda
+  sha256: a2a4f9c90076d70a9e40aebe7ea323e67803af1cf4e6fff759af330726b2644d
+  md5: 8f8c23e8087dbb05828be5abbeac347d
   depends:
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
@@ -20829,17 +20833,18 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=conda-forge-mapping
-  size: 6495445
-  timestamp: 1707226412944
+  size: 6988383
+  timestamp: 1718615911404
 - kind: conda
   name: numpy
-  version: 1.26.4
-  build: py312he3a82b2_0
+  version: 2.0.0
+  build: py312h8813227_0
   subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/numpy-1.26.4-py312he3a82b2_0.conda
-  sha256: 6152b73fba3e227afa4952df8753128fc9669bbaf142ee8f9972bf9df3bf8856
-  md5: 96c61a21c4276613748dba069554846b
+  url: https://conda.anaconda.org/conda-forge/osx-64/numpy-2.0.0-py312h8813227_0.conda
+  sha256: c83b985945095b04c4ba4828eea4ddb0b9b5546518b39d4b91c32afbf7838cde
+  md5: 814dba0ed2de85d89e09a5b811024a86
   depends:
+  - __osx >=10.13
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
   - libcxx >=16
@@ -20852,23 +20857,24 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=conda-forge-mapping
-  size: 6990646
-  timestamp: 1707226178262
+  size: 7479080
+  timestamp: 1718615564653
 - kind: conda
   name: numpy
-  version: 1.26.4
-  build: py312heda63a1_0
-  subdir: linux-64
-  url: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py312heda63a1_0.conda
-  sha256: fe3459c75cf84dcef6ef14efcc4adb0ade66038ddd27cadb894f34f4797687d8
-  md5: d8285bea2a350f63fab23bf460221f3f
+  version: 2.0.0
+  build: py312hb544834_0
+  subdir: osx-arm64
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.0.0-py312hb544834_0.conda
+  sha256: 7015b30c00e8eb6a8abd639a7683f3c57b2abd090e74cda6179ab5f5d6974575
+  md5: 2ee98af1e5c917e3e1410758ab889e7a
   depends:
+  - __osx >=11.0
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
-  - libgcc-ng >=12
+  - libcxx >=16
   - liblapack >=3.9.0,<4.0a0
-  - libstdcxx-ng >=12
   - python >=3.12,<3.13.0a0
+  - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
   constrains:
   - numpy-base <0a0
@@ -20876,8 +20882,8 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=conda-forge-mapping
-  size: 7484186
-  timestamp: 1707225809722
+  size: 6359802
+  timestamp: 1718615501795
 - kind: conda
   name: openjpeg
   version: 2.5.2
@@ -29111,7 +29117,7 @@ packages:
   name: xugrid
   version: 0.10.0
   path: .
-  sha256: 40647e8ddd1dbf3818c42353f9b788e85c1460f407abb89e4b94455b06d4500c
+  sha256: 14ef5813b6580d53e57c9b89cbca6ce1b4dba5477f8256e436b1934fb6e86355
   requires_dist:
   - numba
   - numba-celltree
diff --git a/pyproject.toml b/pyproject.toml
index 88c4d83e9..f5cf00266 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -82,7 +82,7 @@ mapbox_earcut = "*"
 matplotlib-base = "*"
 netcdf4 = "*"
 numba_celltree = "*"
-numpy = "<2.0"
+numpy = "*"
 pip = "*"
 pooch = "*"
 pre-commit = "*"
diff --git a/tests/test_regrid/test_structured.py b/tests/test_regrid/test_structured.py
index 5e3720591..b5006a771 100644
--- a/tests/test_regrid/test_structured.py
+++ b/tests/test_regrid/test_structured.py
@@ -28,6 +28,25 @@ def test_init_2d(grid_data_a_2d):
         StructuredGrid2d(1)
 
