TabulationTest.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:9d030a6d641b9463439eba6275b5d97c2f4d3ce1ac52b7f36248e9e2528410d5"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Module imports"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext autoreload\n",
      "%autoreload 2\n",
      "\n",
      "\n",
      "%matplotlib inline\n",
      "import simulation_brutus as sim_brutus\n",
      "import shutil as sh\n",
      "import numpy as np\n",
      "import re\n",
      "\n",
      "sim_brutus= reload(sim_brutus)\n",
      "\n",
      "import subprocess as sp\n",
      "\n",
      "import os\n",
      "import pickle as pic\n",
      "\n",
      "from kelvin_cell_tools.init_utils import *\n",
      "from kelvin_cell_tools.sim_utils import *\n",
      "#from kelvin_cell_tools.test import *\n",
      "from mpl_toolkits.mplot3d import Axes3D\n",
      "\n",
      "\n",
      "import matplotlib.pyplot as pl\n",
      "\n",
      "pl.rcParams[\"text.usetex\"] = False\n",
      "pl.rcParams[\"text.fontsize\"] = 12\n",
      "\n",
      "import job_configurations as jc\n",
      "\n",
      "import itertools as it\n",
      "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    \n",
      "\n",
      "import cantera as ca"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The autoreload extension is already loaded. To reload it, use:\n",
        "  %reload_ext autoreload\n",
        "Error: Could not import vtkCommonComputationalGeometryPython"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "could not import simulation\n"
       ]
      }
     ],
     "prompt_number": 232
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%javascript\n",
      "IPython.load_extensions('toc');"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "javascript": [
        "IPython.load_extensions('toc');"
       ],
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.Javascript at 0x7ff22e8bb550>"
       ]
      }
     ],
     "prompt_number": 233
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Current Sim"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#current_sim = {\"steady\":True,\"conj\":False,\"temperature\":700,\"inf\":False}\n",
      "#current_sim = {\"steady\":True,\"conj\":False,\"inf\":False}\n",
      "#current_sim = {\"steady\":True,\"conj\":True,\"temperature\":500}\n",
      "#current_sim = {\"steady\":False,\"conj\":True}\n",
      "#current_sim = {\"steady\":True,\"conj\":False,\"inf\":False}\n",
      "#current_sim = {\"inf\":False}\n",
      "#current_sim = {\"conj\":False,\"inf\":False}\n",
      "#current_sim = {\"conj\":True,\"onecell\":True,\"temperature\":500}\n",
      "#current_sim = {\"vel\":10.0}\n",
      "\n",
      "#current_sim = {\"cond\":\"metallic\"}\n",
      "\n",
      "\n",
      "#current_sim = {\"conj\":True,\"onecell\":True,\"temperature\":700,\"cond\":\"ceramic\"}\n",
      "\n",
      "#current_sim = {\"cond\":\"metallic\"}\n",
      "\n",
      "current_sim = {\"temperature\":300,\"geometry\":\"kelvin\",\"dt\":50,\"group\":None,\"mf\":0.01}\n",
      "\n",
      "#current_sim = {\"temperature\":300,\"group\":None,\"cond\":\"ceramic\",\"mf\":0.01,\"dt\":50}\n",
      "\n",
      "#current_sim = {\"temperature\":300,\"porosity\":81}\n",
      "#current_sim = {\"grid\":30}\n",
      "\n",
      "\n",
      "#current_sim = {\"group\":\"grid_convergence\"} #,\"grid\":35}\n",
      "#current_sim = {\"group\":\"paper_sims\",\"steady\":False,\"Dp\":0.001,\"porosity\":81,\"mf\":0.01}\n",
      "\n",
      "#current_sim = {\"steady\":False,\"Dp\":0.002,\"porosity\":81,\"mf\":0.005}\n",
      "current_sim = {\"steady\":False,\"mf\":0.01,\"porosity\":81,\"Dp\":0.002,\"F\":40}\n",
      "\n",
      "#current_sim = {\"cond\":\"low_eps\"}\n",
      "\n",
      "for sim in filter_simulations(sims,**current_sim):\n",
      "    print(sim.sim_info)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'cond': 'fine', 'diff': 2.0954710129698026e-05, 'conj': True, 'inf': False, 'massfraction': [0.01, 0.1, 0.0, 0.89], 'group': 'paper_sims', 'temperature': 300, 'porosity': 81, 'vel': 1.5, 'ncells': 8, 'Dp': 0.002, 'F': 40, 'visc': 1.8319764958622377e-05, 'grid': 30, 'rho': 1.137245148042052, 'dt': 100, 'ds': 0.00046, 'onecell': True, 'mf': 0.01, 'name': 'A', 'kelvin_cell_size': 0.00246, 'geometry': 'kelvin', 'surf_to_volume': 1382.4156945843558, 'steady': False, 'time': array([   200.,    400.,    600.,    800.,   1000.,   1200.,   1400.,\n",
        "         1600.,   1800.,   2000.,   2200.,   2400.,   2600.,   2800.,\n",
        "         3000.,   3200.,   3400.,   3600.,   3800.,   4000.,   4200.,\n",
        "         4400.,   4600.,   4800.,   5000.,   5200.,   5400.,   5600.,\n",
        "         5800.,   6000.,   6200.,   6400.,   6600.,   6800.,   7000.,\n",
        "         7200.,   7400.,   7600.,   7800.,   8000.,   8200.,   8400.,\n",
        "         8600.,   8800.,   9000.,   9200.,   9400.,   9600.,   9800.,\n",
        "        10000.,  10200.,  10400.,  10600.,  10800.,  11000.,  11200.,\n",
        "        11400.,  11600.,  11800.,  12000.,  12200.,  12400.,  12600.,\n",
        "        12800.,  13000.,  13200.,  13400.,  13600.,  13800.,  14000.,\n",
        "        14200.,  14400.,  14600.,  14800.,  15000.,  15200.,  15400.,\n",
        "        15600.,  15800.,  16000.,  16200.,  16400.,  16600.,  16800.,\n",
        "        17000.,  17200.,  17400.,  17600.,  17800.,  18000.,  18200.,\n",
        "        18400.,  18600.,  18800.,  19000.,  19200.,  19400.,  19600.,\n",
        "        19800.,  20000.,  20200.,  20400.,  20600.,  20800.,  21000.,\n",
        "        21200.,  21400.,  21600.,  21800.,  22000.,  22200.,  22400.,\n",
        "        22600.,  22800.,  23000.,  23200.,  23400.,  23600.,  23800.,\n",
        "        24000.,  24200.,  24400.,  24600.,  24800.,  25000.,  25200.,\n",
        "        25400.,  25600.,  25800.,  26000.,  26200.,  26400.,  26600.,\n",
        "        26800.,  27000.,  27200.,  27400.,  27600.,  27800.,  28000.,\n",
        "        28200.,  28400.,  28600.,  28800.,  29000.,  29200.,  29400.,\n",
        "        29600.,  29800.,  30000.])}\n"
       ]
      }
     ],
     "prompt_number": 720
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def write_inital_conditions(folder,massfraction,velocity,temperature):\n",
      "\n",
      "    writer = ini.initalconditions_writer()\n",
      "    writer.add_variable(\"uvelocity\",velocity)\n",
      "    writer.add_variable(\"temperature\",temperature)\n",
      "    \n",
      "    for key,item in massfraction.iteritems():\n",
      "        writer.add_variable(var=\"component_molefraction\",value=item[0],index=item[1])\n",
      "        \n",
      "    writer.write_file(folder)\n",
      "    \n",
      "def filter_simulations(sim_list,**kwargs):\n",
      "  filtered_list = []\n",
      "  for sim in sim_list:\n",
      "      match_all = True\n",
      "      for key, item in kwargs.iteritems():\n",
      "            if not int(sim.sim_info[key]) == int(item):  \n",
      "              match_all = False\n",
      "      if match_all:\n",
      "        filtered_list.append(sim)\n",
      "  return filtered_list\n",
      "    \n",
      "def sort_sims(sim_list,prop): \n",
      "    return sorted(sim_list,key=lambda it: it.sim_info[prop])    \n",
      "\n",
      "def get_array(sim_list,func=None,var=None):\n",
      "    prop_list = []\n",
      "    if func:\n",
      "        for sim in sim_list:\n",
      "           if var:\n",
      "               prop_list.append(func(sim,var))\n",
      "           else:\n",
      "               prop_list.append(func(sim))\n",
      "    return np.array(prop_list)\n",
      "\n",
      "def sh_number(sim,inflow_mf=False):\n",
      "    gas = ca.import_phases(os.path.join(sim.info.local[\"sim_folder\"],\"cantera_input.cti\"),[\"gas\"])[0]\n",
      "    molefraction = [0.003,0.1,0.0,0.897]\n",
      "\n",
      "    gas.TPX = [sim.sim_info[\"temperature\"], 1E5, molefraction]\n",
      "    sh_numbers = []\n",
      "    for cell in range(sim.sim_info[\"ncells\"]-1):\n",
      "       if inflow_mf and cell == 0:\n",
      "           avg_Y_in = gas.Y[0]\n",
      "       else:\n",
      "           avg_Y_in  = sim.result[\"mf_avg\"][cell]\n",
      "       avg_Y_out = sim.result[\"mf_avg\"][cell+1]\n",
      "       sh_numbers.append(np.log(avg_Y_in/avg_Y_out) \n",
      "                              /sim.sim_info[\"surf_to_volume\"]\n",
      "                              /sim.sim_info[\"diff\"]\n",
      "                              *sim.sim_info[\"kelvin_cell_size\"]* sim.sim_info[\"vel\"] \n",
      "                              /(sim.sim_info[\"kelvin_cell_size\"]))\n",
      "    sim.result[\"sh_numbers\"] = sh_numbers\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1732
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Postprocessing scripts"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%file full_3d.py\n",
      "from paraview.simple import *\n",
      "from para_reader import *\n",
      "import pickle as pic\n",
      "import sys\n",
      "\n",
      "\n",
      "iterations = [int(sys.argv[1])]\n",
      "print (iterations[0])\n",
      "\n",
      "\n",
      "try:\n",
      "    os.mkdir(\"processed\")\n",
      "except:\n",
      "    print(\"directory exists already\")\n",
      "    \n",
      "for iteration in  iterations:\n",
      "   \n",
      "   if iteration == -1:\n",
      "       datafile = get_last_modified()\n",
      "   else: \n",
      "       datafile = get_iteration(iteration)\n",
      "   print(datafile)\n",
      "   if datafile:\n",
      "     data = OpenDataFile(datafile)\n",
      "     unstructured = convert_to_unstructured(data)\n",
      "     filename ='processed/output_3d_'+str(iteration)+'.vtu' \n",
      "     pa.UpdatePipeline()\n",
      "     writer=CreateWriter(filename,unstructured)\n",
      "     writer.UpdatePipeline()\n",
      "     Delete(unstructured)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Overwriting full_3d.py\n"
       ]
      }
     ],
     "prompt_number": 323
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%file create_slices.py\n",
      "from paraview.simple import *\n",
      "from para_reader import *\n",
      "import pickle as pic\n",
      "import sys\n",
      "\n",
      "nblocks = 64\n",
      "\n",
      "\n",
      "postprocess = pic.load(open(\"job.pickle\"))\n",
      "iteration_list = postprocess[\"iteration_list\"]\n",
      "kelvin_cell_size = postprocess[\"kelvin_cell_size\"]\n",
      "origin = [kelvin_cell_size/2.,kelvin_cell_size/4.,kelvin_cell_size/4.]\n",
      "\n",
      "\n",
      "iterations = [int(sys.argv[1])]\n",
      "print (iterations[0])\n",
      "\n",
      "\n",
      "try:\n",
      "    os.mkdir(\"processed\")\n",
      "except:\n",
      "    print(\"directory exists already\")\n",
      "    \n",
      "for iteration in  iterations:\n",
      "   \n",
      "   if iteration == -1:\n",
      "       datafile = get_last_modified()\n",
      "   else: \n",
      "       datafile = get_iteration(iteration)\n",
      "   print(datafile)\n",
      "   if datafile:\n",
      "     data = OpenDataFile(datafile)\n",
      "     slice = Slice(data)\n",
      "     slice.SliceType.Origin = origin\n",
      "     slice.SliceType.Normal = [0.0,0.0,1.0]\n",
      "     \n",
      "     print(slice)\n",
      "     unstructured = convert_to_unstructured(slice)\n",
      "     filename ='processed/output_'+str(iteration)+'.vtu' \n",
      "     pa.UpdatePipeline()\n",
      "     writer=CreateWriter(filename,unstructured)\n",
      "     writer.UpdatePipeline()\n",
      "     Delete(unstructured)\n",
      "     Delete(slice)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Overwriting create_slices.py\n"
       ]
      }
     ],
     "prompt_number": 1664
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%file create_contour.py\n",
      "from paraview.simple import *\n",
      "from para_reader import *\n",
      "import pickle as pic\n",
      "import sys\n",
      "\n",
      "postprocess = pic.load(open(\"job.pickle\"))\n",
      "iteration_list = postprocess[\"iteration_list\"]\n",
      "kelvin_cell_size = postprocess[\"kelvin_cell_size\"]\n",
      "origin = [kelvin_cell_size/2.,kelvin_cell_size/4.,kelvin_cell_size/4.]\n",
      "\n",
      "\n",
      "iterations = [int(sys.argv[1])]\n",
      "print (iterations[0])\n",
      "\n",
      "\n",
      "try:\n",
      "    os.mkdir(\"processed\")\n",
      "except:\n",
      "    print(\"directory exists already\")\n",
      "    \n",
      "for iteration in  iterations:\n",
      "   \n",
      "   if iteration == -1:\n",
      "       datafile = get_last_modified()\n",
      "   else: \n",
      "       datafile = get_iteration(iteration)\n",
      "   print(datafile)\n",
      "   if datafile:\n",
      "     data = OpenDataFile(datafile)\n",
      "     unstructured = convert_to_unstructured(data)\n",
      "               \n",
      "     Contour1 = Contour(unstructured)\n",
      "\n",
      "     Contour1.ContourBy = ['POINTS', 'EmbI']\n",
      "     Contour1.Isosurfaces = [0.0]\n",
      "\n",
      "     \n",
      "     print(Contour1)\n",
      "     filename ='processed/contour_'+str(iteration)+'.vtp' \n",
      "     pa.UpdatePipeline()\n",
      "     writer=CreateWriter(filename,Contour1)\n",
      "     writer.UpdatePipeline()\n",
      "     Delete(unstructured)\n",
      "     Delete(Contour1)\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Overwriting create_contour.py\n"
       ]
      }
     ],
     "prompt_number": 373
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%file slice_avg.py\n",
      "\n",
      "from paraview.simple import *\n",
      "from para_reader import *\n",
      "import pickle as pic\n",
      "import os\n",
      "import glob as gl\n",
      "import sys\n",
      "\n",
      "postprocess = pic.load(open(\"job.pickle\"))\n",
      "kelvin_cell_size = postprocess[\"kelvin_cell_size\"]-postprocess[\"kelvin_cell_size\"]*0.0001\n",
      "ncells           = postprocess[\"ncells\"]\n",
      "geometry             = postprocess[\"geometry\"]\n",
      "\n",
      "massflow_weight_Y = postprocess[\"massflow_weight_Y\"]\n",
      "massflow_var_Y    = postprocess[\"massflow_var_Y\"]\n",
      "massflow_weight_P = postprocess[\"massflow_weight_P\"]\n",
      "massflow_var_P    = postprocess[\"massflow_var_P\"]\n",
      "massflow_weight_T    = postprocess[\"massflow_weight_T\"]\n",
      "\n",
      "slice_per_cell = 4\n",
      "\n",
      "filelist = gl.glob(\"RESULT/*.vtm\")\n",
      "iterations = []\n",
      "    \n",
      "for filename in filelist:\n",
      "    iterations.append(int(os.path.basename(filename).split(\".\")[1]))\n",
      "\n",
      "iterations.sort()\n",
      "\n",
      "#servermanager.Disconnect()\n",
      "\n",
      "iterations = [int(sys.argv[1])]\n",
      "print (iterations[0])\n",
      "\n",
      "avg_var    = []\n",
      "for it in iterations[:]:\n",
      "   # servermanager.Connect()\n",
      "\n",
      "    if it == -1:\n",
      "        name = get_last_modified()\n",
      "    else:\n",
      "        name = get_iteration(it)\n",
      "\n",
      "    print(name)\n",
      "\n",
      "    data_file = OpenDataFile(name)\n",
      "    print(\"Reading file \", name)\n",
      "\n",
      "    current_t = []\n",
      "\n",
      "    for sl in range(ncells*slice_per_cell+1):  \n",
      "      print(\"Before slice \")\n",
      "      current_slice = Slice(data_file)\n",
      "\n",
      "      print(\"Current Slice \", current_slice)\n",
      "\n",
      "      #current_slice.SliceType.Origin = [sl*kelvin_cell_size-0.5*kelvin_cell_size,0.0005,0.0005]\n",
      "      if geometry == \"kelvin\":\n",
      "          increment = kelvin_cell_size/slice_per_cell\n",
      "          current_slice.SliceType.Origin = [sl*increment-0.5*kelvin_cell_size,0.0005,0.0005]\n",
      "      else:\n",
      "          current_slice.SliceType.Origin = [sl*kelvin_cell_size,0.0005,0.0005]\n",
      "  \n",
      "      current_slice.SliceType.Normal = [1,0,0]\n",
      "      print(\"Before avgY \")\n",
      "\n",
      "      avg_Y = massflow_avg(current_slice,massflow_weight_Y,massflow_var_Y)\n",
      "      print(\"Before avgP \")\n",
      "\n",
      "      avg_P = massflow_avg(current_slice,massflow_weight_P,massflow_var_P)\n",
      "      print(\"Before avgU \")\n",
      "\n",
      "      avg_U = massflow_avg(current_slice,massflow_weight_P,'U')\n",
      "\n",
      "      print(\"Before avgT \")\n",
      "\n",
      "      avg_T = massflow_avg(current_slice,massflow_weight_T,'T')\n",
      "        \n",
      "        \n",
      "      avg_Ts = massflow_avg(current_slice,['BMR_I',\"(1.0-HembI)\",\"Cp\",\"DEN\"],'T')\n",
      "\n",
      "      avg_var.append([avg_Y, avg_P,avg_U,avg_T,avg_Ts])\n",
      "#    Delete(current_slice)\n",
      "    \n",
      " #   servermanager.Disconnect()\n",
      "try:\n",
      "    os.mkdir(\"processed\")\n",
      "except:\n",
      "    print(\"directory exists already\")\n",
      "    \n",
      "for filename,var in zip([\"slice_averages_time_\"+str(iterations[0])+\".dat\"],[avg_var]):\n",
      "    pickle_file = \"processed/\"+ filename\n",
      "    f = open(pickle_file,'w')\n",
      "    pic.dump(var,f)\n",
      "    f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Overwriting slice_avg.py\n"
       ]
      }
     ],
     "prompt_number": 358
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%file slice_avg_local.py\n",
      "\n",
      "\n",
      "from paraview.simple import *\n",
      "from para_reader import *\n",
      "import pickle as pic\n",
      "import os\n",
      "\n",
      "servermanager.Connect()\n",
      "postprocess = pic.load(open(\"job.pickle\"))\n",
      "kelvin_cell_size = postprocess[\"kelvin_cell_size\"]-postprocess[\"kelvin_cell_size\"]*0.0001\n",
      "ncells           = postprocess[\"ncells\"]\n",
      "\n",
      "massflow_weight_Y = postprocess[\"massflow_weight_Y\"]\n",
      "massflow_var_Y    = postprocess[\"massflow_var_Y\"]\n",
      "massflow_weight_P = postprocess[\"massflow_weight_P\"]\n",
      "massflow_var_P    = postprocess[\"massflow_var_P\"]\n",
      "iteration         = postprocess[\"iteration\"]\n",
      "\n",
