{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo of running AMRClaw Fortran code and plotting results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook can be found in `$CLAW/apps/notebooks/amrclaw_advection_2d_square`\n",
    "\n",
    "The is adapted from $CLAW/amrclaw/examples/advection_2d_square to illustrate the use of IPython notebooks.\n",
    "\n",
    "The setrun.py and setplot.py files are taken from that example but some parameters are modified in the notebook below.\n",
    "\n",
    "The 2-dimensional advection equation $q_t + uq_x + vq_y = 0$ is solved in the unit square with periodic boundary conditions.  The constant advection velocities $u$ and $v$ are specified in setrun.py but can be changed below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have plots appear inline in notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that the CLAW environment variable is set.  (It must be set in the Unix shell before starting the notebook server)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using Clawpack from  /Users/rjl/git/clawpack\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "try:\n",
    "    CLAW = os.environ['CLAW'] \n",
    "    print \"Using Clawpack from \", CLAW\n",
    "except:\n",
    "    print \"*** Environment variable CLAW must be set to run code\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Module with functions used to execute system commands and capture output:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from clawpack.clawutil import nbtools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compile the code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing shell command:   make new\n",
      "Done...  Check this file to see output:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<a href='compile_output.txt' target='_blank'>compile_output.txt</a><br>"
      ],
      "text/plain": [
       "/Users/rjl/git/clawpack/apps/notebooks/amrclaw/advection_2d_square/compile_output.txt"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbtools.make_exe(new=True)  # new=True ==> force recompilation of all code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make documentation files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "See the README.html file for links to input files...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<a href='README.html' target='_blank'>README.html</a><br>"
      ],
      "text/plain": [
       "/Users/rjl/git/clawpack/apps/notebooks/amrclaw/advection_2d_square/README.html"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nbtools.make_htmls()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the code and plot results using the setrun.py and setplot.py files in this directory:\n",
    "\n",
    "First create data files needed for the Fortran code, using parameters specified in setrun.py:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nbtools.make_data(verbose=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now run the code and produce plots.  Specifying a label insures the resulting plot directory will persist after later runs are done below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing shell command:   make output OUTDIR=_output_1\n",
      "Done...  Check this file to see output:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<a href='run_output_1.txt' target='_blank'>run_output_1.txt</a><br>"
      ],
      "text/plain": [
       "/Users/rjl/git/clawpack/apps/notebooks/amrclaw/advection_2d_square/run_output_1.txt"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing shell command:   make plots OUTDIR=_output_1 PLOTDIR=_plots_1\n",
      "Done...  Check this file to see output:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<a href='plot_output_1.txt' target='_blank'>plot_output_1.txt</a><br>"
      ],
      "text/plain": [
       "/Users/rjl/git/clawpack/apps/notebooks/amrclaw/advection_2d_square/plot_output_1.txt"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "View plots created at this link:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<a href='_plots_1/_PlotIndex.html' target='_blank'>_plots_1/_PlotIndex.html</a><br>"
      ],
      "text/plain": [
       "/Users/rjl/git/clawpack/apps/notebooks/amrclaw/advection_2d_square/_plots_1/_PlotIndex.html"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "outdir,plotdir = nbtools.make_output_and_plots(label='1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display the animation inline:\n",
    "\n",
    "Clicking on the _PlotIndex link above, you can view an animation of the results.  \n",
    "\n",
    "After creating all png files in the _plots directory, these can also be combined in an animation that is displayed inline:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "  <form action=\"#n\" name=\"_anim_loop_select774f0c6b47eeb841\" class=\"anim_control\">\n",
