examples/benchmarking/solvers/analyze.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import argparse\n",
    "import glob\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import datreant.core as dtr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b = dtr.discover('../../../solvers/solver_timing/Data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pysparse lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/0c5af030-63aa-46d2-a808-744a24e8ae90/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 82, in <module>\n",
      "\n",
      "    data.categories['sweep {0} - iterations'.format(sweep)] = solver.status['iterations']\n",
      "\n",
      "KeyError: 'iterations'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "trilinos lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/1a993575-d3e0-4948-ad61-f119d2405510/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 82, in <module>\n",
      "\n",
      "    data.categories['sweep {0} - iterations'.format(sweep)] = solver.status['iterations']\n",
      "\n",
      "KeyError: 'iterations'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "pysparse lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/234086f3-7aa3-4db7-8d59-3ce2d5b76382/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 82, in <module>\n",
      "\n",
      "    data.categories['sweep {0} - iterations'.format(sweep)] = solver.status['iterations']\n",
      "\n",
      "KeyError: 'iterations'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/2cedb6f1-c512-4257-a08f-b2783c494e03/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 82, in <module>\n",
      "\n",
      "    data.categories['sweep {0} - iterations'.format(sweep)] = solver.status['iterations']\n",
      "\n",
      "KeyError: 'iterations'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "pysparse lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/2e8d7d7d-adba-4af9-9d48-1c8f7c1a95ca/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 82, in <module>\n",
      "\n",
      "    data.categories['sweep {0} - iterations'.format(sweep)] = solver.status['iterations']\n",
      "\n",
      "KeyError: 'iterations'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "trilinos lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/3922ab54-dec1-41b0-9c7c-07fad037da96/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 82, in <module>\n",
      "\n",
      "    data.categories['sweep {0} - iterations'.format(sweep)] = solver.status['iterations']\n",
      "\n",
      "KeyError: 'iterations'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/3f95a17f-14d3-40d8-a65b-8eb490b4b4df/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 82, in <module>\n",
      "\n",
      "    data.categories['sweep {0} - iterations'.format(sweep)] = solver.status['iterations']\n",
      "\n",
      "KeyError: 'iterations'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy gmres /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/51956080-7f50-47b4-a983-5693fcd0c0c2/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 69, in <module>\n",
      "\n",
      "    solver = fp.LinearGMRESSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy cgs /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/59144a6c-3615-4733-be02-9670db7e896c/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 67, in <module>\n",
      "\n",
      "    solver = fp.LinearCGSSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "trilinos lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/5e31ccdc-3ab9-4ac4-886a-26c37e1c7a93/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 82, in <module>\n",
      "\n",
      "    data.categories['sweep {0} - iterations'.format(sweep)] = solver.status['iterations']\n",
      "\n",
      "KeyError: 'iterations'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy cgs /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/5ea4c41c-ac9e-4b01-a27e-11bb409746d3/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 67, in <module>\n",
      "\n",
      "    solver = fp.LinearCGSSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy gmres /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/62fc4b2f-464d-4625-87ef-eaf06383a549/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 69, in <module>\n",
      "\n",
      "    solver = fp.LinearGMRESSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy pcg /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/6c26c47f-9307-4e1f-9cc8-bdd37afac7f5/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 62, in <module>\n",
      "\n",
      "    solver = fp.LinearPCGSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "pysparse lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/73b423ce-5146-4976-9d17-123092da8d9d/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 82, in <module>\n",
      "\n",
      "    data.categories['sweep {0} - iterations'.format(sweep)] = solver.status['iterations']\n",
      "\n",
      "KeyError: 'iterations'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "trilinos lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/756e7a62-c787-4540-87ad-99550b185243/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 82, in <module>\n",
      "\n",
      "    data.categories['sweep {0} - iterations'.format(sweep)] = solver.status['iterations']\n",
      "\n",
      "KeyError: 'iterations'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy gmres /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/782bad66-5978-4bcd-9f20-0b55b5af9bf6/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 69, in <module>\n",
      "\n",
      "    solver = fp.LinearGMRESSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy pcg /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/790fd00b-c432-47ad-be87-7a678c4dfd2c/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 62, in <module>\n",
      "\n",
      "    solver = fp.LinearPCGSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy pcg /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/7bc041dd-1dba-4527-b8ec-c3a64810cff6/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 62, in <module>\n",
      "\n",
      "    solver = fp.LinearPCGSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy gmres /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/7d6f6c7c-5f52-4235-9dd9-06b106ab1672/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 69, in <module>\n",
      "\n",
      "    solver = fp.LinearGMRESSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/7dfde565-ef7c-43d0-be6b-625872b7a0e6/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 82, in <module>\n",
      "\n",
      "    data.categories['sweep {0} - iterations'.format(sweep)] = solver.status['iterations']\n",
      "\n",
      "KeyError: 'iterations'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy cgs /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/7e41959e-0a96-435f-9a69-72c11cc447ec/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 67, in <module>\n",
      "\n",
      "    solver = fp.LinearCGSSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/7f9fc5e4-d284-47d4-a1f1-512390879ebb/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 82, in <module>\n",
