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    "# Surface-Volume Reactions (Example: IP3 Model)\n",
    "\n",
    "The simulation script described in this chapter is available at [STEPS_Example repository](https://github.com/CNS-OIST/STEPS_Example/blob/master/user_manual/source/ip3.ipynb).\n",
    "\n",
    "In the [previous chapter](well_mixed.ipynb) we used objects of type [steps.model.Reac](API_1/API_model.rst#steps.API_1.model.Reac) to represent\n",
    "a reaction taking place inside a volume. In this chapter we consider another\n",
    "type of kinetic reaction represented by the [steps.model.SReac](API_1/API_model.rst#steps.API_1.model.SReac) class\n",
    "(associated with the [steps.model.Surfsys](API_1/API_model.rst#steps.API_1.model.Surfsys) container) which defines a\n",
    "reaction taking place on a `surface` (or `patch`) connecting two compartments\n",
    "(arbitrarily naming one of them the “inner” compartment, and the other one\n",
    "the “outer” compartment). Reactants and products can therefore be freely moving\n",
    "around in a volume or embedded in a surface.\n",
    "Therefore, it is necessary to firstly specify the location of the reactant\n",
    "and product species.\n",
    "\n",
    "**_Note_**: *Surface reactions are designed to represent reactions where one\n",
    "   reactant is embedded in a membrane, but in fact if all reactants and\n",
    "   products belong to the same compartment and none appear on a patch\n",
    "   it will behave exactly like the equivalent Reac object.*\n",
    "\n",
    "The stoichiometry of the surface reaction is specified by the following lists:\n",
    "\n",
    "* Species on the left hand side of the reaction (the reactants):\n",
    "    * Species on the surface (`slhs`).\n",
    "    * Species in the “outer” compartment (`olhs`).\n",
    "    * **or** Species in the “inner” compartment (`ilhs`). \n",
    "\n",
    "* Species on the right hand side of the reaction (the products):\n",
    "    * Species on the surface (`srhs`).\n",
    "    * Species in the “outer” compartment (`orhs`).\n",
    "    * Species in the “inner” compartment (`irhs`).\n",
    "\n",
    "__Note__: *Reactant species cannot belong to different compartments,\n",
    "   so attempting to create an SReac object with both* `olhs` *and* `ilhs` *will\n",
    "   result in an error.*\n",
    "\n",
    "To become familiar with these objects we will build a simplified version of\n",
    "the inositol 1,4,5-trisphosphate (IP $_{3}$) model\n",
    " (described in Doi T, et al,*Inositol\n",
    "   1,4,5-Triphosphate-Dependent* $Ca^{\\text{2+}}$ *Threshold Dynamics\n",
    "   Detect Spike Timing in Cerebellar Purkinje Cells*, J Neurosci 2005, 25(4):950-961) in STEPS.\n",
    "   \n",
    "![The IP3 receptor model](images/ip3_2.png)\n",
    " \n",
    "In the IP3 receptor model, reactions (i.e. receptor binding of IP3 andcalcium molecules) take place on the membrane separating the endoplasmicreticulum (ER) and the cytosol. Therefore, we will use Surface Reaction objects to describe the reactions.\n",
    "   \n",
    "   \n",
    "In the figure below we can see a  schematic diagram of the states\n",
    "and transitions in the model. We see that our reactions take place on the\n",
    "membrane between the cytosol and the Endoplasmic Reticulum (ER) and therefore\n",
    "must be described by an SReac object, with each “binding” reaction described\n",
    "by a second order surface reaction and each “unbinding” reaction by a first\n",
    "order surface reaction.\n",
    "\n",
    " \n",
    "![The IP3 receptor kinetic scheme](images/IP3_schem_new.png)\n",
    "\n",
    "\n",
    "We will go through the Python code to build this model in STEPS,\n",
    "but providing only brief descriptions of operations we are familiar\n",
    "with from the previous chapter.\n",
    "\n",
    "## Model specification\n",
    "\n",
    "### Model container\n",
    "\n",
    "First we need to import the [steps.model](API_1/API_model.rst) package and create a [steps.model.Model](API_1/API_model.rst#steps.API_1.model.Model)\n",
