{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timeUnit: min\n",
      "Time unit: min\n",
      "Compartments\n",
      "redBloodCells/0:\n",
      "\ttargets\n",
      "\t\tplasma[0,1]: 0.36585365853658536\n",
      "\t\tredBloodCells[0,0]: -0.03048780487804878\n",
      "plasma/1:\n",
      "\ttargets\n",
      "\t\tplasma[1,1]: -5.455487804878048\n",
      "\t\tredBloodCells[1,0]: 0.03048780487804878\n",
      "\t\tvenous[1,2]: 4.91890243902439\n",
      "venous/2:\n",
      "\ttargets\n",
      "\t\tkidney[2,3]: 0.2152439024390244\n",
      "\t\trichlyPerfused[2,5]: 0.9048780487804878\n",
      "\t\tfat[2,6]: 1.7032520325203253\n",
      "\t\tslowlyPerfused[2,7]: 0.6128048780487805\n",
      "\t\tbrainBlood[2,9]: 0.5609756097560976\n",
      "\t\tplacenta[2,11]: 0.05274390243902439\n",
      "\t\tliver[2,12]: 0.2351219512195122\n",
      "\t\tvenous[2,2]: -4.91890243902439\n",
      "kidney/3:\n",
      "\ttargets\n",
      "\t\tkidney[3,3]: -1.2607142857142855\n",
      "\t\tplasma[3,1]: 5.042857142857142\n",
      "urine/4:\n",
      "\ttargets\n",
      "\t\tkidney[4,3]: 0.0\n",
      "richlyPerfused/5:\n",
      "\ttargets\n",
      "\t\trichlyPerfused[5,5]: -0.212\n",
      "\t\tplasma[5,1]: 0.212\n",
      "fat/6:\n",
      "\ttargets\n",
      "\t\tfat[6,6]: -0.1461712890284319\n",
      "\t\tplasma[6,1]: 0.021925693354264784\n",
      "slowlyPerfused/7:\n",
      "\ttargets\n",
      "\t\tslowlyPerfused[7,7]: -0.04104906530612244\n",
      "\t\thair[7,8]: 1.152277557585157e-07\n",
      "\t\tplasma[7,1]: 0.0820408163265306\n",
      "hair/8:\n",
      "\ttargets\n",
      "\t\tslowlyPerfused[8,7]: 0.0050149999999999995\n",
      "\t\thair[8,8]: -2.0164857257740247e-05\n",
      "brainBlood/9:\n",
      "\ttargets\n",
      "\t\tbrainBlood[9,9]: -1.8802945578231294\n",
      "\t\tplasma[9,1]: 1.8775510204081634\n",
      "brain/10:\n",
      "\ttargets\n",
      "\t\tbrainBlood[10,9]: 0.0009602380952380954\n",
      "placenta/11:\n",
      "\ttargets\n",
      "\t\tplacenta[11,11]: -2.0069605568445477\n",
      "\t\tplasma[11,1]: 4.013921113689095\n",
      "liver/12:\n",
      "\ttargets\n",
      "\t\tliver[12,12]: -0.21189250714285712\n",
      "\t\tgut[12,13]: 0.845054945054945\n",
      "\t\tplasma[12,1]: 0.21483516483516485\n",
      "gut/13:\n",
      "\ttargets\n",
      "\t\tintestine[13,14]: 0.0016941176470588236\n",
      "\t\tgut[13,13]: -1.292436974789916\n",
      "\t\tplasma[13,1]: 1.292436974789916\n",
      "intestine/14:\n",
      "\tsource\n",
      "\t\t{'name': 'constant', 'value': 0.0486, 'unit': 'ug/min'}\n",
      "\ttargets\n",
      "\t\tliver[14,12]: 4.11530612244898e-05\n",
      "\t\tintestine[14,14]: -0.0021806020408163267\n",
      "feces/15:\n",
      "\ttargets\n",
      "\t\tintestine[15,14]: 8.066e-05\n",
      "\t\tinorganicMercury[15,16]: 0.0\n",
      "inorganicMercury/16:\n",
      "\ttargets\n",
      "\t\tliver[16,12]: 4.033e-06\n",
      "\t\tintestine[16,14]: 4.033e-05\n",
      "\t\tinorganicMercury[16,16]: 0.0\n",
      "Flows\n",
      "\tplasma:kidney[(plasma,venous):kidney]:kidneyFlow [1.412]\n",
      "\tvenous:kidney[(plasma,venous):kidney]:kidneyFlow [1.412]\n",
      "\tplasma:richlyPerfused[(plasma,venous):richlyPerfused]:richlyPerfusedFlow [1.484]\n",
      "\tvenous:richlyPerfused[(plasma,venous):richlyPerfused]:richlyPerfusedFlow [1.484]\n",
      "\tplasma:fat[(plasma,venous):fat]:fatFlow [0.419]\n",