      "\n",
      "    data.categories['sweep {0} - iterations'.format(sweep)] = solver.status['iterations']\n",
      "\n",
      "KeyError: 'iterations'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy gmres /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/8730c4c5-16ac-4bea-8bfa-9ba7c9df5c4b/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 69, in <module>\n",
      "\n",
      "    solver = fp.LinearGMRESSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "trilinos lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/8b6907ad-a46d-432c-b15b-594895407a72/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 82, in <module>\n",
      "\n",
      "    data.categories['sweep {0} - iterations'.format(sweep)] = solver.status['iterations']\n",
      "\n",
      "KeyError: 'iterations'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/911d570a-1a0f-46f0-abcf-9f05ddc22667/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 82, in <module>\n",
      "\n",
      "    data.categories['sweep {0} - iterations'.format(sweep)] = solver.status['iterations']\n",
      "\n",
      "KeyError: 'iterations'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "trilinos gmres /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/91aed6c0-7a8e-4542-a31f-5e996ec50fe7/\n",
      "/var/spool/gridengine/execd/r033/job_scripts/3099276: line 46:  2794 Segmentation fault      $@\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "pysparse lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/aa62029b-701c-4f25-8a88-0f2a17a0e694/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 82, in <module>\n",
      "\n",
      "    data.categories['sweep {0} - iterations'.format(sweep)] = solver.status['iterations']\n",
      "\n",
      "KeyError: 'iterations'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "pysparse lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/ba503c2c-9b65-43d9-a042-de1a6fabb1bd/\n",
      "Can't expand MemType 0: jcol 9822055\n",
      "\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 80, in <module>\n",
      "\n",
      "    res = eq.sweep(var=var, dt=1., solver=solver)\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/fipy/terms/term.py\", line 241, in sweep\n",
      "\n",
      "    solver._solve()\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/fipy/solvers/pysparse/pysparseSolver.py\", line 116, in _solve\n",
      "\n",
      "    self._solve_(self.matrix, array, self.RHSvector)\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/fipy/solvers/pysparse/linearLUSolver.py\", line 87, in _solve_\n",
      "\n",
      "    LU = superlu.factorize(L.matrix.to_csr())\n",
      "\n",
      "  File \"/users/guyer/miniconda2/envs/fipy/lib/python2.7/site-packages/pysparse/misc/__init__.py\", line 29, in newFunc\n",
      "\n",
      "    return func(*args, **kwargs)\n",
      "\n",
      "  File \"/users/guyer/miniconda2/envs/fipy/lib/python2.7/site-packages/pysparse/__init__.py\", line 47, in factorize\n",
      "\n",
      "    return self.factorizeFnc(*args, **kwargs)\n",
      "\n",
      "MemoryError\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy cgs /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/ba8c4d6f-17d3-4840-b145-d268e4a09ac7/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 67, in <module>\n",
      "\n",
      "    solver = fp.LinearCGSSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "trilinos lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/bbf85436-fd7f-4f32-97a6-914e85ba384c/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 82, in <module>\n",
      "\n",
      "    data.categories['sweep {0} - iterations'.format(sweep)] = solver.status['iterations']\n",
      "\n",
      "KeyError: 'iterations'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy cgs /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/c26c2af3-2645-4f80-b64b-fc09a1f0519d/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 67, in <module>\n",
      "\n",
      "    solver = fp.LinearCGSSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy pcg /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/d2d89f5e-735f-4840-a3c0-52ef86769292/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 62, in <module>\n",
      "\n",
      "    solver = fp.LinearPCGSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy gmres /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/e0300f8c-dbbf-4f44-ba4b-818670123c55/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 69, in <module>\n",
      "\n",
      "    solver = fp.LinearGMRESSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy pcg /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/e65df273-5f33-4df9-a291-daf6d9ec66ee/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 62, in <module>\n",
      "\n",
      "    solver = fp.LinearPCGSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy pcg /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/ebb1ee98-33c0-43d0-ab4d-c0a7d0c44081/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 62, in <module>\n",
      "\n",
      "    solver = fp.LinearPCGSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy cgs /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/fb03d65a-1f3d-4fb9-8f8b-68eaf9f33469/\n",
      "Traceback (most recent call last):\n",
      "\n",
      "  File \"/users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py\", line 67, in <module>\n",
      "\n",
      "    solver = fp.LinearCGSSolver(tolerance=args.tolerance, iterations=args.iterations, precon=fp.JacobiPreconditioner())\n",
      "\n",
      "AttributeError: 'module' object has no attribute 'JacobiPreconditioner'\n",
      "\n",
      "---------------------------------------------------------------------------\n",
      "scipy lu /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/fc5bba11-b5f7-4dc2-b885-9db716c5f770/\n",
      "---------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "for t in b:\n",
    "    if not \"elapsed\" in t.categories:\n",
    "        print t.categories[\"library\"], t.categories[\"solver\"], t.abspath\n",
    "        for f in glob.glob(os.path.join(t.abspath, \"setup.sh.e*\")):\n",
    "            with open(f, 'r') as fp:\n",
    "                for l in fp:\n",
    "                    print l\n",
    "        print \"-\"*75\n",
    "#    !more {t.abspath}/setup.sh.e*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[?1h\u001b=\r",
      "{\"categories\": {\"processes\": 1, \"solver\": \"lu\", \"script\": \"/users/guyer/FiPy/fip \by/examples/benchmarking/solvers/diffusion.py\", \"sweeps\": 10, \"library\": \"pyspars \be\", \"iterations\": 1000, \"tolerance\": 1e-10, \"numberOfElements\": 1000}, \"tags\": [ \b]}\r\n",
      "\r",
      "\u001b[K\u001b[?1l\u001b>"
     ]
    }
   ],
   "source": [