    "container object named `mdl`, as we did in  [previous chapter](well_mixed.ipynb):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import steps.model as smodel\n",
    "mdl = smodel.Model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Species\n",
    "\n",
    "Now we create the species in this model based on the above diagram of the states and transitions,\n",
    "declaring all receptor states as separate [steps.model.Spec](API_1/API_model.rst#steps.API_1.model.Spec) objects. Recall that all\n",
    "identifier strings must be unique and we should also make sure not to reuse\n",
    "a variable name so that we do not lose any references to the objects we create:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
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   "outputs": [],
   "source": [
    "# Calcium\n",
    "Ca = smodel.Spec('Ca', mdl)\n",
    "# IP3\n",
    "IP3 = smodel.Spec('IP3', mdl)\n",
    "############### receptor state objects ###############\n",
    "# receptor state: 'naive' state (no bound ligands)\n",
    "R = smodel.Spec('R', mdl)\n",
    "\n",
    "# receptor state: bound IP3\n",
    "RIP3 = smodel.Spec('RIP3', mdl)\n",
    "\n",
    "# receptor state: bound IP3 and Ca (open)\n",
    "Ropen = smodel.Spec('Ropen', mdl)\n",
    "# receptor state: Ca bound to one inactivation site\n",
    "RCa = smodel.Spec('RCa', mdl)\n",
    "# receptor state: Ca bound to two inactivation sites\n",
    "R2Ca = smodel.Spec('R2Ca', mdl)\n",
    "# receptor state: Ca bound to three inactivation sites\n",
    "R3Ca = smodel.Spec('R3Ca', mdl)\n",
    "\n",
    "# receptor state: Ca bound to four inactivation sites\n",
    "R4Ca = smodel.Spec('R4Ca', mdl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Surface System\n",
    "\n",
    "Next we create a *surface system*. The function of a surface system is similar\n",
    "to the volume system used to group [steps.model.Reac](API_1/API_model.rst#steps.API_1.model.Reac) objects we saw in the [tutorial on well mixed models](well_mixed.ipynb). Basically,\n",
    "surface systems group a set of reaction rules that are described\n",
    "by [steps.model.SReac](API_1/API_model.rst#steps.API_1.model.SReac) objects. It is often the case that such reactions are modeled as\n",
    "taking place on a membrane surface and not within a volume, although this is\n",
    "actually not a necessity. We need to create an object of type\n",
    "[steps.model.Surfsys](API_1/API_model.rst#steps.API_1.model.Surfsys). The arguments to the class constructor are an identifier\n",
    "string and a reference to the parent [steps.model.Model](API_1/API_model.rst#steps.API_1.model.Model) object:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "surfsys = smodel.Surfsys('ssys', mdl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reactions\n",
    "\n",
    "Now it is time to specify the reaction stoichiometry, shown in\n",
    "diagram of states. Unlike in [Well-Mixed Reaction Systems](well_mixed.ipynb),\n",
    "the reactions in this model are defined by the surface reaction objects\n",
    "([steps.model.SReac](API_1/API_model.rst#steps.API_1.model.SReac)) in which the arguments include information about\n",
    "which compartment or patch the reactants and products belong to. Therefore each reactant\n",
    "and product of a surface reaction may be a chemical species within a volume or one which\n",
    "is bound to a surface.\n",
    "\n",
    "Therefore surface reaction objects can deal with three types of reactions, classified by the locations of the reactants, and the object is smart enough to know what type of reaction it is so that the solver knows what kind of reaction it is dealing with. The three types of reactions are;\n",
    "\n",
    "- **Volume-Surface reactions**.   In this case molecules within a volume interact with molecules embedded in a surface and result in products that may reside within in a volume or a surface. The units for the reaction parameters in this case are the same as for ordinary volume reactions, namely: a first order reaction parameter has units $s^{-1}$; a second order reaction parameter has units $\\left(M.s\\right)^{-1}$; a third order reaction $\\left(M^{2}.s\\right)^{-1}$; and so on.\n",