      "\n",
      "data_file = pa.GetActiveSource()\n",
      "\n",
      "print(\"Reading file \", data_file)\n",
      "\n",
      "avg_var    = []\n",
      "for sl in range(ncells+1):  \n",
      "  print(\"Before slice \")\n",
      "  current_slice = Slice(data_file)\n",
      "\n",
      "  print(\"Current Slice \", current_slice)\n",
      "\n",
      "  #current_slice.SliceType.Origin = [sl*kelvin_cell_size-0.5*kelvin_cell_size,0.0005,0.0005]\n",
      "  current_slice.SliceType.Origin = [sl*kelvin_cell_size-0.5*kelvin_cell_size,0.0005,0.0005]\n",
      "\n",
      "  current_slice.SliceType.Normal = [1,0,0]\n",
      "  print(\"Before avgY \")\n",
      "\n",
      "  avg_Y = massflow_avg(current_slice,massflow_weight_Y,massflow_var_Y)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Overwriting slice_avg_local.py\n"
       ]
      }
     ],
     "prompt_number": 199
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "jc.configuration_brutus\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1836,
       "text": [
        "{'cluster_home': '/cluster/home/mavt/vjan/',\n",
        " 'cluster_scratch': '/cluster/scratch_xp/public/vjan/',\n",
        " 'has_remote': True,\n",
        " 'host': 'brutus.ethz.ch',\n",
        " 'jobname': 'kelvin_cells_conjugate_one_cell_ceramic_300_very_coarse_unsteady_low_por',\n",
        " 'paraview_executable': None,\n",
        " 'queue': 'lsf',\n",
        " 'root_folder': '/home/vonrickenbach/testcases_mb/tabulation_tests',\n",
        " 'transat_executable': None,\n",
        " 'transat_init_executable': None,\n",
        " 'transat_version': 'MB',\n",
        " 'username': 'vjan'}"
       ]
      }
     ],
     "prompt_number": 1836
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "3d Simulations"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def create_sim_in_real_folder(jobname,temperature,ref_folder=\"kelvin_cells\",create=False,inf=False,conj=False,\n",
      "                              steady=True,write_ini=True,mf=0.003,velocity=2.0,onecell=True,cond=\"ceramic\",ncells=8,\n",
      "                              geometry=\"kelvin\",dt=50,group=None,grid=25,por=90,Dp=0.002,F=20):\n",
      "\n",
      "    root_folder = \"/home/vonrickenbach/testcases_mb/tabulation_tests\"\n",
      "  \n",
      "    if create:\n",
      "        try:\n",
      "            sh.copytree(os.path.join(root_folder,ref_folder),os.path.join(root_folder,jobname))\n",
      "        except:\n",
      "            print(\"Failed to create folder\",jobname)\n",
      "    massfraction = [mf,0.1,0.0,1-mf]\n",
      "  \n",
      "    massfraction = {}\n",
      "    massfraction[\"CO\"]   = [mf,   [0,0]] \n",
      "    massfraction[\"O2\"]   = [0.1, [0,1]]\n",
      "    massfraction[\"CO2\"]  = [0.0, [0,2]]\n",
      "    massfraction[\"N2\"]   = [0.9-mf,  [0,3]]\n",
      "    if write_ini:\n",
      "        write_inital_conditions(os.path.join(root_folder,jobname),massfraction,velocity,temperature)\n",
      "    #sh.copy(\"/home/vonrickenbach/ipython_notebooks/reaction_rates.dat\",os.path.join(root_folder,jobname))\n",
      "\n",
      "    gas = ca.import_phases(os.path.join(root_folder,jobname,\"cantera_input.cti\"),[\"gas\"])[0]\n",
      "    massfraction = [mf,0.1,0.0,0.9-mf]\n",
      "\n",
      "    gas.TPY = [temperature, 1E5, massfraction]\n",
      "    config = jc.configuration_brutus\n",
      "    \n",
      "    config[\"jobname\"]     = jobname\n",
      "    config[\"root_folder\"] = root_folder\n",
      "    config['cluster_home'] = '/cluster/home/vjan'\n",
      "    config['cluster_scratch'] = '/cluster/scratch/vjan'\n",
      "    config['host'] = 'euler.ethz.ch'\n",
      "\n",
      "\n",
      "    job = sim_brutus.job(**config)\n",
      "    job.sim_info = {}\n",
      "    if por == 81:\n",
      "       job.sim_info[\"ds\"] = 0.00046*Dp/0.002\n",
      "    else:\n",
      "       job.sim_info[\"ds\"] = 0.0003*Dp/0.002\n",
      "    job.sim_info[\"Dp\"] = Dp\n",
      "    Dp = job.sim_info[\"Dp\"]\n",
      "    ds = job.sim_info[\"ds\"]\n",
      "    job.sim_info[\"porosity\"] = por\n",
      "    porosity = job.sim_info[\"porosity\"]/100.0\n",
      "    job.sim_info[\"surf_to_volume\"] = 10.33/(Dp+ds) * np.sqrt(1-porosity) -5.8/(Dp+ds)*(1.0-porosity)\n",
      "    job.sim_info[\"kelvin_cell_size\"] = Dp + ds\n",
      "    job.sim_info[\"ncells\"] = ncells\n",
      "    job.sim_info[\"rho\"]    = gas.density\n",
      "    job.sim_info[\"vel\"]    = velocity\n",
      "    job.sim_info[\"visc\"]   = gas.viscosity\n",
      "    job.sim_info[\"diff\"]   = gas.mix_diff_coeffs[0]\n",
      "    job.sim_info[\"name\"]   = \"A\"\n",
      "    job.sim_info[\"temperature\"] = temperature\n",
      "    job.sim_info[\"inf\"] = inf\n",
      "    job.sim_info[\"conj\"] = conj\n",
      "    job.sim_info[\"steady\"] = steady\n",
      "    job.sim_info[\"mf\"] = mf\n",
      "    job.sim_info[\"onecell\"] = onecell\n",
      "    job.sim_info[\"cond\"] = cond\n",
      "    job.sim_info[\"dt\"] = dt\n",
      "    job.sim_info[\"geometry\"] = geometry\n",
      "    job.sim_info[\"group\"] = group\n",
      "    job.sim_info[\"grid\"] = grid\n",
      "    job.sim_info[\"F\"] = F\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "    \n",
      "    nsteps = 300\n",
      "    job.sim_info[\"time\"] =np.linspace(dt*2,nsteps*dt,nsteps/2)\n",
      "    \n",
      "    if dt == 200 and geometry == \"kelvin\":\n",
      "        nsteps_low = 54\n",
      "        nsteps_high = nsteps\n",
      "        dt_low = 50\n",
      "        job.sim_info[\"time\"] = np.array(list(np.linspace(dt_low*2,nsteps_low*dt_low,nsteps_low/2)) \n",
      "                                      + list(np.linspace(dt_low*nsteps_low+2*dt,(nsteps_high-nsteps_low)*dt+nsteps_low*dt_low,(nsteps_high-nsteps_low)/2)))\n",
      "\n",
      "\n",
      "    job.sim_info[\"massfraction\"] = massfraction\n",
      "\n",
      "\n",
      "    job.postprocess = job.sim_info.copy()\n",
      "    job.postprocess[\"iteration\"] = -1\n",
      "    job.postprocess[\"massflow_weight_Y\"] =  ['U','HembI^100000','BMR_I',\"DEN\"]\n",
      "    job.postprocess[\"massflow_var_Y\"]    =  'CO'\n",
      "    job.postprocess[\"massflow_weight_P\"] =  ['HembI','BMR_I']\n",
      "    job.postprocess[\"massflow_weight_T\"] =  ['U','HembI^100000','BMR_I',\"DEN\",\"Cp\"]\n",
      "\n",
      "    job.postprocess[\"massflow_var_P\"]    =  'P'\n",
      "    job.postprocess[\"iteration_list\"]    =  [-1]\n",
      "\n",
      "    \n",
      "    return job"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 714
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#sims.append(create_sim_in_real_folder(\"channel_washcoat\"))\n",
      "temperatures = [400,450,500,550,600,650,700]\n",
      "for temperature in temperatures:\n",
      "    sims.append(create_sim_in_real_folder(\"kelvin_cells_\"+str(temperature),temperature,create=True))\n",
      "    \n",
      "for temperature in temperatures:\n",
      "    sims.append(create_sim_in_real_folder(\"kelvin_cells_\"+str(temperature)+\"_U_10\",temperature,create=True,velocity=10))\n",
      "    \n",
      "        \n",
      "temperature = 700\n",
      "sims.append(create_sim_in_real_folder(\"kelvin_cells_inf_\"+str(temperature),temperature,create=False,inf=True))\n",
      "\n",
      "temperature = 551\n",
      "sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_\"+str(temperature),temperature,create=True,conj=True,mf=0.01,onecell=False))\n",
      "\n",
      "\n",
      "for temperature in [500,600,700]:\n",
      "    sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_\"+str(temperature),temperature,ref_folder=\"kelvin_cells_conjugate_550\",create=True,conj=True,mf=0.01,onecell=False,ncells=4))\n",
      "\n",
      "    \n",
      "temperature = 700   \n",
      "for cond  in [\"ceramic\",\"metallic\",\"isolated\"]:\n",
      "    sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_one_cell_\"+cond,temperature,ref_folder=\"kelvin_cells_600\",create=True,conj=True,mf=0.01,cond=cond))\n",
      "   \n",
      "    \n",
      "    \n",
      "temperature = 600   \n",
      "for cond  in [\"ceramic\"]:\n",
      "    sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_one_cell_\"+cond+\"_600\",temperature,ref_folder=\"kelvin_cells_conjugate_one_cell_ceramic_500\",create=True,conj=True,mf=0.01,cond=cond))\n",
      "    \n",
      "    \n",
      "temperature = 700   \n",
      "sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_scan_\"+cond+\"_700\",temperature,ref_folder=\"kelvin_cells_conjugate_one_cell_ceramic\",create=True,conj=True,mf=0.01,cond=cond,geometry=\"scan\"))\n",
      "\n",
      "\n",
      "\n",
      "temperature = 300   \n",
      "for cond  in [\"ceramic\"]:\n",
      "    sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_one_cell_ceramic_300\",temperature,ref_folder=\"kelvin_cells_conjugate_one_cell_ceramic\",create=True,conj=True,mf=0.01,cond=cond))\n",
      "    \n",
      "temperature = 300   \n",
      "for cond  in [\"ceramic\"]:\n",
      "    sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_one_cell_ceramic_300_low_dt\",temperature,ref_folder=\"kelvin_cells_conjugate_one_cell_ceramic_300\",create=True,conj=True,mf=0.01,dt=25))\n",
      "\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_one_cell_ceramic_300_dt_200\",temperature,ref_folder=\"kelvin_cells_conjugate_one_cell_ceramic_300\",create=True,conj=True,mf=0.01,dt=200))\n",
      "\n",
      "\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"honeycomb_real_geom\",temperature,ref_folder=\"kelvin_cells_conjugate_one_cell_ceramic_300\",create=True,conj=True,mf=0.01,dt=200,geometry=\"honeycomb\",cond=\"honeycomb\"))\n",
      "\n",
      "\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"honeycomb_real_geom_low_cond\",temperature,ref_folder=\"honeycomb_real_geom\",create=True,conj=True,mf=0.01,dt=200,geometry=\"honeycomb\",cond=\"honeycomb_low\"))\n",
      "\n",
      "\n",
      "temperature = 600   \n",
      "sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_one_cell_ceramic_300_coarse\",temperature,ref_folder=\"kelvin_cells_conjugate_one_cell_ceramic_300_coarse\",create=True,conj=True,mf=0.01,steady=True,geometry=\"kelvin\",cond=\"coarse\",group=\"grid_convergence\",grid=20))\n",
      "\n",
      "\n",
      "temperature = 600   \n",
      "sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_one_cell_ceramic_300_fine\",temperature,ref_folder=\"kelvin_cells_conjugate_one_cell_ceramic_300_fine\",create=True,conj=True,mf=0.01,steady=True,geometry=\"kelvin\",cond=\"fine\",group=\"grid_convergence\",grid=25))\n",
      "\n",
      "temperature = 600   \n",
      "sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_one_cell_ceramic_300_very_fine\",temperature,ref_folder=\"kelvin_cells_conjugate_one_cell_ceramic_300_fine\",create=True,conj=True,mf=0.01,steady=True,geometry=\"kelvin\",cond=\"very_fine\",group=\"grid_convergence\",grid=30))\n",
      "\n",
      "temperature = 600   \n",
      "sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_one_cell_ceramic_300_very_coarse\",temperature,ref_folder=\"kelvin_cells_conjugate_one_cell_ceramic_300_very_coarse\",create=True,conj=True,mf=0.01,steady=True,geometry=\"kelvin\",cond=\"very_coarse\",group=\"grid_convergence\",grid=15))\n",
      "\n",
      "\n",
      "temperature = 600   \n",
      "sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_one_cell_ceramic_300_very_very_coarse\",temperature,ref_folder=\"kelvin_cells_conjugate_one_cell_ceramic_300_very_very_coarse\",create=True,conj=True,mf=0.01,steady=True,geometry=\"kelvin\",cond=\"very_very_coarse\",group=\"grid_convergence\",grid=12))\n",
      "\n",
      "\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_one_cell_ceramic_300_coarse_unsteady\",temperature,ref_folder=\"kelvin_cells_conjugate_one_cell_ceramic_300_very_very_coarse\",create=True,conj=True,mf=0.01,steady=False,geometry=\"kelvin\",cond=\"coarse\",group=\"grid_convergence\",grid=20))\n",
      "\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_one_cell_ceramic_300_very_coarse_unsteady\",temperature,ref_folder=\"kelvin_cells_conjugate_one_cell_ceramic_300_very_very_coarse\",create=True,conj=True,mf=0.01,steady=False,geometry=\"kelvin\",cond=\"very_coarse\",group=\"grid_convergence\",grid=15))\n",
      "\n",
      "\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_one_cell_ceramic_300_coarse_low_mf\",temperature,ref_folder=\"kelvin_cells_conjugate_one_cell_ceramic_300_very_very_coarse\",create=True,conj=True,mf=0.005,steady=False,geometry=\"kelvin\",cond=\"ceramic\"))\n",
      "\n",
      "\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_one_cell_ceramic_300_coarse_unsteady_low_por\",temperature,ref_folder=\"kelvin_cells_conjugate_one_cell_ceramic_300_very_very_coarse\",create=True,conj=True,mf=0.01,steady=False,geometry=\"kelvin\",cond=\"coarse\",group=\"grid_convergence\",grid=20,por=81))\n",
      "\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_one_cell_ceramic_300_fine_unsteady_low_por\",temperature,ref_folder=\"kelvin_cells_conjugate_one_cell_ceramic_300_very_very_coarse\",create=True,conj=True,mf=0.01,steady=False,geometry=\"kelvin\",cond=\"fine\",group=\"grid_convergence\",grid=25,por=81))\n",
      "\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"kelvin_cells_conjugate_one_cell_ceramic_300_very_coarse_unsteady_low_por\",temperature,ref_folder=\"kelvin_cells_conjugate_one_cell_ceramic_300_very_very_coarse\",create=True,conj=True,mf=0.01,steady=False,geometry=\"kelvin\",cond=\"very_coarse\",group=\"grid_convergence\",grid=15,por=81))\n",
      "\n",
      "temperature = 600   \n",
      "sims.append(create_sim_in_real_folder(\"low_por_fine\"\n",
      "                                      ,temperature,create=True,conj=True,mf=0.01,steady=True,geometry=\"kelvin\",\n",
      "                                      cond=\"fine\",group=\"grid_convergence\",grid=30,por=81))\n",
      "\n",
      "\n",
      "#Grid convergence sims\n",
      "\n",
      "temperature = 600   \n",
      "sims.append(create_sim_in_real_folder(\"low_por_coarse\"\n",
      "                                      ,temperature,create=True,conj=True,mf=0.01,steady=True,geometry=\"kelvin\",\n",
      "                                      cond=\"coarse\",group=\"grid_convergence\",grid=25,por=81))\n",
      "\n",
      "temperature = 600   \n",
      "sims.append(create_sim_in_real_folder(\"low_por_very_fine\"\n",
      "                                      ,temperature,create=True,conj=True,mf=0.01,steady=True,geometry=\"kelvin\",\n",
      "                                      cond=\"very_fine\",group=\"grid_convergence\",grid=35,por=81))\n",
      "\n",
      "\n",
      "temperature = 600   \n",
      "sims.append(create_sim_in_real_folder(\"low_por_fine\"\n",
      "                                      ,temperature,create=True,conj=True,mf=0.01,steady=True,geometry=\"kelvin\",\n",
      "                                      cond=\"fine\",group=\"grid_convergence\",grid=30,por=81))\n",
      "\n",
      "temperature = 600   \n",
      "sims.append(create_sim_in_real_folder(\"low_por_very_very_fine\"\n",
      "                                      ,temperature,create=True,conj=True,mf=0.01,steady=True,geometry=\"kelvin\",\n",
      "                                      cond=\"very_very_fine\",group=\"grid_convergence\",grid=40,por=81))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "('Failed to create folder', 'kelvin_cells_400')\n",
        "mkdir: cannot create directory `/cluster/scratch_xp/public/vjan/kelvin_cells_400': File exists"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('Failed to create folder', 'kelvin_cells_450')\n",
        "mkdir: cannot create directory `/cluster/scratch_xp/public/vjan/kelvin_cells_450': File exists"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('Failed to create folder', 'kelvin_cells_500')\n",
        "mkdir: cannot create directory `/cluster/scratch_xp/public/vjan/kelvin_cells_500': File exists"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('Failed to create folder', 'kelvin_cells_550')\n",
        "mkdir: cannot create directory `/cluster/scratch_xp/public/vjan/kelvin_cells_550': File exists"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('Failed to create folder', 'kelvin_cells_600')\n",
        "mkdir: cannot create directory `/cluster/scratch_xp/public/vjan/kelvin_cells_600': File exists"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('Failed to create folder', 'kelvin_cells_650')\n",
        "mkdir: cannot create directory `/cluster/scratch_xp/public/vjan/kelvin_cells_650': File exists"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('Failed to create folder', 'kelvin_cells_700')\n",
        "mkdir: cannot create directory `/cluster/scratch_xp/public/vjan/kelvin_cells_700': File exists"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('Failed to create folder', 'kelvin_cells_400_U_10')\n",
        "mkdir: cannot create directory `/cluster/scratch_xp/public/vjan/kelvin_cells_400_U_10': File exists"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('Failed to create folder', 'kelvin_cells_450_U_10')\n",
        "mkdir: cannot create directory `/cluster/scratch_xp/public/vjan/kelvin_cells_450_U_10': File exists"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('Failed to create folder', 'kelvin_cells_500_U_10')\n",
        "mkdir: cannot create directory `/cluster/scratch_xp/public/vjan/kelvin_cells_500_U_10': File exists"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('Failed to create folder', 'kelvin_cells_550_U_10')\n",
        "mkdir: cannot create directory `/cluster/scratch_xp/public/vjan/kelvin_cells_550_U_10': File exists"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('Failed to create folder', 'kelvin_cells_600_U_10')\n",
        "mkdir: cannot create directory `/cluster/scratch_xp/public/vjan/kelvin_cells_600_U_10': File exists"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('Failed to create folder', 'kelvin_cells_650_U_10')\n",
        "mkdir: cannot create directory `/cluster/scratch_xp/public/vjan/kelvin_cells_650_U_10': File exists"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('Failed to create folder', 'kelvin_cells_700_U_10')\n",
