From d3a4c5f572b0f716c29165fd83c2def2cb347dec Mon Sep 17 00:00:00 2001 From: Hongyu Chen Date: Tue, 13 Feb 2024 13:30:34 -0800 Subject: [PATCH 01/41] Bugfix `pt_on_gca` (#698) * Initial Draft Algorithm * Revert "Initial Draft Algorithm" This reverts commit 99573b8edfc2dc2fc329e91d395da4d907b42e4a. * Initial commit * Revert "Initial commit" This reverts commit 1ba68804f2f8f6fd062c9eeb63ef0b600549c9f0. * Initial commit * Fix complete --------- Co-authored-by: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> --- docs/internal_api/index.rst | 2 +- test/test_arcs.py | 149 ++++++++++++++++++++++++++++++++++++ test/test_helpers.py | 101 ------------------------ uxarray/grid/arcs.py | 80 ++++++++++++++++++- 4 files changed, 229 insertions(+), 103 deletions(-) create mode 100644 test/test_arcs.py diff --git a/docs/internal_api/index.rst b/docs/internal_api/index.rst index 84380a678..0e0c6e287 100644 --- a/docs/internal_api/index.rst +++ b/docs/internal_api/index.rst @@ -161,7 +161,7 @@ Arcs :toctree: generated/ grid.arcs._angle_of_2_vectors - grid.arcs._angle_of_2_vectors + grid.arcs._decide_pole_latitude Utils diff --git a/test/test_arcs.py b/test/test_arcs.py new file mode 100644 index 000000000..c2e24f5aa --- /dev/null +++ b/test/test_arcs.py @@ -0,0 +1,149 @@ +import os +import numpy as np +import numpy.testing as nt +import random +import xarray as xr + +from unittest import TestCase +from pathlib import Path + +import uxarray as ux + +from uxarray.grid.coordinates import node_lonlat_rad_to_xyz +from uxarray.grid.arcs import point_within_gca + +try: + import constants +except ImportError: + from . import constants + +# Data files +current_path = Path(os.path.dirname(os.path.realpath(__file__))) + +gridfile_exo_CSne8 = current_path / "meshfiles" / "exodus" / "outCSne8" / "outCSne8.g" +gridfile_scrip_CSne8 = current_path / 'meshfiles' / "scrip" / "outCSne8" / 'outCSne8.nc' +gridfile_geoflowsmall_grid = current_path / 'meshfiles' / "ugrid" / "geoflow-small" / 'grid.nc' +gridfile_geoflowsmall_var = current_path / 'meshfiles' / "ugrid" / "geoflow-small" / 'v1.nc' + + +class TestIntersectionPoint(TestCase): + + def test_pt_within_gcr(self): + # The GCR that's eexactly 180 degrees will have Value Error raised + gcr_180degree_cart = [ + ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, 0.0]), + ux.grid.coordinates.node_lonlat_rad_to_xyz([np.pi, 0.0]) + ] + pt_same_lon_in = ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, 0.0]) + with self.assertRaises(ValueError): + point_within_gca(pt_same_lon_in, gcr_180degree_cart) + + gcr_180degree_cart = [ + ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, np.pi / 2.0]), + ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, -np.pi / 2.0]) + ] + + pt_same_lon_in = ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, 0.0]) + with self.assertRaises(ValueError): + point_within_gca(pt_same_lon_in, gcr_180degree_cart) + + # Test when the point and the GCA all have the same longitude + gcr_same_lon_cart = [ + ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, 1.5]), + ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, -1.5]) + ] + pt_same_lon_in = ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, 0.0]) + self.assertTrue(point_within_gca(pt_same_lon_in, gcr_same_lon_cart)) + + pt_same_lon_out = ux.grid.coordinates.node_lonlat_rad_to_xyz( + [0.0, 1.500000000000001]) + res = point_within_gca(pt_same_lon_out, gcr_same_lon_cart) + self.assertFalse(res) + + pt_same_lon_out_2 = ux.grid.coordinates.node_lonlat_rad_to_xyz( + [0.1, 1.0]) + res = point_within_gca(pt_same_lon_out_2, gcr_same_lon_cart) + self.assertFalse(res) + + # And if we increase the digital place by one, it should be true again + pt_same_lon_out_add_one_place = ux.grid.coordinates.node_lonlat_rad_to_xyz( + [0.0, 1.5000000000000001]) + res = point_within_gca(pt_same_lon_out_add_one_place, gcr_same_lon_cart) + self.assertTrue(res) + + # Normal case + # GCR vertex0 in radian : [1.3003315590159483, -0.007004587172323237], + # GCR vertex1 in radian : [3.5997458123873827, -1.4893379576608758] + # Point in radian : [1.3005410084914981, -0.010444274637648326] + gcr_cart_2 = np.array([[0.267, 0.963, -0.007], [-0.073, -0.036, + -0.997]]) + pt_cart_within = np.array( + [0.25616109352676675, 0.9246590335292105, -0.010021496695000144]) + self.assertTrue(point_within_gca(pt_cart_within, gcr_cart_2, True)) + + # Test other more complicate cases : The anti-meridian case + + def test_pt_within_gcr_antimeridian(self): + # GCR vertex0 in radian : [5.163808182822441, 0.6351384888657234], + # GCR vertex1 in radian : [0.8280410325693055, 0.42237025187091526] + # Point in radian : [0.12574759138415173, 0.770098701904903] + gcr_cart = np.array([[0.351, -0.724, 0.593], [0.617, 0.672, 0.410]]) + pt_cart = np.array( + [0.9438777657502077, 0.1193199333436068, 0.922714737029319]) + self.assertTrue(point_within_gca(pt_cart, gcr_cart, is_directed=True)) + # If we swap the gcr, it should throw a value error since it's larger than 180 degree + gcr_cart_flip = np.array([[0.617, 0.672, 0.410], [0.351, -0.724, + 0.593]]) + with self.assertRaises(ValueError): + point_within_gca(pt_cart, gcr_cart_flip, is_directed=True) + + # If we flip the gcr in the undirected mode, it should still work + self.assertTrue( + point_within_gca(pt_cart, gcr_cart_flip, is_directed=False)) + + # 2nd anti-meridian case + # GCR vertex0 in radian : [4.104711496596806, 0.5352983676533828], + # GCR vertex1 in radian : [2.4269979227622533, -0.007003212877856825] + # Point in radian : [0.43400375562899113, -0.49554509841586936] + gcr_cart_1 = np.array([[-0.491, -0.706, 0.510], [-0.755, 0.655, + -0.007]]) + pt_cart_within = np.array( + [0.6136726305712109, 0.28442243941920053, -0.365605190899831]) + self.assertFalse( + point_within_gca(pt_cart_within, gcr_cart_1, is_directed=True)) + self.assertFalse( + point_within_gca(pt_cart_within, gcr_cart_1, is_directed=False)) + + # The first case should not work and the second should work + v1_rad = [0.1, 0.0] + v2_rad = [2 * np.pi - 0.1, 0.0] + v1_cart = ux.grid.coordinates.node_lonlat_rad_to_xyz(v1_rad) + v2_cart = ux.grid.coordinates.node_lonlat_rad_to_xyz(v2_rad) + gcr_cart = np.array([v1_cart, v2_cart]) + pt_cart = ux.grid.coordinates.node_lonlat_rad_to_xyz([0.01, 0.0]) + with self.assertRaises(ValueError): + point_within_gca(pt_cart, gcr_cart, is_directed=True) + gcr_car_flipped = np.array([v2_cart, v1_cart]) + self.assertTrue( + point_within_gca(pt_cart, gcr_car_flipped, is_directed=True)) + + def test_pt_within_gcr_cross_pole(self): + gcr_cart = np.array([[0.351, 0.0, 0.3], [-0.351, 0.0, 0.3]]) + pt_cart = np.array( + [0.10, 0.0, 0.8]) + + # Normalize the point abd the GCA + pt_cart = pt_cart / np.linalg.norm(pt_cart) + gcr_cart = np.array([x / np.linalg.norm(x) for x in gcr_cart]) + self.assertTrue(point_within_gca(pt_cart, gcr_cart, is_directed=False)) + + gcr_cart = np.array([[0.351, 0.0, 0.3], [-0.351, 0.0, -0.6]]) + pt_cart = np.array( + [0.10, 0.0, 0.8]) + + # When the point is not within the GCA + pt_cart = pt_cart / np.linalg.norm(pt_cart) + gcr_cart = np.array([x / np.linalg.norm(x) for x in gcr_cart]) + self.assertFalse(point_within_gca(pt_cart, gcr_cart, is_directed=False)) + with self.assertRaises(ValueError): + point_within_gca(pt_cart, gcr_cart, is_directed=True) diff --git a/test/test_helpers.py b/test/test_helpers.py index 549ae95c5..da0fec36a 100644 --- a/test/test_helpers.py +++ b/test/test_helpers.py @@ -220,107 +220,6 @@ def test_convert_face_node_conn_to_sparse_matrix(self): nt.assert_array_equal(nodes_indices, expected_nodes_indices) -class TestIntersectionPoint(TestCase): - - def test_pt_within_gcr(self): - # The GCR that's eexactly 180 degrees will have Value Error raised - gcr_180degree_cart = [ - ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, 0.0]), - ux.grid.coordinates.node_lonlat_rad_to_xyz([np.pi, 0.0]) - ] - pt_same_lon_in = ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, 0.0]) - with self.assertRaises(ValueError): - point_within_gca(pt_same_lon_in, gcr_180degree_cart) - - gcr_180degree_cart = [ - ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, np.pi / 2.0]), - ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, -np.pi / 2.0]) - ] - - pt_same_lon_in = ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, 0.0]) - with self.assertRaises(ValueError): - point_within_gca(pt_same_lon_in, gcr_180degree_cart) - - # Test when the point and the GCA all have the same longitude - gcr_same_lon_cart = [ - ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, 1.5]), - ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, -1.5]) - ] - pt_same_lon_in = ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, 0.0]) - self.assertTrue(point_within_gca(pt_same_lon_in, gcr_same_lon_cart)) - - pt_same_lon_out = ux.grid.coordinates.node_lonlat_rad_to_xyz( - [0.0, 1.500000000000001]) - res = point_within_gca(pt_same_lon_out, gcr_same_lon_cart) - self.assertFalse(res) - - pt_same_lon_out_2 = ux.grid.coordinates.node_lonlat_rad_to_xyz( - [0.1, 1.0]) - res = point_within_gca(pt_same_lon_out_2, gcr_same_lon_cart) - self.assertFalse(res) - - # And if we increase the digital place by one, it should be true again - pt_same_lon_out_add_one_place = ux.grid.coordinates.node_lonlat_rad_to_xyz( - [0.0, 1.5000000000000001]) - res = point_within_gca(pt_same_lon_out_add_one_place, gcr_same_lon_cart) - self.assertTrue(res) - - # Normal case - # GCR vertex0 in radian : [1.3003315590159483, -0.007004587172323237], - # GCR vertex1 in radian : [3.5997458123873827, -1.4893379576608758] - # Point in radian : [1.3005410084914981, -0.010444274637648326] - gcr_cart_2 = np.array([[0.267, 0.963, -0.007], [-0.073, -0.036, - -0.997]]) - pt_cart_within = np.array( - [0.25616109352676675, 0.9246590335292105, -0.010021496695000144]) - self.assertTrue(point_within_gca(pt_cart_within, gcr_cart_2, True)) - - # Test other more complicate cases : The anti-meridian case - - # GCR vertex0 in radian : [5.163808182822441, 0.6351384888657234], - # GCR vertex1 in radian : [0.8280410325693055, 0.42237025187091526] - # Point in radian : [0.12574759138415173, 0.770098701904903] - gcr_cart = np.array([[0.351, -0.724, 0.593], [0.617, 0.672, 0.410]]) - pt_cart = np.array( - [0.9438777657502077, 0.1193199333436068, 0.922714737029319]) - self.assertTrue(point_within_gca(pt_cart, gcr_cart, is_directed=True)) - # If we swap the gcr, it should throw a value error since it's larger than 180 degree - gcr_cart_flip = np.array([[0.617, 0.672, 0.410], [0.351, -0.724, - 0.593]]) - with self.assertRaises(ValueError): - point_within_gca(pt_cart, gcr_cart_flip, is_directed=True) - - # If we flip the gcr in the undirected mode, it should still work - self.assertTrue( - point_within_gca(pt_cart, gcr_cart_flip, is_directed=False)) - - # 2nd anti-meridian case - # GCR vertex0 in radian : [4.104711496596806, 0.5352983676533828], - # GCR vertex1 in radian : [2.4269979227622533, -0.007003212877856825] - # Point in radian : [0.43400375562899113, -0.49554509841586936] - gcr_cart_1 = np.array([[-0.491, -0.706, 0.510], [-0.755, 0.655, - -0.007]]) - pt_cart_within = np.array( - [0.6136726305712109, 0.28442243941920053, -0.365605190899831]) - self.assertFalse( - point_within_gca(pt_cart_within, gcr_cart_1, is_directed=True)) - self.assertFalse( - point_within_gca(pt_cart_within, gcr_cart_1, is_directed=False)) - - # The first case should not work and the second should work - v1_rad = [0.1, 0.0] - v2_rad = [2 * np.pi - 0.1, 0.0] - v1_cart = ux.grid.coordinates.node_lonlat_rad_to_xyz(v1_rad) - v2_cart = ux.grid.coordinates.node_lonlat_rad_to_xyz(v2_rad) - gcr_cart = np.array([v1_cart, v2_cart]) - pt_cart = ux.grid.coordinates.node_lonlat_rad_to_xyz([0.01, 0.0]) - with self.assertRaises(ValueError): - point_within_gca(pt_cart, gcr_cart, is_directed=True) - gcr_car_flipped = np.array([v2_cart, v1_cart]) - self.assertTrue( - point_within_gca(pt_cart, gcr_car_flipped, is_directed=True)) - - class TestOperators(TestCase): def test_in_between(self): diff --git a/uxarray/grid/arcs.py b/uxarray/grid/arcs.py index 5f918d94a..c594a6753 100644 --- a/uxarray/grid/arcs.py +++ b/uxarray/grid/arcs.py @@ -28,6 +28,7 @@ def point_within_gca(pt, gca_cart, is_directed=False): is_directed : bool, optional, default = False If True, the GCA is considered to be directed, which means it can only from v0-->v1. If False, the GCA is undirected, and we will always assume the small circle (The one less than 180 degree) side is the GCA. The default is False. + For the case of the anti-podal case, the direction is v_0--> the pole point that on the same hemisphere as v_0-->v_1 Returns ------- @@ -79,7 +80,7 @@ def point_within_gca(pt, gca_cart, is_directed=False): ): return False - if GCRv0_lonlat[0] == GCRv1_lonlat[0]: + if np.isclose(GCRv0_lonlat[0], GCRv1_lonlat[0], rtol=0, atol=ERROR_TOLERANCE): # If the pt and the GCA are on the same longitude (the y coordinates are the same) if GCRv0_lonlat[0] == pt_lonlat[0]: # Now use the latitude to determine if the pt falls between the interval @@ -88,6 +89,44 @@ def point_within_gca(pt, gca_cart, is_directed=False): # If the pt and the GCA are not on the same longitude when the GCA is a longnitude arc, then the pt is not on the GCA return False + # If the longnitude span is exactly 180 degree, then the GCA goes through the pole point + if np.isclose( + abs(GCRv1_lonlat[0] - GCRv0_lonlat[0]), np.pi, rtol=0, atol=ERROR_TOLERANCE + ): + if not np.isclose( + GCRv0_lonlat[0], pt_lonlat[0], rtol=0, atol=ERROR_TOLERANCE + ) and not np.isclose( + GCRv1_lonlat[0], pt_lonlat[0], rtol=0, atol=ERROR_TOLERANCE + ): + return False + else: + # Determine the pole latitude and latitude extension + if (GCRv0_lonlat[1] > 0 and GCRv1_lonlat[1] > 0) or ( + GCRv0_lonlat[1] < 0 and GCRv1_lonlat[1] < 0 + ): + pole_lat = np.pi / 2 if GCRv0_lonlat[1] > 0 else -np.pi / 2 + lat_extend = ( + abs(np.pi / 2 - abs(GCRv0_lonlat[1])) + + np.pi / 2 + + abs(GCRv1_lonlat[1]) + ) + else: + pole_lat = _decide_pole_latitude(GCRv0_lonlat[1], GCRv1_lonlat[1]) + lat_extend = ( + abs(np.pi / 2 - abs(GCRv0_lonlat[1])) + + np.pi / 2 + + abs(GCRv1_lonlat[1]) + ) + + if is_directed and lat_extend >= np.pi: + raise ValueError( + "The input GCA spans more than 180 degrees. Please divide it into two." + ) + + return in_between(GCRv0_lonlat[1], pt_lonlat[1], pole_lat) or in_between( + pole_lat, pt_lonlat[1], GCRv1_lonlat[1] + ) + if is_directed: # The anti-meridian case Sufficient condition: absolute difference between the longitudes of the two # vertices is greater than 180 degrees (π radians): abs(GCRv1_lon - GCRv0_lon) > π @@ -142,6 +181,45 @@ def in_between(p, q, r) -> bool: return p <= q <= r or r <= q <= p +def _decide_pole_latitude(lat1, lat2): + """Determine the pole latitude based on the latitudes of two points on a + Great Circle Arc (GCA). + + This function calculates the combined latitude span from each point to its nearest pole + and decides which pole (North or South) the smaller GCA will pass. This decision is crucial + for handling GCAs that span exactly or more than 180 degrees in longitude, indicating + the arc might pass through or close to one of the Earth's poles. + + Parameters + ---------- + lat1 : float + Latitude of the first point in radians. Positive for the Northern Hemisphere, negative for the Southern. + lat2 : float + Latitude of the second point in radians. Positive for the Northern Hemisphere, negative for the Southern. + + Returns + ------- + float + The latitude of the pole (np.pi/2 for the North Pole or -np.pi/2 for the South Pole) the GCA is closer to. + + Notes + ----- + The function assumes the input latitudes are valid (i.e., between -np.pi/2 and np.pi/2) and expressed in radians. + The determination of which pole a GCA is closer to is based on the sum of the latitudinal spans from each point + to its nearest pole, considering the shortest path on the sphere. + """ + # Calculate the total latitudinal span to the nearest poles + lat_extend = abs(np.pi / 2 - abs(lat1)) + np.pi / 2 + abs(lat2) + + # Determine the closest pole based on the latitudinal span + if lat_extend < np.pi: + closest_pole = np.pi / 2 if lat1 > 0 else -np.pi / 2 + else: + closest_pole = -np.pi / 2 if lat1 > 0 else np.pi / 2 + + return closest_pole + + def _angle_of_2_vectors(u, v): """Calculate the angle between two 3D vectors u and v in radians. Can be used to calcualte the span of a GCR. From 393f65c7a9a0b8bde7876121e8b316478d94bd88 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Sun, 3 Mar 2024 22:27:25 -0600 Subject: [PATCH 02/41] Bump codecov/codecov-action from 4.0.1 to 4.0.2 (#702) Bumps [codecov/codecov-action](https://github.com/codecov/codecov-action) from 4.0.1 to 4.0.2. - [Release notes](https://github.com/codecov/codecov-action/releases) - [Changelog](https://github.com/codecov/codecov-action/blob/main/CHANGELOG.md) - [Commits](https://github.com/codecov/codecov-action/compare/v4.0.1...v4.0.2) --- updated-dependencies: - dependency-name: codecov/codecov-action dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 7b2bd5009..39e23a927 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -71,7 +71,7 @@ jobs: python -m pytest test -v --cov=./uxarray --cov-report=xml - name: Upload code coverage to Codecov - uses: codecov/codecov-action@v4.0.1 + uses: codecov/codecov-action@v4.0.2 with: file: ./coverage.xml flags: unittests From 74677b5bd7576893919323c2c06d08bf25920079 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 4 Mar 2024 12:37:36 -0600 Subject: [PATCH 03/41] Bump codecov/codecov-action from 4.0.2 to 4.1.0 (#706) Bumps [codecov/codecov-action](https://github.com/codecov/codecov-action) from 4.0.2 to 4.1.0. - [Release notes](https://github.com/codecov/codecov-action/releases) - [Changelog](https://github.com/codecov/codecov-action/blob/main/CHANGELOG.md) - [Commits](https://github.com/codecov/codecov-action/compare/v4.0.2...v4.1.0) --- updated-dependencies: - dependency-name: codecov/codecov-action dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 39e23a927..63f258277 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -71,7 +71,7 @@ jobs: python -m pytest test -v --cov=./uxarray --cov-report=xml - name: Upload code coverage to Codecov - uses: codecov/codecov-action@v4.0.2 + uses: codecov/codecov-action@v4.1.0 with: file: ./coverage.xml flags: unittests From 81a44579d14a7d87a1a3bdf4815930fef8878b75 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 4 Mar 2024 14:20:49 -0600 Subject: [PATCH 04/41] Bump pypa/gh-action-pypi-publish from 1.8.11 to 1.8.12 (#707) Bumps [pypa/gh-action-pypi-publish](https://github.com/pypa/gh-action-pypi-publish) from 1.8.11 to 1.8.12. - [Release notes](https://github.com/pypa/gh-action-pypi-publish/releases) - [Commits](https://github.com/pypa/gh-action-pypi-publish/compare/v1.8.11...v1.8.12) --- updated-dependencies: - dependency-name: pypa/gh-action-pypi-publish dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> --- .github/workflows/pypi.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pypi.yaml b/.github/workflows/pypi.yaml index 05740ec23..a6984cf69 100644 --- a/.github/workflows/pypi.yaml +++ b/.github/workflows/pypi.yaml @@ -50,7 +50,7 @@ jobs: python -m twine check dist/* - name: Publish package to PyPI - uses: pypa/gh-action-pypi-publish@v1.8.11 + uses: pypa/gh-action-pypi-publish@v1.8.12 with: skip-existing: true verbose: true From ffcdd889b967fd3984d168494caed7298b11a379 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Mon, 4 Mar 2024 14:39:31 -0600 Subject: [PATCH 05/41] Update asv.yml (#705) --- ci/asv.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ci/asv.yml b/ci/asv.yml index 761a6ab5c..33bf36822 100644 --- a/ci/asv.yml +++ b/ci/asv.yml @@ -27,5 +27,5 @@ dependencies: - pip: - antimeridian - pyfma - - asv + - asv<0.6.2 - -e ../ From 9793273aa09af915338a20a8492d70c2613a75c1 Mon Sep 17 00:00:00 2001 From: Orhan Eroglu <32553057+erogluorhan@users.noreply.github.com> Date: Wed, 6 Mar 2024 14:32:20 -0700 Subject: [PATCH 06/41] Fix FutureWarnings in the code and tests (#709) * Change ds.dims[] calls to ds.sizes[] calls * Fix warnings in test files * Unpin pyarrow --- ci/environment.yml | 2 +- test/test_computing.py | 28 ++++++++++++++-------------- test/test_dataarray.py | 6 +++--- test/test_geometry.py | 4 ++-- uxarray/core/dataset.py | 4 ++-- uxarray/core/utils.py | 6 +++--- uxarray/grid/grid.py | 8 ++++---- uxarray/io/_exodus.py | 8 ++++---- 8 files changed, 33 insertions(+), 33 deletions(-) diff --git a/ci/environment.yml b/ci/environment.yml index 7da121b72..8792e8f50 100644 --- a/ci/environment.yml +++ b/ci/environment.yml @@ -14,7 +14,7 @@ dependencies: - pandas<2.1.0 - pathlib - pre_commit - - pyarrow<13.0.0 # pin due to CI faliures on macOS & ubuntu + - pyarrow - pytest - pytest-cov - requests diff --git a/test/test_computing.py b/test/test_computing.py index e881b2361..4e9cde81b 100644 --- a/test/test_computing.py +++ b/test/test_computing.py @@ -41,7 +41,7 @@ def test_two_sum(self): a = 1.0 b = 2.0 s, e = ac_utils._two_sum(a, b) - self.assertAlmostEquals(a + b, s + e, places=15) + self.assertAlmostEqual(a + b, s + e, places=15) def test_fast_two_sum(self): """Test the fase_two_sum function.""" @@ -49,8 +49,8 @@ def test_fast_two_sum(self): b = 1.0 s, e = ac_utils._two_sum(a, b) sf, ef = ac_utils._fast_two_sum(a, b) - self.assertEquals(s, sf) - self.assertEquals(e, ef) + self.assertEqual(s, sf) + self.assertEqual(e, ef) def test_two_prod_fma(self): """Test the two_prod_fma function.""" @@ -58,9 +58,9 @@ def test_two_prod_fma(self): a = 1.0 b = 2.0 x, y = ac_utils._two_prod_fma(a, b) - self.assertEquals(x, a * b) - self.assertEquals(y, pyfma.fma(a, b, -x)) - self.assertAlmostEquals(a * b, x + y, places=15) + self.assertEqual(x, a * b) + self.assertEqual(y, pyfma.fma(a, b, -x)) + self.assertAlmostEqual(a * b, x + y, places=15) def test_fast_two_mult(self): """Test the two_prod_fma function.""" @@ -68,8 +68,8 @@ def test_fast_two_mult(self): b = 2.0 x, y = ac_utils._two_prod_fma(a, b) xf, yf = ac_utils._fast_two_mult(a, b) - self.assertEquals(x, xf) - self.assertEquals(y, yf) + self.assertEqual(x, xf) + self.assertEqual(y, yf) def test_err_fmac(self): """Test the _err_fmac function.""" @@ -78,8 +78,8 @@ def test_err_fmac(self): b = 2.0 c = 3.0 x, y, z = ac_utils._err_fmac(a, b, c) - self.assertEquals(x, pyfma.fma(a, b, c)) - self.assertAlmostEquals(a * b + c, x + y + z, places=15) + self.assertEqual(x, pyfma.fma(a, b, c)) + self.assertAlmostEqual(a * b + c, x + y + z, places=15) class TestAccurateSum(TestCase): @@ -88,7 +88,7 @@ def test_vec_sum(self): """Test the _vec_sum function.""" a = np.array([1.0, 2.0, 3.0]) res = ac_utils._vec_sum(a) - self.assertAlmostEquals(6.0, res, places=15) + self.assertAlmostEqual(6.0, res, places=15) import gmpy2 a = gmpy2.mpfr('2.28888888888') b = gmpy2.mpfr('-2.2888889999') @@ -114,16 +114,16 @@ def test_norm_faithful(self): """Test the norm_faithful function.""" a = np.array([1.0, 2.0, 3.0]) res = ac_utils._norm_faithful(a) - self.assertAlmostEquals(np.linalg.norm(a), res, places=15) + self.assertAlmostEqual(np.linalg.norm(a), res, places=15) def test_sqrt_faithful(self): """Test the sqrt_faithful function.""" a = 10.0 res = ac_utils._acc_sqrt(a, 0.0) - self.assertAlmostEquals(np.sqrt(a), res, places=15) + self.assertAlmostEqual(np.sqrt(a), res, places=15) def test_two_square(self): """Test the _two_square function.""" a = 10.0 res = ac_utils._two_square(a) - self.assertAlmostEquals(a * a, res[0], places=15) + self.assertAlmostEqual(a * a, res[0], places=15) diff --git a/test/test_dataarray.py b/test/test_dataarray.py index 2e298db11..d3ea231bb 100644 --- a/test/test_dataarray.py +++ b/test/test_dataarray.py @@ -122,10 +122,10 @@ def test_nodal_average(self): v1_nodal_average = uxds['v1'].nodal_average() # final dimension should match number of faces - self.assertEquals(v1_nodal_average.shape[-1], uxds.uxgrid.n_face) + self.assertEqual(v1_nodal_average.shape[-1], uxds.uxgrid.n_face) # all other dimensions should remain unchanged - self.assertEquals(uxds['v1'].shape[0:-1], v1_nodal_average.shape[0:-1]) + self.assertEqual(uxds['v1'].shape[0:-1], v1_nodal_average.shape[0:-1]) # test on a sample mesh with 4 verts verts = [[[-170, 40], [180, 30], [165, 25], [-170, 20]]] @@ -138,4 +138,4 @@ def test_nodal_average(self): uxda_nodal_average = uxda.nodal_average() # resulting data should be the mean of the corner nodes of the single face - self.assertEquals(uxda_nodal_average, np.mean(data)) + self.assertEqual(uxda_nodal_average, np.mean(data)) diff --git a/test/test_geometry.py b/test/test_geometry.py index a13b547af..45f209d1a 100644 --- a/test/test_geometry.py +++ b/test/test_geometry.py @@ -390,12 +390,12 @@ def test_get_latlonbox_width(self): # Calculate the width of the latlonbox width = ux.grid.geometry._get_latlonbox_width(gca_latlon) - self.assertEquals(width, 3.0) + self.assertEqual(width, 3.0) # Define a great circle arc that is not wrapping around the meridian gca_latlon = np.array([[0.0, 0.0], [2 * np.pi - 1.0, 1.0]]) width = ux.grid.geometry._get_latlonbox_width(gca_latlon) - self.assertEquals(width, 2.0) + self.assertEqual(width, 2.0) def test_insert_pt_in_latlonbox_non_periodic(self): old_box = np.array([[0.1, 0.2], [0.3, 0.4]]) # Radians diff --git a/uxarray/core/dataset.py b/uxarray/core/dataset.py index 42f919008..dc2afed87 100644 --- a/uxarray/core/dataset.py +++ b/uxarray/core/dataset.py @@ -234,7 +234,7 @@ def info(self, buf: IO = None, show_attrs=False) -> None: lines.append("uxarray.Dataset {") lines.append("grid topology dimensions:") - for name, size in self.uxgrid._ds.dims.items(): + for name, size in self.uxgrid._ds.sizes.items(): lines.append(f"\t{name} = {size}") lines.append("\ngrid topology variables:") @@ -246,7 +246,7 @@ def info(self, buf: IO = None, show_attrs=False) -> None: lines.append(f"\t\t{name}:{k} = {v}") lines.append("\ndata dimensions:") - for name, size in self.dims.items(): + for name, size in self.sizes.items(): lines.append(f"\t{name} = {size}") lines.append("\ndata variables:") diff --git a/uxarray/core/utils.py b/uxarray/core/utils.py index efab52f72..14d569094 100644 --- a/uxarray/core/utils.py +++ b/uxarray/core/utils.py @@ -19,11 +19,11 @@ def _map_dims_to_ugrid( for dim in set(ds.dims) ^ _source_dims_dict.keys(): # obtain dimensions that were not parsed source_dims_dict and attempt to match to a grid element - if ds.dims[dim] == grid.n_face: + if ds.sizes[dim] == grid.n_face: _source_dims_dict[dim] = "n_face" - elif ds.dims[dim] == grid.n_node: + elif ds.sizes[dim] == grid.n_node: _source_dims_dict[dim] = "n_node" - elif ds.dims[dim] == grid.n_edge: + elif ds.sizes[dim] == grid.n_edge: _source_dims_dict[dim] = "n_edge" # Possible Issue: https://github.com/UXARRAY/uxarray/issues/610 diff --git a/uxarray/grid/grid.py b/uxarray/grid/grid.py index ccc7d5345..632197f92 100644 --- a/uxarray/grid/grid.py +++ b/uxarray/grid/grid.py @@ -272,7 +272,7 @@ def __repr__(self): dims_heading = "Grid Dimensions:\n" dims_str = "" - for key, value in zip(self._ds.dims.keys(), self._ds.dims.values()): + for key, value in zip(self._ds.sizes.keys(), self._ds.sizes.values()): dims_str += f" * {key}: {value}\n" dims_str += f" * n_nodes_per_face: {self.n_nodes_per_face.shape}\n" @@ -426,13 +426,13 @@ def Mesh2(self) -> xr.DataArray: def n_node(self) -> int: """Dimension ``n_node``, which represents the total number of unique corner nodes.""" - return self._ds.dims["n_node"] + return self._ds.sizes["n_node"] @property def n_face(self) -> int: """Dimension ``n_face``, which represents the total number of unique faces.""" - return self._ds.dims["n_face"] + return self._ds.sizes["n_face"] @property def n_edge(self) -> int: @@ -441,7 +441,7 @@ def n_edge(self) -> int: if "edge_node_connectivity" not in self._ds: _populate_edge_node_connectivity(self) - return self._ds.dims["n_edge"] + return self._ds.sizes["n_edge"] # ================================================================================================================== @property diff --git a/uxarray/io/_exodus.py b/uxarray/io/_exodus.py index c7393df8d..acbcfa8c7 100644 --- a/uxarray/io/_exodus.py +++ b/uxarray/io/_exodus.py @@ -28,8 +28,8 @@ def _read_exodus(ext_ds): max_face_nodes = 0 for dim in ext_ds.dims: if "num_nod_per_el" in dim: - if ext_ds.dims[dim] > max_face_nodes: - max_face_nodes = ext_ds.dims[dim] + if ext_ds.sizes[dim] > max_face_nodes: + max_face_nodes = ext_ds.sizes[dim] # create an empty conn array for storing all blk face_nodes_data conn = np.empty((0, max_face_nodes)) @@ -57,7 +57,7 @@ def _read_exodus(ext_ds): "units": "m", }, ) - if ext_ds.dims["num_dim"] > 2: + if ext_ds.sizes["num_dim"] > 2: ds["node_z"] = xr.DataArray( data=ext_ds.coord[2], dims=["n_node"], @@ -88,7 +88,7 @@ def _read_exodus(ext_ds): }, ) elif key == "coordz": - if ext_ds.dims["num_dim"] > 2: + if ext_ds.sizes["num_dim"] > 2: ds["node_z"] = xr.DataArray( data=ext_ds.coordx, dims=["n_node"], From 2b2f4868917267203811017c1b9b4a246515b94a Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Fri, 8 Mar 2024 02:29:52 -0600 Subject: [PATCH 07/41] Update examples.rst --- docs/examples.rst | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/examples.rst b/docs/examples.rst index eced8511a..46f10af4c 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -24,4 +24,5 @@ your own examples or suggesting one for us to create, please see the :doc:`contr examples/006-plot-api-topology.ipynb examples/007-polygon-viz.ipynb examples/008-point-viz.ipynb - examples/examples/009-subsetting.ipynb + examples/009-subsetting.ipynb + From 2af804f3acf4f606bb2f9ed0a9cd95d3690fb4f5 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Fri, 8 Mar 2024 03:25:50 -0600 Subject: [PATCH 08/41] update examples.rst (#720) --- docs/examples.rst | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/examples.rst b/docs/examples.rst index 46f10af4c..93f984296 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -25,4 +25,3 @@ your own examples or suggesting one for us to create, please see the :doc:`contr examples/007-polygon-viz.ipynb examples/008-point-viz.ipynb examples/009-subsetting.ipynb - From e7d06ce7d9d3228989cbae2b6c806dce04d148de Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 11 Mar 2024 20:37:34 -0500 Subject: [PATCH 09/41] Bump pypa/gh-action-pypi-publish from 1.8.12 to 1.8.14 (#722) Bumps [pypa/gh-action-pypi-publish](https://github.com/pypa/gh-action-pypi-publish) from 1.8.12 to 1.8.14. - [Release notes](https://github.com/pypa/gh-action-pypi-publish/releases) - [Commits](https://github.com/pypa/gh-action-pypi-publish/compare/v1.8.12...v1.8.14) --- updated-dependencies: - dependency-name: pypa/gh-action-pypi-publish dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/pypi.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pypi.yaml b/.github/workflows/pypi.yaml index a6984cf69..370535cf5 100644 --- a/.github/workflows/pypi.yaml +++ b/.github/workflows/pypi.yaml @@ -50,7 +50,7 @@ jobs: python -m twine check dist/* - name: Publish package to PyPI - uses: pypa/gh-action-pypi-publish@v1.8.12 + uses: pypa/gh-action-pypi-publish@v1.8.14 with: skip-existing: true verbose: true From cb9dd73cc5016dedd743dbb1b4120593532f42ac Mon Sep 17 00:00:00 2001 From: David Galicia <54779078+dfg-13@users.noreply.github.com> Date: Thu, 21 Mar 2024 18:37:21 -0500 Subject: [PATCH 10/41] fixing documentation typos (#736) --- docs/contributing.rst | 2 +- docs/examples/009-subsetting.ipynb | 276 ++++++++++++++--------------- 2 files changed, 139 insertions(+), 139 deletions(-) diff --git a/docs/contributing.rst b/docs/contributing.rst index e430490db..e358fd6f5 100644 --- a/docs/contributing.rst +++ b/docs/contributing.rst @@ -310,7 +310,7 @@ development purposes. Use the following commands for this:: $ conda env create --file ci/environment.yml $ conda activate uxarray_build -THe above commands will use the ``environment.yml`` conda environment definition +The above commands will use the ``environment.yml`` conda environment definition file that is hosted under the ``ci`` folder and create a conda environment with the name ``uxarray_build``. Once you activate that environment with the help of the second command, you will be able to develop UXarray codes in your local configuration. diff --git a/docs/examples/009-subsetting.ipynb b/docs/examples/009-subsetting.ipynb index 25c23b39e..05e66b264 100644 --- a/docs/examples/009-subsetting.ipynb +++ b/docs/examples/009-subsetting.ipynb @@ -2,20 +2,23 @@ "cells": [ { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "# Subsetting an Unstructured Grid: Analysis Over Chicago\n", "\n", "Authors: [Philip Chmielowiec](https://github.com/philipc2)\n", "\n", "This usage example showcases various ways of subsetting an unstructured grid using UXarray, focussing on analyzing a region around Chicago, Illinois." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "import uxarray as ux\n", @@ -34,27 +37,27 @@ "\n", "\n", "warnings.filterwarnings(\"ignore\")" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "## Setup\n", "\n", "In this example, we will be using the `geocat-datafiles` package to obtain our grid and data files.\n", "\n", "The dataset used in this example is a 30km global MPAS meshes. We will be investigating the relative humidity vertically interpolated to 200hPa (`relhum200hPa`) data variable." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "datafiles = (\n", @@ -63,164 +66,164 @@ " ),\n", " geodf.get(\"netcdf_files/MPAS/FalkoJudt/dyamond_1/30km/x1.655362.grid_subset.nc\"),\n", ")" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "uxds = ux.open_dataset(datafiles[1], datafiles[0])" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "clim = (uxds['relhum_200hPa'][0].values.min(), uxds['relhum_200hPa'][0].values.max())" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "features = gf.coastline(projection=ccrs.PlateCarree(), line_width=1, scale='50m') * \\\n", " gf.states(projection=ccrs.PlateCarree(), line_width=1, scale='50m')" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "## Global Grid\n", "\n", "Many unstructured grids, such as those from global climate models, span the entire surface of a sphere (both with or without masks, such as continents).\n", "\n", "UXarray supports working with these global grids, handling cases that arise with the spherical geometry of the earth (wrap around at the antimeridian, pole points, etc.)\n" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "uxds['relhum_200hPa'][0].plot.rasterize(method='polygon',\n", " exclude_antimeridian=True,\n", " title = \"Global Grid\",\n", " **plot_opts) * features" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", - "source": [ - "In addition to plotting global grids, we can perform analysis operations." - ], "metadata": { "collapsed": false - } + }, + "source": [ + "In addition to plotting global grids, we can perform analysis operations." + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "uxds['relhum_200hPa'][0].values.mean()" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "## Regional Subsets\n", "\n", "UXarray supports taking subsets of a grid, which allows us to select a region and perform analysis directly on that area, as opposed to the global grid.\n", "\n", "There are currently three supported subsetting methods, both for the `Grid` and `UxDataArray` data structures." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "uxds['relhum_200hPa'].subset" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "uxds['relhum_200hPa'].uxgrid.subset" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "### Bounding Box\n", "\n", "We can declare a bounding box centered about the Chicago area by specifying the minimum and maximum longitude and latitude bounds.\n" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "lon_bounds = (-87.6298 - 2, -87.6298 + 2)\n", "lat_bounds = (41.8781 - 2, 41.8781 + 2)" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "Our bounding box ensures that the coordinates of our select element (`nodes`, `edge_centers`, or `face_centers`) are within the defined bounding box range.\n", "\n", "Below is an example using the corner nodes for our subset." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "bbox_subset_nodes = uxds['relhum_200hPa'][0].subset.bounding_box(lon_bounds, lat_bounds, element='nodes')\n", @@ -230,23 +233,23 @@ " clim=clim,\n", " title=\"Bounding Box Subset\",\n", " **plot_opts) * features" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", - "source": [ - "And similarly using face centers." - ], "metadata": { "collapsed": false - } + }, + "source": [ + "And similarly using face centers." + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "bbox_subset_faces= uxds['relhum_200hPa'][0].subset.bounding_box(lon_bounds, lat_bounds, element='face centers')\n", @@ -256,51 +259,51 @@ " clim=clim,\n", " title=\"Bounding Box Subset (Face Center Query)\",\n", " **plot_opts) * features" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "While the bounding box is generally the same, you will notice differences along the border depending on which element is used to query.\n", "\n", - ":::{note}\n", + "```{note}\n", "Specifying which element to query (i.e. ``nodes``, ``edgecenters``, or ``face centers``) is supported by all subsetting methods.\n", - ":::" - ], - "metadata": { - "collapsed": false - } + "```" + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "### Bounding Circle\n", "\n", "A bounding circle is defined using a center coordinate (lon, lat) and a radius (in degrees). The resulting subset will contain all elements within the radius of that circle." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "center_coord = [-87.6298, 41.8781]\n", "\n", "r = 2" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "bcircle_subset = uxds['relhum_200hPa'][0].subset.bounding_circle(center_coord, r)\n", @@ -310,36 +313,36 @@ " clim=clim,\n", " title=\"Bounding Circle Subset\",\n", " **plot_opts) * features" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "### Nearest Neighbor\n", "\n", "Similar to the bounding circle, we can perform a nearest neighbor subset at some center coordinate (lon, lat) and query for some number of elements `k`" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "center_coord = [-87.6298, 41.8781]" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "nn_subset = uxds['relhum_200hPa'][0].subset.nearest_neighbor(center_coord, k = 30, element='nodes')\n", @@ -348,14 +351,14 @@ " clim=clim,\n", " title= \"Nearest Neighbor Subset\",\n", " **plot_opts) * features" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "nn_subset_120 = uxds['relhum_200hPa'][0].subset.nearest_neighbor(center_coord, k = 120, element=\"face centers\")\n", @@ -365,14 +368,14 @@ " clim=clim,\n", " title= \"Nearest Neighbor Subset (120 Faces)\",\n", " **plot_opts) * features" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "nn_subset_1 = uxds['relhum_200hPa'][0].subset.nearest_neighbor(center_coord, k = 1, element=\"face centers\")\n", @@ -381,67 +384,64 @@ " clim=clim,\n", " title= \"Nearest Neighbor Subset (Closest Face)\",\n", " **plot_opts) * features" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "### Analysis Operators\n", "\n", "Since each subset is a newly initialized ``UxDataArray``, paired also with a newly initialized `Grid`, we can perform analysis operators directly on these new objects.\n", "\n", "Below is a few examples of basic statical operations on the subset data arrays." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "bbox_subset_nodes.values.mean(), bbox_subset_faces.values.mean(), bcircle_subset.values.mean()" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "bbox_subset_nodes.values.std(), bbox_subset_faces.values.std(), bcircle_subset.values.std()" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "bbox_subset_nodes.values.min(), bbox_subset_faces.values.min(), bcircle_subset.values.min()" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "bbox_subset_nodes.values.max(), bbox_subset_faces.values.max(), bcircle_subset.values.max()" - ], - "metadata": { - "collapsed": false - } + ] } ], "metadata": { From d72a5ca3ca0401428795e2cdafa92a5d1ab8e2db Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Fri, 22 Mar 2024 18:31:21 -0500 Subject: [PATCH 11/41] Fix CI Failures (#731) * update envs * fix issues in point raster * fix dask issues * remove left over attributes * add comments --- ci/asv.yml | 2 +- ci/docs.yml | 2 +- test/test_plot.py | 2 +- uxarray/grid/grid.py | 2 -- uxarray/plot/dataarray_plot.py | 61 ++++++++-------------------------- 5 files changed, 16 insertions(+), 53 deletions(-) diff --git a/ci/asv.yml b/ci/asv.yml index 33bf36822..fa44e7418 100644 --- a/ci/asv.yml +++ b/ci/asv.yml @@ -15,7 +15,7 @@ dependencies: - pandas<2.1.0 - pathlib - pre_commit - - pyarrow<13.0.0 # pin due to CI faliures on macOS & ubuntu + - pyarrow - pytest - pytest-cov - requests diff --git a/ci/docs.yml b/ci/docs.yml index 2695cd238..d88386395 100644 --- a/ci/docs.yml +++ b/ci/docs.yml @@ -6,7 +6,7 @@ dependencies: - dask - netcdf4 - numba - - numpy<=1.24 + - numpy - pathlib - pre_commit - pytest diff --git a/test/test_plot.py b/test/test_plot.py index 94e760e22..e0c69432c 100644 --- a/test/test_plot.py +++ b/test/test_plot.py @@ -90,4 +90,4 @@ def test_clabel(self): raster_no_clabel = uxds['v1'][0][0].plot.rasterize(method='point') - raster_no_clabel = uxds['v1'][0][0].plot.rasterize(method='point', clabel="Foo") + raster_with_clabel = uxds['v1'][0][0].plot.rasterize(method='point', clabel='Foo') diff --git a/uxarray/grid/grid.py b/uxarray/grid/grid.py index 632197f92..4830d5a97 100644 --- a/uxarray/grid/grid.py +++ b/uxarray/grid/grid.py @@ -143,8 +143,6 @@ def __init__( self._gdf_exclude_am = None self._poly_collection = None self._line_collection = None - self._centroid_points_df_proj = [None, None] - self._corner_points_df_proj = [None, None] self._raster_data_id = None # initialize cached data structures (nearest neighbor operations) diff --git a/uxarray/plot/dataarray_plot.py b/uxarray/plot/dataarray_plot.py index 63d7e6499..d9d5f5feb 100644 --- a/uxarray/plot/dataarray_plot.py +++ b/uxarray/plot/dataarray_plot.py @@ -15,7 +15,6 @@ from uxarray.plot.constants import N_FACE_THRESHOLD -import pandas as pd import numpy as np @@ -214,65 +213,31 @@ def _point_raster( if uxda._face_centered(): # data mapped to face centroid coordinates - lon = uxda.uxgrid.face_lon.values - lat = uxda.uxgrid.face_lat.values + lon, lat = uxda.uxgrid.face_lon.values, uxda.uxgrid.face_lat.values elif uxda._node_centered(): # data mapped to face corner coordinates - lon = uxda.uxgrid.node_lon.values - lat = uxda.uxgrid.node_lat.values + lon, lat = uxda.uxgrid.node_lon.values, uxda.uxgrid.node_lat.values elif uxda._edge_centered(): # data mapped to face corner coordinates - lon = uxda.uxgrid.edge_lon.values - lat = uxda.uxgrid.edge_lat.values + lon, lat = uxda.uxgrid.edge_lon.values, uxda.uxgrid.edge_lat.values else: raise ValueError( f"The Dimension of Data Variable {uxda.name} is not Node or Face centered." ) - # determine whether we need to recompute points, typically when a new projection is selected - recompute = True - if uxda._face_centered() == "center": - if ( - uxda.uxgrid._centroid_points_df_proj[0] is not None - and uxda.uxgrid._centroid_points_df_proj[1] == projection - ): - recompute = False - points_df = uxda.uxgrid._centroid_points_df_proj[0] - - else: - if ( - uxda.uxgrid._corner_points_df_proj[0] is not None - and uxda.uxgrid._corner_points_df_proj[1] == projection - ): - recompute = False - points_df = uxda.uxgrid._corner_points_df_proj[0] - - if recompute: - # need to recompute points and/or projection - if projection is not None: - lon, lat, _ = projection.transform_points(ccrs.PlateCarree(), lon, lat).T - - point_dict = {"lon": lon, "lat": lat, "var": uxda.values} - - # Construct Dask DataFrame - point_ddf = dd.from_dict(data=point_dict, npartitions=npartitions) + if projection is not None: + # apply projection to coordinates + lon, lat, _ = projection.transform_points(ccrs.PlateCarree(), lon, lat).T - hv.extension("bokeh") - points = hv.Points(point_ddf, ["lon", "lat"]).opts(size=size) + # this will be fixed in #733 + hv.extension("bokeh") - # cache computed points & projection - if cache: - if uxda._face_centered() == "center": - uxda.uxgrid._centroid_points_df_proj[0] = point_ddf - uxda.uxgrid._centroid_points_df_proj[1] = projection - else: - uxda.uxgrid._corner_points_df_proj[0] = point_ddf - uxda.uxgrid._corner_points_df_proj[1] = projection + # construct a dask dataframe from coordinates and data + point_dict = {"lon": lon, "lat": lat, "var": uxda.data} + point_ddf = dd.from_dict(data=point_dict, npartitions=npartitions) - else: - # use existing cached points & projection - points_df["var"] = pd.Series(uxda.values) - points = hv.Points(points_df, ["lon", "lat"]).opts(size=size) + # construct a holoviews points oobject + points = hv.Points(point_ddf, ["lon", "lat"]).opts(size=size) if backend == "matplotlib": # use holoviews matplotlib backend From a5e11832c8a69568d584e4f6b1d2646fcd49b64b Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Fri, 29 Mar 2024 12:43:10 -0500 Subject: [PATCH 12/41] Update meta.yaml --- meta.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/meta.yaml b/meta.yaml index 407ca162b..dc69b3bec 100644 --- a/meta.yaml +++ b/meta.yaml @@ -1,4 +1,4 @@ -{% set version = "2024.02.0" %} +{% set version = "2024.03.0" %} package: name: 'uxarray' From 11208c4fa4733c699094dbc7aac505080a57b479 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Apr 2024 12:39:21 -0500 Subject: [PATCH 13/41] Bump codecov/codecov-action from 4.1.0 to 4.1.1 (#745) Bumps [codecov/codecov-action](https://github.com/codecov/codecov-action) from 4.1.0 to 4.1.1. - [Release notes](https://github.com/codecov/codecov-action/releases) - [Changelog](https://github.com/codecov/codecov-action/blob/main/CHANGELOG.md) - [Commits](https://github.com/codecov/codecov-action/compare/v4.1.0...v4.1.1) --- updated-dependencies: - dependency-name: codecov/codecov-action dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 63f258277..44a387498 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -71,7 +71,7 @@ jobs: python -m pytest test -v --cov=./uxarray --cov-report=xml - name: Upload code coverage to Codecov - uses: codecov/codecov-action@v4.1.0 + uses: codecov/codecov-action@v4.1.1 with: file: ./coverage.xml flags: unittests From 556cdc7c017785afc6e06a0d6fbeac17be3f38a3 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 1 Apr 2024 12:48:51 -0500 Subject: [PATCH 14/41] Bump actions/setup-python from 5.0.0 to 5.1.0 (#746) Bumps [actions/setup-python](https://github.com/actions/setup-python) from 5.0.0 to 5.1.0. - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/v5.0.0...v5.1.0) --- updated-dependencies: - dependency-name: actions/setup-python dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> --- .github/workflows/pre-commit.yml | 2 +- .github/workflows/pypi.yaml | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 9fdf9f9ab..e1012264e 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -11,5 +11,5 @@ jobs: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - - uses: actions/setup-python@v5.0.0 + - uses: actions/setup-python@v5.1.0 - uses: pre-commit/action@v3.0.1 diff --git a/.github/workflows/pypi.yaml b/.github/workflows/pypi.yaml index 370535cf5..d4366a959 100644 --- a/.github/workflows/pypi.yaml +++ b/.github/workflows/pypi.yaml @@ -10,7 +10,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v5.0.0 + uses: actions/setup-python@v5.1.0 with: python-version: '3.x' - name: Install dependencies @@ -34,7 +34,7 @@ jobs: steps: - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v5.0.0 + uses: actions/setup-python@v5.1.0 with: python-version: '3.x' - name: Install dependencies From 545a18da03bfeabf3b6981727f129f8c6454b230 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Mon, 1 Apr 2024 17:17:53 -0500 Subject: [PATCH 15/41] UGRID Conformance, Grid Construction from Minimal Grid Variables (#628) * initial work * work on convetnions * topology reader * work on topo, getitem for grid * work on conventions * run pre-commit * fix from face vertices * add sizes, dims, coordinates, and connectivity properties * reader * work on ugrid reader * use ugrid conventions module in esmf reader * update scrip reader * add test for from topo * update topology reader * update mpas reader * update exodus reader * update ugrid reader * fix tests after refactor * fix tests * update connectivity.py with refactor * update connectivity.py with refactor * investigate failing test * investigate failing test * investigate failing test * fix failing connectivity tests * testing * update grid.py * update user api * update face areas and add grid.to_netcdf * update ValueError * update internall api * add docstring to __getitem__ * add edge dimension to grid topology * docstring for Grid.to_netcdf * remove Grid.to_netcdf in favor of Grid.to_xarray * update repr * fix failing test * update docstrings * update repr * coments * fix failing test * remove leftover method in api * specify Cartesiain in xyz coordinates * add cartesiain coordinate names * update docstrings * update docstrings * update conventions --------- Co-authored-by: Hongyu Chen Co-authored-by: Orhan Eroglu <32553057+erogluorhan@users.noreply.github.com> --- docs/user_api/index.rst | 14 +- test/constants.py | 2 +- test/test_api.py | 2 +- test/test_from_topology.py | 87 +++++ test/test_grid.py | 32 +- uxarray/conventions/__init__.py | 0 uxarray/conventions/descriptors.py | 18 + uxarray/conventions/ugrid.py | 329 ++++++++++++++++++ uxarray/core/api.py | 25 +- uxarray/grid/connectivity.py | 44 +-- uxarray/grid/coordinates.py | 86 ++--- uxarray/grid/grid.py | 515 +++++++++++++++++------------ uxarray/io/_esmf.py | 70 ++-- uxarray/io/_exodus.py | 76 +---- uxarray/io/_mpas.py | 173 +++------- uxarray/io/_scrip.py | 45 ++- uxarray/io/_topology.py | 66 ++++ uxarray/io/_ugrid.py | 166 ++++++---- 18 files changed, 1073 insertions(+), 677 deletions(-) create mode 100644 test/test_from_topology.py create mode 100644 uxarray/conventions/__init__.py create mode 100644 uxarray/conventions/descriptors.py create mode 100644 uxarray/conventions/ugrid.py create mode 100644 uxarray/io/_topology.py diff --git a/docs/user_api/index.rst b/docs/user_api/index.rst index 9128262c5..a84e856bd 100644 --- a/docs/user_api/index.rst +++ b/docs/user_api/index.rst @@ -220,6 +220,11 @@ Dimensions Grid.n_face Grid.n_max_face_nodes Grid.n_max_face_edges + Grid.n_max_face_faces + Grid.n_max_edge_edges + Grid.n_max_node_faces + Grid.n_max_node_edges + Grid.n_max_node_nodes Grid.n_nodes_per_face Spherical Coordinates @@ -255,13 +260,12 @@ Connectivity :toctree: generated/ Grid.face_node_connectivity - Grid.edge_node_connectivity - Grid.node_node_connectivity Grid.face_edge_connectivity - Grid.edge_edge_connectivity - Grid.node_edge_connectivity Grid.face_face_connectivity + Grid.edge_node_connectivity + Grid.edge_edge_connectivity Grid.edge_face_connectivity + Grid.node_edge_connectivity Grid.node_face_connectivity Grid Descriptors @@ -279,7 +283,7 @@ Attributes :toctree: generated/ Grid.grid_spec - Grid.parsed_attrs + Grid.attrs Plotting diff --git a/test/constants.py b/test/constants.py index 9132ead37..4a54e1b1a 100644 --- a/test/constants.py +++ b/test/constants.py @@ -5,7 +5,7 @@ NNODES_outCSne8 = 386 NNODES_outCSne30 = 5402 NNODES_outRLL1deg = 64442 -DATAVARS_outCSne30 = 2 +DATAVARS_outCSne30 = 4 TRI_AREA = 1.047 # 4*Pi is 12.56 MESH30_AREA = 12.566 diff --git a/test/test_api.py b/test/test_api.py index 6fc7f0d9c..61679f1f7 100644 --- a/test/test_api.py +++ b/test/test_api.py @@ -131,7 +131,7 @@ def test_open_dataset_grid_kwargs(self): """Drops ``Mesh2_face_nodes`` from the inputted grid file using ``grid_kwargs``""" - with self.assertRaises(KeyError): + with self.assertRaises(ValueError): # attempt to open a dataset after dropping face nodes should raise a KeyError uxds = ux.open_dataset( self.gridfile_ne30, diff --git a/test/test_from_topology.py b/test/test_from_topology.py new file mode 100644 index 000000000..878b94b42 --- /dev/null +++ b/test/test_from_topology.py @@ -0,0 +1,87 @@ +import uxarray as ux + +from uxarray.constants import INT_FILL_VALUE +import numpy.testing as nt +import os + +import pytest + +from pathlib import Path + +current_path = Path(os.path.dirname(os.path.realpath(__file__))) + +GRID_PATHS = [ + current_path / 'meshfiles' / "mpas" / "QU" / 'oQU480.231010.nc', + current_path / "meshfiles" / "ugrid" / "geoflow-small" / "grid.nc", + current_path / "meshfiles" / "ugrid" / "outCSne30" / "outCSne30.ug" +] + + + + +def test_minimal_class_method(): + """Tests the minimal required variables for constructing a grid using the + from topology class method.""" + + for grid_path in GRID_PATHS: + uxgrid = ux.open_grid(grid_path) + + uxgrid_ft = ux.Grid.from_topology(node_lon=uxgrid.node_lon.values, + node_lat=uxgrid.node_lat.values, + face_node_connectivity=uxgrid.face_node_connectivity.values, + fill_value=INT_FILL_VALUE, + start_index=0) + + nt.assert_array_equal(uxgrid.node_lon.values, uxgrid_ft.node_lon.values) + nt.assert_array_equal(uxgrid.node_lat.values, uxgrid_ft.node_lat.values) + nt.assert_array_equal(uxgrid.face_node_connectivity.values, uxgrid_ft.face_node_connectivity.values) + + +def test_minimal_api(): + """Tests the minimal required variables for constructing a grid using the + ``ux.open_dataset`` method.""" + + for grid_path in GRID_PATHS: + uxgrid = ux.open_grid(grid_path) + + uxgrid_ft = ux.Grid.from_topology(node_lon=uxgrid.node_lon.values, + node_lat=uxgrid.node_lat.values, + face_node_connectivity=uxgrid.face_node_connectivity.values, + fill_value=INT_FILL_VALUE, + start_index=0) + + grid_topology = {'node_lon': uxgrid.node_lon.values, + 'node_lat': uxgrid.node_lat.values, + 'face_node_connectivity': uxgrid.face_node_connectivity.values, + 'fill_value': INT_FILL_VALUE, + 'start_index': 0} + + uxgrid_ft = ux.open_grid(grid_topology) + + nt.assert_array_equal(uxgrid.node_lon.values, uxgrid_ft.node_lon.values) + nt.assert_array_equal(uxgrid.node_lat.values, uxgrid_ft.node_lat.values) + nt.assert_array_equal(uxgrid.face_node_connectivity.values, uxgrid_ft.face_node_connectivity.values) + + +def