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     "source": [
      "\n",
      "\n",
      "Giant low-density lipoprotein (LDL) accumulation in multi-layer artery wall models\n",
      "=============================================================\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "In this document the source code of LDL transport simulations. \n",
      "We solve numerically the transport equations:\n",
      "  \n",
      "\\begin{eqnarray}\n",
      "      \\frac{\\partial c}{\\partial t} +(1-\\sigma)\\vec u\\cdot\\nabla c & = & D_{eff} \\nabla^2 c - k c\n",
      "\\end{eqnarray}\n",
      "\n",
      "in four layers.\n",
      "\n",
      "\n",
      "    \n",
      "    \n",
      "    \n",
      "    \n",
      "    \n",
      "\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "\n",
      "import numpy as np \n",
      "from scipy.sparse import dia_matrix\n",
      "import scipy as sp\n",
      "import scipy.sparse\n",
      "import scipy.sparse.linalg\n",
      "\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "newparams = { 'savefig.dpi': 100, 'figure.figsize': (12/2., 5/2.) }\n",
      "plt.rcParams.update(newparams)\n",
      "\n",
      "params = {'legend.fontsize': 8,\n",
      "          'legend.linewidth': 0.2}\n",
      "\n",
      "plt.rcParams.update(params)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "WSS dependent parameters\n",
      "--------------------------\n",
      "\n",
      "The impact of WSS on the transport properties is given by the equations (8-11) developed by Olgac et al."
     ]
    },
    {
     "cell_type": "code",
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     "input": [
      "def phi(WSS = 1.79):\n",
      "    Rcell = 15e-3 # mm\n",
      "    area=.64 # mm^2\n",
      "    SI = 0.38*np.exp(-0.79*WSS) + 0.225*np.exp(-0.043*WSS)\n",
      "    MC = 0.003797* np.exp(14.75*SI)\n",
      "    LC = 0.307 + 0.805 * MC \n",
      "    phi = (LC*np.pi*Rcell**2)/(area)\n",
      "    return( phi) \n",
      "\n",
      "def Klj(w=14.3e-6,phi=5e-4):\n",
      "    \"permability  in m^2\"\n",
      "    Rcell = 15e-3 # mm\n",
      "    return ( (w**2/3.)*(4.*w*phi)/Rcell * (1e-6) )\n",
      "\n",
      "def Kend(w=14.3e-6,phi=5e-4):\n",
      "    \"permability  w m^2\"\n",
      "    Kend_70mmHg =3.22e-21\n",
      "    Knj = Kend_70mmHg - Klj() # at 70mmHg\n",
      "    return Knj + Klj(w,phi)\n",
      "\n",
      "def sigma_end(phi=5e-4,w=14.3*1e-6,r_m = 11e-6):\n",
      "    a = r_m/w\n",
      "    Kend_70mmHg =3.22e-21\n",
      "    Knj = Kend_70mmHg - Klj() # at 70mmHg\n",
      "    sigma_lj = 1-(1-3/2.*a**2+0.5*a**3)*(1-1/3.*a**2)\n",
      "    return  1 - ((1-sigma_lj)*Klj(phi=phi))/(Knj+Klj(phi=phi))\n",
      "\n",
      "def Diffusivity(w=14.3e-6,phi=5e-4,r_m = 11e-6):\n",
      "    \"Diffusivity  w um^2/s\"\n",
      "    R_cell = 15e-6 # m\n",
      "    a=r_m/w\n",
      "    D_lumen=2.71e-11\n",
      "    return D_lumen*(1-a)*(1.-1.004*a+0.418*a**3-0.16*a**5)*4*w/R_cell*phi*1e-3*1e12"
     ],
     "language": "python",
     "metadata": {},
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    {
     "cell_type": "markdown",
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     "source": [
      "Parameters\n",
      "--------------------\n",
      "\n",
      "In simulation four sets of parameters could be used. Three of them corresond to four layer model. The last one is two layer model.  \n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
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     "input": [
      "class LDL_Parameters_Vafai2012(object):\n",
      "    \"\"\" S. Chung, K. Vafai, International Journal of Biomechanics 45(2012)\"\"\"\n",
      "    names       = [  'endothel' , 'intima', 'IEL'   ,'media' ]\n",
      "    D           = [   5.7e-12     , 5.4e-6  , 3.18e-9 , 5e-8   ]\n",
      "    V           = [   2.3e-2 ]*4\n",
      "    sigma       = [   0.9888    , 0.8272  , 0.9827  , 0.8836 ] \n",
      "    L           = [   2.        , 10.     ,    2.   , 200.   ]\n",
      "    k_react     = [   0.        , 0.      ,    0.   , 3.197e-4 ]\n",
      "    K           = [   3.22e-15  , 2e-10   ,4.392e-13, 2e-12   ]\n",
      "    mu          = [   0.72e-3   , 0.72e-3 , 0.72e-3 , 0.72e-3 ]\n",
      "    \n",
      "    def calculate_filration(self,dPmmHg):\n",
      "        mmHg2Pa = 133.3\n",
      "        dP = mmHg2Pa*dPmmHg\n",
      "        Rw = [L_*mu_/K_ for L_,K_,mu_ in zip(self.L,self.K,self.mu)]\n",
      "        self.Vfiltr = dP/sum(Rw)\n",
      "        \n",
      "    def __init__(self,WSS=1.79):\n",
      "        \"\"\" Change units to mikrometers\n",
      "        Class can be initialized with a value of WSS in Pa\n",
      "        \"\"\"        \n",
      "        dPmmHg=70\n",
      "        self.phi = phi(WSS=WSS)\n",
      "        self.sigma_end = sigma_end(phi=self.phi,w=14.3*1e-6,r_m = 11e-6)\n",
      "        self.Kend = Kend(w=14.3e-6,phi=self.phi)\n",
      "        self.K[0] = self.Kend*1e6 \n",
      "        self.calculate_filration(dPmmHg=dPmmHg)\n",
      "        \n",
      "        self.D = [D_*1e6 for D_ in self.D]\n",
      "        self.V = [ self.Vfiltr*1e6]*4 \n",
      "        self.sigma[0] = self.sigma_end\n",
      "        self.D[0]=Diffusivity(w=14.3e-6,phi=self.phi,r_m = 11e-6)\n",
      "    def get_params(self):\n",
      "        return (self.D,self.V,self.sigma,self.L,self.k_react)\n",
      "    \n",
      "    \n",
      "class LDL_Parameters_Vafai2006_Ai(object):\n",
      "    \"\"\" L. Ai, K. Vafai, International Journal of Heat and Mass Transfer 49 (2006)\n",
      "        Table 2 Physiological parameters used in the numerical simulation\n",
      "         values given in milimeters \n",
      "         With D of endothelium depending on WSS\"\"\"\n",
      "    \n",
      "    names       = [  'endothel' , 'intima', 'IEL'   ,'media' ]\n",
      "    D           = [   6e-11     , 5.0e-6  , 3.18e-9 , 5e-8   ]\n",
      "    V           = [   2.3e-2 ]*4\n",
      "    sigma       = [   0.9886    , 0.8292  , 0.8295  , 0.8660 ] \n",
      "    L           = [   2.        , 10.     ,    2.   , 200.   ]\n",
      "    k_react     = [   0.        , 0.      ,    0.   , 1.4e-4 ]\n",
      "    K           = [   3.2172e-15  , 2.2e-10   ,3.18e-13, 2e-12   ]\n",
      "    mu          = [   0.72e-3   , 0.72e-3 , 0.72e-3 , 0.72e-3 ]\n",
      "    \n",
      "    def calculate_filration(self,dPmmHg):\n",
      "        mmHg2Pa = 133.3\n",
      "        dP = mmHg2Pa*dPmmHg\n",
      "        Rw = [L_*mu_/K_ for L_,K_,mu_ in zip(self.L,self.K,self.mu)]\n",
      "        \n",
      "        self.Vfiltr = dP/sum(Rw)\n",
      "        \n",
