diff --git a/Tests/Data/NodePartitionedMesh/WithIntegrationPointStress/MixedElements/check_point_cloud.ipynb b/Tests/Data/NodePartitionedMesh/WithIntegrationPointStress/MixedElements/check_point_cloud.ipynb index a3b9c38b715..049d410acf8 100644 --- a/Tests/Data/NodePartitionedMesh/WithIntegrationPointStress/MixedElements/check_point_cloud.ipynb +++ b/Tests/Data/NodePartitionedMesh/WithIntegrationPointStress/MixedElements/check_point_cloud.ipynb @@ -1,5 +1,17 @@ { "cells": [ + { + "cell_type": "raw", + "id": "7946bd1a", + "metadata": {}, + "source": [ + "+++\n", + "title = \"Check point cloud\"\n", + "author = \"Christoph Lehmann\"\n", + "note = Dummy frontmatter, for CI testing only\n", + "+++\n" + ] + }, { "cell_type": "markdown", "id": "5867da3a-7df9-4467-a43b-1f63151d45a9", @@ -37,7 +49,8 @@ " assert num_ips == point_cloud.n_points\n", "\n", " # check that values of sigma_ip are equal\n", - " assert np.allclose(sigma_ip_field, sigma_ip_point, atol=1e-15, rtol=0) \n", + " assert np.allclose(sigma_ip_field, sigma_ip_point, atol=1e-15, rtol=0)\n", + "\n", "\n", "def check_all_points_enclosed_by_mesh_surface_3D(mesh, point_cloud):\n", " surface = mesh.extract_surface()\n", @@ -45,16 +58,17 @@ "\n", " # check that each point in the point cloud is contained in the \"original\" mesh\n", " assert points_in_mesh.n_points == point_cloud.n_points\n", - " \n", + "\n", + "\n", "def check_all_points_inside_bbox_2D(mesh, point_cloud):\n", " xmin, xmax, ymin, ymax, zmin, zmax = mesh.bounds\n", "\n", " assert zmin == 0.0 and zmax == 0.0 # make sure we are in 2D\n", - " \n", - " assert np.all(point_cloud.points[:,0] > xmin)\n", - " assert np.all(point_cloud.points[:,0] < xmax)\n", - " assert np.all(point_cloud.points[:,1] > ymin)\n", - " assert np.all(point_cloud.points[:,1] < ymax)" + "\n", + " assert np.all(point_cloud.points[:, 0] > xmin)\n", + " assert np.all(point_cloud.points[:, 0] < xmax)\n", + " assert np.all(point_cloud.points[:, 1] > ymin)\n", + " assert np.all(point_cloud.points[:, 1] < ymax)" ] }, { @@ -69,7 +83,9 @@ "cell_type": "code", "execution_count": 3, "id": "33ca2a9b-d38e-4c2f-a1c3-106ffceb04b9", - "metadata": {}, + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [], "source": [ "mesh = pv.read(\"mesh_with_3D_different_elements_sigma_ip.vtu\")\n", @@ -91,7 +107,9 @@ "cell_type": "code", "execution_count": 4, "id": "bc31ddb1-79c0-486f-bf36-717ffabcd205", - "metadata": {}, + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [], "source": [ "mesh = pv.read(\"TriQuad/quad_tri_THM_t_864000_000000.vtu\")\n", @@ -106,7 +124,9 @@ { "cell_type": "markdown", "id": "5c573198-0ec4-426a-8962-958533164273", - "metadata": {}, + "metadata": { + "lines_to_next_cell": 2 + }, "source": [ "# m1_3Dsquare test" ] @@ -115,7 +135,9 @@ "cell_type": "code", "execution_count": 5, "id": "de8879ef-eb71-425e-9c25-b39abc02085b", - "metadata": {}, + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [], "source": [ "def set_integration_order_to_3(path):\n", @@ -125,15 +147,18 @@ "\n", " ip_meta_str = bytes(mesh.field_data[\"IntegrationPointMetaData\"]).decode(\"utf-8\")\n", " ip_meta = json.loads(ip_meta_str)\n", - " ip_meta[\"integration_point_arrays\"][0]['integration_order'] = 3\n", + " ip_meta[\"integration_point_arrays\"][0][\"integration_order\"] = 3\n", " ip_meta\n", " ip_meta_str = json.dumps(ip_meta)\n", " ip_meta_str.encode(\"utf-8\")\n", "\n", - " mesh.field_data[\"IntegrationPointMetaData\"] = np.frombuffer(ip_meta_str.encode(\"utf-8\"), dtype=np.int8)\n", + " mesh.field_data[\"IntegrationPointMetaData\"] = np.frombuffer(\n", + " ip_meta_str.encode(\"utf-8\"), dtype=np.int8\n", + " )\n", "\n", " mesh.save(path)\n", "\n", + "\n", "# Before 2022-10 the integration point meta data was wrong.