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Last active September 15, 2021 14:29
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adv-0.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "adv-0.ipynb",
"provenance": [],
"collapsed_sections": [
"ExnU6wFQprZ5",
"Y90Y2oMjW9ch"
],
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/likask/0ec7965fc307ad6efd0db15a38b8da9e/adv-0.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ExnU6wFQprZ5"
},
"source": [
"#ADV-0 tutorial\n",
"\n",
"This code supplements [ADV-0](http://mofem.eng.gla.ac.uk/mofem/html/tutorial_plastic_problem.html) tutorial. Follow the link to see implementation.\n",
"\n",
"*Note*: Installation is the most time-consuming party for this example. It is expected to take between 16 minutes."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Y90Y2oMjW9ch"
},
"source": [
"# Source code and other resources\n",
"\n",
"- Source code is under link [link](http://mofem.eng.gla.ac.uk/mofem/html/tutorial_plastic_problem.html)\n",
"- Context of devlopent is presented in [video](https://youtu.be/lYUMaJgFLk8)\n",
"- If you use this work to validate results cite [https://doi.org/10.21105/joss.01441](https://doi.org/10.21105/joss.01441)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YN6d3NtG57jX"
},
"source": [
"# Install prerequisites"
]
},
{
"cell_type": "code",
"metadata": {
"id": "oT5z7zX_ZR1E"
},
"source": [
"%%bash\n",
"apt-get update -qq && \\\n",
"apt-get install -qq -y \\\n",
"curl \\\n",
"git \\\n",
"g++ \\\n",
"gfortran \\\n",
"python \\\n",
"python3 \\\n",
"automake \\\n",
"build-essential \\\n",
"libtool \\\n",
"cmake \\\n",
"ca-certificates \\\n",
"gpg \\\n",
"gpg-agent \\\n",
"libgl1-mesa-glx \\\n",
"libglu1-mesa \\\n",
"xvfb "
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ZmNmM2uwhlG7",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "60c0671a-438f-482e-c141-407a419b73e0"
},
"source": [
"%%bash\n",
"rm /usr/bin/python3\n",
"ln -s /usr/bin/python3.7 /usr/bin/python3\n",
"#apt-get install python3-pip\n",
"#python3 -m pip install --upgrade pip\n",
"pip -q install pip --upgrade && \\\n",
" pip -q install \\\n",
" pandas \\\n",
" scipy \\\n",
" gmsh \\\n",
" piglet \\\n",
" pyvirtualdisplay \\\n",
" pyvista \\\n",
" itkwidgets \\\n",
" ipywidgets \\\n",
" ipyvtklink \\\n",
" ipyvtk-simple==0.1.2"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1XSF13dL5wq0"
},
"source": [
"# Installation MoFEM and dependend libraries (It takes some time ~15 mins)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "6zcHFbZ-u6P0"
},
"source": [
"%%bash\n",
"#rm -rf spack\n",
"#apt-get install python3\n",
"rm /usr/bin/python3\n",
"ln -s /usr/bin/python3.6 /usr/bin/python3\n",
"git clone --single-branch -b develop https://github.com/likask/spack.git\n",
"source spack/share/spack/setup-env.sh\n",
"spack mirror add colab http://userweb.eng.gla.ac.uk/lukasz.kaczmarczyk/Download/colab_mirror\n",
"#spack compiler find\n",
"#spack external find\n",
"curl http://userweb.eng.gla.ac.uk/lukasz.kaczmarczyk/Download/colab_mirror/build_cache/_pgp/47BF84D43F1C1F7F63153D58EC7CD9C7ABFE39B0.pub > key.pub\n",
"spack gpg trust key.pub \n",
"spack mirror list\n",
"spack buildcache list -a"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "83gdcAUYvgop"
},
"source": [
"# Install MoFEM packages is buildcaceh (this takes time)\n",
"%%bash\n",
"source spack/share/spack/setup-env.sh\n",
"spack buildcache install -ao mofem-users-modules@lukasz 2>&1 | tee log"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"collapsed": true,
"id": "1gStIk0j_2wh"
},
"source": [
"# Careate view to installed packages\n",
"%%bash\n",
"source spack/share/spack/setup-env.sh\n",
"spack view --verbose symlink -i um_view mofem-users-modules"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "UFn9jHZRcoj4"
},
"source": [
"# Check system"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ZwJ14QHTcmwg",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "adf6a68f-e869-4aec-ebe0-6a0e709f65c3"
},
"source": [
"!cat /proc/cpuinfo\n",
"!free -h"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"processor\t: 0\n",
"vendor_id\t: GenuineIntel\n",
"cpu family\t: 6\n",
"model\t\t: 79\n",
"model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n",
"stepping\t: 0\n",
"microcode\t: 0x1\n",
"cpu MHz\t\t: 2200.162\n",
"cache size\t: 56320 KB\n",
"physical id\t: 0\n",
"siblings\t: 2\n",
"core id\t\t: 0\n",
"cpu cores\t: 1\n",
"apicid\t\t: 0\n",
"initial apicid\t: 0\n",
"fpu\t\t: yes\n",
"fpu_exception\t: yes\n",
"cpuid level\t: 13\n",
"wp\t\t: yes\n",
"flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n",
"bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa\n",
"bogomips\t: 4400.32\n",
"clflush size\t: 64\n",
"cache_alignment\t: 64\n",
"address sizes\t: 46 bits physical, 48 bits virtual\n",
"power management:\n",
"\n",
"processor\t: 1\n",
"vendor_id\t: GenuineIntel\n",
"cpu family\t: 6\n",
"model\t\t: 79\n",
"model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n",
"stepping\t: 0\n",
"microcode\t: 0x1\n",
"cpu MHz\t\t: 2200.162\n",
"cache size\t: 56320 KB\n",
"physical id\t: 0\n",
"siblings\t: 2\n",
"core id\t\t: 0\n",
"cpu cores\t: 1\n",
"apicid\t\t: 1\n",
"initial apicid\t: 1\n",
"fpu\t\t: yes\n",
"fpu_exception\t: yes\n",
"cpuid level\t: 13\n",
"wp\t\t: yes\n",
"flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n",
"bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa\n",
"bogomips\t: 4400.32\n",
"clflush size\t: 64\n",
"cache_alignment\t: 64\n",
"address sizes\t: 46 bits physical, 48 bits virtual\n",
"power management:\n",
"\n",
" total used free shared buff/cache available\n",
"Mem: 12G 607M 3.5G 1.1M 8.6G 11G\n",
"Swap: 0B 0B 0B\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xHD5foG85bYL"
},
"source": [
"# Beam problem\n",
"\n",
"For details of the porblem see [link](https://thelfer.github.io/mgis/web/mgis_fenics_finite_strain_elastoplasticity.html)."
]
},
{
"cell_type": "code",
"metadata": {
"id": "-dOtEIbdbkAH",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "1f05d5ab-3e6b-490c-c449-82dcda01b522"
},
"source": [
"dir='/content/um_view/tutorials/adv-0'\n",
"bin_dir='/content/um_view/bin'\n",
"nb_proc=1\n",
"import os\n",
"os.chdir(dir)\n",
"!rm -f out_*\n",
"!{bin_dir}/mofem_part -my_file beam_fenics.cub -output_file ab.h5m -my_nparts {nb_proc} -dim 3 -adj_dim 1\n",
"!{bin_dir}/mpirun --allow-run-as-root -np {nb_proc} ./plastic_3d \\\n",
"-file_name ab.h5m \\\n",
"-ts_dt 0.01 \\\n",
"-ts_max_time 0.4 \\\n",
"-ts_type theta \\\n",
"-snes_linesearch_type l2 \\\n",
"-snes_stol 1e-10 \\\n",
"-snes_atol 1e-10 \\\n",
"-snes_rtol 1e-10 \\\n",
"-order 2 \\\n",
"-large_strains 1 \\\n",
"-young_modulus 210e9 \\\n",
"-poisson_ratio 0.3 \\\n",
"-yield_stress 2.5e8 \\\n",
"-hardening 1e6 \\\n",
"-Qinf 0 \\\n",
"-b_iso 0 \\\n",
"-hardening_viscous 0 \\\n",
"-cn 1 \\\n",
"-is_scale 1 \\\n",
"-scale 1e6 2>&1 | tee log_beam"
],
"execution_count": 25,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0mMoFEM version 0.12.1 (MOAB 5.2.1 Petsc Release Version 3.11.4, Sep, 28, 2019 )\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0mgit commit id 569f9b4e466e6def041920581356b154caec42f1\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0mLocal time: 2021-9-15 14:2:47\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0mUTC time: 2021-9-15 14:2:47\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[MeshsetMng] \u001b[0mmeshset 12682136550675316764 type BLOCKSET UNKNOWNNAME msId 3 name FIX_ALL block header: blockCol = 4294967295 blockMat = 0 blockDimension = 2\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[MeshsetMng] \u001b[0mmeshset 12682136550675316765 type BLOCKSET UNKNOWNNAME msId 4 name FIX_X block header: blockCol = 4294967295 blockMat = 0 blockDimension = 2\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[MeshsetMng] \u001b[0mmeshset 12682136550675316766 type BLOCKSET UNKNOWNNAME msId 5 name BODY_FORCE block header: blockCol = 4294967295 blockMat = 0 blockDimension = 3\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[mofem_part] \u001b[0mmeshset 12682136550675316764 type BLOCKSET UNKNOWNNAME msId 3 name FIX_ALL block header: blockCol = 4294967295 blockMat = 0 blockDimension = 2\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[mofem_part] \u001b[0mmeshset 12682136550675316765 type BLOCKSET UNKNOWNNAME msId 4 name FIX_X block header: blockCol = 4294967295 blockMat = 0 blockDimension = 2\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[mofem_part] \u001b[0mmeshset 12682136550675316766 type BLOCKSET UNKNOWNNAME msId 5 name BODY_FORCE block header: blockCol = 4294967295 blockMat = 0 blockDimension = 3\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[ProblemsManager] \u001b[0mFinite elements in problem: row lower 0 row upper 283 nb. elems 283 ( 283 )\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0mMoFEM version 0.12.1 (MOAB 5.2.1 Petsc Release Version 3.11.4, Sep, 28, 2019 )\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0mgit commit id 569f9b4e466e6def041920581356b154caec42f1\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0mLocal time: 2021-9-15 14:2:47\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0mUTC time: 2021-9-15 14:2:47\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[MeshsetMng] \u001b[0mmeshset 12682136550675316764 type BLOCKSET UNKNOWNNAME msId 3 name FIX_ALL block header: blockCol = 4294967295 blockMat = 0 blockDimension = 2\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[MeshsetMng] \u001b[0mmeshset 12682136550675316765 type BLOCKSET UNKNOWNNAME msId 4 name FIX_X block header: blockCol = 4294967295 blockMat = 0 blockDimension = 2\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[MeshsetMng] \u001b[0mmeshset 12682136550675316766 type BLOCKSET UNKNOWNNAME msId 5 name BODY_FORCE block header: blockCol = 4294967295 blockMat = 0 blockDimension = 3\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[FieldCore] \u001b[0mAdd field U field_id 1 space H1 approximation base AINSWORTH_LEGENDRE_BASE rank 3 meshset 12682136550675316768\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[FieldCore] \u001b[0mAdd field TAU field_id 2 space L2 approximation base AINSWORTH_LEGENDRE_BASE rank 1 meshset 12682136550675316769\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[FieldCore] \u001b[0mAdd field EP field_id 4 space L2 approximation base AINSWORTH_LEGENDRE_BASE rank 6 meshset 12682136550675316770\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[FECore] \u001b[0mAdd finite element dFE\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[FECore] \u001b[0mAdd finite element bFE\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[ProblemCore] \u001b[0mAdd problem SimpleProblem\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[FieldCore] \u001b[0mNumber of dofs 9970\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[FECore] \u001b[0mFinite element dFE added. Nb. of elements added 283\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[FECore] \u001b[0mFinite element bFE added. Nb. of elements added 256\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[FECore] \u001b[0mNumber of adjacencies 4932\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[ProblemsManager] \u001b[0mSimpleProblem Nb. local dof 9970 by 9970 nb global dofs 9970 by 9970\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[ProblemsManager] \u001b[0m FEs ghost dofs on problem SimpleProblem Nb. ghost dof 0 by 0 Nb. local dof 9970 by 9970\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mYoung modulus 2.1e+11\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mPoisson ratio 0.3\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mYield stress 2.5e+08\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mHardening 1e+06\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mViscous hardening 0\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mSaturation yield stress 0\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mSaturation exponent 0\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mcn 1\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mScaled Young modulus 1e+06\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mScaled Poisson ratio 0.3\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mScaled Yield stress 1190.48\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mScaled Hardening 4.7619\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mScaled Viscous hardening 0\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mScaled Saturation yield stress 0\n",