      "    def __init__(self,WSS=1.79):\n",
      "        \"\"\" Change units to mikrometers\n",
      "        Class can be initialized with a value of WSS in Pa\n",
      "        \"\"\"\n",
      "        dPmmHg=70\n",
      "\n",
      "        self.phi = phi(WSS=WSS)\n",
      "        self.sigma_end = sigma_end(phi=self.phi,w=14.3*1e-6,r_m = 11e-6)\n",
      "        self.Kend = Kend(w=14.3e-6,phi=self.phi)\n",
      "        self.K[0] = self.Kend*1e6 \n",
      "        self.calculate_filration(dPmmHg=dPmmHg)\n",
      "        \n",
      "        self.D = [D_*1e6 for D_ in self.D]\n",
      "        self.V = [ self.Vfiltr*1e6]*4 \n",
      "        self.sigma[0] = self.sigma_end\n",
      "        self.D[0]=Diffusivity(w=14.3e-6,phi=self.phi,r_m = 11e-6)\n",
      "    def get_params(self):\n",
      "        return (self.D,self.V,self.sigma,self.L,self.k_react)\n",
      "    \n",
      "    \n",
      "class LDL_Parameters_Vafai2006_Ai_without_D(object):\n",
      "    \"\"\" L. Ai, K. Vafai, International Journal of Heat and Mass Transfer 49 (2006)\n",
      "        Table 2 Physiological parameters used in the numerical simulation\n",
      "         values given in milimeters \n",
      "         With D of endothelium from that work.\n",
      "         \"\"\"\n",
      "    names       = [  'endothel' , 'intima', 'IEL'   ,'media' ]\n",
      "    D           = [   8.15e-11     , 5.0e-6  , 3.18e-9 , 5e-8   ]\n",
      "    V           = [   2.3e-2 ]*4\n",
      "    sigma       = [   0.9886    , 0.8292  , 0.8295  , 0.8660 ]\n",
      "    L           = [   2.        , 10.     ,    2.   , 200.   ]\n",
      "    k_react     = [   0.        , 0.      ,    0.   , 1.4e-4 ]\n",
      "    K           = [   3.2172e-15  , 2.2e-10   ,3.18e-13, 2e-12   ]\n",
      "    mu          = [   0.72e-3   , 0.72e-3 , 0.72e-3 , 0.72e-3 ]\n",
      "    \n",
      "    def calculate_filration(self,dPmmHg):\n",
      "        mmHg2Pa = 133.3\n",
      "        dP = mmHg2Pa*dPmmHg\n",
      "        Rw = [L_*mu_/K_ for L_,K_,mu_ in zip(self.L,self.K,self.mu)]\n",
      "        \n",
      "        self.Vfiltr = dP/sum(Rw)\n",
      "        \n",
      "    def __init__(self,WSS=1.79):\n",
      "        \"\"\" Change units to mikrometers\n",
      "        Class can be initialized with a value of WSS in Pa\n",
      "        \"\"\"\n",
      "        dPmmHg=70\n",
      "        \n",
      "        \n",
      "        self.phi = phi(WSS=WSS)\n",
      "        self.sigma_end = sigma_end(phi=self.phi,w=14.3*1e-6,r_m = 11e-6)\n",
      "        self.Kend = Kend(w=14.3e-6,phi=self.phi)\n",
      "        self.K[0] = self.Kend*1e6 \n",
      "        self.calculate_filration(dPmmHg=dPmmHg)\n",
      "        \n",
      "        self.D = [D_*1e6 for D_ in self.D]\n",
      "        self.V = [ self.Vfiltr*1e6]*4 \n",
      "        self.sigma[0] = self.sigma_end\n",
      "    def get_params(self):\n",
      "        return (self.D,self.V,self.sigma,self.L,self.k_react)\n",
      "    \n",
      "    \n",
      "      \n",
      "class LDL_Parameters_Olgac_WSS(object):\n",
      "    \"\"\" U. Olgac, V. Kurtcuoglu, D. Poulikakos, American Journal of Physiology-Heart and Circulatory\n",
      "    Physiology 294\n",
      "    The dependecy of WSS is implemented\n",
      "       \"\"\"\n",
      "    name       = [  'endothel' , 'wall' ]\n",
      "    D           = [   6e-11     ,   8.0e-7 ]\n",
      "    V           = [   2.3e-5 ]*2\n",
      "    sigma       = [   0.988    ,  0.8514   ] \n",
      "    L           = [   2.        , 338.  ]\n",
      "    k_react     = [   0.        ,  3.0e-4  ]\n",
      "    K           = [   3.32e-15  ,  1.2e-12]\n",
      "    mu          = [   0.72e-3   ,0.001]\n",
      "    \n",
      "    def calculate_filration(self,dPmmHg):\n",
      "        mmHg2Pa = 133.3\n",
      "        dP = mmHg2Pa*dPmmHg\n",
      "        Rw = [L_*mu_/K_ for L_,K_,mu_ in zip(self.L,self.K,self.mu)]\n",
      "        self.Vfiltr = dP/sum(Rw)\n",
      "        \n",
      "    def __init__(self,WSS=1.79):\n",
      "        \"\"\" Change units to mikrometers\n",
      "        Class can be initialized with a value of WSS in Pa\n",
      "        \"\"\"\n",
      "        dPmmHg=70\n",
      "        \n",
      "        \n",
      "        self.phi = phi(WSS=WSS)\n",
      "        self.sigma_end = sigma_end(phi=self.phi,w=14.3*1e-6,r_m = 11e-6)\n",
      "        self.Kend = Kend(w=14.3e-6,phi=self.phi)\n",
      "        self.K[0] = self.Kend*1e6 \n",
      "        self.calculate_filration(dPmmHg=dPmmHg)\n",
      "        \n",
      "        self.D = [D_*1e6 for D_ in self.D]\n",
      "        self.V = [ self.Vfiltr*1e6]*4 \n",
      "        self.sigma[0] = self.sigma_end\n",
      "        self.D[0]=Diffusivity(w=14.3e-6,phi=self.phi,r_m = 11e-6)\n",
      "        \n",
      "    def get_params(self):\n",
      "        return (self.D,self.V,self.sigma,self.L,self.k_react)\n",
      "  "
     ],
     "language": "python",
     "metadata": {},
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     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Assembling and solving the discrete system\n",
      "------------------------------------------\n",
      "\n",
      "The LDL_Sim class is responsible for solve the differential equation. \n",
      "\n",
      "In the first step the simulation region is discretized by the method `discretize`. In that function flux continuity between layers and boundary conditions are implemented. It is done in a following way: \n",
      "\n",
      "- the layers are encoded as list `layers` in `discretize`, containing indices of boundaries for given discretization\n",
      "\n",
      "- system parameters depending on space ($k,\\vec u,\\sigma,D_{eff}$) are sampled\n",
      "\n",
      "- the diagonals of matrix are assembled using finite differences in space, neglecting for a moment the space dependence \n",
      "\n",
      "- at regions boudaries the equation is replaced with \n",
      "  $$J_L = J_R$$\n",
      "  it takes a form for a boundary at index `j`:\n",
      "  $$ c_j v_{j-1} - D_{j-1}\\frac{c_j-c_{j-1}}{dx} =  c_j v_{j+1} - D_{j+1}\\frac{c_{j+1}-c_{j}}{dx}  $$\n",
      "  collecting terms with the same $c$:\n",
      "  $$ c_j \\left( v_{j-1}- v_{j+1}  - \\frac{D_{j-1}+D_{j+1}}{dx}\\right) + c_{j-1}\\frac{D_{j-1}}{dx} + c_{j+1}\\frac{D_{j+1}}{dx} = 0\n",
      "    $$\n",
      "  \n",
      "  therefore we need to enforce the above equation on the system for each boundary inside the domain\n",
      "\n",
      "- the system matrix is stored as `scipy.sparse` `dia_matrix`\n",
      "\n",
      "The plot_c function is used for plot the concentration profiles."
