Weakly nonlinear SW simulations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#from exact_solvers.shallow_water_bathymetry import predictor, corrector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "def setup(num_cells=100,solver_type='classic',num_output_times=10):\n",
    "    \n",
    "    from clawpack import riemann\n",
    "    from clawpack import pyclaw\n",
    "\n",
    "    if solver_type == 'classic':\n",
    "        solver = pyclaw.ClawSolver2D(riemann.sw_aug_2D)\n",
    "        solver.dimensional_split=True\n",
    "        #solver.limiters = pyclaw.limiters.tvd.minmod\n",
    "        solver.cfl_max     = 0.9\n",
    "        solver.cfl_desired = 0.8\n",
    "    elif solver_type == 'sharpclaw':\n",
    "        solver = pyclaw.SharpClawSolver2D(riemann.sw_aug_2D)\n",
    "\n",
    "    solver.bc_lower[0] = pyclaw.BC.extrap \n",
    "    solver.bc_upper[0] = pyclaw.BC.extrap\n",
    "    solver.bc_lower[1] = pyclaw.BC.periodic\n",
    "    solver.bc_upper[1] = pyclaw.BC.periodic\n",
    "\n",
    "    solver.aux_bc_lower[0] = pyclaw.BC.extrap\n",
    "    solver.aux_bc_upper[0] = pyclaw.BC.extrap\n",
    "    solver.aux_bc_lower[1] = pyclaw.BC.periodic\n",
    "    solver.aux_bc_upper[1] = pyclaw.BC.periodic\n",
    "\n",
    "    solver.fwave = True\n",
    "\n",
    "    # Domain:\n",
    "    xlower = -300.;  xupper =  100.\n",
    "    ylower = -0.5;  yupper =  0.5\n",
    "\n",
    "    g = 9.81\n",
    "    \n",
    "    mx = num_cells\n",
    "    my = 2\n",
    "\n",
    "    x = pyclaw.Dimension(xlower,xupper,mx,name='x')\n",
    "    y = pyclaw.Dimension(ylower,yupper,my,name='y')\n",
    "    domain = pyclaw.Domain([x,y])\n",
    "\n",
    "    num_aux = 1\n",
    "    state = pyclaw.State(domain,solver.num_eqn,num_aux)\n",
    "    xc = state.p_centers[0]\n",
    "\n",
    "    state.problem_data['grav'] = g   # Gravitational force\n",
    "\n",
    "    unperturbed_depth = 3200\n",
    "    b_l = 0.\n",
    "    b_r = 3000.\n",
    "    edge1 = -50\n",
    "    edge2= 50\n",
    "    eta_l = 0\n",
    "    eta_m = 1\n",
    "    eta_r = 0\n",
    "    b_m = b_l + (xc-edge1)*b_r/(edge2-edge1)\n",
    "    state.aux[0,:,:] = (xc<edge1)*b_l +(xc>=edge1)*(xc<edge2)*b_m +(xc>=edge2)*b_r\n",
    "    state.q[0,:,:] = unperturbed_depth-state.aux[0,:,:] + eta_l*(xc<-3000) \\\n",
    "            + eta_m*(xc>=-80)*(xc<-60) + eta_r*(xc>edge2)\n",
    "    velocity = (xc>-80)*(xc<-60)*np.sqrt(g*unperturbed_depth)*eta_m\n",
    "    state.q[1,:,:] = velocity\n",
    "    state.q[2,:,:] = 0.\n",
    "\n",
    "\n",
    "    #===========================================================================\n",
    "    # Set up controller and controller parameters\n",
    "    #===========================================================================\n",
    "    claw = pyclaw.Controller()\n",
    "    claw.tfinal = 1.5\n",
    "    claw.solution = pyclaw.Solution(state,domain)\n",
    "    claw.solver = solver\n",
    "    claw.num_output_times = num_output_times\n",
    "    claw.keep_copy = True\n",
    "    claw.output_format = None\n",
    "\n",
    "    return claw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "claw = setup(num_cells=4000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-04-12 16:24:37,079 INFO CLAW: Solution 0 computed for time t=0.000000\n",
      "2018-04-12 16:24:51,980 INFO CLAW: Solution 1 computed for time t=0.150000\n",
      "2018-04-12 16:25:06,958 INFO CLAW: Solution 2 computed for time t=0.300000\n",
      "2018-04-12 16:25:22,087 INFO CLAW: Solution 3 computed for time t=0.450000\n",
      "2018-04-12 16:25:37,079 INFO CLAW: Solution 4 computed for time t=0.600000\n",
      "2018-04-12 16:25:52,100 INFO CLAW: Solution 5 computed for time t=0.750000\n",
