diff --git a/CHANGES.md b/CHANGES.md index d9738df00..03cac0a19 100644 --- a/CHANGES.md +++ b/CHANGES.md @@ -1,12 +1,34 @@ CHANGES: Underworld2 ======================= +Release 2.10.0 [] +---------------- +Fixes: +* SWIG4 compatibility changes. +* libxml2 ubuntu20.04 changes. +* Numpy 1.19 compatability changes. +* Fix `LGPLv3.txt` contents. File incorrect contained + GPLv3 text. + +Changes: +* Check for compressibility/penalty clash. +* Update example models for parallel usage. + +New: +* Add Dockerfile for Deepnote. +* Add `test_long.sh` long test runner. + +Release 2.9.5 [2020-07-27] +--------------------------- +Changes: +* Fix `LGPLv3.txt` contents. File incorrect contained + GPLv3 text. + Release 2.9.4 [2020-03-26] --------------------------- Changes: * Updating Dockerfile to include UWGeodynamics-2.9.4. - Release 2.9.3 [2020-03-09] --------------------------- Changes: diff --git a/Dockerfile b/Dockerfile index 2c878be9d..333e2cbbe 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,4 +1,4 @@ -FROM underworldcode/underworld2:2.9.1b +FROM underworldcode/underworld2:2.10.0b # Set the UW_MACHINE env variable for metrics ENV UW_MACHINE binder diff --git a/LGPLv3.txt b/LGPLv3.txt index 3877ae0a7..5357f6918 100644 --- a/LGPLv3.txt +++ b/LGPLv3.txt @@ -1,674 +1,166 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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Such new +versions will be similar in spirit to the present version, but may +differ in detail to address new problems or concerns. + + Each version is given a distinguishing version number. If the +Library as you received it specifies that a certain numbered version +of the GNU Lesser General Public License "or any later version" +applies to it, you have the option of following the terms and +conditions either of that published version or of any later version +published by the Free Software Foundation. If the Library as you +received it does not specify a version number of the GNU Lesser +General Public License, you may choose any version of the GNU Lesser +General Public License ever published by the Free Software Foundation. + + If the Library as you received it specifies that a proxy can decide +whether future versions of the GNU Lesser General Public License shall +apply, that proxy's public statement of acceptance of any version is +permanent authorization for you to choose that version for the +Library. + diff --git a/README.md b/README.md index ade7296bf..8921ba42a 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,6 @@ Underworld 2 [![DOI](https://joss.theoj.org/papers/10.21105/joss.01797/status.svg)](https://doi.org/10.21105/joss.01797) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.1436039.svg)](https://doi.org/10.5281/zenodo.1436039) -[![Build Status](http://115.146.85.138:32779/buildStatus/icon?job=Underworld2%2Fdevelopment)](http://115.146.85.138:32779/job/Underworld2/job/development/) [![Docs Status](https://readthedocs.org/projects/underworld2/badge/?version=latest)](https://underworld2.readthedocs.io/en/latest/?badge=latest) [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/underworldcode/underworld2/master) @@ -34,6 +33,7 @@ You can try out the code immediately via a Jupyter Binder cloud instance. Be awa | | | |-|-| +| [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/underworldcode/underworld2/v2.10.0b) | v2.10.0b (Py3) | | [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/underworldcode/underworld2/v2.9.2b) | v2.9.2b (Py3) | | [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/underworldcode/underworld2/v2.8.2b) | v2.8.2b (Py3) | | [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/underworldcode/underworld2/v2.7.1b) | v2.7.1b (Py2) | @@ -65,11 +65,9 @@ Note that basic usage metrics are dispatched when you use Underworld. To opt out Licensing --------- -Underworld is an open-source project licensed under LGPL-3. See `LICENSE.md` for details. +Underworld is an open-source project licensed under LGPL Version 3. See `LICENSE.md` and `LGPLv3.txt` for details. -Security Notice ---------------- -Earlier versions of our Docker images disabled token authentication for Jupyter Notebook. This potentially left the user's container vulnerable, in particular where running on institution networks. All Docker images have now been updated to enable token authentication. Please ensure you are using an updated image by running `docker pull` for your given Underworld version (or better yet, updating to the latest release). +Note that for releases `2.9.0`, `2.9.1`, `2.9.2`, `2.9.3` and `2.9.4`, the incorrect text was erroneously included within `LGPLv3.txt`, although these releases must still to be considered as licensed under LGPL Version 3. Citing Underworld ----------------- diff --git a/docs/development/docker/deepnote/Dockerfile b/docs/development/docker/deepnote/Dockerfile new file mode 100644 index 000000000..2dfe28d52 --- /dev/null +++ b/docs/development/docker/deepnote/Dockerfile @@ -0,0 +1,12 @@ +FROM gcr.io/deepnote-200602/templates/deepnote + +RUN sudo apt-get update -qq +RUN DEBIAN_FRONTEND=noninteractive sudo apt-get install -yq --no-install-recommends bash-completion build-essential git python3-minimal python3-dev python3-pip libxml2-dev xorg-dev ssh curl libfreetype6-dev libpng-dev libxft-dev xvfb freeglut3 freeglut3-dev libgl1-mesa-dri libgl1-mesa-glx python3-tk rsync vim less xauth swig gdb python3-dbg cmake python3-setuptools wget ca-certificates gfortran petsc-dev + +RUN pip3 install --no-cache-dir packaging h5py appdirs numpy plotly matplotlib pillow pyvirtualdisplay ipython ipyparallel pint sphinx sphinx_rtd_theme sphinxcontrib-napoleon mock scipy tabulate mpi4py scons +RUN pip3 install lavavu +RUN pip3 install git+https://github.com/underworldcode/underworld2 +RUN pip3 install UWGeodynamics + +# hack to force xvfb +RUN sed -i 's/in _os/not in _os/g' /opt/venv/lib/python3.7/site-packages/underworld/visualisation/__init__.py \ No newline at end of file diff --git a/docs/development/guidelines.md b/docs/development/guidelines.md index 1a94de8cd..c369c97eb 100644 --- a/docs/development/guidelines.md +++ b/docs/development/guidelines.md @@ -48,7 +48,7 @@ Version Control We follow the Gitflow Workflow: https://www.atlassian.com/git/tutorials/comparing-workflows/gitflow-workflow/ -Features branches should be created for anything besides quick fixes. Once ready for +Features branches must be created for all changes. Once ready for publishing to the development branch, a pull request should be created and another team member nominated to review the changes. Pull request should only be merged once the following criterion are met: diff --git a/docs/development/release_guidelines.md b/docs/development/release_guidelines.md index ae1c63ce4..7a169d2a4 100644 --- a/docs/development/release_guidelines.md +++ b/docs/development/release_guidelines.md @@ -27,17 +27,19 @@ Documentation review * Review docstrings updates for deprecation warnings. * Check for other DEPRECATE flags in the code. * Check autocomplete to ensure no garbage has slipped in. Non - user relevant objects should be made private so they don't appear in - autocomplete suggestions. + user relevant objects should be made private so they don't appear in + autocomplete suggestions. * Review ``docs/`` for style, relevance and updates. +* Review ``docs/install_guides``, in particular ensure that guides + for Gadi & Magnus are up to date. * Review docs generated at http://underworld2.readthedocs.io/ - make sure builds are passing - review latest content * Review top level `README.md`. * Review `LICENSE.md`. -* Review `.zenodo.json`. +* Review `.zenodo.json` (and validate using `jsonlint`). * Update the copyright information if necessary. -** Add new Binder link, and keep link to old Binder. +* Add new Binder link, and keep link to old Binder. * Generate/update change log (`CHANGES.md`). * Review cheat sheet contents. * Increment version number within ``underworld/_version.py`` @@ -46,18 +48,17 @@ Documentation review Testing ======= -* Ensure 'run_tests.py' script run to completion without raising an exception. -* Test on -** docker -** OSX -** linux -** NCI -** Pawsey -* Scaling tests on NCI and Pawsey machines. -* High resolution production tests on Raijin, Magnus run to completion with - correct results. -* Ensure jenkins and docker are working correctly. -* Ensure metrics are being dispatched. +* Ensure 'test_long.py' script run to completion without raising an exception. + Test on Docker/NCI/Pawsey. Note that parallel jobs within script may need to be + executed by hand on NCI/Pawsey. +* Large parallel tests (NCI/Pawsey). + Compare against previous for timing performance, and confirming expected convergence rates for SolDB3d. + - IO=0, JOBS="1 2 4 6 8 10 12 14 16 18 20 24", BASE=16, ORDER=2, RTOL=1e-11, PENALTY=-1, MODEL=SOLDB3d + - IO=0, JOBS="1 2 4 6 8 10 12 14 16 18 20 24", BASE=32, ORDER=1, RTOL=1e-6, PENALTY=-1, MODEL=SOLH + - IO=1, JOBS="1 2 4 6 8 10", BASE=32, ORDER=1, RTOL=1e-6, PENALTY=-1, MODEL=SOLH +* Confirm Jenkins is running tests and tests are returning expected results. +* Confirm Docker images are being generated correctly. +* Confirm metrics are being dispatched. Creating the release ==================== @@ -74,6 +75,7 @@ Announce new version After the release ============ * Increment version number within ``underworld/_version.py`` on dev branch (eg 2.6.0-dev) +* Update `FROM` tag in top level (binder) Dockerfile to use dev images. * Check `docker/docker.md` for docker related actions. diff --git a/docs/development/run_tests.py b/docs/development/run_tests.py index ad5bdab28..42d8495f4 100755 --- a/docs/development/run_tests.py +++ b/docs/development/run_tests.py @@ -155,7 +155,7 @@ def run_file(fname, dir, exe, job): os.mkdir(dir) # create test log file - logFileName = "testing.log" + logFileName = "test_run.log" logFile = open(dir+logFileName, "w") maxjobs = args.jobs @@ -179,7 +179,7 @@ def run_file(fname, dir, exe, job): # if prefix args found and it's an ipynb, use 'jupyter nbconvert --execute' if is_ipynb and not args.prepend: exe = ['jupyter', 'nbconvert', '--ExecutePreprocessor.kernel_name="python3"', - '--ExecutePreprocessor.timeout=360','--execute', '--stdout'] + '--ExecutePreprocessor.timeout=10000','--execute', '--stdout'] elif is_ipynb and args.prepend: # convert ipynb to py and run with python print("Converting test {} to .py".format(fname)); diff --git a/docs/examples/02_Convection_Example.ipynb b/docs/examples/02_Convection_Example.ipynb index 952d0940c..747e7d36e 100644 --- a/docs/examples/02_Convection_Example.ipynb +++ b/docs/examples/02_Convection_Example.ipynb @@ -358,7 +358,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/docs/examples/03_BlankenbachBenchmark.ipynb b/docs/examples/03_BlankenbachBenchmark.ipynb index 4b8b23fb0..24a936c1e 100644 --- a/docs/examples/03_BlankenbachBenchmark.ipynb +++ b/docs/examples/03_BlankenbachBenchmark.ipynb @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -101,7 +101,7 @@ "metadata": {}, "outputs": [], "source": [ - "inputPath = 'input/1_03_BlankenbachBenchmark/'\n", + "inputPath = 'input/03_BlankenbachBenchmark/'\n", "outputPath = 'output/'\n", "# Make output directory if necessary.\n", "if uw.mpi.rank==0:\n", @@ -206,15 +206,7 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading 64*64 for Ra = 1e4\n" - ] - } - ], + "outputs": [], "source": [ "if(LoadFromFile == True):\n", " # Setup mesh and temperature field for 64*64 data file.\n", @@ -228,13 +220,10 @@ " # read in saved steady state temperature field data\n", " if( case == \"a\" ):\n", " temperatureField64.load(inputPath+'tempfield_inp_64_Ra1e4.h5')\n", - " print('Loading 64*64 for Ra = 1e4')\n", " elif( case == \"b\" ):\n", " temperatureField64.load(inputPath+'tempfield_inp_64_Ra1e5.h5')\n", - " print('Loading 64*64 for Ra = 1e5')\n", " else:\n", " temperatureField64.load(inputPath+'tempfield_inp_64_Ra1e6.h5')\n", - " print('Loading 64*64 for Ra = 1e6')\n", " \n", " if( res==64 ): # no remeshing needed, copy directly\n", " temperatureField.data[:] = temperatureField64.data[:]\n", @@ -282,7 +271,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -443,7 +432,7 @@ ], "source": [ "nu = - nuTop.evaluate()[0]/nuBottom.evaluate()[0]\n", - "print('Nusselt number = {0:.6f}'.format(nu))" + "if uw.mpi.rank == 0 : print('Nusselt number = {0:.6f}'.format(nu))" ] }, { @@ -480,7 +469,7 @@ "intVdotV = uw.utils.Integral( fn.math.dot( velocityField, velocityField ), mesh )\n", "\n", "vrms = math.sqrt( intVdotV.evaluate()[0] )\n", - "print('Initial vrms = {0:.3f}'.format(vrms))" + "if uw.mpi.rank == 0 : print('Initial vrms = {0:.3f}'.format(vrms))" ] }, { @@ -633,7 +622,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -666,7 +655,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -688,95 +677,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Parallel friendly post analysis\n", - "----\n", - "\n", - "When running underworld in parallel the data of each mesh variable is spread across all the processors. However often we will want to calculate a quantity based on data at specific points that may not all be on the same processor.\n", - "\n", - "A solution is presented here which consists of saving the data from all processors to file, then reloading the mesh variable data using a new non-partitioned mesh. This enables all the data to be available to each processor. We will the carry out the post analysis using the first processor.\n", - "\n", - "**Save temperature, pressure and velocity data**\n", - "\n", - "Save the basic mesh variable data to files using the HDF5 format. This is the same file type as is loaded above." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mesh.save(outputPath+\"mesh.h5\")\n", - "temperatureField.save(outputPath+'tempfield.h5')\n", - "pressureField.save(outputPath+'presfield.h5')\n", - "velocityField.save(outputPath+'velfield.h5')" + "Analysis\n", + "----" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**Construct new mesh and variable on non-partitioned mesh**\n", - "\n", - "Read saved mesh variable data into a new mesh variable where the information is not partitioned across multiple processors. This means that we can use a single processor to access all the data and calculate some quantities of interest." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [], - "source": [ - "# build a non-partitioned mesh with same box size\n", - "mesh0 = uw.mesh.FeMesh_Cartesian( elementType = (\"Q1/dQ0\"), \n", - " elementRes = (res, res), \n", - " minCoord = (0., 0.), \n", - " maxCoord = (boxLength, boxHeight),\n", - " partitioned = False ) \n", - "# load previous mesh coordinate data onto new non-partitioned mesh\n", - "mesh0.load(outputPath+'mesh.h5')\n", - "\n", - "# load T, P and V data onto the new mesh\n", - "# note that pressure is always on the submesh\n", - "temperatureField0 = mesh0.add_variable( nodeDofCount=1 ) \n", - "pressureField0 = mesh0.subMesh.add_variable( nodeDofCount=1 ) \n", - "velocityField0 = mesh0.add_variable( nodeDofCount=2 ) \n", - "temperatureField0.load(outputPath+\"tempfield.h5\")\n", - "pressureField0.load(outputPath+\"presfield.h5\")\n", - "velocityField0.load(outputPath+\"velfield.h5\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Temperature gradient**\n", - "\n", "The final benchmarks in the Blankenbach paper involve the temperature gradient in the vertical direction ($\\frac{\\partial T}{\\partial z}$). This is easy to find using the underworld functions, as shown below.\n" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "metadata": { "scrolled": false }, "outputs": [], "source": [ - "if(uw.mpi.rank==0):\n", - " tempgradField = temperatureField0.fn_gradient\n", - " vertTGradField = - boxHeight * tempgradField[1] / tempMax # scaled for direct benchmarking below" + "tempgradField = temperatureField.fn_gradient\n", + "vertTGradField = - boxHeight * tempgradField[1] / tempMax # scaled for direct benchmarking below" ] }, { @@ -802,7 +723,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -816,50 +737,34 @@ } ], "source": [ - "if(uw.mpi.rank==0):\n", - " q1 = vertTGradField.evaluate( (0., boxHeight))[0][0]\n", - " q2 = vertTGradField.evaluate( (boxLength, boxHeight))[0][0]\n", - " q3 = vertTGradField.evaluate( (boxLength, 0.))[0][0]\n", - " q4 = vertTGradField.evaluate( (0., 0.))[0][0]\n", + "q1 = vertTGradField.evaluate_global( ( 0., boxHeight) )\n", + "q2 = vertTGradField.evaluate_global( (boxLength, boxHeight) )\n", + "q3 = vertTGradField.evaluate_global( (boxLength, 0.) )\n", + "q4 = vertTGradField.evaluate_global( ( 0., 0.) )\n", "\n", + "if(uw.mpi.rank==0):\n", " print('Rayleigh number = {0:.1e}'.format(Ra))\n", - " print('q1 = {0:.3f}; q2 = {1:.3f}'.format(q1, q2))\n", - " print('q3 = {0:.3f}; q4 = {1:.3f}'.format(q3, q4))" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's add a test to ensure things are working as expected\n", - "if case == \"a\":\n", - " if not np.isclose(q1,8.020,rtol=1.e-2):\n", - " raise RuntimeError(\"Model did not produce the expected q1.\")\n", - " if not np.isclose(q2,0.589,rtol=1.e-2):\n", - " raise RuntimeError(\"Model did not produce the expected q2.\")" + " print('q1 = {0:.3f}; q2 = {1:.3f}'.format(q1[0][0], q2[0][0]))\n", + " print('q3 = {0:.3f}; q4 = {1:.3f}'.format(q3[0][0], q4[0][0]))\n", + " \n", + " # Let's add a test to ensure things are working as expected\n", + " if case == \"a\":\n", + " if not np.isclose(q1[0][0],8.020,rtol=1.e-2):\n", + " raise RuntimeError(\"Model did not produce the expected q1.\")\n", + " if not np.isclose(q2[0][0],0.589,rtol=1.e-2):\n", + " raise RuntimeError(\"Model did not produce the expected q2.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**Save time and rms values**\n", - "\n", - "The following command uses the ``numpy`` package save to text file function to output all $v_{RMS}$ values as a function of time. This is particularly useful if you have run the simulation from the perturbed initial condition rather than the saved data file, as you can see the system coming to steady state.\n", - "\n", - "The format for this text file is:\n", - "\n", - " timeVal[0], vrmsVal[0]\n", - " timeVal[1], vrmsVal[1]\n", - " ...\n", - " timeVal[N], vrmsVal[N]" + "**Save time and rms values**" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -887,19 +792,21 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ - "# get topography from non-partitioned stress tensor\n", - "if(uw.mpi.rank==0):\n", - " stresstensorFn = 2.* stokes.fn_viscosity*fn.tensor.symmetric( velocityField0.fn_gradient ) - (1.,1.,0.)*pressureField0\n", - " verticalStressFn = stresstensorFn[1]\n", - " stress_zz_top = -verticalStressFn.evaluate(mesh0.specialSets[\"MaxJ_VertexSet\"])\n", + "# get topography\n", + "stresstensorFn = 2.* stokes.fn_viscosity*fn.tensor.symmetric( velocityField.fn_gradient ) - (1.,1.,0.)*pressureField\n", + "verticalStressFn = stresstensorFn[1]\n", "\n", - "# subtract the average value for benchmark.\n", - " mean_sigma_zz_top = np.mean(stress_zz_top)\n", - " sigma_zz_top = stress_zz_top - mean_sigma_zz_top" + "# subtract the average value for benchmark\n", + "top_verts = mesh.specialSets[\"MaxJ_VertexSet\"]\n", + "mean_sigma_zz_top = uw.utils.Integral(-verticalStressFn,\n", + " mesh,\n", + " 'surface',\n", + " top_verts).evaluate()/boxLength\n", + "sigma_zz_top = -verticalStressFn - mean_sigma_zz_top" ] }, { @@ -917,22 +824,21 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "# Set parameters in SI units\n", - "if(uw.mpi.rank==0):\n", - " grav = 10 # m.s^-2\n", - " height = 1.e6 # m \n", - " rho = 4.0e3 # g.m^-3\n", - " kappa = 1.0e-6 # m^2.s^-1\n", + "grav = 10 # m.s^-2\n", + "height = 1.e6 # m \n", + "rho = 4.0e3 # g.m^-3\n", + "kappa = 1.0e-6 # m^2.s^-1\n", "\n", "# dimensionalise \n", - " dim_sigma_zz_top = (eta0 * kappa / (height*height)) * sigma_zz_top\n", + "dim_sigma_zz_top = (eta0 * kappa / (height*height)) * sigma_zz_top\n", "\n", "# find topography in [m]\n", - " topography = dim_sigma_zz_top / (rho * grav) " + "topography = dim_sigma_zz_top / (rho * grav) " ] }, { @@ -941,19 +847,27 @@ "source": [ "**Calculate x-coordinate at zero stress**\n", "\n", - "Calculate the zero point for the stress along the x-axis at the top of the box using the **interpolation function** from ``numpy``. Note that ``numpy`` requires that the first array input for ``np.interp`` must be increasing, so the negative of the topography is used." + "Calculate the zero point for the stress along the x-axis at the top of the box." ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ - "if(uw.mpi.rank==0):\n", - " xCoordFn = fn.input()[0]\n", - " x = xCoordFn.evaluate(mesh0.specialSets[\"MinJ_VertexSet\"])\n", - " xIntercept = np.interp(0.0,-1.0*topography[:, 0],x[:, 0])" + "# First construct abs function\n", + "abs_topo = fn.math.abs(topography)\n", + "# Now create a `min_max` view function.\n", + "# These functions don't modify results, they simply observe\n", + "# calculated results as functions are being evaluated. \n", + "# We will use this to watch out for the minimum value as \n", + "# we traverse function evaluates.\n", + "# We also set `fn_auxiliary` which allows us to also capture\n", + "# the coordinate of the min value.\n", + "abs_topo_min_max = fn.view.min_max(abs_topo,fn_auxiliary=fn.coord()) # create min_max\n", + "ignore = abs_topo_min_max.evaluate(mesh.specialSets[\"MaxJ_VertexSet\"]) # traverse function evaluations\n", + "min_abs_topo_coord = abs_topo_min_max.min_global_auxiliary()[0][0] # get x coord of global min" ] }, { @@ -973,7 +887,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -981,34 +895,32 @@ "output_type": "stream", "text": [ "Rayleigh number = 1.0e+04\n", - "Topography[x=0],[x=max] = 2243.93, -2909.09\n", - "x(topo=0) = 0.540372\n" + "Topography[x=0],[x=max] = 2252.36, -2900.67\n", + "x(topo=0) = 0.539062\n" ] } ], "source": [ + "e1 = topography.evaluate_global( ( 0.,boxHeight) )\n", + "e2 = topography.evaluate_global( (boxLength,boxHeight) )\n", + "\n", "if(uw.mpi.rank==0):\n", - " e1 = float(topography[0])\n", - " e2 = float(topography[len(topography)-1])\n", + " e1 = e1[0][0] # pull values out of list of results\n", + " e2 = e2[0][0] # pull values out of list of results\n", " print('Rayleigh number = {0:.1e}'.format(Ra))\n", " print('Topography[x=0],[x=max] = {0:.2f}, {1:.2f}'.format(e1, e2))\n", - " print('x(topo=0) = {0:.6f}'.format(xIntercept))\n", + " print('x(topo=0) = {0:.6f}'.format(min_abs_topo_coord))\n", " # output a summary file with benchmark values (useful for parallel runs)\n", - " np.savetxt(outputPath+'summary.txt', [Ra, e1, e2, xIntercept, q1, q2, q3, q4])" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's add a test to ensure things are working as expected\n", - "if case == \"a\":\n", - " if not np.isclose(e1,2254.02,rtol=1.e-2):\n", - " raise RuntimeError(\"Model did not produce the expected xi1.\")\n", - " if not np.isclose(e2,-2903.23,rtol=1.e-2):\n", - " raise RuntimeError(\"Model did not produce the expected xi2.\")" + " np.savetxt(outputPath+'summary.txt', [Ra, e1, e2, min_abs_topo_coord, q1, q2, q3, q4])\n", + "\n", + " # Let's add a test to ensure things are working as expected\n", + " if case == \"a\":\n", + " if not np.isclose(e1,2254.02,rtol=1.e-3):\n", + " raise RuntimeError(\"Model did not produce the expected xi1.\")\n", + " if not np.isclose(e2,-2903.23,rtol=1.e-3):\n", + " raise RuntimeError(\"Model did not produce the expected xi2.\")\n", + " if not np.isclose(min_abs_topo_coord,0.539372,rtol=1.e-3):\n", + " raise RuntimeError(\"Model did not produce the expected xi2.\")" ] } ], diff --git a/docs/examples/04_StokesSinker.ipynb b/docs/examples/04_StokesSinker.ipynb index cee5830cd..5f608aedb 100644 --- a/docs/examples/04_StokesSinker.ipynb +++ b/docs/examples/04_StokesSinker.ipynb @@ -32,7 +32,9 @@ "from underworld import function as fn\n", "import underworld.visualisation as vis\n", "import numpy as np\n", - "import math" + "import math\n", + "\n", + "rank = uw.mpi.rank" ] }, { @@ -258,7 +260,7 @@ ], "source": [ "ymin = GetSwarmYMin()\n", - "if(uw.mpi.rank==0):\n", + "if rank==0:\n", " print('Minimum y value for sinker = {0:.3e}'.format(ymin))" ] }, @@ -364,6 +366,18 @@ "execution_count": 13, "metadata": {}, "outputs": [], + "source": [ + "solver.set_inner_method(\"mumps\")\n", + "inner_rtol = 1e-7\n", + "solver.set_inner_rtol(inner_rtol)\n", + "solver.set_outer_rtol(10*inner_rtol)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], "source": [ "top = mesh.specialSets[\"MaxJ_VertexSet\"]\n", "surfaceArea = uw.utils.Integral(fn=1.0,mesh=mesh, integrationType='surface', surfaceIndexSet=top)\n", @@ -374,7 +388,7 @@ " (area,) = surfaceArea.evaluate()\n", " (p0,) = surfacePressureIntegral.evaluate()\n", " offset = p0/area\n", - " print(\"Zeroing pressure using mean upper surface pressure {}\".format( offset ))\n", + " if rank==0: print(\"Zeroing pressure using mean upper surface pressure {}\".format( offset ))\n", " pressureField.data[:] -= offset" ] }, @@ -392,7 +406,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -413,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -429,32 +443,32 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Zeroing pressure using mean upper surface pressure -0.5963863976831097\n", + "Zeroing pressure using mean upper surface pressure -0.5963865446963474\n", "step = 0; time = 0.000e+00; v_rms = 9.022e-03; height = 6.000e-01\n", - "Zeroing pressure using mean upper surface pressure -0.5950600562722993\n", + "Zeroing pressure using mean upper surface pressure -0.5950600430107463\n", "step = 1; time = 2.996e-01; v_rms = 9.164e-03; height = 5.925e-01\n", - "Zeroing pressure using mean upper surface pressure -0.5938677131209781\n", + "Zeroing pressure using mean upper surface pressure -0.5938679921131681\n", "step = 2; time = 5.948e-01; v_rms = 9.316e-03; height = 5.849e-01\n", - "Zeroing pressure using mean upper surface pressure -0.5926534525201347\n", + "Zeroing pressure using mean upper surface pressure -0.5926535037924707\n", "step = 3; time = 8.859e-01; v_rms = 9.454e-03; height = 5.774e-01\n", - "Zeroing pressure using mean upper surface pressure -0.5912657573592763\n", + "Zeroing pressure using mean upper surface pressure -0.5912588507700052\n", "step = 4; time = 1.173e+00; v_rms = 9.571e-03; height = 5.698e-01\n", - "Zeroing pressure using mean upper surface pressure -0.5900389835377379\n", + "Zeroing pressure using mean upper surface pressure -0.5900388239149108\n", "step = 5; time = 1.457e+00; v_rms = 9.696e-03; height = 5.623e-01\n", - "Zeroing pressure using mean upper surface pressure -0.5886541507651477\n", + "Zeroing pressure using mean upper surface pressure -0.5886540412905718\n", "step = 6; time = 1.738e+00; v_rms = 9.797e-03; height = 5.548e-01\n", - "Zeroing pressure using mean upper surface pressure -0.5874560154524642\n", + "Zeroing pressure using mean upper surface pressure -0.5874559408488842\n", "step = 7; time = 2.017e+00; v_rms = 9.921e-03; height = 5.472e-01\n", - "Zeroing pressure using mean upper surface pressure -0.5860024955119602\n", + "Zeroing pressure using mean upper surface pressure -0.586002447443974\n", "step = 8; time = 2.292e+00; v_rms = 1.001e-02; height = 5.397e-01\n", - "Zeroing pressure using mean upper surface pressure -0.5849501369904534\n", + "Zeroing pressure using mean upper surface pressure -0.5849503118787854\n", "step = 9; time = 2.566e+00; v_rms = 1.013e-02; height = 5.321e-01\n" ] } @@ -464,9 +478,10 @@ "time = 0.\n", "step = 0\n", "nsteps = 10\n", - "if(uw.mpi.rank==0):\n", - " tSinker = np.zeros(nsteps)\n", - " ySinker = np.zeros(nsteps)\n", + "\n", + "tSinker = np.zeros(nsteps)\n", + "ySinker = np.zeros(nsteps)\n", + "\n", "# Perform 10 steps\n", "while step" ] @@ -535,7 +557,7 @@ } ], "source": [ - "if(uw.mpi.rank==0):\n", + "if rank==0:\n", " print('Initial position: t = {0:.3f}, y = {1:.3f}'.format(tSinker[0], ySinker[0]))\n", " print('Final position: t = {0:.3f}, y = {1:.3f}'.format(tSinker[nsteps-1], ySinker[nsteps-1]))\n", "\n", @@ -558,13 +580,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -589,13 +611,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -607,7 +629,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -648,7 +670,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/docs/examples/05_Rayleigh_Taylor.ipynb b/docs/examples/05_Rayleigh_Taylor.ipynb index bea97a79a..16190c29a 100644 --- a/docs/examples/05_Rayleigh_Taylor.ipynb +++ b/docs/examples/05_Rayleigh_Taylor.ipynb @@ -72,8 +72,8 @@ "\n", "longtest = False\n", "\n", - "model_end_time = 3.\n", - "res = 64\n", + "model_end_time = 240.\n", + "res = 32\n", "stokes_inner_tol = 1e-6\n", "stokes_outer_tol = 1e-5\n", "# users ignore\n", @@ -81,12 +81,9 @@ "if \"UW_LONGTEST\" in os.environ or longtest:\n", " # to capture the peak V_rms (benchmark) set model_end_time~220\n", " # the peak V_rms. \n", - " \n", - " model_end_time = 800\n", " res = 128\n", " stokes_inner_tol = 1e-7\n", - " stokes_outer_tol = 1e-5\n", - " " + " stokes_outer_tol = 1e-6" ] }, { @@ -152,7 +149,7 @@ "materialIndex = swarm.add_variable( dataType=\"int\", count=1 )\n", "\n", "# Create a layout object, populate the swarm with particles.