{
 "cells": [
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    "# Well-Mixed Reaction System\n",
    "\n",
    "<div class=\"admonition note\">\n",
    "**Topics**: Model declaration, context managers, reaction declaration, simulation paths, data saving, data access.\n",
    "</div>\n",
    "\n",
    "The corresponding python script: [STEPS_Tutorial_wm.py](https://github.com/CNS-OIST/STEPS_Example/tree/master/user_manual/source/API_2/scripts/STEPS_Tutorial_wm.py)\n",
    "\n",
    "In this chapter, we’ll use some simple classical reaction systems as examples to introduce the basics of using STEPS. More specifically, we’ll focus on reaction systems that occur in a single, well-mixed reaction volume. The topics presented in later chapters (such as surface-volume interactions, diffusion, 3D environments, etc) will build on the material presented in this chapter.\n",
    "\n",
    "In our first STEPS simulation, we'll be working with the following simple system,\n",
    "which consists of a single reversible reaction:\n",
    "\n",
    "\\begin{equation}\n",
    "A+B\\underset{{k_{b}}}{\\overset{{k_{f}}}{{\\rightleftarrows}}}C\n",
    "\\end{equation}\n",
    "\n",
    "with 'forward' and 'backward' reaction constants $k_{f}$ and $k_{b}$,\n",
    "respectively.\n",
    "\n",
    "## Model declaration\n",
    "\n",
    "The first thing we need to do, is to write some Python code that “passes” this equation on to STEPS. This is called model specification, which in STEPS consists of building a hierarchy of Python objects that list the species occurring in your model, their relevant chemical and physical properties and their interactions. \n",
    "\n",
    "We first need to import the interface packages and to create a Model container."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import steps.interface\n",
    "\n",
    "from steps.model import *\n",
    "from steps.geom import *\n",
    "from steps.rng import *\n",
    "from steps.sim import *\n",
    "from steps.saving import *\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "mdl = Model()\n",
    "\n",
    "r = ReactionManager()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the first line `import steps.interface` is necessary to use the newest API; without this line, the previous API (see [API_1 corresponding chapter](../well_mixed.ipynb)) would be used.\n",
    "\n",
    "We then import the various STEPS modules that will be used in this chapter as well as the plotting module from [matplotlib](https://matplotlib.org/) and the [numpy](https://numpy.org/) module.\n",
    "\n",
    "Our first actual line of code `mdl = Model()` creates a top-level container object for our model (steps.API_2.model.Model). This top level container is required for all simulations in STEPS but itself does not contain much information and merely acts as a hub that allows the other objects in the model specification to reference each other.\n",
    "\n",
    "In addition to the model container, we also declare a `ReactionManager` object that will be used to declare reactions. It does not have to be named `r` but giving it a short name is preferable.\n",
    "\n",
    "We then proceed to the declaration of Species and Reactions. Instead of specifying, for each object that we will create, which model it is attached to, we wrap all these declarations with a context manager. Everything that will be declared in the block defined by the `with mdl:` line will be declared for model `mdl`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with mdl:\n",
    "    molA, molB, molC = Species.Create()\n",
    "\n",
    "    vsys = VolumeSystem.Create()\n",
    "\n",
    "    with vsys:\n",
    "        molA + molB <r['r1']> molC\n",
    "        r['r1'].K = 0.3e6, 0.7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Species\n",
    "\n",
    "The first line inside this block (`molA, molB, molC = Species.Create()`) declares three chemical species respectively named `molA`, `molB`, and `molC`. In STEPS, objects are uniquely indentified with a name, it is possible to give a custom name explicitely to STEPS objects with e.g. `molD = Species(name='myCustomMolD')`. In most cases however, it is more convenient to let STEPS name the objects automatically.\n",
    "\n",
    "In general, all STEPS objects can be created and implicitely named by using the auto naming syntax: `Class.Create(...)`. Objects are named according to the name of the variable they will be assigned to. If no parameters need to be provided for the object creation, as it is the case for classes `Species` and `VolumeSystem`, all the objects can be created at once without providing any parameter to the `Create` method.\n",
    "\n",
    "### Volume system\n",
    "\n",
    "The second line of the above code (`vsys = VolumeSystem.Create()`) creates a volume system. Volume systems (objects of class steps.API_2.model.VolumeSystem) are container objects that group a number of stoichiometric reaction rules (in later chapters we’ll see how diffusion rules can also be added to these volume systems). The user has the option of grouping all reactions in the entire system into one single big volume system, or using multiple volume systems to organize reaction rules that belong together. The second option may be preferred for larger models, but for our simple example we only require one volume system.\n",
    "\n",
    "Since we used the `Class.Create()` automatic naming syntax, our volume system will be named `vsys`.\n",
    "\n",
    "### Reactions\n",
    "Reactions are then declared inside of a volume system by using the context manager syntax: all reactions declared in the block defined by the `with vsys:` line will be declared for the volume system `vsys`.\n",
    "\n",
    "Reactions are written in a way that is similar to how they would be written down on paper. Both left hand side (`molA + molB`) and right hand side (`molC`) are specified using the addition operator `+` and stoichiometry is optionally specified using the multiplication operator `*`. We could thus declare a reaction like:\n",
    "```python\n",
    "2*molA + molB <r[1]> 3*molC\n",
    "```\n",
    "In between the two sides, we use the `ReactionManager` (`r[id]`) to identify the reaction we are declaring. In our main example, we give an explicit name `'r1'` to the reaction because we will want to modify it during simulation later. For most reactions however, it is simpler to use an integer like `r[1]` that will serve as a temporary identifier until we set the rate constants of the reaction.\n",
    "Reversible reactions are writen using `... <r[id]> ...` while irreversible reactions are writen with `... >r[id]> ...`. The only difference is the orientation of the first comparison operator.\n",
    "\n",
    "If one of the reaction sides is empty, the `None` keyword should be used. For example a zero order reaction:\n",
    "```python\n",
    "None >r[1]> molA\n",
    "```\n",
    "creates `molA` out of thin air while\n",
    "```python\n",
    "molA >r[1]> None\n",
    "```\n",
    "destroys it. Note that both reactions use the same temporary identifier `r[1]`, this is totally ok: `r[1]` designates the first reaction up until the `molA >r[1]> None` line and designates this latter reaction up until the `r[1]` identifier is used to declare a new reaction.\n",
    "\n",
    "Care should be used in the case of empty reaction sides because either situation could break physical laws such as the conservation of mass, although they are available because they can be useful for some simulation approximations.\n",
    "\n",
    "#### Reaction rate constants\n",
    "\n",
    "The `ReactionManager` `r` is used to access the reactions using their identifiers. Just after declaring the reaction, we set its rate constants with\n",
    "```python\n",
    "    r['r1'].K = 0.3e6, 0.7\n",
    "```\n",
    "Since the `'r1'` reaction is reversible, we need to provide two rate constants as a tuple (i.e separated by a comma): the first one sets the forward rate constant while the second sets the backward rate constant. \n",
    "\n",
    "Irreversible reaction only require the forward rate constant, so only one value is given as a value to `r[1].K`. It is also possible to set the forward and backward parts of a reversible reaction individualy:\n",
    "```python\n",
    "    r['r1']['fwd'].K = 0.3e6\n",
    "    r['r1']['bkw'].K = 0.7\n",
    "```\n",
    "The two above lines are equivalent to the single line we used in the main example.\n",
    "\n",
    "If no rate constants are declared, they will default to `0`. Note that the reaction is only truly declared in STEPS when the `with vsys:` block is exited.\n",
    "\n",
    "These rate constants can also be changed later on during the simulation, but values given here will be used as default values when a simulation state is initialized.\n",
    "*Generally speaking, physical constants in STEPS must be specified in SI units*.\n",
    "However, the s.i derived unit for volume is the cubic meter, which means that the s.i derived unit for concentration is mole per cubic meter, and reaction constants would be based on cubic meters, i.e. a second order reaction constant should have units of metres cubed per mole-second ($m^{3}\\left(mol.s\\right)^{-1}$). However, the convention in chemical kinetics is to base reaction parameters on Molar units (M = mol/litre) (i.e. based on the litre rather than the cubic metre) and this convention is followed in STEPS. The actual interpretation of the unit of a reaction rule depends on the order of that reaction.\n",
    "\n",
    "In other words, it depends on the number of species in the left hand side. **The constant for a zero order reaction in STEPS** **has units** $M.s^{-1}$; **for a first order reaction rule has units** $s^{-1}$; **for a second order reaction the units are** $\\left(M.s\\right)^{-1}$; **for a third order reaction** $\\left(M^{2}.s\\right)^{-1}$; and so on (while there is no upper limit on the order of the reaction when working with Reac objects within\n",
    "the context of package steps.model, STEPS simulators will not deal with any\n",
    "reaction rule that has an order larger than 4). These units are not strictly s.i. units, however **all parameters, other than reactions constants, in STEPS must be given in base or derived s.i. units**, which includes the unit of $m^{3}$ for volume.\n",
    "\n",
    "## Geometry declaration\n",
    "\n",
    "Notice that we have said nothing about the actual geometry of our model at this point, nor have we said anything related to the simulation itself (initial conditions, special events during the simulation, etc). We have just created a hierarchy of Python objects that describes the interactions between chemical species and we have done this on a rather abstract level.\n",
    "\n",
    "Before we can start doing simulations, we need to say something about the environment in which our reactions will occur. Specifically, we need to describe the volume compartments in which reactions take place, and sometimes also the surface patches around or in between these compartments (patches are described in more detail in the next chapter). We then link each of these compartments with one or more of the volume systems defined in the kinetic model, in a process called annotation. There are currently two types of geometry that can be specified in STEPS:\n",
