SimulateOptimalControlData.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io\n",
    "import matplotlib.colors as colors\n",
    "from matplotlib.colors import LogNorm\n",
    "import h5py\n",
    "from _create_pdf import create_spins, create_spins_inhomo_g\n",
    "from _helper_functions import tsallis, cauchy, find_nearest, odeintz, find_nearest_sorted_left\n",
    "from _steady_state import a_steady\n",
    "from _mbes import mbes, mbes_soc\n",
    "from _do_calculation import solve_mbes\n",
    "#%qtconsole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.5173208202000295\n"
     ]
    }
   ],
   "source": [
    "gcoll = 31.174474083809773\n",
    "q = 1.8048699796219885\n",
    "quali =2786\n",
    "fr = 3119.2;\n",
    "kappa = np.pi*fr/quali #HWHM\n",
    "delta = 0*2*np.pi #detuning cavity to spin central frequency ws-wc\n",
    "deltac = 0*2*np.pi #detuning drive to cavity wc-wp\n",
    "gperp = 2*np.pi*0.03 #HWHM\n",
    "gpar = 2*np.pi*.0001#0.001 #HWHM\n",
    "gammaq = 48.18060262895924\n",
    "nbins = 1901#701#5000#20\n",
    "g_number = 1 #how many bins for inhomogeneous coupling\n",
    "#g0 = gcoll/sqrt(nspins)\n",
    "print(kappa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def g_inhomo_function(x, sigma):\n",
    "    return np.exp(-x**2/sigma**2)\n",
    "\n",
    "x_start = 5\n",
    "x_stop = 20 #µm\n",
    "x = np.linspace(x_start, x_stop, g_number)\n",
    "sigma = 5\n",
    "g_temp = g_inhomo_function(x, sigma)\n",
    "g_list = g_temp/np.sum(g_temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ffa207ca2b0>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "spins, gs, pdf = create_spins_inhomo_g(gcoll, g_list, q, delta, gammaq, nbins, holes=False, inhomo=True, hyperfine=True)\n",
    "plt.plot(spins[:len(pdf)], pdf/max(pdf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ffa2068aaf0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "initsteady = (0.001,0.001)\n",
    "fstart = 50\n",
    "deltaarr = np.linspace(-fstart*2*np.pi,fstart*2*np.pi, 2000)\n",
    "etasteady = 0.00001\n",
    "losasteady = np.zeros(np.shape(deltaarr))\n",
    "losareal = np.zeros(np.shape(deltaarr))\n",
    "losaimag = np.zeros(np.shape(deltaarr))\n",
    "\n",
    "newinit = initsteady\n",
    "for i, delt in enumerate(deltaarr):\n",
    "    ar, ai = a_steady(etasteady, delt, gs, spins, gpar, gperp, kappa, newinit)\n",
    "    newinit = (ar, ai)\n",
    "    losasteady[i] = ai**2+ar**2\n",
    "    losareal[i] = ar\n",
    "    losaimag[i] = ai\n",
    "\n",
    "fig = plt.figure()\n",
    "plt.xlabel('$\\Delta f$ (MHz)')\n",
    "plt.ylabel('|A|² (dB)');\n",
    "plt.plot(deltaarr/2/np.pi, 10*np.log10(losasteady/0.0000000001))\n",
    "#deltaarr[find(losasteady == max(losasteady))]/2/pi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# SOC pulses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "decaytime = 1220e-3\n",
    "dt = 4e-3\n",
    "tlistdecay = np.arange(0, decaytime+dt, dt)\n",
    "scale = 0.1\n",
    "init = np.ones(len(spins)*2+1)*(-1)\n",
    "init[0] = 0\n",
    "init[1::2] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pulse_rwa(t, args):\n",
    "    wgrund = args[0]\n",
    "    fcomps = args[1]\n",
    "    pulse = 0\n",
    "    for i in range(len(fcomps)):\n",
    "        pulse = pulse+(fcomps[i])*np.sin((i+1)*wgrund*t)\n",
    "    return pulse\n",
    "\n",
    "tlist = np.arange(0, 0.504, dt)\n",
    "\n",
    "fcomps = np.array([-1.177,  1.646, -0.549, -1.668, -0.627,  0.151,  1.68 , -0.024,\n",
    "        0.858,  1.311, -0.15 , -0.355,  0.253,  1.165,  0.069,  0.47 ,\n",
    "       -0.649, -0.814,  0.643, -0.657])\n",
    "wgrund = 2*np.pi\n",
    "nfreqs = int(len(fcomps)/2)\n",
    "\n",
    "ilist = pulse_rwa(tlist, (wgrund, fcomps[:nfreqs]))\n",
    "qlist = pulse_rwa(tlist, (wgrund, fcomps[nfreqs:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "solpump, soldecay, infodict = solve_mbes(mbes_soc, init, pdf, tlist, tlistdecay, ilist, qlist, tlist, kappa, deltac, gs, gperp, spins, gpar)\n",
    "a2pump = abs(solpump[:,0])**2\n",
    "a2decay = abs(soldecay[:,0])**2\n",
    "maxsim = max(max(a2pump), max(a2decay))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dataexp = scipy.io.loadmat('/mnt/samba2/experiments/Quantum InterConnect/Cryogenics/Dilution Fridge/LogFiles/SyncFolderAcqiris/Measurements/2016/20190124_MeasurementRun112/20190327_oct_maxampl1_finer/data.mat')\n",
    "\n",
    "datidx = dataexp['data'][0].dtype.names.index('scan')\n",
    "noiseidx = dataexp['data'][0].dtype.names.index('noisefloor')\n",
    "\n",
    "datalin = dataexp['data'][0][0][datidx][0][30]\n",
    "imeas = datalin[2]-np.mean(datalin[2][3500:4000])\n",
    "qmeas = datalin[3]-np.mean(datalin[3][3500:4000])\n",
    "a2exp = (imeas**2+qmeas**2)\n",
    "texp = datalin[0]\n",
    "\n",
    "noisefloor = dataexp['data'][0][0][noiseidx][0][0]\n",
    "tnoise = np.squeeze(noisefloor[0])\n",
    "inoise = np.squeeze(noisefloor[2]-np.mean(noisefloor[2][3500:4000]))\n",
    "qnoise = np.squeeze(noisefloor[3]-np.mean(noisefloor[3][3500:4000]))\n",
    "a2noise = inoise**2+qnoise**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "toff = 0.72\n",
    "idxstart = 800\n",
    "idx = 3900\n",
    "plt.figure(figsize=(10,8))\n",
    "\n",
    "a2all = (np.squeeze(imeas)-np.squeeze(inoise))**2+(np.squeeze(qmeas)-np.squeeze(qnoise))**2\n",
    "#plt.plot(np.squeeze(texp)[idxstart:idx]*1e6-toff, a2exp[idxstart:idx]/max(a2exp))\n",
    "#plt.plot(np.squeeze(texp)[idxstart:idx]*1e6-toff, a2all[idxstart:idx]/max(a2all[idxstart:idx]))\n",
    "plt.plot(tlist, a2pump/maxsim, tlistdecay+tlist[-1], a2decay/maxsim)\n",
    "\n",
    "plt.xlabel('Time (µs)')\n",
    "plt.ylabel('$|\\mathrm{A}|^2$');\n",
    "%qtconsole"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare with data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "datalin = dataexp['data'][0][0][datidx][0]\n",
    "pin = np.arange(-30, 0.1, 0.5)\n",
    "aref = 90\n",
    "\n",
    "ain = aref*10**(pin/20)\n",
    "texp2 = np.squeeze(datalin[0][0])[idxstart:idx]*1e6-toff\n",
    "tsim = np.hstack((tlist, tlist[-1]+tlistdecay))\n",
    "\n",
    "a2listsimpump = {}\n",
    "a2listsimdecay = {}\n",
    "a2listdat = {}\n",
    "invlistsimpump = {}\n",
    "invlistsimdecay = {}\n",
    "\n",
    "for i, dat in enumerate(pin):\n",
    "    if i<len(datalin):\n",
    "        imeas = datalin[i][2]-np.mean(datalin[i][2][3500:4000])\n",
    "        qmeas = datalin[i][3]-np.mean(datalin[i][3][3500:4000])\n",
    "        a2exp = (np.squeeze(imeas)-np.squeeze(inoise))**2+(np.squeeze(qmeas)-np.squeeze(qnoise))**2\n",
    "        expdat = np.squeeze(a2exp[idxstart:idx])\n",
    "        a2listdat[dat] = expdat\n",
    "    \n",
    "    asolpump, asoldecay, infodict = solve_mbes(mbes_soc, init, pdf, tlist, tlistdecay, ilist*ain[i], qlist*ain[i],tlist, kappa, deltac, gs, gperp, spins, gpar)\n",
    "    a2pump2 = asolpump[:,0]\n",