    "!more /Users/guyer/Documents/research/FiPy/fipy/solvers/solver_timing/Data/0c5af030-63aa-46d2-a808-744a24e8ae90/Treant.333791eb-be75-4cc4-b3b5-f2f1f30a7904.json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(b.categories.any)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x110207090>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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FVqtWraJx48aZCQqAl5cXzz33HN999122Nq01S5Ys4f7778exEM3VF0KIxGuJbI7YnJmY\n/Bv1LwpFA78G9GvQj9aVW9PIvxFOaRrWrIFvhsCvv8Lly1C7Nnz0EXTtCoH2vFtO7mUU0Ra1wtkG\nwAaMGhONsSYKwHSgj9Z6nlLKGxiGMeyzCwjRWl+41xtba7gn2DuYsJdvO5nonu9hDRcuXCA+Pp4a\nNWrc8pyTJ0/SpEmTm9pv7Elp3rw5Tz31FMOGDWPs2LG0aNGCzp078+yzz+JcSOfwCyEKr4xN+jJm\n4fx+8neSUpPw8/CjdeXWfND0Ax4NfBRvszekpsKGDTC8HyxaBBcvQnAwvPUWPPOM8fdCpkgO92it\nf+c2Bb1a6/HAeGvf21o9KWYns1V6OfKD1vdcb5zNvHnz2LZtG0uXLmXVqlX06dOHMWPGsHXrVsxm\n6wxPCSFEXolNjGXtsbWsOrKK1cdWcyr+FC4OLjQLaMaIliMICQqhhk8Nox7PYoE//oCff4YFC+DC\nBahcGfr1MxKT++8vkPvl5FZR7UmxmaJYOOvr60vx4sX5999/b3lOQEAAR44cuan98OHDOZ7fsGFD\nGjZsyPDhw5kzZw7PPfccP//8M3369LFa3EIIYQ2pllT+PvV35vTg7We2Y9EWqvtU5+nqTxNSOYRm\nAc1wc0pfOE1r+PtvmDsX5s2DM2eMupKePY3EpH79Qp2YZFUke1JE/lJK0blzZ2bNmkV4eHiOSVpI\nSAjjx49nz549mXUpsbGxzJ6dfc/GS5cu4eXlla2tdu3aACQnJ+fREwghxJ05celEZk/JumPriEuO\no4RrCR6r/Bgv1XuJ1pVbU94zS0Gr1hAebiQmc+fCyZPGTJynn4Zu3aBRI2NtE2F1kqRkYa3hnoJm\n5MiRrFmzhmbNmvHyyy9TrVo1zpw5w4IFC/jzzz955513mDlzJq1atWLAgAG4u7szefJkAgICuHjx\nYuY05OnTpzN+/HieeOIJKleuzOXLl/nhhx/w9PSkXbt2Nn5KIURRdSXlCr+f+D1zevChmEM4KAca\n+TfizcZvElI5hAZ+DXAw3bBj8N69xlDO3Llw+DCUKgVPPWUkJk2bFvgdhu+VDPfks6I43APg5+fH\n33//zUcffcTs2bOJj4+nXLlytGvXDrPZTPHixdm4cSMDBgxg1KhR+Pj48Nprr+Hm5sbAgQNxdXUF\njMLZ7du3M3fuXM6fP4+npycPPvggs2fPJiAgwMZPKYQoKizawp7zezILXv+I+INrlmsEeAYQUjmE\nUa1G8UilR/By9br5xYcPG0nJzz8bSYqnJ3TpAt9+C488Ak5O+f9AdkqGe0S+8ff3Z+rUqbc8XqtW\nLTZu3Jit7Y033sDV1RVvb28A6tSpw8yZM/MyTCGEyJHWmr8i/2Lyzsn8dvi3zE36WlZsyVetvyIk\nKIT7St5HjvvWnjhh1Jf8/DPs3Gnsj/P44zBqFLRuDS4u+f48wiBJisiV5ORkXLJ8ocbExDBz5kya\nNm2a8xe9EELkg/jkeGbumcmEHRP4J+ofKpeoTM/aPQkJCuGh8g/h4niLBOP0aZg/3+g12boVXF2N\nHYY/+ADatSsSOwwXBJKkZFFUa1Jyo3HjxrRo0YLg4GDOnTvHlClTuHz5Mh999JGtQxNCFEG7z+0m\ndEcos/6ZReK1RDpV7cSXrb/k0cBHMalbFLFGRRlThefOhc2bwdHR2GF41ixjh2EPj/x9iAIuoybl\n0qVLeXYPSVKyKKo1KbnRrl07FixYwKRJk1BKUb9+faZOncpDDz1k69CEEEVEUmoS8/bOY8KOCWw5\ntQU/Dz/eavwWL9Z7Ef/i/jm/KDYWfvnFGMpZv96YHvzYYzBlirHDsFcOdSkiR2kJaSQcSiDhgPFR\n+2Bt7ou8j137d7GZzXlyT0lSRK6MGDGCESNG2DoMIUQRdDjmMBPDJjJ111RiE2N5LPAxFnZdSMcq\nHXFyyKGQNT7eWI5+7lxYvdpYDbZFCwgNNYpg0+voxM201qScTTESkYPXE5KEAwkkR1xfSsLJ1wlz\nsBmPhh74tfSDsXkTjyQpQggh7E6qJZUlB5cQuiOUtcfWUtKtJL3r9KZv/b7cV+q+m19w7hysWAFL\nlxob+iUnw0MPwVdfGdOGy5bN/4ewY5ZkC4lHErMlIRlJSdrlNACUo8ItyA1zsBnf7r6Yg83GR1Uz\nTiWuJ4dXw69KkpIfpCZFCCFs63T8aX4I/4Efwn/gzOUzNPZvzIzOM3iq+lPXV30FY0n6sDBYvtz4\n2LHDGMpp1Ag+/dTYyK+I7zCsteZa9LUcE5Gk40lgMc5zLOGIOdiMe013fJ7yyUxEXANdMTllr+9J\nTobI03DqH5g7dw4bNsg6KflGalKEECL/WbSFtcfWErojlKUHl+Lq6EqPWj3o16AftcvUvn7i5cvG\n7sLLlhm9JufPG+uYtGkDAwYYf/r42O5BbMRyzULSsaQch2hSL6YaJ5nAtZIr5mAz3p29MVc1Z/aM\nOHk7oZQiOdlY5X9fJJwKg8jFcOoUREZe/zMqKuudu1OiRHdKlQoHZJ0UIYQQhUhMQgxTd01lYthE\njsQeoaZvTca1HUePWj0o7lLcOOnwYaOnZNky2LQJrl2DatXg+eehfXto0qTILLB27eK1m5KQxIOJ\nJB5JRKcam8U6eDhkJh+l2pfK/LtDBTfORps4dQr+zUg65mVPQs6fz34/Ly/w9zc6pOrVM5aOyfjc\n39/4KFbM2DEgj9ZykyRFCCFE/tFas+XUFkJ3hDJ/73w0mqerP820x6fRpHwT1LVrxs7Cy5YZycmh\nQ+DsDC1bwpgxRmJSqZKtHyPP6DRN0smkm4ZnEg4kcC3qWuZ5LgEumKuaKdG6BGVeLcdVbzMXXM2c\nSnQm8pQyko8dcGqxkYTcmIB4el5PNurVg06drn+e8WexYvn88DmQJEUIIUSeu5x8mVn/zCJ0Ryh7\nzu8hsEQgw1sOp1edXvhcsRjFrsvGGrNxLl8GPz8jIRk9Glq1so+fmFaUejk1MwFJPJilgPVwAjrZ\n6BUxuZlwq2KGCmbSOpTgotmN045mjiaZOXnewegBmWckIFpfv7an5/Vko04dYwmYG3tACsqSMJKk\nZCGFs0IIYV17zu8hdHsoM/+ZScK1BDpW6cjoRz7jsXhvTCt+gzc7wLZtRtFrw4bwzjtGclKnjtFW\ngGmLJvlU8k1DNAkHE0g5nXL9RG9nkkubiSvuydkHy3I81cy+K2b+Pe/CuT0Kvfv6qcWLZ09AOnS4\nuQckvxIQ2WAwn0nhrBBC3Luk1CQW7FtA6I5Q/or8i7LFyvJe7dfoe7Ey3mu3wpsvwNmzxk/ckBB4\n9VVj5VdfX1uHflfSEtJIPJyYLQm5ut/4UycaU2gsDoornm5EuZo5qctwwNPMv3FmIjCTEO0I0UZy\nkZFoVKgGTfxvTkCKF7fxw2YhGwwKIYQoMI7GHmXCjglM3TWVmMQYepgb831yb2r9EYnp/TGQkgLB\nwfDss0ZvycMPF5iiV601KeeMRc4u70sgOjyBy/8mkHosAVNMMip9uOWKoxORJjNHUzyIoDSRGInI\nFVdXypVWmclGjfIQckMSYk8JiL2QJEUIIcRdS7WksuzQMkJ3hLL+0GranCvGgtiqNN7jicvhLeAc\nBs2bwxdfGIlJ5cq2Dvk/pVy1ELklkbPbErj0TwLJhxMwnUrAHJOAS6qxyFkacBo3IjATiS/nXcxc\nK2PGoZIZn0An/P0huDw8miUJ8fS07XMVVJKkCJtKTEzETXYbFaLAOXP5DD+E/cDCTROos/McgyNL\nsvSgG85XrkCZ00ZC8vmX8OijdlOlmZpqjDJFRsLpfSnE7jKGZDiZgOuFREpcTsAnLRGH9PM1jlww\nmYn1cCepog+qghlzsBsla7pRvpKJell6QAp4+YzdkiRFALBx40befvtt9u7di7+/P4MHD+bMmTMM\nGzYMi8UYUzWZTLz22ms0a9aMIUOGcPz4cerUqcOkSZOoWbMmEydO5Msvv+TUqVM0atSI6dOnU6FC\nhcx7tGjRgtjYWKZNm8Ybb7xBWFgYffv2ZcyYMQD89ttvjBo1ivDwcEwmE82aNWP06NFUr1498xrn\nz5/nvffeY+3atVy4cIGSJUvSsGFDxo0bl+1eQgjrs2gL64+tY+WCzzCv3kD7w4qPTlkwaeCByjC4\ng5Gc1K0LplvsRJyHtIZ//oGDB9MTkQgLcQeSSDuegPO5BLziEyiP8eFDKj4Yi67GublytZSZlKql\niA4y41XLTJmGZmrXdsLLS0kCYkOSpAh27txJ27Zt8fPzY/jw4aSmpjJ8+HC8vb1RN3x1btq0iSVL\nltC/f38ARo4cSYcOHXjnnXcIDQ2lf//+XLx4kc8//5w+ffqwdu3azNcqpYiOjqZdu3Z069aN559/\nntKlSwPw008/0atXL9q0acPo0aNJSEggNDSUpk2bsnPnzswEpEuXLuzfv58BAwYQEBBAVFQUa9as\nISIiQpIUIfJIzIUINk8ZSvKShTz0TzyPXoYUsyuqdWtMw54wil7Tv5ZtISUFFiyAH0cn47/7LEFc\nIUAlUEsn4ohRLJLq7EBqgBnHSm541CyJb30zPvXNuN3nhoOrw23uIGxGa13kP4B6gA4LC9M5CQsL\n0/91vKDr2LGjLlasmD537lxm29GjR7WTk5M2mUyZbUop7ebmpiMiIjLbJk2apJVS2s/PT1+9ejWz\n/f3339cmk0mfPHkys61FixbaZDLpH374Idv9r1y5okuUKKFfeeWVbO1RUVHay8tL9+3bV2ut9aVL\nl7RSSn/11Vd3/IyF/d9QCGuzHD2qjw1/U++u66cTHdEa9Gk/Dx35QldtWbNG6+RkW4eoL1zQ+tNP\nta7ne1W/xQG9Rm3U69026e0tdulDrx3Sp74/pWPXxuqkU0naYrHYOtxCK+P7K1BPW/nns/SkZGG1\ndVISEuDAAesFlpPgYDCb7/kyFouFdevW0aVLl8xeDYDAwEDatm3LsmXLsp3/6KOPUj7Lpl0PPvgg\nAE899RTmLPFktB87dixbD4eLiwu9evXKds01a9YQFxdHt27diImJyWxXSvHggw+yYcMGANzc3HB2\ndmbjxo306dMHLy+ve3x6IUSm1FT46y9Sfl3ElV/mUfL4WcqZYHuQK3/260CdFz7Ar3YjW0cJwN69\n8M038Pf0eJ5OjeBLSzQmb2cqvV0Jv1f8cPSUH235QdZJyWdWWyflwIG828ggQ1iYsZbxPYqKiiIx\nMZGgoKCbjuXUVv6GXUU900vW/f39b2rXWnPx4sVs7eXKlcPRMft/u8OHD6O1pmXLljfdTylF8fR5\nec7Oznz++ee8/fbblC5dmkaNGtGhQ4dsw0ZCiDsQHQ0rV8KyZaStXIFD3GUuFlOsuE9z/o0G1H/+\nPVrVeQKTyv/6khtZ0hel/XqsJm5dLD2dI3k25RLOgW5UfK8Kpf+vtAzb5DNZJ6WgCg42koi8vocN\nODjk/E3gVu0661rNkONMHovFglKKmTNn5phsZE1qBg4cSKdOnVi8eDGrVq3i448/ZtSoUWzYsIHa\ntWvf9FohRBZaw549xp44y5ejt25FWSzsr1iMuXWusLV2KRp2fIUXH3iZCp72UeN15QpMnw7ffm3B\n/8gF+rhF4MdVitXyIOC9Gnh39kY5SGVrYSVJSl4wm63Sy5EffH19cXNz48iRIzcdO3z4cL7EULly\nZbTW+Pj48Mgjj9z2/EqVKjFo0CAGDRrE0aNHqV27Nl999RUzZszIh2iFKGASEmDduszEhFOnsBRz\nZ19tP3540sy8CleoXqsh/Rr044Oqj+PkYB+Lq504Ad99BzN+SKPp5bOMdjtFcZIo0awEFd4NwquF\n102F/aLwkSSliDOZTLRq1YrFixdz7tw5ypQpA8CRI0dYuXJlvsQQEhJC8eLFGTlyJC1atLhpOCg6\nOhpvb28SExMxmUy4uLhkHqtUqRIeHh4kJyfnS6xCFAgnTxoJybJlsGEDJCWhK1fmWMs6TPYrwxin\nHZiLXaBX7RfZ2OAVqnpXtXXEgNHR8+ef8PXXsG7RNbq6nGamOo2zuobv476Uf6cGHnXsY80VkT8k\nSREMHTqU1atX06RJE/r160dqairff/89999/P7t27crz+3t4eBAaGsrzzz9PvXr16NatGz4+PkRE\nRLB8+XL49yqMAAAgAElEQVQefvhhxo0bx6FDh2jVqhVdu3alevXqODo6smjRIqKiomRDSFG0pabC\nli3Xe0v+/RccHaFpU+I/fpeZ/hcZdWEhpy4vo2G5hkxsMJWuNbpidrr34ntrSEmBefOM5CQyLImX\nS0TyqtNZHBWU6VOG8m+Vx62SLPpYFBXqJEUp5QbsB+Zprd+xdTz2ql69eqxcuZK3336bjz/+mPLl\nyzN8+HD27dvHgSyzlJRSOXav/ld7btrAKMAqV64cn332GV9++SXJycmUK1eOpk2b0rt3b8Ao2n32\n2WdZt24dM2fOxNHRkeDgYObPn0/nzp3v9vGFKJhiYzOLXlm5Ei5eBB8faNcO/fHH/F7Fme8O/sSv\nBz/FOdKZZ2s+S78H+lGvrP0MRUdFwcSJMH48uJ67wlt+kdRwiMIJB8q9W55yr5fD2cfZ1mEKGyrU\nSQrwAbDV1kEUBC1atGDHjh3Z2p544olss3bS0tJuel1AQECO7c2bN7+pPWMq8a00a9aMZs2a3fJ4\nyZIlGTdu3H9eQ4hCS2ujhyRjGGfLFmPKS7168Npr0L49sTUrM33PT0wI+5BD+w5R3ac6Y0PG8n+1\n/g9PV/vZPGbPHmMK8ayZmvuJ48syEZQjFhcHF/y/DKTsi2VxLFbYfzyJ3Ci0/wuUUkFAVWApUNPG\n4di95OTkbLUehw8fZsWKFZm9GEIIG0hMNGpKli0zkpOICHB3N/bDmTjR6DUpW5Ztp7cRuiOUuWvn\nkmZJ48nqT/JDxx9oWqGp3RSXWizGI3z9NWxYr+lYKob5ZSLwiIjH7GGmwvRgfLv7YnKy/XRnYT8K\nbZICfAm8DTxk60AKgsDAQHr27ElgYCAnTpxgwoQJuLq6MnjwYFuHJkTREhl5vbdk/XojUQkMhMcf\nhw4djB2FXVy4mnKV2f/MJnRpKDvP7aSiV0WGNB9Cn7p98HX3tfVTZLp8GaZOhXHj4ORRC30Dz/M/\nv0gczyTgWc2T8t/XpFS7UiiTfSRTwr7YXZKilGoKDAbqA2WBzlrrJTec0x8jASkD7AZe11pvz3K8\nE3BQa31EKfUQIP/7b6NNmzb8/PPPnDt3DhcXF5o0acLIkSOpbOfbqgtR4KWlwdat14te9+wBBwdo\n2hSGDTMSk6pVM7fZ3Ru1l9Adofy05ycuJ1+mfZX2jHhkBCGVQ3Aw2c9iZsePw7ffwo8/gr6ayv/u\nP0tjn0g4lkKpTqWo8G5VPJvYzxCUsE92l6QA7sAuYAqw8MaDSqlngK+Al4FtwCBglVKqitY6Ov20\nRkA3pdTTgAfgqJSK01qPyI8HKIh+/PFHW4cgRNFx8SKsWnW96DUmBry9jY36PvgAWreGLNs+JKcm\ns2j/IkJ3hLI5YjOl3UvzesPXeaneSwR4BdjwQbLTGjZtMupNfv0VAoqnMLbmKYL2nkHvTaN0j9KU\nH1we92rutg5VFBB2l6RorVcCKwFUzoOpg4CJWusZ6ee8ArQH+gCj06/xPvB++vGeQA1JUIQQNqM1\n7Nt3vbfkzz+NHpQ6deCVV4zekgceMHpQsjh+8TgTwyYyZecULiRcoEXFFsx9ai6dgzvj7GA/s16S\nk+Hnn416k127oFnlBBY+HEmJv89h2mOibN+y+L/hj6u/q61DFQWM3SUp/0Up5YQxDDQyo01rrZVS\na4HGNgtMCCFysmmTsQDI8uXGEqpms1H0On48tGsHN+x5BZBmSWPF4RWE7ghl5ZGVFHcpTq86vehb\nvy/VfKrl/zP8h/PnYcIE43GioqD3Q5f5qlkEpj8u4BTvhP9HFfF71Q+nEvaxiq0oeApUkgJ4Aw7A\n+Rvaz2PM5LmJ1np6bi+esQtyVt27d6dqVftYjVEIUYBMnQp9+kDFitC+vdFb0qIFuObcm3Duyjkm\nh09mUtgkIuMjaeDXgMmdJtOtZje7WXQtw65dRq/JnDng6KB559GLPBYdQcqfl3ANdKX8d/dRplcZ\nHNzsp0ZGWEfGzsdZyS7It6cAfduzbuNWuyCHh4ff66WFEEXJ4sXw4ovw8stGV8MtpgFrrdl4YiOh\nO0L55cAvOJmcePb+Z3mlwSs08GuQz0H/t7Q0WLrUSE5+/x0Cymu+f/oC9/8bQeLSKzjXK0bQ3Or4\nPOkjG/4VYhk7H2cluyBfFw2kATdulevLzb0rdyyjJyWnfwQhhMiVjRuhWzfo0sUYB8khQbmYeJHp\nu6czYccEDsYcpJp3Nb5q/RXP134eL1evm69pQ/HxMGWKMYX4+HFo3iiNpS+ew3t9JEmzknBp5cV9\na2pRolUJu1mTReSPjF4V6UlJp7W+ppQKA1oBSyCzuLYVcM9Lkd6qJ0UIIXIlPBw6dTKmD8+ceVMh\n7PbT2wndEcrP//5MqiWVLtW6MLHDRJoFNLO7H/BHjhhTiKdMgaQk+L/O1/gp5AwsOsW1bdfweNKH\nGvNq4FFfNvwrqjJ+oS9SPSlKKXcgiOtrmwQqpWoDsVrrSGAMMD09WcmYgmwGpt3rvaUnRQhx1w4d\ngjZtoFo1+OUXSF/B+WrKVeb8O4cJOyYQdjaMAM8APmz2IS/UfYHSxW7sFLYtrY2OoK+/NoZ2SpWC\nd3on0Sn5FPGzz5J6zULZ3mXxf8sfc5B91cmI/FdUe1IaABswakw0xpooANOBPlrreUopb2AYxrDP\nLiBEa33hXm8sPSlCiLty+rSxtom3tzGTp1gxUi2pvL/ufSaFTSI+OZ5297VjWfdltAlqY1eLroHR\nUzJ7trG+yZ49ULMmTPvkKvUPRxI94TyX3R0oN6Ac/gP8cS5tP1OfhW0VyZ4UrfXvwH9u3qC1Hg+M\nt/a9i2pPyrRp0+jTpw8nTpygQoUKtg5HiIIlNtZIULSG1avB2xutNa+teI3J4ZMZ3GQwfRv0paJX\nRVtHepOzZyE01KjtvXDBmIA05oU4fNdFEPNxDHF+zgSOCqTsy2Vx9LC7HxfCxopqT4rNFNWeFKWU\n3Y2HC1EgXLlirHcSFQV//JG57slnf3xmLMLWaQq969rfJp1hYcaQzty54OwMfXprXrw/hrSfIokb\nGEdisJmqP1al9HOlMbnIhn8iZ0WyJ0UIIQqElBR48knYu9co5EhfT2nmnpm8v/59hjYfalcJSmqq\nsVT9118b+VRAAHz2qYXOHlFEfxdB7HcJFG9cnJqLa1Kqo2z4J+yDJClZFNXhHiHEHUpLg+efN5KT\n336D9N8i1x9fT59f+9CnTh8+bv6xbWNMd+mSscnft9/CyZPGxKOFM1Opf+4sZ745RURkMiXbl6RK\naBU8H/aUXlWRazLck8+K6nDPjUwmE0OHDuXjj7N/k61YsSKPPPIIU6ZMsVFkQtgBreH112H+fOPj\nkUcA+Of8Pzwx9wkeqfQIEzpMsPkP+0OHjLVNpk0zOn26d4fXn0+h1O+nOT3gNCfi0/Dt7kv5weUp\ndn8xm8YqCiYZ7hF2xdbfdIWwC0OHGtWmkycbC7YBp+JP0W52OwJLBDL/6fk4OdhmrxqtYd06Y0hn\n+XLw8YG33oIX2ieSNCOScx3PkaCg7EtlKf9meVwryIZ/wr5JkpIHEtLSOJCQkKf3CDabMTvY1zRG\nIQq9ceNg2DD4/HN44QUA4pLiaD+7PSZlYvmzy/Fwyf/FzRITjbXjvvnGKJGpXdvYOqhj9Suc/zqC\nY02icPRypMJ7FSjXvxxOpWTDP1Ew3HGSopTqCURrrZenfz4aeBnYB3TXWp+0boj5x1o1KQcSEqgf\nFmbFyG4WVr8+9TxkpUch8s2sWTBwIAweDO+8A0BKWgpPznuSiLgI/uj9B34efvka0unTxsr7Eyca\nM6E7dYLvvtXU0peIHB3BP6su4lrRlaCvgyjbpywOZvnFRliPvdakvA/0A1BKNQZeA94AOgBjgS5W\niy6fWasmJdhsJiyPxuey3kMIkU+WL4devYxdjT//HDA2B3xp6UtsjtjM6h6rqeFbI9/C2bbN6DWZ\nNw/c3IxOnf79NJ7/RBPxbgR7tl/GvZY71WZVw6erDyZHmUYsrM9ea1LKA0fS/94ZWKC1nqSU+hPY\naK3ACjKzg0Oh7OVIS0uzdQhC5L8//oCnnjJWOps4MXPDwI83fMyM3TOY8+QcmldsnudhpKbCokVG\nvcmWLRAYCF9+CT2ftZCw+ByRHSM5dSgRrxZe3P/b/ZQMKSl1ZKLAu5sk5QpQCogAWmP0ngAkAW5W\nikvYUIkSJbh06VK2tmvXrnH27FkbRSSEjezZYyQnDz4Ic+aAo/Et84ewHxixeQSfP/o53Wp2y9MQ\nYmONGt3vvoPISGjZEhYvhjZNUzn/wxn21TpFyvkUvJ/wptqMahR/sHiexiNEfrqbJGUNMFkptROo\nAixPb68BnLBSXDYh66QYKleuzKZNm7K1TZgwQXpSRNFy7BiEhEDlyrBkCbgaM2FWHF5Bv+X96P9A\nfwY3GZxntz9wwKjTnT7d6EV57jmjJCbYN5lTX59i2/NnsCRZKPN8Gcq/XR5zVRkCFvnLXmtS+gMj\nMIZ9ntRax6S31wfmWCswW5B1Ugwvvvgir7zyCk899RSPPfYYu3fvZvXq1fj4+Ng6NCHyx7lz8Nhj\n4OFhLNZW3OidCDsTRtf5XelQpQPftPnG6sMpGdv/fP01rFwJpUvDu+9C377gEZdA5BeRbJ1xDpOr\nCb9X/PAf6I+Ln4tVYxAit+yyJkVrfQmjWPbG9iFWiUjY3EsvvcSJEyf48ccfWbVqFc2aNWPNmjW0\natVKxrhF4XfpktGDkpwMf/4Jvr4AHL94nPaz21PTtyazn5xt1Z2MExLgp5+MYtj9+6FuXaMH5Zln\nIHl3PBGvRrD/l2icSztTaVgl/F7xw9FTVpAQhZ/8Lxf07NmTnj17Zn6ulGLkyJGMHDky23nHjh3L\n79CEyF8JCdCxI5w6BZs3GxvcALGJsbSd1ZZizsVY2n0pZifrDK1ERsL338OkSRAXB507G7W5Dz2k\nubg6lv1tIrm08RJu97lRZWIVSv9faRxcZRqxKDokSRFCCIBr16BrV9i501i2tXp1AJJSk+g0pxMx\niTFseWELPu73Puy5dasxpLNgAbi7w4svwmuvQUB5CxfmXSCsXgRXd1/F4wEPaiyogXdnb5SD9GKK\nokeSFCGEsFiMNVBWr4Zly4zZPIBFW3j+l+cJPxvO+p7rCSoZdNe3uHYNFi40kpO//4agIOPvPXuC\n2SGNs1POsu2rUySdSKJESAmCxgbh1cJLhlhFkZbrJEUpZdZa5+1a7zYms3uEKIK0hkGDjBVlf/4Z\nWrfOPDR49WAW7FvAomcW0ci/0V1dPibGGM75/ntjhdhWrWDpUmjXDtIuXuP02NPs+fY012Kv4fuM\nLzV+qYFHncK3zpIofOxtdk+0Umo9sARYqrUudItmyOweIYqgTz815vqOH28M96T7Zus3jNk6hm/b\nfkvn4M53fNmEBGP1/ClTjI6aHj2MKcT33w9JEUkcfTOSsz+cBQuUeaEM5d8qj1slWWpKFBz2Nrsn\nGHgc6AqMU0rtxkhYlmit/8mL4IQQIk+FhsJHH8Hw4dCvX2bzwn0LGbRqEIObDOa1hjdNZrytc+eM\nfXT27oX33zemEPv4wJV/r7D/+Uii5kTh4OFA+bfKU+71cjj7OFvzqYQoNHKdpGitI4BvgW+VUp5A\nO4ykZbBSKpb0hAX4XWstq34JIezbvHnQv7/RvfHBB5nNf0X+RY9fevBMzWf47NHP7viye/dC+/aQ\nkgKbNkG9epq4P+LY0zuC2OWxuJR3IfCLQMq+WBbHYlIWKMR/uauvEK11HMbCbXOUUo5AS6ATMBXw\nUEq9rrWeZb0whRDCilavNsZfnnsOxozJ3I/nUMwhOs3pRMNyDZn2+DRM6s425lu71tjmp0IFY09C\n854Ydj50kvgt8ZhrmAmeHoxvd19MTrLhnxC5cc9fKVrrVK31Gq3161rrAKAVcOjeQxNCiDywdSs8\n8YRRIDtlCpiMb4Pnr5ynzcw2+Lr7sviZxbg43tlKrj/+CG3bQqNGsGmdhcRRh/inwz8oB0XNpTV5\nYM8DlHm+jCQoQtwBq/c1aq13WvuaQghhFRljMfXqGcM9Tk4AXE25Soc5HUhMTWRDzw2UcCuR60ta\nLPDhhzBqlFF78tV7SRzouJcrO69QZVIVyr5YVqYRC3GXZEA0C5mCLEQhduKE0Xvi72/MATYbq8am\nWlLptrAbB6IPsKnXJgK8AnJ9ycRE6NUL5s+HL7+E3nUvsqvhPkyuJupurkvxhrIjsSi87G0KcqEn\nU5CFKKSioowExdXV2LnPywsArTWvr3id3w7/xvJnl1O3bN1cX/LCBXj8cdi1CxbM1zQ4Fsmex47h\n1dKL6nOqy4wdUejZ2xRkIYQoeOLjoU0buHzZ2DCwbNnMQ5//+TkTwibwY6cfCQkKyfUlDxwwRo2u\nXIENy1Nx//4AxxZGU+G9ClQcXhGTo9SdCGENd5WkpM/oaQFUBmZrrS8rpfyAeK31FSvGJ4QQdy8p\nyejuOHbMmA8cGJh5aNaeWfxv3f8Y0nwIfer2yfUlN2406m79/OC3iVe59OpeLp5OpsaiGvg8ce/7\n+gghrrvjJEUpFQCsBCoALsAa4DLwbvrnr1gzQCGEuCupqdC9u7FRzurVUKtW5qH1x9fT+9fe9K7T\nmyHNh+T6kjNmGJsBNm8OP/SIIvKJg7hUcKH+9vqYq1pnZ2QhxHV30yf5DbADKAEkZmn/BWP6sRBC\n2JbW8PLLxmaBCxbAww9nHvo36l+emPsELSu1ZGKHibmaeaM1DBlibAbYs4eF8bWOcqLXPkq2LUm9\nv+tJgiJEHrmbJOVhYITWOuWG9hNAuXuOyAqUUp5Kqe1KqXCl1B6l1Iu2jsmeDR06FJPJxMGDB+na\ntSuenp54e3vzxhtvkJycnO3cmTNn8uCDD+Lu7k7JkiVp3rw5a9euzTyutWbo0KGUK1cOd3d3WrVq\nxf79+6lYsSJ9+uS+S12Ie/LuuzB1KkybZuzkl+50/GnazmpLJa9KzH96Pk4OTre9VHKyse7bsGHw\nxQcp9D+xh9PfRFL5q8pUn1tdVo0VIg/dzVeXQ/rHjfwxhn3sQTzQVGudpJRyA/YqpRZqrS/aOjB7\nlPGbZNeuXalUqRKfffYZW7duZdy4cVy6dIlp06YB8Mknn/DJJ5/w0EMPMXz4cJydnfn7779Zv349\njz76KADvvfceX3zxBY8//jitW7dm9+7dhISE3JTsCJFnRo+GL76Ab74xVpRNF58cT7vZ7VAoVjy3\nguIut58eHBNj1J9s2wYLR8RTdsJerqZYqLOuDl7NvfLyKYQQ3F2Sshp4A3g5/XOtlCoGfAKssFZg\n90JrrYGk9E8zthXNt9WU0hLSSDiQkKf3MAebcTDnlCvevcqVK7No0SIA+vXrh4eHB6Ghobz99tu4\nubkxfPhwnnzySebPn5/5mtdeu775WlRUFGPHjqVLly4sWLAgs33YsGEMHTrUqrEKkaPJk41elI8+\nggEDMptT0lJ4ct6TnLx0kj/7/Imfh99tL3X4sDGD52KsZu2AM6R9cgSXBh7UmF8Dl3J3thqtEOLu\n3E2S8hawSim1D3AFZgP3AdGA3ayAlr4J4u9AEDBYax2bX/dOOJBAWP2wPL1H/bD6eNTzsNr1lFL0\n798/W9vrr7/O+PHjWbFiBQ4ODmit+fjjj295jXXr1pGWlka/LLvJZlxHkhSR5xYtMpZ87dcPPvkk\ns1lrzUtLX2LTyU2s6rGKGr41bnupzZuhc2fwK5XG8uaHSPjiPH79/QgaE4TJWaYXC5Ff7jhJ0Vqf\nUkrVBroBtYBiwI/ALK114n++OBeUUk2BwUB9oCzQWWu95IZz+gNvA2WA3cDrWuvtN8QZB9RRSvkA\nvyilFmitL9xrfLlhDjZTPyxvFrbJeg9rCwoKuulzk8nEyZMn0VpjMpmoVq3aLV9/8uTJHK9TokQJ\nSpTI/TLjQtyxDRuMmTxPPw3ffpu5YSDAkI1DmLF7BrO7zKZFxRa3vdTs2dC7N7Srm8i7CXtJ+i2B\n4BnBlPm/Mnn4AEKInNztLsipwEwrx5LBHdgFTAEW3nhQKfUM8BXGcNM2YBBGz04VrXV0DrFeUErt\nAZoCi/Io5mwczA5W7eWwB8YImhB2aMcO6NQJWrQw5gg7XB8G/SHsB4ZvGs7nj35O9/v/u6NXa/j0\nU2Ok6H+tYmgXvh9KOFJvSz2K1S6Wxw8hhMjJ3S7m5ocxy8eXG2YIaa3H3UtAWuuVGOuwoHKeGzgI\nmKi1npF+zitAe6APMDq9rTRwVWt9JX3Ypynw/b3EVRQcPnyYgIDr+5YcOXIEi8VCQEAADg4OWCwW\n9u3bR60s601klfHaI0eOZLtObGwsFy9KzbLIAwcPGlsP16xpDPc4X1+K/rfDv9FveT9ebfAqg5sM\n/s/LpKQYM5ZnTNdMa3mSgPUnKN6uJNV+qoZTidvPABJC5I07HlxVSvUCjmMM8byNkTRkfLxhzeBy\nuLcTxjDQuoy29CLZtUDjLKdWADYrpXZ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Sgh2YSKG1c6eXoEREeAsGxsYCkJaSxtqha9n0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\niIiEJCUpIiIiEpKUpIiIiEhI+n8FGR0TWXLcUgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1101e4d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, results in df[df[\"library\"] == \"pysparse\"].groupby(\"solver\"): \n",