"[\u001b[32m0\u001b[0m] \u001b[31m<warning> \u001b[0m\u001b[1m[example] \u001b[0mREACTION blockset does not exist\n",
"[\u001b[32m0\u001b[0m] \u001b[31m<warning> \u001b[0m\u001b[1m[example] \u001b[0mREACTION blockset does not exist\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 0.0000e+00 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 0.0000e+00 min 0.0000e+00 max 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 0.0000e+00 min 0.0000e+00 max 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 0.0000e+00 min 0.0000e+00 max 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m0 TS dt 0.01 time 0.\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 1.828603464338e-03 [ 1.828603464338e-03 , 2.365919241144e-20 , 0.000000000000e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 2.179437190691e-03 [ 2.179437190691e-03 , 2.628897971104e-18 , 2.148255587829e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 1.006667638298e-03 [ 1.006667638298e-03 , 3.470768126530e-18 , 2.195014987926e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 7.205746914878e-06 [ 7.205746914878e-06 , 3.771709475432e-18 , 1.585910033713e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 1.652726094736e-08 [ 1.652726094736e-08 , 3.834280453787e-18 , 4.420555854182e-20 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 2.420666234715e-12 [ 2.420666234712e-12 , 3.646733554837e-18 , 1.555595940126e-22 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 0 accepted t=0 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 1.0000e-02 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 1.0000e-02 min -3.6546e-05 max 3.6536e-05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 1.0000e-02 min -4.8678e-04 max 7.6708e-06\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 1.0000e-02 min -2.0334e-06 max 2.3143e-06\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m1 TS dt 0.01 time 0.01\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 9.805858935017e-03 [ 9.805858935017e-03 , 3.805907134667e-18 , 1.908841664789e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 2.067423857351e-03 [ 2.067423857351e-03 , 3.921766638460e-18 , 2.868178017870e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 1.050011399765e-03 [ 1.050011399765e-03 , 3.318056885838e-18 , 1.118964345874e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.016391319797e-04 [ 1.016391319797e-04 , 3.939269611817e-18 , 2.795992278978e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 1.090330560185e-06 [ 1.090330560185e-06 , 3.672507596387e-18 , 4.085678532330e-19 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 1.711202169615e-10 [ 1.711202169615e-10 , 3.455091841929e-18 , 1.164390400932e-20 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 2.389830422813e-12 [ 2.389830422810e-12 , 3.674567053084e-18 , 1.485683080873e-23 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 1 accepted t=0.01 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 2.0000e-02 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 2.0000e-02 min -7.3098e-05 max 7.3059e-05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 2.0000e-02 min -9.7352e-04 max 1.5341e-05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 2.0000e-02 min -4.0647e-06 max 4.6333e-06\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m2 TS dt 0.01 time 0.02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 9.804010876472e-03 [ 9.804010876472e-03 , 3.560791341453e-18 , 2.088060751969e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 2.067431208076e-03 [ 2.067431208076e-03 , 3.829464821715e-18 , 1.708370082474e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 1.049799856730e-03 [ 1.049799856730e-03 , 3.397062528941e-18 , 1.818859448314e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.014399334458e-04 [ 1.014399334458e-04 , 4.343457446529e-18 , 1.208039823834e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 1.087795832154e-06 [ 1.087795832154e-06 , 3.745893991908e-18 , 7.451941950740e-19 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 1.702811717228e-10 [ 1.702811717228e-10 , 3.855332432920e-18 , 6.965136511667e-21 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 2.554305896505e-12 [ 2.554305896502e-12 , 4.149501171678e-18 , 9.718539090831e-24 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 2 accepted t=0.02 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 3.0000e-02 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 3.0000e-02 min -1.0965e-04 max 1.0957e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 3.0000e-02 min -1.4602e-03 max 2.3010e-05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 3.0000e-02 min -6.1032e-06 max 6.9569e-06\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m3 TS dt 0.01 time 0.03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 9.800311233229e-03 [ 9.800311233229e-03 , 3.642493075305e-18 , 1.641616366570e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 2.067412040447e-03 [ 2.067412040447e-03 , 3.850003811385e-18 , 1.228934148833e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 1.049464093633e-03 [ 1.049464093633e-03 , 3.470223304501e-18 , 1.934965184686e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.011417295498e-04 [ 1.011417295498e-04 , 4.135653024305e-18 , 1.303553528950e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 1.084036267732e-06 [ 1.084036267732e-06 , 3.686670947293e-18 , 6.473640657197e-19 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 1.683961556425e-10 [ 1.683961556425e-10 , 3.624840247450e-18 , 9.212016554798e-21 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 2.522665857327e-12 [ 2.522665857324e-12 , 3.852855788306e-18 , 2.418395547492e-23 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 3 accepted t=0.03 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 4.0000e-02 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 4.0000e-02 min -1.4621e-04 max 1.4605e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 4.0000e-02 min -1.9467e-03 max 3.0678e-05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 4.0000e-02 min -8.1455e-06 max 9.2848e-06\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m4 TS dt 0.01 time 0.04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 9.794764825159e-03 [ 9.794764825159e-03 , 3.601151912955e-18 , 1.646975026467e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 2.067366304504e-03 [ 2.067366304504e-03 , 3.901774226297e-18 , 1.233779511434e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 1.049004198786e-03 [ 1.049004198786e-03 , 3.379950151651e-18 , 1.068026188898e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.007453935243e-04 [ 1.007453935243e-04 , 4.177331429085e-18 , 7.818836469359e-18 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 1.079051925859e-06 [ 1.079051925859e-06 , 3.508513209661e-18 , 5.087204155730e-19 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 1.659197129653e-10 [ 1.659197129653e-10 , 3.610704699863e-18 , 8.481351793585e-21 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 2.368970022623e-12 [ 2.368970022620e-12 , 3.835236963154e-18 , 3.279945436643e-23 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 4 accepted t=0.04 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 5.0000e-02 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 5.0000e-02 min -1.8275e-04 max 1.8251e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 5.0000e-02 min -2.4330e-03 max 3.8344e-05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 5.0000e-02 min -1.0192e-05 max 1.1617e-05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m5 TS dt 0.01 time 0.05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 9.787378893428e-03 [ 9.787378893428e-03 , 3.753406708230e-18 , 1.857218323609e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 2.067293944693e-03 [ 2.067293944693e-03 , 4.080582919028e-18 , 1.243218803894e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 1.048420302085e-03 [ 1.048420302085e-03 , 3.376418790373e-18 , 9.346416331694e-18 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.002520870483e-04 [ 1.002520870483e-04 , 3.852848341157e-18 , 5.428199482904e-18 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 1.072843853840e-06 [ 1.072843853840e-06 , 3.787013562726e-18 , 4.646711181642e-19 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 1.633030608381e-10 [ 1.633030608381e-10 , 3.620341390568e-18 , 6.657744726218e-21 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 2.270327698955e-12 [ 2.270327698952e-12 , 3.633211655257e-18 , 1.531524669207e-24 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 5 accepted t=0.05 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 6.0000e-02 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 6.0000e-02 min -2.1929e-04 max 2.1895e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 6.0000e-02 min -2.9191e-03 max 4.6007e-05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 6.0000e-02 min -1.2241e-05 max 1.3953e-05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m6 TS dt 0.01 time 0.06\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 9.778426426086e-03 [ 9.778163031370e-03 , 7.177118030368e-05 , 1.624260200979e-17 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 2.080821252266e-03 [ 2.072303481281e-03 , 2.751479169387e-05 , 1.860599408558e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 1.072009168297e-03 [ 1.059832133590e-03 , 4.710696944605e-05 , 1.540793268197e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.382668271993e-04 [ 1.093063119488e-04 , 4.966258280838e-06 , 8.452917810206e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 1.051036583191e-06 [ 1.030579665350e-06 , 6.648390940916e-08 , 1.953544019520e-07 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 2.758631777439e-10 [ 2.466416853034e-10 , 1.080268633487e-11 , 1.230921357097e-10 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 2.440917429964e-12 [ 2.440091215204e-12 , 5.368034264834e-15 , 6.327673819505e-14 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 6 accepted t=0.06 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 7.0000e-02 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 7.0000e-02 min -2.5589e-04 max 2.5542e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 7.0000e-02 min -3.4056e-03 max 5.3753e-05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 7.0000e-02 min -1.4535e-05 max 1.6450e-05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m7 TS dt 0.01 time 0.07\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 9.856381256844e-03 [ 9.792135318098e-03 , 1.123312880151e-03 , 2.248478600273e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 2.181744708400e-03 [ 2.059784043741e-03 , 5.871303131018e-05 , 7.168350198860e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 1.261981864590e-03 [ 1.089978759042e-03 , 2.465967238441e-04 , 5.862888257338e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 6.469158347630e-04 [ 4.265775784124e-04 , 8.784413690628e-05 , 