    "    invpump = np.real(np.sum(asolpump[:,2::2], axis=1))\n",
    "    invdecay= np.real(np.sum(asoldecay[:,2::2], axis=1))\n",
    "    a2decay2 = asoldecay[:,0]\n",
    "    \n",
    "    a2listsimpump[dat] =  abs(a2pump2)**2\n",
    "    a2listsimdecay[dat] = abs(a2decay2)**2\n",
    "    invlistsimpump[dat] = invpump\n",
    "    invlistsimdecay[dat] = invdecay\n",
    "\n",
    "\n",
    "a2dat = []\n",
    "a2sim = []\n",
    "for i, dat in enumerate(pin):\n",
    "    if dat in a2listdat.keys():\n",
    "        a2dat.append(a2listdat[dat])\n",
    "\n",
    "    a2pump2 = a2listsimpump[dat]\n",
    "    a2decay2 = a2listsimdecay[dat]\n",
    "    a2sim.append(np.hstack((a2pump2, a2decay2)))\n",
    "\n",
    "tbef = np.arange(-0.32, 0, dt)\n",
    "a2sim = np.asarray(a2sim)\n",
    "a2empt = np.zeros((len(pin), len(tbef)))\n",
    "a2dat = np.asarray(a2dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input Power: -9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-37-7c4d943fbb8b>:22: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  ax3.pcolormesh(texp2, sorted(list(a2listdat.keys())), a2dat, norm=LogNorm(vmin=minclip, vmax=a2dat.max()))\n",
      "<ipython-input-37-7c4d943fbb8b>:23: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3.  Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading'].  This will become an error two minor releases later.\n",
      "  ax4.pcolormesh(np.hstack((tbef, tsim)), pin, np.clip(np.hstack((a2empt, a2sim)),minclip, None), norm=LogNorm(vmin=minclip, vmax=a2sim.max()))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f2967344e20>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scalem = 2.4e-3\n",
    "pdatlist = [-9]\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2,2,figsize=(16,16))\n",
    "ax1.set_xlabel('Time (µs)')\n",
    "ax2.set_xlabel('Time (µs)')\n",
    "ax1.set_ylabel('|A|^2');\n",
    "ax2.set_ylabel('Inversion');\n",
    "\n",
    "for i, val in enumerate(pdatlist):\n",
    "    print(\"Input Power: %d\"%val)\n",
    "    if val in a2listdat.keys():\n",
    "        ax1.plot(texp2, a2listdat[val])\n",
    "    ax1.plot(tlist, a2listsimpump[val]*scalem, tlistdecay+tlist[-1], a2listsimdecay[val]*scalem)\n",
    "    ax2.plot(tlist, (1+invlistsimpump[val])/2, 'r', tlistdecay+tlist[-1], (1+invlistsimdecay[val])/2, 'k')\n",
    "\n",
    "ax3.set_xlabel('Time (µs)')\n",
    "ax3.set_ylabel('|A|^2');\n",
    "ax4.set_xlabel('Time (µs)')\n",
    "ax4.set_ylabel('|A|^2');\n",
    "\n",
    "minclip = 0.00001\n",
    "ax3.pcolormesh(texp2, sorted(list(a2listdat.keys())), a2dat, norm=LogNorm(vmin=minclip, vmax=a2dat.max()))\n",
    "ax4.pcolormesh(np.hstack((tbef, tsim)), pin, np.clip(np.hstack((a2empt, a2sim)),minclip, None), norm=LogNorm(vmin=minclip, vmax=a2sim.max()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-fc493147caf0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0minitup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mpowerin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1e-16\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0masolpumpup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masoldecayup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfodict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msolve_mbes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmbes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtlistdecayup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpowerin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkappa\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdeltac\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgperp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mspins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpar\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      9\u001b[0m \u001b[0ma2pump2up\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0masolpumpup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\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[0;36m2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0minvpumpup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0masolpumpup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\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[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Dropbox/iPython-Notebooks/_do_calculation.ipynb\u001b[0m in \u001b[0;36msolve_mbes\u001b[0;34m(mbes_func, init, pdf, tlistpump, tlistdecay, *args)\u001b[0m\n\u001b[1;32m     16\u001b[0m    ],\n\u001b[1;32m     17\u001b[0m    \"source\": [\n\u001b[0;32m---> 18\u001b[0;31m     \u001b[0;34m\"%matplotlib inline\\n\"\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     19\u001b[0m     \u001b[0;34m\"import numpy as np\\n\"\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\"import nbimporter\\n\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Dropbox/iPython-Notebooks/_helper_functions.ipynb\u001b[0m in \u001b[0;36modeintz\u001b[0;34m(func, z0, t, **kwargs)\u001b[0m\n\u001b[1;32m     20\u001b[0m    \u001b[0;34m\"outputs\"\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m    \"source\": [\n\u001b[0;32m---> 22\u001b[0;31m     \u001b[0;34m\"def tsallis(f, q, width):\\n\"\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     23\u001b[0m     \u001b[0;34m\"    delta = (width)/2*np.sqrt(2*q-2)/np.sqrt(2**q-2)/np.sqrt(2)\\n\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;34m\"    norm = np.sqrt(q-1)*gamma(1/(q-1))/(np.sqrt(2*np.pi)*delta*gamma((3-q)/(2*(q-1))))\\n\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/scipy/integrate/odepack.py\u001b[0m in \u001b[0;36modeint\u001b[0;34m(func, y0, t, args, Dfun, col_deriv, full_output, ml, mu, rtol, atol, tcrit, h0, hmax, hmin, ixpr, mxstep, mxhnil, mxordn, mxords, printmessg, tfirst)\u001b[0m\n\u001b[1;32m    239\u001b[0m     \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0my0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m     output = _odepack.odeint(func, y0, t, args, Dfun, col_deriv, ml, mu,\n\u001b[0m\u001b[1;32m    242\u001b[0m                              \u001b[0mfull_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrtol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0matol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtcrit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhmax\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhmin\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m                              \u001b[0mixpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmxstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmxhnil\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmxordn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmxords\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Dropbox/iPython-Notebooks/_helper_functions.ipynb\u001b[0m in \u001b[0;36mrealfunc\u001b[0;34m(x, t, *args)\u001b[0m\n\u001b[1;32m     14\u001b[0m    ]\n\u001b[1;32m     15\u001b[0m   },\n\u001b[0;32m---> 16\u001b[0;31m   {\n\u001b[0m\u001b[1;32m     17\u001b[0m    \u001b[0;34m\"cell_type\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"code\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m    \u001b[0;34m\"execution_count\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Dropbox/iPython-Notebooks/_mbes.ipynb\u001b[0m in \u001b[0;36mmbes\u001b[0;34m(Y, t, eta, kappa, deltac, gs, gperp, spins, gpar)\u001b[0m\n\u001b[1;32m      5\u001b[0m    \u001b[0;34m\"execution_count\"\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[0m\n\u001b[1;32m      6\u001b[0m    \u001b[0;34m\"metadata\"\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[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m    \"outputs\": [\n\u001b[0m\u001b[1;32m      8\u001b[0m     {\n\u001b[1;32m      9\u001b[0m      \u001b[0;34m\"name\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"stdout\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "decaytimeup = 1500e-2\n",
