{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.integrate\n",
    "import numpy\n",
    "import matplotlib.pyplot\n",
    "import os\n",
    "import json\n",
    "import scipy.interpolate\n",
    "import cModel\n",
    "\n",
    "def dfdy(t,y,system):\n",
    "    dfdy=system.M.dot(y)+system.u(t)\n",
    "    return dfdy\n",
    "\n",
    "def jacobi(t,y,system):\n",
    "    return system.M\n",
    "\n",
    "#SE post calculation\n",
    "def dfdyS(t,S,system):\n",
    "    #unwrap S to NxM where M is number of parameters\n",
    "    mS=numpy.reshape(S,(system.n,system.m))\n",
    "    mOut=system.M.dot(mS)+system.fS(t)\n",
    "    return numpy.ravel(mOut)\n",
    "\n",
    "def jacobiSE(t,S,system):\n",
    "    N=system.n*(system.m)\n",
    "    fJ=numpy.zeros((N,N))\n",
    "    #print('fJ shape {}'.format(fJ.shape))\n",
    "    for i in range(system.m):\n",
    "        fJ[i*system.n:(i+1)*system.n,i*system.n:(i+1)*system.n]=system.M\n",
    "    return fJ\n",
    "\n",
    "#SE simultaeneous calculation\n",
    "def dfdySFull(t,S,system):\n",
    "    #unwrap S to NxM where M is number of parameters\n",
    "    mS=numpy.reshape(S,(system.n,system.m+1))\n",
    "    #system.fS(y,t) is NxM matrix where M are parameters\n",
    "    y=mS[:,0]\n",
    "    mOut=system.M.dot(mS)+system.fSY(y,t)\n",
    "    return numpy.ravel(mOut)\n",
    "\n",
    "def jacobiSEFull(t,S,system):\n",
    "    N=system.n*(system.m+1)\n",
    "    fJ=numpy.zeros((N,N))\n",
    "    #print('fJ shape {}'.format(fJ.shape))\n",
    "    for i in range(system.m+1):\n",
    "        fJ[i*system.n:(i+1)*system.n,i*system.n:(i+1)*system.n]=system.M\n",
    "    return fJ\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done simultaneous LSODA SE\n",
      "Calculating for adiposePC/0:(73091, 16)\n",
      "Calculating for brainPC/1:(73091, 16)\n"
     ]
    }
   ],
   "source": [
    "sys=model()\n",
    "fh=os.path.expanduser('~')\n",
    "#sys.parse(os.path.join(fh,'software','src','Integra','models','cDiazepam.json'))\n",
    "sys.parse(os.path.join(fh,'software','src','Integra','models','cCotinine.json'))\n",
    "#print(sys.u(10)[sys.lut['venous']])\n",
    "#sys.inspect()         \n",
    "nt=201\n",
    "tmax=4*3600\n",
    "t = numpy.linspace(0,tmax, nt)\n",
    "#first column is the solution y\n",
    "#initial condition\n",
    "y0=numpy.zeros(sys.n)\n",
    "    \n",
    "doSequential=0\n",
    "doSimultaneous=0\n",
    "doIVP=0\n",
    "doIVPSimultaneous=1\n",
    "\n",
    "if doSequential:\n",
    "#sequential SE (first true solution, then parameter derivatives)\n",
    "    y0=numpy.zeros(sys.n)\n",
    "    sol = scipy.integrate.odeint(dfdy, y0=y0, t=t, args=(sys,),Dfun=jacobi,tfirst=True)\n",
    "    print('shape (y) {}'.format(sol.shape))\n",
    "    \n",
    "    #solLSODA = scipy.integrate.LSODA(dfdy,y0,0,tbound=4*3600,min_step=10,max_step=1000,jac=jacobi)\n",
    "    #sol=solLSODA.\n",
    "    sys.setY(t,sol)\n",
    "    S0=numpy.zeros((sys.n,sys.m))\n",
    "    S0=S0.ravel()\n",
    "    #print('lut {}'.format(sys.lut))\n",
    "    #print('lutSE {}'.format(sys.lutSE))\n",