\n", "# This function can be used to correct it.\n", "# set_integration_order_to_3(\"../../../Mechanics/m1_3Dsquare/m1_3Dsquare_ts_1_t_1.000000.vtu\")" @@ -143,11 +168,15 @@ "cell_type": "code", "execution_count": 6, "id": "ed800430-f902-4b16-8b39-44d9fb768b4e", - "metadata": {}, + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [], "source": [ "mesh = pv.read(\"../../../Mechanics/m1_3Dsquare/m1_3Dsquare_ts_1_t_1.000000.vtu\")\n", - "point_cloud = pv.read(\"../../../Mechanics/m1_3Dsquare/m1_3Dsquare_ts_1_t_1.000000_point_cloud.vtu\")\n", + "point_cloud = pv.read(\n", + " \"../../../Mechanics/m1_3Dsquare/m1_3Dsquare_ts_1_t_1.000000_point_cloud.vtu\"\n", + ")\n", "\n", "check_ip_data(mesh, point_cloud, \"sigma_ip\")\n", "check_all_points_enclosed_by_mesh_surface_3D(mesh, point_cloud)" @@ -165,7 +194,9 @@ "cell_type": "code", "execution_count": 8, "id": "06b37fe2-0ca0-493a-8989-099b2f703d9e", - "metadata": {}, + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt" @@ -175,11 +206,13 @@ "cell_type": "code", "execution_count": 9, "id": "d78f6d04-9534-4803-a923-c6eb8327c3a0", - "metadata": {}, + "metadata": { + "lines_to_next_cell": 2 + }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -192,8 +225,8 @@ "fig, ax = plt.subplots()\n", "\n", "sigs_ip = point_cloud.point_data[\"sigma_ip\"]\n", - "sigs_ip_zz = sigs_ip[:,2]\n", - "zs_ip = point_cloud.points[:,2]\n", + "sigs_ip_zz = sigs_ip[:, 2]\n", + "zs_ip = point_cloud.points[:, 2]\n", "\n", "ax.scatter(zs_ip, sigs_ip_zz, label=\"ip data\")\n", "\n", @@ -204,7 +237,7 @@ "\n", "ax.legend()\n", "ax.set_ylabel(\"$\\sigma_{zz}$\")\n", - "ax.set_xlabel(\"$z$\");" + "ax.set_xlabel(\"$z$\")" ] }, { @@ -215,8 +248,10 @@ "outputs": [], "source": [ "# sigma_zz ranges between 0 and -60000, so an absolute tolerance of 0.4 or 0.65 is OK\n", - "assert np.allclose(sigs_ip[:,[0,1,3,4,5]], 0, atol=0.4, rtol=0)\n", - "assert np.allclose(sigs_ip_zz, 3e4 * zs_ip, atol=0.65, rtol=0) # shows that the z order of the points in the point cloud are not messed up" + "assert np.allclose(sigs_ip[:, [0, 1, 3, 4, 5]], 0, atol=0.4, rtol=0)\n", + "assert np.allclose(\n", + " sigs_ip_zz, 3e4 * zs_ip, atol=0.65, rtol=0\n", + ") # shows that the z order of the points in the point cloud are not messed up" ] } ], diff --git a/scripts/cmake/DependenciesExternalProject.cmake b/scripts/cmake/DependenciesExternalProject.cmake index 787d7ef1d9c..417ab7db6b5 100644 --- a/scripts/cmake/DependenciesExternalProject.cmake +++ b/scripts/cmake/DependenciesExternalProject.cmake @@ -132,12 +132,13 @@ if(OGS_USE_PETSC) if(NOT "--download-fc" IN_LIST OGS_PETSC_CONFIG_OPTIONS) list(APPEND _configure_opts --with-fc=0) endif() - if(ENV{CC}) + if(DEFINED ENV{CC}) list(APPEND _configure_opts --with-cc=$ENV{CC}) endif() - if(ENV{CXX}) + if(DEFINED ENV{CXX}) list(APPEND _configure_opts --with-cxx=$ENV{CXX}) endif() + message(STATUS "config opts: ${_configure_opts}") unset(ENV{PETSC_DIR}) BuildExternalProject( diff --git a/web/data/versions.json b/web/data/versions.json index 43efacea260..a67e5d99efb 100644 --- a/web/data/versions.json +++ b/web/data/versions.json @@ -17,7 +17,7 @@ "eigen": "3.4.0", "eigen-unsupported": "9441d94dccccd5db8d64179516fdc5b53994a047", "vtk": "9.1.0", - "petsc": "3.16.1", + "petsc": "3.20.0", "qt": "5.14.2", "python": "3.6", "snakemake": "7.20.0",