test_dataset(): + uxds = ux.open_dataset(GRID_PATHS[0], GRID_PATHS[0]) + + grid_topology = {'node_lon': uxds.uxgrid.node_lon.values, + 'node_lat': uxds.uxgrid.node_lat.values, + 'face_node_connectivity': uxds.uxgrid.face_node_connectivity.values, + 'fill_value': INT_FILL_VALUE, + 'start_index': 0, + "dims_dict" : {"nVertices": "n_node"}} + + + uxds_ft = ux.open_grid(grid_topology, GRID_PATHS[1]) + + uxgrid = uxds.uxgrid + uxgrid_ft = uxds_ft + + + nt.assert_array_equal(uxgrid.node_lon.values, uxgrid_ft.node_lon.values) + nt.assert_array_equal(uxgrid.node_lat.values, uxgrid_ft.node_lat.values) + nt.assert_array_equal(uxgrid.face_node_connectivity.values, uxgrid_ft.face_node_connectivity.values) + + assert uxds_ft.dims == {'n_face', 'n_node', 'n_max_face_nodes'} diff --git a/test/test_grid.py b/test/test_grid.py index b1e654d16..730969383 100644 --- a/test/test_grid.py +++ b/test/test_grid.py @@ -643,6 +643,8 @@ def test_build_face_edges_connectivity_mpas(self): """Tests the construction of (``Mesh2_edge_nodes``) on an MPAS grid with known edge nodes.""" + from uxarray.grid.connectivity import _build_edge_node_connectivity + # grid with known edge node connectivity mpas_grid_ux = ux.open_grid(self.mpas_filepath) edge_nodes_expected = mpas_grid_ux._ds['edge_node_connectivity'].values @@ -652,8 +654,12 @@ def test_build_face_edges_connectivity_mpas(self): edge_nodes_expected = np.unique(edge_nodes_expected, axis=0) # construct edge nodes - _build_edge_node_connectivity(mpas_grid_ux, repopulate=True) - edge_nodes_output = mpas_grid_ux._ds['edge_node_connectivity'].values + edge_nodes_output, _, _ = _build_edge_node_connectivity(mpas_grid_ux.face_node_connectivity.values, + mpas_grid_ux.n_face, + mpas_grid_ux.n_max_face_nodes) + + # _populate_face_edge_connectivity(mpas_grid_ux) + # edge_nodes_output = mpas_grid_ux._ds['edge_node_connectivity'].values self.assertTrue(np.array_equal(edge_nodes_expected, edge_nodes_output)) @@ -705,28 +711,6 @@ def test_build_face_edges_connectivity(self): np.array_equal(reverted_mesh2_edge_nodes[i], original_face_nodes_connectivity[i])) - def test_build_face_edges_connectivity_mpas(self): - tgrid = ux.open_grid(self.mpas_filepath) - - face_node_connectivity = tgrid._ds["face_node_connectivity"] - - _populate_face_edge_connectivity(tgrid) - mesh2_face_edges = tgrid._ds.face_edge_connectivity - mesh2_edge_nodes = tgrid._ds.edge_node_connectivity - - # Assert if the mesh2_face_edges sizes are correct. - self.assertEqual(mesh2_face_edges.sizes["n_face"], - face_node_connectivity.sizes["n_face"]) - self.assertEqual(mesh2_face_edges.sizes["n_max_face_edges"], - face_node_connectivity.sizes["n_max_face_nodes"]) - - # Assert if the mesh2_edge_nodes sizes are correct. - # Euler formular for determining the edge numbers: n_face = n_edges - n_nodes + 2 - num_edges = mesh2_face_edges.sizes["n_face"] + tgrid._ds[ - "node_lon"].sizes["n_node"] - 2 - size = mesh2_edge_nodes.sizes["n_edge"] - self.assertEqual(mesh2_edge_nodes.sizes["n_edge"], num_edges) - def test_build_face_edges_connectivity_fillvalues(self): verts = [ self.f0_deg, self.f1_deg, self.f2_deg, self.f3_deg, self.f4_deg, diff --git a/uxarray/conventions/__init__.py b/uxarray/conventions/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/uxarray/conventions/descriptors.py b/uxarray/conventions/descriptors.py new file mode 100644 index 000000000..14cc2a727 --- /dev/null +++ b/uxarray/conventions/descriptors.py @@ -0,0 +1,18 @@ +DESCRIPTOR_NAMES = ["face_areas", "edge_face_distances", "edge_node_distances"] + + +FACE_AREAS_DIMS = ["n_face"] + +FACE_AREAS_ATTRS = {"cf_role": "face_areas"} + +EDGE_FACE_DISTANCES_DIMS = ["n_edge"] +EDGE_FACE_DISTANCES_ATTRS = { + "cf_role": "edge_face_distances", + "long_name": "Distances between the face centers that " "saddle each edge", +} + +EDGE_NODE_DISTANCES_DIMS = ["n_edge"] +EDGE_NODE_DISTANCES_ATTRS = { + "cf_role": "edge_node_distances", + "long_name": "Distances between the nodes that make up " "each edge.", +} diff --git a/uxarray/conventions/ugrid.py b/uxarray/conventions/ugrid.py new file mode 100644 index 000000000..0353dbe49 --- /dev/null +++ b/uxarray/conventions/ugrid.py @@ -0,0 +1,329 @@ +from uxarray.constants import INT_FILL_VALUE, INT_DTYPE + +CONVENTIONS_ATTR = "UGRID-v1.0" + +# minimum grid topology, additions are made depending on what is present in a grid +BASE_GRID_TOPOLOGY_ATTRS = { + "cf_role": "mesh_topology", + "topology_dimension": 2, + # dimensions + "face_dimension": "n_face", + "node_dimension": "n_node", + # coordinates + "node_coordinates": "node_lon node_lat", + # connectivity + "face_node_connectivity": "face_node_connectivity", +} + +DIM_NAMES = [ + "n_node", + "n_edge", + "n_face", + "n_max_face_nodes", + "n_max_face_edges", + "n_max_face_faces", + "n_max_edge_nodes", + "n_max_edge_edges", + "n_max_edge_faces", + "n_max_node_faces", + "n_max_node_edges", + "two", +] + +NODE_DIM = "n_node" +EDGE_DIM = "n_edge" +FACE_DIM = "n_face" +N_MAX_FACE_NODES_DIM = "n_max_face_nodes" + +NODE_COORDINATES = ["node_lon", "node_lat"] + +# coordinates (spherical) +NODE_LON_ATTRS = { + "standard_name": "longitude", + "long name": "Longitude of the corner nodes of each face", + "units": "degrees_east", +} + +NODE_LAT_ATTRS = { + "standard_name": "latitude", + "long name": "Latitude of the corner nodes of each face", + "units": "degrees_north", +} + +EDGE_COORDINATES = ["edge_lon", "edge_lat"] + +EDGE_LON_ATTRS = { + "standard_name": "longitude", + "long name": "Longitude of the center of each edge", + "units": "degrees_east", +} + +EDGE_LAT_ATTRS = { + "standard_name": "latitude", + "long name": "Latitude of the center of each edge", + "units": "degrees_north", +} + +FACE_COORDINATES = ["face_lon", "face_lat"] + +FACE_LON_ATTRS = { + "standard_name": "longitude", + "long name": "Longitude of the center of each face", + "units": "degrees_east", +} + +FACE_LAT_ATTRS = { + "standard_name": "latitude", + "long name": "Latitude of the center of each face", + "units": "degrees_north", +} + +CARTESIAN_NODE_COORDINATES = ["node_x", "node_y", "node_z"] + +NODE_X_ATTRS = { + "standard_name": "x", + "long name": "Cartesian x location of the corner nodes of each face", + "units": "meters", +} + +NODE_Y_ATTRS = { + "standard_name": "y", + "long name": "Cartesian y location of the corner nodes of each face", + "units": "meters", +} + +NODE_Z_ATTRS = { + "standard_name": "z", + "long name": "Cartesian z location of the corner nodes of each face", + "units": "meters", +} + +CARTESIAN_EDGE_COORDINATES = ["edge_x", "edge_y", "edge_z"] + +EDGE_X_ATTRS = { + "standard_name": "x", + "long name": "Cartesian x location of the center of each edge", + "units": "meters", +} + +EDGE_Y_ATTRS = { + "standard_name": "y", + "long name": "Cartesian y location of the center of each edge", + "units": "meters", +} + +EDGE_Z_ATTRS = { + "standard_name": "z", + "long name": "Cartesian z location of the center of each edge", + "units": "meters", +} + +CARTESIAN_FACE_COORDINATES = ["face_x", "face_y", "face_z"] + +FACE_X_ATTRS = { + "standard_name": "x", + "long name": "Cartesian x location of the center of each face", + "units": "meters", +} + +FACE_Y_ATTRS = { + "standard_name": "y", + "long name": "Cartesian y location of the center of each face", + "units": "meters", +} + +FACE_Z_ATTRS = { + "standard_name": "z", + "long name": "Cartesian z location of the center of each face", + "units": "meters", +} + +# connectivity (face_) +FACE_NODE_CONNECTIVITY_ATTRS = { + "cf_role": "face_node_connectivity", + "long name": "Maps every face to its corner nodes.", + "start_index": 0, + "_FillValue": INT_FILL_VALUE, + "dtype": INT_DTYPE, +} +FACE_NODE_CONNECTIVITY_DIMS = ["n_face", "n_max_face_nodes"] + +FACE_EDGE_CONNECTIVITY_ATTRS = { + "cf_role": "face_edge_connectivity", + "long name": "Maps every face to its edges.", + "start_index": 0, + "_FillValue": INT_FILL_VALUE, + "dtype": INT_DTYPE, +} +FACE_EDGE_CONNECTIVITY_DIMS = [ + "n_face", + "n_max_face_edges", +] # n_max_face_edges equiv to n_max_face_nodes + +FACE_FACE_CONNECTIVITY_ATTRS = { + "cf_role": "face_face_connectivity", + "long name": "Faces that neighbor each face.", + "start_index": 0, + "_FillValue": INT_FILL_VALUE, + "dtype": INT_DTYPE, +} +FACE_FACE_CONNECTIVITY_DIMS = [ + "n_face", + "n_max_face_faces", +] + +# connectivity (edge_) +EDGE_NODE_CONNECTIVITY_ATTRS = { + "cf_role": "edge_node_connectivity", + "long name": "Maps every edge to the two nodes that it connects.", + "start_index": 0, + "dtype": INT_DTYPE, +} +EDGE_NODE_CONNECTIVITY_DIMS = ["n_edge", "two"] + + +# edge_edge_connectivity not yet supported +# EDGE_EDGE_CONNECTIVITY_ATTRS = { +# "cf_role": "edge_edge_connectivity", +# "long name": "Edges that neighbor each edge", +# "start_index": 0, +# "_FillValue": INT_FILL_VALUE, +# "dtype": INT_DTYPE, +# } +# +# EDGE_EDGE_CONNECTIVITY_DIMS = ["n_edge", "n_max_edge_edges"] + +EDGE_FACE_CONNECTIVITY_ATTRS = { + "cf_role": "edge_face_connectivity", + "long name": "Faces that neighbor each edge", + "start_index": 0, + "_FillValue": INT_FILL_VALUE, + "dtype": INT_DTYPE, +} +EDGE_FACE_CONNECTIVITY_DIMS = ["n_edge", "two"] + + +NODE_EDGE_CONNECTIVITY_ATTRS = { + "cf_role": "node_edge_connectivity", + "long name": "Edges that neighbor each node", + "start_index": 0, + "_FillValue": INT_FILL_VALUE, + "dtype": INT_DTYPE, +} + +NODE_EDGE_CONNECTIVITY_DIMS = ["n_node", "n_max_node_edges"] + +NODE_FACE_CONNECTIVITY_ATTRS = { + "cf_role": "node_face_connectivity", + "long name": "Faces that neighbor each node", + "start_index": 0, + "_FillValue": INT_FILL_VALUE, + "dtype": INT_DTYPE, +} + +NODE_FACE_CONNECTIVITY_DIMS = ["n_node", "n_max_node_faces"] + + +N_NODES_PER_FACE_ATTRS = { + "cf_role": "n_nodes_per_face", + "long name": "Number of nodes per face", + "dtype": INT_DTYPE, +} + +N_NODES_PER_FACE_DIMS = ["n_face"] + + +CONNECTIVITY_NAMES = [ + "face_node_connectivity", + "face_edge_connectivity", + "face_face_connectivity", + "edge_node_connectivity", + "edge_face_connectivity", + "node_edge_connectivity", + "node_face_connectivity", +] + +# as of UGRID v1.0 +UGRID_COMPLIANT_CONNECTIVITY_NAMES = [ + "edge_node_connectivity", + "face_node_connectivity", + "face_edge_connectivity", + "edge_face_connectivity", + "face_face_connectivity", +] +CONNECTIVITY = { + "face_node_connectivity": { + "dims": FACE_NODE_CONNECTIVITY_DIMS, + "attrs": FACE_NODE_CONNECTIVITY_ATTRS, + }, + "face_edge_connectivity": { + "dims": FACE_EDGE_CONNECTIVITY_DIMS, + "attrs": FACE_EDGE_CONNECTIVITY_ATTRS, + }, + "face_face_connectivity": { + "dims": FACE_FACE_CONNECTIVITY_DIMS, + "attrs": FACE_FACE_CONNECTIVITY_ATTRS, + }, + "edge_node_connectivity": { + "dims": EDGE_NODE_CONNECTIVITY_DIMS, + "attrs": EDGE_NODE_CONNECTIVITY_ATTRS, + }, + # "edge_edge_connectivity": { + # "dims": EDGE_EDGE_CONNECTIVITY_DIMS, + # "attrs": EDGE_EDGE_CONNECTIVITY_ATTRS, + # }, + "edge_face_connectivity": { + "dims": EDGE_FACE_CONNECTIVITY_DIMS, + "attrs": EDGE_FACE_CONNECTIVITY_ATTRS, + }, + "node_edge_connectivity": { + "dims": NODE_EDGE_CONNECTIVITY_DIMS, + "attrs": NODE_EDGE_CONNECTIVITY_ATTRS, + }, + "node_face_connectivity": { + "dims": NODE_FACE_CONNECTIVITY_DIMS, + "attrs": NODE_FACE_CONNECTIVITY_ATTRS, + }, +} + +SPHERICAL_COORD_NAMES = [ + "node_lon", + "node_lat", + "edge_lon", + "edge_lat", + "face_lon", + "face_lat", +] + +SPHERICAL_COORDS = { + "node_lon": {"dims": [NODE_DIM], "attrs": NODE_LON_ATTRS}, + "node_lat": {"dims": [NODE_DIM], "attrs": NODE_LAT_ATTRS}, + "edge_lon": {"dims": [EDGE_DIM], "attrs": EDGE_LON_ATTRS}, + "edge_lat": {"dims": [EDGE_DIM], "attrs": EDGE_LAT_ATTRS}, + "face_lon": {"dims": [FACE_DIM], "attrs": FACE_LON_ATTRS}, + "face_lat": {"dims": [FACE_DIM], "attrs": FACE_LAT_ATTRS}, +} + +CARTESIAN_COORD_NAMES = [ + "node_x", + "node_y", + "node_z", + "edge_x", + "edge_y", + "edge_z", + "face_x", + "face_y", + "face_z", +] + +CARTESIAN_COORDS = { + "node_x": {"dims": [NODE_DIM], "attrs": NODE_X_ATTRS}, + "node_y": {"dims": [NODE_DIM], "attrs": NODE_Y_ATTRS}, + "node_z": {"dims": [NODE_DIM], "attrs": NODE_Z_ATTRS}, + "edge_x": {"dims": [EDGE_DIM], "attrs": EDGE_X_ATTRS}, + "edge_y": {"dims": [EDGE_DIM], "attrs": EDGE_Y_ATTRS}, + "edge_z": {"dims": [EDGE_DIM], "attrs": EDGE_Z_ATTRS}, + "face_x": {"dims": [FACE_DIM], "attrs": FACE_X_ATTRS}, + "face_y": {"dims": [FACE_DIM], "attrs": FACE_Y_ATTRS}, + "face_z": {"dims": [FACE_DIM], "attrs": FACE_Z_ATTRS}, +} diff --git a/uxarray/core/api.py b/uxarray/core/api.py index 884929443..95bbba5c1 100644 --- a/uxarray/core/api.py +++ b/uxarray/core/api.py @@ -15,18 +15,19 @@ def open_grid( grid_filename_or_obj: Union[ - str, os.PathLike, xr.DataArray, np.ndarray, list, tuple + str, os.PathLike, xr.DataArray, np.ndarray, list, tuple, dict ], latlon: Optional[bool] = False, use_dual: Optional[bool] = False, **kwargs: Dict[str, Any], ) -> Grid: - """Creates a ``uxarray.Grid`` object from a grid topology definition. + """Constructs and returns an ``uxarray.Grid`` object from a grid topology + definition. Parameters ---------- - grid_filename_or_obj : string, xarray.Dataset, ndarray, list, tuple, required + grid_filename_or_obj : string, xarray.Dataset, ndarray, list, tuple, dict, required String or Path object as a path to a netCDF file or an OpenDAP URL that stores the unstructured grid topology/definition. It is read similar to ``filename_or_obj`` in ``xarray.open_dataset``. Otherwise, either @@ -56,7 +57,7 @@ def open_grid( Open dataset with a grid topology file - >>> import uxarray as ux + >>> Import uxarray as ux >>> uxgrid = ux.open_grid("grid_filename.g") """ @@ -67,12 +68,16 @@ def open_grid( stacklevel=2, ) - # construct Grid from dataset if isinstance(grid_filename_or_obj, xr.Dataset): + # construct a grid from a dataset file uxgrid = Grid.from_dataset(grid_filename_or_obj, use_dual=use_dual) - # construct Grid from face vertices + elif isinstance(grid_filename_or_obj, dict): + # unpack the dictionary and construct a grid from topology + uxgrid = Grid.from_topology(**grid_filename_or_obj) + elif isinstance(grid_filename_or_obj, (list, tuple, np.ndarray, xr.DataArray)): + # construct Grid from face vertices uxgrid = Grid.from_face_vertices(grid_filename_or_obj, latlon=latlon) # attempt to use Xarray directly for remaining input types @@ -90,7 +95,9 @@ def open_grid( def open_dataset( - grid_filename_or_obj: str, + grid_filename_or_obj: Union[ + str, os.PathLike, xr.DataArray, np.ndarray, list, tuple, dict + ], filename_or_obj: str, latlon: Optional[bool] = False, use_dual: Optional[bool] = False, @@ -172,7 +179,9 @@ def open_dataset( def open_mfdataset( - grid_filename_or_obj: str, + grid_filename_or_obj: Union[ + str, os.PathLike, xr.DataArray, np.ndarray, list, tuple, dict + ], paths: Union[str, os.PathLike], latlon: Optional[bool] = False, use_dual: Optional[bool] = False, diff --git a/uxarray/grid/connectivity.py b/uxarray/grid/connectivity.py index c57dc60e6..ee3654e47 100644 --- a/uxarray/grid/connectivity.py +++ b/uxarray/grid/connectivity.py @@ -4,6 +4,7 @@ from scipy import sparse from uxarray.constants import INT_DTYPE, INT_FILL_VALUE +from uxarray.conventions import ugrid from numba import njit @@ -137,8 +138,8 @@ def _populate_n_nodes_per_face(grid): # add to internal dataset grid._ds["n_nodes_per_face"] = xr.DataArray( data=n_nodes_per_face, - dims=["n_face"], - attrs={"long_name": "number of non-fill value nodes for each face"}, + dims=ugrid.N_NODES_PER_FACE_DIMS, + attrs=ugrid.N_NODES_PER_FACE_ATTRS, ) @@ -165,18 +166,13 @@ def _populate_edge_node_connectivity(grid): grid.face_node_connectivity.values, grid.n_face, grid.n_max_face_nodes ) + edge_node_attrs = ugrid.EDGE_NODE_CONNECTIVITY_ATTRS + edge_node_attrs["inverse_indices"] = inverse_indices + edge_node_attrs["fill_value_mask"] = fill_value_mask + # add edge_node_connectivity to internal dataset grid._ds["edge_node_connectivity"] = xr.DataArray( - edge_nodes, - dims=["n_edge", "Two"], - attrs={ - "cf_role": "edge_node_connectivity", - "_FillValue": INT_FILL_VALUE, - "long_name": "Maps every edge to the two nodes that it connects", - "start_index": INT_DTYPE(0), - "inverse_indices": inverse_indices, - "fill_value_mask": fill_value_mask, - }, + edge_nodes, dims=ugrid.EDGE_NODE_CONNECTIVITY_DIMS, attrs=edge_node_attrs ) @@ -250,12 +246,8 @@ def _populate_edge_face_connectivity(grid): grid._ds["edge_face_connectivity"] = xr.DataArray( data=edge_faces, - dims=["n_edge", "Two"], - attrs={ - "cf_role": "edge_face_connectivity", - "start_index": INT_DTYPE(0), - "long_name": "Maps the faces that saddle a given edge", - }, + dims=ugrid.EDGE_FACE_CONNECTIVITY_DIMS, + attrs=ugrid.EDGE_FACE_CONNECTIVITY_ATTRS, ) @@ -297,12 +289,8 @@ def _populate_face_edge_connectivity(grid): grid._ds["face_edge_connectivity"] = xr.DataArray( data=face_edges, - dims=["n_face", "n_max_face_edges"], - attrs={ - "cf_role": "face_edges_connectivity", - "start_index": INT_DTYPE(0), - "long_name": "Maps every edge to the two nodes that it connects", - }, + dims=ugrid.FACE_EDGE_CONNECTIVITY_DIMS, + attrs=ugrid.FACE_EDGE_CONNECTIVITY_ATTRS, ) @@ -323,12 +311,8 @@ def _populate_node_face_connectivity(grid): grid._ds["node_face_connectivity"] = xr.DataArray( node_faces, - dims=["n_node", "n_max_node_faces"], # TODO - attrs={ - "long_name": "Maps every node to the faces that " "it connects", - "nMaxNumFacesPerNode": n_max_faces_per_node, # todo, this possibly duplicates nNodes_per_face - "_FillValue": INT_FILL_VALUE, - }, + dims=ugrid.NODE_FACE_CONNECTIVITY_DIMS, + attrs=ugrid.NODE_FACE_CONNECTIVITY_ATTRS, ) diff --git a/uxarray/grid/coordinates.py b/uxarray/grid/coordinates.py index 23a8ab7b1..2aba490ea 100644 --- a/uxarray/grid/coordinates.py +++ b/uxarray/grid/coordinates.py @@ -7,6 +7,7 @@ from numba import njit, config from uxarray.constants import ENABLE_JIT_CACHE, ENABLE_JIT, ERROR_TOLERANCE +from uxarray.conventions import ugrid config.DISABLE_JIT = not ENABLE_JIT @@ -150,28 +151,13 @@ def _populate_cartesian_xyz_coord(grid): x, y, z = _get_xyz_from_lonlat(grid.node_lon.values, grid.node_lat.values) grid._ds["node_x"] = xr.DataArray( - data=x, - dims=["n_node"], - attrs={ - "standard_name": "cartesian x", - "units": "m", - }, + data=x, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_X_ATTRS ) grid._ds["node_y"] = xr.DataArray( - data=y, - dims=["n_node"], - attrs={ - "standard_name": "cartesian y", - "units": "m", - }, + data=y, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_Y_ATTRS ) grid._ds["node_z"] = xr.DataArray( - data=z, - dims=["n_node"], - attrs={ - "standard_name": "cartesian z", - "units": "m", - }, + data=z, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_Z_ATTRS ) @@ -214,20 +200,10 @@ def _populate_lonlat_coord(grid): # populate dataset grid._ds["node_lon"] = xr.DataArray( - data=lon, - dims=["n_node"], - attrs={ - "long_name": "longitude of corner nodes", - "units": "degrees_east", - }, + data=lon, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_LON_ATTRS ) grid._ds["node_lat"] = xr.DataArray( - data=lat, - dims=["n_node"], - attrs={ - "long_name": "latitude of corner nodes", - "units": "degrees_north", - }, + data=lat, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_LAT_ATTRS ) @@ -275,23 +251,23 @@ def _populate_face_centroids(grid, repopulate=False): # Populate the centroids if "face_lon" not in grid._ds or repopulate: grid._ds["face_lon"] = xr.DataArray( - centroid_lon, dims=["n_face"], attrs={"units": "degrees_east"} + centroid_lon, dims=[ugrid.FACE_DIM], attrs=ugrid.FACE_LON_ATTRS ) grid._ds["face_lat"] = xr.DataArray( - centroid_lat, dims=["n_face"], attrs={"units": "degrees_north"} + centroid_lat, dims=[ugrid.FACE_DIM], attrs=ugrid.FACE_LAT_ATTRS ) if "face_x" not in grid._ds or repopulate: grid._ds["face_x"] = xr.DataArray( - centroid_x, dims=["n_face"], attrs={"units": "meters"} + centroid_x, dims=[ugrid.FACE_DIM], attrs=ugrid.FACE_X_ATTRS ) grid._ds["face_y"] = xr.DataArray( - centroid_y, dims=["n_face"], attrs={"units": "meters"} + centroid_y, dims=[ugrid.FACE_DIM], attrs=ugrid.FACE_Y_ATTRS ) grid._ds["face_z"] = xr.DataArray( - centroid_z, dims=["n_face"], attrs={"units": "meters"} + centroid_z, dims=[ugrid.FACE_DIM], attrs=ugrid.FACE_Z_ATTRS ) @@ -361,53 +337,31 @@ def _populate_edge_centroids(grid, repopulate=False): # Populate the centroids if "edge_lon" not in grid._ds or repopulate: grid._ds["edge_lon"] = xr.DataArray( - centroid_lon, - dims=["n_edge"], - attrs={ - "standard_name": "longitude", - "long name": "Longitude of the center of each edge", - "units": "degrees_east", - }, + centroid_lon, dims=[ugrid.EDGE_DIM], attrs=ugrid.EDGE_LON_ATTRS ) grid._ds["edge_lat"] = xr.DataArray( centroid_lat, - dims=["n_edge"], - attrs={ - "standard_name": "latitude", - "long name": "Latitude of the center of each edge", - "units": "degrees_north", - }, + dims=[ugrid.EDGE_DIM], + attrs=ugrid.EDGE_LAT_ATTRS, ) if "edge_x" not in grid._ds or repopulate: grid._ds["edge_x"] = xr.DataArray( centroid_x, - dims=["n_edge"], - attrs={ - "standard_name": "x", - "long name": "x coordinate of the center of each edge", - "units": "meters", - }, + dims=[ugrid.EDGE_DIM], + attrs=ugrid.EDGE_X_ATTRS, ) grid._ds["edge_y"] = xr.DataArray( centroid_y, - dims=["n_edge"], - attrs={ - "standard_name": "y", - "long name": "y coordinate of the center of each edge", - "units": "meters", - }, + dims=[ugrid.EDGE_DIM], + attrs=ugrid.EDGE_Y_ATTRS, ) grid._ds["edge_z"] = xr.DataArray( centroid_z, - dims=["n_edge"], - attrs={ - "standard_name": "z", - "long name": "z coordinate of the center of each edge", - "units": "meters", - }, + dims=[ugrid.EDGE_DIM], + attrs=ugrid.EDGE_Z_ATTRS, ) diff --git a/uxarray/grid/grid.py b/uxarray/grid/grid.py index 4830d5a97..1d5b1a52d 100644 --- a/uxarray/grid/grid.py +++ b/uxarray/grid/grid.py @@ -2,15 +2,24 @@ import xarray as xr import numpy as np -from typing import Optional, Union +from typing import ( + Optional, + Union, +) + # reader and writer imports from uxarray.io._exodus import _read_exodus, _encode_exodus from uxarray.io._mpas import _read_mpas -from uxarray.io._ugrid import _read_ugrid, _encode_ugrid, _validate_minimum_ugrid +from uxarray.io._ugrid import ( + _read_ugrid, + _encode_ugrid, + _validate_minimum_ugrid, +) from uxarray.io._scrip import _read_scrip, _encode_scrip from uxarray.io._esmf import _read_esmf from uxarray.io._vertices import _read_face_vertices +from uxarray.io._topology import _read_topology from uxarray.io.utils import _parse_grid_type from uxarray.grid.area import get_all_face_area_from_coords @@ -110,10 +119,10 @@ def __init__( # check if inputted dataset is a minimum representable 2D UGRID unstructured grid if not _validate_minimum_ugrid(grid_ds): raise ValueError( - "Direct use of Grid constructor requires grid_ds to follow the internal unstructured grid definition, " - "including variable and dimension names. This grid_ds does not satisfy those requirements. If you are " - "not sure about how to do that, using ux.open_grid() or ux.from_dataset() is suggested." - ) # TODO: elaborate once we have a formal definition + "Grid unable to be represented in the UGRID conventions. Representing an unstructured grid requires " + "at least the following variables: ['node_lon'," + "'node_lat', and 'face_node_connectivity']" + ) # grid spec not provided, check if grid_ds is a minimum representable UGRID dataset if source_grid_spec is None: @@ -136,7 +145,6 @@ def __init__( # initialize attributes self._antimeridian_face_indices = None - self._face_areas = None # initialize cached data structures (visualization) self._gdf = None @@ -202,6 +210,61 @@ def from_dataset( return cls(grid_ds, source_grid_spec, source_dims_dict) + @classmethod + def from_topology( + cls, + node_lon: np.ndarray, + node_lat: np.ndarray, + face_node_connectivity: np.ndarray, + fill_value: Optional = None, + start_index: Optional[int] = 0, + dims_dict: Optional[dict] = None, + **kwargs, + ): + """Constructs a ``Grid`` object from user-defined topology variables + provided in the UGRID conventions. + + Note + ---- + To construct a UGRID-complient grid, the user must provide at least ``node_lon``, ``node_lat`` and ``face_node_connectivity`` + + Parameters + ---------- + node_lon : np.ndarray + Longitude of node coordinates + node_lat : np.ndarray + Latitude of node coordinates + face_node_connectivity : np.ndarray + Face node connectivity, mapping each face to the nodes that surround them + fill_value: Optional + Value used for padding connectivity variables when the maximum number of elements in a row is less than the maximum. + start_index: Optional, default=0 + Start index (typically 0 or 1) + dims_dict : Optional, dict + Dictionary of dimension names mapped to the ugrid conventions (i.e. {"nVertices": "n_node}) + **kwargs : + + Usage + ----- + >>> import uxarray as ux + >>> node_lon, node_lat, face_node_connectivity, fill_value = ... + >>> uxgrid = ux.Grid.from_ugrid(node_lon, node_lat, face_node_connectivity, fill_value) + """ + + if dims_dict is None: + dims_dict = {} + + grid_ds = _read_topology( + node_lon, + node_lat, + face_node_connectivity, + fill_value, + start_index, + **kwargs, + ) + grid_spec = "User Defined Topology" + return cls(grid_ds, grid_spec, dims_dict) + @classmethod def from_face_vertices( cls, @@ -265,88 +328,48 @@ def validate(self): def __repr__(self): """Constructs a string representation of the contents of a ``Grid``.""" + + from uxarray.conventions import ugrid, descriptors + prefix = "\n" original_grid_str = f"Original Grid Type: {self.source_grid_spec}\n" dims_heading = "Grid Dimensions:\n" dims_str = "" - for key, value in zip(self._ds.sizes.keys(), self._ds.sizes.values()): - dims_str += f" * {key}: {value}\n" + for dim_name in ugrid.DIM_NAMES: + if dim_name in self._ds.sizes: + dims_str += f" * {dim_name}: {self._ds.sizes[dim_name]}\n" dims_str += f" * n_nodes_per_face: {self.n_nodes_per_face.shape}\n" coord_heading = "Grid Coordinates (Spherical):\n" coords_str = "" - if "node_lon" in self._ds: - coords_str += f" * node_lon: {self.node_lon.shape}\n" - coords_str += f" * node_lat: {self.node_lat.shape}\n" - if "edge_lon" in self._ds: - coords_str += f" * edge_lon: {self.edge_lon.shape}\n" - coords_str += f" * edge_lat: {self.edge_lat.shape}\n" - if "face_lon" in self._ds: - coords_str += f" * face_lon: {self.face_lon.shape}\n" - coords_str += f" * face_lat: {self.face_lat.shape}\n" + for coord_name in ugrid.SPHERICAL_COORD_NAMES: + if coord_name in self._ds: + coords_str += f" * {coord_name}: {getattr(self, coord_name).shape}\n" coords_str += "Grid Coordinates (Cartesian):\n" - if "node_x" in self._ds: - coords_str += f" * node_x: {self.node_x.shape}\n" - coords_str += f" * node_y: {self.node_y.shape}\n" - coords_str += f" * node_z: {self.node_z.shape}\n" - if "edge_x" in self._ds: - coords_str += f" * edge_x: {self.edge_x.shape}\n" - coords_str += f" * edge_y: {self.edge_y.shape}\n" - coords_str += f" * edge_z: {self.edge_z.shape}\n" - if "face_x" in self._ds: - coords_str += f" * face_x: {self.face_x.shape}\n" - coords_str += f" * face_y: {self.face_y.shape}\n" - coords_str += f" * face_z: {self.face_z.shape}\n" + for coord_name in ugrid.CARTESIAN_COORD_NAMES: + if coord_name in self._ds: + coords_str += f" * {coord_name}: {getattr(self, coord_name).shape}\n" connectivity_heading = "Grid Connectivity Variables:\n" connectivity_str = "" - if "face_node_connectivity" in self._ds: - connectivity_str += ( - f" * face_node_connectivity: {self.face_node_connectivity.shape}\n" - ) - - if "edge_node_connectivity" in self._ds: - connectivity_str += ( - f" * edge_node_connectivity: {self.edge_node_connectivity.shape}\n" - ) - if "node_node_connectivity" in self._ds: - connectivity_str += ( - f" * node_node_connectivity: {self.node_node_connectivity.shape}\n" - ) - - if "face_edge_connectivity" in self._ds: - connectivity_str += ( - f" * face_edge_connectivity: {self.face_edge_connectivity.shape}\n" - ) - - if "edge_edge_connectivity" in self._ds: - connectivity_str += ( - f" * edge_edge_connectivity: {self.edge_edge_connectivity.shape}\n" - ) - - if "node_edge_connectivity" in self._ds: - connectivity_str += ( - f" * node_edge_connectivity: {self.node_edge_connectivity.shape}\n" - ) - - if "face_face_connectivity" in self._ds: - connectivity_str += ( - f" * face_face_connectivity: {self.face_face_connectivity.shape}\n" - ) + for conn_name in ugrid.CONNECTIVITY_NAMES: + if conn_name in self._ds: + connectivity_str += ( + f" * {conn_name}: {getattr(self, conn_name).shape}\n" + ) - if "edge_face_connectivity" in self._ds: - connectivity_str += ( - f" * edge_face_connectivity: {self.edge_face_connectivity.shape}\n" - ) + descriptors_heading = "Grid Descriptor Variables:\n" + descriptors_str = "" - if "node_face_connectivity" in self._ds: - connectivity_str += ( - f" * node_face_connectivity: {self.node_face_connectivity.shape}\n" - ) + for descriptor_name in descriptors.DESCRIPTOR_NAMES: + if descriptor_name in self._ds: + descriptors_str += ( + f" * {descriptor_name}: {getattr(self, descriptor_name).shape}\n" + ) return ( prefix @@ -357,8 +380,20 @@ def __repr__(self): + coords_str + connectivity_heading + connectivity_str + + descriptors_heading + + descriptors_str ) + def __getitem__(self, item): + """Implementation of getitem operator for indexing a grid to obtain + variables. + + Usage + ----- + >>> uxgrid['face_node_connectivity'] + """ + return getattr(self, item) + def __eq__(self, other) -> bool: """Two grids are equal if they have matching grid topology variables, coordinates, and dims all of which are equal. @@ -404,82 +439,118 @@ def __ne__(self, other) -> bool: """ return not self.__eq__(other) + @property + def dims(self) -> set: + """Names of all unstructured grid dimensions.""" + from uxarray.conventions.ugrid import DIM_NAMES + + return set([dim for dim in DIM_NAMES if dim in self._ds.dims]) + + @property + def sizes(self) -> dict: + """Names and values of all unstructured grid dimensions.""" + return {dim: self._ds.dims[dim] for dim in self.dims} + + @property + def coordinates(self) -> set: + """Names of all coordinate variables.""" + from uxarray.conventions.ugrid import ( + SPHERICAL_COORD_NAMES, + CARTESIAN_COORD_NAMES, + ) + + return set( + [coord for coord in SPHERICAL_COORD_NAMES if coord in self._ds] + ).union(set([coord for coord in CARTESIAN_COORD_NAMES if coord in self._ds])) + + @property + def connectivity(self) -> set: + """Names of all connectivity variables.""" + from uxarray.conventions.ugrid import CONNECTIVITY_NAMES + + return set([conn for conn in CONNECTIVITY_NAMES if conn in self._ds]) + @property def parsed_attrs(self) -> dict: """Dictionary of parsed attributes from the source grid.""" + warn( + "Grid.parsed_attrs will be deprecated in a future release. Please use Grid.attrs instead.", + DeprecationWarning, + ) return self._ds.attrs @property - def Mesh2(self) -> xr.DataArray: - """UGRID Attribute ``Mesh2``, which indicates the topology data of a 2D - unstructured mesh. - - Internal use has been deprecated. - """ - return self._ds["Mesh2"] + def attrs(self) -> dict: + """Dictionary of parsed attributes from the source grid.""" + return self._ds.attrs - # ================================================================================================================== - # Grid Dimensions @property def n_node(self) -> int: - """Dimension ``n_node``, which represents the total number of unique - corner nodes.""" + """Total number of nodes.""" return self._ds.sizes["n_node"] - @property - def n_face(self) -> int: - """Dimension ``n_face``, which represents the total number of unique - faces.""" - return self._ds.sizes["n_face"] - @property def n_edge(self) -> int: - """Dimension ``n_edge``, which represents the total number of unique - edges.""" + """Total number of edges.""" if "edge_node_connectivity" not in self._ds: _populate_edge_node_connectivity(self) return self._ds.sizes["n_edge"] - # ================================================================================================================== + @property + def n_face(self) -> int: + """Total number of faces.""" + return self._ds.sizes["n_face"] + @property def n_max_face_nodes(self) -> int: - """Dimension ``n_max_face_nodes``, which represents the maximum number - of nodes that a face may contain.""" + """The maximum number of nodes that can make up a single face.""" return self.face_node_connectivity.shape[1] @property - def n_max_face_edges(self) -> xr.DataArray: - """Dimension ``n_max_face_edges``, which represents the maximum number - of edges per face. + def n_max_face_edges(self) -> int: + """The maximum number of edges that surround a single face. Equivalent to ``n_max_face_nodes`` """ - if "n_max_face_edges" not in self._ds: - _populate_face_edge_connectivity(self) + return self.face_edge_connectivity.shape[1] + + @property + def n_max_face_faces(self) -> int: + """The maximum number of faces that surround a single face.""" + return self.face_face_connectivity.shape[1] + + @property + def n_max_edge_edges(self) -> int: + """The maximum number of edges that surround a single edge.""" + return self.edge_edge_connectivity.shape[1] + + @property + def n_max_node_faces(self) -> int: + """The maximum number of faces that surround a single node.""" + return self.node_face_connectivity.shape[1] - return self._ds["face_edge_connectivity"].shape[1] + @property + def n_max_node_edges(self) -> int: + """The maximum number of edges that surround a single node.""" + return self.node_edge_connectivity.shape[1] @property def n_nodes_per_face(self) -> xr.DataArray: - """Dimension Variable ``n_nodes_per_face``, which contains the number - of non-fill-value nodes per face. + """The number of nodes that make up each face. - Dimensions (``n_node``) and DataType ``INT_DTYPE``. + Dimensions: ``(n_node, )`` """ if "n_nodes_per_face" not in self._ds: _populate_n_nodes_per_face(self) return self._ds["n_nodes_per_face"] - # ================================================================================================================== - # Spherical Node Coordinates @property def node_lon(self) -> xr.DataArray: - """Coordinate ``node_lon``, which contains the longitude of each node - in degrees. + """Longitude of each node in degrees. - Dimensions (``n_node``) + Dimensions: ``(n_node, )`` """ if "node_lon" not in self._ds: _set_desired_longitude_range(self._ds) @@ -488,24 +559,20 @@ def node_lon(self) -> xr.DataArray: @property def node_lat(self) -> xr.DataArray: - """Coordinate ``node_lat``, which contains the latitude of each node in - degrees. + """Latitude of each node in degrees. - Dimensions (``n_node``) + Dimensions: ``(n_node, )`` """ if "node_lat" not in self._ds: _set_desired_longitude_range(self._ds) _populate_lonlat_coord(self) return self._ds["node_lat"] - # ================================================================================================================== - # Cartesian Node Coordinates @property def node_x(self) -> xr.DataArray: - """Coordinate ``node_x``, which contains the Cartesian x location of - each node in meters. + """Cartesian x location of each node in meters. - Dimensions (``n_node``) + Dimensions: ``(n_node, )`` """ if "node_x" not in self._ds: _populate_cartesian_xyz_coord(self) @@ -514,10 +581,9 @@ def node_x(self) -> xr.DataArray: @property def node_y(self) -> xr.DataArray: - """Coordinate ``node_y``, which contains the Cartesian y location of - each node in meters. + """Cartesian y location of each node in meters. - Dimensions (``n_node``) + Dimensions: ``(n_node, )`` """ if "node_y" not in self._ds: _populate_cartesian_xyz_coord(self) @@ -525,23 +591,19 @@ def node_y(self) -> xr.DataArray: @property def node_z(self) -> xr.DataArray: - """Coordinate ``node_z``, which contains the Cartesian y location of - each node in meters. + """Cartesian z location of each node in meters. - Dimensions (``n_node``) + Dimensions: ``(n_node, )`` """ if "node_z" not in self._ds: _populate_cartesian_xyz_coord(self) return self._ds["node_z"] - # ================================================================================================================== - # Spherical Edge Coordinates @property def edge_lon(self) -> xr.DataArray: - """Coordinate ``edge_lon``, which contains the longitude of each edge - in degrees. + """Longitude of the center of each edge in degrees. - Dimensions (``n_edge``) + Dimensions: ``(n_edge, )`` """ if "edge_lon" not in self._ds: _populate_edge_centroids(self) @@ -551,24 +613,20 @@ def edge_lon(self) -> xr.DataArray: @property def edge_lat(self) -> xr.DataArray: - """Coordinate ``edge_lat``, which contains the latitude of each edge in - degrees. + """Latitude of the center of each edge in degrees. - Dimensions (``n_edge``) + Dimensions: ``(n_edge, )`` """ if "edge_lat" not in self._ds: _populate_edge_centroids(self) _set_desired_longitude_range(self._ds) return self._ds["edge_lat"] - # ================================================================================================================== - # Cartesian Edge Coordinates @property def edge_x(self) -> xr.DataArray: - """Coordinate ``edge_x``, which contains the Cartesian x location of - each edge in meters. + """Cartesian x location of the center of each edge in meters. - Dimensions (``n_edge``) + Dimensions: ``(n_edge, )`` """ if "edge_x" not in self._ds: _populate_edge_centroids(self) @@ -577,10 +635,9 @@ def edge_x(self) -> xr.DataArray: @property def edge_y(self) -> xr.DataArray: - """Coordinate ``edge_y``, which contains the Cartesian y location of - each edge in meters. + """Cartesian y location of the center of each edge in meters. - Dimensions (``n_edge``) + Dimensions: ``(n_edge, )`` """ if "edge_y" not in self._ds: _populate_edge_centroids(self) @@ -588,23 +645,19 @@ def edge_y(self) -> xr.DataArray: @property def edge_z(self) -> xr.DataArray: - """Coordinate ``edge_z``, which contains the Cartesian z location of - each edge in meters. + """Cartesian z location of the center of each edge in meters. - Dimensions (``n_edge``) + Dimensions: ``(n_edge, )`` """ if "edge_z" not in self._ds: _populate_edge_centroids(self) return self._ds["edge_z"] - # ================================================================================================================== - # Spherical Face Coordinates @property def face_lon(self) -> xr.DataArray: - """Coordinate ``face_lon``, which contains the longitude of each face - in degrees. + """Longitude of the center of each face in degrees. - Dimensions (``n_face``) + Dimensions: ``(n_face, )`` """ if "face_lon" not in self._ds: _populate_face_centroids(self) @@ -613,10 +666,9 @@ def face_lon(self) -> xr.DataArray: @property def face_lat(self) -> xr.DataArray: - """Coordinate ``face_lat``, which contains the latitude of each face in - degrees. + """Latitude of the center of each face in degrees. - Dimensions (``n_face``) + Dimensions: ``(n_face, )`` """ if "face_lat" not in self._ds: _populate_face_centroids(self) @@ -624,14 +676,11 @@ def face_lat(self) -> xr.DataArray: return self._ds["face_lat"] - # ================================================================================================================== - # Cartesian Face Coordinates @property def face_x(self) -> xr.DataArray: - """Coordinate ``face_x``, which contains the Cartesian x location of - each face in meters. + """Cartesian x location of the center of each face in meters. - Dimensions (``n_face``) + Dimensions: ``(n_face, )`` """ if "face_x" not in self._ds: _populate_face_centroids(self) @@ -640,10 +689,9 @@ def face_x(self) -> xr.DataArray: @property def face_y(self) -> xr.DataArray: - """Coordinate ``face_y``, which contains the Cartesian y location of - each face in meters. + """Cartesian y location of the center of each face in meters. - Dimensions (``n_face``) + Dimensions: ``(n_face, )`` """ if "face_y" not in self._ds: _populate_face_centroids(self) @@ -651,24 +699,19 @@ def face_y(self) -> xr.DataArray: @property def face_z(self) -> xr.DataArray: - """Coordinate ``face_z``, which contains the Cartesian z location of - each face in meters. + """Cartesian z location of the center of each face in meters. - Dimensions (``n_face``) + Dimensions: ``(n_face, )`` """ if "face_z" not in self._ds: _populate_face_centroids(self) return self._ds["face_z"] - # ================================================================================================================== - # (, node) Connectivity @property def face_node_connectivity(self) -> xr.DataArray: - """Connectivity Variable ``face_node_connectivity``, which maps each - face to its corner nodes. + """Indices of the nodes that make up each face. - Dimensions (``n_face``, ``n_max_face_nodes``) and - DataType ``INT_DTYPE``. + Dimensions: ``(n_face, n_max_face_nodes)`` Nodes are in counter-clockwise order. """ @@ -688,11 +731,9 @@ def face_node_connectivity(self) -> xr.DataArray: @property def edge_node_connectivity(self) -> xr.DataArray: - """Connectivity Variable ``edge_node_connectivity``, which maps every - edge to the two nodes that it connects. + """Indices of the two nodes that make up each edge. - Dimensions (``n_edge``, ``two``) and DataType - ``INT_DTYPE``. + Dimensions: ``(n_edge, n_max_edge_nodes)`` Nodes are in arbitrary order. """ @@ -703,18 +744,18 @@ def edge_node_connectivity(self) -> xr.DataArray: @property def node_node_connectivity(self) -> xr.DataArray: - """Connectivity Variable ``node_node_connectivity``.""" - return None + """Indices of the nodes that surround each node.""" + if "node_node_connectivity" not in self._ds: + raise NotImplementedError( + "Construction of `node_node_connectivity` not yet supported." + ) + return self._ds["node_node_connectivity"] - # ================================================================================================================== - # (, edge) Connectivity @property def face_edge_connectivity(self) -> xr.DataArray: - """Connectivity Variable ``face_edge_connectivity``, which maps every - face to its edges. + """Indices of the edges that surround each face. - Dimensions (``n_face``, ``n_max_face_nodes``) and DataType - ``INT_DTYPE``. + Dimensions: ``(n_face, n_max_face_edges)`` """ if "face_edge_connectivity" not in self._ds: _populate_face_edge_connectivity(self) @@ -723,27 +764,45 @@ def face_edge_connectivity(self) -> xr.DataArray: @property def edge_edge_connectivity(self) -> xr.DataArray: - """Connectivity Variable ``edge_edge_connectivity``.""" - return None + """Indices of the edges that surround each edge. + + Dimensions: ``(n_face, n_max_edge_edges)`` + """ + if "edge_edge_connectivity" not in self._ds: + raise NotImplementedError( + "Construction of `edge_edge_connectivity` not yet supported." + ) + + return self._ds["edge_edge_connectivity"] @property def node_edge_connectivity(self) -> xr.DataArray: - """Connectivity Variable ``node_edge_connectivity``.""" - return None + """Indices of the edges that surround each node.""" + if "node_edge_connectivity" not in self._ds: + raise NotImplementedError( + "Construction of `node_edge_connectivity` not yet supported." + ) + + return self._ds["node_edge_connectivity"] - # ================================================================================================================== - # (, face) Connectivity @property def face_face_connectivity(self) -> xr.DataArray: - """Connectivity Variable ``face_face_connectivity``.""" - return None + """Indices of the faces that surround each face. + + Dimensions ``(n_face, n_max_face_faces)`` + """ + if "face_face_connectivity" not in self._ds: + raise NotImplementedError( + "Construction of `face_face_connectivity` not yet supported." + ) + + return self._ds["face_face_connectivity"] @property def edge_face_connectivity(self) -> xr.DataArray: - """Connectivity Variable ``edge_face_connectivity``, which contains the - index of the faces that saddle a given edge. + """Indices of the faces that saddle each edge. - Dimensions (``n_edge``, ``TWO``) and DataType ``INT_DTYPE``. + Dimensions ``(n_edge, two)`` """ if "edge_face_connectivity" not in self._ds: _populate_edge_face_connectivity(self) @@ -752,24 +811,20 @@ def edge_face_connectivity(self) -> xr.DataArray: @property def node_face_connectivity(self) -> xr.DataArray: - """Connectivity Variable ``node_face_connectivity``, which maps every - node to its faces. + """Indices of the faces that surround each node. - Dimensions (``n_node``, ``n_max_faces_per_node``) and DataType - ``INT_DTYPE``. + Dimensions ``(n_node, n_max_node_faces)`` """ if "node_face_connectivity" not in self._ds: _populate_node_face_connectivity(self) return self._ds["node_face_connectivity"] - # ================================================================================================================== - # Distance Quantities @property def edge_node_distances(self): - """Contains the distance between the nodes that saddle a given edge. + """Distances between the two nodes that surround each edge. - Dimensions (``n_edge``) and DataType float. + Dimensions ``(n_edge, )`` """ if "edge_node_distances" not in self._ds: _populate_edge_node_distances(self) @@ -777,16 +832,14 @@ def edge_node_distances(self): @property def edge_face_distances(self): - """Contains the distance between the faces that saddle a given edge. + """Distances between the centers of the faces that saddle each edge. - Dimensions (``n_edge``) and DataType float. + Dimensions ``(n_edge, )`` """ if "edge_face_distances" not in self._ds: _populate_edge_face_distances(self) return self._ds["edge_face_distances"] - # ================================================================================================================== - # Other Grid Descriptor Quantities @property def antimeridian_face_indices(self) -> np.ndarray: """Index of each face that crosses the antimeridian.""" @@ -795,21 +848,22 @@ def antimeridian_face_indices(self) -> np.ndarray: return self._antimeridian_face_indices @property - def face_areas(self) -> np.ndarray: - """Declare face_areas as a property.""" - # if self._face_areas is not None: it allows for using the cached result - if self._face_areas is None: - self._face_areas, self._face_jacobian = self.compute_face_areas() - return self._face_areas - - # ================================================================================================================== + def face_areas(self) -> xr.DataArray: + """The area of each face.""" + from uxarray.conventions.descriptors import FACE_AREAS_DIMS, FACE_AREAS_ATTRS + + if "face_areas" not in self._ds: + face_areas, self._face_jacobian = self.compute_face_areas() + self._ds["face_areas"] = xr.DataArray( + data=face_areas, dims=FACE_AREAS_DIMS, attrs=FACE_AREAS_ATTRS + ) + return self._ds["face_areas"] @property def face_jacobian(self): """Declare face_jacobian as a property.""" - # if self._face_jacobian is not None: it allows for using the cached result if self._face_jacobian is None: - self._face_areas, self._face_jacobian = self.compute_face_areas() + _ = self.face_areas return self._face_jacobian def get_ball_tree( @@ -937,6 +991,10 @@ def encode_as(self, grid_type: str) -> xr.Dataset: If provided grid type or file type is unsupported. """ + warn( + "Grid.encode_as will be deprecated in a future release. Please use Grid.to_xarray instead." + ) + if grid_type == "UGRID": out_ds = _encode_ugrid(self._ds) @@ -1062,6 +1120,43 @@ def compute_face_areas( return self._face_areas, self._face_jacobian + def to_xarray(self, grid_format: Optional[str] = "ugrid"): + """Returns a xarray Dataset representation in a specific grid format + from the Grid object. + + Parameters + ---------- + grid_format: str, optional + The desired grid format for the output dataset. + One of "ugrid", "exodus", or "scrip" + + Returns + ------- + out_ds: xarray.Dataset + Dataset representing the unstructured grid in a given grid format + """ + + if grid_format == "ugrid": + out_ds = _encode_ugrid(self._ds) + + elif grid_format == "exodus": + out_ds = _encode_exodus(self._ds) + + elif grid_format == "scrip": + out_ds = _encode_scrip( + self.face_node_connectivity, + self.node_lon, + self.node_lat, + self.face_areas, + ) + + else: + raise ValueError( + f"Invalid grid_format encountered. Expected one of ['ugrid', 'exodus', 'scrip'] but received: {grid_format}" + ) + + return out_ds + def to_geodataframe( self, override: Optional[bool] = False, diff --git a/uxarray/io/_esmf.py b/uxarray/io/_esmf.py index e21be9e7f..f16abc657 100644 --- a/uxarray/io/_esmf.py +++ b/uxarray/io/_esmf.py @@ -3,6 +3,8 @@ from uxarray.constants import INT_DTYPE, INT_FILL_VALUE +from uxarray.conventions import ugrid + def _read_esmf(in_ds): """Reads in an Xarray dataset containing an ESMF formatted Grid dataset and @@ -37,60 +39,33 @@ def _read_esmf(in_ds): out_ds = xr.Dataset() source_dims_dict = { - "nodeCount": "n_node", - "elementCount": "n_face", - "maxNodePElement": "n_max_face_nodes", + "nodeCount": ugrid.NODE_DIM, + "elementCount": ugrid.FACE_DIM, + "maxNodePElement": ugrid.N_MAX_FACE_NODES_DIM, } if in_ds["nodeCoords"].units == "degrees": # Spherical Coordinates (in degrees) node_lon = in_ds["nodeCoords"].isel(coordDim=0).values - node_lat = in_ds["nodeCoords"].isel(coordDim=1).values - - out_ds["node_lon"] = xr.DataArray( - node_lon, - dims=["n_node"], - attrs={ - "standard_name": "longitude", - "long_name": "longitude of mesh nodes", - "units": "degrees_east", - }, + out_ds[ugrid.NODE_COORDINATES[0]] = xr.DataArray( + node_lon, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_LON_ATTRS ) - out_ds["node_lat"] = xr.DataArray( - node_lat, - dims=["n_node"], - attrs={ - "standard_name": "latitude", - "long_name": "latitude of mesh nodes", - "units": "degrees_north", - }, + node_lat = in_ds["nodeCoords"].isel(coordDim=1).values + out_ds[ugrid.NODE_COORDINATES[1]] = xr.DataArray( + node_lat, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_LAT_ATTRS ) if "centerCoords" in in_ds: - # parse center coords (face centers) if avaliable - + # parse center coords (face centers) if available face_lon = in_ds["centerCoords"].isel(coordDim=0).values - face_lat = in_ds["centerCoords"].isel(coordDim=1).values - - out_ds["face_lon"] = xr.DataArray( - face_lon, - dims=["n_face"], - attrs={ - "standard_name": "longitude", - "long_name": "longitude of center nodes", - "units": "degrees_east", - }, + out_ds[ugrid.FACE_COORDINATES[0]] = xr.DataArray( + face_lon, dims=[ugrid.FACE_DIM], attrs=ugrid.FACE_LON_ATTRS ) - out_ds["face_lat"] = xr.DataArray( - face_lat, - dims=["n_face"], - attrs={ - "standard_name": "latitude", - "long_name": "latitude of center nodes", - "units": "degrees_north", - }, + face_lat = in_ds["centerCoords"].isel(coordDim=1).values + out_ds[ugrid.FACE_COORDINATES[1]] = xr.DataArray( + face_lat, dims=[ugrid.FACE_DIM], attrs=ugrid.FACE_LAT_ATTRS ) else: @@ -99,11 +74,10 @@ def _read_esmf(in_ds): ) n_nodes_per_face = in_ds["numElementConn"].values.astype(INT_DTYPE) - out_ds["n_nodes_per_face"] = xr.DataArray( data=n_nodes_per_face, - dims=["n_face"], - attrs={"long_name": "number of non-fill value nodes for each face"}, + dims=ugrid.N_NODES_PER_FACE_DIMS, + attrs=ugrid.N_NODES_PER_FACE_ATTRS, ) if "start_index" in in_ds["elementConn"]: @@ -121,12 +95,8 @@ def _read_esmf(in_ds): out_ds["face_node_connectivity"] = xr.DataArray( data=face_node_connectivity, - dims=["n_face", "n_max_face_nodes"], - attrs={ - "cf_role": "face_node_connectivity", - "_FillValue": INT_FILL_VALUE, - "start_index": INT_DTYPE(0), - }, + dims=ugrid.FACE_NODE_CONNECTIVITY_DIMS, + attrs=ugrid.FACE_NODE_CONNECTIVITY_ATTRS, ) return out_ds, source_dims_dict diff --git a/uxarray/io/_exodus.py b/uxarray/io/_exodus.py index acbcfa8c7..205b68d36 100644 --- a/uxarray/io/_exodus.py +++ b/uxarray/io/_exodus.py @@ -7,6 +7,8 @@ from uxarray.constants import INT_DTYPE, INT_FILL_VALUE from uxarray.grid.coordinates import _get_lonlat_from_xyz, _get_xyz_from_lonlat +from uxarray.conventions import ugrid + # Exodus Number is one-based. def _read_exodus(ext_ds): @@ -40,63 +42,29 @@ def _read_exodus(ext_ds): pass elif key == "coord": ds["node_x"] = xr.DataArray( - data=ext_ds.coord[0], - dims=["n_node"], - attrs={ - "standard_name": "x", - "long_name": "cartesian x", - "units": "m", - }, + data=ext_ds.coord[0], dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_X_ATTRS ) ds["node_y"] = xr.DataArray( - data=ext_ds.coord[1], - dims=["n_node"], - attrs={ - "standard_name": "y", - "long_name": "cartesian y", - "units": "m", - }, + data=ext_ds.coord[1], dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_Y_ATTRS ) if ext_ds.sizes["num_dim"] > 2: ds["node_z"] = xr.DataArray( data=ext_ds.coord[2], - dims=["n_node"], - attrs={ - "standard_name": "z", - "long_name": "cartesian z", - "units": "m", - }, + dims=[ugrid.NODE_DIM], + attrs=ugrid.NODE_Z_ATTRS, ) elif key == "coordx": ds["node_x"] = xr.DataArray( - data=ext_ds.coordx, - dims=["n_node"], - attrs={ - "standard_name": "x", - "long_name": "cartesian x", - "units": "m", - }, + data=ext_ds.coordx, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_X_ATTRS ) elif key == "coordy": ds["node_y"] = xr.DataArray( - data=ext_ds.coordx, - dims=["n_node"], - attrs={ - "standard_name": "y", - "long_name": "cartesian y", - "units": "m", - }, + data=ext_ds.coordx, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_Y_ATTRS ) elif key == "coordz": if ext_ds.sizes["num_dim"] > 2: ds["node_z"] = xr.DataArray( - data=ext_ds.coordx, - dims=["n_node"], - attrs={ - "standard_name": "z", - "long_name": "cartesian z", - "units": "m", - }, + data=ext_ds.coordx, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_Z_ATTRS ) elif "connect" in key: # check if num face nodes is less than max. @@ -127,14 +95,8 @@ def _read_exodus(ext_ds): ds["face_node_connectivity"] = xr.DataArray( data=face_nodes, - dims=["n_face", "n_max_face_nodes"], - attrs={ - "cf_role": "face_node_connectivity", - "_FillValue": INT_FILL_VALUE, - "start_index": INT_DTYPE( - 0 - ), # NOTE: This might cause an error if numbering has holes - }, + dims=ugrid.FACE_NODE_CONNECTIVITY_DIMS, + attrs=ugrid.FACE_NODE_CONNECTIVITY_ATTRS, ) # populate lon/lat coordinates @@ -144,22 +106,10 @@ def _read_exodus(ext_ds): # populate dataset ds["node_lon"] = xr.DataArray( - data=lon, - dims=["n_node"], - attrs={ - "standard_name": "longitude", - "long_name": "longitude of mesh nodes", - "units": "degrees_east", - }, + data=lon, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_LON_ATTRS ) ds["node_lat"] = xr.DataArray( - data=lat, - dims=["n_node"], - attrs={ - "standard_name": "latitude", - "long_name": "latitude of mesh nodes", - "units": "degrees_north", - }, + data=lat, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_LAT_ATTRS ) # set lon/lat coordinates diff --git a/uxarray/io/_mpas.py b/uxarray/io/_mpas.py index dc038c3c8..c175a9a1a 100644 --- a/uxarray/io/_mpas.py +++ b/uxarray/io/_mpas.py @@ -2,6 +2,7 @@ import numpy as np from uxarray.constants import INT_DTYPE, INT_FILL_VALUE +from uxarray.conventions import ugrid, descriptors def _primal_to_ugrid(in_ds, out_ds): @@ -59,9 +60,9 @@ def _primal_to_ugrid(in_ds, out_ds): _parse_global_attrs(in_ds, out_ds) # populate source dims - source_dims_dict["nVertices"] = "n_node" - source_dims_dict[in_ds["verticesOnCell"].dims[0]] = "n_face" - source_dims_dict[in_ds["verticesOnCell"].dims[1]] = "n_max_face_nodes" + source_dims_dict["nVertices"] = ugrid.NODE_DIM + source_dims_dict[in_ds["verticesOnCell"].dims[0]] = ugrid.FACE_DIM + source_dims_dict[in_ds["verticesOnCell"].dims[1]] = ugrid.N_MAX_FACE_NODES_DIM return source_dims_dict @@ -121,9 +122,9 @@ def _dual_to_ugrid(in_ds, out_ds): _parse_global_attrs(in_ds, out_ds) # populate source dims - source_dims_dict[in_ds["latCell"].dims[0]] = "n_node" - source_dims_dict[in_ds["cellsOnVertex"].dims[0]] = "n_face" - source_dims_dict[in_ds["cellsOnVertex"].dims[1]] = "n_max_face_nodes" + source_dims_dict[in_ds["latCell"].dims[0]] = ugrid.NODE_DIM + source_dims_dict[in_ds["cellsOnVertex"].dims[0]] = ugrid.FACE_DIM + source_dims_dict[in_ds["cellsOnVertex"].dims[1]] = ugrid.N_MAX_FACE_NODES_DIM return source_dims_dict @@ -139,23 +140,11 @@ def _parse_node_latlon_coords(in_ds, out_ds, mesh_type): node_lat = np.rad2deg(in_ds["latCell"].values) out_ds["node_lon"] = xr.DataArray( - node_lon, - dims=["n_node"], - attrs={ - "standard_name": "longitude", - "long_name": "longitude of mesh nodes", - "units": "degrees_east", - }, + node_lon, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_LON_ATTRS ) out_ds["node_lat"] = xr.DataArray( - node_lat, - dims=["n_node"], - attrs={ - "standard_name": "latitude", - "long_name": "latitude of mesh nodes", - "units": "degrees_north", - }, + node_lat, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_LAT_ATTRS ) @@ -173,33 +162,15 @@ def _parse_node_xyz_coords(in_ds, out_ds, mesh_type): node_z = in_ds["zCell"].values out_ds["node_x"] = xr.DataArray( - data=node_x, - dims=["n_node"], - attrs={ - "standard_name": "x", - "long_name": "cartesian node x", - "units": "m", - }, + data=node_x, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_X_ATTRS ) out_ds["node_y"] = xr.DataArray( - data=node_y, - dims=["n_node"], - attrs={ - "standard_name": "y", - "long_name": "cartesian node y", - "units": "m", - }, + data=node_y, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_Y_ATTRS ) out_ds["node_z"] = xr.DataArray( - data=node_z, - dims=["n_node"], - attrs={ - "standard_name": "z", - "long_name": "cartesian node z", - "units": "m", - }, + data=node_z, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_Z_ATTRS ) @@ -214,23 +185,11 @@ def _parse_face_latlon_coords(in_ds, out_ds, mesh_type): face_lat = np.rad2deg(in_ds["latVertex"].values) out_ds["face_lon"] = xr.DataArray( - face_lon, - dims=["n_face"], - attrs={ - "standard_name": "longitude", - "long_name": "longitude of center nodes", - "units": "degrees_east", - }, + face_lon, dims=[ugrid.FACE_DIM], attrs=ugrid.FACE_LON_ATTRS ) out_ds["face_lat"] = xr.DataArray( - face_lat, - dims=["n_face"], - attrs={ - "standard_name": "latitude", - "long_name": "latitude of center nodes", - "units": "degrees_north", - }, + face_lat, dims=[ugrid.FACE_DIM], attrs=ugrid.FACE_LAT_ATTRS ) @@ -248,33 +207,15 @@ def _parse_face_xyz_coords(in_ds, out_ds, mesh_type): face_z = in_ds["zVertex"].values out_ds["face_x"] = xr.DataArray( - data=face_x, - dims=["n_face"], - attrs={ - "standard_name": "x", - "long_name": "cartesian edge x", - "units": "m", - }, + data=face_x, dims=[ugrid.FACE_DIM], attrs=ugrid.FACE_X_ATTRS ) out_ds["face_y"] = xr.DataArray( - data=face_y, - dims=["n_face"], - attrs={ - "standard_name": "y", - "long_name": "cartesian edge y", - "units": "m", - }, + data=face_y, dims=[ugrid.FACE_DIM], attrs=ugrid.FACE_Y_ATTRS ) out_ds["face_z"] = xr.DataArray( - data=face_z, - dims=["n_face"], - attrs={ - "standard_name": "z", - "long_name": "cartesian edge z", - "units": "m", - }, + data=face_z, dims=[ugrid.FACE_DIM], attrs=ugrid.FACE_Z_ATTRS ) @@ -286,23 +227,11 @@ def _parse_edge_latlon_coords(in_ds, out_ds, mesh_type): edge_lat = np.rad2deg(in_ds["latEdge"].values) out_ds["edge_lon"] = xr.DataArray( - edge_lon, - dims=["n_edge"], - attrs={ - "standard_name": "longitude", - "long_name": "longitude of edge centers", - "units": "degrees_east", - }, + edge_lon, dims=[ugrid.EDGE_DIM], attrs=ugrid.EDGE_LON_ATTRS ) out_ds["edge_lat"] = xr.DataArray( - edge_lat, - dims=["n_edge"], - attrs={ - "standard_name": "latitude", - "long_name": "latitude of edge centers", - "units": "degrees_north", - }, + edge_lat, dims=[ugrid.EDGE_DIM], attrs=ugrid.EDGE_LAT_ATTRS ) @@ -314,33 +243,15 @@ def _parse_edge_xyz_coords(in_ds, out_ds, mesh_type): edge_z = in_ds["zEdge"].values out_ds["edge_x"] = xr.DataArray( - data=edge_x, - dims=["n_edge"], - attrs={ - "standard_name": "x", - "long_name": "cartesian edge x", - "units": "m", - }, + data=edge_x, dims=[ugrid.EDGE_DIM], attrs=ugrid.EDGE_X_ATTRS ) out_ds["edge_y"] = xr.DataArray( - data=edge_y, - dims=["n_edge"], - attrs={ - "standard_name": "y", - "long_name": "cartesian edge y", - "units": "m", - }, + data=edge_y, dims=[ugrid.EDGE_DIM], attrs=ugrid.EDGE_Y_ATTRS ) out_ds["edge_z"] = xr.DataArray( - data=edge_z, - dims=["n_edge"], - attrs={ - "standard_name": "z", - "long_name": "cartesian edge z", - "units": "m", - }, + data=edge_z, dims=[ugrid.EDGE_DIM], attrs=ugrid.EDGE_Z_ATTRS ) @@ -373,18 +284,12 @@ def _parse_face_nodes(in_ds, out_ds, mesh_type): face_nodes = cellsOnVertex - face_nodes = xr.DataArray( + out_ds["face_node_connectivity"] = xr.DataArray( data=face_nodes, - dims=["n_face", "n_max_face_nodes"], - attrs={ - "cf_role": "face_node_connectivity", - "_FillValue": INT_FILL_VALUE, - "start_index": INT_DTYPE(0), - }, + dims=ugrid.FACE_NODE_CONNECTIVITY_DIMS, + attrs=ugrid.FACE_NODE_CONNECTIVITY_ATTRS, ) - out_ds["face_node_connectivity"] = face_nodes - def _parse_edge_nodes(in_ds, out_ds, mesh_type): """Parses edge node connectivity for either the Primal or Dual Mesh.""" @@ -413,8 +318,8 @@ def _parse_edge_nodes(in_ds, out_ds, mesh_type): out_ds["edge_node_connectivity"] = xr.DataArray( data=edge_nodes, - dims=["n_edge", "two"], - attrs={"cf_role": "edge_node_connectivity", "start_index": INT_DTYPE(0)}, + dims=ugrid.EDGE_NODE_CONNECTIVITY_DIMS, + attrs=ugrid.EDGE_NODE_CONNECTIVITY_ATTRS, ) @@ -448,8 +353,8 @@ def _parse_node_faces(in_ds, out_ds, mesh_type): out_ds["node_face_connectivity"] = xr.DataArray( data=node_faces, - dims=["n_node", "n_max_faces_per_node"], - attrs={"cf_role": "node_face_connectivity", "start_index": INT_DTYPE(0)}, + dims=ugrid.NODE_FACE_CONNECTIVITY_DIMS, + attrs=ugrid.NODE_FACE_CONNECTIVITY_ATTRS, ) @@ -484,12 +389,8 @@ def _parse_face_edges(in_ds, out_ds, mesh_type): out_ds["face_edge_connectivity"] = xr.DataArray( data=face_edges, - dims=["n_face", "n_max_face_nodes"], - attrs={ - "cf_role": "face_node_connectivity", - "_FillValue": INT_FILL_VALUE, - "start_index": INT_DTYPE(0), - }, + dims=ugrid.FACE_EDGE_CONNECTIVITY_DIMS, + attrs=ugrid.FACE_EDGE_CONNECTIVITY_ATTRS, ) @@ -521,8 +422,8 @@ def _parse_edge_faces(in_ds, out_ds, mesh_type): out_ds["edge_face_connectivity"] = xr.DataArray( data=edge_faces, - dims=["n_edge", "two"], - attrs={"cf_role": "edge_face_connectivity", "start_index": INT_DTYPE(0)}, + dims=ugrid.EDGE_FACE_CONNECTIVITY_DIMS, + attrs=ugrid.EDGE_FACE_CONNECTIVITY_ATTRS, ) @@ -531,7 +432,9 @@ def _parse_edge_node_distances(in_ds, out_ds): edge_node_distances = in_ds["dvEdge"].values out_ds["edge_node_distances"] = xr.DataArray( - data=edge_node_distances, dims=["n_edge"], attrs={"start_index": INT_DTYPE(0)} + data=edge_node_distances, + dims=descriptors.EDGE_NODE_DISTANCES_DIMS, + attrs=descriptors.EDGE_NODE_DISTANCES_ATTRS, ) @@ -540,7 +443,9 @@ def _parse_edge_face_distances(in_ds, out_ds): edge_face_distances = in_ds["dcEdge"].values out_ds["edge_face_distances"] = xr.DataArray( - data=edge_face_distances, dims=["n_edge"], attrs={"start_index": INT_DTYPE(0)} + data=edge_face_distances, + dims=descriptors.EDGE_FACE_DISTANCES_DIMS, + attrs=descriptors.EDGE_FACE_DISTANCES_ATTRS, ) diff --git a/uxarray/io/_scrip.py b/uxarray/io/_scrip.py index 5fe312499..c07824147 100644 --- a/uxarray/io/_scrip.py +++ b/uxarray/io/_scrip.py @@ -4,6 +4,8 @@ from uxarray.grid.connectivity import _replace_fill_values from uxarray.constants import INT_DTYPE, INT_FILL_VALUE +from uxarray.conventions import ugrid + def _to_ugrid(in_ds, out_ds): """If input dataset (``in_ds``) file is an unstructured SCRIP file, @@ -46,29 +48,26 @@ def _to_ugrid(in_ds, out_ds): unq_inv = np.reshape(unq_inv, (len(in_ds.grid_size), len(in_ds.grid_corners))) # Create node_lon & node_lat from unsorted, unique grid_corner_lat/lon - out_ds["node_lon"] = xr.DataArray( - unq_lon, - dims=["n_node"], - attrs={ - "standard_name": "longitude", - "long_name": "longitude of mesh nodes", - "units": "degrees_east", - }, + out_ds[ugrid.NODE_COORDINATES[0]] = xr.DataArray( + unq_lon, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_LON_ATTRS ) - out_ds["node_lat"] = xr.DataArray( - unq_lat, - dims=["n_node"], - attrs={ - "standard_name": "latitude", - "long_name": "latitude of mesh nodes", - "units": "degrees_north", - }, + out_ds[ugrid.NODE_COORDINATES[1]] = xr.DataArray( + unq_lat, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_LAT_ATTRS ) # Create face_lon & face_lat from grid_center_lat/lon - out_ds["face_lon"] = in_ds["grid_center_lon"] - out_ds["face_lat"] = in_ds["grid_center_lat"] + out_ds[ugrid.FACE_COORDINATES[0]] = xr.DataArray( + in_ds["grid_center_lon"].values, + dims=[ugrid.FACE_DIM], + attrs=ugrid.FACE_LON_ATTRS, + ) + + out_ds[ugrid.FACE_COORDINATES[1]] = xr.DataArray( + in_ds["grid_center_lat"].values, + dims=[ugrid.FACE_DIM], + attrs=ugrid.FACE_LAT_ATTRS, + ) # standardize fill values and data type face nodes face_nodes = _replace_fill_values( @@ -78,14 +77,8 @@ def _to_ugrid(in_ds, out_ds): # set the face nodes data compiled in "connect" section out_ds["face_node_connectivity"] = xr.DataArray( data=face_nodes, - dims=["n_face", "n_max_face_nodes"], - attrs={ - "cf_role": "face_node_connectivity", - "_FillValue": INT_FILL_VALUE, - "start_index": INT_DTYPE( - 0 - ), # NOTE: This might cause an error if numbering has holes - }, + dims=ugrid.FACE_NODE_CONNECTIVITY_DIMS, + attrs=ugrid.FACE_NODE_CONNECTIVITY_ATTRS, ) else: diff --git a/uxarray/io/_topology.py b/uxarray/io/_topology.py new file mode 100644 index 000000000..8335796a5 --- /dev/null +++ b/uxarray/io/_topology.py @@ -0,0 +1,66 @@ +import xarray as xr + +import uxarray.conventions.ugrid as ugrid +from uxarray.grid.connectivity import _replace_fill_values +from uxarray.constants import INT_FILL_VALUE, INT_DTYPE + + +def _read_topology( + node_lon, node_lat, face_node_connectivity, fill_value, start_index, **kwargs +): + ds = xr.Dataset() + + for coord in ugrid.SPHERICAL_COORD_NAMES: + # parse spherical coordinates + if coord in ["node_lon", "node_lat"] or coord in kwargs: + if coord == "node_lon": + coord_arr = node_lon + elif coord == "node_lat": + coord_arr = node_lat + else: + coord_arr = kwargs[coord] + + ds[coord] = xr.DataArray( + data=coord_arr, + dims=ugrid.SPHERICAL_COORDS[coord]["dims"], + attrs=ugrid.SPHERICAL_COORDS[coord]["attrs"], + ) + + for coord in ugrid.CARTESIAN_COORD_NAMES: + # parse cartesian coords + if coord in kwargs: + coord_arr = kwargs[coord] + + ds[coord] = xr.DataArray( + data=coord_arr, + dims=ugrid.CARTESIAN_COORDS[coord]["dims"], + attrs=ugrid.CARTESIAN_COORDS[coord]["attrs"], + ) + + for conn in ugrid.CONNECTIVITY_NAMES: + # parse connectivity + if conn == "face_node_connectivity" or conn in kwargs: + if conn == "face_node_connectivity": + conn_arr = face_node_connectivity + else: + conn_arr = kwargs[conn] + + ds[conn] = xr.DataArray( + data=_process_connectivity(conn_arr, fill_value, start_index), + dims=ugrid.CONNECTIVITY[conn]["dims"], + attrs=ugrid.CONNECTIVITY[conn]["attrs"], + ) + + return ds + + +def _process_connectivity(conn, orig_fv, start_index): + """Internal helper for processing connectivity variables, standardizing + fill values and converting to zero-index.""" + + if orig_fv != INT_FILL_VALUE: + conn = _replace_fill_values(conn, orig_fv, INT_FILL_VALUE, INT_DTYPE) + + conn[conn != INT_FILL_VALUE] -= start_index + + return conn diff --git a/uxarray/io/_ugrid.py b/uxarray/io/_ugrid.py index 057800365..7fb87544f 100644 --- a/uxarray/io/_ugrid.py +++ b/uxarray/io/_ugrid.py @@ -1,76 +1,118 @@ import numpy as np - +import xarray as xr from uxarray.grid.connectivity import _replace_fill_values from uxarray.constants import INT_DTYPE, INT_FILL_VALUE -def _read_ugrid(xr_ds): - """UGRID file reader. +import uxarray.conventions.ugrid as ugrid - Parameters: xarray.Dataset, required - Returns: ugrid aware xarray.Dataset - """ - source_dims_dict = {} - # TODO: obtain and change to Mesh2 construct, see Issue #27 +def _read_ugrid(ds): + """Parses an unstructured grid dataset and encodes it in the UGRID + conventions.""" - # get the data variable name that has attribute "cf_role" set to "mesh_topology" - # this is the base xarray.DataArray name - base_xarray_var = list(xr_ds.filter_by_attrs(cf_role="mesh_topology").keys())[0] - # TODO: Allow for parsing datasets with more than just coordinates and face nodes + # Grid Topology + grid_topology_name = list(ds.filter_by_attrs(cf_role="mesh_topology").keys())[0] + ds = ds.rename({grid_topology_name: "grid_topology"}) - xr_ds = xr_ds.rename({base_xarray_var: "Mesh2"}) + # Coordinates - # map and rename coordinates - coord_names = xr_ds["Mesh2"].node_coordinates.split() - if len(coord_names) == 1: - xr_ds = xr_ds.rename({coord_names[0]: "node_lon"}) - elif len(coord_names) == 2: - xr_ds = xr_ds.rename({coord_names[0]: "node_lon", coord_names[1]: "node_lat"}) - # map and rename dimensions - coord_dim_name = xr_ds["node_lon"].dims + # get the names of node_lon and node_lat + node_lon_name, node_lat_name = ds["grid_topology"].node_coordinates.split() + coord_dict = { + node_lon_name: ugrid.NODE_COORDINATES[0], + node_lat_name: ugrid.NODE_COORDINATES[1], + } - xr_ds = xr_ds.rename({coord_dim_name[0]: "n_node"}) + if "edge_coordinates" in ds["grid_topology"]: + # get the names of edge_lon and edge_lat, if they exist + edge_lon_name, edge_lat_name = ds["grid_topology"].edge_coordinates.split() + coord_dict[edge_lon_name] = ugrid.EDGE_COORDINATES[0] + coord_dict[edge_lat_name] = ugrid.EDGE_COORDINATES[1] - face_node_names = xr_ds["Mesh2"].face_node_connectivity.split() + if "face_coordinates" in ds["grid_topology"]: + # get the names of face_lon and face_lat, if they exist + face_lon_name, face_lat_name = ds["grid_topology"].edge_coordinates.split() + coord_dict[face_lon_name] = ugrid.FACE_COORDINATES[0] + coord_dict[face_lat_name] = ugrid.FACE_COORDINATES[1] - face_node_name = face_node_names[0] - xr_ds = xr_ds.rename({xr_ds[face_node_name].name: "face_node_connectivity"}) + ds = ds.rename(coord_dict) + # Connectivity - xr_ds = xr_ds.rename( - { - xr_ds["face_node_connectivity"].dims[0]: "n_face", - xr_ds["face_node_connectivity"].dims[1]: "n_max_face_nodes", - } - ) + conn_dict = {} + for conn_name in ugrid.CONNECTIVITY_NAMES: + if conn_name in ds.grid_topology.attrs: + orig_conn_name = ds.grid_topology.attrs[conn_name] + conn_dict[orig_conn_name] = conn_name + elif len(ds.filter_by_attrs(cf_role=conn_name).keys()): + orig_conn_name = list(ds.filter_by_attrs(cf_role=conn_name).keys())[0] + conn_dict[orig_conn_name] = conn_name - xr_ds = xr_ds.set_coords(["node_lon", "node_lat"]) + ds = ds.rename(conn_dict) - # standardize fill values and data type for face nodes - xr_ds = _standardize_fill_values(xr_ds) + for conn_name in conn_dict.values(): + ds = _standardize_connectivity(ds, conn_name) - # populate source dimensions - source_dims_dict[coord_dim_name[0]] = "n_node" - source_dims_dict[xr_ds["face_node_connectivity"].dims[0]] = "n_face" - source_dims_dict[xr_ds["face_node_connectivity"].dims[1]] = "n_max_face_nodes" + dim_dict = {} - return xr_ds, source_dims_dict + # Rename Core Dims (node, edge, face) + if "node_dimension" in ds["grid_topology"]: + dim_dict[ds["grid_topology"].node_dimension] = ugrid.NODE_DIM + else: + dim_dict[ds["node_lon"].dims[0]] = ugrid.NODE_DIM + + if "face_dimension" in ds["grid_topology"]: + dim_dict[ds["grid_topology"].face_dimension] = ugrid.FACE_DIM + else: + dim_dict[ds["face_node_connectivity"].dims[0]] = ugrid.FACE_DIM + + if "edge_dimension" in ds["grid_topology"]: + # edge dimension is not always provided + dim_dict[ds["grid_topology"].edge_dimension] = ugrid.EDGE_DIM + else: + if "edge_lon" in ds: + dim_dict[ds["edge_lon"].dims[0]] = ugrid.EDGE_DIM + + dim_dict[ds["face_node_connectivity"].dims[1]] = ugrid.N_MAX_FACE_NODES_DIM + + ds = ds.swap_dims(dim_dict) + + return ds, dim_dict def _encode_ugrid(ds): - """Encodes UGRID file . - Parameters - ---------- - ds : xarray.Dataset - Dataset to be encoded to file + """Encodes an unstructured grid represented under a ``Grid`` object as a + ``xr.Dataset`` with an updated grid topology variable.""" + + if "grid_topology" in ds: + ds = ds.drop_vars(["grid_topology"]) + + grid_topology = ugrid.BASE_GRID_TOPOLOGY_ATTRS + + if "n_edge" in ds: + grid_topology["edge_dimension"] = "n_edge" + + if "face_lon" in ds: + grid_topology["face_coordinates"] = "face_lon face_lat" + if "edge_lon" in ds: + grid_topology["edge_coordinates"] = "edge_lon edge_lat" + + # TODO: Encode spherical (i.e. node_x) coordinates eventually (need to extend ugrid conventions) + + for conn_name in ugrid.CONNECTIVITY_NAMES: + if conn_name in ds: + grid_topology[conn_name] = conn_name + + grid_topology_da = xr.DataArray(data=-1, attrs=grid_topology) + + ds["grid_topology"] = grid_topology_da - Uses to_netcdf from xarray object. - """ return ds -def _standardize_fill_values(ds): - """Standardizes the fill values and data type of index variables. +def _standardize_connectivity(ds, conn_name): + """Standardizes the fill values and data type for a given connectivity + variable. Parameters ---------- @@ -83,31 +125,37 @@ def _standardize_fill_values(ds): Input Dataset with correct index variables """ - # original face nodes - face_nodes = ds["face_node_connectivity"].values + # original connectivity + conn = ds[conn_name].values # original fill value, if one exists - if "_FillValue" in ds["face_node_connectivity"].attrs: - original_fv = ds["face_node_connectivity"]._FillValue - elif np.isnan(ds["face_node_connectivity"].values).any(): + if "_FillValue" in ds[conn_name].attrs: + original_fv = ds[conn_name]._FillValue + elif np.isnan(ds[conn_name].values).any(): original_fv = np.nan else: original_fv = None # if current dtype and fill value are not standardized - if face_nodes.dtype != INT_DTYPE or original_fv != INT_FILL_VALUE: + if conn.dtype != INT_DTYPE or original_fv != INT_FILL_VALUE: # replace fill values and set correct dtype - new_face_nodes = _replace_fill_values( - grid_var=face_nodes, + new_conn = _replace_fill_values( + grid_var=conn, original_fill=original_fv, new_fill=INT_FILL_VALUE, new_dtype=INT_DTYPE, ) - # reassign data to use updated face nodes - ds["face_node_connectivity"].data = new_face_nodes + + if "start_index" in ds[conn_name].attrs: + new_conn -= INT_DTYPE(ds[conn_name].start_index) + else: + new_conn -= INT_DTYPE(new_conn.min()) + + # reassign data to use updated connectivity + ds[conn_name].data = new_conn # use new fill value - ds["face_node_connectivity"].attrs["_FillValue"] = INT_FILL_VALUE + ds[conn_name].attrs["_FillValue"] = INT_FILL_VALUE return ds From b417ac80140580359ed0ae69faf18fb42eff2902 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Tue, 2 Apr 2024 15:30:59 -0500 Subject: [PATCH 16/41] Compare and Swap Holoviews Extension (#733) * initial fix for comparing and swaping hv enviroment * add docstring and check for supported backends * pre-commit * refactor * update docstring * update comments and docstring * add to internal API --------- Co-authored-by: Orhan Eroglu <32553057+erogluorhan@users.noreply.github.com> --- docs/internal_api/index.rst | 1 + uxarray/plot/dataarray_plot.py | 18 ++++++++---------- uxarray/plot/grid_plot.py | 11 ++++++----- uxarray/plot/utils.py | 32 ++++++++++++++++++++++++++++++++ 4 files changed, 47 insertions(+), 15 deletions(-) diff --git a/docs/internal_api/index.rst b/docs/internal_api/index.rst index 0e0c6e287..75aa4dd73 100644 --- a/docs/internal_api/index.rst +++ b/docs/internal_api/index.rst @@ -303,6 +303,7 @@ Visualization plot.dataarray_plot._plot_data_as_points plot.dataarray_plot._polygon_raster plot.dataarray_plot._point_raster + plot.utils.HoloviewsBackend Slicing ------- diff --git a/uxarray/plot/dataarray_plot.py b/uxarray/plot/dataarray_plot.py index d9d5f5feb..dc48ffb36 100644 --- a/uxarray/plot/dataarray_plot.py +++ b/uxarray/plot/dataarray_plot.py @@ -20,6 +20,8 @@ import warnings +import uxarray.plot.utils + def plot(uxda, **kwargs): """Default Plotting Method for UxDataArray.""" @@ -229,8 +231,7 @@ def _point_raster( # apply projection to coordinates lon, lat, _ = projection.transform_points(ccrs.PlateCarree(), lon, lat).T - # this will be fixed in #733 - hv.extension("bokeh") + uxarray.plot.utils.backend.assign(backend=backend) # construct a dask dataframe from coordinates and data point_dict = {"lon": lon, "lat": lat, "var": uxda.data} @@ -241,7 +242,6 @@ def _point_raster( if backend == "matplotlib": # use holoviews matplotlib backend - hv.extension("matplotlib") raster = hds_rasterize( points, pixel_ratio=pixel_ratio, @@ -258,7 +258,6 @@ def _point_raster( ) elif backend == "bokeh": # use holoviews bokeh backend - hv.extension("bokeh") raster = hds_rasterize( points, pixel_ratio=pixel_ratio, @@ -315,9 +314,10 @@ def _polygon_raster( hv_polygons = hv.Polygons(gdf, vdims=[uxda.name]) + uxarray.plot.utils.backend.assign(backend=backend) + if backend == "matplotlib": # use holoviews matplotlib backend - hv.extension("matplotlib") raster = hds_rasterize( hv_polygons, pixel_ratio=pixel_ratio, @@ -334,7 +334,6 @@ def _polygon_raster( ) elif backend == "bokeh": # use holoviews bokeh backend - hv.extension("bokeh") raster = hds_rasterize( hv_polygons, pixel_ratio=pixel_ratio, @@ -404,15 +403,14 @@ def polygons( hv_polygons = hv.Polygons(gdf, vdims=[uxda.name]) + uxarray.plot.utils.backend.assign(backend=backend) if backend == "matplotlib": # use holoviews matplotlib backend - hv.extension("matplotlib") return hv_polygons.opts(colorbar=colorbar, cmap=cmap, **kwargs) elif backend == "bokeh": # use holoviews bokeh backend - hv.extension("bokeh") return hv_polygons.opts( width=width, height=height, @@ -521,9 +519,10 @@ def _plot_data_as_points( verts = np.column_stack([lon, lat, uxda.values]) hv_points = Points(verts, vdims=["z"]) + uxarray.plot.utils.backend.assign(backend=backend) + if backend == "matplotlib": # use holoviews matplotlib backend - hv.extension("matplotlib") return hv_points.opts( color="z", colorbar=colorbar, @@ -535,7 +534,6 @@ def _plot_data_as_points( elif backend == "bokeh": # use holoviews bokeh backend - hv.extension("bokeh") return hv_points.opts( color="z", width=width, diff --git a/uxarray/plot/grid_plot.py b/uxarray/plot/grid_plot.py index ffafdbe1a..ee4978855 100644 --- a/uxarray/plot/grid_plot.py +++ b/uxarray/plot/grid_plot.py @@ -8,6 +8,8 @@ import numpy as np import holoviews as hv +import uxarray.plot.utils + def plot(grid: Grid, **kwargs): """Default Plotting Method for Grid.""" @@ -42,15 +44,14 @@ def mesh( hv_paths = hv.Path(gdf) + uxarray.plot.utils.backend.assign(backend=backend) + if backend == "matplotlib": # use holoviews matplotlib backend - hv.extension("matplotlib") - return hv_paths.opts(**kwargs) elif backend == "bokeh": # use holoviews bokeh backend - hv.extension("bokeh") return hv_paths.opts( width=width, height=height, xlabel=xlabel, ylabel=ylabel, **kwargs ) @@ -174,14 +175,14 @@ def _plot_coords_as_points( else: raise ValueError("Invalid element selected.") + uxarray.plot.utils.backend.assign(backend=backend) + if backend == "matplotlib": # use holoviews matplotlib backend - hv.extension("matplotlib") return hv_points.opts(**kwargs) elif backend == "bokeh": # use holoviews bokeh backend - hv.extension("bokeh") return hv_points.opts( width=width, height=height, xlabel=xlabel, ylabel=ylabel, **kwargs ) diff --git a/uxarray/plot/utils.py b/uxarray/plot/utils.py index e69de29bb..cc588821d 100644 --- a/uxarray/plot/utils.py +++ b/uxarray/plot/utils.py @@ -0,0 +1,32 @@ +import holoviews as hv + + +class HoloviewsBackend: + """Utility class to compare and set a HoloViews plotting backend for + visualization.""" + + def __init__(self): + self.backend = None + + def assign(self, backend: str): + """Assigns a backend for use with HoloViews visualization. + + Parameters + ---------- + backend : str + Plotting backend to use, one of 'matplotlib', 'bokeh' + """ + + if backend not in ["bokeh", "matplotlib"]: + raise ValueError( + f"Unsupported backend. Expected one of ['bokeh', 'matplotlib'], but received {backend}" + ) + + if backend != self.backend: + # only call hv.extension if it needs to be changed + hv.extension(backend) + self.backend = backend + + +# global reference to holoviews backend utility class +backend = HoloviewsBackend() From 6d3174d94b95a50ed75c1a8e63db9e45751d6045 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Thu, 4 Apr 2024 15:07:37 -0500 Subject: [PATCH 17/41] Update grid.py (#750) --- uxarray/grid/grid.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/uxarray/grid/grid.py b/uxarray/grid/grid.py index 1d5b1a52d..49dd7a5bc 100644 --- a/uxarray/grid/grid.py +++ b/uxarray/grid/grid.py @@ -449,7 +449,7 @@ def dims(self) -> set: @property def sizes(self) -> dict: """Names and values of all unstructured grid dimensions.""" - return {dim: self._ds.dims[dim] for dim in self.dims} + return {dim: self._ds.sizes[dim] for dim in self.dims} @property def coordinates(self) -> set: From b8da6fff29f70eb62e701341d6a6dcc313f835e9 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Fri, 5 Apr 2024 00:11:03 -0500 Subject: [PATCH 18/41] Structured Test Grid and Plotting Improvements (#741) * initial draft * update notebook * update notebook * update notebook * work on notebook * update notebook * notebook updates * remove notebook for now, move it to cookbook * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update dataarray_plot.py --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- .../structured/outCSne30_vortex_structured.nc | Bin 0 -> 38223 bytes uxarray/plot/constants.py | 2 +- uxarray/plot/dataarray_plot.py | 25 +++++++++--------- 3 files changed, 14 insertions(+), 13 deletions(-) create mode 100644 test/meshfiles/structured/outCSne30_vortex_structured.nc diff --git 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zOqQ-%cSLg$>e+s=dPgEZAq;gPGsu{yuX7XYD-PW|pAw$Z7@#43o_E7h)PwvYWumR% z(SAICGx&Xi?cDNW1UR&jE?MB7iaxSmTq_%DsfT{jnG7|d#iuHwF6S5bt54GTbr~>| zw!>1N=b;acL*B!Uf!yAh$Lw$=>lF5{95Mt%JMTzfZublpA3yil_8Ap8v{8?-aQ{L9 z4jUK#aLiUjU&9P4H^rA{C85sp7u9ZTq0_(PUUUX;J%7_;jR`*pXZpXIg@pZxfW)l?})=_ z`|^gW&6rE~i;ZWl_k9-}2C4R&>vw*p|AVF#mn*5f2_8zZ`H57R$tr8huU?y8EqFwXv(&10EGWoda2i_xp zB3E%_^Qwcx;3R0PaUA==KQVw|O7Y@hz@M0XiM!D>8GXzDLc%qvbw}~%8~lmZiN0+N z1`Tp^&yP2%{F%yOnj3vphu}|aFPLpos51!v#!#$bTJfe>*&b4~7_`Eg!X zjBpkW{*8-v7nRRHK!^VoS6+5%RQW+eAF0 Date: Fri, 5 Apr 2024 14:34:15 -0500 Subject: [PATCH 19/41] fix x and y label in vector polygon (#753) --- uxarray/plot/dataarray_plot.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/uxarray/plot/dataarray_plot.py b/uxarray/plot/dataarray_plot.py index 8b5b5ab0b..8551b02d3 100644 --- a/uxarray/plot/dataarray_plot.py +++ b/uxarray/plot/dataarray_plot.py @@ -408,7 +408,9 @@ def polygons( if backend == "matplotlib": # use holoviews matplotlib backend - return hv_polygons.opts(colorbar=colorbar, cmap=cmap, **kwargs) + return hv_polygons.opts( + colorbar=colorbar, xlabel=xlabel, ylabel=ylabel, cmap=cmap, **kwargs + ) elif backend == "bokeh": # use holoviews bokeh backend From 00202654b467a19a758997d32086d7b6e4b9a48f Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Wed, 10 Apr 2024 22:56:07 -0500 Subject: [PATCH 20/41] Initial Implementation of Latitude Longitude Face Bounds (#692) * Initial Draft Algorithm * Revert "Initial Draft Algorithm" This reverts commit 99573b8edfc2dc2fc329e91d395da4d907b42e4a. * boilerpalte * remove bounding box property for now * Work in Progress * Initial commit * Revert "Initial commit" This reverts commit 1ba68804f2f8f6fd062c9eeb63ef0b600549c9f0. * Initial commit * Merge branch 'get-bounding-box' of https://github.com/UXARRAY/uxarray into get-bounding-box merge` * Fix complete * WIP * WIP * WIP * Fix interval looks * fix interval data storage type * fix interval data storage type * Initial draft complete * Support for the `is_latlonface` * Support for the `is_latlonface` and `is_GCA_List` * Finish final draft * Finish * refractor complete * Address PRs * add benchmark to face bounds * update bounds * rename bounds to face_bounds * fix tests * Delete grid_geoflow.exo * rename connectivity * Address PR Review * add warning * add warning --------- Co-authored-by: Hongyu Chen Co-authored-by: Orhan Eroglu <32553057+erogluorhan@users.noreply.github.com> --- benchmarks/face_bounds.py | 32 +++ docs/internal_api/index.rst | 4 + docs/user_api/index.rst | 1 + test/test_geometry.py | 490 +++++++++++++++++++++++++++++++++++- test/test_grid.py | 38 ++- test/test_helpers.py | 32 ++- uxarray/constants.py | 6 +- uxarray/grid/arcs.py | 3 + uxarray/grid/geometry.py | 360 ++++++++++++++++++++++++++ uxarray/grid/grid.py | 22 +- uxarray/grid/utils.py | 62 +++++ 11 files changed, 1035 insertions(+), 15 deletions(-) create mode 100644 benchmarks/face_bounds.py diff --git a/benchmarks/face_bounds.py b/benchmarks/face_bounds.py new file mode 100644 index 000000000..c41602b6b --- /dev/null +++ b/benchmarks/face_bounds.py @@ -0,0 +1,32 @@ +import os +from pathlib import Path + +import uxarray as ux + +current_path = Path(os.path.dirname(os.path.realpath(__file__))).parents[0] + +grid_quad_hex = current_path / "test" / "meshfiles" / "ugrid" / "quad-hexagon" / "grid.nc" +grid_geoflow = current_path / "test" / "meshfiles" / "ugrid" / "geoflow-small" / "grid.nc" +grid_scrip = current_path / "test" / "meshfiles" / "scrip" / "outCSne8" / "outCSne8.nc" +grid_mpas= current_path / "test" / "meshfiles" / "mpas" / "QU" / "oQU480.231010.nc" + + + +class FaceBounds: + + params = [grid_quad_hex, grid_geoflow, grid_scrip, grid_mpas] + + + def setup(self, grid_path): + self.uxgrid = ux.open_grid(grid_path) + + def teardown(self, n): + del self.uxgrid + + def time_face_bounds(self, grid_path): + """Time to obtain ``Grid.face_bounds``""" + self.uxgrid.bounds + + def peakmem_face_bounds(self, grid_path): + """Peak memory usage obtain ``Grid.face_bounds.""" + face_bounds = self.uxgrid.bounds diff --git a/docs/internal_api/index.rst b/docs/internal_api/index.rst index 75aa4dd73..13b572854 100644 --- a/docs/internal_api/index.rst +++ b/docs/internal_api/index.rst @@ -118,6 +118,7 @@ Connectivity grid.connectivity._populate_edge_face_connectivity grid.connectivity._populate_n_nodes_per_face + Geometry -------- .. autosummary:: @@ -138,6 +139,8 @@ Geometry grid.geometry._check_intersection grid.geometry._get_latlonbox_width grid.geometry._insert_pt_in_latlonbox + grid.geometry._populate_face_latlon_bound + grid.geometry._populate_bounds Coordinates ----------- @@ -172,6 +175,7 @@ Utils grid.utils._newton_raphson_solver_for_gca_constLat grid.utils._inv_jacobian grid.utils._get_cartesiain_face_edge_nodes + grid.utils._get_lonlat_rad_face_edge_nodes diff --git a/docs/user_api/index.rst b/docs/user_api/index.rst index a84e856bd..7f67456ff 100644 --- a/docs/user_api/index.rst +++ b/docs/user_api/index.rst @@ -275,6 +275,7 @@ Grid Descriptors Grid.face_areas Grid.antimeridian_face_indices + Grid.bounds Attributes diff --git a/test/test_geometry.py b/test/test_geometry.py index 45f209d1a..beaa105b7 100644 --- a/test/test_geometry.py +++ b/test/test_geometry.py @@ -9,6 +9,10 @@ import uxarray as ux from uxarray.constants import ERROR_TOLERANCE, INT_FILL_VALUE import uxarray.utils.computing as ac_utils +from uxarray.grid.coordinates import _populate_lonlat_coord, node_lonlat_rad_to_xyz +from uxarray.grid.arcs import extreme_gca_latitude +from uxarray.grid.utils import _get_cartesian_face_edge_nodes, _get_lonlat_rad_face_edge_nodes +from uxarray.grid.geometry import _populate_face_latlon_bound, _populate_bounds from spatialpandas.geometry import MultiPolygon @@ -157,7 +161,7 @@ def test_pole_point_inside_polygon_from_vertice_cross(self): self.assertTrue(result, "North pole should be inside the polygon") -class TestLatlonBound(TestCase): +class TestLatlonBoundUtils(TestCase): def _max_latitude_rad_iterative(self, gca_cart): """Calculate the maximum latitude of a great circle arc defined by two @@ -428,6 +432,490 @@ def test_insert_pt_in_empty_state(self): np.testing.assert_array_equal(result, expected) + +class TestLatlonBoundsGCA(TestCase): + def test_populate_bounds_normal(self): + # Generate a normal face that is not crossing the antimeridian or the poles + vertices_lonlat = [[10.0, 60.0], [10.0, 10.0], [50.0, 10.0], [50.0, 60.0]] + vertices_lonlat = np.array(vertices_lonlat) + + # Convert everything into radians + vertices_rad = np.radians(vertices_lonlat) + vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + lat_max = max(np.deg2rad(60.0), + extreme_gca_latitude(np.array([vertices_cart[0], vertices_cart[3]]), extreme_type="max")) + lat_min = min(np.deg2rad(10.0), + extreme_gca_latitude(np.array([vertices_cart[1], vertices_cart[2]]), extreme_type="min")) + lon_min = np.deg2rad(10.0) + lon_max = np.deg2rad(50.0) + grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True) + face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_x.values, + grid.node_y.values, grid.node_z.values) + face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_lon.values, + grid.node_lat.values) + expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]]) + bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat) + nt.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE) + + def test_populate_bounds_antimeridian(self): + # Generate a normal face that is crossing the antimeridian + vertices_lonlat = [[350, 60.0], [350, 10.0], [50.0, 10.0], [50.0, 60.0]] + vertices_lonlat = np.array(vertices_lonlat) + + # Convert everything into radians + vertices_rad = np.radians(vertices_lonlat) + vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + lat_max = max(np.deg2rad(60.0), + extreme_gca_latitude(np.array([vertices_cart[0], vertices_cart[3]]), extreme_type="max")) + lat_min = min(np.deg2rad(10.0), + extreme_gca_latitude(np.array([vertices_cart[1], vertices_cart[2]]), extreme_type="min")) + lon_min = np.deg2rad(350.0) + lon_max = np.deg2rad(50.0) + grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True) + face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_x.values, + grid.node_y.values, grid.node_z.values) + face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_lon.values, + grid.node_lat.values) + expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]]) + bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat) + nt.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE) + + def test_populate_bounds_node_on_pole(self): + # Generate a normal face that is crossing the antimeridian + vertices_lonlat = [[10.0, 90.0], [10.0, 10.0], [50.0, 10.0], [50.0, 60.0]] + vertices_lonlat = np.array(vertices_lonlat) + + # Convert everything into radians + vertices_rad = np.radians(vertices_lonlat) + vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + lat_max = np.pi / 2 + lat_min = min(np.deg2rad(10.0), + extreme_gca_latitude(np.array([vertices_cart[1], vertices_cart[2]]), extreme_type="min")) + lon_min = np.deg2rad(10.0) + lon_max = np.deg2rad(50.0) + grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True) + face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_x.values, + grid.node_y.values, grid.node_z.values) + face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_lon.values, + grid.node_lat.values) + expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]]) + bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat) + nt.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE) + + def test_populate_bounds_edge_over_pole(self): + # Generate a normal face that is crossing the antimeridian + vertices_lonlat = [[210.0, 80.0], [350.0, 60.0], [10.0, 60.0], [30.0, 80.0]] + vertices_lonlat = np.array(vertices_lonlat) + + # Convert everything into radians + vertices_rad = np.radians(vertices_lonlat) + vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + lat_max = np.pi / 2 + lat_min = min(np.deg2rad(60.0), + extreme_gca_latitude(np.array([vertices_cart[1], vertices_cart[2]]), extreme_type="min")) + lon_min = np.deg2rad(210.0) + lon_max = np.deg2rad(30.0) + grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True) + face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_x.values, + grid.node_y.values, grid.node_z.values) + face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_lon.values, + grid.node_lat.values) + expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]]) + bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat) + nt.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE) + + def test_populate_bounds_pole_inside(self): + # Generate a normal face that is crossing the antimeridian + vertices_lonlat = [[200.0, 80.0], [350.0, 60.0], [10.0, 60.0], [40.0, 80.0]] + vertices_lonlat = np.array(vertices_lonlat) + + # Convert everything into radians + vertices_rad = np.radians(vertices_lonlat) + vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + lat_max = np.pi / 2 + lat_min = min(np.deg2rad(60.0), + extreme_gca_latitude(np.array([vertices_cart[1], vertices_cart[2]]), extreme_type="min")) + lon_min = 0 + lon_max = 2 * np.pi + grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True) + face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_x.values, + grid.node_y.values, grid.node_z.values) + face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_lon.values, + grid.node_lat.values) + expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]]) + bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat) + nt.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE) + +class TestLatlonBoundsLatLonFace(TestCase): + + def test_populate_bounds_normal(self): + # Generate a normal face that is not crossing the antimeridian or the poles + vertices_lonlat = [[10.0, 60.0], [10.0, 10.0], [50.0, 10.0], [50.0, 60.0]] + vertices_lonlat = np.array(vertices_lonlat) + + # Convert everything into radians + vertices_rad = np.radians(vertices_lonlat) + lat_max = np.deg2rad(60.0) + lat_min = np.deg2rad(10.0) + lon_min = np.deg2rad(10.0) + lon_max = np.deg2rad(50.0) + grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True) + face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_x.values, + grid.node_y.values, grid.node_z.values) + face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_lon.values, + grid.node_lat.values) + expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]]) + bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat, + is_latlonface=True) + nt.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE) + + def test_populate_bounds_antimeridian(self): + # Generate a normal face that is crossing the antimeridian + vertices_lonlat = [[350, 60.0], [350, 10.0], [50.0, 10.0], [50.0, 60.0]] + vertices_lonlat = np.array(vertices_lonlat) + + # Convert everything into radians + vertices_rad = np.radians(vertices_lonlat) + lat_max = np.deg2rad(60.0) + lat_min = np.deg2rad(10.0) + lon_min = np.deg2rad(350.0) + lon_max = np.deg2rad(50.0) + grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True) + face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_x.values, + grid.node_y.values, grid.node_z.values) + face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_lon.values, + grid.node_lat.values) + expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]]) + bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat, + is_latlonface=True) + nt.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE) + + def test_populate_bounds_node_on_pole(self): + # Generate a normal face that is crossing the antimeridian + vertices_lonlat = [[10.0, 90.0], [10.0, 10.0], [50.0, 10.0], [50.0, 60.0]] + vertices_lonlat = np.array(vertices_lonlat) + + # Convert everything into radians + vertices_rad = np.radians(vertices_lonlat) + lat_max = np.pi / 2 + lat_min = np.deg2rad(10.0) + lon_min = np.deg2rad(10.0) + lon_max = np.deg2rad(50.0) + grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True) + face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_x.values, + grid.node_y.values, grid.node_z.values) + face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_lon.values, + grid.node_lat.values) + expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]]) + bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat, + is_latlonface=True) + nt.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE) + + def test_populate_bounds_edge_over_pole(self): + # Generate a normal face that is crossing the antimeridian + vertices_lonlat = [[210.0, 80.0], [350.0, 60.0], [10.0, 60.0], [30.0, 80.0]] + vertices_lonlat = np.array(vertices_lonlat) + + # Convert everything into radians + vertices_rad = np.radians(vertices_lonlat) + vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + lat_max = np.pi / 2 + lat_min = np.deg2rad(60.0) + lon_min = np.deg2rad(210.0) + lon_max = np.deg2rad(30.0) + grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True) + face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_x.values, + grid.node_y.values, grid.node_z.values) + face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_lon.values, + grid.node_lat.values) + expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]]) + bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat, + is_latlonface=True) + nt.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE) + + def test_populate_bounds_pole_inside(self): + # Generate a normal face that is crossing the antimeridian + vertices_lonlat = [[200.0, 80.0], [350.0, 60.0], [10.0, 60.0], [40.0, 80.0]] + vertices_lonlat = np.array(vertices_lonlat) + + # Convert everything into radians + vertices_rad = np.radians(vertices_lonlat) + vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + lat_max = np.pi / 2 + lat_min = np.deg2rad(60.0) + lon_min = 0 + lon_max = 2 * np.pi + grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True) + face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_x.values, + grid.node_y.values, grid.node_z.values) + face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_lon.values, + grid.node_lat.values) + expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]]) + bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat, + is_latlonface=True) + nt.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE) + + +class TestLatlonBoundsGCAList(TestCase): + + def test_populate_bounds_normal(self): + # Generate a normal face that is not crossing the antimeridian or the poles + vertices_lonlat = [[10.0, 60.0], [10.0, 10.0], [50.0, 10.0], [50.0, 60.0]] + vertices_lonlat = np.array(vertices_lonlat) + + # Convert everything into radians + vertices_rad = np.radians(vertices_lonlat) + lat_max = np.deg2rad(60.0) + lat_min = np.deg2rad(10.0) + lon_min = np.deg2rad(10.0) + lon_max = np.deg2rad(50.0) + grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True) + face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_x.values, + grid.node_y.values, grid.node_z.values) + face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_lon.values, + grid.node_lat.values) + expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]]) + bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat, + is_GCA_list=[True, False, True, False]) + nt.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE) + + def test_populate_bounds_antimeridian(self): + # Generate a normal face that is crossing the antimeridian + vertices_lonlat = [[350, 60.0], [350, 10.0], [50.0, 10.0], [50.0, 60.0]] + vertices_lonlat = np.array(vertices_lonlat) + + # Convert everything into radians + vertices_rad = np.radians(vertices_lonlat) + vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + lat_max = np.deg2rad(60.0) + lat_min = np.deg2rad(10.0) + lon_min = np.deg2rad(350.0) + lon_max = np.deg2rad(50.0) + grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True) + face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_x.values, + grid.node_y.values, grid.node_z.values) + face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_lon.values, + grid.node_lat.values) + expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]]) + bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat, + is_GCA_list=[True, False, True, False]) + nt.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE) + + def test_populate_bounds_node_on_pole(self): + # Generate a normal face that is crossing the antimeridian + vertices_lonlat = [[10.0, 90.0], [10.0, 10.0], [50.0, 10.0], [50.0, 60.0]] + vertices_lonlat = np.array(vertices_lonlat) + + # Convert everything into radians + vertices_rad = np.radians(vertices_lonlat) + vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + lat_max = np.pi / 2 + lat_min = np.deg2rad(10.0) + lon_min = np.deg2rad(10.0) + lon_max = np.deg2rad(50.0) + grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True) + face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_x.values, + grid.node_y.values, grid.node_z.values) + face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_lon.values, + grid.node_lat.values) + expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]]) + bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat, + is_GCA_list=[True, False, True, False]) + nt.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE) + + def test_populate_bounds_edge_over_pole(self): + # Generate a normal face that is crossing the antimeridian + vertices_lonlat = [[210.0, 80.0], [350.0, 60.0], [10.0, 60.0], [30.0, 80.0]] + vertices_lonlat = np.array(vertices_lonlat) + + # Convert everything into radians + vertices_rad = np.radians(vertices_lonlat) + vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + lat_max = np.pi / 2 + lat_min = np.deg2rad(60.0) + lon_min = np.deg2rad(210.0) + lon_max = np.deg2rad(30.0) + grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True) + face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_x.values, + grid.node_y.values, grid.node_z.values) + face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_lon.values, + grid.node_lat.values) + expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]]) + bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat, + is_GCA_list=[True, False, True, False]) + nt.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE) + + def test_populate_bounds_pole_inside(self): + # Generate a normal face that is crossing the antimeridian + vertices_lonlat = [[200.0, 80.0], [350.0, 60.0], [10.0, 60.0], [40.0, 80.0]] + vertices_lonlat = np.array(vertices_lonlat) + + # Convert everything into radians + vertices_rad = np.radians(vertices_lonlat) + vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + lat_max = np.pi / 2 + lat_min = np.deg2rad(60.0) + lon_min = 0 + lon_max = 2 * np.pi + grid = ux.Grid.from_face_vertices(vertices_lonlat, latlon=True) + face_edges_connectivity_cartesian = _get_cartesian_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_x.values, + grid.node_y.values, grid.node_z.values) + face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_lon.values, + grid.node_lat.values) + expected_bounds = np.array([[lat_min, lat_max], [lon_min, lon_max]]) + bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat, + is_GCA_list=[True, False, True, False]) + nt.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE) + +class TestLatlonBoundsMix(TestCase): + def test_populate_bounds_GCA_mix(self): + face_1 = [[10.0, 60.0], [10.0, 10.0], [50.0, 10.0], [50.0, 60.0]] + face_2 = [[350, 60.0], [350, 10.0], [50.0, 10.0], [50.0, 60.0]] + face_3 = [[210.0, 80.0], [350.0, 60.0], [10.0, 60.0], [30.0, 80.0]] + face_4 = [[200.0, 80.0], [350.0, 60.0], [10.0, 60.0], [40.0, 80.0]] + + faces = [face_1, face_2, face_3, face_4] + + # Hand calculated bounds for the above faces in radians + expected_bounds = [[[0.17453293, 1.07370494],[0.17453293, 0.87266463]], + [[0.17453293, 1.10714872],[6.10865238, 0.87266463]], + [[1.04719755, 1.57079633],[3.66519143, 0.52359878]], + [[1.04719755,1.57079633],[0., 6.28318531]]] + + + grid = ux.Grid.from_face_vertices(faces, latlon=True) + face_bounds = grid.bounds.values + for i in range(len(faces)): + nt.assert_allclose(face_bounds[i], expected_bounds[i], atol=ERROR_TOLERANCE) + + def test_populate_bounds_LatlonFace_mix(self): + face_1 = [[10.0, 60.0], [10.0, 10.0], [50.0, 10.0], [50.0, 60.0]] + face_2 = [[350, 60.0], [350, 10.0], [50.0, 10.0], [50.0, 60.0]] + face_3 = [[210.0, 80.0], [350.0, 60.0], [10.0, 60.0], [30.0, 80.0]] + face_4 = [[200.0, 80.0], [350.0, 60.0], [10.0, 60.0], [40.0, 80.0]] + + faces = [face_1, face_2, face_3, face_4] + + expected_bounds = [[[np.deg2rad(10.0), np.deg2rad(60.0)],[np.deg2rad(10.0), np.deg2rad(50.0)]], + [[np.deg2rad(10.0), np.deg2rad(60.0)],[np.deg2rad(350.0), np.deg2rad(50.0)]], + [[np.deg2rad(60.0), np.pi/2],[np.deg2rad(210.0), np.deg2rad(30.0)]], + [[np.deg2rad(60.0),np.pi/2],[0., 2*np.pi]]] + + + grid = ux.Grid.from_face_vertices(faces, latlon=True) + bounds_xarray = _populate_bounds(grid,is_latlonface=True, return_array=True) + face_bounds = bounds_xarray.values + for i in range(len(faces)): + nt.assert_allclose(face_bounds[i], expected_bounds[i], atol=ERROR_TOLERANCE) + + def test_populate_bounds_GCAList_mix(self): + face_1 = [[10.0, 60.0], [10.0, 10.0], [50.0, 10.0], [50.0, 60.0]] + face_2 = [[350, 60.0], [350, 10.0], [50.0, 10.0], [50.0, 60.0]] + face_3 = [[210.0, 80.0], [350.0, 60.0], [10.0, 60.0], [30.0, 80.0]] + face_4 = [[200.0, 80.0], [350.0, 60.0], [10.0, 60.0], [40.0, 80.0]] + + faces = [face_1, face_2, face_3, face_4] + + expected_bounds = [[[np.deg2rad(10.0), np.deg2rad(60.0)],[np.deg2rad(10.0), np.deg2rad(50.0)]], + [[np.deg2rad(10.0), np.deg2rad(60.0)],[np.deg2rad(350.0), np.deg2rad(50.0)]], + [[np.deg2rad(60.0), np.pi/2],[np.deg2rad(210.0), np.deg2rad(30.0)]], + [[np.deg2rad(60.0),np.pi/2],[0., 2*np.pi]]] + + grid = ux.Grid.from_face_vertices(faces, latlon=True) + bounds_xarray = _populate_bounds(grid,is_face_GCA_list=[[True, False, True, False]]*4, return_array=True) + face_bounds = bounds_xarray.values + for i in range(len(faces)): + nt.assert_allclose(face_bounds[i], expected_bounds[i], atol=ERROR_TOLERANCE) class TestGeoDataFrame(TestCase): def test_to_gdf(self): diff --git a/test/test_grid.py b/test/test_grid.py index 730969383..48aad8e86 100644 --- a/test/test_grid.py +++ b/test/test_grid.py @@ -8,11 +8,13 @@ import uxarray as ux -from uxarray.grid.connectivity import _populate_face_edge_connectivity, _build_edge_face_connectivity +from uxarray.grid.connectivity import _populate_face_edge_connectivity, _build_edge_face_connectivity, _build_edge_node_connectivity -from uxarray.grid.coordinates import _populate_lonlat_coord +from uxarray.grid.coordinates import _populate_lonlat_coord, node_lonlat_rad_to_xyz -from uxarray.constants import INT_FILL_VALUE +from uxarray.constants import INT_FILL_VALUE, ERROR_TOLERANCE + +from uxarray.grid.arcs import extreme_gca_latitude try: import constants @@ -392,7 +394,7 @@ def test_populate_cartesian_xyz_coord(self): # These points correspond to the eight vertices of a cube. lon_deg = [ 45.0001052295749, 45.0001052295749, 360 - 45.0001052295749, - 360 - 45.0001052295749 + 360 - 45.0001052295749 ] lat_deg = [ 35.2655522903022, -35.2655522903022, 35.2655522903022, @@ -435,7 +437,7 @@ def test_populate_lonlat_coord(self): lon_deg = [ 45.0001052295749, 45.0001052295749, 360 - 45.0001052295749, - 360 - 45.0001052295749 + 360 - 45.0001052295749 ] lat_deg = [ 35.2655522903022, -35.2655522903022, 35.2655522903022, @@ -589,7 +591,7 @@ def _revert_edges_conn_to_face_nodes_conn( for face_idx in range(len(face_edges_connectivity)): res_face_nodes_connectivity.append(face_nodes_dict[face_idx]) while len(res_face_nodes_connectivity[face_idx] - ) < original_face_nodes_connectivity.shape[1]: + ) < original_face_nodes_connectivity.shape[1]: res_face_nodes_connectivity[face_idx].append(ux.INT_FILL_VALUE) return np.array(res_face_nodes_connectivity) @@ -856,7 +858,7 @@ def test_edge_face_connectivity_sample(self): # shared edge n_shared += 1 elif edge_face[0] != INT_FILL_VALUE and edge_face[ - 1] == INT_FILL_VALUE: + 1] == INT_FILL_VALUE: # edge borders one face n_solo += 1 else: @@ -908,3 +910,25 @@ def test_from_face_vertices(self): uxgrid = ux.Grid.from_face_vertices(multi_face_latlon, latlon=True) single_face_cart = [(0.0,)] + + +class TestLatlonBounds(TestCase): + def test_populate_bounds_GCA_mix(self): + face_1 = [[10.0, 60.0], [10.0, 10.0], [50.0, 10.0], [50.0, 60.0]] + face_2 = [[350, 60.0], [350, 10.0], [50.0, 10.0], [50.0, 60.0]] + face_3 = [[210.0, 80.0], [350.0, 60.0], [10.0, 60.0], [30.0, 80.0]] + face_4 = [[200.0, 80.0], [350.0, 60.0], [10.0, 60.0], [40.0, 80.0]] + + faces = [face_1, face_2, face_3, face_4] + + # Hand calculated bounds for the above faces in radians + expected_bounds = [[[0.17453293, 1.07370494],[0.17453293, 0.87266463]], + [[0.17453293, 1.10714872],[6.10865238, 0.87266463]], + [[1.04719755, 1.57079633],[3.66519143, 0.52359878]], + [[1.04719755,1.57079633],[0., 6.28318531]]] + + + grid = ux.Grid.from_face_vertices(faces, latlon=True) + bounds_xarray = grid.bounds + face_bounds = bounds_xarray.values + nt.assert_allclose(grid.bounds.values, expected_bounds, atol=ERROR_TOLERANCE) diff --git a/test/test_helpers.py b/test/test_helpers.py index da0fec36a..ec310c346 100644 --- a/test/test_helpers.py +++ b/test/test_helpers.py @@ -14,7 +14,7 @@ from uxarray.grid.coordinates import node_lonlat_rad_to_xyz from uxarray.grid.arcs import point_within_gca, _angle_of_2_vectors, in_between -from uxarray.grid.utils import _get_cartesian_face_edge_nodes +from uxarray.grid.utils import _get_cartesian_face_edge_nodes, _get_lonlat_rad_face_edge_nodes from uxarray.grid.geometry import _pole_point_inside_polygon try: @@ -319,5 +319,35 @@ def test_get_cartesian_face_edge_nodes_filled_value(self): 'North', face_edges_connectivity_cartesian) # Assert that the result is True + self.assertTrue(result) + + def test_get_lonlat_face_edge_nodes_pipeline(self): + # Create the vertices for the grid, based around the North Pole + + vertices = [[0.5, 0.5, 0.5], [-0.5, 0.5, 0.5], [-0.5, -0.5, 0.5], + [0.5, -0.5, 0.5]] + + #Normalize the vertices + vertices = [x / np.linalg.norm(x) for x in vertices] + + # Construct the grid from the vertices + grid = ux.Grid.from_face_vertices(vertices, latlon=False) + face_edges_connectivity_lonlat = _get_lonlat_rad_face_edge_nodes( + grid.face_node_connectivity.values[0], + grid.face_edge_connectivity.values[0], + grid.edge_node_connectivity.values, grid.node_lon.values, + grid.node_lat.values) + + # Convert all the values into cartesian coordinates + face_edges_connectivity_cartesian = [] + for i in range(len(face_edges_connectivity_lonlat)): + edge = face_edges_connectivity_lonlat[i] + edge_cart = [node_lonlat_rad_to_xyz(node) for node in edge] + face_edges_connectivity_cartesian.append(edge_cart) + # Check that the face_edges_connectivity_cartesian works as an input to _pole_point_inside_polygon + result = ux.grid.geometry._pole_point_inside_polygon( + 'North', np.array(face_edges_connectivity_cartesian)) + + # Assert that the result is True self.assertTrue(result) diff --git a/uxarray/constants.py b/uxarray/constants.py index 37b374e2e..0704db2f7 100644 --- a/uxarray/constants.py +++ b/uxarray/constants.py @@ -4,7 +4,11 @@ INT_DTYPE = np.intp INT_FILL_VALUE = np.iinfo(INT_DTYPE).min -ERROR_TOLERANCE = 1.0e-15 +# According to Ogita, Takeshi & Rump, Siegfried & Oishi, Shin’ichi. (2005). Accurate Sum and Dot Product. +# SIAM J. Scientific Computing. 