    "    #fJ=sys.fSS[sys.lutSE['brainPC']]\n",
    "    #print('X shape {}\\n {}'.format(fJ.shape,fJ))\n",
    "    solSE=scipy.integrate.odeint(dfdyS, S0, t, args=(sys,),Dfun=jacobiSE,tfirst=True)\n",
    "    s1=numpy.reshape(solSE,(len(t),sys.n,sys.m))\n",
    "    print('Done sequential SE')\n",
    "    \n",
    "\n",
    "if doSimultaneous:\n",
    "#simultaneous SE\n",
    "    S1=numpy.zeros((sys.n,sys.m+1))\n",
    "    #set initial condition\n",
    "    S1[:,0]=y0\n",
    "    S1=S1.ravel()\n",
    "    solSE1=scipy.integrate.odeint(dfdySFull, S1, t, args=(sys,),Dfun=jacobiSEFull,tfirst=True)\n",
    "    sFull=numpy.reshape(solSE1,(len(t),sys.n,sys.m+1))\n",
    "    s1=sFull[:,:,1:]\n",
    "    sol=sFull[:,:,0]\n",
    "    print('Done simultaneous SE')\n",
    "\n",
    "if doIVP:\n",
    "    solIVP=scipy.integrate.solve_ivp(dfdy,[0, tmax],y0, args=(sys,), jac=jacobi,\n",
    "                                  method='LSODA', atol=1e-4, rtol=1e-8)\n",
    "    #y is n x nt (odeint nt x n)\n",
    "    sol=numpy.transpose(solIVP.y)\n",
    "    t=solIVP.t\n",
    "    print('shape (y) {}'.format(sol.shape))\n",
    "    sys.setY(t,sol)\n",
    "    solIVPSE=scipy.integrate.solve_ivp(dfdyS,[0, tmax],S0, args=(sys,), jac=jacobiSE,\n",
    "                                  method='LSODA', atol=1e-4, rtol=1e-8)\n",
    "    sraw=numpy.reshape(numpy.transpose(solIVPSE.y),(len(solIVPSE.t),sys.n,sys.m))\n",
    "    #interpolate on t\n",
    "    s1=numpy.zeros((len(t),sys.n,sys.m))\n",
    "    for i in range(sys.n):\n",
    "        for j in range(sys.m):\n",
    "            tck = scipy.interpolate.splrep(solIVPSE.t, sraw[:,i,j], s=0)\n",
    "            s1[:,i,j]=scipy.interpolate.splev(t, tck, der=0)\n",
    "        \n",
    "if doIVPSimultaneous:\n",
    "    S1=numpy.zeros((sys.n,sys.m+1))\n",
    "    #set initial condition\n",
    "    S1[:,0]=y0\n",
    "    S1=S1.ravel()\n",
    "    solIVP1=scipy.integrate.solve_ivp(dfdySFull,[0, tmax],S1, args=(sys,), jac=jacobiSEFull,\n",
    "                                  method='LSODA', atol=10, rtol=1e-2)\n",
    "    t=solIVP1.t\n",
    "    sFull=numpy.reshape(numpy.transpose(solIVP1.y),(len(t),sys.n,sys.m+1))\n",
    "    s1=sFull[:,:,1:]\n",
    "    sol=sFull[:,:,0]\n",
    "    print('Done simultaneous LSODA SE')\n",
    "    \n",
    "    \n",
    "#calculate uncertainty\n",
    "#s1 is nt x nvar x npar\n",
    "\n",
    "se=sys.calculateUncertainty(sol,s1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(73091, 16, 2)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1800 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#copy output of Thompson et al\n",
    "print(s1.shape)\n",
    "fig, axs = matplotlib.pyplot.subplots(5, 3,figsize=(15,25))\n",
    "name=['arterial','adipose','brain','heart','kidney','liver','lung','muscle','skin',\n",
    "      'splanchnic','stomach','excrement']\n",
    "#diazepam\n",
    "max=[1.5,2.6,3,4,5,2.5,6.8,1.5,1.5,4,4.2,3]\n",
    "#cotinine\n",
    "max=[9]*12\n",
    "max[11]=90\n",
    "\n",
    "max=[1000*x for x in max]\n",
    "for i in range(len(name)):\n",