\n", - "swarmLayout = uw.swarm.layouts.PerCellSpaceFillerLayout( swarm=swarm, particlesPerCell=20 )\n", + "swarmLayout = uw.swarm.layouts.PerCellSpaceFillerLayout( swarm=swarm, particlesPerCell=30 )\n", "swarm.populate_using_layout( layout=swarmLayout )" ] }, @@ -213,7 +210,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -312,9 +309,8 @@ "# Optional solver settings\n", "if(uw.mpi.size==1):\n", " solver.set_inner_method(\"lu\")\n", - "# solver.set_penalty(1.0e6) \n", - "# solver.set_inner_rtol = stokes_inner_tol\n", - "solver.set_outer_rtol = stokes_outer_tol\n", + "solver.set_inner_rtol(stokes_inner_tol) \n", + "solver.set_outer_rtol(stokes_outer_tol) \n", "\n", "# Create a system to advect the swarm\n", "advector = uw.systems.SwarmAdvector( swarm=swarm, velocityField=velocityField, order=2 )" @@ -369,8 +365,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "step = 0; time = 0.000e+00; v_rms = 1.833e-04\n", - "step = 1; time = 1.697e+01; v_rms = 1.833e-04\n" + "step = 0; time = 0.000e+00; v_rms = 1.872e-04\n", + "step = 20; time = 1.805e+02; v_rms = 1.879e-03\n", + "step = 40; time = 2.171e+02; v_rms = 3.007e-03\n", + "step = 56; time = 2.409e+02; v_rms = 3.062e-03\n" ] } ], @@ -416,9 +414,7 @@ "metadata": {}, "source": [ "Post simulation analysis\n", - "-----\n", - "\n", - "In two sections - 'demo' or 'longtest'" + "-----" ] }, { @@ -429,7 +425,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -454,21 +450,13 @@ "output_type": "stream", "text": [ "Viscosity ratio = 1.00\n", - " t(max vrms) = 0.00\n", - " vrms = 1.8334e-04\n" + " t(max vrms) = 227.53\n", + " vrms = 3.1447e-03\n" ] } ], "source": [ - "if longtest == False:\n", - " # This cell is for testing purposes. It ensures the example computes the expected flow velocity rms values\n", - " if not np.isclose(stokes.velocity_rms(), 1.83340219912e-4):\n", - " raise RuntimeError(\"The Velocity RMS {} is not close to expected value\".format(stokes.velocity_rms()))\n", - "\n", - "# import os\n", - "# os.write(1, \"Vrms is {:.5e} \\n\".format(stokes.velocity_rms()))\n", - "\n", - "if(uw.mpi.rank==0):\n", + "if uw.mpi.rank==0:\n", " \n", " maxIndex = np.argmax(vrmsVal)\n", "\n", @@ -507,7 +495,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -545,6 +533,29 @@ " ax.set_ylabel('RMS velocity')\n", " ax.set_xlim([0.0,1000.0])" ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "t_max = 212. # expected vrms max time\n", + "vrms_max = 3.1183e-03 # expected vrms max\n", + "if uw.mpi.rank==0:\n", + " if longtest: \n", + " rtol = 1.e-2\n", + " else:\n", + " rtol = 1.e-1\n", + " if not np.allclose(timeVal[maxIndex], t_max, rtol=rtol):\n", + " raise RuntimeError(\"Expected t_max ({0:.2f}) is different to encountered value ({1:.2f})\".format(t_max,timeVal[maxIndex]))\n", + " if longtest: \n", + " rtol = 1.e-3\n", + " else:\n", + " rtol = 1.e-2\n", + " if not np.allclose(vrmsVal[maxIndex], vrms_max, rtol=rtol):\n", + " raise RuntimeError(\"Expected vrms_max ({0:.4e}) is different to encountered value ({1:.4e})\".format(vrms_max,vrmsVal[maxIndex]))" + ] } ], "metadata": { diff --git a/docs/examples/06_SlabSubduction.ipynb b/docs/examples/06_SlabSubduction.ipynb index 3577d378c..0b0a038bd 100644 --- a/docs/examples/06_SlabSubduction.ipynb +++ b/docs/examples/06_SlabSubduction.ipynb @@ -25,7 +25,9 @@ "from underworld import function as fn\n", "import underworld.visualisation as vis\n", "import numpy as np\n", - "import os" + "import os\n", + "\n", + "rank = uw.mpi.rank" ] }, { @@ -36,7 +38,7 @@ "source": [ "outputPath = os.path.join(os.path.abspath(\".\"),\"output/\")\n", "\n", - "if uw.mpi.rank==0:\n", + "if rank==0:\n", " if not os.path.exists(outputPath):\n", " os.makedirs(outputPath)\n", "uw.mpi.barrier()" @@ -168,7 +170,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -328,9 +330,8 @@ "metadata": {}, "outputs": [], "source": [ - "# use \"lu\" direct solve if running in serial\n", - "if(uw.mpi.size==1):\n", - " solver.set_inner_method(\"lu\")" + "# use \"mumps\" direct solve, best for 2D models.\n", + "solver.set_inner_method(\"mumps\")" ] }, { @@ -440,7 +441,7 @@ " figStress.save( outputPath + \"stress\" + str(step).zfill(4))\n", " \n", " vrms = stokes.velocity_rms()\n", - " print('step = {0:6d}; time = {1:.3e}; Vrms = {2:.3e}'.format(step,time,vrms))\n", + " if rank==0: print('step = {0:6d}; time = {1:.3e}; Vrms = {2:.3e}'.format(step,time,vrms))\n", "\n", " # update\n", " time,step = update()" @@ -453,7 +454,7 @@ "outputs": [], "source": [ "# Add test to ensure model is producing expected results.\n", - "if (step==3) and not np.isclose(vrms,4.068e-5):\n", + "if (step==3) and not np.isclose(vrms,4.068e-5, rtol=5e-2):\n", " raise RuntimeError(\"VRMS differs from expected value.\")" ] }, @@ -496,7 +497,7 @@ "output_type": "stream", "text": [ "Saved database 'output/subduction.gldb'\n", - " - 5 figures : ['Particles', 'Figure_4', 'Figure_1', 'Figure_3', 'Figure_2']\n", + " - 5 figures : dict_keys(['Particles', 'Figure_1', 'Figure_2', 'Figure_3', 'Figure_4'])\n", " - 2 timesteps (final = 2) : [0, 2]\n" ] } @@ -504,9 +505,10 @@ "source": [ "figs = viewer.figures\n", "steps = viewer.steps\n", - "print(\"Saved database '%s'\" % (viewer.filename))\n", - "print(\" - %d figures : %s\" % (len(figs), str(figs.keys())))\n", - "print(\" - %d timesteps (final = %d) : %s\" % (len(steps), steps[-1], steps))" + "if rank==0:\n", + " print(\"Saved database '%s'\" % (viewer.filename))\n", + " print(\" - %d figures : %s\" % (len(figs), str(figs.keys())))\n", + " print(\" - %d timesteps (final = %d) : %s\" % (len(steps), steps[-1], steps))" ] }, { @@ -524,7 +526,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -536,7 +538,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -548,7 +550,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -560,7 +562,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -572,7 +574,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -609,7 +611,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/docs/examples/07_ShearBandsPureShear.ipynb b/docs/examples/07_ShearBandsPureShear.ipynb index 166c71ae0..b2befb3c6 100644 --- a/docs/examples/07_ShearBandsPureShear.ipynb +++ b/docs/examples/07_ShearBandsPureShear.ipynb @@ -105,8 +105,8 @@ "maxX = 2.0\n", "meshV = 1.0\n", "\n", - "resX = 64\n", - "resY = 16\n", + "resX = 48\n", + "resY = 12\n", "\n", "mesh = uw.mesh.FeMesh_Cartesian( elementType = \"Q2/dPc1\", \n", " elementRes = ( resX, resY), \n", @@ -171,8 +171,12 @@ "# track deformation and history dependence of particles.\n", "swarm = uw.swarm.Swarm( mesh=mesh )\n", "parts_per_cell = 50\n", - "swarmLayout = uw.swarm.layouts.PerCellSpaceFillerLayout( swarm=swarm, particlesPerCell=parts_per_cell )\n", - "swarm.populate_using_layout( layout=swarmLayout )\n", + "if uw.mpi.size==1:\n", + " swarmLayout = uw.swarm.layouts.PerCellSpaceFillerLayout( swarm=swarm, particlesPerCell=parts_per_cell )\n", + " swarm.populate_using_layout( layout=swarmLayout )\n", + "else:\n", + " # load in parallel for deterministic results\n", + " swarm.load(\"input/07_ShearBandsPureShear/particle_coords.h5\")\n", "# create pop control object\n", "pop_control = uw.swarm.PopulationControl(swarm, aggressive=True, aggressiveThreshold=0.8, particlesPerCell=parts_per_cell)\n", "\n", @@ -266,23 +270,27 @@ " zz = (xx - minX) / (maxX - minX)\n", " return (np.tanh(zz*width) + np.tanh((1.0-zz)*width) - math.tanh(width))**power\n", "\n", - "np.random.seed(0) # fix the random seed to ensure deterministic results for testing\n", - "plasticStrain.data[:,0] = 0.1 * np.random.rand(len(plasticStrain.data))\n", - "plasticStrain.data[:,0] *= gaussian(swarm.particleCoordinates.data[:,0], 0.0, 0.025) \n", - "plasticStrain.data[:,0] *= gaussian(swarm.particleCoordinates.data[:,1], 0.0, 0.025) \n", - "plasticStrain.data[:,0] *= boundary(swarm.particleCoordinates.data[:,0], minX, maxX, 10.0, 2) \n", + "if uw.mpi.size==1:\n", + " np.random.seed(0) # fix the random seed to ensure deterministic results for testing\n", + " plasticStrain.data[:,0] = 0.1 * np.random.rand(len(plasticStrain.data))\n", + " plasticStrain.data[:,0] *= gaussian(swarm.particleCoordinates.data[:,0], 0.0, 0.025) \n", + " plasticStrain.data[:,0] *= gaussian(swarm.particleCoordinates.data[:,1], 0.0, 0.025) \n", + " plasticStrain.data[:,0] *= boundary(swarm.particleCoordinates.data[:,0], minX, maxX, 10.0, 2) \n", + "else:\n", + " # load in parallel for deterministic results\n", + " plasticStrain.load(\"input/07_ShearBandsPureShear/plastic_strain.h5\")\n", "\n", "deformationVariable.data[:,0] = deformationSwarm.particleCoordinates.data[:,1]" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -310,13 +318,13 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -365,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -394,13 +402,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -460,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -490,7 +498,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -508,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -535,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -544,19 +552,19 @@ "text": [ "\u001b[1;35m\n", " \n", - "Pressure iterations: 5\n", - "Velocity iterations: 1 (presolve) \n", - "Velocity iterations: 5 (pressure solve)\n", - "Velocity iterations: 1 (backsolve) \n", - "Velocity iterations: 7 (total solve) \n", + "Pressure iterations: 12\n", + "Velocity iterations: 6 (presolve) \n", + "Velocity iterations: 87 (pressure solve)\n", + "Velocity iterations: 5 (backsolve) \n", + "Velocity iterations: 98 (total solve) \n", " \n", - "SCR RHS setup time: 5.1742e-02\n", - "SCR RHS solve time: 1.0736e-03\n", - "Pressure setup time: 5.1141e-04\n", - "Pressure solve time: 5.9183e-03\n", - "Velocity setup time: 8.1062e-06 (backsolve)\n", - "Velocity solve time: 9.0551e-04 (backsolve)\n", - "Total solve time : 7.9954e-02\n", + "SCR RHS setup time: 5.3653e-03\n", + "SCR RHS solve time: 9.4970e-03\n", + "Pressure setup time: 1.5734e-04\n", + "Pressure solve time: 9.3357e-02\n", + "Velocity setup time: 7.3297e-07 (backsolve)\n", + "Velocity solve time: 5.6888e-03 (backsolve)\n", + "Total solve time : 1.2474e-01\n", " \n", "Velocity solution min/max: 0.0000e+00/0.0000e+00\n", "Pressure solution min/max: 0.0000e+00/0.0000e+00\n", @@ -574,15 +582,6 @@ "\n", "solver = uw.systems.Solver( stokes )\n", "\n", - "# \"mumps\" is a good alternative for \"lu\" but \n", - "# not every petsc installation has mumps !\n", - "# It also works fine in parallel\n", - "# use \"lu\" direct solve and large penalty (if running in serial)\n", - "if(uw.mpi.size==1):\n", - " solver.set_inner_method(\"lu\")\n", - " solver.set_penalty(1.0e6) \n", - " \n", - "\n", "# Define a function to perform pressure calibration. \n", "# It will be used as a callback during non-linear \n", "# iteration to ensure pressure is calibrated per\n", @@ -601,13 +600,13 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -628,13 +627,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -661,7 +660,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": { "scrolled": true }, @@ -669,7 +668,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -700,7 +699,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -712,7 +711,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -755,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -769,7 +768,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": { "scrolled": false }, @@ -778,56 +777,46 @@ "name": "stdout", "output_type": "stream", "text": [ - "step = 0; time = 0.000e+00; vrms = 6.343e-01; max_pstrain = 9.458e-02\n", - "step = 1; time = 1.360e-02; vrms = 6.358e-01; max_pstrain = 9.458e-02\n", - "step = 2; time = 2.732e-02; vrms = 6.377e-01; max_pstrain = 9.458e-02\n", - "step = 3; time = 4.115e-02; vrms = 6.397e-01; max_pstrain = 9.458e-02\n", - "step = 4; time = 5.503e-02; vrms = 6.430e-01; max_pstrain = 9.458e-02\n", - "step = 5; time = 6.904e-02; vrms = 6.469e-01; max_pstrain = 9.458e-02\n", - "step = 6; time = 8.319e-02; vrms = 6.502e-01; max_pstrain = 9.544e-02\n", - "step = 7; time = 9.739e-02; vrms = 6.526e-01; max_pstrain = 1.037e-01\n", - "step = 8; time = 1.117e-01; vrms = 6.546e-01; max_pstrain = 1.122e-01\n", - "step = 9; time = 1.261e-01; vrms = 6.560e-01; max_pstrain = 1.212e-01\n", - "step = 10; time = 1.405e-01; vrms = 6.571e-01; max_pstrain = 1.309e-01\n", - "step = 11; time = 1.550e-01; vrms = 6.580e-01; max_pstrain = 1.412e-01\n", - "step = 12; time = 1.695e-01; vrms = 6.594e-01; max_pstrain = 1.512e-01\n", - "step = 13; time = 1.840e-01; vrms = 6.600e-01; max_pstrain = 1.615e-01\n", - "step = 14; time = 1.985e-01; vrms = 6.603e-01; max_pstrain = 1.726e-01\n", - "step = 15; time = 2.131e-01; vrms = 6.621e-01; max_pstrain = 1.843e-01\n", - "step = 16; time = 2.277e-01; vrms = 6.632e-01; max_pstrain = 1.971e-01\n", - "step = 17; time = 2.423e-01; vrms = 6.629e-01; max_pstrain = 2.091e-01\n", - "step = 18; time = 2.570e-01; vrms = 6.640e-01; max_pstrain = 2.220e-01\n", - "step = 19; time = 2.716e-01; vrms = 6.657e-01; max_pstrain = 2.355e-01\n", - "step = 20; time = 2.863e-01; vrms = 6.674e-01; max_pstrain = 2.497e-01\n", - "step = 21; time = 3.010e-01; vrms = 6.682e-01; max_pstrain = 2.649e-01\n", - "step = 22; time = 3.157e-01; vrms = 6.690e-01; max_pstrain = 2.809e-01\n", - "step = 23; time = 3.305e-01; vrms = 6.705e-01; max_pstrain = 3.018e-01\n", - "step = 24; time = 3.452e-01; vrms = 6.720e-01; max_pstrain = 3.321e-01\n", - "step = 25; time = 3.600e-01; vrms = 6.738e-01; max_pstrain = 3.661e-01\n", - "step = 26; time = 3.748e-01; vrms = 6.756e-01; max_pstrain = 4.052e-01\n", - "step = 27; time = 3.896e-01; vrms = 6.774e-01; max_pstrain = 4.493e-01\n", - "step = 28; time = 4.045e-01; vrms = 6.792e-01; max_pstrain = 4.989e-01\n", - "step = 29; time = 4.194e-01; vrms = 6.821e-01; max_pstrain = 5.553e-01\n", - "step = 30; time = 4.343e-01; vrms = 6.844e-01; max_pstrain = 6.163e-01\n", - "step = 31; time = 4.492e-01; vrms = 6.869e-01; max_pstrain = 6.821e-01\n", - "step = 32; time = 4.641e-01; vrms = 6.898e-01; max_pstrain = 7.510e-01\n", - "step = 33; time = 4.791e-01; vrms = 6.930e-01; max_pstrain = 8.231e-01\n", - "step = 34; time = 4.941e-01; vrms = 6.974e-01; max_pstrain = 9.054e-01\n", - "step = 35; time = 5.091e-01; vrms = 7.008e-01; max_pstrain = 9.858e-01\n", - "step = 36; time = 5.241e-01; vrms = 7.042e-01; max_pstrain = 1.062e+00\n", - "step = 37; time = 5.392e-01; vrms = 7.081e-01; max_pstrain = 1.137e+00\n", - "step = 38; time = 5.543e-01; vrms = 7.110e-01; max_pstrain = 1.210e+00\n", - "step = 39; time = 5.694e-01; vrms = 7.144e-01; max_pstrain = 1.284e+00\n", - "step = 40; time = 5.845e-01; vrms = 7.170e-01; max_pstrain = 1.353e+00\n", - "step = 41; time = 5.996e-01; vrms = 7.204e-01; max_pstrain = 1.421e+00\n", - "step = 42; time = 6.148e-01; vrms = 7.229e-01; max_pstrain = 1.478e+00\n", - "step = 43; time = 6.300e-01; vrms = 7.248e-01; max_pstrain = 1.559e+00\n", - "step = 44; time = 6.451e-01; vrms = 7.262e-01; max_pstrain = 1.668e+00\n", - "step = 45; time = 6.603e-01; vrms = 7.283e-01; max_pstrain = 1.772e+00\n", - "step = 46; time = 6.755e-01; vrms = 7.296e-01; max_pstrain = 1.868e+00\n", - "step = 47; time = 6.908e-01; vrms = 7.307e-01; max_pstrain = 1.960e+00\n", - "step = 48; time = 7.060e-01; vrms = 7.319e-01; max_pstrain = 2.054e+00\n", - "step = 49; time = 