dedalus_1d_turb_momentum.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Solve zero pressure gradient viscous 1D momentum problem\n",
    "\n",
    "$$\n",
    "\\frac{\\partial u}{\\partial t} + 0 - = -\\frac{1}{\\rho_0}\\frac{\\partial P}{\\partial x} - \\nu \\frac{\\partial^2 u}{\\partial z^2}  \n",
    "$$\n",
    "\n",
    "with boundary conditions\n",
    "\n",
    "$$\n",
    "u_z(z=H) = 0\n",
    "$$\n",
    "\n",
    "$$\n",
    "u(z=0) = 0\n",
    "$$\n",
    "\n",
    "We scale the Pressure gradient term to equal a cosine of amplitude $u_\\infty$.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gotta find my FFTW_PATH\n",
    "!export FFTW_PATH=/path/to/your/fftw_prefix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[sudo] password for suntans: \n"
     ]
    }
   ],
   "source": [
    "!sudo pip install dedalus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Bad key \"backend.qt5\" on line 8 in\n",
      "/home/suntans/.config/matplotlib/matplotlibrc.\n",
      "You probably need to get an updated matplotlibrc file from\n",
      "http://github.com/matplotlib/matplotlib/blob/master/matplotlibrc.template\n",
      "or from the matplotlib source distribution\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import dedalus.public as de\n",
    "from dedalus.tools  import post\n",
    "\n",
    "from dedalus.extras.plot_tools import quad_mesh, pad_limits\n",
    "\n",
    "\n",
    "import logging\n",
    "import matplotlib.pyplot as plt\n",
    "logger = logging.getLogger(__name__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Domain\n",
    "H = 1. # Depth\n",
    "nz = 64\n",
    "nz = 200\n",
    "\n",
    "# Constants\n",
    "# Physical constants\n",
    "nu_m = 1e-2 # Mollecular\n",
    "nu_t = 1e-1\n",
    "\n",
    "# Forcing\n",
    "omega = 1\n",
    "u_infty  =1\n",
    "u_bottom = 0\n",
    "\n",
    "# Closure parameters\n",
    "c_mu_0 = 0.285\n",
    "k_0 = 1e-1\n",
    "l_0 = 1\n",
    "\n",
    "# Simulation parameters\n",
    "k_ramp = -1/(2*np.pi)\n",
    "\n",
    "uniform_diffusion = True\n",
    "hot_start = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CReate the domain\n",
    "\n",
    "z = de.Chebyshev('z', nz, interval=[0.,H])\n",
    "domain = de.Domain([z],grid_dtype='float')\n",
    "\n",
    "# def initialise_ncc(domain, problem, name, value):\n",
    "#     # Initialise a vertically variable ncc\n",
    "    \n",
    "#     ncc = domain.new_field(name = name)\n",
    "#     ncc['g'] = (z*0.)+float(value)\n",
    "#     problem.parameters[name] = ncc\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.54211776e-05, 1.38784891e-04, 3.85481880e-04, 7.55451275e-04,\n",
       "       1.24860179e-03, 1.86481175e-03, 2.60392912e-03, 3.46577152e-03,\n",
       "       4.45012632e-03, 5.55675063e-03, 6.78537141e-03, 8.13568553e-03,\n",
       "       9.60735980e-03, 1.12000311e-02, 1.29133065e-02, 1.47467633e-02,\n",
       "       1.66999490e-02, 1.87723818e-02, 2.09635503e-02, 2.32729138e-02,\n",
       "       2.56999027e-02, 2.82439180e-02, 3.09043320e-02, 3.36804884e-02,\n",
       "       3.65717022e-02, 3.95772599e-02, 4.26964201e-02, 4.59284131e-02,\n",
       "       4.92724414e-02, 5.27276801e-02, 5.62932765e-02, 5.99683509e-02,\n",
       "       6.37519965e-02, 6.76432797e-02, 7.16412406e-02, 7.57448925e-02,\n",
       "       7.99532231e-02, 8.42651938e-02, 8.86797410e-02, 9.31957752e-02,\n",
       "       9.78121824e-02, 1.02527823e-01, 1.07341535e-01, 1.12252128e-01,\n",
       "       1.17258393e-01, 1.22359094e-01, 1.27552972e-01, 1.32838745e-01,\n",
       "       1.38215110e-01, 1.43680741e-01, 1.49234287e-01, 1.54874380e-01,\n",
       "       1.60599627e-01, 1.66408617e-01, 1.72299915e-01, 1.78272068e-01,\n",
       "       1.84323602e-01, 1.90453025e-01, 1.96658824e-01, 2.02939469e-01,\n",
       "       2.09293408e-01, 2.15719075e-01, 2.22214883e-01, 2.28779232e-01,\n",
       "       2.35410500e-01, 2.42107051e-01, 2.48867233e-01, 2.55689379e-01,\n",
       "       2.62571805e-01, 2.69512813e-01, 2.76510690e-01, 2.83563709e-01,\n",
       "       2.90670131e-01, 2.97828202e-01, 3.05036156e-01, 3.12292214e-01,\n",
       "       3.19594587e-01, 3.26941471e-01, 3.34331056e-01, 3.41761516e-01,\n",
       "       3.49231020e-01, 3.56737724e-01, 3.64279775e-01, 3.71855313e-01,\n",
       "       3.79462470e-01, 3.87099367e-01, 3.94764120e-01, 4.02454839e-01,\n",