    "dtup = 8e-2\n",
    "tlistdecayup = np.arange(0, decaytimeup+dtup, dtup)\n",
    "initup = np.ones(nbins*2+1)*1\n",
    "initup[0] = 0\n",
    "initup[1::2] = 0\n",
    "powerin = 1e-16\n",
    "asolpumpup, asoldecayup, infodict = solve_mbes(mbes, initup, pdf, tlist, tlistdecayup, powerin, kappa, deltac, gs, gperp, spins, gpar)\n",
    "a2pump2up = abs(asolpumpup[:,0])**2\n",
    "invpumpup = np.real(np.sum(asolpumpup[:,2::2], axis=1))\n",
    "invdecayup = np.real(np.sum(asoldecayup[:,2::2], axis=1))\n",
    "a2decay2up = abs(asoldecayup[:,0])**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9302622fd0>,\n",
       " <matplotlib.lines.Line2D at 0x7f930262f040>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error in callback <function install_repl_displayhook.<locals>.post_execute at 0x7f93729203a0> (for post_execute):\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "\n|\\mathrm A|^2\n ^\nUnknown symbol: \\mathrm, found '\\'  (at char 1), (line:1, col:2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mParseFatalException\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/mathtext.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self, s, fonts_object, fontsize, dpi)\u001b[0m\n\u001b[1;32m   2616\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2617\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_expression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\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   2618\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseString\u001b[0;34m(self, instring, parseAll)\u001b[0m\n\u001b[1;32m   1946\u001b[0m                 \u001b[0;31m# catch and re-raise exception from here, clears out pyparsing internal stack trace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1947\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1948\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/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseString\u001b[0;34m(self, instring, parseAll)\u001b[0m\n\u001b[1;32m   1936\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1937\u001b[0;31m             \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\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[0m\u001b[1;32m   1938\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mparseAll\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1676\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1677\u001b[0;31m                     \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   1678\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4444\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4445\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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   4446\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/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1676\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1677\u001b[0;31m                     \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   1678\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4051\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-> 4052\u001b[0;31m                 \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexprtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   4053\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mexprtokens\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mexprtokens\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhaskeys\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[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1676\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1677\u001b[0;31m                     \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   1678\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4763\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4764\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mZeroOrMore\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   4765\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mParseException\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\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/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4679\u001b[0m             \u001b[0mtry_not_ender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4680\u001b[0;31m         \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself_expr_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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   4681\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1676\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1677\u001b[0;31m                     \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   1678\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4034\u001b[0m         \u001b[0;31m# pre-parsed the string as part of our And pre-parsing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4035\u001b[0;31m         \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresultlist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexprs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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   4036\u001b[0m         \u001b[0merrorStop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1709\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1710\u001b[0;31m                         \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokensStart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretTokens\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   1711\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mparse_action_exc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m   1315\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1316\u001b[0;31m                 \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlimit\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[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1317\u001b[0m                 \u001b[0mfoundArity\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/mathtext.py\u001b[0m in \u001b[0;36mmath_string\u001b[0;34m(self, s, loc, toks)\u001b[0m\n\u001b[1;32m   2679\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmath_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoks\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-> 2680\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_math_expression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseString\u001b[0;34m(self, instring, parseAll)\u001b[0m\n\u001b[1;32m   1946\u001b[0m                 \u001b[0;31m# catch and re-raise exception from here, clears out pyparsing internal stack trace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1947\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1948\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/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseString\u001b[0;34m(self, instring, parseAll)\u001b[0m\n\u001b[1;32m   1936\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1937\u001b[0;31m             \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\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[0m\u001b[1;32m   1938\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mparseAll\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1676\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1677\u001b[0;31m                     \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   1678\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4444\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4445\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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   4446\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/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1676\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1677\u001b[0;31m                     \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   1678\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4689\u001b[0m                     \u001b[0mpreloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4690\u001b[0;31m                 \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtmptokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself_expr_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   4691\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtmptokens\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtmptokens\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhaskeys\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[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1676\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1677\u001b[0;31m                     \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   1678\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4444\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4445\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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   4446\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/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1676\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1677\u001b[0;31m                     \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   1678\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4236\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4237\u001b[0;31m                 \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   4238\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1709\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1710\u001b[0;31m                         \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokensStart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretTokens\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   1711\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mparse_action_exc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m   1315\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1316\u001b[0;31m                 \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlimit\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[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1317\u001b[0m                 \u001b[0mfoundArity\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/mathtext.py\u001b[0m in \u001b[0;36munknown_symbol\u001b[0;34m(self, s, loc, toks)\u001b[0m\n\u001b[1;32m   2774\u001b[0m         \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtoks\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[0;32m-> 2775\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mParseFatalException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Unknown symbol: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mc\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   2776\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mParseFatalException\u001b[0m: Unknown symbol: \\mathrm, found '\\'  (at char 1), (line:1, col:2)",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mpost_execute\u001b[0;34m()\u001b[0m\n\u001b[1;32m    136\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mpost_execute\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    137\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_interactive\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[0;32m--> 138\u001b[0;31m                     \u001b[0mdraw_all\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    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m             \u001b[0;31m# IPython >= 2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/_pylab_helpers.py\u001b[0m in \u001b[0;36mdraw_all\u001b[0;34m(cls, force)\u001b[0m\n\u001b[1;32m    136\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmanager\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_all_fig_managers\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    137\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mforce\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m                 \u001b[0mmanager\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_idle\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    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mdraw_idle\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2010\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_idle_drawing\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2011\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_idle_draw_cntx\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[0;32m-> 2012\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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[0m\n\u001b[0m\u001b[1;32m   2013\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2014\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeprecated\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"3.2\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    405\u001b[0m              (self.toolbar._wait_cursor_for_draw_cm() if self.toolbar\n\u001b[1;32m    406\u001b[0m               else nullcontext()):\n\u001b[0;32m--> 407\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrenderer\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    408\u001b[0m             \u001b[0;31m# A GUI class may be need to update a window using this draw, so\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    409\u001b[0m             \u001b[0;31m# don't forget to call the superclass.