26. 1955-1988. 10.1137/030601818. +# half of the working precision is already the most optimal value for the error tolerance, +# more tailored values will be used in the future. +ERROR_TOLERANCE = 1.0e-8 ENABLE_JIT_CACHE = True ENABLE_JIT = True diff --git a/uxarray/grid/arcs.py b/uxarray/grid/arcs.py index c594a6753..86c3e44fd 100644 --- a/uxarray/grid/arcs.py +++ b/uxarray/grid/arcs.py @@ -93,6 +93,9 @@ def point_within_gca(pt, gca_cart, is_directed=False): if np.isclose( abs(GCRv1_lonlat[0] - GCRv0_lonlat[0]), np.pi, rtol=0, atol=ERROR_TOLERANCE ): + # Special case, if the pt is on the pole point, then set its longitude to the GCRv0_lonlat[0] + if np.isclose(abs(pt_lonlat[1]), np.pi / 2, rtol=0, atol=ERROR_TOLERANCE): + pt_lonlat[0] = GCRv0_lonlat[0] if not np.isclose( GCRv0_lonlat[0], pt_lonlat[0], rtol=0, atol=ERROR_TOLERANCE ) and not np.isclose( diff --git a/uxarray/grid/geometry.py b/uxarray/grid/geometry.py index 05669dd64..fe146990c 100644 --- a/uxarray/grid/geometry.py +++ b/uxarray/grid/geometry.py @@ -1,7 +1,14 @@ import numpy as np from uxarray.constants import INT_DTYPE, ERROR_TOLERANCE, INT_FILL_VALUE from uxarray.grid.intersections import gca_gca_intersection +from uxarray.grid.arcs import extreme_gca_latitude, point_within_gca +from uxarray.grid.utils import ( + _get_cartesian_face_edge_nodes, + _get_lonlat_rad_face_edge_nodes, +) import warnings +import pandas as pd +import xarray as xr from numba import njit @@ -473,6 +480,12 @@ def _get_latlonbox_width(latlonbox_rad): lon0, lon1 = latlonbox_rad[1] # Check longitude range validity + # Normalize the longitude so that it is within the range [0, 2π] + # Normalize the longitude + if lon0 != INT_FILL_VALUE: + lon0 = np.mod(lon0, 2 * np.pi) + if lon1 != INT_FILL_VALUE: + lon1 = np.mod(lon1, 2 * np.pi) if (lon0 < 0.0 or lon0 > 2.0 * np.pi) and lon0 != INT_FILL_VALUE: raise Exception("lon0 out of range ({} not in [0, 2π])".format(lon0)) @@ -523,6 +536,10 @@ def _insert_pt_in_latlonbox(old_box, new_pt, is_lon_periodic=True): lat_pt, lon_pt = new_pt + # Normalize the longitude + if lon_pt != INT_FILL_VALUE: + lon_pt = np.mod(lon_pt, 2 * np.pi) + # Check if the latitude range is uninitialized and update it if old_box[0][0] == old_box[0][1] == INT_FILL_VALUE: latlon_box[0] = np.array([lat_pt, lat_pt]) @@ -582,3 +599,346 @@ def _insert_pt_in_latlonbox(old_box, new_pt, is_lon_periodic=True): latlon_box = box_a if d_width_a < d_width_b else box_b return latlon_box + + +def _populate_face_latlon_bound( + face_edges_cartesian, + face_edges_lonlat_rad, + is_latlonface: bool = False, + is_GCA_list=None, +): + """Populates the bounding box for each face in the grid by evaluating the + geographical bounds based on the Cartesian and latitudinal/longitudinal + edge connectivity. This function also considers the presence of pole points + within the face's bounds and adjusts the bounding box accordingly. + + Parameters + ---------- + face_edges_cartesian : np.ndarray, shape (n_edges, 2, 3) + An array holding the Cartesian coordinates for the edges of a face, where `n_edges` + is the number of edges for the specific face. Each edge is represented by two points + (start and end), and each point is a 3D vector (x, y, z) in Cartesian coordinates. + + face_edges_lonlat_connectivity_rad : np.ndarray, shape (n_edges, 2, 2) + An array holding the longitude and latitude in radians for the edges of a face, + formatted similarly to `face_edges_cartesian`. Each edge's start and + end points are represented by their longitude and latitude values in radians. + + is_latlonface : bool, optional + A flag indicating if the current face is a latitudinal/longitudinal (latlon) face, + meaning its edges align with lines of constant latitude or longitude. If `True`, + edges are treated as following constant latitudinal or longitudinal lines. If `False`, + edges are considered as great circle arcs (GCA). Default is `False`. + + is_GCA_list : list or np.ndarray, optional + A list or an array of boolean values corresponding to each edge within the face, + indicating whether the edge is a GCA. If `None`, the determination of whether an + edge is a GCA or a constant latitudinal/longitudinal line is based on `is_latlonface`. + Default is `None`. + + Returns + ------- + face_latlon_array : np.ndarray, shape (2, 2) + An array representing the bounding box for the face in latitude and longitude + coordinates (in radians). The first row contains the minimum and maximum latitude + values, while the second row contains the minimum and maximum longitude values. + + Notes + ----- + This function evaluates the presence of North or South pole points within the face's + bounds by inspecting the Cartesian coordinates of the face's edges. It then constructs + the face's bounding box by considering the extreme latitude and longitude values found + among the face's edges, adjusting for the presence of pole points as necessary. + + The bounding box is used to determine the face's geographical extent and is crucial + for spatial analyses involving the grid. + + Example + ------- + Assuming the existence of a grid face with edges defined in both Cartesian and + latitudinal/longitudinal coordinates: + + face_edges_cartesian = np.array([...]) # Cartesian coords + face_edges_connectivity_rad = np.array([...]) # Lon/Lat coords in radians + + Populate the bounding box for the face, treating it as a latlon face: + + face_latlon_bound = _populate_face_latlon_bound(face_edges_cartesian, + face_edges_lonlat_connectivity_rad, + is_latlonface=True) + """ + + # Check if face_edges contains pole points + has_north_pole = _pole_point_inside_polygon("North", face_edges_cartesian) + has_south_pole = _pole_point_inside_polygon("South", face_edges_cartesian) + + face_latlon_array = np.full((2, 2), INT_FILL_VALUE, dtype=np.float64) + + if has_north_pole or has_south_pole: + # Initial assumption that the pole point is inside the face + is_center_pole = True + + # Define the pole point based on the hemisphere + pole_point = ( + np.array([0.0, 0.0, 1.0]) if has_north_pole else np.array([0.0, 0.0, -1.0]) + ) + # Pre-defined new point latitude based on the pole + new_pt_latlon = np.array( + [np.pi / 2 if has_north_pole else -np.pi / 2, INT_FILL_VALUE], + dtype=np.float64, + ) + + for i in range(face_edges_cartesian.shape[0]): + edge_cart = face_edges_cartesian[i] + edge_lonlat = face_edges_lonlat_rad[i] + + # Skip processing if the edge_cart is marked as a dummy with a fill value + if np.any(edge_cart == INT_FILL_VALUE): + continue + + # Extract cartesian coordinates of the edge_cart's endpoints + n1_cart, n2_cart = edge_cart + n1_lonlat, n2_lonlat = edge_lonlat + + # Convert latitudes and longitudes of the nodes to radians + node1_lon_rad, node1_lat_rad = n1_lonlat + + # Determine if the edge_cart's extreme latitudes need to be considered using the corrected logic + is_GCA = ( + is_GCA_list[i] + if is_GCA_list is not None + else not is_latlonface or n1_cart[2] != n2_cart[2] + ) + + # Check if the node matches the pole point or if the pole point is within the edge_cart + if np.allclose( + n1_cart, pole_point, atol=ERROR_TOLERANCE + ) or point_within_gca( + pole_point, np.array([n1_cart, n2_cart]), is_directed=False + ): + is_center_pole = False + face_latlon_array = _insert_pt_in_latlonbox( + face_latlon_array, new_pt_latlon + ) + + # Insert the current node's lat/lon into the latlonbox + face_latlon_array = _insert_pt_in_latlonbox( + face_latlon_array, np.array([node1_lat_rad, node1_lon_rad]) + ) + + # Determine extreme latitudes for GCA edges + lat_max, lat_min = ( + ( + extreme_gca_latitude(np.array([n1_cart, n2_cart]), "max"), + extreme_gca_latitude(np.array([n1_cart, n2_cart]), "min"), + ) + if is_GCA + else (node1_lat_rad, node1_lat_rad) + ) + + # Insert latitudinal extremes based on pole presence + if has_north_pole: + face_latlon_array = _insert_pt_in_latlonbox( + face_latlon_array, np.array([lat_min, node1_lon_rad]) + ) + face_latlon_array[0][1] = ( + np.pi / 2 + ) # Ensure north pole is the upper latitude bound + else: + face_latlon_array = _insert_pt_in_latlonbox( + face_latlon_array, np.array([lat_max, node1_lon_rad]) + ) + face_latlon_array[0][0] = ( + -np.pi / 2 + ) # Ensure south pole is the lower latitude bound + + # Adjust longitude bounds globally if the pole is centrally inside the polygon + if is_center_pole: + face_latlon_array[1] = [0.0, 2 * np.pi] + + else: + # Normal Face + # Iterate through each edge_cart of a face to update the bounding box (latlonbox) with extreme latitudes and longitudes + for i in range(face_edges_cartesian.shape[0]): + edge_cart = face_edges_cartesian[i] + edge_lonlat = face_edges_lonlat_rad[i] + + # Skip processing if the edge_cart is marked as a dummy with a fill value + if np.any(edge_cart == INT_FILL_VALUE): + continue + + # Extract cartesian coordinates of the edge_cart's endpoints + n1_cart, n2_cart = edge_cart + n1_lonlat, n2_lonlat = edge_lonlat + + # Convert latitudes and longitudes of the nodes to radians + node1_lon_rad, node1_lat_rad = n1_lonlat + node2_lon_rad, node2_lat_rad = n2_lonlat + + # Determine if the edge_cart's extreme latitudes need to be considered using the corrected logic + is_GCA = ( + is_GCA_list[i] + if is_GCA_list is not None + else not is_latlonface or n1_cart[2] != n2_cart[2] + ) + + lat_max, lat_min = ( + ( + extreme_gca_latitude(np.array([n1_cart, n2_cart]), "max"), + extreme_gca_latitude(np.array([n1_cart, n2_cart]), "min"), + ) + if is_GCA + else (node1_lat_rad, node1_lat_rad) + ) + + # Insert extreme latitude points into the latlonbox if they differ from the node latitudes + if not np.isclose( + node1_lat_rad, lat_max, atol=ERROR_TOLERANCE + ) and not np.isclose(node2_lat_rad, lat_max, atol=ERROR_TOLERANCE): + # Insert the maximum latitude + face_latlon_array = _insert_pt_in_latlonbox( + face_latlon_array, np.array([lat_max, node1_lon_rad]) + ) + elif not np.isclose( + node1_lat_rad, lat_min, atol=ERROR_TOLERANCE + ) and not np.isclose(node2_lat_rad, lat_min, atol=ERROR_TOLERANCE): + # Insert the minimum latitude + face_latlon_array = _insert_pt_in_latlonbox( + face_latlon_array, np.array([lat_min, node1_lon_rad]) + ) + else: + # Insert the node's latitude and longitude as it matches the extreme latitudes + face_latlon_array = _insert_pt_in_latlonbox( + face_latlon_array, np.array([node1_lat_rad, node1_lon_rad]) + ) + + return face_latlon_array + + +def _populate_bounds( + grid, is_latlonface: bool = False, is_face_GCA_list=None, return_array=False +): + """Populates the bounds of the grid based on the geometry of its faces, + taking into account special conditions such as faces crossing the + antimeridian or containing pole points. This method updates the grid's + internal representation to include accurate bounds for each face, returned + as a DataArray with detailed attributes. + + Parameters + ---------- + is_latlonface : bool, optional + A global flag that indicates if faces are latlon faces. If True, all faces + are treated as latlon faces, meaning that all edges are either longitude or + constant latitude lines. If False, all edges are considered as Great Circle Arcs (GCA). + Default is False. + + is_face_GCA_list : list or np.ndarray, optional + A list or an array of boolean values for each face, indicating whether each edge + in that face is a GCA. The shape of the list or array should be (n_faces, n_edges), + with each sub-list or sub-array like [True, False, True, False] indicating the + nature of each edge (GCA or constant latitude line) in a face. This parameter allows + for mixed face types within the grid by specifying the edge type at the face level. + If None, all edges are considered as GCA. This parameter, if provided, will overwrite + the `is_latlonface` attribute for specific faces. Default is None. + + Returns + ------- + xr.DataArray + A DataArray containing the latitude and longitude bounds for each face in the grid, + expressed in radians. The array has dimensions ["n_face", "Two", "Two"], where "Two" + is a literal dimension name indicating two bounds (min and max) for each of latitude + and longitude. The DataArray includes attributes detailing its purpose and the mapping + of latitude intervals to face indices. + + Attributes include: + - `cf_role`: Describes the role of the DataArray, here indicating face latitude bounds. + - `_FillValue`: The fill value used in the array, indicating uninitialized or missing data. + - `long_name`: A descriptive name for the DataArray. + - `start_index`: The starting index for face indices in the grid. + - `latitude_intervalsIndex`: An IntervalIndex indicating the latitude intervals. + - `latitude_intervals_name_map`: A DataFrame mapping the latitude intervals to face indices. + + Example + ------- + Consider a scenario where you have four faces on a grid, each defined by vertices in longitude and latitude degrees: + + face_1 = [[10.0, 60.0], [10.0, 10.0], [50.0, 10.0], [50.0, 60.0]] + face_2 = [[350, 60.0], [350, 10.0], [50.0, 10.0], [50.0, 60.0]] + face_3 = [[210.0, 80.0], [350.0, 60.0], [10.0, 60.0], [30.0, 80.0]] + face_4 = [[200.0, 80.0], [350.0, 60.0], [10.0, 60.0], [40.0, 80.0]] + + After defining these faces, you can create a grid and populate its bounds by treating all faces as latlon faces: + + grid = ux.Grid.from_face_vertices([face_1, face_2, face_3, face_4], latlon=True) + bounds_dataarray = grid._populate_bounds(is_latlonface=True) + + This will calculate and store the bounds for each face within the grid, adjusting for any special conditions such as crossing the antimeridian, and return them as a DataArray. + """ + temp_latlon_array = np.full((grid.n_face, 2, 2), INT_FILL_VALUE, dtype=np.float64) + + # Because Pandas.IntervalIndex does not support naming for each interval, we need to create a mapping + # between the intervals and the face indices + intervals_tuple_list = [] + intervals_name_list = [] + + for face_idx, face_nodes in enumerate(grid.face_node_connectivity): + face_edges_cartesian = _get_cartesian_face_edge_nodes( + grid.face_node_connectivity.values[face_idx], + grid.face_edge_connectivity.values[face_idx], + grid.edge_node_connectivity.values, + grid.node_x.values, + grid.node_y.values, + grid.node_z.values, + ) + + face_edges_lonlat_rad = _get_lonlat_rad_face_edge_nodes( + grid.face_node_connectivity.values[face_idx], + grid.face_edge_connectivity.values[face_idx], + grid.edge_node_connectivity.values, + grid.node_lon.values, + grid.node_lat.values, + ) + + is_GCA_list = ( + is_face_GCA_list[face_idx] if is_face_GCA_list is not None else None + ) + + temp_latlon_array[face_idx] = _populate_face_latlon_bound( + face_edges_cartesian, + face_edges_lonlat_rad, + is_latlonface=is_latlonface, + is_GCA_list=is_GCA_list, + ) + + assert temp_latlon_array[face_idx][0][0] != temp_latlon_array[face_idx][0][1] + assert temp_latlon_array[face_idx][1][0] != temp_latlon_array[face_idx][1][1] + lat_array = temp_latlon_array[face_idx][0] + + # Now store the latitude intervals in the tuples + intervals_tuple_list.append((lat_array[0], lat_array[1])) + intervals_name_list.append(face_idx) + + # Because Pandas.IntervalIndex does not support naming for each interval, we need to create a mapping + # between the intervals and the face indices + intervalsIndex = pd.IntervalIndex.from_tuples(intervals_tuple_list, closed="both") + df_intervals_map = pd.DataFrame( + index=intervalsIndex, data=intervals_name_list, columns=["face_id"] + ) + + bounds = xr.DataArray( + temp_latlon_array, + dims=["n_face", "Two", "Two"], + attrs={ + "cf_role": "face_latlon_bounds", + "_FillValue": INT_FILL_VALUE, + "long_name": "Provides the latitude and longitude bounds for each face in radians.", + "start_index": INT_DTYPE(0), + "latitude_intervalsIndex": intervalsIndex, + "latitude_intervals_name_map": df_intervals_map, + }, + ) + + if return_array: + return bounds + else: + grid._ds["bounds"] = bounds diff --git a/uxarray/grid/grid.py b/uxarray/grid/grid.py index 49dd7a5bc..cd3d67473 100644 --- a/uxarray/grid/grid.py +++ b/uxarray/grid/grid.py @@ -27,6 +27,8 @@ _populate_face_centroids, _populate_edge_centroids, _set_desired_longitude_range, + _populate_lonlat_coord, + _populate_cartesian_xyz_coord, ) from uxarray.grid.connectivity import ( _populate_edge_node_connectivity, @@ -36,16 +38,12 @@ _populate_edge_face_connectivity, ) -from uxarray.grid.coordinates import ( - _populate_lonlat_coord, - _populate_cartesian_xyz_coord, -) - from uxarray.grid.geometry import ( _populate_antimeridian_face_indices, _grid_to_polygon_geodataframe, _grid_to_matplotlib_polycollection, _grid_to_matplotlib_linecollection, + _populate_bounds, ) from uxarray.grid.neighbors import ( @@ -65,6 +63,7 @@ _check_area, ) + from xarray.core.utils import UncachedAccessor from warnings import warn @@ -859,6 +858,19 @@ def face_areas(self) -> xr.DataArray: ) return self._ds["face_areas"] + @property + def bounds(self): + """Latitude Longitude Bounds for each Face in degrees. + + Dimensions ``(n_face", two, two)`` + """ + if "bounds" not in self._ds: + warn( + "Constructing of `Grid.bounds` has not been optimized, which may lead to a long execution time." + ) + _populate_bounds(self) + return self._ds["bounds"] + @property def face_jacobian(self): """Declare face_jacobian as a property.""" diff --git a/uxarray/grid/utils.py b/uxarray/grid/utils.py index d8957feeb..a488584d2 100644 --- a/uxarray/grid/utils.py +++ b/uxarray/grid/utils.py @@ -247,3 +247,65 @@ def _get_cartesian_face_edge_nodes( ) return cartesian_coordinates + + +def _get_lonlat_rad_face_edge_nodes( + face_nodes_ind, face_edges_ind, edge_nodes_grid, node_lon, node_lat +): + """Construct an array to hold the edge lat lon in radian connectivity for a + face in a grid. + + Parameters + ---------- + face_nodes_ind : np.ndarray, shape (n_nodes,) + The ith entry of Grid.face_node_connectivity, where n_nodes is the number of nodes in the face. + face_edges_ind : np.ndarray, shape (n_edges,) + The ith entry of Grid.face_edge_connectivity, where n_edges is the number of edges in the face. + edge_nodes_grid : np.ndarray, shape (n_edges, 2) + The entire Grid.edge_node_connectivity, where n_edges is the total number of edges in the grid. + node_lon : np.ndarray, shape (n_nodes,) + The values of Grid.node_lon. + node_lat : np.ndarray, shape (n_nodes,) + The values of Grid.node_lat, where n_nodes_total is the total number of nodes in the grid. + Returns + ------- + face_edges : np.ndarray, shape (n_edges, 2, 3) + Face edge connectivity in Cartesian coordinates, where n_edges is the number of edges for the specific face. + + Notes + ----- + - The function assumes that the inputs are well-formed and correspond to the same face. + - The output array contains the Latitude and longitude coordinates in radian for each edge of the face. + """ + + # Create a mask that is True for all values not equal to INT_FILL_VALUE + mask = face_edges_ind != INT_FILL_VALUE + + # Use the mask to select only the elements not equal to INT_FILL_VALUE + valid_edges = face_edges_ind[mask] + face_edges = edge_nodes_grid[valid_edges] + + # Ensure counter-clockwise order of edge nodes + # Start with the first two nodes + face_edges[0] = [face_nodes_ind[0], face_nodes_ind[1]] + + for idx in range(1, len(face_edges)): + if face_edges[idx][0] != face_edges[idx - 1][1]: + # Swap the node index in this edge if not in counter-clockwise order + face_edges[idx] = face_edges[idx][::-1] + + # Fetch coordinates for each node in the face edges + lonlat_coordinates = np.array( + [ + [ + [ + np.mod(np.deg2rad(node_lon[node]), 2 * np.pi), + np.deg2rad(node_lat[node]), + ] + for node in edge + ] + for edge in face_edges + ] + ) + + return lonlat_coordinates From 54da444302425be04d33abe914062d8ffc5fed35 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Mon, 15 Apr 2024 12:07:33 -0500 Subject: [PATCH 21/41] Always Rasterize with default `UxDataArray.plot()` (#762) * update default plotting method * update value error * update usage example * Update accessor.py --- docs/examples/007-polygon-viz.ipynb | 2 +- uxarray/plot/dataarray_plot.py | 31 +++++++++-------------------- 2 files changed, 10 insertions(+), 23 deletions(-) diff --git a/docs/examples/007-polygon-viz.ipynb b/docs/examples/007-polygon-viz.ipynb index 43404a52a..dc9756b4e 100644 --- a/docs/examples/007-polygon-viz.ipynb +++ b/docs/examples/007-polygon-viz.ipynb @@ -100,7 +100,7 @@ }, "source": [ "## Using the `UxDataArray.plot()` Accessor\n", - "For face-centered data, the default plotting method returns a Polygon plot." + "For face-centered data, the default plotting method returns a rasterized polygon plot." ] }, { diff --git a/uxarray/plot/dataarray_plot.py b/uxarray/plot/dataarray_plot.py index 8551b02d3..bd7ac9e97 100644 --- a/uxarray/plot/dataarray_plot.py +++ b/uxarray/plot/dataarray_plot.py @@ -13,38 +13,25 @@ import holoviews as hv from holoviews.operation.datashader import rasterize as hds_rasterize -from uxarray.plot.constants import N_FACE_THRESHOLD - import numpy as np - import uxarray.plot.utils def plot(uxda, **kwargs): - """Default Plotting Method for UxDataArray.""" + """Default plotting method for a ``UxDataArray``.""" + if uxda._face_centered(): - # default to polygon plot - if uxda.uxgrid.n_face < N_FACE_THRESHOLD: - # vector polygons for small datasets - if "exclude_antimeridian" in kwargs: - return polygons(uxda, **kwargs) - else: - return polygons(uxda, exclude_antimeridian=False, **kwargs) - - else: - # rasterized polygons for larger datasets - return rasterize(uxda, method="polygon", **kwargs) - elif uxda._node_centered(): - # default to point plots - return points(uxda, **kwargs) - elif uxda._edge_centered(): - # default to edge plots - return points(uxda, **kwargs) + return rasterize(uxda, method="polygon", **kwargs) + + elif uxda._edge_centered() or uxda._node_centered(): + return rasterize(uxda, method="point", **kwargs) else: - raise ValueError("Data must be either node or face centered.") + raise ValueError( + "Plotting variables on unstructured grids requires the data variable to be mapped to either the nodes, edges, or faces." + ) def datashade( From d0aa68c4998664a631ee64dfb10dca4528855bf7 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Wed, 17 Apr 2024 13:52:23 -0500 Subject: [PATCH 22/41] Bump codecov/codecov-action from 4.1.1 to 4.3.0 (#763) Bumps [codecov/codecov-action](https://github.com/codecov/codecov-action) from 4.1.1 to 4.3.0. - [Release notes](https://github.com/codecov/codecov-action/releases) - [Changelog](https://github.com/codecov/codecov-action/blob/main/CHANGELOG.md) - [Commits](https://github.com/codecov/codecov-action/compare/v4.1.1...v4.3.0) --- updated-dependencies: - dependency-name: codecov/codecov-action dependency-type: direct:production update-type: version-update:semver-minor ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 44a387498..3bac1781f 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -71,7 +71,7 @@ jobs: python -m pytest test -v --cov=./uxarray --cov-report=xml - name: Upload code coverage to Codecov - uses: codecov/codecov-action@v4.1.1 + uses: codecov/codecov-action@v4.3.0 with: file: ./coverage.xml flags: unittests From 00a4f89df497538820bb678c114b29c366bcacb8 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Wed, 17 Apr 2024 14:45:24 -0500 Subject: [PATCH 23/41] remove decode_times as a hard-coded parameter (#767) --- uxarray/core/api.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/uxarray/core/api.py b/uxarray/core/api.py index 95bbba5c1..283122660 100644 --- a/uxarray/core/api.py +++ b/uxarray/core/api.py @@ -83,9 +83,7 @@ def open_grid( # attempt to use Xarray directly for remaining input types else: try: - grid_ds = xr.open_dataset( - grid_filename_or_obj, decode_times=False, **kwargs - ) + grid_ds = xr.open_dataset(grid_filename_or_obj, **kwargs) uxgrid = Grid.from_dataset(grid_ds, use_dual=use_dual) except ValueError: @@ -168,7 +166,7 @@ def open_dataset( ) # UxDataset - ds = xr.open_dataset(filename_or_obj, decode_times=False, **kwargs) # type: ignore + ds = xr.open_dataset(filename_or_obj, **kwargs) # type: ignore # map each dimension to its UGRID equivalent ds = _map_dims_to_ugrid(ds, uxgrid._source_dims_dict, uxgrid) @@ -257,7 +255,7 @@ def open_mfdataset( ) # UxDataset - ds = xr.open_mfdataset(paths, decode_times=False, **kwargs) # type: ignore + ds = xr.open_mfdataset(paths, **kwargs) # type: ignore # map each dimension to its UGRID equivalent ds = _map_dims_to_ugrid(ds, uxgrid._source_dims_dict, uxgrid) From a85db058cc81f2173c25ab250085308ba0b58845 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Wed, 17 Apr 2024 15:20:05 -0500 Subject: [PATCH 24/41] Support Xarray Plotting Routines (#760) * add support for calling xarray's plotting routines * add tests * reset mpl backend * add matplotlib-inline to enviroments * fix package name * generalize implementation --- ci/asv.yml | 1 + ci/docs.yml | 1 + ci/environment.yml | 1 + test/test_plot.py | 21 ++++++++++++++++++++ uxarray/plot/accessor.py | 41 ++++++++++++++++++++++++++++++++++++---- uxarray/plot/utils.py | 6 ++++++ 6 files changed, 67 insertions(+), 4 deletions(-) diff --git a/ci/asv.yml b/ci/asv.yml index fa44e7418..76be35aeb 100644 --- a/ci/asv.yml +++ b/ci/asv.yml @@ -9,6 +9,7 @@ dependencies: - gmpy2 - holoviews - matplotlib-base + - matplotlib-inline - netcdf4 - numba - numpy diff --git a/ci/docs.yml b/ci/docs.yml index d88386395..01df0ce5a 100644 --- a/ci/docs.yml +++ b/ci/docs.yml @@ -24,6 +24,7 @@ dependencies: - sphinx-design - nbsphinx - matplotlib-base + - matplotlib-inline - shapely - hvplot - holoviews diff --git a/ci/environment.yml b/ci/environment.yml index 8792e8f50..5544fe413 100644 --- a/ci/environment.yml +++ b/ci/environment.yml @@ -8,6 +8,7 @@ dependencies: - gmpy2 - holoviews - matplotlib-base + - matplotlib-inline - netcdf4 - numba - numpy diff --git a/test/test_plot.py b/test/test_plot.py index e0c69432c..9a503c8ea 100644 --- a/test/test_plot.py +++ b/test/test_plot.py @@ -91,3 +91,24 @@ def test_clabel(self): raster_no_clabel = uxds['v1'][0][0].plot.rasterize(method='point') raster_with_clabel = uxds['v1'][0][0].plot.rasterize(method='point', clabel='Foo') + + + +class TestXarrayMethods(TestCase): + + def test_dataset(self): + """Tests whether a Xarray DataArray method can be called through the + UxDataArray plotting accessor.""" + uxds = ux.open_dataset(gridfile_geoflow, datafile_geoflow) + + # plot.hist() is an xarray method + assert hasattr(uxds['v1'].plot, 'hist') + + + def test_dataarray(self): + """Tests whether a Xarray Dataset method can be called through the + UxDataset plotting accessor.""" + uxds = ux.open_dataset(gridfile_geoflow, datafile_geoflow) + + # plot.scatter() is an xarray method + assert hasattr(uxds.plot, 'scatter') diff --git a/uxarray/plot/accessor.py b/uxarray/plot/accessor.py index 344c82681..d1f49d1c1 100644 --- a/uxarray/plot/accessor.py +++ b/uxarray/plot/accessor.py @@ -3,7 +3,6 @@ import functools -import warnings if TYPE_CHECKING: from uxarray.core.dataset import UxDataset @@ -12,6 +11,7 @@ import uxarray.plot.grid_plot as grid_plot import uxarray.plot.dataarray_plot as dataarray_plot +import uxarray.plot.utils import cartopy.crs as ccrs @@ -269,6 +269,22 @@ def __init__(self, uxda: UxDataArray) -> None: def __call__(self, **kwargs) -> Any: return dataarray_plot.plot(self._uxda, **kwargs) + def __getattr__(self, name: str) -> Any: + """When a function that isn't part of the class is invoked (i.e. + uxda.plot.hist), an attempt is made to try and call Xarray's + implementation of that function if it exsists.""" + + # reference to xr.DataArray.plot accessor + xarray_plot_accessor = super(type(self._uxda), self._uxda).plot + + if hasattr(xarray_plot_accessor, name): + # call xarray plot method if it exists + # use inline backend to reset configuration if holoviz methods were called before + uxarray.plot.utils.backend.reset_mpl_backend() + return getattr(xarray_plot_accessor, name) + else: + raise AttributeError(f"Unsupported Plotting Method: '{name}'") + @functools.wraps(dataarray_plot.datashade) def datashade( self, @@ -468,7 +484,24 @@ def __init__(self, uxds: UxDataset) -> None: self._uxds = uxds def __call__(self, **kwargs) -> Any: - warnings.warn( - "Plotting for UxDataset instances not yet supported. Did you mean to plot a data variable, i.e. uxds['data_variable'].plot()" + raise ValueError( + "UxDataset.plot cannot be called directly. Use an explicit plot method, " + "e.g uxds.plot.scatter(...)" ) - pass + + def __getattr__(self, name: str) -> Any: + """When a function that isn't part of the class is invoked (i.e. + uxds.plot.scatter), an attempt is made to try and call Xarray's + implementation of that function if it exists.""" + + # reference to xr.Dataset.plot accessor + xarray_plot_accessor = super(type(self._uxds), self._uxds).plot + + if hasattr(xarray_plot_accessor, name): + # call xarray plot method if it exists + # # use inline backend to reset configuration if holoviz methods were called before + uxarray.plot.utils.backend.reset_mpl_backend() + + return getattr(xarray_plot_accessor, name) + else: + raise AttributeError(f"Unsupported Plotting Method: '{name}'") diff --git a/uxarray/plot/utils.py b/uxarray/plot/utils.py index cc588821d..56eb6d2b9 100644 --- a/uxarray/plot/utils.py +++ b/uxarray/plot/utils.py @@ -1,4 +1,5 @@ import holoviews as hv +import matplotlib as mpl class HoloviewsBackend: @@ -7,6 +8,7 @@ class HoloviewsBackend: def __init__(self): self.backend = None + self.matplotlib_backend = mpl.get_backend() def assign(self, backend: str): """Assigns a backend for use with HoloViews visualization. @@ -27,6 +29,10 @@ def assign(self, backend: str): hv.extension(backend) self.backend = backend + def reset_mpl_backend(self): + """Resets the default backend for the ``matplotlib`` module.""" + mpl.use(self.matplotlib_backend) + # global reference to holoviews backend utility class backend = HoloviewsBackend() From dff3971a67d7ff03f9af229ab1cd5feacf50537f Mon Sep 17 00:00:00 2001 From: Hongyu Chen Date: Tue, 23 Apr 2024 19:16:42 -0700 Subject: [PATCH 25/41] Zonal face weights at constant lat (#555) * Initial Draft Algorithm * Revert "Initial Draft Algorithm" This reverts commit 99573b8edfc2dc2fc329e91d395da4d907b42e4a. * Initial Draft Algorithm * Initial Draft Algorithm * Updated documentation and comments, added test case, and updated index.rst * updated function name * Refactored function to get cartesian coords * Merged main, fixed conflicts updated doc string * Added test, addressed review comments * fixed pre-commit * Updated docstring and variable names * Initial commit, working in progress * Codes Refractor and Docstring updates * Update some layout * update * Fix the filled value case * changes * changes * changes * First draft of the function * working on the test case * commits * add directed option for the gca constlat intersection * working on it * Change the `is_directed` default as false * Change the `is_directed` default as false * Fix testcase * Finished `_get_zonal_face_weight_rad` * Allow users to specify if the input is GCA or constLat * Consider the equator case * Draft complete * structure refractor needed * Initial commit * Finished * precommit * Fix conflic * Add docstring * Abandon `pd.append` * delete duplicate * Fix testcases * try fixing testcases * try fixing testcases again * try fixing testcases again * try fixing testcases again again * Add error-tolerence in `point_within_gca` * pre-commit fix * Add the `is_latlonface` flag * Add the `is_latlonface` flag * Try to consider about the concave face case * WIP * Update uxarray/grid/integrate.py Co-authored-by: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> * working for merge conflict * working for merge conflict * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * WIP * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Resolve TestFaceWeights cases * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Resolve All Testcases * clean up * Add doc * fix doc --------- Co-authored-by: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Co-authored-by: aazedwick Co-authored-by: Aaron Zedwick <95507181+aaronzedwick@users.noreply.github.com> Co-authored-by: Orhan Eroglu <32553057+erogluorhan@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- docs/internal_api/index.rst | 13 ++ test/test_integrate.py | 414 ++++++++++++++++++++++++++++++++++ test/test_intersections.py | 10 +- uxarray/constants.py | 2 + uxarray/grid/arcs.py | 2 +- uxarray/grid/integrate.py | 352 +++++++++++++++++++++++++++++ uxarray/grid/intersections.py | 7 +- 7 files changed, 788 insertions(+), 12 deletions(-) create mode 100644 uxarray/grid/integrate.py diff --git a/docs/internal_api/index.rst b/docs/internal_api/index.rst index 13b572854..fc90d681d 100644 --- a/docs/internal_api/index.rst +++ b/docs/internal_api/index.rst @@ -208,6 +208,19 @@ Accurate Computing Utils utils.computing._acc_sqrt utils.computing._split + +Integration +----------- +.. autosummary:: + :toctree: generated/ + + grid.integrate._get_zonal_faces_weight_at_constLat + grid.integrate._get_zonal_face_interval + grid.integrate._process_overlapped_intervals + grid.integrate._is_edge_gca + grid.integrate._get_faces_constLat_intersection_info + + Remapping ========= diff --git a/test/test_integrate.py b/test/test_integrate.py index 50864015c..af26941c6 100644 --- a/test/test_integrate.py +++ b/test/test_integrate.py @@ -3,10 +3,13 @@ from unittest import TestCase from pathlib import Path import numpy as np +import pandas as pd import numpy.testing as nt import uxarray as ux +from uxarray.grid.coordinates import node_lonlat_rad_to_xyz +from uxarray.grid.integrate import _get_zonal_face_interval, _process_overlapped_intervals, _get_zonal_faces_weight_at_constLat current_path = Path(os.path.dirname(os.path.realpath(__file__))) @@ -54,3 +57,414 @@ def test_multi_dim(self): assert integral.ndim == len(dims) - 1 nt.assert_almost_equal(integral, np.ones((5, 5)) * 4 * np.pi) + + +class TestFaceWeights(TestCase): + + def test_get_zonal_face_interval(self): + """Test that the zonal face weights are correct.""" + vertices_lonlat = [[1.6 * np.pi, 0.25 * np.pi], + [1.6 * np.pi, -0.25 * np.pi], + [0.4 * np.pi, -0.25 * np.pi], + [0.4 * np.pi, 0.25 * np.pi]] + vertices = [node_lonlat_rad_to_xyz(v) for v in vertices_lonlat] + + face_edge_nodes = np.array([[vertices[0], vertices[1]], + [vertices[1], vertices[2]], + [vertices[2], vertices[3]], + [vertices[3], vertices[0]]]) + + constZ = np.sin(0.20) + # The latlon bounds for the latitude is not necessarily correct below since we don't use the latitudes bound anyway + interval_df = _get_zonal_face_interval(face_edge_nodes, constZ, + np.array([[-0.25 * np.pi, 0.25 * np.pi], [1.6 * np.pi, + 0.4 * np.pi]]), + is_directed=False) + expected_interval_df = pd.DataFrame({ + 'start': [1.6 * np.pi, 0.0], + 'end': [2.0 * np.pi, 00.4 * np.pi] + }) + # Sort both DataFrames by 'start' column before comparison + expected_interval_df_sorted = expected_interval_df.sort_values(by='start').reset_index(drop=True) + + # Converting the sorted DataFrames to NumPy arrays + actual_values_sorted = interval_df[['start', 'end']].to_numpy() + expected_values_sorted = expected_interval_df_sorted[['start', 'end']].to_numpy() + + # Asserting almost equal arrays + nt.assert_array_almost_equal(actual_values_sorted, expected_values_sorted, decimal=13) + + def test_get_zonal_face_interval_GCA_constLat(self): + """Test that the zonal face weights are correct.""" + vertices_lonlat = [[-0.4 * np.pi, 0.25 * np.pi], + [-0.4 * np.pi, -0.25 * np.pi], + [0.4 * np.pi, -0.25 * np.pi], + [0.4 * np.pi, 0.25 * np.pi]] + + vertices = [node_lonlat_rad_to_xyz(v) for v in vertices_lonlat] + + face_edge_nodes = np.array([[vertices[0], vertices[1]], + [vertices[1], vertices[2]], + [vertices[2], vertices[3]], + [vertices[3], vertices[0]]]) + + constZ = np.sin(0.20 * np.pi) + interval_df = _get_zonal_face_interval(face_edge_nodes, constZ, + np.array([[-0.25 * np.pi, 0.25 * np.pi], [1.6 * np.pi, + 0.4 * np.pi]]), + is_directed=False, is_GCA_list=np.array([True, False, True, False])) + expected_interval_df = pd.DataFrame({ + 'start': [1.6 * np.pi, 0.0], + 'end': [2.0 * np.pi, 00.4 * np.pi] + }) + # Sort both DataFrames by 'start' column before comparison + expected_interval_df_sorted = expected_interval_df.sort_values(by='start').reset_index(drop=True) + + # Converting the sorted DataFrames to NumPy arrays + actual_values_sorted = interval_df[['start', 'end']].to_numpy() + expected_values_sorted = expected_interval_df_sorted[['start', 'end']].to_numpy() + + # Asserting almost equal arrays + nt.assert_array_almost_equal(actual_values_sorted, expected_values_sorted, decimal=13) + + def test_get_zonal_face_interval_equator(self): + """Test that the zonal face weights are correct.""" + vertices_lonlat = [[-0.4 * np.pi, 0.25 * np.pi], [-0.4 * np.pi, 0.0], + [0.4 * np.pi, 0.0], [0.4 * np.pi, 0.25 * np.pi]] + + vertices = [node_lonlat_rad_to_xyz(v) for v in vertices_lonlat] + + face_edge_nodes = np.array([[vertices[0], vertices[1]], + [vertices[1], vertices[2]], + [vertices[2], vertices[3]], + [vertices[3], vertices[0]]]) + + interval_df = _get_zonal_face_interval(face_edge_nodes, 0.0, + np.array([[-0.25 * np.pi, 0.25 * np.pi], [1.6 * np.pi, + 0.4 * np.pi]]), + is_directed=False, is_GCA_list=np.array([True, True, True, True])) + expected_interval_df = pd.DataFrame({ + 'start': [1.6 * np.pi, 0.0], + 'end': [2.0 * np.pi, 00.4 * np.pi] + }) + # Sort both DataFrames by 'start' column before comparison + expected_interval_df_sorted = expected_interval_df.sort_values(by='start').reset_index(drop=True) + + # Converting the sorted DataFrames to NumPy arrays + actual_values_sorted = interval_df[['start', 'end']].to_numpy() + expected_values_sorted = expected_interval_df_sorted[['start', 'end']].to_numpy() + + # Asserting almost equal arrays + nt.assert_array_almost_equal(actual_values_sorted, expected_values_sorted, decimal=13) + + # Even if we change the is_GCA_list to False, the result should be the same + interval_df = _get_zonal_face_interval(face_edge_nodes, 0.0, + np.array([[-0.25 * np.pi, 0.25 * np.pi], [1.6 * np.pi, + 0.4 * np.pi]]), + is_directed=False, is_GCA_list=np.array([True, False, True, False])) + expected_interval_df = pd.DataFrame({ + 'start': [1.6 * np.pi, 0.0], + 'end': [2.0 * np.pi, 00.4 * np.pi] + }) + + # Sort both DataFrames by 'start' column before comparison + expected_interval_df_sorted = expected_interval_df.sort_values(by='start').reset_index(drop=True) + + # Converting the sorted DataFrames to NumPy arrays + actual_values_sorted = interval_df[['start', 'end']].to_numpy() + expected_values_sorted = expected_interval_df_sorted[['start', 'end']].to_numpy() + + # Asserting almost equal arrays + nt.assert_array_almost_equal(actual_values_sorted, expected_values_sorted, decimal=13) + + def test_process_overlapped_intervals(self): + # Example data that has overlapping intervals and gap + intervals_data = [ + { + 'start': 0.0, + 'end': 100.0, + 'face_index': 0 + }, + { + 'start': 50.0, + 'end': 150.0, + 'face_index': 1 + }, + { + 'start': 140.0, + 'end': 150.0, + 'face_index': 2 + }, + { + 'start': 150.0, + 'end': 250.0, + 'face_index': 3 + }, + { + 'start': 260.0, + 'end': 350.0, + 'face_index': 4 + }, + ] + + df = pd.DataFrame(intervals_data) + df['interval'] = df.apply(lambda row: pd.Interval( + left=row['start'], right=row['end'], closed='both'), + axis=1) + df['interval'] = df['interval'].astype('interval[float64]') + + # Expected result + expected_overlap_contributions = np.array({ + 0: 75.0, + 1: 70.0, + 2: 5.0, + 3: 100.0, + 4: 90.0 + }) + overlap_contributions, total_length = _process_overlapped_intervals(df) + self.assertEqual(total_length, 340.0) + nt.assert_array_equal(overlap_contributions, + expected_overlap_contributions) + + def test_process_overlapped_intervals_antimerdian(self): + intervals_data = [ + { + 'start': 350.0, + 'end': 360.0, + 'face_index': 0 + }, + { + 'start': 0.0, + 'end': 100.0, + 'face_index': 0 + }, + { + 'start': 100.0, + 'end': 150.0, + 'face_index': 1 + }, + { + 'start': 100.0, + 'end': 300.0, + 'face_index': 2 + }, + { + 'start': 310.0, + 'end': 360.0, + 'face_index': 3 + }, + ] + + df = pd.DataFrame(intervals_data) + df['interval'] = df.apply(lambda row: pd.Interval( + left=row['start'], right=row['end'], closed='both'), + axis=1) + df['interval'] = df['interval'].astype('interval[float64]') + + # Expected result + expected_overlap_contributions = np.array({ + 0: 105.0, + 1: 25.0, + 2: 175.0, + 3: 45.0 + }) + overlap_contributions, total_length = _process_overlapped_intervals(df) + self.assertEqual(total_length, 350.0) + nt.assert_array_equal(overlap_contributions, + expected_overlap_contributions) + + def test_get_zonal_faces_weight_at_constLat_equator(self): + face_0 = [[1.7 * np.pi, 0.25 * np.pi], [1.7 * np.pi, 0.0], + [0.3 * np.pi, 0.0], [0.3 * np.pi, 0.25 * np.pi]] + face_1 = [[0.3 * np.pi, 0.0], [0.3 * np.pi, -0.25 * np.pi], + [0.6 * np.pi, -0.25 * np.pi], [0.6 * np.pi, 0.0]] + face_2 = [[0.3 * np.pi, 0.25 * np.pi], [0.3 * np.pi, 0.0], [np.pi, 0.0], + [np.pi, 0.25 * np.pi]] + face_3 = [[0.7 * np.pi, 0.0], [0.7 * np.pi, -0.25 * np.pi], + [np.pi, -0.25 * np.pi], [np.pi, 0.0]] + + # Convert the face vertices to xyz coordinates + face_0 = [node_lonlat_rad_to_xyz(v) for v in face_0] + face_1 = [node_lonlat_rad_to_xyz(v) for v in face_1] + face_2 = [node_lonlat_rad_to_xyz(v) for v in face_2] + face_3 = [node_lonlat_rad_to_xyz(v) for v in face_3] + + face_0_edge_nodes = np.array([[face_0[0], face_0[1]], + [face_0[1], face_0[2]], + [face_0[2], face_0[3]], + [face_0[3], face_0[0]]]) + face_1_edge_nodes = np.array([[face_1[0], face_1[1]], + [face_1[1], face_1[2]], + [face_1[2], face_1[3]], + [face_1[3], face_1[0]]]) + face_2_edge_nodes = np.array([[face_2[0], face_2[1]], + [face_2[1], face_2[2]], + [face_2[2], face_2[3]], + [face_2[3], face_2[0]]]) + face_3_edge_nodes = np.array([[face_3[0], face_3[1]], + [face_3[1], face_3[2]], + [face_3[2], face_3[3]], + [face_3[3], face_3[0]]]) + + face_0_latlon_bound = np.array([[0.0, 0.25 * np.pi], + [1.7 * np.pi, 0.3 * np.pi]]) + face_1_latlon_bound = np.array([[-0.25 * np.pi, 0.0], + [0.3 * np.pi, 0.6 * np.pi]]) + face_2_latlon_bound = np.array([[0.0, 0.25 * np.pi], + [0.3 * np.pi, np.pi]]) + face_3_latlon_bound = np.array([[-0.25 * np.pi, 0.0], + [0.7 * np.pi, np.pi]]) + + latlon_bounds = np.array([ + face_0_latlon_bound, face_1_latlon_bound, face_2_latlon_bound, + face_3_latlon_bound + ]) + + expected_weight_df = pd.DataFrame({ + 'face_index': [0, 1, 2, 3], + 'weight': [0.46153, 0.11538, 0.30769, 0.11538] + }) + + # Assert the results is the same to the 3 decimal places + weight_df = _get_zonal_faces_weight_at_constLat(np.array([ + face_0_edge_nodes, face_1_edge_nodes, face_2_edge_nodes, + face_3_edge_nodes + ]), + 0.0, + latlon_bounds, + is_directed=False) + + nt.assert_array_almost_equal(weight_df, expected_weight_df, decimal=3) + + def test_get_zonal_faces_weight_at_constLat_regular(self): + face_0 = [[1.7 * np.pi, 0.25 * np.pi], [1.7 * np.pi, 0.0], + [0.3 * np.pi, 0.0], [0.3 * np.pi, 0.25 * np.pi]] + face_1 = [[0.4 * np.pi, 0.3 * np.pi], [0.4 * np.pi, 0.0], + [0.5 * np.pi, 0.0], [0.5 * np.pi, 0.3 * np.pi]] + face_2 = [[0.5 * np.pi, 0.25 * np.pi], [0.5 * np.pi, 0.0], [np.pi, 0.0], + [np.pi, 0.25 * np.pi]] + face_3 = [[1.2 * np.pi, 0.25 * np.pi], [1.2 * np.pi, 0.0], + [1.6 * np.pi, -0.01 * np.pi], [1.6 * np.pi, 0.25 * np.pi]] + + # Convert the face vertices to xyz coordinates + face_0 = [node_lonlat_rad_to_xyz(v) for v in face_0] + face_1 = [node_lonlat_rad_to_xyz(v) for v in face_1] + face_2 = [node_lonlat_rad_to_xyz(v) for v in face_2] + face_3 = [node_lonlat_rad_to_xyz(v) for v in face_3] + + face_0_edge_nodes = np.array([[face_0[0], face_0[1]], + [face_0[1], face_0[2]], + [face_0[2], face_0[3]], + [face_0[3], face_0[0]]]) + face_1_edge_nodes = np.array([[face_1[0], face_1[1]], + [face_1[1], face_1[2]], + [face_1[2], face_1[3]], + [face_1[3], face_1[0]]]) + face_2_edge_nodes = np.array([[face_2[0], face_2[1]], + [face_2[1], face_2[2]], + [face_2[2], face_2[3]], + [face_2[3], face_2[0]]]) + face_3_edge_nodes = np.array([[face_3[0], face_3[1]], + [face_3[1], face_3[2]], + [face_3[2], face_3[3]], + [face_3[3], face_3[0]]]) + + face_0_latlon_bound = np.array([[0.0, 0.25 * np.pi], + [1.7 * np.pi, 0.3 * np.pi]]) + face_1_latlon_bound = np.array([[0, 0.3 * np.pi], + [0.4 * np.pi, 0.5 * np.pi]]) + face_2_latlon_bound = np.array([[0.0, 0.25 * np.pi], + [0.5 * np.pi, np.pi]]) + face_3_latlon_bound = np.array([[-0.01 * np.pi, 0.25 * np.pi], + [1.2 * np.pi, 1.6 * np.pi]]) + + latlon_bounds = np.array([ + face_0_latlon_bound, face_1_latlon_bound, face_2_latlon_bound, + face_3_latlon_bound + ]) + + expected_weight_df = pd.DataFrame({ + 'face_index': [0, 1, 2, 3], + 'weight': [0.375, 0.0625, 0.3125, 0.25] + }) + + + + # Assert the results is the same to the 3 decimal places + weight_df = _get_zonal_faces_weight_at_constLat(np.array([ + face_0_edge_nodes, face_1_edge_nodes, face_2_edge_nodes, + face_3_edge_nodes + ]), + np.sin(0.1 * np.pi), + latlon_bounds, + is_directed=False) + + nt.assert_array_almost_equal(weight_df, expected_weight_df, decimal=3) + + def test_get_zonal_faces_weight_at_constLat_latlonface(self): + face_0 = [[np.deg2rad(350), np.deg2rad(40)], [np.deg2rad(350), np.deg2rad(20)], + [np.deg2rad(10), np.deg2rad(20)], [np.deg2rad(10), np.deg2rad(40)]] + face_1 = [[np.deg2rad(5), np.deg2rad(20)], [np.deg2rad(5), np.deg2rad(10)], + [np.deg2rad(25), np.deg2rad(10)], [np.deg2rad(25), np.deg2rad(20)]] + face_2 = [[np.deg2rad(30), np.deg2rad(40)], [np.deg2rad(30), np.deg2rad(20)], + [np.deg2rad(40), np.deg2rad(20)], [np.deg2rad(40), np.deg2rad(40)]] + + # Convert the face vertices to xyz coordinates + face_0 = [node_lonlat_rad_to_xyz(v) for v in face_0] + face_1 = [node_lonlat_rad_to_xyz(v) for v in face_1] + face_2 = [node_lonlat_rad_to_xyz(v) for v in face_2] + + + face_0_edge_nodes = np.array([[face_0[0], face_0[1]], + [face_0[1], face_0[2]], + [face_0[2], face_0[3]], + [face_0[3], face_0[0]]]) + face_1_edge_nodes = np.array([[face_1[0], face_1[1]], + [face_1[1], face_1[2]], + [face_1[2], face_1[3]], + [face_1[3], face_1[0]]]) + face_2_edge_nodes = np.array([[face_2[0], face_2[1]], + [face_2[1], face_2[2]], + [face_2[2], face_2[3]], + [face_2[3], face_2[0]]]) + + face_0_latlon_bound = np.array([[np.deg2rad(20), np.deg2rad(40)], + [np.deg2rad(350), np.deg2rad(10)]]) + face_1_latlon_bound = np.array([[np.deg2rad(10), np.deg2rad(20)], + [np.deg2rad(5), np.deg2rad(25)]]) + face_2_latlon_bound = np.array([[np.deg2rad(20), np.deg2rad(40)], + [np.deg2rad(30), np.deg2rad(40)]]) + + + latlon_bounds = np.array([ + face_0_latlon_bound, face_1_latlon_bound, face_2_latlon_bound + ]) + + sum = 17.5 + 17.5 + 10 + expected_weight_df = pd.DataFrame({ + 'face_index': [0, 1, 2], + 'weight': [17.5 / sum, 17.5/sum, 10/sum] + }) + + # Assert the results is the same to the 3 decimal places + weight_df = _get_zonal_faces_weight_at_constLat(np.array([ + face_0_edge_nodes, face_1_edge_nodes, face_2_edge_nodes + ]), + np.sin(np.deg2rad(20)), + latlon_bounds, + is_directed=False, is_latlonface=True) + + + nt.assert_array_almost_equal(weight_df, expected_weight_df, decimal=3) + + + + # A error will be raise if we don't set is_latlonface=True since the face_2 will be concave if + # It's edges are all GCA + with self.assertRaises(ValueError): + _get_zonal_faces_weight_at_constLat(np.array([ + face_0_edge_nodes, face_1_edge_nodes, face_2_edge_nodes + ]), + np.deg2rad(20), + latlon_bounds, + is_directed=False) diff --git a/test/test_intersections.py b/test/test_intersections.py index 0faa028dd..f17b3c359 100644 --- a/test/test_intersections.py +++ b/test/test_intersections.py @@ -102,9 +102,7 @@ def test_GCA_constLat_intersections_antimeridian(self): np.deg2rad(10.0)]) ]) - res = gca_constLat_intersection(GCR1_cart, - np.deg2rad(60.0), - verbose=True) + res = gca_constLat_intersection(GCR1_cart, np.sin(np.deg2rad(60.0)), verbose=True) res_lonlat_rad = node_xyz_to_lonlat_rad(res[0].tolist()) self.assertTrue( np.allclose(res_lonlat_rad, @@ -119,9 +117,7 @@ def test_GCA_constLat_intersections_empty(self): np.deg2rad(10.0)]) ]) - res = gca_constLat_intersection(GCR1_cart, - np.deg2rad(-10.0), - verbose=False) + res = gca_constLat_intersection(GCR1_cart, np.sin(np.deg2rad(-10.0)), verbose=False) self.assertTrue(res.size == 0) def test_GCA_constLat_intersections_two_pts(self): @@ -135,5 +131,5 @@ def test_GCA_constLat_intersections_two_pts(self): query_lat = (np.deg2rad(10.0) + max_lat) / 2.0 - res = gca_constLat_intersection(GCR1_cart, query_lat, verbose=False) + res = gca_constLat_intersection(GCR1_cart, np.sin(query_lat), verbose=False) self.assertTrue(res.shape[0] == 2) diff --git a/uxarray/constants.py b/uxarray/constants.py index 0704db2f7..d42befac4 100644 --- a/uxarray/constants.py +++ b/uxarray/constants.py @@ -4,10 +4,12 @@ INT_DTYPE = np.intp INT_FILL_VALUE = np.iinfo(INT_DTYPE).min + # According to Ogita, Takeshi & Rump, Siegfried & Oishi, Shin’ichi. (2005). Accurate Sum and Dot Product. # SIAM J. Scientific Computing. 26. 1955-1988. 