7.213e-01; vrms = 7.330e-01; max_pstrain = 2.149e+00\n" + "step = 0; time = 0.000e+00; vrms = 6.368e-01; max_pstrain = 9.157e-02\n", + "step = 1; time = 1.814e-02; vrms = 6.390e-01; max_pstrain = 9.157e-02\n", + "step = 2; time = 3.645e-02; vrms = 6.417e-01; max_pstrain = 9.157e-02\n", + "step = 3; time = 5.492e-02; vrms = 6.452e-01; max_pstrain = 9.157e-02\n", + "step = 4; time = 7.360e-02; vrms = 6.481e-01; max_pstrain = 9.892e-02\n", + "step = 5; time = 9.240e-02; vrms = 6.508e-01; max_pstrain = 1.084e-01\n", + "step = 6; time = 1.114e-01; vrms = 6.527e-01; max_pstrain = 1.180e-01\n", + "step = 7; time = 1.305e-01; vrms = 6.545e-01; max_pstrain = 1.280e-01\n", + "step = 8; time = 1.498e-01; vrms = 6.557e-01; max_pstrain = 1.386e-01\n", + "step = 9; time = 1.691e-01; vrms = 6.568e-01; max_pstrain = 1.506e-01\n", + "step = 10; time = 1.884e-01; vrms = 6.579e-01; max_pstrain = 1.637e-01\n", + "step = 11; time = 2.078e-01; vrms = 6.581e-01; max_pstrain = 1.781e-01\n", + "step = 12; time = 2.272e-01; vrms = 6.589e-01; max_pstrain = 1.930e-01\n", + "step = 13; time = 2.467e-01; vrms = 6.611e-01; max_pstrain = 2.084e-01\n", + "step = 14; time = 2.662e-01; vrms = 6.625e-01; max_pstrain = 2.245e-01\n", + "step = 15; time = 2.858e-01; vrms = 6.622e-01; max_pstrain = 2.413e-01\n", + "step = 16; time = 3.054e-01; vrms = 6.638e-01; max_pstrain = 2.594e-01\n", + "step = 17; time = 3.250e-01; vrms = 6.658e-01; max_pstrain = 2.889e-01\n", + "step = 18; time = 3.446e-01; vrms = 6.678e-01; max_pstrain = 3.086e-01\n", + "step = 19; time = 3.644e-01; vrms = 6.683e-01; max_pstrain = 3.292e-01\n", + "step = 20; time = 3.841e-01; vrms = 6.692e-01; max_pstrain = 3.488e-01\n", + "step = 21; time = 4.038e-01; vrms = 6.707e-01; max_pstrain = 3.673e-01\n", + "step = 22; time = 4.236e-01; vrms = 6.722e-01; max_pstrain = 3.892e-01\n", + "step = 23; time = 4.434e-01; vrms = 6.741e-01; max_pstrain = 4.144e-01\n", + "step = 24; time = 4.633e-01; vrms = 6.769e-01; max_pstrain = 4.426e-01\n", + "step = 25; time = 4.832e-01; vrms = 6.788e-01; max_pstrain = 4.730e-01\n", + "step = 26; time = 5.031e-01; vrms = 6.802e-01; max_pstrain = 5.053e-01\n", + "step = 27; time = 5.231e-01; vrms = 6.822e-01; max_pstrain = 5.430e-01\n", + "step = 28; time = 5.431e-01; vrms = 6.835e-01; max_pstrain = 5.851e-01\n", + "step = 29; time = 5.631e-01; vrms = 6.854e-01; max_pstrain = 6.285e-01\n", + "step = 30; time = 5.831e-01; vrms = 6.871e-01; max_pstrain = 6.725e-01\n", + "step = 31; time = 6.032e-01; vrms = 6.888e-01; max_pstrain = 7.176e-01\n", + "step = 32; time = 6.233e-01; vrms = 6.922e-01; max_pstrain = 7.644e-01\n", + "step = 33; time = 6.434e-01; vrms = 6.937e-01; max_pstrain = 8.132e-01\n", + "step = 34; time = 6.636e-01; vrms = 6.975e-01; max_pstrain = 8.656e-01\n", + "step = 35; time = 6.837e-01; vrms = 6.995e-01; max_pstrain = 9.232e-01\n", + "step = 36; time = 7.039e-01; vrms = 7.031e-01; max_pstrain = 9.951e-01\n", + "step = 37; time = 7.242e-01; vrms = 7.050e-01; max_pstrain = 1.092e+00\n", + "step = 38; time = 7.444e-01; vrms = 7.072e-01; max_pstrain = 1.179e+00\n", + "step = 39; time = 7.646e-01; vrms = 7.101e-01; max_pstrain = 1.231e+00\n" ] } ], @@ -835,7 +824,7 @@ "# Stepping. Initialise time and timestep.\n", "time = 0.\n", "step = 0\n", - "nsteps = 50\n", + "nsteps = 40\n", "\n", "frictionValueAsString = str(frictionInf.evaluate([0.0,0.0])[0,0]) # For tagging in file names\n", "\n", @@ -872,7 +861,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": { "scrolled": true }, @@ -880,7 +869,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -896,13 +885,13 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -918,13 +907,13 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -940,13 +929,13 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -972,13 +961,13 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -1009,13 +998,13 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -1031,7 +1020,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1041,8 +1030,10 @@ "# here are consistent with the results obtained by the \n", "# original version of this notebook, a more rigorous review\n", "# is warranted to establish numerical and mathematical accuracy.\n", - "if not np.isclose(stokes.velocity_rms(),0.73295,rtol=1e-4):\n", - " raise RuntimeError(\"VRMS differs from expected value.\")" + "vrms_obtained = stokes.velocity_rms()\n", + "vrms_expected = 0.7101\n", + "if not np.isclose(vrms_obtained,vrms_expected,rtol=1e-4):\n", + " raise RuntimeError(\"VRMS value ({}) differs from expected value ({}).\".format(vrms_obtained,vrms_expected))" ] } ], diff --git a/docs/examples/09_Groundwater_Flow.ipynb b/docs/examples/09_Groundwater_Flow.ipynb index 57092a240..e9763df1a 100644 --- a/docs/examples/09_Groundwater_Flow.ipynb +++ b/docs/examples/09_Groundwater_Flow.ipynb @@ -61,9 +61,9 @@ " minCoord = (-1., -1.), \n", " maxCoord = (0., 0.)) \n", "\n", - "gwPressureField = mesh.add_variable( nodeDofCount=1 )\n", - "hydraulicDiffusivityField = mesh.add_variable( nodeDofCount=1 )\n", - "velocityField = mesh.add_variable(nodeDofCount=2)" + "gwPressureField = mesh.add_variable( nodeDofCount=1 )\n", + "hydraulicDiffusivityField = mesh.add_variable( nodeDofCount=1 )\n", + "velocityField = mesh.add_variable( nodeDofCount=2 )" ] }, { @@ -81,13 +81,11 @@ "metadata": {}, "outputs": [], "source": [ - "iWalls = mesh.specialSets[\"MinI_VertexSet\"] + mesh.specialSets[\"MaxI_VertexSet\"]\n", - "jWalls = mesh.specialSets[\"MinJ_VertexSet\"] + mesh.specialSets[\"MaxJ_VertexSet\"]\n", "topWall = mesh.specialSets[\"MaxJ_VertexSet\"]\n", "bottomWall = mesh.specialSets[\"MinJ_VertexSet\"]\n", "\n", "gwPressureBC = uw.conditions.DirichletCondition( variable = gwPressureField, \n", - " indexSetsPerDof = ( topWall+ bottomWall ) )" + " indexSetsPerDof = ( topWall+ bottomWall ) )" ] }, { @@ -128,7 +126,7 @@ "metadata": {}, "outputs": [], "source": [ - "swarm = uw.swarm.Swarm( mesh=mesh )\n", + "swarm = uw.swarm.Swarm(mesh=mesh)\n", "swarmLayout = uw.swarm.layouts.PerCellGaussLayout(swarm=swarm,gaussPointCount=1)\n", "swarm.populate_using_layout(swarmLayout)" ] @@ -147,10 +145,10 @@ "outputs": [], "source": [ "materialIndex = swarm.add_variable( dataType=\"int\", count=1 )\n", - "materialVelocity = swarm.add_variable( dataType=\"double\", count=2 )\n", + "materialVelocity = swarm.add_variable( dataType=\"double\", count=2 )\n", "\n", - "materialPorous = 0\n", - "materialImpermeable = 1" + "materialPorous = 0\n", + "materialImpermeable = 1" ] }, { @@ -172,7 +170,7 @@ "interfaceY = -0.25\n", "\n", "conditions = [ (yCoordFn > interfaceY, materialPorous),\n", - " ( True , materialImpermeable )]\n", + " ( True , materialImpermeable ) ]\n", "\n", "materialfn = uw.function.branching.conditional( conditions )\n", "materialIndex.data[:] = materialfn.evaluate(swarm)" @@ -194,8 +192,7 @@ "Ka = 1.\n", "Kb = 1e-3\n", "\n", - "hydraulicDiffusivityMap = { materialPorous : Ka, \n", - " materialImpermeable : Kb}\n", + "hydraulicDiffusivityMap = { materialPorous : Ka, materialImpermeable : Kb}\n", "hydraulicDiffusivityMapFn = uw.function.branching.map( fn_key = materialIndex, mapping = hydraulicDiffusivityMap )" ] }, @@ -216,7 +213,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -248,12 +245,13 @@ "outputs": [], "source": [ "g = uw.function.misc.constant((0.,-1.))\n", - "\n", - "\n", "gwadvDiff = uw.systems.SteadyStateDarcyFlow(velocityField=velocityField,\n", - " pressureField = gwPressureField, fn_diffusivity = hydraulicDiffusivityMapFn,\n", - " conditions=[gwPressureBC],fn_bodyforce=g, \n", - " voronoi_swarm=swarm,swarmVarVelocity=materialVelocity)\n", + " pressureField=gwPressureField, \n", + " fn_diffusivity = hydraulicDiffusivityMapFn,\n", + " conditions=[gwPressureBC],\n", + " fn_bodyforce=g, \n", + " voronoi_swarm=swarm,\n", + " swarmVarVelocity=materialVelocity)\n", "gwsolver = uw.systems.Solver(gwadvDiff)" ] }, @@ -286,7 +284,7 @@ "outputs": [ { "data": { - "image/png": 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0uOPZWwEBAXTv3p3Q0FDq1atHhQoV7rjs9u3bExISckuH3R49ehAWFkb79u1vmbd+/fo0btyYiIgIypQpk3hq79tvv02lSpWoWrUqJUqUSO236RLu3UZ+eAGI6AL1hlsbKjViYuD999kweQEDaj/P8ZzBrP6iOyFFC8CkSVCmjNUJlUoTd24j37t3b8qWLZt4sDy9cXQbefc+RpKR+fjAG2/wSLNm/NDtWdadSyDk0inYfoq/6jahaM9OMHAg3LQvVyllvfLly+Pv78/IkSOtjuIy7r1ryx2EhuK3fg31erQAX18iC5Tg8S6f0H/LBS5Wqgq//GJ1QqXUTSIjI1m7du0tB+zdnXsXEle2kXcmLy945RXYsYOHH8hLz03zmVu6NnWr9mFF+xfg1Vfh2jWrUyqlMin3LiTgXm1HihXDd/VK+reKYOG8QQReu0TXFm/y2s5rEB4O69dbnVAplQm5eSFxky2Sm3l4QO/ehK1YxOIji3hp/QyKnTkMf/yBqV4d+vSBqCirUyqlMhE3LyRurEgRfH76kZc61aLL/tUAfFv8UXoezcapCo/AihXW5lNKZRpufdZWgkngyuXLXHBgc7J0p359KFsWPvyQg2e8WflgRTYUDqfP0M+ou2AB0qsXZMtmdUqlbpMemjYCLFq0iNatW7Njx45UX6/RrVs3nnzySZo3b37Hed5//3369++f+LxGjRqsWbPmnsd6++238ff35+WXX05VVnB800ZLtkhEJEhEfhKRP+xfA5OYp4yIbBSR3SKyU0TuufNibL5yxOUo6JjQ6VnevPDhhzR7qjoT146h0KWTDHukC/0vFuRU1+fBjdpVK+Voc+bMoWrVqsyZM8ep47z//vu3PE9NEUm3jDEufwAfAAPs0wOA95OYpxjwkH26AHACCEhu2eXLlzeZ2okTJq5Zc/NVRGNT/OV5ZmnxqsaAMR07GnP2rNXplEq0Z88eqyOYy5cvmwIFCph9+/aZYsWKGWOMWbVqlalRo4Zp0aKFKV68uGnXrp1JSEgwxhgzdOhQExERYR5++GHTvXv3xNc7depk5s6da1asWGGaNGmSuPwff/zRNG3a1PTv3994eHiY8PBw065dO2OMMf7+/onzvffeeyY0NNSEhYWZ/v37G2OMGT9+vImIiDBhYWGmefPm5sqVK8YYYwYPHmw+/PDDNH3fSa17YKtJ7f/pqf1gWh7APiC/fTo/sC8Fn9lxo7Dc7ZHpC4kxxiQkGDNnjjlZ6EHbPzGYpcWrmiMPlDLmm2+sTqeUMSZ9FJLp06ebZ555xhhjTJUqVczWrVvNqlWrTI4cOczRo0dNfHy8qVy5slm3bp0xxpizN/0x1qFDB7N48WJjzL+FJCEhwRQvXtycOnXKGGNM27ZtE+e5uXDc/Py7774zVapUSSwUN8Y4c+ZM4rwDBw40Y8eONcakz0Ji1cH2vMaYE/bpf4C8d5tZRCoCPsCfd3i/h4hsFZGtp0+f/veN8TVhXea5ujSRCLRqRXDkRmjXjmteWXijznPUazyUKcMmkvDUU3DqlNUplbLcrFmzaNOmDQBt2rRJ7LNVsWJFChYsiIeHB2XKlOHQoUMArFq1ikqVKlG6dGlWrlzJ7t27b1meiPD0008zffp0Lly4wMaNG3niiSfummH58uV06dIFPz8/AIKCbLcH37VrF9WqVaN06dLMmDHjtrHSE6cdbBeR5UBSt/obePMTY4wRkTte7CEi+YFpQCdjTEJS8xhjxgPjwdZrK/GN4FKQPf+9h3cXuXPDjBlkfeopFvd7ndfKtmZwnZ58e3Q371euRdHhg6BNG/e5cFOpe6Bt5B3HaVskxpjaxpjQJB6LgJP2AnGjUCT557GI5ACWAgONMZvuOUTTT6FMuzR8F26icWPu27KeKdkP8+HS0ezLcz8Nmw7lTLfnbHdi/PtvqxMq5XLaRt5xrNq1tRjoZJ/uBNx2ByoR8QEWAFONMUn/i6mUCwhAJkyg1chXWf79MN766TNyX70IixdzplxlmDjRvboAKJUMbSPvQKk9uJKWB5ALWAH8ASwHguyvRwBf2ac7ALHA9pseZZJb9i0H20eWMubHN1N4+CkTuXTJmOeeMwbM5oIPm2IvzzcfV2ltYurWN+bQIavTqUwiPRxsd5ZevXqZr776yuoYd+QWB9uNMWeNMY8bYx4ytl1g5+yvbzXGdLNPTzfGeBtjytz02H5PA12/DHHXnfAdZHDZs8Onn8KqVRTN4UWdA5sZUb0jTQo8we6aDeCzzyAhycNRSqlklC9fnp07d9KhQwero7iMW1/ZbqO7a+6oZk1y//Izn7zxBg0XDGdQnedp0nI4L02bSe/Zs+Grr+DBB61OqTKLSQ2Sn6dYPaja59/5y7SDsu3hylmY0/HWebssdXzGFIiMjLRkXCu5d68tPRkpef7+MGoU9SePZPnaUTTeswbf2OuwZg2EhcHo0bp1opS6u9TuE0uvj1uOkbwbYszS/91tV6G62bVrxrz+uknw9DQGzKKS1c27NTqZ2AYNjTl3zup0ys2kh2MkV69eNdWrVzdxcXHm4MGDBki88M8Y27GOSZMmWRfwJlWqVDHGGHPw4EEzY8aMZOdfsmSJeeONN5J8zy2OkbiMfx64cNjqFBmHry8MH45s3gxhYezO+wCfV27F09mrcObRWrBzp9UJlXKoiRMn0rx5czw9PQEIDg7mo48+cmozybi4uFR9boO9Z96hQ4eYOXNmsvM3aNCAJUuWJJ5W7ExufYzkWt4IsvyxhOOH/wIPb6vjZBzBwbBgAR2+/JJcG6YwokJbnszdj/e6vchDY4ZDSIjVCZUbSA/df6dPn86UKVOIiYkhJiaG3LlzU6VKFSZMmEDXrl2Jj49PzLljxw569+7N1atXKVq0KOPHjycw8NZ+s3/++SedO3fmypUrNGrUiI8//phz586xZs0ahg4dSkBAAPv27WP37t20bNmSY8eOER0dTe/evenWrRvjx4/nr7/+4r333gNg6tSpREZG8tFHHxEUFMS5c+fo378/v//+O+Hh4XTo0IHFixczatQowsPDAahVqxYfffQRYWFhVKtWjYULF9KyZctbcrpF919XuV6kNh6xV8i1egAk3H4hkLoLb294/nme6NqQz9Z+TIJ4MLxsGxK++srqZEo5RExMDAcPHqRw4cK3vP7KK68wevRo4uPjb3n9mWeeYfjw4URGRhIaGsqwYcNuW2a/fv3o3bs327Zt47777rvlvV9//ZWRI0cmtjoZP348mzZtYuPGjYwbN46zZ8/SrFkzFi3697K6uXPn0rp161uWM2zYMKpWrcqWLVt48cUX6dy5M1OnTgVg//79REdHExYWBtjOIFvvgjunuvUWSUCFVhB7Er8fB+JXpavtjA91bzp2JOSBBxjW5X8cyxlMgV+X4v3yyxAaanUylcFFRUXh4+Nj2fhnzpwhICAgMYOPjw8iQokSJahcuTLz5s3D09MTLy8vrl27xsWLF6lduzZgKyqtWrW6Lf/mzZtZvHgxXl5edOzYkQEDBuDj44O3tzcVK1akePHiifN+/vnnLFiwAIBjx45x+PBhKleuzAMPPMC2bdt46KGH2L9/PzVr1kTsbYxuLMvDwyNx7LZt2/Luu+8yatQopk+fTpcuXRLfK1CgACdPnrwtp5eXFyEO3LPg1oUEgEd6Q5HqkD/M6iQZV9Wq1C+RG5bY/1IaOhTmzrU2k1JplDVr1iR7ZQG8/vrrtGzZkho1ajhsPH9//8Tp1atXs3z5cjZu3Iifnx81a9ZMzNKmTRvmzJlDiRIlaNasWWIRuRM/Pz/q1KnDokWLmDNnzi2nH0dHR5M1a1aHfQ934ta7thLdKCKHN8LcLhCb9A+PuoshQ4gXD+aFPsbKbYf0wLvK8AIDA4mPj0+ymJQoUYJSpUqxZMkSAHLmzElgYGBiH65p06YlWWQqV67M/PnzAfj666/vOPbFixcJDAzEz8+P33//nU2b/m0leGP31s2diW+WPXt2Ll++fMtr3bp1o0+fPlSoUOGW4zb79+8n1AV7DzJHIbnh7B9wcpftind1b8qVQ5o05suKzRn2WFfihwy1OpFSaVa3bt07HkMYOHAgx44dS3w+ZcoU/ve//xEWFsb27dt58803b/vMmDFjGDVqFGFhYRw4cICcOXMmuez69esTFxdHyZIlGTBgAJUrV058LzAwkJIlS3L48GEqVqx422fDwsLw9PQkPDyc0aNHA7ZjITly5LilSSTY2t43aJCCCz3TKrXnDafXR7I3toq5ZvsaF2tMzNW7z6tutX27+b5YFXN//2/NN6VqGrNtm9WJVAaWHq4jiYyMNB06dHDY8q5cuZJ418RZs2aZxo0bO2zZd3P8+HHz0EMPmfj4+MTX/vnnH/PYY48lOb9eR5JW3r62LrcLn4OZrSHG+edYu43wcOqWLkCpk3/yUdV2xA0eYnUipdKkXLly1KpV67YztFIrMjKSMmXKEBYWxqeffsrIkc6/sd7UqVOpVKkSw4cPx8Pj3//Sjxw54pLxAcRWiNxHRESE2bp1a/Iz7pgNC3vC/VWh7deQJZvzw7mDXbv4qXk3ujd/gw+XjqbVpPcgIsLqVCoD2rt37y1t1pXrJLXuRSTSGJOqX+bMt0VyQ/hT0PxLOPwzzGipx01SKjSU2mXvp+nuVeS+egGGDLE6kcrA3O0P2YzAGevc/U//vZvSLcHDE+Z1hWnNocN88M1hdap0TwYPZkxoqG0X4V/A5s1QqZLVsVQG4+vry9mzZ8mVK1eyp7gqxzDGcPbsWXx9fR263My7a+tmexbDvC6QPxw6fANZA5wTzp20a8fl+QuZVaY+nYKiyfL9d1YnUhlMbGxsYosQ5Tq+vr4ULFgQb+9b20alZddW5t4iuaFUY2g9FeZ0gmlN4ekFkDUw+c9lZm++yfZN+3inVleyLhvH0xs3QpUqVqdSGYi3tzdFihSxOoZyALcuJKdOneL69RTeIdE/DN/HR5N7RV+uzuvFuZrvOzdcRufvz/2PlyPs1AHGVmtHlZGjyTK6oNWplFIWyLwH25MQXagmp+t9zoVKr1odJUOQzp3p+tu3nPYLZMmFrLBjh9WRlFIWcOstkuDg4Hv/0I1GZvGxsGIoPPIiZMvj2GDuIiSEkIfvY9bhncwsUZueY8aSdfmPVqdSSrmYbpHcyak9sGUC/LnC6iTp2xtv0HfjLEqcPsSFX7bZbtGrlMpU9Kytu7n0N+QoYJtOSAAPrbtJ6tYNJkywTVevDqtXg57OqVSGohckOsuNInJkE3xZ01ZY1O0GDQIvL47lyMPqo1GwapXViZRSLqSFJEUEzv4Fk56Ei8eSnz2zKVwYnnmGIbV70rdhP6KGDrNdrKiUyhS0kKREoUq2a0uunrUVk/OHrU6U/gwcyAu/zOO8X04mx+aB5cutTqSUchEtJCkVUgE6LoToCzC5IZw/ZHWi9KVQIcIb16L2H5v5skIzLg0drlslSmUSWkjuxX3loeNiuH4JJjWAc39ZnSh9ee01Xto8h4tZszORAvDDD1YnUkq5gBaSe1WgDHT+FmKv2orJ2T+tTpR+FCxIaLM6NNi7ligfPxg8WLdKlMoE9PTf1Dq5G6Y0Bk9v6L0FsmR3/pgZwd9/k1D0ATyu2xvxLVkCDRtam0kplSw9/dcKeR+2bZk8NkiLyM0KFMDjuZ4A/Jq/GOeGva9bJUq5OS0kaRFcEsp2sE0fXAcn91ibJ73o35/jeUJo0eFDxvs+AIsWWZ1IKeVEbt1r6566/6ZFQhz5FvQi3j8vp5+c6PzxMoLObXj8SCSTIxrz5KdfEli2rHYGUMpN6W+2I3h4cabeZ5yt9YHVSdKPDh3ovG85MR7ezMz6EKxda3UipZSTuPUWSaq6/6baTV2Dl7wEFbtBgbIuHD+dCQkhpF41mv62igUlqvPymA8Jbt9et0qUckOW/FaLSJCI/CQif9i/3vF2hCKSQ0SOicgnrsyYalfOwKG1MKUJHIu0Oo21XnmFPjsW4xcbzZ7zMTB3rtWJlFJOYNWfhwOAFcaYh4AV9ud38jaQcfaL5MgPnb8Dv0DbbXuP/mJ1Iuvkzk3hTk+x6dNO1Dy4DYYOhfh4q1MppRzMqkLSBJhin54CNE1qJhG7NK7IAAAgAElEQVQpD+QFMtbdkgJCoPNS8M8N05rB4Y1WJ7JOv35k8ctKAsLus9Ewe7bViZRSDmZVIclrjDlhn/4HW7G4hYh4ACOBV5JbmIj0EJGtIrL19OnTjk2aWjkL2opJ9nwwvQUc+tnqRNYICoK+ffn4kado2nEUxz/4COLirE6llHIgpxUSEVkuIruSeDS5eT5ju7Q+qSvWnge+M8Yk27fdGDPeGBNhjInIkycd3RY3RwFbMclZEGa0hIMZZw+dQ/XtS6tDmxEDn+SrADNnWp1IKeVATiskxpjaxpjQJB6LgJMikh/A/vVUEouoAvQWkUPACKCjiLznrLxOkz2f7Qr4gPthRuvMuZsrIIACz3amzY5lzC1dh6MjPtGtEqXciFW7thYDnezTnYDbLn02xrQ3xhQyxhTGtntrqjHmbgfl069swbZiUrIR5CludRprvPgiz+/9EQ+TwMcFq8C0aVYnUko5iFWF5D2gjoj8AdS2P0dEIkTkK4syOZd/bmjxJfgFQdx1OLrF6kSulTMn+Z7vSodfv2NLwYeJHvYOxMRYnUop5QDa/dcKP74Bm7+APttsx08yi8uXiSpWkiyn/sE7IR7Gj4fu3a1OpZRCu/9mPNVehuZfZK4iApA9O9leegHvhHiivXw48+EY3SpRyg24dYsUlzVtTI0cEXD0KFn+/gWP6xe4VqSu1Ylco2lT4mbO4umqfSh+7ghDPv8cmjWzOpVSKg10i8RKxpB951fkWvU/sv71vdVpXMPPD6/27ahx9FdW3l+eg/O+g/Ra7JVSKaLHSKx2/bLttOCjm6DZeAhrZXUi57t6lfMlS/No8/eocXAbnz5eAF54wepUSmVqTj9GIiJZRKSdiLwuIm/eeKRmQPUfWbJDh3lwf1VY0AO2z7I6kfP5+RHY9wWeiVzMdyUeZc/n0+DaNatTKaVSKaW7thZh648VB1y56aEcwccf2s2BwtVg4XOwLRNcY/Hss3Q7uons0VEsyV0KPv/c6kRKqVRK6cH2gsaY+k5Nktn5+EG72fB1O1jcG0w8lO9sdSrnyZqVnK+8xHcDX6TgxZPwezD06AH+/lYnU0rdo5RukWwQkdJOTaLAOyu0mQUP1oElL8KWCVYncq5u3QjJ7o0AFy9egc8+szqRUioV7lpIROQ3EdkJPApsE5F9IrLzpteVo3n7QpsZULwBeGWxOo1zZckCgwaxoVAYlZ+fwq8T50JUlNWplFL36K5nbYnI/Xf7sDHmsMMTpVGGO2vrTowBEdv0haO2e5y4o5gYokqVptqTgyn9zwGmlvWG116zOpVSmY7Tztoyxhy2F4thN6Zvfi01A6oUulFE/t4On0TAdjdtve7jQ7bXXuXZzd+wtmh5IqcuhEuXrE6llLoHKT1G8vDNT0TEEyjv+DjqNnkfhiq9oJgbn+vQsSMdz+8m95XzjApvDB99ZHUipdQ9SO4YyWsichkIE5FLInLZ/vwUSbR+V07g6Q2Pv/lv1+Df5lmdyPG8vfF7vT89N81jw/1hHJj4NVy4YHUqpVQKJbdr611jTHbgQ2NMDmNMdvsjlzFGd2S7WuRkmN8VVr1rO4biTjp0oMOl/fww8QUePLQHxoyxOpFSKoVSumvrdRFpLiKjRGSkiDR1aiqVtArdoEx7WPMerBzmXsXEywvfN16n+Bnb+RsxYz6Cc+csDqWUSomUXpA4DngQuNG/o6eI1DHG9HJOLMdI191/U6tcfwKvRpNt3QguXTzPxYiX/j0wn9E9+ihUrcrY3BXZF3Q/n4wajTzbw+pUSqlkpHSL5DGgnjFmkjFmEvCk/TXlauLB+UcHE1WiNTl2TiDnlpHus2Xi6Qldu1Lo0kl2Bj/IL2t26LESpTKAlG6RHAAKATeuGwmxv5auBQcHWx3BeULGw/cB5PhlPDn8/aDeO+6xZdKpE8+O/oivL55kWpGatJgxA3nvPatTKaXuIqVbJNmBvSKyWkRWAXuAHCKyWEQWOy+euiMReOIDqPw8bPoUvn/VPbZMPDzwGfwGL2yYzY4CxVm55Gc4dcrqVEqpu0jR/UhEpMbd3jfGrHFYojRymyvbU8oY+HEQHP4ZOn0LWbJZnSjtEhKILR/B4xWfJ2/UOebmOwkjR1qdSim3lpYr21N8Yyt7u5SHjDHLRSQr4GWMuZyaQZ0p0xUSsBWT2Gu2DsKx0eDpAx4Z/OaXixax/blXue/iKfIkRMNff0H+/FanUsptueLGVt2BecAX9pcKAgtTM6ByAhFbEYmPha/bwrcvWp0o7Ro3pkz+bOS5egETHY3R4yRKpVsp/bO1F1AVuARgjPkDcOMj2RmUhxeEVLI9MjoReOstTvkH0LL9B3y/YgccO2Z1KqVUElJaSK4bY2JuPBERL8ANjuy6GRGoOcB20SLAiR0QH2dtprR48klyPVycC1mzM7pya+LffdfqREqpJKS0kKwRkdeBrCJSB5gLLHFeLJUmInDhCEyoC990t+3yyohE8HxrKC+tn8kfue9n6dq9cOSI1amUUv+R0kIyADgN/AY8C3wHDHJWKOUAAYWg1kDY/Q3MeybjFpO6dWkQFE/x04cYU7k18cP07gVKpTcpKiTGmARsB9efN8a0NMZ8aVJ6upeyTtU+UO9d2LsY5naGuJhkP5LuiODx1lv0XT+Dv3KF8O3GP+HgQatTKaVuklwbeRGRISJyBtgH7BOR0yLypmviqTSr8rztwsXfv4U5HW2t6DOaxx6jbn4fPvhuDPV+Xw9vv211IqXUTZLbIumL7WytCsaYIGNMEFAJqCoifZ2eTjlGpWfhyRGw/3uY3cF2rUlGYt8qaf3bcnzjYmDqVDiQ7jv0KJVpJHfP9l+BOsaYM/95PQ/wozGmrJPz3bObL0h0y+6/aeD/+1yCfh7Ktfse4WztsRgvX6sj3Zs+fdhw4jqzSzzOCO99eL/5htWJlHIbhQoVctoFid7/LSIAxpjTgHdqBlTWuVKiFeeqvYXv37/gc3Kb1XHuXffuCIZt+Urw/Z9RcPhw8p9RSjldclsk24wx5e71PStlyhYp9+r8YQi83zZtTIbqGmyeeIJmeWpzKlsQq84vJ8usGVZHUsotOLNFSrj9Xu3/fVwGSqdmQJUO3Cgi+36AaU3hepS1ee6BDB3Ky+tm8HeOYGbvvwi7dlkdSalML7l7tnva79X+30d2Y4zu2sroYq9AzBVIyEBXv1esSLXQ+4g4tptxlVsT/ZZeV6KU1VLc/Tej0F1b9yghHjw8bd2D42PAN6fViZK3bRuRjdpzMnsu6u/bgMev26BMGatTKZWhOb37r6OJSJCI/CQif9i/Bt5hvkIi8qOI7BWRPSJS2LVJMwEPT9txkrldYGpTuJYBbm1brhzlK5XkyX0/44GBIUOsTqRUpmbVTSsGACuMMQ8BK+zPkzIV+NAYUxKoCOit8pxBBMp1hH9+g6lN4Oo5qxMlb8gQEhA+eqQNMw5dB90KVcoyVhWSJsAU+/QUoOl/ZxCRUthunvUTgDEmyhhz1XURM5kST0KbmXBqL0xtnP6LSVgYHq1bsSXkYUZV68DVoXqsRCmrWFVI8hpjTtin/wHyJjFPMeCCiHwjIr+KyIci4pnUwkSkh4hsFZGtp0+fdlZm91esLrSdCaf3w5RGcOW2S4jSl8GD6bt+Bmf9A5hy2gs2bbI6kVKZktMKiYgsF5FdSTya3DyfvfljUkf8vYBqwCtABaAo0DmpsYwx440xEcaYiDx58jj2G8lsHqwN7WbD2T9hckOISsd7E0uVonyNctT8cyvjKzYnaoj24FLKCk4rJMaY2saY0CQei4CTIpIfwP41qf+tjgHbjTF/GWPisHUfTncXQLqlB2rZismFw7Zicvmk1Ynu7M036bthJuf9cjL5UjZYt87qREplOlbt2loMdLJPdwIWJTHPFiDA3tcL4DFgjwuyKYCiNaD9PLh4DJYPtjrNnRUvTnidKvRdN51qB3+Fwek4q1JuypLrSEQkFzAHKAQcBlobY86JSATQ0xjTzT5fHWAkIEAk0OPmW/4mRZs2Opb36V3E5SyM8clmdZQ7O34c2rSBhATb87FjoXx5azMplcE4s2mjUxhjzhpjHjfGPGTfBXbO/vrWG0XE/vwnY0yYMaa0MaZzckVEOV5snlCMTzYk7hpBawfhGXUi+Q+52n33wZNPciprAKMi2nBp0jTbtTFKKZfwsjqAMwUHB1sdwX2c3A3H1uBftgWEVLQ6ze1efZWohT+xoFgN7ttwglf27YM6daxOpVSmYNUxEpXR5H0Y+myHUo1tz9PbbXsLF6Zkk9o02LuWSeUbc+6td3SrRCkX0UKiUi5rgO3rHz/BuIq2U4TTk4ED6fvLXK76+DLe8374/nurEymVKWghUfcuez64fgkmN4Azf1id5l8FC/Jgq4Y03rOWKeUacXrY+7pVopQLaCFR9y5faej0LcTH2orJ6f1WJ/rXa6/RZ+s3NNm7BrNzJyxebHUipdyeFhKVOnlLQeeltr/4Jz9p69GVHuTPzwPtmvLeDx8TfOWC7bqSG6cFK6WcQguJSr3gErZiIp62K+BP7rY6kU3//uDnx/b8xZhJPliwwOpESrk1LSQqbfIUgy7fgaePrZic2Gl1IggOhhdeYFZ4PYbUfpYT747SrRKlnEgLiUq7XA9Al6Xg7QfTW6SPe8C/8gq9d3yLEeHToDCYM8fqREq5LS0kyjGCitqKSaMxkCUdtFPJnZuQru1ptXM5X4fX49j7H0F8vNWplHJLWkiU4wQWhhINbNN7l8DRLZbG4eWX6f3bUsTAJ/kqwMyZ1uZRyk1pIVGOFx8LK96GNe9ZmyMwkALPdqbHL/MJufAPDB0KcXHWZlLKDbl1ry3t/msdj9qfYbz9MEePWhukdWuemtMSLl/mKMCXX0LDhtZmUsrN6BaJcooE/+DErsG5f+xFlhO/WBMkWzZo25YEhFUhZTn+9UKIjbUmi1Juyq23SLT7bzoQdRqiT5L1x17Q7msoWtP1GV58kTOfT+Cdyh15Yt8GRv/0Ezz7rOtzKOWmdItEOVe2PLZ2KkFFYOZTcGCF6zNkz07uPs/RcdtSFpWqwYGxX4Lu8lTKYbSQKOfLlgc6LYFcD8Kstrbuwa7WqxfP/rkW37gYxjz4OHz1leszKOWmtJAo1/DPbSsmeYrD1+1g/zIXj+9Prpd70ynyW5aWeJR94ybBtWuuzaCUm9JColzHLwg6LbbdJOvr9vD7UteO/+yz9DiygZKnDnL2Sgx88YVrx1fKTWkhUa6VNRCeXgj5w2BORzi41oVjZyXwlRdZOvlFHjnyG7z7Lly54rrxlXJTWkiU62UNgKcXQIXucF95147drRsSEkK0lw/LchaFceNcO75SbkgLibKGb0544j3w8Yfrl+GP5a4ZN0sWGDSIyeUa8WzzQfw2YQ5cvuyasZVyU1pIlPVWv2c7AH/pb9eM17kz7c7vJue1y4wp3QDGjnXNuEq5KS0kynq1Xof2cyBHAdeM5+NDjgH/o8cv37DiwYr8OnUBXLzomrGVckNaSJT1fPz/veJ9z2LYNtX5Y3bsSOfzuwm6epHR4Y1hzBjnj6mUm3LrFinatDGDMYbcmyaQ9chqzp09zZUSrZ07Xr+XeGreCtYVDGffnK/wa9kScuRw7phKuSHdIlHphwhnao3kWkh1gn5+i2x7vnbueHXq8NSV/Xz60wj8Ll+Ar508nlJuSowxVmdwqIiICLN161arY6i0iLsOczvDvu/giQ+gkhMbLM6eDW3acMo/gMuBeXhg28+QJ4/zxlMqnRKRSGNMRGo+q1skKv3xygKtpkCJhvD9q7DRidd6tGpFQmhpWrf7gNeqPYMZMcJ5YynlprSQqPTJywdaTYZSTWDZ67DeSQfDPTzwGDqELpGL+aVQaX5etAZOnnTOWEq5KS0kKv3y9IYWEyG0BSwfDOtHO2ecpk1pY06Q/9JpRke0wLz/vnPGUcpNaSFR6ZunFzQbD2U6QO7izhnDw4MsQ96k18Y5RBYsxdqlG+BvF10cqZQb0EKi0j9PL2g6Dko8aXt+cjc4+iSRRo1o7X2OkAv/sD13EVtDR6VUimghURnLsa3w+aPw6zTHLlcEn6GDWTaxFy9u+BrGj4ejRx07hlJuypJCIiJBIvKTiPxh/xp4h/k+EJHdIrJXRMaKiLg6q0pnCpSDOm/Dw80dv+z69fErXxaAA9mCMcOHO34MpdyQVVskA4AVxpiHgBX257cQkUeAqkAYEApUAGq4MqRKhzw84JHekCWbrWvwL186bjeXCLz9NhtDSlOn26f8uGYXHDzomGUr5casKiRNgCn26SlA0yTmMYAv4ANkAbwBPS9T/Wv7TPjuFfhhgOOKyeOPU6FwIIXPn2B0lTYkvP22Y5arlBuzqpDkNcacsE//A+T97wzGmI3AKuCE/bHMGLM3qYWJSA8R2SoiW0+fPu2szCq9qdgDKveCzZ/bCkpCQtqXKYLXW0N58edZ/B5chO83HYADB9K+XKXcmNMKiYgsF5FdSTya3DyfsfVoue3PSRF5ECgJFATuAx4TkWpJjWWMGW+MiTDGROTR9haZhwjUGw6P9IEtX8HSvo4pJjVr0iifJw+eOcKYR9oQ/5ZulSh1N07r/muMqX2n90TkpIjkN8acEJH8wKkkZmsGbDLGRNk/8z1QBViX0gza/TeTKNGdnFFXyBE5gajLlzj/6BCQNP6N9HJfnn73S96p3Ik1Px3gofXr4f77HRJXKXdj1a6txUAn+3QnYFES8xwBaoiIl4h4YzvQnuSuLZXJiXAx4iUululJtv3fELTuTUiIT9syw8Komc+LuYsG8eD5o5iJEx2TVSk3ZEn3XxHJBcwBCgGHgdbGmHMiEgH0NMZ0ExFP4FOgOrZdXz8YY15Obtna/TeTW/0+rH4Hwp6Cpp+Bh2fql/XLL1CpEgY44x9Ink1rITTUYVGVSk/S0v1X28gr97P2Qzi0HtrOBm/ftC2rUSMGxBVhY6HSrDj3E15z5zgmo1LpjLaRV+pm1f8H7efbikj0JYiPTf2yhg7l8QObORxYgG/2XYDt2x2XUyk3oYVEuSdPL1sBmdYMFqThxljlylG79H2EndjP2EeeInbwEIdFVMpdaCFR7svTG0q3gtCWaVqMDBlC3/UzOBaQj7mHo0F3nSp1Cy0kyr1V7vlv1+C/1kBs9L0vIyyMmhEPUPb478wJqwNvvunYjEplcE67jkSpdOX8IZjeHIrWhKdm3PNBeBkyhLGPPEaeqHMQFwMbN0KVKs5IqlSGo1skKnMILAwNRsGBFTCrDcReu7fPlyxJSIPH8Y2LIdbDk5ghQ50SU6mMSAuJyjzKd4Im4+Cv1TCzNcRcubfPv/km5/xzUqv7F0w/mwXWpbjJglJuTQuJylzKtodmX9iuM5nRGq5HpfyzxYoR1KoZIRdP8lnlllwbPNTxd2pUKgPSQqIyn/CnoPmXcGQDzGhpu69JSr3xBn03zuZ0tiCmR+WAlSudl1OpDEILicqcSreEFhPg6C8wrbntwsWUKFqUinUrUe3gNj6v1IIrQ4fpVonK9Nz6rC3t/qvuKmcFstYaQdC6gZz+bSUx+cqn7HPdu9P+uVfpWaQckz3z03j+fKhUyblZlUrH3LqQKJWca0XqcCJ/BAm+gbYXEuLAI5lfi3z5eLhqGB//NJKw03/Cl2ehYkXb/VGUyoTcupAEBwdbHUFlCCG2Lzu+tt1tscM34Bd094+8+iohXzwA168Tf+wYnr/9Bg0aOD+qUumQHiNR6ga/XJAtL3hnTX7e++6Dnj1ZWrwqNXqM5+Jb7+ixEpVpaSFR6oaH6kDbr22FJPoiRJ2++/wDBnD/tXMcC8jHRO/CsCip+7Mp5f60kCh1MxHblsXX7WFKQ4hK6i7QdvnyEdq6AfX3/czECk248Pa7jrlnvFIZjBYSpf5LBGr0hwtHYHIDuPzPned99VX6Ri4gyicrX/kXg3nzXJdTqXRCC4lSSSlSDTrMh4vHbcXk0t9Jz5cnD8U7NKPB7+uZVL4xl4e9C/FpvF+8UhmMFhKl7uT+R+DpBXD5JEx6Ei4eS3q+fv34X+R8ZsweRPbftsPs2a7NqZTFtJAodTeFKtmKydWztmJy4cjt8+TKxf1d21PmxH4AzJAhEBfn2pxKWUgLiVLJCakAHRdC9AWY1MB2b5P/6tsXExDA63V78V6BqjBjhstjKmUVLSRKpcR95aHjYrh+CdaNvP39gADk5Ze57uXN5PKNOPXBGIiNdX1OpSyghUSplCpQBrothyc+TPr9F1+kz+4fiPP04tP8FWHKFNfmU8oibt0iRZs2KsfzhWun8bh+kaA1r3Gh4v+ICyiS+K7Hsx2pH7mJGWWfoNHkjwmuVQt8fCzMq5Tz6RaJUqngce0s3uf+wDPqP6cFN29Op6PrMQjTg8vD0qXWBFTKhcS4WX+giIgIs3XrVqtjqMwg7jp4ZbFNX78MWbLbpkeOZOHEb4k4tpuC2X3gwAHw9bUup1IpICKRxpiI1HxWt0iUSq0bRWTXfBhbDv7ZZXv+3HM0Pfc7BS+dhuPH4YsvrMuolAtoIVEqrfKXAU9vW2+uEzvAzw9ee42DgQXo3HIIRz7+Eq5etTqlUk6jhUSptMr1AHT+Frz9YUpjOL4NevTAL1cAGwuV5uNiteHTT61OqZTTaCFRyhGCikKXpZAlB0xtCmd2k/fl3rTf/j3fhD7Gwc+nQFSU1SmVcgotJEo5SmBhWzHJGgDTmkLd0vQ8tgnv+FjGlqgLn3xidUKlnEILiVKOFFAIunwH/rlh9lME92lEp21LWfhwTQ58OQMuXbI6oVIOp4VEKUfLWRA6L4Xs+eDKbHqc3kK/ddPJe+xPGDPG6nRKOZxeR6KUs1z+B84egDV/QufOttdy5oRDhyAgwMpkSt1GryNRKj3Kng8KPwrt20P9gkQ++jDvlWkKo0ZZnUwph7KkkIhIKxHZLSIJInLHCigi9UVkn4gcEJEBrsyolMOIgeo5yFE6ls8rt2LPtG/g7FmrUynlMFZtkewCmgNr7zSDiHgC44AngFJAWxEp5Zp4SjmQpze8sIbgjX5kj47io7JNYMQIq1Mp5TCWdP81xuwFEJG7zVYROGCM+cs+79dAE2BPSsfR7r8qXXnpJTrO+4H7i53hjwUH8G3bFgIDrU6lVJql5zby9wFHb3p+DKiU1Iwi0gPoYX8aJSL7bno7N3DGKQkzFl0PNulnPYSHW50g/awLa+l6sCme2g86rZCIyHIgXxJvDTTGLHLkWMaY8cD4O+TYmtozEdyJrgcbXQ//0nVho+vBRkRSfbqr0wqJMaZ2GhdxHAi56XlB+2tKKaXSkfR8+u8W4CERKSIiPkAbYLHFmZRSSv2HVaf/NhORY0AVYKmILLO/XkBEvgMwxsQBvYFlwF5gjjFmdyqGS3KXVyak68FG18O/dF3Y6HqwSfV6cLsr25VSSrlWet61pZRSKgPQQqKUUipN3KaQJNdORUSyiMhs+/ubRaSw61M6XwrWw8siskdEdorIChG534qczpbS9joi0kJEzN1a9WRkKVkPItLa/jOxW0Rmujqjq6Tgd6OQiKwSkV/tvx9PWpHTmURkooicEpFdd3hfRGSsfR3tFJFyKVqwMSbDPwBP4E+gKOAD7ABK/Wee54HP7dNtgNlW57ZoPdQC/OzTz2XW9WCfLzu2Nj2bgAirc1v08/AQ8CsQaH8ebHVuC9fFeOA5+3Qp4JDVuZ2wHqoD5YBdd3j/SeB7QIDKwOaULNddtkgS26kYY2KAG+1UbtYEmGKfngc8Lsn0aMmAkl0PxphVxpir9qebsF2f425S8vMA8DbwPhDtynAulJL10B0YZ4w5D2CMOeXijK6SknVhgBz26ZzA3y7M5xLGmLXAubvM0gSYamw2AQEikj+55bpLIUmqncp9d5rH2E4tvgjkckk610nJerhZV2x/fbibZNeDfZM9xBiz1JXBXCwlPw/FgGIi8rOIbBKR+i5L51opWRdDgA72SxO+A15wTbR05V7/DwHSd68t5UQi0gGIAGpYncXVRMQDGAV0tjhKeuCFbfdWTWxbp2tFpLQx5oKlqazRFphsjBkpIlWAaSISaoxJsDpYeucuWyQpaaeSOI+IeGHbdHW3m0KkqK2MiNQGBgKNjTHu2B45ufWQHQgFVovIIWz7ghe74QH3lPw8HAMWG2NijTEHgf3YCou7Scm66ArMATDGbAR8sTV0zExS1ZrKXQpJStqpLAY62adbAiuN/eiSG0l2PYhIWeALbEXEXfeH33U9GGMuGmNyG2MKG2MKYztW1NgY4273aE7J78VCbFsjiEhubLu6/nJlSBdJybo4AjwOICIlsRWS0y5Nab3FQEf72VuVgYvGmBPJfcgtdm0ZY+JE5EY7FU9gojFmt4i8BWw1xiwGJmDbVD2A7WBTG+sSO0cK18OHQDZgrv1cgyPGmMaWhXaCFK4Ht5fC9bAMqCsie4B44H/GGHfbUk/puugHfCkifbEdeO/sbn9sisgsbH845LYfCxoMeAMYYz7HdmzoSeAAcBXokqLlutl6Ukop5WLusmtLKaWURbSQKKWUShMtJEoppdJEC4lSSqk00UKilFIqTdzi9F+lnEFE4oHfsP2e7AU63dSnTCllp1skSt3ZNWNMGWNMKBAD9Lz5TftFWy77HRIRT1eNpdS90EKiVMqsAx4UkcL2e1pMBXYBISJSV0Q2isg2EZkrItkAROS9m+79MsL+WisR2SUiO0Rkrf21ziLyyY2BRORbEalpn44SkZEisgOoIiLlRWSNiESKyLKUdGZVytl015ZSybD3ZnsC+MH+0kPYdnNtsrcVGQTUNsZcEZH+wMsiMg5oBpQwxhgRCbB/9k2gnjHm+E2v3Y0/tntC9BMRb2AN0MQYc1pEngKGA8847JtVKhW0kCh1Z1lFZLt9eh22NjsFgMP2ezWAreFjKeBne8sZH2AjtgUyngoAAAFnSURBVNsURAMTRORb4Nv/t3fHKnkEURiG308RhRRWKa0StbBQEAySztbWJp2lRZqkFm8gjeQK7AIBhTRpxFJICtMpWCX3IJIgkZNip/gNSgyj3ftUw8wus1PsHmZmOdOuPwb2knwEDu7xDNfAfivPMySbPGx9jQP/zIMkPTYDiXS3n1W1NFrRPuCXo1XAYVW9+vvmJCsMSQA3gNfAWlVtJXkBrAMnSZaB39xcZp4aKf+qquuRvk6rarVvWNLDco9E6vMFeJnkOUCSJ0nm2j7JdFV9Bt4Ai639WVV9raodhsyyM8APYCnJWJIZhtP8bnMOPG1nZZBkIsnCYw5Oug9nJFKHtlexCXxIMtmqt4EL4FOSKYaZxNvW9i7JbKs7Yjg7HOA7cMbwm/G3O/q6SrIBvE8yzfD+7gKnDz4w6T+Y/VeS1MWlLUlSFwOJJKmLgUSS1MVAIknqYiCRJHUxkEiSuhhIJEld/gAU2rH4KVVtyAAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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-4.99300000e+03 4.96946190e-02 2.10589518e+02 -4.99400000e+03 4.97085339e-02 2.10734748e+02 -4.99500000e+03 4.97224855e-02 2.11531621e+02 -4.99600000e+03 4.97363816e-02 2.12107321e+02 -4.99700000e+03 4.97502337e-02 2.14114011e+02 -4.99800000e+03 4.97638982e-02 2.16162056e+02 -4.99900000e+03 4.97775098e-02 2.18694887e+02 -5.00000000e+03 4.97910367e-02 2.19269699e+02 -5.00100000e+03 4.98046424e-02 2.19328430e+02 -5.00200000e+03 4.98181490e-02 2.23219444e+02 -5.00300000e+03 4.98315622e-02 2.22618659e+02 -5.00400000e+03 4.98450194e-02 2.22997304e+02 -5.00500000e+03 4.98584869e-02 2.23364820e+02 -5.00600000e+03 4.98719742e-02 2.28002043e+02 -5.00700000e+03 4.98852742e-02 2.28953849e+02 -5.00800000e+03 4.98985722e-02 2.29364941e+02 -5.00900000e+03 4.99118820e-02 2.30969840e+02 -5.01000000e+03 4.99251745e-02 2.33146361e+02 -5.01100000e+03 4.99383468e-02 2.36988713e+02 -5.01200000e+03 4.99515057e-02 2.37605552e+02 -5.01300000e+03 4.99646611e-02 2.39248177e+02 -5.01400000e+03 4.99778239e-02 2.41444694e+02 -5.01500000e+03 4.99909512e-02 2.42689513e+02 diff --git a/docs/install_guides/nci_gadi.sh b/docs/install_guides/nci_gadi.sh index f0c3c3420..6a6e14f67 100644 --- a/docs/install_guides/nci_gadi.sh +++ b/docs/install_guides/nci_gadi.sh @@ -3,11 +3,6 @@ # Note, swig will need to be installed and in your path. # Also, swig4 doesn't seem to work, so use swig3. # -# You will need to build your own PETSc, as there appears -# to be an issue with Gadi & OpenMPI3 & Underworld, and -# the system PETSc does not work with OpenMPI4. Please ensure -# the PETSC_DIR environment variable is set and points to a -# valid petsc build. # # Usage: # sh ./nci_gadi.sh @@ -28,7 +23,7 @@ git checkout $1 # checkout the requested version # setup modules module purge -RUN_MODS='pbs openmpi/4.0.2 hdf5/1.10.5p python3/3.7.4' +RUN_MODS='pbs openmpi/4.0.2 hdf5/1.10.5p python3/3.7.4 petsc/3.12.2' module load scons/3.1.1 $RUN_MODS echo "*** The module list is: ***" module list -t @@ -39,6 +34,7 @@ export LD_PRELOAD=/apps/openmpi-mofed4.7-pbs19.2/4.0.2/lib/libmpi_usempif08_GNU. pip3 install --user mpi4py +export OMPI_MCA_io=ompio export HDF5_VERSION=1.10.5 CC=h5pcc HDF5_MPI="ON" pip3 install --user --no-cache-dir --global-option=build_ext --global-option="-L/apps/hdf5/1.10.5p/lib/ompi3/" --no-binary=h5py h5py @@ -53,7 +49,8 @@ echo "#####################################################################" echo "Underworld2 built " echo "Remember to set the required environment before running Underworld2. " echo " module load $RUN_MODS " -echo "You will also need to preload the OpenMPI libraries: " +echo "You will also need to set the following environment variables: " +echo " export OMPI_MCA_io=ompio " echo " export LD_PRELOAD=${LD_PRELOAD}:/apps/openmpi-mofed4.7-pbs19.2/4.0.2/lib/libmpi_usempif08_GNU.so.40" echo " export LD_PRELOAD=${LD_PRELOAD}:/apps/openmpi-mofed4.7-pbs19.2/4.0.2/lib/libmpi_usempi_ignore_tkr_GNU.so.40" echo " export LD_PRELOAD=${LD_PRELOAD}:/apps/openmpi-mofed4.7-pbs19.2/4.0.2/lib/libmpi_cxx.so.40" diff --git a/docs/install_guides/pawsey_magnus_container.md b/docs/install_guides/pawsey_magnus_container.md index 0eafea1a6..23596382b 100644 --- a/docs/install_guides/pawsey_magnus_container.md +++ b/docs/install_guides/pawsey_magnus_container.md @@ -8,7 +8,7 @@ Basic instructions for Singularity usage on Magnus is provided below. Note that your home directory will not be mounted into your Singularity container. You should instead run from `/scratch` or `/group`, which are by default mounted. Please review the Singularity documentation at Pawsey for full usage instructions: -https://support.pawsey.org.au/documentation/display/US/Singularity +https://support.pawsey.org.au/documentation/display/US/Containers 1. Load the Singularity module: @@ -19,14 +19,17 @@ user@magnus-1:~$ module load singularity 2. Pull down the required image. Note that images are generally around 1GB, so you might consider storing them somewhere in `/group` or `/scratch`. ```shell -user@magnus-1:~$ singularity pull /WHERE/TO/STORE/YOUR/IMAGE/IMAGENAME.sif docker://underworldcode/underworld2:2.9.0b +user@magnus-1:~$ singularity pull /WHERE/TO/STORE/YOUR/IMAGE/IMAGENAME.sif docker://underworldcode/underworld2 ``` 3. Use the image. Note that this will normally be a command line in your queue submission script. Don't forget to also load the Singularity module. Note also the setting of the environment variable `UW_VIS_PORT` which is required for correct vis operation. + Note also that we currently need to set the `SINGULARITYENV_LD_LIBRARY_PATH` + variable which prevents unwanted host libraries being loaded at runtime. ```shell +user@magnus-1:~$ export SINGULARITYENV_LD_LIBRARY_PATH=/opt/cray/pe/mpt/7.7.0/gni/mpich-gnu-abi/4.9/lib:/opt/cray/xpmem/2.2.15-6.0.7.1_5.16__g7549d06.ari/lib64:/opt/cray/ugni/6.0.14.0-6.0.7.1_3.18__gea11d3d.ari/lib64:/opt/cray/udreg/2.3.2-6.0.7.1_5.18__g5196236.ari/lib64:/opt/cray/pe/pmi/5.0.13/lib64:/opt/cray/alps/6.6.43-6.0.7.1_5.54__ga796da32.ari/lib64:/opt/cray/wlm_detect/1.3.3-6.0.7.1_5.8__g7109084.ari/lib64:/usr/lib64 user@magnus-1:~$ export UW_VIS_PORT=0 user@magnus-1:~$ srun singularity exec /WHERE/TO/STORE/YOUR/IMAGE/IMAGENAME.sif python3 YOURSCRIPT.py ``` @@ -45,6 +48,7 @@ user@magnus-1:~$ module load singularity 3. Launch the Singularity shell. Note the `--pty` required for pseudo terminal mode. ```shell +user@magnus-1:~$ export SINGULARITYENV_LD_LIBRARY_PATH=/opt/cray/pe/mpt/7.7.0/gni/mpich-gnu-abi/4.9/lib:/opt/cray/xpmem/2.2.15-6.0.7.1_5.16__g7549d06.ari/lib64:/opt/cray/ugni/6.0.14.0-6.0.7.1_3.18__gea11d3d.ari/lib64:/opt/cray/udreg/2.3.2-6.0.7.1_5.18__g5196236.ari/lib64:/opt/cray/pe/pmi/5.0.13/lib64:/opt/cray/alps/6.6.43-6.0.7.1_5.54__ga796da32.ari/lib64:/opt/cray/wlm_detect/1.3.3-6.0.7.1_5.8__g7109084.ari/lib64:/usr/lib64 user@magnus-1:~$ export UW_VIS_PORT=0 user@magnus-1:~$ srun --pty singularity shell /WHERE/TO/STORE/YOUR/IMAGE/IMAGENAME.sif ``` diff --git a/docs/install_guides/xsede_stampede2_singularity.md b/docs/install_guides/xsede_stampede2_singularity.md index 25528df64..a55c4d15b 100644 --- a/docs/install_guides/xsede_stampede2_singularity.md +++ b/docs/install_guides/xsede_stampede2_singularity.md @@ -33,7 +33,7 @@ or for the standard Underworld image: $ singularity pull docker://underworldcode/underworld2:2.7.1b ``` -Available Underworld images are listed on [our DockerHub page](https://cloud.docker.com/u/underworldcode/repository/docker/underworldcode/underworld2/tags). Note that +Available Underworld images are listed on [our DockerHub page](https://hub.docker.com/r/underworldcode/underworld2/tags). Note that only Underworld 2.7+ images will operate correctly on Stampede2. The location of the pulled down image will be reported at the conclusion of the `pull` diff --git a/docs/test/2DCosineHillAdvection.ipynb b/docs/test/2DCosineHillAdvection.ipynb index a395c180f..a0e1f5090 100644 --- a/docs/test/2DCosineHillAdvection.ipynb +++ b/docs/test/2DCosineHillAdvection.ipynb @@ -207,7 +207,7 @@ " if diffuseOnly: method += \" (diffusion only)\"\n", " print(\"{}: t_max={:.15f}, t_avg={:.15f}, wall time={:.4e}, its={}, dt={:.4e}\".format(\n", " method, maxT, avgT, num_t, steps, dt))\n", - " \n", + " \n", " return maxT, avgT, num_t, steps, nEls, dt" ] }, @@ -221,45 +221,7 @@ { "data": { "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SLCN: t_max=1.000000000000000, t_avg=0.039831195790397, wall time=2.7290e-06, its=0, dt=3.1416e-02\n" - ] - }, - { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SLCN (diffusion only): t_max=0.998415344757853, t_avg=0.039831185188472, wall time=1.5593e+01, its=200, dt=3.1416e-02\n" - ] - }, - { - "data": { - "text/html": [ - "" + "" ], "text/plain": [ "" @@ -272,13 +234,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "SLCN: t_max=0.852184812161098, t_avg=0.040071167501389, wall time=1.6513e+01, its=200, dt=3.1416e-02\n" + "SLCN: t_max=0.852184812161144, t_avg=0.040071167501391, wall time=4.3059e+00, its=200, dt=3.1416e-02\n" ] }, { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -291,13 +253,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "SUPG: t_max=0.494862381241538, t_avg=0.039927886622725, wall time=6.6796e+00, its=200, dt=3.1416e-02\n" + "SUPG: t_max=0.494862381241538, t_avg=0.039927886622725, wall time=2.0663e+00, its=200, dt=3.1416e-02\n" ] } ], "source": [ - "results_slcn = runModel(\"SLCN\", nEls = nEls, dt = dt, viz = viz, rotations=0.) # initial conditions only\n", - "results_slcn = runModel(\"SLCN\", nEls = nEls, dt = dt, viz = viz, diffuseOnly=True) # diffuse only\n", + "# uncomment the following 2 line to test no rotation scenarios: \n", + "# 1. the initial condition\n", + "# 2. heat diffusion only\n", + "#results_slcn = runModel(\"SLCN\", nEls = nEls, dt = dt, viz = viz, rotations=0.) # initial conditions only\n", + "#results_slcn = runModel(\"SLCN\", nEls = nEls, dt = dt, viz = viz, diffuseOnly=True) # diffuse only\n", "results_slcn = runModel(\"SLCN\", nEls = nEls, dt = dt, viz = viz)\n", "results_supg = runModel(\"SUPG\", nEls = nEls, dt = dt, viz = viz)" ] @@ -341,7 +306,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/docs/test/Analytic Soln Convergence Tests.ipynb b/docs/test/Analytic Soln Convergence Tests.ipynb index f523277e5..88cdfe153 100755 --- a/docs/test/Analytic Soln Convergence Tests.ipynb +++ b/docs/test/Analytic Soln Convergence Tests.ipynb @@ -31,11 +31,11 @@ " (\"B\", OD(), {\"itol\":1.e-5, \"otol\":1.e-5}, False ),\n", " (\"C\", OD(), {\"itol\":1.e-5, \"otol\":1.e-5}, False ),\n", " (\"DA\", OD(), {\"itol\":1.e-7, \"otol\":1.e-7}, True ),\n", - " (\"DB2d\", OD(), {\"itol\":1.e-6, \"otol\":1.e-6}, False ),\n", - " (\"DB3d\", OD(), {\"itol\":1.e-8, \"otol\":1.e-8}, False ), \n", + " (\"DB2d\", OD(), {\"itol\":1.e-6, \"otol\":1.e-6}, True ),\n", + " (\"DB3d\", OD(), {\"itol\":1.e-8, \"otol\":1.e-8}, True ), \n", "# (\"H\", OD([(\"nmodes\",240),]), {\"itol\":1.e-6, \"otol\":1.e-6}, True ), # needs parallel for analytic\n", " (\"Kz\", OD(), {\"itol\":1.e-4, \"otol\":1.e-4}, False ),\n", - " (\"M\", OD(), {\"itol\":1.e-6, \"otol\":1.e-6}, False ), \n", + " (\"M\", OD(), {\"itol\":1.e-6, \"otol\":1.e-6}, True ), \n", " ]\n", " \n", "\n", @@ -261,7 +261,49 @@ "Performing simulations for solution: SolNL 1 32\n", "Performing simulations for solution: SolNL 2 8\n", "Performing simulations for solution: SolNL 2 16\n", - "Performing simulations for solution: SolNL 2 32\n" + "Performing simulations for solution: SolNL 2 32\n", + "Performing simulations for solution: SolB 1 8\n", + "Performing simulations for solution: SolB 1 16\n", + "Performing simulations for solution: SolB 1 32\n", + "Performing simulations for solution: SolB 2 8\n", + "Performing simulations for solution: SolB 2 16\n", + "Performing simulations for solution: SolB 2 32\n", + "Performing simulations for solution: SolC 1 8\n", + "Performing simulations for solution: SolC 1 16\n", + "Performing simulations for solution: SolC 1 32\n", + "Performing simulations for solution: SolC 2 8\n", + "Performing simulations for solution: SolC 2 16\n", + "Performing simulations for solution: SolC 2 32\n", + "Performing simulations for solution: SolDA 1 8\n", + "Performing simulations for solution: SolDA 1 16\n", + "Performing simulations for solution: SolDA 1 32\n", + "Performing simulations for solution: SolDA 2 8\n", + "Performing simulations for solution: SolDA 2 16\n", + "Performing simulations for solution: SolDA 2 32\n", + "Performing simulations for solution: SolDB2d 1 8\n", + "Performing simulations for solution: SolDB2d 1 16\n", + "Performing simulations for solution: SolDB2d 1 32\n", + "Performing simulations for solution: SolDB2d 2 8\n", + "Performing simulations for solution: SolDB2d 2 16\n", + "Performing simulations for solution: SolDB2d 2 32\n", + "Performing simulations for solution: SolDB3d 1 4\n", + "Performing simulations for solution: SolDB3d 1 8\n", + "Performing simulations for solution: SolDB3d 1 16\n", + "Performing simulations for solution: SolDB3d 2 4\n", + "Performing simulations for solution: SolDB3d 2 8\n", + "Performing simulations for solution: SolDB3d 2 16\n", + "Performing simulations for solution: SolKz 1 8\n", + "Performing simulations for solution: SolKz 1 16\n", + "Performing simulations for solution: SolKz 1 32\n", + "Performing simulations for solution: SolKz 2 8\n", + "Performing simulations for solution: SolKz 2 16\n", + "Performing simulations for solution: SolKz 2 32\n", + "Performing simulations for solution: SolM 1 8\n", + "Performing simulations for solution: SolM 1 16\n", + "Performing simulations for solution: SolM 1 32\n", + "Performing simulations for solution: SolM 2 8\n", + "Performing simulations for solution: SolM 2 16\n", + "Performing simulations for solution: SolM 2 32\n" ] } ], @@ -283,6 +325,10 @@ "\n", "\n", " for res in resolutions:\n", + " # use lower res for 3d. \n", + " dim = solnguy.dim\n", + " if dim==3:\n", + " res = round(res/2.)\n", " # do not rerun existing results\n", " if res in err_vel:\n", " continue\n", @@ -323,7 +369,7 @@ }, { "data": { - "image/png": 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\n", "text/plain": [ "
" ] @@ -336,6 +382,7 @@ ], "source": [ "if (uw.mpi.rank==0) and do_analysis:\n", + " err_messages = []\n", " print(\"Performing analysis.\", flush=True)\n", "\n", " errtype = 1 # {0:\"absolute\", 1:\"scaled absolute\"}\n", @@ -415,8 +462,6 @@ " lines = collections.OrderedDict()\n", " for key, err in sorted(soln_results.items(), key=lambda x: x[0][0]):\n", " soln_name = key[0]\n", - " if (soln_name not in solns) or (solns[soln_name].graph==False):\n", - " continue\n", " order = key[1]\n", " velpres = key[2]\n", " ax = axes[order]\n", @@ -424,8 +469,11 @@ " fitdata = get_fit_line(np.reciprocal(list(err.keys()),dtype='double'),fit) \n", "\n", " expected_order = order if (key[2]==pressure_key) else order+1\n", - " if not np.isclose(fit[1],expected_order,rtol=1.5e-1):\n", - " raise RuntimeError(\"Rejecting {} fit = {}, expected = {}.\".format(soln_name,fit[1],expected_order))\n", + " if not np.isclose(fit[1],expected_order,rtol=2.e-1):\n", + " err_messages.append(\"Rejecting {} fit = {}, expected = {}.\".format(soln_name,fit[1],expected_order))\n", + "\n", + " if (soln_name not in solns) or (solns[soln_name].graph==False):\n", + " continue\n", "\n", " col,ls = scheme[(soln_name,velpres)]\n", " line = ax.plot(*fitdata, linewidth=1., color=col, linestyle=ls)\n", @@ -448,15 +496,20 @@ " leg.set_title(\"Q1 dQ0 Q2 dPc1 \", \n", " {'family': 'monospace', 'size':6 })\n", " leg._legend_box.align = \"right\"\n", - " #fig.savefig(\"Analytic_Convergence_Graph.png\")" + " fig.savefig(\"Analytic_Convergence_Graph.png\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "if len(err_messages)>0:\n", + " for err in err_messages:\n", + " print(err)\n", + " raise RuntimeError(\"Not all configurations produced the expected convergence rates.\")" + ] } ], "metadata": { diff --git a/docs/test/SteadyState.ipynb b/docs/test/SteadyState.ipynb index 33b39fd24..3beebf260 100644 --- a/docs/test/SteadyState.ipynb +++ b/docs/test/SteadyState.ipynb @@ -67,11 +67,13 @@ "rank = uw.mpi.rank\n", "\n", "# for machines without matplotlib #\n", - "make_graphs = True\n", - "try:\n", - " import matplotlib\n", - "except ImportError:\n", - " make_graphs=False\n", + "make_graphs = False\n", + "if uw.utils.is_kernel(): \n", + " make_graphs = True\n", + " try:\n", + " import matplotlib\n", + " except ImportError:\n", + " make_graphs=False\n", "\n", "# depth range\n", "y0 = -.60\n", @@ -145,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -162,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -189,7 +191,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -199,27 +201,29 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 8, "metadata": { "scrolled": false }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ - "Numerical flux at y = -0.6 is 0.241786540077\n", - "Exact flux at y = -0.6 is 0.298507462687\n", + "Numerical flux at y = -0.6 is 0.241786540078\n", + "Exact flux at y = -0.6 is 0.2985074626865672\n", "\n", "Abs. error = 2.581e-04\n", "Rel. error = 6.156e-06\n" @@ -239,10 +243,6 @@ "ycoord = tField.mesh.data[ids.data,[1]]\n", "analytic = analyticTemperature(ycoord, h, k, c0, c1)\n", "\n", - "abserr = uw.utils._nps_2norm(analytic - yvals)\n", - "mag = uw.utils._nps_2norm(analytic)\n", - "relerr = abserr / mag\n", - "\n", "# measure border flux, analytic is easy, parallel check needed for numeric result\n", "yspot = y0\n", "ana_flux = exactDeriv(yspot,h,k,c0)\n", @@ -258,8 +258,7 @@ "coords = comm.allgather(ycoord)\n", "numerical = comm.allgather(yvals)\n", "\n", - "if rank == 0:\n", - " # build matplot lib graph of result only on proc 0\n", + "if make_graphs:\n", "\n", " # 1st build exact solution hiRes\n", " big = np.linspace(y0,y1)\n", @@ -276,6 +275,9 @@ "if rank == 0:\n", " threshold = 1.0e-4\n", " yspot = y0\n", + " abserr = np.linalg.norm(analytic - yvals)\n", + " mag = np.linalg.norm(analytic)\n", + " relerr = abserr / mag\n", " print(\"Numerical flux at y = \" ,yspot,\"is\", num_flux)\n", " print(\"Exact flux at y = \" ,yspot,\"is\", ana_flux)\n", " print(\"\\nAbs. error = {0:.3e}\".format(abserr))\n", @@ -403,9 +405,11 @@ "ycoord = tField.mesh.data[ids.data,[1]]\n", "analytic = analyticTemperature(ycoord, h, k, c0, c1)\n", "\n", + "'''\n", "abserr = uw.utils._nps_2norm(analytic - yvals)\n", "mag = uw.utils._nps_2norm(analytic)\n", "relerr = abserr / mag\n", + "'''\n", "\n", "# measure border flux, analytic is easy, parallel check needed for numeric result\n", "yspot = y0\n", @@ -421,8 +425,7 @@ "coords = comm.allgather(ycoord)\n", "numerical = comm.allgather(yvals)\n", "\n", - "if rank == 0:\n", - " # build matplot lib graph of result only on proc 0\n", + "if make_graphs:\n", "\n", " # 1st build exact solution hiRes\n", " big = np.linspace(y0,y1)\n", @@ -439,6 +442,9 @@ "if rank == 0:\n", " threshold = 1.0e-4\n", " yspot = y1\n", + " abserr = np.linalg.norm(analytic - yvals)\n", + " mag = np.linalg.norm(analytic)\n", + " relerr = abserr / mag\n", " print(\"Numerical flux at y = \" ,yspot,\"is\", num_flux)\n", " print(\"Exact flux at y = \" ,yspot,\"is\", ana_flux)\n", " print(\"\\nAbs. error = {0:.3e}\".format(abserr))\n", @@ -537,10 +543,11 @@ "ycoord = tField.mesh.data[ids.data,[1]]\n", "analytic = analyticTemperature(ycoord, h, k, c0, c1)\n", "\n", + "'''\n", "abserr = uw.utils._nps_2norm(analytic - yvals)\n", "mag = uw.utils._nps_2norm(analytic)\n", "relerr = abserr / mag\n", - "\n", + "'''\n", "# measure border flux, analytic is easy, parallel check needed for numeric result\n", "yspot = y0\n", "ana_flux = exactDeriv(yspot,h,k,c0)\n", @@ -579,6 +586,9 @@ "if rank == 0:\n", " threshold = 1.0e-4\n", " yspot = y0\n", + " abserr = np.linalg.norm(analytic - yvals)\n", + " mag = np.linalg.norm(analytic)\n", + " relerr = abserr / mag\n", " print(\"Numerical flux at y = \" ,yspot,\"is\", num_flux)\n", " print(\"Exact flux at y=\" ,yspot,\"is\", ana_flux)\n", " print(\"\\nAbs. error = {0:.3e}\".format(abserr))\n", @@ -607,8 +617,7 @@ ], "source": [ "\n", - "if rank == 0:\n", - " # build matplot lib graph of result only on proc 0\n", + "if make_graphs:\n", "\n", " # 1st build exact solution hiRes\n", " big = np.linspace(y0,y1)\n", @@ -646,7 +655,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/docs/test/StokesEq_PoiseuilleFlow.ipynb b/docs/test/StokesEq_PoiseuilleFlow.ipynb index 7d2f742dd..0fd6f1009 100644 --- a/docs/test/StokesEq_PoiseuilleFlow.ipynb +++ b/docs/test/StokesEq_PoiseuilleFlow.ipynb @@ -65,11 +65,13 @@ "import numpy as np\n", "\n", "# for machines without matplotlib #\n", - "make_graphs = True\n", - "try:\n", - " import matplotlib\n", - "except ImportError:\n", - " make_graphs=False" + "make_graphs = False\n", + "if uw.utils.is_kernel(): \n", + " make_graphs = True\n", + " try:\n", + " import matplotlib\n", + " except ImportError:\n", + " make_graphs=False" ] }, { @@ -193,7 +195,7 @@ { "data": { "text/html": [ - "" + "" ], "text/plain": [ "" @@ -216,19 +218,36 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ids, 16641\n" + ] + } + ], "source": [ "ids = mesh.specialSets[\"MaxI_VertexSet\"]\n", "coords = mesh.data[ids.data] # xcoords\n", + "print(\"Ids, \", ids.size)\n", "V = velocityField.evaluate(ids)\n", "gradV = velocityField.fn_gradient.evaluate(ids)\n", "\n", + "from mpi4py import MPI\n", + "comm = MPI.COMM_WORLD\n", + "# assuming order in the allgather is the same\n", + "# some squashing of arrays required for parallel comms.