    "    results = results.sort_values(by=\"numberOfElements\")\n",
    "    plt.loglog(results[\"numberOfElements\"], results[\"elapsed\"], label=name)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"number of elements\")\n",
    "plt.ylabel(\"elapsed time / s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>elapsed</th>\n",
       "      <th>iterations</th>\n",
       "      <th>library</th>\n",
       "      <th>numberOfElements</th>\n",
       "      <th>processes</th>\n",
       "      <th>script</th>\n",
       "      <th>solver</th>\n",
       "      <th>sweep 0 - iterations</th>\n",
       "      <th>sweep 1 - iterations</th>\n",
       "      <th>sweep 2 - iterations</th>\n",
       "      <th>sweep 3 - iterations</th>\n",
       "      <th>sweep 4 - iterations</th>\n",
       "      <th>sweep 5 - iterations</th>\n",
       "      <th>sweep 6 - iterations</th>\n",
       "      <th>sweep 7 - iterations</th>\n",
       "      <th>sweep 8 - iterations</th>\n",
       "      <th>sweep 9 - iterations</th>\n",
       "      <th>sweeps</th>\n",
       "      <th>tolerance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4996.49</td>\n",
       "      <td>1000</td>\n",
       "      <td>trilinos</td>\n",
       "      <td>100000</td>\n",
       "      <td>1</td>\n",
       "      <td>/users/guyer/FiPy/fipy/examples/benchmarking/s...</td>\n",
       "      <td>cgs</td>\n",
       "      <td>747.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>897.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18406.32</td>\n",
       "      <td>1000</td>\n",
       "      <td>trilinos</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1</td>\n",
       "      <td>/users/guyer/FiPy/fipy/examples/benchmarking/s...</td>\n",
       "      <td>cg</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1000</td>\n",
       "      <td>pysparse</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>/users/guyer/FiPy/fipy/examples/benchmarking/s...</td>\n",
       "      <td>lu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50.21</td>\n",
       "      <td>1000</td>\n",
       "      <td>pysparse</td>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "      <td>/users/guyer/FiPy/fipy/examples/benchmarking/s...</td>\n",
       "      <td>gmres</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.38</td>\n",
       "      <td>1000</td>\n",
       "      <td>pysparse</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>/users/guyer/FiPy/fipy/examples/benchmarking/s...</td>\n",
       "      <td>gmres</td>\n",
       "      <td>55.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000e-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    elapsed  iterations   library  numberOfElements  processes  \\\n",
       "0   4996.49        1000  trilinos            100000          1   \n",
       "1  18406.32        1000  trilinos           1000000          1   \n",
       "2       NaN        1000  pysparse              1000          1   \n",
       "3     50.21        1000  pysparse             10000          1   \n",
       "4      0.38        1000  pysparse               100          1   \n",
       "\n",
       "                                              script solver  \\\n",
       "0  /users/guyer/FiPy/fipy/examples/benchmarking/s...    cgs   \n",
       "1  /users/guyer/FiPy/fipy/examples/benchmarking/s...     cg   \n",
       "2  /users/guyer/FiPy/fipy/examples/benchmarking/s...     lu   \n",
       "3  /users/guyer/FiPy/fipy/examples/benchmarking/s...  gmres   \n",
       "4  /users/guyer/FiPy/fipy/examples/benchmarking/s...  gmres   \n",
       "\n",
       "   sweep 0 - iterations  sweep 1 - iterations  sweep 2 - iterations  \\\n",
       "0                 747.0                1000.0                 891.0   \n",
       "1                1000.0                1000.0                1000.0   \n",
       "2                   NaN                   NaN                   NaN   \n",
       "3                1000.0                1000.0                1000.0   \n",
       "4                  55.0                  59.0                  60.0   \n",
       "\n",
       "   sweep 3 - iterations  sweep 4 - iterations  sweep 5 - iterations  \\\n",
       "0                1000.0                 897.0                1000.0   \n",
       "1                1000.0                1000.0                1000.0   \n",
       "2                   NaN                   NaN                   NaN   \n",
       "3                1000.0                1000.0                1000.0   \n",
       "4                  60.0                  60.0                  60.0   \n",
       "\n",
       "   sweep 6 - iterations  sweep 7 - iterations  sweep 8 - iterations  \\\n",
       "0                1000.0                1000.0                1000.0   \n",
       "1                1000.0                1000.0                1000.0   \n",
       "2                   NaN                   NaN                   NaN   \n",
       "3                1000.0                1000.0                1000.0   \n",
       "4                  60.0                  60.0                  60.0   \n",
       "\n",
       "   sweep 9 - iterations  sweeps     tolerance  \n",
       "0                1000.0      10  1.000000e-10  \n",
       "1                1000.0      10  1.000000e-10  \n",
       "2                   NaN      10  1.000000e-10  \n",
       "3                1000.0      10  1.000000e-10  \n",
       "4                  60.0      10  1.000000e-10  "
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x111ad42d0>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lBuBSADPrAFwBXAX0AkYBfSMWXTFmVh3IALbvqOHubmazgA6FDm0GPJKf4xhw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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f266e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, results in df[df[\"library\"] == \"scipy\"].groupby(\"solver\"): \n",
    "    results = results.sort_values(by=\"numberOfElements\")\n",
    "    plt.loglog(results[\"numberOfElements\"], results[\"elapsed\"], label=name)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"number of elements\")\n",
    "plt.ylabel(\"elapsed time / s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>elapsed</th>\n",