    "\n",
    "1. *Well-mixed geometry*. In this type of geometry description, compartments are described\n",
    "   only by their volume in cubic meters and patches by their area in\n",
    "   square meters and connectivity to compartments. Nothing is said\n",
    "   about the actual shape.\n",
    "\n",
    "2. *Tetrahedral mesh geometry*. In this type of geometry, a compartment is a collection of 3D tetrahedral\n",
    "   voxels and a patch is a 2D section between compartments composed of\n",
    "   triangular surface connecting tetrahedrons.\n",
    "\n",
    "We will talk about tetrahedral meshes (and their relationship with\n",
    "well-mixed geometry) in the chapter on [Simulating Diffusion in Volumes](STEPS_Tutorial_Diffusion.ipynb).\n",
    "In this chapter, however, we will restrict ourselves to well-mixed geometry,\n",
    "because we will only use the well-mixed stochastic solver. Specifying a\n",
    "well-mixed compartment that can be used together with the kinetic model\n",
    "from the previous section is very easy. We will make use of classes defined in the [steps.geom](API_geom.rst) \n",
    "module that we already imported. \n",
    "\n",
    "The declaration of the geometry works in a similar way to the model declaration, involving a `geom` container object and making use of the context manager syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "geom = Geometry()\n",
    "\n",
    "with geom:\n",
    "    comp = Compartment.Create(vsys, 1.6667e-21)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since our model is very simple, we only create one [compartment](API_geom.rst#steps.API_2.geom.Compartment) called comp. The compartment is created using the automatic naming syntax but this time, several arguments are passed. The created compartment is associated to the volume system `vsys` and has volume $1.6667e-21 m^{\\text{3}}$ (volume is given in SI units). It is also possible to first declare the compartment and set these later:\n",
    "```python\n",
    "comp = Compartment.Create()\n",
    "comp.addSystem(vsys)\n",
    "comp.Vol = 1.6667e-21\n",
    "```\n",
    "or\n",
    "```python\n",
    "comp = Compartment.Create(vsys)\n",
    "comp.Vol = 1.6667e-21\n",
    "```\n",
    "If several compartments should be created, and only one argument should be passed to the constructor of each compartment, the automatic naming syntax can be used as follows:\n",
    "```python\n",
    "comp1, comp2 = Compartment.Create(vsys1, vsys2)\n",
    "```\n",
    "`comp1` will be associated to `vsys1` and `comp2` to `vsys2`. If more than one argument needs to be passed, they need to be grouped with the `Params` class from the `utils` package:\n",
    "```python\n",
    "from steps.utils import Params\n",
    "\n",
    "comp1, comp2 = Compartment.Create(\n",
    "    Params(vsys1, 1.5e-21),\n",
    "    Params(vsys2, 2e-21)\n",
    ")\n",
    "```\n",
    "\n",
    "Note that each compartment can be associated to more than one volume system by using e.g. `comp.addSystem(vsys2)`.\n",
    "\n",
    "## Simulation declaration\n",
    "\n",
    "With all this in place, we can finally start performing simulations. The simulator (or *solver*) we'll be using here is the `'Wmdirect'` solver.\n",
    "`'Wmdirect'` is an implementation of Gillespie's Direct Method (see Gillespie, *Exact stochastic simulation of coupled chemical reactions*, J Phys Chem 1977, 81:2340-2361) for stochastic simulation and has the following properties:\n",
    "\n",
    "* It's a *well-mixed* solver, meaning that you will need to present\n",
    "  it with well-mixed geometry.  **Note**: *if you present a well-mixed solver in STEPS with a tetrahedral\n",
    "   mesh, the solver will automatically extract the well-mixed properties\n",
    "   (i.e. the volumes of compartments, the areas of patches and their connectivity)\n",
    "   from the mesh.*    Well-mixed solvers have no\n",
    "  concept of concentration gradients within a given compartment, but rather\n",
    "  assume that all molecules in any given compartment are kept uniformly\n",
    "  distributed by elastic (non-reactive) collisions between reaction events.\n",
    "  Therefore there is also no concept of diffusion within a compartment.\n",
    "  However, we will later see that even in simulations with well-mixed solvers,\n",
    "  it is possible to implement diffusive fluxes in between compartments,\n",
    "  by linking them with patches.\n",
    "\n",
    "* It's a *stochastic* solver, meaning that it uses random numbers to create\n",
    "  possible “realizations” (also called “iterations”) of the stochastic\n",
    "  interpretation of the reaction system. In other words, for the same set\n",
    "  of initial conditions, running the simulation multiple times (with different\n",
    "  initial seed values for the random number generator) will generate different\n",
    "  results each time.\n",
    "\n",
    "* It's a *discrete* stochastic solver, meaning that the amount of mass in the\n",
    "  system is (at least internally) not being tracked over time as continuous\n",
    "  concentrations, but as integer molecular counts. This may be a negligible\n",
    "  distinction with large numbers of molecules present in the system, but it\n",
    "  becomes very important when any species involved in the system has a small\n",
    "  population of only a few molecules (especially when these particular molecules\n",
    "  are involved in some feedback mechanism). Consequently, each realization is a\n",
    "  sequence of discrete, singular reaction events.\n",
    "\n",
    "* It's an *exact* stochastic solver, which means that each iteration is exact\n",
    "  with respect to the master equation governing the reaction system.\n",
    "\n",
    "To perform a simulation of the above kinetic model and geometry with the `'Wmdirect'` solver, we first need to create a random number generator. This must be done explicitly by the user, because this allows you to choose which random number generator to use (even though that choice is rather limited right now) and, more importantly, how to use it. Random number generation objects can be found in package [steps.rng](API_rng.rst):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = RNG('mt19937', 256, 1234)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The random number generator requires three arguments.\n",
    "The first argument selects which type of random number generator we want. STEPS currently only implements two pseudo RNG algorithm, 'mt19937', also known as the “Mersenne Twister” and `'r123'` (see [API reference](API_rng.rst)). The Mersenne Twister is supported because it is considered to be quite simply the current\n",
    "best choice for numerical simulations, because of its large period and fast runtime.\n",
    "The second argument selects how many random numbers are pre-generated and stored in a buffer.\n",
    "The third argument is the seed value used to initialize the random number generator.\n",
    "\n",
    "Note that one can use `rng.initialize(seed)` to explicitely set the seed.\n",
    "\n",
    "We then create a `Simulation` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model checking:\n",
      "No errors were found\n"
     ]
    }
   ],
   "source": [
    "sim = Simulation('Wmdirect', mdl, geom, rng)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Simulation` object is created by specifying the solver (`'Wmdirect'` here), the model, the geometry, and the random number generator. After creating the simulation, the model is automatically checked for potential errors or mistakes (partially declared reactions, reactions with peculiar rates, species that are only ever present on the RHS of reactions, etc.).\n",
    "\n",
    "## Running a simulation\n",
    "\n",
    "We first need to signal the start of a new run by calling `sim.newRun()`, which resets the solver and handles data saving related tasks. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.newRun()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This method sets all values within the simulation “state” to their default values.\n",
    "This state includes the concentration of species in all compartments (set to 0\n",
    "everywhere), rate constants (set to their defaults from the [steps.model.Reaction](API_model.rst#steps.API_2.model.Reaction) objects)\n",
    "etc. If you want to re-initialize the random number generator prior to each\n",
    "individual iteration, setting the seed value right before calling the `newRun`\n",
    "method would be a good choice.\n",
    "\n",
    "After the `newRun` method call, we can start manipulating the “state” of the simulation, i.e. setting up the initial conditions of the simulation.\n",
    "\n",
    "Setting the initial conditions can be done in various ways, depending on which solver is used. While some solvers allow setting the concentration in individual tetrahedron, this does not make sense in a well-mixed solver. STEPS will raise errors if the user tries to set values that cannot be set with the solver they use.\n",
    "A detailed list of which values can be set in each solver is available in the [API reference](API_sim.rst).\n",
    "\n",
    "### Simulation paths\n",
    "\n",
    "With the `'Wmdirect'` well-mixed solver, we can set the concentrations of species in our compartment with simulation paths:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim.comp.molA.Conc = 31.4e-6\n",
    "sim.comp.molB.Conc = 22.3e-6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This means we're setting the concentration of molA to $31.4 \\mu M$ and the concentration of molB to $22.3 \\mu M$ in our compartment comp.\n",
    "We're setting these concentration values at simulation time $t = 0$, but these values can be set at any point in time, to control the concentration of species during simulation.\n",
    "\n",
    "The syntax for setting values in the simulation is called a \"simulation path\" and has the following general form:\n",
    "```python\n",
    "sim.Location.Object.Property = value\n",
    "```\n",
    "We essentially describe a dot-separated path whose root is the simulation object `sim`; we then need to write which `Location` (compartment in our case) should be considered, which `Object` (species in our case) and which `Property` (concentration in our case) we want to set. Getting a value uses the exact same syntax:\n",
    "```python\n",
    "value = sim.Location.Object.Property\n",
    "```\n",
    "\n",
    "All available paths are documented in the [API references](API_sim.rst#simulation-paths). In addition to setting single values in single locations, simulation paths allow to set or get values in a grouped fashion. At each step of the path, several methods ([ALL()](API_sim.rst#steps.API_2.sim.SimPath.ALL), [LIST()](API_sim.rst#steps.API_2.sim.SimPath.LIST) and [MATCH()](API_sim.rst#steps.API_2.sim.SimPath.MATCH)) can be used to group objects. If we had more than one compartment, we could set the concentration of `molB` in all these compartments by using:\n",