4.783461868501e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 8.958496846439e-05 [ 2.326234615656e-05 , 8.500533464865e-06 , 8.609338393205e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 5.342207090144e-07 [ 3.947951532763e-07 , 9.526653083986e-08 , 3.470631657079e-07 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 1.071193831612e-10 [ 2.862707607438e-11 , 1.064511677886e-11 , 1.026729480099e-10 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 2.299254579416e-12 [ 2.297740204151e-12 , 7.825051352537e-15 , 8.306830781813e-14 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 7 accepted t=0.07 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 8.0000e-02 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 8.0000e-02 min -2.9398e-04 max 2.9341e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 8.0000e-02 min -3.9159e-03 max 6.4008e-05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 8.0000e-02 min -1.8741e-05 max 2.2580e-05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m8 TS dt 0.01 time 0.08\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 1.135438945905e-02 [ 1.061096338828e-02 , 1.609024609458e-03 , 3.706839053241e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 2.323912478245e-03 [ 2.066390370846e-03 , 1.126768554524e-04 , 1.057309778666e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 1.689986053300e-03 [ 1.387752877864e-03 , 3.412465025580e-04 , 9.020785081256e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.280892462209e-03 [ 9.912137838911e-04 , 2.299216887074e-04 , 7.780210481920e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 9.779574753957e-04 [ 6.436089787383e-04 , 1.394016274093e-04 , 7.230044899207e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 6.190339403390e-04 [ 2.809038704909e-04 , 5.236357278719e-05 , 5.491394095125e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 7.753452178340e-05 [ 3.105201393163e-05 , 1.564696184967e-05 , 6.930041185913e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 2.401991288047e-06 [ 1.265873982498e-06 , 4.758552778120e-07 , 1.985116360032e-06 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 1.956356257768e-09 [ 8.499395398516e-10 , 4.267277863109e-10 , 1.709630364229e-09 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 2.450953001600e-12 [ 2.449293219659e-12 , 3.750741494882e-15 , 9.010700372744e-14 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 8 accepted t=0.08 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 9.0000e-02 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 9.0000e-02 min -3.3735e-04 max 3.3675e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 9.0000e-02 min -4.5097e-03 max 8.1149e-05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 9.0000e-02 min -2.6826e-05 max 3.4487e-05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m9 TS dt 0.01 time 0.09\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 2.126786309821e-02 [ 1.394278110858e-02 , 2.691124566351e-03 , 1.583283626805e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 3.848417923585e-03 [ 2.128455055400e-03 , 5.028082958848e-05 , 3.205849564451e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 3.221328795354e-03 [ 1.726415593204e-03 , 2.112682846971e-04 , 2.711422895676e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 2.703236192394e-03 [ 1.375343692105e-03 , 1.811978078654e-04 , 2.320147192503e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 2.197411460702e-03 [ 1.029215335604e-03 , 1.287953566295e-04 , 1.937200215954e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 1.598294340255e-03 [ 6.476946958029e-04 , 6.623576429957e-05 , 1.459674348150e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 6.357278374424e-04 [ 1.622376597887e-04 , 2.515925156652e-06 , 6.146726731896e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 2.775281335388e-07 [ 9.498508220565e-08 , 6.363955216431e-09 , 2.606898523877e-07 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 5.955500767092e-12 [ 2.916786025379e-12 , 4.942459309698e-15 , 5.192333217455e-12 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 9 accepted t=0.09 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 1.0000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 1.0000e-01 min -3.9572e-04 max 3.9490e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 1.0000e-01 min -5.3436e-03 max 1.1651e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 1.0000e-01 min -4.3489e-05 max 5.3369e-05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m10 TS dt 0.01 time 0.1\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 5.318431760303e-02 [ 2.754437094853e-02 , 2.966913625786e-03 , 4.539908249610e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 9.109510374662e-03 [ 2.087039150812e-03 , 1.690314366971e-04 , 8.865600669016e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 6.497993464864e-03 [ 1.840488817078e-03 , 2.895451640014e-04 , 6.225165345726e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 5.165639178849e-03 [ 1.550198265665e-03 , 1.927451950266e-04 , 4.923775254110e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 3.731679355487e-03 [ 1.145511419087e-03 , 1.440272283343e-04 , 3.548589939456e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 1.804611496785e-03 [ 5.248711225744e-04 , 1.874468112944e-05 , 1.726494018508e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 6.178647330747e-06 [ 1.573941942384e-06 , 2.597130915574e-07 , 5.969165662790e-06 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 5.832485029215e-09 [ 1.459246676416e-09 , 4.167125428901e-11 , 5.646834888674e-09 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 2.550588975252e-12 [ 2.491812705869e-12 , 4.482963180623e-15 , 5.443835620089e-13 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 10 accepted t=0.1 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 1.1000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 1.1000e-01 min -4.7237e-04 max 4.7350e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 1.1000e-01 min -6.4797e-03 max 1.7666e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 1.1000e-01 min -6.4426e-05 max 8.2869e-05\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m11 TS dt 0.01 time 0.11\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 1.044786035544e-01 [ 5.296918708073e-02 , 2.838982200403e-03 , 9.001102155151e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 1.907992643324e-02 [ 2.861595233994e-03 , 3.366969764634e-04 , 1.886111079869e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 1.622893867060e-02 [ 1.700437389222e-03 , 6.936011166544e-04 , 1.612469784369e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.129359008048e-02 [ 1.289874177043e-03 , 5.545009003234e-04 , 1.120597743460e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 8.740598751898e-03 [ 1.280684989112e-03 , 3.580968608954e-04 , 8.638847095450e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 5.671292273312e-03 [ 1.112521046297e-03 , 2.119071885673e-04 , 5.557062921571e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 1.276756719021e-03 [ 2.584005452145e-04 , 5.477695663268e-05 , 1.249134245316e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 9.260205024192e-06 [ 4.440490251051e-07 , 4.986934865435e-07 , 9.236098871268e-06 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 9.010506089473e-10 [ 2.137947315331e-10 , 5.171681035252e-11 , 8.737902403781e-10 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 2.544425490749e-12 [ 2.443281037469e-12 , 4.531692006131e-15 , 7.102522887581e-13 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 11 accepted t=0.11 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 1.2000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 1.2000e-01 min -5.7168e-04 max 5.7526e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 1.2000e-01 min -7.9224e-03 max 2.6014e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 1.2000e-01 min -9.7247e-05 max 1.2997e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m12 TS dt 0.01 time 0.12\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 1.587992300681e-01 [ 8.664814663329e-02 , 6.865591577488e-03 , 1.328990511912e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 2.377979783649e-02 [ 2.003241272479e-03 , 3.422552180905e-04 , 2.369279787012e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 1.955413692575e-02 [ 2.969248546940e-03 , 5.920316485038e-04 , 1.931831598521e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.671225944034e-02 [ 3.768700196166e-03 , 5.243714702598e-04 , 1.627333859397e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 1.377310231817e-02 [ 4.294715802374e-03 , 4.077884781074e-04 , 1.308004098620e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 1.055414641370e-02 [ 4.223427569409e-03 , 3.649984669480e-04 , 9.665373360967e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 6.589668421564e-03 [ 3.305467742365e-03 , 2.463205183428e-04 , 5.695343634285e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 9.189072097582e-04 [ 3.813901237278e-04 , 1.220345062496e-04 , 8.270668733258e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 5.266306139909e-05 [ 4.853625292483e-06 , 2.103980770680e-06 , 5.239669476562e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 4.994028881346e-07 [ 1.638574257183e-09 , 4.991524675024e-07 , 1.572812569242e-08 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 10 SNES Function norm 1.235334380609e-09 [ 2.920304161634e-12 , 2.291755827607e-12 , 1.235328803030e-09 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 11 SNES Function norm 2.526150327676e-12 [ 2.198386222409e-12 , 8.578333659617e-15 , 1.244371289982e-12 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 12 accepted t=0.12 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 1.3000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 1.3000e-01 min -7.5507e-04 max 7.7008e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 1.3000e-01 min -1.0761e-02 max 4.3134e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 1.3000e-01 min -2.8506e-04 max 2.2066e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m13 TS dt 0.01 time 0.13\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 4.392308871676e-01 [ 2.996396295839e-01 , 1.039440722374e-02 , 3.209857020544e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 6.533169163354e-02 [ 1.834313734677e-02 , 5.753137467324e-04 , 6.270110252675e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 6.036402016948e-02 [ 2.501736073431e-02 , 7.806966724742e-04 , 5.493029315065e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 5.648239248072e-02 [ 2.912528053536e-02 , 6.849666427883e-04 , 4.838914666307e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 5.195414515542e-02 [ 3.005327645809e-02 , 5.639261590577e-04 , 4.237588654236e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 4.661254530811e-02 [ 2.915999668441e-02 , 5.832460274923e-04 , 3.636047026012e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 4.158602066319e-02 [ 2.759513020437e-02 , 4.868334381152e-04 , 3.110737688727e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 3.584879688745e-02 [ 2.487981337298e-02 , 4.307327722812e-04 , 2.580592168635e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 2.988581201928e-02 [ 2.148976048417e-02 , 3.537064095550e-04 , 2.076600217087e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 2.396358648053e-02 [ 1.769898024719e-02 , 2.687492703691e-04 , 1.615324577443e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 10 SNES Function norm 1.784058599954e-02 [ 1.338458683201e-02 , 2.301466504621e-04 , 1.179348874009e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 11 SNES Function norm 9.532546126697e-03 [ 6.692550709916e-03 , 2.231279605148e-04 , 