 
+def assert_expected_overlap(
+    actual_source,
+    actual_target,
+    actual_weights,
+    expected_source,
+    expected_target,
+    expected_weights,
+):
+    # Numpy 2.0 release has change sorting behavior of non-stable sorting:
+    # https://numpy.org/doc/stable/release/2.0.0-notes.html#minor-changes-in-behavior-of-sorting-functions
+    # So the comparison method must be robust to work for numpy <2.0 and >=2.0.
+    actual_mapping = np.column_stack((actual_target, actual_source))
+    expected_mapping = np.column_stack((expected_target, expected_source))
+    actual, actual_sorter = np.unique(actual_mapping, axis=0, return_index=True)
+    expected, expected_sorter = np.unique(expected_mapping, axis=0, return_index=True)
+    assert np.array_equal(actual, expected)
+    assert np.allclose(actual_weights[actual_sorter], expected_weights[expected_sorter])
+
+
 def test_overlap_1d(
     grid_data_a_1d, grid_data_b_1d, grid_data_b_flipped_1d, grid_data_e_1d
 ):
@@ -37,11 +56,12 @@ def test_overlap_1d(
     # node 1   1, 2     25 m
     # node 2   2, 3     25 m
     # --------
-    source, target, weights = grid_data_a_1d.overlap(grid_data_b_1d, relative=False)
-    sorter = np.argsort(source)
-    assert np.array_equal(source[sorter], np.array([0, 0, 1, 1, 2, 2]))
-    assert np.array_equal(target[sorter], np.array([0, 1, 1, 2, 2, 3]))
-    assert np.array_equal(weights[sorter], np.array([25, 25, 25, 25, 25, 25]))
+    assert_expected_overlap(
+        *grid_data_a_1d.overlap(grid_data_b_1d, relative=False),
+        np.array([0, 0, 1, 1, 2, 2]),
+        np.array([0, 1, 1, 2, 2, 3]),
+        np.array([25, 25, 25, 25, 25, 25]),
+    )
 
     # flipped axis (y-axis)
     # --------
@@ -50,13 +70,12 @@ def test_overlap_1d(
     # 1        1, 2     25 m
     # 2        2, 3     25 m
     # --------
-    source, target, weights = grid_data_a_1d.overlap(
-        grid_data_b_flipped_1d, relative=False
+    assert_expected_overlap(
+        *grid_data_a_1d.overlap(grid_data_b_flipped_1d, relative=False),
+        np.array([0, 0, 1, 1, 2, 2]),
+        np.array([2, 3, 1, 2, 0, 1]),
+        np.array([25, 25, 25, 25, 25, 25]),
     )
-    sorter = np.argsort(source)
-    assert np.array_equal(source[sorter], np.array([0, 0, 1, 1, 2, 2]))
-    assert np.array_equal(target[sorter], np.array([2, 3, 1, 2, 0, 1]))
-    assert np.array_equal(weights[sorter], np.array([25, 25, 25, 25, 25, 25]))
 
     # non-equidistant
     # --------
@@ -64,11 +83,12 @@ def test_overlap_1d(
     # node 0   0, 1     17.5 m, 32.5 m
     # node 1   1, 2     17.5 m, 25.0 m
     # --------
-    source, target, weights = grid_data_a_1d.overlap(grid_data_e_1d, relative=False)
-    sorter = np.argsort(source)
-    assert np.array_equal(source[sorter], np.array([0, 0, 1, 1]))
-    assert np.array_equal(target[sorter], np.array([0, 1, 1, 2]))
-    assert np.array_equal(weights[sorter], np.array([17.5, 32.5, 17.5, 25.0]))
+    assert_expected_overlap(
+        *grid_data_a_1d.overlap(grid_data_e_1d, relative=False),
+        np.array([0, 0, 1, 1]),
+        np.array([0, 1, 1, 2]),
+        np.array([17.5, 32.5, 17.5, 25.0]),
+    )
 
     # relative
     # --------
@@ -76,12 +96,11 @@ def test_overlap_1d(
     # node 0   0, 1     17.5 m, 32.5 m
     # node 1   1, 2     17.5 m, 25.0 m
     # --------
-    source, target, weights = grid_data_a_1d.overlap(grid_data_e_1d, relative=True)
-    sorter = np.argsort(source)
-    assert np.array_equal(source[sorter], np.array([0, 0, 1, 1]))
-    assert np.array_equal(target[sorter], np.array([0, 1, 1, 2]))
-    assert np.array_equal(
-        weights[sorter], np.array([17.5 / 50.0, 32.5 / 50.0, 17.5 / 50.0, 25.0 / 50.0])
+    assert_expected_overlap(
+        *grid_data_a_1d.overlap(grid_data_e_1d, relative=True),
+        np.array([0, 0, 1, 1]),
+        np.array([0, 1, 1, 2]),
+        np.array([17.5 / 50.0, 32.5 / 50.0, 17.5 / 50.0, 25.0 / 50.0]),
     )
 