        "mkdir: cannot create directory `/cluster/scratch_xp/public/vjan/kelvin_cells_700_U_10': File exists"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "mkdir: cannot create directory `/cluster/scratch_xp/public/vjan/kelvin_cells_inf_700': File exists"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('Failed to create folder', 'kelvin_cells_conjugate_551')\n",
        "mkdir: cannot create directory `/cluster/scratch_xp/public/vjan/kelvin_cells_conjugate_551': File exists"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "('Failed to create folder', 'kelvin_cells_conjugate_500')\n"
       ]
      },
      {
       "ename": "KeyboardInterrupt",
       "evalue": "",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-1800-b9ab5a7d000c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtemperature\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m500\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m600\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m700\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m     \u001b[0msims\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcreate_sim_in_real_folder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"kelvin_cells_conjugate_\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtemperature\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mref_folder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"kelvin_cells_conjugate_550\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mconj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.01\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0monecell\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mncells\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m<ipython-input-1799-93e0c2be0b33>\u001b[0m in \u001b[0;36mcreate_sim_in_real_folder\u001b[1;34m(jobname, temperature, ref_folder, create, inf, conj, steady, write_ini, mf, velocity, onecell, cond, ncells, geometry, dt, group, grid, por)\u001b[0m\n\u001b[0;32m     28\u001b[0m     \u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"jobname\"\u001b[0m\u001b[1;33m]\u001b[0m     \u001b[1;33m=\u001b[0m \u001b[0mjobname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m     \u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"root_folder\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mroot_folder\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 30\u001b[1;33m     \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msim_brutus\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     31\u001b[0m     \u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msim_info\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m     \u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msim_info\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ds\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.0003\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/vonrickenbach/contrib_git/python/cluster_interface/simulation_brutus.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m    641\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_remote\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    642\u001b[0m          \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommand_runner_remote\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcommand_runner_paramiko\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 643\u001b[1;33m          \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommunicator\u001b[0m          \u001b[1;33m=\u001b[0m \u001b[0mcommunicator_remote\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    644\u001b[0m          \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpostprocessor\u001b[0m         \u001b[1;33m=\u001b[0m \u001b[0mapplication_paraview_remote\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"paraview_executable\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    645\u001b[0m          \u001b[1;32mif\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"transat_version\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"MB\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/vonrickenbach/contrib_git/python/cluster_interface/simulation_brutus.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, sim_class)\u001b[0m\n\u001b[0;32m    485\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msim_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msim_class\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    486\u001b[0m       \u001b[0mmkdir_command\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'mkdir '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msim_class\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcluster\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"dir\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 487\u001b[1;33m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msim_class\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommand_runner_remote\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmkdir_command\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    488\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    489\u001b[0m    \u001b[1;32mdef\u001b[0m \u001b[0msync_input\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/home/vonrickenbach/contrib_git/python/cluster_interface/simulation_brutus.pyc\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, command, quiet)\u001b[0m\n\u001b[0;32m     63\u001b[0m       \u001b[0mstdin\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstdout\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstderr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mssh\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexec_command\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m       \u001b[1;32mwhile\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mstdout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchannel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexit_status_ready\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 65\u001b[1;33m          \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     66\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     67\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mquiet\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
       ]
      }
     ],
     "prompt_number": 1800
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Create Sims"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sims = []\n",
      "\n",
      "\n",
      "#Low porosity cases\n",
      "\n",
      "\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"low_por_ref_case\"\n",
      "                                      ,temperature,create=True,conj=True,mf=0.01,steady=True,geometry=\"kelvin\",\n",
      "                                      cond=\"fine\",group=\"paper_sims\",grid=30,por=81,velocity=1.5))\n",
      "\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"low_por_ref_case_unsteady\"\n",
      "                                      ,temperature,create=True,conj=True,mf=0.01,steady=False,geometry=\"kelvin\",\n",
      "                                      cond=\"fine\",group=\"paper_sims\",grid=30,por=81,velocity=1.5,dt=100))\n",
      "\n",
      "\n",
      "#High porosity cases\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"high_por_ref_case\"\n",
      "                                      ,temperature,create=True,conj=True,mf=0.01,steady=True,geometry=\"kelvin\",\n",
      "                                      cond=\"fine\",group=\"paper_sims\",grid=30,por=90,velocity=1.5))\n",
      "\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"high_por_ref_case_unsteady\"\n",
      "                                      ,temperature,create=True,conj=True,mf=0.01,steady=False,geometry=\"kelvin\",\n",
      "                                      cond=\"fine\",group=\"paper_sims\",grid=30,por=90,velocity=1.5,dt=100))\n",
      "\n",
      "\n",
      "# High ppi cases\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"low_por_high_ppi\"\n",
      "                                      ,temperature,create=True,conj=True,mf=0.01,steady=True,geometry=\"kelvin\",\n",
      "                                      cond=\"fine\",group=\"paper_sims\",grid=30,por=81,velocity=1.5,Dp=0.001))\n",
      "\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"low_por_high_ppi_unsteady\"\n",
      "                                      ,temperature,create=True,conj=True,mf=0.01,steady=False,geometry=\"kelvin\",\n",
      "                                      cond=\"fine\",group=\"paper_sims\",grid=30,por=81,velocity=1.5,Dp=0.001,dt=100))\n",
      "\n",
      "# Low mf cases\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"low_por_low_mf\"\n",
      "                                      ,temperature,create=True,conj=True,mf=0.005,steady=True,geometry=\"kelvin\",\n",
      "                                      cond=\"fine\",group=\"paper_sims\",grid=30,por=81,velocity=1.5,Dp=0.002))\n",
      "\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"low_por_low_mf_unsteady\"\n",
      "                                      ,temperature,create=True,conj=True,mf=0.005,steady=False,geometry=\"kelvin\",\n",
      "                                      cond=\"fine\",group=\"paper_sims\",grid=30,por=81,velocity=1.5,Dp=0.002,dt=100))\n",
      "\n",
      "temperature = 300   \n",
      "sims.append(create_sim_in_real_folder(\"low_por_high_F_unsteady\"\n",
      "                                      ,temperature,create=True,conj=True,mf=0.01,steady=False,geometry=\"kelvin\",\n",
      "                                      cond=\"fine\",group=\"paper_sims\",grid=30,por=81,velocity=1.5,Dp=0.002,dt=100,F=40))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "('Failed to create folder', 'low_por_ref_case')\n",
        "Autoloading openblas/0.2.8_seq"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "mkdir: cannot create directory `/cluster/scratch/vjan/low_por_ref_case': File exists\n",
        "('Failed to create folder', 'low_por_ref_case_unsteady')\n",
        "Autoloading openblas/0.2.8_seq"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "mkdir: cannot create directory `/cluster/scratch/vjan/low_por_ref_case_unsteady': File exists\n",
        "('Failed to create folder', 'high_por_ref_case')\n",
        "Autoloading openblas/0.2.8_seq"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "mkdir: cannot create directory `/cluster/scratch/vjan/high_por_ref_case': File exists\n",
        "('Failed to create folder', 'high_por_ref_case_unsteady')\n",
        "Autoloading openblas/0.2.8_seq"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "mkdir: cannot create directory `/cluster/scratch/vjan/high_por_ref_case_unsteady': File exists\n",
        "('Failed to create folder', 'low_por_high_ppi')\n",
        "Autoloading openblas/0.2.8_seq"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "mkdir: cannot create directory `/cluster/scratch/vjan/low_por_high_ppi': File exists\n",
        "('Failed to create folder', 'low_por_high_ppi_unsteady')\n",
        "Autoloading openblas/0.2.8_seq"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "mkdir: cannot create directory `/cluster/scratch/vjan/low_por_high_ppi_unsteady': File exists\n",
        "('Failed to create folder', 'low_por_low_mf')\n",
        "Autoloading openblas/0.2.8_seq"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "mkdir: cannot create directory `/cluster/scratch/vjan/low_por_low_mf': File exists\n",
        "('Failed to create folder', 'low_por_low_mf_unsteady')\n",
        "Autoloading openblas/0.2.8_seq"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "mkdir: cannot create directory `/cluster/scratch/vjan/low_por_low_mf_unsteady': File exists\n",
        "('Failed to create folder', 'low_por_high_F_unsteady')\n",
        "Need to have exactly one .stt file in the template directory"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Could not find stt file, probably not a TransAT case\n",
        "Autoloading openblas/0.2.8_seq"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "bash: line 0: cd: /cluster/scratch/vjan/low_por_high_F_unsteady: No such file or directory\n"
       ]
      }
     ],
     "prompt_number": 715
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for sim in filter_simulations(sims,**current_sim):\n",
      "    sim.sync_input()\n",
      "   # sim.cp(\"surf_reaction_kelvin.runb\")\n",
      "  # print(sim.sim_info[\"time\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "===================================================\n",
        " Syncing Current Working Directory with Cluster \n",
        "===================================================\n",
        "Autoloading openblas/0.2.8_seq"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 718
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "14*60"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1995,
       "text": [
        "840"
       ]
      }
     ],
     "prompt_number": 1995
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Run sim"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "init = False\n",
      "depend = False\n",
      "restart = True\n",
      "\n",
      "endtime = 60\n",
      "njobs = 8\n",
      "nproc = 60\n",
      "\n",
      "endtime = 240\n",
      "njobs = 4\n",
      "nproc =500\n",
      "\n",
      "runtime = '1440'\n",
      "\n",
      "\n",
      "#for temperature in [500,600,700]:\n",
      "#for temperature in [600,700]:\n",
      "#    current_sim = {\"conj\":True,\"temperature\":temperature,\"steady\":True,\"onecell\":False}\n",
      "if True:\n",
      "    for sim in filter_simulations(sims,**current_sim):\n",
      "       sim.application.load      = \"source ~/.bashrc_boost\"\n",
      "       sim.init_application.load = \"source ~/.bashrc_boost\"  \n",
      "       sim.compile_init_application.load = \"source ~/.bashrc_boost\"  \n",
      "\n",
      "       sim.sync_input()\n",
      "       if init:\n",
      "           sim.compile_init(nproc=\"1\",memory=\"1024\",time='60') \n",
      "\n",
      "           sim.run_init(nproc=str(nproc),memory=\"2048\",time='60',depend=True) \n",
      "           depend = True\n",
      "       sim.run_sim(nproc=str(nproc),memory=\"1024\",time=runtime,njobs=njobs,restart=restart,depend=depend,endtime=endtime) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "===================================================\n",
        " Syncing Current Working Directory with Cluster \n",
        "===================================================\n",
        "Autoloading openblas/0.2.8_seq"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Autoloading openblas/0.2.8_seq"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "===================================================\n",
        " Syncing Current Working Directory with Cluster \n",
        "===================================================\n",
        "Autoloading openblas/0.2.8_seq"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "==================================================="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        " Syncing Current Working Directory with Cluster \n",
        "===================================================\n",
        "Autoloading openblas/0.2.8_seq"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "bsub -n 1 -W 60 -R \"rusage[mem=1024]\" -J low_por_high_F_unsteady -o post_command.log -e std_err.log   ./post_command.py \n",
        "Job <2599353> is submitted to queue <beta.4h>."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Autoloading openblas/0.2.8_seq\n",
        "Generic job.\n",
        "source ~/.bashrc_boost; bsub -n 500 -W 1440 -R \"rusage[mem=1024]\" -J low_por_high_F_unsteady -o std_out.log -e std_err.log -w \"ended(low_por_high_F_unsteady)\" mpirun ./transatmbDP -pc_hypre_boomeramg_max_iter -pc_hypre_boomeramg_coarsen_type HMIS -pc_hypre_boomeramg_interp_type ext+i -pc_hypre_boomeramg_P_max 4 -pc_hypre_boomeramg_strong_treshold 0 -pc_hypre_boomeramg_agg_nl 2\n",
        "Job <2599354> is submitted to queue <beta.24h>."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Autoloading openblas/0.2.8_seq\n",
        "MPI job.\n",
        "bsub -n 1 -W 60 -R \"rusage[mem=1024]\" -J low_por_high_F_unsteady -o post_command.log -e std_err.log -w \"ended(low_por_high_F_unsteady)\"  ./post_command.py \n",
        "source ~/.bashrc_boost; bsub -n 500 -W 1440 -R \"rusage[mem=1024]\" -J low_por_high_F_unsteady -o std_out.log -e std_err.log -w \"ended(low_por_high_F_unsteady)\" mpirun ./transatmbDP -pc_hypre_boomeramg_max_iter -pc_hypre_boomeramg_coarsen_type HMIS -pc_hypre_boomeramg_interp_type ext+i -pc_hypre_boomeramg_P_max 4 -pc_hypre_boomeramg_strong_treshold 0 -pc_hypre_boomeramg_agg_nl 2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Job <2599356> is submitted to queue <beta.24h>."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Autoloading openblas/0.2.8_seq\n",
        "MPI job.\n",
        "bsub -n 1 -W 60 -R \"rusage[mem=1024]\" -J low_por_high_F_unsteady -o post_command.log -e std_err.log -w \"ended(low_por_high_F_unsteady)\"  ./post_command.py \n",
        "source ~/.bashrc_boost; bsub -n 500 -W 1440 -R \"rusage[mem=1024]\" -J low_por_high_F_unsteady -o std_out.log -e std_err.log -w \"ended(low_por_high_F_unsteady)\" mpirun ./transatmbDP -pc_hypre_boomeramg_max_iter -pc_hypre_boomeramg_coarsen_type HMIS -pc_hypre_boomeramg_interp_type ext+i -pc_hypre_boomeramg_P_max 4 -pc_hypre_boomeramg_strong_treshold 0 -pc_hypre_boomeramg_agg_nl 2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Job <2599358> is submitted to queue <beta.24h>."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Autoloading openblas/0.2.8_seq\n",
        "MPI job.\n",
        "bsub -n 1 -W 60 -R \"rusage[mem=1024]\" -J low_por_high_F_unsteady -o post_command.log -e std_err.log -w \"ended(low_por_high_F_unsteady)\"  ./post_command.py \n",
        "source ~/.bashrc_boost; bsub -n 500 -W 1440 -R \"rusage[mem=1024]\" -J low_por_high_F_unsteady -o std_out.log -e std_err.log -w \"ended(low_por_high_F_unsteady)\" mpirun ./transatmbDP -pc_hypre_boomeramg_max_iter -pc_hypre_boomeramg_coarsen_type HMIS -pc_hypre_boomeramg_interp_type ext+i -pc_hypre_boomeramg_P_max 4 -pc_hypre_boomeramg_strong_treshold 0 -pc_hypre_boomeramg_agg_nl 2"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Job <2599360> is submitted to queue <beta.24h>."