    "\n",
    "- **Surface-Surface reactions**.   In this case the reactants are all embedded in a surface. Quite clearly, the dimensions of the reaction are different from a volume reaction and the reaction parameter is assumed to be two-dimensional. This is an important point because the reaction parameter will be treated differently from a volume-volume or volume-surface interaction. A further complication is that parameters for ordinary volume reactions are based on the litre, where there is no convenient equivalent 2D concentration unit.   Surface-surface reaction parameters are based on units of area of `square meters`. **A first order surface-surface reaction parameter is therefore required in units of** $s^{-1}$; **a second-order surface-surface reaction parameter has units** $\\left(mol.m^{-2}\\right)^{-1}.s^{-1}$; **a third-order surface-surface reaction parameter has units** $\\left(mol.m^{-2}\\right)^{-2}.s^{-1}$; and so on.   Zero-order surface reactions are not supported because of the ambiguity of interpreting the reaction parameter.\n",
    "\n",
    "- **Volume-Volume reactions**. \n",
    "   It is possible for a surface reaction to contain reactant species that are all in a volume, in which case the reaction behaves similarly to an ordinary volume reaction ([steps.model.Reac](API_1/API_model.rst#steps.API_1.model.Reac)), though products may belong to connected volumes or surfaces.\n",
    "\n",
    "\n",
    "As mentioned previously, to create our surface reaction objects we have to include some information about the location of the reaction:\n",
    "which compartment are the reactants to be found in, and are any molecules embedded in a surface and which of the two compartments that the surface connects are the products injected into? We supply this information to STEPS by labelling our compartments that a patch connects, arbitrarily choosing the labels 'inner' and 'outer'. When the surface reaction's parent surface system object is added to a certain patch, the compartment labelling in the surface reaction stoichiometry will match the compartment labelling in the patch definition. We will come to creating a patch later in this chapter.\n",
    "\n",
    "So, at this stage we must chose which compartment we will label 'outer'\n",
    "and which we will label 'inner' and make sure to maintain this labelling\n",
    "throughout our definitions, and also in our geometry description.\n",
    "We chose to label the cytosol as the 'outer' compartment and the ER\n",
    "as the 'inner' compartment, so should be very careful that this ties in correctly to our description when\n",
    "we create our [steps.geom.Patch](API_1/API_geom.rst#steps.API_1.geom.Patch) object to represent a surface to connect the two compartments.\n",
    "\n",
    "We will first complete all “forward” binding reactions, recalling that\n",
    "“forward” and “backward” reactions must be declared separately:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# The 'forward' binding reactions:\n",
    "R_bind_IP3_f = smodel.SReac('R_bind_IP3_f', surfsys,\n",
    "                            olhs=[IP3], slhs=[R], srhs=[RIP3])\n",
    "\n",
    "RIP3_bind_Ca_f = smodel.SReac('RIP3_bind_Ca_f', surfsys,\n",
    "                              olhs=[Ca], slhs=[RIP3], srhs=[Ropen])\n",
    "\n",
    "R_bind_Ca_f = smodel.SReac('R_bind_Ca_f', surfsys,\n",
    "                           olhs=[Ca], slhs=[R], srhs=[RCa])\n",
    "\n",
    "RCa_bind_Ca_f = smodel.SReac('RCa_bind_Ca_f', surfsys,\n",
    "                             olhs=[Ca], slhs=[RCa],srhs=[R2Ca])\n",
    "\n",
    "R2Ca_bind_Ca_f = smodel.SReac('R2Ca_bind_Ca_f', surfsys,\n",
    "                              olhs=[Ca], slhs=[R2Ca], srhs=[R3Ca])\n",
    "\n",
    "R3Ca_bind_Ca_f = smodel.SReac('R3Ca_bind_ca_f', surfsys,\n",
    "                              olhs=[Ca], slhs=[R3Ca], srhs=[R4Ca])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# The 'backward' unbinding reactions:\n",
    "R_bind_IP3_b = smodel.SReac('R_bind_IP3_b', surfsys,\n",
    "                            slhs=[RIP3], orhs=[IP3], srhs=[R])\n",