    "```python\n",
    "sim.ALL(Compartment).molB.Conc = 1.5e-6\n",
    "```\n",
    "When getting values, this grouping syntax outputs a list of values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[32.0, 22.0, 0.0]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.comp.ALL(Species).Count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we wanted to get a specific order, we could use:\n",
    "```python\n",
    "sim.comp.LIST(molC, molB, molA).Count\n",
    "```\n",
    "This will return a list in which the first element is the number of `molC` in `comp`, the second element the number of `molB`, etc.\n",
    "\n",
    "Here are additional examples of simulation paths:\n",
    "```python\n",
    "sim.comp.molA.Count         # The count of molA in comp\n",
    "sim.comp.r1['fwd'].K        # The rate constant of forward reaction r1 in comp\n",
    "sim.comp.r1['bkw'].K        # The rate constant of backward reaction r1 in comp\n",
    "sim.comp.r1['fwd'].Active   # Whether forward reaction r1 is active in comp\n",
    "sim.comp.r1.Active          # A list of 2 booleans indicating whether forward and backward\n",
    "                            # reactions in r1 are active in comp\n",
    "sim.comp.ALL(Species).Count # A list of integers giving the count of all species in comp\n",
    "```\n",
    "\n",
    "Rather than geting into all the details of this syntax now, we will instead introduce the different possibilities as they become useful in the example models.\n",
    "\n",
    "### Manual data saving\n",
    "\n",
    "Before introducing the automatic data saving features implemented in STEPS, we will quickly go over manual data saving. \n",
    "\n",
    "Simulation objects have a [run(ENDT)](API_sim.rst#steps.API_2.sim.Simulation.run) method that will advance the simulation until time `ENDT` (in seconds). If we want to record data every 1 ms, we will need to call this method repeatedly like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tpnts = np.arange(0.0, 2.001, 0.001)\n",
    "values = []\n",
    "for t in tpnts:\n",
    "    sim.run(t)\n",
    "    values.append(sim.comp.LIST(molA, molB, molC).Count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first array, `tpnts`, contains the time points at which we will pause the simulation. This range of numbers starts at 0.0 and runs to 2.0 seconds with $1ms$ intervals. That gives us a total of 2001 “time points”. For each of these time points `t`, we advance the simulation to `t` and we then get the counts of `molA`, `molB`, and `molC` in `comp`.\n",
    "\n",
    "Finally, we can plot these values using Matplotlib. Due to the low numbers of molecules, we can clearly see the reactions occurring as discrete events."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(tpnts, values)\n",
    "plt.legend(['molA', 'molB', 'molC'])\n",
    "plt.xlabel('Time [s]')\n",
    "plt.ylabel('#molecules')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Saving data automatically\n",
    "\n",
    "Although values can be retrieved during the simulation with simulation paths, as we just saw, STEPS implements an automatic data saving mechanism. This allows users to specify in advance which data should be saved and at which frequency. STEPS will then take care of saving the data as the simulation advances.\n",
    "\n",
    "This automatic data saving mechanism uses the [ResultSelector](API_saving.rst#steps.API_2.saving.ResultSelector) class to declare paths to the data that should be saved. These paths work in the same way as simulation paths:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "rs = ResultSelector(sim)\n",
    "\n",
    "saver = rs.comp.LIST(molA, molB, molC).Count\n",
    "\n",
    "sim.toSave(saver, dt=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we first create a `ResultSelector` object that will be used as a root of all paths. This is necessary in order to distinguish between paths like `sim.comp.molA.Count`, that should return the current number of `molA` in `comp`, and `rs.comp.molA.Count` that returns a `ResultSelector` object that represents this path.\n",
    "\n",
    "In the above code, this `ResultSelector` object is then added to the simulation with the [toSave](API_sim.rst#steps.API_2.sim.Simulation.toSave) method. The `dt` parameter specifies how frequently should the data be saved. One can then call the `run` method on the simulation until the end time, the data will be saved automatically. \n",
    "\n",
    "If we’re using a stochastic simulation algorithm such as that implemented in solver Wmdirect, we’re usually interested in analysing the range of behaviours produced by different iterations. We can easily save data from several runs with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "NITER = 100\n",
    "\n",
    "for i in range(NITER):\n",
    "    sim.newRun()\n",
    "\n",
    "    sim.comp.molA.Conc = 31.4e-6\n",
    "    sim.comp.molB.Conc = 22.3e-6\n",
    "\n",
    "    sim.run(2.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This basically runs `NITER` iterations of the simulation. Note that `sim.newRun()` is called at the start of each loop and the initial conditions are set before calling `sim.run` for the full 2 seconds of simulation.\n",
    "\n",
    "## Accessing the results\n",
    "\n",
    "We now ran all simulations and we want to access the data that was saved. \n",
    "\n",
    "### Saved data\n",
    "\n",
    "We can do so from the `ResultSelector` object that we declared earlier:\n",
    "```python3\n",
    "saver.data[runId, timeId, colId]\n",
    "```\n",
    "\n",
    "<img src=\"images/saver_data_structure.png\" />\n",
    "\n",
    "`saver.data` can be seen as a numpy array with 3 dimensions (see above figure): the first dimension corresponds to the number of runs, the second to the number of saved timesteps and the last one to the number of value saved. Like for numpy arrays, the [basic slicing syntax](https://numpy.org/doc/1.18/reference/arrays.indexing.html) works (see  row / column selection on the figure).\n",
    "\n",
    "Each time we call `sim.newRun()`, it implicitely creates a new matrix for all `ResultSelector` objects that have been linked to the simulation with the `toSave` method. Then, every `dt = 0.001`s, a new column is added to these matrices. The first two dimensions are thus straightforward to understand but the third one is a bit more tricky. In our case, we declared the saver with `saver = rs.comp.LIST(molA, molB, molC).Count`; since there are three species in the `LIST(...)` method, the third dimension has length 3. Equivalently, we could have declared a saver with:\n",
    "```python\n",
    "saver = sim.comp.molA.Count << sim.comp.molB.Conc << sim.comp.molC.Conc\n",
    "```\n",
    "We can use the `<<` operator to concatenate `sim.comp.molA.Count`, `sim.comp.molB.Conc`, and `sim.comp.molC.Conc`, each of these corresponds to a single value so the third dimension of `saver.data` will be 3 as well. If we had two compartments, we could declare a different `ResultSelector` like this:\n",
    "```python\n",
    "saver2 = sim.LIST(comp1, comp2).LIST(molA, molB, molC).Count\n",
    "```\n",
    "Which would be equivalent to:\n",
    "```python\n",
    "saver2 = sim.comp1.molA.Count << simp.comp1.molB.Count << sim.comp1.molC.Count << sim.comp2.molA.Count << simp.comp2.molB.Count << sim.comp2.molC.Count\n",
    "```\n",
    "or to\n",
    "```python\n",
    "saver2 = sim.comp1.LIST(molA, molB, molC).Count << sim.comp2.LIST(molA, molB, molC).Count \n",
    "```\n",
    "In all of these cases, the third dimension of `saver2.data` would be 2 * 3 = 6.\n",
    "\n",
    "### Saved time points\n",
    "\n",
    "To get the time at which each data was saved, one can use:\n",
    "```python\n",
    "saver.time[runId, timeId]\n",
    "```\n",
    "In most cases, we will want to use `saver.time[0]` that returns a vector with all saved time points for the first run. Since, in our example, we always save data at the same time, all the other runs will have the same `time` vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.000e+00, 1.000e-03, 2.000e-03, ..., 1.998e+00, 1.999e+00,\n",
       "       2.000e+00])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "saver.time[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Labels\n",
    "\n",
    "Finally, to get a string description of saved data, one can use:\n",
    "```python\n",
    "saver.labels\n",
    "```\n",
    "Since all runs share the same saved data for a given `ResultSelector`, there is no need for indexing. `saver.labels` returns a list of strings describing each saved data. For example, in our case:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['comp.molA.Count', 'comp.molB.Count', 'comp.molC.Count']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "saver.labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "There are other possible operations for the declation of `ResultSelector` objects but they will be introduced later. \n",
    "\n",
    "## Plotting the results\n",
    "\n",
    "The structure of saver.data makes it easy to plot all saved data simultaneously, to plot all saved data for run 0, one can write:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(saver.time[0], saver.data[0])\n",
    "plt.legend(saver.labels)\n",
    "plt.xlabel('Time [s]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we want to look at the average across all runs, we can write:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(saver.time[0], np.mean(saver.data, axis=0))\n",
    "plt.legend(saver.labels)\n",
    "plt.xlabel('Time [s]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`np.mean(saver.data, axis=0)` computes the average across the first dimension (`axis=0`), the one of runs.\n",
    "\n",
    "## Controlling the simulation\n",
    "\n",
    "In the previous section, we ran each simulation until 2s in one go, by calling `sim.run(2.0)`. The only time we actively changed the simulation state was at t=0, to set the initial conditions. However, the simulation paths calls we used to set initial conditions can be used at any time during the simulation.\n",
    "\n",
    "As an example, let's interrupt our simulation at t=1sec to add 10 molecules of species molA. We plot the mean behaviour of multiple (n = 100) iterations of our second order reaction, with an injection of 10 molecules of species A at t = 1.0.\n",
    "\n",
    "Newly simulated runs will simply be added to the `saver` result selector:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(NITER):\n",
    "    sim.newRun()\n",
    "\n",
    "    sim.comp.molA.Conc = 31.4e-6\n",
    "    sim.comp.molB.Conc = 22.3e-6\n",
    "\n",
    "    sim.run(1.0)\n",
    "\n",
    "    sim.comp.molA.Count += 10\n",
    "\n",
    "    sim.run(2.0)\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(saver.time[0], np.mean(saver.data[NITER:], axis=0))\n",
    "plt.legend(saver.labels)\n",
    "plt.xlabel('Time [s]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "instead of `saver.data`, we use `saver.data[NITER:]` which returns the data relative to all runs whose index is higher than or equal to `NITER`.\n",