    "    row=i//3\n",
    "    col=i%3\n",
    "    fy=sol[:,sys.lut[name[i]]]\n",
    "    fe=se[:,sys.lut[name[i]]]\n",
    "    ax=axs[row,col]\n",
    "    ax.plot(t/60,fy)\n",
    "    ax.fill_between(t/60, fy-fe, fy + fe, color='red',alpha=0.1)\n",
    "    ax.plot(t/60,fy-fe,color='red',linewidth=1,alpha=0.2)\n",
    "    ax.plot(t/60,fy+fe,color='red',linewidth=1,alpha=0.2)\n",
    "    axs[row,col].set_title(name[i])\n",
    "    axs[row,col].set_ylim([0,max[i]])\n",
    "    axs[row,col].set_xlim([0,250])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  message: 'The solver successfully reached the end of the integration interval.'\n",
       "     nfev: 1135\n",
       "     njev: 25\n",
       "      nlu: 25\n",
       "      sol: None\n",
       "   status: 0\n",
       "  success: True\n",
       "        t: array([0.00000000e+00, 3.20591951e-04, 6.41183902e-04, 1.28236780e-03,\n",
       "       1.92355170e-03, 2.56473561e-03, 8.97657462e-03, 1.53884136e-02,\n",
       "       2.18002527e-02, 2.82120917e-02, 5.40150345e-02, 7.98179774e-02,\n",
       "       1.05620920e-01, 1.31423863e-01, 1.57226806e-01, 1.99625129e-01,\n",
       "       2.42023452e-01, 2.84421775e-01, 3.26820098e-01, 3.69218420e-01,\n",
       "       4.11616743e-01, 4.79107559e-01, 5.46598375e-01, 6.14089191e-01,\n",
       "       6.81580007e-01, 7.49070823e-01, 8.16561639e-01, 8.84052455e-01,\n",
       "       9.51550514e-01, 1.01904857e+00, 1.08654663e+00, 1.15404469e+00,\n",
       "       1.22154275e+00, 1.28904081e+00, 1.35653887e+00, 1.44133130e+00,\n",
       "       1.52612374e+00, 1.61091617e+00, 1.69570861e+00, 1.78050104e+00,\n",
       "       1.86529348e+00, 1.96079513e+00, 2.05629678e+00, 2.15179843e+00,\n",
       "       2.24730008e+00, 2.34280173e+00, 2.46429320e+00, 2.58578468e+00,\n",
       "       2.70727615e+00, 2.82876763e+00, 2.95025910e+00, 3.07228543e+00,\n",
       "       3.19431176e+00, 3.31633809e+00, 3.43836441e+00, 3.56039074e+00,\n",
       "       3.70535909e+00, 3.85032743e+00, 3.99529578e+00, 4.14026413e+00,\n",
       "       4.28523247e+00, 4.61325179e+00, 4.94127111e+00, 5.26929043e+00,\n",
       "       5.59730975e+00, 5.92532907e+00, 6.25334839e+00, 6.49130662e+00,\n",
       "       6.72926484e+00, 6.96722306e+00, 7.20518128e+00, 7.44313950e+00,\n",
       "       7.68109773e+00, 7.91905595e+00, 8.15701417e+00, 8.38073440e+00,\n",
       "       8.60445462e+00, 8.82817485e+00, 9.05189507e+00, 9.27561530e+00,\n",
       "       9.49933552e+00, 9.72305574e+00, 9.94677597e+00, 1.00874168e+01,\n",
       "       1.02280576e+01, 1.03686985e+01, 1.05093393e+01, 1.07277551e+01,\n",
       "       1.09461710e+01, 1.11645868e+01, 1.13830026e+01, 1.16014185e+01,\n",
       "       1.20719265e+01, 1.25424345e+01, 1.30129425e+01, 1.34834505e+01,\n",
       "       1.39539585e+01, 1.40376119e+01, 1.41212653e+01, 1.42049187e+01,\n",
       "       1.42885721e+01, 1.43722255e+01, 1.44558790e+01, 1.45634334e+01,\n",
       "       1.46709877e+01, 1.47785421e+01, 1.48860965e+01, 1.49936509e+01,\n",
       "       1.51396824e+01, 1.52857140e+01, 1.54317455e+01, 1.55777770e+01,\n",