       "       4.10169626e-01, 4.17906577e-01, 4.25663783e-01, 4.33439331e-01,\n",
       "       4.41231301e-01, 4.49037772e-01, 4.56856817e-01, 4.64686507e-01,\n",
       "       4.72524910e-01, 4.80370092e-01, 4.88220118e-01, 4.96073050e-01,\n",
       "       5.03926950e-01, 5.11779882e-01, 5.19629908e-01, 5.27475090e-01,\n",
       "       5.35313493e-01, 5.43143183e-01, 5.50962228e-01, 5.58768699e-01,\n",
       "       5.66560669e-01, 5.74336217e-01, 5.82093423e-01, 5.89830374e-01,\n",
       "       5.97545161e-01, 6.05235880e-01, 6.12900633e-01, 6.20537530e-01,\n",
       "       6.28144687e-01, 6.35720225e-01, 6.43262276e-01, 6.50768980e-01,\n",
       "       6.58238484e-01, 6.65668944e-01, 6.73058529e-01, 6.80405413e-01,\n",
       "       6.87707786e-01, 6.94963844e-01, 7.02171798e-01, 7.09329869e-01,\n",
       "       7.16436291e-01, 7.23489310e-01, 7.30487187e-01, 7.37428195e-01,\n",
       "       7.44310621e-01, 7.51132767e-01, 7.57892949e-01, 7.64589500e-01,\n",
       "       7.71220768e-01, 7.77785117e-01, 7.84280925e-01, 7.90706592e-01,\n",
       "       7.97060531e-01, 8.03341176e-01, 8.09546975e-01, 8.15676398e-01,\n",
       "       8.21727932e-01, 8.27700085e-01, 8.33591383e-01, 8.39400373e-01,\n",
       "       8.45125620e-01, 8.50765713e-01, 8.56319259e-01, 8.61784890e-01,\n",
       "       8.67161255e-01, 8.72447028e-01, 8.77640906e-01, 8.82741607e-01,\n",
       "       8.87747872e-01, 8.92658465e-01, 8.97472177e-01, 9.02187818e-01,\n",
       "       9.06804225e-01, 9.11320259e-01, 9.15734806e-01, 9.20046777e-01,\n",
       "       9.24255107e-01, 9.28358759e-01, 9.32356720e-01, 9.36248004e-01,\n",
       "       9.40031649e-01, 9.43706724e-01, 9.47272320e-01, 9.50727559e-01,\n",
       "       9.54071587e-01, 9.57303580e-01, 9.60422740e-01, 9.63428298e-01,\n",
       "       9.66319512e-01, 9.69095668e-01, 9.71756082e-01, 9.74300097e-01,\n",
       "       9.76727086e-01, 9.79036450e-01, 9.81227618e-01, 9.83300051e-01,\n",
       "       9.85253237e-01, 9.87086693e-01, 9.88799969e-01, 9.90392640e-01,\n",
       "       9.91864314e-01, 9.93214629e-01, 9.94443249e-01, 9.95549874e-01,\n",
       "       9.96534228e-01, 9.97396071e-01, 9.98135188e-01, 9.98751398e-01,\n",
       "       9.99244549e-01, 9.99614518e-01, 9.99861215e-01, 9.99984579e-01])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "SymbolicParsingError",
     "evalue": "LHS is constant but RHS is nonconstant along z axis.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mSymbolicParsingError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-ea51431f2672>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;31m# Boundary conditions for TKE, k\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m \u001b[0mrans_problem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_bc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'left(k) = u**2 * 0.0002'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     38\u001b[0m \u001b[0mrans_problem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_bc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'right(kz) = 0'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dedalus/lib/python3.7/site-packages/dedalus/core/problems.py\u001b[0m in \u001b[0;36madd_bc\u001b[0;34m(self, equation, condition)\u001b[0m\n\u001b[1;32m    132\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_basic_dictionary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mequation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcondition\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_object_forms\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 134\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_bc_conditions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    135\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_matrix_expressions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbcs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dedalus/lib/python3.7/site-packages/dedalus/core/problems.py\u001b[0m in \u001b[0;36m_check_bc_conditions\u001b[0;34m(self, temp)\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_conditions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_differential_order\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_meta_consistency\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'LHS'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'RHS'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    