{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.integrate\n",
    "import numpy\n",
    "import matplotlib.pyplot\n",
    "import os\n",
    "import json\n",
    "import scipy.interpolate\n",
    "\n",
    "def dfdy(y,t,system):\n",
    "    dfdy=system.M.dot(y)+system.u(t)\n",
    "    return dfdy\n",
    "\n",
    "def jacobi(y,t,system):\n",
    "    return system.M\n",
    "\n",
    "#SE post calculation\n",
    "def dfdyS(S,t,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(S,t,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(S,t,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(S,t,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",
    "class model:\n",
    "    def __init__(self):\n",
    "        self.compartments={}\n",
    "        self.seJ={}\n",
    "        \n",
    "    def add_source(self,compartmentName,formula):\n",
    "        self.compartments[compartmentName]['source']=formula\n",
    "\n",
    "    def add_compartment(self,compartmentName):\n",
    "        self.compartments[compartmentName]={}\n",
    "        self.compartments[compartmentName]['targets']={}\n",
    "        self.compartments[compartmentName]['sensTargets']={}\n",
    "\n",
    "    def bind(self,sourceCompartment,targetCompartment,k,pc,scaleToVolume=0):\n",
    "        #establish a flow from one compartment to the other\n",
    "        cSrc=self.compartments[sourceCompartment]\n",
    "        cTarg=self.compartments[targetCompartment]\n",
    "        \n",
    "        vSPar=self.getVolumePar(sourceCompartment)\n",
    "        vTPar=self.getVolumePar(targetCompartment)\n",
    "        \n",
    "        #the source equation (where we subtract the current)\n",
    "        addValue(cSrc['targets'],sourceCompartment,-get(k)/get(pc)/get(vSPar))\n",
    "        #the target equation (where we add the current)\n",
    "        addValue(cTarg['targets'],sourceCompartment,get(k)/get(pc)/get(vTPar))\n",
    "        \n",
    "                    \n",
    "    def getDerivative(self,variable, sign, qPar, pcPar, vPar):\n",
    "        \n",
    "        #for flow based transfer, k=Q/vS/pc, and jacobian derivative is -Q/V/pc/pc\n",
    "        #for diffusion dominated transfer, k=k1/pc\n",
    "        q=get(qPar)\n",
    "        pc=get(pcPar)\n",
    "        v=get(vPar)\n",
    "        if variable==\"partitionCoefficient\":\n",
    "            return sign*(-1)*q/v/pc/pc\n",
    "        if variable==\"diffusionCoefficient\":\n",
    "            return sign/pc/v\n",
    "        if variable==\"flow\":\n",
    "            return sign/pc/v\n",
    "        if variable==\"volume\":\n",
    "            return sign*(-1)*q/pc/v/v\n",
    "            \n",
    "    def getVolumePar(self,compartment,useVolume=1):\n",
    "        if not useVolume:\n",
    "            return {\"value\":1}\n",
    "        try:\n",
    "            parVName=self.mod[\"volumes\"][compartment]\n",
    "            parV=self.mod[\"parameters\"][parVName]\n",
    "        except KeyError:\n",
    "            return {\"value\":1}\n",
    "        \n",
    "        parV[\"name\"]=parVName\n",
    "        return parV\n",
    "    \n",
    "    def bindDerivative(self,parameterName,variable,src,target,q,pc,useVolume=1):\n",
    "        parName=parameterName\n",
    "        srcV=self.getVolumePar(src,useVolume)\n",
    "        doAdd=(variable!=\"volume\" or \"range\" in srcV)\n",
    "            \n",
    "        if doAdd:\n",
    "            #the source equation (where we subtract the current)\n",
    "            if variable==\"volume\":\n",
    "                parName=srcV[\"name\"]\n",
    "        \n",
    "            csrc=self.getSEJ_comp(parName,src)\n",
    "            addValue(csrc,src,self.getDerivative(variable,-1,q,pc,srcV))\n",
    "        \n",
