sw_fwave_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import interact"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from clawpack.pyclaw import examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-03-07 15:00:16,478 INFO CLAW: Solution 0 computed for time t=0.000000\n",
      "2017-03-07 15:00:16,523 INFO CLAW: Solution 1 computed for time t=0.100000\n",
      "2017-03-07 15:00:16,569 INFO CLAW: Solution 2 computed for time t=0.200000\n",
      "2017-03-07 15:00:16,609 INFO CLAW: Solution 3 computed for time t=0.300000\n",
      "2017-03-07 15:00:16,659 INFO CLAW: Solution 4 computed for time t=0.400000\n",
      "2017-03-07 15:00:16,712 INFO CLAW: Solution 5 computed for time t=0.500000\n",
      "2017-03-07 15:00:16,759 INFO CLAW: Solution 6 computed for time t=0.600000\n",
      "2017-03-07 15:00:16,803 INFO CLAW: Solution 7 computed for time t=0.700000\n",
      "2017-03-07 15:00:16,842 INFO CLAW: Solution 8 computed for time t=0.800000\n",
      "2017-03-07 15:00:16,886 INFO CLAW: Solution 9 computed for time t=0.900000\n",
      "2017-03-07 15:00:16,918 INFO CLAW: Solution 10 computed for time t=1.000000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'cflmax': 0.9014577115207897,\n",
       " 'dtmax': 0.0011485652165592229,\n",
       " 'dtmin': 0.0010716263579387897,\n",
       " 'numsteps': 908}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f=examples.shallow_1d.sill.setup(kernel_language='Fortran')\n",
    "f.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-03-07 15:00:17,499 INFO CLAW: Solution 0 computed for time t=0.000000\n",
      "2017-03-07 15:00:17,601 INFO CLAW: Solution 1 computed for time t=0.100000\n",
      "2017-03-07 15:00:17,688 INFO CLAW: Solution 2 computed for time t=0.200000\n",
      "2017-03-07 15:00:17,774 INFO CLAW: Solution 3 computed for time t=0.300000\n",
      "2017-03-07 15:00:17,864 INFO CLAW: Solution 4 computed for time t=0.400000\n",
      "2017-03-07 15:00:17,948 INFO CLAW: Solution 5 computed for time t=0.500000\n",
      "2017-03-07 15:00:18,035 INFO CLAW: Solution 6 computed for time t=0.600000\n",
      "2017-03-07 15:00:18,123 INFO CLAW: Solution 7 computed for time t=0.700000\n",
      "2017-03-07 15:00:18,206 INFO CLAW: Solution 8 computed for time t=0.800000\n",
      "2017-03-07 15:00:18,289 INFO CLAW: Solution 9 computed for time t=0.900000\n",
      "2017-03-07 15:00:18,369 INFO CLAW: Solution 10 computed for time t=1.000000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'cflmax': 0.90145771152078968,\n",
       " 'dtmax': 0.001148565216559225,\n",
       " 'dtmin': 0.0010716263579387895,\n",
       " 'numsteps': 908}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = examples.shallow_1d.sill.setup(kernel_language='Python')\n",
    "\n",
    "p.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b178f932a28c40789c8fd7a9d160d074"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def compare(i):\n",
    "    plt.plot(f.frames[i].q[0,:]+f.frames[i].aux[0,:],lw=3)\n",
    "    plt.plot(p.frames[i].q[0,:]+p.frames[i].aux[0,:],'.')\n",
    "    plt.show()\n",
    "    \n",
    "interact(compare,i=(0,10,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a638d97104146459e40e331fea825b2"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def diff(i):\n",
    "    plt.plot(f.frames[i].q[0,:]+f.frames[i].aux[0,:]-(p.frames[i].q[0,:]+p.frames[i].aux[0,:]))\n",
    "    plt.show()\n",
    "interact(diff,i=(0,10,1));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x110f90a50>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Sv9Alya4hy6hRK4aQI3b56qbkhG2UlbC3jlLwWrvBMBlF6+hOYdfmF7EDwMkj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\nS+MLu1vFY/f7sRdt/5JfTnqdPDKAhz5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7gtsiMMYuY4xtYoxtGhsba8LTKo4UhvvD/tp/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\nKxQKRZehhF2hUCi6jP8PhVhcxoiKnH4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110d22c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 5\n",
    "plt.plot(f.frames[i].q[0,:]+f.frames[i].aux[0,:]-(p.frames[i].q[0,:]+p.frames[i].aux[0,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x110f4eed0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m7rWU3nvwyeNomHi1eg/AzmWzWPfjizi39vuc1UYvwdRLEDlhuoNbodlQi8+4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\nRCSFgkFERFIoGEREJMX/BwDOD1vY2YiVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110f4e1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "    plt.plot(f.frames[i].q[0,:]+f.frames[i].aux[0,:],lw=3)\n",
    "    plt.plot(p.frames[i].q[0,:]+p.frames[i].aux[0,:],'.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": 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": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}