 
@@ -98,11 +117,9 @@ def test_overlap_2d(grid_data_a_2d, grid_data_b_2d):
     # 7        9,  10, 13, 14     625 m
     # 8        10, 11, 14, 15     625 m
     # --------
-    source, target, weights = grid_data_a_2d.overlap(grid_data_b_2d, relative=False)
-    sorter = np.argsort(source)
-    assert np.array_equal(
-        source[sorter],
-        np.array(
+    assert_expected_overlap(
+        *grid_data_a_2d.overlap(grid_data_b_2d, relative=False),
+        expected_source=np.array(
             [
                 0,
                 0,
@@ -142,10 +159,7 @@ def test_overlap_2d(grid_data_a_2d, grid_data_b_2d):
                 8,
             ]
         ),
-    )
-    assert np.array_equal(
-        target[sorter],
-        np.array(
+        expected_target=np.array(
             [
                 0,
                 4,
@@ -185,8 +199,8 @@ def test_overlap_2d(grid_data_a_2d, grid_data_b_2d):
                 15,
             ]
         ),
+        expected_weights=np.full(36, 625.0),
     )
-    assert np.array_equal(weights[sorter], np.array([625] * source.size))
 
 
 def test_locate_centroids_1d(
@@ -197,11 +211,12 @@ def test_locate_centroids_1d(
     # 0        1       1
     # 1        2       1
     # --------
-    source, target, weights = grid_data_a_1d.locate_centroids(grid_data_b_1d)
-    sorter = np.argsort(source)
-    assert np.array_equal(source[sorter], np.array([0, 1]))
-    assert np.array_equal(target[sorter], np.array([1, 2]))
-    assert np.allclose(weights[sorter], np.ones(2))
+    assert_expected_overlap(
+        *grid_data_a_1d.locate_centroids(grid_data_b_1d),
+        np.array([0, 1]),
+        np.array([1, 2]),
+        np.ones(2),
+    )
 
     # flipped axis (y-axis)
     # --------
@@ -209,11 +224,12 @@ def test_locate_centroids_1d(
     # 0        1       1
     # 1        2       1
     # --------
-    source, target, weights = grid_data_a_1d.locate_centroids(grid_data_b_flipped_1d)
-    sorter = np.argsort(source)
-    assert np.array_equal(source[sorter], np.array([0, 1]))
-    assert np.array_equal(target[sorter], np.array([2, 1]))
-    assert np.allclose(weights[sorter], np.ones(2))
+    assert_expected_overlap(
+        *grid_data_a_1d.locate_centroids(grid_data_b_flipped_1d),
+        np.array([0, 1]),
+        np.array([2, 1]),
+        np.ones(2),
+    )
 
     # non-equidistant
     # --------
@@ -221,11 +237,12 @@ def test_locate_centroids_1d(
     # 0        0, 1    1, 1
     # 1        2       1
     # --------
-    source, target, weights = grid_data_a_1d.locate_centroids(grid_data_e_1d)
-    sorter = np.argsort(source)
-    assert np.array_equal(source[sorter], np.array([0, 0, 1]))
-    assert np.array_equal(target[sorter], np.array([0, 1, 2]))
-    assert np.allclose(weights[sorter], np.ones(3))
+    assert_expected_overlap(
+        *grid_data_a_1d.locate_centroids(grid_data_e_1d),
+        np.array([0, 0, 1]),
+        np.array([0, 1, 2]),
+        np.ones(3),
+    )
 