    "        targetV=self.getVolumePar(target,useVolume)\n",
    "        doAdd=(variable!=\"volume\" or \"range\" in targetV)\n",
    "        \n",
    "        #the target equation (where we add the current)\n",
    "        if doAdd:\n",
    "            if variable==\"volume\":\n",
    "                parName=targetV[\"name\"]\n",
    "\n",
    "            ctarget=self.getSEJ_comp(parName,target)\n",
    "            addValue(ctarget,src,self.getDerivative(variable,+1,q,pc,targetV))\n",
    "        \n",
    "        \n",
    "        \n",
    "    def build(self):\n",
    "        comps=self.compartments\n",
    "        self.n=len(comps)\n",
    "        self.M=numpy.zeros((self.n,self.n))\n",
    "        self.fu=[lambda t:0]*self.n\n",
    "        self.lut={c:i for (i,c) in zip(range(self.n),comps.keys())}\n",
    "        for c in comps:\n",
    "            comp=comps[c]\n",
    "            if 'source' in comp:\n",
    "                self.fu[self.lut[c]]=parseFunction(comp['source'])\n",
    "                               \n",
    "            for t in comp['targets']:\n",
    "                self.M[self.lut[c],self.lut[t]]=comp['targets'][t]\n",
    "        #build SE part\n",
    "        self.m=len(self.seJ)\n",
    "        #MxNxN matrix\n",
    "        self.lutSE={c:i for (i,c) in zip(range(self.m),self.seJ.keys())}\n",
    "        self.fSS=numpy.zeros((self.m,self.n,self.n))\n",
    "        for par in self.seJ:\n",
    "            sources=self.seJ[par]\n",
    "            for compartment in sources:\n",
    "                targets=sources[compartment]\n",
    "                for t in targets:\n",
    "                    #print('FSS: Adding {}/{},{}/{},{}/{}:{}'.\\\n",
    "                    #      format(par,self.lutSE[par],compartment,self.lut[compartment],t,self.lut[t],targets[t]))\n",
    "                    self.fSS[self.lutSE[par],self.lut[compartment],self.lut[t]]=targets[t]\n",
    "        \n",
    "                      \n",
    "        \n",
    "        #print('Done')\n",
    "    \n",
    "    def inspect(self):\n",
    "        comps=self.compartments\n",
    "        pars=self.mod['parameters']\n",
    "        print('Compartments')\n",
    "        for c in comps:\n",
    "            print('{}/{}:'.format(c,self.lut[c]))\n",
    "            comp=comps[c]\n",
    "            if 'source' in comp:\n",
    "                print('\\tsource\\n\\t\\t{}'.format(comp['source']))\n",
    "            print('\\ttargets')\n",
    "            for t in comp['targets']:\n",
    "                print('\\t\\t{}[{},{}]: {}'.format(t,self.lut[c],self.lut[t],comp['targets'][t]))\n",
    "        print('Flows')\n",
    "        for f in self.flows:\n",
    "            fName=self.flows[f]\n",
    "            fParName=self.mod['flows'][fName]\n",
    "            fPar=pars[fParName]\n",
    "            print('\\t{}[{}]:{} [{}]'.format(f,fName,fParName,get(fPar)))\n",
    "        \n",
    "        print('Volumes')\n",
    "        for v in self.mod['volumes']:\n",
    "            vParName=self.mod['volumes'][v]\n",
    "            vPar=pars[vParName]\n",
    "            print('\\t{}:{} [{}]'.format(v,vParName,get(vPar)))\n",
    "        \n",
    "        print('Partition coefficients')\n",
    "        for pc in self.mod['partitionCoefficients']:\n",
    "            pcParName=self.mod['partitionCoefficients'][pc]\n",
    "            pcPar=pars[pcParName]\n",
    "            print('\\t{}:{} [{}]'.format(pc,pcParName,get(pcPar)))\n",
    "        \n",
    "        \n",
    "        print('SE parameters')\n",
    "        for p in self.seJ:\n",
    "            print(p)\n",
    "            sources=self.seJ[p]\n",
    "            for compartment in sources:\n",
    "                targets=sources[compartment]\n",