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Autoloading openblas/0.2.8_seq\n",
        "MPI job.\n"
       ]
      }
     ],
     "prompt_number": 719
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Run postprocess"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filename = \"create_slices.py\" #\"postprocess.py\"\n",
      "#filename = \"create_contour.py\" #\"postprocess.py\"\n",
      "\n",
      "#filename = \"slice_avg.py\" #\"postprocess.py\"\n",
      "#filename = \"full_3d.py\"\n",
      "#for sim in filter_simulations(sims,temperature=700,inf=False,steady=False,conj=True):\n",
      "#current_sim = {\"conj\":True,\"temperature\":551}\n",
      "\n",
      "#current_sim = {\"conj\":True,\"steady\":True,\"onecell\":False}\n",
      "#for sim in filter_simulations(sims,temperature=300,geometry=\"honeycomb\"):\n",
      "for sim in filter_simulations(sims,**current_sim):\n",
      "\n",
      "#for sim in sims:\n",
      "   # sim.sim_info[\"iterations\"] = np.linspace(2,400,200)\n",
      "    maxit = 200\n",
      "    sim.sim_info[\"iterations\"] = np.linspace(2,maxit,maxit/2)\n",
      "    sim.sim_info[\"iterations\"] = [-1]\n",
      "\n",
      "#    sim.sim_info[\"iterations\"] = [64,100,200,300,400,500,600,700]\n",
      "\n",
      "#    sim.sim_info[\"iterations\"] = [-1]\n",
      "\n",
      "\n",
      "    for iteration in sim.sim_info[\"iterations\"]:\n",
      "        sh.copy(filename,os.path.join(sim.info.local[\"sim_folder\"]))\n",
      "        sim.run_postprocess(filename + \" \" + str(int(iteration)),nproc=\"1\",memory=\"32000\",time=\"90\",args=\"10\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "===================================================\n",
        " Syncing Current Working Directory with Cluster \n",
        "===================================================\n",
        "Autoloading openblas/0.2.8_seq"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "source $HOME/.bashrc_paraview; bsub -n 1 -W 90 -R \"rusage[mem=32000]\" -J low_por_high_F_unsteady -o postprocess.out -e std_err.log  mpirun /cluster/home/vjan/ParaView-4.0.1-Linux-64bit/bin/pvbatch --use-offscreen-rendering create_slices.py -1\n"
       ]
      }
     ],
     "prompt_number": 721
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for sim in filter_simulations(sims,**current_sim):\n",
      "#for sim in filter_simulations(sims):\n",
      "\n",
      "#for sim in sims:\n",
      "   # if sim.sim_info[\"cond\"] == \"metallic\":\n",
      "        sim.kill()\n",
      " \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Job <2405361> is being terminated\n",
        "Job <2405359> is being terminated\n",
        "Job <2405362> is being terminated\n",
        "Autoloading openblas/0.2.8_seq\n"
       ]
      }
     ],
     "prompt_number": 1924
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def write_pvd_file(sim):\n",
      "    \n",
      "    vtu_filename = \"processed/output\"\n",
      "    filename = os.path.join(sim.info.local[\"sim_folder\"],\"test.pvd\")\n",
      "    pvd_file = open(filename,'w')\n",
      "    pvd_file.write(r'<?xml version=\"1.0\"?>')\n",
      "    pvd_file.write(os.linesep)\n",
      "\n",
      "    pvd_file.write(r'<VTKFile type=\"Collection\" version=\"0.1\">')\n",
      "    pvd_file.write(os.linesep)\n",
      "\n",
      "    pvd_file.write(r'<Collection>')\n",
      "    pvd_file.write(os.linesep)\n",
      "\n",
      "\n",
      "    sim.sim_info[\"iterations\"] = np.linspace(2,68,34)\n",
      "    for it,time in zip(sim.sim_info[\"iterations\"],sim.sim_info[\"time\"]):\n",
      "        pvd_file.write(r'<DataSet timestep=\"'+str(time)+ r'\" file=\"'+vtu_filename+ \"_\" + str(int(it)) + '.vtu\"' + \"/>\")\n",
      "        pvd_file.write(os.linesep)\n",
      "\n",
      "    pvd_file.write(r'</Collection>')\n",
      "    pvd_file.write(os.linesep)\n",
      "\n",
      "    pvd_file.write(r'</VTKFile>')\n",
      "    pvd_file.write(os.linesep)\n",
      "\n",
      "    pvd_file.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1704
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim  = filter_simulations(sims,**current_sim)\n",
      "\n",
      "write_pvd_file(sim[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1705
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Get processsed results"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "local_sims =  filter_simulations(sims,group=\"paper_sims\",steady=False)\n",
      "\n",
      "for sim in local_sims:\n",
      "#for sim in filter_simulations(sims,**current_sim):\n",
      "\n",
      "    print(sim.info.local[\"jobname\"])\n",
      "    sim.sync_result(\"processed\")\n",
      "    sim.sim_info[\"iterations\"] = np.linspace(2,220,110)\n",
      "  #  sim.sim_info[\"iterations\"] = [1]\n",
      "\n",
      "    try:\n",
      "        sim.result = {}\n",
      "        try:\n",
      "            sim.data = pic.load(open(os.path.join(sim.info.local[\"sim_folder\"],\"processed/slice_averages_time_-1.dat\"))) \n",
      "        except:\n",
      "            print(\"No steady info for: \" + sim.info.local[\"jobname\"])\n",
      "\n",
      "        sim.data_time = []\n",
      "        for iteration in sim.sim_info[\"iterations\"]:\n",
      "            try:\n",
      "                sim.data_time.append(pic.load(open(os.path.join(sim.info.local[\"sim_folder\"],\"processed/slice_averages_time_\"+str(int(iteration))+\".dat\")))) \n",
      "            except:\n",
      "                print(\"reached iteration \"+str(int(iteration)))\n",
      "                break\n",
      "    except:\n",
      "        print(\"failed to open: \" + sim.info.local[\"jobname\"])\n",
      "        sim.data= None\n",
      "        continue "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "low_por_ref_case_unsteady\n",
        "===================================================\n",
        " Syncing processed folder  with Cluster      \n",
        "===================================================\n",
        "reached iteration 120"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "high_por_ref_case_unsteady\n",
        "===================================================\n",
        " Syncing processed folder  with Cluster      \n",
        "===================================================\n",
        "No steady info for: high_por_ref_case_unsteady"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "reached iteration 96"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "low_por_high_ppi_unsteady\n",
        "===================================================\n",
        " Syncing processed folder  with Cluster      \n",
        "===================================================\n",
        "No steady info for: low_por_high_ppi_unsteady"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "reached iteration 102"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "low_por_low_mf_unsteady\n",
        "===================================================\n",
        " Syncing processed folder  with Cluster      \n",
        "===================================================\n",
        "reached iteration 82"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "low_por_high_F_unsteady\n",
        "===================================================\n",
        " Syncing processed folder  with Cluster      \n",
        "===================================================\n",
        "reached iteration 120"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 723
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_data(sim,time=False):\n",
      " \n",
      "    sim.result_time = {}\n",
      "    if not time:\n",
      "        data_list = [sim.data]\n",
      "    else:\n",
      "     #   data_list = zip(*[iter(sim.data_time)]*9)\n",
      "       data_list = sim.data_time\n",
      "    variables = [\"T\",\"CO\",\"Ts\"]\n",
      "    transat_variables = [\"T\",\"CO\",\"T\"]\n",
      "\n",
      "    calc_index = [3,0,4]\n",
      " \n",
      "    for idx,var,t_var in zip(calc_index,variables,transat_variables):\n",
      "        dict_key = var+\"_avg\"\n",
      "        sim.result_time[dict_key] = []\n",
      "        for idx_time,time_dat in enumerate(data_list): \n",
      "            try:\n",
      "               spatial_data =  [(item[idx][\"massflow_t_\"+t_var]/item[idx][\"massflow\"])[0] for item in time_dat]\n",
      "               sim.result_time[dict_key].append(spatial_data)\n",
      "            except:\n",
      "                print(\"failed to open: \" + sim.info.local[\"jobname\"]+\" \"+var+\" \"+str(idx_time))\n",
      "                print(time_dat[0][0])\n",
      "                continue"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 251
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def interpolate_array(time_val,time_array,value_array):\n",
      "    values = np.array(value_array)\n",
      "    nx = len(values[0,:])\n",
      "    time_array = time_array[values[:,0]>0]\n",
      "    return np.array([np.interp(time_val,time_array,values[:,idx]) for idx in range(nx)])\n",
      "\n",
      "def get_figs(nfigs):\n",
      "    figs = []\n",
      "    axs  = []\n",
      "\n",
      "    for idx in range(nfigs):\n",
      "      fig,ax = pl.subplots(figsize=(6,4))\n",
      "      axs.append(ax)\n",
      "      figs.append(fig) \n",
      "    return figs,axs\n",
      "\n",
      "\n",
      "\n",
      "def plot_sim(sim,color,figs,axs,plot_time=0,clear=True,tmax=1000,ncol=2):\n",
      "\n",
      "    get_data(sim,time=True)\n",
      "\n",
      "    dt = sim.sim_info[\"dt\"] \n",
      "    nt = len(sim.result_time[\"CO_avg\"])\n",
      "    print(sim,nt)\n",
      "\n",
      "   \n",
      "    plot_sim_at_time(sim,color,figs,axs,plot_time,clear=clear,tmax=tmax,ncol=ncol)\n",
      "\n",
      "    \n",
      "def plot_sim_at_time(sim,color,figs,axs,plot_time,label=\"case\",clear=True,tmax=1000,ncol=2):\n",
      "    if clear:\n",
      "        axs[1].clear()\n",
      "    \n",
      "    xcoord = np.linspace(0.0,1,33)\n",
      "    label = \" \".join([str(item) for item in[sim.sim_info[\"Dp\"],sim.sim_info[\"porosity\"]]])\n",
      "    ref_sim = filter_simulations(sims,group=\"paper_sims\",steady=False,Dp=0.002,porosity=81,mf=0.01)[0]\n",
      "    ref_surf_area = ref_sim.sim_info[\"surf_to_volume\"]\n",
      "\n",
      "    \n",
      "    dp_fac =  (sim.sim_info[\"Dp\"]+sim.sim_info[\"ds\"])/0.00246\n",
      "    area_fac = 1.0 #ref_surf_area / sim.sim_info[\"surf_to_volume\"] \n",
      "    por_frac = (1.0-sim.sim_info[\"porosity\"]/100.0) /0.19 \n",
      "    time_norm = area_fac*por_frac* dp_fac\n",
      "    time_norm = 3110*880*(1-sim.sim_info[\"porosity\"]/100.0)* (sim.sim_info[\"Dp\"]+sim.sim_info[\"ds\"])/(1.5*884)\n",
      "\n",
      "  #  time_norm = 1.0\n",
      "    print(time_norm)\n",
      "    plot_time = plot_time* time_norm\n",
      "    idx = np.min(np.where(sim.sim_info[\"time\"]>plot_time))\n",
      "    time = sim.sim_info[\"time\"][idx]\n",
      "    temps = [\"Ts\",\"T\"]\n",
      "    for var,ls,lab in zip(temps,[\"-\",\"--\"],[\"fl:  \",\"sol: \"]): \n",
      "        try:\n",
      "            array_to_plot = interpolate_array(plot_time,sim.sim_info[\"time\"],sim.result_time[var+\"_avg\"])\n",
      "            axs[0].plot(xcoord[::2],array_to_plot[::2],ls,label=lab+\" \"+label,color=color)\n",
      "\n",
      "        except:\n",
      "            print(\"Failed to plot \"+sim.info.local[\"sim_folder\"])\n",
      "\n",
      "        axs[0].set_xlabel(\"x Coordinate (m)\")\n",
      "        axs[0].set_ylabel(\"Temperature (K)\")\n",
      "        axs[0].set_ylim((axs[0].get_ylim()[0],tmax))\n",
      "\n",
      "        y_arr = np.array(sim.result_time[var+\"_avg\"])[:,0]\n",
      "        indices = np.where(y_arr>0)\n",
      " \n",
      "\n",
      "        axs[1].plot(sim.sim_info[\"time\"][indices]/1000.0/time_norm,y_arr[indices],ls,label=lab+label,color=color)\n",
      "        y_arr = np.array(sim.result_time[var+\"_avg\"])[:,16]\n",
      "\n",
      "        axs[1].plot(sim.sim_info[\"time\"][indices]/1000.0/time_norm,y_arr[indices],ls,label=lab+label,color=color)\n",
      "        y_arr = np.array(sim.result_time[var+\"_avg\"])[:,-1]\n",
      "        axs[1].plot(sim.sim_info[\"time\"][indices]/1000.0/time_norm,y_arr[indices],ls,label=lab+label,color=color)\n",
      "\n",
      " #       axs[1].set_xlim((0,5))\n",
      "\n",
      "        axs[1].set_ylabel(\"Temperature (K)\")\n",
      "        axs[1].set_xlabel(\"Time (s)\")\n",
      "        \n",
      "    for var in [\"CO\"]:\n",
      "  \n",
      "      try:\n",
      "         array_to_plot = interpolate_array(plot_time,sim.sim_info[\"time\"],sim.result_time[var+\"_avg\"])\n",
      "         axs[3].plot(xcoord,array_to_plot,'-',label=label,color=color)\n",
      "      except:\n",
      "         print(\"Failed to plot \"+sim.info.local[\"sim_folder\"])\n",
      "\n",
      "      axs[3].set_xlabel(\"x Coordinate (m)\")\n",
      "      axs[3].set_ylabel(\"CO mass fraction (-)\")\n",
      "      axs[3].set_ylim((0.0,0.011))\n",
      "      y_arr = np.array(sim.result_time[var+\"_avg\"])[:,-1]\n",
      "      axs[2].plot(sim.sim_info[\"time\"][y_arr>0]/1000.0/time_norm,y_arr,'-',label=label,color=color)\n",
      "      axs[2].set_xlabel(\"$t'$\")\n",
      "      axs[2].set_xlim([3,13])\n",
      "      axs[2].set_ylabel(\"CO mass fraction (-)\")\n",
      "    for col,ax in zip([2,2,1,2],axs):\n",
      "        ax.legend(loc=0,frameon=False,ncol=ncol)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 699
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Plot all sims"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "color = [\"k\",\"b\",\"r\",\"m\",\"y\",\"r\"]\n",
      "\n",
      "figs,axs = get_figs(4)\n",
      "\n",