       "       1.59004276e+01, 1.62230782e+01, 1.65457289e+01, 1.68683795e+01,\n",
       "       1.71910301e+01, 1.77869542e+01, 1.83828783e+01, 1.85318593e+01,\n",
       "       1.86808404e+01, 1.88298214e+01, 1.89788024e+01, 1.91277834e+01,\n",
       "       1.92767645e+01, 1.93982262e+01, 1.95196879e+01, 1.96411496e+01,\n",
       "       1.97626114e+01, 1.98840731e+01, 2.00155725e+01, 2.01470719e+01,\n",
       "       2.02785713e+01, 2.04100707e+01, 2.05938577e+01, 2.07776447e+01,\n",
       "       2.09614317e+01, 2.11452187e+01, 2.16199944e+01, 2.20947702e+01,\n",
       "       2.25695459e+01, 2.30443216e+01, 2.35190974e+01, 2.43992497e+01,\n",
       "       2.46192878e+01, 2.48393258e+01, 2.50593639e+01, 2.52794020e+01,\n",
       "       2.54994401e+01, 2.57194782e+01, 2.58285538e+01, 2.59376295e+01,\n",
       "       2.60467052e+01, 2.61557809e+01, 2.62648566e+01, 2.64129177e+01,\n",
       "       2.65609789e+01, 2.67090400e+01, 2.68571012e+01, 2.70099722e+01,\n",
       "       2.71628433e+01, 2.73157143e+01, 2.74685853e+01, 2.80819963e+01,\n",
       "       2.86954072e+01, 2.93088181e+01, 2.99222290e+01, 2.99605672e+01,\n",
       "       2.99989054e+01, 2.99990971e+01, 2.99992887e+01, 2.99994804e+01,\n",
       "       2.99996721e+01, 2.99998638e+01, 2.99998744e+01, 2.99998849e+01,\n",
       "       2.99999061e+01, 2.99999272e+01, 2.99999483e+01, 2.99999561e+01,\n",
       "       2.99999640e+01, 2.99999797e+01, 2.99999953e+01, 2.99999975e+01,\n",
       "       2.99999996e+01, 2.99999997e+01, 2.99999998e+01, 2.99999999e+01,\n",
       "       3.00000000e+01, 3.00000000e+01, 3.00000002e+01, 3.00000004e+01,\n",
       "       3.00000006e+01, 3.00000023e+01, 3.00000040e+01, 3.00000057e+01,\n",
       "       3.00000228e+01, 3.00000399e+01, 3.00000570e+01, 3.00002278e+01,\n",
       "       3.00003987e+01, 3.00005696e+01, 3.00007405e+01, 3.00024492e+01,\n",
       "       3.00041580e+01, 3.00058667e+01, 3.00075755e+01, 3.00092843e+01,\n",
       "       3.00263719e+01, 3.00434595e+01, 3.00605471e+01, 3.00776348e+01,\n",
       "       3.00947224e+01, 3.01118100e+01, 3.01673762e+01, 3.02229424e+01,\n",
       "       3.02785086e+01, 3.03340747e+01, 3.03896409e+01, 3.04452071e+01,\n",
       "       3.05007733e+01, 3.05682713e+01, 3.06357694e+01, 3.07032675e+01,\n",
       "       3.07707655e+01, 3.08382636e+01, 3.09057616e+01, 3.09732597e+01,\n",
       "       3.10407577e+01, 3.11082558e+01, 3.11757538e+01, 3.12552883e+01,\n",
       "       3.13348228e+01, 3.14143573e+01, 3.14938918e+01, 3.15734263e+01,\n",
       "       3.16529608e+01, 3.17474570e+01, 3.18419532e+01, 3.19364493e+01,\n",
       "       3.20309455e+01, 3.21254416e+01, 3.22199378e+01, 3.23414413e+01,\n",
       "       3.24629448e+01, 3.25844483e+01, 3.27059518e+01, 3.28274553e+01,\n",
       "       3.29489589e+01, 3.30704624e+01, 3.31919659e+01, 3.33153927e+01,\n",
       "       3.34388196e+01, 3.35622464e+01, 3.36856733e+01, 3.38091001e+01,\n",
       "       3.39982846e+01, 3.41874690e+01, 3.43766535e+01, 3.45658379e+01,\n",
       "       3.47550224e+01, 3.51264554e+01, 3.54978884e+01, 3.58693215e+01,\n",