       "      <th>iterations</th>\n",
       "      <th>library</th>\n",
       "      <th>numberOfElements</th>\n",
       "      <th>processes</th>\n",
       "      <th>script</th>\n",
       "      <th>solver</th>\n",
       "      <th>sweep 0 - iterations</th>\n",
       "      <th>sweep 1 - iterations</th>\n",
       "      <th>sweep 2 - iterations</th>\n",
       "      <th>sweep 3 - iterations</th>\n",
       "      <th>sweep 4 - iterations</th>\n",
       "      <th>sweep 5 - iterations</th>\n",
       "      <th>sweep 6 - iterations</th>\n",
       "      <th>sweep 7 - iterations</th>\n",
       "      <th>sweep 8 - iterations</th>\n",
       "      <th>sweep 9 - iterations</th>\n",
       "      <th>sweeps</th>\n",
       "      <th>tolerance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4996.49</td>\n",
       "      <td>1000</td>\n",
       "      <td>trilinos</td>\n",
       "      <td>100000</td>\n",
       "      <td>1</td>\n",
       "      <td>/users/guyer/FiPy/fipy/examples/benchmarking/s...</td>\n",
       "      <td>cgs</td>\n",
       "      <td>747.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>891.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>897.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>27903.67</td>\n",
       "      <td>1000</td>\n",
       "      <td>trilinos</td>\n",
       "      <td>10000000</td>\n",
       "      <td>1</td>\n",
       "      <td>/users/guyer/FiPy/fipy/examples/benchmarking/s...</td>\n",
       "      <td>cgs</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>29.58</td>\n",
       "      <td>1000</td>\n",
       "      <td>trilinos</td>\n",
       "      <td>10000</td>\n",
       "      <td>1</td>\n",
       "      <td>/users/guyer/FiPy/fipy/examples/benchmarking/s...</td>\n",
       "      <td>cgs</td>\n",
       "      <td>258.0</td>\n",
       "      <td>494.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>369.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>464.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>507.0</td>\n",
       "      <td>422.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>14902.93</td>\n",
       "      <td>1000</td>\n",
       "      <td>trilinos</td>\n",
       "      <td>1000000</td>\n",
       "      <td>1</td>\n",
       "      <td>/users/guyer/FiPy/fipy/examples/benchmarking/s...</td>\n",
       "      <td>cgs</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000e-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>0.38</td>\n",
       "      <td>1000</td>\n",
       "      <td>trilinos</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>/users/guyer/FiPy/fipy/examples/benchmarking/s...</td>\n",
       "      <td>cgs</td>\n",
       "      <td>25.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000e-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     elapsed  iterations   library  numberOfElements  processes  \\\n",
       "0    4996.49        1000  trilinos            100000          1   \n",
       "15  27903.67        1000  trilinos          10000000          1   \n",
       "22     29.58        1000  trilinos             10000          1   \n",
       "26  14902.93        1000  trilinos           1000000          1   \n",
       "46      0.38        1000  trilinos               100          1   \n",
       "\n",
       "                                               script solver  \\\n",
       "0   /users/guyer/FiPy/fipy/examples/benchmarking/s...    cgs   \n",
       "15  /users/guyer/FiPy/fipy/examples/benchmarking/s...    cgs   \n",
       "22  /users/guyer/FiPy/fipy/examples/benchmarking/s...    cgs   \n",
       "26  /users/guyer/FiPy/fipy/examples/benchmarking/s...    cgs   \n",
       "46  /users/guyer/FiPy/fipy/examples/benchmarking/s...    cgs   \n",
       "\n",
       "    sweep 0 - iterations  sweep 1 - iterations  sweep 2 - iterations  \\\n",
       "0                  747.0                1000.0                 891.0   \n",
       "15                1000.0                1000.0                1000.0   \n",
       "22                 258.0                 494.0                 320.0   \n",
       "26                1000.0                1000.0                1000.0   \n",
       "46                  25.0                  33.0                  36.0   \n",
       "\n",
       "    sweep 3 - iterations  sweep 4 - iterations  sweep 5 - iterations  \\\n",
       "0                 1000.0                 897.0                1000.0   \n",
       "15                1000.0                   0.0                   0.0   \n",
       "22                 427.0                 369.0                 386.0   \n",
       "26                1000.0                1000.0                   0.0   \n",
       "46                  37.0                  41.0                  37.0   \n",
       "\n",
       "    sweep 6 - iterations  sweep 7 - iterations  sweep 8 - iterations  \\\n",
       "0                 1000.0                1000.0                1000.0   \n",
       "15                   0.0                   0.0                   0.0   \n",
       "22                 464.0                 472.0                 507.0   \n",
       "26                   0.0                   0.0                   0.0   \n",
       "46                  38.0                  41.0                  37.0   \n",
       "\n",
       "    sweep 9 - iterations  sweeps     tolerance  \n",
       "0                 1000.0      10  1.000000e-10  \n",
       "15                   0.0      10  1.000000e-10  \n",
       "22                 422.0      10  1.000000e-10  \n",
       "26                   0.0      10  1.000000e-10  \n",
       "46                  39.0      10  1.000000e-10  "
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"library\"] == \"trilinos\") & (df[\"solver\"] == \"cgs\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x111f913d0>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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U3igCBweSciuFOivrUG5wOeldFUYjISUdGe7J3qxZs5g1K9+K9gohDhzQw8m2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TAAAE\nkklEQVSkj/N6Z0XkExHxE5FBzu9JhIgMzuZ1ezuHoCJFJFhEiqd6zW2BF52vOVlEqopIoPN79a/z\nNf4tIo9m9PqUyis0SVHKNz0CxAA3AIOA4SLSIdXXM9srMQLoB7QEqgLfAy8APbFDKHcAz6d5zmNA\nItDCee7LIvJ4qq9/CtwI9AAaAz8Av4lIrVTnFHPG/TjQEPg3bWAiUgmYD2wAmgDPOM8f6jzlBWA4\ncBgo74znEs77/uaMoxHwIHAz8HEGbQLwI3AV0BFoBmwBFotIYKpzagGdnOf0BJ5wxlsJaAO8BowR\nkRbZuO492PbvjE1KXnd+7UVgHTAFqIAd4joMjAHqO69bH3gWOHmZ16dU3mCM0Yc+9OFDD2AZsCLN\nsQ3AOOfH1QAH0CTV1wOcx9o4P28LJAPtUp3zmvNYtVTHJgEL0tz7rzT3fivlGDbRSQQqpDnnD2CM\n8+NHnfdpdIXXORbYkebYs0BUqs9fBPZe4TpTgElpjt0CJAH+zs/3AS+k+toZoFCa5+wGnnB+PAI4\nCxRL9fXfgH/SPCccGJTD674DrE3zPfggzTV+Ab7w9s+mPvTh6YfOSVHKN21N8/kxoFw2rrMt1ccR\nwHljzIE0x9L2UKxP8/k6bG+KYHsqCgC7nJ+n8Ofiv+wTjDFXmuRa33nt1NYAJUSksjHm8BWenyII\naCwivVMdS4mtBvB3mvObACWB0xe/BIpgezlS7DfGnE/1eQQ28SHNsZTvS3avm5nv7SRgtog0BxYB\nc4wxadtOqTxHkxSlfFNims8N/w3POpz/pn4nLJSJ65grXDczSmDfqJuliiNFTKqPYzNxLeHSYauU\n15SVSbYlgMnARC5uE4CDGZx/FNvblPb8yFQfp9dWl2u/nFz3st8DY8xCEamKHR66DVgiIp8YYwZd\n7nlK5XaapCiV+5xw/lsRCHN+fB1Ze2O/nJvSfN4S2G2MMSLyJ7YnpbwxZk0O77MDuDfNsZuBs8aY\nI1m4zhagoTFmXxbOrwAkG2PSS2Kyy1XXTcC28UWMMaeAr4GvRWQ18C523o9SeZZOnFUqlzHGxGGH\nZF4TkfrOlTVvpnNq2r/mM6uKc/VOXRHpBTwHTHDeezcwE/tG2V1EqovIDSLyuojcmcX7fOa818ci\nUk9E7gFGAu9n8TrvAC2d1wkSkdoico+IpDtx1hizGDvMNEdEbhe7WqqViIwRkWZZvLc7rrsfuNH5\n/KvEGiUiXUWklog0BLpgkzyl8jRNUpTyPZnpEemLnQeyGfgAGJLN66T3nK+BosBG7AqZD40xX6Q6\n5zHnOeOBncDPwPWkP7SS8Y2MOYpd4dICCMUmLVOwE2qzcp1t2CGWOsBKbI/GSCB1b0zatrjLee5U\n7JyVmdhJwRFZubebrjseO/F4B3ZVVBVs78o4bM/ZcuyQW68sxqpUriPGuKqHWCmllFLKdbQnRSml\nlFI+SZMUpZRSSvkkTVKUUkop5ZM0SVFKKaWUT9IkRSmllFI+SZMUpZRSSvkkTVKUUkop5ZM0SVFK\nKaWUT9IkRSmllFI+SZMUpZRSSvkkTVKUUkop5ZP+H0z2xpT0SHmaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11069c3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, results in df[df[\"library\"] == \"trilinos\"].groupby(\"solver\"): \n",