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1862\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1863\u001b[0;31m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   1864\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   1865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\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    132\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    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[1;32m    409\u001b[0m                          \u001b[0;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m                 **kwargs)\n\u001b[0;32m--> 411\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\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    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2746\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\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   2747\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2748\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\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   2749\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2750\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\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    132\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    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1176\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_label_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1178\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\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   1179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1180\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_offset_text_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticklabelBoxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticklabelBoxes2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/text.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    678\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_wrap_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 679\u001b[0;31m             \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\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    680\u001b[0m             \u001b[0mtrans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\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    681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    291\u001b[0m             \u001b[0mclean_line\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mismath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_preprocess_math\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[1;32m    292\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mclean_line\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m                 w, h, d = renderer.get_text_width_height_descent(\n\u001b[0m\u001b[1;32m    294\u001b[0m                     clean_line, self._fontproperties, ismath=ismath)\n\u001b[1;32m    295\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/usr/local/lib/python3.8/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mismath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0mox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfonts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mused_characters\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmathtext_parser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprop\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    234\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/mathtext.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self, s, dpi, prop)\u001b[0m\n\u001b[1;32m   3338\u001b[0m         \u001b[0;31m# mathtext.fontset rcParams also affect the parse (e.g. by affecting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3339\u001b[0m         \u001b[0;31m# the glyph metrics).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3340\u001b[0;31m         return self._parse_cached(\n\u001b[0m\u001b[1;32m   3341\u001b[0m             s, dpi, prop, rcParams['ps.useafm'], rcParams['mathtext.fontset'])\n\u001b[1;32m   3342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/mathtext.py\u001b[0m in \u001b[0;36m_parse_cached\u001b[0;34m(self, s, dpi, prop, ps_useafm, fontset)\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParser\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   3363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3364\u001b[0;31m         \u001b[0mbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfont_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\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   3365\u001b[0m         \u001b[0mfont_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_canvas_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdepth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3366\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfont_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbox\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/mathtext.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self, s, fonts_object, fontsize, dpi)\u001b[0m\n\u001b[1;32m   2617\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_expression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2618\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2619\u001b[0;31m             raise ValueError(\"\\n\".join([\"\",\n\u001b[0m\u001b[1;32m   2620\u001b[0m                                         \u001b[0merr\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[1;32m   2621\u001b[0m                                         \u001b[0;34m\" \"\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn\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[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: \n|\\mathrm A|^2\n ^\nUnknown symbol: \\mathrm, found '\\'  (at char 1), (line:1, col:2)"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "\n|\\mathrm A|^2\n ^\nUnknown symbol: \\mathrm, found '\\'  (at char 1), (line:1, col:2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mParseFatalException\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/mathtext.