      "\tvenous:fat[(plasma,venous):fat]:fatFlow [0.419]\n",
      "\tplasma:slowlyPerfused[(plasma,venous):slowlyPerfused]:slowlyPerfusedFlow [2.01]\n",
      "\tvenous:slowlyPerfused[(plasma,venous):slowlyPerfused]:slowlyPerfusedFlow [2.01]\n",
      "\tplasma:brainBlood[(plasma,venous):brainBlood]:brainBloodFlow [0.92]\n",
      "\tvenous:brainBlood[(plasma,venous):brainBlood]:brainBloodFlow [0.92]\n",
      "\tplasma:placenta[(plasma,venous):placenta]:placentaFlow [0.173]\n",
      "\tvenous:placenta[(plasma,venous):placenta]:placentaFlow [0.173]\n",
      "\tplasma:liver[plasma:liver]:liverInFlow [0.391]\n",
      "\tliver:venous[liver:venous]:liverOutFlow [1.928]\n",
      "\tplasma:gut[(plasma,liver):gut]:gutFlow [1.538]\n",
      "\tliver:gut[(plasma,liver):gut]:gutFlow [1.538]\n",
      "\tplasma:venous[plasma:venous]:plasmaFlow [8.067]\n",
      "Volumes\n",
      "\tplasma:plasmaVolume [1.64]\n",
      "\tvenous:plasmaVolume [1.64]\n",
      "\tkidney:kidneyVolume [0.28]\n",
      "\trichlyPerfused:richlyPerfusedVolume [7]\n",
      "\tfat:fatVolume [19.11]\n",
      "\tslowlyPerfused:slowlyPerfusedVolume [24.5]\n",
      "\tbrainBlood:brainBloodVolume [0.49]\n",
      "\tplacenta:placentaVolume [0.0431]\n",
      "\tgut:gutVolume [1.19]\n",
      "\tintestine:intestineVolume [0.98]\n",
      "\tredBloodCells:redBloodCellsVolume [1.64]\n",
      "\thair:hairVolume [0.14]\n",
      "\tbrain:brainVolume [1.4]\n",
      "\tliver:liverVolume [1.82]\n",
      "Partition coefficients\n",
      "\tkidney:kidneyPC [4.0]\n",
      "\trichlyPerfused:richlyPerfusedPC [1]\n",
      "\tfat:fatPC [0.15]\n",
      "\tslowlyPerfused:slowlyPerfusedPC [2]\n",
      "\tbrainBlood:brainBloodPC [1]\n",
      "\tplacenta:placentaPC [2]\n",
      "\tliver:liverPC [5]\n",
      "\tgut:gutPC [1]\n",
      "\tbrainBlood->brain:brainPC [3]\n",
      "\thair->slowlyPerfused:hairPC [248.7]\n",
      "\tredBloodCells->plasma:rbcPC [12]\n",
      "SE parameters\n",
      "Done simultaneous LSODA SE\n"
     ]
    }
   ],
   "source": [
    "sys=cModel.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','PBPK','models','humanHG.json'))\n",
    "#print(sys.u(10)[sys.lut['venous']])\n",
    "sys.inspect()         \n",
    "nt=201\n",
    "tmax=24*60*365*2\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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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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",
    "tscale=24*60*365\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','testes','excrement']\n",
    "name=['plasma','gut','hair','kidney','liver','inorganicMercury','redBloodCells']\n",
    "\n",
    "#diazepam\n",
    "max=[1.5,2.6,3,4,5,2.5,6.8,1.5,1.5,4,4.2,3,25]\n",
    "#cotinine\n",
    "max=[9]*13\n",
    "max[12]=90\n",
    "\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/tscale,fy)\n",
    "    ax.fill_between(t/tscale, fy-fe, fy + fe, color='red',alpha=0.1)\n",
    "    ax.plot(t/tscale,fy-fe,color='red',linewidth=1,alpha=0.2)\n",
    "    ax.plot(t/tscale,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([0,1.1*tmax/tscale])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13140000"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "25*24*60*365\n",
    "\n",
    "\n"
   ]
  },
  {
   "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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