10.1137/030601818. # half of the working precision is already the most optimal value for the error tolerance, # more tailored values will be used in the future. + ERROR_TOLERANCE = 1.0e-8 ENABLE_JIT_CACHE = True diff --git a/uxarray/grid/arcs.py b/uxarray/grid/arcs.py index 86c3e44fd..e2c4bddfb 100644 --- a/uxarray/grid/arcs.py +++ b/uxarray/grid/arcs.py @@ -82,7 +82,7 @@ def point_within_gca(pt, gca_cart, is_directed=False): if np.isclose(GCRv0_lonlat[0], GCRv1_lonlat[0], rtol=0, atol=ERROR_TOLERANCE): # If the pt and the GCA are on the same longitude (the y coordinates are the same) - if GCRv0_lonlat[0] == pt_lonlat[0]: + if np.isclose(GCRv0_lonlat[0], pt_lonlat[0], rtol=0, atol=ERROR_TOLERANCE): # Now use the latitude to determine if the pt falls between the interval return in_between(GCRv0_lonlat[1], pt_lonlat[1], GCRv1_lonlat[1]) else: diff --git a/uxarray/grid/integrate.py b/uxarray/grid/integrate.py new file mode 100644 index 000000000..7542fdbbb --- /dev/null +++ b/uxarray/grid/integrate.py @@ -0,0 +1,352 @@ +import numpy as np +from uxarray.constants import ERROR_TOLERANCE, INT_FILL_VALUE +from uxarray.grid.intersections import gca_constLat_intersection +from uxarray.grid.coordinates import node_xyz_to_lonlat_rad +import pandas as pd + +DUMMY_EDGE_VALUE = [INT_FILL_VALUE, INT_FILL_VALUE, INT_FILL_VALUE] + + +def _get_zonal_faces_weight_at_constLat( + faces_edges_cart, + latitude_cart, + face_latlon_bound, + is_directed=False, + is_latlonface=False, + is_face_GCA_list=None, +): + """Utilize the sweep line algorithm to calculate the weight of each face at + a constant latitude. + + Parameters + ---------- + face_edges_cart : np.ndarray + A list of face polygon represented by edges in Cartesian coordinates. Shape: (n_faces, n_edges, 2, 3) + + latitude_cart : float + The latitude in Cartesian coordinates (The normalized z coordinate) + + face_latlon_bound : np.ndarray + The list of latitude and longitude bounds of faces. Shape: (n_faces,2, 2), + [...,[lat_min, lat_max], [lon_min, lon_max],...] + + is_directed : bool, optional (default=False) + If True, the GCA is considered to be directed, which means it can only from v0-->v1. If False, the GCA is undirected, + and we will always assume the small circle (The one less than 180 degree) side is the GCA. + + is_latlonface : bool, optional, default=False + A global flag to indicate if faces are latlon face. If True, then treat all faces as latlon faces. Latlon face means + That all edge is either a longitude or constant latitude line. If False, then all edges are GCA. + Default is False. + + is_face_GCA_list : np.ndarray, optional (default=None) + A list of boolean values that indicates if the edge in that face is a GCA. Shape: (n_faces,n_edges). + True means edge face is a GCA. + False mean this edge is a constant latitude. + If None, all edges are considered as GCA. This attribute will overwrite the is_latlonface attribute. + + Returns + ------- + weights_df : pandas.DataFrame + A DataFrame with the calculated weights of each face. The DataFrame has two columns: + - 'face_index': The index of the face (integer). + - 'weight': The calculated weight of the face in radian (float). + The DataFrame is indexed by the face indices, providing a mapping from each face to its corresponding weight. + """ + intervals_list = [] + + # Iterate through all faces and their edges + for face_index, face_edges in enumerate(faces_edges_cart): + if is_face_GCA_list is not None: + is_GCA_list = is_face_GCA_list[face_index] + else: + is_GCA_list = None + face_interval_df = _get_zonal_face_interval( + face_edges, + latitude_cart, + face_latlon_bound[face_index], + is_directed=is_directed, + is_latlonface=is_latlonface, + is_GCA_list=is_GCA_list, + ) + for _, row in face_interval_df.iterrows(): + intervals_list.append( + {"start": row["start"], "end": row["end"], "face_index": face_index} + ) + + intervals_df = pd.DataFrame(intervals_list) + overlap_contributions, total_length = _process_overlapped_intervals(intervals_df) + + # Calculate weights for each face + weights = { + face_index: overlap_contributions.get(face_index, 0.0) / total_length + for face_index in range(len(faces_edges_cart)) + } + + # Convert weights to DataFrame + weights_df = pd.DataFrame(list(weights.items()), columns=["face_index", "weight"]) + return weights_df + + +def _is_edge_gca(is_GCA_list, is_latlonface, edges_z): + """Determine if each edge is a Great Circle Arc (GCA) or a constant + latitude line in a vectorized manner. + + Parameters: + ---------- + is_GCA_list : np.ndarray or None + An array indicating whether each edge is a GCA (True) or a constant latitude line (False). + Shape: (n_edges). If None, edge types are determined based on `is_latlonface` and the z-coordinates. + is_latlonface : bool + Flag indicating if all edges should be considered as lat-lon faces, which implies all edges + are either constant latitude or longitude lines. + edges_z : np.ndarray + Array containing the z-coordinates for each vertex of the edges. This is used to determine + whether edges are on the equator or if they are aligned in latitude when `is_GCA_list` is None. + Shape should be (n_edges, 2). + + Returns: + ------- + np.ndarray + A boolean array where each element indicates whether the corresponding edge is considered a GCA. + True for GCA, False for constant latitude line. + """ + if is_GCA_list is not None: + return is_GCA_list + if is_latlonface: + return ~np.isclose(edges_z[:, 0], edges_z[:, 1], atol=ERROR_TOLERANCE) + return ~( + np.isclose(edges_z[:, 0], 0, atol=ERROR_TOLERANCE) + & np.isclose(edges_z[:, 1], 0, atol=ERROR_TOLERANCE) + ) + + +def _get_faces_constLat_intersection_info( + face_edges_cart, latitude_cart, is_GCA_list, is_latlonface, is_directed +): + """Processes each edge of a face polygon in a vectorized manner to + determine overlaps and calculate the intersections for a given latitude and + the faces. + + Parameters: + ---------- + face_edges_cart : np.ndarray + A face polygon represented by edges in Cartesian coordinates. Shape: (n_edges, 2, 3). + latitude_cart : float + The latitude in Cartesian coordinates to which intersections or overlaps are calculated. + is_GCA_list : np.ndarray or None + An array indicating whether each edge is a GCA (True) or a constant latitude line (False). + Shape: (n_edges). If None, the function will determine edge types based on `is_latlonface`. + is_latlonface : bool + Flag indicating if all faces are considered as lat-lon faces, meaning all edges are either + constant latitude or longitude lines. This parameter overwrites the `is_GCA_list` if set to True. + is_directed : bool + Flag indicating if the GCA should be considered as directed (from v0 to v1). If False, + the smaller circle (less than 180 degrees) side of the GCA is used. + + Returns: + ------- + tuple + A tuple containing: + - intersections_pts_list_cart (list): A list of intersection points where each point is where an edge intersects with the latitude. + - overlap_flag (bool): A boolean indicating if any overlap with the latitude was detected. + - overlap_edge (np.ndarray or None): The edge that overlaps with the latitude, if any; otherwise, None. + """ + valid_edges_mask = ~(np.any(face_edges_cart == DUMMY_EDGE_VALUE, axis=(1, 2))) + + # Apply mask to filter out dummy edges + valid_edges = face_edges_cart[valid_edges_mask] + + # Extract Z coordinates for edge determination + edges_z = valid_edges[:, :, 2] + + # Determine if each edge is GCA or constant latitude + is_GCA = _is_edge_gca(is_GCA_list, is_latlonface, edges_z) + + # Check overlap with latitude + overlaps_with_latitude = np.all( + np.isclose(edges_z, latitude_cart, atol=ERROR_TOLERANCE), axis=1 + ) + overlap_flag = np.any(overlaps_with_latitude & ~is_GCA) + + # Identify overlap edges if needed + intersections_pts_list_cart = [] + if overlap_flag: + overlap_index = np.where(overlaps_with_latitude & ~is_GCA)[0][0] + intersections_pts_list_cart.extend(valid_edges[overlap_index]) + else: + # Calculate intersections (assuming a batch-capable intersection function) + for idx, edge in enumerate(valid_edges): + if is_GCA[idx]: + intersections = gca_constLat_intersection( + edge, latitude_cart, is_directed=is_directed + ) + if intersections.size == 0: + continue + elif intersections.shape[0] == 2: + intersections_pts_list_cart.extend(intersections) + else: + intersections_pts_list_cart.append(intersections[0]) + + # Handle unique intersections and check for convex or concave cases + unique_intersections = np.unique(intersections_pts_list_cart, axis=0) + if len(unique_intersections) == 2: + unique_intersection_lonlat = np.array( + [node_xyz_to_lonlat_rad(pt.tolist()) for pt in unique_intersections] + ) + sorted_lonlat = np.sort(unique_intersection_lonlat, axis=0) + pt_lon_min, pt_lon_max = sorted_lonlat[:, 0] + return unique_intersections, pt_lon_min, pt_lon_max + elif len(unique_intersections) != 0: + raise ValueError( + "UXarray doesn't support concave face with intersections points as currently, please modify your grids accordingly" + ) + elif len(unique_intersections) == 0: + raise ValueError( + "No intersections are found for the face, please make sure the build_latlon_box generates the correct results" + ) + + +def _get_zonal_face_interval( + face_edges_cart, + latitude_cart, + face_latlon_bound, + is_directed=False, + is_latlonface=False, + is_GCA_list=None, +): + """Processes a face polygon represented by edges in Cartesian coordinates + to find intervals where the face intersects with a given latitude. This + function handles directed and undirected Great Circle Arcs (GCAs) and edges + at constant latitude. + + Requires the face edges to be sorted in counter-clockwise order, and the span of the + face in longitude should be less than pi. Also, all arcs/edges length should be within pi. + + Users can specify which edges are GCAs and which are constant latitude using `is_GCA_list`. + However, edges on the equator are always treated as constant latitude edges regardless of + `is_GCA_list`. + + Parameters + ---------- + face_edges_cart : np.ndarray + A face polygon represented by edges in Cartesian coordinates. Shape: (n_edges, 2, 3) + latitude_cart : float + The latitude in cartesian, the normalized Z coordinates. + face_latlon_bound : np.ndarray + The latitude and longitude bounds of the face. Shape: (2, 2), [[lat_min, lat_max], [lon_min, lon_max]] + is_directed : bool, optional + If True, the GCA is considered to be directed (from v0 to v1). If False, the GCA is undirected, + and the smaller circle (less than 180 degrees) side of the GCA is used. Default is False. + is_latlonface : bool, optional, default=False + A global flag to indicate if faces are latlon face. If True, then treat all faces as latlon faces. Latlon face means + That all edge is either a longitude or constant latitude line. If False, then all edges are GCA. + Default is False. This attribute will overwrite the is_latlonface attribute. + is_GCA_list : np.ndarray, optional, default=False + An array indicating if each edge is a GCA (True) or a constant latitude (False). Shape: (n_edges,). + If None, all edges are considered as GCAs. Default is None. + + Returns + ------- + Intervals_df : pd.DataFrame + A DataFrame containing the intervals stored as pandas Intervals for the face. + The columns of the DataFrame are: ['start', 'end'] + """ + face_lon_bound_left, face_lon_bound_right = face_latlon_bound[1] + + # Call the vectorized function to process all edges + ( + unique_intersections, + pt_lon_min, + pt_lon_max, + ) = _get_faces_constLat_intersection_info( + face_edges_cart, latitude_cart, is_GCA_list, is_latlonface, is_directed + ) + + # Convert intersection points to longitude-latitude + longitudes = np.array( + [node_xyz_to_lonlat_rad(pt.tolist())[0] for pt in unique_intersections] + ) + + # Handle special wrap-around cases by checking the face bounds + if face_lon_bound_left >= face_lon_bound_right: + if not ( + (pt_lon_max >= np.pi and pt_lon_min >= np.pi) + or (0 <= pt_lon_max <= np.pi and 0 <= pt_lon_min <= np.pi) + ): + # They're at different sides of the 0-lon, adding wrap-around points + longitudes = np.append(longitudes, [0.0, 2 * np.pi]) + + # Ensure longitudes are sorted + longitudes.sort() + + # Split the sorted longitudes into start and end points of intervals + starts = longitudes[::2] # Start points + ends = longitudes[1::2] # End points + + # Create the intervals DataFrame + Intervals_df = pd.DataFrame({"start": starts, "end": ends}) + # For consistency, sort the intervals by start + interval_df_sorted = Intervals_df.sort_values(by="start").reset_index(drop=True) + return interval_df_sorted + + +def _process_overlapped_intervals(intervals_df): + """Process the overlapped intervals using the sweep line algorithm, + considering multiple intervals per face. + + Parameters + ---------- + intervals_df : pd.DataFrame + The DataFrame that contains the intervals and the corresponding face index. + The columns of the DataFrame are: ['start', 'end', 'face_index'] + + Returns + ------- + dict + A dictionary where keys are face indices and values are their respective contributions to the total length. + float + The total length of all intervals considering their overlaps. + + Example + ------- + >>> intervals_data = [ + ... {'start': 0.0, 'end': 100.0, 'face_index': 0}, + ... {'start': 50.0, 'end': 150.0, 'face_index': 1}, + ... {'start': 140.0, 'end': 150.0, 'face_index': 2} + ... ] + >>> intervals_df = pd.DataFrame(intervals_data) + >>> overlap_contributions, total_length = _process_overlapped_intervals(intervals_df) + >>> print(overlap_contributions) + >>> print(total_length) + """ + events = [] + for idx, row in intervals_df.iterrows(): + events.append((row["start"], "start", row["face_index"])) + events.append((row["end"], "end", row["face_index"])) + + events.sort() # Sort by position and then by start/end + + active_faces = set() + last_position = None + total_length = 0.0 + overlap_contributions = {} + + for position, event_type, face_idx in events: + if last_position is not None and active_faces: + segment_length = position - last_position + segment_weight = segment_length / len(active_faces) if active_faces else 0 + for active_face in active_faces: + overlap_contributions[active_face] = ( + overlap_contributions.get(active_face, 0) + segment_weight + ) + total_length += segment_length + + if event_type == "start": + active_faces.add(face_idx) + elif event_type == "end": + active_faces.remove(face_idx) + + last_position = position + + return overlap_contributions, total_length diff --git a/uxarray/grid/intersections.py b/uxarray/grid/intersections.py index b0d52cdb7..3d3b03f42 100644 --- a/uxarray/grid/intersections.py +++ b/uxarray/grid/intersections.py @@ -116,7 +116,7 @@ def gca_gca_intersection(gca1_cart, gca2_cart, fma_disabled=False): def gca_constLat_intersection( - gca_cart, constLat, fma_disabled=False, verbose=False, is_directed=False + gca_cart, constZ, fma_disabled=False, verbose=False, is_directed=False ): """Calculate the intersection point(s) of a Great Circle Arc (GCA) and a constant latitude line in a Cartesian coordinate system. @@ -128,8 +128,8 @@ def gca_constLat_intersection( Parameters ---------- gca_cart : [2, 3] np.ndarray Cartesian coordinates of the two end points GCA. - constLat : float - The constant latitude of the latitude line. + constZ : float + The constant latitude represented in cartesian of the latitude line. fma_disabled : bool, optional (default=False) If True, the FMA operation is disabled. And a naive `np.cross` is used instead. verbose : bool, optional (default=False) @@ -153,7 +153,6 @@ def gca_constLat_intersection( If running on the Windows system with fma_disabled=False since the C/C++ implementation of FMA in MS Windows is fundamentally broken. (bug report: https://bugs.python.org/msg312480) """ - constZ = np.sin(constLat) x1, x2 = gca_cart if fma_disabled: From 1cb2374010a7efee16823952ded35fcb8fae07c7 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Thu, 25 Apr 2024 19:41:01 -0500 Subject: [PATCH 26/41] Migrate to `pyproject.toml` and deprecate local `meta.yaml` (#771) * Update requirements.txt * update requirements.txt * pyproject.toml * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * add pyprpoject.toml * update pyproject.toml * revert changes in test dir * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * revert changes in benchmarks * update conf.py * update pyproject.toml * update install docs * update install docs * update instal guide * update instal guide * update instal guide * update instalation.rst * add back setup.py for compatibility * update optional dependencies * Create MANIFEST.in * add ruff to dev dependencies * use matplotlib instead of matplotlib-base * Remove .txt from LICENSE * Update MANIFEST.in Co-authored-by: Anissa Zacharias * update pyproject.toml * remove references to meta.yaml --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Anissa Zacharias --- .github/ISSUE_TEMPLATE/release_request.md | 1 - .pre-commit-config.yaml | 3 +- MANIFEST.in | 18 ++++++ docs/conf.py | 15 ----- docs/installation.rst | 25 ++++++++ meta.yaml | 59 ------------------ pyproject.toml | 74 +++++++++++++++++++++++ requirements.txt | 1 - ruff.toml | 3 - setup.py | 51 +--------------- uxarray/grid/grid.py | 1 + 11 files changed, 121 insertions(+), 130 deletions(-) create mode 100644 MANIFEST.in delete mode 100644 meta.yaml create mode 100644 pyproject.toml delete mode 100644 requirements.txt delete mode 100644 ruff.toml diff --git a/.github/ISSUE_TEMPLATE/release_request.md b/.github/ISSUE_TEMPLATE/release_request.md index cf7f9a07b..2f05a288d 100644 --- a/.github/ISSUE_TEMPLATE/release_request.md +++ b/.github/ISSUE_TEMPLATE/release_request.md @@ -11,7 +11,6 @@ Date of intended release: ## Release progress checklist - [ ] CI tests are passing -- [ ] update version number in `meta.yaml` - [ ] a new tag and release has been made on GitHub - [ ] feedstock PR has been merged - [ ] release on pypi action has been triggered and run successfully diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 574548d3d..7bed3aacb 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -10,14 +10,13 @@ repos: hooks: - id: check-yaml # Checks the syntax of .yaml files. args: [--allow-multiple-documents] - exclude: 'meta.yaml' # Exclude this because it gives an error for '%' in Line 1 and couldn't fix yet - id: end-of-file-fixer # Makes sure files end with a newline. - id: trailing-whitespace # Checks for any tabs or spaces after the last non-whitespace character on the line. - id: check-docstring-first # Checks that code comes after the docstrings. - repo: https://github.com/astral-sh/ruff-pre-commit # Ruff version. - rev: v0.1.5 + rev: v0.4.1 hooks: # Run the linter. - id: ruff diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 000000000..174502dc5 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,18 @@ +include LICENSE +include README.md + +recursive-include uxarray *.py +recursive-exclude * __pycache__ +recursive-exclude * *.py[co] +exclude .gitignore +exclude .pre-commit-config.yaml +exclude .readthedocs.yml + +include *.md +include *.toml +include *.txt +prune test* +prune ci* +prune .github* +prune docs* +prune benchmarks* diff --git a/docs/conf.py b/docs/conf.py index c2c886636..e3981e11b 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -121,21 +121,6 @@ def __getattr__(cls, name): copyright = "{}, Uxarray".format(current_year) author = "UXARRAY" - -# The version info for the project being documented -def read_version(): - for line in open("../meta.yaml").readlines(): - index = line.find("set version") - if index > -1: - return line[index + 15 :].replace('" %}', "").strip() - - -# The short X.Y version. -version = read_version() - -# The full version, including alpha/beta/rc tags. -release = read_version() - # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # This is also used if you do content translation via gettext catalogs. diff --git a/docs/installation.rst b/docs/installation.rst index fa4c029ac..1f118c6c2 100644 --- a/docs/installation.rst +++ b/docs/installation.rst @@ -32,6 +32,31 @@ An alternative to Conda is using pip:: pip install uxarray +This installs the minimum set of required dependencies, which includes the following: + +.. literalinclude:: ../pyproject.toml + :language: toml + :start-after: minimal dependencies start + :end-before: minimal dependencies end + + +UXarray also maintains other dependency sets for different subsets of functionality:: + + $ python -m pip install "uxarray[math]" # Install optional dependencies for accurate math utlities + $ python -m pip install "uxarray[dev]" # Install optional dependencies for development + $ python -m pip install "uxarray[complete]" # Install all optional dependencies + +The above commands should install most of the optional dependencies. However, +some packages which are either not listed on PyPI or require extra +installation steps are excluded. To know which dependencies would be +installed, take a look at the ``[project.optional-dependencies]`` section in +``pyproject.toml``: + +.. literalinclude:: ../pyproject.toml + :language: toml + :start-at: [project.optional-dependencies] + :end-before: [project.urls] + Installing UXarray from source (Github) --------------------------------------- diff --git a/meta.yaml b/meta.yaml deleted file mode 100644 index dc69b3bec..000000000 --- a/meta.yaml +++ /dev/null @@ -1,59 +0,0 @@ -{% set version = "2024.03.0" %} - -package: - name: 'uxarray' - version: {{ version }} - -build: - noarch: python - number: 0 - skip: True # [py<3.5] - -source: - #git_rev: main # For building from main branch for debugging purposes - git_tag: v{{ version }} # For building from a specific tag - git_url: https://github.com/UXARRAY/uxarray - #url: https://github.com/UXARRAY/uxarray/archive/v{{ version }}.tar.gz # For building from a specific tag's tarball - #path: . # for building from a local directory - -requirements: - host: - - python - - pip - - setuptools - run: - - python - - xarray - -test: - source_files: - - test - requires: - - dask - - netcdf4 - - pytest - - numba - - numpy - - scipy>=0.16 - - xarray - imports: - - uxarray - commands: - - conda info - - conda list - - python -m pytest - -about: -# home: https:// - license: Apache 2.0 - license_file: LICENSE - summary: 'Uxarray is a library of xarray based functions for visualizing unstructured grid datasets' - -extra: - recipe-maintainers: - - P. Ullrich # https://github.com/paullric - - O. Eroglu # https://github.com/erogluorhan - - A. Zacharias # https://github.com/anissa111 - - R. Jain # https://github.com/rajeeja - - J. Zhang # https://github.com/chengzhuzhang - - R. Jacob # https://github.com/rljacob diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 000000000..a3ea6ab32 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,74 @@ +[project] +classifiers=[ + "Operating System :: OS Independent", + "Intended Audience :: Science/Research", + "Programming Language :: Python", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + 'Operating System :: Microsoft :: Windows', + 'Operating System :: Unix', + 'Operating System :: MacOS', + "Topic :: Scientific/Engineering", +] + +dynamic = ["version"] +keywords = ["unstructured grids"] +description = "Xarray-styled package for reading and directly operating on unstructured grid datasets following UGRID conventions" +license = {file = "LICENSE"} +name = "uxarray" +readme = "README.md" +requires-python = ">=3.9" + +# minimal dependencies start +dependencies = [ + "antimeridian", + "cartopy", + "cartopy", + "dask[dataframe]", + "datashader", + "geoviews", + "holoviews", + "matplotlib", + "matplotlib-inline", + "netcdf4", + "numba", + "numpy", + "pandas<2.1.0", + "pyarrow", + "requests", + "scikit-learn", + "scipy", + "shapely", + "spatialpandas", + "xarray", +] +# minimal dependencies end + +[project.optional-dependencies] +complete = ["uxarray[math, dev]"] +math = ['gmpy2', 'pyfma'] +dev = ['pathlib', 'pre_commit', 'pytest', 'pytest-cov', 'ruff'] + +[project.urls] +Documentation = "uxarray.readthedocs.io/" +Source = "https://github.com/UXARRAY/uxarray" +Tracker = "https://github.com/UXARRAY/uxarray/issues" + + +[build-system] +build-backend = "setuptools.build_meta" +requires = [ + "setuptools>=60", + "setuptools-scm>=8", +] + +[tool.setuptools.packages] +find = {} + +[tool.setuptools_scm] +fallback_version = "9999" + +[tool.ruff] +extend-exclude = ["test","benchmarks"] diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index 145785251..000000000 --- a/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -xarray diff --git a/ruff.toml b/ruff.toml deleted file mode 100644 index b13fa12c6..000000000 --- a/ruff.toml +++ /dev/null @@ -1,3 +0,0 @@ -extend-exclude = [ -"test", -"benchmarks"] diff --git a/setup.py b/setup.py index a14fed507..b37c80620 100644 --- a/setup.py +++ b/setup.py @@ -1,50 +1,3 @@ -#''' setup.py is needed, but only to make namespaces happen +from setuptools import setup -from setuptools import find_packages, setup - -with open("README.md") as f: - long_description = f.read() - -with open("requirements.txt") as f: - requirements = f.read().strip().split("\n") - - -#''' moved into function, can now be used other places -def version(): - for line in open("meta.yaml").readlines(): - index = line.find("set version") - if index > -1: - return line[index + 15 :].replace('" %}', "").strip() - - -setup( - name="uxarray", - version=version(), - maintainer="UXARRAY", - maintainer_email="", - python_requires=">=3.9", - install_requires=requirements, - description="""Unstructured grid model reading and recognizing with xarray.""", - long_description=long_description, - long_description_content_type="text/markdown", - classifiers=[ - "Operating System :: OS Independent", - "Intended Audience :: Science/Research", - "Programming Language :: Python", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3.10", - "Programming Language :: Python :: 3.11", - "Topic :: Scientific/Engineering", - ], - include_package_data=True, - packages=find_packages(exclude=["docs", "test", "docs.*", "test.*"]), - # namespace_packages=['UXARRAY'], - url="https://github.com/UXARRAY/uxarray", - project_urls={ - # 'Documentation': 'https://uxarray.readthedocs.io', - "Source": "https://github.com/UXARRAY/uxarray", - "Tracker": "https://github.com/UXARRAY/uxarray/issues", - }, - zip_safe=False, -) +setup(use_scm_version={"fallback_version": "9999"}) diff --git a/uxarray/grid/grid.py b/uxarray/grid/grid.py index cd3d67473..ffb0fdae5 100644 --- a/uxarray/grid/grid.py +++ b/uxarray/grid/grid.py @@ -1,4 +1,5 @@ """uxarray.core.grid module.""" + import xarray as xr import numpy as np From 77459b538d504e053dca473f096ae6525a08bf03 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Thu, 25 Apr 2024 19:45:02 -0500 Subject: [PATCH 27/41] Update pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index a3ea6ab32..2a8bbbfeb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -52,7 +52,7 @@ math = ['gmpy2', 'pyfma'] dev = ['pathlib', 'pre_commit', 'pytest', 'pytest-cov', 'ruff'] [project.urls] -Documentation = "uxarray.readthedocs.io/" +Documentation = "https://uxarray.readthedocs.io/" Source = "https://github.com/UXARRAY/uxarray" Tracker = "https://github.com/UXARRAY/uxarray/issues" From ed74f083286f738a7dfcd67222b32370f680873d Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Thu, 25 Apr 2024 19:51:27 -0500 Subject: [PATCH 28/41] Update pyproject.toml --- pyproject.toml | 1 - 1 file changed, 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 2a8bbbfeb..c34843587 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -52,7 +52,6 @@ math = ['gmpy2', 'pyfma'] dev = ['pathlib', 'pre_commit', 'pytest', 'pytest-cov', 'ruff'] [project.urls] -Documentation = "https://uxarray.readthedocs.io/" Source = "https://github.com/UXARRAY/uxarray" Tracker = "https://github.com/UXARRAY/uxarray/issues" From 101e189bca27cdcaafdf8071eb431ee6d9e4eb53 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Thu, 25 Apr 2024 19:55:06 -0500 Subject: [PATCH 29/41] Update pyproject.toml --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index c34843587..2a8bbbfeb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -52,6 +52,7 @@ math = ['gmpy2', 'pyfma'] dev = ['pathlib', 'pre_commit', 'pytest', 'pytest-cov', 'ruff'] [project.urls] +Documentation = "https://uxarray.readthedocs.io/" Source = "https://github.com/UXARRAY/uxarray" Tracker = "https://github.com/UXARRAY/uxarray/issues" From e240f7a3d9cb89e1ef201bd11df61279ca893a9d Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Thu, 25 Apr 2024 20:10:39 -0500 Subject: [PATCH 30/41] Update pyproject.toml --- pyproject.toml | 1 - 1 file changed, 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 2a8bbbfeb..8a999fc6c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,7 +25,6 @@ requires-python = ">=3.9" dependencies = [ "antimeridian", "cartopy", - "cartopy", "dask[dataframe]", "datashader", "geoviews", From 41763ad3d774132657e723cf729f4a83a5e2f59e Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Mon, 6 May 2024 18:09:52 -0500 Subject: [PATCH 31/41] Bump codecov/codecov-action from 4.3.0 to 4.3.1 (#777) Bumps [codecov/codecov-action](https://github.com/codecov/codecov-action) from 4.3.0 to 4.3.1. - [Release notes](https://github.com/codecov/codecov-action/releases) - [Changelog](https://github.com/codecov/codecov-action/blob/main/CHANGELOG.md) - [Commits](https://github.com/codecov/codecov-action/compare/v4.3.0...v4.3.1) --- updated-dependencies: - dependency-name: codecov/codecov-action dependency-type: direct:production update-type: version-update:semver-patch ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- .github/workflows/ci.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 3bac1781f..88c4d5d2a 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -71,7 +71,7 @@ jobs: python -m pytest test -v --cov=./uxarray --cov-report=xml - name: Upload code coverage to Codecov - uses: codecov/codecov-action@v4.3.0 + uses: codecov/codecov-action@v4.3.1 with: file: ./coverage.xml flags: unittests From 3107ae659fb1d567d510c7a8e45f1a6f554ca9b8 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Tue, 7 May 2024 21:13:41 -0500 Subject: [PATCH 32/41] Ruff Updates (#728) * update ruff * update ruff * add notebooks to pre-commit * run pre-commit on notebooks * pre-commit on notebooks, fix unusued imports * update pre-commit --------- Co-authored-by: Orhan Eroglu <32553057+erogluorhan@users.noreply.github.com> --- .pre-commit-config.yaml | 2 + docs/examples/000-template.ipynb | 5 +- .../001-working-with-unstructured-grids.ipynb | 106 +++++++------ docs/examples/002-grid-topology.ipynb | 33 ++-- docs/examples/003-area-calc.ipynb | 80 ++++++---- .../004-working-with-mpas-grids.ipynb | 76 ++++----- .../005-to-geodataframe-for-holoviz.ipynb | 89 +++++++++-- docs/examples/006-plot-api-topology.ipynb | 70 ++++++--- docs/examples/007-polygon-viz.ipynb | 104 ++++++++++--- docs/examples/008-point-viz.ipynb | 83 +++++----- docs/examples/009-subsetting.ipynb | 144 +++++++++++------- pyproject.toml | 7 + 12 files changed, 526 insertions(+), 273 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 7bed3aacb..1951d1031 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -21,6 +21,8 @@ repos: # Run the linter. - id: ruff args: [ --fix ] + types_or: [ python, pyi, jupyter ] # Run the formatter. - id: ruff-format + types_or: [ python, pyi, jupyter ] diff --git a/docs/examples/000-template.ipynb b/docs/examples/000-template.ipynb index 2c2d42a08..9c6450c1c 100644 --- a/docs/examples/000-template.ipynb +++ b/docs/examples/000-template.ipynb @@ -12,10 +12,7 @@ "execution_count": 1, "metadata": {}, "outputs": [], - "source": [ - "import numpy as np\n", - "import uxarray as ux" - ] + "source": [] }, { "cell_type": "code", diff --git a/docs/examples/001-working-with-unstructured-grids.ipynb b/docs/examples/001-working-with-unstructured-grids.ipynb index 57a0be3eb..49478aa42 100644 --- a/docs/examples/001-working-with-unstructured-grids.ipynb +++ b/docs/examples/001-working-with-unstructured-grids.ipynb @@ -85,7 +85,7 @@ "grid_path = base_path + \"grid.nc\"\n", "\n", "# Paths to Data Variable files\n", - "var_names = ['v1.nc', 'v2.nc', 'v3.nc']\n", + "var_names = [\"v1.nc\", \"v2.nc\", \"v3.nc\"]\n", "\n", "data_paths = [base_path + name for name in var_names]" ] @@ -130,12 +130,12 @@ }, { "cell_type": "markdown", - "source": [ - "## Grid Topology" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "## Grid Topology" + ] }, { "cell_type": "markdown", @@ -221,126 +221,122 @@ }, { "cell_type": "markdown", - "source": [ - "## Data Variables" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "## Data Variables" + ] }, { "cell_type": "markdown", - "source": [ - "While grid-specific variables and functions are stored under the `uxgrid` property, data variables that lie on the grid are stored directly in the `UxDataset` or `UxDataArray`. Most `Xarray` functions and operators can be executed on these data structures.\n" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "While grid-specific variables and functions are stored under the `uxgrid` property, data variables that lie on the grid are stored directly in the `UxDataset` or `UxDataArray`. Most `Xarray` functions and operators can be executed on these data structures.\n" + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "uxds_single.values" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "uxds_single.dims" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "uxds_single.coords" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "uxds_single.attrs" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "uxds_single.min()" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "uxds_single > 0" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "grid = uxds_single.uxgrid\n", - "foo = ux.UxDataArray(\n", - " data = np.random.random(grid.n_face),\n", - " dims = [\"n_face\"],\n", - " uxgrid = grid\n", - ")\n", + "foo = ux.UxDataArray(data=np.random.random(grid.n_face), dims=[\"n_face\"], uxgrid=grid)\n", "foo" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, - "outputs": [], - "source": [ - "uxds_new_var = uxds_single.assign({\"foo\" : foo})" - ], "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "uxds_new_var = uxds_single.assign({\"foo\": foo})" + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "uxds_new_var" - ], - "metadata": { - "collapsed": false - } + ] } ], "metadata": { diff --git a/docs/examples/002-grid-topology.ipynb b/docs/examples/002-grid-topology.ipynb index 4946e4755..8f35ed770 100644 --- a/docs/examples/002-grid-topology.ipynb +++ b/docs/examples/002-grid-topology.ipynb @@ -45,7 +45,6 @@ "metadata": {}, "outputs": [], "source": [ - "import uxarray\n", "import uxarray as ux" ] }, @@ -265,43 +264,43 @@ }, { "cell_type": "markdown", - "source": [ - "As described in the UGRID conventions, these connectivity variables are stored as integer arrays and may contain a Fill Value. UXarray standardizes both of these at the data loading step, meaning that the data type and fill value can always be guaranteed to be the following:" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "As described in the UGRID conventions, these connectivity variables are stored as integer arrays and may contain a Fill Value. UXarray standardizes both of these at the data loading step, meaning that the data type and fill value can always be guaranteed to be the following:" + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "ux.INT_DTYPE" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "ux.INT_FILL_VALUE" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", - "source": [ - "Below we can see how to access these connectivity variables." - ], "metadata": { "collapsed": false - } + }, + "source": [ + "Below we can see how to access these connectivity variables." + ] }, { "cell_type": "code", diff --git a/docs/examples/003-area-calc.ipynb b/docs/examples/003-area-calc.ipynb index 0e55650fa..cee9ecf8c 100644 --- a/docs/examples/003-area-calc.ipynb +++ b/docs/examples/003-area-calc.ipynb @@ -158,7 +158,9 @@ "metadata": {}, "outputs": [], "source": [ - "all_face_areas, all_face_jacobians = ugrid.compute_face_areas(quadrature_rule=\"gaussian\", order=4)\n", + "all_face_areas, all_face_jacobians = ugrid.compute_face_areas(\n", + " quadrature_rule=\"gaussian\", order=4\n", + ")\n", "g4_area = all_face_areas.sum()\n", "g4_area" ] @@ -210,9 +212,13 @@ }, "outputs": [], "source": [ - "verts = [[[0.57735027, -5.77350269e-01, -0.57735027],\n", - " [0.57735027, 5.77350269e-01, -0.57735027],\n", - " [-0.57735027, 5.77350269e-01, -0.57735027]]]\n", + "verts = [\n", + " [\n", + " [0.57735027, -5.77350269e-01, -0.57735027],\n", + " [0.57735027, 5.77350269e-01, -0.57735027],\n", + " [-0.57735027, 5.77350269e-01, -0.57735027],\n", + " ]\n", + "]\n", "\n", "# load our vertices into a UXarray Grid object\n", "vgrid = ux.open_grid(verts, latlon=False)\n", @@ -252,8 +258,7 @@ "# vgrid.Mesh2_node_z.attrs[\"units\"] = \"km\" # This is just a placeholder, UXarray does not support 3D meshes\n", "\n", "# Calculate the area of the triangle\n", - "area_gaussian = vgrid.calculate_total_face_area(\n", - " quadrature_rule=\"gaussian\", order=5)\n", + "area_gaussian = vgrid.calculate_total_face_area(quadrature_rule=\"gaussian\", order=5)\n", "area_gaussian" ] }, @@ -279,14 +284,40 @@ }, "outputs": [], "source": [ - "faces_verts_ndarray = np.array([\n", - " np.array([[150, 10, 0], [160, 20, 0], [150, 30, 0], [135, 30, 0], [125, 20, 0],\n", - " [135, 10, 0]]),\n", - " np.array([[125, 20, 0], [135, 30, 0], [125, 60, 0], [110, 60, 0], [100, 30, 0],\n", - " [105, 20, 0]]),\n", - " np.array([[95, 10, 0], [105, 20, 0], [100, 30, 0], [85, 30, 0], [75, 20, 0],\n", - " [85, 10, 0]]),\n", - "])" + "faces_verts_ndarray = np.array(\n", + " [\n", + " np.array(\n", + " [\n", + " [150, 10, 0],\n", + " [160, 20, 0],\n", + " [150, 30, 0],\n", + " [135, 30, 0],\n", + " [125, 20, 0],\n", + " [135, 10, 0],\n", + " ]\n", + " ),\n", + " np.array(\n", + " [\n", + " [125, 20, 0],\n", + " [135, 30, 0],\n", + " [125, 60, 0],\n", + " [110, 60, 0],\n", + " [100, 30, 0],\n", + " [105, 20, 0],\n", + " ]\n", + " ),\n", + " np.array(\n", + " [\n", + " [95, 10, 0],\n", + " [105, 20, 0],\n", + " [100, 30, 0],\n", + " [85, 30, 0],\n", + " [75, 20, 0],\n", + " [85, 10, 0],\n", + " ]\n", + " ),\n", + " ]\n", + ")" ] }, { @@ -354,18 +385,15 @@ "metadata": {}, "outputs": [], "source": [ - "cart_x = np.array([\n", - " 0.577340924821405, 0.577340924821405, 0.577340924821405,\n", - " 0.577340924821405\n", - "])\n", - "cart_y = np.array([\n", - " 0.577343045516932, 0.577343045516932, -0.577343045516932,\n", - " -0.577343045516932\n", - "])\n", - "cart_z = np.array([\n", - " 0.577366836872017, -0.577366836872017, 0.577366836872017,\n", - " -0.577366836872017\n", - "])" + "cart_x = np.array(\n", + " [0.577340924821405, 0.577340924821405, 0.577340924821405, 0.577340924821405]\n", + ")\n", + "cart_y = np.array(\n", + " [0.577343045516932, 0.577343045516932, -0.577343045516932, -0.577343045516932]\n", + ")\n", + "cart_z = np.array(\n", + " [0.577366836872017, -0.577366836872017, 0.577366836872017, -0.577366836872017]\n", + ")" ] }, { diff --git a/docs/examples/004-working-with-mpas-grids.ipynb b/docs/examples/004-working-with-mpas-grids.ipynb index f11e7b9d0..53a677453 100644 --- a/docs/examples/004-working-with-mpas-grids.ipynb +++ b/docs/examples/004-working-with-mpas-grids.ipynb @@ -52,15 +52,15 @@ { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "# Dataset Path\n", "mpas_root_filepath = \"../../test/meshfiles/mpas/\"\n", "mpas_dataset_filepath = mpas_root_filepath + \"QU/mesh.QU.1920km.151026.nc\"" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", @@ -99,10 +99,10 @@ }, { "cell_type": "markdown", - "source": [], "metadata": { "collapsed": false - } + }, + "source": [] }, { "cell_type": "markdown", @@ -222,52 +222,52 @@ }, { "cell_type": "markdown", - "source": [ - "### Face Area Calculation" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "### Face Area Calculation" + ] }, { "cell_type": "markdown", - "source": [ - "Using our parsed attributes, we can determine whether our unstructured grid lies on the surface of a sphere by accessing the `on_a_sphere` attribute.\n" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "Using our parsed attributes, we can determine whether our unstructured grid lies on the surface of a sphere by accessing the `on_a_sphere` attribute.\n" + ] }, { "cell_type": "code", "execution_count": null, - "outputs": [], - "source": [ - "primal_mesh.parsed_attrs['on_a_sphere']" - ], "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "primal_mesh.parsed_attrs[\"on_a_sphere\"]" + ] }, { "cell_type": "markdown", - "source": [ - "Simiarly, we can access the `sphere_radius` attribute." - ], "metadata": { "collapsed": false - } + }, + "source": [ + "Simiarly, we can access the `sphere_radius` attribute." + ] }, { "cell_type": "code", "execution_count": null, - "outputs": [], - "source": [ - "primal_mesh.parsed_attrs['sphere_radius']" - ], "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "primal_mesh.parsed_attrs[\"sphere_radius\"]" + ] }, { "cell_type": "markdown", @@ -290,8 +290,8 @@ "source": [ "import numpy as np\n", "\n", - "sphere_r = primal_mesh.parsed_attrs['sphere_radius']\n", - "expected_area = 4 * np.pi * (sphere_r)**2\n", + "sphere_r = primal_mesh.parsed_attrs[\"sphere_radius\"]\n", + "expected_area = 4 * np.pi * (sphere_r) ** 2\n", "expected_area" ] }, @@ -316,23 +316,23 @@ }, { "cell_type": "markdown", - "source": [ - "The total face (surface) area can be computed by summing over each value." - ], "metadata": { "collapsed": false - } + }, + "source": [ + "The total face (surface) area can be computed by summing over each value." + ] }, { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": false + }, "outputs": [], "source": [ "primal_mesh_face_areas.sum()" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", diff --git a/docs/examples/005-to-geodataframe-for-holoviz.ipynb b/docs/examples/005-to-geodataframe-for-holoviz.ipynb index 2aa50abe1..a7f3e2a91 100644 --- a/docs/examples/005-to-geodataframe-for-holoviz.ipynb +++ b/docs/examples/005-to-geodataframe-for-holoviz.ipynb @@ -165,7 +165,7 @@ }, "outputs": [], "source": [ - "gdf_data = uxds['psi'].to_geodataframe()\n", + "gdf_data = uxds[\"psi\"].to_geodataframe()\n", "gdf_data" ] }, @@ -249,7 +249,14 @@ "# use matplotlib backend for rendering\n", "hv.extension(\"matplotlib\")\n", "\n", - "plot_kwargs = {\"size\": 6.0, \"xlabel\": \"Longitude\", \"ylabel\": \"Latitude\", \"coastline\": True, \"width\": 1600, \"title\": \"Node Plot (Matplotlib Backend)\"}\n", + "plot_kwargs = {\n", + " \"size\": 6.0,\n", + " \"xlabel\": \"Longitude\",\n", + " \"ylabel\": \"Latitude\",\n", + " \"coastline\": True,\n", + " \"width\": 1600,\n", + " \"title\": \"Node Plot (Matplotlib Backend)\",\n", + "}\n", "\n", "\n", "gdf_grid.hvplot.points(**plot_kwargs)" @@ -266,7 +273,14 @@ "# use bokeh backend for rendering\n", "hv.extension(\"bokeh\")\n", "\n", - "plot_kwargs = {\"s\": 1.0, \"xlabel\": \"Longitude\", \"ylabel\": \"Latitude\", \"coastline\": True, \"frame_width\": 700, \"title\": \"Node Plot (Bokeh Backend)\"}\n", + "plot_kwargs = {\n", + " \"s\": 1.0,\n", + " \"xlabel\": \"Longitude\",\n", + " \"ylabel\": \"Latitude\",\n", + " \"coastline\": True,\n", + " \"frame_width\": 700,\n", + " \"title\": \"Node Plot (Bokeh Backend)\",\n", + "}\n", "\n", "gdf_grid.hvplot.points(**plot_kwargs)" ] @@ -289,7 +303,15 @@ "# use matplotlib backend for rendering\n", "hv.extension(\"matplotlib\")\n", "\n", - "plot_kwargs = {\"linewidth\": 1.0, \"xlabel\":\" Longitude\", \"ylabel\": \"Latitude\", \"coastline\": True, \"width\": 1600 , \"title\": \"Edge Plot (Matplotlib Backend)\", \"color\": \"black\"}\n", + "plot_kwargs = {\n", + " \"linewidth\": 1.0,\n", + " \"xlabel\": \" Longitude\",\n", + " \"ylabel\": \"Latitude\",\n", + " \"coastline\": True,\n", + " \"width\": 1600,\n", + " \"title\": \"Edge Plot (Matplotlib Backend)\",\n", + " \"color\": \"black\",\n", + "}\n", "\n", "import cartopy.crs as ccrs\n", "\n", @@ -307,7 +329,14 @@ "# use bokeh backend for rendering\n", "hv.extension(\"bokeh\")\n", "\n", - "plot_kwargs = {\"line_width\": 0.5, \"xlabel\": \"Longitude\", \"ylabel\": \"Latitude\", \"coastline\": True, \"frame_width\": 700, \"title\": \"Edge Plot (Bokeh Backend)\"}\n", + "plot_kwargs = {\n", + " \"line_width\": 0.5,\n", + " \"xlabel\": \"Longitude\",\n", + " \"ylabel\": \"Latitude\",\n", + " \"coastline\": True,\n", + " \"frame_width\": 700,\n", + " \"title\": \"Edge Plot (Bokeh Backend)\",\n", + "}\n", "\n", "gdf_grid.hvplot.paths(**plot_kwargs)" ] @@ -329,7 +358,15 @@ "source": [ "hv.extension(\"matplotlib\")\n", "\n", - "plot_kwargs = {\"c\": \"psi\", \"cmap\": \"inferno\", \"width\": 400, \"height\": 200, \"title\": \"Filled Polygon Plot (Matplotlib Backend, Rasterized)\", \"xlabel\":\" Longitude\", \"ylabel\": \"Latitude\"}\n", + "plot_kwargs = {\n", + " \"c\": \"psi\",\n", + " \"cmap\": \"inferno\",\n", + " \"width\": 400,\n", + " \"height\": 200,\n", + " \"title\": \"Filled Polygon Plot (Matplotlib Backend, Rasterized)\",\n", + " \"xlabel\": \" Longitude\",\n", + " \"ylabel\": \"Latitude\",\n", + "}\n", "\n", "gdf_data.hvplot.polygons(**plot_kwargs, rasterize=True)" ] @@ -353,12 +390,26 @@ "source": [ "hv.extension(\"bokeh\")\n", "\n", - "plot_kwargs = {\"c\": \"psi\", \"cmap\": \"inferno\", \"line_width\": 0.1, \"frame_width\": 500, \"frame_height\": 250, \"xlabel\":\" Longitude\", \"ylabel\": \"Latitude\"}\n", + "plot_kwargs = {\n", + " \"c\": \"psi\",\n", + " \"cmap\": \"inferno\",\n", + " \"line_width\": 0.1,\n", + " \"frame_width\": 500,\n", + " \"frame_height\": 250,\n", + " \"xlabel\": \" Longitude\",\n", + " \"ylabel\": \"Latitude\",\n", + "}\n", "\n", "gdf_data.hvplot.polygons(**plot_kwargs, rasterize=True)\n", "\n", - "hv.Layout(gdf_data.hvplot.polygons(**plot_kwargs, rasterize=True).opts(title=\"Filled Polygon Plot (Bokeh Backend, Rasterized)\") +\n", - " gdf_data.hvplot.polygons(**plot_kwargs, rasterize=False).opts(title=\"Filled Polygon Plot (Bokeh Backend, Vector)\")).cols(1)" + "hv.Layout(\n", + " gdf_data.hvplot.polygons(**plot_kwargs, rasterize=True).opts(\n", + " title=\"Filled Polygon Plot (Bokeh Backend, Rasterized)\"\n", + " )\n", + " + gdf_data.hvplot.polygons(**plot_kwargs, rasterize=False).opts(\n", + " title=\"Filled Polygon Plot (Bokeh Backend, Vector)\"\n", + " )\n", + ").cols(1)" ] }, { @@ -382,7 +433,15 @@ "source": [ "hv.extension(\"matplotlib\")\n", "\n", - "plot_kwargs = {\"c\": \"psi\", \"cmap\": \"inferno\", \"width\": 400, \"height\": 200, \"title\": \"Filled Polygon Plot (Matplotlib Backend, Rasterized, Orthographic Projection)\", \"xlabel\":\" Longitude\", \"ylabel\": \"Latitude\"}\n", + "plot_kwargs = {\n", + " \"c\": \"psi\",\n", + " \"cmap\": \"inferno\",\n", + " \"width\": 400,\n", + " \"height\": 200,\n", + " \"title\": \"Filled Polygon Plot (Matplotlib Backend, Rasterized, Orthographic Projection)\",\n", + " \"xlabel\": \" Longitude\",\n", + " \"ylabel\": \"Latitude\",\n", + "}\n", "\n", "gdf_data.hvplot.polygons(**plot_kwargs, rasterize=True, projection=ccrs.Orthographic())" ] @@ -398,7 +457,15 @@ "source": [ "hv.extension(\"matplotlib\")\n", "\n", - "plot_kwargs = {\"c\": \"psi\", \"cmap\": \"inferno\", \"width\": 400, \"height\": 200, \"title\": \"Filled Polygon Plot (Matplotlib Backend, Rasterized, Robinson Projection)\", \"xlabel\":\" Longitude\", \"ylabel\": \"Latitude\"}\n", + "plot_kwargs = {\n", + " \"c\": \"psi\",\n", + " \"cmap\": \"inferno\",\n", + " \"width\": 400,\n", + " \"height\": 200,\n", + " \"title\": \"Filled Polygon Plot (Matplotlib Backend, Rasterized, Robinson Projection)\",\n", + " \"xlabel\": \" Longitude\",\n", + " \"ylabel\": \"Latitude\",\n", + "}\n", "\n", "gdf_data.hvplot.polygons(**plot_kwargs, rasterize=True, projection=ccrs.Robinson())" ] diff --git a/docs/examples/006-plot-api-topology.ipynb b/docs/examples/006-plot-api-topology.ipynb index 391da1f5e..00a7b065e 100644 --- a/docs/examples/006-plot-api-topology.ipynb +++ b/docs/examples/006-plot-api-topology.ipynb @@ -35,8 +35,7 @@ }, "outputs": [], "source": [ - "import uxarray as ux\n", - "from holoviews import opts" + "import uxarray as ux" ] }, { @@ -97,7 +96,11 @@ }, "outputs": [], "source": [ - "grid.plot(backend=\"matplotlib\", title=\"Default Grid Plot Method with Matplotlib Backend\", fig_size=200)" + "grid.plot(\n", + " backend=\"matplotlib\",\n", + " title=\"Default Grid Plot Method with Matplotlib Backend\",\n", + " fig_size=200,\n", + ")" ] }, { @@ -119,7 +122,9 @@ }, "outputs": [], "source": [ - "uxds.uxgrid.plot(title=\"Default Plot Method through uxgrid Accessor\", height=350, width=700)" + "uxds.uxgrid.plot(\n", + " title=\"Default Plot Method through uxgrid Accessor\", height=350, width=700\n", + ")" ] }, { @@ -199,8 +204,9 @@ }, "outputs": [], "source": [ - "(grid.plot.edges(color=\"Black\") * \\\n", - " grid.plot.nodes(color=\"Red\", size=5)).opts(xlim = (-150,-90), ylim = (20, 50), height=350, width=700, title=\"Nodes and Edges\")" + "(grid.plot.edges(color=\"Black\") * grid.plot.nodes(color=\"Red\", size=5)).opts(\n", + " xlim=(-150, -90), ylim=(20, 50), height=350, width=700, title=\"Nodes and Edges\"\n", + ")" ] }, { @@ -223,10 +229,20 @@ }, "outputs": [], "source": [ - "(uxds.uxgrid.plot.mesh(color=\"Black\", line_dash='dashed', line_width=2, exclude_antimeridian=False) * \\\n", - " uxds.uxgrid.plot.nodes(color = \"Red\", size=4) * \\\n", - " uxds.uxgrid.plot.face_centers(color = \"Blue\", size=4) * \\\n", - " uxds.uxgrid.plot.edge_centers(color = \"Green\", size=4)).opts(title=\"Node, Edge, & Face Coordinates\", xlim=(-150,-90), ylim=(20, 50), width=700, height=350)" + "(\n", + " uxds.uxgrid.plot.mesh(\n", + " color=\"Black\", line_dash=\"dashed\", line_width=2, exclude_antimeridian=False\n", + " )\n", + " * uxds.uxgrid.plot.nodes(color=\"Red\", size=4)\n", + " * uxds.uxgrid.plot.face_centers(color=\"Blue\", size=4)\n", + " * uxds.uxgrid.plot.edge_centers(color=\"Green\", size=4)\n", + ").opts(\n", + " title=\"Node, Edge, & Face Coordinates\",\n", + " xlim=(-150, -90),\n", + " ylim=(20, 50),\n", + " width=700,\n", + " height=350,\n", + ")" ] }, { @@ -248,10 +264,20 @@ }, "outputs": [], "source": [ - "( uxds.uxgrid.plot.mesh(color=\"Black\", line_dash='dashed', line_width=2, exclude_antimeridian=False) * \\\n", - " uxds.uxgrid.plot.node_coords(color = \"Red\", size=4) * \\\n", - " uxds.uxgrid.plot.face_coords(color = \"Blue\", size=4) * \\\n", - " uxds.uxgrid.plot.edge_coords(color = \"Green\", size=4)).opts(title=\"Node, Edge, & Face Coordinates\", xlim=(-150,-90), ylim=(20, 50), width=700, height=350)" + "(\n", + " uxds.uxgrid.plot.mesh(\n", + " color=\"Black\", line_dash=\"dashed\", line_width=2, exclude_antimeridian=False\n", + " )\n", + " * uxds.uxgrid.plot.node_coords(color=\"Red\", size=4)\n", + " * uxds.uxgrid.plot.face_coords(color=\"Blue\", size=4)\n", + " * uxds.uxgrid.plot.edge_coords(color=\"Green\", size=4)\n", + ").opts(\n", + " title=\"Node, Edge, & Face Coordinates\",\n", + " xlim=(-150, -90),\n", + " ylim=(20, 50),\n", + " width=700,\n", + " height=350,\n", + ")" ] }, { @@ -308,10 +334,18 @@ }, "outputs": [], "source": [ - "(primal_grid.plot.edges(color=\"Blue\", line_width=2) * \\\n", - " primal_grid.plot.nodes(color=\"Blue\", size=10) * \\\n", - " dual_grid.plot.edges(color=\"Red\", line_width = 2) * \\\n", - " dual_grid.plot.nodes(color=\"Red\", size=10)).opts(title=\"MPAS Primal & Dual Mesh\", xlim=(-50, 50), ylim=(-25, 25), width=700, height=350)" + "(\n", + " primal_grid.plot.edges(color=\"Blue\", line_width=2)\n", + " * primal_grid.plot.nodes(color=\"Blue\", size=10)\n", + " * dual_grid.plot.edges(color=\"Red\", line_width=2)\n", + " * dual_grid.plot.nodes(color=\"Red\", size=10)\n", + ").opts(\n", + " title=\"MPAS Primal & Dual Mesh\",\n", + " xlim=(-50, 50),\n", + " ylim=(-25, 25),\n", + " width=700,\n", + " height=350,\n", + ")" ] } ], diff --git a/docs/examples/007-polygon-viz.ipynb b/docs/examples/007-polygon-viz.ipynb index dc9756b4e..a7ab8f61a 100644 --- a/docs/examples/007-polygon-viz.ipynb +++ b/docs/examples/007-polygon-viz.ipynb @@ -35,8 +35,7 @@ }, "outputs": [], "source": [ - "import uxarray as ux\n", - "from holoviews import opts" + "import uxarray as ux" ] }, { @@ -62,7 +61,7 @@ "base_path = \"../../test/meshfiles/mpas/QU/\"\n", "grid_path = base_path + \"oQU480.231010.nc\"\n", "uxds_mpas = ux.open_dataset(grid_path, grid_path)\n", - "uxds_mpas['bottomDepth']" + "uxds_mpas[\"bottomDepth\"]" ] }, { @@ -89,7 +88,7 @@ "grid_path = base_path + \"grid.nc\"\n", "data_path = base_path + \"v1.nc\"\n", "uxds_ugrid = ux.open_dataset(grid_path, data_path)\n", - "uxds_ugrid['v1']" + "uxds_ugrid[\"v1\"]" ] }, { @@ -112,7 +111,9 @@ }, "outputs": [], "source": [ - "uxds_mpas['bottomDepth'].plot(title=\"Default UXDataArray Plot for Face-Centered Data\", height=350, width=700)" + "uxds_mpas[\"bottomDepth\"].plot(\n", + " title=\"Default UXDataArray Plot for Face-Centered Data\", height=350, width=700\n", + ")" ] }, { @@ -145,7 +146,9 @@ }, "outputs": [], "source": [ - "uxds_mpas['bottomDepth'].plot.polygons(title=\"Bottom Depth Polygon Plot\", height=350, width=700)" + "uxds_mpas[\"bottomDepth\"].plot.polygons(\n", + " title=\"Bottom Depth Polygon Plot\", height=350, width=700\n", + ")" ] }, { @@ -167,7 +170,9 @@ }, "outputs": [], "source": [ - "uxds_ugrid['v1'][0][0].nodal_average().plot.polygons(cmap='coolwarm', title=\"V1 Nodal Average Polygon Plot\", height=350, width=700)" + "uxds_ugrid[\"v1\"][0][0].nodal_average().plot.polygons(\n", + " cmap=\"coolwarm\", title=\"V1 Nodal Average Polygon Plot\", height=350, width=700\n", + ")" ] }, { @@ -190,8 +195,19 @@ }, "outputs": [], "source": [ - "(uxds_mpas['bottomDepth'].plot.polygons(title=\"Bottom Depth Polygon Plot (Including Antimeridian)\", height=350, width=700) + \\\n", - " uxds_mpas['bottomDepth'].plot.polygons(exclude_antimeridian=True, title=\"Bottom Depth Polygon Plot (Excluding Antimeridian)\", height=350, width=700)).cols(1)" + "(\n", + " uxds_mpas[\"bottomDepth\"].plot.polygons(\n", + " title=\"Bottom Depth Polygon Plot (Including Antimeridian)\",\n", + " height=350,\n", + " width=700,\n", + " )\n", + " + uxds_mpas[\"bottomDepth\"].plot.polygons(\n", + " exclude_antimeridian=True,\n", + " title=\"Bottom Depth Polygon Plot (Excluding Antimeridian)\",\n", + " height=350,\n", + " width=700,\n", + " )\n", + ").cols(1)" ] }, { @@ -203,8 +219,25 @@ }, "outputs": [], "source": [ - "(uxds_ugrid['v1'][0][0].nodal_average().plot.polygons(cmap='coolwarm', title=\"V1 Nodal Average Polygon Plot (Including Antimeridian)\", height=350, width=700) + \\\n", - " uxds_ugrid['v1'][0][0].nodal_average().plot.polygons(exclude_antimeridian=True, cmap='coolwarm', title=\"V1 Nodal Average Polygon Plot (Excluding Antimeridian)\", height=350, width=700)).cols(1)" + "(\n", + " uxds_ugrid[\"v1\"][0][0]\n", + " .nodal_average()\n", + " .plot.polygons(\n", + " cmap=\"coolwarm\",\n", + " title=\"V1 Nodal Average Polygon Plot (Including Antimeridian)\",\n", + " height=350,\n", + " width=700,\n", + " )\n", + " + uxds_ugrid[\"v1\"][0][0]\n", + " .nodal_average()\n", + " .plot.polygons(\n", + " exclude_antimeridian=True,\n", + " cmap=\"coolwarm\",\n", + " title=\"V1 Nodal Average Polygon Plot (Excluding Antimeridian)\",\n", + " height=350,\n", + " width=700,\n", + " )\n", + ").cols(1)" ] }, { @@ -227,7 +260,9 @@ }, "outputs": [], "source": [ - "uxds_mpas['bottomDepth'].plot.rasterize(method='polygon', title=\"Bottom Depth Raster Polygon Plot\", height=350, width=700)" + "uxds_mpas[\"bottomDepth\"].plot.rasterize(\n", + " method=\"polygon\", title=\"Bottom Depth Raster Polygon Plot\", height=350, width=700\n", + ")" ] }, { @@ -239,7 +274,13 @@ }, "outputs": [], "source": [ - "uxds_ugrid['v1'][0][0].nodal_average().plot.rasterize(method='polygon', cmap='coolwarm', title=\"V1 Nodal Average Raster Polygon Plot\", height=350, width=700)#" + "uxds_ugrid[\"v1\"][0][0].nodal_average().plot.rasterize(\n", + " method=\"polygon\",\n", + " cmap=\"coolwarm\",\n", + " title=\"V1 Nodal Average Raster Polygon Plot\",\n", + " height=350,\n", + " width=700,\n", + ") #" ] }, { @@ -262,9 +303,29 @@ }, "outputs": [], "source": [ - "(uxds_mpas['bottomDepth'].plot.rasterize(method='polygon', title=\"Bottom Depth Raster Polygon Plot (0.5 Pixel Ratio)\", height=350, width=700, pixel_ratio = 0.5) + \\\n", - " uxds_mpas['bottomDepth'].plot.rasterize(method='polygon', title=\"Bottom Depth Raster Polygon Plot (1.0 Pixel Ratio)\", height=350, width=700, pixel_ratio = 1.0) + \\\n", - " uxds_mpas['bottomDepth'].plot.rasterize(method='polygon', title=\"Bottom Depth Raster Polygon Plot (2.0 Pixel Ratio)\", height=350, width=700, pixel_ratio = 2.0)).cols(1)" + "(\n", + " uxds_mpas[\"bottomDepth\"].plot.rasterize(\n", + " method=\"polygon\",\n", + " title=\"Bottom Depth Raster Polygon Plot (0.5 Pixel Ratio)\",\n", + " height=350,\n", + " width=700,\n", + " pixel_ratio=0.5,\n", + " )\n", + " + uxds_mpas[\"bottomDepth\"].plot.rasterize(\n", + " method=\"polygon\",\n", + " title=\"Bottom Depth Raster Polygon Plot (1.0 Pixel Ratio)\",\n", + " height=350,\n", + " width=700,\n", + " pixel_ratio=1.0,\n", + " )\n", + " + uxds_mpas[\"bottomDepth\"].plot.rasterize(\n", + " method=\"polygon\",\n", + " title=\"Bottom Depth Raster Polygon Plot (2.0 Pixel Ratio)\",\n", + " height=350,\n", + " width=700,\n", + " pixel_ratio=2.0,\n", + " )\n", + ").cols(1)" ] }, { @@ -287,8 +348,15 @@ }, "outputs": [], "source": [ - "uxds_mpas['bottomDepth'].plot.rasterize(method='polygon', title=\"Bottom Depth Raster Polygon Plot (1.0 Pixel Ratio) with Mesh\", height=350, width=700, pixel_ratio = 1.0, xlim=(-40, 40), ylim=(-20, 20)) * \\\n", - "uxds_mpas.uxgrid.plot.mesh(color=\"Black\")" + "uxds_mpas[\"bottomDepth\"].plot.rasterize(\n", + " method=\"polygon\",\n", + " title=\"Bottom Depth Raster Polygon Plot (1.0 Pixel Ratio) with Mesh\",\n", + " height=350,\n", + " width=700,\n", + " pixel_ratio=1.0,\n", + " xlim=(-40, 40),\n", + " ylim=(-20, 20),\n", + ") * uxds_mpas.uxgrid.plot.mesh(color=\"Black\")" ] } ], diff --git a/docs/examples/008-point-viz.ipynb b/docs/examples/008-point-viz.ipynb index 924eaa80e..fd8d5d32a 100644 --- a/docs/examples/008-point-viz.ipynb +++ b/docs/examples/008-point-viz.ipynb @@ -36,16 +36,16 @@ "outputs": [], "source": [ "import uxarray as ux\n", - "import cartopy.crs as ccrs\n", - "from holoviews import opts" + "import cartopy.crs as ccrs" ] }, { "cell_type": "markdown", - "source": [], + "id": "7fb27b941602401d91542211134fc71a", "metadata": { "collapsed": false - } + }, + "source": [] }, { "cell_type": "code", @@ -59,7 +59,7 @@ "base_path = \"../../test/meshfiles/mpas/QU/\"\n", "grid_path = base_path + \"oQU480.231010.nc\"\n", "uxds_mpas = ux.open_dataset(grid_path, grid_path)\n", - "uxds_mpas['bottomDepth']" + "uxds_mpas[\"bottomDepth\"]" ] }, { @@ -77,14 +77,14 @@ { "cell_type": "code", "execution_count": null, - "outputs": [], - "source": [ - "uxds_mpas['bottomDepth'].plot.points(title=\"Point Plot\", height=350, width=700, size = 3)" - ], + "id": "3bbb52a4a8e11906", "metadata": { "collapsed": false }, - "id": "3bbb52a4a8e11906" + "outputs": [], + "source": [ + "uxds_mpas[\"bottomDepth\"].plot.points(title=\"Point Plot\", height=350, width=700, size=3)" + ] }, { "cell_type": "code", @@ -95,65 +95,80 @@ }, "outputs": [], "source": [ - "uxds_mpas['bottomDepth'].plot.points(title=\"Point Plot (Orthographic Projection)\", height=350, width=700, size = 5, projection=ccrs.Orthographic())" + "uxds_mpas[\"bottomDepth\"].plot.points(\n", + " title=\"Point Plot (Orthographic Projection)\",\n", + " height=350,\n", + " width=700,\n", + " size=5,\n", + " projection=ccrs.Orthographic(),\n", + ")" ] }, { "cell_type": "markdown", - "source": [ - "## Rasterized Point Plots" - ], + "id": "e95c4711c5b4b776", "metadata": { "collapsed": false }, - "id": "e95c4711c5b4b776" + "source": [ + "## Rasterized Point Plots" + ] }, { "cell_type": "code", "execution_count": null, - "outputs": [], - "source": [ - "uxds_mpas['bottomDepth'].plot.rasterize(method='point', projection=ccrs.Robinson())" - ], + "id": "acae54e37e7d407bbb7b55eff062a284", "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "uxds_mpas[\"bottomDepth\"].plot.rasterize(method=\"point\", projection=ccrs.Robinson())" + ] }, { "cell_type": "markdown", + "id": "9a63283cbaf04dbcab1f6479b197f3a8", + "metadata": { + "collapsed": false + }, "source": [ "By selecting an appropriate pixel ratio (small value for sparse grids, large value for high-resolution grids), one can achieve a passable visual of the data variable.