    "                for t in targets:\n",
    "                    print('\\t SE bind {}/{}:{}'.format(compartment,t,targets[t]))\n",
    "        #print('Done')\n",
    "    \n",
    "    def parse(self,file):\n",
    "                    \n",
    "        with open(file,'r') as f:\n",
    "            self.mod=json.load(f)\n",
    "        for m in self.mod['compartments']:\n",
    "            self.add_compartment(m)\n",
    "        for s in self.mod['sources']:\n",
    "            #src=mod['sources'][s]\n",
    "            self.add_source(s,self.mod['sources'][s])\n",
    "        self.flows={}\n",
    "        pars=self.mod['parameters']\n",
    "        for f in self.mod['flows']:\n",
    "            #skip comments\n",
    "            if f.find(':')<0:\n",
    "                continue\n",
    "            \n",
    "            comps=f.split(':')\n",
    "            c0=splitVector(comps[0])\n",
    "            c1=splitVector(comps[1])\n",
    "            for x in c0:\n",
    "                for y in c1:\n",
    "                    pairName='{}:{}'.format(x,y)\n",
    "                    self.flows[pairName]=f\n",
    "                    \n",
    "        for b in self.mod['bindings']['diffusion']:\n",
    "            comps=b.split('->')\n",
    "            try:\n",
    "                pcParName=self.mod['partitionCoefficients'][b]\n",
    "                pcPar=pars[pcParName]\n",
    "            except KeyError:\n",
    "                pcPar={\"value\":1}\n",
    "            \n",
    "            kParName=self.mod['bindings']['diffusion'][b]\n",
    "            kPar=pars[kParName]\n",
    "            self.bind(comps[0],comps[1],kPar,pcPar)\n",
    "            \n",
    "            #add derivatives if range is specified\n",
    "            if \"range\" in kPar:\n",
    "                self.bindDerivative(kParName,\"diffusionParameter\",comps[0],comps[1],kPar,pcPar,0)\n",
    "            if \"range\" in pcPar:\n",
    "                self.bindDerivative(pcParName,\"partitionCoefficient\",comps[0],comps[1],kPar,pcPar,0)\n",
    "            \n",
    "        for q in self.mod['bindings']['flow']:\n",
    "            comps=q.split('->')\n",
    "            srcs=splitVector(comps[0])\n",
    "            tgts=splitVector(comps[1])\n",
    "            for cs in srcs:\n",
    "                for ct in tgts:\n",
    "                    #get partition coefficient\n",
    "                    try:\n",
    "                        pcParName=self.mod['partitionCoefficients'][cs]\n",
    "                        pcPar=pars[pcParName]\n",
    "                    except KeyError:\n",
    "                        pcPar={\"value\":1}\n",
    "                    \n",
    "                    #get flow (direction could be reversed)\n",
    "                    try:\n",
    "                        qName=self.flows['{}:{}'.format(cs,ct)]\n",
    "                    except KeyError:\n",
    "                        qName=self.flows['{}:{}'.format(ct,cs)]\n",
    "                    \n",
    "                    flowParName=self.mod['flows'][qName]\n",
    "                    flowPar=pars[flowParName]\n",
    "                    \n",
    "                    self.bind(cs,ct,flowPar,pcPar,1)\n",
    "                    \n",
    "                    if \"range\" in pcPar:\n",
    "                        self.bindDerivative(pcParName,\"partitionCoefficient\",cs,ct,flowPar,pcPar)\n",
    "                    if \"range\" in flowPar:\n",
    "                        self.bindDerivative(flowParName,\"flow\",cs,ct,flowPar,pcPar)\n",
    "                    self.bindDerivative(\"x\",\"volume\",cs,ct,flowPar,pcPar)\n",
    "                         \n",
    "                    #print('bind: {}:(q={},vt={},pc={}):{}'.format(bind,q,vt,pc,q/vt/pc))\n",