198\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_boundary_form\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'LHS'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'RHS'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dedalus/lib/python3.7/site-packages/dedalus/core/problems.py\u001b[0m in \u001b[0;36m_check_meta_consistency\u001b[0;34m(self, LHS, RHS)\u001b[0m\n\u001b[1;32m    238\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdefault_meta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m                 \u001b[0mcheck\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_check_meta_%s'\u001b[0m \u001b[0;34m%\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 240\u001b[0;31m                 \u001b[0mcheck\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLHS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmeta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRHS\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmeta\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_meta_scale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLHS_scale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRHS_scale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dedalus/lib/python3.7/site-packages/dedalus/core/problems.py\u001b[0m in \u001b[0;36m_check_meta_constant\u001b[0;34m(self, LHS_constant, RHS_constant, axis)\u001b[0m\n\u001b[1;32m    252\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mLHS_constant\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mRHS_constant\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 254\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mSymbolicParsingError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"LHS is constant but RHS is nonconstant along {} axis.\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdomain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbases\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_meta_parity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLHS_parity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRHS_parity\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mSymbolicParsingError\u001b[0m: LHS is constant but RHS is nonconstant along z axis."
     ]
    }
   ],
   "source": [
    "# Create the RANS problem\n",
    "rans_problem = de.IVP(domain, variables=['u', 'uz', 'Fu', 'PF',\n",
    "                                        'k','kz','Fk','P','nu']) # u and viscous stress\n",
    "\n",
    "# Physical constants - this will be updated at the end of each time step, initialise at mollecular\n",
    "ncc = domain.new_field(name = 'l')\n",
    "ncc['g'] = 0.4*z.grid()\n",
    "rans_problem.parameters['l'] = ncc\n",
    "\n",
    "# Forcing\n",
    "rans_problem.parameters['omega'] = omega\n",
    "rans_problem.parameters['u_infty'] = u_infty\n",
    "rans_problem.parameters['u_bottom'] = u_bottom\n",
    "rans_problem.parameters['cmu'] = 0.285\n",
    "\n",
    "\n",
    "\n",
    "# Primitive equations\n",
    "rans_problem.add_equation('PF = u_infty*cos(omega*t)')\n",
    "# rans_problem.add_equation('uzz - dz(uz) = 0')\n",
    "rans_problem.add_equation('uz - dz(u) = 0')\n",
    "rans_problem.add_equation('dt(u) - dz(Fu) = PF ')\n",
    "rans_problem.add_equation('Fu  = nu*uz')\n",
    "rans_problem.add_equation('dt(k) - dz(Fk) = P')\n",
    "rans_problem.add_equation('Fk  = nu*kz')\n",
    "# rans_problem.add_equation('kzz - dz(kz) = 0')\n",
    "rans_problem.add_equation('kz - dz(k) = 0')\n",
    "rans_problem.add_equation('P  = uz*k*cmu')\n",
    "rans_problem.add_equation('nu =   sqrt(k)*l')\n",
    "\n",
    "# Call this a substitution\n",
    "rans_problem.add_equation('tau_bed = left(u)**2')\n",
    "\n",
    "\n",
    "# Add the time varying boundary conditions\n",
    "rans_problem.add_bc('left(Fu) = tau_bed')\n",
    "rans_problem.add_bc('right(uz) = 0')\n",
    "\n",
    "# Boundary conditions for TKE, k\n",
    "rans_problem.add_bc('left(k) = left(u)**2 * 0.0002')\n",
    "rans_problem.add_bc('right(kz) = 0')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the GLS problem\n",
    "# At the moment this is entirely algebraic and has no value being inside a dedalus problem\n",
    "\n",
    "gls_problem = de.IVP(domain, variables=['c_mu', 'k', 'l', 'M'])\n",
    "\n",
    "gls_problem.parameters['z0'] = 1e-5\n",
    "gls_problem.parameters['kappa'] = 0.41\n",
    "\n",