     ]
    },
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     "collapsed": false,
     "input": [
      "class LDL_Sim(object):\n",
      "   \n",
      "    def __init__(self, pars):\n",
      "        self.pars = pars\n",
      "        self.c_st = None\n",
      "    def discretize(self,N=2000):\n",
      "        self.N = N\n",
      "        k = np.ones(N)\n",
      "        v = np.ones(N)\n",
      "        Dyf = np.ones(N)\n",
      "\n",
      "        D,V,sigma,L,k_react = self.pars.get_params()\n",
      "        l = np.sum(L) \n",
      "        self.l = l\n",
      "        self.x=np.linspace(0,l,N)\n",
      "        \n",
      "        layers=[0]+list( np.ceil( (N*(np.cumsum(L)/sum(L)))).astype(np.int32) )\n",
      "    \n",
      "        for i,(l1,l2) in enumerate(zip(layers[:],layers[1:])):\n",
      "            k[l1:l2] = k_react[i]\n",
      "            v[l1:l2] = (1.0-sigma[i])*V[i]\n",
      "            Dyf[l1:l2] = D[i]\n",
      "        dx2_1 = (N-1)**2/l**2\n",
      "        dx_1 = (N-1)/l\n",
      "\n",
      "        diag_l = np.ones(N)*(np.roll(Dyf,-1)*dx2_1)\n",
      "        diag   = np.ones(N)*(-2.*Dyf*dx2_1 - k + v*dx_1)\n",
      "        diag_u = np.ones(N)*(np.roll(Dyf,1)*dx2_1 - np.roll(v,1)*dx_1)\n",
      "\n",
      "        # Layer's junctions\n",
      "        for j in layers[1:-1]:\n",
      "            diag[j] = v[j-1]-v[j+1]-(Dyf[j-1]+Dyf[j+1])*dx_1\n",
      "            diag_l[j-1] = Dyf[j-1]*dx_1\n",
      "            diag_u[j+1] = Dyf[j+1]*dx_1\n",
      "        #Boundary Conditions\n",
      "        diag[0] = 1\n",
      "        diag[-1] = 1\n",
      "        diag_u[0+1] = 0\n",
      "        diag_l[0-2] = 0\n",
      "   \n",
      "        self.L = dia_matrix((np.array([diag_l,diag,diag_u]),np.array([-1,0,1])), shape=(N,N))\n",
      "        \n",
      "    def solve_stationary(self,bc=[1,0]):\n",
      "        b = np.zeros(self.N)\n",
      "        b[0],b[-1] = bc\n",
      "        L = self.L.tocsr()\n",
      "        self.c_st = sp.sparse.linalg.linsolve.spsolve(L,b)\n",
      "\n",
      "\n",
      "        \n",
      "    def plot_c(self,yrange=(0,0.2),xrange=(0,214),filename=None, color='red', alpha=0.2, style='-'):\n",
      "        \n",
      "        i1,i2 = int(xrange[0]/self.l*self.N),int(xrange[1]/self.l*self.N)\n",
      "        plt.plot(self.x[i1:i2],self.c_st[i1:i2],color=color,linewidth=2, ls=style)\n",
      "        plt.ylim( *yrange)\n",
      "        plt.xlim( *xrange)\n",
      "        L=self.pars.L\n",
      "        d=[0]+np.cumsum(self.pars.L).tolist()\n",
      "        colors=['m','g','b','w']\n",
      "        for i,(l1,l2) in enumerate(zip(d[:],d[1:])):\n",
      "            \n",
      "            plt.bar([l1,],yrange[1],l2-l1, color=colors[i], linewidth=0.3,  alpha=alpha)\n",
      "        \n",
      "        plt.grid(True,axis='y', which='major')\n",
      "        plt.xlabel(r\"$x \\left[\\mu m\\right]$\")\n",
      "        plt.ylabel(r\"$c(x)$\")\n",
      "        if filename!=None:\n",
      "            plt.savefig(filename)\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
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     "source": [
      "Class that performs simulation \n",
      "------------------------------\n",
      "\n",
      "In that class the whole simulation is performed: parameters are initiated and then the equation is solved. As a result we get the object that contains the values of LDL concentration in discretized points and the function plot_c which can plot the concentration profile.\n",
      "\n",
      "Class can be iitiated with name of one of the parameters sets:\n",
      "\n",
      " - `\"4L_2012\"` for S. Chung, K. Vafai, International Journal of Biomechanics 45(2012)\n",
      " - `\"4L_2006_Ai\"` for L. Ai, K. Vafai, International Journal of Heat and Mass Transfer 49 (2006) with D(WSS)\n",
      " - `\"4L_2006_Ai_without_D\"` for L. Ai, K. Vafai, International Journal of Heat and Mass Transfer 49 (2006) with D from this publication\n",
      " - `\"2L\"` for U. Olgac, V. Kurtcuoglu, D. Poulikakos, American Journal of Physiology-Heart and \n",
      "Circulatory Physiology 294\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def LDL_simulation(wss=1.79, parameters=\"2012\", bc=[1,0.0047],verbose=True):\n",
      "    if (parameters==\"4L_2012\"):\n",
      "        pars = LDL_Parameters_Vafai2012(WSS=wss)\n",
      "    elif (parameters==\"4L_2006_Ai\"):\n",
      "        pars = LDL_Parameters_Vafai2006_Ai(WSS=wss)\n",
      "    elif (parameters==\"4L_2006_Ai_without_D\"):\n",
      "        pars = LDL_Parameters_Vafai2006_Ai_without_D(WSS=wss)\n",