    "                \n",
    "        self.build()\n",
    "        \n",
    "    def u(self,t):\n",
    "        ub=[f(t) for f in self.fu]\n",
    "        return numpy.array(ub)\n",
    "                     \n",
    "    def fSY(self,y,t):\n",
    "        #M number of sensitivity parameters\n",
    "        #N number of equations\n",
    "        #fSS is MxNxN\n",
    "        \n",
    "        #assume a tabulated solution y(t) at t spaced intervals\n",
    "        \n",
    "        qS=self.fSS.dot(y)\n",
    "        #qS is MxN\n",
    "        #but NxM is expected, so do a transpose\n",
    "        \n",
    "        #for simultaneous calculation, a Nx(M+1) matrix is expected\n",
    "        tS=numpy.zeros((self.n,self.m+1))\n",
    "        #columns from 2..M+1 are the partial derivatives \n",
    "        tS[:,1:]=numpy.transpose(qS)\n",
    "        #first column is the original function\n",
    "        tS[:,0]=self.u(t)\n",
    "        return tS\n",
    "    \n",
    "    def fS(self,t):\n",
    "        #M number of sensitivity parameters\n",
    "        #N number of equations\n",
    "        #fSS is MxNxN\n",
    "        \n",
    "        #assume a tabulated solution y(t) at t spaced intervals\n",
    "        \n",
    "        qS=self.fSS.dot(self.getY(t))\n",
    "        return numpy.transpose(qS)\n",
    "                     \n",
    "    def getSEJ(self,parName):\n",
    "        #find the sensitivity (SE) derivative of Jacobi with respect to parameter  \n",
    "        try:\n",
    "            return self.seJ[parName]\n",
    "        except KeyError:\n",
    "            self.seJ[parName]={}\n",
    "            return self.seJ[parName]\n",
    "    \n",
    "    def getSEJ_comp(self,parName,compartmentName):\n",
    "        #find equation dictating concentration in compartmentName for jacobi-parameter derivative\n",
    "        seJ=self.getSEJ(parName)\n",
    "        \n",
    "        try:\n",
    "            return seJ[compartmentName]\n",
    "        except KeyError:\n",
    "            seJ[compartmentName]={}\n",
    "            return seJ[compartmentName]\n",
    "    def setY(self,t,y):\n",
    "        self.tck=[None]*self.n\n",
    "        for i in range(self.n):\n",
    "            self.tck[i] = scipy.interpolate.splrep(t, y[:,i], s=0)\n",
    "    \n",
    "    def getY(self,t):\n",
    "        fY=numpy.zeros(self.n)\n",
    "        for i in range(self.n):\n",
    "            fY[i]=scipy.interpolate.splev(t, self.tck[i], der=0)\n",
    "        return fY\n",
    "        \n",
    "def splitVector(v):\n",
    "    if v.find('(')<0:\n",
    "        return [v]\n",
    "    return v[1:-1].split(',')\n",
    "\n",
    "def parseFunction(formula):\n",
    "    if formula['name']=='exponential':\n",
    "        c0=formula['constant']\n",
    "        k=formula['k']\n",
    "        return lambda t:c0*numpy.exp(k*t)\n",
    "    if formula['name']=='constant':\n",
    "        c0=formula['value']\n",
    "        return lambda t:c0\n",
    "    if formula['name']=='Heavyside':\n",
    "        t0=formula['limit']\n",
    "        v=formula['value']\n",
    "        return lambda t:v if t<t0 else 0\n",
    "    return lambda t:1\n",
    "\n",
    "def addValue(qdict,compName,v):\n",
    "    #add real number v to attribute compName of dictionary qdict, check if compName exists and handle the potential error\n",
    "    try:\n",
    "        qdict[compName]+=v\n",
    "    except KeyError:\n",
    "        qdict[compName]=v\n",
    "\n",
    "def get(par):\n",
    "    v=par[\"value\"]\n",
    "    #convert to seconds\n",
    "    try:\n",
    "        if par['unit'].split('/')[1]=='min':\n",
    "            return v/60\n",
    "    except (KeyError,IndexError):\n",