6.784498107160e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 12 SNES Function norm 8.192729631878e-04 [ 1.562527879877e-04 , 8.116256194182e-06 , 8.041936214880e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 13 SNES Function norm 8.217581199097e-07 [ 1.286126425628e-07 , 1.506970094390e-08 , 8.114912814837e-07 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 14 SNES Function norm 6.259253248630e-12 [ 2.651858270413e-12 , 1.528500484706e-14 , 5.669714747036e-12 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 13 accepted t=0.13 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 1.4000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 1.4000e-01 min -1.2814e-03 max 1.3366e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 1.4000e-01 min -1.9880e-02 max 8.9233e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 1.4000e-01 min -3.4507e-04 max 4.9760e-04\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m14 TS dt 0.01 time 0.14\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 3.579283805576e+00 [ 2.751030997660e+00 , 2.093155433642e-02 , 2.289686197016e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 4.816827311588e-01 [ 7.747488019844e-02 , 1.723314807754e-03 , 4.754081684415e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 4.273855385113e-01 [ 1.571344409004e-01 , 1.744548114447e-03 , 3.974470059812e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 3.797142505655e-01 [ 1.742392993356e-01 , 1.398720738650e-03 , 3.373746022300e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 3.234691514115e-01 [ 1.677391592307e-01 , 1.077321296642e-03 , 2.765767628603e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 2.521397231606e-01 [ 1.428942771528e-01 , 6.900119732307e-04 , 2.077382714763e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 1.347935406947e-01 [ 8.061123780375e-02 , 3.135743580187e-04 , 1.080325350248e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 1.983996118897e-03 [ 8.791598221588e-04 , 1.936221559398e-05 , 1.778466674275e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 1.065590662495e-06 [ 8.352270947961e-08 , 4.235624744908e-09 , 1.062303853180e-06 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 1.066177630215e-11 [ 2.879825090204e-12 , 1.161896020601e-14 , 1.026547350913e-11 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 14 accepted t=0.14 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 1.5000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 1.5000e-01 min -2.3399e-03 max 2.5241e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 1.5000e-01 min -3.8920e-02 max 1.7933e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 1.5000e-01 min -7.8766e-04 max 1.0875e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m15 TS dt 0.01 time 0.15\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 2.122296041042e+01 [ 1.130597989413e+01 , 2.713721167661e-02 , 1.796073859249e+01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 5.491594942303e+00 [ 7.714146985696e-01 , 4.339550852444e-03 , 5.437142221927e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 7.217272050506e-01 [ 3.834220591339e-01 , 2.217570882437e-03 , 6.114513598473e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.018720466443e-01 [ 2.205073640158e-02 , 5.681588584080e-04 , 9.945529702919e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 1.176716411018e-02 [ 2.276124830129e-03 , 1.747931992666e-04 , 1.154360664138e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 3.274116011828e-04 [ 1.654386658387e-05 , 1.113889094127e-04 , 3.074364453467e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 3.411534973218e-04 [ 1.380029668637e-05 , 9.037769480931e-05 , 3.286748131927e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 3.362818021887e-04 [ 1.168513566583e-05 , 7.526241974811e-05 , 3.275430906940e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 3.189861465477e-04 [ 9.920870593479e-06 , 6.310290046564e-05 , 3.125248181647e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 2.951352330751e-04 [ 8.325461882020e-06 , 5.234492118793e-05 , 2.903368762534e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 10 SNES Function norm 2.661882486831e-04 [ 6.762529054840e-06 , 4.196068480536e-05 , 2.627731966324e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 11 SNES Function norm 2.308983326501e-04 [ 5.111993850877e-06 , 3.112483730474e-05 , 2.287338017045e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 12 SNES Function norm 1.844573815221e-04 [ 3.242737672702e-06 , 1.902197163630e-05 , 1.834452911509e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 13 SNES Function norm 1.084149460914e-04 [ 8.721142573318e-07 , 4.056980106392e-06 , 1.083355014072e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 14 SNES Function norm 7.110410033212e-07 [ 2.219594837240e-08 , 1.660830052977e-07 , 6.910159792879e-07 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 15 SNES Function norm 9.331473396063e-09 [ 3.671364393513e-11 , 1.023866057794e-10 , 9.330839449521e-09 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 16 SNES Function norm 7.103338173627e-12 [ 2.871713489226e-12 , 1.315696012593e-14 , 6.496960961796e-12 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 15 accepted t=0.15 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 1.6000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 1.6000e-01 min -2.9753e-03 max 3.2275e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 1.6000e-01 min -4.8960e-02 max 2.2852e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 1.6000e-01 min -1.0466e-03 max 1.4209e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m16 TS dt 0.01 time 0.16\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 4.601040629333e+00 [ 3.205417593057e+00 , 1.275245358954e-02 , 3.300713604938e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 1.100242583218e+00 [ 1.637342744980e-01 , 1.582403096627e-03 , 1.087990039146e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 1.086109524435e-01 [ 3.621959807932e-02 , 3.169228676331e-03 , 1.023446906061e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.158127806460e-02 [ 1.871279845409e-03 , 3.128598798468e-04 , 1.142481649942e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 4.725630877735e-04 [ 1.220065721179e-05 , 4.494540676553e-05 , 4.702626141852e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 1.131251066775e-05 [ 5.151686339157e-08 , 1.290076113766e-07 , 1.131165773249e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 6.663974145014e-11 [ 2.915983210627e-12 , 1.302417292944e-12 , 6.656317218742e-11 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 16 accepted t=0.16 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 1.7000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 1.7000e-01 min -3.4824e-03 max 3.8055e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 1.7000e-01 min -5.6458e-02 max 2.6725e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 1.7000e-01 min -1.2544e-03 max 1.6988e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m17 TS dt 0.01 time 0.17\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 2.611853644543e+00 [ 1.854024220553e+00 , 1.202439313605e-02 , 1.839627425346e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 5.844478687874e-01 [ 9.279525476279e-02 , 1.250817591465e-03 , 5.770327438534e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 1.969554742122e-01 [ 6.946683815348e-02 , 4.666886316305e-03 , 1.842390767216e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 3.432204301965e-02 [ 1.183711710438e-02 , 4.456077925673e-04 , 3.221314528876e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 7.375555484991e-04 [ 1.607594483580e-04 , 1.202976800538e-05 , 7.197220793940e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 1.110566396241e-06 [ 4.127967975453e-08 , 3.136967004698e-09 , 1.109794516087e-06 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 7.021423936427e-12 [ 3.139834901861e-12 , 1.545233189841e-14 , 6.280254143705e-12 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 17 accepted t=0.17 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 1.8000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 1.8000e-01 min -4.0070e-03 max 4.3931e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 1.8000e-01 min -6.3734e-02 max 3.0562e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 1.8000e-01 min -1.4648e-03 max 1.9867e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m18 TS dt 0.01 time 0.18\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 2.643748269872e+00 [ 1.790857842635e+00 , 9.843183840942e-03 , 1.944771506798e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 6.651568220959e-01 [ 1.011881039724e-01 , 1.286443723252e-03 , 6.574138047362e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 2.026257158156e-01 [ 9.164799366030e-02 , 4.326225283627e-03 , 1.806629728047e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 2.894384352912e-02 [ 8.188807289143e-03 , 7.037176628977e-04 , 2.775237458075e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 1.159221201618e-03 [ 1.185212885650e-04 , 2.120332257620e-05 , 1.152951394270e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 2.561113058249e-06 [ 6.436723554755e-08 , 1.157148604729e-08 , 2.560277925701e-06 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 7.179885562923e-12 [ 3.227689470501e-12 , 2.611008979791e-14 , 6.413430879170e-12 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 18 accepted t=0.18 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 1.9000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 1.9000e-01 min -4.5377e-03 max 4.9785e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 1.9000e-01 min -7.0553e-02 max 3.4225e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 1.9000e-01 min -1.6673e-03 max 2.2689e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m19 TS dt 0.01 time 0.19\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 2.497155179024e+00 [ 1.638198278984e+00 , 8.322829645682e-03 , 1.884680640683e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 6.948237599266e-01 [ 8.145253558576e-02 , 1.288026959128e-03 , 6.900317983917e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 2.251791987129e-01 [ 9.720987039359e-02 , 3.815457756118e-03 , 2.030796762681e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 2.589130707874e-02 [ 7.087193811626e-03 , 2.572726081231e-04 , 2.490110995372e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 2.027666576758e-04 [ 4.657008515406e-05 , 2.494084587968e-06 , 1.973304947945e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 1.009308343545e-06 [ 5.037014050503e-09 , 8.184058182233e-10 , 1.009295442896e-06 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 6.196944389739e-12 [ 3.103777623777e-12 , 1.634735785188e-14 , 5.363619766124e-12 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 19 accepted t=0.19 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 2.0000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 2.0000e-01 min -5.0568e-03 max 5.5741e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 2.0000e-01 min -7.6828e-02 max 3.7742e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 2.0000e-01 min -1.8628e-03 max 2.5509e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m20 TS dt 0.01 time 0.2\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 2.400531957160e+00 [ 1.516327652095e+00 , 9.726988925591e-03 , 1.860970046649e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 6.808425281709e-01 [ 9.115956850350e-02 , 1.087445149238e-03 , 6.747112706184e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 2.279245606720e-01 [ 8.858083858754e-02 , 6.188619139916e-03 , 2.099160341322e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 