       "\n",
       "    <input id=\"_frame_no774f0c6b47eeb841\" type=\"textbox\" size=\"1\" onchange=\"anim774f0c6b47eeb841.set_frame(parseInt(this.value));\" onpaste=\"this.onchange();\" oninput=\"this.onchange();\"></input>\n",
       "    \n",
       "    <input type=\"radio\" name=\"state\" value=\"once\" > Once </input>\n",
       "    <input type=\"radio\" name=\"state\" value=\"loop\" checked> Loop </input>\n",
       "    <input type=\"radio\" name=\"state\" value=\"reflect\" > Reflect </input>\n",
       "  </form>\n",
       "\n",
       "</div>\n",
       "\n",
       "\n",
       "<script language=\"javascript\">\n",
       "  /* Instantiate the Animation class. */\n",
       "  /* The IDs given should match those used in the template above. */\n",
       "  (function() {\n",
       "    var img_id = \"_anim_img774f0c6b47eeb841\";\n",
       "    var slider_id = \"_anim_slider774f0c6b47eeb841\";\n",
       "    var frame_id = \"_frame_no774f0c6b47eeb841\"\n",
       "    var loop_select_id = \"_anim_loop_select774f0c6b47eeb841\";\n",
       "    var frames = new Array(0);\n",
       "    \n",
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       "\"\n",
       "\n",
       "\n",
       "    /* set a timeout to make sure all the above elements are created before\n",
       "       the object is initialized. */\n",
       "    setTimeout(function() {\n",
       "        anim774f0c6b47eeb841 = new Animation(frames, img_id, slider_id, frame_id, 200, loop_select_id);\n",
       "    }, 0);\n",
       "  })()\n",
       "</script>\n"
      ],
      "text/plain": [
       "<matplotlib.animation.FuncAnimation at 0x1086c3ad0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import clawpack.visclaw.JSAnimation.JSAnimation_frametools as J\n",
    "anim = J.make_anim(plotdir, figno=0, figsize=(6,4))\n",
    "anim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Illustrate how to adjust some parameters and rerun the code: \n",
    "\n",
    "First read in the current rundata.  (See the <a href=\"README.html\">README.html</a> file for a link to `setrun.py`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import setrun\n",
    "rundata = setrun.setrun()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we adjust the mesh to be $40 \\times 40$ and set two gauges at $(0.6, 0.4)$ and $(0.6,0.8)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rundata.clawdata.num_cells = [40,40]\n",
    "rundata.gaugedata.gauges = [[1, 0.6, 0.4, 0., 10.], [2, 0.6, 0.8, 0., 10.]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The advection velocities can also be changed (in setrun.py, $(u,v) = (0.5,1)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rundata.probdata.u = -1.0\n",
    "rundata.probdata.v = 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the limiter if you want to experiment with other methods.  A list of 1 limiter is required since this is a scalar problem with only 1 wave in the Riemann solution.\n",
    "\n",
    "Here we set the limiter to ['none'] to see the oscillations that arise if Lax-Wendroff is used with no limiter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rundata.clawdata.limiter = ['none']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Write the data out (for the Fortran code to read in) and run the code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing shell command:   make output OUTDIR=_output_2\n",
      "Done...  Check this file to see output:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<a href='run_output_2.txt' target='_blank'>run_output_2.txt</a><br>"
      ],
      "text/plain": [
       "/Users/rjl/git/clawpack/apps/notebooks/amrclaw/advection_2d_square/run_output_2.txt"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing shell command:   make plots OUTDIR=_output_2 PLOTDIR=_plots_2\n",
      "Done...  Check this file to see output:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<a href='plot_output_2.txt' target='_blank'>plot_output_2.txt</a><br>"
      ],
      "text/plain": [
       "/Users/rjl/git/clawpack/apps/notebooks/amrclaw/advection_2d_square/plot_output_2.txt"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "View plots created at this link:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<a href='_plots_2/_PlotIndex.html' target='_blank'>_plots_2/_PlotIndex.html</a><br>"
      ],
      "text/plain": [
       "/Users/rjl/git/clawpack/apps/notebooks/amrclaw/advection_2d_square/_plots_2/_PlotIndex.html"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rundata.write()\n",
    "outdir, plotdir = nbtools.make_output_and_plots(label='2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing plot parameters and plotting inline\n",