       "       3.62407545e+01, 3.66121875e+01, 3.69836205e+01, 3.72054366e+01,\n",
       "       3.74272527e+01, 3.76490687e+01, 3.78708848e+01, 3.80927009e+01,\n",
       "       3.82360839e+01, 3.83794670e+01, 3.85228501e+01, 3.86662331e+01,\n",
       "       3.88403885e+01, 3.90145439e+01, 3.91886992e+01, 3.93628546e+01,\n",
       "       3.96674704e+01, 3.99720861e+01, 4.02767019e+01, 4.05813177e+01,\n",
       "       4.08859335e+01, 4.14733614e+01, 4.20607893e+01, 4.24024682e+01,\n",
       "       4.27441471e+01, 4.30858260e+01, 4.34275049e+01, 4.35129246e+01,\n",
       "       4.35983443e+01, 4.36837641e+01, 4.37691838e+01, 4.38546035e+01,\n",
       "       4.39400232e+01, 4.40608926e+01, 4.41817619e+01, 4.43026312e+01,\n",
       "       4.44235006e+01, 4.45443699e+01, 4.46912355e+01, 4.48381011e+01,\n",
       "       4.49849667e+01, 4.51318323e+01, 4.53290529e+01, 4.55262734e+01,\n",
       "       4.57234940e+01, 4.59207145e+01, 4.63338678e+01, 4.67470211e+01,\n",
       "       4.71601743e+01, 4.75733276e+01, 4.79864809e+01, 4.87601600e+01,\n",
       "       4.89535798e+01, 4.91469996e+01, 4.93404193e+01, 4.95338391e+01,\n",
       "       4.97272589e+01, 4.99206787e+01, 5.00421128e+01, 5.01635469e+01,\n",
       "       5.02849810e+01, 5.04064152e+01, 5.05278493e+01, 5.06757793e+01,\n",
       "       5.08237093e+01, 5.09716394e+01, 5.11195694e+01, 5.12536191e+01,\n",
       "       5.13876688e+01, 5.15217185e+01, 5.16557682e+01, 5.21486586e+01,\n",
       "       5.26415490e+01, 5.31344395e+01, 5.36273299e+01, 5.41202203e+01,\n",
       "       5.47944536e+01, 5.54686869e+01, 5.60045516e+01, 5.65404162e+01,\n",
       "       5.70762808e+01, 5.76121454e+01, 5.81480100e+01, 5.86838746e+01,\n",
       "       5.92197392e+01, 5.97556038e+01, 6.02914684e+01, 6.08273331e+01,\n",
       "       6.13631977e+01, 6.18990623e+01, 6.24349269e+01, 6.29707915e+01,\n",
       "       6.35066561e+01, 6.40425207e+01, 6.45783853e+01, 6.51142500e+01,\n",
       "       6.57794325e+01, 6.64446150e+01, 6.71097975e+01, 6.77749800e+01,\n",
       "       6.84401625e+01, 6.91053450e+01, 6.98529979e+01, 7.06006508e+01,\n",
       "       7.13483037e+01, 7.20959566e+01, 7.28436094e+01, 7.35912623e+01,\n",
       "       7.44334931e+01, 7.52757238e+01, 7.61179545e+01, 7.69601853e+01,\n",
       "       7.78024160e+01, 7.86446467e+01, 7.94868775e+01, 8.03291082e+01,\n",
       "       8.11713389e+01, 8.21778788e+01, 8.31844187e+01, 8.41909586e+01,\n",
       "       8.51974985e+01, 8.62040384e+01, 8.72105782e+01, 8.85692586e+01,\n",
       "       8.99279389e+01, 9.12866192e+01, 9.26452995e+01, 9.40039798e+01,\n",
       "       9.53626601e+01, 9.68856694e+01, 9.84086787e+01, 9.99316880e+01,\n",
       "       1.01454697e+02, 1.02977707e+02, 1.04500716e+02, 1.06290777e+02,\n",
       "       1.08080838e+02, 1.09870899e+02, 1.11660959e+02, 1.13451020e+02,\n",
       "       1.15241081e+02, 1.17473382e+02, 1.19705683e+02, 1.21937984e+02,\n",
       "       1.24170285e+02, 1.26402586e+02, 1.28634887e+02, 1.31735011e+02,\n",