4.505163715013e-02 [ 1.520361037118e-02 , 5.523336569744e-04 , 4.240513140081e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 1.021887079112e-03 [ 2.511602299038e-04 , 3.564853162415e-05 , 9.898994512394e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 1.341740424557e-04 [ 1.072009803905e-05 , 1.503968049133e-06 , 1.337366488553e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 1.356449209331e-07 [ 6.177312391988e-09 , 8.224691363961e-08 , 1.076885814900e-07 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 4.993513719945e-11 [ 4.016076221778e-12 , 4.677739079902e-12 , 4.955308079231e-11 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 20 accepted t=0.2 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 2.1000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 2.1000e-01 min -5.6060e-03 max 6.1987e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 2.1000e-01 min -8.3084e-02 max 4.1366e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 2.1000e-01 min -2.0652e-03 max 2.8475e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m21 TS dt 0.01 time 0.21\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 2.635053000546e+00 [ 1.595082655717e+00 , 8.955138816526e-03 , 2.097411605434e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 7.804665793581e-01 [ 1.072474676467e-01 , 1.111445011163e-03 , 7.730619812591e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 2.910987730064e-01 [ 1.059230798588e-01 , 3.473872738190e-03 , 2.711212441091e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 2.796099221714e-02 [ 6.437016752114e-03 , 8.524787539611e-04 , 2.719660238107e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 3.307579605913e-03 [ 1.112950761424e-04 , 3.495527549525e-05 , 3.305521802105e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 2.776961947976e-05 [ 2.934977594495e-07 , 1.586999370325e-05 , 2.278615643268e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 1.256818249844e-05 [ 2.258565336506e-07 , 4.634177426349e-06 , 1.168043662375e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 1.197122023740e-06 [ 1.088978119120e-08 , 4.230542009703e-07 , 1.119824850336e-06 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 7.977316461088e-09 [ 1.331896340330e-10 , 3.599646714825e-09 , 7.117751187713e-09 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 6.881308192117e-12 [ 3.656143732648e-12 , 5.404220900075e-14 , 5.829416341347e-12 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 21 accepted t=0.21 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 2.2000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 2.2000e-01 min -6.1876e-03 max 6.8475e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 2.2000e-01 min -8.9248e-02 max 4.5113e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 2.2000e-01 min -2.2757e-03 max 3.1553e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m22 TS dt 0.01 time 0.22\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 2.907626261154e+00 [ 1.671366089120e+00 , 7.048487542383e-03 , 2.379238573476e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 9.168088850936e-01 [ 1.025596594011e-01 , 1.033243588987e-03 , 9.110537747344e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 2.909366352868e-01 [ 1.161733962022e-01 , 3.860328772763e-03 , 2.667076407391e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 4.064653982725e-02 [ 8.893504918930e-03 , 2.335583402739e-04 , 3.966096595756e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 6.353815897443e-04 [ 8.752682331114e-05 , 6.447078061269e-06 , 6.292910733289e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 2.325856166455e-07 [ 1.883579306329e-08 , 1.351404545969e-09 , 2.318177207976e-07 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 8.191617920963e-12 [ 3.816646196228e-12 , 1.993792784108e-14 , 7.248132066601e-12 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 22 accepted t=0.22 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 2.3000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 2.3000e-01 min -6.7613e-03 max 7.5078e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 2.3000e-01 min -9.5112e-02 max 4.8876e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 2.3000e-01 min -2.4876e-03 max 3.4679e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m23 TS dt 0.01 time 0.23\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 3.043444594164e+00 [ 1.649199964783e+00 , 6.924631638701e-03 , 2.557859754440e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 9.672254943501e-01 [ 1.267434640546e-01 , 8.491789652920e-04 , 9.588850453184e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 3.298661760603e-01 [ 1.171652529628e-01 , 4.476896669087e-03 , 3.083244314079e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 3.855565959719e-02 [ 8.671118272535e-03 , 3.770593579214e-04 , 3.756605410632e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 7.086649585662e-04 [ 9.362478336159e-05 , 4.067527711519e-05 , 7.012745149167e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 5.547396944272e-05 [ 1.425873222127e-06 , 1.699929014070e-05 , 5.278591010865e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 6.873939548822e-06 [ 1.115469815750e-07 , 7.772582319899e-07 , 6.828943683511e-06 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 2.458163550010e-07 [ 1.668683638893e-10 , 2.455479823872e-07 , 1.148219868056e-08 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 6.768946167598e-10 [ 6.290163031835e-12 , 1.274187351052e-12 , 6.768641905833e-10 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 8.776566372578e-12 [ 4.475528150857e-12 , 2.129434941179e-14 , 7.549656390449e-12 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 23 accepted t=0.23 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 2.4000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 2.4000e-01 min -7.3654e-03 max 8.1831e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 2.4000e-01 min -1.0084e-01 max 5.2694e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 2.4000e-01 min -2.7040e-03 max 3.7867e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m24 TS dt 0.01 time 0.24\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 3.250189981015e+00 [ 1.674596440490e+00 , 6.413864009609e-03 , 2.785573645865e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 1.063451453877e+00 [ 1.332866011943e-01 , 1.146442028722e-03 , 1.055065098639e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 4.036184883563e-01 [ 1.261476394039e-01 , 2.575308123386e-03 , 3.833901733273e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 2.105689282128e-02 [ 3.720983451548e-03 , 3.227301003439e-04 , 2.072300322643e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 8.397700231858e-04 [ 3.258700063261e-05 , 1.010389761941e-04 , 8.330323550265e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 2.564336162946e-04 [ 5.572917373139e-06 , 3.561840823732e-05 , 2.538867289807e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 8.376252834223e-05 [ 1.894578703635e-06 , 5.937370100037e-06 , 8.353034994607e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 1.502850792238e-06 [ 1.867477633203e-08 , 1.767820811399e-07 , 1.492300188382e-06 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 2.093707435901e-09 [ 4.605136999633e-11 , 1.575053570436e-10 , 2.087266672222e-09 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 9.263653765249e-12 [ 4.099669179525e-12 , 2.369642929943e-14 , 8.307071215543e-12 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 24 accepted t=0.24 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 2.5000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 2.5000e-01 min -7.9753e-03 max 8.8730e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 2.5000e-01 min -1.0651e-01 max 5.6562e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 2.5000e-01 min -2.9236e-03 max 4.1096e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m25 TS dt 0.01 time 0.25\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 3.492583705228e+00 [ 1.716380152910e+00 , 6.128274452510e-03 , 3.041733478293e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 1.177778064191e+00 [ 1.400849617021e-01 , 8.165862318334e-04 , 1.169417250249e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 4.095536787325e-01 [ 1.326521225656e-01 , 3.054646015856e-03 , 3.874639328762e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 2.234247781996e-02 [ 3.452767695237e-03 , 3.324208757093e-04 , 2.207157010137e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 9.750070201803e-04 [ 2.009594425448e-05 , 1.324828997968e-04 , 9.657552089876e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 3.006145725188e-04 [ 6.519000501061e-06 , 4.288203941780e-05 , 2.974689135666e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 2.128897282889e-05 [ 2.300211683296e-07 , 1.418990221720e-06 , 2.124038420373e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 1.011871017687e-08 [ 1.696298751167e-10 , 1.608613008411e-09 , 9.988587764950e-09 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 1.042478199846e-11 [ 4.554848315130e-12 , 2.198678969251e-14 , 9.377043943734e-12 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 25 accepted t=0.25 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 2.6000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 2.6000e-01 min -8.5384e-03 max 9.5713e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 2.6000e-01 min -1.1199e-01 max 6.0521e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 2.6000e-01 min -3.1410e-03 max 4.4331e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m26 TS dt 0.01 time 0.26\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 3.613422809294e+00 [ 1.702014542972e+00 , 5.261739828941e-03 , 3.187466581524e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 1.266673754318e+00 [ 1.335619436265e-01 , 5.893956628071e-04 , 1.259612345011e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 4.009772424949e-01 [ 1.501715279016e-01 , 3.000801474175e-03 , 3.717825391234e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 2.177409200815e-02 [ 3.459155292984e-03 , 1.658942757611e-04 , 2.149692551338e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 3.741071525569e-04 [ 1.653654060092e-05 , 8.843300491168e-05 , 3.631285007562e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 1.899685215955e-04 [ 6.260032881571e-06 , 3.073447087281e-05 , 1.873612646351e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 3.490944492587e-05 [ 9.210608340712e-07 , 9.916999998977e-06 , 3.345854304946e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 4.262023967437e-07 [ 7.081434789044e-09 , 8.645075941521e-08 , 4.172824013398e-07 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 2.721345481019e-11 [ 4.321785242278e-12 , 7.505565586849e-12 , 2.579846468779e-11 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 26 accepted t=0.26 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 2.7000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 2.7000e-01 min -9.1502e-03 max 1.0273e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 2.7000e-01 min -1.1729e-01 max 6.4602e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 2.7000e-01 min -3.3553e-03 max 4.7553e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m27 TS dt 0.01 time 0.27\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 3.666998984903e+00 [ 1.654499750816e+00 , 5.537669900186e-03 , 3.272534409910e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 1.297905865327e+00 [ 1.374318077064e-01 , 6.675272244286e-04 , 1.290609037582e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 4.296770801023e-01 [ 1.473330739140e-01 , 3.086192182432e-03 , 4.036159485379e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.315217583435e-02 [ 2.630535106512e-03 , 2.148036846869e-04 , 1.288463711588e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 8.484707608702e-04 [ 3.079570425864e-05 , 5.515147220292e-05 , 8.461161691900e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 3.120252139107e-05 [ 3.251101525582e-07 , 5.265729581601e-08 , 3.120078319142e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 3.179242422823e-10 [ 6.447376163822e-12 , 8.013136217670e-13 , 3.178578504109e-10 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 27 accepted t=0.27 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 2.8000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 2.8000e-01 min -9.7230e-03 max 1.0984e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 2.8000e-01 min -1.2239e-01 max 6.8786e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 2.8000e-01 min -3.5693e-03 max 5.0794e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m28 TS dt 0.01 time 0.28\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 3.844702445667e+00 [ 1.640877163958e+00 , 5.472440510801e-03 , 3.476956870730e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 1.418823120275e+00 [ 1.236472511104e-01 , 6.573485017508e-04 , 1.413424908445e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 4.532433565443e-01 [ 1.584609898753e-01 , 2.917902963886e-03 , 4.246305933180e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.645438780669e-02 [ 3.028457123583e-03 , 2.103604232890e-04 , 1.617192239765e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 3.581331415997e-04 [ 1.568889412263e-05 , 1.202300609864e-04 , 3.369835873577e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 2.816465810560e-04 [ 1.128045187954e-05 , 8.270043879622e-05 , 2.689947684414e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 2.200760421440e-04 [ 7.616952149970e-06 , 5.291796933975e-05 , 2.134833363209e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 1.645267029865e-04 [ 4.853247186301e-06 , 3.395946637758e-05 , 1.609106479718e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 1.079430988556e-04 [ 2.902558966576e-06 , 1.289859030605e-05 , 1.071303603564e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 1.187015867715e-05 [ 3.780506032501e-07 , 4.145004399362e-06 , 1.111650499444e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 10 SNES Function norm 8.111524712759e-07 [ 2.014273647677e-08 , 1.717903707251e-07 , 7.924964797085e-07 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 11 SNES Function norm 7.842281477819e-10 [ 2.683154372337e-11 , 3.003069944067e-10 , 7.239541181216e-10 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 12 SNES Function norm 1.081016869198e-11 [ 6.115517411214e-12 , 9.661361783833e-14 , 8.913521175778e-12 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 28 accepted t=0.28 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 2.9000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 2.9000e-01 min -1.0218e-02 max 1.1713e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 2.9000e-01 min -1.2735e-01 max 7.3131e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 2.9000e-01 min -3.7850e-03 max 5.4090e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m29 TS dt 0.01 time 0.29\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 4.124428699699e+00 [ 1.660126147618e+00 , 5.008777099678e-03 , 3.775561969436e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 1.539601294435e+00 [ 1.331498805339e-01 , 6.941804052763e-04 , 1.533832707062e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 4.808965087904e-01 [ 1.639392186370e-01 , 2.893360163906e-03 , 4.520807596287e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 2.497864464879e-02 [ 3.778934985360e-03 , 2.737533642205e-04 , 2.468962126001e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 8.538067009119e-04 [ 2.894996573919e-05 , 1.862486904390e-04 , 8.327419812376e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 5.204172904277e-04 [ 1.482746113791e-05 , 8.035325782973e-05 , 5.139626995498e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 3.477278337025e-04 [ 1.189403710161e-05 , 4.261952210265e-05 , 3.449010793672e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 1.896853379274e-04 [ 6.954787928879e-06 , 1.101569745523e-05 , 1.892374507308e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 6.743584130037e-06 [ 1.686069938541e-07 , 9.053052355744e-07 , 6.680413238038e-06 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 9.038898501811e-08 [ 3.092009302331e-09 , 5.715894645061e-09 , 9.015506995996e-08 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 10 SNES Function norm 1.350815384513e-11 [ 6.204441045713e-12 , 3.256786740006e-14 , 1.199891957419e-11 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 29 accepted t=0.29 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 3.0000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 3.0000e-01 min -1.0785e-02 max 1.2457e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 3.0000e-01 min -1.3218e-01 max 7.7657e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 3.0000e-01 min -4.0045e-03 max 5.7443e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m30 TS dt 0.01 time 0.3\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 4.431267714658e+00 [ 1.646232500018e+00 , 5.367116081383e-03 , 4.114124853346e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 1.705856302702e+00 [ 1.272963592554e-01 , 6.551695720398e-04 , 1.701099918624e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 5.634536574748e-01 [ 1.716179858603e-01 , 2.374483902925e-03 , 5.366764880979e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.965689684347e-02 [ 3.107013122045e-03 , 2.804237165921e-04 , 1.940776714394e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 1.685247874654e-03 [ 6.509128621917e-05 , 2.380593159622e-04 , 1.667078668080e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 1.301869304283e-03 [ 5.953399818671e-05 , 1.749046911931e-04 , 1.288692258646e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 1.106180789643e-03 [ 5.231284748237e-05 , 1.312917812646e-04 , 1.097115205225e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 9.282154648884e-04 [ 4.395528788806e-05 , 9.381222328255e-05 , 9.224159304173e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 7.307996886226e-04 [ 3.402405188402e-05 , 5.735482673894e-05 , 7.277506253066e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 4.587604989425e-04 [ 2.013825703892e-05 , 1.838359833075e-05 , 4.579494396828e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 10 SNES Function norm 2.115060558659e-05 [ 5.897035602235e-07 , 2.411437078499e-06 , 2.100441233663e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 11 SNES Function norm 8.330221699979e-07 [ 3.452313795756e-08 , 3.519568747787e-08 , 8.315619954258e-07 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 12 SNES Function norm 6.931165587028e-11 [ 6.417849597565e-12 , 7.503617233437e-12 , 6.860475620857e-11 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 30 accepted t=0.3 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 3.1000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 3.1000e-01 min -1.1287e-02 max 1.3231e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 3.1000e-01 min -1.3696e-01 max 8.2488e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 3.1000e-01 min -4.2345e-03 max 6.0918e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m31 TS dt 0.01 time 0.31\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 5.044427737411e+00 [ 1.680464889738e+00 , 4.771615884042e-03 , 4.756287016571e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 2.072780726114e+00 [ 9.158595387383e-02 , 7.333716592629e-04 , 2.070756241997e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 5.702608434674e-01 [ 1.797842373049e-01 , 1.846048655940e-03 , 5.411761725291e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.461699241181e-02 [ 2.137946231996e-03 , 4.842261235677e-04 , 1.445168426646e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 7.048913762340e-03 [ 7.805322095817e-04 , 2.562571340946e-04 , 7.000877586413e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 3.689752112923e-03 [ 3.148537398583e-04 , 1.601967684290e-04 , 3.672802032876e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 2.427146298901e-03 [ 1.962594831164e-04 , 8.898886914931e-05 , 2.417561240739e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 1.508337024618e-03 [ 9.548508994585e-05 , 3.488833113633e-05 , 1.504907300063e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 2.291077675345e-04 [ 1.047356504299e-05 , 6.067673314543e-06 , 2.287877988890e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 5.544412400559e-06 [ 2.901770536514e-07 , 1.800589865231e-07 , 5.533885154788e-06 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 10 SNES Function norm 5.840830532250e-09 [ 2.769616174456e-10 , 1.541389153073e-10 , 5.832223826613e-09 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 11 SNES Function norm 1.192070285331e-11 [ 6.292492859750e-12 , 2.839010328934e-14 , 1.012456834284e-11 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 31 accepted t=0.31 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 3.2000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 3.2000e-01 min -1.1723e-02 max 1.4021e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 3.2000e-01 min -1.4166e-01 max 8.7570e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 3.2000e-01 min -4.4719e-03 max 6.4446e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m32 TS dt 0.01 time 0.32\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 5.500105950418e+00 [ 1.661529584319e+00 , 5.215027885517e-03 , 5.243134340234e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 2.350860747061e+00 [ 6.489433931740e-02 , 5.007348680364e-04 , 2.349964835069e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 6.188903311420e-01 [ 1.747290076943e-01 , 2.178257008211e-03 , 5.937089110394e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.666167487424e-02 [ 5.336665932432e-03 , 4.697133011679e-04 , 1.577690640637e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 1.920569073992e-03 [ 1.420141847139e-04 , 2.699669627614e-04 , 1.896189699986e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 1.229869655216e-03 [ 6.846852816267e-05 , 2.146052401451e-04 , 1.209064109290e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 8.768701065086e-04 [ 3.709712531931e-05 , 1.269064516821e-04 , 8.668447032213e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 6.362498305844e-04 [ 2.168534057069e-05 , 6.817510291124e-05 , 6.322149541620e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 4.743794714057e-04 [ 1.375457443767e-05 , 3.820216218750e-05 , 4.726386456664e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 2.602266597113e-04 [ 6.063220773086e-06 , 7.919800340730e-06 , 2.600354370868e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 10 SNES Function norm 3.270964511636e-06 [ 1.360842375762e-07 , 3.974359399897e-07 , 3.243876475803e-06 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 11 SNES Function norm 2.965221292911e-08 [ 6.331497708724e-10 , 8.922921047660e-10 , 2.963202098677e-08 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 12 SNES Function norm 1.094717698384e-11 [ 5.850966934514e-12 , 2.704972843180e-14 , 9.252358518719e-12 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 32 accepted t=0.32 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 3.3000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 3.3000e-01 min -1.2205e-02 max 1.4848e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 3.3000e-01 min -1.4644e-01 max 9.3100e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 3.3000e-01 min -4.7241e-03 