 
 def test_locate_centroids_2d(grid_data_a_2d, grid_data_b_2d):
@@ -236,11 +253,12 @@ def test_locate_centroids_2d(grid_data_a_2d, grid_data_b_2d):
     # 3        9       1
     # 4        10      1
     # --------
-    source, target, weights = grid_data_a_2d.locate_centroids(grid_data_b_2d)
-    sorter = np.argsort(source)
-    assert np.array_equal(source[sorter], np.array([0, 1, 3, 4]))
-    assert np.array_equal(target[sorter], np.array([5, 6, 9, 10]))
-    assert np.allclose(weights[sorter], np.ones(4))
+    assert_expected_overlap(
+        *grid_data_a_2d.locate_centroids(grid_data_b_2d),
+        np.array([0, 1, 3, 4]),
+        np.array([5, 6, 9, 10]),
+        np.ones(4),
+    )
 
 
 def test_linear_weights_1d(
@@ -258,11 +276,12 @@ def test_linear_weights_1d(
     # 1   ->   2       50%
     # 2   ->   2       50%
     # --------
-    source, target, weights = grid_data_a_1d.linear_weights(grid_data_b_1d)
-    sorter = np.argsort(target)
-    assert np.array_equal(source[sorter], np.array([0, 1, 1, 2]))
-    assert np.array_equal(target[sorter], np.array([1, 1, 2, 2]))
-    assert np.allclose(weights[sorter], np.array([0.5, 0.5, 0.5, 0.5]))
+    assert_expected_overlap(
+        *grid_data_a_1d.linear_weights(grid_data_b_1d),
+        np.array([0, 1, 1, 2]),
+        np.array([1, 1, 2, 2]),
+        np.array([0.5, 0.5, 0.5, 0.5]),
+    )
 
     # flipped axis (y-axis)
     # --------
@@ -272,11 +291,12 @@ def test_linear_weights_1d(
     # 1   ->   1       50%
     # 2   ->   1       50%
     # --------
-    source, target, weights = grid_data_a_1d.linear_weights(grid_data_b_flipped_1d)
-    sorter = np.argsort(target)
-    assert np.array_equal(source[sorter], np.array([1, 2, 0, 1]))
-    assert np.array_equal(target[sorter], np.array([1, 1, 2, 2]))
-    assert np.allclose(weights[sorter], np.array([0.5, 0.5, 0.5, 0.5]))
+    assert_expected_overlap(
+        *grid_data_a_1d.linear_weights(grid_data_b_flipped_1d),
+        np.array([2, 1, 1, 0]),
+        np.array([1, 1, 2, 2]),
+        np.array([0.5, 0.5, 0.5, 0.5]),
+    )
 
     # --------
     # source   target  weight
@@ -285,11 +305,12 @@ def test_linear_weights_1d(
     # 2        2       80%
     # 1        2       20%
     # --------
-    source, target, weights = grid_data_a_1d.linear_weights(grid_data_c_1d)
-    sorter = np.argsort(target)
-    assert np.array_equal(source[sorter], np.array([1, 0, 2, 1]))
-    assert np.array_equal(target[sorter], np.array([1, 1, 2, 2]))
-    assert np.allclose(weights[sorter], np.array([0.8, 0.2, 0.8, 0.2]))
+    assert_expected_overlap(
+        *grid_data_a_1d.linear_weights(grid_data_c_1d),
+        np.array([1, 0, 2, 1]),
+        np.array([1, 1, 2, 2]),
+        np.array([0.8, 0.2, 0.8, 0.2]),
+    )
 
     # --------
     # source   target  weight
@@ -300,11 +321,12 @@ def test_linear_weights_1d(
     # 1        3       90%
     # 2        3       10%
     # --------
-    source, target, weights = grid_data_a_1d.linear_weights(grid_data_d_1d)
-    sorter = np.argsort(target)
-    assert np.array_equal(source[sorter], np.array([0, 1, 1, 0, 1, 2]))
-    assert np.array_equal(target[sorter], np.array([1, 1, 2, 2, 3, 3]))
-    assert np.allclose(weights[sorter], np.array([0.9, 0.1, 0.6, 0.4, 0.9, 0.1]))
+    assert_expected_overlap(
+        *grid_data_a_1d.linear_weights(grid_data_d_1d),
+        np.array([0, 1, 1, 0, 1, 2]),
+        np.array([1, 1, 2, 2, 3, 3]),
+        np.array([0.9, 0.1, 0.6, 0.4, 0.9, 0.1]),
+    )
 
