avo_explorer_v2_mono.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AVO Explorer v2 (mono)\n",
    "\n",
    "Jupyter notebook to explore AVO classes.\n",
    "\n",
    "Monolytic version, made to be run without any Python installed on local pc using [colab.research.google.com](https://colab.research.google.com): no dependency from external libraries, no interactive widgets (ipywidgets are not yet supported by colab).\n",
    "\n",
    "Bookmark the following link to reuse this notebook: <https://colab.research.google.com/github/aadm/geophysical_notes/blob/master/avo_explorer_v2_mono.ipynb>\n",
    "\n",
    "2016-2018, Alessandro Amato del Monte (alessandro.adm@gmail.com)\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell below defines the elastic properties for the various AVO classes, taken from Castagna & Swan, \"Principles of AVO crossplotting\" (1997) and Hilterman, \"Seismic Amplitude Interpretation\" (2001)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "shale = np.array([[3094,1515,2.40], [2643,1167,2.29], [2192,818,2.16], [3240,1620,2.34]])\n",
    "ssgas = np.array([[4050,2526,2.21,.2], [2781,1665,2.08,.25], [1542,901,1.88,.33], [1650,1090,2.07,.18]])\n",
    "ssbri = np.array([[4115,2453,2.32,.2], [3048,1595,2.23,.25], [2134,860,2.11,.33], [2590,1060,2.21,.18]])\n",
    "avocl=['class 1','class 2','class 3','class 4']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following cell define some support functions: `ricker` (to create wavelets), `gassmann` (to perform fluid substitution), `shuey` and `ruger` (reflectivity equations for the isotropic and anisotropic case)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ricker(f, length, dt):\n",
    "    '''\n",
    "    (taken from https://github.com/seg/tutorials-2014/blob/master/1406_Make_a_synthetic/how_to_make_synthetic.ipynb)\n",
    "    (author: Evan Bianco)\n",
    "    '''\n",
    "    t = np.linspace(-length/2, (length-dt)/2, int(length/dt))\n",
    "    y = (1. - 2.*(np.pi**2)*(f**2)*(t**2))*np.exp(-(np.pi**2)*(f**2)*(t**2))\n",
    "    return t, y\n",
    "\n",
    "def gassmann(vp1, vs1, rho1, rho_fl1, k_fl1, rho_fl2, k_fl2, k0, phi):\n",
    "    '''\n",
    "    written by Alessandro Amato del Monte, 2016\n",
    "    '''\n",
    "    vp1 = vp1/1000\n",
    "    vs1 = vs1/1000\n",
    "    rho2 = rho1-phi*rho_fl1+phi*rho_fl2\n",
    "    mu1 = rho1*vs1**2.\n",
    "    k1 = rho1*vp1**2-(4./3.)*mu1\n",
    "    kdry= (k1 * ((phi*k0)/k_fl1+1-phi)-k0) / ((phi*k0)/k_fl1+(k1/k0)-1-phi)\n",
    "    k2 = kdry + (1- (kdry/k0))**2 / ( (phi/k_fl2) + ((1-phi)/k0) - (kdry/k0**2) )\n",
    "    mu2 = mu1\n",
    "    vp2 = np.sqrt(((k2+(4./3)*mu2))/rho2)\n",
    "    vs2 = np.sqrt((mu2/rho2))\n",
    "    return [vp2*1000, vs2*1000, rho2, k2, kdry]\n",
    "\n",
    "def ruger(vp1,vs1,rho1,d1,e1,vp2,vs2,rho2,d2,e2,theta):\n",
    "    '''\n",
    "    written by Alessandro Amato del Monte, 2016\n",
    "    '''\n",
    "    a = np.radians(theta)\n",
    "    vp  = np.mean([vp1,vp2])\n",
    "    vs  = np.mean([vs1,vs2])\n",
    "    rho = np.mean([rho1,rho2])\n",
    "    z   = np.mean([vp1*rho1,vp2*rho2])\n",
    "    g   = np.mean([rho1*vs1**2,rho2*vs2**2])\n",
    "    dvp = vp2-vp1\n",
    "    dvs = vs2-vs1\n",
    "    drho= rho2-rho1\n",
    "    z2, z1 = vp2*rho2, vp1*rho1\n",
    "    dz  = z2-z1\n",
    "    dg  = rho2*vs2**2 - rho1*vs1**2\n",
    "    dd = d2-d1\n",
    "    de = e2-e1\n",
    "    A = 0.5*(dz/z)\n",
    "    B = 0.5*(dvp/vp - (2*vs/vp)**2 * (dg/g) + dd) * np.sin(a)**2\n",
    "    C = 0.5*(dvp/vp + de) * np.sin(a)**2 * np.tan(a)**2\n",
    "    R = A+B+C\n",
    "    return R\n",
    "\n",
    "def shuey(vp1, vs1, rho1, vp2, vs2, rho2, theta, approx=True, terms=False):\n",
    "    '''\n",
    "    shuey (C) aadm 2016\n",
    "    Calculates P-wave reflectivity with Shuey's equation\n",
    "\n",
    "    reference:\n",
    "    Avseth et al. (2005), Quantitative Seismic Interpretation, Cambridge University Press (p.182)\n",
    "\n",
    "    INPUT\n",
    "    vp1, vs1, rho1: P-, S-wave velocity (m/s) and density (g/cm3) of upper medium\n",
    "    vp2, vs2, rho2: P-, S-wave velocity (m/s) and density (g/cm3) of lower medium\n",
    "    theta: angle of incidence (degree)\n",
    "    approx: returns approximate (2-term) form (default: True)\n",
    "    terms: returns reflectivity, intercept and gradient (default: False)\n",
    "\n",
    "    OUTPUT\n",
    "    reflectivity (and optionally intercept, gradient; see terms option) at angle theta\n",
    "    '''\n",
    "    a = np.radians(theta)\n",
    "    dvp = vp2-vp1\n",
    "    dvs = vs2-vs1\n",
    "    drho = rho2-rho1\n",
    "    vp  = np.mean([vp1,vp2])\n",
    "    vs  = np.mean([vs1,vs2])\n",
    "    rho = np.mean([rho1,rho2])\n",
    "    R0 = 0.5*(dvp/vp + drho/rho)\n",
    "    G  = 0.5*(dvp/vp) - 2*(vs**2/vp**2)*(drho/rho+2*(dvs/vs))\n",
    "    F =  0.5*(dvp/vp)\n",
    "    if approx:\n",
    "        R = R0 + G*np.sin(a)**2\n",
    "    else:\n",
    "        R = R0 + G*np.sin(a)**2 + F*(np.tan(a)**2-np.sin(a)**2)\n",
    "    if terms:\n",
    "        return R,R0,G\n",
    "    else:\n",
    "        return R"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The four main functions are `make_avoclasses` (plots a reference chart with the AVO classes), `avomod1` (creation of AVO curves, synthetic gather and Intercept-Gradient crossplot for a half-space model), `avomod2` (like the previous but allows for two different configurations for the lower medium, usually different fluids), `make_avo_explorer` (uses `avomod2` to give ... a simple selection to plot the reference AVO classes given by Hilterman (2001) for shale/brine sand scenarios and performs fluid replacement to make gas and oil sands)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_avoclasses(brine=False):\n",
    "    '''\n",
    "    written by Alessandro Amato del Monte, 2016\n",
    "    '''\n",
    "    ang = np.arange(0,50,1)\n",
    "    cc = ['m','c','r','g']\n",
    "    f,ax=plt.subplots(1,2, figsize=(10, 5))\n",
    "    if brine:\n",
    "        print('In the Intercept-Gradient crossplot, circles are gas sands, squares are brine sands.')\n",
    "    for i, val in enumerate(avocl):\n",
    "        amp0 = shuey(shale[i,0],shale[i,1],shale[i,2],ssbri[i,0],ssbri[i,1],ssbri[i,2],ang)\n",
    "        amp1 = shuey(shale[i,0],shale[i,1],shale[i,2],ssgas[i,0],ssgas[i,1],ssgas[i,2],ang)\n",
    "        tmp0 = shuey(shale[i,0],shale[i,1],shale[i,2],ssbri[i,0],ssbri[i,1],ssbri[i,2],30,terms=True)\n",
    "        tmp1 = shuey(shale[i,0],shale[i,1],shale[i,2],ssgas[i,0],ssgas[i,1],ssgas[i,2],30,terms=True)\n",
    "        Ib, Gb = tmp0[1],tmp0[2]\n",
    "        Ig, Gg = tmp1[1],tmp1[2]\n",
    "        ax[0].plot(ang, amp1, color=cc[i], lw=2, ls='-', label=val+' (gas)')\n",
    "        ax[0].axhline(0, color='k')\n",
    "        ax[0].set_xlabel('angle of incidence'), ax[0].set_ylabel('amplitude')\n",
    "        ax[0].set_xlim(0, 40)\n",
    "        ax[0].text(2,amp1[0]-.02,avocl[i], style='italic', fontsize=10, ha='left', va='top')\n",
    "        ax[1].plot(Ig, Gg, color=cc[i], marker='o', ms=10, label=val+' (gas)')\n",
    "        ax[1].axhline(0, color='k'), ax[1].axvline(0, color='k')\n",
    "        ax[1].set_xlabel('intercept'), ax[1].set_ylabel('gradient')\n",
    "        ax[1].set_xlim(-.5, .5)\n",
    "        if brine:\n",
    "            ax[0].plot(ang, amp0, color=cc[i], lw=2, ls='--', label=val+' (brine)')\n",
    "            ax[1].plot(Ib, Gb, color=cc[i], marker='s', ms=10, label=val+' (brine)')\n",
    "        # draw avo classes areas\n",