      "    elif (parameters==\"2L\"):\n",
      "        pars = LDL_Parameters_Olgac_WSS(WSS=wss)\n",
      "    else:\n",
      "        print \"Parameters error\"\n",
      "        return\n",
      "    sim = LDL_Sim(pars)\n",
      "    sim.discretize(130*214)\n",
      "    sim.solve_stationary(bc=bc)\n",
      "    if verbose:\n",
      "        print \"The total surfaced LDL concentration:\",np.sum(sim.c_st)*(sim.l/(sim.N-1))\n",
      "    return sim\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Examples\n",
      "========\n",
      "\n",
      "In this section the results presented in publications are calculated\n",
      "\n",
      "Small WSS=0.02\n",
      "------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LDL_simulation(wss=0.02, parameters=\"4L_2012\").plot_c(yrange=(0,5.0),xrange=(0,214), color='green', alpha=0.1)\n",
      "LDL_simulation(wss=0.02, parameters=\"4L_2006_Ai\").plot_c(yrange=(0,5.0),xrange=(0,214), color='blue', alpha=0.1, style='--')\n",
      "legend(('4LC parametrs','4LA parametrs'));"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The total surfaced LDL concentration: 74.2308568103\n",
        "The total surfaced LDL concentration: 65.8469132801\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pefPm36WmppZ/L/zrX//q/cgjjzwgiqLa5XLptFptrntbRERE9uzZs48AQMOGDQ+aTKY6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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fba980355d0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LDL_simulation(wss=0.02, parameters=\"4L_2012\").plot_c(yrange=(0,5.0),xrange=(0,25), color='green', alpha=0.1)\n",
      "LDL_simulation(wss=0.02, parameters=\"4L_2006_Ai\").plot_c(yrange=(0,5.0),xrange=(0,25), color='blue', alpha=0.1, style='--')\n",
      "legend(('4LC parametrs','4LA parametrs'));"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The total surfaced LDL concentration: 74.2308568103\n",
        "The total surfaced LDL concentration: 65.8469132801\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3AugMQA0gDUAOxKaNhMdEgugsHBtkg8v4EwBA7joPA5/vEOeIiKgBEjGRaL4k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\nRERERERERERERERERERERK3Y/wd1YgHv+1ZM4gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fba98036cd0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "High WSS=2.2\n",
      "------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim = LDL_simulation(wss=2.2, parameters=\"4L_2012\")\n",
      "sim.plot_c(yrange=(0,1.1),xrange=(0,214), color='green', alpha=0.1)\n",
      "sim = LDL_simulation(wss=2.2, parameters=\"4L_2006_Ai\")\n",
      "sim.plot_c(yrange=(0,1.1),xrange=(0,214), color='blue', alpha=0.1, style='--')\n",
      "legend(('4LC parametrs','4LA parametrs'));\n",
      "# use e.g. xlim(0,10) to zoom"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The total surfaced LDL concentration: 4.37625312868\n",
        "The total surfaced LDL concentration: 4.28078869686\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.legend.Legend at 0x7fba91809f50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ainBcFvEAkwFrQFp8GMoVERGRRigcIxezgCeAPcDbmJdHIvf2UxEREalT4Zpz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\nNBn/D4Bkzh5TbyB2AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fba91809490>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim = LDL_simulation(wss=2.2, parameters=\"4L_2012\")\n",
      "sim.plot_c(yrange=(0,1.1),xrange=(0,25), color='green', alpha=0.1)\n",
      "sim = LDL_simulation(wss=2.2, parameters=\"4L_2006_Ai_without_D\")\n",
      "sim.plot_c(yrange=(0,1.1),xrange=(0,25), color='orange', alpha=0.1)\n",
      "sim = LDL_simulation(wss=2.2, parameters=\"4L_2006_Ai\")\n",
      "sim.plot_c(yrange=(0,1.1),xrange=(0,25), color='blue', alpha=0.1, style='--')\n",
      "\n",
      "legend(('4LC parametrs','4LA parametrs without D(WSS)','4LA parametrs with D(WSS)'));"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The total surfaced LDL concentration: 4.37625312868\n",
        "The total surfaced LDL concentration: 3.95620489143\n",
        "The total surfaced LDL concentration: 4.28078869686\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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oAeJTgOWzgbhW/rfdcd0unSEkJGRUdnZ2wPXr15cYuyzk0Q0aNGhaz5498zUD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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fba91b44bd0>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Concentration in intima in function of WSS\n",
      "-------------------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "WSSs = np.arange(0.0,3.0,0.1) \n",
      "print WSSs\n",