    "\n",
    "Quite often, one does not want to simulate the sudden injection of molecules, but rather keep the concentration of some species constant at a controlled value.\n",
    "This means that any reaction involving the buffered molecule will still occur if the reactants are present in sufficiently large numbers, but the occurrence of this reaction will not actually change the amount of the buffered species that is present.\n",
    "The following code snippet shows how, during the time interval $0.1\\leq t<0.6$, the concentration of species `molA` is clamped to whatever its value was at $t=0.5$. We plot the result of a single iteration of the second order reaction, where the concentration of A is clamped during the interval $0.1\\leq t<0.6$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fzdr2UprKxVq7xxgzH5gIdDbGlESqUH2BTVmLKss8HgipAiUiItKmvv3tbzNw4EDWrFmDx+Phs88+Y9WqVU2W+/GPf8yWLVtYsWIFZWVlbN26lTfeeCOrsUQTqOhcgplK5iq8HsaYzpG/2wGnAquB+cCFkcVmAs9lJaIcCFeglECJiEhheeihhxg9ejRjxozhsssuo7q6mlNOOYXRo0czffp01q9fD8CsWbO4+uqrOfHEExk0aBCvv/46l19+OcOHD2fWrFmx7VVUVPD973+fkSNHMn36dBJ1rbnllluYOXMmU6ZMoX///jz99NP86Ec/oqqqihkzZuD3+wGYN28exx57LFVVVVx++eXU1tY22M66detYtGgRt99+Ox5PON0YOHAgZ511VoPlampquP/++7nnnntic9j16tWLr33tawA89thjVFVVMWrUKG644YYGxxL15JNPxo5z1qxZXHvttUyaNIlBgwbFpq658cYbeeuttxg7dmxWRiJPpgLVB/izMcZLOOH6i7X2RWPMKuBxY8ztwAfAHzOOJkc8HiVQIiKSnp+/+3M+2vVRVrd5TNdjuGHCDS0us3LlSm6//Xbeeecdunfvzq5du5g5c2bs58EHH+Taa6/l2WefBWD37t0sXLiQ559/nnPPPZe3336bBx54gOOPP56lS5cyduxYDh48yPjx47nrrru47bbbuPXWW7n33nub7HvdunXMnz+fVatWMXHiRJ566il+8YtfcMEFF/DSSy8xY8YMZs2axbx58xg6dCjf+ta3+N3vfsf111/fIP6xY8fi9XpbPM61a9fSr1+/hBMJb968mRtuuIElS5bQpUsXTjvtNJ599lnOP//8Fre5ZcsWFixYwEcffcS5557LhRdeyOzZs7nzzjt58cUXW1w3WclchbfcWnustXa0tXaUtfa2yO2fWmsnWGuHWGsvstbWtratfPF6jJrwRESkoLz22mtcdNFFdO/eHYCuXbuycOHCWBPUZZddxoIFC2LLn3POORhjqKqqolevXrE56kaOHEl1dTUAHo+Hiy++GIBvfvObDdaPd8YZZ+Dz+aiqqiIYDDJjxgwAqqqqqK6u5uOPP2bgwIEMHToUgJkzZ/Lmm29m/TF47733mDp1Kj169KCkpIRLL700qf2cf/75eDweRowYwdatW7MeF6TYB6pQqQlPRETS1VqlyCmizV8ejyf2d/T/QCCQcB0TGSuxpW35fL7Yci1tq7GRI0eybNkygsFgi1WoIUOGsH79evbt25ewCtWc+NgPHz6cMH6AXM35W/RTuUC4CU8VKBERKSSnnHIKc+fOZefOnQDs2rWLSZMm8fjjjwPwyCOPMGXKlJS2GQqFYn2CHn30USZPnpxWbMOGDaO6upq1a9cC8PDDD3PyySc3WGbw4MGMHz+en/zkJ7Ekprq6mpdeeqnBcu3bt+eKK67guuuuo66uDoDt27czd+5cJkyYwBtvvMGOHTsIBoM89thjsf306tWL1atXEwqFeOaZZ1qNubKykv3796d1vIm4IoFSBUpERArNyJEjuemmmzj55JMZM2YMP/jBD7jnnnuYM2cOo0eP5uGHH+buu+9OaZsdOnTg3XffZdSoUbz22mvcfPPNANx3333cd999SW+nvLycOXPmcNFFF8WaCq+66qomyz3wwANs3bqVIUOGMGrUKGbNmkXPnj0BOPPMM9m8eTMAt99+Oz169GDEiBGMGjWKs88+m44dO9KnTx9mz57NtGnTGDNmDOPGjeO8884DYPbs2Zx99tlMmjSJPn36tBrz6NGj8Xq9jBkzJiudyE2uSluJjB8/3kbHhmhLJ81+jRMHdeNXXxvT5vsWEZHCs3r1aoYPH57vMLKuoqJCEwo3I9E5N8YssdaOT7S8KypQxsBT72/ksD+Y71BERESkCLgigTqiUzsANuyqyXMkIiIi+aPqU/a4IoG6fPJAAOo0n4uIiCSpLbu4SH6lc65dkUCVloQvdfQH9WIQEZHWlZeXs3PnTiVRLmCtZefOnZSXl6e0nivGgfJ5w3miXxUoERFJQt++fdm4cWPCqU6k+JSXl9O3b9+U1nFXAhVQAiUiIq3z+XwMHDgw32GIg7miCS+aQKkPlIiIiGSDKxKo0lgTntqyRUREJHOuSKB8sU7kqkCJiIhI5tyRQKkTuYiIiGSRKxKoaBNenTqRi4iISBa4I4EqCR/m31ZuzXMkIiIiUgxckUD1rCwDwOc1eY5EREREioErEihjDCOP6Kg+UCIiIpIVrkigINyRvE7DGIiIiEgWuCaBKvV6NBK5iIiIZIVrEihfiVETnoiIiGSFexIor0cJlIiIiGSFqxIo9YESERGRbHBNAlWqCpSIiIhkiWsSKJ9XfaBEREQkO1yUQHk4VBdk4+4agiE15YmIiEj6XJNAdSgrYdv+Wib/fD7/9eyH+Q5HREREClhJvgNoK/82bTAjj+jI3fPW8MXew/kOR0RERAqYaypQPSvLuWj8UfSsLMOvq/FEREQkA65JoKLCwxmoM7mIiIikz3UJVGmJhjMQERGRzLgugdKI5CIiIpIpFyZQBn9AfaBEREQkfa5LoEpLvKpAiYiISEZcl0D5vEadyEVERCQjrkugNCeeiIiIZMp1CZTP62HrvloO+4P5DkVEREQKlOsSqI7twoOvz128Ic+RiIiISKFyXQL13WlDANh3OJDnSERERKRQuS6BKi/xAqgflIiIiKTNdQmUx2Mo8RglUCIiIpI21yVQEB2NXINpioiISHpcmkAZ6gKqQImIiEh6XJlAaUJhERERyYQrEyhNKCwiIiKZaDWBMsYcZYyZb4xZZYxZaYy5LnL7LcaYTcaYpZGfM3MfbnaoD5SIiIhkoiSJZQLAD6217xtjKoElxphXI/fdZa29M3fh5YbmwxMREZFMtFqBstZusda+H/l7P7AaODLXgeWSz+vhpeVb2LrvcL5DERERkQKUUh8oY8wA4FhgUeSma4wxy40xDxpjujSzzpXGmMXGmMXbt2/PLNosGdKzAoBnP9iU50hERESkECWdQBljKoCngOuttfuA3wGDgbHAFuBXidaz1v7BWjveWju+R48emUecBXdfciwAtRrKQERERNKQVAJljPERTp4esdY+DWCt3WqtDVprQ8D9wITchZldXo/Bq9HIRUREJE3JXIVngD8Cq621v467vU/cYhcAK7IfXu6oI7mIiIikK5mr8E4CLgM+NMYsjdz2/4CvG2PGAhaoBr6Tg/hyxuf14A9oKAMRERFJXasJlLV2AWAS3PVy9sNpOxpMU0RERNLlypHIIdyEpwRKRERE0uHiBMqjCYVFREQkLa5NoEq9HnUiFxERkbS4NoFSHygRERFJl3sTqBLDks/38O0/L2b2Kx/lOxwREREpIK5NoM4Y1YeelWUs27iH+95YRzCkIQ1EREQkOa5NoL47bQgvXzeFfzlpAICa80RERCRprk2gokq94YdACZSIiIgky/UJlC+WQKkJT0RERJKjBEoVKBEREUmREihveJYaDaopIiIiyXJ9AlVaogqUiIiIpMb1CZT6QImIiEiqlECpD5SIiIikSAlUtA+UEigRERFJUkm+A8i3aB+oKx9aTFmJlx+fPZwZo/rkOSqR7AsEQ3z9/n+yafehhPf379aBR759Ah6PaePIREQKj+sTqDF9OzNr0gAO1gZ45oNNLPl8txIoKUr7Dgd4r3o3x/brzJAeFQ3u+3jrfhZ+upPDgSDtS13/tiAi0irXv1N2KCvhlnNHAvDq6q0azkCKVvS5/bXxR/H1Cf0a3Pfggs9YvnEv/oCF0nxEJyJSWFzfByqez+uhTlfjSZGKXigRvXAini/SlK2+gCIiyVECFafU69HVeFK06mIJVNM+TqWR2/T8FxFJjhKoOD6v0QeIFK3oc7s0UQVKw3mIiKRECVQcnypQUsT8gXDzdMImPCVQIiIpUQIVx+f1UBdQHygpTtEmvJIETXjRBErPfxGR5CiBiqMmPClmLTXhlZaoD5SISCqUQMVRE54Us9hVeCVqwhMRyVRxjQPlPwz+mvr/fe3BV5706j6vh5q6IHtq6nIQnBST0hJP3gec3HvIj7XJN7ntrvEDLfeB2nWwLqPnvzGGTu18Ka+395CfyrISx46Cbq1lX92+fIeRF+1L2uPzpn5OnSYYCnLAfyDfYeRVeUk5Zd6yfIfRxIHaAIHIl7dO7XwY48z3gcaKK4Fa/CD87T/r/y+thB+uhrLKpFZvX+pl4ac7GXvbqzkKUIqF12N48XuTGd6nY172/+ii9fy/Zz5Ma912Pm+T29qXhm+78uElGcUFcOu5I5k5aUDSyz+8sJofP7eSC8f15c6LxmS8/1y4Y9EdPPHxE/kOIy8GdhrI8+c/n+8wMnbNa9ewYNOCfIeRVxW+Cv5x0T/o4OuQ71BiFq7byTce+CfR74KzJg2IDW7tdMWVQA04CWb8PPz3piXw4V+gZlfSCdSNZxzD5KO75zBAKQab9xzi/rc+Y8veQ3lLoNbvqsHrMfzXWcNTWq9TOx9De1U0uX3UEZ2486Ix7D/szyiu/37lIzbsqml9wTjrI8uvT3G9trTxwEb6dOjDzJEz8x1Km3p9w+u8v/X9fIeRFRv3b2R41+GcN+S8fIeSF8u3L+flz15mb+1eRyVQG3fXYC1cN/1o/rJ4g6PfBxorrgSqz5jwD8DyueEEKpj8B8LRvSo5uldyyZa416rN+7j/rc/yesVaIBiivMTDv5w0MCvb83gMF47rm/F27p63JuV+VP7I6P9O7n8VCAXo3aE3lw6/NN+htKndh3ezaMsirLUF06zSHH/IT1XnKtedw6hOZZ14+bOX8Ycy+5KUbdHX/zdO6Meba7Y7+n2gseLtRB5tsw+qP5NklxOuWPMHQwk7g+dbOtMhRYdXcPIbpz/ox+cp/H5AqfJ5fFgsQRvMdygZ84f8RdGXK13R568/haJCW4ifYqrQLuRy3jtwtngjM6IqgZIsc8IVa3VBm7AzeL6lMx2SPzLJsd/BY1AFQgF3JlCRhMNpVYt0uPUcRsUSKIedS3/cFFPh9w/nvg805rx34GyJJVDOerJI4XNCAuUPhhKO55Rv6Yyl5i+EClTIvRUocN6HbjrcWkWMcuq5rGtQgSqssRid9w6cLWrCkxyJjdqdx29K/mAo4aTA+ZZOCT76jbPOwW+cbm3+cWqzTzrcmgRHObWaGD/FVHg2EOe+DzRWxAmUmvAkN6KVH38eX+jhBMp5L990pkMqiD5QIT8lnuK65iYZTq1apMOt5zDKqefSHwzh9Ri8HoOvRH2gnEFNeJIjPgd0Iq8LOLMPVDpvgPVNeM7t++DW5h+nVi1SFQwFCdqgK6uIUU6tJsZX09UHyinUhCc54pQ+UE68Cq80kz5QDi7du7X5x6lVi1QFbADAlecwyqnnsi6uml5ofaCKt54ZTaC2LIX2XaHvBPAW7+FK2ymJTDfy6faDvL12R0bbGtqrkh6VzU+tcLA2wLINe2j8nWzHgdrY6OFOUuLxsPNAXexx6V5RxrDeTcdWC4UsH2zYzWF/iJ0Hwl9yagOhJo9nz8qylMZm23loJ2v2rMngCBI7FDjkyg/faJPXsm3LKKcHn22vjd1X7vNy7FGd2V8bYMWmvc1uo1M7H6OO7NTs/Xtq6li5OTxNzuAeFfTuFJ5+KxgKsnzHcsq95QzvltqAsY1t2r8JcHcCFT2Xq3auor2vfez2Ed1G0LG0fkBgay1LN+yhpq7p0BXxr+cvDn5B9b7qhNtoyYpNe9l7qD6J27CrJi6B8nCwNpD0+2pr75+5VrwZRXnn8O83fxn++eofoerCvIYkxcEYQ+f2Pp7+YBNPf7Apo21NHtKd//v2Cc3ef9ern/DAgs8S3vfl4b0y2ncudOngY+GnO7n0gUVAeMqbpTefSmV5ww+uBWt38K0H321wW10wFFsvyuc1LPvJaUnPO3jT2zfx9qa3MziC5nUqaz4JKFadyzoDcPM7N9M3tJrVHzecauexfz2RuUs28PT7Lb8OXv/3qQzonnj06x8/t5IXlm0GYHTfTjx/zWQA3tj4BtfNvw6Av331bxxRcUTax/HDN34IuPMcRkWP/ffLf8/vl/8+dvu5g8/ljsl3xP5ftnEvF/zvOwm3Ef96vvofV7N2z1oAzh9yPj896aetxrB+Zw1n39N0Op1BPcLPjS7tS9l3ONDkfaA5937jWM4enf7zIlPFm0B17AP/tgh2roEnvgm17pwIVHLjhWsms2Xv4Yy2MfuV1Q2+iSWy55Cf7hWl/O+l45rcN8yBo+b/91dGM2tSeHT0eau38vs3P+VgbbBJArUnctx3XTyGIzu3Z1CPDny+8yDx1fu/rfyCPy74jJq6YNIJ1L7afVR1r+KH43+YnQOK8BgPI7qNyOo2C8H4XuN5/OzHueTFSzjo38+Yozpz05nDqd55kB89uZy9h/zsrfEzsHsHfv7V0U3WX/L5bn7+149afJ7vPeTn6J7hylP8NB57a+urWvvq9nEE6X9QHgocYkDHAZw96Oy0t1HoerbvybPnPcue2j2x225+++Ymk2RHJxP/6fmjGrzHNH4976vdx8l9T+bTvZ+yL8nP1+jz4D9OH8bxA7rGbu/XNVwRu+aUIZw8rAfJzpE+uEd+p6Qp3gQKoOcx4eY7gFDhj6QrznFU1/Yc1bV96wu2oEdlGZ/vbHneJ38wRIeyEiYM7Nrick7RqZ0vFmv0wzBRn4Zof6fj+nWhf7fwm2D3ioal+HXbDzS7fnP8IT+92/dmXK+mCaekzhjDyG4j8RovAeunR0UZEwZ2pUv7aIfkEHXBUIPzHu+wPxhbrjn+QIgu7Uvp3bGcddsO1N8e11cn047PgVCA43sf7+qr8AAGdx7c4P+OpR2b9ImKduIe27czVX3rK3af7zwYuT/SZzHkp3eH3uw4tCPpflXRK25HHtEx4fOl3OdtkFg5nfN6oWabifQTsYXTMU3cITztScvPS6cOV5CM6JU1iY4xfvqG5tePDheR/FU5/qA7x2vKNZ/HR9AGYtMYxV9I0dKgrvVjprWQQAVD+ErCl7DHj63WIIHKsOOzWy8CaI3P6yMQDDS4LfbaLGk4zlxpScNzGX1MfR5f0ucnum0nDgKcjuI4ipZ4IoeoCpQ4TDLTnvgdOmVLMkpbuFrRH6ofPK85LSVgzQnYgOurDLng8/gI2UB9Z9+S+nMbCNomH7ZR9fNGNp8ER78kNH49BEL1H+zZSKBKo0PbSEyi5Ke5LzeNrz6ODi7r8yafQAUizwMnXkGcjuI4ipbEKlBKoMRZfF5Pq9WV8Ld75404noyWKkjRJryWvolG7wuEUmjCc+l4Tbnm84YrUPGXm0M4MWqpShq9PdBCEhyd17HxJezxH8rxyVQ63D4PXnN8Hl+Txzaa7DZ+bdafy/D9mVSgCvVLYWPFcRQt8UQSKFWgxGF8Ja2PeVLQTXglzTffNNdM0GD9dJrw1FSTEyWeEkLUJ1Dx1cWWJrZOZsy0aBNg42mA4vs9ZVyBUmKdUGoVqPqKcDAUJGRD9QlUkn3U6ue9K8wvhY0V5jtzKlSBEodKqg+UQ0ccT0Z9lSLNPlAtJGDNUQKVG9EmvGg1NPU+UK014ZlIAmWxtr7CEVsmgwQqZEMErCpQiSRqfvM3k+TET2EVXUd9oFphjDnKGDPfGLPKGLPSGHNd5PauxphXjTFrIr+75D7cNMQqUOpELs6STB+oOoeOOJ6MlvpART9Qo4OSJtJSAtYct076m2s+j69BBao+gbKxBCiRZOaN9AcifaBK6rcJ2bsKL9pEpedFUyWekibJT3Qy38bvO76489M4gUq2idWNTXgB4IfW2hHAicB3jTEjgBuBedbao4F5kf+dRxUocajoN+6WFEUfqGYqUKVeD8Y0f2wtJWDNUVNNbvi8kQSqpGEfqLpAKJYAJVwviXkj64IWX4mnScKcrQpU/Ie9NJSo+a21PlD+YFwFKsVO5NHm+EL9UthYq5erWGu3AFsif+83xqwGjgTOA6ZGFvsz8DpwQ06izISuwhOHCpp9eDq9xVUvrMaQOJHYbHfSwzuxjSPLjhKPoaTTYn67ZDlz1zQc42nN1v2UVnYBzmh+fS/4Or3H/yxezqMfJTddQ12oDn+gON6cnSQQ8OCtWEmA8ICJxhjKKz/h5fVL2Fd2mIDnywnXK/V6KKlcziMffcAbWxOPm1bTbjsBM4Mg4OvyNte9shqf18Pag4tjy/zpg5f56+p16cUeqo0dgzTk93vYUbObq1/4dey2T3ccxFtRjs97ZoNlfdHX8/vL6fRxuOIUDHqo9Ru2H2y4jeZs3F1DSaWHUm/i50uhSel6X2PMAOBYYBHQK5JcAXwBJJxXwhhzJXAlQL9+/dIONCPGqwqUOM523qa894u8vauFhTrDTt9uoPBGUK41X9DuiCf5JACfND5GH3h7VQDXNrv+ITZTfsRTfFQHtPQYNbJxW7t0wpUW7NjdEUpgm30TmABA6ZGP8oU5jOkGOzwApzRd0XOIdn0fpdpCdTPn0NsdthkflbY/5b1fYFHctHr28JHYkt2srXmHtTWJpxdJhrWGTTv0vGhs4/YO1NlDLNg1p/5GD7Tv68Fj/r3BstHX88d+YE/4Md28oz0bt7WnztY03EYL2vUF67kayN8cdtmSdAJljKkAngKut9buiy+9W2utMSZhW4S19g/AHwDGjx+f/OU02eTxqgIljjOoZylsgX989S1opgJ15T9mMrBTYY5fU14Wfs3dNvG/mXTElAb3/eHD3/L8umdaXL9Debgp52cn/ZIJvZOrwk367/n0+1JmE89KU50OzGR/5/cY3Kv+uejx+Ll42Lf4W/XLDW6PZ024UnHdsf/OOYPOT7jMV144iyG9SulX6YNqePacV6goreTOv33E3NXbgBD/dc7RnDPmyLRi33/Iz5d//RZHDhmb1vrF7CjPmazZVMWL106O3fbIR39mzsr7wx3vTX2zZ/zreWjHCZx19zscccxotnv68enmMbzwvclNtt/YS589x13v/xJMZldVOkVSCZQxxkc4eXrEWvt05Oatxpg+1totxpg+wLZcBZkxVaDEgfwhPyWeEnpVdG52mfKSsowv4c6XaNzd23eiV0XDSVw7lVW02vE0un6PDp2brN8cn6cspT5TkpxgCDyUxs5ZMBQkaIN0Lq+gzFva7HM02r+mW7uOzZ7DMm94u9FtHNGxG+1K2lFZWgnsADx0bWH91lSWBsCW6nmRgD8YorykXYPHtkt5ZeS+hv0J41+PR1R2BuuLXYVZ7m2f1Pnp2q5jg20VumSuwjPAH4HV1tr4Rs7ngZmRv2cCz2U/vCzxeHUVnjhOMh2eUxljxWmicSe6+snn8RGwAUItTLGUTuffZIaGkNT5gxYP9VdsBWzkyjZPy52Io7e3NDp89DL4xuc7foywTK7a8iVxJaBb1SUYZy76+Dcd3iDuyru4kejDsyUkd6FLbNsF+p7WWDIVqJOAy4APjTFLI7f9P2A28BdjzBXA58DXchJhNqgCJQ6UzJhFqYyx4jQtJUDRpCoQCjQ7xUb8G3aykhkaQlJXFwhh4hKoBh+mLVzGHn+1VnN83vCXBH/Ij8HgjVw5HX8VWCYJVHSoDD0vmko0hlezCVT80AVxI9EnSsKa09y2C1UyV+EtoLkOGjA9u+HkiMejPlDiOMkmUDWBmjaKKLtaTKDi3kibTaDSrEClMnK5JMcfDOE13voEqvFAis1UFJI5h/EVKJ/HFxvaIv5DubSFEetbY4yh1OtpcTBPt0o012b8l5sGyzY455EBUgORgVSTHJag2BIod1zXqQqUOFAygz6mMsaK07T04Rlt0mmplJ9WApXE9DiSOn8whMfUJ0oNxgFqoUqaTBUxlkAFG74efFmqQIXX1/MikUSDoDbXzBZ/zj0eQ4nHxPpAJV2B8iqBKjy6Ck8cyB/yU+pp+Qq7Qm7Ci36DTVRhSuabaDLNP022qz5QOeEPWrzxTXhxyW2i0axj6yVTgYp8SQiEAg1eD/FVjUwTqNISNe0mUpdgENRkmvCg/jH1B23SU7MUWx8odyRQqkCJAxV9J/Ikm/CaXV99oByjLhjCa0piSXGDPlBJdCJvrQIVvQovfrn4Efgzr0DpeZFIoua3ZDqRQ/1MCv4UpptSE14h0lV44kBJNeEVcAWqpQQomVJ+2n2g1Nclq6wNf0h6PYkrUEn1gWqpE3l8H6hmmvAynXzW5/VQp75xTbTUB6rxazM2p2CDBCo6mXSKV+EV6HtaYymNRF6wjIFlj8K594DXHYcsufXHD//Imj1rMtrGyh0r6dUh4QD+MT6vj52HdnLjW02nmjy136lM7++s6zhe3/A6f63+KwDr960Hmh/GAOCX7/2SitIKBnUaRPXeakLUf9Gp3lvdYNlk+LyGDzft5frHP0jzCBIzxjBr0gDGHNU5q9t1ut0H6/j5Xz/CWigxPlbtXMWNb93I3trwcOHRBOrz/Z8nfI5uPbg1tlxzfB4fq3etZlvNtgbLNegDlUEn8vC2DE+9v5Hrph9Nv26Jp5QpdoFgiDteXs3ug3Wx2z7feZAjOpc3WC7aP/GeD+6hS3mX2O2NX8+lXsOCtTvYdbCOo7ok95hG131wxYO8/NnLsdvH9hjLJcdcwoodK3hk9SNYkkt2v37M1xnTY0xSy+aCO7KJssgAX7vWQY9h+Y1FisI9H9xDe197Opd1TnsbZSVlnHTkSS0uM6H3BBZ/sZjl25c3uH1bzTa21WxzXAL16OpHWbJ1SSwxHN9rPJ3Kmg6wN6zLMIZ0HsKnez9l68Gt1IXCb+pHVR7VYLkTep9ARWlF0vufOqwnT72/kQ827En/IBJYv6uGzu19rkug3qvexePvbaBvl3ac0HsSH+z+R+y5OLTLUIZ0GcLEIyayds/aJs/RqJHdRtK3sm+z+5h0xCSq91UDMKVv/Yj1o47sxNE9K2hX6uXIzplNw3LCwG5U76zhH6u3cvnkgRltq1BV76xhztvVdK8opUNZ+KO/S4dSJg/p3mC5wZ0Gc0zXY9iwfwMb9m9ocF/863n68F68uWY7XTuUclKjbTTnyIojGdVtVOz9C2DX4V0s2rKIS465hOfXPc/Ln73MkRXJjTp/5sAzW18oh4y1bVfWHD9+vF28eHHrC2bb6hfgiW/CVQugd1Xb71+KSjAUZOzDY/m3Mf/G1WOvzksM//r3f+Vw4DAPn/lwXvbfnH/5679gsfxpxp9SWmfx1sW0K2nHu5e+m7vgMjDup69yRlVvbj/fXe8fLy3fwncffZ+/Xf8lhvWuzHc4aaupCzDi5r/xn2ccw3dOHpzvcPJi9ZZ9nHH3W/zu0uM4o6pPvsOJueOfd/BK9SssuGQBty68ldc3vM78r83Pd1gxxpgl1trxie5zRx+o6FVAwbqWlxNJQmwU5hSuDss2p/aNSmZsq8aiy7c0WnW+uXV8qWjH62RHmnaq2GjkLu5IXn8unfWxHx1IFZK7sMZJnPVI5kr0g65Ar2YSZ0nn6rBsK6oEKvL6dPIbp1vHl6pz6IduqqKjkbt5MM1YApXkFXNtJf69LJ33j3xy1iOZK6pASRalc3VYtjl1gM2WRhZvTnTsn1TXa0tuHV8q+qFb5rAP3VRFRyN3YxIcFb0KMdMrGrOtNDIZtbVWCZQjKYGSLEpngMdsc+r4UOmU4GOXRTv4jdOtH77RCXgLvQIFkdHIXTyhcPT5m8m0OLkQfd0HbCCpoV2cpPBfFclQE55kkSMqUGrCa1NuHV8qesxOa/ZJh8/lo5E7tg9U3OjkqkA5kSpQkkXqA9W8dL5BFkIFyq1zqdUVSSdyiDbDui8JjnJ8AhXyEwgGHP0+0JizHslciSVQzvvAkcLjiAqUQ/tABUKpvwFGr75z8htneCRr9yVQsQ9dT+F/VLi1GTYqmjw6OYFSBcqJYk14qkBJ5hyRQEXmD3OajPpAObjvg1sno/UHQ5R4DB5PMVSg3FlFjIr2/3JaJ/Lo6z46H2JJAc0WUjiRZiJagfrkbzD2G/mNxYGCoSDXvHYNWw5sSXldn9fHT0/6Kcd0PSajGD7d8yk3vnUjdcE6OpZ15N7p93LTgpvYsG9D6ys3o7K0knun30unsk68s/kd7lx8J6EszIl4OHgYyO+4RT6Pj0OBQ5z/7PkprdexrCO/nf5bKksbDoq4+/Buvvfa9zhQdyCjuA74D6T8uETfQJ0+DtTKzfs49ddv5DuUNrXjQC0lRdB8B+FzOG/1Ntedw6i9h8Jf/Jx2PqNfoK742xV8cfALJh0xKc8RJc+571jZVNE78od7279bsq9uHws2LeCYrsc0mUqjJXXBOt7Y+AYf7vgw4wRq5c6VrN61mmFdhvHBtg/4ZNcnvL7hdYZ2GUr/jv1T3t6OQzv4YNsHbNi/gU5lnVj8xWLW7F7Dqf1PzSjOqHG9xjG6x+isbCsdp/Y/lQ37NxC0waTX2V6zPfaYjOg2osF91fuqWbZ9Gcf1PI5u7bqlHdeQLkM4fcDpKa1zev/T2Xxgc9bOTS58fUI/yn3O+ubeFo7uVcHII5pOxVOIrpg8kDfXbM93GHnVs7Kc3h3LW1+wDR3f+3jOHnQ2tcFahnUdxnmDz8t3SElzRwLl8UDv0eoD1Yxok9TXhn2Ni4ZelPR6uw/v5ktPfCkrl9NHm6MuPuZiblt4GzWBGgC+cvRXuHT4pSlv753N7/CdV78TO7ZAKEC5t5xfT/11xrE6wfBuw/nlyb9MaZ0FmxZw9T+uTth3KnoOrzn2Go7vfXxWYkzWyO4jufPkO9t0n6k6dUQvTh3R8sTP4myXTOjHJRP65TsMaaR3h97895T/zncYaXHPVypvqfpANSPdPj3xnf+yFUP7kvCs3tEEKt1+RvGXxka3X0idE3Oh8WMSzwn9ukRECokSKEn7svxo35VcJFCH/IfSiikWW6PkrtAGaMuFlhJeJwwOKiJSSFyUQJWoCa8Z6VYfSkxJg/UziiFybtr7Glag0u1Y3Di584f8ju6k3BaSSqBUgRIRSYqLEihVoJqT7oen1+PFa7xZ6QPVpAnPH2nCS7Mi0qQCVWCzfOdCSxVDJwwOKiJSSJRASUbNN9kajygaQ7uSdoD6QOWCKlAiItnjogTKBwElUIlkUn3weXzUhTJ/XKNNbKWRMbsO+g+mHVP8etHEoC5Y5/r+PS11Io+eQyVQIiLJcVECpQpUczKpPvi8vuw04UWa2KIxZDuBUgUqbtbzBBXDWBLt8iRTRCRZ7ulVG02ggv76qV0ECFdnIL1kpcRTQm2wNuOO5LXB2nAC5c1SAhXZTjS2ulCdEqjIY3I4eLjJ+aoN1oaXcfljJCKSLPckUCXlsG8T/LQ7TPoenHZ7viNyhNpgLde8dg0AZSVlKa9f7i3nuXXP8dy65zKOpWe7npR5wzHMWz8vvP2S9EbNjW5n9ruzmf3ubABO6H1CxjEWsuhj8rNFP+Nni36WcJloE6qIiLTMPQnUxO9CxyPgvQdg+yf5jsYx9tXuA2B41+EM6Twk5fX/68T/4sMdH2YlluFdh1NZWsnPJv+MLQe3UOGrSHuKmA6+DrHtRE3sMzErcRaq+Mc2kb4VfWNJloiItMw9CVS3wfClfw9PKKy+UDHRppyvH/N1PCb1LnETj5jIxCOym5icM/gcR22nmOgxERHJDvd0Io/ylmpAzTgagVpERCR1LkygfKpAxdEAiiIiIqlzYQKl4QziaQBFERGR1LkwgfKpCS+OEigREZHUuTCBUgUqnvpAiYiIpE4JlMupAiUiIpI6FyZQasKLp07kIiIiqXPPOFBR3lLYvxlCQfB48x1NA/vr9lO9t7pN97luzzpACZSIiEgq3JdAlbYP//7gYRg3K6+hNPafb/0nb2x8Iy/7riityMt+RURECpH7EqiTrod37oGaXfmOpIndtbsZ3nU41xx7TZvut1NZJ46qPKpN9ykiIlLI3JdAtesS/u3AflD+oJ+e7Xvypb5fyncoIiIi0gL3dSL3eMF4HXklnj/kV18kERGRAuC+BAocO5RBIBRQAiUiIlIAXJxAObAJL+TXgJYiIiIFwKUJlDMnFPYH1YQnIiJSCFyaQDmzCc8f8lPicV+/fhERkULTagJljHnQGLPNGLMi7rZbjDGbjDFLIz9n5jbMLHPoaOTqRC4iIlIYkqlA/QmYkeD2u6y1YyM/L2c3rBxzcAVKfaBEREScr9UEylr7JuC8UScz4fXByqfh0O58RxITCAWoDdaqAiUiIlIAMukDdY0xZnmkia9LcwsZY640xiw2xizevn17BrvLoso+4d8fPJLfOOJ8sO0DAKy1eY5EREREWpNuAvU7YDAwFtgC/Kq5Ba21f7DWjrfWju/Ro0eau8uySx4N//bX5DeOOIcChwCYdtS0PEciIiIirUkrgbLWbrXWBq21IeB+YEJ2w8oxXzkYj6P6QflD4U7t6gMlIiLifGklUMaYPnH/XgCsaG5Zx3JYR/JYAqU+UCIiIo7X6qBDxpjHgKlAd2PMRuAnwFRjzFjAAtXAd3IXYo44bDRyf1AJlIiISKFoNYGy1n49wc1/zEEsbctho5EHQgFACZSIiEghcOdI5ODcJjz1gRIREXE8FydQzhqNXH2gRERECoeLEyiHVaDUB0pERKRgKIFyCFWgRERECkernciLlqcENi+DF78P3YfCiVe36e4/2vURT37yZGzk8ZU7VwJQ4nHvKRERESkU7v20HjAFPvwLLHs8PCL5hCvB422z3T/1yVP85eO/0KW8fhacE3qfgDGmzWIQERGR9Lg3gZrxs/DPW7+CebeFO5S3YQLlD/np0a4H8742r832KSIiItnh3j5QUd7S8O827g/lD/k1ZIGIiEiBUgIVS6DadkgDf9CvDuMiIiIFSglUtAoUauMEKuRXh3EREZECpQQqn014qkCJiIgUJCVQ+WrCUx8oERGRgqUEKprEqAIlIiIiSVICla8mPHUiFxERKVhKoPLZhKcESkREpCDpMrBoAvXcd6GsI0z7fzB4Wk525Q/5+eHrP2TnoZ2s27OOnkf0zMl+REREJLdUgepdBSPOg45HwqYlsPYfOdvVtpptzN8wn5pADeN6jeOcwefkbF8iIiKSO6pAte8KX3so/Pfs/jltyvNHtn1F1RWcPejsnO1HREREcksVqHje0px2JvdHButU3ycREZHCpgQqnrc0txUoJVAiIiJFQQlUPK9PFSgRERFplRKoeLluwotUtzQCuYiISGFTAhVPTXgiIiKSBCVQ8dqoCa/UU5qzfYiIiEjuKYGK11ZX4akJT0REpKApgYrn9akJT0RERFqlgTTjlZTD2lfhpylMseIpga/8AYa3PDBmjb+G/3jjPwA14YmIiBQ6JVDxvvQf0GtkCitYePtu2La61QRqx6EdAIzsNpK+lX0zCFJERETyTQlUvH4nhH+SZS28/T9J9ZuKNt/NGjULY0y6EYqIiIgDqA9UJoxJuuO5+j+JiIgUDyVQmUpy7KjYIJpKoERERAqeEqhMJTl2lCpQIiIixUMJVKZKypRAiYiIuIwSqEwlOXaUBtEUEREpHkqgMpVsJ3L1gRIRESkaSqAypavwREREXEfjQGXokLeELYe2wWfzWlxu07algBIoERGRYqAEKkP/XnaYN80eePP6pJbvEMjdXHsiIiLSNpRAZWhH5yMZXneQy3tPbmXBNXT95O/0UKupiIhIwVMClSG/x0O/HiOZ8aWftLzgiqdh+QtJ9ZcSERERZ1M5JEP+oJ9ST2nrC5aUhX8rgRIRESl4SqAy5A/5kxvbyRtJspIYM0pEREScTQlUhvwhf3JX1kWTLFWgRERECp4SqAwFQgFKPEl0JYtVoJRAiYiIFDolUBnyB5OtQKkJT0REpFi0mkAZYx40xmwzxqyIu62rMeZVY8yayO8uuQ3TuZLvA6UmPBERkWKRTAXqT8CMRrfdCMyz1h4NzIv870p1oboUK1BKoERERApdq513rLVvGmMGNLr5PGBq5O8/A68DN2QzsHzYfGAzSyNTriTDYgnZUGoJ1Cd/h5EXpBegiIiIOEK6A2n2stZuifz9BdCruQWNMVcCVwL069cvzd21jdnvzmb+hvkpr9e9XffWF2oXaeVc9iicchN06pvyfkRERMQZMh6J3FprjTG2hfv/APwBYPz48c0u5wQ1/hqGdx3Oz7/086TXKTEl9K1MIhlq3xXO+AW88iOoO5hBlCIiIpJv6SZQW40xfay1W4wxfYBt2QwqX/whPx1LOzKw08Dc7KDjEeHf6gclIiJS0NIdxuB5YGbk75nAc9kJJ7/8IT8l3hxOD6iO5CIiIkUhmWEMHgMWAsOMMRuNMVcAs4FTjTFrgC9H/i94SY8qnq7YUAYaC0pERKSQJXMV3tebuWt6lmPJu6QHxUyXKlAiIiJFQSORx8l9BSqSQAWUQImIiBQyJVBx2q4JTwmUiIhIIVMCFSfpaVnSpSY8ERGRoqAEKk6bNeGpE7mIiEhBy+E1+4WhNljLnBVzqPHXcNB/sG2a8JY/AV8shxHnQ99xudufiIiI5ITrE6jl25fz26W/xefx4fP4OKbrMbnbWYce0Lk/VC+Ata/C7s/g4v/L3f5EREQkJ1yfQNVF+iPNmTGHMT3G5HZnpR3g+uXhv39/sq7GExERKVCu7wPlD4X7I+W06S4Rb6k6k4uIiBQo1ydQ0QpUfhIodSYXEREpRK5PoPJXgfKpAiUiIlKglEBFEqgSTxt3B1MTnoiISMFSApXXCpSa8ERERAqREqhIEpPTEcgTUQVKRESkYCmB0lV4IiIikiIlUGrCExERkRS5diDNJz56gnnr57HxwEYgTxWog9vhofPrb+s5Amb8rG3jEBERkZS5tgL19NqnWbFzBV3Lu3LBkAvwerxtG8DQGXDkcVB3MPyz4xP452/B2raNQ0RERFLm2gqUP+RnQu8J/Gbab/ITwNDTwj9Rb/4SXrsdQkHwuva0iIiIFATXVqD8QX/bN9u1xFsa/h2szW8cIiIi0ir3JlAhpyZQujJPRETE6dydQLX12E8ticaiK/NEREQcz7UJVCAUUAVKRERE0uLaBMq5faCUQImIiDidexMox/WBUhOeiIhIoXB1AlXicdBwAdFkThUoERERx3NlAhUMBQnaoMM6kasJT0REpFC4MoG6Y9EdAJR6SvMcSZySSCyfvZnfOERERKRVrkyglm5fCsDpA07PbyDx+k4I/66ryW8cIiIi0ipXJlCBUIDT+p9G38q++Q6lXllFuBlPTXgiIiKO58oEyh902CCaUd5SXYUnIiJSANyZQDltCIMor08VKBERkQKgBMpJ1IQnIiJSEJRAOYma8ERERAqCKxMox82DF6UmPBERkYLgygTK2Z3IlUCJiIg4nesSqJANEbABZw2iGaUmPBERkYLgoMng2oY/FE5QHFuBqt0Peze17X5LO0C7zm27TxERkQLmugTqwRUPAtCupF2eI0mgtH14Kpe7RrTtfj0++P4KqOzdtvsVEREpUK5LoHYe2gnAuYPPzXMkCZx5J6z/Z9vu84sP4b374cA2JVAiIiJJcl0CFQgF6NmuJ5WllfkOpakew8I/bemTv4cTKPW9EhERSZrrOpH7Qw69Ai9foo+Frv4TERFJmvsSqKBDB9HMF2/kakQlUCIiIklzXwIV8lPicV3LZfNiCZSa8ERERJLlygRKFag4asITERFJmTsTKPWBqqcmPBERkZRl1JZljKkG9gNBIGCtHZ+NoHJJFahG1IQnIiKSsmx0Bppmrd2Rhe20CX/QT3lJeb7DcA414YmIiKTMNb2p99XtY+m2pew6vIv+HfvnOxzniFagqt+CYy9Nff1dn8L2T7ITS7ch0H0IbF4K+7+AskroPwmMSW970e1E9RkDHftkI9LC1fgxidfxCOgzuk3DEREpVJkmUBb4uzHGAr+31v6h8QLGmCuBKwH69euX4e7Sd9+y+3h41cMAjO05Nm9xOE5ZZEDRZY/BlH8PJzCpePxS2LYqO7F07g9XvwP3nwI2GL7tqreh96jUt1VXAw9Mh1Cg/rYhp8I3n8xOrIWo9kDDx7Yxbyn85yYoceBE2yIiDpNpAjXZWrvJGNMTeNUY85G19s34BSJJ1R8Axo8fbzPcX9r21+2na3lX/nf6/zKo86B8heE8ZRVw1q/gpR9C7d7U1z+8F4aeASf/KLM43vkf+PR18NeEP+AHTAlXxWr3pbc9/6Fw8jTpWhh5QeT40txWsYg+tiddDyPOa3jfh3Phn/8LgcNKoEREkpBRAmWt3RT5vc0Y8wwwAXiz5bXywx/y076kPSO7j8x3KM7TNZJQptORPFgXnkPvyOMyi6HjkeH9R/tidTyyfvvpiK7XdVA4tvbdoKZguurlRvQx6Ta46fna+F5kGV1MICKSjLSHMTDGdDDGVEb/Bk4DVmQrsGzzBzV8QbMyGcogWFe/fkYx+MLbisZQ2iGy/TQ/0KPbicbmLVVy0PgxiaeLCUREUpJJBaoX8IwJd/AtAR611v41K1HlgIYvaEFGCZS//sM30xiCdRBonEClW4Hy128X6hM0N4s9JgnOl8YDExFJSdoJlLX2U2BMFmPJKSVQLYhVH9JswstWBQrC/XSgvnN7pk140e1GEzQ3a7ECpfHARERS4ZqRyAOhAKXZ+KAvRulWH0KhcEftkrLsxVB3MPy7tCISU5aa8ErUhJdcAuXyJFNEJEmuSaBUgWpButWHUAtNQunGEEugst2EpwqUmvBERLJHCZSk34G4pYpGujHUHQj/zjiBUhNeE2rCExHJGvckUEElUM1Kt/rQuMqTjRiifaCy3YTn9Sk50FV4IiJZ45qpXFbvWk3fyr75DsOZoh+oH78CB7Ylv160WpTNJrzVL4R/RytQa/4eHqwzVTvXNtyutzQ8SOQbv8gszihfezj+2+DL07yKezfCssfBhpJfZ8ea8O+WmvCWPgKfv51+XMbA6IuhcwqzDuzZAMsfh0GnQN9x6e9bRArP4X2wZA4EasP/D51RMFNKuSKB2nEoPIDiocChPEfiUGUdofII+OSv4Z9UeEqg6+DMY+g6KPwhvubv4Xi6DQ5/CK/9R/gnHaWV4fndALoPA2th/h2ZxxrVawQMPiV720vF+w/BGz9Pfb3ouW6sU99w1W/pI5nHVncQvnxL8ssvmQNv/QqqF8C3nst8/yJSONb8HV69uf7/Lcvgkiy8D7UBVyRQtcFwZnv6gNPzHIlDlZTCD1alVs2IMeDJQkvwURPgpq2Ep1eMbPO65WnGlCC2MRdD1YWZxwmwZWl4TrnoN6Z88B8CbxnctCXFFZs5X52Pghs3EH78M/DzAak/Lv7D4d/5fDxFJD/8kcLGdcvgLzML6n3AFQmUP9L3RX2gWmAMGG9+Y2j8wZ7tmDxZ2lZJpNkun/2Fgv7w8BHZOibITiKczoCl0eXV/0rEfaKv+5J24fe0AnofcEUncn9ICZRkkROuWMvWAKbZls7VjkqgRNwrfniVAptySwmUSKqccMWaYxOoNK52jC5fQG+cIpIl8VcHF9hwM+5KoDSZsGSDEwadzNYchNmmCpSIpEIJlLOpD5RklZrwmpdRAqUKlIjrNGjCK6zx+tyRQKkJT7JJTXjNy6gJr3C+eYpIlgTrwOMLXzSkCpTzKIGSrFITXvPUhCciqYj/MqhO5M6jPlCSVdEXe0AVqCa8pak/LtHEKZ+Pp4jkR7Aubs7SNIZBySNXjAM195O5gCpQkiUeLxgPfDgXtn6Y2baGnQVjv978/dULYNHvaTLA5RfLocfwzPadC15feCThJ74Z/r/jkTBjdrg8H2//Vvj7f0HgEGxdEb7NX1O/XlSnfnD6HU3Xl7a3fC6sjhsp3tceTv9v2PgeLP2/5tcr7wRn/qr5aY8+egmWPRb+e9A0OP6K7MUsqTu0B/56Y/1UXfGaez0nK1ALL/0QDu+pv23L8oYVqEO7mr4PNGfiNdDvxPRiyQJXJFDVe6sB6NOhT34DkeIx6sLwB//OdelvY8962Le55QRq+RPw8cvQfWjD2yt6wbAZ6e87V4adGZ5Pcec6OLQb9r8Ak38Alb0aLrd+IXz4l/AUPpV9oO+E8GMR/3jW7ArPjfilf4f2Xdv2OKSp9+6HL1ZAl/7h0aN3fwYjzg9P/7Pm1fD0S43V7oe9G+D4f4Ujxibe7pI/w2dvhEfW37ZaCVS+bf4gnNB27l8/JymEX48Hmnk9J2vHJ/DBw9DpKCirDN9W2gGGRmYJGTwNPn8n+ffVREleG3JFAmWt5dzB59Le1z7foUix+Or9mW/jsa+HJwVuSdAfns/v3xZmvr+2cMJ3wj8A7z8Mz18DoQR9GqL9HL4xF7oPSbyt9/4IL/2goPpEFLVgHQw4CS6dC1tXwu8mhc9tsC48L+SVrzddZ82r8MiFEAq0vN3eo8MJ2Ofv5Cx8SVL09XbhnIaTe7//EDz/vcSv56S3HWmeO+tX9UlTvGPOCv8UCFf0gQraIB7jikOVQpLMFWtO7euUjJauVoyN/dJCs7oTOutLvaC/YVNL9LaWnqPJXLEa3a6nRMmyEzT32szG6zF+yIIi4IqsImRDePM9z5tIY8lcsVYUCVSiClTc4HnNrq8EylEad/aN3hafWDWWzDmMbrfALmEvWs29Nlt6PWe67QLligRKFShxpGQu2XXqcAXJaOnDM/ZNtKUEKgtv2JI9jS83j94Wn1g1lsygs9HtFtgl7EWruSpRVipQSqAKTsiGlECJ8yRzyW5BV6BaSqDUhFdw4pP5rDfh+ZJ7PUjuNVuBUhNeY67IKoI2qCY8cR5vKQRrW16mpeYRp4u+SSYa3ymlJjxVJRyhQQWqcRNeaxWolhKo2rgKVCuvB8k9NeElzRUJlCpQ4khJNeG10DzidEk14bVUgXLAlDlSr8UmvNb6QCXZhGdDEApmL2ZJXU6b8JJoui8grsgq1IlcHMk1TXjNdCI33vCgpK2urwTKEdqiCa+1ZSX3ctqEl0TTfQFxRQIVDAXxeFxxqFJIolcdWdv8MsXQhNdcH6jWjktNeM4Sf86io/FnpQkvrgLV2rKSe2rCS5orBtJUBUocKfqGdPdooJmpEfZtgu5Ht1lIWRV9k3zh2vDUEPEO7U4igYo8Ps9eFZ42JBkeL5z5Sxjy5dRilZa9dnt4MMz4c+YthUV/gLr9rTfhzbsNFvwm8TI1uxomUL87KZycAfSbGBmxf1Nm8Xt9cP59cNTxmW2n2Hz4JLz204Zf4qLTrDTXhPf898Kvx5Iy+Mr9sGMNzL+95S+CUbX7G26rwLkigdIwBuJIw8+DXdUtj9IMcOylbRJO1nUfCidcHU6WEjni2JbX7zkCTrgqPDdXspY/DhveUwKVbWv/Ef494vz62075MXzxYXhetDHfSLxeuy7wpf+APRua33b/k2DMxdCua3iOx2iFY+N74fMJ0Pd46JpgqphkBGth5TPhKUqUQDVUvQD2f9HwvAL0GNZ0vrv417O/BlY/H573ctOS8NyWI85Lbp+djoT23bISfr4VfQIVsiEsVhUocZ4eQ+H83+Y7itzx+uCM2emvX1IKZ/w8tXVWPq0moFwIBsITX/c8pv62Sde0vp4xcMp/Jb+f8+6t//vlH8G7kTnRxv1L+l8kDu8LJ1B6XjQV9EOHHvCV37e+bPzr+cD2cAIVbcJNdhtFpujLMkEbvqJDFSgRF9Bo1rmRj6tB4/eXSZOP+lY1L93zGt8fqpCvFM5Q0WcVIRsCUAVKxA00GGNuRMdqaksN+ltl8AGtEe2bl+55TXYYiyJX9AlUMKQKlIhrqAKVG/m4GjRbCZTHGx4yQ8+LpoJ+8KRTgYofxqKAp5vKUNFnFapAibiIx6dKQy4UchNedH0lUE2le149XsCoApXvAHJNfaBEXERNeLmRjw/JbFWgottSYt1UuufVmPqkVAlU8YpVoFoa8VhEioMqDbmRj2aaxmNOZbQtJdYJZXJeo0mpmvCKlypQIi6iSkNu5KUCpSa8nMvkvEaTUhdXoIp6HKi6YB0H6g4A6gMl4gpeX3iQv8P7srtdY6CsMrvbLBR1NU1HIW8LWW3CK6kfYdvNArXhnyj/YeiQbgJVCnUHIXBYCVSx2Vu7l9OePI2aQA0ApS49wSKu4msPn74Os4/K/rZnzIYTr87+dp1s81J4YHr4b1+7tt13/P6SncqnOd4yWP0CrH4Rhp+d2bYKVc0uuGsU+A82vL3HsPS252sHyx4N/91zRGaxFaiiTaB2HtpJTaCGcwadQ1WPKr7cT1M7iBS90+8IT0+RbfPvgF2fZn+7Trd3Q7j6dNL1cOw323bfQ2fAmXdCaQfoNiSzbZ3+M3j0ItjzeXZiK0QHtoWTp9GXQO+q+tuPPi297Z332/D0OABDT888vgJUtAmUPxTuBzG933Sm95+e52hEpE0cMTb8k23v/I87+9BEj3nsN6BD97bdd1kFTPjX7Gxr4JTwbzeew6josQ8/JztVuAEnhX9crGh7VkcTKJ9Lrw4QkSxya+f06DEX+vto/MCPbhU7l+rOki1Fn0CVeIq2yCYibcWtl8FHj7nQP3Q9XjAed57DqNi5LPBk2EGKN4GKZNu+dIapFxGJ59bL4IslgQL3nsOoYjqXDlG8CVRICZSIZInXpVPEFEsTHri3GTZKTXhZV/wJVDG88EUkv9xavSimqoVbm2Gj1ISXdRklUMaYGcaYj40xa40xN2YrqGxQBUpEssbj0g/fokqgXJoERxXTuXSItBMoY4wX+C1wBjAC+LoxxjGjaakPlIhkjdub8IrhYhy3nsMoNeFlXSavignAWmvtpwDGmMeB84BV2QgsHV8c/IIN+zcAsGbPGkAJlIhkgbcUDmzNzSCdTrbr0/CxG5PvSDLnLYV9m9x3DqO2rQz/VhNe1mSSQB0JbIj7fyNwQmbhZObVz1/lF+/9Iva/x3io8FXkMSIRKQrtusC6efCns/IdSdvr0DPfEWRHuy7w2ZvhH7cyHvfO6ZgDOa/LGmOuBK4E6NevX073dWr/UxnWpX5en67lXelc3jmn+xQRFzjrVzBuZr6jyI/OuX3fbjMX/x/s+CTfUeRXhx7Qvmu+oygamSRQm4D4GTv7Rm5rwFr7B+APAOPHj7cZ7K9VvTv0pneH3rnchYi4UbvOMPBL+Y5CMlHZO/wjkiWZXIX3HnC0MWagMaYUuAR4PjthiYiIiDhX2hUoa23AGHMN8DfACzxorV2ZtchEREREHCqjPlDW2peBl7MUi4iIiEhBKNqRyEVERERyRQmUiIiISIqUQImIiIikSAmUiIiISIqUQImIiIikSAmUiIiISIqUQImIiIikSAmUiIiISIqUQImIiIikSAmUiIiISIqUQImIiIikSAmUiIiISIqMtbbtdmbMduDzHO+mO7Ajx/twMjcfv5uPHdx9/Dp293Lz8bv52KFtjr+/tbZHojvaNIFqC8aYxdba8fmOI1/cfPxuPnZw9/Hr2N157ODu43fzsUP+j19NeCIiIiIpUgIlIiIikqJiTKD+kO8A8szNx+/mYwd3H7+O3b3cfPxuPnbI8/EXXR8oERERkVwrxgqUiIiISE4pgRIRERFJUUElUMaYGcaYj40xa40xNya4v8wY80Tk/kXGmAFx9/1n5PaPjTGnt2ngWZDEsf/AGLPKGLPcGDPPGNM/7r6gMWZp5Of5to08O5I4/lnGmO1xx/ntuPtmGmPWRH5mtm3kmUvi2O+KO+5PjDF74u4r6HNvjHnQGLPNGLOimfuNMeZ/Io/NcmPMcXH3Ffp5b+3YL40c84fGmHeMMWPi7quO3L7UGLO47aLOniSOf6oxZm/c8/vmuPtafM04XRLH/h9xx70i8jrvGrmvoM+9MeYoY8z8yOfZSmPMdQmWccbr3lpbED+AF1gHDAJKgWXAiEbL/BtwX+TvS4AnIn+PiCxfBgyMbMeb72PK8rFPA9pH/r46euyR/w/k+xja4PhnAfcmWLcr8Gnkd5fI313yfUzZPPZGy38PeLCIzv2XgOOAFc3cfybwCmCAE4FFxXDekzz2SdFjAs6IHnvk/2qge76PIcfHPxV4McHtKb1mnPjT2rE3WvYc4LViOfdAH+C4yN+VwCcJ3u8d8bovpArUBGCttfZTa20d8DhwXqNlzgP+HPn7SWC6McZEbn/cWltrrf0MWBvZXqFo9dittfOttTWRf/8J9G3jGHMpmXPfnNOBV621u6y1u4FXgRk5ijMXUj32rwOPtUlkbcBa+yawq4VFzgMesmH/BDobY/pQ+Oe91WO31r4TOTYovtd8Mue+OZm8XzhCisdebK/5Ldba9yN/7wdWA0c2WswRr/tCSqCOBDbE/b+Rpg9qbBlrbQDYC3RLcl0nSzX+Kwhn51HlxpjFxph/GmPOz0F8uZbs8X81Us590hhzVIrrOlXS8UeabQcCr8XdXOjnvjXNPT6Fft5T1fg1b4G/G2OWGGOuzFNMbWGiMWaZMeYVY8zIyG2uOffGmPaEE4Sn4m4umnNvwt1wjgUWNbrLEa/7klxtWPLDGPNNYDxwctzN/a21m4wxg4DXjDEfWmvX5SfCnHkBeMxaW2uM+Q7hSuQpeY6prV0CPGmtDcbd5oZz72rGmGmEE6jJcTdPjpz3nsCrxpiPIlWNYvI+4ef3AWPMmcCzwNH5DanNnQO8ba2Nr1YVxbk3xlQQTgyvt9buy3c8iRRSBWoTcFTc/30jtyVcxhhTAnQCdia5rpMlFb8x5svATcC51tra6O3W2k2R358CrxPO6AtJq8dvrd0Zd8wPAOOSXdfhUon/EhqV8ovg3Lemucen0M97Uowxowk/38+z1u6M3h533rcBz1BYXRaSYq3dZ609EPn7ZcBnjOmOS859REuv+YI998YYH+Hk6RFr7dMJFnHG674tO4dl8kO4WvYp4SaKaMfAkY2W+S4NO5H/JfL3SBp2Iv+UwupEnsyxH0u44+TRjW7vApRF/u4OrKHwOlQmc/x94v6+APhn5O+uwGeRx6FL5O+u+T6mbB57ZLljCHceNcV07iOxD6D5jsRn0bAz6bvFcN6TPPZ+hPtzTmp0ewegMu7vd4AZ+T6WHBx/7+jznXCSsD7yPEjqNeP0n5aOPXJ/J8L9pDoU07mPnMOHgN+0sIwjXvcF04RnrQ0YY64B/kb4KosHrbUrjTG3AYuttc8DfwQeNsasJfzEuiSy7kpjzF+AVUAA+K5t2MzhaEke+y+BCmBuuN8866215wLDgd8bY0KEK46zrbWr8nIgaUry+K81xpxL+PzuInxVHtbaXcaYnwLvRTZ3m21Y7na0JI8dws/1x23kXSSi4M+9MeYxwldbdTfGbAR+AvgArLX3AS8TviJnLVAD/EvkvoI+75DUsd9MuI/n/0Ze8wEbnpm+F/BM5LYS4FFr7V/b/AAylMTxXwhcbYwJAIeASyLP/4SvmTwcQtqSOHYIf1H8u7X2YNyqxXDuTwIuAz40xiyN3Pb/CH9hcNTrXlO5iIiIiKSokPpAiYiIiDiCEigRERGRFCmBEhEREUmREigRERGRFCmBEhEREUmREigRERGRFCmBEpE2Z4zpZoxZGvn5whizKfL3AWPM/+Zgf38yxnxmjLmqhWWmGGNWGWNWZHv/IlJ8NA6UiOSVMeYW4IC19s4c7uNPwIvW2idbWW5AZLlRuYpFRIqDKlAi4hjGmKnGmBcjf99ijPmzMeYtY8znxpivGGN+YYz50Bjz18h8WRhjxhlj3ojMPv83Y0yfJPZzkTFmhTFmmTGm4CZaFZH8UwIlIk42GDgFOBf4P2C+tbaK8NQdZ0WSqHuAC62144AHgTuS2O7NwOnW2jGRbYuIpKRg5sITEVd6xVrrN8Z8SHhes+i8Xh8Snmx1GDAKeDUy/5cX2JLEdt8G/hSZIzPRbO8iIi1SAiUiTlYLYK0NGWP8cZMlhwi/fxlgpbV2YiobtdZeZYw5gfCs7kuMMeOstTuzGbiIFDc14YlIIfsY6GGMmQhgjPEZY0a2tpIxZrC1dpG19mZgO3BUjuMUkSKjCpSIFCxrbZ0x5kLgf4wxnQi/p/0GWNnKqr80xhxNuII1D1iW00BFpOhoGAMRKXoaxkBEsk1NeCLiBnuBn7Y2kCbwArCjzaISkYKlCpSIiIhIilSBEhEREUmREigRERGRFCmBEhEREUmREigRERGRFP1/KmEFyiMAWKQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim.newRun()\n",
    "sim.comp.molA.Conc = 31.4e-6\n",
    "sim.comp.molB.Conc = 22.3e-6\n",
    "\n",
    "sim.run(0.1)\n",
    "\n",
    "sim.comp.molA.Clamped = True\n",
    "\n",
    "sim.run(0.6)\n",
    "\n",
    "sim.comp.molA.Clamped = False\n",
    "\n",
    "sim.run(2.0)\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(saver.time[0], saver.data[-1])\n",
    "plt.legend(saver.labels)\n",
    "plt.xlabel('Time [s]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`saver.data[-1]` returns the data relative to the last run. The `Clamped` property is a boolean which is used to turn on or off the clamping of the species in the specified compartment.\n",
    "\n",
    "A final way in which we will control our simulation in this chapter is\n",
    "by activating/inactivating a reaction channel. Inactivating a reaction channel\n",
    "means that it will never occur, regardless of whether the required reactants\n",
    "are present in sufficient numbers. In the following simulation:\n",
    "\n",
    "* we will turn off the forward reaction of the above equation  during\n",
    "  interval $2.0\\leq t<4.0$;\n",
    "\n",
    "* turn it back on and let everything recover during $4.0\\leq t<6.0$;\n",
    "\n",
    "* turn off the backward reaction during $6.0\\leq t<8.0$;\n",
    "\n",
    "* turn it back on and let everything recover again during $8.0\\leq t<10.0$;\n",
    "\n",
    "* and finally turn off both the forward and backward channel during a final\n",
    "  interval $10.0\\leq t<12.0$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TiUOHDrFr165i+/33v//l+PHj7NixA39/fxITE/nzzz9dGkt+ApW/lmB1Ga4FympW2JzShSeEEML3ff7553Tr1o3o6GgmTpxIXFwcQ4cOpVu3bgwbNoz4+HgAJk+ezF133UXfvn1p3bo1y5cv5+abb6ZTp05Mnjy54HwhISE8+OCDdOnShWHDhlHS/IxPP/00kyZNYuDAgbRo0YIffviBxx57jKioKEaMGIHNZgNgyZIldO/enaioKG6++WZycnKKnOfAgQOsW7eOadOmYTLp6UarVq0YNWpUkf0yMzP5+OOPeeeddwrWsIuMjOSaa64BYM6cOURFRdG1a1cef/zxIj9Lvu+++67g55w8eTL33Xcf/fv3p3Xr1gVL1zzxxBOsXLmSmJgYl8xEbsgWKOnCE0II4SovrX+JPSl7XHrOjvU68njvx8vcZ+fOnUybNo01a9YQERFBSkoKkyZNKvg3c+ZM7rvvPubPnw/A6dOnWbt2LQsWLGDMmDGsXr2aGTNm0KtXL7Zu3UpMTAwZGRnExsbyxhtv8Oyzz/LMM8/w7rvvFrv2gQMHWLZsGbt27aJfv358//33vPzyy4wbN46FCxcyYsQIJk+ezJIlS2jfvj033ngjH3zwAQ888ECR+GNiYjCbzWX+nPv376d58+YlLiR87NgxHn/8cTZt2kTdunW55JJLmD9/PldccUWZ5zx+/DirVq1iz549jBkzhquvvprp06fz6quv8vPPP5d5bEUZrgXKYlYyD5QQQgift3TpUsaPH09ERAQA9erVY+3atQVdUBMnTmTVqlUF+19++eUopYiKiiIyMrJgjbouXboQFxcHgMlk4tprrwXghhtuKHJ8YSNHjsRqtRIVFYXD4WDEiBEAREVFERcXx969e2nVqhXt27cHYNKkSaxYscLlr8GGDRsYPHgwDRo0wGKxMGHChApd54orrsBkMtG5c2cSExNdHhcYtAVKaqCEEEK4SnktRd4iv/vLZDIV3M9/bLfbSzxGKVXuuaxWa8F+ZZ3rfF26dGHbtm04HI4yW6Hatm1LfHw8qampJbZClaZw7NnZ2SXGD1CZNX8rw3AtUFaTJFBCCCF839ChQ5k7dy7JyckApKSk0L9/f77++msAvvrqKwYOHFipczqdzoKaoNmzZzNgwIAqxdahQwfi4uLYv38/AF988QWDBg0qsk+bNm2IjY3lqaeeKkhi4uLiWLhwYZH9goKCuOWWW7j//vvJzc0FICkpiblz59K7d2/+/PNPTp06hcPhYM6cOQXXiYyMZPfu3TidTubNm1duzKGhoaSlpVXp5y2J8RIoi5LFhIUQQvi8Ll26MGXKFAYNGkR0dDQPPfQQ77zzDrNmzaJbt2588cUXvPXWW5U6Z3BwMOvXr6dr164sXbqUqVOnAvDhhx/y4YcfVvg8AQEBzJo1i/Hjxxd0Fd55553F9psxYwaJiYm0bduWrl27MnnyZBo2bAjAZZddxrFjxwCYNm0aDRo0oHPnznTt2pXRo0cTFhZG48aNmT59OkOGDCE6OpqePXsyduxYAKZPn87o0aPp378/jRs3Ljfmbt26YTabiY6OdkkRuXJX01ZJYmNjtfy5IdzlivdWExpg4Ytb+rj1OkIIIYxr9+7ddOrUydNhuFxISIgsKFyKkv7PlVKbNE2LLWl/47VAmaUFSgghhBDuZcAESmqghBBCiJJI65PrGC6BsphNMpGmEEKIaqvJEhfhWVX5vzZcAuVnVtjs0gIlhBCi6gICAkhOTpYkqhbQNI3k5GQCAgIqdZzh5oGymEzYnZJACSGEqLqmTZuSkJBQ4lInwngCAgJo2rRppY4xXAJltZhkJnIhhBDVYrVaadWqlafDEF7McF14VpOSInIhhBBCuJXxEiizSaYxEEIIIYRbGS6B0hcTlhYoIYQQQriP4RIomQdKCCGEEO5mwARKSRG5EEIIIdzKgAmUTGMghBBCCPcyZAJlc2g4ZTZyIYQQQriJ4RIof6v+I+VKHZQQQggh3MR4CZTFDECOLOcihBBCCDcxXALlZ8lrgZIESgghhBBuYrgEyj8vgcqxOzwciRBCCCGMysAJlLRACSGEEMI9DJhA5dVA2SSBEt7n0KkMftx6FE2TUaJCCOHLLJ4OwNXyR+FJF57wJg6nxvMLdzNz9SEAujWtQ6uIYA9HJYQQoqqM1wJlliJy4X2e/WknM1cfIthPbyHNzLV7OCIhhBDVYbwEyio1UMK7fLXuMJ+tPcyobo15d0IPQN6fQgjh6wyXQPmZ9W/40gIlPM3h1Hj9971MmbeDmGZ1eOmqbgRY5P0phBBGYOAaKPmAEp6Tlevg1s83sHp/Mhd3iuT9CT3ws5gK5imT96cQQvg2wyVQfvk1UA4pIheekZKRyz2zN7PmQDIPXNyOBy5uX/BcwTQbNnl/CiGELzNcAlXQAiXTGAgPOJmazfA3VpCabeO5sV2Y2K9lkefzEyhZq1EIIXyb8RIoWQtPeEh8ciZj31vF2Swbs27qxZAODYvtI/OUCSGEMRiviFzWwhMe4HRq3Pr5Bk5n2njnX91LTJ5AavSEEMIoDJdAyVp4whNe/GU3/ySmc+uAVlwefUGp+xXU6Mn7UwghfJrhEiiLSaGUfMMXNef13/fy8cpDRDUJZ8qoTmXuKy1QQghhDIZLoJRS+FtM0oUnasTOY2d5e+l+ujUNZ/ZtfVBKlbl/fguUJFBCCOHbDJdAgf4hJR9Qwt2W7z3JqLdXoRS8MC6K0ABrucdYzCbMJiUJvhBC+DjDjcID8LeapQZKuNXh5Azu/HITrSKC+fSmXrSoX/GFgf0tJnl/CiGEjzNkC5T+ASXf8IV7aJrG0wt24nRS6eQJ9JGi0gIlhBC+zZAJlJ8kUMKNPl97mGV7k7j9otaVTp5AEnwhhDACQyZQ/hazTFQo3OK7TQk8tWAnMc3qcO+wtlU6h7/FLAmUEEL4OIMmUFJjIlzvSEomz/28i/rBfsy+rU/BrOKVJV14Qgjh+8pNoJRSzZRSy5RSu5RSO5VS9+dtf1opdVQptTXv32XuD7digvzMZOVKAiVc52RaNhM/WYfDqTHn9r4E+VV9/IUk+EII4fsq8ilgBx7WNG2zUioU2KSUWpz33Buapr3qvvCqJtBq5myWzdNhCIPIsTu4/uN1xCVn8sa10bSPDK3W+aRGTwghfF+5CZSmaceB43n305RSu4Em7g6sOgL8zGTZ5Bu+cI13l+5n/8l03rouhrEx1X/rSxG5EEL4vkrVQCmlWgLdgXV5m+5RSv2tlJqplKpbyjG3K6U2KqU2JiUlVS/aCgqySheecI1fdxzn3WX76dWyLpd3K32Nu8qQInIhhPB9FU6glFIhwPfAA5qmpQIfAG2AGPQWqtdKOk7TtI80TYvVNC22QYMG1Y+4AgKlBUq4wLcbjnDnl5uJDA1gxqRemExlL9NSUX4WEzny/hRCCJ9WoQRKKWVFT56+0jTtBwBN0xI1TXNomuYEPgZ6uy/Mygn0M5MpLVCiGpLScnjl9720jwzhl/sHEh5Y/jItFeVvMZHrkBYoIYTwZRUZhaeAT4Ddmqa9Xmh740K7jQN2uD68qgm0msm1O3E4NU+HInyQpmk8OX87ZzJzeXV8NHWD/Vx6fpmnTAghfF9FRuFdCEwEtiultuZt+w/wL6VUDKABccAdboivSoL89Pl5smwOQvwNudyfcKOv1sXz285E7h3alm5N67j8/DIKTwghfF9FRuGtAkoq/ljk+nBcI9Cal0DlSgIlKudISibP/ryL9pEh3D+snVuu4W8xkSvzQAkhhE8z5EzkgXmTHGZLoa6opC/XHSbX7uR/E2OxmN3z6yHTGAghhO8zZgKV1wIlheSiMhJTs/nfnwe5LKoRrSIqv0hwReUnUJomNXpCCOGrDJlAFa6BEqKi3vzjHwDuuKiNW6/jn5fg2xySQAkhhK8yZAIVUNACZfdwJMJX/Lj1KHPWH6Ff6/p0axru1mv55XUNynp4QgjhuwxZYR2Y1wIlNVCiPFm5Du77eguLdyXSINSfj27siT5zh/v4W/MTKCfVW1VPCCGEpxgygcrvwpMaKFGeT1YdZPGuRK6IuYB7hrYlNMB1E2aWxt+iJ1C5UkguhBA+y5AJVOFpDIQozbYjZ/jfioNEN6vDm9d1r7Hr+lnOtUAJIYTwTYasgZIuPFGetGwbd365CT+ziRfHRdXotf0t+vtTWqCEEMJ3GboFSrrwRGk+/PMAx89m8+Utfeh8QViNXluKyIUQwvcZswXKKtMYiNK99cc+3lt2gBFdGnFh2/o1fv3CReRCCCF8kyETKJNJ4W8xSQ2UKGbH0bO88cc/dG0SxjvXd3f7iLuSSBeeEEL4PkMmUKCPxJMWKFGY3eHkntmbMSl489oYrG5aqqU8+aPwpEZPCCF8l2ETqECrWWqgRBFPLdhJXHImN1/YirYNPTcDU7C/3gKVIe9PIYTwWcZNoKQFShRyNsvGV+viGd45kidHd/ZoLEF5i11n5shM+UII4auMnUDJN3yR57M1cQDc1L+lR+OAcxO9SheeEEL4LuMmUFZJoIRu2d6TvPnHPwxsF0H/thGeDufcWo2SQAkhhM8ybgLlZ5EuPIHN4eTRuX/TKCyAt2pwtvGy+FtMKAXZkuALIYTPMm4CZZVpDAR8u/EIp9JzeHREB+oF+3k6HACUUnoLqST4QgjhswybQAVJC1St53RqvL1kH60bBHNFTBNPh1OEJFBCCOHbDJtABcg0BrXekj0nSUzN4f8Gt/XIhJllCbCaycqViTSFEMJXGTaBCvIzyyinWizb5uDFX3bTsn4QY2Mu8HQ4xQTK+1MIIXyasRYTPhMPp/6BVoPzJtK0o2ma17U+CPeb/sseDiZl8OENPT0243hZpAtPuIumaaTZ0nA4HTg0R5HbLEcWKdkpKBQmZcKszOduTeceB1mCaBra1NM/ihBezVgJ1O6f4Lf/wOOHCfQz49Qg1+EsWHtM1A7ZNgefrokjulkdRnRt5OlwSiTTbNQONoeNQ6mHsDls2Jw27E47ds3OmZwzHE07SnxaPFm2LHKduWTbs0nMTMTu1CdYdWpONDT9VtPO3UdD04reL7xfliOr4BzV8fElH9O3cd9qn0cIozJWAmXOG2XlsBGYN9dOdq4kULXNj1uPAnDHRa09HEnpAvzMpGbZPB2GcBGbw0ZCegIbEzcSdzaOpKwkDqce5p+Uf7BrpSczwdZg6gfUx8/sh5/Zj4jACML9wzFhAgUmZcKECaUUCoVSestRwf1SnqsfWJ9ASyAWZcFsMmNWZiwmC2ZlJtw/HD+zH07NiUNz4NSc+n2nfj/Tnsl/Vv2HFQkrJIESogzGSqBMeT+O01Yw23OmzU44Vg8GJWqSw6kxc1UcHRuFMtJLW59An2bjZKq0QPmipMwkVh5dycEzB0lIT+BY+jH2pOxBQwPAarJS178uLcJbcHX7q+kS0YU6/nWwmCz6P2XBz+xHi7AWhPmFeWWJwXf/fMe2k9s8HYYQXs1YCVThFqi89cakm6T2cDo1Xv19L3sT03jruhiv/GDKJ9Ns+JZcRy5z/5nLsiPLWHd8XcH2lmEtqR9Yn9GtR9MlogtREVFERUR59XuvIqIbRPPl7i/JdeTiZ/aO+dOE8DYGS6DyWpocNgKs/gAylUEt8tJve/jfnwcZ3jmSy7t538i7wgKkBsonpOamsmD/AmbumElSVhJNQppwXYfrGNh0IP0u6IfVZMzW7eiG0czaOYtdybuIaRjj6XCE8ErGTKAKdeHJUPHaIcfu4PtNCXRsFMpHE3t6fQuAFJF7txUJK/gt7jf+OPwHmfZMGgY25Jn+z3Bluys9HVqNiG4QDcCGExskgRKiFMZKoPK/DTpyC4rIpQWqdpi9Lp5T6bm8fo13d93lC/QzSReeF0rPTefptU/zW9xvBFoC6RHZg2vbX8vgZoN94n3lKhGBEbSt05YNJzZwW7fbPB2OEF7JWAlUQQ2UncC8Fij5kDK+w8kZTP9lD71b1mNA2whPh1MhgVYzdqeGzeH0ynmqahuH08EnOz7hk+2fkGnPZFLnSdzT/R4CLAGeDs1jYhrG8Fvcbzg1JyYl71Ehzmes3wpzXj5YqAVKuvCMb/a6eOxOjbf+FYPJ5ButBAFWSfC9hVNz8uHfH/LOlnfoEtGFL0Z+wSO9HqnVyRNA2zptSctNY+KiiZ4ORQivZKwEylS4BkpPpqQLz9iych18veEIF7WLoHF4oKfDqbD8FtJseX96jKZpLDm8hJHfj+TDbR8yoMkAZl46U2p+8lzc/GIA/j71t4cjEcI7GSuBKujCO9cCJYW6xvbj1qOczbIxqX9LT4dSKYHSAuVRTs3JG5ve4IHlD6CU4tn+z/LWkLc8HZZXiQyO5P4e9wNwLP2Yh6MRwvsYLIHK78KTGqjaYsaqQ3RsFOoztU/5JIHyrCmrpjBr5ywGNBnAT+N+Yly7cTLfUQl6N+oNwCsbXvFwJEJ4H4MlUOdaoKxmhdmkyMyt/ppQwjsdTs5g/8l0ru7ZFIuPFWIH+EkLqaesTFjJzwd/pk/jPrw79F3DzuXkCt0adAMgPi3ew5EI4X1861OnPIVqoJRSBPmZyciRDyij+vnv4wCMjGrs4UgqT7qYPeNU1ileXP8ijYIb8f6w9zGbZJ3M8twadSv/nP6H9Nx0T4cihFcxVgJVMBO53uoU4m8hPUdaoIxI0zRmr4unZ4u6NKnjO8Xj+UL89e7mNHl/1qgnVjzBkbQjPNnnSemyq6BBTQcB8MK6FzwciRDexaAJVC4AoQEW0rJlxXsjemrBTo6eyWJc9yaeDqVKwgP192patiRQNWXOnjmsO7GOyV0mM6jZIE+H4zPyRyX+dPAnbA75eypEPoMlUHnfKJ36L3l4oJWzWfILbzR/J5zh87WHGdqxIRP6NPd0OFUSGqC3QKXK+7NGnMo6xQvrXqBD3Q4FI8tExV3X4ToA5h+Y79lAhPAixkqgTOcWEwY9gTqTKR9QRnIqPYe7Z2/GYlI8fXkXn11eo6ALT1qg3C7bns2Dyx5EoXi89+NYTMZagKEm3NP9HgDm7Zvn4UiE8B7GSqDM5ydQfvIN32CeX7ibIylZvDehB83rB3k6nCqzmE0E+5lJlS5mt/tw24dsTdrK8wOep1ejXp4OxyeF+4dTP6A+209t51TWKU+HI4RXMGgCpddASReesew+nsr8rUe5ZUArLu3SyNPhVFtYoFVq9Nws05bJt/98y/AWw7m8zeWeDsenvTBALyIf8u0QD0cihHcwWAKVXwOld4uEB1rJyHVgczg9GJRwBYdT4/YvNhLiZ+HeoW09HY5LhAZYSM2SLjx3emfLO6TlpnFj5xs9HYrP69+kf8H9szlnPRiJEN7BWAmUyQyoghaoOkF6i5S0Qvm+bzce4UhKFk+O7kSdIGMMPw8LsJKWI+9Nd9mbspfv/vmOqIgoWd/ORd4f9j4AH//9sYcjEcLzjJVAgd6NV6iIHCSB8nU5dgfvLt1Pq4hgrolt5ulwXEZaoNzH5rRx79J78TP78fJFL3s6HMPoe0FfQJ8SQqY0ELWdARMoP0mgDObXHSc4eiaLx0d09NlRdyWRGij3WXRwEcczjvPshc/SNLSpp8MxDKvJypuD3yTXmcsnOz7xdDhCeFS5CZRSqplSaplSapdSaqdS6v687fWUUouVUvvybuu6P9wKMFnOzQOV34UnUxn4tK/WxdOifhCXdI70dCguFRpgIVWmMXCLefvnEe4fzpBmUvDsagObDgTgva3veTgSITyrIi1QduBhTdM6A32Bu5VSnYEngCWaprUDluQ99jyzX5FReCAtUL5sX2Ia6w+lcH3v5phMxml9AggN0FugNE3zdCiGcvDsQTYlbmJsm7GYlPEa2T3Nz+xHoEVfPmnjiY0ejkYIzyn3r4umacc1Tducdz8N2A00AcYCn+Xt9hlwhZtirByztWAtPEmgfN+Xfx3Gz2zi6p7G64YJC7Bic2hk22SUqCu9u+VdAi2B3NT1Jk+HYli3d7sdgJt+u4kz2Wc8G4wQHlKpr2dKqZZAd2AdEKlp2vG8p04AJfavKKVuV0ptVEptTEpKqk6sFWO2FlnKBZDZyH1UwulMvt5whNHdGlM/xN/T4bhcwXIuUgflMgfOHGDx4cX8q+O/iAiM8HQ4hnV9x+sL7r+w7gVy81r9hahNKpxAKaVCgO+BBzRNSy38nKb3QZTYD6Fp2keapsVqmhbboEGDagVbISZrQReeNW+2Z2mB8k0zVh5C0+DhSzt4OhS3CCtYUFjen66QP/LOYrIU+YAXrhdkDeLvG/8G4Je4X7j252s9HJEQNa9CCZRSyoqePH2ladoPeZsTlVKN855vDJx0T4iVVGgUHshs5L4qPcfOd5sSGNWtMU3qBHo6HLfIb4E6K1MZuMTfSX9zJO0Iz1/4PJHBxhpw4I2UUnx8iT4f1P4z+z0cjRA1ryKj8BTwCbBb07TXCz21AJiUd38S8KPrw6sCs6VoAhXkJwmUD/p+UwLpOXYm9mvh6VDcJixAWqBcafXR1ZiVmQubXOjpUGqNvo378mDPBwH4avdXODUnNue597PNYWPKqil8uetLnJrU+gljqUgL1IXARGCoUmpr3r/LgOnAcKXUPuDivMeeZ/YrqIECCA+0cDZL+ud9zbcbj9CkTiDdm9XxdChuE1ZQAyUtUNVlc9iYt38esY1iCfcP93Q4tcrgZoMBmL5+OtGfR9Pjix58u/dbjqUfo8eXPVhwYAEvbXiJsfPHejZQIVysIqPwVmmapjRN66ZpWkzev0WapiVrmjZM07R2mqZdrGlaSk0EXC6TVbrwXORM9hmPrLy+9kAyO4+lck1sM0NNnHk+qYFynf1n9nMq6xTj2o7zdCi1Tuvw1lzboWgN1HN/Pcel319aZFtcahxRn0Xx2sbXOJFxgrizcXy1+6siLVaFJWUmseboGrfFLUR1GW+SlPO68OoEelcX3qmsUyRnJQP6SvH5nJqTqM+iiPosyiVN3Rd9fRFXL7i6yLYTGScYNncY25K2lXnsiYwT/HX8LwZ+M5Ah3w7hZGbNlre9v3w/of4Wbh7QskavW9NklGjl7EnZw6BvBrH8yPJiz20+uRmALvW71GxQAoAn+z7JuuvXYVGWYs9tnbiV1wefq/74dOenDP9uOJfPv5zp66fT44seXPbDZUWSpSx7FuN/Gs8df9zBj/u9ozpEiPMVf7f7OrMf5KQXPAwPsnrNB9SKhBXcveRuAKb0mcLz655nQJMBrDq6qsh+0Z9H87+L/1dk9fPypOemE2gJJOaLmIJtp3NO8/0/33NV+6tIyU5h+HfDAbhh0Q1sn7S9YL89KXtISEug/wX9ybRnFuyX76X1L/Ha4Ncq++NWyeHkDFbuO8UtA1oRmlcjZFQBVjNBfmZSMryziznDlsGM7TP4dMendKjXgQmdJnB5m8s9EoumaYz/aTwA9y69t2D72DZj+U+f//DzgZ9pHNyYFmHGrZnzdkHWILbcuAW7084/p/+hc/3OBc8NbzGcm7rcxKyds0o89kjaEe74444Sn3ty9ZP0adyHRsGN3BJ3ZeQ4cjApE1aTsf82iYpRNTkLcmxsrLZxo5tnrp19HaQmwJ16UvLesv288tte9jw3ggCr2b3XPs8Dyx5gSfwSAD64+APu+uOuSh3/bP9n2XJyC/P2z+PZ/s8yrp3ePbE9aTstwlvgdDoZ+M3AKse3deJW9p3ZV/DBVJIu9buQlJXEkvFLqnydynh6wU4+XRPHikeH0Lx+UI1c05MGvLSUXi3r8ca1MR6NIy03jf5zKpawzxszj7Z127o5Il22PZtJv05iV/KuMvczKRNj2ozhuQufq5G4RNXtO72P7/75jotbXMzelL0cTT/Kl7u/LPe4ZqHNWHTlonL3y/9M6/Z5NwA23bCJuNQ4FIp2ddsV7JeYkcjsPbP54/AfxKfFE2gJZPk1ywmynvu7szt5N9/s/YYHejxAqF9okS+oT/R+ggmdJgAwc8dM5u2bx/djvsfP7Feh10H4BqXUJk3TYkt8znAJ1DcT4dQ/cPc6QJ/J+sn5O1j/n2E0DAtw22W3ntxK6zqtCfMLA/Rv731n963Qsa3CW5Gem86vV/2KU3PS66te1Yolf36Wh/98mMWHFxd5bvHVi4u1MJVk243bMCkTX+3+iunrp7P46sVu/wZ4MjWbAS8vY1D7Bnx8Y4nvV8MZ++4qwoP8+Pzm3h6LYW/KXq7+6eryd8wzrPkwBjYZSPeG3Wldp3Wx509mniTEGsJnOz/j/W3vM+vSWcQ2Kvv/8+K5F5OYmVjweETLEWw+ublY9/HP437mj8N/kOvM5f2t7xd5btG4RTQLa1bhn0N4jw0nNpCQlsDUNVMLtl3e+nJeGPgCUZ9FFWz7ZvQ3RVq2DqceZvS80QWPW4a1JC41rtTrjG49mhcGvMA7W97h4+0fl7jPsmuWcejsIW7+7eYKxf73jX8XJGsA/xv+P/pf0L9I3DXx91O4R+1KoL67GY5thfv0moifth3j3jlbWPzgRbSLDHXLJTclbmLyr5MBfYmDW6NupfdX+gfitR2upW5AXT7c9iEAq65bRbh/ODaHDaUUFlPxXlSH01Hkm055+jTuw6hWo5i6ZipvDn6TYS2GAXpdVY4jB6vJyqRfJnFn9J0MbDqwxD86bw99mzHzxwDwVL+nuLq9/oG689ROrlt4Ha9c9AojWo2o9GtTGRM/Wce6Qyl8cXNv+rSu79ZreYubP91AYmo2C++rektidWiaVuSPP0B0g2gCzAGsO7GOv67/i2BrMJqmse7EOm77/bYi+84ZNYeuEV255LtLOJ5xnNK8NPAlMu2ZBe+rpfFLybJncSTtCJ/t/Ix0W3qpxwI80/8Zekb2LNZF98amN5i5YyZAkW5pYRw7k3fyv23/Y9mRZQBsvmEzVrPehVY4SakpzUObE58WX+Y+P479kbE/Fh11GNMghlbhrdwZWq1zTYdr6BrR1a3XqF0J1Lw7IW41PKj/MV3xTxI3zlzP3Dv70atlPZdd5kTGiXJbcsL9w1l13aoy9ylLriMXszKTac8s0r3y2qDXWHNsDc1CmzGu3TjqBVT+59p6cisTf5nI/T3u59aoW0vdz+a0ceGcC7my3ZU80dt960VvOnyaqz5Yw5TLOnHbRcVbNYzqkbnbWL3/FGv/PazGr30q6xRDvh1S8Hj5NcupH1h24jph0QT+Tvq7yLZxbccxb/+8Cl2ze8PubDm5pdTnh7cYzubEzSRnJxdsK+89+tXurxjZamSVfg+E7ygrWbo16lZmbJ8BQMd6HdmTsoep/abSvm57bv/9duZePpcvdn3B13u/Ljjm5YteZmSrkaVeY0ybMTzd72lOZJzgsnmX0btRb94e+jbB1mAAlsUv475l9xXs/0jsI7y68dUi53tt0Gs8/OfDBY8jg2SCV1d6su+TBdNouEvtSqB+vAf2/wEP7wFg25EzjH1vNTNujOXiztV/82bbsxn0zSAy7Znl7rtl4pYSW5iqw+a01XgB482/3UyGLYNvRn/jtmvcO2cLy/ee5K9/DyPY33hjG0rz4i+7mbU6jr3PjXDblA1Pr3ma7/d9T6vwVsy8dGbBGnFTVk1hwYEFAGyeuLlC76tMWyYTFk2gUXCjYoMf8l3a8lJ+i/sN0H8Hun/RvdzzFq7xy5e/vprUlAjQa5Yu/u7iYtt/v+p3Goc0JseRw8EzB+lUv1Op50jNTeWp1U/Rv0l/xrcvufbz5Q0v07Nhz4KW/LLEp8bz1e6veCT2Eaxma7EvJfmtoqeyTlHHv47LPw+E+5WVQBnvf/O8pVzqBOkfCq6ayqC0+qTPR35O94bd+enAT9iddgY1G+SWXxZPjP6IaRDDzB0zybBlFHz7cqUcu4PFu04wvmezWpU8AdQP9iPX7iQ9x+6WUYfT/prG9/u+B+DQ2UMM+XYIQZYgll+7vCB5goq/r4KsQcwbq7c22Z123tnyToldaP8X/X80DGqIxWTh29Hf4mf2Y9pf09iYqH+Berb/s1ze5vIyf0ckcRKFRQZHsn7Cenp/1ZtGwY24pv013Nz1ZswmfXCQv9m/zOQJIMwvjDeGvFHmPo/1eqzCMTUPa86/+/y74HFEYAQfX/Ixt/1+G5O7TC6yXRiP8T6tzMUn0gQ4nVn9oeIHzxwsuD+y1UhevuhlHE5HwS8w4LFh3u7U74J+fLz9Y5bGL3XLz/fOkv1k25wM7lADi017mfrB/gAkp+e6JIFKz03nj/g/6FSvU6mF4Zn2zIIavQBzACuvW1mla1lMFh7s+SATO0+kjn+dIs8VLi7P/1D7aPhH/LDvB65uf3WR3xkhKirQEuj1tW59G/f1+hiFaxgzgSo0s21YgBU/s4mktJwqn7LPV32KdNkVHsZdGz4Iekb25ILgC/jpwE8uT6D+SUzj3WX7GdyhAYPa174EqmGYnkCdTMuhZUT1WveOpB7hsnmXFdt+T8w93BF9B5qmMWHRBLafOvfH/Y/xfxBgqd7o1Ip+u7aarVzb8drydxRCCB9gvJnITVZwnGttMpkUdYOrPpnmk6ueLFbv1KZOm2qF6GtMysSIViNYd2JdQV2Kq7y4aDeBVjOvXxODxWy8t2N5IvOm1khMza7WefILXUtyR7Q+QaFSitmjZvPxJfrw7fUT1su6cUIIUUXGa4Gy+IPTDk4nmPQP5DqBfpXuwtM0jVVHV/HjAX0ZgUtbXsoDPR6gaWhTl4fsC9rWaYtTc3Ik7YjLEsjjZ7NYse8UV/doSr3g2lnvkv9zV7WL+Vj6sWJrji0at4jF8YsZ3348oX7Fp+6QLgYhhKg+433lt+hdIjjOddnl2B2s3l+5RXFn75nN/y35P0CffO3VQa/W2uQJ9KHBACsTqlYvU5I56+Jxahr3DK2ZWa29UZ28Gr2qLOcyf//8YsnTthu30SysGTd3vbnE5EkIIYRrGC+BMuclUPZzXSJHz2SRkeuo8ClOZJxg+vrpBY+n9JnisvB8Vbu67ehYryPLE5a75HzpOXY+XRPH0A4NaVbP+Eu2lMZiNhEeaOV0JROoe5fey39X/7fg8YUXXMj3Y77HpIz3Ky2EEN7IeH9t81ug7Oc+kP7Vuzlwbo2ksmiaVjBB5pXtrmT7pO2E+IW4Pk4fFBsZy6bETZzIOFHtc83fcpTUbDv/N6T2tj7lqxfsR3IlEqhpf01j+ZHlgD5535aJW/hw+Ie0r9vePQEKIYQoxoAJVN6IokItUE3qBAKQWYFWqJc3vFxw//Fej7s2Nh+XP/Fc4fmDqkLTNL5aF0+XC8Lo0byOCyLzbXWDrJWqgfpmrz6h6TP9n2FSl0kyOZ8QQniAAROo/BaoczVQ+ZNpVuRDKn9V8BEtRxRZlVvoc/t0qteJ1UdXV+s8W46cYffxVCb0aeG22bd9Sd0gP05nlD9K1Kk5uW/puaUjrmx3pTvDEkIIUQYDJlB5LVCOwgmUPtKpvKkM4lP1BSK7N+zOK4NecU98Pq57w+5sPrmZTFv5S9mU5r2l+wnxtzAm5gIXRuYj4tfBmnf1UaJ56gT5lTtTfrY9m+jPowsWVK3OGotCCCGqz4AJVF4LlO1cF17+SKfyEqjPd30OUObCpbVd94b6umaFJ2OsjKS0HP78J4khHRsSUpuWbbHnwNybYOYl8PsUSN5f8FTDMH9OpmVjdzhLPfyznZ8V3H+i9xMyf5MQQniYcROoQi1Q9UP0FqjkjNJnI7c5bAW1JTENY9wWnq/r36Q/CsWWk1uqdPyvO09gd2rcOah1+TsbyQ+3w84foF7eHFr2rIKn2jQIwebQiE8pvVXvs116AvXH1X8wodMEt4YqhBCifMZLoMzFa6Aah+tF5Amns0o6gkxbJj2+7AHATV1uIswvzL0x+rAwvzDa1m3LV7u/wuao/OzuH604QJM6gXRuXEteY02DZS/CrvnQczJcljdIodAo0eZ50zgcPVPy+/N09mnSctOIaRBDZHCkmwMWQghREcZLoEooIg/2txAR4seRUr7h70zeWXD/zug73RqeEUzoOIEzOWf4Je6XSh234+hZjqRkcU1ss9pRPG7Lhp/uhz+nQ/uRcNmrJc5TFpHXQrrrWGqJp9l6citgzIWqhRDCVxkwgSo+jQHo3/IPJ5ecQL2/9X0APhvxmYy8q4Ar211JZFAkPx34qcLHaJrGC4t242cxMbl/S/cF5y2yTsOMi2HzZ9DzJrhutr7QdcH781yCf0HeNBs59pJroL7c/SXB1mBGtx7t9rCFEEJUjAETqLw11c5b9LZ5vaBSa0w2Jm4EoEdkD7eGZhRKKbpGdGXLyS04nBWb4X3LkTOsOZDMLQNaEZ43rYRhJR+Azy6HxB1w+Vtw+ZsF6zKWVKMXYDUT6m8pcTmXg2cPsv7EeiZ0miDJvRBCeBEDJlCltEDVD+b42Sxyz/uWn5SZBMDwFsNrJDyjGNhkIDmOHDYlbqrQ/l+v16eIuMnorU8pB+GD/nBqH4z/VK97KsxSvAsPoF5IybOR37vkXgD6Ne7nhmCFEEJUlfESKHPxpVxAb4FyasULdV/f9DoAPRpK61NlDGo2CIBNJ8tPoHLsDpbsPkmvlnVpGBbg7tA85/g2eLc3OB0weRF0uaL4PiXU6IG+nEtJ6+FFBEYAENso1tXRCiGEqAbjJVClfMNvUV/v/ijcjedwOthycgtNQ5pyQ+cbaixEI4gIjKBjvY4VmpX8l+0nSM7I5f5hBl6rzWGHbyaC0wYTvoWmPUver4RRogCNwwNYH5dSZFu2PZvNJzdzVbur3BGxEEKIajBwAlX0Ayp/qHh8ckbBtvUn1nM0/SgP9nywxsIzkoFNBrLj1I4yZyXXNI2Zqw/ROiKY/m3q12B0NUjTYP5dcOYwDH8O2gwtfd9S3p8RIf7k2p2k59gLtv1++HcA+jbu6/KQhRBCVI/xEiiTBZSpSJEuQIMQf/wtpiItUFtObsGkTFzY5MKajtIQYiNjcWgOViSsKHWfX3ac4O+Es9x0YUtMJoNOXfDzg7D9W2g9BC68r+x9S6nR69taTy7jC40U3ZuyF6vJyiUtL3FpuEIIIarPeAmUUvqH1HkfUCaTKjaVwdaTW2lXpx3B1uCajtIQejfuTb2AeiyNX1rqPr/uOEG9YD/+1bt5DUZWgw4shU2zoNUg+NfX5e9fMAqveI0eQHzKuRbS+LR4WoS1wKSM92sqhBC+zph/mc1+xbpIoOhUBnannb9P/U10g+iajs4wLCYLvRr14pe4Xzibc7bY82czbfy+6wSXdmmExWzAt9q2r+GLcXrCPuo1sFagQN5k1ltJi40S1ROowgl+fKqeQAkhhPA+BvxUI68FqoQEqn4QR1Iy0TSNHad2kGHLoO8FUl9SHSNbjgTOLcRc2Ncb4sm2ObmhrwFbnxJ3wo93Q+MYeHgvRLSr+LFm/2Lvz7AAK3WDrBzOS/AdTgdH0o7QPNSAr50QQhiAQROo0lugMnIdJGfksv3UdgC6N+xe09EZypDmQwBYfmQ5mqYVbHc6NWavj6d3y3p0uSDcQ9G5idMJPz+kJ0LXfwOBdSp3vKV4AgX6XGX5NVCP/PkINqeNAIuBp30QQggfZtAEqngNFECHRqEAbI0/w3tb3wPOzbMjqsakTPynz3/45/Q/rD2+tmD7yv2nOJycyQQjtj79OR2O/AVD/g2hjSp/fCnvzxb1gjickkGuI5c/4v8AYESrEdWNVgghhBsYM4Ey+xcr0gWIaqK3hOxPSifDlkGQRZbGcIVLWuijxDYnbgb0qQte/nUPESF+jOhahQTDm615F/58CdpdCn3/r2rnKKOF9NiZbFYf1RPR94a9R+vw1tWJVgghhJsYM4GyBkAJcxOFBlipE2Rl/6kTANwVfVdNR2ZI9QPr06leJ7ac3ALoI+92HkvloeEd8LeYPRydCyXugmUvQJ0WcO2XekF4VVgCik2zAXqNnsOpsfLIBszKTO9GvasZsBBCCHcxaAIVBLasEp9qVjeIfWf3AtC5fueajMrQekb25O+kv8myZzFrTRyBVjNX9mji6bBcJ/kAfD5WT85vWnRu0eqqKKUGqkXeVAZrj60mukG01D8JIYQXM3ACVfLs2K0bBHM0Yx8AHet3rMmoDG1ws8FkO7KZ/88vrD+UwoiujQiwGqT1KTcTZo2EzGS44gMIb1q985UwCg+gRf1gULkkZO4nyCrdy0II4c0MmkAFltoC1aZBCOnE0TSkGWF+YTUcmHH1jOxJoCWQb3f/AsDl0Y09HJELLf4vpCfC9d9C+0urf75SWqAahvoTEHaw+ucXQgjhdsZMoPyC9FaDErRrGII54CiNA9vWcFDGZjFZ6Nu4LwfObqdLkxCGdGjo6ZCqT9Ng/cewYQa0Hwlth7nmvBb/EkfhmUyKuuGnAXhhwAuuuZYQQgi3MGYCVUYXXrvGJkx+p7Ham9VwUMbXxNofzZzB8B5nUcoA696tfRcWPQIRHWD8LH2ZIFewBJQ4ShQgMOQEJkdd6gbUdc