      "2018-04-12 16:26:07,030 INFO CLAW: Solution 6 computed for time t=0.900000\n",
      "2018-04-12 16:26:22,041 INFO CLAW: Solution 7 computed for time t=1.050000\n",
      "2018-04-12 16:26:37,057 INFO CLAW: Solution 8 computed for time t=1.200000\n",
      "2018-04-12 16:26:52,168 INFO CLAW: Solution 9 computed for time t=1.350000\n",
      "2018-04-12 16:27:07,964 INFO CLAW: Solution 10 computed for time t=1.500000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'cflmax': 0.8000000445870639,\n",
       " 'dtmax': 0.0004515186486329485,\n",
       " 'dtmin': 0.00045131212876842793,\n",
       " 'numsteps': 3330}"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "claw.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10c3f9b38>]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xc = claw.solution.state.grid.x.centers\n",
    "h = claw.frames[0].q[0,:,0]\n",
    "b = claw.frames[0].aux[0,:,0]\n",
    "plt.plot(xc,h+b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10c4cd780>]"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xc,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.959634672505672\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 9\n",
    "plt.figure(figsize=(10,4))\n",
    "xc = claw.solution.state.grid.x.centers\n",
    "h = claw.frames[i].q[0,:,0]\n",
    "b = claw.frames[0].aux[0,:,0]\n",
    "plt.plot(xc,h+b-3200)\n",
    "print(np.max(h+b)-3200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-04-12 16:28:28,355 INFO CLAW: Solution 0 computed for time t=0.000000\n",
      "2018-04-12 16:30:22,176 INFO CLAW: Solution 1 computed for time t=0.150000\n",
      "2018-04-12 16:32:17,116 INFO CLAW: Solution 2 computed for time t=0.300000\n",
      "2018-04-12 16:34:10,255 INFO CLAW: Solution 3 computed for time t=0.450000\n",
      "2018-04-12 16:36:05,453 INFO CLAW: Solution 4 computed for time t=0.600000\n",
      "2018-04-12 16:38:02,619 INFO CLAW: Solution 5 computed for time t=0.750000\n",
      "2018-04-12 16:39:55,259 INFO CLAW: Solution 6 computed for time t=0.900000\n",
      "2018-04-12 16:41:47,951 INFO CLAW: Solution 7 computed for time t=1.050000\n",
      "2018-04-12 16:43:40,319 INFO CLAW: Solution 8 computed for time t=1.200000\n",
      "2018-04-12 16:45:35,857 INFO CLAW: Solution 9 computed for time t=1.350000\n",
      "2018-04-12 16:47:31,221 INFO CLAW: Solution 10 computed for time t=1.500000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'cflmax': 0.8000000261622681,\n",
       " 'dtmax': 0.00022575931083209787,\n",
       " 'dtmin': 0.00022565606438421397,\n",
       " 'numsteps': 6650}"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "claw = setup(num_cells=8000)\n",
    "claw.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 9\n",
    "plt.figure(figsize=(10,4))\n",
    "xc = claw.solution.state.grid.x.centers\n",
    "h = claw.frames[i].q[0,:,0]\n",
    "b = claw.frames[0].aux[0,:,0]\n",
    "plt.plot(xc,h+b-3200)\n",
    "print(np.max(h+b)-3200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018-04-12 16:22:04,494 INFO CLAW: Animation.save using <class 'clawpack.visclaw.JSAnimation.html_writer.HTMLWriter'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<script language=\"javascript\">\n",
       "  /* Define the Animation class */\n",
       "  function Animation(frames, img_id, slider_id, frame_id, interval, loop_select_id){\n",
       "    this.img_id = img_id;\n",
       "    this.slider_id = slider_id;\n",
       "    this.frame_id = frame_id;\n",
       "    this.loop_select_id = loop_select_id;\n",
       "    this.interval = interval;\n",
       "    this.current_frame = 0;\n",
       "    this.direction = 0;\n",
       "    this.timer = null;\n",
       "    this.frames = new Array(frames.length);\n",