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self, s, fonts_object, fontsize, dpi)\u001b[0m\n\u001b[1;32m   2616\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2617\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_expression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\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   2618\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseString\u001b[0;34m(self, instring, parseAll)\u001b[0m\n\u001b[1;32m   1946\u001b[0m                 \u001b[0;31m# catch and re-raise exception from here, clears out pyparsing internal stack trace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1947\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1948\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/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseString\u001b[0;34m(self, instring, parseAll)\u001b[0m\n\u001b[1;32m   1936\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1937\u001b[0;31m             \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\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[0m\u001b[1;32m   1938\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mparseAll\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1676\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1677\u001b[0;31m                     \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   1678\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4444\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4445\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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   4446\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/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1676\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1677\u001b[0;31m                     \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   1678\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4051\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-> 4052\u001b[0;31m                 \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexprtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   4053\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mexprtokens\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mexprtokens\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhaskeys\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[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1676\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1677\u001b[0;31m                     \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   1678\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4763\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4764\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mZeroOrMore\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   4765\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mParseException\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\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/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4679\u001b[0m             \u001b[0mtry_not_ender\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4680\u001b[0;31m         \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself_expr_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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   4681\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1676\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1677\u001b[0;31m                     \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   1678\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4034\u001b[0m         \u001b[0;31m# pre-parsed the string as part of our And pre-parsing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4035\u001b[0;31m         \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresultlist\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexprs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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   4036\u001b[0m         \u001b[0merrorStop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1709\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1710\u001b[0;31m                         \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokensStart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretTokens\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   1711\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mparse_action_exc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m   1315\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1316\u001b[0;31m                 \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlimit\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[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1317\u001b[0m                 \u001b[0mfoundArity\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/mathtext.py\u001b[0m in \u001b[0;36mmath_string\u001b[0;34m(self, s, loc, toks)\u001b[0m\n\u001b[1;32m   2679\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmath_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoks\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-> 2680\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_math_expression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoks\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseString\u001b[0;34m(self, instring, parseAll)\u001b[0m\n\u001b[1;32m   1946\u001b[0m                 \u001b[0;31m# catch and re-raise exception from here, clears out pyparsing internal stack trace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1947\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1948\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/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseString\u001b[0;34m(self, instring, parseAll)\u001b[0m\n\u001b[1;32m   1936\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1937\u001b[0;31m             \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\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[0m\u001b[1;32m   1938\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mparseAll\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1676\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1677\u001b[0;31m                     \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   1678\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4444\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4445\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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   4446\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/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1676\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1677\u001b[0;31m                     \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   1678\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4689\u001b[0m                     \u001b[0mpreloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4690\u001b[0;31m                 \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtmptokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself_expr_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   