    "    results = results.sort_values(by=\"numberOfElements\")\n",
    "    plt.loglog(results[\"numberOfElements\"], results[\"elapsed\"], label=name)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"number of elements\")\n",
    "plt.ylabel(\"elapsed time / s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11179fcd0>"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5D0khSYf/UGkpfPWVPpXz88/68uCGOwyHHaYu5Qzj9VbicGw5pG7E7c6uO8KAzda6LgDp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\nKRWX/h+MOEwmpGNfTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111fa8f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, results in df.groupby((\"library\", \"solver\")): \n",
    "    results = results.sort_values(by=\"numberOfElements\")\n",
    "    plt.loglog(results[\"numberOfElements\"], results[\"elapsed\"], label=' - '.join(name))\n",
    "# plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"number of elements\")\n",
    "plt.ylabel(\"elapsed time / s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x113a7d690>"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KCop9Xy9evIivry8uLi7xfvhlz56d5s2bs3DhQh4+fMi7775L9uzZTfMihRDP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\nOpHvJHqDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111b8fc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, results in df[df[\"solver\"] == \"cg\"].groupby(\"library\"): \n",
    "    results = results.sort_values(by=\"numberOfElements\")\n",
    "    plt.loglog(results[\"numberOfElements\"], results[\"elapsed\"], label=name)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"number of elements\")\n",
    "plt.ylabel(\"elapsed time / s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11431b850>"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m2XTw8PBg7NixCd8vWLAADw8Pzp8/n3CtUaNGNG7c2BHhCeG2TpwwTV/r1oXr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\nS6X/D3L+HsffcT0aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11413ae50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, results in df[df[\"solver\"] == \"pcg\"].groupby(\"library\"): \n",
    "    results = results.sort_values(by=\"numberOfElements\")\n",
    "    plt.loglog(results[\"numberOfElements\"], results[\"elapsed\"], label=name)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"number of elements\")\n",
    "plt.ylabel(\"elapsed time / s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1148a1050>"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GW2un+jsgERGRlPjzz7ikZNkyOHEC8uVzUzfPPONGS4oX93eU3hNwSYox5l7gZaA6cDPQ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\ns1z6f5waOPNkFtCzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141887d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, results in df[df[\"solver\"] == \"cgs\"].groupby(\"library\"): \n",
    "    results = results.sort_values(by=\"numberOfElements\")\n",
    "    plt.loglog(results[\"numberOfElements\"], results[\"elapsed\"], label=name)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"number of elements\")\n",
    "plt.ylabel(\"elapsed time / s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x114d5b490>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Np+9B/Et9UvzMa31Sdu0CH60ZjxMeDn7o6ZKc6Z2kKp5ga9K8efMCcPz4cQB+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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114816490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, results in df[df[\"solver\"] == \"gmres\"].groupby(\"library\"): \n",
    "    results = results.sort_values(by=\"numberOfElements\")\n",
    "    plt.loglog(results[\"numberOfElements\"], results[\"elapsed\"], label=name)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"number of elements\")\n",
    "plt.ylabel(\"elapsed time / s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Data has no positive values, and therefore can not be log-scaled.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-144-2fe1b08fc630>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"solver\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"lu\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"library\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"numberOfElements\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloglog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"numberOfElements\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"elapsed\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"upper left\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"number of elements\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/guyer/anaconda/envs/condafipy/lib/python2.7/site-packages/matplotlib/pyplot.pyc\u001b[0m in \u001b[0;36mloglog\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   3044\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3045\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3046\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloglog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3047\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3048\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwashold\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/guyer/anaconda/envs/condafipy/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mloglog\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1518\u001b[0m         \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1519\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTrue\u001b[0m  \u001b[0;31m# we've already processed the hold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1520\u001b[0;31m         \u001b[0ml\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1521\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m  \u001b[0;31m# restore the hold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/guyer/anaconda/envs/condafipy/lib/python2.7/site-packages/matplotlib/__init__.pyc\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1817\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1818\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1819\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1820\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1821\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/guyer/anaconda/envs/condafipy/lib/python2.7/site-packages/matplotlib/axes/_axes.pyc\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1384\u001b[0m             \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1385\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1386\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoscale_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1387\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/guyer/anaconda/envs/condafipy/lib/python2.7/site-packages/matplotlib/axes/_base.pyc\u001b[0m in \u001b[0;36mautoscale_view\u001b[0;34m(self, tight, scalex, scaley)\u001b[0m\n\u001b[1;32m   2166\u001b[0m                 \u001b[0mx1\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mdelta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2167\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_tight\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2168\u001b[0;31m                 \u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxlocator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2169\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_xbound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/guyer/anaconda/envs/condafipy/lib/python2.7/site-packages/matplotlib/ticker.pyc\u001b[0m in \u001b[0;36mview_limits\u001b[0;34m(self, vmin, vmax)\u001b[0m\n\u001b[1;32m   1615\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mminpos\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mminpos\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1616\u001b[0m             raise ValueError(\n\u001b[0;32m-> 1617\u001b[0;31m                 \u001b[0;34m\"Data has no positive values, and therefore can not be \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1618\u001b[0m                 \"log-scaled.\")\n\u001b[1;32m   1619\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Data has no positive values, and therefore can not be log-scaled."
     ]
    },
    {
     "data": {
      "image/png": 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S65IcSXIiyZkke9Zpsz/J80leTXI0ybUbXPZDVXUt8JvAbUneP25ckiSpf11GLHYAx4H9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114816410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, results in df[df[\"solver\"] == \"lu\"].groupby(\"library\"): \n",
    "    results = results.sort_values(by=\"numberOfElements\")\n",
    "    plt.loglog(results[\"numberOfElements\"], results[\"elapsed\"], label=name)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"number of elements\")\n",
    "plt.ylabel(\"elapsed time / s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b = dtr.discover('../../../solvers/solver_timing/memory/Data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(b.categories.all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10b738dd0>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OYMfglwfjV8uP/K75n3jMgwfwxRfw+bQHtL+xgKPOn5Cby6gm7WH0Sqhe3YKf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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109ab5790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, library in df.groupby(\"library\"): \n",
    "    library = library.sort_values(by=\"numberOfElements\")\n",
    "    plt.loglog(library[\"numberOfElements\"], library[\"elapsed\"], label=name)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"number of elements\")\n",
    "plt.ylabel(\"elapsed time / s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Usage: __main__.py <command> <options> <arguments>\n",
      "\n",
      "Available commands:\n",
      "\n",
      "    run      run a given command or python file\n",
      "    rm       remove a given file generated by mprof\n",
      "    clean    clean the current directory from files created by mprof\n",
      "    list     display existing profiles, with indices\n",
      "    plot     plot memory consumption generated by mprof run\n",
      "\n",
      "Type mprof <command> --help for usage help on a specific command.\n",
      "For example, mprof plot --help will list all plotting options.\n",
      "\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "1",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 1\n"
     ]
    }
   ],
   "source": [
    "mprof = !which mprof\n",
    "execfile(mprof[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def parse_args(mprofile):\n",
    "    parser = argparse.ArgumentParser()\n",
    "    parser.add_argument(\"--output\", help=\"directory to store results in\")\n",
    "    parser.add_argument(\"--numberOfElements\", help=\"number of total cells in a Grid2D\",\n",
    "                        type=int, default=10000)\n",
    "    parser.add_argument(\"--solver\", help=\"solver class to use\",\n",
    "                        choices=(\"cg\", \"pcg\", \"cgs\", \"gmres\", \"lu\"), default=\"cg\")\n",
    "    parser.add_argument(\"--sweeps\", help=\"number of nonlinear sweeps to take\",\n",
    "                        type=int, default=10)\n",
    "    parser.add_argument(\"--iterations\", help=\"maximum number of linear iterations to take for each sweep\",\n",
    "                        type=int, default=1000)\n",
    "    parser.add_argument(\"--tolerance\", help=\"linear solver tolerance\",\n",
    "                        type=float, default=1e-10)\n",
    "    parser.add_argument(\"--writeFiles\", help=\"whether to write solution values and matrix to OUTPUT\",\n",
    "                        action='store_true')\n",
    "    \n",
    "    parser.add_argument(\"--pysparse\", help=\"use PySparse solvers\", dest=\"library\",\n",
    "                        action=\"store_const\", const=\"pysparse\")\n",
    "    parser.add_argument(\"--trilinos\", help=\"use PyTrilinos solvers\", dest=\"library\",\n",
    "                        action=\"store_const\", const=\"trilinos\")\n",
    "    parser.add_argument(\"--scipy\", help=\"use SciPy solvers\", dest=\"library\",\n",
    "                        action=\"store_const\", const=\"scipy\")\n",
    "    parser.set_defaults(library=\"pysparse\")\n",
    "\n",
    "    return parser.parse_known_args(mprofile['cmd_line'].split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "highwater = []\n",
    "\n",
    "for f in glob.glob('../../../solvers/solver_timing/memory/mprofile_*.dat'):\n",
    "    mprofile = read_mprofile_file(f)\n",
    "    \n",
    "    args, unknowns = parse_args(mprofile)\n",
    "    args.__dict__['mem_usage'] = max(mprofile['mem_usage'])\n",
    "    highwater.append(args.__dict__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "highwater = pd.DataFrame(highwater)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.merge(df, highwater, on=(\"library\", \"solver\", \"iterations\", \"sweeps\", \"tolerance\", \"numberOfElements\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10865e390>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m159M17e6Olw3apuKijLLfCdONMt+X38dhgwxmUWWhG1MeP++Wf47ZgzkzWua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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10825f650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, library in df.groupby(\"library\"): \n",
    "    library = library.sort_values(by=\"numberOfElements\")\n",
    "    plt.loglog(library[\"numberOfElements\"], library[\"mem_usage\"], label=name)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"number of elements\")\n",
    "plt.ylabel(\"high memory / MiB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}