    "\n",
    "RIP3_bind_Ca_b = smodel.SReac('RIP3_bind_Ca_b', surfsys,\n",
    "                              slhs=[Ropen], orhs=[Ca], srhs=[RIP3])\n",
    "\n",
    "R_bind_Ca_b = smodel.SReac('R_bind_Ca_b', surfsys,\n",
    "                           slhs=[RCa], orhs=[Ca], srhs=[R])\n",
    "\n",
    "RCa_bind_Ca_b = smodel.SReac('RCa_bind_Ca_b', surfsys,\n",
    "                             slhs=[R2Ca], orhs=[Ca], srhs=[RCa])\n",
    "\n",
    "R2Ca_bind_Ca_b = smodel.SReac('R2Ca_bind_Ca_b', surfsys,\n",
    "                              slhs=[R3Ca], orhs=[Ca], srhs=[R2Ca])\n",
    "\n",
    "R3Ca_bind_Ca_b = smodel.SReac('R3Ca_bind_ca_b', surfsys,\n",
    "                              slhs=[R4Ca], orhs=[Ca], srhs=[R3Ca])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We model our calcium flux from the ER to the cytosol simply as a second order reaction.\n",
    "In effect we are saying, when such a reaction takes place, a calcium ion from\n",
    "the ER passes instantaneously through an open receptor to the cytosol."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# Ca ions passing through open IP3R channel\n",
    "R_Ca_channel_f = smodel.SReac('R_Ca_channel_f', surfsys,\\\n",
    "                              ilhs=[Ca], slhs=[Ropen], orhs=[Ca], srhs=[Ropen])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that it is vital that when we come to describing our geometry\n",
    "that our compartment labelling is maintained. At this level we have specified\n",
    "whether some reactants and products belong to the 'inner' or 'outer' compartment,\n",
    "with the intention to label the cytosol as the 'outer' compartment and the ER as\n",
    "the 'inner' compartment, but we will not actually make that distinction until\n",
    "we come to describing our geometry.\n",
    "\n",
    "Next, we set all reaction constants' default values (see Doi T, et al,*Inositol\n",
    "   1,4,5-Triphosphate-Dependent* $Ca^{\\text{2+}}$ *Threshold Dynamics\n",
    "   Detect Spike Timing in Cerebellar Purkinje Cells*, J Neurosci 2005, 25(4):950-961). These constants\n",
    "could have been passed to the initializer when we were creating our [steps.model.SReac](API_1/API_model.rst#steps.API_1.model.SReac) objects,\n",
    "but for clarity we chose to set them here with method `setKcst`. Since these are volume-surface interactions, we must make\n",
    "sure to supply our values in Molar units as  discussed previously in this chapter.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "R_bind_IP3_f.setKcst(1000e6)\n",
    "R_bind_IP3_b.setKcst(25800)\n",
    "RIP3_bind_Ca_f.setKcst(8000e6)\n",
    "RIP3_bind_Ca_b.setKcst(2000)\n",
    "R_bind_Ca_f.setKcst(8.889e6)\n",
    "R_bind_Ca_b.setKcst(5)\n",
    "RCa_bind_Ca_f.setKcst(20e6)\n",
    "RCa_bind_Ca_b.setKcst(10)\n",
    "R2Ca_bind_Ca_f.setKcst(40e6)\n",
    "R2Ca_bind_Ca_b.setKcst(15)\n",
    "R3Ca_bind_Ca_f.setKcst(60e6)\n",
    "R3Ca_bind_Ca_b.setKcst(20)\n",
    "R_Ca_channel_f.setKcst(2e8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geometry specification\n",
    "\n",
    "The next step is to create the geometry for the model. We will chose well-mixed\n",
    "geometry, as in the [chapter on well-mixed models](well_mixed.ipynb), but we now have two compartments which\n",
    "are connected by a surface 'patch'. We create two [steps.geom.Comp](API_1/API_geom.rst#steps.API_1.geom.Comp) objects\n",
    "to represent the Endoplasmic Reticulum (which we intend to label the 'inner'\n",
    "compartment) and the cytosol ('outer' compartment), and a [steps.geom.Patch](API_1/API_geom.rst#steps.API_1.geom.Patch)\n",
    "object to represent the ER membrane between the ER and cytosol.\n",
    "We then add the stoichiometry we previously defined and grouped in our\n",
    "surface system object to the patch object. Note that any volume-reactions we defined with `Reac` objects and grouped in `Volsys`containers would be added to the `Compartments` at this stage.\n",
    "\n",
    "First we create the two well-mixed compartments. With more than one compartment\n",
    "in the model we must make sure that the identifier strings are be unique amongst\n",