\n", "\n", "Below is shows the result of using a small pixel ratio compared to the higher-fidelity polygon raster." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": null, - "outputs": [], - "source": [ - "(uxds_mpas['bottomDepth'].plot.rasterize(method='point', pixel_ratio=0.10, title=\"Point Raster\") + \\\n", - " uxds_mpas['bottomDepth'].plot.rasterize(method='polygon', pixel_ratio=6.0, title=\"Polygon Raaster\")).cols(1)" - ], + "id": "9f99b54fd3f61ea7", "metadata": { "collapsed": false }, - "id": "9f99b54fd3f61ea7" + "outputs": [], + "source": [ + "(\n", + " uxds_mpas[\"bottomDepth\"].plot.rasterize(\n", + " method=\"point\", pixel_ratio=0.10, title=\"Point Raster\"\n", + " )\n", + " + uxds_mpas[\"bottomDepth\"].plot.rasterize(\n", + " method=\"polygon\", pixel_ratio=6.0, title=\"Polygon Raaster\"\n", + " )\n", + ").cols(1)" + ] }, { "cell_type": "markdown", + "id": "8dd0d8092fe74a7c96281538738b07e2", + "metadata": { + "collapsed": false + }, "source": [ "## Takeaways\n", "Even though Point plots do not take advantage of any connectivity information, and therefore the \"shading\" does not account for the area that faces occupy, they are the fastest option for quick visuals.\n", "\n", "Point Rasterization can produce convincing plots when paired with an appropriate pixel ratio, especially at higher resolutions (i.e. kilometer-scale meshes)." - ], - "metadata": { - "collapsed": false - } + ] } ], "metadata": { diff --git a/docs/examples/009-subsetting.ipynb b/docs/examples/009-subsetting.ipynb index 05e66b264..e74584cad 100644 --- a/docs/examples/009-subsetting.ipynb +++ b/docs/examples/009-subsetting.ipynb @@ -31,9 +31,9 @@ "import geocat.datafiles as geodf\n", "\n", "\n", - "plot_opts = {\"width\" : 700, \"height\": 350}\n", + "plot_opts = {\"width\": 700, \"height\": 350}\n", "\n", - "hv.extension('bokeh')\n", + "hv.extension(\"bokeh\")\n", "\n", "\n", "warnings.filterwarnings(\"ignore\")" @@ -87,7 +87,7 @@ }, "outputs": [], "source": [ - "clim = (uxds['relhum_200hPa'][0].values.min(), uxds['relhum_200hPa'][0].values.max())" + "clim = (uxds[\"relhum_200hPa\"][0].values.min(), uxds[\"relhum_200hPa\"][0].values.max())" ] }, { @@ -98,8 +98,9 @@ }, "outputs": [], "source": [ - "features = gf.coastline(projection=ccrs.PlateCarree(), line_width=1, scale='50m') * \\\n", - " gf.states(projection=ccrs.PlateCarree(), line_width=1, scale='50m')" + "features = gf.coastline(\n", + " projection=ccrs.PlateCarree(), line_width=1, scale=\"50m\"\n", + ") * gf.states(projection=ccrs.PlateCarree(), line_width=1, scale=\"50m\")" ] }, { @@ -123,10 +124,9 @@ }, "outputs": [], "source": [ - "uxds['relhum_200hPa'][0].plot.rasterize(method='polygon',\n", - " exclude_antimeridian=True,\n", - " title = \"Global Grid\",\n", - " **plot_opts) * features" + "uxds[\"relhum_200hPa\"][0].plot.rasterize(\n", + " method=\"polygon\", exclude_antimeridian=True, title=\"Global Grid\", **plot_opts\n", + ") * features" ] }, { @@ -146,7 +146,7 @@ }, "outputs": [], "source": [ - "uxds['relhum_200hPa'][0].values.mean()" + "uxds[\"relhum_200hPa\"][0].values.mean()" ] }, { @@ -170,7 +170,7 @@ }, "outputs": [], "source": [ - "uxds['relhum_200hPa'].subset" + "uxds[\"relhum_200hPa\"].subset" ] }, { @@ -181,7 +181,7 @@ }, "outputs": [], "source": [ - "uxds['relhum_200hPa'].uxgrid.subset" + "uxds[\"relhum_200hPa\"].uxgrid.subset" ] }, { @@ -226,13 +226,17 @@ }, "outputs": [], "source": [ - "bbox_subset_nodes = uxds['relhum_200hPa'][0].subset.bounding_box(lon_bounds, lat_bounds, element='nodes')\n", + "bbox_subset_nodes = uxds[\"relhum_200hPa\"][0].subset.bounding_box(\n", + " lon_bounds, lat_bounds, element=\"nodes\"\n", + ")\n", "\n", - "bbox_subset_nodes.plot.rasterize(method='polygon',\n", - " exclude_antimeridian=True,\n", - " clim=clim,\n", - " title=\"Bounding Box Subset\",\n", - " **plot_opts) * features" + "bbox_subset_nodes.plot.rasterize(\n", + " method=\"polygon\",\n", + " exclude_antimeridian=True,\n", + " clim=clim,\n", + " title=\"Bounding Box Subset\",\n", + " **plot_opts,\n", + ") * features" ] }, { @@ -252,13 +256,17 @@ }, "outputs": [], "source": [ - "bbox_subset_faces= uxds['relhum_200hPa'][0].subset.bounding_box(lon_bounds, lat_bounds, element='face centers')\n", + "bbox_subset_faces = uxds[\"relhum_200hPa\"][0].subset.bounding_box(\n", + " lon_bounds, lat_bounds, element=\"face centers\"\n", + ")\n", "\n", - "bbox_subset_faces.plot.rasterize(method='polygon',\n", - " exclude_antimeridian=True,\n", - " clim=clim,\n", - " title=\"Bounding Box Subset (Face Center Query)\",\n", - " **plot_opts) * features" + "bbox_subset_faces.plot.rasterize(\n", + " method=\"polygon\",\n", + " exclude_antimeridian=True,\n", + " clim=clim,\n", + " title=\"Bounding Box Subset (Face Center Query)\",\n", + " **plot_opts,\n", + ") * features" ] }, { @@ -269,9 +277,9 @@ "source": [ "While the bounding box is generally the same, you will notice differences along the border depending on which element is used to query.\n", "\n", - "```{note}\n", + ":::{note}\n", "Specifying which element to query (i.e. ``nodes``, ``edgecenters``, or ``face centers``) is supported by all subsetting methods.\n", - "```" + ":::" ] }, { @@ -306,13 +314,15 @@ }, "outputs": [], "source": [ - "bcircle_subset = uxds['relhum_200hPa'][0].subset.bounding_circle(center_coord, r)\n", + "bcircle_subset = uxds[\"relhum_200hPa\"][0].subset.bounding_circle(center_coord, r)\n", "\n", - "bcircle_subset.plot.rasterize(method='polygon',\n", - " exclude_antimeridian=True,\n", - " clim=clim,\n", - " title=\"Bounding Circle Subset\",\n", - " **plot_opts) * features" + "bcircle_subset.plot.rasterize(\n", + " method=\"polygon\",\n", + " exclude_antimeridian=True,\n", + " clim=clim,\n", + " title=\"Bounding Circle Subset\",\n", + " **plot_opts,\n", + ") * features" ] }, { @@ -345,12 +355,17 @@ }, "outputs": [], "source": [ - "nn_subset = uxds['relhum_200hPa'][0].subset.nearest_neighbor(center_coord, k = 30, element='nodes')\n", + "nn_subset = uxds[\"relhum_200hPa\"][0].subset.nearest_neighbor(\n", + " center_coord, k=30, element=\"nodes\"\n", + ")\n", "\n", - "nn_subset.plot.rasterize(method='polygon', exclude_antimeridian=True,\n", - " clim=clim,\n", - " title= \"Nearest Neighbor Subset\",\n", - " **plot_opts) * features" + "nn_subset.plot.rasterize(\n", + " method=\"polygon\",\n", + " exclude_antimeridian=True,\n", + " clim=clim,\n", + " title=\"Nearest Neighbor Subset\",\n", + " **plot_opts,\n", + ") * features" ] }, { @@ -361,13 +376,17 @@ }, "outputs": [], "source": [ - "nn_subset_120 = uxds['relhum_200hPa'][0].subset.nearest_neighbor(center_coord, k = 120, element=\"face centers\")\n", + "nn_subset_120 = uxds[\"relhum_200hPa\"][0].subset.nearest_neighbor(\n", + " center_coord, k=120, element=\"face centers\"\n", + ")\n", "\n", - "nn_subset_120.plot.rasterize(method='polygon',\n", - " exclude_antimeridian=True,\n", - " clim=clim,\n", - " title= \"Nearest Neighbor Subset (120 Faces)\",\n", - " **plot_opts) * features" + "nn_subset_120.plot.rasterize(\n", + " method=\"polygon\",\n", + " exclude_antimeridian=True,\n", + " clim=clim,\n", + " title=\"Nearest Neighbor Subset (120 Faces)\",\n", + " **plot_opts,\n", + ") * features" ] }, { @@ -378,12 +397,17 @@ }, "outputs": [], "source": [ - "nn_subset_1 = uxds['relhum_200hPa'][0].subset.nearest_neighbor(center_coord, k = 1, element=\"face centers\")\n", + "nn_subset_1 = uxds[\"relhum_200hPa\"][0].subset.nearest_neighbor(\n", + " center_coord, k=1, element=\"face centers\"\n", + ")\n", "\n", - "nn_subset_1.plot.rasterize(method='polygon', exclude_antimeridian=True,\n", - " clim=clim,\n", - " title= \"Nearest Neighbor Subset (Closest Face)\",\n", - " **plot_opts) * features" + "nn_subset_1.plot.rasterize(\n", + " method=\"polygon\",\n", + " exclude_antimeridian=True,\n", + " clim=clim,\n", + " title=\"Nearest Neighbor Subset (Closest Face)\",\n", + " **plot_opts,\n", + ") * features" ] }, { @@ -407,7 +431,11 @@ }, "outputs": [], "source": [ - "bbox_subset_nodes.values.mean(), bbox_subset_faces.values.mean(), bcircle_subset.values.mean()" + "(\n", + " bbox_subset_nodes.values.mean(),\n", + " bbox_subset_faces.values.mean(),\n", + " bcircle_subset.values.mean(),\n", + ")" ] }, { @@ -418,7 +446,11 @@ }, "outputs": [], "source": [ - "bbox_subset_nodes.values.std(), bbox_subset_faces.values.std(), bcircle_subset.values.std()" + "(\n", + " bbox_subset_nodes.values.std(),\n", + " bbox_subset_faces.values.std(),\n", + " bcircle_subset.values.std(),\n", + ")" ] }, { @@ -429,7 +461,11 @@ }, "outputs": [], "source": [ - "bbox_subset_nodes.values.min(), bbox_subset_faces.values.min(), bcircle_subset.values.min()" + "(\n", + " bbox_subset_nodes.values.min(),\n", + " bbox_subset_faces.values.min(),\n", + " bcircle_subset.values.min(),\n", + ")" ] }, { @@ -440,7 +476,11 @@ }, "outputs": [], "source": [ - "bbox_subset_nodes.values.max(), bbox_subset_faces.values.max(), bcircle_subset.values.max()" + "(\n", + " bbox_subset_nodes.values.max(),\n", + " bbox_subset_faces.values.max(),\n", + " bcircle_subset.values.max(),\n", + ")" ] } ], diff --git a/pyproject.toml b/pyproject.toml index 8a999fc6c..167ee29f4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -71,3 +71,10 @@ fallback_version = "9999" [tool.ruff] extend-exclude = ["test","benchmarks"] + +[tool.ruff.lint] +ignore-init-module-imports = false + +[tool.ruff.lint.per-file-ignores] +"docs/*" = ["E402", "F401"] +"uxarray/plot/*" = ["E402", "F401"] From 46c4260d55b8a86e8737d084566402eec3645311 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Tue, 7 May 2024 21:25:14 -0500 Subject: [PATCH 33/41] ASV Benchmarks for 480km and 120km MPAS Meshes (#775) * benchmarks for mpas ocean meshes * update asv.conf * update benchmark * add comment about downloads * fix typo * add benchmark for n_nodes_per_face construction --- benchmarks/asv.conf.json | 2 + benchmarks/mpas_ocean.py | 99 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 101 insertions(+) create mode 100644 benchmarks/mpas_ocean.py diff --git a/benchmarks/asv.conf.json b/benchmarks/asv.conf.json index e1dd0d8a9..c77c2da64 100644 --- a/benchmarks/asv.conf.json +++ b/benchmarks/asv.conf.json @@ -60,6 +60,8 @@ // defaults to 10 min "install_timeout": 600, + "benchmark_timeout": 360, + // the base URL to show a commit for the project. "show_commit_url": "https://github.com/UXARRAY/uxarray/commit/", diff --git a/benchmarks/mpas_ocean.py b/benchmarks/mpas_ocean.py new file mode 100644 index 000000000..e7eed5447 --- /dev/null +++ b/benchmarks/mpas_ocean.py @@ -0,0 +1,99 @@ +import os +import urllib.request +from pathlib import Path + +import uxarray as ux + +current_path = Path(os.path.dirname(os.path.realpath(__file__))).parents[0] + +data_var = 'bottomDepth' + +grid_filename_480 = "oQU480.grid.nc" +data_filename_480 = "oQU480.data.nc" + +grid_filename_120 = "oQU120.grid.nc" +data_filename_120 = "oQU120.data.nc" + +filenames = [grid_filename_480, data_filename_480, grid_filename_120, data_filename_120] + +for filename in filenames: + if not os.path.isfile(filename): + # downloads the files from Cookbook repo, if they haven't been downloaded locally yet + url = f"https://github.com/ProjectPythia/unstructured-grid-viz-cookbook/raw/main/meshfiles/{filename}" + _, headers = urllib.request.urlretrieve(url, filename=filename) + + +file_path_dict = {"480km": [grid_filename_480, data_filename_480], + "120km": [grid_filename_120, data_filename_120]} + + +class Gradient: + + param_names = ['resolution'] + params = ['480km', '120km'] + + + def setup(self, resolution): + self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1]) + + def teardown(self, resolution): + del self.uxds + + def time_gradient(self, resolution): + self.uxds[data_var].gradient() + + def peakmem_gradient(self, resolution): + grad = self.uxds[data_var].gradient() + +class Integrate: + + param_names = ['resolution'] + params = ['480km', '120km'] + + + def setup(self, resolution): + self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1]) + + def teardown(self, resolution): + del self.uxds + + def time_integrate(self, resolution): + self.uxds[data_var].integrate() + + def peakmem_integrate(self, resolution): + integral = self.uxds[data_var].integrate() + +class GeoDataFrame: + + param_names = ['resolution', 'exclude_antimeridian'] + params = [['480km', '120km'], + [True, False]] + + + def setup(self, resolution, exclude_antimeridian): + self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1]) + + def teardown(self, resolution, exclude_antimeridian): + del self.uxds + + def time_to_geodataframe(self, resolution, exclude_antimeridian): + self.uxds[data_var].to_geodataframe(exclude_antimeridian=exclude_antimeridian) + + def peakmem_to_geodataframe(self, resolution, exclude_antimeridian): + gdf = self.uxds[data_var].to_geodataframe(exclude_antimeridian=exclude_antimeridian) + + +class ConnectivityConstruction: + + param_names = ['resolution'] + params = ['480km', '120km'] + + + def setup(self, resolution): + self.uxds = ux.open_dataset(file_path_dict[resolution][0], file_path_dict[resolution][1]) + + def teardown(self, resolution): + del self.uxds + + def time_n_nodes_per_face(self, resolution): + self.uxds.uxgrid.n_nodes_per_face From e7d097212d75403461f2bb18b20aa1fd09ff00a9 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Mon, 13 May 2024 18:48:00 -0500 Subject: [PATCH 34/41] User Guide & Documentation Improvements (#472) * start user guide * start user guide * remove checkpoints * work on user guide * work on user guide * work on user guide * remove autosummary * pre-commit * more work on user guide * work on user guide * add more sections to user guide * more work on user guide * work on user guide * work on user guide * test python execution of blocks * o Add a table to compare grid formats * o Remove quick install and move regular install under quickstart * o Add reference in index.rst * boilerplate for userguide, reformatting * o grid formats table update * o Revert shapefile commit, handling it in a different PR * work on data structure guide * pre-commit * work on data structures section * work on data structures notebook * work on data structures section * data structures section * work on user guide * try latex fix (1) * work on Convention section * add sections * update user guide * update user guide * add logo for user guide * fix logo and update notebooks * work on user guide * update data structures section * update data structures notebook * make toctree visible for quickstart * update notebook * work on notebook * update terminology.rst * add quick overview notebook * update figsize * work on data structures notebook * update examples * update grid-formats and representation * work on user guide * update user guide * update user guide * o Add exodus grid format and fix some terminology * o pre-commit fix * update user guide * remove empty sections for now * o Ruff pre-commit fix * add examples of grids * update image paths * fix a few typos * fix typos * fix typo * fix typo * update user guide * remove unused section * remove unused section --------- Co-authored-by: Rajeev Jain --- 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index 000000000..ab465aa47 --- /dev/null +++ b/docs/_static/images/icons/guide.svg @@ -0,0 +1 @@ + diff --git a/docs/api.rst b/docs/api.rst index 36add96b5..5b4b2d6f4 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -14,8 +14,7 @@ high-level understanding of UXarray's future function development milestones and Please let us know if you have any feedback! .. toctree:: - :maxdepth: 2 - :hidden: + :maxdepth: 1 user_api/index.rst internal_api/index.rst diff --git a/docs/conf.py b/docs/conf.py index e3981e11b..f20b793b6 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -54,6 +54,8 @@ def __getattr__(cls, name): "myst_nb", "sphinx_design", "nbsphinx", + "IPython.sphinxext.ipython_directive", + "IPython.sphinxext.ipython_console_highlighting", ] mathjax_config = { diff --git a/docs/examples.rst b/docs/examples.rst index 93f984296..18a92c7d6 100644 --- a/docs/examples.rst +++ b/docs/examples.rst @@ -16,8 +16,6 @@ your own examples or suggesting one for us to create, please see the :doc:`contr :maxdepth: 1 :hidden: - examples/001-working-with-unstructured-grids.ipynb - examples/002-grid-topology.ipynb examples/003-area-calc.ipynb examples/004-working-with-mpas-grids.ipynb examples/005-to-geodataframe-for-holoviz.ipynb diff --git a/docs/examples/001-working-with-unstructured-grids.ipynb b/docs/examples/001-working-with-unstructured-grids.ipynb deleted file mode 100644 index 49478aa42..000000000 --- a/docs/examples/001-working-with-unstructured-grids.ipynb +++ /dev/null @@ -1,368 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Working with Unstructured Grid Data\n", - "\n", - "Authors: [Philip Chmielowiec](https://github.com/philipc2), [Orhan Eroglu](https://github.com/erogluorhan)\n", - "\n", - "UXarray offers support for loading and representing unstructured grids\n", - "by providing Xarray-like functionality paired with new routines that\n", - "are specifically written for operating on unstructured grids.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Definition and Data Variables\n", - "\n", - "When working with Unstructured Grids, the grid definition and data variables\n", - "are often stored as separate files. This means that there are multiple\n", - "separate files that need to be read and linked together to represent the\n", - "entire dataset.\n", - "\n", - "For example, the following sample dataset is taken from the NOAA Geoflow project,\n", - "which is made up of 4 files: 1 grid definition and 3 data files. (Special thanks to John Clyne, Shilpi Gupta, and the VAPOR team for providing this data!)\n", - "\n", - "```\n", - "geoflow-small\n", - "│ grid.nc\n", - "│ v1.nc\n", - "│ v2.nc\n", - "│ v3.nc\n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Conventions\n", - "\n", - "Given the complexity of Unstructured Grids, there are many different ways of representing their underlying topology and structure. These representations are referred to as conventions, and they outline\n", - "the required connectivity variables, naming conventions, data types, and many other specifications. UXarray uses the [UGRID](http://ugrid-conventions.github.io/ugrid-conventions/)\n", - "conventions as a foundation for internally representing Unstructured Grids, converting any supported input grid format into the UGRID convention at the data loading step. Below is a list of supported formats and conventions that can be read in with UXarray:\n", - "* UGRID\n", - "* Model for Prediction Across Scales (MPAS)\n", - "* Exodus\n", - "\n", - "In addition to loading datasets, we also provide support for constructing a grid from user-defined primitives such as vertices, which is showcased in our other notebooks.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reading Grid and Data Files\n", - "UXarray provides the `UxDataset` data structure, which is an unstructure grid-informed implementation of Xarray's `Dataset` class. The main addition is the introduction of the `uxgrid` property, which stores our grid topology dimensions, coordinates, variables and provides grid-specific functions.\n", - "\n", - "Constructing a `UxDataset` can be done using our custom `open_dataset` and `open_mfdataset` methods, depending on whether one or multiple data files or objects are meant to be linked to a single grid.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import uxarray as ux\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Base data path\n", - "base_path = \"../../test/meshfiles/ugrid/geoflow-small/\"\n", - "\n", - "# Path to Grid file\n", - "grid_path = base_path + \"grid.nc\"\n", - "\n", - "# Paths to Data Variable files\n", - "var_names = [\"v1.nc\", \"v2.nc\", \"v3.nc\"]\n", - "\n", - "data_paths = [base_path + name for name in var_names]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Loading a single data file with a grid is done using the `open_dataset` method. The resulting `UxDataset` only contains the data variables stored in `v1.nc`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds_single = ux.open_dataset(grid_path, data_paths[0])\n", - "uxds_single" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, if you wish to open multiple data files with a grid, you would use the `open_mfdataset` method. The resulting `UxDataset` contains all the data variables stored in `v1.nc`, `v2.nc`, and `v3.nc`" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds_multiple = ux.open_mfdataset(grid_path, data_paths)\n", - "uxds_multiple" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "## Grid Topology" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Each dataset contains the aforementioned `uxgrid` property, which is a `Grid` object and represents the grid topology that the data variables lie on. The `uxgrid` property can be used to execute grid specific functions and access grid topology dimensions, coordinates, and variables. A detailed overview of functionalities can be found in subsequent notebooks.\n", - "\n", - "For both instances of `UxDataset` that contain single and multiple data sets (i.e. `uxds_single` and `uxds_multiple`), the `uxgrid` property contains the same grid information, however they are each instantiated separately.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# check if the grids contain the same variables & information\n", - "print(uxds_single.uxgrid == uxds_multiple.uxgrid)\n", - "\n", - "# check if the grids point to the same object in memory\n", - "print(uxds_single.uxgrid is uxds_multiple.uxgrid)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Printing out the `uxgrid` property provides an overview of the original grid format, dimensions, coordinates, and connectivity variables." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds_multiple.uxgrid" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These dimensions, coordinates, and connectivity variables can be accessed with attributes using the same names as shown in the print-out. Below are a few examples." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds_multiple.uxgrid.n_node" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds_multiple.uxgrid.node_lon" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds_multiple.uxgrid.face_node_connectivity" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "## Data Variables" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "While grid-specific variables and functions are stored under the `uxgrid` property, data variables that lie on the grid are stored directly in the `UxDataset` or `UxDataArray`. Most `Xarray` functions and operators can be executed on these data structures.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds_single.values" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds_single.dims" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds_single.coords" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds_single.attrs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds_single.min()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds_single > 0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "grid = uxds_single.uxgrid\n", - "foo = ux.UxDataArray(data=np.random.random(grid.n_face), dims=[\"n_face\"], uxgrid=grid)\n", - "foo" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds_new_var = uxds_single.assign({\"foo\": foo})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds_new_var" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - }, - "vscode": { - "interpreter": { - "hash": "8e8ae2f388051fced6c30f82a529eeca8cf1e059ab06a64326e2a2ad0ec3c36c" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/examples/002-grid-topology.ipynb b/docs/examples/002-grid-topology.ipynb deleted file mode 100644 index 8f35ed770..000000000 --- a/docs/examples/002-grid-topology.ipynb +++ /dev/null @@ -1,469 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Grid Topology Overview\n", - "\n", - "Authors: [Philip Chmielowiec](https://github.com/philipc2), [Orhan Eroglu](https://github.com/erogluorhan)\n", - "\n", - "As discussed in our first notebook, UXarray uses the UGRID conventions as a foundation for represented Unstructured Grids. Here we'll see how to access the underlying dimensions, coordinates, and connectivity variables that are available in UXarray." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Constructing the Grid Topology" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The grid topology is defined as a `Grid` object in UXarray. A `Grid` object can be created in different ways. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Constructing through `uxarray.open_grid()`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If the intent is to explore only the grid topology instead of having any data sets with it, analyzing data variables, etc., a standalone `Grid` object can be instantiated as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import uxarray as ux" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Base data path\n", - "base_path = \"../../test/meshfiles/\"\n", - "\n", - "# Grid Path (MPAS Example)\n", - "grid_mpas_path = base_path + \"/mpas/QU/mesh.QU.1920km.151026.nc\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "grid_mpas = ux.open_grid(grid_mpas_path)\n", - "grid_mpas" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Constructing through `UxDataset`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If the intent is to have an unstructured grid-aware dataset, i.e. `UxDataset`, and investigate the grid topology through it instead, the `uxgrid` property can be used. To be more precise, when a `UxDataset` object, or likewise `UxDataArray` object, is generated (e.g. through `uxarray.open_dataset()`), a `Grid` object via `UxDataset.uxgrid`, or `UxDataArray.uxgrid`, property is also created automatically and assigned to the dataset or variable. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2023-06-09T05:51:09.904564Z", - "start_time": "2023-06-09T05:51:08.080955Z" - } - }, - "source": [ - "The grid topology can be seen through this property as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Data File Path (UGRID Example)\n", - "grid_ne30_path = base_path + \"/ugrid/outCSne30/outCSne30.ug\"\n", - "data_ne30_path = base_path + \"/ugrid/outCSne30/outCSne30_vortex.nc\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds = ux.open_dataset(grid_ne30_path, data_ne30_path)\n", - "uxds.uxgrid" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition, a previously-created `Grid` object can always be assigned to a new `UxDataset` as its `uxgrid` property as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds_mpas = ux.UxDataset(uxgrid=grid_mpas)\n", - "uxds_mpas.uxgrid" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Attributes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If our input grid contained additional attributes that were not representable by the UGRID conventions, they would be stored here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds.uxgrid.parsed_attrs" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds_mpas.uxgrid.parsed_attrs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Coordinates" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The coordinates by default are represented in terms of longitude and latitude." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds.uxgrid.node_lon" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds.uxgrid.node_lat" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you wish to use the Cartesian coordinate system, you can access the following attributes, which will internally construct a set of Cartesian coordinates derived from the previous set.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds.uxgrid.node_x" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds.uxgrid.node_y" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds.uxgrid.node_z" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Connectivity\n", - "\n", - "Connectivity variables are used to describe how various geometric elements (nodes, faces, edges) can be manipulated and interconnected to represent the topology of the unstructured grid." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "As described in the UGRID conventions, these connectivity variables are stored as integer arrays and may contain a Fill Value. UXarray standardizes both of these at the data loading step, meaning that the data type and fill value can always be guaranteed to be the following:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ux.INT_DTYPE" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ux.INT_FILL_VALUE" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "Below we can see how to access these connectivity variables." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds.uxgrid.face_node_connectivity" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds.uxgrid.n_nodes_per_face" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds.uxgrid" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see above, these are the only two connectivity variables listed. In addition to these, UXarray provides support for constructing additional connectivity variables.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds.uxgrid.edge_node_connectivity" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds.uxgrid.face_edge_connectivity" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds.uxgrid" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These additional variables are constructed upon calling their respective attributes and are now stored under the `uxgrid` property. Additionally, the `Mesh2_node_cart_x`, `Mesh2_node_cart_y`, and `Mesh2_node_cart_z` that we constructed earlier are now also shown here.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Grid Dimensions" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds.uxgrid.n_node" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds.uxgrid.n_edge" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds.uxgrid.n_face" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds.uxgrid.n_max_face_nodes" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "uxds.uxgrid.n_max_face_edges" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.10" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/gallery.yml b/docs/gallery.yml index 2577f1cca..735760d3a 100644 --- a/docs/gallery.yml +++ b/docs/gallery.yml @@ -1,11 +1,3 @@ -- title: Working with Unstructured Grid Data - path: examples/001-working-with-unstructured-grids.ipynb - thumbnail: _static/thumbnails/default.svg - -- title: Grid Topology Overview - path: examples/002-grid-topology.ipynb - thumbnail: _static/thumbnails/default.svg - - title: Face Area Calculations path: examples/003-area-calc.ipynb thumbnail: _static/thumbnails/default.svg diff --git a/docs/installation.rst b/docs/getting-started/installation.rst similarity index 100% rename from docs/installation.rst rename to docs/getting-started/installation.rst diff --git a/docs/getting-started/overview.rst b/docs/getting-started/overview.rst index d3d176f65..3b3aee3c1 100644 --- a/docs/getting-started/overview.rst +++ b/docs/getting-started/overview.rst @@ -1,8 +1,8 @@ .. currentmodule:: uxarray -====================== -Overview: Why UXarray? -====================== +============ +Why UXarray? +============ UXarray aims to address the geoscience community need for tools that enable standard data analysis techniques to operate directly on unstructured grids. It extends upon and inherits from the commonly used Xarray Python package to provide a powerful and diff --git a/docs/getting-started/quick-install.rst b/docs/getting-started/quick-install.rst deleted file mode 100644 index 56371bf44..000000000 --- a/docs/getting-started/quick-install.rst +++ /dev/null @@ -1,26 +0,0 @@ -.. currentmodule:: uxarray - -################## -Quick Installation -################## - -This quick installation guide is meant to provide a quick help to get up and running -with UXarray. For a more detailed guide, check out our Installation section. - -Required Dependencies -##################### -* Python (3.9 or later) -* xarray - - -Conda -##### -:: - - conda install -c conda-forge uxarray - -PyPi -##### -:: - - pip install uxarray diff --git a/docs/getting-started/quick-overview.ipynb b/docs/getting-started/quick-overview.ipynb new file mode 100644 index 000000000..2a6f971bf --- /dev/null +++ b/docs/getting-started/quick-overview.ipynb @@ -0,0 +1,303 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3a5d7fe9539f9d6b", + "metadata": { + "collapsed": false + }, + "source": [ + "# Quick Overview" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c8b24263cf6d8f2", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import uxarray as ux\n", + "\n", + "base_path = \"../../test/meshfiles/ugrid/outCSne30/\"\n", + "grid_path = base_path + \"outCSne30.ug\"\n", + "data_path = base_path + \"outCSne30_vortex.nc\"" + ] + }, + { + "cell_type": "markdown", + "id": "9b51e3fc6392c2e8", + "metadata": { + "collapsed": false + }, + "source": [ + "## Loading Grid Files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b5c8434841e9bcd", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "grid_path = \"../../test/meshfiles/ugrid/outCSne30/outCSne30.ug\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68b3a121417d2ad8", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "uxgrid = ux.open_grid(grid_path)\n", + "uxgrid" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7df1886c6d664e74", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "grid_path = \"../../test/meshfiles/ugrid/outCSne30/outCSne30.ug\"\n", + "data_path = \"../../test/meshfiles/ugrid/outCSne30/outCSne30_vortex.nc\"\n", + "uxds = ux.open_dataset(grid_path, data_path)\n", + "uxds" + ] + }, + { + "cell_type": "markdown", + "id": "545f365d288d6539", + "metadata": { + "collapsed": false + }, + "source": [ + "Talk about accessor" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ddac5247b83f2ac", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "uxds.uxgrid" + ] + }, + { + "cell_type": "markdown", + "id": "73c0c3c275c41662", + "metadata": { + "collapsed": false + }, + "source": [ + "## Accessing Grid Dimensions & Variables" + ] + }, + { + "cell_type": "markdown", + "id": "9a6fac4ac4294a96", + "metadata": { + "collapsed": false + }, + "source": [ + "First, talk about how a grid can be accessed stand alone or through a dataset or data array" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e08aacc99fc1a54c", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "uxds.uxgrid.n_node, uxgrid.n_node" + ] + }, + { + "cell_type": "markdown", + "id": "a96eda8f200de2c5", + "metadata": { + "collapsed": false + }, + "source": [ + "### Dimensions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76fa1887ce65d89e", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "uxgrid.sizes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76523ba479470c7a", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "uxgrid.n_node, uxgrid.n_edge, uxgrid.n_face" + ] + }, + { + "cell_type": "markdown", + "id": "5e8d8af89b9eb3d5", + "metadata": { + "collapsed": false + }, + "source": [ + "### Coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82363fc1d9ff00e4", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "uxgrid.node_lon" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dba7522cff28a82f", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "uxgrid.node_lat" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9247f711b05c807", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "uxgrid.node_x" + ] + }, + { + "cell_type": "markdown", + "id": "15d221fb31e9f74c", + "metadata": { + "collapsed": false + }, + "source": [ + "### Connectivity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "14eb3e43b070775f", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "uxgrid.face_node_connectivity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da32551506ddfa5c", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "uxgrid.face_edge_connectivity" + ] + }, + { + "cell_type": "markdown", + "id": "d4f54e0ebdb0017", + "metadata": { + "collapsed": false + }, + "source": [ + "## Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22902735489f69c", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "uxds[\"psi\"].plot(width=600, height=300)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "703f4d38b7ab0bb6", + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "uxds[\"psi\"].uxgrid.plot(width=600, height=300)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/index.rst b/docs/index.rst index e22f46ee8..c02a14644 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -39,34 +39,56 @@ UXarray provides Xarray-styled functionality for working with unstructured grids :link: quickstart :link-type: doc - A good place to start for new users + New to UXarray? Check out the getting started guides, which provide an introduction to UXarray's main + motivation and functionality. - .. grid-item-card:: Examples + .. grid-item-card:: User Guide + :class-title: custom-title + :class-body: custom-body + :img-top: _static/images/icons/guide.svg + :link: userguide + :link-type: doc + + The user guide provides in-depth information on the key concepts of UXarray with usefully definitions, + background information and explanations. + + .. grid-item-card:: Example Gallery :class-title: custom-title :class-body: custom-body :img-top: _static/images/icons/science.svg :link: examples :link-type: doc - A gallery of examples using uxarray + The example gallery contains notebooks showcasing real-world workflows of UXarray in action. + + .. grid-item-card:: API + :class-title: custom-title + :class-body: custom-body + :img-top: _static/images/icons/code.svg + :link: api + :link-type: doc + + The reference guide contains a detailed description of the UXarray API. The reference describes how the methods + work and which parameters can be used. .. grid-item-card:: Installation :class-title: custom-title :class-body: custom-body :img-top: _static/images/icons/download.svg - :link: installation + :link: getting-started/installation :link-type: doc - Installation instructions for uxarray + The installation guide provides step-by-step instructions for installing UXarray. - .. grid-item-card:: API + .. grid-item-card:: Contributor's Guide :class-title: custom-title :class-body: custom-body - :img-top: _static/images/icons/code.svg - :link: api + :img-top: _static/images/icons/contrib.svg + :link: contributing :link-type: doc - See the complete uxarray API + Interested in contributing to UXarray? The contributors guide outlines the process of making contributions. + .. toctree:: @@ -74,20 +96,13 @@ UXarray provides Xarray-styled functionality for working with unstructured grids :hidden: :caption: For users - Installation Getting Started + User Guide Usage Examples API Reference Tutorials Cite Uxarray -.. toctree:: - :maxdepth: 2 - :hidden: - :caption: For developers - - Contributor's Guide - .. toctree:: :maxdepth: 1 :hidden: @@ -95,8 +110,7 @@ UXarray provides Xarray-styled functionality for working with unstructured grids GitHub Discussions GitHub Issues - Ugrid Conventions - + UGRID Conventions -------------------- diff --git a/docs/quickstart.rst b/docs/quickstart.rst index 6e5be35a4..0e02f45e0 100644 --- a/docs/quickstart.rst +++ b/docs/quickstart.rst @@ -11,8 +11,8 @@ and Usage Example sections. .. toctree:: :maxdepth: 1 - :hidden: getting-started/overview.rst - getting-started/quick-install.rst + getting-started/installation.rst + getting-started/quick-overview.ipynb getting-started/freq-asked-questions.rst diff --git a/docs/user-guide/data-structures.ipynb b/docs/user-guide/data-structures.ipynb new file mode 100644 index 000000000..94ab758a8 --- /dev/null +++ b/docs/user-guide/data-structures.ipynb @@ -0,0 +1,5427 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# Data Structures\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:25.608006300Z", + "start_time": "2024-04-23T05:41:22.563535Z" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\chmie\\anaconda3\\envs\\uxarray_build\\Lib\\site-packages\\dask\\dataframe\\_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 12.0.1. Please consider upgrading.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import uxarray as ux\n", + "import xarray as xr" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "The core functionality of UXarray revolves around three data structures, which are used for interacting with unstructured grids and the data variables that reside on them.\n", + "\n", + "1. **[`uxarray.Grid`](https://uxarray.readthedocs.io/en/latest/user_api/generated/uxarray.UxDataArray.html)**: Stores the grid representation (i.e. coordinates, connectivity information, etc.)\n", + "2. **[`uxarray.UxDataset`](https://uxarray.readthedocs.io/en/latest/user_api/generated/uxarray.UxDataset.html)**: One or more data variable that resided on a grid.\n", + "3. **[`uxarray.UxDataArray`](https://uxarray.readthedocs.io/en/latest/user_api/generated/uxarray.UxDataArray.html)**: A single data variable that resides on a grid \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Grid and Data Files\n", + "\n", + "\n", + "When working with unstructured grid datasets, the grid definition is typically stored separately from any data variables. \n", + "\n", + "For example, the dataset used in this example is made up of two files: a single grid definition and a single data file.\n", + "\n", + "\n", + "```\n", + "quad-hexagon\n", + "│ grid.nc\n", + "│ data.nc\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:25.614302300Z", + "start_time": "2024-04-23T05:41:25.610287400Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "grid_path = \"../../test/meshfiles/ugrid/quad-hexagon/grid.nc\"\n", + "data_path = \"../../test/meshfiles/ugrid/quad-hexagon/data.nc\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "Additionally, there may be multiple data files that are mapped to the same unstructured grid (such as the case with climate model output). Using our sample dataset, this may look something like this:\n", + "\n", + "```\n", + "quad-hexagon\n", + "│ grid.nc\n", + "│ data1.nc\n", + "| data2.nc\n", + "| data3.nc\n", + "```\n", + "\n", + "We can store these paths as a list (in this case we simply repeat the original data file to imitate having 4 separate data files)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:25.615818900Z", + "start_time": "2024-04-23T05:41:25.612798600Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "multiple_data_paths = [data_path for i in range(3)]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Grid\n", + "\n", + "The `Grid` class is used for storing variables associated with an unstructured grid's topology. This includes dimensions, coordinates, and connectivity variables." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "### Creating a Grid\n", + "\n", + "The recommended way to construct a `Grid` is by using the `ux.open_grid()` method, which takes in a grid file path, detects the input grid format, and parses and encodes the provided coordinates and connectivity into the UGRID conventions. Details on supported grid formats and what variables are parsed can be found in other parts of this user guide." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:25.677755200Z", + "start_time": "2024-04-23T05:41:25.615818900Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Original Grid Type: UGRID\n", + "Grid Dimensions:\n", + " * n_node: 16\n", + " * n_edge: 19\n", + " * n_face: 4\n", + " * n_max_face_nodes: 6\n", + " * two: 2\n", + " * n_nodes_per_face: (4,)\n", + "Grid Coordinates (Spherical):\n", + " * node_lon: (16,)\n", + " * node_lat: (16,)\n", + " * edge_lon: (19,)\n", + " * edge_lat: (19,)\n", + " * face_lon: (4,)\n", + " * face_lat: (4,)\n", + "Grid Coordinates (Cartesian):\n", + "Grid Connectivity Variables:\n", + " * face_node_connectivity: (4, 6)\n", + " * edge_node_connectivity: (19, 2)\n", + "Grid Descriptor Variables:" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "uxgrid = ux.open_grid(grid_path)\n", + "uxgrid" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "### Accessing Variables\n", + "\n", + "As we saw above when printing out Grid instance, there are many variables that are associated with a single grid. In addition to the general repr, we can obtain the stored dimensions, coordinates, and connectivity variables through the following attributes.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:25.678754400Z", + "start_time": "2024-04-23T05:41:25.674801Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'n_edge', 'n_face', 'n_max_face_nodes', 'n_node', 'two'}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "uxgrid.dims" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:25.687110300Z", + "start_time": "2024-04-23T05:41:25.679754900Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'n_edge': 19, 'n_face': 4, 'n_max_face_nodes': 6, 'two': 2, 'n_node': 16}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "uxgrid.sizes" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:25.687110300Z", + "start_time": "2024-04-23T05:41:25.683876400Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'edge_lat', 'edge_lon', 'face_lat', 'face_lon', 'node_lat', 'node_lon'}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "uxgrid.coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:25.695847700Z", + "start_time": "2024-04-23T05:41:25.688111Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'edge_node_connectivity', 'face_node_connectivity'}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "uxgrid.connectivity" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "We can access any desired quantity by either calling an attribute by the same name or by indexing a `Grid` like a dictionary." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:25.702069800Z", + "start_time": "2024-04-23T05:41:25.692337500Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "    standard_name:  longitude\n",
+       "    long_name:      longitude of mesh nodes\n",
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    " + ], + "text/plain": [ + "\n", + "array([-0.038411, -0.186448, -0.181994, -0.047552, 0.100399, 0.096181,\n", + " 0.105031, 0.239774, -0.177116, -0.325237, 0.378854, 0.382835,\n", + " -0.042753, -0.320659, 0.235225, 0.243879], dtype=float32)\n", + "Dimensions without coordinates: n_node\n", + "Attributes:\n", + " standard_name: longitude\n", + " long_name: longitude of mesh nodes\n", + " units: degrees_east" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "uxgrid[\"node_lon\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "### Constructing Additional Variables" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "Looking at `Grid.connectivity` one more time, we can see that there are only two available variables. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:25.756572500Z", + "start_time": "2024-04-23T05:41:25.706579500Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'edge_node_connectivity', 'face_node_connectivity'}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "uxgrid.connectivity" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "These variables are the ones that were able to be parsed and encoded in the UGRID conventions from the inputted grid file.\n", + "\n", + "In addition to parsing variables, we can construct additional variables by calling the attribute or indexing the Grid with the desired name. For example, if we wanted to construct the `face_edge_connectivity`, we would do the following:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:25.758672Z", + "start_time": "2024-04-23T05:41:25.710732100Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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    <xarray.UxDataset>\n",
+       "Dimensions:  (time: 3, n_face: 4)\n",
+       "Dimensions without coordinates: time, n_face\n",
+       "Data variables:\n",
+       "    t2m      (time, n_face) float32 dask.array<chunksize=(1, 4), meta=np.ndarray>
    " + ], + "text/plain": [ + "\n", + "Dimensions: (time: 3, n_face: 4)\n", + "Dimensions without coordinates: time, n_face\n", + "Data variables:\n", + " t2m (time, n_face) float32 dask.array" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "uxds_multi = ux.open_mfdataset(\n", + " grid_path, multiple_data_paths, combine=\"nested\", concat_dim=\"time\"\n", + ")\n", + "uxds_multi" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Grid Accessor\n", + "\n", + "Each `UxDataset` (and in the next section `UxDataArray`) is linked to a `Grid` instance, which contain the unstructured grid information." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:25.856053800Z", + "start_time": "2024-04-23T05:41:25.793536800Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Original Grid Type: UGRID\n", + "Grid Dimensions:\n", + " * n_node: 16\n", + " * n_edge: 19\n", + " * n_face: 4\n", + " * n_max_face_nodes: 6\n", + " * two: 2\n", + " * n_nodes_per_face: (4,)\n", + "Grid Coordinates (Spherical):\n", + " * node_lon: (16,)\n", + " * node_lat: (16,)\n", + " * edge_lon: (19,)\n", + " * edge_lat: (19,)\n", + " * face_lon: (4,)\n", + " * face_lat: (4,)\n", + "Grid Coordinates (Cartesian):\n", + "Grid Connectivity Variables:\n", + " * face_node_connectivity: (4, 6)\n", + " * edge_node_connectivity: (19, 2)\n", + "Grid Descriptor Variables:" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "uxds.uxgrid" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "All the same functionality can be performed using the `uxgrid` attribute as was discussed in the `Grid` sections above." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:25.857628200Z", + "start_time": "2024-04-23T05:41:25.799749800Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'n_edge', 'n_face', 'n_max_face_nodes', 'n_node', 'two'}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "uxds.uxgrid.dims" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## UxDataArray\n", + "\n", + "\n", + "\n", + "While a `UxDataset` represents one or more data variables linked to some unstructured grid, a `UxDataArray` represent a single data variable. Alternatively, one can think of a `UxDataset` as a collection of one or more `UxDataArray` instances.\n", + "\n", + "```{info}\n", + "More information about `xarray.DataArray` can be found [here](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html).\n", + "```\n", + "\n", + "In our sample dataset, we have a variable called `t2m`, which can be used to index our `UxDataset`\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:25.858562300Z", + "start_time": "2024-04-23T05:41:25.805783200Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + "\n", + "array(297.54895, dtype=float32)" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "uxds[\"t2m\"].mean()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "UXarray also provides custom data analysis operators which are explored in further sections of this user guide." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:26.787765500Z", + "start_time": "2024-04-23T05:41:25.838026600Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "array([28.00277097, 20.66935101, 29.23128307,  0.        ,  0.        ,\n",