      "c_endo_wss=[LDL_simulation(wss=x, parameters=\"4L_2012\",verbose=False).c_st[2*130] for x in WSSs]\n",
      "c_endo_wss2=[LDL_simulation(wss=x, parameters=\"4L_2006_Ai\",verbose=False).c_st[2*130] for x in WSSs]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 0.   0.1  0.2  0.3  0.4  0.5  0.6  0.7  0.8  0.9  1.   1.1  1.2  1.3  1.4\n",
        "  1.5  1.6  1.7  1.8  1.9  2.   2.1  2.2  2.3  2.4  2.5  2.6  2.7  2.8  2.9]\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot (WSSs,c_endo_wss2)\n",
      "plot (WSSs,c_endo_wss, ls='--')\n",
      "title(\"WSS dependence of intima side LDL concentration at the endothelium\", fontsize=10)\n",
      "xlim([0.0,2.5])\n",
      "xlabel('WSS [Pa]')\n",
      "ylabel('$c_{w}end^{*}$')\n",
      "grid(True)\n",
      "legend(('4LA parametrs','4LC parametrs'))\n",
      "\n",
      " \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.legend.Legend at 0x7fba91979dd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WZse/VfAr2AkiqjdQsBP59Vi8Frhlg633T6Y4yYrj845yHz1vhQveDhecNw87uF5Oz2Rh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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fba9178c110>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Two layer model \n",
      "===============\n",
      "\n",
      "High WSS=2.2\n",
      "--------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim = LDL_simulation(wss=2.2, parameters=\"2L\")\n",
      "sim.plot_c(yrange=(0,0.012),xrange=(0,340));\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The total surfaced LDL concentration: 2.7040541751\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4jGC2glmNdtEMHvZ1nUREREpbSczueQczRgVMsjKMMxs02w8YB7wEtAeWAPOB+l7iGwHf\nAr8440cDE4Eb3WI6A3OAGUBb4APgE+ACt5hqwK9ANnAl0BJzW+vIGbSlRKyrRdb4TjztgByAxkfo\n/+0sevi6XiIiIqWpJJKUr4Bbne9Tgbfctk/HSOBdYBpmttAjwC68D8IdCmx3HrcReM957GNuMSOA\nhUA88C/wCvCjs9xlFGaW0t2Y3pudwE+YWUs+90wPNi+vz+uu7Z5beO7JJTT0ZZ1ERERKU0kkKcmY\nX/gPAa0x41ZO9wm+oZjbKws9yhcCF3k5prOX+I6A1bndqQjnvA5zy+hTTJtWAfcUo+6lrvtd/G9P\nNN8CWPOIfHYx8W2TCPd1vUREREpDSSQpl2N6T+pjxnOkAZVO81wxmMQi2aM8BYjzckxsIfHJmEQp\nxrkd5yXG/ZyNMb01G4GewGTMbaM7i1790mWzQu/bGO16WnKlXJp8+yFPav0UERGpiEoiSfkT0/vw\nOHAu0A4/GMdxGoIwPSnPAqsxY23ewdxO8htr4sgafTGj7BYyAeoe45pF72v9FBERqXhKIknZANzB\niVs812IGnZ6OA4Ad0zviLhazQFxhkijYyxIL2DgxsyjJyzmT3Lb3Ymb/uNsANDhZhRexaAyf8TLx\nJBx/jWY6s+meL/AzOhFPQoETjGcU07g+X9kCWhBPAhs9BiBPYgiTGTj6Erb/0Nisn7ITeHUnTw+c\nxeX5Yt+hHxM9ZgGlEE48CXxJ+3zlM+nFWJ4vULfXGFOa7chXtpI44knAcx0YtUPtUDvUDrWjfLdj\nJr2O/258kQXEk8Bahhc4phCWogQVQTQmKcjEJCk1MDNpTsdvmB4N90Xg1gNzgWcKiX/F+Zmt3com\nY2bxdHFuz3HW8Wq3mPnAIcwKuQAfYm5ZXeIWMw44n8KnQJ8HJF7BFUO/v+P7TTQxK8OWpa3jeaLR\nEfoC5ASx/64buGN229Oa8i0iIlJ2vqItiUwDOmDGgBaquD0pzTFTfj0dwyQo5qPzJyhXFvMzEjAD\nVgdhemTGAfXAPHAPGAO87xY/BbMs/1hn/GDn63W3mAmYcSZPAC0wM3kuB8a7xYzDDLB9CmiKmaF0\nL/BmMetfZi4eTMLRMP4CCHVQc/I3xGuhNxERqSiKm6RsBK4BBnDqXphY4L/kv6VSFJ9gpgY/hxnv\n0hXojZmGDObWjvuaKdud+7s745/BPOBwrlvMcqA/JvFZjRkM2xdY4RazEuiDadta53keBmYXs/5l\nZk9lbPdcxxPZVnONq2TTdsk0ntJAWhERqQhO93ZPL8yibbsxv+hTMD0p1TBjOLpgkpOX8T6WpLzz\n+e0el9cX0OKR5bwXBGEAv9fltU736nEEIiLip0rpdo/LAsw4kMlAFUwvxi2YmT1JmNs1D1JxExS/\n8lgvNvzQmBdd2xfsYeTM/9HRl3USERE5U6e76JrLGudLfKzXnSzY8AbNmh/kLgtYB6zltX9rMOjl\nbmz3dd1EREROR0lMQb4XWAzUdG5fT8HpvlIGLriXtw5EsBQgOI/oZxYzoc96qvm6XiIiIqejJJKU\nPOA1IMO5/SXmVpCUsdRwHL1v4+m0EDYChNupO+MLxrVO0dL5IiJS/pREklIZM+043bmdB2SVwHnl\nNKyoR8aDVzMi20oKQOUczlk0g5cqZ5XI/2sREZEyUxK/uOyYJxBb3coiSuC8cppmtmf/2M48ZLeY\nxLFWBpcmTuUhX9dLRESkOEoiSZmIWeRtD2Zl12ngscSulLlnerD501aMyjNJJE0Pc/sfb9Pf1/US\nEREpqpIakzIEMw5lJfA9hS9fL2VswC38tqw+r7i2z9/LYz+8z1W+rJOIiEhRleQ4hRWYpej9doXW\nQNT1buauj+Ed1/Zl23jhs48LfRaRiIiIX9FgygDQ7n6mbq/KpwAWsPb5h1ffmefxJEsRERE/oyQl\nANis0OpB4vdFsQAgCMLu+ovxY36gma/rJiIi4o2SlACRGULe+ffx/MEIlgME5xE1chmTnlxCQ1/X\nTUREpDBKUgLInsrYLhvI46mhrAUIdVDjvz8xZeQy6vm6biIiIp6UpASYNXFk3dKXh12r0oY6qDn6\nR6Y+9Bt1fV03EREJDBemUqMocUpSAtDCpqQOuJkH00PYDBBmJzb+e6Y+8Ae1fV03ERGp2Gb+j45v\nbGJMUWKVpASor5tz5I4+3J8RzFaAMDtxYxcw9d6VxPm6biIiUjHFL6T5rWsZa4XgosQrSQlgc1tx\neGAfhmYEsx0g3E6difOZoh4VEREpaSOWU+/h35hozaNSUY9RkhLgPmvNoXuuY2hmMDsAwu3US1jA\nu08spYGv6yYiIhXDzeuoPvpHJoU6zFiUlGA2FeU4JSnC7LYcGHrNiR6VMDuxLy3inZd+pKmPqyYi\nIuVc511UmjaPiRE2M5M0I5itD5zNhKIcqyRFAPPk5Ntv5N60EP4FMz35yaW8PX4+rXxdNxERKZ/O\n20vE/FlMiM6hBUC2laRhVzNsWwQZRTleSYocN7cVh28YwJDUUP4GCM6j8rA/mPzePM71dd1ERKR8\naX6AsB9mMq5KtnkMS24QR16+hGHTzyWlqOfw1yTlAWAbkIl5svKpHojXDUh0xm/BPJXZ003AeiAL\nWAfccJLzPQk4gHHFqnUF8GNjjvW4kweOhJEIYM2j0l1/MmnebLr7uGoiIlJONDxCyK/v8Vq1LDoC\n2CykTbyQYS93M8MKisofk5R+mOTgJaA9sASYD9T3Et8I+Bb4xRk/GpgI3OgW0xmYA8wA2gIfAJ8A\nFxRyvvOB+4A1QN4ZtaScWlGPjK538/DBCJaBedbPtRuJ//VdbvJ13URExL/FphH8x9uMqZHJRQB2\nCxlTOzLssV5sKO65/DFJGQm8C0wDNgKPALuA+73EDwW2O4/bCLznPPYxt5gRwEIgHvgXeAX40Vnu\nLgqYBdwDHD7jlpRj62qR1e5+Ru6NYj6ABYIu2s1T6ydxf7Dd17UTERF/FJtG8F+TeblWhul9t1vI\ner89Dw+72gwjKC5/S1JCgfMwCYW7hWAyskJ09hLfEbA6tzsV8ZxvAl8DiwBLkWtdQe2pjK3pwzy3\nsQYzXGUtD3D3tvE8H5tWtIV4REQkMDQ8Qsiat3glLp0eAA7ImXMOI+++nj9P95z+lqTEYBKLZI/y\nFPC6EmpsIfHJmNXsYpzbcV5i3M/ZH3O76CnndkDe6vGUGUJei+FM+r0ur+U5r0m9Y1y77k3Gdd1B\nlK/rJyIivtf8AGErp/K6qwfFATmft+Kx22/ijzM5r78lKb5SH5gA3A7kOMssqDfluE738vG3zRjl\ncF6fGpl0XvAB00cs1xOURUQCWdskwpe9y7iYTLqAucUzuw0j+vY14xrPhL8lKQcAO6Z3xF0ssM/L\nMUkU7GWJBWzO87liTnbODkBNYBWQ63xdAjyE+aXsNVlZxKIxfMbLxJNw/DWa6XjOhvmMTsSTUOAE\n4xnFNK7PV7aAFsSTwEaq5iufxBAmMzBf2UriiCeBxZyVr/wd+jGRh/OVpRBOPAl8aaaDHTeTXozl\n+QJ1e40x7u245jYW9Ylj0lXm+hBpo1H897w/fS4dylM7gArx/0PtUDvUDrXD1+2oNo3rznqbL6tn\nmYkodgsZ09sz7Pat9Dnejpn0Ov678UUWEE8CaxleoD2F8Meegt8w04kfdCtbD8wFnikk/hXgWqC1\nW9lkzCyeLs7tOUA0cLVbzHzgEHAbZsCs+zLwFmA68A/wqvPzPZ0HJF7BFUO/v+P7TTThaFEaVxE8\n9Bt1x/zAuEgbjQEcYFten1e73s1cX9dNRETKRr+/qfHuPCZG5dIczDTjqR0ZVqRBsl/RlkSmYToJ\nVnkL87eeFIAEzOyaQUBLzHTkesAU5/4xwPtu8VOAhsBYZ/xg5+t1t5gJQE/gCaAFMAq4HBjv3J+G\nSURcr3VABiaJKSxBCWgTO7Gnx0AGHYjgV4AgCO6yi2e2jOcxDagVEan4Hv+V+jPmMs2VoOQGcXTi\nhQw93Vk83vhjkvIJZmrwc8CfmIXcemOmIYO5teO+Zsp25/7uzvhngOGQ76/65ZiBsYOA1cCdQF9g\nxUnqkYcGz3q1vD7pTR7mkc3V+NBV1vgI/Te+wZQBa44PWBYRkQpm3He0/L8fmRZupy6Ype5f6M7d\nj15Z/HVQTsUfb/eUFwF7u8fT4mnc0GUnTwaZGVXkBHFgVjueOpNpZyIi4n8++oxOfdfxmjWPCID0\nEDYPvYZhs9odHwNaNOX4do+UM5cM5ou3zueebKuZ5h3qIGbQn0xZOZXbtfCbiEjFsPQ9buz/NxNc\nCcqRMFZdfSv3FDtBKQYlKVIihl/N331v4bZD4WZOvAWsHfYxYncCr2g9FRGR8qt6BtbNE3i0yy6e\ntjgXSU2uxKKOQxj2SyPSSvOzlaRIifmyBUcajWDYhhpMc5XFptPj+5l8NOkbzvFl3UREpPi67iBq\n4xskNDnMAFfZpurMOns4T26pfnxdsVKjJEVKVGo4jpbDeeuLFoy0WUyGHW6nzgMreG/1WwyqnKXv\nnIhIeTDsd+p8N4v3XIu0OcC2uAEvnf0Q41PDcZRFHfQLQ0pFn/4sHt6b/kfDWA3m9k/bFB7cPp63\n7vyLmr6un4iIeDfnUzonLOCDSrk0ATPFeGY7Huw2mHllWQ8lKVJqppxPUuOHuW99DO/kYbLuall0\nfPdL5nz9IZf5un4iIpJfRC6WNW9xT991TAxxUAUgM5gdz1zOXYP6kFjW9VGSIqXqUCT21sOYOv1c\nhrhm/4Q4qHL1JuL3vs7LV20yPwQiIuJbl28lemcC49qkMNTiXKJkfySLr7qNga91Ob5WWZlSkiJl\n4u7r+fO6AQxIrsSPrrLaaVz5xWw+mTuHS3xZNxGRQPf6Alp8/SEfxGTSFSAPHGtq8WbDR3i0tGfw\nnIySFCkzC5uSWm8ko75vzLM2C6kAoQ5q3LCBhN1j+e/lW4n2dR1FRAJJRC6WFVO59ZHlzAi3m6fa\n5wZxZPY5DG/3ANMzQ3y78rqSFClTNiv0vJPv7uxD3/2RLHGV1z3G1d/O4tNvZ9FDC8CJiJS+m9dR\nfVcCEzruY6RrxfBjoax7sDe333Yzv/u6fqAkRXxkdlsO1HmUR346ixdcU5VDHcRctZlX9r3O+Af+\noLav6ygiUlHN/pROH37O7BqZXOQq21SdD1oO4+53OpLky7q5U5IiPmOzwmV38fV919F3fySLXeUx\nmXSdOJ9PE6dwu56qLCJSclqnEL55Ao/2X8ekUAc1AHKCODj7HIad/RAT9lTG5us6ulOSIj43/VxS\n6jzKyK/P5vGcIPYDWPMIPy+JEVsm8MHUr2jn6zqKiJR3b39J+xVvM9t99dgDEfza/2b633ozv/my\nbt4oSRG/YLPCtbfyU/dB3Ly1KnPyMIO1KuXS7L5E3tv7Oi8P+pNavq6niEh50zqF8E0TeOSeVbwT\nYaM+gAOyV9Th9QYjGTG3FYd9XUdvlKSIX1len/QmI3h90gXclRbCRld57TSufOdL/rfmLe5ufoAw\nX9ZRRKS8cPaefNj0MLe51j45GsaaZy7n1gvuY46vZ++cipIU8UsP9WZd3Ue5Y1k9xuQGcQTMLaA2\nKdz/12Q+++ZDLtcsIBGRwl21iSo7E3j23lW8G2GjIYADclbFMb7BI9zzysXs8HUdi0JJivit1HAc\nXe7h86tup4/zFpAdINxO7d6bePXgq0yf9gXn+bqeIiL+ItgOi2ZwzbzZfF4/lRtc5amh/P2fy7i1\nw1BmldXDAUuCkhTxez825liTEbz+YjcGHAo/MXe/cg5tBv3F2/tfZcKYH2jmyzqKiPjas79w1v54\npl66nRdCHFQFsFtI/70u8fVHMnj0JWz3cRWLTUmKlBsvXMrW2Md5cG4LHkkPYYurPCaTLqOW8tHu\nsbw47Hfq+LKOIiJlredmKm8Zz2P//Yk5VbPp4CrfF8XCu27gpk738kl56j1xpzUopFyxWeHG/iyp\nnMWvX8yh90W7GBpmJ84ClrrH6D1hPj0f/5WvX+vCtEkXstfX9RURKS2xaQTPm81NHfYyJDiPyq7y\nLCu757Xg1f63sNyX9SsJ6kmRcik1HMdld/F16we5MbE243KDOAoQBMENUrlh4nzm7kzguRHLzbMo\nREQqimA9dRHAAAAZb0lEQVQ7fPoJXbaOZ86Fe3jclaDYLWT9XZMp7e+nX0VIUMC/k5QHgG1AJrAS\nzJMZT6IbkOiM3wIMKSTmJmA9kAWsgxODipyeAlYAqUAyMBc4+/SqL2VhS3VyOg7hw8sGcv36GN51\nLbFvAWv9VK5LWMDnu8fywhNLaeDruoqInKmpX9Fufzxv37yeCZE2znKV74nm68HX06fNg7y7MYZs\nH1axRPlrktIPGAe8BLQHlgDzwSxCU4hGwLfAL8740cBE4Ea3mM7AHGAG0Bb4APgEuMAt5hLgDeBC\n4ArM7bCFQOSZN0lK09KGpLUexpQed3LNuppMtVk4BiZZqXuMa175gc+TXiP+ra9p4+u6iogU1yvf\nc/b+Vxl/XyLvVc0+MavxaBir37iAgfUe5YWZ7c2K3RWJxdcV8OJ3TO/Jg25l64EvgKcLiX8VuAZo\n7VY2GWgHxx+e9DEQBVztFjMfOAzc6qUeMUAKJnlZ6rHvPCDxCq4Y+v0d32+iibndIP6h6w6i3vqG\nfi33c5v7vVowP9S/nMUHd/RhcXkdTCYigeHJJTR86HeG1E6jp3t5ZjA7f2nI5Gtv5Xub1Ve1OwNf\n0ZZEpgEdgFXewvxx4GwoJgEY7VG+kBMJh6fOzv2e8XcDVsz6Gp2AhEJiHj5JXao6/3vo5FUWf7O0\nIWltH+C9zruYM/lrbmm5n/6hDmIAqmTT7rqNtEt6nZ1/xvHh/dfwzZo4snxdZxERlzE/0OyuPxkc\nm04Pi1uHQraVpN/r8k6f/nx9KJIKv6SlPyYpMZjEItmjPAWI83JMbCHxyZj2xTjfx3mJ8XZOC+aW\n0xJML46UQ8vrk97+fmY0PMKHM+bS6/y93FEplyYAETYaXLSbpxKnMnxHVb56uwOfxndlp6/rLCKB\n662vaXPLOu6Oycw/DjM3iMN/xfHegJv535bq5PiqfmXNH5MUfzEJc/voVAN2pRzYUZXcSwfxdbCd\nrz/6nIt6bOWOalmcDxCcR1STwwx49QcGjFrKb0sb8tmg61kSCH+liIjvReRimf4FnXpuYWC1LDq6\n78sN4tC6mnx437V8uqIeGb6qo6/448DZA5jbM7Ee5bHAPi/HJFGwRyQWsDnP54op7JxJhZzvDcwY\nl0vh5GttLGLRGD7jZeJJOP4azXRm0z1f4Gd0Ir7A7SYYzyimcX2+sgW0IJ4ENh6/3WRMYgiTGZiv\nbCVxxJPA4hOjvAF4h35M9LiVlUI48STwJe3zlc+kF2N5vkDdXmNMRWuHzQp9+7KselPerxHGql2V\n+dLBiZHw/8mi0/6NvL53LPPWvMU9964kzh/bcVw5//+hdqgdgdyOkLG8+Ou73HTgVT7tt443qmXR\nsR9m8KXztk58mwe49tyabFzxES/7aztO+f9jJr2O/258