    "# Functions to be updated from RANS model\n",
    "ncc = domain.new_field(name = 'M')\n",
    "ncc['g'] = 0\n",
    "gls_problem.parameters['M'] = ncc\n",
    "\n",
    "ncc = domain.new_field(name = 'N')\n",
    "ncc['g'] = 0\n",
    "gls_problem.parameters['N'] = ncc\n",
    "\n",
    "gls_problem.parameters['c_mu_0'] = c_mu_0 \n",
    "# gls_problem.parameters['k'] = k_0 \n",
    "# gls_problem.parameters['l'] = l_0 \n",
    "\n",
    "gls_problem.substitutions['c_mu'] = f'c_mu_0' #Need to code still\n",
    "\n",
    "gls_problem.substitutions['epsilon'] = f'(c_mu_0**(3/4))*(k**(3/2))/l' #UB05 eq 32\n",
    "gls_problem.substitutions['k'] = f'(c_mu_0**(-3/2))*(c_mu*M**2 - 0 )*l**2' #UB05 eq 34\n",
    "\n",
    "gls_problem.substitutions['l'] = f'(0 + 1/((kappa*z+z0)) + 1/0.2)**(-1)' #BLACKADAR - do better\n",
    "\n",
    "gls_problem.substitutions['nu_t'] = f'c_mu*(k**(1/2))*l'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def asm_calculate_nu(rans_problem, rans_solver, z):\n",
    "    \n",
    "    # Get u at the 5th bin\n",
    "#     u5  = np.copy(rans_solver.state['u']['g'])[5]\n",
    "    M  = np.copy(rans_solver.state['uz']['g'])\n",
    "    \n",
    "#     tau_ = 20*u5**2\n",
    "#     u_star = np.sqrt(tau_)\n",
    "#     k = 10*u_star**2\n",
    "    \n",
    "    nu_t = asm_calculate_c_mu() * (asm_calculate_k(M)**(1/2)) * 1\n",
    "    \n",
    "    return nu_t \n",
    "\n",
    "def asm_calculate_k(M):\n",
    "    \n",
    "    l = 1\n",
    "    k = (c_mu_0**(-3/2)) * (asm_calculate_c_mu()*M**2 - 0 ) * asm_calculate_l()**2 # UB05 eq 34\n",
    "\n",
    "    return k\n",
    "\n",
    "def asm_calculate_l():\n",
    "    # Just returns 1 at the moment. No good. \n",
    "    \n",
    "    return 1\n",
    "    \n",
    "def asm_calculate_c_mu():\n",
    "    # This is no good. Gotta code up a better c_mu.\n",
    "    \n",
    "    c_mu = c_mu_0\n",
    "    \n",
    "    return c_mu\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-11-08 16:35:18,946 pencil 0/1 INFO :: Building pencil matrix 1/1 (~100%) Elapsed: 0s, Remaining: 0s, Rate: 5.0e+01/s\n",
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      "2019-11-08 16:36:03,180 solvers 0/1 INFO :: Simulation stop time reached.\n",
      "2019-11-08 16:36:03,185 __main__ 0/1 INFO :: Total time: 44.230595\n",
      "2019-11-08 16:36:03,188 __main__ 0/1 INFO :: Iterations: 6284\n",
      "2019-11-08 16:36:03,196 __main__ 0/1 INFO :: Average timestep: 0.010000\n"
     ]
    }
   ],
   "source": [
    "rans_solver = rans_problem.build_solver(de.timesteppers.RK443)\n",
    "\n",
    "rans_solver.stop_sim_time = 20*np.pi\n",
    "rans_solver.stop_wall_time = 1000.\n",
    "rans_solver.stop_iteration = np.inf\n",
    "dt = 0.01\n",
    "\n",
    "# Store data for final plot\n",
    "# Initial conditions\n",
    "z = domain.grid(0)\n",
    "u = rans_solver.state['u']\n",
    "uz = rans_solver.state['uz']\n",
    "nu = rans_problem.parameters['nu']\n",
    "\n",
    "nu_t_solve = gls_problem.substitutions['nu_t']\n",
    "\n",
    "u.set_scales(1, keep_data=True)\n",
    "\n",
    "u_list = [np.copy(u['g'])]\n",
    "t_list = [rans_solver.sim_time]\n",
    "\n",
    "nu_list = [np.copy(nu['g'])]\n",
    "    \n",
    "def update_profile(problem, name, profile):\n",
    "    \n",
    "    problem.parameters[name]['g'][:] = profile\n",
    "\n",
    "start_time = time.time()\n",
    "while rans_solver.ok:\n",
    "    rans_solver.step(dt)\n",
    "    if rans_solver.iteration % 20 == 0:\n",
    "        logger.info(\"Time step {}\".format(rans_solver.iteration))\n",
    "        \n",
    "        u.set_scales(1, keep_data=True)\n",
    "        u_list.append(np.copy(u['g']))\n",
    "        t_list.append(rans_solver.sim_time)\n",
    "        nu_list.append(np.copy(nu['g']))\n",
    "        \n",
    "    # Update M in GLS problem\n",
    "    update_profile(gls_problem, 'M', np.copy(uz['g']))\n",
    "#   update_profile(gls_problem, 'nu', nu_val)\n",
    "\n",
    "    # Update nu in rans_problem\n",
    "    nu_val = asm_calculate_nu(rans_problem, rans_solver, z)\n",
    "    update_profile(rans_problem, 'nu', nu_val)\n",
    "        \n",
    "end_time = time.time()\n",
    "\n",
    "# Print statistics\n",
    "logger.info('Total time: %f' %(end_time-start_time))\n",
    "logger.info('Iterations: %i' %rans_solver.iteration)\n",