       "       1.34835135e+02, 1.37935259e+02, 1.41035384e+02, 1.44135508e+02,\n",
       "       1.47235632e+02, 1.50998305e+02, 1.54760979e+02, 1.58523652e+02,\n",
       "       1.62286326e+02, 1.66048999e+02, 1.69811673e+02, 1.74086293e+02,\n",
       "       1.78360913e+02, 1.82635533e+02, 1.86910153e+02, 1.91184773e+02,\n",
       "       1.95459393e+02, 2.00340233e+02, 2.05221073e+02, 2.10101912e+02,\n",
       "       2.14982752e+02, 2.19863592e+02, 2.24744432e+02, 2.29625272e+02,\n",
       "       2.34506112e+02, 2.39386952e+02, 2.44267792e+02, 2.49148632e+02,\n",
       "       2.54029472e+02, 2.58910312e+02, 2.63791151e+02, 2.68671991e+02,\n",
       "       2.75508564e+02, 2.82345136e+02, 2.89181708e+02, 2.96018280e+02,\n",
       "       3.02854852e+02, 3.09691425e+02, 3.17582683e+02, 3.25473942e+02,\n",
       "       3.33365201e+02, 3.41256460e+02, 3.49147719e+02, 3.57038978e+02,\n",
       "       3.64930237e+02, 3.72821496e+02, 3.80712755e+02, 3.88604014e+02,\n",
       "       3.96495273e+02, 4.04386532e+02, 4.12277790e+02, 4.20169049e+02,\n",
       "       4.28060308e+02, 4.35951567e+02, 4.43842826e+02, 4.51734085e+02,\n",
       "       4.61149736e+02, 4.70565388e+02, 4.79981039e+02, 4.89396691e+02,\n",
       "       4.98812342e+02, 5.08227994e+02, 5.17643645e+02, 5.27059297e+02,\n",
       "       5.36474948e+02, 5.47097513e+02, 5.57720077e+02, 5.68342642e+02,\n",
       "       5.78965206e+02, 5.89587770e+02, 6.12387555e+02, 6.35187339e+02,\n",
       "       6.52316298e+02, 6.69445257e+02, 6.86574217e+02, 7.03703176e+02,\n",
       "       7.20832135e+02, 7.37961095e+02, 7.55090054e+02, 7.72219013e+02,\n",
       "       7.75644805e+02, 7.76266240e+02, 7.76887675e+02, 7.77509109e+02,\n",
       "       7.78751979e+02, 7.79994848e+02, 7.81237718e+02, 7.82480587e+02,\n",
       "       7.88083218e+02, 7.93685849e+02, 7.99288481e+02, 8.04891112e+02,\n",
       "       8.19221953e+02, 8.33552795e+02, 8.47883636e+02, 8.62214478e+02,\n",
       "       8.86883807e+02, 9.11553137e+02, 9.36222466e+02, 9.60891796e+02,\n",
       "       9.85561125e+02, 1.01434242e+03, 1.04312371e+03, 1.07190500e+03,\n",
       "       1.10068629e+03, 1.12946758e+03, 1.16998037e+03, 1.21049315e+03,\n",
       "       1.25100594e+03, 1.29151873e+03, 1.33203151e+03, 1.39046177e+03,\n",
       "       1.44889202e+03, 1.50732228e+03, 1.56575253e+03, 1.62418279e+03,\n",
       "       1.69696214e+03, 1.76974149e+03, 1.84252084e+03, 1.91530019e+03,\n",
       "       1.98807954e+03, 2.06085889e+03, 2.16122300e+03, 2.26158711e+03,\n",
       "       2.36195123e+03, 2.46231534e+03, 2.56267945e+03, 2.66304357e+03,\n",
       "       2.79861731e+03, 2.93419105e+03, 3.06976480e+03, 3.20533854e+03,\n",
       "       3.34091228e+03, 3.47648603e+03, 3.61205977e+03, 3.74763351e+03,\n",
       "       3.88320726e+03, 4.01878100e+03, 4.15435474e+03, 4.28992849e+03,\n",
       "       4.42550223e+03, 4.56107597e+03, 4.69664972e+03, 4.84924465e+03,\n",
       "       5.00183958e+03, 5.15443450e+03, 5.30702943e+03, 5.45962436e+03,\n",
       "       5.61221929e+03, 5.76481422e+03, 5.91740915e+03, 6.07000407e+03,\n",