\n", + "V = np.concatenate(\n", + " comm.allgather( V.ravel() )\n", + " ).reshape(-1,mesh.dim)\n", + "gradV = np.concatenate(\n", + " comm.allgather( gradV.ravel() )\n", + " ).reshape(-1,mesh.dim*velocityField.data.shape[1])\n", + "\n", "u = V[:,0] ; v = V[:,1]\n", "du_dx = gradV[:,0] ; du_dy = gradV[:,1]\n", - "dv_dx = gradV[:,2] ; dv_dy = gradV[:,3]\n", - "\n", - "strainRate = fn.tensor.symmetric( velocityField.fn_gradient )\n", - "devstress = 2.0 * viscosity * strainRate" + "dv_dx = gradV[:,2] ; dv_dy = gradV[:,3]" ] }, { @@ -239,11 +258,7 @@ "source": [ "def exact_vx(y):\n", " ana_u = 1.0 / (2.0 * viscosity)* dp_dx * (y**2 - h*y)\n", - " return ana_u\n", - "\n", - "def exact_shearSR(y):\n", - " shearSR = dp_dx / (2.0*viscosity) * (y - h/2 )\n", - " return shearSR" + " return ana_u" ] }, { @@ -253,12 +268,14 @@ "outputs": [ { "data": { - "image/png": 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T4rt9T1KtmW2T9AVJiRKAH5W0xcx+J6lZ0oXu/kquxgoMFSvhAaAwOP+ilOS0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\nkANl04LdzF6WFBTo6Q+Q9EqBnhv5w+t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\n", "text/plain": [ - "" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -289,22 +306,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "Relative error against analytic solution: 1.4047969477270738e-05\n" + "Relative error against analytic solution: 1.416719019592425e-05\n" ] } ], "source": [ "ana_u = exact_vx(coords[:,1])\n", "\n", - "abserr = uw.utils._nps_2norm(ana_u - u)\n", - "mag = uw.utils._nps_2norm(ana_u)\n", - "relerr = abserr / mag\n", - "\n", - "from mpi4py import MPI\n", - "comm = MPI.COMM_WORLD\n", - "\n", - "\n", "if uw.mpi.rank == 0:\n", + " abserr = np.linalg.norm(ana_u - u)\n", + " mag = np.linalg.norm(ana_u)\n", + " relerr = abserr / mag\n", " threshold = 1.0e-4\n", " print(\"Relative error against analytic solution: {}\".format(relerr))\n", " if relerr > threshold:\n", @@ -335,7 +347,12 @@ "metadata": {}, "outputs": [], "source": [ - "# Further analytics\n", + "## Further analytics\n", + "# def exact_shearSR(y):\n", + "# shearSR = dp_dx / (2.0*viscosity) * (y - h/2 )\n", + "# return shearSR\n", + "# strainRate = fn.tensor.symmetric( velocityField.fn_gradient )\n", + "# devstress = 2.0 * viscosity * strainRate\n", "# pyplot.plot(coords[:,1], sr, '-', label='exact_shearSR')\n", "# pyplot.plot(coords[:,1], strainRate.evaluate(ids)[:,2], 'o', color = 'purple', label='sr_shear')\n", "# pyplot.plot(coords[:,1], devstress.evaluate(ids)[:,2], '+', label='tau_shear')\n", @@ -362,7 +379,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.7.3" } }, "nbformat": 4, diff --git a/docs/test/doctests.py b/docs/test/doctests.py index 965cbe86d..e196a2965 100644 --- a/docs/test/doctests.py +++ b/docs/test/doctests.py @@ -16,7 +16,7 @@ for itthing, modName, bs in modIter: mod=sys.modules.get(modName) - if modName.startswith("underworld.visualisation"): + if modName.startswith("underworld.visualisation") and (not test_vis): print("Not testing `{}` as `lavavu` not found.".format(modName)) continue print("Testing "+modName) diff --git a/docs/test/image_tests/notebooks/runtest.py b/docs/test/image_tests/notebooks/runtest.py index 770c9bb7d..20223ae61 100644 --- a/docs/test/image_tests/notebooks/runtest.py +++ b/docs/test/image_tests/notebooks/runtest.py @@ -8,7 +8,11 @@ import shutil #Viewer instance for running image tests -lv = vis.lavavu.Viewer(quality=1) + +kwargs = {} +if 'UW_VIS_PORT' in os.environ: + kwargs['port'] = int(os.environ['UW_VIS_PORT']) +lv = vis.lavavu.Viewer(quality=1,**kwargs) #files = glob.glob("*.ipynb") nbdir = "../../../user_guide/" diff --git a/docs/test/image_tests/raytay/runtest.py b/docs/test/image_tests/raytay/runtest.py index 8352c682a..93e9b4e0d 100644 --- a/docs/test/image_tests/raytay/runtest.py +++ b/docs/test/image_tests/raytay/runtest.py @@ -171,6 +171,9 @@ def doOutput(): #Check the image results if uw.mpi.rank == 0: - lv = vis.lavavu.Viewer(quality=1) + kwargs = {} + if 'UW_VIS_PORT' in os.environ: + kwargs['port'] = int(os.environ['UW_VIS_PORT']) + lv = vis.lavavu.Viewer(quality=1,**kwargs) lv.testimages(tolerance=1e-3) diff --git a/docs/test/solver_penalty_test.py b/docs/test/solver_penalty_test.py new file mode 100644 index 000000000..314cbf2b9 --- /dev/null +++ b/docs/test/solver_penalty_test.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python3 +''' +This script simply configures a system with compressibilty and +ensures that it flags a warning if the user attempts to use a +penalty solver. +''' + +import underworld as uw +from underworld import function as fn + +mesh = uw.mesh.FeMesh_Cartesian("Q1/DQ0", (2,2), (0.,0.), (1.,1.)) +velocityField = uw.mesh.MeshVariable(mesh,2) +pressureField = uw.mesh.MeshVariable(mesh.subMesh,1) +stokesSystem = uw.systems.Stokes(velocityField,pressureField,1.,(0.,0.),fn_one_on_lambda=1.) +solver=uw.systems.Solver(stokesSystem) +solver.set_penalty(1.) + +# catch warning to check it was generated correctly. +import warnings +with warnings.catch_warnings(record=True) as w: + # Cause all warnings to always be triggered. + warnings.simplefilter("always") + # Trigger warning. + solver.solve() + # Verify message exists + assert len(w) == 1 + # Verify message is correct + assert uw.systems._bsscr.StokesSolver._warning_penalty_compressibility == str(w[-1].message) + # if broken, check the generated message: + # print(str(w[-1].message)) \ No newline at end of file diff --git a/test_long.sh b/test_long.sh new file mode 100644 index 000000000..6ea18bacb --- /dev/null +++ b/test_long.sh @@ -0,0 +1,12 @@ +#!/bin/sh + +# ensure we bail on errors +set -e + +# update the PYTHONPATH +. ./updatePyPath.sh + +export UW_LONGTEST=1 +./docs/development/run_tests.py ./docs/examples/* ./docs/user_guide/* docs/test/* +./docs/development/run_tests.py --prepend="mpirun -np 2" ./docs/examples/* +./docs/development/run_tests.py --prepend="mpirun -np 4" ./docs/examples/* diff --git a/underworld/__init__.py b/underworld/__init__.py index 7497bd86e..3068db79e 100644 --- a/underworld/__init__.py +++ b/underworld/__init__.py @@ -179,11 +179,6 @@ def _set_init_sig_as_sig(mod): _id = str(_uuid.uuid4()) from . import _net -# DEPRECATE JM 20200203 -def matplotlib_inline(): - raise RuntimeError("This function has been moved to `underworld.utils.matplotlib_inline()`.") -matplotlib_inline.DO_NOT_DOC = True # this disables documentation of this function - # lets handle exceptions differently in parallel to ensure we call. # add isinstance so that this acts correctly for Mocked classes used in sphinx docs generation if isinstance(underworld.mpi.size, int) and underworld.mpi.size > 1: diff --git a/underworld/_version.py b/underworld/_version.py index 9ac69ef32..f033a6d0e 100644 --- a/underworld/_version.py +++ b/underworld/_version.py @@ -1 +1 @@ -__version__ = "2.9.4b" +__version__ = "2.10.0b" diff --git a/underworld/function/analytic.py b/underworld/function/analytic.py index 1d8b90c4c..abfb0bc0b 100644 --- a/underworld/function/analytic.py +++ b/underworld/function/analytic.py @@ -196,7 +196,7 @@ class SolC(_SolBaseFreeSlipBc): Fourier terms required. """ - eqn_bodyforce = "(0,-\sigma_0 \operatorname{step}(1,0,x_c; x)" + eqn_bodyforce = "(0,-\sigma_0 \operatorname{step}(1,0,x_c; x))" eqn_viscosity = "\eta_0" def __init__(self, sigma_0=1., x_c=0.5, eta_0=1., nmodes=200, *args, **kwargs): if not isinstance(eta_0, float) or eta_0<=0: diff --git a/underworld/libUnderworld/Solvers/KSPSolvers/src/BSSCR/list_operations.c b/underworld/libUnderworld/Solvers/KSPSolvers/src/BSSCR/list_operations.c index 2632ac140..e59b35a9b 100644 --- a/underworld/libUnderworld/Solvers/KSPSolvers/src/BSSCR/list_operations.c +++ b/underworld/libUnderworld/Solvers/KSPSolvers/src/BSSCR/list_operations.c @@ -201,10 +201,10 @@ PetscErrorCode BSSCR_MatListOperations( Mat A, PetscViewer v ) BSSCR_report_op( ops->getvecs, v, i++, "MatGetVec" ); PetscViewerASCIIPrintf(v, "------------------------------------------------\n"); - BSSCR_report_op( ops->matmult, v, i++, "MatMatMult" ); + // BSSCR_report_op( ops->matmult, v, i++, "MatMatMult" ); removed for petsc 3.13.2 compat. BSSCR_report_op( ops->matmultsymbolic, v, i++, "MatMatMultSymbolic" ); BSSCR_report_op( ops->matmultnumeric, v, i++, "MatMatMultNumeric" ); - BSSCR_report_op( ops->ptap, v, i++, "MatPtAP" ); + // BSSCR_report_op( ops->ptap, v, i++, "MatPtAP" ); removed for petsc 3.13.2 compat. BSSCR_report_op( ops->ptapsymbolic, v, i++, "MatPtAPSymbolic" ); BSSCR_report_op( ops->ptapnumeric, v, i++, "MatPtAPNumeric" ); diff --git a/underworld/libUnderworld/StGermain/Base/Context/src/Init.c b/underworld/libUnderworld/StGermain/Base/Context/src/Init.c index ea1b9dac2..0d95d724f 100644 --- a/underworld/libUnderworld/StGermain/Base/Context/src/Init.c +++ b/underworld/libUnderworld/StGermain/Base/Context/src/Init.c @@ -32,11 +32,22 @@ #include #define NUMPY_IMPORT_ARRAY_RETVAL False +/* Note that the following is required as `import_array()` is */ +/* actually a macro which calls `return` under certain */ +/* circumstances. In itself that is fine, but because */ +/* `BaseContext_Init()` returns `Bool` some compilers */ +/* complain. This was encoutered first for `numpy` 1.19.0. */ +/* Perhaps this call should go somewhere else altogether, */ +/* like `main()`. */ +void* import_numpy() { + import_array(); + return NULL; +}; + Bool BaseContext_Init( int* argc, char** argv[] ) { Stream* typedStream; - import_array(); - + import_numpy(); Journal_Printf( Journal_Register( DebugStream_Type, (Name)"Context" ), "In: %s\n", __func__ ); diff --git a/underworld/libUnderworld/config/packages/Python.py b/underworld/libUnderworld/config/packages/Python.py index d75456b06..2177bc029 100644 --- a/underworld/libUnderworld/config/packages/Python.py +++ b/underworld/libUnderworld/config/packages/Python.py @@ -25,3 +25,7 @@ def gen_envs(self, loc): env.AppendUnique(CPPPATH=[os.path.join(self.location[1][0],'python3.7m')]) env.PrependUnique(LIBS=['python3.7m']) yield env + if self.find_libraries(loc[2], 'python3.8'): + env.AppendUnique(CPPPATH=[os.path.join(self.location[1][0],'python3.8')]) + env.PrependUnique(LIBS=['python3.8']) + yield env diff --git a/underworld/libUnderworld/config/packages/libXML2.py b/underworld/libUnderworld/config/packages/libXML2.py index e7291bfe3..30e15821c 100644 --- a/underworld/libUnderworld/config/packages/libXML2.py +++ b/underworld/libUnderworld/config/packages/libXML2.py @@ -10,6 +10,8 @@ def gen_base_extensions(self): def gen_envs(self, loc): for env in Package.gen_envs(self, loc): + # the following is required to force libxml2 to not use the cpp-api + env.AppendUnique(CPPDEFINES=["U_SHOW_CPLUSPLUS_API=0",]) self.headers = [os.path.join('libxml', 'parser.h')] if self.find_libraries(loc[2], 'xml2'): env.PrependUnique(LIBS=['xml2']) diff --git a/underworld/libUnderworld/libUnderworldPy/Function.i b/underworld/libUnderworld/libUnderworldPy/Function.i index ef1c01e57..0bb177f12 100644 --- a/underworld/libUnderworld/libUnderworldPy/Function.i +++ b/underworld/libUnderworld/libUnderworldPy/Function.i @@ -6,7 +6,11 @@ ** located at the project root, or contact the authors. ** ** ** **~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*/ +#if (SWIG_VERSION <= 0x040000) %module (package="underworld.libUnderworld.libUnderworldPy") Function +#else +%module (package="underworld.libUnderworld.libUnderworldPy", moduleimport="import $module") Function +#endif %include "exception.i" %import "StGermain.i" diff --git a/underworld/libUnderworld/libUnderworldPy/PICellerator.i b/underworld/libUnderworld/libUnderworldPy/PICellerator.i index 6f4d4a1bf..a6e07982b 100644 --- a/underworld/libUnderworld/libUnderworldPy/PICellerator.i +++ b/underworld/libUnderworld/libUnderworldPy/PICellerator.i @@ -8,7 +8,12 @@ **~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*/ /* -*- C -*- (not really, but good for syntax highlighting) */ +#if (SWIG_VERSION <= 0x040000) %module (package="underworld.libUnderworld.libUnderworldPy") PICellerator +#else +%module (package="underworld.libUnderworld.libUnderworldPy", moduleimport="import $module") PICellerator +#endif + %{ /* Includes the header in the wrapper code */ diff --git a/underworld/libUnderworld/libUnderworldPy/Solvers.i b/underworld/libUnderworld/libUnderworldPy/Solvers.i index f734cc36b..1a0f9306f 100644 --- a/underworld/libUnderworld/libUnderworldPy/Solvers.i +++ b/underworld/libUnderworld/libUnderworldPy/Solvers.i @@ -8,7 +8,13 @@ **~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*/ /* -*- C -*- (not really, but good for syntax highlighting) */ + +#if (SWIG_VERSION <= 0x040000) %module (package="underworld.libUnderworld.libUnderworldPy") Solvers +#else +%module (package="underworld.libUnderworld.libUnderworldPy", moduleimport="import $module") Solvers +#endif + %{ /* Includes the header in the wrapper code */ diff --git a/underworld/libUnderworld/libUnderworldPy/Spherical.i b/underworld/libUnderworld/libUnderworldPy/Spherical.i index 8b42e9979..47bbd8b06 100644 --- a/underworld/libUnderworld/libUnderworldPy/Spherical.i +++ b/underworld/libUnderworld/libUnderworldPy/Spherical.i @@ -8,7 +8,7 @@ **~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*/ /* -*- C -*- (not really, but good for syntax highlighting) */ -%module (package="underworld.libUnderworld.libUnderworldPy") Spherical +%module (package="underworld.libUnderworld.libUnderworldPy", moduleimport="import $module") Spherical %import "StGermain.i" %import "StgDomain.i" diff --git a/underworld/libUnderworld/libUnderworldPy/StGermain.i b/underworld/libUnderworld/libUnderworldPy/StGermain.i index 705a93fbb..e5814cf83 100644 --- a/underworld/libUnderworld/libUnderworldPy/StGermain.i +++ b/underworld/libUnderworld/libUnderworldPy/StGermain.i @@ -8,7 +8,12 @@ **~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*/ /* -*- C -*- (not really, but good for syntax highlighting) */ + +#if (SWIG_VERSION <= 0x040000) %module (package="underworld.libUnderworld.libUnderworldPy") StGermain +#else +%module (package="underworld.libUnderworld.libUnderworldPy", moduleimport="import $module") StGermain +#endif %{ /* Includes the header in the wrapper code */ diff --git a/underworld/libUnderworld/libUnderworldPy/StGermain_Tools.i b/underworld/libUnderworld/libUnderworldPy/StGermain_Tools.i index 36c92fbb4..dbb01d807 100644 --- a/underworld/libUnderworld/libUnderworldPy/StGermain_Tools.i +++ b/underworld/libUnderworld/libUnderworldPy/StGermain_Tools.i @@ -8,7 +8,7 @@ **~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*/ /* -*- C -*- (not really, but good for syntax highlighting) */ -%module (package="underworld.libUnderworld.libUnderworldPy") StGermain_Tools +%module (package="underworld.libUnderworld.libUnderworldPy", moduleimport="import $module") StGermain_Tools %{ diff --git a/underworld/libUnderworld/libUnderworldPy/StgDomain.i b/underworld/libUnderworld/libUnderworldPy/StgDomain.i index 81b77d3da..701145104 100644 --- a/underworld/libUnderworld/libUnderworldPy/StgDomain.i +++ b/underworld/libUnderworld/libUnderworldPy/StgDomain.i @@ -8,8 +8,11 @@ **~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*/ /* -*- C -*- (not really, but good for syntax highlighting) */ +#if (SWIG_VERSION <= 0x040000) %module (package="underworld.libUnderworld.libUnderworldPy") StgDomain - +#else +%module (package="underworld.libUnderworld.libUnderworldPy", moduleimport="import $module") StgDomain +#endif %include "exception.i" %import "StGermain.i" diff --git a/underworld/libUnderworld/libUnderworldPy/StgFEM.i b/underworld/libUnderworld/libUnderworldPy/StgFEM.i index 94728ced2..72b943812 100644 --- a/underworld/libUnderworld/libUnderworldPy/StgFEM.i +++ b/underworld/libUnderworld/libUnderworldPy/StgFEM.i @@ -8,7 +8,11 @@ **~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*/ /* -*- C -*- (not really, but good for syntax highlighting) */ +#if (SWIG_VERSION <= 0x040000) %module (package="underworld.libUnderworld.libUnderworldPy") StgFEM +#else +%module (package="underworld.libUnderworld.libUnderworldPy", moduleimport="import $module") StgFEM +#endif %{ diff --git a/underworld/libUnderworld/libUnderworldPy/Underworld.i b/underworld/libUnderworld/libUnderworldPy/Underworld.i index c2d8431bb..7680d9154 100644 --- a/underworld/libUnderworld/libUnderworldPy/Underworld.i +++ b/underworld/libUnderworld/libUnderworldPy/Underworld.i @@ -8,7 +8,11 @@ **~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*/ /* -*- C -*- (not really, but good for syntax highlighting) */ +#if (SWIG_VERSION <= 0x040000) %module (package="underworld.libUnderworld.libUnderworldPy") Underworld +#else +%module (package="underworld.libUnderworld.libUnderworldPy", moduleimport="import $module") Underworld +#endif %include "exception.i" %import "StGermain.i" diff --git a/underworld/libUnderworld/libUnderworldPy/c_arrays.i b/underworld/libUnderworld/libUnderworldPy/c_arrays.i index 771d45176..297799e98 100644 --- a/underworld/libUnderworld/libUnderworldPy/c_arrays.i +++ b/underworld/libUnderworld/libUnderworldPy/c_arrays.i @@ -8,7 +8,11 @@ **~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*/ /* -*- C -*- (not really, but good for syntax highlighting) */ +#if (SWIG_VERSION <= 0x040000) %module (package="underworld.libUnderworld.libUnderworldPy") c_arrays +#else +%module (package="underworld.libUnderworld.libUnderworldPy", moduleimport="import $module") c_arrays +#endif %{ /* Includes the header in the wrapper code */ diff --git a/underworld/libUnderworld/libUnderworldPy/c_pointers.i b/underworld/libUnderworld/libUnderworldPy/c_pointers.i index d63e24561..06e236ec4 100644 --- a/underworld/libUnderworld/libUnderworldPy/c_pointers.i +++ b/underworld/libUnderworld/libUnderworldPy/c_pointers.i @@ -8,7 +8,7 @@ **~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*/ /* -*- C -*- (not really, but good for syntax highlighting) */ -%module (package="underworld.libUnderworld.libUnderworldPy") c_pointers +%module (package="underworld.libUnderworld.libUnderworldPy", moduleimport="import $module") c_pointers /* this guy is an interface to malloc */ %include "cpointer.i" diff --git a/underworld/libUnderworld/libUnderworldPy/gLucifer.i b/underworld/libUnderworld/libUnderworldPy/gLucifer.i index 061db4447..2713a562e 100644 --- a/underworld/libUnderworld/libUnderworldPy/gLucifer.i +++ b/underworld/libUnderworld/libUnderworldPy/gLucifer.i @@ -8,7 +8,12 @@ **~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*/ /* -*- C -*- (not really, but good for syntax highlighting) */ +#if (SWIG_VERSION <= 0x040000) %module (package="underworld.libUnderworld.libUnderworldPy") gLucifer +#else +%module (package="underworld.libUnderworld.libUnderworldPy", moduleimport="import $module") gLucifer +#endif + %{ /* Includes the header in the wrapper code */ diff --git a/underworld/libUnderworld/libUnderworldPy/petsc.i b/underworld/libUnderworld/libUnderworldPy/petsc.i index 71af1e497..ce140ebdd 100644 --- a/underworld/libUnderworld/libUnderworldPy/petsc.i +++ b/underworld/libUnderworld/libUnderworldPy/petsc.i @@ -8,7 +8,7 @@ **~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*~*/ /* -*- C -*- (not really, but good for syntax highlighting) */ -%module (package="underworld.libUnderworld.libUnderworldPy") petsc +%module (package="underworld.libUnderworld.libUnderworldPy", moduleimport="import $module") petsc %{ /* Includes the header in the wrapper code */ diff --git a/underworld/scaling/_scaling.py b/underworld/scaling/_scaling.py index a2d39a5bc..21c8b8f4a 100644 --- a/underworld/scaling/_scaling.py +++ b/underworld/scaling/_scaling.py @@ -124,7 +124,6 @@ def check(length, time, mass, temperature, substance): else: raise ValueError('Dimension Error') - def dimensionalise(value, units): """ Dimensionalise a value. @@ -190,3 +189,19 @@ def check(length, time, mass, temperature, substance): else: return (value * factor).to(units) + +def ndargs(f): + """ Decorator used to non-dimensionalise the arguments of a function""" + + def convert(obj): + if isinstance(obj, (list, tuple)): + return type(obj)([convert(val) for val in obj]) + else: + return non_dimensionalise(obj) + + def new_f(*args, **kwargs): + nd_args = [convert(arg) for arg in args] + nd_kwargs = {name:convert(val) for name, val in kwargs.items()} + return f(*nd_args, **nd_kwargs) + new_f.__name__ = f.__name__ + return new_f diff --git a/underworld/swarm/_swarmvariable.py b/underworld/swarm/_swarmvariable.py index 66ebb978b..42736e388 100644 --- a/underworld/swarm/_swarmvariable.py +++ b/underworld/swarm/_swarmvariable.py @@ -21,10 +21,9 @@ import weakref class SwarmVariable(_stgermain.StgClass, function.Function): - """ - The SwarmVariable class allows users to add data to swarm particles. The data - can be of type "char", "short", "int", "long, "float" or "double". + The SwarmVariable class allows users to add data to swarm particles. The data + can be of type "char", "short", "int", "long, "float" or "double". Note that the swarm allocates one block of contiguous memory for all the particles. The per particle variable datums is then interlaced across this memory block. diff --git a/underworld/systems/_bsscr.py b/underworld/systems/_bsscr.py index cf05b85bb..1761f1274 100644 --- a/underworld/systems/_bsscr.py +++ b/underworld/systems/_bsscr.py @@ -317,6 +317,8 @@ def _add_to_stg_dict(self,componentDictionary): ### the solve function ######################################################################## + _warning_penalty_compressibility = "Stokes system appears to have set the `fn_one_on_lambda` parameter which is incompatible "\ + "with the penalty method. If parameter is non-zero, erroneous results are likely to be generated." def solve(self, nonLinearIterate=None, nonLinearTolerance=1.0e-2, nonLinearKillNonConvergent=False, nonLinearMinIterations=1, @@ -355,6 +357,9 @@ def solve(self, nonLinearIterate=None, nonLinearTolerance=1.0e-2, Solvers.SBKSP_SetSolver(self._cself, self._stokesSLE._cself) if isinstance(self.options.main.penalty,float) and self.options.main.penalty > 0.0: + if hasattr(self._stokesSLE, "_mmatrix") and (uw.mpi.rank==0): + import warnings + warnings.warn(self._warning_penalty_compressibility) Solvers.SBKSP_SetPenalty(self._cself, self.options.main.penalty) if self.options.main.ksp_k2_type == "NULL": self.options.main.ksp_k2_type = "GMG" diff --git a/underworld/visualisation/__init__.py b/underworld/visualisation/__init__.py index b853ee07a..07f801f8e 100644 --- a/underworld/visualisation/__init__.py +++ b/underworld/visualisation/__init__.py @@ -74,7 +74,6 @@ def __del__(self): pass import os as _os -# disable collection of data if requested if "UW_USE_XVFB" in _os.environ: if _uw.mpi.rank == 0: _display = _xvfb_runner() diff --git a/underworld/visualisation/_glucifer.py b/underworld/visualisation/_glucifer.py index b451f8e26..3ac2a9bb8 100644 --- a/underworld/visualisation/_glucifer.py +++ b/underworld/visualisation/_glucifer.py @@ -167,23 +167,20 @@ def save(self,filename): libUnderworld.gLucifer.lucDatabase_BackupDbFile(self._db, filename) return filename - def lvget(self, db=None, *args, **kwargs): - #Use a single LavaVu instance per db(store) to save resources + def lvget(self): + #Get existing viewer, if any, otherwuse create one if not self.viewer: - self.viewer = self.lvrun(db=db, *args, **kwargs) - else: - if not db: - db = self._db.path - self.viewer.setup(cache=False, clearstep=True, database=db, timestep=self.step, *args, **kwargs) - + self.lvrun() return self.viewer def lvrun(self, db=None, *args, **kwargs): + #Create and execute a new viewer with passed args if not db: db = self._db.path if 'UW_VIS_PORT' in os.environ: kwargs['port'] = int(os.environ['UW_VIS_PORT']) - return lavavu.Viewer(cache=False, clearstep=True, database=db, timestep=self.step, *args, **kwargs) + self.viewer = lavavu.Viewer(cache=False, clearstep=True, database=db, timestep=self.step, *args, **kwargs) + return self.viewer def _generate(self, figname, objects, props): #First merge object list with active @@ -240,7 +237,7 @@ def _generate(self, figname, objects, props): #Output any custom geometry on objects if lavavu and uw.mpi.rank == 0 and any(x.geomType is not None for x in self._objects): - lv = self.lvget() #Open the viewer + lv = self.lvget() #Open/get the viewer for obj in self._objects: #Create/Transform geometry by object obj.render(lv) @@ -255,7 +252,7 @@ def _generate(self, figname, objects, props): self.filename = comm.bcast(self.filename, root=0) #print uw.mpi.rank,self.filename #Open the viewer with db filename - lv = self.lvget(self.filename) + lv = self.lvrun(self.filename) #Loop through objects and run their parallel_render method if present for obj in self._objects: if hasattr(obj, "parallel_render"): @@ -285,24 +282,6 @@ def _get_state(self, objects, props): return json.dumps(export, indent=2) - def _read_state(self): - #Read state from database (DEPRECATED) - if uw.mpi.rank > 0: - return - if not self._db.db: - libUnderworld.gLucifer.lucDatabase_OpenDatabase(self._db) - try: - lv = self.lvget() - #Also save the step data - self.timesteps = json.loads(lv.app.getTimeSteps()) - #Get figures/states - return lv.app.figures - except RuntimeError as e: - print("LavaVu error: " + str(e)) - import traceback - traceback.print_exc() - pass - def empty(self): """ Empties all the cached drawing objects """ @@ -445,7 +424,6 @@ def __init__(self, store=None, name=None, figsize=None, boundingBox=None, faceco self.draw = objects.Drawing() self._drawingObjects = [] - self._script = [] #Types of all Drawing derived classes def all_subclasses(cls): @@ -470,9 +448,6 @@ def method(*args, **kwargs): super(Figure, self).__init__(*args) - def __del__(self): - self.close_viewer() - def _getProperties(self): #Convert properties to string return '\n'.join(['%s=%s' % (k,v) for k,v in self.items()]); @@ -691,7 +666,7 @@ def save(self, filename=None, size=(0,0), type="Image"): try: if type.lower() == "webgl": - lv = self.db.lvget(script=self._script) + lv = self.db.lvrun() return lv.webgl(filename + '.html') else: return self._generate_image(filename, size) @@ -713,7 +688,7 @@ def _generate_image(self, filename="", size=(0,0)): return try: #Render with viewer - lv = self.db.lvget(quality=self["quality"], script=self._script) + lv = self.db.lvrun(quality=self["quality"]) imagestr = lv.image(filename, resolution=size) #Return the generated filename return imagestr @@ -724,25 +699,6 @@ def _generate_image(self, filename="", size=(0,0)): pass return "" - def script(self, cmd=None): - """ - Append to or get contents of the saved script. - - Parameters - ---------- - cmd: str - Command to add to script. - """ - if cmd: - if isinstance(cmd, list): - self._script += cmd - else: - self._script += [cmd] - else: - self._script = [] - #Returns contents as newline separated string - return '\n'.join(self._script) - def window(self, *args, **kwargs): """ Open an inline viewer. @@ -778,36 +734,6 @@ def viewer(self, new=False, *args, **kwargs): else: return self.db.lvget(*args, **kwargs) - def open_viewer(self, args=[], background=True): - """ Open the external viewer. - """ - fname = self.db.filename - if not fname: - fname = os.path.join(tmpdir,"gluciferDB"+self.db._id+".gldb") - self.save_database(fname) - #Already open? - if self._viewerProc and self._viewerProc.poll() == None: - return - - if uw.mpi.rank == 0: - #Open viewer with local web server for interactive/iterative use - if background: - self._viewerProc = subprocess.Popen(["LV", "-" + str(self.db.step), "-p9999", "-q90", fname] + self._script + args, - stdout=PIPE, stdin=PIPE, stderr=STDOUT) - from IPython.display import HTML - return HTML('''Open Viewer Interface''') - else: - self.db.lvget(db=fname, port=9999) - - def close_viewer(self): - """ Close the viewer. - """ - #poll() returns None if the process is alive - if self._viewerProc and self._viewerProc.poll() == None: - self.send_command("quit") - self._viewerProc.kill() - self._viewerProc = None - def send_command(self, cmd, retry=True): """ Run command on an open viewer instance.