    "logger.info('Average timestep: %f' %(rans_solver.sim_time/rans_solver.iteration))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.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",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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 / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.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.\n",
       "mpl.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\n",
       "mpl.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",
       " */\n",
       "function 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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.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",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.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 overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"799.5333333333333\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "u_array = np.array(u_list)\n",
    "nu_array = np.array(nu_list)\n",
    "t_array = np.array(t_list)\n",
    "zmesh, tmesh = quad_mesh(x=z, y=t_array)\n",
    "\n",
    "plt.figure(figsize=(8, 9))\n",
    "plt.subplot2grid((3, 20), (1, 0), colspan=19)\n",
    "plt.pcolormesh(tmesh.T, zmesh.T, nu_array.T, cmap='inferno', vmin=0)\n",
    "plt.ylabel('z')\n",
    "plt.xlabel('t')\n",
    "plt.title('nu_t')\n",
    "cax = plt.subplot2grid((3, 20), (1, 19), colspan=1)\n",
    "plt.colorbar(cax=cax)\n",
    "\n",
    "plt.subplot2grid((3, 20), (2, 0), colspan=19)\n",
    "plt.pcolormesh(tmesh.T, zmesh.T, u_array.T, cmap='RdBu_r', vmin = -1, vmax = 1)\n",
    "plt.axis(pad_limits(tmesh, zmesh))\n",
    "plt.ylabel('z')\n",
    "plt.xlabel('t')\n",
    "plt.title('u')\n",
    "cax = plt.subplot2grid((3, 20), (2, 19), colspan=1)\n",
    "plt.colorbar(cax=cax)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((63, 200), (200,), 63)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u_array.shape, z.shape, len(t_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.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",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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 / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.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.\n",
       "mpl.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\n",
       "mpl.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",
       " */\n",
       "function 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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.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",
       "\n",
       "mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
       "\n",
       "mpl.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 overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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",
       "\n",
       "mpl.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; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"639.85\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "TypeError",
     "evalue": "float() argument must be a string or a number, not 'UnaryGridFunctionField'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-346-2b18e0518fad>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     19\u001b[0m \u001b[0;31m# plt.plot(u_array[-1,:],z, 'k')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdelta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelta\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'k--'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dedalus/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2793\u001b[0m     return gca().plot(\n\u001b[1;32m   2794\u001b[0m         *args, scalex=scalex, scaley=scaley, **({\"data\": data} if data\n\u001b[0;32m-> 2795\u001b[0;31m         is not None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2796\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2797\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dedalus/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1666\u001b[0m         \u001b[0mlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1667\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1668\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1669\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoscale_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscalex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaley\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscaley\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1670\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dedalus/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36madd_line\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   