      "local_sims = filter_simulations(sims,group=\"paper_sims\",steady=False,mf=0.01,F=20) \n",
      "print(local_sims)\n",
      "\n",
      "for sim,co in zip(local_sims,color):\n",
      "  print(sim.sim_info[\"porosity\"],sim.sim_info[\"surf_to_volume\"])\n",
      "\n",
      "\n",
      "  get_data(sim,time=True)\n",
      "\n",
      "  dt = sim.sim_info[\"dt\"] \n",
      "  nt = len(sim.result_time[\"CO_avg\"])\n",
      "  print(sim,nt)\n",
      "  idx = [28]\n",
      "  for time in sim.sim_info[\"time\"][idx]:\n",
      "    print(time-1)\n",
      "    plot_sim_at_time(sim,co,figs,axs,time-1,tmax=600,clear=False,ncol=1)\n",
      "\n",
      "extension = \".pdf\"\n",
      "names = [\"temp\",\"temp\",\"time_CO\",\"temp\"]\n",
      "\n",
      "for name, fig in zip(names,figs):\n",
      "    fig.tight_layout()\n",
      "    fig.savefig(os.path.join(\"pics_3\",name+extension))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[<simulation_brutus.job instance at 0x7ff21e039908>, <simulation_brutus.job instance at 0x7ff21e0bc5f0>, <simulation_brutus.job instance at 0x7ff22c3deef0>]\n",
        "(81, 1382.4156945843558)\n",
        "(<simulation_brutus.job instance at 0x7ff21e039908>, 59)\n",
        "5799.0\n",
        "0.964691040724\n",
        "(90, 1168.1012273712763)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(<simulation_brutus.job instance at 0x7ff21e0bc5f0>, 47)\n",
        "5799.0\n",
        "0.474708898944\n",
        "(81, 2764.8313891687117)\n",
        "(<simulation_brutus.job instance at 0x7ff22c3deef0>, 50)"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "5799.0\n",
        "0.482345520362\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MFjwoEAieIIQwaeGxE6Z8cnKklbUBAZprojp2lFQnJ4f79++nt7e3fA3e3t7c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\nzSzs1TRwd64AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff22ebd4190>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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s6AWE/PsDZB+vFNHGr10rHtrc3l7rRFkiBw+KY+TIh3/AEmBRx02VSnhA5+eL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qwlgQwC5KBB6jIbwOPj49kj8GMurDmLqbBkKxxj586kkJpGPXnvzp1JzfJvTEwM33jjDenv0bbt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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff21d4642d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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k5OjOZWdn0/Dhw6l79+4GB6JvspepJ44ZOhGMk1LVUJrENGTIEAJAXbp0Mf+m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\nNc3arzVGNgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff22e8d3990>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WLFiA6urqFut//vlnpKSkICEhwWHBkfWqq6vh4+PTrNzHx6fFW8m3197S/v7y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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff234448510>"
       ]
      }
     ],
     "prompt_number": 726
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "local_sim = filter_simulations(sims,group=\"paper_sims\",steady=False,porosity=81,mf=0.01,Dp=0.002)[0]\n",
      "\n",
      "fig, ax = get_figs(2)\n",
      "indices = [4,35,48,54]\n",
      "colors = np.loadtxt(\"colors\")[:,-3:]/255\n",
      "\n",
      "for co,idx in zip(colors,indices):\n",
      "    time = local_sim.sim_info[\"time\"][idx]/1000.0\n",
      "    label = \" \".join([\"t=\",str(time),\"s\"])\n",
      "    x_coord = np.linspace(0,1,len(local_sim.result_time[\"Ts_avg\"][idx]))\n",
      "    ax[0].plot(x_coord,local_sim.result_time[\"Ts_avg\"][idx][::1],'--',color=co)\n",
      "    ax[0].plot(x_coord[::2],local_sim.result_time[\"T_avg\"][idx][::2],color=co,label=label)\n",
      "    ax[1].plot(x_coord[::2],local_sim.result_time[\"CO_avg\"][idx][::2],color=co,label=label)\n",
      "\n",
      "ax[0].set_xlabel(\"$x'$ \")\n",
      "ax[0].set_ylabel(\"Temperature (K)\")\n",
      "\n",
      "ax[1].set_xlabel(\"$x'$ \")\n",
      "ax[1].set_ylabel(\"$Y^b_{CO}$ \")\n",
      "\n",
      "\n",
      "ax[0].legend(frameon=False,loc=0)\n",
      "ax[1].legend(frameon=False,loc=0)\n",
      "\n",
      "ext = \".pdf\"\n",
      "names = [\"spatial_temp\",\"spatial_CO\"]\n",
      "for fi,name in zip(fig,names):\n",
      "    fi.tight_layout()\n",
      "    fi.savefig(os.path.join(\"pics_3\",name+ext))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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odHDhCuw9Bn/9A/9eKjrerD70aA/dO0jRfFC+NWt0cOyGDJg4ckMKxuwu0noy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LAAAM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\nIpFIVIUUJolEIpGoiv8HdFUd3xPOtHcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff21d8d5fd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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f1J5NSt4BfUbBwi8gL98z2xRCaEwmE/v27ePs2bNuWV953cSVNpZoqWkZGRml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\nuR8WkQSoV6hOrgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff22c843e90>"
       ]
      }
     ],
     "prompt_number": 713
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(local_sim.result_time[\"Ts_avg\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 547,
       "text": [
        "50"
       ]
      }
     ],
     "prompt_number": 547
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Grid refinement"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_val(sim):\n",
      "    try:\n",
      "        result = sim.result_time[\"Ts_avg\"][0][0]\n",
      "    except:\n",
      "        result = np.nan\n",
      "    return result\n",
      "\n",
      "def get_info(sim,info):\n",
      "     return sim.sim_info[info]\n",
      "\n",
      "\n",
      "\n",
      "local_sims = filter_simulations(sims,group=\"grid_convergence\",steady=True)\n",
      "\n",
      "for sim,co in zip(local_sims,color):\n",
      "\n",
      "    get_data(sim)\n",
      "\n",
      "\n",
      "pl.plot(get_array(sort_sims(local_sims,\"grid\"),get_info,\"grid\"),get_array(sort_sims(local_sims,\"grid\"),get_val))\n",
      "pl.gca().set_xlim([24,40])\n",
      "pl.gca().set_ylim([665,670])\n",
      "\n",
      "pl.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5To/2mTFjBgAgPz8f06ZNA6D08QFg5syZHc7Fxsbi2LFjmDt3LoqKijBq1CgA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\nYvgTEakQw5+ISIX+H6nToRyXycsLAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9b15accdd0>"
       ]
      }
     ],
     "prompt_number": 2017
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figs,axs = get_figs(4)\n",
      "\n",
      "local_sims =  filter_simulations(sims,temperature=300,geometry=\"honeycomb\") #+  filter_simulations(sims,temperature=300,cond=\"ceramic\",geometry=\"kelvin\",dt=200)\n",
      "\n",
      "for sim,co in zip(local_sims,color):\n",
      "    plot_sim(sim,co,figs,axs,plot_time=4999,clear=False,tmax=700,ncol=1)\n",
      "    \n",
      "    \n",
      "extension = \".png\"\n",
      "names = [\"spatial_different_lambda\",\"T_over_time\",\"time_CO_different_lambda_with_foam\",\"dummy\"]\n",
      "\n",
      "for name, fig in zip(names,figs):\n",
      "    fig.tight_layout()\n",
      "    fig.savefig(os.path.join(\"pics_3\",name+extension))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7ff22ee8cd90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7ff22ee1ae90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7ff22fe1d3d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UiURUXFwc31ZcXKxIJKKmpqZea6dNm6a6urp4jD7tww8/HPKAAIBLQ8IgnTp1Sm63Wx6P\nJ74tLy9PLpdLzc3NvdZ6vV7dfvvtvbZt2LBBV111laZMmZLCkQEAmSgr0Q+j0Wivj+U+lpOTo1gs\nlvCBH3/8ce3YsUPBYHB4EwIALgkJ3yHl5uZeMDyxWEwul6vf+61bt06/+tWvtHHjRpWUlAx/SgBA\nxkv4DqmoqEiRSESRSERut1uSFA6HFY1GVVhYeMH7rF69Wps2bVJlZaV+8pOfJNz52rVr+/2Z3++X\n3+9PPD0AIG1CoZBCodCIPX7Cq+za29tVUFCg7du3x9/p7N69W6WlpWppaVF2dnav9b/97W/1yCOP\nqKqqSkuWLOl/p1xlBwAZJRXn9YTvkHJzc1VWVqby8nJVVVWpp6dHq1atUnl5ubKzs9Xa2irpowsa\njhw5oocffli/+MUv9M1vflPvv/9+/HF8Pp/GjBkz5CEBAJkv4TskSers7FQgEFBNTY2ysrK0dOlS\nrV+/Xk6nU36/X06nU/X19fr1r3+tRx55pO8OHA7985//1Je+9KVe2yTeIQFApkjFeX3AII0EggQA\nmWXEvxgLAEC6ECQAgBUIEgDACgQJAGAFggQAsAJBAgBYgSABAKxAkAAAViBIAAArECQAgBUIEgDA\nCgQJAGAFggQAsAJBAgBYgSABAKxAkAAAViBIAAArECQAgBUIEgDACgQJAGAFggQAsAJBAgBYgSAB\nAKxAkAAAViBIAAArECQAgBUIEgDACgQJAGAFggQAsAJBAgBYgSABAKxAkAAAViBIAAArECQAgBUI\nEgDACgTpIhAKhUZ7hIsGx2rwOFaDw3FKnwGD1N3drUAgIJ/PJ6/Xq0AgoK6urmGvxeDxF2LwOFaD\nx7EaHI5T+mQNtKCiokJ1dXUKBoOKxWJavny5srOz9cQTTwxrLQAAn5bwHVJHR4cqKyv11FNP6aab\nblJxcbE2bdqkZ599VrFYbMhrAQD4rIRBampqUiQSUXFxcXxbcXGxIpGImpqahrwWAIDPShikU6dO\nye12y+PxxLfl5eXJ5XKpubl5yGsBAPishEGKRqPKycnpsz0nJ6fPx3DJrAUAoA+TwJ///Gczbty4\nPtvdbrf5y1/+MuS1krhx48aNWwbehiPhO6SioiJFIhFFIpH4tnA4rGg0qsLCwiGv/ahJAIBMsmHD\nhmHdP+Fl3zNnzpTb7da+fftUUlIiSdq7d6/cbrdmzpw55LUSUQIA9JYwSLm5uSorK1N5ebmqqqrU\n09OjVatWqby8XNnZ2WptbZUkeb3eAdcCAJCIwwzwVqWzs1OBQEA1NTXKysrS0qVLtX79ejmdTvn9\nfjmdTtXX1w+4FgCARAYsRXZ2tiorK/Xhhx/qf//7nzZs2BAPTCgUisfos2vfe+899fT06IorruBX\nDg0gmeff3NyshQsXyufzadKkSVq5cqXa2trSPPHoGOrrZM2aNZo2bVoaJrRHMsfq/Pnzqqio0KRJ\nk5Sfn69FixappaUlzROPnmSO1enTp1VaWqqJEyeqoKBAd955p86ePZvmiUeXMUYlJSXavHlzv2uG\nfE4f1iURCfz85z8311xzjTl48KDZu3evmTp1qrn//vuHvTYTDfb5d3d3mxtuuMF8+9vfNv/617/M\n66+/bmbPnm3uuOOOUZg6/YbyOjl06JDJysoy06ZNS9OUdkjmWP3yl780kyZNMnv27DFHjhwxs2bN\nMt///vfTPPHoSeZYzZs3z8yZM8ccOXLENDY2mhtuuMH84Ac/SPPEo+f8+fPmnnvuMQ6Hw2zevLnf\ndUM9p49IkNrb2824ceNMMBiMb9u9e7cZN26c6ejoGPLaTJTM8z906JBxOBzm9OnT8W2vvfaacTgc\npq2tLW0zj4ahvE5isZi57rrrzNy5c83UqVPTNeqoS+ZYtbW1mcsuu8zs2rUrvq2hocHMmDHDdHZ2\npm3m0ZLs62rcuHGmpqYm/ufq6mrj8XjSMutoO378uLn55pvN1KlTTX5+fr9BGs45fUSCdODAAeNw\nOEw4HI5va2trMw6Hwxw8eHDIazNRMs+/paXFvPrqq722fRyk//73v2mZd7QM5XWyZs0as2DBAvPM\nM89cUkFK5lgFg0HjcrlMd3d3use0QrKvq1tvvdXMnz/ftLW1mba2NjN//nxz2223pXPkUVNdXW3K\nyspMa2urmTp1ar9BGs45fUSuNuBXDg1eMs/f6/Xq9ttv77Vtw4YNuuqqqzRlypS0zDtakn2dNDY2\nasuWLaqsrLzkvmKQzLE6duyYJk+erNraWs2aNUtFRUVauXKlwuFwusceFcm+rqqrq3X06FHl5+fL\n6/XqzTff1IsvvpjOkUdNaWmpnnvuOeXn5ydcN5xz+ogEiV85NHjDef6PP/64duzYoY0bN47UeNZI\n5jh1dnZqxYoVevLJJ1VQUJCuEa2RzLE6d+6c3n33XT300EN6+umntW3bNh0+fFiLFi1K17ijKtm/\nf0uWLJHH41FDQ4P27Nmj8ePHq7S0NB2jXjSGc04b8P+HNBS5ubkX3HEsFpPL5Rry2kw01Oe/bt06\nPfjgg9q0aVP8i8iZLJnjtG7dOhUVFWnZsmXpGs8qyRyrsWPH6ty5c9q6dauuv/56SdILL7yg2bNn\n6+2339b06dPTMvNoSeZY7d+/Xw0NDXrrrbd09dVXS5J27NihadOmae/evZo7d25aZrbdcM7pI/IO\naaR+5VAmGsrzX716tdauXavKykrdc8896Rp1VCVznKqrq1VfXy+PxyOPx6P7779f77zzjjwej157\n7bV0j552yRyrK6+8UpJ07bXXxrfNmDFDknTy5Mk0TDu6kjlWzc3Ncrvd8RhJ0uTJkzVx4kSdOHEi\nbTPbbljn9FT+o9fHotGoGTdunHnllVfi22pra824ceNMLBYb8tpMlOzz/81vfmPGjBlj/vSnP6Vz\nzFGXzHE6efKkOX78ePy2du1aU1RUZI4fP27a29vTPXraJXOsjh07ZhwOh3n99dfj2/7xj39cEhfK\nGJPcsWpsbOxzXN5//30zZswYs2/fvrTNbINEFzUM55w+Yt9D+tnPfmamT59u/va3v8WvQ3/ggQeM\nMR9dLdbS0jKotZeCwR6rw4cPG6fTaR544AHz/vvvm/feey9+uxSukkrmNfVpmzZtuqSusjMmuWP1\nwx/+0Fx33XXmwIED5tChQ+bLX/6y+c53vjNao6ddMsfq1ltvNTfffLNpbGw0hw8fNrfccov56le/\nOlqjj5rPBilV5/QRC1IsFjN33XWXmTBhgpk4caJZvXq1OX/+vDHGmLlz55pbbrllUGsvBYM9VmvW\nrDEOh6PPzel0mn//+9+j+RTSIpnX1Kc988wzl9wXY5M5VpFIxNx9993G6/Wa8ePHm+XLl2f899o+\nLZlj9eGHH5oVK1aYz33uc6agoMAsW7as3/8QymSfDVKqzukD/i47AADSgd96CgCwAkECAFiBIAEA\nrECQAABWIEgAACsQJACAFQgSAMAKBAkAYAWCBACwwv8B6uGM4r61pDQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff22ecc07d0>"
       ]
      }
     ],
     "prompt_number": 725
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figs2, axs2 = get_figs(4)\n",
      "\n",
      "for sim,co in zip(filter_simulations(sims,dt=50,group=\"paper_sims\",steady=False,cond=\"fine\"),color):\n",
      "    plot_sim(sim,co,figs2,axs2,plot_time=100)\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(<simulation_brutus.job instance at 0x7f9b17d8bb00>, 2)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BWloaoqOjcefOHWu0tc6qO9ScOxdISADc3ICUFKB7dys3jojITtnNabrCwkK4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\njIiIyOb+H9OXXninr1owAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9b179383d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Tg6ioKMhkMpPiRERkHiYVDq1WW+8XtE6ne+QuIYlEYmwaAfd+8qvVajFx4kRY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\nicLCQUREorBwEBGRKCwcREQkyv8BcU1src9t2DMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9b17af1f50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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bQ4cCK1ZwAXkjIvlXmlW5evWqwU9baYw44TFmhJISYNEiccelRgN4eQHLlwPD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\nHgDACQQeAMAJH8xd66Jcx6HuAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9b17cc4510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4dyo2WSwpr7i4GCNHjkT37t1bOhQiIrvxFl8bFRYWhuzsbCxatAguLi4IDw/H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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9b16cf88d0>"
       ]
      }
     ],
     "prompt_number": 2046
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plot profiles"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_filter = {\"steady\":True,\"conj\":True,\"inf\":False,\"onecell\":False}\n",
      "\n",
      "x_coord = np.linspace(0,8*0.0023,9)\n",
      "fig = pl.figure(figsize=(16,9))\n",
      "ax = fig.add_subplot(1,1,1)\n",
      "for sim in filter_simulations(sims,**plot_filter):\n",
      "    try:\n",
      "        sim.result[\"mf_avg\"] =   [(item[0][\"massflow_t_CO\"]/item[0][\"massflow\"])[0] for item in sim.data]\n",
      "        sh_number(sim,inflow_mf=True) \n",
      "    except:\n",
      "        print(\"failed to open: \" + sim.info.local[\"jobname\"])\n",
      "        continue\n",
      "\n",
      "    #pl.plot(data_dict[\"arc_length\"],data_dict[\"CO\"],label=\"Wall mass fraction\")\n",
      "    prop_list = [\"temperature\",\"inf\",\"conj\"]\n",
      "    label = \" \".join([str(sim.sim_info[prop]) for prop in prop_list])\n",
      "    ax.plot(x_coord,sim.result[\"mf_avg\"],label=label)\n",
      "\n",
      "plot_temp = [sim.sim_info[\"temperature\"] for sim in filter_simulations(sims,**plot_filter)]\n",
      "print plot_temp\n",
      "\n",
      "sim_to_plot = filter_simulations(sim_list_py,wc_geo_area=100,dp=10,sh_number=8000,wc_thickness=100,cov_CO=1,vel=2.0)\n",