     # non-equidistant
     # --------
@@ -314,11 +336,12 @@ def test_linear_weights_1d(
     # 1        2       90%
     # 2        2       10%
     # --------
-    source, target, weights = grid_data_a_1d.linear_weights(grid_data_e_1d)
-    sorter = np.argsort(target)
-    assert np.array_equal(source[sorter], np.array([0, 1, 1, 2]))
-    assert np.array_equal(target[sorter], np.array([1, 1, 2, 2]))
-    assert np.allclose(weights[sorter], np.array([0.65, 0.35, 0.9, 0.1]))
+    assert_expected_overlap(
+        *grid_data_a_1d.linear_weights(grid_data_e_1d),
+        np.array([0, 1, 1, 2]),
+        np.array([1, 1, 2, 2]),
+        np.array([0.65, 0.35, 0.9, 0.1]),
+    )
 
     # 1-1 grid
     # --------
@@ -328,11 +351,12 @@ def test_linear_weights_1d(
     # 2        2       100%
     # 1        2       0%
     # --------
-    source, target, weights = grid_data_b_1d.linear_weights(grid_data_b_1d)
-    sorter = np.argsort(target)
-    assert np.array_equal(source[sorter], np.array([1, 0, 2, 1]))
-    assert np.array_equal(target[sorter], np.array([1, 1, 2, 2]))
-    assert np.allclose(weights[sorter], np.array([1.0, 0.0, 1.0, 0.0]))
+    assert_expected_overlap(
+        *grid_data_b_1d.linear_weights(grid_data_b_1d),
+        np.array([1, 0, 2, 1]),
+        np.array([1, 1, 2, 2]),
+        np.array([1.0, 0.0, 1.0, 0.0]),
+    )
 
 
 def test_linear_weights_2d(
@@ -345,15 +369,12 @@ def test_linear_weights_2d(
     # 9        3, 4, 6, 7  25%
     # 10       4, 5, 7, 8  25%
     # --------
-    source, target, weights = grid_data_a_2d.linear_weights(grid_data_b_2d)
-    sorter = np.argsort(target)
-    assert np.array_equal(
-        source[sorter], np.array([0, 1, 3, 4, 1, 2, 4, 5, 3, 4, 6, 7, 4, 5, 7, 8])
-    )
-    assert np.array_equal(
-        target[sorter], np.array([5, 5, 5, 5, 6, 6, 6, 6, 9, 9, 9, 9, 10, 10, 10, 10])
+    assert_expected_overlap(
+        *grid_data_a_2d.linear_weights(grid_data_b_2d),
+        np.array([3, 4, 1, 0, 5, 4, 1, 2, 6, 7, 4, 3, 8, 7, 4, 5]),
+        np.array([5, 5, 5, 5, 6, 6, 6, 6, 9, 9, 9, 9, 10, 10, 10, 10]),
+        np.array([0.25] * 16),
     )
-    assert np.allclose(weights[sorter], np.array([0.25] * 16))
 
     # --------
     # source   targets      weight
@@ -362,15 +383,12 @@ def test_linear_weights_2d(
     # 9        3, 4, 6, 7   10%	40%	10%	40%
     # 10       4, 5, 7, 8   10%	40%	10%	40%
     # --------
-    source, target, weights = grid_data_a_layered_2d.linear_weights(grid_data_c_2d)
-    sorter = np.argsort(target)
-    assert np.array_equal(
-        source[sorter], np.array([1, 0, 4, 3, 2, 1, 5, 4, 4, 3, 7, 6, 5, 4, 8, 7])
-    )
-    assert np.array_equal(
-        target[sorter], np.array([5, 5, 5, 5, 6, 6, 6, 6, 9, 9, 9, 9, 10, 10, 10, 10])
+    assert_expected_overlap(
+        *grid_data_a_layered_2d.linear_weights(grid_data_c_2d),
+        np.array([1, 0, 4, 3, 2, 1, 5, 4, 4, 3, 7, 6, 5, 4, 8, 7]),
+        np.array([5, 5, 5, 5, 6, 6, 6, 6, 9, 9, 9, 9, 10, 10, 10, 10]),
+        np.array([0.4, 0.1, 0.4, 0.1] * 4),
     )
-    assert np.allclose(weights[sorter], np.array([0.4, 0.1, 0.4, 0.1] * 4))
 