4691\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtmptokens\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mtmptokens\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhaskeys\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[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1676\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1677\u001b[0;31m                     \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   1678\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4444\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4445\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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   4446\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/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1676\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1677\u001b[0;31m                     \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseImpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   1678\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mparseImpl\u001b[0;34m(self, instring, loc, doActions)\u001b[0m\n\u001b[1;32m   4236\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4237\u001b[0;31m                 \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\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   4238\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1837\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1838\u001b[0;31m                     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parseNoCache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoActions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallPreParse\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   1839\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpe\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36m_parseNoCache\u001b[0;34m(self, instring, loc, doActions, callPreParse)\u001b[0m\n\u001b[1;32m   1709\u001b[0m                     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1710\u001b[0;31m                         \u001b[0mtokens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokensStart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretTokens\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   1711\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mparse_action_exc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3/dist-packages/pyparsing.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m   1315\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1316\u001b[0;31m                 \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlimit\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[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1317\u001b[0m                 \u001b[0mfoundArity\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/mathtext.py\u001b[0m in \u001b[0;36munknown_symbol\u001b[0;34m(self, s, loc, toks)\u001b[0m\n\u001b[1;32m   2774\u001b[0m         \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtoks\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[0;32m-> 2775\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mParseFatalException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Unknown symbol: %s\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mc\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   2776\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mParseFatalException\u001b[0m: Unknown symbol: \\mathrm, found '\\'  (at char 1), (line:1, col:2)",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    339\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    340\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--> 341\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\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    342\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 248\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\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[0m\u001b[1;32m    249\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    250\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\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[0;32m/usr/local/lib/python3.8/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0mFigureCanvasBase\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\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    133\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\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    134\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2191\u001b[0m                            else suppress())\n\u001b[1;32m   2192\u001b[0m                     \u001b[0;32mwith\u001b[0m \u001b[0mctx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2193\u001b[0;31m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\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   2194\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2195\u001b[0m                     bbox_inches = self.figure.get_tightbbox(\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/figure.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m   1861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1862\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1863\u001b[0;31m             mimage._draw_list_compositing_images(\n\u001b[0m\u001b[1;32m   1864\u001b[0m                 renderer, self, artists, self.suppressComposite)\n\u001b[1;32m   1865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\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    132\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    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/cbook/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*inner_args, **inner_kwargs)\u001b[0m\n\u001b[1;32m    409\u001b[0m                          \u001b[0;32melse\u001b[0m \u001b[0mdeprecation_addendum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m                 **kwargs)\n\u001b[0;32m--> 411\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minner_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0minner_kwargs\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    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, inframe)\u001b[0m\n\u001b[1;32m   2746\u001b[0m             \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_rasterizing\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   2747\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2748\u001b[0;31m         \u001b[0mmimage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_draw_list_compositing_images\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0martists\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   2749\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2750\u001b[0m         \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_group\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'axes'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/image.py\u001b[0m in \u001b[0;36m_draw_list_compositing_images\u001b[0;34m(renderer, parent, artists, suppress_composite)\u001b[0m\n\u001b[1;32m    129\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnot_composite\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_images\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m             \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\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    132\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    133\u001b[0m         \u001b[0;31m# Composite any adjacent images together\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/axis.