max 6.8106e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m33 TS dt 0.01 time 0.33\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 6.544460440631e+00 [ 1.719165435717e+00 , 5.286098161556e-03 , 6.314618335322e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 2.964169388535e+00 [ 1.737015429745e-02 , 5.256706178013e-04 , 2.964118446577e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 6.827763266073e-01 [ 1.664244835413e-01 , 1.606503188534e-03 , 6.621811101208e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 2.036979043412e-02 [ 7.752638672405e-03 , 3.491596954740e-04 , 1.883356162419e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 2.178666459963e-03 [ 1.540465115602e-04 , 2.254963879647e-04 , 2.161482962009e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 7.611336478864e-04 [ 2.138533993169e-05 , 1.415356856449e-04 , 7.475525044235e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 3.117175801745e-04 [ 5.764247154749e-06 , 6.099294411626e-05 , 3.056378314486e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 7.551941833404e-05 [ 7.674390523380e-07 , 1.209475531739e-05 , 7.454066324245e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 2.749611221114e-06 [ 1.818604496638e-08 , 3.280254865928e-08 , 2.749355402244e-06 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 1.839369947852e-11 [ 6.304301093733e-12 , 2.634398132861e-13 , 1.727757412632e-11 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 33 accepted t=0.33 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 3.4000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 3.4000e-01 min -1.2633e-02 max 1.5715e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 3.4000e-01 min -1.5131e-01 max 9.9168e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 3.4000e-01 min -4.9902e-03 max 7.1902e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m34 TS dt 0.01 time 0.34\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 7.912763631508e+00 [ 1.757688917544e+00 , 5.696423953464e-03 , 7.715071322289e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 3.895760357611e+00 [ 1.018690245246e-01 , 7.272911816986e-04 , 3.894428191253e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 7.747068070783e-01 [ 1.509428079376e-01 , 9.184916229473e-04 , 7.598592383057e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 5.578732782934e-02 [ 1.610860608461e-02 , 1.409776017144e-04 , 5.341084984968e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 6.593782659404e-04 [ 1.108344656186e-04 , 4.048462872384e-05 , 6.487344708451e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 5.970645628932e-05 [ 1.678366682345e-06 , 1.944162162746e-05 , 5.642754076161e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 1.301214641365e-05 [ 2.229893694581e-07 , 1.562309235299e-06 , 1.291609150961e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 3.392782109118e-08 [ 8.588051302447e-10 , 1.041248286662e-08 , 3.227909072907e-08 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 1.419260272186e-11 [ 6.836744097251e-12 , 3.015041404132e-13 , 1.243374430421e-11 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 34 accepted t=0.34 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 3.5000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 3.5000e-01 min -1.2975e-02 max 1.6642e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 3.5000e-01 min -1.5642e-01 max 1.0600e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 3.5000e-01 min -5.2748e-03 max 7.5895e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m35 TS dt 0.01 time 0.35\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 1.032920565039e+01 [ 1.859919312909e+00 , 5.505001862785e-03 , 1.016037200168e+01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 5.668883309328e+00 [ 2.909157224210e-01 , 1.230445922351e-03 , 5.661413648836e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 8.698631354244e-01 [ 1.397598754781e-01 , 5.474807524335e-04 , 8.585620256228e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.604912121910e-01 [ 3.561892020639e-02 , 2.314745582663e-04 , 1.564885559183e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 3.950339748071e-03 [ 6.071517257803e-04 , 1.040403716254e-04 , 3.902015698090e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 1.297624543446e-04 [ 9.788191982108e-07 , 5.898629702069e-05 , 1.155766119693e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 7.319706282873e-05 [ 7.263261376445e-07 , 3.424525648554e-05 , 6.468805813544e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 5.346778272235e-05 [ 7.182926889515e-07 , 1.929000530063e-05 , 4.986164397971e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 3.050894870869e-05 [ 4.772154672210e-07 , 5.117216712891e-06 , 3.007294980245e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 9.417183351780e-07 [ 4.765436708707e-09 , 4.483021414986e-07 , 8.281521015799e-07 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 10 SNES Function norm 1.745244544903e-08 [ 2.652631392215e-10 , 3.219602133101e-09 , 1.715084982509e-08 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 11 SNES Function norm 1.469549286351e-11 [ 7.466533273824e-12 , 6.895264503826e-14 , 1.265715753655e-11 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 35 accepted t=0.35 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 3.6000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 3.6000e-01 min -1.3346e-02 max 1.7626e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 3.6000e-01 min -1.6176e-01 max 1.1371e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 3.6000e-01 min -5.5722e-03 max 8.0054e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m36 TS dt 0.01 time 0.36\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 1.352133427175e+01 [ 1.954614323829e+00 , 6.366581981865e-03 , 1.337930950386e+01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 8.197990607992e+00 [ 5.121304212101e-01 , 1.952173287985e-03 , 8.181978283362e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 9.969654499230e-01 [ 1.437431596936e-01 , 6.319225400572e-04 , 9.865483328532e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 3.300320707649e-01 [ 6.438406196771e-02 , 3.173390192098e-04 , 3.236908395272e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 4.116632614083e-02 [ 2.229301890205e-03 , 2.153438009933e-04 , 4.110535546694e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 7.521156845998e-04 [ 2.972866940671e-05 , 1.095289540448e-04 , 7.435036095825e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 3.481883786836e-04 [ 1.070133097876e-05 , 5.968482340744e-05 , 3.428678322918e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 1.693935223062e-04 [ 3.716913896538e-06 , 1.938769265963e-05 , 1.682393156302e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 1.242549364437e-05 [ 1.735032794955e-07 , 7.703355244680e-06 , 9.747877045412e-06 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 7.532164959010e-07 [ 2.248000704063e-09 , 7.582596563487e-08 , 7.493867220106e-07 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 10 SNES Function norm 2.042077355365e-11 [ 7.797700987603e-12 , 6.892947800792e-12 , 1.756960791973e-11 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 36 accepted t=0.36 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 3.7000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 3.7000e-01 min -1.3747e-02 max 1.8684e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 3.7000e-01 min -1.6742e-01 max 1.2251e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 3.7000e-01 min -5.8817e-03 max 8.4414e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m37 TS dt 0.01 time 0.37\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 1.850047128463e+01 [ 2.095700768987e+00 , 6.351354717157e-03 , 1.838138829633e+01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 1.239319738005e+01 [ 8.030092176326e-01 , 2.760698338748e-03 , 1.236715447772e+01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 1.022893135317e+00 [ 1.259800464775e-01 , 8.599328682615e-04 , 1.015105243157e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 5.217947971115e-01 [ 8.736585643254e-02 , 4.887815569662e-04 , 5.144285941849e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 3.061637942945e-01 [ 4.356589887734e-02 , 2.425240411834e-04 , 3.030482182325e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 3.413091424924e-02 [ 2.015198977016e-03 , 2.059159267739e-04 , 3.407074814563e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 2.017325317899e-03 [ 1.891707293934e-04 , 1.618397666648e-04 , 2.001905033537e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 2.894973553308e-04 [ 8.411871812784e-06 , 5.743551594520e-05 , 2.836179131583e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 2.921279702653e-05 [ 2.544759484924e-07 , 3.585705214059e-06 , 2.899078250449e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 8.076214841922e-08 [ 3.037791474107e-10 , 3.698440005507e-10 , 8.076073025260e-08 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 10 SNES Function norm 1.470912495152e-11 [ 6.100035521520e-12 , 2.930208238005e-14 , 1.338458310383e-11 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 37 accepted t=0.37 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 3.8000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 3.8000e-01 min -1.4099e-02 max 1.9823e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 3.8000e-01 min -1.7348e-01 max 1.3264e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 3.8000e-01 min -6.2033e-03 max 8.8978e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m38 TS dt 0.01 time 0.38\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 2.593703346817e+01 [ 2.247994489942e+00 , 6.882103221323e-03 , 2.583943069301e+01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 1.892652608377e+01 [ 1.116225299826e+00 , 3.783388382331e-03 , 1.889358135362e+01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 1.378061557378e+00 [ 3.502040266408e-02 , 1.688441275239e-03 , 1.377615467569e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 8.684364582540e-01 [ 5.959235983860e-02 , 1.052896770183e-03 , 8.663887834466e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 6.639800915100e-01 [ 5.261492883235e-02 , 6.854963312536e-04 , 6.618918048144e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 4.859695620443e-01 [ 3.890630125921e-02 , 4.029581859195e-04 , 4.844094885327e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 2.773485726073e-01 [ 1.925366952196e-02 , 2.643542569595e-04 , 2.766793397673e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 6.845332665212e-03 [ 4.347681317911e-04 , 2.229824076649e-04 , 6.827871909671e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 5.272438236581e-04 [ 1.054338552287e-05 , 9.260513450756e-05 , 5.189404355706e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 1.180609513288e-04 [ 1.606555002781e-06 , 3.464216205231e-05 , 1.128526819266e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 10 SNES Function norm 1.051444386675e-05 [ 7.021320344591e-08 , 8.575627484532e-07 , 1.047917869232e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 11 SNES Function norm 2.328351277132e-09 [ 1.581399008862e-11 , 3.839775615264e-10 , 2.296416952490e-09 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 38 accepted t=0.38 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 3.9000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 3.9000e-01 min -1.4698e-02 max 2.1056e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 3.9000e-01 min -1.8004e-01 max 1.4438e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 3.9000e-01 min -6.5385e-03 max 9.3772e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m39 TS dt 0.01 time 