    "\n",
    "In addition to producing a _plots directory using the parameters in setplot.py, plot parameters can be changed and plots produced inline as the examples below illustrate..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import setplot\n",
    "plotdata = setplot.setplot()\n",
    "plotdata.outdir = outdir"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot one frame of the solution:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Reading  Frame 2 at t = 0.4  from outdir = /Users/rjl/git/clawpack/apps/notebooks/amrclaw/advection_2d_square/_output_2\n"
     ]
    },
    {
     "data": {
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8ZhPtuo6i7z/Gs3nrXr/hhLamaK1nSeby9NMQge5UKc6yyF9zNLM9S3IjbqsK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\nRAyUFEVEDJQURUQMlBRFRAz+H24erxeO1RI+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a565490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jhuEwZ+Yc1+9xPeKI6xF3pfULIQqJj99EfPymK15OieHQoX0zDh9N4PjJc9Su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\nCQchhCEJByGEIQkHIYQhCQchhKH/B3QQRv9NUZYAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ae09150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JyDCyLYfBfdsjPu79Qrd/t+cYLqWk4uIvX2Llh+MxYuISRQPUl4gLb4jMmWxyaBFcD46V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\niCQxORCRJCYHIpLE5EBEkpgciEjS/wM6mX4iEn445gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10af7bfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotdata.plotframe(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adjust some plot parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Current plot figures, axes, and items:\n",
      "---------------------------------------\n",
      "  figname = pcolor, figno = 0\n",
      "     axesname = AXES1, axescmd = subplot(1,1,1)\n",
      "        itemname = ITEM1,  plot_type = 2d_pcolor\n",
      " \n",
      "  figname = contour, figno = 1\n",
      "     axesname = AXES1, axescmd = subplot(1,1,1)\n",
      "        itemname = ITEM1,  plot_type = 2d_contour\n",
      " \n",
      "  figname = cells, figno = 2\n",
      "     axesname = AXES1, axescmd = subplot(1,1,1)\n",
      "        itemname = ITEM1,  plot_type = 2d_patch\n",
      " \n",
      "  figname = q, figno = 300\n",
      "     axesname = AXES1, axescmd = subplot(1,1,1)\n",
      "        itemname = ITEM1,  plot_type = 1d_plot\n",
      " \n"
     ]
    }
   ],
   "source": [
    "plotdata.showitems()   # shows the currently defined figures, axes, items"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the color limits for the pcolor plot to show the overshoots and undershoots with Lax-Wendroff.  Also suppress showing the other plots..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FFbo2x0l93XNUls/cncxKFkrZontYs3HNbKvTatcK4dLlDOrUDuXi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\ngf8H9J1u2ROgApYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10af7bf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotitem = plotdata.getitem('ITEM1','AXES1','pcolor')\n",
    "plotitem.pcolor_cmin = -0.2\n",
    "plotitem.pcolor_cmax = 1.2\n",
    "plotdata.getfigure('contour').show = False\n",
    "plotdata.getfigure('cells').show = False\n",
    "plotdata.plotframe(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explore the gauge output:\n",
    "\n",
    "The overshoots and undershoots with Lax-Wendroff are even more visible in the gauge output..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gauge 1 at [0.6, 0.4] has a maximum value of q = 1.16098 at t = 1.84500\n",
      "Gauge 2 at [0.6, 0.8] has a maximum value of q = 1.15636 at t = 1.54062\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10ae9a190>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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r98Ri3vYbNLwOJYF5DEZ+MWEIAH7lGPIdShIpjXCSL6GkDCuTTBgMLzFhCAC+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\nhmEYxihMGAzDMIxRmDAYhmEYo/j/bLh+v/XCom4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bcd4c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for gaugeno in [1,2]:\n",
    "    gauge = plotdata.getgauge(gaugeno)\n",
    "    q = gauge.q[0,:]\n",
    "    t = gauge.t\n",
    "    qmax = q.max()\n",
    "    tmax = t[q.argmax()]\n",
    "    print \"Gauge %s at %s has a maximum value of q = %7.5f at t = %7.5f\" \\\n",
    "        % (gaugeno, gauge.location, qmax, tmax)\n",
    "    plot(t,q,label=\"Gauge %s\" % gaugeno)\n",
    "legend()\n",
    "xlim(0,3)\n",
    "ylim(-0.1,1.1)\n",
    "xlabel('time')\n",
    "ylabel('q at gauge')\n",
    "title(\"Gauge output\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "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": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}