       "\n",
       "    for (var i=0; i<frames.length; i++)\n",
       "    {\n",
       "     this.frames[i] = new Image();\n",
       "     this.frames[i].src = frames[i];\n",
       "    }\n",
       "    document.getElementById(this.slider_id).max = this.frames.length - 1;\n",
       "    this.set_frame(this.current_frame);\n",
       "  }\n",
       "\n",
       "  Animation.prototype.get_loop_state = function(){\n",
       "    var button_group = document[this.loop_select_id].state;\n",
       "    for (var i = 0; i < button_group.length; i++) {\n",
       "        var button = button_group[i];\n",
       "        if (button.checked) {\n",
       "            return button.value;\n",
       "        }\n",
       "    }\n",
       "    return undefined;\n",
       "  }\n",
       "\n",
       "  Animation.prototype.set_frame = function(frame){\n",
       "    this.current_frame = frame;\n",
       "    document.getElementById(this.img_id).src = this.frames[this.current_frame].src;\n",
       "    document.getElementById(this.slider_id).value = this.current_frame;\n",
       "    document.getElementById(this.frame_id).value = this.current_frame;\n",
       "  }\n",
       "\n",
       "  Animation.prototype.next_frame = function()\n",
       "  {\n",
       "    this.set_frame(Math.min(this.frames.length - 1, this.current_frame + 1));\n",
       "  }\n",
       "\n",
       "  Animation.prototype.previous_frame = function()\n",
       "  {\n",
       "    this.set_frame(Math.max(0, this.current_frame - 1));\n",
       "  }\n",
       "\n",
       "  Animation.prototype.first_frame = function()\n",
       "  {\n",
       "    this.set_frame(0);\n",
       "  }\n",
       "\n",
       "  Animation.prototype.last_frame = function()\n",
       "  {\n",
       "    this.set_frame(this.frames.length - 1);\n",
       "  } \n",
       "\n",
       "  Animation.prototype.slower = function()\n",
       "  {\n",
       "    this.interval /= 0.7;\n",
       "    if(this.direction > 0){this.play_animation();}\n",
       "    else if(this.direction < 0){this.reverse_animation();}\n",
       "  }\n",
       "\n",
       "  Animation.prototype.faster = function()\n",
       "  {\n",
       "    this.interval *= 0.7;\n",
       "    if(this.direction > 0){this.play_animation();}\n",
       "    else if(this.direction < 0){this.reverse_animation();}\n",
       "  }\n",
       "\n",
       "  Animation.prototype.anim_step_forward = function()\n",
       "  {\n",
       "    this.current_frame += 1;\n",
       "    if(this.current_frame < this.frames.length){\n",
       "      this.set_frame(this.current_frame);\n",
       "    }else{\n",
       "      var loop_state = this.get_loop_state();\n",
       "      if(loop_state == \"loop\"){\n",
       "        this.first_frame();\n",
       "      }else if(loop_state == \"reflect\"){\n",
       "        this.last_frame();\n",
       "        this.reverse_animation();\n",
       "      }else{\n",
       "        this.pause_animation();\n",
       "        this.last_frame();\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "\n",
       "  Animation.prototype.anim_step_reverse = function()\n",
       "  {\n",
       "    this.current_frame -= 1;\n",
       "    if(this.current_frame >= 0){\n",
       "      this.set_frame(this.current_frame);\n",
       "    }else{\n",
       "      var loop_state = this.get_loop_state();\n",
       "      if(loop_state == \"loop\"){\n",
       "        this.last_frame();\n",
       "      }else if(loop_state == \"reflect\"){\n",
       "        this.first_frame();\n",
       "        this.play_animation();\n",
       "      }else{\n",
       "        this.pause_animation();\n",
       "        this.first_frame();\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "\n",