    "        cl1_area = patches.Rectangle((0.02,-1),.98,1,edgecolor='None',facecolor='m',alpha=0.2)\n",
    "        cl2_area = patches.Rectangle((-0.02,-1),.04,2,edgecolor='None',facecolor='c',alpha=0.2)\n",
    "        cl3_area = patches.Rectangle((-1,-1),.98,1,edgecolor='None',facecolor='r',alpha=0.2)\n",
    "        cl4_area = patches.Rectangle((-1,0),.98,1,edgecolor='None',facecolor='g',alpha=0.2)\n",
    "        for aa in ax:\n",
    "            aa.grid()\n",
    "            aa.set_ylim(-.5, .5)\n",
    "    background = patches.Polygon([[-1, 1], [1, -1], [1, 1]],facecolor='w')\n",
    "    ax[1].add_patch(cl1_area)\n",
    "    ax[1].add_patch(cl2_area)\n",
    "    ax[1].add_patch(cl3_area)\n",
    "    ax[1].add_patch(cl4_area)\n",
    "    ax[1].add_patch(background)\n",
    "    ax[1].text(.15,-.3,'Class 1',ha='center',va='center',color='m',style='italic')\n",
    "    ax[1].text(0,-.25,'Class 2/2p',ha='center',va='center', color='c',style='italic')\n",
    "    ax[1].text(-.35,-.3,'Class 3',ha='center',va='center', color='r',style='italic')\n",
    "    ax[1].text(-.35,.15,'Class 4',ha='center',va='center', color='g',style='italic')\n",
    "\n",
    "def avomod1(vp1,vs1,rho1,vp2,vs2,rho2,angmin=0,angmax=30,polarity='normal',black=False,mx=0.5):\n",
    "    '''\n",
    "    written by Alessandro Amato del Monte, 2016\n",
    "    '''\n",
    "    n_samples = 500\n",
    "    gain=10\n",
    "    interface=int(n_samples/2)\n",
    "    ang = np.arange(angmin,angmax+1,1)\n",
    "    z = np.arange(n_samples)\n",
    "\n",
    "    # build Ip and Vp/Vs logs\n",
    "    ip, vpvs = (np.zeros(n_samples) for _ in range(2))\n",
    "    ip[:interface] = vp1*rho1\n",
    "    ip[interface:] = vp2*rho2\n",
    "    vpvs[:interface] = vp1/vs1\n",
    "    vpvs[interface:] = vp2/vs2\n",
    "\n",
    "    # calculate avo curve, intercept and gradient\n",
    "    avo = shuey(vp1,vs1,rho1,vp2,vs2,rho2,ang)\n",
    "    _,I,G = shuey(vp1,vs1,rho1,vp2,vs2,rho2,30,terms=True)\n",
    "\n",
    "    # create synthetic gather\n",
    "    _,wavelet = ricker(f=10, length=.250, dt=0.001)\n",
    "    if polarity is not 'normal':\n",
    "        avo *= -1\n",
    "\n",
    "    # builds prestack gather model\n",
    "    rc, syn = (np.zeros((n_samples,ang.size)) for _ in range(2))\n",
    "    rc[interface,:]=  avo\n",
    "    for i in range(ang.size):\n",
    "        syn[:,i] = np.convolve(rc[:,i],wavelet,mode='same')\n",
    "\n",
    "    # do the plot\n",
    "    f=plt.subplots(figsize=(10, 5))\n",
    "    ax0 = plt.subplot2grid((1,7), (0,0), colspan=1)\n",
    "    ax1 = plt.subplot2grid((1,7), (0,1), colspan=1)\n",
    "    ax2 = plt.subplot2grid((1,7), (0,2), colspan=1)\n",
    "    ax3 = plt.subplot2grid((1,7), (0,3), colspan=2)\n",
    "    ax4 = plt.subplot2grid((1,7), (0,5), colspan=2)\n",
    "    ax0.plot(ip, z, '-k', lw=4)\n",
    "    ax0.set_xlabel('AI [m/s*g/cc]')\n",
    "    ax0.margins(x=0.5)\n",
    "    ax1.plot(vpvs, z, '-k', lw=4)\n",
    "    ax1.set_xlabel('Vp/Vs')\n",
    "    ax1.margins(x=0.5)\n",
    "    opz1={'color':'k','linewidth':2}\n",
    "    opz2={'linewidth':0, 'alpha':0.6}\n",
    "    for i in range(0,int(ang.size),10):\n",
    "        trace=gain*syn[:,i] / np.max(np.abs(syn))\n",
    "        ax2.plot(i+trace,z,**opz1)\n",
    "        if black==False:\n",
    "            ax2.fill_betweenx(z,trace+i,i,where=trace+i>i,facecolor=[0.6,0.6,1.0],**opz2)\n",
    "            ax2.fill_betweenx(z,trace+i,i,where=trace+i<i,facecolor=[1.0,0.7,0.7],**opz2)\n",
    "        else:\n",
    "            ax2.fill_betweenx(z,trace+i,i,where=trace+i>i,facecolor='black',**opz2)\n",
    "        ax2.set_xticklabels([])\n",
    "    ax2.margins(x=0.05)\n",
    "    ax3.plot(ang, avo,'-k', lw=4)\n",
    "    ax3.axhline(0, color='k', lw=1)\n",
    "    ax3.set_xlabel('angle of incidence')\n",
    "    ax3.set_ylim(-mx,mx)\n",
    "    ax4.plot(I,G,'ko',ms=10,mfc='none',mew=2)\n",
    "    ax4.axhline(0, color='k', lw=1), ax4.axvline(0, color='k', lw=1)\n",
    "    ax4.set_xlabel('intercept'), ax4.set_ylabel('gradient')\n",
    "    ax4.set_xlim(-mx,mx), ax4.set_ylim(-mx,mx)\n",
    "    ax4.xaxis.set_label_position('top'), ax4.xaxis.tick_top()\n",
    "    ax4.yaxis.set_label_position('right'), ax4.yaxis.tick_right()\n",
    "    for aa in [ax0, ax1, ax2]:\n",
    "        aa.invert_yaxis()\n",
    "        aa.xaxis.tick_top()\n",
    "        plt.setp(aa.xaxis.get_majorticklabels(), rotation=90, fontsize=8)\n",
    "        aa.set_yticklabels([])\n",
    "    plt.tight_layout()\n",
    "\n",
    "def avomod2(vp1,vs1,rho1,vp2A,vs2A,rho2A,vp2B,vs2B,rho2B,angmin=0,angmax=30):\n",
    "    '''\n",
    "    written by Alessandro Amato del Monte, 2016\n",
    "    '''\n",
    "    n_samples = 500\n",
    "    interface = int(n_samples/2)\n",
    "    ang = np.arange(angmin,angmax+1,1)\n",
    "    z = np.arange(n_samples)\n",
    "\n",
    "    # builds Ip and Vp/Vs logs\n",
    "    ipA,ipB,vpvsA,vpvsB = (np.zeros(n_samples) for _ in range(4))\n",
    "    ipA[:interface] = vp1*rho1\n",
    "    ipA[interface:] = vp2A*rho2A\n",
    "    ipB[:interface] = vp1*rho1\n",
    "    ipB[interface:] = vp2B*rho2B\n",
    "    vpvsA[:interface] = vp1/vs1\n",
    "    vpvsA[interface:] = vp2A/vs2A\n",
    "    vpvsB[:interface] = vp1/vs1\n",
    "    vpvsB[interface:] = vp2B/vs2B\n",
    "\n",
    "\n",
    "    # calculates avo curve, intercept and gradient\n",
    "    avoA = shuey(vp1,vs1,rho1,vp2A,vs2A,rho2A,ang)\n",
    "    avoB = shuey(vp1,vs1,rho1,vp2B,vs2B,rho2B,ang)\n",
    "    tmp0= shuey(vp1,vs1,rho1,vp2A,vs2A,rho2A,30,terms=True)\n",
    "    IA, GA = tmp0[1],tmp0[2]\n",
    "    tmp0= shuey(vp1,vs1,rho1,vp2B,vs2B,rho2B,30,terms=True)\n",
    "    IB, GB = tmp0[1],tmp0[2]\n",
    "\n",
    "    # do the plot\n",
    "    f=plt.subplots(figsize=(10, 5))\n",
    "    ax0 = plt.subplot2grid((1,6), (0,0), colspan=1)\n",
    "    ax1 = plt.subplot2grid((1,6), (0,1), colspan=1)\n",
    "    ax2 = plt.subplot2grid((1,6), (0,2), colspan=2)\n",
    "    ax3 = plt.subplot2grid((1,6), (0,4), colspan=2)\n",
    "    ax0.plot(ipB, z, '-r', lw=4)\n",
    "    ax0.plot(ipA, z, '-k', lw=4)\n",
    "    ax0.set_xlabel('AI [m/s*g/cc]')\n",
    "    ax0.margins(x=0.5)\n",
    "    ax1.plot(vpvsB, z, '-r', lw=4)\n",
    "    ax1.plot(vpvsA, z, '-k', lw=4)\n",
    "    ax1.set_xlabel('Vp/Vs')\n",
    "    ax1.margins(x=0.5)\n",
    "    ax2.plot(ang, avoB,'-r', lw=4)\n",
    "    ax2.plot(ang, avoA,'-k', lw=4)\n",
    "    ax2.axhline(0, color='k', lw=1)\n",
    "    ax2.set_xlabel('angle of incidence')\n",
    "    ax2.margins(y=0.5)\n",
    "    ax3.plot(IB,GB,'ro',ms=15,mfc='r',mew=1)\n",
    "    ax3.plot(IA,GA,'ko',ms=15,mfc='k',mew=1)\n",
    "    ax3.axhline(0, color='k', lw=1), ax3.axvline(0, color='k', lw=1)\n",
    "    ax3.set_xlabel('intercept'), ax3.set_ylabel('gradient')\n",
    "    ax3.margins(0.5)\n",
    "    ax3.xaxis.set_label_position('top'), ax3.xaxis.tick_top()\n",
    "    ax3.yaxis.set_label_position('right'), ax3.yaxis.tick_right()\n",
    "    for aa in [ax0, ax1]:\n",
    "        aa.invert_yaxis()\n",
    "        aa.xaxis.tick_top()\n",
    "        plt.setp(aa.xaxis.get_majorticklabels(), rotation=90, fontsize=8)\n",
    "        aa.set_yticklabels([])\n",
    "    plt.tight_layout()\n",
    "\n",
    "def make_avo_explorer(avoclass=3, fluid='gas', phimod=0.0):\n",
    "    '''\n",
    "    written by Alessandro Amato del Monte, 2016\n",
    "    '''\n",
    "    shale = np.array([[3094,1515,2.40], [2643,1167,2.29], [2192,818,2.16], [3240,1620,2.34]])\n",
    "    ssbri = np.array([[4115,2453,2.32,.2], [3048,1595,2.23,.25], [2134,860,2.11,.33], [2590,1060,2.21,.18]])\n",