      "for sim in it.ifilterfalse(lambda sim:sim.sim_info[\"temperature\"] not in plot_temp,sim_to_plot):\n",
      "    pl.plot(sim.wc_data[\"coords\"],sim.wc_data[\"CO\"],'o',label=sim.sim_info[\"temperature\"])\n",
      "\n",
      "ax = pl.gca()\n",
      "ax.set_xlabel(\"x (m)\")\n",
      "ax.set_ylabel(\"CO mass fraction (-)\")\n",
      "ax.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "failed to open: kelvin_cells_conjugate_551\n"
       ]
      },
      {
       "ename": "ValueError",
       "evalue": "x and y must have same first dimension",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-14-5e43af8a3bc1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     15\u001b[0m     \u001b[0mprop_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"temperature\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"inf\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"conj\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m     \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\" \"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msim_info\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mprop\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mprop\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mprop_list\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m     \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_coord\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"mf_avg\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[0mplot_temp\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0msim\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msim_info\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"temperature\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msim\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfilter_simulations\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msims\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mplot_filter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/axes.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   4135\u001b[0m         \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4136\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4137\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4138\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4139\u001b[0m             \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/axes.pyc\u001b[0m in \u001b[0;36m_grab_next_args\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    315\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    316\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 317\u001b[1;33m                 \u001b[1;32mfor\u001b[0m \u001b[0mseg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    318\u001b[0m                     \u001b[1;32myield\u001b[0m \u001b[0mseg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    319\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/axes.pyc\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    293\u001b[0m             \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    294\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 295\u001b[1;33m         \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    296\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    297\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'plot'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/pymodules/python2.7/matplotlib/axes.pyc\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[1;34m(self, x, y)\u001b[0m\n\u001b[0;32m    235\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    236\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 237\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"x and y must have same first dimension\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    238\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    239\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"x and y can be no greater than 2-D\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mValueError\u001b[0m: x and y must have same first dimension"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CFQAAgKIIVQAAAIoiVAEAACiKUAUAAKAoQhUAAICiCFUAAACKIlQBAAAoilAFAACgKEIV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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9b36769cd0>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot_filter = {\"steady\":True,\"conj\":True,\"inf\":False,\"onecell\":False}\n",
      "\n",
      "ncells = 4\n",
      "x_coord = np.linspace(0,ncells*0.0023,ncells+1)\n",
      "fig = pl.figure(figsize=(16,9))\n",
      "ax = fig.add_subplot(1,1,1)\n",
      "for sim in filter_simulations(sims,**plot_filter):\n",
      "    try:\n",
      "#       print(sim.result[\"mf_avg\"])\n",
      "       #print(\"mf_avg\")\n",
      "\n",
      "\n",
      "       sim.result[\"T_avg\"] =   [(item[3][\"massflow_t_T\"]/item[3][\"massflow\"])[0] for item in sim.data]\n",
      "       sim.result[\"mf_avg\"] =   [(item[0][\"massflow_t_CO\"]/item[0][\"massflow\"])[0] for item in sim.data]\n",
      "\n",
      "       # sim.result[\"T_s_avg\"] =   [(item[3][\"massflow_t_Ts\"]/item[3][\"massflow\"])[0] for item in sim.data]\n",
      "\n",
      "    except:\n",
      "        print(\"failed to open: \" + sim.info.local[\"jobname\"])\n",
      "        continue\n",
      "\n",
      "    #pl.plot(data_dict[\"arc_length\"],data_dict[\"CO\"],label=\"Wall mass fraction\")\n",
      "    prop_list = [\"temperature\",\"inf\",\"conj\"]\n",
      "    label = \" \".join([str(sim.sim_info[prop]) for prop in prop_list])\n",
      "    ax.plot(x_coord,sim.result[\"T_avg\"],label=label)\n",
      "#    ax.plot(x_coord,sim.result[\"T_s_avg\"],label=label)\n",
      "\n",
      "\n",
      "ax = pl.gca()\n",
      "ax.set_xlabel(\"x (m)\")\n",
      "ax.set_ylabel(\"T  (-)\")\n",
      "ax.legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "failed to open: kelvin_cells_conjugate_551\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<matplotlib.legend.Legend at 0x7f9b366a6ed0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jbgsh1t/vIcQCQIsyeTyhedjkT3/6k6ZOnaqjR4+qd+/eeuaZZ1RWVqYNGzbc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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9b366a68d0>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gas = ca.import_phases(os.path.join(sim.info.local[\"sim_folder\"],\"cantera_input.cti\"),[\"gas\"])[0]\n",
      "interface = ca.Interface(os.path.join(sim.info.local[\"sim_folder\"],\"cantera_input.cti\"),\"pt_surf\",[gas])\n",
      "\n",
      "gas.TPX = [sim_test.sim_info[\"temperature\"],1E5,sim.sim_info[\"massfraction\"]]\n",
      "interface.TPX = [sim_test.sim_info[\"temperature\"],1E5,[1]]\n",
      "deltaH = interface.delta_enthalpy\n",
      "cp = gas.cp\n",
      "u = 2.0\n",
      "\n",
      "deltaT = sim_test.result[\"T_avg\"][0]  - sim_test.result[\"T_avg\"][4]\n",
      "deltaY = sim_test.result[\"mf_avg\"][0] - sim_test.result[\"mf_avg\"][4]\n",
      "Q_delta_T =  cp * u * deltaT \n",
      "Q_reac = - deltaY*deltaH/28.0*2.0\n",
      "Q_dot = Q_delta_T + Q_reac\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Q_dot,  Q_delta_T, Q_reac\n",
      "print(-Q_dot/Q_delta_T)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.66483624]\n"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cantera_file = \"/home/vonrickenbach/testcases_python_h_ref/default_0/cantera_input.cti\"\n",
      "gas = ca.import_phases(cantera_file,[\"gas\"])[0]\n",
      "interface = ca.Interface(cantera_file,\"pt_surf\",[gas])\n",
      "\n",
      "interface.TPX = [550,1E5,[1,0,0,0]]\n",
      "gas.TPX = [300,1E5,[0.1,0.0,0.0,0.89,0.01]]\n",
      "\n",
      "nt= 1000\n",
      "DT = 0.001\n",
      "dt = DT/nt\n",
      "CO_conc = []\n",
      "surf_conc = []\n",
      "\n",
      "time_array = np.linspace(0,DT,nt)\n",
      "\n",
      "print(gas.mix_diff_coeffs)\n",
      "print(gas.species_names)\n",
      "diff_t = gas.thermal_conductivity/(gas.density*gas.cp_mass)\n",
      "\n",
      "diff_t_solid = 14.1/(3110*880)\n",
      "rhocp_s = 3110*880\n",
      "lengthscale_solid_r = 0.0001\n",
      "lengthscale_solid_ax = 0.01\n",
      "lengthscale = 0.001\n",
      "\n",
      "Dp = 0.002\n",
      "ds = 0.00046\n",
      "porosity = 0.903\n",
      "a_s = 10.33/(Dp+ds) * np.sqrt(1-porosity) -5.8/(Dp+ds)*(1.0-porosity)\n",
      "Nu = 8.0\n",
      "porosity = 0.81\n",
      "\n",
      "print(\"species tscale\",lengthscale**2/np.min(gas.mix_diff_coeffs))\n",
      "print(\"temperature tscale\",lengthscale**2/diff_t)\n",
      "print(\"temperature tscale solid r\",lengthscale_solid_r**2/diff_t_solid)\n",
      "print(\"temperature tscale solid ax\",lengthscale_solid_ax**2/diff_t_solid)\n",
      "print(\"flow tscale\",lengthscale_solid_ax/1.5)\n",
      "print(\"solid heating\",1/(a_s*Nu*gas.thermal_conductivity/0.002/((1-porosity)*rhocp_s)))\n",
      "\n",
      "\n",
      "\n",
      "for time in time_array:\n",
      "\n",
      "    interface.advance_coverages_test(dt)\n",
      "    CO_conc.append(gas.X[4])\n",
      "    surf_conc.append(interface.X[0])\n",
      "\n",
      "\n",
      "#pl.plot(time_array,CO_conc)\n",
      "\n",
      "pl.plot(time_array,surf_conc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[  2.08352791e-05   1.59710709e-05   2.30094546e-05   2.35137462e-05\n",
        "   2.09548545e-05]\n",
        "['O2', 'CO2', 'H2O', 'N2', 'CO']\n",
        "('species tscale', 0.062613209103444048)\n",
        "('temperature tscale', 0.04402538524765535)\n",
        "('temperature tscale solid r', 0.0019409929078014183)\n",
        "('temperature tscale solid ax', 19.409929078014184)\n",
        "('flow tscale', 0.006666666666666667)\n",
        "('solid heating', 4.5448313814382146)\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2085,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f9b14648690>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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R3KQOEc5EiIjkJnWIEBGR3KQOEc5EiIjkJnWIEBGR3BgiREQUM6lDhI+ziIjkJnWIEBGR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\niGL2/zp1N4jDAAAAA0lEQVSS3yQ9/h89AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9b1500ded0>"
       ]
      }
     ],
     "prompt_number": 2085
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "default_inputs = {}\n",
      "\n",
      "default_inputs[\"wc_thickness\"] = 20E-6\n",
      "default_inputs[\"h\"]=8000\n",
      "default_inputs[\"h_temp\"]=4000\n",
      "default_inputs[\"porosity\"]=0.35\n",
      "default_inputs[\"lambda_solid\"] = 10.\n",
      "default_inputs[\"tortuosity\"] = 8.0\n",
      "default_inputs[\"atol\"] = 1E-20\n",
      "default_inputs[\"rtol\"]   = 1E-6\n",
      "default_inputs[\"d_p\"] = 1E-8\n",
      "default_inputs[\"nx\"]=256\n",
      "default_inputs[\"x_cells\"]=1\n",
      "default_inputs[\"L_reactor\"] = 0.005\n",
      "default_inputs[\"vel\"] = 5.0\n",
      "default_inputs[\"A_V\"] = 1000.\n",
      "default_inputs[\"mintemp\"] = 380\n",
      "default_inputs[\"maxtemp\"] = 520\n",
      "default_inputs[\"trate\"]= 0.1\n",
      "default_inputs[\"from_file\"] = False\n",
      "default_inputs[\"with_energy\"] = False\n",
      "default_inputs[\"maxiter\"] = 10000\n",
      "default_inputs[\"istorf\"] = 1\n",
      "default_inputs[\"wc_geo_area\"] = 20\n",
      "default_inputs[\"t_fac\"] = 1\n",
      "default_inputs[\"gas\"] = None\n",
      "default_inputs[\"mf\"] = 3000\n",
      "default_inputs[\"dt\"] = 1.0\n",
      "default_inputs[\"n_output\"] = 1000\n",
      "default_inputs[\"area_to_volume\"] = default_inputs[\"wc_geo_area\"]/ default_inputs[\"wc_thickness\"]\n",
      "default_inputs[\"maxtemp\"] = -1\n",
      "default_inputs[\"mintemp\"] = -1\n",
      "default_inputs[\"trate\"] = -1\n",
      "default_inputs[\"rhocp\"] = 1E6\n",
      "default_inputs[\"rhocp_st\"] = 0.001\n",
      "default_inputs[\"inf_ext_mt\"] = False\n",
      "default_inputs[\"bulk_pressure\"] = 1E5\n",
      "default_inputs[\"heat_source\"] = 0.0\n",
      "default_inputs[\"cell_ratio\"] = 1.000001\n",
      "\n",
      "default_inputs[\"inf_ext_mt\"] = True\n",
      "input_file = os.path.join(\"/home/vonrickenbach/python_validation/test_mod_f_50_more_mf\",\"cantera_input.cti\")\n",
      "\n",
      "del default_inputs[\"wc_geo_area\"]\n",
      "del default_inputs[\"t_fac\"]\n",
      "del default_inputs[\"mf\"]\n",
      "\n",
      "\n",
      "gas=ca.import_phases(input_file,[\"gas\"])\n",
      "gas = gas[0]\n",
      "i =ca.Interface(input_file,\"pt_surf\",[gas])\n",
      "   \n",
      "\n",
      "default_inputs[\"gas\"] =gas\n",
      "\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 309
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_timescale(temp=500,massfrac=0.01):\n",
      "\n",
      "    default_inputs[\"gas\"].TPY = (temp,1E5,\n",
      "                               [0.1,0.0,0.0,0.9-massfrac,massfrac])\n",
      "    i.TPX =[temp,1E5,[0,0,1,0]]\n",
      "    i.coverages =[0,0,1,0]\n",
      "\n",
      "\n",
      "    default_inputs[\"temperature\"] = temp\n",
      "    print(default_inputs)\n",
      "\n",
      "\n",
      "    #i.advance_coverages_test(100000)\n",
      "    print(i.TPX)\n",
      "\n",
      "    i.advance_coverages(**default_inputs)\n",
      "\n",
      "    gas.TPY\n",
      "\n",
      "    time = np.linspace(2,101,99)\n",
      "    data = []\n",
      "    for ti in time:\n",
      "        data.append(np.genfromtxt(\"grid_0_\"+str(int(ti))+\".dat\"))\n",
      "        \n",
      "    data = np.array(data)\n",
      "    steady_state = np.sum(data[-1,4,:])\n",
      "    sum_data = np.sum(data[:,4,:],axis=1)\n",
      "   \n",
      "    char_time = np.where(np.abs((sum_data-steady_state)/steady_state) > 0.001)\n",
      "\n",
      "    try:\n",
      "        char_time = time[np.max(char_time)]\n",
      "    except: \n",
      "        char_time = 0.0\n",
      "\n",
      "    return (data,char_time)\n",