       "  Animation.prototype.pause_animation = function()\n",
       "  {\n",
       "    this.direction = 0;\n",
       "    if (this.timer){\n",
       "      clearInterval(this.timer);\n",
       "      this.timer = null;\n",
       "    }\n",
       "  }\n",
       "\n",
       "  Animation.prototype.play_animation = function()\n",
       "  {\n",
       "    this.pause_animation();\n",
       "    this.direction = 1;\n",
       "    var t = this;\n",
       "    if (!this.timer) this.timer = setInterval(function(){t.anim_step_forward();}, this.interval);\n",
       "  }\n",
       "\n",
       "  Animation.prototype.reverse_animation = function()\n",
       "  {\n",
       "    this.pause_animation();\n",
       "    this.direction = -1;\n",
       "    var t = this;\n",
       "    if (!this.timer) this.timer = setInterval(function(){t.anim_step_reverse();}, this.interval);\n",
       "  }\n",
       "</script>\n",
       "\n",
       "<div class=\"animation\" align=\"center\">\n",
       "    <img id=\"_anim_img433843022d197b70\" style=\"width:Nonepx\">\n",
       "    <br>\n",
       "    <input id=\"_anim_slider433843022d197b70\" type=\"range\" style=\"width:350px\" name=\"points\" min=\"0\" max=\"1\" step=\"1\" value=\"0\" onchange=\"anim433843022d197b70.set_frame(parseInt(this.value));\"></input>\n",
       "    <br>\n",
       "    \n",
       "    <button onclick=\"anim433843022d197b70.slower()\">&#8211;</button>\n",
       "    <button onclick=\"anim433843022d197b70.first_frame()\"><img class=\"anim_icon\" src=\"data:image/png;base64,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\"></button>\n",
       "    <button onclick=\"anim433843022d197b70.previous_frame()\"><img class=\"anim_icon\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+Hj8y/AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3QURCAgyTCyQ6wAAANRJREFUKM9jYBjO4AiUfgzFGGAp4+yayUvX6jMwMDCsYmBgOCS4OAOrSYmMgcc8/pd5Q3irC+Neh/1AlmeBMVgZmP8yMLD8/c/cqv9r90whzv/MX7Eq/MfAwMDIwCuZdfSV8U8WDgZGRmYGrAoZGRgY/jO8b3sj/J2F6T8j4z80pzEhmIwMjAxsSbqqlkeZGP//Z8SlkJnhPwMjwx/Guoe1NhmRwk+YGH5jV8jOwMPHzcDBysAwh8FrxQwtPU99HrwBXsnAwMDAsJiBgYGBoZ1xmKYqALHhMpn1o7igAAAAAElFTkSuQmCC\"></button>\n",
       "    <button onclick=\"anim433843022d197b70.reverse_animation()\"><img class=\"anim_icon\" src=\"data:image/png;base64,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\"></button>\n",
       "    <button onclick=\"anim433843022d197b70.pause_animation()\"><img class=\"anim_icon\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+Hj8y/AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3QURCAkR91DQ2AAAAKtJREFUKM9jYCANTEVib2K4jcRbzQihGWEC00JuNjN8Z2Q0Zo3VYWA4lL005venH9+c3ZK5IfIsMIXMBtc12Bj+MMgxMDAwMPzWe2TBzPCf4SLcZCYY4/9/RgZGBiaYFf8gljFhKiQERhUOeoX/Gf8y/GX4y/APmlj+Mfxj+MfwH64Qnnq0zr9fyfLrPzP3eQYGBobvk5x4GX4xMIij23gdib0cRWYHiVmAAQDK5ircshCbHQAAAABJRU5ErkJggg==\"></button>\n",
       "    <button onclick=\"anim433843022d197b70.play_animation()\"><img class=\"anim_icon\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+Hj8y/AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3QURCAkEmo00MwAAAS9JREFUKM+tkj1IQmEUhp9j94LQj0FD4RRBLdLQ3ftb26PRcCiQIIiIDFwKC0OhaAiam5wVDBpqCKohQojMLYzaAiUatOtpuQrKVQl64fu+4Xt4OLwc+Fs+nNM16jsPAWS6gZXggoZfXmfhog3hcZ6aTXF87Sp68OmH4/YggAo8bmfyyeh6Z1AAKPVldyO1+Iz2uILq3AriJSe3l+H7aj+cuRnrTsVDxSxay+VYbMDnCtZxxQOU9G4nlU9E1HQBxRkCQMRGRnIbpxMARkvxCIoAorYMMrq0mJ0qu4COUW3xyVDqJC4P+86P0ewDQbQqgevhlc2C8ETApXAEFLzvwa3EXG9BoIE1GQUbv1h7k4fTXxBu6cKgUbX5M3ZzNC+a7rQ936HV56SlRpcle+Mf8wvgJ16zo/4BtQAAAABJRU5ErkJggg==\"></button>\n",