    "all compartments in the geometry container. We create the cytosol compartment\n",
    "with the minimum information (identifier string and reference to container),\n",
    "setting the volume with class method [steps.geom.Comp.setVol](API_1/API_geom.rst#steps.API_1.geom.Comp.setVol), but set the volume of the ER during\n",
    "object construction purely to demonstrate the two possible ways to achieve the task:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import steps.geom as swm\n",
    "wmgeom = swm.Geom()\n",
    "\n",
    "# Create the cytosol compartment\n",
    "cyt = swm.Comp('cyt', wmgeom)\n",
    "cyt.setVol(1.6572e-19)\n",
    "\n",
    "# Create the Endoplasmic Reticulum compartment\n",
    "ER = swm.Comp('ER', wmgeom, vol=1.968e-20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now create a [steps.geom.Patch](API_1/API_geom.rst#steps.API_1.geom.Patch) object, defining the 'inner' and 'outer' compartments.\n",
    "We wish to label the ER as the 'inner' compartment and the cytosol as the 'outer' compartment,\n",
    "which we achieve by their order to the patch object constructor.\n",
    "The 3rd (required) argument to the constructor (here I am calling the string Id the 1st argument, not the 'zeroth') is a reference to the 'inner'\n",
    "compartment and the 4th (optional) argument is a reference to the 'outer'\n",
    "compartment. It is vital that care is taken in the order of the [steps.geom.Comp](API_1/API_geom.rst#steps.API_1.geom.Comp) objects to the constructor, so that the required labelling from or surface reaction\n",
    "definitions is maintained. **Note**: *A Patch must have an inner compartment by convention,\n",
    "   but does not require an outer compartment. This is an easy way to remember the order to the constructor; since an inner compartment is always required it must come first to the constructor, and the optional outer compartment comes after. Obviously any surface reaction\n",
    "   rules that contain reactants or products in the outer compartment cannot be\n",
    "   added to a Patch that doesn't have an outer compartment.*\n",
    "\n",
    "We can check the labelling is as desired after\n",
    "object construction if we like with methods [steps.geom.Patch.getOComp](API_1/API_geom.rst#steps.API_1.geom.Patch.getOComp) and [steps.geom.Patch.getIComp](API_1/API_geom.rst#steps.API_1.geom.Patch.getIComp). **Note**: *Typically, get functions return references to the object, not the identifier\n",
    "   string, so we can use any of the object methods on the returned reference to\n",
    "   access information about the object. Here we use method getID, which returns\n",
    "   the identifier string of the object.*\n",
    "\n",
    "We also set the surface area of the patch:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "# ER is the 'inner' compartment, cyt is the 'outer' compartment\n",
    "memb = swm.Patch('memb', wmgeom, ER, cyt)\n",
    "memb.addSurfsys('ssys')\n",
    "memb.setArea(0.4143e-12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inner compartment to memb is ER\n",
      "Outer compartment to patch is cyt\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function # for backward compatibility with Py2\n",
    "\n",
    "print('Inner compartment to memb is', memb.getIComp().getID())\n",
    "print('Outer compartment to patch is', memb.getOComp().getID())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation with `Wmdirect`\n",
    "\n",
    "Now the model is completed and ready for simulation. To run the simulation,\n",
    "we create a random number generator object, as we did previously in\n",
    "[Well-Mixed Reaction Systems](well_mixed.ipynb):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import steps.rng as srng\n",
    "r = srng.create('mt19937', 512)\n",
    "r.initialize(7233)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and use the [steps.solver.Wmdirect](API_1/API_solver.rst#steps.API_1.solver.Wmdirect) solver as we also did in [Well-Mixed Reaction Systems](well_mixed.ipynb):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "import steps.solver as ssolver\n",