      "    \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 312
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "char_times = []\n",
      "all_data = []\n",
      "temperatures = np.linspace(400,600,41)\n",
      "massfractions = [0.01,0.005]\n",
      "\n",
      "for massfrac in massfractions:\n",
      "    char_times_loc = []\n",
      "    data_loc = []\n",
      "    for temp in temperatures:\n",
      "\n",
      "        data, char_time = get_timescale(temp=temp,massfrac=massfrac)\n",
      "        char_times_loc.append(char_time)\n",
      "        data_loc.append(data)\n",
      "\n",
      "    char_times.append(char_times_loc)    \n",
      "    all_data.append(data_loc)    \n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{'maxtemp': -1, 'mintemp': -1, 'cell_ratio': 1.000001, 'inf_ext_mt': True, 'wc_thickness': 2e-05, 'tortuosity': 8.0, 'area_to_volume': 999999.9999999999, 'temperature': 600.0, 'nx': 256, 'rtol': 1e-06, 'n_output': 1000, 'maxiter': 10000, 'istorf': 1, 'vel': 5.0, 'porosity': 0.35, 'A_V': 1000.0, 'rhocp_st': 0.001, 'bulk_pressure': 100000.0, 'gas': <cantera._cantera.Solution object at 0x7ff22feeecf8>, 'lambda_solid': 10.0, 'x_cells': 1, 'atol': 1e-20, 'dt': 1.0, 'with_energy': False, 'from_file': False, 'rhocp': 1000000.0, 'L_reactor': 0.005, 'h': 8000, 'd_p': 1e-08, 'h_temp': 4000, 'heat_source': 0.0, 'trate': -1}\n",
        "(400.0, 100000.0, array([ 0.,  0.,  1.,  0.]))\n",
        "{'maxtemp': -1, 'mintemp': -1, 'cell_ratio': 1.000001, 'inf_ext_mt': True, 'wc_thickness': 2e-05, 'tortuosity': 8.0, 'area_to_volume': 999999.9999999999, 'temperature': 400.0, 'nx': 256, 'rtol': 1e-06, 'n_output': 1000, 'maxiter': 10000, 'istorf': 1, 'vel': 5.0, 'porosity': 0.35, 'A_V': 1000.0, 'rhocp_st': 0.001, 'bulk_pressure': 100000.0, 'gas': <cantera._cantera.Solution object at 0x7ff22feeecf8>, 'lambda_solid': 10.0, 'x_cells': 1, 'atol': 1e-20, 'dt': 1.0, 'with_energy': False, 'from_file': False, 'rhocp': 1000000.0, 'L_reactor': 0.005, 'h': 8000, 'd_p': 1e-08, 'h_temp': 4000, 'heat_source': 0.0, 'trate': -1}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(405.0, 100000.0, array([ 0.,  0.,  1.,  0.]))\n",
        "{'maxtemp': -1, 'mintemp': -1, 'cell_ratio': 1.000001, 'inf_ext_mt': True, 'wc_thickness': 2e-05, 'tortuosity': 8.0, 'area_to_volume': 999999.9999999999, 'temperature': 405.0, 'nx': 256, 'rtol': 1e-06, 'n_output': 1000, 'maxiter': 10000, 'istorf': 1, 'vel': 5.0, 'porosity': 0.35, 'A_V': 1000.0, 'rhocp_st': 0.001, 'bulk_pressure': 100000.0, 'gas': <cantera._cantera.Solution object at 0x7ff22feeecf8>, 'lambda_solid': 10.0, 'x_cells': 1, 'atol': 1e-20, 'dt': 1.0, 'with_energy': False, 'from_file': False, 'rhocp': 1000000.0, 'L_reactor': 0.005, 'h': 8000, 'd_p': 1e-08, 'h_temp': 4000, 'heat_source': 0.0, 'trate': -1}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(410.0, 100000.0, array([ 0.,  0.,  1.,  0.]))\n",
        "{'maxtemp': -1, 'mintemp': -1, 'cell_ratio': 1.000001, 'inf_ext_mt': True, 'wc_thickness': 2e-05, 'tortuosity': 8.0, 'area_to_volume': 999999.9999999999, 'temperature': 410.0, 'nx': 256, 'rtol': 1e-06, 'n_output': 1000, 'maxiter': 10000, 'istorf': 1, 'vel': 5.0, 'porosity': 0.35, 'A_V': 1000.0, 'rhocp_st': 0.001, 'bulk_pressure': 100000.0, 'gas': <cantera._cantera.Solution object at 0x7ff22feeecf8>, 'lambda_solid': 10.0, 'x_cells': 1, 'atol': 1e-20, 'dt': 1.0, 'with_energy': False, 'from_file': False, 'rhocp': 1000000.0, 'L_reactor': 0.005, 'h': 8000, 'd_p': 1e-08, 'h_temp': 4000, 'heat_source': 0.0, 'trate': -1}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(415.0, 100000.0, array([ 0.,  0.,  1.,  0.]))\n",
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(550.0, 100000.0, array([ 0.,  0.,  1.,  0.]))\n",
        "{'maxtemp': -1, 'mintemp': -1, 'cell_ratio': 1.000001, 'inf_ext_mt': True, 'wc_thickness': 2e-05, 'tortuosity': 8.0, 'area_to_volume': 999999.9999999999, 'temperature': 550.0, 'nx': 256, 'rtol': 1e-06, 'n_output': 1000, 'maxiter': 10000, 'istorf': 1, 'vel': 5.0, 'porosity': 0.35, 'A_V': 1000.0, 'rhocp_st': 0.001, 'bulk_pressure': 100000.0, 'gas': <cantera._cantera.Solution object at 0x7ff22feeecf8>, 'lambda_solid': 10.0, 'x_cells': 1, 'atol': 1e-20, 'dt': 1.0, 'with_energy': False, 'from_file': False, 'rhocp': 1000000.0, 'L_reactor': 0.005, 'h': 8000, 'd_p': 1e-08, 'h_temp': 4000, 'heat_source': 0.0, 'trate': -1}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(555.0, 100000.0, array([ 0.,  0.,  1.,  0.]))\n",
        "{'maxtemp': -1, 'mintemp': -1, 'cell_ratio': 1.000001, 'inf_ext_mt': True, 'wc_thickness': 2e-05, 'tortuosity': 8.0, 'area_to_volume': 999999.9999999999, 'temperature': 555.0, 'nx': 256, 'rtol': 1e-06, 'n_output': 1000, 'maxiter': 10000, 'istorf': 1, 'vel': 5.0, 'porosity': 0.35, 'A_V': 1000.0, 'rhocp_st': 0.001, 'bulk_pressure': 100000.0, 'gas': <cantera._cantera.Solution object at 0x7ff22feeecf8>, 'lambda_solid': 10.0, 'x_cells': 1, 'atol': 1e-20, 'dt': 1.0, 'with_energy': False, 'from_file': False, 'rhocp': 1000000.0, 'L_reactor': 0.005, 'h': 8000, 'd_p': 1e-08, 'h_temp': 4000, 'heat_source': 0.0, 'trate': -1}"
       ]
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(560.0, 100000.0, array([ 0.,  0.,  1.,  0.]))\n",
        "{'maxtemp': -1, 'mintemp': -1, 'cell_ratio': 1.000001, 'inf_ext_mt': True, 'wc_thickness': 2e-05, 'tortuosity': 8.0, 'area_to_volume': 999999.9999999999, 'temperature': 560.0, 'nx': 256, 'rtol': 1e-06, 'n_output': 1000, 'maxiter': 10000, 'istorf': 1, 'vel': 5.0, 'porosity': 0.35, 'A_V': 1000.0, 'rhocp_st': 0.001, 'bulk_pressure': 100000.0, 'gas': <cantera._cantera.Solution object at 0x7ff22feeecf8>, 'lambda_solid': 10.0, 'x_cells': 1, 'atol': 1e-20, 'dt': 1.0, 'with_energy': False, 'from_file': False, 'rhocp': 1000000.0, 'L_reactor': 0.005, 'h': 8000, 'd_p': 1e-08, 'h_temp': 4000, 'heat_source': 0.0, 'trate': -1}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(565.0, 100000.0, array([ 0.,  0.,  1.,  0.]))\n",
        "{'maxtemp': -1, 'mintemp': -1, 'cell_ratio': 1.000001, 'inf_ext_mt': True, 'wc_thickness': 2e-05, 'tortuosity': 8.0, 'area_to_volume': 999999.9999999999, 'temperature': 565.0, 'nx': 256, 'rtol': 1e-06, 'n_output': 1000, 'maxiter': 10000, 'istorf': 1, 'vel': 5.0, 'porosity': 0.35, 'A_V': 1000.0, 'rhocp_st': 0.001, 'bulk_pressure': 100000.0, 'gas': <cantera._cantera.Solution object at 0x7ff22feeecf8>, 'lambda_solid': 10.0, 'x_cells': 1, 'atol': 1e-20, 'dt': 1.0, 'with_energy': False, 'from_file': False, 'rhocp': 1000000.0, 'L_reactor': 0.005, 'h': 8000, 'd_p': 1e-08, 'h_temp': 4000, 'heat_source': 0.0, 'trate': -1}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(570.0, 100000.0, array([ 0.,  0.,  1.,  0.]))\n",
        "{'maxtemp': -1, 'mintemp': -1, 'cell_ratio': 1.000001, 'inf_ext_mt': True, 'wc_thickness': 2e-05, 'tortuosity': 8.0, 'area_to_volume': 999999.9999999999, 'temperature': 570.0, 'nx': 256, 'rtol': 1e-06, 'n_output': 1000, 'maxiter': 10000, 'istorf': 1, 'vel': 5.0, 'porosity': 0.35, 'A_V': 1000.0, 'rhocp_st': 0.001, 'bulk_pressure': 100000.0, 'gas': <cantera._cantera.Solution object at 0x7ff22feeecf8>, 'lambda_solid': 10.0, 'x_cells': 1, 'atol': 1e-20, 'dt': 1.0, 'with_energy': False, 'from_file': False, 'rhocp': 1000000.0, 'L_reactor': 0.005, 'h': 8000, 'd_p': 1e-08, 'h_temp': 4000, 'heat_source': 0.0, 'trate': -1}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(575.0, 100000.0, array([ 0.,  0.,  1.,  0.]))\n",
        "{'maxtemp': -1, 'mintemp': -1, 'cell_ratio': 1.000001, 'inf_ext_mt': True, 'wc_thickness': 2e-05, 'tortuosity': 8.0, 'area_to_volume': 999999.9999999999, 'temperature': 575.0, 'nx': 256, 'rtol': 1e-06, 'n_output': 1000, 'maxiter': 10000, 'istorf': 1, 'vel': 5.0, 'porosity': 0.35, 'A_V': 1000.0, 'rhocp_st': 0.001, 'bulk_pressure': 100000.0, 'gas': <cantera._cantera.Solution object at 0x7ff22feeecf8>, 'lambda_solid': 10.0, 'x_cells': 1, 'atol': 1e-20, 'dt': 1.0, 'with_energy': False, 'from_file': False, 'rhocp': 1000000.0, 'L_reactor': 0.005, 'h': 8000, 'd_p': 1e-08, 'h_temp': 4000, 'heat_source': 0.0, 'trate': -1}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(580.0, 100000.0, array([ 0.,  0.,  1.,  0.]))\n",
        "{'maxtemp': -1, 'mintemp': -1, 'cell_ratio': 1.000001, 'inf_ext_mt': True, 'wc_thickness': 2e-05, 'tortuosity': 8.0, 'area_to_volume': 999999.9999999999, 'temperature': 580.0, 'nx': 256, 'rtol': 1e-06, 'n_output': 1000, 'maxiter': 10000, 'istorf': 1, 'vel': 5.0, 'porosity': 0.35, 'A_V': 1000.0, 'rhocp_st': 0.001, 'bulk_pressure': 100000.0, 'gas': <cantera._cantera.Solution object at 0x7ff22feeecf8>, 'lambda_solid': 10.0, 'x_cells': 1, 'atol': 1e-20, 'dt': 1.0, 'with_energy': False, 'from_file': False, 'rhocp': 1000000.0, 'L_reactor': 0.005, 'h': 8000, 'd_p': 1e-08, 'h_temp': 4000, 'heat_source': 0.0, 'trate': -1}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(585.0, 100000.0, array([ 0.,  0.,  1.,  0.]))\n",
        "{'maxtemp': -1, 'mintemp': -1, 'cell_ratio': 1.000001, 'inf_ext_mt': True, 'wc_thickness': 2e-05, 'tortuosity': 8.0, 'area_to_volume': 999999.9999999999, 'temperature': 585.0, 'nx': 256, 'rtol': 1e-06, 'n_output': 1000, 'maxiter': 10000, 'istorf': 1, 'vel': 5.0, 'porosity': 0.35, 'A_V': 1000.0, 'rhocp_st': 0.001, 'bulk_pressure': 100000.0, 'gas': <cantera._cantera.Solution object at 0x7ff22feeecf8>, 'lambda_solid': 10.0, 'x_cells': 1, 'atol': 1e-20, 'dt': 1.0, 'with_energy': False, 'from_file': False, 'rhocp': 1000000.0, 'L_reactor': 0.005, 'h': 8000, 'd_p': 1e-08, 'h_temp': 4000, 'heat_source': 0.0, 'trate': -1}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(590.0, 100000.0, array([ 0.,  0.,  1.,  0.]))\n",
        "{'maxtemp': -1, 'mintemp': -1, 'cell_ratio': 1.000001, 'inf_ext_mt': True, 'wc_thickness': 2e-05, 'tortuosity': 8.0, 'area_to_volume': 999999.9999999999, 'temperature': 590.0, 'nx': 256, 'rtol': 1e-06, 'n_output': 1000, 'maxiter': 10000, 'istorf': 1, 'vel': 5.0, 'porosity': 0.35, 'A_V': 1000.0, 'rhocp_st': 0.001, 'bulk_pressure': 100000.0, 'gas': <cantera._cantera.Solution object at 0x7ff22feeecf8>, 'lambda_solid': 10.0, 'x_cells': 1, 'atol': 1e-20, 'dt': 1.0, 'with_energy': False, 'from_file': False, 'rhocp': 1000000.0, 'L_reactor': 0.005, 'h': 8000, 'd_p': 1e-08, 'h_temp': 4000, 'heat_source': 0.0, 'trate': -1}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(595.0, 100000.0, array([ 0.,  0.,  1.,  0.]))\n",
        "{'maxtemp': -1, 'mintemp': -1, 'cell_ratio': 1.000001, 'inf_ext_mt': True, 'wc_thickness': 2e-05, 'tortuosity': 8.0, 'area_to_volume': 999999.9999999999, 'temperature': 595.0, 'nx': 256, 'rtol': 1e-06, 'n_output': 1000, 'maxiter': 10000, 'istorf': 1, 'vel': 5.0, 'porosity': 0.35, 'A_V': 1000.0, 'rhocp_st': 0.001, 'bulk_pressure': 100000.0, 'gas': <cantera._cantera.Solution object at 0x7ff22feeecf8>, 'lambda_solid': 10.0, 'x_cells': 1, 'atol': 1e-20, 'dt': 1.0, 'with_energy': False, 'from_file': False, 'rhocp': 1000000.0, 'L_reactor': 0.005, 'h': 8000, 'd_p': 1e-08, 'h_temp': 4000, 'heat_source': 0.0, 'trate': -1}"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "(600.0, 100000.0, array([ 0.,  0.,  1.,  0.]))\n"
       ]
      }
     ],
     "prompt_number": 326
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_data = np.array(all_data)\n",
      "\n",
      "fig, ax = pl.subplots()\n",
      "ax2 = ax.twinx()\n",
      "\n",
      "for idx,massfrac in enumerate(massfractions):\n",
      "    ax.plot(temperatures,all_data[idx,:,9,4,-1],\"--\",label=\"$Y_s: $\"+str(massfrac))\n",
      "    ax2.plot(temperatures,char_times[idx],label=\"$Y_s: $\"+str(massfrac))\n",
      "\n",
      "    \n",
      "\n",
      "ax.set_xlabel(\"Temperature (K)\")\n",
      "ax2.set_ylabel(\"Time to steady state (ms)\")\n",
      "ax.set_ylabel(\"$Y_{CO}$ at $w=t_{wc}$\")\n",
      "\n",
      "ax.set_xlim([450,600])\n",
      "\n",
      "pl.legend(frameon=False)\n",
      "\n",
      "fig = pl.gcf()\n",
      "fig.tight_layout()\n",
      "fig.savefig(os.path.join(\"pics_3\",\"timescales.pdf\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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D6OhoiMViBAUFwcjICGvXrmVdV9XxKVOm4MmTJzh9+jSkUimCg4MxdepUHDp0SKc/M0Vp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\nic8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff22dabbcd0>"
       ]
      }
     ],
     "prompt_number": 335
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pl.plot(data[9,4,:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 190,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7ff2343fa0d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7ff22f89edd0>"
       ]
      }
     ],
     "prompt_number": 190
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(np.array([]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[]\n"
       ]
      }
     ],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Dp = 0.002\n",
      "ds = 0.00046\n",
      "porosity = 0.903\n",
      "10.33/(Dp+ds) * np.sqrt(1-porosity) -5.8/(Dp+ds)*(1.0-porosity)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2083,
       "text": [
        "1079.130169266386"
       ]
      }
     ],
     "prompt_number": 2083
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Plot Light-off"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig,ax = pl.subplots()\n",
      "\n",
      "def get_conv(sim):\n",
      "    try:\n",
      "        return 1-sim.result[\"mf_avg\"][4]/0.00297\n",
      "       # return sim.result[\"mf_avg\"][4]\n",
      "\n",
      "    except:\n",
      "        return 0.0\n",
      "    \n",
      "def get_conv_1d(sim):\n",
      "#    try:\n",
      "        return 1-np.interp(4*0.0023,sim.wc_data[\"coords\"],sim.wc_data[\"CO\"])/0.00297\n",
      "       # return np.interp(4*0.0023,sim.wc_data[\"coords\"],sim.wc_data[\"CO\"])\n",
      "#        return 0.0\n",
      "    \n",
      "def get_var(sim,var):\n",
      "    return sim.sim_info[var]\n",
      "colors = [\"b\",\"r\"]\n",
      "\n",
      "for vel,col in zip([2.0,10.0],colors):\n",