       "    <button onclick=\"anim433843022d197b70.next_frame()\"><img class=\"anim_icon\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABQAAAAUCAQAAAAngNWGAAAAAXNSR0IArs4c6QAAAAJiS0dEAP+Hj8y/AAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH3QURCAkd/uac8wAAAMhJREFUKM9jYBie4DEUQ8B+fEq3+3UrMzAwMFxjYGBgYJizYubaOUxYFUaXh/6vWfRfEMIL/+//P5gZJoei4/f/7wxnY1PeNUXdE2RgYGZgYoCrY2BBVsjKwMDAwvCS4f3SG/dXxm5gYESSQ1HIwvCPgZmB8f8Pxv+Kxxb/YfiPJIdi9T8GJgaG/38ZFd4Fx0xUYsZt4h8GBgb2D2bLy7KnMTAwMEIxFoVCXIYr1IoDnkF4XAysqNIwUMDAwMDAsADKS2NkGL4AAIARMlfNIfZMAAAAAElFTkSuQmCC\"></button>\n",
       "    <button onclick=\"anim433843022d197b70.last_frame()\"><img class=\"anim_icon\" src=\"data:image/png;base64,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\"></button>\n",
       "    <button onclick=\"anim433843022d197b70.faster()\">+</button>\n",
       "  <form action=\"#n\" name=\"_anim_loop_select433843022d197b70\" class=\"anim_control\">\n",
       "\n",
       "    <input id=\"_frame_no433843022d197b70\" type=\"textbox\" size=\"1\" onchange=\"anim433843022d197b70.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_img433843022d197b70\";\n",
       "    var slider_id = \"_anim_slider433843022d197b70\";\n",
       "    var frame_id = \"_frame_no433843022d197b70\"\n",
       "    var loop_select_id = \"_anim_loop_select433843022d197b70\";\n",
       "    var frames = new Array(0);\n",
       "    \n",
       "  frames[0] = \"data:image/png;base64,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\"\n",
       "  frames[1] = 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\"\n",
       "  frames[2] = 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\"\n",
       "  frames[3] = 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\"\n",
       "  frames[4] = 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\"\n",
       "  frames[5] = 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\"\n",
       "  frames[6] = 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\"\n",
       "  frames[7] = 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XXbZZZo+fbrC4bDq6upUXl4uSSovL9f27dslSXV1dVq+fLlcLpdmz56tkydPqq2tTbt27ZLf71diYqImTJggv9+v+vp6x/YLAAAAQGwRwL6iI0eO6O2339asWbN09OhRpaSkSJImT56so0ePSpLC4bCmTJlirZOamqpwODxgOwAAAICRye10AcPZp59+qiVLlujRRx/VuHHj+sxzuVxyuVxD8jmBQECBQKBnqn1ItgkAAADAfpwBG6SzZ89qyZIluvXWW3XTTTdJkiZNmqS2tjZJUltbm5KTkyVJHo9HLS0t1rqtra3yeDwDtn9RZWWlgsGggsGgpIkx3CsAAAAAsUQAGwRjjFauXKnp06frzjvvtNqLi4utkQxramq0aNEiq/3ZZ5+VMUYHDhzQ+PHjlZKSogULFmj37t3q6OhQR0eHdu/erQULFjiyTwAAAABij0sQB+H111/Xli1bNGPGDBUUFEiSHn74Yd13330qKSnR5s2bNXXqVG3btk2StHDhQu3YsUNer1djx47V008/LUlKTEzU/fffr6KiIknSAw88oMTERGd2CgAAAEDMuYwxxukicOFcLp+koNNlAEDUCgt9PZdSXxwqKir04osvKjk5We+++66k7seDLFu2TEeOHFFaWpq2bdumCRMmyBijH/3oR9qxY4fGjh2rZ555xhoN98v4fBfXPgNANOjDYoNLEAEAo9KKFSv6Pfoj2uc5AgAQLQIYAGBUmjNnTr/LvqN9niMAANEigAEA0CPa5zkCABAtBuEAAOA8Bvs8x97Pbmxv59mNAIC+OAMGAECPaJ/neD69n904cSLPbgQA9EUAAwCgR7TPcwQAIFpcgggAGJXKysq0f/9+HTt2TKmpqVq/fn3Uz3MEACBaBDAAwKi0devW87bv3bu3X5vL5VJ1dXWsSwIAjAJcgggAAAAANiGAAQAAAIBNCGAAAAAAYBMCGAAAAADYhAAGAAAAADYhgAEAAACATQhgAAAAAGATAhgAAAAA2IQABgAAAAA2IYABAAAAgE0IYAAAAABgEwIYAAAAANiEAAYAAAAANiGADUJFRYWSk5OVl5dnta1bt04ej0cFBQUqKCjQjh07rHkbNmyQ1+tVdna2du3aZbXX19crOztbXq9XVVVVtu4DAAAAAPsRwAZhxYoVqq+v79e+Zs0ahUIhhUIhLVy4UJLU2Nio2tpaHTx4UPX19brtttsUiUQUiUR0++23a+fOnWpsbNTWrVvV2Nho964AAAAAsJHb6QKGozlz5ujIkSMXtGxdXZ1KS0uVkJCg9PR0eb1eNTQ0SJK8Xq8yMjIkSaWlpaqrq1NOTk6sygaAPi51d+qS+C5d4o7IHd+lS+LP9xqxpt3xEbnjer3GRRQf1yV3fKcuietS/Lnpntf4uIjiXX9+ffWY03sMAIDzCGBDaOPGjXr22Wfl8/n0yCOPaMKECQqHw5o9e7a1TGpqqsLhsCRpypQpfdrfeOMN22sGhhuXy+iS+IgVHC6Jj/S8dvWEhZ6AEN8dBLqDRHfQcMdFFH+uvScgnHu9JL4nNLjOFx4icsdFFOfqnt//NWJNx+nPry51Kc4VUZy65FKke17Pe5d6ljE90ybS89o9LdPV3Wa6pF6v6vrzPJkuqSsi0/Oqru5lTKR7OdPVZb2ac6+R7uVMV9fgfgFGUufgVn11zJjBrQgAwAhCABsiq1ev1v333y+Xy6X7779fa9eu1VNPPTUk2w4EAgoEAj1T7UOyTdgvPq7njIK7qydARHTpF848WAEirvd0r7MN8b3OOlivnVaI6BMgXH8OEN1BodNq7x0U+kyr7zyXeoeHXsHBdMp1btp0DRAcvhAUTPc//PsFh0jnn6e7+r+aSKRveDBmcL+Arp6fQYaHWDM9PwAAYGQjgA2RSZMmWe9XrVqlG2+8UZLk8XjU0tJizWttbZXH45GkAdu/qLKyUpWVlZIkl8s35LX/Jb2Dw6XxnbrE3f9Spb7BoddZiPMGh87udXqfaegTHLoDRVy/APHncGC97zm7YL1awaHTmnb1erWCwrmzDfrzWYbe4UFdkb7BwWrvdYahd2gw584q9ExHIv2Dw2BFen4uUgQHjDQffPCBli1bZk0fPnxY//iP/6iTJ0/q3//93zVx4kRJ0sMPP2zd7wsAwIUigA2RtrY2paSkSJJeeOEFa4TE4uJi3XLLLbrzzjv1ySefqKmpSTNnzpQxRk1NTWpubpbH41Ftba3+67/+6y9+TvqEVj00f03MgoOJdEqRLoIDgFErOztboVBIkhSJROTxeLR48WI9/fTTWrNmje666y6HKwQADGcEsEEoKyvT/v37dezYMaWmpmr9+vXav3+/QqGQXC6X0tLStGnTJklSbm6uSkpKlJOTI7fbrerqasXHx0vqvmdswYIFikQiqqioUG5u7l/+8D/9UaZxN8EBAGywd+9eZWZmaurUqU6XAgAYIVzGDPaGCjghY8wYPdgzciIADCc/HTNGwWDQ6TKiUlFRoauvvlo//OEPtW7dOj3zzDMaN25cn8GWvozP5xt2+wwA59CHxQbPAQMA4DzOnDmjX/7yl7r55psldQ+2dOjQIYVCIaWkpGjt2rXnXS8QCMjn88nn86m9nYGTAAB9EcAAADiPnTt36uqrr7YGWZo0aZLi4+MVFxenVatWWc90/KLKykoFg0EFg0FrwA4AAM4hgAEAcB5bt25VWVmZNd3W1ma97z3YEgAA0WAQDgAAvuCzzz7Tnj17rAGVJOmee+4572BLAABEgwAGAMAXfO1rX9Px48f7tG3ZssWhagAAIwmXIAIAAACATQhgAAAAAGATAhgAAAAA2IQABgAAAAA2IYABAAAAgE0IYAAAAABgEwIYAAAAANiEAAYAAAAANiGAAQAAAIBNCGAAAAAAYBMCGAAAAADYhAAGAAAAADYhgAEAAACATQhgAAAAAGATAhgAAAAA2IQABgAAAAA2IYABAAAAgE0IYINQUVGh5ORk5eXlWW0nTpyQ3+9XVlaW/H6/Ojo6JEnGGN1xxx3yer3Kz8/XW2+9Za1TU1OjrKwsZWVlqaamxvb9AAAAAGAvAtggrFixQvX19X3aqqqqNH/+fDU1NWn+/PmqqqqSJO3cuVNNTU1qampSIBDQ6tWrJXUHtvXr1+uNN95QQ0OD1q9fb4U2AICz0tLSNGPGDBUUFMjn80ka+Is2AACiQQAbhDlz5igxMbFPW11dncrLyyVJ5eXl2r59u9W+fPlyuVwuzZ49WydPnlRbW5t27dolv9+vxMRETZgwQX6/v1+oAwA45+WXX1YoFFIwGJQ08BdtAABEgwA2RI4ePaqUlBRJ0uTJk3X06FFJUjgc1pQpU6zlUlNTFQ6HB2wHAFycBvqiDQCAaBDAYsDlcsnlcg3Z9gKBgHw+n3w+n05FIkO2XQDA+blcLl1//fUqLCxUIBCQNPAXbQAARMPtdAEjxaRJk9TW1qaUlBS1tbUpOTlZkuTxeNTS0mIt19raKo/HI4/Ho/379/dpv+6668677crKSlVWVkqSMsaMidk+AAC6/frXv5bH49Ef/vAH+f1+TZs2rc/8L/uiLRAIWKGtvb095rUCAIYXzoANkeLiYmskw5qaGi1atMhqf/bZZ2WM0YEDBzR+/HilpKRowYIF2r17tzo6OtTR0aHdu3drwYIFTu4CAKCHx+ORJCUnJ2vx4sVqaGiwvmiT1OeLti+qrKxUMBhUMBjUxIkTbasZADA8EMAGoaysTNdcc40++OADpaamavPmzbrvvvu0Z88eZWVl6aWXXtJ9990nSVq4cKEyMjLk9Xq1atUqPf7445KkxMRE3X///SoqKlJRUZEeeOCBfgN7AADs99lnn+nUqVPW+927dysvL2/AL9oAAIiGyxhjnC4CFy5jzBg9mJHhdBkAELWfjhljjSh4MTt8+LAWL14sSers7NQtt9yiH//4xzp+/LhKSkr08ccfa+rUqdq2bdtf/OLM5/MNi30GgPOhD4sN7gEDAKCXjIwMvfPOO/3ak5KStHfvXgcqAgCMJFyCCAAAAAA2IYABAAAAgE0IYAAAAABgEwIYAAAAANiEAAYAAAAANiGAAQAAAIBNCGAAAAAAYBMCGAAAAADYhAAGAAAAADYhgAEAAACATQhgAAAAAGATAhgAAAAA2IQABgAAAAA2IYABAAAAgE0IYAAAAABgEwIYAAAAANiEAAYAAAAANiGAAQAAAIBNCGAAAPTS0tKiuXPnKicnR7m5uXrsscckSevWrZPH41FBQYEKCgq0Y8cOhysFAAxHbqcLAADgYuJ2u/XII4/o6quv1qlTp1RYWCi/3y9JWrNmje666y6HKwQADGcEMAAAeklJSVFKSook6bLLLtP06dMVDocdrgoAMFJwCeIQS0tL04wZM1RQUCCfzydJOnHihPx+v7KysuT3+9XR0SFJMsbojjvukNfrVX5+vt566y0nSwcAfMGRI0f09ttva9asWZKkjRs3Kj8/XxUVFVZfDgBANAhgMfDyyy8rFAopGAxKkqqqqjR//nw1NTVp/vz5qqqqkiTt3LlTTU1NampqUiAQ0OrVq50sGwDQy6effqolS5bo0Ucf1bhx47R69WodOnRIoVBIKSkpWrt27XnXCwQC8vl88vl8am9vt7lqAMDFjgBmg7q6OpWXl0uSysvLtX37dqt9+fLlcrlcmj17tk6ePKm2tjYnSwUASDp79qyWLFmiW2+9VTfddJMkadKkSYqPj1dcXJxWrVqlhoaG865bWVmpYDCoYDCoiRMn2lk2AGAYIIANMZfLpeuvv16FhYUKBAKSpKNHj1r3E0yePFlHjx6VJIXDYU2ZMsVaNzU1lfsMAMBhxhitXLlS06dP15133mm19/6C7IUXXlBeXp4T5QEAhjkG4Rhiv/71r+XxePSHP/xBfr9f06ZN6zPf5XLJ5XJFtc1AIGCFuVORyJDVCgDo7/XXX9eWLVus+3kl6eGHH9bWrVsVCoXkcrmUlpamTZs2OVwpAGA4IoANMY/HI0lKTk7W4sWL1dDQoEmTJqmtrU0pKSlqa2tTcnKytWxLS4u1bmtrq7V+b5WVlaqsrJQkZYwZY8NeAMDo9Td/8zcyxvRrX7hwoQPVAABGGi5BHEKfffaZTp06Zb3fvXu38vLyVFxcrJqaGklSTU2NFi1aJEkqLi7Ws88+K2OMDhw4oPHjx1uXKgIAAAAYeTgDNoSOHj2qxYsXS5I6Ozt1yy236Fvf+paKiopUUlKizZs3a+rUqdq2bZuk7m9Td+zYIa/Xq7Fjx+rpp592snwAAAAAMUYAG0IZGRl65513+rUnJSVp7969/dpdLpeqq6vtKA0AAADARYBLEAEAAADAJgQwAAAAALAJAQwAAAAAbEIAAwAAAACbEMAAAAAAwCYEMAAAAACwCQEMAAAAAGxCAAMAAAAAmxDAAAAAAMAmBDAAAAAAsAkBDAAAAABsQgADAAAAAJsQwAAAAADAJgQwAAAAALAJAQwAAAAAbEIAAwAgCvX19crOzpbX61VVVZXT5QAAhhkCGAAAFygSiej222/Xzp071djYqK1bt6qxsdHpsgAAwwgBDACAC9TQ0CCv16uMjAxdeumlKi0tVV1dndNlAQCGEbfTBQAAMFyEw2FNmTLFmk5NTdUbb7wx4PIffPCBrrvuOhsqAwAMFwQwAACGUCAQUCAQkCSdPXvW4WoAABcbAhgAABfI4/GopaXFmm5tbZXH4+mzTGVlpSorKyVJPp9P+/fvt7NEABgyPp/P6RJGJO4BAwDgAhUVFampqUnNzc06c+aMamtrVVxc7HRZAIBhhADmMIYzBoDhw+12a+PGjVqwYIGmT5+ukpIS5ebmOl0WAGAY4RJEB50bznjPnj1KTU1VUVGRiouLlZOT43RpAIABLFy4UAsXLnS6DADAMEUAc1Dv4YwlWcMZE8AAYGT4/HPpvfdit32Xa3huO9bbp3Zntk/tzmw/1rVj6BHAHBTtcMYAgOHl4EGJ79QADFeFhU5XMDIRwIaB3kMadyYladGePQ5XBADR++koHKwiI0N6+OHYbNuY2Gw31tuO9fap3ZntU7sz24/1tp94InbbH80IYA66kOGMpf5DGn/tiitsqxEAMHgTJkjLljldBQAMDgEsNhgF0UEMZwwAAACtyuWmAAAKiklEQVSMLpwBc1Dv4YwjkYgqKioYzhgAAAAYwQhgDmM4YwAAAGD04BJEAAAAALAJAQwAAAAAbEIAAwAAAACbEMAAAAAAwCYEMAAAAACwicuYWD//G0Pp61//uqZNm+Z0Gf20t7dr4sSJTpfRD3VFh7qiQ13Ref/99/Xpp586XYat6LOjQ13Roa7oXay1Xax1jcZ+2w4MQz/MTJs2TcFg0Oky+vH5fNQVBeqKDnVF52Kua7Shz44OdUWHuqJ3sdZ2MdeFoccliAAAAABgEwIYAAAAANgkft26deucLgLRKSwsdLqE86Ku6FBXdKgrOtR18bhY95m6okNd0blY65Iu3tqoa/RgEA4AAAAAsAmXIAIAAACATQhgF6Ff/OIXys3NVVxc3JeOiFNfX6/s7Gx5vV5VVVVZ7c3NzZo1a5a8Xq+WLVumM2fODEldJ06ckN/vV1ZWlvx+vzo6Ovot8/LLL6ugoMD6+au/+itt375dkrRixQqlp6db80KhkG11SVJ8fLz12cXFxVa7k8crFArpmmuuUW5urvLz8/Xzn//cmjfUx2ugv5dzTp8+rWXLlsnr9WrWrFk6cuSINW/Dhg3yer3Kzs7Wrl27vlId0db105/+VDk5OcrPz9f8+fP10UcfWfMG+p3aUdczzzyjiRMnWp//5JNPWvNqamqUlZWlrKws1dTU2FrXmjVrrJquvPJKXX755da8WB2viooKJScnKy8v77zzjTG644475PV6lZ+fr7feesuaF8tjZRf67KGvS6LPps8e2rros/sa7f224wwuOo2Njeb999831157rfntb3973mU6OztNRkaGOXTokDl9+rTJz883Bw8eNMYYc/PNN5utW7caY4z5/ve/bx5//PEhqevuu+82GzZsMMYYs2HDBnPPPfd86fLHjx83EyZMMJ999pkxxpjy8nLzi1/8YkhqGUxdX/va187b7uTx+uCDD8yHH35ojDEmHA6byZMnm46ODmPM0B6vL/t7Oae6utp8//vfN8YYs3XrVlNSUmKMMebgwYMmPz/ffP755+bw4cMmIyPDdHZ22lbXvn37rL+hxx9/3KrLmIF/p3bU9fTTT5vbb7+937rHjx836enp5vjx4+bEiRMmPT3dnDhxwra6evu3f/s3873vfc+ajtXxeuWVV8ybb75pcnNzzzv/V7/6lfnWt75lurq6zG9+8xszc+ZMY0xsj5Wd6LNjUxd9Nn32UNZFn93XaO+3ncYZsIvQ9OnTlZ2d/aXLNDQ0yOv1KiMjQ5deeqlKS0tVV1cnY4z27dunpUuXSpLKy8utbzO/qrq6OpWXl1/wdp977jndcMMNGjt27JB8/lDV1ZvTx+vKK69UVlaWJOmKK65QcnKy2tvbh+Tzexvo72WgepcuXaq9e/fKGKO6ujqVlpYqISFB6enp8nq9amhosK2uuXPnWn9Ds2fPVmtr65B89letayC7du2S3+9XYmKiJkyYIL/fr/r6ekfq2rp1q8rKyobks7/MnDlzlJiYOOD8uro6LV++XC6XS7Nnz9bJkyfV1tYW02NlJ/rs2NbVm9PHiz6bPjuWddnVZ0v0204jgA1T4XBYU6ZMsaZTU1MVDod1/PhxXX755XK73X3ah8LRo0eVkpIiSZo8ebKOHj36pcvX1tb260h+/OMfKz8/X2vWrNHp06dtrevzzz+Xz+fT7Nmzrf+xXkzHq6GhQWfOnFFmZqbVNlTHa6C/l4GWcbvdGj9+vI4fP35B68ayrt42b96sG264wZo+3+/Uzrr++7//W/n5+Vq6dKlaWlqiWjeWdUnSRx99pObmZs2bN89qi9Xx+ksGqjuWx+piQ58dfV302fTZQ10XffaFo9+OLbfTBYxW3/zmN/X73/++X/tDDz2kRYsWOVBRty+rqzeXyyWXyzXgdtra2vS///u/WrBggdW2YcMGTZ48WWfOnFFlZaX+6Z/+SQ888IBtdX300UfyeDw6fPiw5s2bpxkzZmj8+PEX9PmxrEvqPl7f/e53VVNTo7i47u9FvsrxGon+4z/+Q8FgUK+88orVdr7fae9/DMXSt7/9bZWVlSkhIUGbNm1SeXm59u3bZ8tnX4ja2lotXbpU8fHxVpuTx2u4o8+mz+6NPvsvo8+ODn326EIAc8hLL730ldb3eDzWtzeS1NraKo/Ho6SkJJ08eVKdnZ1yu91W+1DUNWnSJLW1tSklJUVtbW1KTk4ecNlt27Zp8eLFuuSSS6y2c98sJiQk6Hvf+57+5V/+xda6zh2HjIwMXXfddXr77be1ZMkSx4/X//3f/+lv//Zv9dBDD2n27NlW+1c5Xl800N/L+ZZJTU1VZ2en/vjHPyopKemC1o1lXVL3cX7ooYf0yiuvKCEhoc/6Ut/f6VD8z+lC6kpKSrLe/93f/Z3uuecea939+/f3Wfe66677yjVdaF3n1NbWqrq6ut/60tAfr79koLpjeayGGn02ffY59Nn02UNZ1zkXU5997rOHe799UXPyBjR8uS+7ofvs2bMmPT3dHD582Lqx89133zXGGLN06dI+NyhXV1cPST133XVXnxuU77777gGXnTVrltm3b1+ftk8++cQYY0xXV5f50Y9+ZO69917b6jpx4oT5/PPPjTHGtLe3G6/Xa90I6+TxOn36tJk3b57513/9137zhvJ4fdnfyzkbN27sc0P3zTffbIwx5t133+1zQ3d6evqQ3dB9IXW99dZbJiMjw7rx/Zwv+53aUde5348xxjz//PNm1qxZxpjuG5TT0tLMiRMnzIkTJ0xaWpo5fvy4bXUZY8x7771npk6darq6uqy2WB4vY4xpbm4e8GbuF198sc/N3EVFRcaY2B4rJ9BnD11d9Nn02UNdF312f/TbziGAXYSef/554/F4zKWXXmqSk5PN9ddfb4zpHnHphhtusJb71a9+ZbKyskxGRoZ58MEHrfZDhw6ZoqIik5mZaZYuXWr9B/xVHTt2zMybN894vV4zf/586z+43/72t2blypXWcs3NzeaKK64wkUikz/pz5841eXl5Jjc319x6663m1KlTttX1+uuvm7y8PJOfn2/y8vLMk08+aa3v5PHasmWLcbvd5hvf+Ib18/bbbxtjhv54ne/v5f777zd1dXXGGGP+9Kc/maVLl5rMzExTVFRkDh06ZK374IMPmoyMDHPllVeaHTt2fKU6oq1r/vz5Jjk52To+3/72t40xX/47taOu++67z+Tk5Jj8/Hxz3XXXmffee89ad/PmzSYzM9NkZmaap556yta6jDHmJz/5Sb9//MXyeJWWlprJkycbt9ttPB6PefLJJ83PfvYz87Of/cwY0/0P0ttuu81kZGSYvLy8PiEllsfKLvTZQ18XfTZ99lDXRZ/d12jvt53mMsYYp8/CAQAAAMBowCiIAAAAAGATAhgAAAAA2IQABgAAAAA2IYABAAAAgE0IYAAAAABgEwIYAAAAANiEAAYAAAAANiGAAQAAAIBNCGAAAAAAYBMCGAAAAADYhAAGAAAAADYhgAEAAACATQhgAAAAAGATAhgAAAAA2IQABgAAAAA2IYABAAAAgE0IYAAAAABgEwIYAAAAANiEAAYAAAAANiGAAQAAAIBN/h9Ur3fFMjLWygAAAABJRU5ErkJggg==\"\n",
