From e677cd16f385d31c0fd8b85d6c2d40a894c42058 Mon Sep 17 00:00:00 2001 From: Luca Carniato Date: Fri, 18 Aug 2023 18:28:24 +0200 Subject: [PATCH 1/4] Add face edges --- meshkernel/c_structures.py | 6 ++++++ meshkernel/py_structures.py | 6 ++++++ 2 files changed, 12 insertions(+) diff --git a/meshkernel/c_structures.py b/meshkernel/c_structures.py index ff1e7d91..c5f29f1a 100644 --- a/meshkernel/c_structures.py +++ b/meshkernel/c_structures.py @@ -28,7 +28,9 @@ class CMesh2d(Structure): Used for communicating with the MeshKernel dll. The ``_fields_`` attribute of this class contains the following fields: + - edge_faces (POINTER(c_int)): The face indices for each edge. - edge_nodes (POINTER(c_int)): The nodes composing each mesh 2d edge. + - face_edges (POINTER(c_int)): The edge indices for each face. - face_nodes (POINTER(c_int)): The nodes composing each mesh 2d face. - nodes_per_face (POINTER(c_int)): The nodes composing each mesh 2d face. - node_x (POINTER(c_double)): The x-coordinates of the nodes. @@ -44,7 +46,9 @@ class CMesh2d(Structure): """ _fields_ = [ + ("edge_faces", POINTER(c_int)), ("edge_nodes", POINTER(c_int)), + ("face_edges", POINTER(c_int)), ("face_nodes", POINTER(c_int)), ("nodes_per_face", POINTER(c_int)), ("node_x", POINTER(c_double)), @@ -86,7 +90,9 @@ def from_mesh2d(mesh2d: Mesh2d) -> CMesh2d: c_mesh2d = CMesh2d() # Set the pointers + c_mesh2d.edge_faces = as_ctypes(mesh2d.edge_faces) c_mesh2d.edge_nodes = as_ctypes(mesh2d.edge_nodes) + c_mesh2d.face_edges = as_ctypes(mesh2d.face_edges) c_mesh2d.face_nodes = as_ctypes(mesh2d.face_nodes) c_mesh2d.nodes_per_face = as_ctypes(mesh2d.nodes_per_face) c_mesh2d.node_x = as_ctypes(mesh2d.node_x) diff --git a/meshkernel/py_structures.py b/meshkernel/py_structures.py index 2dbcd308..4fd03f6c 100644 --- a/meshkernel/py_structures.py +++ b/meshkernel/py_structures.py @@ -92,6 +92,8 @@ class Mesh2d: Attributes: node_x (ndarray): A 1D double array describing the x-coordinates of the nodes. node_y (ndarray): A 1D double array describing the y-coordinates of the nodes. + edge_faces (ndarray, optional): A 1D integer array describing for each edge the indices of the faces. + face_edges (ndarray, optional): A 1D integer array describing for each face the indices of the edges. edge_nodes (ndarray, optional): A 1D integer array describing the nodes composing each mesh 2d edge. face_nodes (ndarray, optional): A 1D integer array describing the nodes composing each mesh 2d face. nodes_per_face (ndarray, optional): A 1D integer array describing the nodes composing each mesh 2d face. @@ -106,6 +108,8 @@ def __init__( self, node_x=np.empty(0, dtype=np.double), node_y=np.empty(0, dtype=np.double), + edge_faces=np.empty(0, dtype=np.int32), + face_edges=np.empty(0, dtype=np.int32), edge_nodes=np.empty(0, dtype=np.int32), face_nodes=np.empty(0, dtype=np.int32), nodes_per_face=np.empty(0, dtype=np.int32), @@ -116,6 +120,8 @@ def __init__( ): self.node_x: ndarray = node_x self.node_y: ndarray = node_y + self.edge_faces: ndarray = edge_faces + self.face_edges: ndarray = face_edges self.edge_nodes: ndarray = edge_nodes self.face_nodes: ndarray = face_nodes self.nodes_per_face: ndarray = nodes_per_face From b6bd19b88e577c7793065d02269b77fc93da3b7b Mon Sep 17 00:00:00 2001 From: Luca Carniato Date: Mon, 21 Aug 2023 11:23:30 +0200 Subject: [PATCH 2/4] Correct unit tests --- meshkernel/py_structures.py | 12 ++++++------ tests/test_c_structures.py | 4 ++++ tests/test_curvilinear_basics.py | 4 ++-- 3 files changed, 12 insertions(+), 8 deletions(-) diff --git a/meshkernel/py_structures.py b/meshkernel/py_structures.py index 4fd03f6c..d51e2bd3 100644 --- a/meshkernel/py_structures.py +++ b/meshkernel/py_structures.py @@ -92,8 +92,6 @@ class Mesh2d: Attributes: node_x (ndarray): A 1D double array describing the x-coordinates of the nodes. node_y (ndarray): A 1D double array describing the y-coordinates of the nodes. - edge_faces (ndarray, optional): A 1D integer array describing for each edge the indices of the faces. - face_edges (ndarray, optional): A 1D integer array describing for each face the indices of the edges. edge_nodes (ndarray, optional): A 1D integer array describing the nodes composing each mesh 2d edge. face_nodes (ndarray, optional): A 1D integer array describing the nodes composing each mesh 2d face. nodes_per_face (ndarray, optional): A 1D integer array describing the nodes composing each mesh 2d face. @@ -101,6 +99,8 @@ class Mesh2d: edge_y (ndarray, optional): A 1D double array describing x-coordinates of the mesh edges' middle points. face_x (ndarray, optional): A 1D double array describing x-coordinates of the mesh faces' mass centers. face_y (ndarray, optional): A 1D double array describing y-coordinates of the mesh faces' mass centers. + edge_faces (ndarray, optional): A 1D integer array describing for each edge the indices of the faces. + face_edges (ndarray, optional): A 1D integer array describing for each face the indices of the edges. """ @@ -108,8 +108,6 @@ def __init__( self, node_x=np.empty(0, dtype=np.double), node_y=np.empty(0, dtype=np.double), - edge_faces=np.empty(0, dtype=np.int32), - face_edges=np.empty(0, dtype=np.int32), edge_nodes=np.empty(0, dtype=np.int32), face_nodes=np.empty(0, dtype=np.int32), nodes_per_face=np.empty(0, dtype=np.int32), @@ -117,11 +115,11 @@ def __init__( edge_y=np.empty(0, dtype=np.double), face_x=np.empty(0, dtype=np.double), face_y=np.empty(0, dtype=np.double), + edge_faces=np.empty(0, dtype=np.int32), + face_edges=np.empty(0, dtype=np.int32), ): self.node_x: ndarray = node_x self.node_y: ndarray = node_y - self.edge_faces: ndarray = edge_faces - self.face_edges: ndarray = face_edges self.edge_nodes: ndarray = edge_nodes self.face_nodes: ndarray = face_nodes self.nodes_per_face: ndarray = nodes_per_face @@ -129,6 +127,8 @@ def __init__( self.edge_y: ndarray = edge_y self.face_x: ndarray = face_x self.face_y: ndarray = face_y + self.edge_faces: ndarray = edge_faces + self.face_edges: ndarray = face_edges def plot_edges(self, ax, *args, **kwargs): """Plots the edges at a given axes. diff --git a/tests/test_c_structures.py b/tests/test_c_structures.py index 71b569aa..98f42ae6 100644 --- a/tests/test_c_structures.py +++ b/tests/test_c_structures.py @@ -35,6 +35,8 @@ def test_cmesh2d_from_mesh2d(): edge_y = np.array([0.0, 0.5, 1.0, 0.5], dtype=np.double) face_x = np.array([0.5], dtype=np.double) face_y = np.array([0.5], dtype=np.double) + edge_faces = np.array([0, -1, 0, -1, 0, -1, 0, -1], dtype=np.int32) + face_edges = np.array([0, 1, 2, 3], dtype=np.int32) mesh2d = Mesh2d(node_x, node_y, edge_nodes) mesh2d.face_nodes = face_nodes @@ -43,6 +45,8 @@ def test_cmesh2d_from_mesh2d(): mesh2d.edge_y = edge_y mesh2d.face_x = face_x mesh2d.face_y = face_y + mesh2d.edge_faces = edge_faces + mesh2d.face_edges = face_edges c_mesh2d = CMesh2d.from_mesh2d(mesh2d) diff --git a/tests/test_curvilinear_basics.py b/tests/test_curvilinear_basics.py index 92c4a3ff..1552669c 100644 --- a/tests/test_curvilinear_basics.py +++ b/tests/test_curvilinear_basics.py @@ -255,8 +255,8 @@ def test_curvilinear_make_uniform_with_polygon(): curvilinear_grid = mk.curvilineargrid_get() # Test the number of m and n - assert curvilinear_grid.num_m == 6 - assert curvilinear_grid.num_n == 6 + assert curvilinear_grid.num_m == 11 + assert