     # 1-1
     # --------
@@ -380,14 +398,9 @@ def test_linear_weights_2d(
     # 9        8, 9,12,13   0% 100% 0% 0%
     # 10       9,10,13,14   0% 100% 0% 0%
     # --------
-    source, target, weights = grid_data_b_2d.linear_weights(grid_data_b_2d)
-    sorter = np.argsort(target)
-    assert np.array_equal(
-        source[sorter], np.array([5, 4, 9, 8, 6, 5, 10, 9, 9, 8, 13, 12, 10, 9, 14, 13])
-    )
-    assert np.array_equal(
-        target[sorter], np.array([5, 5, 5, 5, 6, 6, 6, 6, 9, 9, 9, 9, 10, 10, 10, 10])
-    )
-    assert np.allclose(
-        weights[sorter], np.array([1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0])
+    assert_expected_overlap(
+        *grid_data_b_2d.linear_weights(grid_data_b_2d),
+        np.array([5, 4, 9, 8, 6, 5, 10, 9, 9, 8, 13, 12, 10, 9, 14, 13]),
+        np.array([5, 5, 5, 5, 6, 6, 6, 6, 9, 9, 9, 9, 10, 10, 10, 10]),
+        np.array([1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0]),
     )
diff --git a/xugrid/conversion.py b/xugrid/conversion.py
index 20a955e9a..465fcc46c 100644
--- a/xugrid/conversion.py
+++ b/xugrid/conversion.py
@@ -69,6 +69,7 @@ def linestrings_to_edges(edges: LineArray) -> Tuple[FloatArray, FloatArray, IntA
     segments = np.column_stack([linear_index[:-1], linear_index[1:]])
     segments = segments[np.diff(index) == 0]
     unique, inverse = np.unique(xy, return_inverse=True, axis=0)
+    inverse = inverse.ravel()
     segments = inverse[segments]
     x, y = contiguous_xy(unique)
     return x, y, segments
@@ -93,6 +94,7 @@ def polygons_to_faces(
         *shapely.get_coordinates(polygons, return_index=True)
     )
     unique, inverse = np.unique(xy, axis=0, return_inverse=True)
+    inverse = inverse.ravel()
     n = len(polygons)
     m_per_row = np.bincount(indices)
     m = m_per_row.max()
diff --git a/xugrid/ugrid/connectivity.py b/xugrid/ugrid/connectivity.py
index c16d181c7..1c1caaa6f 100644
--- a/xugrid/ugrid/connectivity.py
+++ b/xugrid/ugrid/connectivity.py
@@ -436,6 +436,7 @@ def edge_connectivity(
     edge_node_connectivity, inverse_indices = np.unique(
         ar=edge_node_connectivity, return_inverse=True, axis=0
     )
+    inverse_indices = inverse_indices.ravel()
 
     if prior is not None:  # prior edge_node_connectivity exists
         unique, index = np.unique(np.sort(prior, axis=1), axis=0, return_index=True)
diff --git a/xugrid/ugrid/partitioning.py b/xugrid/ugrid/partitioning.py
index c57ac4fbd..6b4797544 100644
--- a/xugrid/ugrid/partitioning.py
+++ b/xugrid/ugrid/partitioning.py
@@ -84,6 +84,7 @@ def merge_nodes(grids):
     _, index, inverse = np.unique(
         node_xy, axis=0, return_index=True, return_inverse=True
     )
+    inverse = inverse.ravel()
     # We want to maintain order, so create an inverse index to the new numbers.
     inverse = renumber(index)[inverse]
     # Maintain order.
diff --git a/xugrid/ugrid/ugrid2d.py b/xugrid/ugrid/ugrid2d.py
index 37c63a241..74b111074 100644
--- a/xugrid/ugrid/ugrid2d.py
+++ b/xugrid/ugrid/ugrid2d.py
@@ -1586,6 +1586,7 @@ def to_periodic(self, obj=None):
         _, node_index, inverse = np.unique(
             coordinates, return_index=True, return_inverse=True, axis=0
         )
+        inverse = inverse.ravel()
         # Create a mapping of the inverse index to the new node index.
         new_index = connectivity.renumber(node_index)
         new_faces = new_index[inverse[self.face_node_connectivity]]

From f3e23de431927dbac740b321e48c16bb4880e42c Mon Sep 17 00:00:00 2001
From: Huite Bootsma <huitebootsma@gmail.com>
Date: Fri, 12 Jul 2024 13:48:50 +0200
Subject: [PATCH 2/2] Numpy <2 for now. mapbox_earcut needs to be rebuilt.