       "       6.24122942e+03, 6.41245476e+03, 6.58368011e+03, 6.75490545e+03,\n",
       "       6.92613079e+03, 7.09735614e+03, 7.26858148e+03, 7.43980683e+03,\n",
       "       7.61103217e+03, 7.80531658e+03, 7.99960098e+03, 8.19388539e+03,\n",
       "       8.38816980e+03, 8.58245420e+03, 8.77673861e+03, 8.99236271e+03,\n",
       "       9.20798682e+03, 9.42361092e+03, 9.63923503e+03, 9.85485913e+03,\n",
       "       1.00704832e+04, 1.03087344e+04, 1.05469856e+04, 1.07852368e+04,\n",
       "       1.10234879e+04, 1.12617391e+04, 1.14999903e+04, 1.17673860e+04,\n",
       "       1.20347817e+04, 1.23021774e+04, 1.25695732e+04, 1.28369689e+04,\n",
       "       1.31043646e+04, 1.33717603e+04, 1.36391561e+04, 1.39065518e+04,\n",
       "       1.41739475e+04, 1.44000000e+04])\n",
       " t_events: None\n",
       "        y: array([[0.00000000e+00, 0.00000000e+00, 1.19224476e-13, ...,\n",
       "        2.27072093e-01, 1.98648405e-01, 1.77412864e-01],\n",
       "       [0.00000000e+00, 0.00000000e+00, 3.03909825e-13, ...,\n",
       "        1.54477792e+00, 1.35140976e+00, 1.20694535e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 1.96363686e-12, ...,\n",
       "        1.58519348e+00, 1.38676803e+00, 1.23851862e+00],\n",
       "       ...,\n",
       "       [0.00000000e+00, 2.91933365e-08, 1.16661187e-07, ...,\n",
       "        5.65008407e+00, 4.94283188e+00, 4.41444553e+00],\n",
       "       [0.00000000e+00, 3.91109887e-01, 7.82207482e-01, ...,\n",
       "        5.63491242e+00, 4.92955788e+00, 4.40259621e+00],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])\n",
       " y_events: None"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00000000e+000, 9.46314230e+003, 6.45365940e+003, 5.67275261e+003,\n",
       "       6.26418952e-007, 1.79899429e-007, 1.79899429e-007, 3.22904947e-009,\n",
       "       0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "       0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "       0.00000000e+000, 5.56433140e+115, 0.00000000e+000, 0.00000000e+000,\n",
       "                   inf, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "       0.00000000e+000, 0.00000000e+000, 4.49748572e-008, 4.49748572e-008,\n",
       "       7.19597716e-007, 4.49748572e-008, 4.49748572e-008, 0.00000000e+000,\n",
       "       0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "       0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "       0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "       0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "       0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "       0.00000000e+000, 0.00000000e+000, 2.15076749e+017, 0.00000000e+000,\n",
       "       0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "       3.11391673e-008, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "       0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "       0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "       0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "       0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "       0.00000000e+000,             inf, 0.00000000e+000, 0.00000000e+000,\n",