curvilinear_grid.num_n == 11 def test_curvilinear_refine_derefine(): From 8e579eff4398f7085e12c83bbda9e16a7485f6c1 Mon Sep 17 00:00:00 2001 From: Luca Carniato Date: Mon, 21 Aug 2023 15:41:45 +0200 Subject: [PATCH 3/4] initialize edge_faces and face_edges --- meshkernel/c_structures.py | 24 +++++++++++++++--------- 1 file changed, 15 insertions(+), 9 deletions(-) diff --git a/meshkernel/c_structures.py b/meshkernel/c_structures.py index c5f29f1a..c15ce4d1 100644 --- a/meshkernel/c_structures.py +++ b/meshkernel/c_structures.py @@ -128,6 +128,8 @@ def allocate_memory(self) -> Mesh2d: edge_y = np.empty(self.num_edges, dtype=np.double) face_x = np.empty(self.num_faces, dtype=np.double) face_y = np.empty(self.num_faces, dtype=np.double) + edge_faces = np.empty(self.num_edges * 2, dtype=np.int32) + face_edges = np.empty(self.num_face_nodes, dtype=np.int32) self.edge_nodes = as_ctypes(edge_nodes) self.face_nodes = as_ctypes(face_nodes) @@ -138,17 +140,21 @@ def allocate_memory(self) -> Mesh2d: self.edge_y = as_ctypes(edge_y) self.face_x = as_ctypes(face_x) self.face_y = as_ctypes(face_y) + self.edge_faces = as_ctypes(edge_faces) + self.face_edges = as_ctypes(face_edges) return Mesh2d( - node_x, - node_y, - edge_nodes, - face_nodes, - nodes_per_face, - edge_x, - edge_y, - face_x, - face_y, + node_x=node_x, + node_y=node_y, + edge_nodes=edge_nodes, + face_nodes=face_nodes, + nodes_per_face=nodes_per_face, + edge_x=edge_x, + edge_y=edge_y, + face_x=face_x, + face_y=face_y, + edge_faces=edge_faces, + face_edges=face_edges, ) From 927c57c00bd92e246d551e92f2e77c785116778c Mon Sep 17 00:00:00 2001 From: Luca Carniato Date: Mon, 21 Aug 2023 15:43:27 +0200 Subject: [PATCH 4/4] Ass a single cell for mk.mesh2d_get() in 01_mesh2d_basics.ipynb --- docs/examples/01_mesh2d_basics.ipynb | 67 ++++++++++++++-------------- 1 file changed, 34 insertions(+), 33 deletions(-) diff --git a/docs/examples/01_mesh2d_basics.ipynb b/docs/examples/01_mesh2d_basics.ipynb index d89d52bc..ab0da81e 100644 --- a/docs/examples/01_mesh2d_basics.ipynb +++ b/docs/examples/01_mesh2d_basics.ipynb @@ -79,7 +79,15 @@ "metadata": {}, "outputs": [], "source": [ - "mk.curvilinear_convert_to_mesh2d()\n", + "mk.curvilinear_convert_to_mesh2d()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ "mesh2d_input = mk.mesh2d_get()" ] }, @@ -95,12 +103,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -142,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -152,7 +160,7 @@ " 1, 1, 2, 2, 3, 4, 5, 5, 6, 6, 7, 8, 9, 9, 10, 10, 11])" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -170,12 +178,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -202,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -219,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -228,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -246,12 +254,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -282,12 +290,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -312,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -329,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -347,7 +355,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -363,12 +371,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -399,7 +407,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -416,12 +424,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -448,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -465,7 +473,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -482,12 +490,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -502,13 +510,6 @@ "fig, ax = plt.subplots()\n", "mesh2d_output_3.plot_edges(ax, color=\"blue\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": {