    "        pass\n",
    "    \n",
    "    return v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compartments\n",
      "adipose/0:\n",
      "\ttargets\n",
      "\t\tadipose[0,0]: -0.07083333333333333\n",
      "\t\tarterial[0,13]: 0.0042499999999999994\n",
      "brain/1:\n",
      "\ttargets\n",
      "\t\tbrain[1,1]: -0.04012345679012346\n",
      "\t\tarterial[1,13]: 0.010833333333333335\n",
      "heart/2:\n",
      "\ttargets\n",
      "\t\theart[2,2]: -0.25\n",
      "\t\tarterial[2,13]: 0.07\n",
      "kidney/3:\n",
      "\ttargets\n",
      "\t\tkidney[3,3]: -0.19224537037037037\n",
      "\t\tarterial[3,13]: 0.13841666666666666\n",
      "liver/4:\n",
      "\ttargets\n",
      "\t\tliver[4,4]: -0.10079914529914531\n",
      "\t\tsplanchnic[4,5]: 0.09897360703812318\n",
      "\t\tstomach[4,6]: 0.01599326599326599\n",
      "\t\tarterial[4,13]: 0.005378787878787879\n",
      "splanchnic/5:\n",
      "\ttargets\n",
      "\t\tsplanchnic[5,5]: -0.07258064516129033\n",
      "\t\tarterial[5,13]: 0.022500000000000003\n",
      "stomach/6:\n",
      "\ttargets\n",
      "\t\tstomach[6,6]: -0.1599326599326599\n",
      "\t\tarterial[6,13]: 0.028787878787878782\n",
      "muscle/7:\n",
      "\ttargets\n",
      "\t\tmuscle[7,7]: -0.007471264367816092\n",
      "\t\tarterial[7,13]: 0.0021666666666666666\n",
      "skin/8:\n",
      "\ttargets\n",
      "\t\tskin[8,8]: -0.015009301538981905\n",
      "\t\tarterial[8,13]: 0.0027016742770167427\n",
      "testes/9:\n",
      "\ttargets\n",
      "\t\ttestes[9,9]: -0.07037037037037036\n",
      "\t\tarterial[9,13]: 0.012666666666666665\n",
      "remainder/10:\n",
      "\ttargets\n",
      "\t\tremainder[10,10]: -0.028774027191748715\n",
      "\t\tarterial[10,13]: 0.005179324894514769\n",
      "lung/11:\n",
      "\ttargets\n",
      "\t\tvenous[11,14]: 1.1111111111111112\n",
      "\t\tlung[11,11]: -3.703703703703704\n",
      "excrement/12:\n",
      "\ttargets\n",
      "\t\tliver[12,4]: 0.0105\n",
      "arterial/13:\n",
      "\tsource\n",
      "\t\t{'name': 'exponential', 'k': -0.005, 'constant': 0}\n",
      "\ttargets\n",
      "\t\tarterial[13,13]: -0.19607843137254902\n",
      "\t\tlung[13,11]: 0.6535947712418301\n",
      "venous/14:\n",
      "\tsource\n",
      "\t\t{'name': 'Heavyside', 'limit': 30, 'value': 1220.0}\n",
      "\ttargets\n",
      "\t\tadipose[14,0]: 0.05208333333333333\n",
      "\t\tbrain[14,1]: 0.0035403050108932465\n",
      "\t\theart[14,2]: 0.01838235294117647\n",
      "\t\tkidney[14,3]: 0.028271377995642704\n",
      "\t\tliver[14,4]: 0.08075666163901458\n",
      "\t\tmuscle[14,7]: 0.06866970926301555\n",
      "\t\tskin[14,8]: 0.04833877995642702\n",
      "\t\ttestes[14,9]: 0.012935729847494552\n",
      "\t\tremainder[14,10]: 0.033428649237472775\n",
      "\t\tvenous[14,14]: -0.09803921568627451\n",
      "source/15:\n",
      "\ttargets\n",
      "Flows\n",
      "\tarterial:adipose[(arterial,venous):adipose]:adiposeFlow [0.042499999999999996]\n",
      "\tvenous:adipose[(arterial,venous):adipose]:adiposeFlow [0.042499999999999996]\n",
      "\tarterial:brain[(arterial,venous):brain]:brainFlow [0.013000000000000001]\n",
      "\tvenous:brain[(arterial,venous):brain]:brainFlow [0.013000000000000001]\n",
      "\tarterial:heart[(arterial,venous):heart]:heartFlow [0.07]\n",
      "\tvenous:heart[(arterial,venous):heart]:heartFlow [0.07]\n",
      "\tarterial:kidney[(arterial,venous):kidney]:kidneyFlow [0.2768333333333333]\n",
      "\tvenous:kidney[(arterial,venous):kidney]:kidneyFlow [0.2768333333333333]\n",
      "\tarterial:liver[arterial:liver]:liverInFlow [0.059166666666666666]\n",
      "\tliver:venous[liver:venous]:liverOutFlow [0.42833333333333334]\n",
      "\tarterial:lung[(arterial,venous):lung]:totalFlow [1.3333333333333333]\n",