0.39\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 0 SNES Function norm 3.730063071210e+01 [ 2.442732178387e+00 , 7.940389708365e-03 , 3.722055947961e+01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 1 SNES Function norm 2.892728167231e+01 [ 1.414553584275e+00 , 5.149959836413e-03 , 2.889267444499e+01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 2 SNES Function norm 3.219854393557e+00 [ 1.440188228969e-01 , 4.205201032411e-03 , 3.216629168966e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 3 SNES Function norm 1.338687189923e+00 [ 4.553935330738e-02 , 3.670527718773e-03 , 1.337907353665e+00 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 4 SNES Function norm 9.386031812764e-01 [ 3.976990392280e-02 , 2.736261991568e-03 , 9.377562580514e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 5 SNES Function norm 8.122586188321e-01 [ 3.739761958839e-02 , 2.119909900364e-03 , 8.113944712027e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 6 SNES Function norm 7.145382526871e-01 [ 3.395228441494e-02 , 1.642519277886e-03 , 7.137292617419e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 7 SNES Function norm 6.238232265687e-01 [ 2.977616032940e-02 , 1.260648275109e-03 , 6.231109123170e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 8 SNES Function norm 5.324460914891e-01 [ 2.505157432399e-02 , 9.265440796663e-04 , 5.318556199590e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 9 SNES Function norm 4.312679550797e-01 [ 1.957754817094e-02 , 6.074666420594e-04 , 4.308229330833e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 10 SNES Function norm 3.033260692735e-01 [ 1.270578287857e-02 , 2.879073807933e-04 , 3.030597045001e-01 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 11 SNES Function norm 6.850911015354e-02 [ 2.203606501360e-03 , 1.880923149822e-04 , 6.847340296567e-02 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 12 SNES Function norm 5.724902380411e-03 [ 2.759893434829e-04 , 4.460524945223e-05 , 5.718072010673e-03 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 13 SNES Function norm 2.949681265561e-04 [ 1.092362344021e-05 , 7.121508580823e-05 , 2.860337072589e-04 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 14 SNES Function norm 2.945592798264e-05 [ 1.407692839126e-07 , 1.546618090290e-05 , 2.506848869809e-05 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 15 SNES Function norm 1.092818269283e-06 [ 4.241251658878e-09 , 2.234682532123e-07 , 1.069717589493e-06 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m 16 SNES Function norm 8.810796177690e-11 [ 7.424116546852e-12 , 5.090670536881e-11 , 7.152903445785e-11 ]\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m TSAdapt none theta 0: step 39 accepted t=0.39 + 1.000e-02 dt=1.000e-02 \n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mreaction time 4.0000e-01 0.0000e+00\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUx time 4.0000e-01 min -1.5990e-02 max 2.2399e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUy time 4.0000e-01 min -1.8724e-01 max 1.5830e-02\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[example] \u001b[0mUz time 4.0000e-01 min -6.8915e-03 max 9.8831e-03\n",
"[\u001b[32m0\u001b[0m] \u001b[34m<inform> \u001b[0m\u001b[1m[petsc] \u001b[0m40 TS dt 0.01 time 0.4\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 621
},
"id": "t_hYcjUxtKw7",
"outputId": "e5c95285-ced1-4f99-8566-b3e97f4cf39f"
},
"source": [
"# Plot convergence\n",
"!grep SNES log_beam > snes\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"newton_data=pd.read_fwf('snes', header=None)\n",
"newton_data=newton_data.drop([0,1,2,3,5,6,7,9,11,13,15], axis=1)\n",
"newton_data=newton_data.rename(columns={0: \"setp\", 4: \"it\", 8: \"res\", 10: 'equilibrium', 12: 'constrain', 14: 'flow'})\n",
"plt.rcParams['figure.figsize'] = [15, 10] \n",
"plt.plot(newton_data['res'].to_numpy(),'r^-')\n",
"plt.title('Neton method convergence')\n",
"plt.ylabel('absolute residial')\n",
"plt.xlabel('accumulated iterations')\n",
"plt.yscale('log')\n",
"plt.grid(True)\n",
"plt.show()"
],
"execution_count": 26,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 621
},
"id": "uOj9_Lho4vrb",
"outputId": "673e223d-933b-4794-c6b7-0383ef5cd3a6"
},
"source": [
"dir='/content/um_view/tutorials/adv-0'\n",
"bin_dir='/content/um_view/bin'\n",
"import os\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"os.chdir(dir)\n",
"# Plot load disp-path\n",
"!grep reaction 'log_beam' > reaction\n",
"!grep Ux 'log_beam' > ux\n",
"!grep Uy 'log_beam' > uy\n",
"!grep Uz 'log_beam' > uz\n",
"\n",
"data_reaction=pd.read_csv('reaction',sep='\\s+',header=None)\n",
"data_ux=pd.read_csv('ux',sep='\\s+',header=None)\n",
"data_uy=pd.read_csv('uy',sep='\\s+',header=None)\n",
"data_uz=pd.read_csv('uz',sep='\\s+',header=None)\n",
"\n",
"data_reaction=data_reaction.drop([0,1,2,3,4], axis=1)\n",
"data_reaction=data_reaction.rename(columns={5: \"time\", 6: \"force\"})\n",
"data_ux=data_ux.drop([0,1,2,3,4,6,8], axis=1)\n",
"data_ux=data_ux.rename(columns={5: \"time\", 7: \"min\", 9: \"max\"})\n",
"data_uy=data_uy.drop([0,1,2,3,4,6,8], axis=1)\n",
"data_uy=data_uy.rename(columns={5: \"time\", 7: \"min\", 9: \"max\"})\n",
"data_uz=data_uz.drop([0,1,2,3,4,6,8], axis=1)\n",
"data_uz=data_uz.rename(columns={5: \"time\", 7: \"min\", 9: \"max\"})\n",
"\n",
"def get_data_from_string(data_string, columns = 2, delimiter=' '):\n",
" my_data=np.fromstring(data_string, dtype=float, sep=delimiter)\n",
" my_data = my_data.reshape(int(my_data.size/columns),columns)\n",
" return my_data\n",
"\n",
"fenics_force=\"0 0 0.00028513238289205184 0.03221884498480243 0.000610997963340118 0.06869300911854093 0.001262729124236247 0.09969604863221893 0.001588594704684313 0.13252279635258368 0.0020773930753564124 0.1671732522796353 0.002403258655804475 0.20000000000000007 0.0027291242362525413 0.2346504559270517 0.003217922606924637 0.26747720364741634 0.0035437881873727033 0.3003039513677812 0.0040325865580448025 0.33313069908814597 0.004684317718940935 0.36960486322188457 0.005336048879837064 0.4024316109422492 0.006150712830957226 0.4352583586626141 0.007454175152749487 0.46808510638297873 0.009246435845213848 0.5027355623100305 0.012993890020366595 0.5355623100303951 0.024236252545824847 0.5702127659574469 0.03873727087576376 0.6030395136778116 0.048839103869653774 0.6358662613981764 0.05665987780040735 0.670516717325228 0.0635030549898167 0.7033434650455928 0.06953156822810591 0.7343465045592705 0.07507128309572303 0.7689969604863223 0.08061099796334013 0.801823708206687 0.08598778004073322 0.8328267477203648 0.0908757637474542 0.8693009118541034 0.0959266802443992 0.9039513677811551 0.10048879837067211 0.9313069908814591 0.10521384928716905 0.9677811550151977 0.10961303462321795 1.0006079027355623\"\n",
"fenics_force=get_data_from_string(fenics_force)\n",
"data_fenics=pd.DataFrame(fenics_force)\n",
"data_fenics=data_fenics.rename(columns={0: \"displacement\", 1: \"force\"})\n",
"\n",
"body_force=50e6\n",
"body_volume=0.004\n",
"data_fenics['force']=data_fenics['force'].to_numpy()*body_force*body_volume*0.25*1e-3\n",
"data_uy['min']=-data_uy['min'].to_numpy()\n",
"# Calculate reaction force from reaction from time scaling and know value of body force\n",
"data_reaction['force'] = data_reaction['time']*body_force*body_volume*1e-3\n",
"\n",
"plt.rcParams['figure.figsize'] = [15, 10]\n",
"\n",
"fig, ax = plt.subplots()\n",
"ax.plot(data_uy['min'].to_numpy(), \n",
" data_reaction['force'].to_numpy(), 'ro-', label='multifield platicity(MoFEM)')\n",
"ax.plot(data_fenics['displacement'].to_numpy(), \n",
" data_fenics['force'].to_numpy(), 'bo-', label='FeniCS + MFront')\n",
"ax.set(xlabel='u [mm]', ylabel='force [kN]',\n",
" title='Load displacement path')\n",
"#Set limit to fit Mieche paper\n",
"ax.axis(xmin=0)\n",
"#ax.axis(xmax=0.01)\n",
"ax.legend(loc='best')\n",
"ax.grid(True)"
],
"execution_count": 37,
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 1080x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "s3RErkQZulgx"
},
"source": [
"dir='/content/um_view/tutorials/adv-0'\n",
"bin_dir='/content/um_view/bin'\n",
"import os\n",
"os.chdir(dir)\n",
"list_of_files=!ls -c1 out*h5m\n",
"for f in list_of_files:\n",
" !{bin_dir}/mbconvert {f} {f}.vtk 2>/dev/null"
],
"execution_count": 28,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yT4hZ-o5uMdC",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "68fae898-42fe-455c-d016-536797aaa904"
},
"source": [
"#from pyvirtualdisplay import Display\n",
"#display = Display(visible=0, size=(1200, 800))\n",
"#display.start()\n",
"import os\n",
"os.system('/usr/bin/Xvfb :98 -screen 0 1024x768x24 &')\n",
"os.environ['DISPLAY'] = ':98'\n",
"os.environ['PYVISTA_USE_IPYVTK'] = 'true'\n",
"\n",
"import pyvista as pv\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.colors import ListedColormap\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.image as mpimg\n",
"\n",
"from IPython.display import HTML, Image\n",
"\n",
"dir='/content/um_view/tutorials/adv-0'\n",
"bin_dir='/content/um_view/bin'\n",
"import os\n",
"os.chdir(dir)\n",
"\n",
"nb_steps=!ls -c1 out*h5m.vtk | wc -l\n",
"nb_steps=int(nb_steps[0])\n",
"print(nb_steps)\n",
"\n",
"file='out_plastic_%d.h5m.vtk' % (nb_steps-1)\n",
"mesh = pv.read(file)\n",
"\n",
"# Scale displacements\n",
"scale_factor=1\n",
"\n",
"# Create a plotter object \n",
"plotter = pv.Plotter(notebook=True)\n",
"my_cmap = plt.cm.get_cmap(\"twilight\", 32)\n",
"\n",
"plotter.add_mesh(mesh, \n",
" scalars='PLASTIC_MULTIPLIER',\n",
" cmap=my_cmap,\n",
" show_edges=True, \n",
" smooth_shading=False, \n",
" clim=[0,0.125])\n",
"#mesh=mesh.warp_by_vector('U',factor=scale_factor)\n",
"plotter.add_axes()\n",
"plotter.view_xy()\n",
"\n",
"print(mesh.point_arrays)\n",
"\n",
"# setup camera and close\n",
"plotter.render()\n",
"cpos=plotter.camera_position\n",
"#plotter.update()\n",
"\n",
"camera_shift=-0.2\n",
"plotter.camera_position=[\n",
" (cpos[0][0]+camera_shift,cpos[0][1]+4*camera_shift,cpos[0][2]),\n",
" (cpos[1][0],cpos[1][1]+camera_shift,cpos[1][2]),\n",
" cpos[2]\n",
" ]\n",
"print(cpos)\n",
"\n",
"# # Open a gif\n",
"out_gif='fenics_beam.gif'\n",
"plotter.open_gif(out_gif)\n",
"\n",
"list_of_files=!ls -c1 out*h5m.vtk\n",
"for f in list_of_files:\n",
" #print('Render file ',f)\n",
" n_mesh=pv.read(f)\n",
" n_mesh=n_mesh.warp_by_vector('U',factor=scale_factor)\n",
" plotter.update_coordinates(n_mesh.points, mesh, render=True)\n",
" plotter.update_scalars(n_mesh.point_arrays['PLASTIC_MULTIPLIER'], mesh, render=True)\n",
" plotter.write_frame() # this will trigger the render\n",
"\n",
"# Close movie and delete object\n",
"plotter.close()\n",
"from IPython.display import HTML, Image\n",
"dir='/content/um_view/tutorials/adv-0'\n",
"bin_dir='/content/um_view/bin'\n",
"import os\n",
"os.chdir(dir)\n",
"Image(open('fenics_beam.gif','rb').read())"
],
"execution_count": 29,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"41\n",
"pyvista DataSetAttributes\n",
"Association : POINT\n",
"Active Scalars : PLASTIC_MULTIPLIER\n",
"Active Vectors : U\n",
"Active Texture : None\n",
"Active Normals : None\n",
"Contains arrays :\n",
" GLOBAL_ID int32 (2830,)\n",
" GRAD float64 (2830, 9) TENSORS\n",
" HARDENING float64 (2830,)\n",
" PLASTIC_MULTIPLIER float64 (2830,) SCALARS\n",
" PLASTIC_SURFACE float64 (2830,)\n",
" TEMPERATURE float64 (2830,)\n",
" U float64 (2830, 3) VECTORS\n",
"[(0.0, 0.0, 1.9430240855266505),\n",
" (0.0, 0.0, 0.0),\n",
" (0.0, 1.0, 0.0)]\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/pyvista/core/dataset.py:1195: PyvistaDeprecationWarning: Use of `point_arrays` is deprecated. Use `point_data` instead.\n",
" PyvistaDeprecationWarning\n"
]
},
{
"output_type": "execute_result",
"data": {
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