       "  frames[8] = 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\"\n",
       "  frames[9] = 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\"\n",
       "  frames[10] = 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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",
       "        anim433843022d197b70 = new Animation(frames, img_id, slider_id, frame_id, 100, loop_select_id);\n",
       "    }, 0);\n",
       "  })()\n",
       "</script>\n"
      ],
      "text/plain": [
       "<matplotlib.animation.FuncAnimation at 0x10b6d6c18>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from matplotlib import animation\n",
    "from clawpack.visclaw.JSAnimation import IPython_display\n",
    "\n",
    "def plot_waves(claw,ylim=None,save_plots=False):\n",
    "    fig = plt.figure(figsize=[12,4])\n",
    "    ax1 = fig.add_subplot(1, 2, 1)\n",
    "    ax2 = fig.add_subplot(1, 2, 2)\n",
    "    fills = []\n",
    "    frame = claw.frames[0]\n",
    "    b = frame.aux[0,:,:]\n",
    "    h = frame.q[0,:,:]\n",
    "    hu = frame.q[1, :, :]\n",
    "    u = np.where(h > 1e-3, frame.q[1, :, :] / frame.q[0, :, :], 0.0)\n",
    "    surface = np.maximum(b,h+b)\n",
    "\n",
    "    x, y = frame.state.grid.p_centers    \n",
    "    slice = 1\n",
    "    fill = ax1.fill_between(x[:,0],b[:,slice],surface[:,slice],facecolor='blue')\n",
    "    fill2 = ax1.fill_between(x[:,0],0*b[:,slice],b[:,slice],facecolor='brown')\n",
    "    fills = [fill,fill2]\n",
    "    ax1.set_xlim(-1,1)\n",
    "    if ylim: ax1.set_ylim(ylim)\n",
    "    \n",
    "    velocity_plots = []\n",
    "    velocity_plots.append(ax2.plot(x[:, 0], hu[:, slice], 'b', label=\"momentum\")[0])\n",
    "    velocity_plots.append(ax2.plot(x[:, 0], u[:, slice], 'k-', label=\"velocity\")[0])\n",
    "    ax2.set_xlim(-1, 1)\n",
    "\n",
    "    def fplot(frame_number):\n",
    "        fills[-2].remove()\n",
    "        fills[-1].remove()\n",
    "        velocity_plots[-2].remove()\n",
    "        velocity_plots[-1].remove()\n",
    "        \n",
    "        frame = claw.frames[frame_number]\n",
    "        b = frame.aux[0,:,:]\n",
    "        h = frame.q[0,:,:]\n",
    "        hu = frame.q[1, :, :]\n",
    "        u = np.where(h > 1e-3, frame.q[1, :, :] / frame.q[0, :, :], 0.0)\n",
    "        surface = np.maximum(b,h+b)\n",
    "        #line.set_data(x[:,0],surface[:,slice])\n",
    "        fill = ax1.fill_between(x[:,0],b[:,slice],surface[:,slice],facecolor='blue',where=b[:,slice]<surface[:,slice])\n",
    "        fill2 = ax1.fill_between(x[:,0],0*b[:,slice],b[:,slice],facecolor='brown')\n",
    "        fills.append(fill)\n",
    "        fills.append(fill2)\n",
    "        \n",
    "        l1, = ax2.plot(x[:, 0], hu[:, slice], 'b', label=\"momentum\")\n",
    "        l2, = ax2.plot(x[:, 0], u[:, slice], 'k-', label=\"velocity\")\n",
    "        velocity_plots.append(l1)\n",
    "        velocity_plots.append(l2)\n",
    "        \n",
    "        if save_plots:\n",
    "            fname = 'frame'+str(frame_number).zfill(4)+'.eps'\n",
    "            fig.savefig(fname)   \n",
    "        return fill,\n",
    "\n",
    "    return animation.FuncAnimation(fig, fplot, frames=len(claw.frames), interval=100)\n",
    "\n",
    "plot_waves(claw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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