      "\tvenous:lung[(arterial,venous):lung]:totalFlow [1.3333333333333333]\n",
      "\tarterial:muscle[(arterial,venous):muscle]:muscleFlow [0.2708333333333333]\n",
      "\tvenous:muscle[(arterial,venous):muscle]:muscleFlow [0.2708333333333333]\n",
      "\tarterial:skin[(arterial,venous):skin]:skinFlow [0.11833333333333333]\n",
      "\tvenous:skin[(arterial,venous):skin]:skinFlow [0.11833333333333333]\n",
      "\tarterial:splanchnic[(arterial,liver):splanchnic]:splanchnicFlow [0.3375]\n",
      "\tliver:splanchnic[(arterial,liver):splanchnic]:splanchnicFlow [0.3375]\n",
      "\tarterial:stomach[(arterial,liver):stomach]:stomachFlow [0.03166666666666666]\n",
      "\tliver:stomach[(arterial,liver):stomach]:stomachFlow [0.03166666666666666]\n",
      "\tarterial:testes[(arterial,venous):testes]:testesFlow [0.03166666666666666]\n",
      "\tvenous:testes[(arterial,venous):testes]:testesFlow [0.03166666666666666]\n",
      "\tarterial:remainder[(arterial,venous):remainder]:remainderFlow [0.08183333333333334]\n",
      "\tvenous:remainder[(arterial,venous):remainder]:remainderFlow [0.08183333333333334]\n",
      "Volumes\n",
      "\tadipose:adiposeVolume [10]\n",
      "\tbrain:brainVolume [1.2]\n",
      "\theart:heartVolume [1]\n",
      "\tkidney:kidneyVolume [2]\n",
      "\tliver:liverVolume [11]\n",
      "\tlung:lungVolume [1.2]\n",
      "\tmuscle:muscleVolume [125]\n",
      "\tskin:skinVolume [43.8]\n",
      "\tsplanchnic:splanchnicVolume [15]\n",
      "\tstomach:stomachVolume [1.1]\n",
      "\ttestes:testesVolume [2.5]\n",
      "\tremainder:remainderVolume [15.8]\n",
      "\tvenous:venousVolume [13.6]\n",
      "\tarterial:arterialVolume [6.8]\n",
      "Partition coefficients\n",
      "\tadipose:adiposePC [0.06]\n",
      "\tbrain:brainPC [0.27]\n",
      "\theart:heartPC [0.28]\n",
      "\tkidney:kidneyPC [0.72]\n",
      "\tliver:liverPC [0.39]\n",
      "\tlung:lungPC [0.3]\n",
      "\tmuscle:musclePC [0.29]\n",
      "\tremainder:remainderPC [0.18]\n",
      "\tskin:remainderPC [0.18]\n",
      "\tsplanchnic:splanchnicPC [0.31]\n",
      "\tstomach:stomachPC [0.18]\n",
      "\ttestes:testesPC [0.18]\n",
      "SE parameters\n",
      "adiposePC\n",
      "\t SE bind adipose/adipose:1.1805555555555556\n",
      "\t SE bind venous/adipose:-0.8680555555555555\n",
      "Done sequential SE\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",
    "t = numpy.linspace(0, 4*3600, nt)\n",
    "#first column is the solution y\n",
    "#initial condition\n",
    "y0=numpy.zeros(sys.n)\n",
    "    \n",
    "doSequential=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, t, args=(sys,),Dfun=jacobi)\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)\n",
    "    s1=numpy.reshape(solSE,(len(t),sys.n,sys.m))\n",
    "    print('Done sequential SE')\n",
    "    \n",
    "\n",
    "else:\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)\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(201, 16, 1)\n"
     ]
    },
    {
     "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",
    "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",
    "\n",
    "max=[1000*x for x in max]\n",
    "for i in range(len(name)):\n",
    "    row=i//3\n",
    "    col=i%3\n",
    "    axs[row,col].plot(t/60,sol[:,sys.lut[name[i]]])\n",
    "    axs[row,col].plot(t/60,s1[:,sys.lut[name[i]],0])\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": "markdown",
   "metadata": {},
   "source": [
    "#### 422/(9.1/0.15)/60"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0004898989898989898"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "422/(9.1/0.15)/60/11\n",
    "0.13/(0.39/0.97)/60/11\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)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}