       "       0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "       0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "       0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "       0.00000000e+000, 0.00000000e+000, 0.00000000e+000, 0.00000000e+000,\n",
       "                   inf, 7.42698527e-313, 6.93839637e-310, 0.00000000e+000,\n",
       "       2.70839593e-316, 3.92400703e-013, 6.36598738e-313, 1.04267605e-017,\n",
       "       2.70844099e-316, 2.54639495e-313, 4.03179201e-313, 1.69759664e-313,\n",
       "       6.15378780e-313, 2.70852044e-316, 1.31563739e-312, 2.70883190e-316,\n",
       "       1.18575755e-322, 2.70855087e-316, 7.00258612e-313, 2.70857854e-316,\n",
       "                   nan, 5.09278990e-313, 0.00000000e+000, 0.00000000e+000,\n",
       "       2.70865008e-316, 2.70866984e-316, 1.33685735e-312, 0.00000000e+000,\n",
       "       6.93840202e-310, 1.69759664e-313, 1.18831764e-312, 1.18575755e-322,\n",
       "       4.94065646e-324, 8.06358401e-313, 6.93839888e-310, 9.54898107e-313,\n",
       "       0.00000000e+000, 0.00000000e+000, 1.61271680e-312, 6.93839890e-310,\n",
       "       2.70938723e-316, 1.18575755e-322, 3.18299369e-313, 2.70888368e-316,\n",
       "       6.93840200e-310, 2.54639496e-313, 0.00000000e+000, 0.00000000e+000,\n",
       "       4.94065646e-324, 0.00000000e+000, 4.94065646e-324, 6.93832955e-310,\n",
       "       4.29464389e-317, 2.54639495e-313, 6.93832870e-310, 4.29472294e-317,\n",
       "       4.94065646e-324, 6.93832948e-310, 4.29476246e-317, 8.29771096e+039,\n",
       "       4.94065646e-324, 1.48219694e-323, 1.18575755e-322, 2.54639496e-313,\n",
       "       2.70915561e-316, 2.70916510e-316, 4.94065646e-324, 0.00000000e+000,\n",
       "       2.70919553e-316, 7.85138443e-313, 9.88131292e-324, 4.94065646e-324,\n",
       "       1.08221785e-312, 6.93839637e-310, 6.79038654e-313, 6.57818696e-313,\n",
       "       4.94065646e-324, 4.94065646e-324, 1.48219694e-323, 1.48219694e-323,\n",
       "       1.48219694e-323, 4.24399159e-313, 1.90979621e-312, 0.00000000e+000,\n",
       "       4.94065646e-324, 6.93839886e-310, 7.42698528e-313, 1.08694442e-322,\n",
       "       0.00000000e+000, 4.94065646e-324, 8.39911598e-323, 0.00000000e+000,\n",
       "       9.88131292e-323, 6.93840203e-310, 2.70952043e-316, 2.54639496e-313,\n",
       "       0.00000000e+000, 4.45619117e-313, 0.00000000e+000, 0.00000000e+000,\n",
       "       1.90979824e-313])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fse=s1[:,:,0]\n",
    "numpy.argwhere(numpy.isnan(fse))\n",
    "fse[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3. 2.]\n",
      " [2. 4.]\n",
      " [2. 6.]]\n",
      "[[3. 2.]\n",
      " [2. 4.]\n",
      " [2. 6.]]\n"
     ]
    }
   ],
   "source": [
    "M=numpy.ones((3,2,2))\n",
    "M[0,0,1]=2\n",
    "M[1,1,0]=3\n",
    "M[2,1,1]=5\n",
    "v=numpy.ones(2)\n",
    "q=M.dot(v)\n",
    "q1=q.ravel()\n",
    "q2=numpy.reshape(q1,q.shape)\n",
    "print(q)\n",
    "print(q2)"
   ]
  }
 ],
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