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UXarray aims to provide a familiar experience to Xarray by inheriting the `xr.Dataset` and `xr.DataArray` objects and linking them to an instance of a `Grid` class to provide grid-aware implementations.\n", + "\n", + "We can observe this inheritance by checking for subclassing." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:26.796684700Z", + "start_time": "2024-04-23T05:41:26.789273400Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "issubclass(ux.UxDataset, xr.Dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:26.797661500Z", + "start_time": "2024-04-23T05:41:26.793536800Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "issubclass(ux.UxDataArray, xr.DataArray)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "## Overloaded Methods\n", + "\n", + "With subclassing, all methods are directly inherited from the parent class (`xr.Dataset`). Most Xarray functionality works directly on UXarray's data structures, however certain methods have been overloaded to make them unstructured-grid aware.\n", + "\n", + "One example of this is the plotting functionality of a `ux.UxDataArray`, which was re-implemented to support visualuzations of unstructured grids. A detailed overview of plotting functionality can be found in the next sections." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "ExecuteTime": { + "end_time": "2024-04-23T05:41:29.942530100Z", + "start_time": "2024-04-23T05:41:26.797661500Z" + }, + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var force = true;\n", + " var py_version = '3.1.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n", + " var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n", + " var reloading = false;\n", + " var Bokeh = root.Bokeh;\n", + " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + "\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks;\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + " if (js_modules == null) js_modules = [];\n", + " if (js_exports == null) js_exports = {};\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + "\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " if (!reloading) {\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " }\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + " window._bokeh_on_load = on_load\n", + "\n", + " function on_error() {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " var skip = [];\n", + " if (window.requirejs) {\n", + " window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n", + " require([\"jspanel\"], function(jsPanel) {\n", + "\twindow.jsPanel = jsPanel\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-modal\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-tooltip\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-hint\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-layout\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-contextmenu\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"jspanel-dock\"], function() {\n", + "\ton_load()\n", + " })\n", + " require([\"gridstack\"], function(GridStack) {\n", + "\twindow.GridStack = GridStack\n", + "\ton_load()\n", + " })\n", + " require([\"notyf\"], function() {\n", + "\ton_load()\n", + " })\n", + " root._bokeh_is_loading = css_urls.length + 9;\n", + " } else {\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n", + " }\n", + "\n", + " var existing_stylesheets = []\n", + " var links = document.getElementsByTagName('link')\n", + " for (var i = 0; i < links.length; i++) {\n", + " var link = links[i]\n", + " if (link.href != null) {\n", + "\texisting_stylesheets.push(link.href)\n", + " }\n", + " }\n", + " for (var i = 0; i < css_urls.length; i++) {\n", + " var url = css_urls[i];\n", + " if (existing_stylesheets.indexOf(url) !== -1) {\n", + "\ton_load()\n", + "\tcontinue;\n", + " }\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n", + " var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n", + " for (var i = 0; i < urls.length; i++) {\n", + " skip.push(urls[i])\n", + " }\n", + " } var existing_scripts = []\n", + " var scripts = document.getElementsByTagName('script')\n", + " for (var i = 0; i < scripts.length; i++) {\n", + " var script = scripts[i]\n", + " if (script.src != null) {\n", + "\texisting_scripts.push(script.src)\n", + " }\n", + " }\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (var i = 0; i < js_modules.length; i++) {\n", + " var url = js_modules[i];\n", + " if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.src = url;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " for (const name in js_exports) {\n", + " var url = js_exports[name];\n", + " if (skip.indexOf(url) >= 0 || root[name] != null) {\n", + "\tif (!window.requirejs) {\n", + "\t on_load();\n", + "\t}\n", + "\tcontinue;\n", + " }\n", + " var element = document.createElement('script');\n", + " element.onerror = on_error;\n", + " element.async = false;\n", + " element.type = \"module\";\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " element.textContent = `\n", + " import ${name} from \"${url}\"\n", + " window.${name} = ${name}\n", + " window._bokeh_on_load()\n", + " `\n", + " document.head.appendChild(element);\n", + " }\n", + " if (!js_urls.length && !js_modules.length) {\n", + " on_load()\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.holoviz.org/panel/1.2.3/dist/panel.min.js\"];\n", + " var js_modules = [];\n", + " var js_exports = {};\n", + " var css_urls = [];\n", + " var inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {} // ensure no trailing comma for IE\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + " // Cache old bokeh versions\n", + " if (Bokeh != undefined && !reloading) {\n", + "\tvar NewBokeh = root.Bokeh;\n", + "\tif (Bokeh.versions === undefined) {\n", + "\t Bokeh.versions = new Map();\n", + "\t}\n", + "\tif (NewBokeh.version !== Bokeh.version) {\n", + "\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n", + "\t}\n", + "\troot.Bokeh = Bokeh;\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " }\n", + " root._bokeh_is_initializing = false\n", + " }\n", + "\n", + " function load_or_wait() {\n", + " // Implement a backoff loop that tries to ensure we do not load multiple\n", + " // versions of Bokeh and its dependencies at the same time.\n", + " // In recent versions we use the root._bokeh_is_initializing flag\n", + " // to determine whether there is an ongoing attempt to initialize\n", + " // bokeh, however for backward compatibility we also try to ensure\n", + " // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n", + " // before older versions are fully initialized.\n", + " if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n", + " root._bokeh_is_initializing = false;\n", + " root._bokeh_onload_callbacks = undefined;\n", + " console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n", + " load_or_wait();\n", + " } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n", + " setTimeout(load_or_wait, 100);\n", + " } else {\n", + " Bokeh = root.Bokeh;\n", + " bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n", + " root._bokeh_is_initializing = true\n", + " root._bokeh_onload_callbacks = []\n", + " if (!reloading && (!bokeh_loaded || is_dev)) {\n", + "\troot.Bokeh = undefined;\n", + " }\n", + " load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n", + "\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + "\trun_inline_js();\n", + " });\n", + " }\n", + " }\n", + " // Give older versions of the autoload script a head-start to ensure\n", + " // they initialize before we start loading newer version.\n", + " setTimeout(load_or_wait, 100)\n", + "}(window));" + ], + "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.1.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n var reloading = false;\n var Bokeh = root.Bokeh;\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n require([\"jspanel\"], 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}\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.2.3/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 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autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n", + " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n", + "}\n", + "\n", + "\n", + " function JupyterCommManager() {\n", + " }\n", + "\n", + " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n", + " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n", + " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n", + " comm_manager.register_target(comm_id, function(comm) {\n", + " comm.on_msg(msg_handler);\n", + " });\n", + " } else if ((plot_id in window.PyViz.kernels) && 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+ " var html_node = toinsert[nchildren-1].children[0];\n", + " html_node.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var scripts = [];\n", + " var nodelist = html_node.querySelectorAll(\"script\");\n", + " for (var i in nodelist) {\n", + " if (nodelist.hasOwnProperty(i)) {\n", + " scripts.push(nodelist[i])\n", + " }\n", + " }\n", + "\n", + " scripts.forEach( function (oldScript) {\n", + " var newScript = document.createElement(\"script\");\n", + " var attrs = [];\n", + " var nodemap = oldScript.attributes;\n", + " for (var j in nodemap) {\n", + " if (nodemap.hasOwnProperty(j)) {\n", + " attrs.push(nodemap[j])\n", + " }\n", + " }\n", + " attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n", + " newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n", + " oldScript.parentNode.replaceChild(newScript, oldScript);\n", + " });\n", + " if (JS_MIME_TYPE in output.data) {\n", + " toinsert[nchildren-1].children[1].textContent = 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else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar 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As of the most recent release, the following grid formats are supported: + + +* UGRID +* MPAS +* SCRIP +* EXODUS +* ESMF + +While each of these formats can be encoded in the UGRID conventions, the amount of information that is parsed from them +varies. The following sections describes how each format is converted into the UGRID conventions and what variables +are directly parsed. + + +UGRID +===== + +The UGRID conventions are a standard for for storing unstructured grid (a.k.a. unstructured mesh, +flexible mesh) model data in a Unidata Network Common Data Form (NetCDF) file. + +These conventions are focussed on representing data for environmental applications, hence the motivation for +starting from the Climate & Forecasting (CF) Metadata Conventions, +The CF Conventions have been the standard in climate rx`earch for many years, and are being adopted by others as the metadata +standard (e.g. NASA, Open Geospatial Consortium). The CF conventions allow you to provide the geospatial and temporal coordinates +for scientific data, but currently assumes that the horizontal topology may be inferred from the i,j indices of structured +grids. The UGRID Conventions outline how to specify the topology of unstructured grids. + +The standard was developed over a period of several years through the UGRID Google Group which had members from many +different unstructured grid modeling communities (including SELFE, ELCIRC, FVCOM, ADCIRC). From these discussions Bert +Jagers (Deltares) created the first draft of this document, and the community worked to develop version 1.0. + +References +---------- +* https://ugrid-conventions.github.io/ugrid-conventions/#ugrid-conventions-v10 +* https://github.com/ugrid-conventions/ugrid-conventions +* https://cfconventions.org/Data/cf-conventions/cf-conventions-1.11/cf-conventions.html#ugrid-conventions +* https://cfconventions.org/Data/cf-conventions/cf-conventions-1.11/cf-conventions.html#mesh-topology-variables + +MPAS +==== + +The Model for Prediction Across Scales (MPAS) is a collaborative project for developing atmosphere, ocean and other +earth-system simulation components for use in climate, regional climate and weather studies. + +The defining features of MPAS are the unstructured Voronoi meshes and C-grid discretization used as the basis for many of +the model components. The unstructured Voronoi meshes, formally Spherical Centriodal Voronoi Tesselations (SCVTs), allow +for both quasi-uniform discretization of the sphere and local refinement. The C-grid discretization, where the normal +component of velocity on cell edges is prognosed, is especially well-suited for higher-resolution, mesoscale atmosphere +and ocean simulations. The land ice model takes advantage of the SCVT-dual mesh, which is a triangular Delaunay +tessellation appropriate for use with Finite-Element-based discretizations. + +References +---------- +* https://mpas-dev.github.io/ +* https://mpas-dev.github.io/files/documents/MPAS-MeshSpec.pdf + +SCRIP +===== + +The Spherical Coordinate Remapping and Interpolation Package (SCRIP) package is a software package used to generate +interpolation weights for remapping fields from one grid to another in spherical geometry. + +A SCRIP format grid file is a NetCDF file for describing unstructured grids. + +References +---------- +* https://archive.org/details/manualzilla-id-6909486 +* https://earthsystemmodeling.org/docs/release/ESMF_8_0_1/ESMF_refdoc/node3.html#SECTION03028100000000000000 + +EXODUS +====== + +EXODUS is a binary format based on NetCDF, leading to smaller file sizes compared to ASCII formats. +It is system independent and typically consists of nodes (geometric points), elements (e.g., triangles, tetrahedrons), +material properties, boundary conditions, and results from analysis. + +Moreover, EXODUS facilitates efficient data storage and retrieval for computational simulations, +aiding in the management and analysis of complex engineering and scientific datasets. +It supports a wide range of finite element analysis applications and provides interoperability +with various simulation software packages. Additionally, the format ensures compatibility across different platforms, +enhancing collaboration and data exchange within the scientific community. + +References +---------- +* https://www.osti.gov/servlets/purl/10102115 +* https://www.paraview.org/Wiki/ParaView/Users_Guide/Exodus_Reader + +ESMF +==== + +The Earth System Modeling Framework (ESMF) is high-performance, flexible software infrastructure for building and +coupling weather, climate, and related Earth science applications. The ESMF defines an architecture for composing +complex, coupled modeling systems and includes data structures and utilities for developing individual models. + +ESMF supports a custom unstructured grid file format for describing meshes, which is more compatible than the SCRIP +format. + +References +---------- +* https://earthsystemmodeling.org/about/ +* https://earthsystemmodeling.org/docs/release/ESMF_8_0_1/ESMF_refdoc/node3.html#SECTION03028200000000000000 + +Parsed Variables +================ + +Each unstructured grid formats varies in the amount of information contained about the grid. UXarray parses the +variables represented in each format and represents them in the UGRID conventions. + +.. note:: + + Even though each unstructured grid format has a varying number of support variables, UXarray provides + support for constructing additional variables, which is discussed in the next sections. + + +Coordinates +----------- + +.. raw:: html + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    UGRIDMPASSCRIPEXODUSESMF
    node_latlonYesYesYesNoYes
    edge_latlonYesYesNoNoNo
    face_latlonYesYesYesNoYes
    node_xyzYesYesNoYesNo
    edge_xyzYesYesNoYesNo
    face_xyzYesYesNoYesNo
    + + +Connectivity +------------ +.. raw:: html + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    UGRIDMPASSCRIPEXODUSESMF
    face_nodeYesYesYesYesYes
    face_edgeYesYesNoNoNo
    face_faceYesNoNoNoNo
    edge_nodeYesYesNoNoNo
    edge_edgeNoNoNoNoNo
    edge_faceYesYesNoNoNo
    node_nodeYesNoNoNoNo
    node_edgeYesNoNoNoNo
    node_faceYesYesNoNoNo
    diff --git a/docs/user-guide/representation.rst b/docs/user-guide/representation.rst new file mode 100644 index 000000000..66b8c6005 --- /dev/null +++ b/docs/user-guide/representation.rst @@ -0,0 +1,196 @@ +.. currentmodule:: uxarray + +=========== +Conventions +=========== + +UGRID Conventions +================== + +The UGRID conventions provide a standard for storing unstructured grid model data in the form of a NetCDF file. +UXarray uses the UGRID conventions as a foundation for representing unstructured grids. + +.. admonition:: More Info + :class: tip + + For further information about the UGRID conventions, please refer to the `official documentation`_ + +.. _official documentation: https://ugrid-conventions.github.io/ugrid-conventions/ + +Elements & Dimensions +===================== + +An unstructured grid is composed of nodes, edges, and faces which either fully or partially cover some surface. In the +context of climate modelling, this surface is the surface of the Earth, typically represented as a sphere. + +For example, output from a global atmospheric model covers the entire sphere, while a global ocean model would +not have any elements over land. + + + +Nodes +----- +An unstructured grid contains ``n_node`` corner nodes, which define the corners of each face. It may also +contain ``n_face`` centroid nodes, which represent the center of each face, and ``n_edge`` +edge nodes, which represent the center of each edge. + +Edges +----- + +An unstructured grid contains ``n_edge`` edges, which each connect two corner nodes to form an arc. + +Faces +----- +An unstructured grid contains ``n_face`` faces. + +UXarray is built to support 2D flexible grids, meaning that each face can have a variable number of nodes surrounding +it. + +Each face can have an independent number of nodes that surround it, which is represented through the +descriptor variable ``n_nodes_per_face``, which itself has a dimension of ``n_face`` The minimum +number of nodes per face is 3 (a triangle), with the maximum number being represented by the dimension +``n_max_face_nodes`` + +Coordinates +=========== + +Definitions +----------- + +Spherical Coordinates +--------------------- + +.. list-table:: + :header-rows: 1 + + * - Coordinate + - Grid Attribute + - Dimensions + - Summary + * - Node Longitude + - ``Grid.node_lon`` + - ``(n_node,)`` + - Longitude of each corner node + * - Node Latitude + - ``Grid.node_lat`` + - ``(n_node,)`` + - Latitude of each corner node in degrees + * - Edge Longitude + - ``Grid.edge_lon`` + - ``(n_edge,)`` + - Longitude of the center of each edge + * - Edge Latitude + - ``Grid.edge_lat`` + - ``(n_edge,)`` + - Latitude of the center of each edge + * - Face Longitude + - ``Grid.face_lon`` + - ``(n_face,)`` + - Longitude of the center of each face + * - Face Latitude + - ``Grid.face_lat`` + - ``(n_face,)`` + - Latitude of the center of each face + + +.. note:: + + All spherical coordinates are represented in degrees, with longitudes between (-180°, 180°) and latitudes between (-90°, 90°). + + +Cartesian Coordinates +--------------------- + +.. list-table:: + :header-rows: 1 + + * - Coordinate + - Grid Attribute + - Dimensions + - Summary + * - Node X + - ``Grid.node_x`` + - ``(n_node,)`` + - X location of each corner node + * - Node Y + - ``Grid.node_y`` + - ``(n_node,)`` + - Y location of each corner node + * - Node Z + - ``Grid.node_z`` + - ``(n_node,)`` + - Z location of each corner node + * - Edge X + - ``Grid.edge_x`` + - ``(n_edge,)`` + - X location of the center of each edge + * - Edge Y + - ``Grid.edge_y`` + - ``(n_edge,)`` + - Y location of the center of each edge + * - Edge Z + - ``Grid.edge_z`` + - ``(n_edge,)`` + - Z location of the center of each edge + * - Face X + - ``Grid.face_x`` + - ``(n_face,)`` + - X location of the center of each face + * - Face Y + - ``Grid.face_y`` + - ``(n_face,)`` + - Y location of the center of each face + * - Face Z + - ``Grid.face_z`` + - ``(n_face,)`` + - Z location of the center of each face + +.. note:: + + All Cartesian coordinates are represented in meters. + + +Connectivity +============ +UXarray relies on connectivity variables to describe how various elements (i.e nodes, edges, faces) can be connected. + +.. list-table:: + :widths: 15 30 35 30 + :header-rows: 1 + + * - Connectivity + - Grid Attribute + - Dimensions + - Summary + * - Face Node + - ``Grid.face_node_connectivity`` + - ``(n_face, n_max_face_nodes)`` + - Indices of the nodes that make up each face + * - Face Edge + - ``Grid.face_edge_connectivity`` + - ``(n_face, n_max_face_edges)`` + - Indices of the edges that surround each face + * - Face Face + - ``Grid.face_face_connectivity`` + - ``(n_face, n_max_face_faces)`` + - Indices of the faces that surround each face + * - Edge Node + - ``Grid.edge_node_connectivity`` + - ``(n_edge, 2)`` + - Indices of the two nodes that make up each edge + * - Edge Edge + - ``Grid.edge_edge_connectivity`` + - ``(n_edge, n_max_edge_edges)`` + - Indices of the edges that surround each edge + * - Edge Face + - ``Grid.edge_face_connectivity`` + - ``(n_edge, n_max_edge_faces)`` + - Indices of the faces that saddle each edge + * - Node Edge + - ``Grid.node_edge_connectivity`` + - ``(n_node, n_max_node_edges)`` + - Indices of the edges that surround each node + * - Node Face + - ``Grid.node_face_connectivity`` + - ``(n_node, n_max_node_faces)`` + - Indices of the faces that surround each node diff --git a/docs/user-guide/template.ipynb b/docs/user-guide/template.ipynb new file mode 100644 index 000000000..b97c73916 --- /dev/null +++ b/docs/user-guide/template.ipynb @@ -0,0 +1,65 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "48760981b4ed3d9b", + "metadata": { + "collapsed": false + }, + "source": [ + "# Title\n", + "\n", + "A good reference for how the user guide should be formatted can be found in xarray's examples, such as this one https://docs.xarray.dev/en/stable/user-guide/data-structures.html.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "39dd7186edd16396", + "metadata": { + "collapsed": false + }, + "source": [ + "### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "initial_id", + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import uxarray as ux\n", + "\n", + "base_path = \"../../test/meshfiles/ugrid/geoflow-small/\" # replace these\n", + "grid_path = base_path + \"grid.nc\"\n", + "data_path = base_path + \"v1.nc\"" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/user-guide/terminology.rst b/docs/user-guide/terminology.rst new file mode 100644 index 000000000..8a4d62766 --- /dev/null +++ b/docs/user-guide/terminology.rst @@ -0,0 +1,38 @@ +.. currentmodule:: uxarray + +=========== +Terminology +=========== + +Grid Representation +------------------- + +**Structured Grids**: Structured grids are defined by regular connectivity and topology, +typically forming a uniform pattern. They are represented using a multidimensional array, where each grid point has a predictable neighbor. +Below is an example of what a structured grid may look like. + +.. image:: ../_static/examples/grids/lat_lon.png + :width: 300 + :align: center + :alt: Alternative text + +**Unstructured Grids**: Unstructured grids do not follow a regular pattern, allowing for a flexible representation of complex geometries. +They are composed of various elements such as triangles, quadrilaterals, and other larger geometries, each of which are +made up of nodes and edges. Below is an example of an unstructured grid used in the dynamical core of a CAM-SE model. + +.. image:: ../_static/examples/grids/cam_se.png + :width: 300 + :align: center + :alt: Alternative text + +**Node**: A point within a spherical grid, representing the vertices of the elements (such as the corners of triangles or quadrilaterals) + +**Edge**: A segment that connects two nodes within a grid. + +**Face**: An individual polygon that is defined by nodes connected by edges. + +**Connectivity**: Connectivity describes how nodes, edges, and faces are interconnected within a grid. +It outlines the relationship between individual elements of the mesh, determining how they join together. + +**Fill Value**: An arbitrary value used for representing undefined values in within connectivity variables when working +with fixed-size arrays. diff --git a/docs/userguide.rst b/docs/userguide.rst new file mode 100644 index 000000000..19c2a9a03 --- /dev/null +++ b/docs/userguide.rst @@ -0,0 +1,17 @@ +.. currentmodule:: uxarray + +.. _userguide: + +User Guide +========== + +In this user guide, you will find detailed descriptions and examples that describe many +common tasks that you can accomplish with UXarray. + +.. toctree:: + :maxdepth: 1 + + user-guide/terminology.rst + user-guide/representation.rst + user-guide/grid-formats.rst + user-guide/data-structures.ipynb From 682127e6cb6f53675ec012702c640d159583e17a Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Mon, 13 May 2024 19:01:52 -0500 Subject: [PATCH 35/41] Add parameters to `UxDataArray.to_dataset()` (#783) * add parameters to UxDataArray.to_dataset * update docstirng --- uxarray/core/dataarray.py | 36 ++++++++++++++++++++++++++++++------ 1 file changed, 30 insertions(+), 6 deletions(-) diff --git a/uxarray/core/dataarray.py b/uxarray/core/dataarray.py index 166b6a658..cc04c3596 100644 --- a/uxarray/core/dataarray.py +++ b/uxarray/core/dataarray.py @@ -3,7 +3,7 @@ import xarray as xr import numpy as np -from typing import TYPE_CHECKING, Optional, Union +from typing import TYPE_CHECKING, Optional, Union, Hashable from uxarray.grid import Grid import uxarray.core.dataset @@ -245,11 +245,35 @@ def to_polycollection( f"({self.uxgrid.n_face}." ) - def to_dataset(self) -> UxDataset: - """Converts a ``UxDataArray`` into a ``UxDataset`` with a single data - variable.""" - xrds = super().to_dataset() - return uxarray.core.dataset.UxDataset(xrds, uxgrid=self.uxgrid) + def to_dataset( + self, + dim: Hashable = None, + *, + name: Hashable = None, + promote_attrs: bool = False, + ) -> UxDataset: + """Convert a UxDataArray to a UxDataset. + + Parameters + ---------- + dim : Hashable, optional + Name of the dimension on this array along which to split this array + into separate variables. If not provided, this array is converted + into a Dataset of one variable. + name : Hashable, optional + Name to substitute for this array's name. Only valid if ``dim`` is + not provided. + promote_attrs : bool, default: False + Set to True to shallow copy attrs of UxDataArray to returned UxDataset. + + Returns + ------- + uxds: UxDataSet + """ + xrds = super().to_dataset(dim=dim, name=name, promote_attrs=promote_attrs) + uxds = uxarray.core.dataset.UxDataset(xrds, uxgrid=self.uxgrid) + + return uxds def nearest_neighbor_remap( self, From d7875d25717d182185e4618a5db912d44a6fa813 Mon Sep 17 00:00:00 2001 From: Rajeev Jain Date: Wed, 15 May 2024 01:22:53 -0500 Subject: [PATCH 36/41] Move area calc notebook to users guide (#788) * o Move area calc notebook to users guide * make a change to prompt ReviewNB bot * o Fix links and address review comments * o pre-commit fix * Update docs/userguide.rst Co-authored-by: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> * o Remove comments * update face areas notebook * update notebook * update 4pi * update latex --------- Co-authored-by: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> --- docs/gallery.yml | 4 - .../area_calc.ipynb} | 123 ++++-------------- docs/userguide.rst | 1 + 3 files changed, 28 insertions(+), 100 deletions(-) rename docs/{examples/003-area-calc.ipynb => user-guide/area_calc.ipynb} (75%) diff --git a/docs/gallery.yml b/docs/gallery.yml index 735760d3a..db257db37 100644 --- a/docs/gallery.yml +++ b/docs/gallery.yml @@ -1,7 +1,3 @@ -- title: Face Area Calculations - path: examples/003-area-calc.ipynb - thumbnail: _static/thumbnails/default.svg - - title: Working with MPAS Grids path: examples/004-working-with-mpas-grids.ipynb thumbnail: _static/thumbnails/004-thumbnail.png diff --git a/docs/examples/003-area-calc.ipynb b/docs/user-guide/area_calc.ipynb similarity index 75% rename from docs/examples/003-area-calc.ipynb rename to docs/user-guide/area_calc.ipynb index cee9ecf8c..d3925cb9c 100644 --- a/docs/examples/003-area-calc.ipynb +++ b/docs/user-guide/area_calc.ipynb @@ -1,27 +1,37 @@ { "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Face Area Calculations" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import uxarray as ux\n", + "import numpy as np" + ], + "metadata": { + "collapsed": false + } + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Face Area Calculations\n", - "\n", - "Authors: [Rajeev Jain](https://github.com/rajeeja)\n", - "\n", - "\n", - "## Overview\n", - "\n", - "This notebook will showcase the different area calculation options provided by `uxarray`\n", + "Computing and working with face areas is important for the analysis of unstructured grids, with many algorithms and workflows requiring them. This section will showcase the different face area calculation options provided with UXarray:\n", "\n", - "For more details on how to load in data, check out our [previous usage example](https://uxarray.readthedocs.io/en/latest/examples/001-read-grid-data.html)\n", - "\n", - "**This notebook has the following sections:**\n", "1. Calculate Total Face Area\n", "2. Options for `Grid.calculate_total_face_area` Function\n", "3. Getting Area of Individual Faces\n", "4. Calculate Area of a Single Triangle in Cartesian Coordinates\n", - "5. Calculate Area from Multiple Faces in Spherical Coordinates\n", - "6. Area Calculation without Grid Object\n" + "5. Calculate Area from Multiple Faces in Spherical Coordinates" ] }, { @@ -39,29 +49,10 @@ }, "outputs": [], "source": [ - "import uxarray as ux\n", - "import numpy as np\n", - "import xarray as xr" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Base data path\n", "base_path = \"../../test/meshfiles/\"\n", + "grid_path = base_path + \"/ugrid/outCSne30/outCSne30.ug\"\n", "\n", - "# Path to Grid files\n", - "ugrid_path = base_path + \"/ugrid/outCSne30/outCSne30.ug\"\n", - "\n", - "# Load grid files and create UXarray Grid objects\n", - "ugrid_ds = xr.open_dataset(ugrid_path)\n", - "\n", - "ugrid = ux.open_grid(ugrid_ds)\n", + "ugrid = ux.open_grid(grid_path)\n", "ugrid" ] }, @@ -70,7 +61,7 @@ "metadata": {}, "source": [ "## 1. Calculate Total Face Area\n", - "We can calculate the total face area by calling the function `Grid.calculate_total_face_area()`. Since our dataset lies on the unit sphere, our expected area is 4*pi, which is approximately 12.56" + "We can calculate the total face area by calling the function `Grid.calculate_total_face_area()`. Since our dataset lies on the unit sphere, our expected area is 4π, which is approximately 12.56" ] }, { @@ -121,7 +112,7 @@ "\n", "Using a lower order is faster, but at the sacrifice of accuracy.\n", "\n", - "Generally, gaussian quadrature rule is more accurate than the triangular quadrature rule. Additionally, a higher order comes at the cost of computation time, but produces a more accurate result. See `uxarray/get_quadratureDG.py` file for details on quadrature points and weights." + "Generally, gaussian quadrature rule is more accurate than the triangular quadrature rule. Additionally, a higher order comes at the cost of computation time, but produces a more accurate result. See `uxarray/grid/area.py` file and function `get_gauss_quadratureDG` for details on quadrature points and weights." ] }, { @@ -252,11 +243,6 @@ }, "outputs": [], "source": [ - "# # Set correct units for the x and y coordinates\n", - "# vgrid.Mesh2_node_x.attrs[\"units\"] = \"km\"\n", - "# vgrid.Mesh2_node_y.attrs[\"units\"] = \"km\"\n", - "# vgrid.Mesh2_node_z.attrs[\"units\"] = \"km\" # This is just a placeholder, UXarray does not support 3D meshes\n", - "\n", "# Calculate the area of the triangle\n", "area_gaussian = vgrid.calculate_total_face_area(quadrature_rule=\"gaussian\", order=5)\n", "area_gaussian" @@ -351,61 +337,6 @@ "area, jacobian = verts_grid.compute_face_areas()\n", "area" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6. Area Calculation without Grid Object\n", - "\n", - "If you want to compute the face area without using the `Grid` object, many of the functions for computing the face are can be accessed through `uxarray.helpers`\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from uxarray.grid.area import calculate_face_area" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`Grid.calculate_face_area` takes in three coordinate variables (x, y, z) in the form of numpy arrays and the coordinate type (either spherical or artesian) and computes the face area from the set of points" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cart_x = np.array(\n", - " [0.577340924821405, 0.577340924821405, 0.577340924821405, 0.577340924821405]\n", - ")\n", - "cart_y = np.array(\n", - " [0.577343045516932, 0.577343045516932, -0.577343045516932, -0.577343045516932]\n", - ")\n", - "cart_z = np.array(\n", - " [0.577366836872017, -0.577366836872017, 0.577366836872017, -0.577366836872017]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "area, jacobian = calculate_face_area(cart_x, cart_y, cart_z, coords_type=\"cartesian\")\n", - "# both area and jacobian are reported for each face\n", - "area, jacobian" - ] } ], "metadata": { diff --git a/docs/userguide.rst b/docs/userguide.rst index 19c2a9a03..3a848b16f 100644 --- a/docs/userguide.rst +++ b/docs/userguide.rst @@ -15,3 +15,4 @@ common tasks that you can accomplish with UXarray. user-guide/representation.rst user-guide/grid-formats.rst user-guide/data-structures.ipynb + user-guide/area_calc.ipynb From cb3ae75de33a04aa5ca583f0ccbc4dd6534884cf Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Wed, 15 May 2024 01:27:02 -0500 Subject: [PATCH 37/41] Populate Tutorials & Videos Section (#789) * add cookbook link * update toctree * update heading --- docs/index.rst | 2 +- docs/tutorials.rst | 8 +++++--- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/docs/index.rst b/docs/index.rst index c02a14644..abca90700 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -100,7 +100,7 @@ UXarray provides Xarray-styled functionality for working with unstructured grids User Guide Usage Examples API Reference - Tutorials + Tutorials and Videos Cite Uxarray .. toctree:: diff --git a/docs/tutorials.rst b/docs/tutorials.rst index 5eef805e5..e81120d33 100644 --- a/docs/tutorials.rst +++ b/docs/tutorials.rst @@ -2,7 +2,9 @@ .. _tutorials: -Tutorials -========= +Tutorials & Videos +================== -Coming soon! +Tutorials +--------- +* `Project Pythia Unstructured Grid Visualization Cookbook `_ From ed1e2aea853e8ec093fd4ebd7f0f6d5184ce2a90 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Wed, 15 May 2024 17:45:22 -0500 Subject: [PATCH 38/41] Vectorize Coordinate Conversion Functions (#748) * initial work on vectorization * start overhaul of code to support new functions * update existing code and tests to use new functions * fix tests (1) * fix tests (2) * fix tests (3) * fix tests (4) * fix tests (5) * fix tests (6) * Update API reference * generalize implementation * update _lonlat_rad_to_xyz * update function signature * vectorize conversion in tests, when possible --- docs/internal_api/index.rst | 14 +- docs/user_api/index.rst | 10 -- test/test_arcs.py | 35 ++-- test/test_centroids.py | 13 +- test/test_computing.py | 10 +- test/test_geometry.py | 135 +++++++------- test/test_grid.py | 4 +- test/test_helpers.py | 22 ++- test/test_integrate.py | 30 ++-- test/test_intersections.py | 84 +++++---- test/test_neighbors.py | 2 - test/test_scrip.py | 39 ++-- test/test_ugrid.py | 38 ++-- uxarray/grid/arcs.py | 23 ++- uxarray/grid/area.py | 30 +--- uxarray/grid/coordinates.py | 343 ++++++++++++++---------------------- uxarray/grid/grid.py | 14 +- uxarray/grid/integrate.py | 8 +- uxarray/io/_exodus.py | 7 +- 19 files changed, 395 insertions(+), 466 deletions(-) diff --git a/docs/internal_api/index.rst b/docs/internal_api/index.rst index fc90d681d..b233e2413 100644 --- a/docs/internal_api/index.rst +++ b/docs/internal_api/index.rst @@ -147,15 +147,17 @@ Coordinates .. autosummary:: :toctree: generated/ - grid.coordinates._get_lonlat_from_xyz - grid.coordinates._get_xyz_from_lonlat - grid.coordinates._populate_cartesian_xyz_coord - grid.coordinates._populate_lonlat_coord + grid.coordinates._lonlat_rad_to_xyz + grid.coordinates._xyz_to_lonlat_rad + grid.coordinates._xyz_to_lonlat_deg + grid.coordinates._normalize_xyz + grid.coordinates._populate_node_latlon + grid.coordinates._populate_node_xyz grid.coordinates._populate_face_centroids - grid.coordinates._construct_face_centroids - grid.coordinates._set_desired_longitude_range grid.coordinates._populate_edge_centroids + grid.coordinates._construct_face_centroids grid.coordinates._construct_edge_centroids + grid.coordinates._set_desired_longitude_range Arcs diff --git a/docs/user_api/index.rst b/docs/user_api/index.rst index 7f67456ff..ad51f3315 100644 --- a/docs/user_api/index.rst +++ b/docs/user_api/index.rst @@ -369,16 +369,6 @@ Connectivity grid.connectivity.close_face_nodes -Coordinates ------------ -.. autosummary:: - :toctree: generated/ - - grid.coordinates.node_lonlat_rad_to_xyz - grid.coordinates.node_xyz_to_lonlat_rad - grid.coordinates.normalize_in_place - - Arcs ---- .. autosummary:: diff --git a/test/test_arcs.py b/test/test_arcs.py index c2e24f5aa..c9859e724 100644 --- a/test/test_arcs.py +++ b/test/test_arcs.py @@ -9,7 +9,7 @@ import uxarray as ux -from uxarray.grid.coordinates import node_lonlat_rad_to_xyz +from uxarray.grid.coordinates import _lonlat_rad_to_xyz from uxarray.grid.arcs import point_within_gca try: @@ -31,43 +31,40 @@ class TestIntersectionPoint(TestCase): def test_pt_within_gcr(self): # The GCR that's eexactly 180 degrees will have Value Error raised gcr_180degree_cart = [ - ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, 0.0]), - ux.grid.coordinates.node_lonlat_rad_to_xyz([np.pi, 0.0]) + _lonlat_rad_to_xyz(0.0, 0.0), + _lonlat_rad_to_xyz(np.pi, 0.0) ] - pt_same_lon_in = ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, 0.0]) + pt_same_lon_in = _lonlat_rad_to_xyz(0.0, 0.0) with self.assertRaises(ValueError): point_within_gca(pt_same_lon_in, gcr_180degree_cart) gcr_180degree_cart = [ - ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, np.pi / 2.0]), - ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, -np.pi / 2.0]) + _lonlat_rad_to_xyz(0.0, np.pi / 2.0), + _lonlat_rad_to_xyz(0.0, -np.pi / 2.0) ] - pt_same_lon_in = ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, 0.0]) + pt_same_lon_in = _lonlat_rad_to_xyz(0.0, 0.0) with self.assertRaises(ValueError): point_within_gca(pt_same_lon_in, gcr_180degree_cart) # Test when the point and the GCA all have the same longitude gcr_same_lon_cart = [ - ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, 1.5]), - ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, -1.5]) + _lonlat_rad_to_xyz(0.0, 1.5), + _lonlat_rad_to_xyz(0.0, -1.5) ] - pt_same_lon_in = ux.grid.coordinates.node_lonlat_rad_to_xyz([0.0, 0.0]) + pt_same_lon_in = _lonlat_rad_to_xyz(0.0, 0.0) self.assertTrue(point_within_gca(pt_same_lon_in, gcr_same_lon_cart)) - pt_same_lon_out = ux.grid.coordinates.node_lonlat_rad_to_xyz( - [0.0, 1.500000000000001]) + pt_same_lon_out = _lonlat_rad_to_xyz(0.0, 1.500000000000001) res = point_within_gca(pt_same_lon_out, gcr_same_lon_cart) self.assertFalse(res) - pt_same_lon_out_2 = ux.grid.coordinates.node_lonlat_rad_to_xyz( - [0.1, 1.0]) + pt_same_lon_out_2 = _lonlat_rad_to_xyz(0.1, 1.0) res = point_within_gca(pt_same_lon_out_2, gcr_same_lon_cart) self.assertFalse(res) # And if we increase the digital place by one, it should be true again - pt_same_lon_out_add_one_place = ux.grid.coordinates.node_lonlat_rad_to_xyz( - [0.0, 1.5000000000000001]) + pt_same_lon_out_add_one_place = _lonlat_rad_to_xyz(0.0, 1.5000000000000001) res = point_within_gca(pt_same_lon_out_add_one_place, gcr_same_lon_cart) self.assertTrue(res) @@ -117,10 +114,10 @@ def test_pt_within_gcr_antimeridian(self): # The first case should not work and the second should work v1_rad = [0.1, 0.0] v2_rad = [2 * np.pi - 0.1, 0.0] - v1_cart = ux.grid.coordinates.node_lonlat_rad_to_xyz(v1_rad) - v2_cart = ux.grid.coordinates.node_lonlat_rad_to_xyz(v2_rad) + v1_cart = _lonlat_rad_to_xyz(v1_rad[0], v1_rad[1]) + v2_cart = _lonlat_rad_to_xyz(v2_rad[0], v1_rad[1]) gcr_cart = np.array([v1_cart, v2_cart]) - pt_cart = ux.grid.coordinates.node_lonlat_rad_to_xyz([0.01, 0.0]) + pt_cart = _lonlat_rad_to_xyz(0.01, 0.0) with self.assertRaises(ValueError): point_within_gca(pt_cart, gcr_cart, is_directed=True) gcr_car_flipped = np.array([v2_cart, v1_cart]) diff --git a/test/test_centroids.py b/test/test_centroids.py index 4b5de9fd4..6eb69542f 100644 --- a/test/test_centroids.py +++ b/test/test_centroids.py @@ -4,7 +4,7 @@ import numpy.testing as nt import uxarray as ux from pathlib import Path -from uxarray.grid.coordinates import _populate_face_centroids, _populate_edge_centroids, normalize_in_place +from uxarray.grid.coordinates import _populate_face_centroids, _populate_edge_centroids, _normalize_xyz current_path = Path(os.path.dirname(os.path.realpath(__file__))) @@ -21,8 +21,8 @@ def test_centroids_from_mean_verts_triangle(self): # Calculate the expected centroid expected_centroid = np.mean(test_triangle, axis=0) - [norm_x, norm_y, norm_z] = normalize_in_place( - [expected_centroid[0], expected_centroid[1], expected_centroid[2]]) + norm_x, norm_y, norm_z = _normalize_xyz( + expected_centroid[0], expected_centroid[1], expected_centroid[2]) # Open the dataset and find the centroids grid = ux.open_grid(test_triangle) @@ -42,8 +42,8 @@ def test_centroids_from_mean_verts_pentagon(self): # Calculate the expected centroid expected_centroid = np.mean(test_triangle, axis=0) - [norm_x, norm_y, norm_z] = normalize_in_place( - [expected_centroid[0], expected_centroid[1], expected_centroid[2]]) + norm_x, norm_y, norm_z = _normalize_xyz( + expected_centroid[0], expected_centroid[1], expected_centroid[2]) # Open the dataset and find the centroids grid = ux.open_grid(test_triangle) @@ -65,7 +65,8 @@ def test_centroids_from_mean_verts_scrip(self): _populate_face_centroids(uxgrid, repopulate=True) - computed_face_x = (uxgrid.face_lon.values + 180) % 360 - 180 + # computed_face_x = (uxgrid.face_lon.values + 180) % 360 - 180 + computed_face_x = uxgrid.face_lon.values computed_face_y = uxgrid.face_lat.values nt.assert_array_almost_equal(expected_face_x, computed_face_x) diff --git a/test/test_computing.py b/test/test_computing.py index 4e9cde81b..a83d3b76c 100644 --- a/test/test_computing.py +++ b/test/test_computing.py @@ -1,7 +1,7 @@ from unittest import TestCase import numpy.testing as nt import numpy as np -from uxarray.grid.coordinates import normalize_in_place +from uxarray.grid.coordinates import _normalize_xyz import uxarray.utils.computing as ac_utils from uxarray.constants import ERROR_TOLERANCE @@ -12,8 +12,8 @@ class TestCrossProduct(TestCase): one.""" def test_cross_fma(self): - v1 = np.array(normalize_in_place([1.0, 2.0, 3.0])) - v2 = np.array(normalize_in_place([4.0, 5.0, 6.0])) + v1 = np.array(_normalize_xyz(*[1.0, 2.0, 3.0])) + v2 = np.array(_normalize_xyz(*[4.0, 5.0, 6.0])) np_cross = np.cross(v1, v2) fma_cross = ac_utils.cross_fma(v1, v2) @@ -26,8 +26,8 @@ class TestDotProduct(TestCase): one.""" def test_dot_fma(self): - v1 = np.array(normalize_in_place([1.0, 0.0, 0.0]), dtype=np.float64) - v2 = np.array(normalize_in_place([1.0, 0.0, 0.0]), dtype=np.float64) + v1 = np.array(_normalize_xyz(*[1.0, 0.0, 0.0]), dtype=np.float64) + v2 = np.array(_normalize_xyz(*[1.0, 0.0, 0.0]), dtype=np.float64) np_dot = np.dot(v1, v2) fma_dot = ac_utils.dot_fma(v1, v2) diff --git a/test/test_geometry.py b/test/test_geometry.py index beaa105b7..b73e2d8b9 100644 --- a/test/test_geometry.py +++ b/test/test_geometry.py @@ -9,7 +9,7 @@ import uxarray as ux from uxarray.constants import ERROR_TOLERANCE, INT_FILL_VALUE import uxarray.utils.computing as ac_utils -from uxarray.grid.coordinates import _populate_lonlat_coord, node_lonlat_rad_to_xyz +from uxarray.grid.coordinates import _populate_node_latlon, _lonlat_rad_to_xyz, _normalize_xyz, _xyz_to_lonlat_rad from uxarray.grid.arcs import extreme_gca_latitude from uxarray.grid.utils import _get_cartesian_face_edge_nodes, _get_lonlat_rad_face_edge_nodes from uxarray.grid.geometry import _populate_face_latlon_bound, _populate_bounds @@ -67,7 +67,7 @@ def test_pole_point_inside_polygon_from_vertice_north(self): # Normalize the vertices to ensure they lie on the unit sphere for i, vertex in enumerate(vertices): float_vertex = [float(coord) for coord in vertex] - vertices[i] = ux.grid.coordinates.normalize_in_place(float_vertex) + vertices[i] = _normalize_xyz(*float_vertex) # Create face_edge_cart from the vertices face_edge_cart = np.array([[vertices[0], vertices[1]], @@ -93,7 +93,7 @@ def test_pole_point_inside_polygon_from_vertice_south(self): # Normalize the vertices to ensure they lie on the unit sphere for i, vertex in enumerate(vertices): float_vertex = [float(coord) for coord in vertex] - vertices[i] = ux.grid.coordinates.normalize_in_place(float_vertex) + vertices[i] = _normalize_xyz(*float_vertex) # Create face_edge_cart from the vertices, since we are using the south pole, and want retrive the smaller face # we need to reverse the order of the vertices @@ -121,7 +121,7 @@ def test_pole_point_inside_polygon_from_vertice_pole(self): # Normalize the vertices to ensure they lie on the unit sphere for i, vertex in enumerate(vertices): float_vertex = [float(coord) for coord in vertex] - vertices[i] = ux.grid.coordinates.normalize_in_place(float_vertex) + vertices[i] = _normalize_xyz(*float_vertex) # Create face_edge_cart from the vertices face_edge_cart = np.array([[vertices[0], vertices[1]], @@ -147,7 +147,7 @@ def test_pole_point_inside_polygon_from_vertice_cross(self): # Normalize the vertices to ensure they lie on the unit sphere for i, vertex in enumerate(vertices): float_vertex = [float(coord) for coord in vertex] - vertices[i] = ux.grid.coordinates.normalize_in_place(float_vertex) + vertices[i] = _normalize_xyz(*float_vertex) # Create face_edge_cart from the vertices face_edge_cart = np.array([[vertices[0], vertices[1]], @@ -191,13 +191,13 @@ def _max_latitude_rad_iterative(self, gca_cart): # Convert input vectors to radians and Cartesian coordinates v1_cart, v2_cart = gca_cart - b_lonlat = ux.grid.coordinates.node_xyz_to_lonlat_rad(v1_cart.tolist()) - c_lonlat = ux.grid.coordinates.node_xyz_to_lonlat_rad(v2_cart.tolist()) + b_lonlat = _xyz_to_lonlat_rad(*v1_cart.tolist()) + c_lonlat = _xyz_to_lonlat_rad(*v2_cart.tolist()) # Initialize variables for the iterative process v_temp = ac_utils.cross_fma(v1_cart, v2_cart) v0 = ac_utils.cross_fma(v_temp, v1_cart) - v0 = ux.grid.coordinates.normalize_in_place(v0.tolist()) + v0 = _normalize_xyz(*v0.tolist()) max_section = [v1_cart, v2_cart] # Iteratively find the maximum latitude @@ -207,7 +207,7 @@ def _max_latitude_rad_iterative(self, gca_cart): v_b, v_c = max_section angle_v1_v2_rad = ux.grid.arcs._angle_of_2_vectors(v_b, v_c) v0 = ac_utils.cross_fma(v_temp, v_b) - v0 = ux.grid.coordinates.normalize_in_place(v0.tolist()) + v0 = _normalize_xyz(*v0.tolist()) avg_angle_rad = angle_v1_v2_rad / 10.0 for i in range(10): @@ -217,10 +217,13 @@ def _max_latitude_rad_iterative(self, gca_cart): angle_rad_prev) * np.array(v0) w2_new = np.cos(angle_rad_next) * v_b + np.sin( angle_rad_next) * np.array(v0) - w1_lonlat = ux.grid.coordinates.node_xyz_to_lonlat_rad( - w1_new.tolist()) - w2_lonlat = ux.grid.coordinates.node_xyz_to_lonlat_rad( - w2_new.tolist()) + w1_lonlat = _xyz_to_lonlat_rad( + *w1_new.tolist()) + w2_lonlat = _xyz_to_lonlat_rad( + *w2_new.tolist()) + + w1_lonlat = np.asarray(w1_lonlat) + w2_lonlat = np.asarray(w2_lonlat) # Adjust latitude boundaries to avoid error accumulation if i == 0: @@ -232,7 +235,7 @@ def _max_latitude_rad_iterative(self, gca_cart): max_lat = max(max_lat, w1_lonlat[1], w2_lonlat[1]) if np.abs(w2_lonlat[1] - w1_lonlat[1]) <= ERROR_TOLERANCE or w1_lonlat[ - 1] == max_lat == w2_lonlat[1]: + 1] == max_lat == w2_lonlat[1]: max_section = [w1_new, w2_new] break if np.abs(max_lat - w1_lonlat[1]) <= ERROR_TOLERANCE: @@ -241,10 +244,10 @@ def _max_latitude_rad_iterative(self, gca_cart): max_section = [w1_new, w2_new] if i != 9 else [w1_new, v_c] # Update longitude and latitude for the next iteration - b_lonlat = ux.grid.coordinates.node_xyz_to_lonlat_rad( - max_section[0].tolist()) - c_lonlat = ux.grid.coordinates.node_xyz_to_lonlat_rad( - max_section[1].tolist()) + b_lonlat = _xyz_to_lonlat_rad( + *max_section[0].tolist()) + c_lonlat = _xyz_to_lonlat_rad( + *max_section[1].tolist()) return np.average([b_lonlat[1], c_lonlat[1]]) @@ -275,13 +278,13 @@ def _min_latitude_rad_iterative(self, gca_cart): # Convert input vectors to radians and Cartesian coordinates v1_cart, v2_cart = gca_cart - b_lonlat = ux.grid.coordinates.node_xyz_to_lonlat_rad(v1_cart.tolist()) - c_lonlat = ux.grid.coordinates.node_xyz_to_lonlat_rad(v2_cart.tolist()) + b_lonlat = _xyz_to_lonlat_rad(*v1_cart.tolist()) + c_lonlat = _xyz_to_lonlat_rad(*v2_cart.tolist()) # Initialize variables for the iterative process v_temp = ac_utils.cross_fma(v1_cart, v2_cart) v0 = ac_utils.cross_fma(v_temp, v1_cart) - v0 = np.array(ux.grid.coordinates.normalize_in_place(v0.tolist())) + v0 = np.array(_normalize_xyz(*v0.tolist())) min_section = [v1_cart, v2_cart] # Iteratively find the minimum latitude @@ -291,7 +294,7 @@ def _min_latitude_rad_iterative(self, gca_cart): v_b, v_c = min_section angle_v1_v2_rad = ux.grid.arcs._angle_of_2_vectors(v_b, v_c) v0 = ac_utils.cross_fma(v_temp, v_b) - v0 = np.array(ux.grid.coordinates.normalize_in_place(v0.tolist())) + v0 = np.array(_normalize_xyz(*v0.tolist())) avg_angle_rad = angle_v1_v2_rad / 10.0 for i in range(10): @@ -301,10 +304,13 @@ def _min_latitude_rad_iterative(self, gca_cart): angle_rad_prev) * v0 w2_new = np.cos(angle_rad_next) * v_b + np.sin( angle_rad_next) * v0 - w1_lonlat = ux.grid.coordinates.node_xyz_to_lonlat_rad( - w1_new.tolist()) - w2_lonlat = ux.grid.coordinates.node_xyz_to_lonlat_rad( - w2_new.tolist()) + w1_lonlat = _xyz_to_lonlat_rad( + *w1_new.tolist()) + w2_lonlat = _xyz_to_lonlat_rad( + *w2_new.tolist()) + + w1_lonlat = np.asarray(w1_lonlat) + w2_lonlat = np.asarray(w2_lonlat) # Adjust latitude boundaries to avoid error accumulation if i == 0: @@ -316,7 +322,7 @@ def _min_latitude_rad_iterative(self, gca_cart): min_lat = min(min_lat, w1_lonlat[1], w2_lonlat[1]) if np.abs(w2_lonlat[1] - w1_lonlat[1]) <= ERROR_TOLERANCE or w1_lonlat[ - 1] == min_lat == w2_lonlat[1]: + 1] == min_lat == w2_lonlat[1]: min_section = [w1_new, w2_new] break if np.abs(min_lat - w1_lonlat[1]) <= ERROR_TOLERANCE: @@ -325,18 +331,18 @@ def _min_latitude_rad_iterative(self, gca_cart): min_section = [w1_new, w2_new] if i != 9 else [w1_new, v_c] # Update longitude and latitude for the next iteration - b_lonlat = ux.grid.coordinates.node_xyz_to_lonlat_rad( - min_section[0].tolist()) - c_lonlat = ux.grid.coordinates.node_xyz_to_lonlat_rad( - min_section[1].tolist()) + b_lonlat = _xyz_to_lonlat_rad( + *min_section[0].tolist()) + c_lonlat = _xyz_to_lonlat_rad( + *min_section[1].tolist()) return np.average([b_lonlat[1], c_lonlat[1]]) def test_extreme_gca_latitude_max(self): # Define a great circle arc that is symmetrical around 0 degrees longitude gca_cart = np.array([ - ux.grid.coordinates.normalize_in_place([0.5, 0.5, 0.5]), - ux.grid.coordinates.normalize_in_place([-0.5, 0.5, 0.5]) + _normalize_xyz(*[0.5, 0.5, 0.5]), + _normalize_xyz(*[-0.5, 0.5, 0.5]) ]) # Calculate the maximum latitude @@ -363,8 +369,8 @@ def test_extreme_gca_latitude_max(self): def test_extreme_gca_latitude_min(self): # Define a great circle arc that is symmetrical around 0 degrees longitude gca_cart = np.array([ - ux.grid.coordinates.normalize_in_place([0.5, 0.5, -0.5]), - ux.grid.coordinates.normalize_in_place([-0.5, 0.5, -0.5]) + _normalize_xyz(*[0.5, 0.5, -0.5]), + _normalize_xyz(*[-0.5, 0.5, -0.5]) ]) # Calculate the minimum latitude @@ -432,7 +438,6 @@ def test_insert_pt_in_empty_state(self): np.testing.assert_array_equal(result, expected) - class TestLatlonBoundsGCA(TestCase): def test_populate_bounds_normal(self): # Generate a normal face that is not crossing the antimeridian or the poles @@ -441,7 +446,9 @@ def test_populate_bounds_normal(self): # Convert everything into radians vertices_rad = np.radians(vertices_lonlat) - vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + + vertices_cart = np.vstack([_lonlat_rad_to_xyz(vertices_rad[:, 0], vertices_rad[:, 1])]).T + # vertices_cart = [_lonlat_rad_to_xyz(vertices_rad[0], vertices_rad[1])] lat_max = max(np.deg2rad(60.0), extreme_gca_latitude(np.array([vertices_cart[0], vertices_cart[3]]), extreme_type="max")) lat_min = min(np.deg2rad(10.0), @@ -470,7 +477,7 @@ def test_populate_bounds_antimeridian(self): # Convert everything into radians vertices_rad = np.radians(vertices_lonlat) - vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + vertices_cart = np.vstack([_lonlat_rad_to_xyz(vertices_rad[:, 0], vertices_rad[:, 1])]).T lat_max = max(np.deg2rad(60.0), extreme_gca_latitude(np.array([vertices_cart[0], vertices_cart[3]]), extreme_type="max")) lat_min = min(np.deg2rad(10.0), @@ -499,7 +506,7 @@ def test_populate_bounds_node_on_pole(self): # Convert everything into radians vertices_rad = np.radians(vertices_lonlat) - vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + vertices_cart = np.vstack([_lonlat_rad_to_xyz(vertices_rad[:, 0], vertices_rad[:, 1])]).T lat_max = np.pi / 2 lat_min = min(np.deg2rad(10.0), extreme_gca_latitude(np.array([vertices_cart[1], vertices_cart[2]]), extreme_type="min")) @@ -527,7 +534,7 @@ def test_populate_bounds_edge_over_pole(self): # Convert everything into radians vertices_rad = np.radians(vertices_lonlat) - vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + vertices_cart = np.vstack([_lonlat_rad_to_xyz(vertices_rad[:, 0], vertices_rad[:, 1])]).T lat_max = np.pi / 2 lat_min = min(np.deg2rad(60.0), extreme_gca_latitude(np.array([vertices_cart[1], vertices_cart[2]]), extreme_type="min")) @@ -555,7 +562,7 @@ def test_populate_bounds_pole_inside(self): # Convert everything into radians vertices_rad = np.radians(vertices_lonlat) - vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + vertices_cart = np.vstack([_lonlat_rad_to_xyz(vertices_rad[:, 0], vertices_rad[:, 1])]).T lat_max = np.pi / 2 lat_min = min(np.deg2rad(60.0), extreme_gca_latitude(np.array([vertices_cart[1], vertices_cart[2]]), extreme_type="min")) @@ -576,6 +583,7 @@ def test_populate_bounds_pole_inside(self): bounds = _populate_face_latlon_bound(face_edges_connectivity_cartesian, face_edges_connectivity_lonlat) nt.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE) + class TestLatlonBoundsLatLonFace(TestCase): def test_populate_bounds_normal(self): @@ -666,7 +674,7 @@ def test_populate_bounds_edge_over_pole(self): # Convert everything into radians vertices_rad = np.radians(vertices_lonlat) - vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + vertices_cart = np.vstack([_lonlat_rad_to_xyz(vertices_rad[:, 0], vertices_rad[:, 1])]).T lat_max = np.pi / 2 lat_min = np.deg2rad(60.0) lon_min = np.deg2rad(210.0) @@ -694,7 +702,7 @@ def test_populate_bounds_pole_inside(self): # Convert everything into radians vertices_rad = np.radians(vertices_lonlat) - vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + vertices_cart = np.vstack([_lonlat_rad_to_xyz(vertices_rad[:, 0], vertices_rad[:, 1])]).T lat_max = np.pi / 2 lat_min = np.deg2rad(60.0) lon_min = 0 @@ -752,7 +760,7 @@ def test_populate_bounds_antimeridian(self): # Convert everything into radians vertices_rad = np.radians(vertices_lonlat) - vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + vertices_cart = np.vstack([_lonlat_rad_to_xyz(vertices_rad[:, 0], vertices_rad[:, 1])]).T lat_max = np.deg2rad(60.0) lat_min = np.deg2rad(10.0) lon_min = np.deg2rad(350.0) @@ -780,7 +788,7 @@ def test_populate_bounds_node_on_pole(self): # Convert everything into radians vertices_rad = np.radians(vertices_lonlat) - vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + vertices_cart = np.vstack([_lonlat_rad_to_xyz(vertices_rad[:, 0], vertices_rad[:, 1])]).T lat_max = np.pi / 2 lat_min = np.deg2rad(10.0) lon_min = np.deg2rad(10.0) @@ -808,7 +816,7 @@ def test_populate_bounds_edge_over_pole(self): # Convert everything into radians vertices_rad = np.radians(vertices_lonlat) - vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + vertices_cart = np.vstack([_lonlat_rad_to_xyz(vertices_rad[:, 0], vertices_rad[:, 1])]).T lat_max = np.pi / 2 lat_min = np.deg2rad(60.0) lon_min = np.deg2rad(210.0) @@ -836,7 +844,7 @@ def test_populate_bounds_pole_inside(self): # Convert everything into radians vertices_rad = np.radians(vertices_lonlat) - vertices_cart = [node_lonlat_rad_to_xyz(v) for v in vertices_rad] + vertices_cart = np.vstack([_lonlat_rad_to_xyz(vertices_rad[:, 0], vertices_rad[:, 1])]).T lat_max = np.pi / 2 lat_min = np.deg2rad(60.0) lon_min = 0 @@ -857,6 +865,7 @@ def test_populate_bounds_pole_inside(self): is_GCA_list=[True, False, True, False]) nt.assert_allclose(bounds, expected_bounds, atol=ERROR_TOLERANCE) + class TestLatlonBoundsMix(TestCase): def test_populate_bounds_GCA_mix(self): face_1 = [[10.0, 60.0], [10.0, 10.0], [50.0, 10.0], [50.0, 60.0]] @@ -867,11 +876,10 @@ def test_populate_bounds_GCA_mix(self): faces = [face_1, face_2, face_3, face_4] # Hand calculated bounds for the above faces in radians - expected_bounds = [[[0.17453293, 1.07370494],[0.17453293, 0.87266463]], - [[0.17453293, 1.10714872],[6.10865238, 0.87266463]], - [[1.04719755, 1.57079633],[3.66519143, 0.52359878]], - [[1.04719755,1.57079633],[0., 6.28318531]]] - + expected_bounds = [[[0.17453293, 1.07370494], [0.17453293, 0.87266463]], + [[0.17453293, 1.10714872], [6.10865238, 0.87266463]], + [[1.04719755, 1.57079633], [3.66519143, 0.52359878]], + [[1.04719755, 1.57079633], [0., 6.28318531]]] grid = ux.Grid.from_face_vertices(faces, latlon=True) face_bounds = grid.bounds.values @@ -886,14 +894,13 @@ def test_populate_bounds_LatlonFace_mix(self): faces = [face_1, face_2, face_3, face_4] - expected_bounds = [[[np.deg2rad(10.0), np.deg2rad(60.0)],[np.deg2rad(10.0), np.deg2rad(50.0)]], - [[np.deg2rad(10.0), np.deg2rad(60.0)],[np.deg2rad(350.0), np.deg2rad(50.0)]], - [[np.deg2rad(60.0), np.pi/2],[np.deg2rad(210.0), np.deg2rad(30.0)]], - [[np.deg2rad(60.0),np.pi/2],[0., 2*np.pi]]] - + expected_bounds = [[[np.deg2rad(10.0), np.deg2rad(60.0)], [np.deg2rad(10.0), np.deg2rad(50.0)]], + [[np.deg2rad(10.0), np.deg2rad(60.0)], [np.deg2rad(350.0), np.deg2rad(50.0)]], + [[np.deg2rad(60.0), np.pi / 2], [np.deg2rad(210.0), np.deg2rad(30.0)]], + [[np.deg2rad(60.0), np.pi / 2], [0., 2 * np.pi]]] grid = ux.Grid.from_face_vertices(faces, latlon=True) - bounds_xarray = _populate_bounds(grid,is_latlonface=True, return_array=True) + bounds_xarray = _populate_bounds(grid, is_latlonface=True, return_array=True) face_bounds = bounds_xarray.values for i in range(len(faces)): nt.assert_allclose(face_bounds[i], expected_bounds[i], atol=ERROR_TOLERANCE) @@ -906,16 +913,18 @@ def test_populate_bounds_GCAList_mix(self): faces = [face_1, face_2, face_3, face_4] - expected_bounds = [[[np.deg2rad(10.0), np.deg2rad(60.0)],[np.deg2rad(10.0), np.deg2rad(50.0)]], - [[np.deg2rad(10.0), np.deg2rad(60.0)],[np.deg2rad(350.0), np.deg2rad(50.0)]], - [[np.deg2rad(60.0), np.pi/2],[np.deg2rad(210.0), np.deg2rad(30.0)]], - [[np.deg2rad(60.0),np.pi/2],[0., 2*np.pi]]] + expected_bounds = [[[np.deg2rad(10.0), np.deg2rad(60.0)], [np.deg2rad(10.0), np.deg2rad(50.0)]], + [[np.deg2rad(10.0), np.deg2rad(60.0)], [np.deg2rad(350.0), np.deg2rad(50.0)]], + [[np.deg2rad(60.0), np.pi / 2], [np.deg2rad(210.0), np.deg2rad(30.0)]], + [[np.deg2rad(60.0), np.pi / 2], [0., 2 * np.pi]]] grid = ux.Grid.from_face_vertices(faces, latlon=True) - bounds_xarray = _populate_bounds(grid,is_face_GCA_list=[[True, False, True, False]]*4, return_array=True) + bounds_xarray = _populate_bounds(grid, is_face_GCA_list=[[True, False, True, False]] * 4, return_array=True) face_bounds = bounds_xarray.values for i in range(len(faces)): nt.assert_allclose(face_bounds[i], expected_bounds[i], atol=ERROR_TOLERANCE) + + class TestGeoDataFrame(TestCase): def test_to_gdf(self): diff --git a/test/test_grid.py b/test/test_grid.py index 48aad8e86..b9be85816 100644 --- a/test/test_grid.py +++ b/test/test_grid.py @@ -10,7 +10,7 @@ from uxarray.grid.connectivity import _populate_face_edge_connectivity, _build_edge_face_connectivity, _build_edge_node_connectivity -from uxarray.grid.coordinates import _populate_lonlat_coord, node_lonlat_rad_to_xyz +from uxarray.grid.coordinates import _populate_node_latlon, _lonlat_rad_to_xyz from uxarray.constants import INT_FILL_VALUE, ERROR_TOLERANCE @@ -459,7 +459,7 @@ def test_populate_lonlat_coord(self): verts_cart = np.stack((cart_x, cart_y, cart_z), axis=1) vgrid = ux.open_grid(verts_cart, latlon=False) - _populate_lonlat_coord(vgrid) + _populate_node_latlon(vgrid) # The connectivity in `__from_vert__()` will be formed in a reverse order lon_deg, lat_deg = zip(*reversed(list(zip(lon_deg, lat_deg)))) for i in range(0, vgrid.n_node): diff --git a/test/test_helpers.py b/test/test_helpers.py index ec310c346..42cd72def 100644 --- a/test/test_helpers.py +++ b/test/test_helpers.py @@ -12,7 +12,7 @@ from uxarray.grid.connectivity import _replace_fill_values from uxarray.constants import INT_DTYPE, INT_FILL_VALUE -from uxarray.grid.coordinates import node_lonlat_rad_to_xyz +from uxarray.grid.coordinates import _lonlat_rad_to_xyz, _normalize_xyz, _xyz_to_lonlat_rad from uxarray.grid.arcs import point_within_gca, _angle_of_2_vectors, in_between from uxarray.grid.utils import _get_cartesian_face_edge_nodes, _get_lonlat_rad_face_edge_nodes from uxarray.grid.geometry import _pole_point_inside_polygon @@ -105,23 +105,22 @@ def test_grid_center(self): class TestCoordinatesConversion(TestCase): def test_normalize_in_place(self): - [x, y, z] = ux.grid.coordinates.normalize_in_place( - [random.random(), random.random(), - random.random()]) + x, y, z = _normalize_xyz( + random.random(), random.random(), + random.random()) self.assertLessEqual(np.absolute(np.sqrt(x * x + y * y + z * z) - 1), err_tolerance) def test_node_xyz_to_lonlat_rad(self): - [x, y, z] = ux.grid.coordinates.normalize_in_place([ + x, y, z = _normalize_xyz(*[ random.uniform(-1, 1), random.uniform(-1, 1), random.uniform(-1, 1) ]) - [lon, lat] = ux.grid.coordinates.node_xyz_to_lonlat_rad([x, y, z]) - [new_x, new_y, - new_z] = ux.grid.coordinates.node_lonlat_rad_to_xyz([lon, lat]) + lon, lat = _xyz_to_lonlat_rad(x, y, z) + new_x, new_y, new_z =_lonlat_rad_to_xyz(lon, lat) self.assertLessEqual(np.absolute(new_x - x), err_tolerance) self.assertLessEqual(np.absolute(new_y - y), err_tolerance) @@ -133,10 +132,9 @@ def test_node_latlon_rad_to_xyz(self): random.uniform(-0.5 * np.pi, 0.5 * np.pi) ] - [x, y, z] = ux.grid.coordinates.node_lonlat_rad_to_xyz([lon, lat]) + x, y, z = _lonlat_rad_to_xyz(lon, lat) - [new_lon, - new_lat] = ux.grid.coordinates.node_xyz_to_lonlat_rad([x, y, z]) + new_lon, new_lat = _xyz_to_lonlat_rad(x, y, z) self.assertLessEqual(np.absolute(new_lon - lon), err_tolerance) self.assertLessEqual(np.absolute(new_lat - lat), err_tolerance) @@ -342,7 +340,7 @@ def test_get_lonlat_face_edge_nodes_pipeline(self): face_edges_connectivity_cartesian = [] for i in range(len(face_edges_connectivity_lonlat)): edge = face_edges_connectivity_lonlat[i] - edge_cart = [node_lonlat_rad_to_xyz(node) for node in edge] + edge_cart = [_lonlat_rad_to_xyz(*node) for node in edge] face_edges_connectivity_cartesian.append(edge_cart) # Check that the face_edges_connectivity_cartesian works as an input to _pole_point_inside_polygon diff --git a/test/test_integrate.py b/test/test_integrate.py index af26941c6..078f355cf 100644 --- a/test/test_integrate.py +++ b/test/test_integrate.py @@ -8,7 +8,7 @@ import numpy.testing as nt import uxarray as ux -from uxarray.grid.coordinates import node_lonlat_rad_to_xyz +from uxarray.grid.coordinates import _lonlat_rad_to_xyz from uxarray.grid.integrate import _get_zonal_face_interval, _process_overlapped_intervals, _get_zonal_faces_weight_at_constLat current_path = Path(os.path.dirname(os.path.realpath(__file__))) @@ -67,7 +67,7 @@ def test_get_zonal_face_interval(self): [1.6 * np.pi, -0.25 * np.pi], [0.4 * np.pi, -0.25 * np.pi], [0.4 * np.pi, 0.25 * np.pi]] - vertices = [node_lonlat_rad_to_xyz(v) for v in vertices_lonlat] + vertices = [_lonlat_rad_to_xyz(*v) for v in vertices_lonlat] face_edge_nodes = np.array([[vertices[0], vertices[1]], [vertices[1], vertices[2]], @@ -101,7 +101,7 @@ def test_get_zonal_face_interval_GCA_constLat(self): [0.4 * np.pi, -0.25 * np.pi], [0.4 * np.pi, 0.25 * np.pi]] - vertices = [node_lonlat_rad_to_xyz(v) for v in vertices_lonlat] + vertices = [_lonlat_rad_to_xyz(*v) for v in vertices_lonlat] face_edge_nodes = np.array([[vertices[0], vertices[1]], [vertices[1], vertices[2]], @@ -132,7 +132,7 @@ def test_get_zonal_face_interval_equator(self): vertices_lonlat = [[-0.4 * np.pi, 0.25 * np.pi], [-0.4 * np.pi, 0.0], [0.4 * np.pi, 0.0], [0.4 * np.pi, 0.25 * np.pi]] - vertices = [node_lonlat_rad_to_xyz(v) for v in vertices_lonlat] + vertices = [_lonlat_rad_to_xyz(*v) for v in vertices_lonlat] face_edge_nodes = np.array([[vertices[0], vertices[1]], [vertices[1], vertices[2]], @@ -284,10 +284,10 @@ def test_get_zonal_faces_weight_at_constLat_equator(self): [np.pi, -0.25 * np.pi], [np.pi, 0.0]] # Convert the face vertices to xyz coordinates - face_0 = [node_lonlat_rad_to_xyz(v) for v in face_0] - face_1 = [node_lonlat_rad_to_xyz(v) for v in face_1] - face_2 = [node_lonlat_rad_to_xyz(v) for v in face_2] - face_3 = [node_lonlat_rad_to_xyz(v) for v in face_3] + face_0 = [_lonlat_rad_to_xyz(*v) for v in face_0] + face_1 = [_lonlat_rad_to_xyz(*v) for v in face_1] + face_2 = [_lonlat_rad_to_xyz(*v) for v in face_2] + face_3 = [_lonlat_rad_to_xyz(*v) for v in face_3] face_0_edge_nodes = np.array([[face_0[0], face_0[1]], [face_0[1], face_0[2]], @@ -347,10 +347,10 @@ def test_get_zonal_faces_weight_at_constLat_regular(self): [1.6 * np.pi, -0.01 * np.pi], [1.6 * np.pi, 0.25 * np.pi]] # Convert the face vertices to xyz coordinates - face_0 = [node_lonlat_rad_to_xyz(v) for v in face_0] - face_1 = [node_lonlat_rad_to_xyz(v) for v in face_1] - face_2 = [node_lonlat_rad_to_xyz(v) for v in face_2] - face_3 = [node_lonlat_rad_to_xyz(v) for v in face_3] + face_0 = [_lonlat_rad_to_xyz(*v) for v in face_0] + face_1 = [_lonlat_rad_to_xyz(*v) for v in face_1] + face_2 = [_lonlat_rad_to_xyz(*v) for v in face_2] + face_3 = [_lonlat_rad_to_xyz(*v) for v in face_3] face_0_edge_nodes = np.array([[face_0[0], face_0[1]], [face_0[1], face_0[2]], @@ -410,9 +410,9 @@ def test_get_zonal_faces_weight_at_constLat_latlonface(self): [np.deg2rad(40), np.deg2rad(20)], [np.deg2rad(40), np.deg2rad(40)]] # Convert the face vertices to xyz coordinates - face_0 = [node_lonlat_rad_to_xyz(v) for v in face_0] - face_1 = [node_lonlat_rad_to_xyz(v) for v in face_1] - face_2 = [node_lonlat_rad_to_xyz(v) for v in face_2] + face_0 = [_lonlat_rad_to_xyz(*v) for v in face_0] + face_1 = [_lonlat_rad_to_xyz(*v) for v in face_1] + face_2 = [_lonlat_rad_to_xyz(*v) for v in face_2] face_0_edge_nodes = np.array([[face_0[0], face_0[1]], diff --git a/test/test_intersections.py b/test/test_intersections.py index f17b3c359..b134d9029 100644 --- a/test/test_intersections.py +++ b/test/test_intersections.py @@ -3,7 +3,9 @@ import uxarray as ux from uxarray.constants import ERROR_TOLERANCE -from uxarray.grid.coordinates import node_lonlat_rad_to_xyz, node_xyz_to_lonlat_rad +# from uxarray.grid.coordinates import node_lonlat_rad_to_xyz, node_xyz_to_lonlat_rad + +from uxarray.grid.coordinates import _lonlat_rad_to_xyz, _xyz_to_lonlat_rad from uxarray.grid.intersections import gca_gca_intersection, gca_constLat_intersection @@ -11,16 +13,20 @@ class TestGCAGCAIntersection(TestCase): def test_get_GCA_GCA_intersections_antimeridian(self): # Test the case where the two GCAs are on the antimeridian - GCA1 = node_lonlat_rad_to_xyz([np.deg2rad(170.0), np.deg2rad(89.99)]) + + + + + GCA1 = _lonlat_rad_to_xyz(np.deg2rad(170.0), np.deg2rad(89.99)) GCR1_cart = np.array([ - node_lonlat_rad_to_xyz([np.deg2rad(170.0), - np.deg2rad(89.99)]), - node_lonlat_rad_to_xyz([np.deg2rad(170.0), - np.deg2rad(10.0)]) + _lonlat_rad_to_xyz(np.deg2rad(170.0), + np.deg2rad(89.99)), + _lonlat_rad_to_xyz(np.deg2rad(170.0), + np.deg2rad(10.0)) ]) GCR2_cart = np.array([ - node_lonlat_rad_to_xyz([np.deg2rad(70.0), 0.0]), - node_lonlat_rad_to_xyz([np.deg2rad(179.0), 0.0]) + _lonlat_rad_to_xyz(np.deg2rad(70.0), 0.0), + _lonlat_rad_to_xyz(np.deg2rad(179.0), 0.0) ]) res_cart = gca_gca_intersection(GCR1_cart, GCR2_cart) @@ -28,14 +34,14 @@ def test_get_GCA_GCA_intersections_antimeridian(self): self.assertTrue(np.array_equal(res_cart, np.array([]))) GCR1_cart = np.array([ - node_lonlat_rad_to_xyz([np.deg2rad(170.0), - np.deg2rad(89.0)]), - node_lonlat_rad_to_xyz([np.deg2rad(170.0), - np.deg2rad(-10.0)]) + _lonlat_rad_to_xyz(np.deg2rad(170.0), + np.deg2rad(89.0)), + _lonlat_rad_to_xyz(np.deg2rad(170.0), + np.deg2rad(-10.0)) ]) GCR2_cart = np.array([ - node_lonlat_rad_to_xyz([np.deg2rad(70.0), 0.0]), - node_lonlat_rad_to_xyz([np.deg2rad(175.0), 0.0]) + _lonlat_rad_to_xyz(np.deg2rad(70.0), 0.0), + _lonlat_rad_to_xyz(np.deg2rad(175.0), 0.0) ]) res_cart = gca_gca_intersection(GCR1_cart, GCR2_cart) @@ -45,7 +51,7 @@ def test_get_GCA_GCA_intersections_antimeridian(self): np.allclose(np.linalg.norm(res_cart, axis=0), 1.0, atol=ERROR_TOLERANCE)) - res_lonlat_rad = node_xyz_to_lonlat_rad(res_cart.tolist()) + res_lonlat_rad = _xyz_to_lonlat_rad(res_cart[0], res_cart[1], res_cart[2]) # res_cart should be [170, 0] self.assertTrue( @@ -56,12 +62,12 @@ def test_get_GCA_GCA_intersections_antimeridian(self): def test_get_GCA_GCA_intersections_parallel(self): # Test the case where the two GCAs are parallel GCR1_cart = np.array([ - node_lonlat_rad_to_xyz([0.3 * np.pi, 0.0]), - node_lonlat_rad_to_xyz([0.5 * np.pi, 0.0]) + _lonlat_rad_to_xyz(0.3 * np.pi, 0.0), + _lonlat_rad_to_xyz(0.5 * np.pi, 0.0) ]) GCR2_cart = np.array([ - node_lonlat_rad_to_xyz([0.5 * np.pi, 0.0]), - node_lonlat_rad_to_xyz([-0.5 * np.pi - 0.01, 0.0]) + _lonlat_rad_to_xyz(0.5 * np.pi, 0.0), + _lonlat_rad_to_xyz(-0.5 * np.pi - 0.01, 0.0) ]) res_cart = gca_gca_intersection(GCR1_cart, GCR2_cart) self.assertTrue(np.array_equal(res_cart, np.array([]))) @@ -69,14 +75,14 @@ def test_get_GCA_GCA_intersections_parallel(self): def test_get_GCA_GCA_intersections_perpendicular(self): # Test the case where the two GCAs are perpendicular to each other GCR1_cart = np.array([ - node_lonlat_rad_to_xyz([np.deg2rad(170.0), - np.deg2rad(0.0)]), - node_lonlat_rad_to_xyz([np.deg2rad(170.0), - np.deg2rad(10.0)]) + _lonlat_rad_to_xyz(np.deg2rad(170.0), + np.deg2rad(0.0)), + _lonlat_rad_to_xyz(np.deg2rad(170.0), + np.deg2rad(10.0)) ]) GCR2_cart = np.array([ - node_lonlat_rad_to_xyz([0.5 * np.pi, 0.0]), - node_lonlat_rad_to_xyz([-0.5 * np.pi - 0.01, 0.0]) + _lonlat_rad_to_xyz(*[0.5 * np.pi, 0.0]), + _lonlat_rad_to_xyz(*[-0.5 * np.pi - 0.01, 0.0]) ]) res_cart = gca_gca_intersection(GCR1_cart, GCR2_cart) @@ -85,7 +91,7 @@ def test_get_GCA_GCA_intersections_perpendicular(self): np.allclose(np.linalg.norm(res_cart, axis=0), 1.0, atol=ERROR_TOLERANCE)) - res_lonlat_rad = node_xyz_to_lonlat_rad(res_cart.tolist()) + res_lonlat_rad = _xyz_to_lonlat_rad(*res_cart) self.assertTrue( np.allclose(res_lonlat_rad, np.array([np.deg2rad(170.0), @@ -96,14 +102,14 @@ class TestGCAconstLatIntersection(TestCase): def test_GCA_constLat_intersections_antimeridian(self): GCR1_cart = np.array([ - node_lonlat_rad_to_xyz([np.deg2rad(170.0), - np.deg2rad(89.99)]), - node_lonlat_rad_to_xyz([np.deg2rad(170.0), - np.deg2rad(10.0)]) + _lonlat_rad_to_xyz(np.deg2rad(170.0), + np.deg2rad(89.99)), + _lonlat_rad_to_xyz(np.deg2rad(170.0), + np.deg2rad(10.0)) ]) res = gca_constLat_intersection(GCR1_cart, np.sin(np.deg2rad(60.0)), verbose=True) - res_lonlat_rad = node_xyz_to_lonlat_rad(res[0].tolist()) + res_lonlat_rad = _xyz_to_lonlat_rad(*(res[0].tolist())) self.assertTrue( np.allclose(res_lonlat_rad, np.array([np.deg2rad(170.0), @@ -111,10 +117,10 @@ def test_GCA_constLat_intersections_antimeridian(self): def test_GCA_constLat_intersections_empty(self): GCR1_cart = np.array([ - node_lonlat_rad_to_xyz([np.deg2rad(170.0), - np.deg2rad(89.99)]), - node_lonlat_rad_to_xyz([np.deg2rad(170.0), - np.deg2rad(10.0)]) + _lonlat_rad_to_xyz(np.deg2rad(170.0), + np.deg2rad(89.99)), + _lonlat_rad_to_xyz(np.deg2rad(170.0), + np.deg2rad(10.0)) ]) res = gca_constLat_intersection(GCR1_cart, np.sin(np.deg2rad(-10.0)), verbose=False) @@ -122,10 +128,10 @@ def test_GCA_constLat_intersections_empty(self): def test_GCA_constLat_intersections_two_pts(self): GCR1_cart = np.array([ - node_lonlat_rad_to_xyz([np.deg2rad(10.0), - np.deg2rad(10)]), - node_lonlat_rad_to_xyz([np.deg2rad(170.0), - np.deg2rad(10.0)]) + _lonlat_rad_to_xyz(np.deg2rad(10.0), + np.deg2rad(10)), + _lonlat_rad_to_xyz(np.deg2rad(170.0), + np.deg2rad(10.0)) ]) max_lat = ux.grid.arcs.extreme_gca_latitude(GCR1_cart, 'max') diff --git a/test/test_neighbors.py b/test/test_neighbors.py index 87736fae4..74321c247 100644 --- a/test/test_neighbors.py +++ b/test/test_neighbors.py @@ -10,8 +10,6 @@ from uxarray.grid.connectivity import _populate_face_edge_connectivity, _build_edge_face_connectivity -from uxarray.grid.coordinates import _populate_lonlat_coord - from uxarray.constants import INT_FILL_VALUE try: diff --git a/test/test_scrip.py b/test/test_scrip.py index b0c4426fb..9f234f6f7 100644 --- a/test/test_scrip.py +++ b/test/test_scrip.py @@ -38,25 +38,26 @@ def test_read_ugrid(self): nt.assert_equal(uxgrid_RLL10deg_ne4.node_lon.size, constants.NNODES_ov_RLL10deg_CSne4) - def test_read_ugrid_opendap(self): - """Read an ugrid model from an OPeNDAP URL.""" - - try: - # make sure we can read the ugrid file from the OPeNDAP URL - url = "http://test.opendap.org:8080/opendap/ugrid/NECOFS_GOM3_FORECAST.nc" - uxgrid_url = ux.open_grid(url, drop_variables="siglay") - - except OSError: - # print warning and pass if we can't connect to the OPeNDAP server - warnings.warn(f'Could not connect to OPeNDAP server: {url}') - pass - - else: - - assert isinstance(getattr(uxgrid_url, "node_lon"), xr.DataArray) - assert isinstance(getattr(uxgrid_url, "node_lat"), xr.DataArray) - assert isinstance(getattr(uxgrid_url, "face_node_connectivity"), - xr.DataArray) + # TODO: UNCOMMENT + # def test_read_ugrid_opendap(self): + # """Read an ugrid model from an OPeNDAP URL.""" + # + # try: + # # make sure we can read the ugrid file from the OPeNDAP URL + # url = "http://test.opendap.org:8080/opendap/ugrid/NECOFS_GOM3_FORECAST.nc" + # uxgrid_url = ux.open_grid(url, drop_variables="siglay") + # + # except OSError: + # # print warning and pass if we can't connect to the OPeNDAP server + # warnings.warn(f'Could not connect to OPeNDAP server: {url}') + # pass + # + # else: + # + # assert isinstance(getattr(uxgrid_url, "node_lon"), xr.DataArray) + # assert isinstance(getattr(uxgrid_url, "node_lat"), xr.DataArray) + # assert isinstance(getattr(uxgrid_url, "face_node_connectivity"), + # xr.DataArray) def test_encode_ugrid(self): """Read an Exodus dataset and encode that as a UGRID format.""" diff --git a/test/test_ugrid.py b/test/test_ugrid.py index b0c4426fb..9f7576ce2 100644 --- a/test/test_ugrid.py +++ b/test/test_ugrid.py @@ -38,25 +38,25 @@ def test_read_ugrid(self): nt.assert_equal(uxgrid_RLL10deg_ne4.node_lon.size, constants.NNODES_ov_RLL10deg_CSne4) - def test_read_ugrid_opendap(self): - """Read an ugrid model from an OPeNDAP URL.""" - - try: - # make sure we can read the ugrid file from the OPeNDAP URL - url = "http://test.opendap.org:8080/opendap/ugrid/NECOFS_GOM3_FORECAST.nc" - uxgrid_url = ux.open_grid(url, drop_variables="siglay") - - except OSError: - # print warning and pass if we can't connect to the OPeNDAP server - warnings.warn(f'Could not connect to OPeNDAP server: {url}') - pass - - else: - - assert isinstance(getattr(uxgrid_url, "node_lon"), xr.DataArray) - assert isinstance(getattr(uxgrid_url, "node_lat"), xr.DataArray) - assert isinstance(getattr(uxgrid_url, "face_node_connectivity"), - xr.DataArray) + # def test_read_ugrid_opendap(self): + # """Read an ugrid model from an OPeNDAP URL.""" + # + # try: + # # make sure we can read the ugrid file from the OPeNDAP URL + # url = "http://test.opendap.org:8080/opendap/ugrid/NECOFS_GOM3_FORECAST.nc" + # uxgrid_url = ux.open_grid(url, drop_variables="siglay") + # + # except OSError: + # # print warning and pass if we can't connect to the OPeNDAP server + # warnings.warn(f'Could not connect to OPeNDAP server: {url}') + # pass + # + # else: + # + # assert isinstance(getattr(uxgrid_url, "node_lon"), xr.DataArray) + # assert isinstance(getattr(uxgrid_url, "node_lat"), xr.DataArray) + # assert isinstance(getattr(uxgrid_url, "face_node_connectivity"), + # xr.DataArray) def test_encode_ugrid(self): """Read an Exodus dataset and encode that as a UGRID format.""" diff --git a/uxarray/grid/arcs.py b/uxarray/grid/arcs.py index e2c4bddfb..381d02e41 100644 --- a/uxarray/grid/arcs.py +++ b/uxarray/grid/arcs.py @@ -1,6 +1,8 @@ import numpy as np -from uxarray.grid.coordinates import node_xyz_to_lonlat_rad, normalize_in_place +# from uxarray.grid.coordinates import node_xyz_to_lonlat_rad, normalize_in_place + +from uxarray.grid.coordinates import _xyz_to_lonlat_rad, _normalize_xyz from uxarray.constants import ERROR_TOLERANCE @@ -58,9 +60,13 @@ def point_within_gca(pt, gca_cart, is_directed=False): Please ensure that the input coordinates are in radians and adhere to the ERROR_TOLERANCE value for floating-point comparisons. """ # Convert the cartesian coordinates to lonlat coordinates - pt_lonlat = node_xyz_to_lonlat_rad(_to_list(pt)) - GCRv0_lonlat = node_xyz_to_lonlat_rad(_to_list(gca_cart[0])) - GCRv1_lonlat = node_xyz_to_lonlat_rad(_to_list(gca_cart[1])) + pt_lonlat = np.array(_xyz_to_lonlat_rad(pt[0], pt[1], pt[2])) + GCRv0_lonlat = np.array( + _xyz_to_lonlat_rad(gca_cart[0][0], gca_cart[0][1], gca_cart[0][2]) + ) + GCRv1_lonlat = np.array( + _xyz_to_lonlat_rad(gca_cart[1][0], gca_cart[1][1], gca_cart[1][2]) + ) # Convert the list to np.float64 gca_cart[0] = np.array(gca_cart[0], dtype=np.float64) @@ -284,14 +290,13 @@ def extreme_gca_latitude(gca_cart, extreme_type): if np.isclose(d_a_max, [0, 1], atol=ERROR_TOLERANCE).any() else d_a_max ) - lat_n1, lat_n2 = ( - node_xyz_to_lonlat_rad(n1.tolist())[1], - node_xyz_to_lonlat_rad(n2.tolist())[1], - ) + + _, lat_n1 = _xyz_to_lonlat_rad(n1[0], n1[1], n1[2]) + _, lat_n2 = _xyz_to_lonlat_rad(n2[0], n2[1], n2[2]) if 0 < d_a_max < 1: node3 = (1 - d_a_max) * n1 + d_a_max * n2 - node3 = np.array(normalize_in_place(node3.tolist())) + node3 = np.array(_normalize_xyz(node3[0], node3[1], node3[2])) d_lat_rad = np.arcsin(np.clip(node3[2], -1, 1)) return ( diff --git a/uxarray/grid/area.py b/uxarray/grid/area.py index 086a22334..6e1692feb 100644 --- a/uxarray/grid/area.py +++ b/uxarray/grid/area.py @@ -1,14 +1,8 @@ import numpy as np -from numba import njit, config -from uxarray.constants import ENABLE_JIT_CACHE, ENABLE_JIT -from uxarray.grid.coordinates import node_lonlat_rad_to_xyz +from uxarray.grid.coordinates import _lonlat_rad_to_xyz -config.DISABLE_JIT = not ENABLE_JIT - - -@njit(cache=ENABLE_JIT_CACHE) def calculate_face_area( x, y, z, quadrature_rule="gaussian", order=4, coords_type="spherical" ): @@ -67,15 +61,14 @@ def calculate_face_area( node3 = np.array([x[j + 2], y[j + 2], z[j + 2]], dtype=np.float64) if coords_type == "spherical": - node1 = np.array( - node_lonlat_rad_to_xyz([np.deg2rad(x[0]), np.deg2rad(y[0])]) - ) - node2 = np.array( - node_lonlat_rad_to_xyz([np.deg2rad(x[j + 1]), np.deg2rad(y[j + 1])]) - ) - node3 = np.array( - node_lonlat_rad_to_xyz([np.deg2rad(x[j + 2]), np.deg2rad(y[j + 2])]) - ) + node1 = _lonlat_rad_to_xyz(np.deg2rad(x[0]), np.deg2rad(y[0])) + node1 = np.asarray(node1) + + node2 = _lonlat_rad_to_xyz(np.deg2rad(x[j + 1]), np.deg2rad(y[j + 1])) + node2 = np.asarray(node2) + + node3 = _lonlat_rad_to_xyz(np.deg2rad(x[j + 2]), np.deg2rad(y[j + 2])) + node3 = np.asarray(node3) for p in range(len(dW)): if quadrature_rule == "gaussian": @@ -99,7 +92,6 @@ def calculate_face_area( return area, jacobian -@njit(cache=ENABLE_JIT_CACHE) def get_all_face_area_from_coords( x, y, @@ -174,7 +166,6 @@ def get_all_face_area_from_coords( return area, jacobian -@njit(cache=ENABLE_JIT_CACHE) def calculate_spherical_triangle_jacobian(node1, node2, node3, dA, dB): """Calculate Jacobian of a spherical triangle. This is a helper function for calculating face area. @@ -264,7 +255,6 @@ def calculate_spherical_triangle_jacobian(node1, node2, node3, dA, dB): return dJacobian -@njit(cache=ENABLE_JIT_CACHE) def calculate_spherical_triangle_jacobian_barycentric(node1, node2, node3, dA, dB): """Calculate Jacobian of a spherical triangle. This is a helper function for calculating face area. @@ -343,7 +333,6 @@ def calculate_spherical_triangle_jacobian_barycentric(node1, node2, node3, dA, d return 0.5 * dJacobian -@njit(cache=ENABLE_JIT_CACHE) def get_gauss_quadratureDG(nCount): """Gauss Quadrature Points for integration. @@ -588,7 +577,6 @@ def get_gauss_quadratureDG(nCount): return dG, dW -@njit(cache=ENABLE_JIT_CACHE) def get_tri_quadratureDG(nOrder): """Triangular Quadrature Points for integration. diff --git a/uxarray/grid/coordinates.py b/uxarray/grid/coordinates.py index 2aba490ea..7032d8d22 100644 --- a/uxarray/grid/coordinates.py +++ b/uxarray/grid/coordinates.py @@ -1,204 +1,138 @@ -import warnings - import xarray as xr import numpy as np -import math -from numba import njit, config +import warnings -from uxarray.constants import ENABLE_JIT_CACHE, ENABLE_JIT, ERROR_TOLERANCE +from uxarray.constants import ERROR_TOLERANCE from uxarray.conventions import ugrid -config.DISABLE_JIT = not ENABLE_JIT +from typing import Union + +def _lonlat_rad_to_xyz( + lon: Union[np.ndarray, float], + lat: Union[np.ndarray, float], +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Converts Spherical latitude and longitude coordinates into Cartesian x, + y, z coordinates.""" + x = np.cos(lon) * np.cos(lat) + y = np.sin(lon) * np.cos(lat) + z = np.sin(lat) -@njit(cache=ENABLE_JIT_CACHE) -def node_lonlat_rad_to_xyz(node_coord): - """Helper function to Convert the node coordinate from 2D - longitude/latitude to normalized 3D xyz. + return x, y, z + + +def _xyz_to_lonlat_rad( + x: Union[np.ndarray, float], + y: Union[np.ndarray, float], + z: Union[np.ndarray, float], + normalize: bool = True, +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Converts Cartesian x, y, z coordinates in Spherical latitude and + longitude coordinates in degrees. Parameters ---------- - node: float list - 2D coordinates[longitude, latitude] in radiance + x : Union[np.ndarray, float] + Cartesian x coordinates + y: Union[np.ndarray, float] + Cartesiain y coordinates + z: Union[np.ndarray, float] + Cartesian z coordinates + normalize: bool + Flag to select whether to normalize the coordinates Returns - ---------- - float list - the result array of the unit 3D coordinates [x, y, z] vector where :math:`x^2 + y^2 + z^2 = 1` - - Raises - ---------- - RuntimeError - The input array doesn't have the size of 3. + ------- + lon : Union[np.ndarray, float] + Longitude in radians + lat: Union[np.ndarray, float] + Latitude in radians """ - if len(node_coord) != 2: - raise RuntimeError( - "Input array should have a length of 2: [longitude, latitude]" - ) - lon = node_coord[0] - lat = node_coord[1] - return [np.cos(lon) * np.cos(lat), np.sin(lon) * np.cos(lat), np.sin(lat)] + if normalize: + x, y, z = _normalize_xyz(x, y, z) + denom = np.abs(x * x + y * y + z * z) + x /= denom + y /= denom + z /= denom -@njit(cache=ENABLE_JIT_CACHE) -def node_xyz_to_lonlat_rad(node_coord): - """Calculate the latitude and longitude in radiance for a node represented - in the [x, y, z] 3D Cartesian coordinates. + lon = np.arctan2(y, x, dtype=np.float64) + lat = np.arcsin(z, dtype=np.float64) - Parameters - ---------- - node_coord: float list - 3D Cartesian Coordinates [x, y, z] of the node + # set longitude range to [0, pi] + lon = np.mod(lon, 2 * np.pi) - Returns - ---------- - float list - the result array of longitude and latitude in radian [longitude_rad, latitude_rad] + z_mask = np.abs(z) > 1.0 - ERROR_TOLERANCE - Raises - ---------- - RuntimeError - The input array doesn't have the size of 3. - """ - if len(node_coord) != 3: - raise RuntimeError("Input array should have a length of 3: [x, y, z]") - - [dx, dy, dz] = normalize_in_place(node_coord) - dx /= np.absolute(dx * dx + dy * dy + dz * dz) - dy /= np.absolute(dx * dx + dy * dy + dz * dz) - dz /= np.absolute(dx * dx + dy * dy + dz * dz) - - if np.absolute(dz) < (1.0 - ERROR_TOLERANCE): - d_lon_rad = math.atan2(dy, dx) - d_lat_rad = np.arcsin(dz) - - if d_lon_rad < 0.0: - d_lon_rad += 2.0 * np.pi - elif dz > 0.0: - d_lon_rad = 0.0 - d_lat_rad = 0.5 * np.pi - else: - d_lon_rad = 0.0 - d_lat_rad = -0.5 * np.pi + lat = np.where(z_mask, np.sign(z) * np.pi / 2, lat) + lon = np.where(z_mask, 0.0, lon) - return [d_lon_rad, d_lat_rad] + return lon, lat -@njit(cache=ENABLE_JIT_CACHE) -def normalize_in_place(node): - """Helper function to project an arbitrary node in 3D coordinates [x, y, z] - on the unit sphere. It uses the `np.linalg.norm` internally to calculate - the magnitude. +def _xyz_to_lonlat_deg( + x: Union[np.ndarray, float], + y: Union[np.ndarray, float], + z: Union[np.ndarray, float], + normalize: bool = True, +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Converts Cartesian x, y, z coordinates in Spherical latitude and + longitude coordinates in degrees. Parameters ---------- - node: float list - 3D Cartesian Coordinates [x, y, z] + x : Union[np.ndarray, float] + Cartesian x coordinates + y: Union[np.ndarray, float] + Cartesiain y coordinates + z: Union[np.ndarray, float] + Cartesian z coordinates + normalize: bool + Flag to select whether to normalize the coordinates Returns - ---------- - float list - the result unit vector [x, y, z] where :math:`x^2 + y^2 + z^2 = 1` - - Raises - ---------- - RuntimeError - The input array doesn't have the size of 3. - """ - if len(node) != 3: - raise RuntimeError("Input array should have a length of 3: [x, y, z]") - return list(np.array(node) / np.linalg.norm(np.array(node), ord=2)) - - -def _get_xyz_from_lonlat(node_lon, node_lat): - nodes_lon_rad = np.deg2rad(node_lon) - nodes_lat_rad = np.deg2rad(node_lat) - nodes_rad = np.stack((nodes_lon_rad, nodes_lat_rad), axis=1) - nodes_cart = np.asarray(list(map(node_lonlat_rad_to_xyz, list(nodes_rad)))) - - return nodes_cart[:, 0], nodes_cart[:, 1], nodes_cart[:, 2] - - -def _populate_cartesian_xyz_coord(grid): - """A helper function that populates the xyz attribute in UXarray.Grid._ds. - This function is called when we need to use the cartesian coordinates for - each node to do the calculation but the input data only has the - ``node_lon`` and ``node_lat`` in degree. - - Note - ---- - In the UXarray, we abide the UGRID convention and make sure the following attributes will always have its - corresponding units as stated below: - - node_lon - unit: "degree_east" for longitude - node_lat - unit: "degrees_north" for latitude - node_x - unit: "m" - node_y - unit: "m" - node_z - unit: "m" + ------- + lon : Union[np.ndarray, float] + Longitude in degrees + lat: Union[np.ndarray, float] + Latitude in degrees """ + lon_rad, lat_rad = _xyz_to_lonlat_rad(x, y, z, normalize=normalize) - # Check if the cartesian coordinates are already populated - if "node_x" in grid._ds.keys(): - return + lon = np.rad2deg(lon_rad) + lat = np.rad2deg(lat_rad) - # get Cartesian (x, y, z) coordinates from lon/lat - x, y, z = _get_xyz_from_lonlat(grid.node_lon.values, grid.node_lat.values) + lon = (lon + 180) % 360 - 180 + return lon, lat - grid._ds["node_x"] = xr.DataArray( - data=x, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_X_ATTRS - ) - grid._ds["node_y"] = xr.DataArray( - data=y, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_Y_ATTRS - ) - grid._ds["node_z"] = xr.DataArray( - data=z, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_Z_ATTRS + +def _normalize_xyz( + x: Union[np.ndarray, float], + y: Union[np.ndarray, float], + z: Union[np.ndarray, float], +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Normalizes a set of Cartesiain coordinates.""" + denom = np.linalg.norm( + np.asarray(np.array([x, y, z]), dtype=np.float64), ord=2, axis=0 ) + x_norm = x / denom + y_norm = y / denom + z_norm = z / denom + return x_norm, y_norm, z_norm -def _get_lonlat_from_xyz(x, y, z): - nodes_cart = np.stack((x, y, z), axis=1).tolist() - nodes_rad = list(map(node_xyz_to_lonlat_rad, nodes_cart)) - nodes_degree = np.rad2deg(nodes_rad) - - return nodes_degree[:, 0], nodes_degree[:, 1] - - -def _populate_lonlat_coord(grid): - """Helper function that populates the longitude and latitude and store it - into the ``node_lon`` and ``node_lat``. This is called when the input data - has ``node_x``, ``node_y``, ``node_z`` in meters. Since we want - ``node_lon`` and ``node_lat`` to always have the "degree" units. For more - details, please read the following. - - Note - ---- - In the UXarray, we abide the UGRID convention and make sure the following attributes will always have its - corresponding units as stated below: - - node_lon - unit: "degree_east" for longitude - node_lat - unit: "degrees_north" for latitude - node_x - unit: "m" - node_y - unit: "m" - node_z - unit: "m" - """ - # get lon/lat coordinates from Cartesian (x, y, z) - lon, lat = _get_lonlat_from_xyz( +def _populate_node_latlon(grid) -> None: + """Populates the latitude and longitude coordinates of a Grid (`node_lon`, + `node_lat`)""" + lon_rad, lat_rad = _xyz_to_lonlat_rad( grid.node_x.values, grid.node_y.values, grid.node_z.values ) - # populate dataset + lon = np.rad2deg(lon_rad) + lat = np.rad2deg(lat_rad) + grid._ds["node_lon"] = xr.DataArray( data=lon, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_LON_ATTRS ) @@ -207,6 +141,25 @@ def _populate_lonlat_coord(grid): ) +def _populate_node_xyz(grid) -> None: + """Populates the Cartesiain node coordinates of a Grid (`node_x`, `node_y` + and `node_z`)""" + + node_lon_rad = np.deg2rad(grid.node_lon.values) + node_lat_rad = np.deg2rad(grid.node_lat.values) + x, y, z = _lonlat_rad_to_xyz(node_lon_rad, node_lat_rad) + + grid._ds["node_x"] = xr.DataArray( + data=x, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_X_ATTRS + ) + grid._ds["node_y"] = xr.DataArray( + data=y, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_Y_ATTRS + ) + grid._ds["node_z"] = xr.DataArray( + data=z, dims=[ugrid.NODE_DIM], attrs=ugrid.NODE_Z_ATTRS + ) + + def _populate_face_centroids(grid, repopulate=False): """Finds the centroids of faces using cartesian averaging based off the vertices. The centroid is defined as the average of the x, y, z @@ -237,14 +190,14 @@ def _populate_face_centroids(grid, repopulate=False): # If there are cartesian centroids already use those instead centroid_x, centroid_y, centroid_z = grid.face_x, grid.face_y, grid.face_z - # Convert from xyz to latlon - centroid_lon, centroid_lat = _get_lonlat_from_xyz( - centroid_x, centroid_y, centroid_z + # Convert from xyz to latlon TODO + centroid_lon, centroid_lat = _xyz_to_lonlat_deg( + centroid_x, centroid_y, centroid_z, normalize=False ) else: # Convert to xyz if there are latlon centroids already stored centroid_lon, centroid_lat = grid.face_lon.values, grid.face_lat.values - centroid_x, centroid_y, centroid_z = _get_xyz_from_lonlat( + centroid_x, centroid_y, centroid_z = _lonlat_rad_to_xyz( centroid_lon, centroid_lat ) @@ -271,29 +224,21 @@ def _populate_face_centroids(grid, repopulate=False): ) -@njit() def _construct_face_centroids(node_x, node_y, node_z, face_nodes, n_nodes_per_face): """Constructs the xyz centroid coordinate for each face using Cartesian Averaging.""" - centroids = np.zeros((3, face_nodes.shape[0]), dtype=np.float64) - for face_idx, n_max_nodes in enumerate(n_nodes_per_face): - # compute cartesian average - centroid_x = np.mean(node_x[face_nodes[face_idx, 0:n_max_nodes]]) - centroid_y = np.mean(node_y[face_nodes[face_idx, 0:n_max_nodes]]) - centroid_z = np.mean(node_z[face_nodes[face_idx, 0:n_max_nodes]]) - - # normalize coordinates - centroid_normalized_xyz = normalize_in_place( - [centroid_x, centroid_y, centroid_z] - ) + centroid_x = np.zeros((face_nodes.shape[0]), dtype=np.float64) + centroid_y = np.zeros((face_nodes.shape[0]), dtype=np.float64) + centroid_z = np.zeros((face_nodes.shape[0]), dtype=np.float64) - # store xyz - centroids[0, face_idx] = centroid_normalized_xyz[0] - centroids[1, face_idx] = centroid_normalized_xyz[1] - centroids[2, face_idx] = centroid_normalized_xyz[2] + for face_idx, n_max_nodes in enumerate(n_nodes_per_face): + # Compute Cartesian Average + centroid_x[face_idx] = np.mean(node_x[face_nodes[face_idx, 0:n_max_nodes]]) + centroid_y[face_idx] = np.mean(node_y[face_nodes[face_idx, 0:n_max_nodes]]) + centroid_z[face_idx] = np.mean(node_z[face_nodes[face_idx, 0:n_max_nodes]]) - return centroids[0, :], centroids[1, :], centroids[2, :] + return _normalize_xyz(centroid_x, centroid_y, centroid_z) def _populate_edge_centroids(grid, repopulate=False): @@ -324,13 +269,13 @@ def _populate_edge_centroids(grid, repopulate=False): centroid_x, centroid_y, centroid_z = grid.edge_x, grid.edge_y, grid.edge_z # Convert from xyz to latlon - centroid_lon, centroid_lat = _get_lonlat_from_xyz( - centroid_x, centroid_y, centroid_z + centroid_lon, centroid_lat = _xyz_to_lonlat_deg( + centroid_x, centroid_y, centroid_z, normalize=False ) else: # Convert to xyz if there are latlon centroids already stored centroid_lon, centroid_lat = grid.edge_lon.values, grid.edge_lat.values - centroid_x, centroid_y, centroid_z = _get_xyz_from_lonlat( + centroid_x, centroid_y, centroid_z = _lonlat_rad_to_xyz( centroid_lon, centroid_lat ) @@ -365,29 +310,15 @@ def _populate_edge_centroids(grid, repopulate=False): ) -@njit() -def _construct_edge_centroids(node_x, node_y, node_z, edge_nodes_con): +def _construct_edge_centroids(node_x, node_y, node_z, edge_node_conn): """Constructs the xyz centroid coordinate for each edge using Cartesian Averaging.""" - centroids = np.zeros((3, edge_nodes_con.shape[0]), dtype=np.float64) - - for edge_idx, connectivity in enumerate(edge_nodes_con): - # compute cartesian average - centroid_x = np.mean(node_x[connectivity[0:]]) - centroid_y = np.mean(node_y[connectivity[0:]]) - centroid_z = np.mean(node_z[connectivity[0:]]) - - # normalize coordinates - centroid_normalized_xyz = normalize_in_place( - [centroid_x, centroid_y, centroid_z] - ) - # store xyz - centroids[0, edge_idx] = centroid_normalized_xyz[0] - centroids[1, edge_idx] = centroid_normalized_xyz[1] - centroids[2, edge_idx] = centroid_normalized_xyz[2] + centroid_x = np.mean(node_x[edge_node_conn], axis=1) + centroid_y = np.mean(node_y[edge_node_conn], axis=1) + centroid_z = np.mean(node_z[edge_node_conn], axis=1) - return centroids[0, :], centroids[1, :], centroids[2, :] + return _normalize_xyz(centroid_x, centroid_y, centroid_z) def _set_desired_longitude_range(ds): diff --git a/uxarray/grid/grid.py b/uxarray/grid/grid.py index ffb0fdae5..716a1983b 100644 --- a/uxarray/grid/grid.py +++ b/uxarray/grid/grid.py @@ -28,8 +28,8 @@ _populate_face_centroids, _populate_edge_centroids, _set_desired_longitude_range, - _populate_lonlat_coord, - _populate_cartesian_xyz_coord, + _populate_node_latlon, + _populate_node_xyz, ) from uxarray.grid.connectivity import ( _populate_edge_node_connectivity, @@ -554,7 +554,7 @@ def node_lon(self) -> xr.DataArray: """ if "node_lon" not in self._ds: _set_desired_longitude_range(self._ds) - _populate_lonlat_coord(self) + _populate_node_latlon(self) return self._ds["node_lon"] @property @@ -565,7 +565,7 @@ def node_lat(self) -> xr.DataArray: """ if "node_lat" not in self._ds: _set_desired_longitude_range(self._ds) - _populate_lonlat_coord(self) + _populate_node_latlon(self) return self._ds["node_lat"] @property @@ -575,7 +575,7 @@ def node_x(self) -> xr.DataArray: Dimensions: ``(n_node, )`` """ if "node_x" not in self._ds: - _populate_cartesian_xyz_coord(self) + _populate_node_xyz(self) return self._ds["node_x"] @@ -586,7 +586,7 @@ def node_y(self) -> xr.DataArray: Dimensions: ``(n_node, )`` """ if "node_y" not in self._ds: - _populate_cartesian_xyz_coord(self) + _populate_node_xyz(self) return self._ds["node_y"] @property @@ -596,7 +596,7 @@ def node_z(self) -> xr.DataArray: Dimensions: ``(n_node, )`` """ if "node_z" not in self._ds: - _populate_cartesian_xyz_coord(self) + _populate_node_xyz(self) return self._ds["node_z"] @property diff --git a/uxarray/grid/integrate.py b/uxarray/grid/integrate.py index 7542fdbbb..16cc13194 100644 --- a/uxarray/grid/integrate.py +++ b/uxarray/grid/integrate.py @@ -1,7 +1,7 @@ import numpy as np from uxarray.constants import ERROR_TOLERANCE, INT_FILL_VALUE from uxarray.grid.intersections import gca_constLat_intersection -from uxarray.grid.coordinates import node_xyz_to_lonlat_rad +from uxarray.grid.coordinates import _xyz_to_lonlat_rad import pandas as pd DUMMY_EDGE_VALUE = [INT_FILL_VALUE, INT_FILL_VALUE, INT_FILL_VALUE] @@ -191,9 +191,11 @@ def _get_faces_constLat_intersection_info( # Handle unique intersections and check for convex or concave cases unique_intersections = np.unique(intersections_pts_list_cart, axis=0) if len(unique_intersections) == 2: + # TODO: vectorize? unique_intersection_lonlat = np.array( - [node_xyz_to_lonlat_rad(pt.tolist()) for pt in unique_intersections] + [_xyz_to_lonlat_rad(pt[0], pt[1], pt[2]) for pt in unique_intersections] ) + sorted_lonlat = np.sort(unique_intersection_lonlat, axis=0) pt_lon_min, pt_lon_max = sorted_lonlat[:, 0] return unique_intersections, pt_lon_min, pt_lon_max @@ -265,7 +267,7 @@ def _get_zonal_face_interval( # Convert intersection points to longitude-latitude longitudes = np.array( - [node_xyz_to_lonlat_rad(pt.tolist())[0] for pt in unique_intersections] + [_xyz_to_lonlat_rad(*pt.tolist())[0] for pt in unique_intersections] ) # Handle special wrap-around cases by checking the face bounds diff --git a/uxarray/io/_exodus.py b/uxarray/io/_exodus.py index 205b68d36..8c91913fa 100644 --- a/uxarray/io/_exodus.py +++ b/uxarray/io/_exodus.py @@ -5,7 +5,8 @@ from uxarray.grid.connectivity import _replace_fill_values from uxarray.constants import INT_DTYPE, INT_FILL_VALUE -from uxarray.grid.coordinates import _get_lonlat_from_xyz, _get_xyz_from_lonlat + +from uxarray.grid.coordinates import _lonlat_rad_to_xyz, _xyz_to_lonlat_deg from uxarray.conventions import ugrid @@ -100,7 +101,7 @@ def _read_exodus(ext_ds): ) # populate lon/lat coordinates - lon, lat = _get_lonlat_from_xyz( + lon, lat = _xyz_to_lonlat_deg( ds["node_x"].values, ds["node_y"].values, ds["node_z"].values ) @@ -181,7 +182,7 @@ def _encode_exodus(ds, outfile=None): # Set the dim to 3 as we will always have x/y/z for cartesian grid # Note: Don't get orig dimension from Mesh2 attribute topology dimension if "node_x" not in ds: - x, y, z = _get_xyz_from_lonlat(ds["node_lon"].values, ds["node_lat"].values) + x, y, z = _lonlat_rad_to_xyz(ds["node_lon"].values, ds["node_lat"].values) c_data = xr.DataArray([x, y, z]) else: c_data = xr.DataArray( From 318f3f68ca447d5e14a5e778d65f6b51053f638f Mon Sep 17 00:00:00 2001 From: Orhan Eroglu <32553057+erogluorhan@users.noreply.github.com> Date: Wed, 15 May 2024 21:04:58 -0600 Subject: [PATCH 39/41] Add CODECOV_TOKEN to ci.yml to fix Codecov uploads (#791) * Update ci.yml to add CODECOV_TOKEN * Update .github/workflows/ci.yml Co-authored-by: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> --------- Co-authored-by: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> --- .github/workflows/ci.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 88c4d5d2a..47c558298 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -71,7 +71,10 @@ jobs: python -m pytest test -v --cov=./uxarray --cov-report=xml - name: Upload code coverage to Codecov + if: github.repository == 'UXARRAY/uxarray' uses: codecov/codecov-action@v4.3.1 + env: + CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} with: file: ./coverage.xml flags: unittests From 2a4bcee70afc7d5326bf70774d5f8f84d39986e5 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Fri, 17 May 2024 13:39:46 -0500 Subject: [PATCH 40/41] Fix ASV CI Workflow (#792) * update workflow * update env file * update env file (2) * update asv-bencnhmarking.yml * fetch branches * update asv-benchmarking.yml * try different version scheme * revert to old version scheme * drop 'v' from version * try to remove shell spec * add v * add bash * Update .github/workflows/asv-benchmarking.yml Co-authored-by: Anissa Zacharias * update mpas_ocean benchmark to use current path * fix dataset download (1) * fix dataset download (2) * fix dataset download (3) * fix dataset download (4) * add compressed mesh files using zarr * use zarr files in benchmark * delete meshfiles and update benchmark to version working on fork * attempt to fix env cache (1) * attempt to fix env cache (2) * attempt to fix env cache (3) * attempt to fix env cache (4) * attempt to fix env cache (5) * attempt to fix env cache (5) * attempt to fix env cache (6) --------- Co-authored-by: Anissa Zacharias --- .github/workflows/asv-benchmarking.yml | 54 +++++++++++++++++--------- benchmarks/mpas_ocean.py | 10 ++--- 2 files changed, 41 insertions(+), 23 deletions(-) diff --git a/.github/workflows/asv-benchmarking.yml b/.github/workflows/asv-benchmarking.yml index aa06f2441..c2ce43479 100644 --- a/.github/workflows/asv-benchmarking.yml +++ b/.github/workflows/asv-benchmarking.yml @@ -9,6 +9,9 @@ on: jobs: benchmark: runs-on: ubuntu-latest + defaults: + run: + shell: bash -el {0} env: CONDA_ENV_FILE: ./ci/asv.yml ASV_DIR: ./benchmarks @@ -18,36 +21,51 @@ jobs: uses: actions/checkout@v4 with: repository: UXARRAY/uxarray + ref: main fetch-depth: 0 + + - name: Fetch all tags and branches for uxarray + run: | + git fetch --all --tags + - name: Checkout uxarray-asv uses: actions/checkout@v4 with: - repository: uxarray/uxarray-asv + repository: UXARRAY/uxarray-asv persist-credentials: false fetch-depth: 0 ref: main path: uxarray-asv - - name: Set environment variables - run: | - echo "TODAY=$(date +'%Y-%m-%d')" >> $GITHUB_ENV - - name: Set up conda environment - id: env-setup - continue-on-error: true - uses: mamba-org/setup-micromamba@v1 + - name: Setup Miniforge + uses: conda-incubator/setup-miniconda@v3 with: - environment-file: ./ci/asv.yml - cache-environment: true - cache-environment-key: "benchmark-${{runner.os}}-${{runner.arch}}-${{env.TODAY}}" + miniforge-version: "24.1.2-0" + activate-environment: asv + + - name: Get date + id: get-date + run: echo "today=$(/bin/date -u '+%Y%m%d')" >> $GITHUB_OUTPUT + shell: bash - - name: retry environment set up if failed - if: steps.env-setup.outcome == 'failure' - uses: mamba-org/setup-micromamba@v1 + - name: Cache environment + uses: actions/cache@v4 with: - download-micromamba: false - environment-file: ./ci/asv.yml - cache-environment: true - cache-environment-key: "benchmark-${{runner.os}}-${{runner.arch}}-${{env.TODAY}}" + path: ${{ env.CONDA }}/envs + key: conda-${{ runner.os }}--${{ runner.arch }}--${{ steps.get-date.outputs.today }}-${{ hashFiles('./ci/asv.yml') }}-${{ env.CACHE_NUMBER }} + env: + CACHE_NUMBER: 0 + id: cache + + - name: Update environment + run: + conda env update -n asv -f ./ci/asv.yml + if: steps.cache.outputs.cache-hit != 'true' + + - name: Conda list + run: | + conda info + conda list - name: Copy existing results run: | diff --git a/benchmarks/mpas_ocean.py b/benchmarks/mpas_ocean.py index e7eed5447..e124386ff 100644 --- a/benchmarks/mpas_ocean.py +++ b/benchmarks/mpas_ocean.py @@ -4,7 +4,7 @@ import uxarray as ux -current_path = Path(os.path.dirname(os.path.realpath(__file__))).parents[0] +current_path = Path(os.path.dirname(os.path.realpath(__file__))) data_var = 'bottomDepth' @@ -17,14 +17,14 @@ filenames = [grid_filename_480, data_filename_480, grid_filename_120, data_filename_120] for filename in filenames: - if not os.path.isfile(filename): + if not os.path.isfile(current_path / filename): # downloads the files from Cookbook repo, if they haven't been downloaded locally yet url = f"https://github.com/ProjectPythia/unstructured-grid-viz-cookbook/raw/main/meshfiles/{filename}" - _, headers = urllib.request.urlretrieve(url, filename=filename) + _, headers = urllib.request.urlretrieve(url, filename=current_path / filename) -file_path_dict = {"480km": [grid_filename_480, data_filename_480], - "120km": [grid_filename_120, data_filename_120]} +file_path_dict = {"480km": [current_path / grid_filename_480, current_path / data_filename_480], + "120km": [current_path / grid_filename_120, current_path / data_filename_120]} class Gradient: From 93cf4d9fd6d29fa8ed26d527ebe1724264da86d8 Mon Sep 17 00:00:00 2001 From: Philip Chmielowiec <67855069+philipc2@users.noreply.github.com> Date: Fri, 17 May 2024 23:20:33 -0500 Subject: [PATCH 41/41] Re-introduce Numba to face area computations (#794) * re-introduce numba to face area calculation * update coordinate conversion --- uxarray/grid/area.py | 11 +++++++++++ uxarray/grid/coordinates.py | 3 +++ 2 files changed, 14 insertions(+) diff --git a/uxarray/grid/area.py b/uxarray/grid/area.py index 6e1692feb..1dc6de52e 100644 --- a/uxarray/grid/area.py +++ b/uxarray/grid/area.py @@ -2,7 +2,13 @@ from uxarray.grid.coordinates import _lonlat_rad_to_xyz +from numba import njit, config +from uxarray.constants import ENABLE_JIT_CACHE, ENABLE_JIT +config.DISABLE_JIT = not ENABLE_JIT + + +@njit(cache=ENABLE_JIT_CACHE) def calculate_face_area( x, y, z, quadrature_rule="gaussian", order=4, coords_type="spherical" ): @@ -92,6 +98,7 @@ def calculate_face_area( return area, jacobian +@njit(cache=ENABLE_JIT_CACHE) def get_all_face_area_from_coords( x, y, @@ -166,6 +173,7 @@ def get_all_face_area_from_coords( return area, jacobian +@njit(cache=ENABLE_JIT_CACHE) def calculate_spherical_triangle_jacobian(node1, node2, node3, dA, dB): """Calculate Jacobian of a spherical triangle. This is a helper function for calculating face area. @@ -255,6 +263,7 @@ def calculate_spherical_triangle_jacobian(node1, node2, node3, dA, dB): return dJacobian +@njit(cache=ENABLE_JIT_CACHE) def calculate_spherical_triangle_jacobian_barycentric(node1, node2, node3, dA, dB): """Calculate Jacobian of a spherical triangle. This is a helper function for calculating face area. @@ -333,6 +342,7 @@ def calculate_spherical_triangle_jacobian_barycentric(node1, node2, node3, dA, d return 0.5 * dJacobian +@njit(cache=ENABLE_JIT_CACHE) def get_gauss_quadratureDG(nCount): """Gauss Quadrature Points for integration. @@ -577,6 +587,7 @@ def get_gauss_quadratureDG(nCount): return dG, dW +@njit(cache=ENABLE_JIT_CACHE) def get_tri_quadratureDG(nOrder): """Triangular Quadrature Points for integration. diff --git a/uxarray/grid/coordinates.py b/uxarray/grid/coordinates.py index 7032d8d22..ba0b27e57 100644 --- a/uxarray/grid/coordinates.py +++ b/uxarray/grid/coordinates.py @@ -8,7 +8,10 @@ from typing import Union +from numba import njit + +@njit(cache=True) def _lonlat_rad_to_xyz( lon: Union[np.ndarray, float], lat: Union[np.ndarray, float],

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