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1176\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_label_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1178\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\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   1179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1180\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_offset_text_position\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mticklabelBoxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mticklabelBoxes2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mdraw_wrapper\u001b[0;34m(artist, renderer, *args, **kwargs)\u001b[0m\n\u001b[1;32m     39\u001b[0m                 \u001b[0mrenderer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_filter\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     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrenderer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0martist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_agg_filter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/text.py\u001b[0m in \u001b[0;36mdraw\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    677\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    678\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0m_wrap_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 679\u001b[0;31m             \u001b[0mbbox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minfo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_layout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrenderer\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    680\u001b[0m             \u001b[0mtrans\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtextobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_transform\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    681\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/text.py\u001b[0m in \u001b[0;36m_get_layout\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    291\u001b[0m             \u001b[0mclean_line\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mismath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_preprocess_math\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[1;32m    292\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mclean_line\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 293\u001b[0;31m                 w, h, d = renderer.get_text_width_height_descent(\n\u001b[0m\u001b[1;32m    294\u001b[0m                     clean_line, self._fontproperties, ismath=ismath)\n\u001b[1;32m    295\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/usr/local/lib/python3.8/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mget_text_width_height_descent\u001b[0;34m(self, s, prop, ismath)\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mismath\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m             \u001b[0mox\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfonts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mused_characters\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmathtext_parser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprop\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    234\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdescent\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/mathtext.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self, s, dpi, prop)\u001b[0m\n\u001b[1;32m   3338\u001b[0m         \u001b[0;31m# mathtext.fontset rcParams also affect the parse (e.g. by affecting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3339\u001b[0m         \u001b[0;31m# the glyph metrics).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3340\u001b[0;31m         return self._parse_cached(\n\u001b[0m\u001b[1;32m   3341\u001b[0m             s, dpi, prop, rcParams['ps.useafm'], rcParams['mathtext.fontset'])\n\u001b[1;32m   3342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/mathtext.py\u001b[0m in \u001b[0;36m_parse_cached\u001b[0;34m(self, s, dpi, prop, ps_useafm, fontset)\u001b[0m\n\u001b[1;32m   3362\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParser\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   3363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3364\u001b[0;31m         \u001b[0mbox\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfont_output\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\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   3365\u001b[0m         \u001b[0mfont_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_canvas_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdepth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3366\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mfont_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbox\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/mathtext.py\u001b[0m in \u001b[0;36mparse\u001b[0;34m(self, s, fonts_object, fontsize, dpi)\u001b[0m\n\u001b[1;32m   2617\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_expression\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparseString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2618\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mParseBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2619\u001b[0;31m             raise ValueError(\"\\n\".join([\"\",\n\u001b[0m\u001b[1;32m   2620\u001b[0m                                         \u001b[0merr\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[1;32m   2621\u001b[0m                                         \u001b[0;34m\" \"\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn\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[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: \n|\\mathrm A|^2\n ^\nUnknown symbol: \\mathrm, found '\\'  (at char 1), (line:1, col:2)"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ((ax1, ax2)) = plt.subplots(1,2 ,figsize=(14,5))\n",
    "ax1.set_xlabel('Time (µs)')\n",
    "ax2.set_xlabel('Time (µs)')\n",
    "ax1.set_ylabel('$|\\mathrm A|^2$');\n",
    "ax2.set_ylabel('Inversion');\n",
    "\n",
    "ax1.plot(tlist, a2pump2up, tlistdecayup+tlist[-1], a2decay2up)\n",
    "ax2.plot(tlist, invpumpup, tlistdecayup+tlist[-1], invdecayup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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",
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