kQXEk8Bahhc4phD+OnD2N8x0Ys+Bs3OB\nZwqJfwW4loIDZ9sCXZzbc4BoCg6cPQTc5ty2YBKU64HucGJV00Jo4GwFcdUmqry0iGtb7efmCFv+\ndVXyIO9wOL+vrMNXD/Xm54o0tU9EfGPQn9QatZS+TQ5xo+fA/sxgdq6oy4xbb+LbPZWx+aqOpa4c\nD5wFM8D1A8wMn9+A+4B6wBTn/jFAHTieFU4BhgFjgXcxA2kHA/3dzjkBWAw8AXyJSUQu50QSA/Am\nMMC5L50TvTNHQAMrK6r5zTg6vxmzInL5cPoXdLpsG7fEZHCxBSwWsFTPolPPrXT6+02O7a7Mgnkt\n+PKxnqwvlyPqRcQnInKxTPuCCy/dzo210ulmMWMvjzsSRuLis5itWYf5+WuS8glQA3gOqA2sBXoD\nu5z748i/Zsp25/5xmN6XPcBwTM+Ly3JM0vIyZv2VzUBfzOJtLkOBPOBnj/rcBcw8kwaJ/8sMIc+5\nSuPyuxOJffh3rml2kGvD7aZ3JTiP6LOOcvPDv3PzfYns2FqNBR+1YWF5fGiXiJSN21cT8/ivXHf2\nQW4It+d/tpgDbPui+W7OOcx5rBcbfFVHf+avt3vKA93uCQARuVje/IZzr9jCdbXT6GHNI9wzJi2E\njZtqsODtDiyccn6hY5xEJIDUTSV40rdcdNFOrqtpemXz9ZrkBHFgc3W+eLE7n358Dgd9VE3fKue3\ne0T8QmYIeYNvYBWw6vzdxL++kCvapHBV1Sw6uNYuiMql+blJNJ/8DQ+98gOrN1fnp1lt+Xl8Z3b7\nuPoiUkaC7TBxPm2u2sTVdY9xRYiDKu778yDvYATLljVg7n3XsjQ5qgKPNylBSlJEimhFPTK6DWYe\nMO/Ov6g5/A96nH2AXpVzOMcVUyWbdh320a7DPka8vIjNO6rw8/xm/PxkDzZoDItIxfPEUhoM/Iur\nGh/mKtetYXc5QezfXJ15b1zIF+ppLT4lKSKnYWZ79s9sz2xg9ojl1Bv0J1c0OUyvSrk0dcVUyqVp\nqwM0bXWAe4b9QdKeaH7+vR5L/3MZqwJpMSaRiiTYDs//TNM+G7jsrCNc5v4z72K3kJVSiZ+X1ee7\n+65ludZcOn1KUkTO0PjO7B7fmenA9CeX0LDf33RvfJjulXNOPMwwzE5c4yP0b3yE/n3XkXU4nJVb\nqrP8k9b8mnCRbguJ+LNgO7z2PS2v2sTlDY5yWYSt4FPV88BxOJw//opj/pM9+CkQF14rDRo4e/o0\ncFZOasAaYob9QbfmB+leLZPzg7z8UZAZzO6kKJb9WZtl4zrx19KGpJV1XUUkv/P2EvHcL1zQPoku\ncWl0CbMXWAwUgNRQ1m6qwQ/jO7FgVrvji4fKqWjgrIhvzW7Lgdlt+Rz4vOsOop5eQqdzUrgoNo2L\nXA87BIiwUa/REfo2OkLfPv/gSAvln31RrFwdR+JrF/GX/iITKX3Bdhi5nAa3rKdL40N0qZpFhyAI\n8YzLA8eRcFb9W4Ofpnbkp+nnkuKL+gYKJSkiZWBpQ9J6N+QH4IdgO7z0E82u3MRFDY/SuUoW7V29\nLBYIis6hdfQhWp99iIE3r8d+LJT1+6JJ/CuOxMkdWftLI/W0iJSE21cTM/AvOjY/yPk10+kYbqdu\nYXEOyDkcwcp/YliU0Jlf5rbicFnXNVApSREpYzYrPNWDTU/1YBPwfuddVHpyKee3Seb8Wul0dD2l\nGcAC1so5tKl8kDbND3JX33XkZYSw9WAEa7dVY/XCJqyN78J2zRwSObVrNlL1/pV0aJViftYibR7P\nrHGTZWVfUjRLE2vz6/OXsnJdLa067gtKUkR8bHl90q8fwM84Vzrus55q966iQ8v9dKyVTodIG41c\nsRawVMqlSaVcmjRI5YZuO+CFnzl6NIy1+6JZuyGGf2afwz/6S08CXUQulsd/5aweW2l31hHa1sig\n7cmSEgfkHA1nzbaqLJ3Xgl9HX8w2Jf++pyRFxM/MbcXhua3MrSGAfn9TY9CfdGh2kLY1M2gXlcPZ\n7itYhjioEpNJ15hMurZJgVvWQ7aV5KNh/JMcxYZ/a7Dh09b8E7ArW0pA6LmZyneupkWbZM6pc4y2\nVbNo6/nwPnd5YE8N4++90axcWYcV/3cJa/UAUf+jJEXEz318Dgc/PoeFwEKAtkmEj/iNVufto63r\nH+MQR/7HsofZia2VQWytDLq3SYGb/oGZ/2N/ahgbD0WwZXcVtiTWZsvUjmzTmi1S3ly3gaoD1tKi\n1X5axqbTokoWLT2fi+PJAba0UDYkR/HXX3H8oUHp5YOSFJFyZk0cWa6l+sHMSnjkN+r33Ezrs47Q\nMiaDFtE5tLDmUcn9uFAHNWMyqRmTSdezD8Fl2+CxZTgyg9l5LIwtByLZsr0qm/+oy7b327N7R1Vy\nfdJAEacmhwi9ZxUNO+6hSb1UmtTIpEnlbJp7mw7sLjeIQ4fDWbOnMmtW1mHN2Iv4Rz0l5Y/WSTl9\nWidF/FZELpbhv1P/8q20bHSEFjEZtIzOpkVwHlFFOT4PHNlW9qaHsvNoGDtTKrFjS3V2/NKQnbPa\nkZwZQl5pt0ECR9skwgf8Tf12STRseMQkI9HZNI6w0cACQac63m4hMy2UjQcj2LC9Guu/Ops1ky5g\nt8aU+LEirpOiJOX0KUmRciXYDgP/IvbKLTRtcogmtdJpUiWLppG5NAqC0KKexwHZmSHsyghh77FQ\n9h6KYO++aPZuiGHfN83YoynSUpjmBwi7fTX12yVTr14qDWpk0KByNvUjc2kQ6qBmUc9jt5B+LJQN\nByL5Z3tVNvzUiA2TLmBnajiO0qy/lDAt5iYi7mxWeK8Dye91IBn41VVePQPrfYnU67qTJmcdoWn1\nTBpE5dAwMpeG1jwiPc8TBGGVcmlaKZemNTOg8RFgH1z7Lzy+DGwW0rKC2ZsRwt60UJKOhpNyMIKU\n3ZXZv74WKQuasH9NnKZzViTBdui2g+grtlC7+UHiah+jdvVM4qKzqR2ZS1y4jTj3BQyLwgE5GSFs\nPRrO1v2RbNlanS0/NmLL9HNJUk9e4FCSIhLgDkVif+VidgA7gEWu8mA73PQPNXpuoUGzgzSMTaNB\n1SyTwITbqFfYapwAwXlEReVydlQuZ9cqZFhi/PcmkcmxkpIVzP6MEPanhZJyLIxDh8M5vL8Sh3dV\n4fCaWhxe1JgjeqS977iSjy47iWl6iBq1jxFTLYuYytnEVMqhRoSNmDAbMWF2YgtLaIsiN4jDGSHs\nSgtl56EIdm2vypYlDdkytQN71DsiSlJEpFA26/GZRQeBP933ReRiuWUdMV12UafxIWrXSqdOtSzq\nVMqhToSNOmE24rw9qwhMIhNsIyrSRuPqp+hTsVk4lmvlUI6Vw9lWjmQFczgrmNTMEI6lh3DsWBjH\njoRz7EAkx/ZGk/ZvDVJX1OWYBv7m1/AIIe2SqNT0EFGNDlM1Lp0q1TOpUjmbKpVyqBKRS9UwO5XD\nbFQJtVMlxEHVEDvVi3Mr0JucIA5kBbPvWBi7Doezc280u9bXYucXzdml24NyMkpSRKTYMkPIm9me\n/TPbsx9Y7bm/chZBt6yj5gV7iat/lJo1M6hZJYtaUTnUjMilVpidmqF2alrzCD/VZwXnER1sIzrC\nRsPi1NEB2bYgjtmDyLRbyLQHkWELIstuITPXSkZuEJm5VrJyrGTkWMnMCiYz20pWrhVbjpWcHCu5\n2VZys4KdrxBy00LITQ8lNy2U3CPh5GQF47BbyMu1kmcPIi/HiiPb+T7biiM7mDxbEHlZwThsQeRF\n5WCtnIU1MpegSBvWSjlYw20EReQSHGYnKNROULiN4FA7QZG5hETlEBqVQ1iEjbBwG6HhuYSF2gkL\nsxMWYic0xEFYiJ2wEAfhoXYiQ+xUCnFQKdhBZLCDKKuDSGselawOKp0saTwTdgtZOVZSMoPZlx5K\n0tEw9h2IJGlHVZL+iiNpbkuSlTDK6VKSIiIlLjUch9v4l0K5biV0206tpoeoWTOdqlWzqB6VQ7XI\nXKpF2KgWajevEDvVijozySUIwkIdhOmGQfHlgd0WxNEcK4eyrRzIDOFgeggHUsM5cDCCg3ujOfBv\nDQ7+chYHVtQhXbNopLQoSRERn7BZ4cfGHPuxMceALaeKb3iEkG7bqdpqP9Vi04iunkV05SyionKo\nHJlLdLiN6DA70aF2okPsRIc4iA52EG11EBGUR2Rp9ST4KwdkO3uP0u0W0m1BpNuCyMi1kpYVzNGM\nEI6mhXL0WChHD0ZyNCmKo1urcXRVbY7+Vo80JR7iDwLqh1ZEyq8dVcl13l7afzrHx6YR3GI/4Y2P\nEBmbRkRMBhFVsgivkk1kpVwiInKJCLMRFuwgJMRBiNVBSLCDEGue830eoUHO7SBTFmoxyzhYLHkE\nARbndpDFzD0JcttvAYLyLNjzwJ5nweH8rz0PHA6Lee9wbjvf22xBZOcGkW0LIjvX3ILKzg0iOyeY\nnKxgsrOCyc4IITsjhJyUSNL3RZO+uTrpq+PI0IBjqQiUpIhIQEiOwpYcRdovaKCmSHlxypX8fOQB\nYBuQCawEup4ivhuQ6IzfAgwpJOYmYD2QBawDbiiBzxV3M+nl6yr4FV2PgnRN8tP1KEjXJL8Avx7+\nmKT0A8YBLwHtgSXAfKC+l/hGwLfAL8740cBE4Ea3mM7AHGAG0Bb4APgEuOAMPlc8JQX2D1MBuh4F\n6Zrkp+tRkK5JfgF+PfwxSRkJvAtMAzYCjwC7gPu9xA8FtjuP2wi85zz2MbeYEZgnyMYD/wKvAD86\ny0/3c0VERKQU+VuSEop5Js5Cj/KFwEVejunsJb4j4Bqf3ukU5zydzxUREZFS5G9JSgwmsfBcWyEF\niPNyTGwh8cmYQcGuZ0XEeYlxnfN0PldERERKkWb3nKGd7GzEKkJYT7qv6+JzNqL5ira+robf0PUo\nSNckP12PgnRN8quo1yOZJkUJ87ck5QBgx/SOuIsF9nk5JomCvR2xgM15PldMYedMOoPP3Qfs2cjG\nUazzEhGIzKO3xUXXoyBdk/x0PQrSNcmv4l6Pf/D+OxbwvyQlBzOVuCcwz638CmCul2OWA9d6lPUE\nVmASD1dMT2CCR4zrcfWn87n7gPOB2l72i4iIiHf7OEWS4o/6AtnAIKAlZlpwKiemAo8B3neLPwuz\nONNYZ/xg5/F93GI6A7nAE0ALYBQmMTm/GJ8rIiIiwv2YRdWyMD0i7ouqTQcWecRfgukJycIs5nZf\nIee8CdO1lI33xdxO9rkiIiIiIiIiIiIiIiJSYQTqM35eABwer72FxOwBMoCfgFZlV71SdwnwFaZ9\nDuD6QmJe4OTtDwPewDzJNw0zULtu6VS3TJzqmsyg4HdmmUdMRbomT2FuFadi1l2aC5xdSNwLBMb3\npCjXYwaB9R25H1gNHHW+lgFXesS8QGB8P6QU9MOMaxkMNMcMsD1GYAywfQFYA9Rye9Vw2z8KOIIZ\n79MamI35QYsq01qWniuBFzHtcwDXeewvSvsnYx63cBnmGVE/An/ifwsrFtWprsl04Bvyf2eqesRU\npGsyH7gTM/i+LSaB2w5EusUE0vekKNcj0L4j12B+bpoATYGXMRM5Wjv3B9L3Q0rB78CbHmXrMQ82\nrOhewPwgFMaCmUr2uFtZKHCYwgcyl3eev5CL0v4qmAT3FreY2pg1fXqWWk3LTmFJygy8T+OHin9N\nYjDXxdXbGujfE8/rAfqOABzEzCwN9O9HAcq6ikfP+IFmmKx+KybDb+Qsb4RZ/M792uRgnk4dCNem\nKO3vAIR4xOwD/qbiXqM8oDumq38j8DZQ021/Rb8mrh6BQ87/Bvr3xPN6QGB/R6xAf8ztmyXo+1GA\nkpTiCfRn/PwG3IHJ1u/FtHkZUJ0T7Q/Ua1OU9sdh/sE56hGTTMHVjiuK+cCtwKXAo5i1iRZhEn6o\n2NfEgrkdvATT2wqB/T0p7HpAYH5H2mDGkmRhkrK+wGYC+/tRKH9bcVb823du79dhVvLdAgzE3Abz\nJq80K1UOBHL7P3F7vx4z0Hw7cDUn7+KvCCZhxhQUdWB9Rf+eeLsegfgd2YAZo1MFc9tmDqY36WQq\n+vejUOpJKZ7TecZPRZYBrMUM/nK1/2TPSKrIXG08WfuTMH8dVvGIiSMwrhGYdu7EfGdc2xXxmryB\nGSB5KflnwAXq98Tb9ShMIHxHcjG3zP8Ensb8kXc/Rft3tCJeD6+UpBSP+zN+3F1BwSlzgSAMMzVu\nH2ZKdhL5r00o0I3AuDZFaX8i5h8n95jamL8uA+EagbllWp8T/xhXtGtiwfQY3ICZebHDY3+gfU9O\ndT0KU9G/I4UJcr4C7fshpSCQn/HzOmZdjEbAhZjphEc40fYnMKPQbwDOAT4CdgOVyrympaMSZrpf\ne8wMhRHO98Vp/1uYvxIvA87FTB1chfnHvDw62TWphPnOdMI8Y6s75h/RnVTca/IW5jtwCeYvW9cr\n3C0mkL4np7oegfgdGQNcjGlvG+D/MDNzLnPuD6Tvh5SSQH3Gj2u+fjbmh+ZTzAMb3T2P6c7NpOIt\n5tadE4tN2d3euz9G/VTtDwUmYm4dplP+F2HqjvdrEo4Zx5SM+c5sd5Z7trciXRPP6+B63ekRFyjf\nk1Ndj0D8jrzLid8fyZhZOpd7xATK90NERERERERERERERERERERERERERERERERERERERERERERE\nRERERERERERERERERERERERERCqIx4HqpXDe3gTOQ0NF/JrV1xUQETmJTkAPYBDmya+XAC8Ai4Ca\nwB9usc8CjYG/nNuzMY+zTwceBEZhnj7bG/OI+1uAXOBioC/QHlgObHKWrS21VomIiEi5Fg3c4Xx/\nC+aR9UHAHKAKcJtH/EqgjfN9GHAYiMAkOCHAP8Dtzv2RQBbQ2bndEljndi7Pc4uIiIgcFwEEO9+/\nBjzksd89kagK7HPbvhT41fm+MhAH7Hbb3xlY6rZ9J/CZl3OLiI8E+boCIiJeZAI25/sewI/O95UL\nib0EWOy2fQXmllANINXjeIDLgR/ctm/F9NBUPeNai0iJUZIiIv7qWmAE0ARohrkdE4Tp9fB0KbDL\n+T4UuAHTU9LPWeaZpLhvV8OMffkKGFxy1ReRMxV86hAREZ+ogRkI2xP4P8ztHhswq5DYSzEDXu/A\n/Ls2BzND53fn/iaYgbMAFiAGM0gWTI/N75jxKl+XdCNEREQk8LjGjVQHdpTSuUXEh3S7R0TKu27k\nHwQrIhWEkhQRKa/qYW4JtQW+KcHzXkfJ98yIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiI\niIiIiIiIiIiIiIiIiIiIiIiIiIiIiJzS/wNZLH9n5RKNbwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fba9178c5d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "Low WSS=0.02\n",
      "--------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sim = LDL_simulation(wss=0.02, parameters=\"2L\")\n",
      "sim.plot_c(yrange=(0,0.25),xrange=(0,340))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The total surfaced LDL concentration: 14.9909714672\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jq38YZ/1MXHWzccVn46qVi3PBPMb841zGxeTjisnHFe3GFenGFenB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\nnAdelBwxAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fba9178cd50>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Concentration in intima in function of WSS\n",
      "-------------------------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c_endo_wss=[LDL_simulation(wss=x, parameters=\"2L\",verbose=False).c_st[2*130] for x in WSSs]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot (WSSs,c_endo_wss)\n",
      "xlim([0.0,1.6])\n",
      "ylim([0,0.3])\n",
      "xlabel('WSS [Pa]')\n",
      "ylabel('$c_{w}end^{*}$')\n",
      "grid(True)\n",
      "plt.title(\"WSS dependence of intima side LDL concentration at the endothelium\", fontsize=10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.text.Text at 0x7fba918be5d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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LgL8BnwPuAHavc52jsITju1iLzUPAvcC2ZeZfB5jn5/8/OncAjlqMJTbF11a0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\nDNzD4K70dxp1qMOuiDRAUOf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8gd1KDTboW5qKl5ZWp7xeERERERERERERERERERERERER\nERERERERERERERERERERERERERERkdb2/wFOBHuQxPH1IgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fba91771c50>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    }
   ],
   "metadata": {}
  }
 ]
}