    "    vp1,vs1,rho1 = shale[avoclass-1,0],shale[avoclass-1,1],shale[avoclass-1,2]\n",
    "    vp2,vs2,rho2 = ssbri[avoclass-1,0],ssbri[avoclass-1,1],ssbri[avoclass-1,2]\n",
    "    phi2 = ssbri[avoclass-1,3]+phimod\n",
    "\n",
    "    # elastic parameters for toy-fluid replacement\n",
    "    k0 = 37.00\n",
    "    rhob, kb = 1.09,  2.20\n",
    "    if fluid is 'gas':\n",
    "        rhof_new, kf_new = 0.40,  0.02 # gas density & bulk modulus\n",
    "    else:\n",
    "        rhof_new, kf_new = 0.80,  1.02 # oil density & bulk modulus\n",
    "\n",
    "    vp2B,vs2B,rho2B,_,_ = gassmann(vp2, vs2, rho2, rhob, kb, rhof_new, kf_new, k0, phi2)\n",
    "\n",
    "    print('Shale:  Vp={:.0f}, Vs={:.0f}, rho={:.2f}'.format(vp1,vs1,rho1))\n",
    "    print('Sand (brine): Vp={:.0f}, Vs={:.0f}, rho={:.2f}, porosity={:.2f}'.format(vp2,vs2,rho2,phi2))\n",
    "    print('Sand ({:s}): Vp={:.0f}, Vs={:.0f}, rho={:.2f}'.format(fluid,vp2B,vs2B,rho2B))\n",
    "\n",
    "    avomod2(vp1,vs1,rho1,vp2,vs2,rho2,vp2B,vs2B,rho2B,angmin=0,angmax=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AVO classes\n",
    "\n",
    "The function `make_avoclasses` simply plots all the AVO classes in the amplitude vs angle domain and intercept vs gradient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 317,
       "width": 618
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "make_avoclasses()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AVO explorer\n",
    "\n",
    "`avomod1` is the main function that allows the user to explore AVO classes by studying the reflectivity (amplitude) variations with angle of incidence at an interface between two half-spaces.\n",
    "\n",
    "The syntax is:\n",
    "\n",
    "```\n",
    "avomod1(vp1, vs1, rho1, vp2, vs2, rho2, angmin=0, angmax=30, polarity='normal', black=False, mx=0.5)\n",
    "```\n",
    "\n",
    "The upper medium is characterized by the elastic properties `vp1`, `vs1`, `rho1` (P and S-wave velocity in m/s, density in g/cc) and the lower medium by `vp2`, `vs2`, `rho2`.\n",
    "\n",
    "The maximum angle of incidence in degrees is `angmax`.\n",
    "\n",
    "Optional parameters are `polarity` that when set to `normal` means SEG-normal, i.e. increase in acoustic impedance is a peak or positive number; `black` is a boolean flag to draw the synthetic seismogram in black and white when set to `True`, otherwise peaks are blue and troughs are red. Finally, `mx` controls the scale of the intercept and gradient crossplot as well as the vertical scale (i.e., reflection magnitude) of the AVO plot.\n",
    "\n",
    "We can use the elastic properties from the above array defining the various classes or input our own values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VT3aM2AeAt1MmIfyloSwRsS8lNdMulKd1PgWsQPl9nDZ23Zw2/oXyRfQzgV9TUm2dDmxBWd/hW3VSy2KO+UAcDqwC/IySauxrwCMsWWB8g/bCjrkkSTPPIILJrbzI89p31lmDPwDWAfYdwHn6sajLvkf6ONYZ7H4O8Hjgu+07M/O7lMXzLqM8qn8acGNEXBwRe7SVu6lHUK6VT/nG+uMadXtL95fTc3/X/gFr1m3jB5xhltU0kJmLF9ejzk6uH8LeSNvCe130ug5vrts12vb1ujZbus2OXibFRZtXURazXKluzwb+FhFfqbnaJUnL+jSwNvDOzNwvM9+bmc+jBHc2AT46ob2bug4HNqY82TXkQsURsTrl/7V/UNKjvTEz/40SiP4V8IqIePUY93equ5byBfX6mXlAZr4vM98AbArcSPky+mWtwo75wKyemc/KzDfUfzvekZk7AMcATwDe1yromEuSNDONKphcF9/ar/74jfq43+I/lDd5MP4L8Y3WSynBtWUe+c/M+fUD2WOA3SkfzDYHfhgRT+/WWGZGZi7scujuuu0VFOu1v1f/WjNBn9ij3kjLavr4IiUFxetrmpXnARsC5wyR27zXdbhu3bZ/UdPz3qm+C9wO7BMR60TE04BdKTOmz+4snJn3Z+bczNyYMqP5tZQZ+68FvtPjHJI0Y0XEhsCewELKDMF2RwP3Aq+LiFXGuWtTXmaek5nX1S9nm7yC8uXqafXJtFYbD1BmOENDQHqmy8yzM/MHNW1d+/6bKSnlAOa0HXLMB6COVzffqtuN2vY55pIkzUCjnZl8IOUxpkuAU3v8uQ14fkQ8ZZTnGk/7ARdl5k29CmTmvfVN7hGUb+pXoCxUNhxXU/IvbxURq3U5/pxh9u+iuu2nH8Mpq2kiM28Bvg88jnIdtWYFd1t4r2XXzh11cbytgQeAq9oODXnvZGYrL/LylH8/DqHkRf5c04fzzLyx5j3fC7gOeI6PakvSMp5Xtz/tEoS7B7gAWJkl6a40Nlq/hx93OXYucB+wU5d1B9Sf1hOAj7Ttc8zH1j51e2XbPsdckqQZaLTB5Naj6m/NzEO6/QE+w/gvxDdiEbEd8GRKzq/OY7tHxEpdqrVmbt43nHPVwNo3KWkCPtB+LCKeAbx+OP2jBOnuBg6NiF261F1/hGU1vbTSSfw/SvD3drpfTy2vi4htOvbNpVy338jMB6Hx2ux2/jdRAsoP05Emp7b3+Ih4Zpf6qwCrUT5APtRwLkmaaTap22t7HL+ubjceh77MZD1/D5n5CHADZb2CDcezU9NBRMxiyXvk9iCmYz5AEfHuiJgbEcdHxHnAhymB5I+1FXPMJUmagWaNtGJEzKG8gfjfzPzNEEVPBY4CDo6Io+sbi8nspXXbLSD2CWB2RCygPD76ELAd5Vv5PzGyRSbeW+u/pwbOLgTWA14J/IgS7GufWdSzf5l5e0TsT3n8/5yIOIvypm91YCtgA+Apwy2raeenlDf3O9afP1m/2OjlLOCCiPgWcBNlxvxzKPfAe9vKDXXvLJaZ10REa6EWgNPrjOlOTwQuioirgEsp+RFXp+QtXxc4scfClZI0k7Xy2HdbK6J9/5o9jmsw/D2MnY9RFuH7UWb+pG2/Yz5Y72bpVGc/Bg5qXywZx1ySpBlpNDOTW4/H91q0C4CaK/jnlADpPkOVnSReSgmQd8sfewwlsLY5Zab1Wyhvso4BdsjMO4d7shpE2wn4cm33cGAb4K2U1ZNhSW7lpv6RmfOB7WvdbShvBP+Fksf22JGW1fRR00mc2rar28J37Y6nXI9bA4dRFr6ZB+yUmbe2lRvy2uzw2R5/b7eQkt/zZmA34AjKQjs3APvXvkiShifqtp+8vxo7/h5GICLeSXmy6mrgdcOtXreOeR8yc93MDMoX+C+jzC6+LCK2HUYzjrkkSdPQiGcmZ+YBwAF9lt2z4+d5dHmsfRR96dleZi5gyRuZzmML249FxMbA04H/6FH+WyxZfGJgMvMvlMf9lxIRrdXWr+qnf23t/Y4uKTJGW1bTR2Z+FPhoY8El5ecxxD3b77XZ1t7XWPJlSa8yd9X2+mpTkgQsmQm4Ro/jq3eU09jw9zBgEfE24H+A3wO7Z+YdHUUc8zFQJ758LyIupaSz+DJlZjg45pIkzUijzZk83o6OiIyIq8eo/b4e0x+0iHhCl31bAu8E7gB+WXdPSP9qf/4eEUmXoLfEBF6bkqSlXFO3vXIib1S3vXIqazB6/h5qzt+nUHL//3E8OzVVRcRhwCeB3wK7ZebNXYo55mMoM/9ECeRvHhGPq7sdc0mSZqARz0weZwuBD7X9fPtYnCQzjwOOG4u2G1wcEX+gvEG+l/JB70WUYP9bMvOBCe4flFQeK7T9fNcE9UOT0ARfm5KkJc6p2z0j4lGZuXjdhYhYDdgZuB+4aCI6N4OcTXmC7wXANzqO7QKsDJzbWsRWvUXEkZQ8yZcDe2Rmr88BjvnYa02A+UfdOuaSJM1AUyKYXNNRzJ3gboylz1AW2nsNsBolUPsT4L9qmo4Jl5nHTHQfJEnS0DLz+oj4KbAn8DbgpLbDHwJWAT6TmfdORP9mkO9QvmR9dUSclJkXA0TEisBHapmTJ6pzU0VEfJCS7uoSYM8uqS3aOeajFBGbAnd1zvyOiEcBHwbWBi5sWyfGMZckaQaKshaXJEmSpoOIeCpwISXwcyZl7YVnUhYzvZaygOrfJq6HU1NE7Ef58h/KomR7UR7fP6/uuz0z391R/jvAA8BplNRlLwE2qftfmb4R7ykiDqSs1/APypci3fLuLqzrOrTqOOajUNOJ/CdwLnA98DfKYuO7Uhbgu5mSr/r3bXUcc0mSZhiDyZIkSdNMRGxAmdH5AmAt4CbgDOBDDbM71UNEzAWOHqLInzJzdkednYGjgGcDKwJ/AL4AnJiZ/1imBS3Wx3gD/DIz53TUc8xHKCK2AA6lpMNZH1iTkoLvWmA+ZQyX+ffDMZckaWYxmCxJkiRJkiRJavSoie6AJEmSJEmSJGnyM5gsSZIkSZIkSWpkMFmSJEmSJEmS1MhgsiRJkiRJkiSpkcFkSZIkSZIkSVIjg8mSJEmSJEmSpEYGkyVJkiRJkiRJjQwmS5IkSZIkSZIaGUyWJEmSJEmSJDUymCxJkiRJkiRJamQwWZIkSZIkSZLUyGCyJEmSNEwRMSciMiLmTnRf2kXE9hHxs4i4vfbv8obyE/I66jkXDKP8pBxvSZKkmWbWRHdAkiRJ0uhFxOrAfGBF4CvA7cDNE9opSZIkTSsGkyVJkqTpYUdgbeCozDymzzq/ATajBJ7H02bAfeN8TkmSJI2SwWRJkiRpenhC3f613wqZeR9w9dh0Z8jzjvs5JUmSNHrmTJYkSdKkFhEHRcTpEfHHiLg/Iu6OiAsi4rU9yi+o+XVnRcT7I+K6iHgwIm6MiOMiYoUe9Q6IiEvrOW6NiK9ExBNa7Q2jv4+NiGMj4qra1qKI+EVE7DmC1757RPw4Iu6IiAci4tqI+FhErNFWZnbt35fqri/W158RcVBD+11zEY9iDDeNiC9ExMJa/taIOC8iDu0o1zVnckSsExGnRsQtdewuj4gDG15D3+Ndr6Ws293q67ynXlPzI2KzHudYOSKOjIiLa/m/1/OdGBHrdCn7vtr3e2vZX0XEa4Z6HZIkSVOBM5MlSZI02Z0M/B44F7gJWAvYG/hKRGySmR/sUe/rwHOBs4C7a533UFJBHNxeMCL+Dfg4cCclKLsI2AO4oP69LxHxZGABMBs4D/gxsArwYuDHEfGvmfm5Ptv6V8prvxf4NnArMAc4EtgnInbOzLuAu4APAVsD+wJnAq2F94ZcgK8PwxnDF9V+Ppryur8BrAk8o9Y5eagTRcRawIXAhsD59c96wCnAT3vUGel4v5gyVmfV9p9eX9sOEfH0zFyc9iMiHgOcU1/HNcAXgIeApwJvAL4L3FLLrgmcDWwDXFrLPgrYC/h6RGyemR8YahwkSZImM4PJkiRJmuy2yMzr23fUmbFnAe+NiFMy8y9d6j0V2Dwz76h1jgKuAF4fEe/LzJvr/g2BYyh5g7fNzBvr/vdSgqmvHkZfvwQ8GXhNZp7W1t81KUHPEyPi+5l5y1CN1CDpicDfgR3b00JExKeBQynB7zfXgPLcOgt5X+CMzJw3jD4Ppd8xfBxlrGYBz8vMX3a8nvX7ONexlEDyCZl5eFvdTwK/6lFnpOO9H7BXZv6irc6xwHspAeKPt5X9FCWQfArwtsz8Z1ud1Vj6ac8TKIHkIzPz423lVgTOAN4fEd/JzNEG+SVJkiaEaS4kSZI0qXUGkuu+hyhBvlnA7j2qHtkKgtY69wJfo7wH3r6t3P61nZNageRaPinBxX/008+IeAawK3B6e2CztnUXcDSwIvDyPpp7LbAC8Mku+YWPAu4BXhcRj+6nb6PQ7xgeCKwOnNwZSK71/jzUSSJieeAAyuua21H34nrOzjqjGe/T2gPJ1Wfrdse2c6wNvIoyI/7d7YHkep57MnNRLbsW5fd2cXsguZZ7gDKjPCjXmyRJ0pTkzGRJkiRNahHxJEogbnfgScBKHUWe2KPqxV32tYLFj2nbt03dnt9ZODP/FBE3UtIoNHl23a7RmYO4enzdds3L22Hbuj27S5/ujIjLgF2ATSkzhcdKv2P4rLo9a4Tn2RRYGTivFZztsIASsG43mvHu93XtQAmcn1sD6UPZAVgOWCYHdbX8EP2RJEmaEgwmS5IkadKqKSh+QwnwnUfJnbuIMlt4NiXA2HV2bp2d2umRul2ubV9rMbteqSduob9g8lp1u0f908uqfbTV6tNNPY639q/ZR1sjNowxbPWjW7qRfjT9Dm7usm80473M68rMRyICRv66Wv3Zof4ZTn8kSZKmBIPJkiRJmsyOoATpDu7MAxwRr2HZ2aojcXfdrgP8rsvxdfpspzWj9l2ZeeIo+9Rqa90efVqvo9xEawVnnwj87wjqt15Hr7Fed4g6gxjvXtpfV5NWf47PzCPGqD+SJEkTypzJkiRJmsyeVrendzm264DOcVndPqfzQF0Ib4M+27mobp87wD7N6dKnNYGtgQeAqwZwrkFovfYXjrD+1cB9wNYRsUaX43OGOOcgxruX3wD/BHaJiFX6LDuW/ZEkSZpQBpMlSZI0mS2s2zntOyNiL+CQAZ3j65TUDe+IiMWB4yg5D45l6bQHPdWF4s4DXhYRb+hWJiK2rIu6Nfkq8HDt09M6jn2YstjdVzPzwX76Ng6+RJnhfWhE7NJ5MCLWH6pyZj5MWWRvNToW4IuI7SmL83XWGeR49+rXbcBplJng/xURS31+iohVW8HvzLy1vobtI+KDEbHMU6AR8dSIeMpI+yNJkjTRTHMhSZKkyezTwMHAtyPidEru2i2AFwDfAl412hNk5vUR8e/AMcAVEfFNSsqCPYDHUha426rP5vanLJp3akS8E/g1JVXC+rWNLSgLx93a0KeFEXEY8Cng0oj4FnAbZTb2sykzeY8czuscS5l5e0TsD3wHOCcizgKupAS9t6LM7m4Kor6fssjiYTWAfD4liPsq4EfAS7rUGch4N3h7bectwJyI+AnwUH09e9V+LWgruxHwH8DrIuJ8Sh7oJ1AW3tsBeA1wwyj6I0mSNGEMJkuSJGnSyswrI2I34CPA3pT3r1cAL6MEDUcdTK7nOTYi/kzJ0XwwcA/wE+A9lEX/7h6iens7f46I7YB3AC+nzKhdjrKA3O+Bk+gzp3Bmfjoi/gC8u7a1MnAj8J/AMT0Wx5swmTm/BoGPpASF9wTupAS+j+2j/u0RsTMlqL8PsD1wDXAoZYb6MsHkQY73EP26MyJ2Ag6jXG9vpiwAeSPwhXqeVtm7I2LXWmb/2qcVKQHl64DDgZ+Npj+SJEkTKTJzovsgSZIkTUoRsTolEHh5Zj57ovsjSZIkTSRzJkuSJGnGi4jHR8TyHftmAZ+gzCz93oR0TJIkSZpEnJksSZKkGS8i3kLJc/tzSvqCxwK7ABsDlwM7Zeb9E9dDSZIkaeKZM1mSJEkqC7edTwkgr1X33QB8FDjOQLIkSZLkzGRJkiRJkiRJUh/MmSxJkiRJkiRJamQwWZIkSZIkSZLUyGCyJEmSJEmSJKmRwWRJkiRJkiRJUiODyZIkSZIkSZKkRgaTJUmSJEmSJEmNDCZLkiRJkiRJkhoZTJYkSZIkSZIkNTKYLEmSJEmSJElqZDBZkiRJkiRJktTIYLIkSZIkSZIkqZHBZEmSJEmSJElSI4PJkiRJkiRJkqRG/wcEkJ65M4xxOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 350,
       "width": 713
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avomod1(shale[2,0],shale[2,1],shale[2,2],ssgas[2,0],ssgas[2,1],ssgas[2,2],angmin=0,angmax=30,polarity='normal',black=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make it easier to investigate the predefined AVO classes and how they change according to fluid content and porosity variations, use `make_avo_explorer`, that also allows the user to change the saturating fluid in the sand (lower half-space) and its porosity.\n",
    "\n",
    "In the plot below, *black* is always brine, *red* is either gas or oil according to the selection in the input parameters.\n",
    "\n",
    "Porosity variations should stay within a range of 10% positive or negative variations, i.e. from -0.1 to +0.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shale:  Vp=2192, Vs=818, rho=2.16\n",
      "Sand (brine): Vp=2134, Vs=860, rho=2.11, porosity=0.33\n",
      "Sand (gas): Vp=1521, Vs=911, rho=1.88\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
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44AHOP/30MmuLi4u54IILmDZtGvvn5PD2qFEcctBBlX9TIiIiySvdzFoAbmaN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\ngP79+5OZmVlh/cyZr7J9+xa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' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        this.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overriden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; 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xCuhmZh8ABlxuZjcDX7j76zHWuQd41sx6EDmL0D+UTkVEJNmq1DFAAVkkg1WnIRbuvgu4\nutTsz8qoOzjq8VqgR2o7ExGRVKtqxwANsRAJWSZcrzgTehAREclUCsgiVUAGD5sQERGpdhSQRdIk\nVXfHU5gWERGpHAVkkZClKvQqTIuIiCSHArJIDaQxyCIiIrEpIIukic4Ki4iIZCYFZJGQaYiFiIhI\nZlNAFqmBNMRCREQkNgVkkTRJ1VlhERERqRwFZJGQpXooRDW5LbWIiEjaKCCLZLBU3WpaZ6ZFRERi\nU0AWSROdvRUREclMCsgiIdMQCxERkcymgCySwTTEQkREJHwKyCJporO3IiIimUkBWSRkGmIhIiKS\n2RSQRTKYhliIiIiETwFZJE109lZERCQzKSCLhCzVQywyYbsiIiJVmQKySAZLVZjWEAsREZHYFJBF\n0iTZZ28VekVERJJDAVkkZJk0xEJERER2p4AsksEy6ZJwIiIiNYUCskiapHOIhYZjiIiIxBY3IJvZ\nQDObZWazVq5cGUZPEoP2RWbZ0/2hIRYiIiKZLW5AdvcCd8939/wmTZqE0ZPEoH2RWcLYH6k+06vg\nLSIisjsNsRBJk3Re21hDLERERGJTQBYJWU29trGZZZnZ42Y21cwmmFmrMmqamNlCM8sNpuua2Stm\nNtnM3jIz/elERKQKqmrHAAVkkQxWzYZYnAvkuntH4FbgH9ELzexM4B2gadTsa4B57n4y8DQwOKRe\nRUQkuarUMUABWSRNauAQi5OAsQDuPg3IL7V8F9AVWFPWOsCYYLmIiFQ9VeoYUCusJxKRiJo6xAJo\nAKyPmt5pZrXcvRjA3cfBbq8jep2NQMOyNmxmA4GBAC1atEhu1yIikojGZjYrarrA3QuiplN2DEgF\nBWSRDFbNhlhsAPKiprNK3hgTXCcPWFdWUfAmXACQn5+vS3OIiIRvlbuXPiscLWXHgFTQEAuRNEnn\nJdbSdLZ5CtA9eP4OwLyKrAOcDUxOTWsiIpJiVeoYoDPIIiGrwbePHgV0M7MPAAMuN7ObgS/c/fUY\n6wwBRphZIVAE9A2nVRERSbIqdQxQQBbJYKm+fXSYYdrddwFXl5r9WRl1B0c93gJcmNrOREQk1ara\nMUBDLERqoCrwgT4REZG0UUAWSZNUDZvI4CEWIiIiVYICskjIUjVsQmeFRUREkkMBWaQG09lmERGR\n3Skgi6RJOodY6GyziIhIbArIIiHTEAsREZHMpoAsUoNpiIWIiMjuFJBF0iSdV6bQ2WYREZHYFJBF\nQladbv4hIiJSHSkgi1QTCtMiIiLJoYAskiYaYiEiIpKZFJBFQqYhFiIiIplNAVmkmlCYFhERSQ4F\nZJE00RALERGRzKSALBIy3fxDREQksykgi1Qz6TwzLSIiUh0oIIukSbKDrM5Mi4iIJIcCskjIFGRF\nREQymwKySDWjIRYiIiKVo4AskiYaYiEiIpKZFJBFQqYgKyIiktkUkEVEREREoiggi4Ss5Kzwrl27\nklqbk5MDQFFRUcK127dvj1srIiJS0yggi4Rsr732AmDz5s1Jra1fvz4AGzdujFubl5eXcK2IiEhN\no4AsErKKhNOS2g0bNiRcu2nTpri1DRo0SLgHERGRmkYBWSRkJeE0kdBbkSC7J8FbAVlERGR3Csgi\nIatI6K1ImK7I2eaS7a5fvz5urYiISE2jgCwSsn322QeAVatWJbW2adOmAHz77bdJrRUREalpFJBF\nQnbQQQcBsGzZsoRrly5dmpLtLl++PG5tsphZlpk9bmZTzWyCmbUqtfxKM5tlZtPMrGcwr5GZrQrq\nJ5jZb0NrWEREkqaqHQNqhfVEIhLRokULAJYsWRK3tmXLlkDyA/KBBx6Y8HaT6Fwg1907mlkH4B/A\nOQBm1gy4AcgHcoFCMxsHHAe84O6/CbNRERFJuip1DNAZZJGQlQTkxYsXJ7W2JEx/+eWXcW9Nfeih\nh5Kdnc2XX37J1q1b4247SU4CxgK4+zQib4Ql2gNT3H27u68HvgCOBtoBx5nZRDP7j5ntX9aGzWxg\ncOZh1sqVK1P7KkREpCyNS96Hg6+BpZan7BiQCgrIIiE7/PDDAViwYEHcG3Uccsgh5OTUZvHixXE/\nfNesWTP23rsx69ati3tmuG7durRs2Zpdu3Yxf/78ir2APdcAiP5U4E4zqxVj2UagIfAZcKe7nwq8\nCjxS1obdvcDd8909v0mTJsnvXERE4llV8j4cfBWUWp6yY0AqKCCLhCwvL48WLX7Gjh07+OSTT8qt\nrV27Nocd9nMA5s6dW26tmdG69bEJ1QL87GfHJFybJBuAvKjpLHcvjrEsD1gHvA+MD+aNAo5NdZMi\nIpISVeoYoIAskgatW7cDYPbs2XFrjzgiUjtr1qwEtnscANOnT49b+/OfdwBgwoQJcWuTZArQHSAY\nfzYvatkM4GQzyzWzhkAbYD7wJHB+UHM6EP8bJiIimahKHQMUkEXS4KijEg+nP/95JwDGjx8fpxKO\nP74LAO+8807c2g4dzgBg3Lhx7Nq1K259EowCtpnZB8ADwE1mdrOZ9Xb374CHgclEzhj8wd23AbcC\n15jZBOBqQFexEBGpmqrUMUBXsRBJg/btfxpOs7Ji/67avn03IBKQt2/fTp06dWLWHnPMydSuXYc5\nc+awcuVKyhuP27Llz2jatAUrVizlww8/5LjjjtvDV5MYd99F5A0u2mdRy58Anii1ziLgtJQ2JiIi\nKVfVjgE6gyySBi1btma//Q5i5cqVfPjhh+XWNmnSnEMP/TlbtmxhypQp5dbm5tbl6KNPAeDtt98u\nt9bMOOGE7gC8/PLLFeheRESkelNAFkkDM+P4488E4K233opb3779WQC8+eabcWs7duwBwBtvvBG3\ntkuXXwDw4osvxr00nIiISE2hgCySJp069QRg9OjRCdT2AhILvSW1Y8eOpaioqNzao48+mcaNm7No\n0SJmzJgRd9siIiI1gQKySJq0a9eV2rXrMGPGDFasWFFu7VFHdaRBg0YsXLiQzz//vNza5s0P5ZBD\njmLDhg1MmjSp3Nrs7GxOO+0iAF544YWKvQAREZFqKm5A1h2qMof2RWap7P6oW3cvjj22C+7OmDFj\nyq2tVavWD+OFEzuL3BuA119/PW7taadFhlmMHDlSwyxERERIICDrDlWZQ/sisyRjf3TsmJphFiee\n+GNAjhd627Rpz7777s+yZcuYM2dO3G2LiIhUdxpiIZJGJeOQ33777bjjhdu3P5Ps7FpMmTKFNWvW\nlFvbpk179tlnP5YsWRL3VtJZWVmcfPJ5AIwaNaoC3YuIiFRPCsgiadS0aQsOO+xoNm3aFPcSbvXr\nN+SYY05l586djBs3rtzarKysH85Ov/baa3H7KDk7/f777yfYuYiISPWlgCySZm3anADAf//737i1\nRx4ZuQPfJ598Erf2+OMjNyNJ5LbTbdq0B+DDDz8M6656IiIiGUsBWSTNmjVrCcDy5csTqD044dr9\n9z804doGDRpRr14eW7duZePGjXHrRUREqjMFZJE0y87OAWDHjh1xa2vVSrw2O7tid5LPyYncwnrr\n1q0VWk9ERKS6UUAWSbNVq74GoGnTpnFrV65MvHbNmm8B2HvvvePWbt++jQ0bVpOVlUXjxo3j1ouI\niFRnCsgiaTZ/fuTDeUceeWTCtUcddVTc2o8+itwk5Pjjj49bO3fu+7g7bdu2pVatip15FhERqW4U\nkEXS6Kuv5vP557PJy8ujS5cu5dZ+//1yZs58m1q1atGrV69ya4uKtjNmzL8A6NGjR9w+Ro58FIBz\nzz03wc5FRESqLwVkkTRxd4YOvQWAfv36Ubdu3XLrn3jidnbu3EmfPn3iDoN4/vl7WLv2e9q2bcsp\np5xSbu3UqW8yffoY8vLyuPbaayv2IkRERDKYmeWXmj41kfX0t1SRNBk9+kmmTXuLvLw87rzzznJr\nx4//D++88wy1a9fmb3/7W7m18+d/wNNP/wWABx98EDOLWfvtt4u5++7LAPjjH/+o8cciIlItmNnJ\nwJHATWZ2fzA7G7gO+J946ysgi6TB3LmTeOih6wF47LHH2G+//WLWfvrpLP7+98sBuPfeezn00ENj\n1i5ZsoDbb+/Nzp3F3HjjjXTu3Dlm7YoVy7n55tPZsGENPXr04He/+92evRgREZHMsxZoBtQB9g/m\n7QIGJbKyArJIyKZNm8b//m8Pduwo4rrrruPSSy+NWTt37lxuuKEbW7du5tJLL+WGG26IWTt//nyu\nuqob69evpkePHtx3330xaxcsWMDAgd355puvaNeuHc8++yxZWRpxJSIi1YO7zwfmm9kT7v5NRdfX\nEVEkRGPHjqVr165s2bKJX/7ylzz00EMxa9977z06d+7Mxo3rOOecc3jqqadiDpeYOHEip556KqtX\nf0eXLl148cUXY16N4r333uOEE05g+fIvadeuHe+8805Cl4ITERGpgrqa2adm9pWZLTKzrxJZSQFZ\nJCQjRoygV69ebN68mUv79mXEiBFkZ2eXWfvss89y1llnsWHDBi48/3xeeuklcnJyyqwdOnQoXbt2\nZc2aNfTu2ZM333yT+vXr71bn7gwZMoSzzjqLdevWcU7PnkyYMIFGjRol9XWKiIhkkFuAXkAb4Ijg\n37gUkEVSzN3505/+RP/+/SkuLubWK67g6eHDywy87s5dd91Fv379KC4u5uZ+/Xjx2WepU6fObrU7\nduzg+uuv5+qrr6a4uJjfXXYZI196idzc3N1qt23bxoABA7j22mspLi5mUP/+jHzxxTKDtIiISDXy\nlbt/4e7bS74SWUljkEVSqKioiAEDBvDMM8+QlZXFQ4MGcf0vfwllDJXYvn07v/71r3nuuefIysri\ngd//nhsuuQTKGBu8du1aLrzwQt577z1q5+RQcMcd/Kp3byjjjPSyZcs4//zzmTlzJnVzc3nyzjvp\n2717mdsVERGpZraY2RjgQ8AB3P32eCspIIukyNq1a+nTpw8TJkygXm4uL917Lz1PLfvyi6tWreK8\n886jsLCQverW5cW//z1m7YIFC+jVqxcLFixgv0aNePXBB+nYtm2ZtZMmTeLCCy/k+++/5+DmzRn1\nwAMcc8QRSXuNIiIiGe6tPVlJp5BEUmDRokV06tSJCRMmsH+TJkwePrzcwNuhQwcKCws5YL/9KCyn\ntuQDdgsWLKBt69bMfP75MsOxu/PII49w+umn8/3339O1QwdmvfCCwrGIiNQ0zwE5wKHAEuDNRFZS\nQBZJsiVLlnDyySfz2Wef8fPDD2f6s89yXJuyPxOwYMECTj75ZL788kuOPeIIpj/7bMwQO3r0aM4+\n++zIB+xOO43C4cNpsf/+u9Xt3LmTK6+8khtuuIHi4mL+t39/xvzzn+yrK1WIiEjN8zjQAjgDyAOe\nTmQlBWSRJFq9ejXdunXj66+/5qRjj6Vw+HAOataszNpvvvmGrl27/nCGd9K//sUBTZuWWTt+/HjO\nP/98duzYwQ19+zLy/vupX6/ebnXuznXXXcewYcOol5vLi3//O/fedFPMS76JiIhUc4e5+x3ANnd/\nA2iYyEo6aookibszYMAAFi5cyDGtWzP6kUdoEOMqEbt27eKSSy5h2bJldDrmGF594AH2KiPwQiRI\nX3TRRRQVFXHdL37Bg4MGxbwe8kOPPsrQoUPJrVOHt/75T07Nzy+zTkREpIaoZWaNATezPCJ304u/\nUio72rBhA9988w07duxI2VdhYWEqX0K14e4sWrSIrVu37tH3ubi4OG7NypUr0/0y0+rdd9/l1Vdf\npUH9+rz64IM0zMuLWfvv//yHCRMmsF+jRoy8//6Y4Rjg9jvuYNWqVXTr0IGHbrklZjhevmIFtw4e\nDMCzd9+dceHYzLKAx4C2wHZggLt/EbX8SuAqoBj4i7uPDt7UngfqAt8Al7v7ltCbFxGRSknjMWAw\nMIXI7aanATcmslLKAvL999/PLbfcQnFxcaqeQhK0dOlSunbtysKFC9PdSrX2+OOPA3DL5ZfTsnnz\ncmuHDB0KwJ+vvZam++4bs27N+vU898ILZGdnM2Tw4Jg3FgF47KWX2L59Oxd068b5XbvuwStIuXOB\nXHfvaGYdgH8A5wCYWTPgBiAfyAUKzWwccAfwvLsPN7Nbibx5PpCW7kVEpDLScgxw94lAazNrAqxy\nd09kvZT/bAG1AAAgAElEQVSMQS4qKmLw4MFpCccNGjQI/Tkz3eOPP56WcFzT9sWUKVMAuPiss8qt\n27VrFx9MnZpQ7fR58yguLqZT27YcdtBB5dZOmj0bgAHnnZdoy2E7CRgL4O7TiLwRlmgPTAku4r4e\n+AI4OnodYAyQkclfRETiCvUYYGaPBv9ONbMPgNeAKcHjuFJyBnnjxo1s3bo1FZsu1+GHH87pp58e\n+vNmuu+++y4tz3v11Ven5XnTZdOmTQDsE+cXg+1FRRQXF5NTqxZ5e+1Vbu3GzZsB2Ldh/M8UbAp+\n5jL4ahUNgPVR0zvNrJa7F5exbCORD1JEzy+ZtxszGwgMjJpOYtsi1cPs2bP1s1FB+n5VSGMzmxU1\nXeDuBVHTKTsGxPB/wb8XV2CdH4TyIb2srCyOPvpocnJykvZVq1atn0zvs88+nHrqqeSVM+5TIg48\n8ECaNm1a7vezsl/HHnssbWJc2qy6Ouyww/j444+ZMW8eZ554Ysy6urm5NG/enG+++Ya5n31GuyOP\njFl7eIsWAMyYP5/i4uJyr0bRumVLPvr8cybNnk3+UUft+QtJnQ1ELrFTIit4YyxrWR6wLmr+1qh5\nuwnehAsA8vPzfdasWWWViVRaVQ5M7dq1Qz8biTMzEvxrvABmtsrdy/vwS8qOATFcVc7P65/jrRxK\nQN57772ZO3duGE8lCbjrrru44oor0t1GtXP++efz8ccf87ennuKMTp3KPZBe0KcPDz/6KH994glG\nPhB7ONUxRxzB4a1asfCLL3hy5EiuvuiimLUXn3UW/37nHe4bMYL+55xDowTOOodsCtAL+Hcw/mxe\n1LIZwF/NLBeoA7QB5gfrdAeGA2cDk8NsWEREkibsY8CK4N9zgUXBto4nck3kuHQdZJEkuf7669l3\n332ZOGsWf3/qqXJrB/3+9+y1116Mev99hvz73zHrzIy//jnyi+7N//gHsz75JGbtOaedRqcOHfhu\n1SouHjSI7UVFe/ZCUmcUsC0Y//UAcJOZ3Wxmvd39O+BhIm9+7wN/cPdtwF+Ai81sCtAReDRNvYuI\nSOWEegxw96HuPpTImepr3f05d7+Rn56pjkkBWSRJGjVqxFNBML79kUd4aezYmLUHHHAAQ4YMAeA3\n99zD6xMmxKy9oE8frrjiCrZu20bP3/yGT774osy6rKwsnh8xgiZNmjBu2jQu/P3vMyoku/sud7/a\n3Tu5e0d3/8zd73f314PlT7j78e7ezt1fCeatcPez3P1Edz/H3Ten91WIiMieSOMxYF8zOwzAzFoT\nGdcclwKySBL17t2bu+++G3en72238fQbb8Ss7devH7feeis7d+7k/N/9jn+//XaZdWbGkCFD6Nq1\nKytWr+aUK65g+scfl1nbsmVL3n33XRo1asQbEydy2oABrFi9OimvTUREpAq6EXjBzL4GngP6J7KS\nArJIkt16663cdddd7Nq1i18NHsxfCgpiftDj7rvvZtCgQRQXF3PxLbdw77/+VWZt7dq1ef311+nZ\nsydr1q+n84ABPPfmm2Vu8+ijj2b8+PEcdNBBTP3oI47v25e5n36a1NcoIiJSFbh7obu3d/cDgg8R\nlv1n2FIUkEWSzMy44447uP/++zEz/vjPf/KrwYPLHO5gZtxzzz3cc889uDu3PPggV9xxR5m1devW\nZeTIkQwYMIBt27dz6e23M+iBB9i5c+dutUcffTQzZ86kY8eOLPvuO07s3z9moBYREamuzOwqM/vc\nzL4ys0XAfxNZTwFZJEVuuukmXn31VerVq8czo0fT7aqrWLV27W51ZsYtt9zCK6+8Qr169Rj++usx\na3NycigoKODRRx8lOzub+4YPp+dvfsO6DRt2q23atCnjx4/n8ssvZ+u2bVx6++3ceO+97NixIyWv\nV0REJANdCXQmcqORy4HYn3aPooAskkK9e/emsLCQAw44gMlz5nDir37F4q+/LrO2T58+TJ48mebN\nmzN5zhw6XnYZXy5btludmXHdddfx7rvvsu+++zJ2yhQ6XnYZXy1fvlttnTp1GDZsGEOGDCEnJ4eH\nnnuOrlddpXHJIiJSU6xy92+BPHefADRKZCUFZJEUO/bYY5k+fTpt27ZlwZIldLzsspjXBT/uuOOY\nOXMmxx57LF8sXUrHfv2YGePC/p07d2bWrFn8z//8D58tWkSHSy9l6rRpu9WZGVdffTUTJ05k//33\nZ9Ls2Rx38cVMmzEjqa9TREQkA603s3MBN7OrgCaJrKSALBKCAw44gIkTJ9KlSxe+W7WKU08/nUmT\nJpVZ27x5cyZOnMgZZ5zByrVr6dytG++8806ZtQcffDCFhYWceeaZrFy7ltPOPJNXX321zNqOHTsy\nZ84cTjrpJL75/ntO6daNp59+OmmvUUREJANdCSwBbgV+BlyTyEoKyCIhadiwIWPGjOGXv/wlGzdu\npGfPnsyePbvM2ry8PEaPHk3//v3ZsmUL5557LoWFhTG3O3r0aK655hq2b9/ORRddxBsxLi/XrFkz\n3nvvPa6//np27NhB//79efLJJ5P2GkVERDLMy+4+192/dfffBcMs4lJAFglR7dq1eeaZZzj99F+w\nceNGevTowXfffVdmbU5ODsOGDaNXryvYunUrvXr1YtGiRWXW1qpVi3/+859ceunv2bFjBxdeeGHM\n8F27dm0eeeQRbrjh77g7V155Ja+88krSXqOIiEgGWWdm55jZEWb2MzP7WSIrKSCLhCw7O5s77nia\nY47pzIoVK7jssstiXic5KyuLW28toFOnXqxbt46LL76Y4uLiMmvNjBtuuJcePQawfft2LrjgAtat\nWxezj1/9ahBXXnl38PhXfFLObaxFRESqqCbAb4HHgMeDr7gUkEXSICenNn/843M0bNiYcePG8dpr\nr8Wszc7O5rbbhtO0aQtmzJjB8OHDY9aaGTfe+CitW+ezePFi/vrXv5bbxyWX3Eq3bpewefNmrr32\n2phBXUREpIr6GXAK0AY4GTjezBaaWbfyVlJAFkmTxo2b07//nQDcdddd5dY2aNCIq6++F4A///nP\nZd4cpETt2nX43e8ivyA//PDDrFixImatmfHb3z5Cw4aNmTRpEm+99VZFX4aIiEgmmwQc5e77A0cA\nI4Gzgf8rbyUFZJE06tnzSurX35sPP/yQBQsWlFvbufOFNGt2MMuWLWNaGZdzi9a6dTs6dOhOUVER\no0aNKrc2L28ffvGL3wHwzDPPVOwFiIiIZLYD3f1zAHf/Emjp7l8AZY9XDCggi6RR7dp1aN/+LAAm\nTpxYbm1WVhYnntgbgPHjx8fd9imn9AHg/fffj1vbpcsvABg3blzcWhERkSrkWzO7x8x6m9k9wHfB\n8Iqi8lZSQBZJs5YtjwBg8eLFCdS2qUDtkQAxr3wRrVmzg6lbdy/WrFlT7gf7REREqpjLgG+IDKtY\nBvQHNgG/LG+lWilvS0TKVbt2LgA7duxIam2dOnUBKCoq95dkIDIWuXbtumzdujmhbYuIiFQF7r4N\neLjU7Knx1tMZZJE027BhDRC5OUgyazduXAtAgwYN4tYWFxezefP6hLctIiJSnSkgi6TZV1/NA6BN\nmzYJ1x555JFxaxcvjlzXuHX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          "\u001b[1;32m~\\GoogleDrive\\PYTHON\\geophysical_notes\\avo_explorer_library.py\u001b[0m in \u001b[0;36mmake_avo_explorer\u001b[1;34m(avoclass, fluid, phimod)\u001b[0m\n\u001b[0;32m    344\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    345\u001b[0m                 \u001b[0mrhof_new\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkf_new\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.80\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;36m1.02\u001b[0m \u001b[1;31m# oil density & bulk modulus\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 346\u001b[1;33m         \u001b[0mvp2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvs2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrho2B\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrockphysics\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mavseth_fluidsub\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvp2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvs2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrho2\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1e3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mphi2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrhob\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1e3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrhof_new\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1e3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mk0\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1e9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkb\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1e9\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mkf_new\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1e9\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    347\u001b[0m         \u001b[0mrho2B\u001b[0m \u001b[1;33m/=\u001b[0m \u001b[1;36m1e3\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    348\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
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xM0Zedx0jfvpTMho23GX9li1buO6663jiiScAuObss/n7rbdSr5Ir5m3bto1b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\n9SCLSGjcfQtwzi5ev2UXr12ejDaJiEh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        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Shale:  Vp=2192, Vs=818, rho=2.16\nSand (brine): Vp=2134, Vs=860, rho=2.11, porosity=0.33\nSand (gas): Vp=1521, Vs=911, rho=1.88\n"