      "    sim_to_plot = []\n",
      "    for temp in [400,450,500,550,600,650,700]:\n",
      "        sim_to_plot.append(filter_simulations(sims,steady=True,inf=False,conj=False,temperature=temp,vel=vel)[0])\n",
      "    ax.plot(get_array(sim_to_plot,get_var,\"temperature\"),get_array(sim_to_plot,get_conv),'o',color=col,label=\"U= \"+str(vel)+\" 3D\")\n",
      "\n",
      "    sim_to_plot_1d = sort_sims(filter_simulations(sim_list_py,wc_geo_area=100,cov_CO=1,dp=10,sh_number=8000,wc_thickness=100,vel=vel),\"temperature\")\n",
      "    ax.plot(get_array(sim_to_plot_1d,get_var,\"temperature\"),get_array(sim_to_plot_1d,get_conv_1d),color=col,label=\"U= \"+str(vel)+\" 1D\")\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "ax.set_xlabel(\"Temperature (K)\")\n",
      "ax.set_ylabel(\"Conversion (-)\")\n",
      "ax.legend(loc=0,frameon=False)\n",
      "fig.tight_layout()\n",
      "fig.savefig(\"paper_3/light_off_CO.pdf\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PEULMAwUl0iikpAAvvwwkJ/P0PsuXAx98QOsOEWJuaEo4MXvZ2Xwad2Ym0KkTX/qhe3dj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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f43dceaca90>"
       ]
      }
     ],
     "prompt_number": 513
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_conv(filter_simulations(sims,steady=True,inf=False,conj=False,temperature=500)[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 298,
       "text": [
        "0.017243546154506739"
       ]
      }
     ],
     "prompt_number": 298
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for sim in filter_simulations(sims,inf=True):\n",
      "    print(sim.result[\"sh_numbers\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[19.894683108267607, 14.300897895273174, 14.280314001957352, 14.28172518662789, 14.25950451703928, 14.24752044579507, 14.282696002959245]\n"
       ]
      }
     ],
     "prompt_number": 299
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filter_simulations(sim_list_py,wc_geo_area=100,cov_CO=1,dp=10,sh_number=8000,wc_thickness=100,vel=2.0)[0].sim_info"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 328,
       "text": [
        "{'a_v': 1000,\n",
        " 'converged': False,\n",
        " 'cov_CO': 1,\n",
        " 'default': 0,\n",
        " 'dp': 10,\n",
        " 'folder': 'default_0',\n",
        " 'mf': 3000,\n",
        " 'nx': 64,\n",
        " 'sh_number': 8000,\n",
        " 't_fac': 1,\n",
        " 'temperature': 300,\n",
        " 'vel': 2.0,\n",
        " 'wc_geo_area': 100,\n",
        " 'wc_porosity': 0.35,\n",
        " 'wc_thickness': 100,\n",
        " 'wc_tortuosity': 8.0,\n",
        " 'x_cells': 20}"
       ]
      }
     ],
     "prompt_number": 328
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "1D Sims"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def create_simulation_python(input_dict,folder,root_folder=\".\",ref_folder=None):\n",
      "     \n",
      "    try:\n",
      "        sh.copytree(ref_folder,os.path.join(root_folder,folder))\n",
      "    except:\n",
      "        print(\"directory exists already\")\n",
      "\n",
      "       \n",
      "    config_sim = jc.configuration_mb_local\n",
      "    config_sim[\"transat_version\"] = \"wc_python\"\n",
      "\n",
      "    config_sim[\"jobname\"] = folder\n",
      "    config_sim[\"root_folder\"] = root_folder\n",
      "    simulation = sim_brutus.job(**config_sim)\n",
      "\n",
      "    simulation.sim_info = input_dict.copy()\n",
      "    simulation.sim_info[\"converged\"] = False\n",
      "\n",
      "\n",
      "    return simulation"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 277
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_data(sim,time=False,flux=False):\n",
      "    idx_t = 1\n",
      "    if time:\n",
      "        idx_t = 0\n",
      "    key = \"grid\"\n",
      "    if flux:\n",
      "        key = \"fluxes\"\n",
      "    return np.array([item for item in sim.all_data[key]])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "default_inputs = {}\n",
      "default_inputs[\"sh_number\"] = 8000\n",
      "default_inputs[\"vel\"] = 2.0\n",
      "default_inputs[\"a_v\"] = 1000\n",
      "default_inputs[\"dp\"] = 10\n",
      "default_inputs[\"mf\"] = 3000\n",
      "default_inputs[\"wc_thickness\"] = 100\n",
      "default_inputs[\"wc_geo_area\"] = 100\n",
      "default_inputs[\"nx\"] = 64\n",
      "default_inputs[\"t_fac\"] = 1\n",
      "default_inputs[\"x_cells\"] = 20\n",
      "\n",
      "cov_co = [1,0]\n",
      "\n",
      "    \n",
      "default_inputs[\"wc_tortuosity\"] = 8.0\n",
      "default_inputs[\"wc_porosity\"]   = 0.35\n",
      "default_inputs[\"cov_CO\"]  = 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 287
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ref_folder = \"/home/vonrickenbach/testcases_python_vol_avg/default_0\"\n",
      "\n",
      "\n",
      "par_inputs_py = {}\n",
      "\n",
      "par_inputs_py[\"default\"] = [0]\n",
      "par_inputs_py[\"vel\"] = [10.0]\n",
      "\n",
      "\n",
      "\n",
      "inputs = {}\n",
      "\n",
      "sim_list_py  = []\n",
      "trate = 0.1\n",
      "mintemp = 380\n",
      "maxtemp = 1000\n",
      "temperatures = [300,360,370,385,390,400,410,420,430,440,450,460,470,480,490,500,510,520,530,540,550,560,570,600,700,800,900,1000]\n",
      "temperatures_ext = [300,360,370,380,390,400,410,420,430,440,450,460,470,480,490,500,510,520,530,540,550,560,570,600,620,640,660,680\n",
      "                ,700,720,740,760,780,800,820,840,860,880,900,920,940,960,980,1000]\n",
      "\n",
      "\n",
      "\n",
      "root_folder= \"/home/vonrickenbach/testcases_python_vol_avg/\"\n",
      "\n",
      "for par_key,par_value in par_inputs_py.iteritems():\n",
      "    for val in par_value:\n",
      "        folder = par_key+\"_\"+str(int(val))\n",
      "        try:\n",
      "            sh.copytree(ref_folder,os.path.join(root_folder,folder))\n",
      "        except:\n",
      "            pass\n",
      "        input_file = open(os.path.join(root_folder,folder,\"input.pic\"),\"w\")\n",
      "        file_dict = {}\n",
      "        if par_key != \"default\":\n",
      "            file_dict[par_key] = val\n",
      "            if par_key == \"vel\" and val == 10.0:\n",
      "                file_dict[\"h\"] = -20\n",
      "\n",
      "        pic.dump(file_dict,input_file)\n",
      "        input_file.close()\n",
      "        for cov in [1]:\n",
      "            temp_array = temperatures_ext\n",
      "\n",
      "            for temperature in temp_array:\n",
      "                inputs = default_inputs.copy()\n",
      "                inputs[\"temperature\"] = temperature\n",
      "                inputs[\"folder\"] = folder\n",
      "                if folder = \"default_0\":\n",
      "                    \n",
      "\n",
      "                inputs[par_key] = val\n",
      "                sim_list_py.append(create_simulation_python(inputs,folder,root_folder=root_folder,ref_folder=ref_folder))\n",
      "                \n",
      "sim_list_run = filter_simulations(sim_list_py,temperature=mintemp,cov_CO=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "directory exists already\n",
        "Warning Initial conditions with single block code not supported\n",
        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
        "Warning Initial conditions with single block code not supported"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "directory exists already\n",
        "Warning Initial conditions with single block code not supported\n",
        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
        "Warning Initial conditions with single block code not supported"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "directory exists already\n",
        "Warning Initial conditions with single block code not supported\n",
        "directory exists already\n",
        "Warning Initial conditions with single block code not supported\n",
        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
        "Warning Initial conditions with single block code not supported\n",
        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
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        "directory exists already\n",
        "Warning Initial conditions with single block code not supported\n",
        "directory exists already\n",
        "Warning Initial conditions with single block code not supported\n",
        "directory exists already\n",
        "Warning Initial conditions with single block code not supported\n",
        "directory exists already\n",
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        "directory exists already\n",
        "Warning Initial conditions with single block code not supported\n",
        "directory exists already\n",
        "Warning Initial conditions with single block code not supported\n"
       ]
      }
     ],
     "prompt_number": 337
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for sim in sim_list_py:\n",
      " try:\n",
      "    load_wc(sim,L_r=0.0184)\n",
      " except:\n",
      "       print(sim.sim_info)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 382
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def load_wc(sim,L_r=0.005):\n",
      "    wc_data = []\n",
      "        \n",
      "    time_idx = int(sim.sim_info[\"temperature\"])\n",
      "\n",
      "    nx = sim.sim_info[\"x_cells\"]\n",
      "    dx = L_r/ nx\n",
      "    for point in range(sim.sim_info[\"x_cells\"]):\n",
      "      wc_data.append([point*dx+dx/2,np.genfromtxt(os.path.join(sim.info.local[\"sim_folder\"],\"result\",\"grid_\"+str(point)+\"_\"+str(time_idx)+\"_\"+str(sim.sim_info[\"cov_CO\"])+\".dat\"))])\n",
      "    \n",
      "    sim.wc_data = {}\n",
      "    sim.wc_data[\"coords\"] = np.linspace(0.0,L_r,100)\n",
      "    sim.wc_data[\"CO\"]     = np.interp(sim.wc_data[\"coords\"],[item[0] for item in wc_data],[np.array(item[1][4][0]) for item in wc_data])\n",
      "    sim.all_data = wc_data  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 285
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filter_simulations(sims,temperature=500)[0].sim_info"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 221,
       "text": [
        "{'Dp': 0.002,\n",
        " 'conj': False,\n",
        " 'diff': 5.0767065038843225e-05,\n",
        " 'ds': 0.0003,\n",
        " 'inf': False,\n",
        " 'kelvin_cell_size': 0.0023,\n",
        " 'name': 'A',\n",
        " 'ncells': 8,\n",
        " 'porosity': 0.903,\n",
        " 'rho': 0.6823476206610333,\n",
        " 'steady': True,\n",
        " 'surf_to_volume': 1154.2000940849175,\n",
        " 'temperature': 500,\n",
        " 'vel': 2.0,\n",
        " 'visc': 2.6482057271786968e-05}"
       ]
      }
     ],
     "prompt_number": 221
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interface.advance_coverages_test(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 905
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%writefile colors   \n",
      "    \n",
      "\n",
      "   0.164   0.043   0.850 249.000   0.950   0.850   0.686   0.808   0.000   0.150      41      10     216\n",
      "   0.150   0.306   1.000 229.000   0.850   1.000   0.850   0.694   0.000   0.000      38      77     255\n",
      "   0.250   0.630   1.000 209.600   0.750   1.000   0.750   0.370   0.000   0.000      63     160     255\n",
      "   0.450   0.853   1.000 196.000   0.550   1.000   0.550   0.147   0.000   0.000     114     217     255\n",
      "   0.670   0.973   1.000 185.000   0.330   1.000   0.330   0.027   0.000   0.000     170     247     255\n",
      "   0.880   1.000   1.000 180.000   0.120   1.000   0.120   0.000   0.000   0.000     224     255     255\n",
      "   1.000   1.000   0.750  60.000   0.250   1.000   0.000   0.000   0.250   0.000     255     255     191\n",
      "   1.000   0.880   0.600  42.000   0.400   1.000   0.000   0.120   0.400   0.000     255     224     153\n",
      "   1.000   0.679   0.450  25.000   0.550   1.000   0.000   0.321   0.550   0.000     255     173     114\n",
      "   0.970   0.430   0.370   6.000   0.619   0.970   0.000   0.540   0.600   0.030     247     109      94\n",
      "   0.850   0.150   0.196 356.000   0.824   0.850   0.000   0.700   0.654   0.150     216      38      50\n",
      "   0.650   0.000   0.130 348.008   1.000   0.650   0.000   0.650   0.520   0.350     165       0      33"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Writing colors\n"
       ]
      }
     ],
     "prompt_number": 504
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colors = np.loadtxt(\"colors\")[:,-3:]/255"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 508
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colors"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 509,
       "text": [
        "array([[ 0.16078431,  0.03921569,  0.84705882],\n",
        "       [ 0.14901961,  0.30196078,  1.        ],\n",
        "       [ 0.24705882,  0.62745098,  1.        ],\n",
        "       [ 0.44705882,  0.85098039,  1.        ],\n",
        "       [ 0.66666667,  0.96862745,  1.        ],\n",
        "       [ 0.87843137,  1.        ,  1.        ],\n",
        "       [ 1.        ,  1.        ,  0.74901961],\n",
        "       [ 1.        ,  0.87843137,  0.6       ],\n",
        "       [ 1.        ,  0.67843137,  0.44705882],\n",
        "       [ 0.96862745,  0.42745098,  0.36862745],\n",
        "       [ 0.84705882,  0.14901961,  0.19607843],\n",
        "       [ 0.64705882,  0.        ,  0.12941176]])"
       ]
      }
     ],
     "prompt_number": 509
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colors = np.array([[12,40,64],[28,97,140],[166,155,134],[242,86,29],[242,39,38]])/256.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 524
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}