---
 pixi.lock      | 246 ++++++++++++++++++++++++-------------------------
 pyproject.toml |   2 +-
 2 files changed, 121 insertions(+), 127 deletions(-)

diff --git a/pixi.lock b/pixi.lock
index 202504793..4d2f2c08e 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -217,7 +217,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/numba-0.60.0-py312h83e6fd3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/numba_celltree-0.1.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/numcodecs-0.12.1-py312h7070661_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.0.0-py312h22e1c76_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py312heda63a1_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.2-h488ebb8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-h4ab18f5_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.0.1-h17fec99_1.conda
@@ -542,7 +542,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-64/numba-0.60.0-py312hc3b515d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/numba_celltree-0.1.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/numcodecs-0.12.1-py312h28f332c_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/numpy-2.0.0-py312h8813227_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/numpy-1.26.4-py312he3a82b2_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/openjpeg-2.5.2-h7310d3a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/openssl-3.3.1-h87427d6_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/orc-2.0.1-hf43e91b_1.conda
@@ -856,7 +856,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numba-0.60.0-py312h41cea2d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/numba_celltree-0.1.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numcodecs-0.12.1-py312h5c2e7bc_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.0.0-py312hb544834_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-1.26.4-py312h8442bc7_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openjpeg-2.5.2-h9f1df11_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.3.1-hfb2fe0b_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.0.1-h47ade37_1.conda
@@ -1161,7 +1161,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/win-64/numba-0.60.0-py312hcccf92d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/numba_celltree-0.1.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/numcodecs-0.12.1-py312h275cf98_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.0.0-py312h49bc9c5_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-1.26.4-py312h8753938_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.2-h3d672ee_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/openssl-3.3.1-h2466b09_1.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/orc-2.0.1-h7e885a9_1.conda
@@ -1495,7 +1495,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/numba-0.60.0-py310h5dc88bb_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/numba_celltree-0.1.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/numcodecs-0.12.1-py310h76e45a6_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-2.0.0-py310h515e003_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py310hb13e2d6_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.5.2-h488ebb8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-h4ab18f5_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/orc-2.0.1-h17fec99_1.conda
@@ -1821,7 +1821,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-64/numba-0.60.0-py310h89a1501_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/numba_celltree-0.1.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/numcodecs-0.12.1-py310he0a0c5d_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/numpy-2.0.0-py310he367959_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/numpy-1.26.4-py310h4bfa8fc_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/openjpeg-2.5.2-h7310d3a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/openssl-3.3.1-h87427d6_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/orc-2.0.1-hf43e91b_1.conda
@@ -2136,7 +2136,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numba-0.60.0-py310h0628f0e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/numba_celltree-0.1.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numcodecs-0.12.1-py310hcf9f62a_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.0.0-py310h52bbd9b_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-1.26.4-py310hd45542a_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openjpeg-2.5.2-h9f1df11_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/openssl-3.3.1-hfb2fe0b_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/orc-2.0.1-h47ade37_1.conda