    "sim = ssolver.Wmdirect(mdl, wmgeom, r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To run the simulation and plot the data, we import modules from `numpy` \n",
    "and create arrays to store the data, as we also did previously. We also import modules from `matplotlib` in order to plot the results. This time we also want to plot the standard deviation so we\n",
    "create arrays to store that data too. Here we create variable `NITER`\n",
    "and assign it the value 100, the number of iterations we wish to run. **Note**: *It is perhaps better to group simulation parameters together at the\n",
    "   beginning of the script (such as number of iterations, simulation end time,\n",
    "   data collection time step, etc) so we can change a parameter simply by\n",
    "   changing one variable, which reduces the amount of typing and reduces the\n",
    "   scope for error. This is an approach we will adopt in the* [next chapter](diffusion.ipynb).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "NITER = 100\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "tpnt = np.arange(0.0, 0.201, 0.001)\n",
    "res = np.zeros([NITER, 201, 2])\n",
    "res_std = np.zeros([201, 2])\n",
    "res_std1 = np.zeros([201, 2])\n",
    "res_std2 = np.zeros([201, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At the beginning of the simulation, we reset the solver state and set the\n",
    "initial concentration or count (by “count” we mean the number of molecules)\n",
    "of each species (any species we don't explicitly assign a concentration or\n",
    "count to will be initialized with the default value of zero which was set\n",
    "when we called the reset function) and run the simulation for `NITER` number\n",
    "of iterations. In the following example code, we record the number of IP3\n",
    "receptors in open state ('Ropen' in 'memb') and the concentration of calcium\n",
    "in the cytosol ('Ca' in 'cyt'). We include a `pylab.plot` call within our main\n",
    "loop to plot the number of open receptors for each individual iteration:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qpCrXz6vPBWsAIsU6wuvuQ8R/Q/C6ou0oHcP0g4BO5nLnwt2G1zmK2JZw+bK0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\njvA6ODg4ODg4ODhUNP4/nm/OdZ7Ytz0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f7fbf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "for i in range (0, NITER):\n",
    "    sim.reset()\n",
    "    sim.setCompConc('cyt', 'Ca', 3.30657e-8)\n",
    "    sim.setCompCount('cyt', 'IP3', 6)\n",
    "    sim.setCompConc('ER', 'Ca', 150e-6)\n",
    "    sim.setCompClamped('ER', 'Ca', True)\n",
    "    sim.setPatchCount('memb', 'R', 160)\n",
    "    for t in range(0, 201):\n",
    "        sim.run(tpnt[t])\n",
    "        res[i, t, 0] = sim.getPatchCount('memb', 'Ropen')\n",
    "        res[i, t, 1] = sim.getCompConc('cyt', 'Ca')\n",
    "    plt.plot(tpnt, res[i,:,0], color='blue', linewidth=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also calculate the mean and standard deviation of our data using\n",
    "NumPy functions and plot them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12009d610>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vU+0dOnSIXbt2UV1dHY1QRUREZBASYx2AjDydnZ00NjZ2SagzMzOpq6tj165d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wAAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1200f6550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "res_mean = np.mean(res, 0)\n",
    "res_std = np.std(res, 0)\n",
    "res_std1 = res_mean[:,0] + res_std[:,0]\n",
    "res_std2 = res_mean[:,0]- res_std[:,0]\n",
    "\n",
    "plt.plot(tpnt, res_mean[:,0], color='black', linewidth=2.0, label='mean')\n",
    "plt.plot(tpnt, res_std1, color='gray', linewidth=1.0, label='std')\n",
    "plt.plot(tpnt, res_std2,color='gray', linewidth=1.0)\n",
    "\n",
    "plt.xlabel('Time (sec)')\n",
    "plt.ylabel('# IP3 receptors in open state')\n",
    "plt.title('IP3 receptor model: %d iterations with Wmdirect' % NITER)\n",
    "plt.ylim(0)\n",
    "plt.legend()"
   ]
  }
 ],
 "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
}