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j7C0tz1dFRJxHsQ3kFuCMzPxq68PrOW2br0GTmdsi4i4azw1l+YOZ+esm55vObUQcRLF9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Sh/keoH2ccSxJyp16KadQL+Osgv8oLC9MhSe5sPwGkIBPteUkhT80fw4sAJ4t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\nDAAIGP369VOTJk0cY2+NGaPXR41yLdMYo08++US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582vWrFlq2rRptjMkacaM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PAAAg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\n7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      "state": {
       "layout": "IPY_MODEL_02479e00172749b9a3dfb015f4d23832",
       "outputs": [
        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Shale:  Vp=2192, Vs=818, rho=2.16\nSand (brine): Vp=2134, Vs=860, rho=2.11, porosity=0.33\nSand (gas): Vp=1521, Vs=911, rho=1.88\n"
        },
        {
         "ename": "TypeError",
         "evalue": "avomod2() got an unexpected keyword argument 'method'",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
          "\u001b[1;32m~\\AppData\\Local\\Continuum\\Miniconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    248\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 250\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    251\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m~\\GoogleDrive\\PYTHON\\geophysical_notes\\avo_explorer_library.py\u001b[0m in \u001b[0;36mmake_avo_explorer\u001b[1;34m(avoclass, fluid, phimod)\u001b[0m\n\u001b[0;32m    356\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Sand ({:s}): Vp={:.0f}, Vs={:.0f}, rho={:.2f}'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfluid\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvp2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvs2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrho2B\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    357\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 358\u001b[1;33m         \u001b[0mavomod2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvp1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvs1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrho1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvp2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvs2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrho2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvp2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvs2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrho2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mangmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mangmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'shuey'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
          "\u001b[1;31mTypeError\u001b[0m: avomod2() got an unexpected keyword argument 'method'"
         ]
        }
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        {
         "name": "stdout",
         "output_type": "stream",
         "text": "Shale:  Vp=3094, Vs=1515, rho=2.40\nSand (brine): Vp=4115, Vs=2453, rho=2.32, porosity=0.20\nSand (gas): Vp=4099, Vs=2529, rho=2.18\n"
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giZlxxRVXMGvWLA444ICEvo0bN3L66adz0003sXXr1ogSiojULSqQRaTmSkoK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\n+AGtrYVJ5iZZK8mTSaYn2abFcDtwYq/NQ1v53Uk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          "text/plain": "<matplotlib.figure.Figure at 0xa1f7e10>"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "e55e846fbf0241428ebd78f2bd06fcc1": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.0.0",
      "model_name": "FloatSliderModel",
      "state": {
       "description": "rho1",
       "layout": "IPY_MODEL_94c8b767f2fd44d8ab5f236daf648101",
       "max": 3,
       "min": 1.5,
       "step": 0.1,
       "style": "IPY_MODEL_c843f9aac8dd41478b79ce5e64f2d9f9",
       "value": 2.16
      }
     },
     "e5761ed97ed04db19a0ab9db8d85c201": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_27b832e8781c45b7b24535443d54ed0a",
       "outputs": [
        {
         "data": {
          "image/png": 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OcAzAQOqXAHfYVv72y/M+Az6Y5N1JVid5b5KfJ/mLJJ/ZhWO9PslpM1caAADQ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\n+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       "style": "IPY_MODEL_f7cb3a118ca44926b68bbdd1c486468b"
      }
     },
     "f0c295ebabe540838051be3b5898cd96": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.0.0",
      "model_name": "IntSliderModel",
      "state": {
       "description": "vs1",
       "layout": "IPY_MODEL_6038535946014ed7a9ab98b06a6a8ca5",
       "max": 3000,
       "min": 1000,
       "step": 100,
       "style": "IPY_MODEL_d111ab33f4894f45bff1e586ecabcb94",
       "value": 1000
      }
     },
     "f0d49c87eb134479af1b263b2a7b3b38": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.0.0",
      "model_name": "CheckboxModel",
      "state": {
       "description": "black",
       "disabled": false,
       "layout": "IPY_MODEL_cf1e7df697224301bf1961640efb9528",
       "style": "IPY_MODEL_bddf589f92714f88bb2b008fc57e3591",
       "value": false
      }
     },
     "f0ecf998d69a42cfb54356502389debe": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.0.0",
      "model_name": "CheckboxModel",
      "state": {
       "description": "black",
       "disabled": false,
       "layout": "IPY_MODEL_05f5a57e9a274efe9b7b83015579139a",
       "style": "IPY_MODEL_4d06fcae9cb54077abdf3f965d373347",
       "value": false
      }
     },
     "f14cccc5011148cb859c714279e2658f": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_82aedb43e02d42879570c17cfbf55c5b",
       "outputs": [
        {
         "data": {
          "image/png": 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44AHOP/30MmuLi4u54IILmDZtGvvn5PD2qFEcctBBlX9TIiIiySvdzFoAbmaN\niFxNr+KZ4tumyJf0smXLKCwsZOfOnRQVFQW6rUxt7O3cuXPj/ZaS2rp161i3bl2VP9/KbJuCgoKw\n326t2rJly+6r1f3+llsqDMcFBQVce+21FBcXc+e111YYjld99x033XQTAH+7++5ywzHAY//8J1Om\nTKF506ZMHT263CEeYTKzNOAx4EigELjG3RfHvH4tcB1QBPzG3f8dSkNFRKRG1dL+/05gJpHLTb8P\n3BxkprgG5C+++ILevXuzatWqeK5GAnB3hg4dysiRI8NuSsr65z//SV5eHsd16cItl11WYf2EyZNZ\ntmwZh7Vvz91Dh1ZY/8jf/8727ds5t1cvrujfv9za4uJifvv44wCM/L//S9hwHHUOkOXuJ5jZ8cBD\nQH8AM2sFDANygSxghpm9GfQ8liIiktDivv9396lAZzPLAda5uweZL64H6f3xj38MPRzrcrkRX3/9\ndejhONW3xdSpUwG4esCAQBfbeHfGDACuPOcc0tMr/r/qu7NmATD0/PMrrF24ZAnfrV1Lm1atGNCr\nV4X1IesJTAFw9/eJ7AxLHAvMjJ7cPR9YDJR+NKKIiCSbuO3/zezR6O1sM5sFTABmRu9XKK49yGvW\nrInn4iuUk5PDwIEDQ21Dogh7WwBcf/31YTchrrZs2QJAi6ZNA9Vv2rwZgJzs7GDL37oVgJbNmlVc\nu3377tokOI1bYyA/5vEuM0t396JSXtsMNNl7AWY2BBgS8zhOTRVJXvPmzdPfRjXos6uyFmYWO/51\npLuX9NhVe/9fjhHR24sq22Co5YP0DjzwQJo3b056ejoZGRk1fht7v0GDBpx00kk0b968Nt9i0qhf\nvz4HH3xw3LbF3redO3fm6KOPDvttx1Xnzp3597//zdR58wL12nbu2BGAqXPncnm/fhXWH9KxIwsX\nLeKdDz7gyM6dy63t1LYtaWlpfLxoEWvWraNVixbB3kQ4NhE59U6JtOjOsbTXGgEb915AdGc7EiA3\nN9d1LILUtmQIT927d9dxOlVkZgT8ZV72Ymbr3D23jJervf8vx3Xl/F3eW9HMtRqQH3nkEc4u58IH\nUnu6d+/OtGnTwm5GSrnooot46KGHGDl2LD+/+GI6tGlTbv2ggQP59YMP8o9Jk7j50kvLPO9xiUvP\nPZdXXnuN+596iovPPLPc0Nu8aVP69erF+DffZNjvfseYBx9M5C/wmcDZwL+iY9A+jXntA+A+M8sC\n6gKHAp8HuA/xAAAgAElEQVTVfhNFRCQO4rn//0/09hxgWXRdxxA5J3KFdKEQkRqSm5vLRRddxPaC\nAgb+4hds2LSp3PpOHTowdOhQioqKGHDLLaxdv77c+nP69OGUU04hb8MGBtxyC5uiQzrK8vvhw2nY\nsCEvvfEGw//850Tu/RgHFETHhf0JuMXMbjWzfu6+BngYmA68A/zK3fet06OIiKSuuO3/3f1Jd3+S\nSK/0z9z9OXe/mR/2SpdJAVmkBj3++ON06tSJj7/6itOHDq0wJP/+97+ne/fuLF25kpOuuooV5YwV\nNzNeeOEF2rRpw/uffELv665j/cayf23q2K4dzz33HHXq1OHBp5/muhEj2LlzZ5XfW7y4e7G7D3X3\nH7v7Ce7+pbv/0d0nRl8f5e7HuHt3dx8bdntFRKRm1NL+v7mZdQAws85ExjZXSAFZpAY1bdqUd955\nh/bt2zP388857pJL+Oqbb8qsb9CgAZMmTaJr16589c03/HjwYOZ+/nmZ9a1atWLq1Km0a9eODz77\njGMGDWLBl1+WWd+vXz/Gjx9PVlYWo8aO5ZRrrik3hIuIiKSYm4EXzGwV8BxwRZCZFJBFaljr1q15\n7733OPLII/l6+XKOu/RSpsycWWb9fvvtx3vvvUePHj1Y+Z//0POKKxg9blyZ9QcddBDTp08nNzeX\nZatWccLgwTz76qtl1p911lm8/fbbHHDAAcxasICjLryQ16ZPr9Z7FBERSQbuPsPdj3X3A6IHCy6u\ncCYUkEXiok2bNsyYMYMBAwaQv3kzfW+4gftGjaK4uPQrXGZnZ/P2229z3XXXUbhjB1ffcw/X3Xsv\nBYWlnw+9devWTJ8+nauuuoqCwkIuv/NOhtx7L9vLuHrhj3/8Y+bPn0+fPn1Yv3EjP73xRm5+8MEy\nly8iIpIKzOw6M/vKzJaa2TJgYZD5FJBF4qRhw4a8/PLL3HPPPQDc+eijnHvrreRHz3+8t7p16/LE\nE0/w1FNPUbduXUaOHUvPK65g2cqVpdZnZWXxt7/9jZEjR1K3bl1GjR3L8ZddVuaQjpycHCZNmsQD\nDzxAeno6f3nuOY679FI+XxzoP9MiIiLJ6FrgFGAycCVQ9jjGGArIInGUlpbG3Xffzb///W+aNm3K\nhHff5dhLLik3lF511VXMmjWLgw46iHkLF9L94ouZVMYp+cyMa6+9ljlz5tCpUyc+WbSI3Isv5oXJ\nk8tsz+23386sWbPo2LFjpH7QIB4bMyaRz3IhIiJSVevc/Tugkbu/B1R8tS0UkEVqRd++fZk7dy5d\nu3Zl0bffctyll/Kv8ePLrD/66KOZN28eZ599Nhs2beKsn/+cXz3wALt27Sq1/sgjj2Tu3LlceOGF\nbNm2jUHDhzP0zjspKGPIxTHHHMP8+fO58sorKSgs5Ibf/pb+P/85eXl5NfJ+RUREEkS+mZ0DuJld\nB+QEmUkBWaSWdOjQgdmzZ3PppZeydft2Lrz6au64444ye26zs7MZP348999/P2lpafz2kUc4//zz\nywy9jRs35oUXXuDxxx+nbt26PPnCC5xwwgmsLGOIRsOGDRk9ejRjxoyhSZMmvPree3Tt2pVZswJd\npl5ERCQZXAt8CwwHDgauDzKTArJILapfvz7PPvssjzzyCOnp6dx///0MGzaszIP30tLSGD58OG+9\n9RZNmjRl3Lhx9OnTh61bt5Zab2YMHTqU2bNn0759RxYsWEDPnj35+uuvy2zTBRdcwCeffELPniey\nZs0aevXqxbhyzqIhIiKSRF529/nu/p27/yI6zKJCCsgitczMuPHGG3nkkXFkZGTy6KOPctddd5U7\nz6mnnsrLL0+nRYv9mTp1KhdeeCFFRUVl1h911FG89dYcDjvsOL799ltOPfVUVq1aVWZ927ZteeON\ndzj77CEUFBRw3nnnMb6cISAiIiJJYqOZ9TezQ8zsYDM7OMhMCsgiITn11LP4zW/GkZaWxn333cfL\nL79cbv0hhxzBn/70Dk2aNGfSpEnccccd5dZnZzfjoYfeokuXHqxatYp+/fqVOTwDID09nV/84gku\nu+xXFBcXc/HFF/P+++9X6b2JiIgkiBzgJuAx4InoVCEFZJEQHX98X66//g8A3HDDDWyq4NLUbdt2\n5r77JpCWlsYf/vCHCgNs/foN+c1vxnPAAR346KOPeOCBB8qtNzOuvnrE7p7kyy+/vNxQLSIikuAO\nBk4CDgVOBI4xs6/NrHd5Mykgi4Ts/PNv5ogjfszatWt56KGHKqzv0qUHF174P7j77nMsl6dp0xbc\nfvtoAB544AFWr15dbr2ZMWzYwxx44KEsWrSIP/3pT4Heh4iISAKaBhzu7j8CDgFeAc4ERpQ3kwKy\nSMhKem0B/vGPfwQ6H/GgQbdTt249Xn/9dZYuXVph/ZFHnkTPnudQWFjIM888U2F9ZmZdfv7zPwPw\n2GOP6RzJIiKSrFq7+1cA7r4EONDdFwNlH8iDArJIQjjyyJPJzm7JsmXLWLJkSYX1jRs349hj+wAw\nY8aMQOvo1esiAKZPnx6oPje3Ny1a7M/KlSv5/PNAFx4SERFJNN+Z2QNm1s/MHgDWRIdX7ChvJgVk\nkQRQp04d2rU7HCBQQAbo2PFIAL788stA9Z06HQXAwoWBLkOPmdG+fVcAvv3220DziIiIJJjBwGoi\nwypWAFcAW4CLy5spPe7NEpFAsrLqA7Bz585A9ZmZ9SpVX7du5ZYPUK9eQ4AKDx4UEamq5cuXM3Hi\nRDZu3EjTpk3p378/bdq0CbtZkiLcvQB4eK+nZ1c0nwKySILIz18PRK6IF8TGjWsBaNq0aaXqs7Oz\nA7dpw4b/ALDffvsFnkdEJIg1a9Zw4403Mm7cuB9cLOmmm25iwIABPProo7Rq1SrEFsq+TAFZJAHs\n2rWLZcs+A+DQQw8NNE9J/cEHBzrnOYsXfwxA586dA9UXFRWxePGCSs0jIhLEmjVr6NGjB0uXLiUj\nI4MBAwbQuXNnvvrqKyZMmMDYsWOZP38+s2bN0n/QJRQKyCIJ4Kuv5rJ9+xbatWtHTk5OhfU7dhTy\n8cfTADjxxBMDrePDD98A4OSTTw5U/8UXc9i2bTPt27fngAMOCDSPiEgQN954I0uXLuXoo49mwoQJ\ntG7devdrK1eupH///nz00UfccMMNFV5ESSQedJCeSAKYPXsSAD/96U8D1S9Y8B6Fhdvp0qVLoJ8g\nd+wo5MMPXwfgjDPOCLSOt99+AYBzzz03UL2ISBDLly9n3LhxZGRk/Fc4BmjdujXjx48nPT2dcePG\nsWLFipBaKvsyBWSRBDB9+jgAzjrrrED1b731PAADBw4MVP/++6+xefMGunbtGmi4RFFREe+++y8A\nBg0aFGgdIiJBTJw4keLiYvr16/df4bhEmzZt6N+/P8XFxUycOLGWWygSICCb2RAzm2tmc/Py8mqj\nTVIGbYvEUlPb49tvv2TZss9o2rQpp512WoX1hYXbmTbtFSB4eH399WcBGDx4cKD6Dz98nY0b8zjk\nkEPo1q1boHlERILYuHEjUPGxDSXHV2zYsCHubRLZW4UB2d1Hunuuu+cGGRsp8aNtkVhqantMmzYW\ngP79+5OZmVlh/cyZr7J9+xaOOeYYOnXqVGF9fv563n9/EmlpaYED9ZQpkavtDR48GDMLNI+ISBAl\nZ9756quvyq1btGgRULkz74jUFA2xEAnZe+9FDkA577zzAtWXjA2+5JJLAi7/JYqKdtK7d29+9KMf\nVVi/efMGZs2aiJlx6aWXBlqHiEhQ/fr1Iy0tjYkTJ7Jy5cpSa1asWMGECRNIS0ujX79+tdxCEQVk\nkVCtXLmYxYsX0LhxY3r37l1h/bZtW/jggylA8EBd2bHE7777L3bsKOS0007TyfpFpMa1bduWAQMG\nsHPnTvr37/9fB+GtWLGCc845h6KiIgYMGKD9kIRCp3kTCdHUqZHe4379+lG3bt0K6+fMmcyOHQWc\ncMIJgU699v33/+Hjj6eSmZkZuBemZHjF5ZdfHqheRKSyHn30UebPn89HH31E+/bt6d+/PwcffDCL\nFi1iwoQJFBUV0b59e/7617+G3VTZRykgi4SoJCAH7Q2eMWM8EPzsFTNmTKC4uJjTTz890BX3vvtu\nGZ9/PpsGDRowYMCAQOsQEamsVq1aMXPmzN1X0hs7duzu19LS0hg4cCB//etfdZEQCY0CskhIVq9e\nzldfzaNhw4acfvrpFda7OwsWvAdAnz59Aq1j3ry3gOCnj/vgg9d3L79hw4aB5hERqYpWrVrx8ssv\ns2LFCiZOnMiGDRvIzs6mX79+GlYhoVNAFgnJggWzgciV8OrVq1dh/bffLmXdutU0b96cww47rML6\n4uJi5s9/FyDQ6eMA5s59EyDQeGgRkZrQpk0bbrjhhrCbIfIDOkhPJCSffvohAMcdd1yg+g8/nAFE\nAnWQU68tXbqY/Px1/OhHP6Jjx44V1rs7n3wyHYBevXoFapOIiEgqUkAWCUlJQD7mmGMC1X/55acA\ndO/ePVD9woWR+m7dugUK1KtXr2bjxjyaNm1Khw4dAq1DREQkFSkgi4Tk228jJ8E/4ogjAtUvXRo5\nqf6hhx4aqL4kIHfp0iVQ/ccfLwCCB2oREZFUpYAsEoJt27aRl7eGjIyMQKdrA1i8+EsADjnkkED1\nixZF6g8//PBA9UuXLqnU8kVERFKVArJICL755hsADjzwQOrUqVNhvbvz3XeRK061bds20DrWrFkN\nQOvWrQPVr1ixfHebRERE9mUKyCIhWLt2LUCgSz8DbNmyhcLCAurVqxf49Gv/+c93lVrH8uUKyCIi\nIqCALBKKDRs2AJCdnR2oPi8vD4CWLVsGHh9c2YCclxcJ7Toxv4iI7OsUkEVCsHHjRiB4QF63bh0A\nOTk5gep37tzJ1q1bSUtLo0mTJoHm2bRpE0DgehERkVSlgCwSgpIe5CCXf4bIQX0ADRo0qHR90B7n\nTZvyAWjcuHGgehERkVSlgCwSgq1btwIEHk+8fft2ALKysgLVlwTk+vXrB25Tfn4kIKsHWURE9nUK\nyCIh2LlzJwAZGRmB6gsKCgACXZIaqhaQS0J4ZeYRERFJRQrIIiHYsWMHAJmZmYHqa6MHuaioCAge\n2kVERFKVArJICKragxw0IO/atQuA9PT0wG0qCciVmUdERCQVKSCLhKCyAbkk8Aa5qAhAcXExAGlp\nwf7Ei4uLKz2PiIhIqtI3oUgIKhuQ3R0g8BkpKht2Y3ucg65DREQkVSkgi4SgssMZ4h2QNbxCRERk\nDwVkkRDFu0e4sgE56BAOERGRVKaALJIE4t2DXFKvgCwiIqKALBKKksBb2frKBmQFXhERkcpTQBYJ\nUdDAG+8e5LCYWT0zG2tm083sNTPLKaXm92Y228w+NLNrw2iniIjUjkT5Xkjsb08RAfYE5MoOmUiC\nM1JcD3zq7icCzwJ3xr5oZqcCHd39BKAncLuZZdd+M0VEpJYkxPeCArJICCo7xKKqgTcJAnJPYEr0\n/mTgJ3u9Phu4KnrfgTrAztppmoiIhCAhvhd0TieREMVriEUiMrOrgVv2evo/QH70/magSeyL7l4A\nFJhZBvAMMNLdt5Sy7CHAEIC2bdvWcMtFRKQaWpjZ3JjHI919JMT3e6G6FJBFkkC8A3Jle7SruI6n\ngKdinzOzV4BG0YeNgI17zxf96exl4D13v7+MZY8ERgLk5ubG/82IiEhQ69w9t7QX4vm9UF0aYiES\ngqoG0nj3IIfQQz0T6Bu9fyYwfa/21APeBka7+4habpuIiNS+hPheUA+ySIiSechEDXkceMbMZgA7\ngEEAZvYgkd6BHkB74NqYI5WvdPdlYTRWRETiLiG+FxSQRSQ07r4NOL+U52+L3v0A+FOtNkpEREKT\nKN8LGmIhEoLaGPMrIiIiVaOALBIiDbEQERFJPArIIqIebRERkRgKyCKym3q0RUREFJBFQqEeWxER\nkcSls1iIhEg9trXjrrvuYuHChcCezzz2NnZKS0v7wVSnTp3dt7H309PTy5wyMjL+a8rMzNx9WzLV\nrVt3923JlJWVtfu2Tp06oX1mIiL7MgVkEUl5U6dOZdq0aWE3o9LS09PJysr6wVSvXr0fTPXr1989\nxT5u0KDBf902aNCAhg0b7r4tuZ+Wph8TRURiKSCLSMpL1iEtRUVFbNmyhS1btsR1PfXr198dmBs1\navSDqXHjxv91Gzs1adJk922jRo0UtkUkJSggi4js47Zt28a2bdtYu3ZttZZjZjRq1IgmTZrQpEkT\nmjZtuvs2dsrOzv6v2+zsbBo3bqyALSIJQQFZRJK2hzWoe++9l3Xr1u1+n7Hvt7i4GHffPZU83rVr\nF8XFxRQXF7Nr164fTMXFxRQVFbFr1y6Kiop2Tzt37vyv2507d7Jjx47dtyVTYWHhD+4XFhZSUFDw\ng/vJtl3cnU2bNrFp0yZWrFhR6fnT0tJo0qQJzZo1o1mzZmRnZ+++Hzs1b978B/ezs7NJT9fXmYjU\nHO1RRGS3VD1o8JRTTgm7CZXm7uzcuZOCgoLd0/bt23dPsY9LeoC3bdvG1q1b2b59O1u3bt39eO+p\nZNhGyeNEUVxczIYNG9iwYQNLliyp1LxNmzalefPmtGjRgubNm+++XzKV9lihWkTKor2DiEgCMrPd\nZ7to3Lhx3NZTXFzM1q1b2bx58+7b0qaSnuHYKT8/f/dtfn5+qGF748aNbNy4sVLBOjs7m5ycHFq0\naEFOTk6pU8uWLXffr1u3bhzfgYgkEgVkEZF9WFpa2u4D8qqrqKiIzZs3k5+fz8aNG39wu2HDht23\nGzdu3N1THDvVdsAuWe+iRYsC1Tdu3JiuXbsyffr0OLdMRMKmgCwiIjUiPT199wF3VbFz584fBObv\nv/9+97RhwwbWr1/P999//4Pb9evXs3Hjxhp+J6XbtGlT3M8oIiKJQQFZREQSQkZGBi1btqRly5aV\nmq+oqGh3gC6Z1q1bx7p1635wP3b6/vvvq9TGyrZNRJKTArKIiCS19PT03eOEgyoqKuL7778nLy+v\n1GndunWsXbv2B88VFxdXah0ikrwqDMhmNgQYAtC2bdu4N0jKpm2RWLQ9RJJXenp6pXqrS86wsWvX\nrji3TEQSQYVnZHf3ke6e6+65+p9zuLQtEou2h8i+Iy0tjebNm2uIhcg+QpcsEhERERGJoYAsIiIi\nIhJDAVlEREREJIYCsoiIiIhIDAVkEREREZEYCsgiIiIiIjEUkEVEREREYiggi4iIiIjEUEAWERER\nEYmhgCwiIiIiEkMBWUREREQkhgKyiIiIiEgMBWSRFOTuYTdBREQkaSkgi6QwMwu7CSIiIklHAVlE\nREREJIYCsoiIiIhIDAVkEREREZEYCsgiIiIiIjEUkEVEREREYiggi4iIiIjEUEAWEREREYmhgCwi\nIiIiEkMBWUREREQkhgKyiIiIiEgMBWQRERERkRgKyCIiIiIiMRSQRURERERiKCCLiIiIiMRQQBYR\nERERiaGALCIiIiISQwFZRERERCSGArKIhMbM6pnZWDObbmavmVlOGXX1zWyBmfWp7TaKiEjtSZTv\nBQVkEQnT9cCn7n4i8CxwZxl1fwW81lolIiJhSYjvBQVkEQlTT2BK9P5k4Cd7F5jZ/wCzgI9rsV0i\nIhKOhPheUEAWkVphZleb2WexE9AEyI+WbI4+jp2nF9DJ3UdVsOwhZjbXzObm5eXFpf0iIlIlLUr2\nz9FpSMkL8fxeqK70eC5cRKSEuz8FPBX7nJm9AjSKPmwEbNxrtquBA83sPeAQ4GgzW+PuC/Za9khg\nJEBubq6GYoiIJI517p5b2gvx/F6oLgVkEQnTTKAv8AFwJjA99kV3H1Ry38yeBl6s6Z2giIgklIT4\nXtAQCxEJ0+PA4WY2AxgC/BrAzB40s2NDbZmIiIQhIb4X1IMsIqFx923A+aU8f1spz11RG20SEZHw\nJMr3gnqQRURERERiKCCLiIiIiMRQQBYRERERiaGALCIiIiISQwFZRERERCSGArKIiIiISAwFZBER\nERGRGArIIiIiIiIxFJBFRERERGIoIIuIiIiIxKgwIJvZEDOba2Zz8/LyaqNNUgZti8RSne2RkZFB\nVlYWderUCVRfp04dsrKyyMjICFSflpZGVlYWmZmZgduUlZVFVlZW4HoREZFUlV5RgbuPBEYC5Obm\nemUWnpmZ+YMv3LQ0dVhXR3W2RUlgKlGZ4CSlq872GDVqFKNGjQpcP2zYMIYNGxa4/pRTTmH79u2B\n65s1a1apehERkVRWYUCujrFjx8Zz8VIJPXv2VAASERERCUBduiIiIiIiMRSQRURERERiKCCLiIiI\niMRQQBYRERERiaGALCIiIiISQwFZRERERCSGArKIiIiISAwFZBERERGRGArIIiIiIiIxFJBFRERE\nRGKYuwcvNssDvo1fcxLGge6eE3YjyrMPbQvQ9kgkyb4tWgDrarE5NU3tD1d57U/2vw2ReEn4v43S\nVCogi4gkMzOb6+65YbejqtT+cCV7+0UkOA2xEBERERGJoYAsIiIiIhJDAVlE9iUjw25ANan94Ur2\n9otIQNUKyGZ2u5l9Z2ZZ0cf3mNnQvWquMLPlZnZrwGXWMbNxZbzWxsx+Zma9zaxlBct52MwOCrjO\nG83sm73bnsrMbJqZnbbXc38xs2vKqK9jZuPMbKmZddjrtQlm9pN4tlekJrh7UgcctT9cyd5+EQmu\nuj3IlwAvAhdVUPe8u/8x4DJ7ArPKeO1QYAhwB9C2guUc5O7LgqzQ3R8Fng7YvlQxEhhc8sDMMoGz\ngRfKqC/ZLqOBy2Lm2w/oDLwdt5aKiIiI1KIqB2QzOwVYAjwB3BBwnqfNbJSZvWFmU83sejN7zcw+\ni+mVPAv4t5n1MLP3zWy6mU00s0bAR8DzwIfAJ2bWImZZI81scXQ9hwMLo/fvNLO5ZrbAzK4r67l9\n0MvAqWZWP/q4P/AGMMnMnjCz96Kfa6vo62cB/wb+Dlwcs5zBwNPu7mZ2n5nNNrM5ZnZzbb0RERER\nkZpUnR7ka4C/uftXQKGZHRdwvm/c/XTgCyK9vH2BsUR6LwEOdfcvgHOAV4CTifRaZrv7Ond/0N1v\nc/cdwK+A8e5+MvASkB5dRknIPgo4EzgO+DFwWBnPWTU+h6Tk7gXABGBA9Kkr2TO+bpa7nwKMIdJb\nD9Ht4u6rgK/MrEf0+UuIhGaIhOVBwEnA9vi+A5HgzCwt+h+/2dH//HUMu01BmdlxZvZe9H5HM5sR\n7Th43MwS9jgSM8sws39E2/qBmfVLsvbXMbPRZjYzOiStQzK1X0Sqp0p/3GaWDfQFbjKzKUAT4MaA\ns38Uvd1ItJcX2ABkmVl7YHH0ud8CLYn8dH8esLOUZR3KnuEY02OePyH6fGfgA3ff5e7b3P2m0p7z\nffdk0KOAy8xsfyL/ASnZNu9Eb2cBnffaLiXzDTaz44Gv3f0/0ecvAu4HXgeaxr31IsGdA2S5+wnA\ncOChkNsTiJndBvwNyIo+9UfgTnc/ETAiv/wkqkuB9dG2ngk8SnK1/2wAd+8B3EWk7cnUfhGphqr+\n7/dS4Cl3P93d+xDpjT0dCHKllPLC6NnApOj9S4j8dH8q8DmRscd7+4xIGAY4HsDMmgH57r4L+BI4\nOtp7lGFmbwLL9n7OzOoGaHfKcfdPgUbATUR66Ut0j972IPLZx24XgNeIfO6XE+11jn6G5xMZfnEa\ncIWZHRjP9otUQk9gCoC7vw8ky8UelgDnxjzuDkyN3p8MJPLBsS8B/xfzuIgkar+7j2fP986BwH9I\novaLSPVUNSBfA/yj5IG7byMyTOLaarbnJPbsfD4EnjGzqUQC17Ol1D8A9DOzd6Pr3gn0Yc8X4YLo\n/ZnADOA5d59TynOF1Wx3MhtN5LOLPTjviujn/lPgPn64XYj+52MCcCrwVvS5QuB7YAGRHug3gOW1\n0H6RIBoD+TGPd5lZelnFicLdx/LDX88s5hevzUR+vUtI7r7F3TdHjx95GbiTJGo/gLsXmdkzwCNE\n3kNStV9Eqi7ul5o2syuAQ9x9eByW3RfIc/cPo6cZu8PdT6tovjKWdQ+wxt2fqMk2JpvoWMeh7v5l\n2G0RqSlm9kfgfXf/V/TxSndvHXKzAjGzdsCL7n58bLvNrD/Q292DDm+rdWbWBhgHPObuo5Ot/SWi\nByvPARq7e3b0uaRpv4hUXm0dYDDIAp4HuZKWAQ+b2XTgXuC2qizEzG4ErqjBdolIYplJ5LgJomPn\nPw23OVU2P3oGIYiM651eTm2ooqeAfAO43d1LhnAlU/svM7P/jT7cBhQDc5Ol/SJSPXHvQRYRCVv0\nbAOPAV2JHFx1ZbL8SrJXD/LBRA6SzSRyJqBro0OeEo6Z/QW4kMixICVuAh4mOdrfgMgZeloBGUSG\n9H1Bknz+IlI9CsgiIiIiIjF0DkcRERERkRgKyCIiIiIiMRSQRURERERiKCCLiIiIiMRQQBYRERER\niaGALCKSRMzsHjMbWo3565jZ62Y2w8yyY57/s5m1reSyXjSzzL2e62NmT1e1fSIiiSDhL7UqIv/f\n3v2EWF2FYRz/PppkZCGpgZRooZCFRMgIojBmqQwKhSQURrSTmY1jJARSoiG6clGRJFG0bFq0qCY0\nYcb/NRO1iJEQKdEE0YkiFyWlb4vzThxuIffmdJV4PjDM/Z1zfu95l+99OZdjNq5mAtMjYmE9GBG9\nrQaKiKfHLSszs5uIO8hmZm0i6U5JfZL2S/pKUneOD2YH94CkIUmzc/zlXLdP0uHqFrexeDslHZV0\nXNK6f9hvvaTh7Ba/K2kSsBeYJ+mthrWDkh7IDvV7kj6VdELSqpxfk7GGJe2VNEHSaUmTJc3PHA4A\n3VXMdTl+RNKuHGslfme+e1DSO5m/mdl/zgWymVn7zKXcircSWAO8UM0NRcTjwGfAM5Ieplxn3AE8\nSen8/kVSF3BfRCwBHgW2SJpazU8DtgHLI2Ip8DOwAegBTkTEhmvkeTkiuig3322SdAvwBrA6IjqA\nHyUtnLUAAAHhSURBVIB7q/WvAq9k/sdy/7ty/8dy/3skrWgh/izKrXVrI6ITOAc8f42czczGjY9Y\nmJm1z3mgV9Ja4BfKFcZjvs7/ZynXG8+nFM1XgF8lfdkQawGwUNJgPk8CZlMKYYD7gZGIuJTPh4CV\nwMdN5FnnMhmYDvwUERcAImI7gKSx9Q8BQ/n5aOY+F5gB9Oe6OzKnpuJLupvypaAv378N2N9E7mZm\n180dZDOz9nkROB4RzwIfAKrmomHtCNCRRw1uBR5pmP8WGIiIZcByoA/4rpr/HnhQ0u353AmcbDLP\nxlwuAFOzK4yk1yQtashlcX7uqPY/C6zIHF8Hvmg2PjCH0kl+It/fAQw0mb+Z2XVxB9nMrH0+AvZI\nWg/8CPyRxe/fRMQ3kvqBz4FR4Pf8q2Mtk3QYmAJ8WHWLiYhRSVuBAUlXgVPAS5TudEsi4qqkHuAT\nSVcoHeDhakkP8L6kzcBF4LeIuChpN3BQ0kTgNKWIbyX+xhybQOm4P9dq7mZm/4YiGr/Im5nZjZZH\nDJ6KiDeziB6hnCc+c4NTMzP733MH2czs5jRKOWIxTDmS8LaLYzOz9nAH2czMzMys4h/pmZmZmZlV\nXCCbmZmZmVVcIJuZmZmZVVwgm5mZmZlVXCCbmZmZmVX+BFA2HBaKJ4xkAAAAAElFTkSuQmCC\n",
          "text/plain": "<matplotlib.figure.Figure at 0xa253898>"
         },
         "metadata": {},
         "output_type": "display_data"
        },
        {
         "data": {
          "text/plain": "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]),\n array([-0.24338299, -0.24341179, -0.24349814, -0.24364195, -0.24384303,\n        -0.24410115, -0.24441598, -0.24478715, -0.24521421, -0.24569662,\n        -0.24623381, -0.24682511, -0.24746982, -0.24816714, -0.24891623,\n        -0.24971616, -0.25056598, -0.25146463, -0.25241104, -0.25340403,\n        -0.25444241, -0.25552491, -0.25665021, -0.25781694, -0.25902367,\n        -0.26026894, -0.26155124, -0.26286899, -0.26422059, -0.2656044 ,\n        -0.26701872]),\n -0.24338298853988244,\n -0.094542937487458734)"
         },
         "metadata": {},
         "output_type": "display_data"
        }
       ]
      }
     },
     "f153db1168ab407797290934e3f740ea": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.0.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "f2b61954ba1d4f8094ba4cf2a2b8b2a2": {
      "model_module": "@jupyter-widgets/output",
      "model_module_version": "1.0.0",
      "model_name": "OutputModel",
      "state": {
       "layout": "IPY_MODEL_73941b473daa4ada9578893a20b30c71",
       "outputs": [
        {
         "ename": "ValueError",
         "evalue": "too many values to unpack (expected 3)",
         "output_type": "error",
         "traceback": [
          "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
          "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
          "\u001b[1;32m~\\AppData\\Local\\Continuum\\Miniconda3\\lib\\site-packages\\ipywidgets\\widgets\\interaction.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    248\u001b[0m                     \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_interact_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    249\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mwidget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_kwarg\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 250\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    251\u001b[0m                 \u001b[0mshow_inline_matplotlib_plots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mauto_display\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;32m~\\GoogleDrive\\PYTHON\\geophysical_notes\\avo_explorer_library.py\u001b[0m in \u001b[0;36mmake_avo_explorer\u001b[1;34m(avoclass, fluid, phimod)\u001b[0m\n\u001b[0;32m    347\u001b[0m                 \u001b[0mrhof_new\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkf_new\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.80\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;36m1.02\u001b[0m \u001b[1;31m# oil density & bulk modulus\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    348\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 349\u001b[1;33m         \u001b[0mvp2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mvs2B\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrho2B\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgassmann\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvp2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvs2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrho2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrhob\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrhof_new\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkf_new\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mk0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mphi2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    350\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    351\u001b[0m         \u001b[1;31m# vp2B,vs2B,rho2B=b.rockphysics.avseth_fluidsub(vp2,vs2,rho2*1e3,phi2,rhob*1e3,rhof_new*1e3,k0*1e9,kb*1e9,kf_new*1e9)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
          "\u001b[1;31mValueError\u001b[0m: too many values to unpack (expected 3)"
         ]
        }
       ]
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