@@ -2442,7 +2442,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/win-64/numba-0.60.0-py310h7793332_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/numba_celltree-0.1.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/numcodecs-0.12.1-py310h9e98ed7_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-2.0.0-py310h1ec8c79_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/numpy-1.26.4-py310hf667824_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/openjpeg-2.5.2-h3d672ee_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/openssl-3.3.1-h2466b09_1.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/orc-2.0.1-h7e885a9_1.conda
@@ -2777,7 +2777,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/numba-0.60.0-py311h4bc866e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/numba_celltree-0.1.6-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/numcodecs-0.12.1-py311h4332511_1.conda
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   subdir: win-64
-  url: https://conda.anaconda.org/conda-forge/win-64/numpy-2.0.0-py312h49bc9c5_0.conda
-  sha256: a2a4f9c90076d70a9e40aebe7ea323e67803af1cf4e6fff759af330726b2644d
-  md5: 8f8c23e8087dbb05828be5abbeac347d
+  url: https://conda.anaconda.org/conda-forge/win-64/numpy-1.26.4-py312h8753938_0.conda
+  sha256: 73570817a5109d396b4ebbe5124a89525959269fd33fa33fd413700289fbe0ef
+  md5: f9ac74c3b07c396014434aca1e58d362
   depends:
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
@@ -20833,18 +20829,17 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=conda-forge-mapping
-  size: 6988383
-  timestamp: 1718615911404
+  size: 6495445
+  timestamp: 1707226412944
 - kind: conda
   name: numpy
-  version: 2.0.0
-  build: py312h8813227_0
+  version: 1.26.4
+  build: py312he3a82b2_0
   subdir: osx-64
-  url: https://conda.anaconda.org/conda-forge/osx-64/numpy-2.0.0-py312h8813227_0.conda
-  sha256: c83b985945095b04c4ba4828eea4ddb0b9b5546518b39d4b91c32afbf7838cde
-  md5: 814dba0ed2de85d89e09a5b811024a86
+  url: https://conda.anaconda.org/conda-forge/osx-64/numpy-1.26.4-py312he3a82b2_0.conda
+  sha256: 6152b73fba3e227afa4952df8753128fc9669bbaf142ee8f9972bf9df3bf8856
+  md5: 96c61a21c4276613748dba069554846b
   depends:
-  - __osx >=10.13
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
   - libcxx >=16
@@ -20857,24 +20852,23 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=conda-forge-mapping
-  size: 7479080
-  timestamp: 1718615564653
+  size: 6990646
+  timestamp: 1707226178262
 - kind: conda
   name: numpy
-  version: 2.0.0
-  build: py312hb544834_0
-  subdir: osx-arm64
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/numpy-2.0.0-py312hb544834_0.conda
-  sha256: 7015b30c00e8eb6a8abd639a7683f3c57b2abd090e74cda6179ab5f5d6974575
-  md5: 2ee98af1e5c917e3e1410758ab889e7a
+  version: 1.26.4
+  build: py312heda63a1_0
+  subdir: linux-64
+  url: https://conda.anaconda.org/conda-forge/linux-64/numpy-1.26.4-py312heda63a1_0.conda
+  sha256: fe3459c75cf84dcef6ef14efcc4adb0ade66038ddd27cadb894f34f4797687d8
+  md5: d8285bea2a350f63fab23bf460221f3f
   depends:
-  - __osx >=11.0
   - libblas >=3.9.0,<4.0a0
   - libcblas >=3.9.0,<4.0a0
-  - libcxx >=16
+  - libgcc-ng >=12
   - liblapack >=3.9.0,<4.0a0
+  - libstdcxx-ng >=12
   - python >=3.12,<3.13.0a0
-  - python >=3.12,<3.13.0a0 *_cpython
   - python_abi 3.12.* *_cp312
   constrains:
   - numpy-base <0a0
@@ -20882,8 +20876,8 @@ packages:
   license_family: BSD
   purls:
   - pkg:pypi/numpy?source=conda-forge-mapping
-  size: 6359802
-  timestamp: 1718615501795
+  size: 7484186
+  timestamp: 1707225809722
 - kind: conda
   name: openjpeg
   version: 2.5.2
@@ -29117,7 +29111,7 @@ packages:
   name: xugrid
   version: 0.10.0
   path: .
-  sha256: 14ef5813b6580d53e57c9b89cbca6ce1b4dba5477f8256e436b1934fb6e86355
+  sha256: 567497ff9ff7d3505065c130e92745e7e3a4a8c7128ea50ec7f4f5fd702888be
   requires_dist:
   - numba
   - numba-celltree
diff --git a/pyproject.toml b/pyproject.toml
index d7aa834ce..f22e54fad 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -82,7 +82,7 @@ mapbox_earcut = "*"
 matplotlib-base = "*"
 netcdf4 = "*"
 numba_celltree = "*"
-numpy = "*"
+numpy = "<2.0"
 pip = "*"
 pooch = "*"
 pre-commit = "*"