1900\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1901\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1902\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_line_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1903\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1904\u001b[0m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'_line%d'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dedalus/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_update_line_limits\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   1922\u001b[0m         \u001b[0mFigures\u001b[0m \u001b[0mout\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdata\u001b[0m \u001b[0mlimit\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdating\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataLim\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1923\u001b[0m         \"\"\"\n\u001b[0;32m-> 1924\u001b[0;31m         \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1925\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1926\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dedalus/lib/python3.7/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mget_path\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1025\u001b[0m         \"\"\"\n\u001b[1;32m   1026\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1027\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1028\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1029\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dedalus/lib/python3.7/site-packages/matplotlib/lines.py\u001b[0m in \u001b[0;36mrecache\u001b[0;34m(self, always)\u001b[0m\n\u001b[1;32m    673\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0malways\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invalidy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    674\u001b[0m             \u001b[0myconv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_yunits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_yorig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 675\u001b[0;31m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_to_unmasked_float_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0myconv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    676\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    677\u001b[0m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dedalus/lib/python3.7/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_to_unmasked_float_array\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1388\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1389\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1390\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/dedalus/lib/python3.7/site-packages/numpy/core/_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[0;34m(a, dtype, order)\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m     \"\"\"\n\u001b[0;32m---> 85\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: float() argument must be a string or a number, not 'UnaryGridFunctionField'"
     ]
    }
   ],
   "source": [
    "def get_ind(t_array, val):\n",
    "    \n",
    "    dt = np.abs(t_array-val)\n",
    "    return np.where(dt==np.min(dt))[0][0]\n",
    "\n",
    "delta = np.sqrt(2*nu) # assumes zero freq\n",
    "\n",
    "plt.figure()\n",
    "# plt.plot(u_array[0,:], z, 'k:')\n",
    "plt.plot(u_array[get_ind(t_array, 1.00*np.pi),:], z)\n",
    "plt.plot(u_array[get_ind(t_array, 1.25*np.pi),:], z)\n",
    "plt.plot(u_array[get_ind(t_array, 1.50*np.pi),:], z)\n",
    "plt.plot(u_array[get_ind(t_array, 1.75*np.pi),:], z)\n",
    "plt.plot(u_array[get_ind(t_array, 2.00*np.pi),:], z)\n",
    "plt.plot(u_array[get_ind(t_array, 2.25*np.pi),:], z)\n",
    "plt.plot(u_array[get_ind(t_array, 2.50*np.pi),:], z)\n",
    "plt.plot(u_array[get_ind(t_array, 2.75*np.pi),:], z)\n",
    "plt.plot(u_array[get_ind(t_array, 3.00*np.pi),:], z)\n",
    "# plt.plot(u_array[-1,:],z, 'k')\n",
    "\n",
    "plt.plot([-1, 1], [delta, delta], 'k--')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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