21hBBCuEWtS6DiM3cCkJl2QU1GVCskJLQBexibz37n6VCq7/RhWPEKNO0Fd67Uu4VdpZQWKACH5Ti5mZGczbK57npCCCFczrgJlCMXHPZiTx04cwCAI4n1azoqQ3M6Nf7YlUwz80i2JG1hw4kNng6p6pxOWHAP2HP1ovH8BYBdpZQick3TSHOcxJlbnynztrv2mkIIIVzKmAmUX94IphJaoQ6dPUSwuT6Hkxyk5xRPsETVbI4/TZbNwZi2owFYeHChhyOqhlWvw6EVMPTJyq1xV1GlFJEnZydjc2bjtNXnTKa0QAkhhDczZgKV391Swki8uNQ4mgQ3R9Ng17HUGg7MuH7flYif2cSE2C50qteJRYcWYXP6YBKQ9A8sex6a9YV+d7vnGqUsdn0k7QgAjYOb4Cy0rqAQQgjvY9AEKli/LaEF6mj6UdrVawnA9qNnazAoY1t/KIXoZuGEBli5ofMNZNmz+PXQr54Oq/LWfaCPvrt6puuKxs9n8SuxBio/gerfvAMb4lLItTvdc30hhBDVVm4CpZSaqZQ6qZTaUWjb00qpo0qprXn/LnNvmJVU0AJVNIHKtGWSkp1C23r6ArefrjlU05EZ0pnMXLYeOUNsy3oAjG49mtbhrXl7y9vYnT7UTXo6DjbOhO4TINyNs6jnL+VyXivTkbQjKBQXtWqPzaGxIS7FfTEIIYSoloq0QH0KlLQk/BuapsXk/Vvk2rCqyS+/BapoF96elD0AtAxriVJwJKXkyTZF5fy+MxGA2Bb60HuTMnFf9/s4kXGCP4/86cnQKue3KfptVRcJrihz3jIw501lcCTtCI2CGzGkgz5CdMKMdRw/K+9RIYTwRuUmUJqmrQB866twfgtUbkaRzXtP62vgRTeI5pFLOgCQklHyfDyiYjRN48n5O2gdEVxk8sxBzQYRGRTJN3u/8WB0lbD6LdjzM3S6HCK7uPda+WvcndeNl5CWQLPQZgT6menbWm/Nm7Mu3r2xCCGEqJLq1EDdo5T6O6+Lr9RZ/5RStyulNiqlNiYlJVXjcpVQShF5QloCAeYAIgIj6JnXWrLp8OmaicmgVuw7Ra7DyfDOkZhM52qGLCYLV7W/irXH13LwrBcvT5KZAh8PhcVToV5ruOoT918zf1oEe/EWqGah+gSvX97SBwCbU4rJhRDCG1U1gfoAaAPEAMeB10rbUdO0jzRNi9U0LbZBgwZVvFwlFRSRF22BOpJ2hKahTVFK0b15HQKtZlbvP1UzMRnUD5sTALh1YOtiz41vPx6rycq3e7+t6bAqbu17cHQT9L8XJv3s+jmfSlKQQJ1rgcqwZZCSnULTUH2hYovZROuIYOJOZZR0BiGEEB5WpQRK07RETdMcmqY5gY+B3q4Nq5pKa4FKTyj4gPK3mIluFs7meGmBqiqnU2PdwRQu7RJJg9DiiUdEYASDmg7i97jf0bxxWP6BpbDmHWg5EC6Z5t7C8cIKuvDOTWWQkKYnovktUAAtI4KJSy55Rn0hhBCeVaUESinVuNDDccCO0vb1iBKKyDVNIyEtgaYhTQu29WxRl13HUsnKddR0hIbw16FkTqRmM7xzo1L3GdJ8CElZSfx44McajKwCMk7Bd7dAaCMY807NXju/BcpxLoHKn8KgSAJVX2+B8srkUwgharmKTGMwB1gLdFBKJSilbgFeVkptV0r9DQwBHnRznJVTQhF5cnYyWfasIh9QsS3qYXdqrDkg3XhVsf5QCkrBJV0iS93n8taX07ZOWz7f9bn3JAJOJyydBlkpes1TvVY1e31z8S68TYmbgKIJVPvIELJsDpbtPVmj4QkhhChfRUbh/UvTtMaaplk1TWuqadonmqZN1DQtStO0bpqmjdE07XhNBFthluJdePldJPldeAAXto2gYag/b/6xr0bDM4ple5PoekE4YQHWUvdRSnFDpxvYd3ofW05uqcHoyvDLo7BpFvScDM161fz1SygiT85KxqRMhPqFFmwb1U1v6F28SxIoIYTwNsacidxk0pOoQkXk+V0khRMoP4uJvq3rs/3oWb7ZIMPFK+N0Ri7bjpxhcIfyBwZc1voyQq2hfLjtwxqIrBwHlsGGGdC8H4x+0zMxlFBEfizjGL0iiyZzoQFWopuGM2e9vDeFEMLbGDOBAn1B4fNaoBSKJiFFC4UfGt4egFX7k2s0PF+3Yp8+JUX+7ONlCbQEMqDpANYeX8vpbA8W7edmwoJ7wWSB8Z+6b6mW8pQwD9SZnDPUDSg+G8iZLH09wS0y2EEIIbyKcRMoa5D+gZknIT2BhkEN8TcXHS3WMkIvOP9p27EaDc/XLdp+HH+LiQvb1K/Q/uPbjwdg7j9z3RlW2X66D84egWFT9eJxT/EL0W8L1eilZKeUmEC9cW0MAKv2SZ2eEEJ4E2MnUIXWwis8SeH52kfqH2h7TqTWSGi+TtM0dh9Po2uTcCzmir2FYiNjiYqI4tMdn5JZwiLPbpeWCNvnQvS/4ML7a/76heWPEs1NB8DmsJGWm0Zd/+IJVI/mdYlpVoeZqw+RbZPRokII4S0MnEAFFkmgEtISitQ/FTZ1tL50x4g3V5Jrd9ZIeL7s57+PE5+SydU9S349S6KU4rFej5FmS2POnjlujK4Ua9/Vb2Nvqflrn88vSL+16V14xzL01k+rueRi/DsHteZ0po2f//ausRpCCFGbGTiBOlcDlWXPIikrqcgcUIVFNQ0vuP/FX4drJDxf9tGKgwRazVwRU7mJJ2MaxhARGMH8/fNrdkqD7d/Bmrehy5WeGXV3Pmt+AqV34aVk60tNdqrXqcTdezTXW6a+3XDE/bEJIYSoEOMmUH7nuvCOZ+jf3C8IuaDEXcMDrex45lIAnvt5F+k59pqJ0Qel59jZeewstw1sRaCfudLH39z1ZuJS4zh09pAboitBbgb8dD/Ubwdj362Za5bH7AfKVJDg50+x0Ti4cYm7NwwLIDzQyvq4FM7mFZULIYTwLOMmUNbAgiLy/JFf9QNLL3gO8bdwcSd9QsjtCWfdH5+PWrP/FE4NelZg9F1JhrcYjkIxa+csF0dWio2z9FqjS547V3vkaUoVaSE9dPYQFmWhWVjJNXoA1/XSn4t+5neW7E6skTCFEEKUzsAJVHBBC9TZHD0hquNfp8xDHhvRAYD/rTjg1tB82V8H9e6m7s3rVOn4RsGN6N24N0vjl5JdaBi/WzjssPI1aBILHUa691qVVahGLykrifqB9bGaSp+Q9J6hbQvu3/LZRjJzpZVUCCE8ycAJVGCxBCrcP7ysI2gfqc8CvXxvEq8v/se98fmoTfGniWpS9uzj5bk96nZSc1P5/fDvLoysBDvn6cu19L/XvdepCmtgQQvUmZwz5Sb3oQFW9j0/koZ5izb3eWGJuyMUQghRBuMmUH7BBR9Qqbn69AThfmUnUACj85bPeHvJPhk2fp4jKZlsO3KGoR0bVus8vRr1omVYS77d+62LIitB1hn442mIjIJOY9x3naoqNM3Gmewz5Sb3AFaziXX/GQZAWradNftlbighhPAU4yZQ1kC9gFjTSM7SZxkPtpZfA/P2dd3P3V8ia+QVlj9C8coelRt9dz6lFOPbj2db0jb2pux1RWjF/TYFUo/CpdP0pX28zXktUCVNolkSpVTB8jnXz1gnE2wKIYSHeOEni4tYgwAN7DkF8w6pCizdYTIplj8yGID3lx+g5RMLpSUqz7pDKfRoXocW9atfjD227VgCLYH87+//uSCy82SnwtYvocWF0Hqw68/vCoWKyCvShVfYzEm9aNNA/z+44ZN1tHxiIS8s2k18sgcmKBVCiFrK4AkUYMusUPdIYS0jgpncv2XB46cX7HRhYL7pYFI6246cYVjeSMXqCvcP59oO17I0fiknM0+65JwFtuVN1DnwQdee15XyavQcTgdnc85WuAUK9CR/ycODuXtIm4JtH604yEWvLKvZ+bWEEKIWM24C5Vc0gRrSbEilDn/q8s5MuUyf2PDrDUeYsz6es5m1dw6e7Uf1QvwhHapX/1TYNe2vwak5+WT7Jy47Jw4b/PW+PvKu7cWuO6+r5XXhpeamoqFVqgUq36OXduSPhwYV2dbq34s4k5nLsj0nybFLy6kQQriLcROoghaoLM7mnK10K5RSitsuas3wznqLy79/2E70s7/T8omFro7UJ+w/mY5S0LqB6+ZSahbWjN6NerPgwALSctOqf0J7Lvz8AJyOg4serf753Clvmo38OcrqBVRtXq22DUOImz6KxQ9eVLAt5tnF3PTpBjo8+Suj3l5Z7Jjdx1MZ/vqfsmyREEJUg4ETqEAAtJx0zuScqdAIvJJ8fGNssW1HUmpXrYmmafz893F6tahHgLXys4+X5YGeD5BuS+e5tc/h1Kr5gf7r47DlS+h5E7S/1DUBukteC1RSVhIAEYER1Tpdu8hQZt/Wp9j2ncdSmb/laMHjR+duY+RbK9l3Mp32T/4is+4LIUQVGTiB0lugUrOSyXHk0CCoQZVPFTd9FHHTR7HwvgEADHx5Wa2aJ2rrkTMcOpVR7dF3Jeka0ZVrO1zLL3G/8Nexv6p+otwM2PE9NO8Pl7+pz/btzaxBkJtZUP/VILDq7898/dtEsGHKxdw2sFWRZOqBb7bS78UltHxiIXM3JRQ5Zsy7q3A4q1c3lW1zVKj2yuHU+HXHcanTEkIYgvETqGx9mHdlu/BK0rlxWMH9t5fso+UTC2n5xEIGvLSUvSf0LqjtCWfZeuSMoUbu/flPEkrBiK6N3HL+R2Ifwc/kx8qjxbubKmzNu5B9FoZNdV1g7pRXRJ6/kHBZywxVRoNQf6aM6kz/NhHETR9FbAu9OP342aKzvm96Uq8PO5iUQZv/LKry9T5bE0fH//5Kq38XPcfZTBt2h96iGJ+cydEzWUyZt507v9xMq38vYtPh01W+pqi9HE6NK95bzaNzt7F4l3cuabTrWCotn1goC9PXAhZPB+A2fvkJ1BmgYpNolkcpxY5nLqXrU78V2Z5wOotL31xBsJ+ZjNxzidOhFy+r0NQJ3m7T4dN0iAylTpCfW84fYAngwiYXsujQIh7o+QD+Zv/KnSBxF/z5ErQfCc37uiVGl7MGguYgJTMJi8lCiDXELZf57q7+jHp7JTuP6ZPJ/vrAQDo20r8IXB59AT9tOwaA06lhMpX/Xs21O7nji420aRDCjFVFF4TOrw985JL2vPq73kJ74IXLuOiVZcXOc9UHa4o8nnNbX/q1KZpEappmiN8f4RopGbn0eG4xoLeK57embvnvcOoG+2F3ODl+Nptm9YJKPUeu3cnbS/YR1TScS7uU/IVw5qpDRDcLp2eL8usST5zNZuW+JK7q0RSTSXE6I5fL8uoO/zt/BxP7tgD0Vlp/i0nezwZj3ATKWjSBCvMPK2PnigvxtxA3fRRb4k8z7v2iHwKFkyeAYa/9ydK8OaWqI9vmoPfzf5CarderfH9XP/1bTkQw3ZvXJcS/8v+NcacyGPzqcp4f15UJfVqUup/DqbEl/gxXdL+gyvFXxIROE1h2ZBnz983n2o7XVu7gjTPBZIYr3vf+rrt8ee/P01mnqOdfz61/WBfeN7DE7Y+P6FCQQLX+zyJ2PXspQX5lv5funbOZZXuTWLY3qWBb1yZh7DiaWvA4P3kCKty69a+P/6J1g2Cu7tmULfFnirQuvHx1N66JLX2h5TX7T9G9eV0C/Vxbnye8S37ydL7uzy3mfxN7cscXmwCY1K8Fn609zOxb+xAZHsAzP+3itfHRLNp+nKcKTUnzxS29GdiuaNd5j+cWk5KRC8DdQ9rw6KUdSUzN5pI3VnBJ50ieu6JrQR3opsOnC74IPPrd30ViyLfnRCoj3tQTqlB/C83rl57cicp7fERHLmpf/fKHqlI1WY8QGxurbdy4sWYulnocXu/IrwPv4tGEhfww5gfa1W3nlkv9vvMEt+f94ozq1pgXr4yi29P6Om//Hd2ZOoFWHp67DaBCH1JOp0brQh88/hYTOWWMmHrw4vb0alWXZ3/axfSruhHTrA5AkVqTpxfs5MoeTYluVoczmbnEPHvuj9GFbevz4Q09Gfban5xMy+GDCT0YGaUvabPrWCqXvb2SN66NZlz3ppV4VSpH0zQm/TqJfaf3MeOSGXSJ6FKxA3PS4NUO0G44XPOZ2+JzuY2z4OcHuKffeBJzzzL38rkeCeP891qIv4XxsU0JD7SyYNsxfrl/IP4W/QPj0KkMhry6vMjxH97QkxFdG9H2P4uwl1FLdX2f5lhNiqfHdEEpxZz18aRk5LJ6/ynWHEguN87Zt/ahbcMQGoYFFNn+/vL9vPyrPpt93PRRFf2xhQ/ZfzKd7zcn8MFyfZH3nc9cSrC/BU3TinUd14QrYi5g/tZjZe7z870DGP3OqiLbmtYNpGOjUHeGVuvcMagNvVpWbQRzRSmlNmmaVnw0GUZOoLLOwEst+LbPDTx3cgVLxi+hYZDr5jA63687jhPVtA5N6uij/5LTc+g57Y8KHXttbDNMJsXzV3RFo+Lf2kuT33X41I87+Gxt0X74rVOHF0meyjvHF38d5r/zd7Di0SFu//Z0JPUIY+aPYViLYbw66NWKHbT2PfjtPzB5IbQc4Nb4XGrbNzDvdiZ0v5jgwPp8dMlHHgul8DfpkgT5mcks1Lrap1U9hnRsSL/W9YnOS9bzaZrG6v3JtG0Ywmu/72XupoQiCXlpzp8epEX9IA6XMLP6+inDWLL7JAFWEw9+s63Ic3/9exiNwgOKHSO8X9ypDPacSOPhb7cWtOQ/ckl77hnarsh7Y/atfejf9tyI1d3HUxn5VsVrJyPD/Pn9gUE889NOfig0OrWwlY8NYeW+U/xn3vYKnfOfaSNp/+QvBY+fHduFG/u1LBL3D//Xnx7NKz5ZrvAetTOBctjguQhm9BjLW6e3sGHCBgIsNfvHdehryzmYlAHAo5d24JXfKrfu2796N2P1/mTiUzJ56vLO3HRhKwB+3XGCHi3qYHdo9J++tMrx7XluBKv3n+KWz0r/PxnYLoLdx9PYMGVYjfTfP73maebvn8/vV/9efsKrafBWNIQ0hFsW+073HcCuBfDtREZ2jqVbo1heuuglj4aTmJpNnxeWVGjfn+4ZQFTT6tcUVkRqtq2gNbc8N/ZrwbNju7o5IlFdWbkOftiSQJ9W9Th0KpMTqdn8d/6OCh1bXiuj06mR63DiZzYVtKxue+oS1h9KIdBqZkC7c8lX3KkMHv1uGxvizg1o2PjkxUSE6DWYmqaxbO9JZq2O49Xx0YQGWOg89Vz967juTXj9mmiUUrz06x4+WH6AHc9cWqSkIsfuwM8stU++rHYmUADP1uf1LoP5KjOOTRM3lb+/m+W35gD83+A2vL/8AE3qBHL0TFaxfV8bH81VPSveZXYkJZP6IX5FfsFBT9zuGtSGk2k59H3x3Adk4QL3vw4msz3hLFf3bMrpzFyGvvZnkXNc3CmSGZNKfP+43P7T+xm3YBy3dL2FB3o+UPbOJ3fD+31h1GvQ69Yaic9l9v0BX11Fn7btuLL9eB7v/binIwL0Ils/iz44Nzk9h3tmb2HtQb2L7aHh7blvmHu6wctTWndNu4YhfHtHP26cuZ4zWbmsfGyoB6IThWmaRnJGbkEikm/BtmPcN2dLlc65+MGLaNswxOOJyNksG2aTqlLdqfBNZSVQxn4X+IWQas90WQF5dU3s24KezesSYDXRukEIN/RtwQV5XX5ZuQ46Tf0VgH3Pj8RqrtwME/kjTw6+cBkOTaPdlF+wmBT/N7gNSikahQew4J4LGfPuat6f0KPIH6K+revTt7U+AqpusB9LHx7E9qNnuf/rrQBMHd25uj96hbWt25boBtF8v+977oi+g0BLYOk77/gBlAk6jamx+FzGGkiOgkxHTpVnIXeH/OQJoH6IP3Nu945RjUopvr69L9d99FeR1th8I6Ma8fKvezmSklnmKCzhPnaHkzHvrmbX8XMDCjo3DmPhfQNYse9UuclT4S+NJ85mF3zhe2h4e9pFekftUHig1dMhCC9i7ATKP5RUexbhwXU8HUmBzhecS+bykyeAQD8zB1+4DKWo1rcsk0lhQpXY1N2taZ0KFdq2bhBC6wYhdGtaB4dTq/GRI7dF3cY9S+/h273fMqnLpJJ3cthh0yy97inEfbVtbhMQRqpJT1ZcMUdZbdC3df1S378XtokA9vJ3wllJoDzg3aX7ioy+zLfreGqxlsNhHRvyn1GdOHYmi5+3HeeZsV2KrXDQKDyAPx8dzLaEs4yJdu8IYCGqyvAJ1FlHtte0QJWnIvPw1KRWEa5b964yLmp6Ee3qtmPOnjnc2PnGkhPK7yZDRhJ0f7HG43MJ/zDS8hIod80BVZt0aBRKnSArP2xOYFS3sgvWhWvFJ2cWS54euLgd3ZvXZdLM9QXbwgOtbHvqkoLHbRqEFJtGoLAW9YNpUd8zf4OEqAjjzkQOEFiXVGcuYX6+kUAJnVKKUa1GcTT9KHtPl1B4f2gF7P4JosZDt/E1H6ArBIQXJFChft7RPeHLAqxmxvdsyop9SbK+Xw1bvFufs+va2Gb8M20km568mAcubs+g9g3Y9eylNAoL4LKoRgWz3wthFMZOoALqkKrZpYvEB13d/moCzAF8vefrok+cjoO5N4FfCFzyvEdicwn/UNIlgXKpIR0aYnNorN5/ytOh1BrbE87y3M+7AHjp6m74WUzUL1Q8HuRn4a//DOP9CT2xVLKuUwhvZ+x3dGAdzuKUFigfFO4fzqjWo1h4cCGnsgp9IP76H8hKgeu/gdBIzwVYXSYzaX5694QkUK7Ro0VdIsP8efjbbSSn53g6nFohf/6wBqGVXH5JCAMwdAJlDwgnw6QkgfJRk7pMwu6088HWD/QN8etg70K46FHfmjSzFGl++iACqYFyjQCrmY8mxpKeY5eFXN0sx+6g5RMLyXU46dY0nKUPD/J0SELUOEMnUGl5CwqHWWRUji9qFd6Ky9tczoIDC0jNSII510FwQ+h/n6dDc4k0P31iV2mBcp3oZnUY2rEhn6w6xOm8Nc2E681ZF19wf8akWEIDZHi/qH0MnUCdterNymEmYw82NLJrO15LtiObn1ZM1bvuRr0K/sZosUm3+GHWKHuuK1FpDw1vT1q2nU/XxHk6FMN6e+l+AN66LoaGobJ8jqidDJ1ApVr9AAjXvGt6AFFxXep3oWu9zsw9tgKtSQ/oPNbTIblMqsVKKMrjsysbTdcm4fRsUZcv/jpMTa60UFvYHU5S8lr3xsY08XA0QniOsRMokz45W1gZq8QL73dNYHMOWEys7X6Np0NxqXSTmRB5b7rF2JgLSMnIZfX+ZE+HYjgr9+mDOm4Z0KqcPYUwNkMnUGfzfrowp9OzgYhqGXFwPfWc8NaJ5YZqUUhTilCnw9NhGNI1sc2ICPHnoW+3cjI129PhGIbDqXHLZxtQSl9nU4jazNAJVGpez0iYTYY0+6yjmwk8tpXbGw9iV/Iufjv8W/nH+Ih0pRFqt4OBkkJvEWA188ENPTidmcsD32z1dDiGse5QMk4NJvRpXmz5FSFqG2MnUJo+I3G4JFC+a8MMsAYxfuBT1Auox4vrXiTDluHpqFwiFYfeAmXL9HQohtSrZT2u6tGUNQeS2XnsrKfDMYTrP14HwIMXt/dwJEJ4nqETqLOOHAKdTqw5aZ4ORVRF0l74+xuIuR6/4Aa8PfRtUrJT+GT7J56OzCXSNTshTidky4e7u/z7sk4E+Zn5ZOUhT4fi8zbHny64X3i2cSFqK0MnUKm2dMI0IOuMp0MRVbHmbTD7weB/AxDdIJqLm1/MjO0zWHd8nYeDq740Rw6hkkC5VXiglWtim/HDlqP8sSvR0+H4tHtnbwFg1eNDPByJEN7B2AlUbiphmgmyTpe/s/AumSmw/Tvodi0ERxRsfqzXY9QLqMe9S+/lZOZJDwZYPQ6ngwxnLqFODbJTPR2OoT10SXs6Ngrl/77azN8JZzwdjk9Ky7Zx9EwWzesF0bSuTEwsBBg8gTqbc5YwZYbsM54ORVTWxplgz4ZetxTZ3DikMa8MeoUsexavbHjFZ0flpdvSAaQFqgaEBVj5ZHIvch1OnlqwE6dMHVFp7+RNnDm5f0vPBiKEFzF0ApWam0qY2U+68HyNpsGO76FuS2gUVezpXo16MaLlCH6N+5WfD/5c8/G5QH4CFeJ0Qo60QLlbkzqBPHhxe7bEnylIBkTFfbTiIAD/6t3cw5EI4T0MnUCl29IJNQVIF56v2fsLnNwFfe4sdZfpA6dTP6A+8/bPq8HAXCctVx/YEOZ0SgtpDblvWFt6NK/DW0v+Ye0BmWCzonYc1VtIL4tqRKCfTF0gRD5DJ1AZtgxCrEHyAeVrVr4GQRHQc3Kpu5hNZsZ3GM/GExtJykyqudhcJD1Xb4EKdjolwa8hSileu0Zfu+0/87Zjd8gEuxXx+84TADw7tquHIxHCu5SbQCmlZiqlTiqldhTaVk8ptVgptS/vtq57w6yaLFsWQdZgvQvPR2tlap3EnXB0I/S4EaxlL7I7suVINDT+iP+jhoJznWyHPjt2oDkQMqQ1pKa0igjmv6M7c+hUBg/P3eazNXQ16at18bSPDCFCpi4QooiKtEB9Cow4b9sTwBJN09oBS/IeexWbw4ZdsxNoDQFHDtiyPB2SqIi/PgCTBfrcUe6ureu0plloM3468BNOzbdaE7LtegIVEBAOmac8HE3tcllUI4Z2bMiPW4/x0LeSRJVl57GzJGfk0rxesKdDEcLrlJtAaZq2Akg5b/NY4LO8+58BV7g2rOrLcugJU6B/qL5BuvG8X/IB2DobOo2B0EYVOuT6jtez/dR2NiVucnNwrpVlz3t/BtSFDEmgapJSig9v6Mn4nk2Zt+Uoo99ZxR+7EmV0XgnWH9L/9D82Qta9E+J8Va2BitQ07Xje/RNAZGk7KqVuV0ptVEptTEqquVqVgm/4/uH6BhmJ5900DebdCX4hcOH9FT5sXLtxBFmCmLNnjhuDc738LryAoAhJoDzAz2Lipau68cK4KI6eyeLWzzdyv6yZV8Sp9Bye+WkXAO0jQz0cjRDep9pF5Jre/l3qVzdN0z7SNC1W07TYBg0aVPdyFVbwDT8/gZIWKO92cDkkrIfhT8MFMRU+LNgazBVtr2DZkWWkZJ/fUOq9ChL8oAjpwvMQk0lxfZ/mrH1iGJP7t+Snbce4+6vN7EuUpZ8AftmhF483qVN2LaIQtVVVE6hEpVRjgLxbr5sSOv8DKjAwr75dRjp5t3X/00feRV9f6UPHtx8PGjy56kmfqWcpeH+GROotUD4StxEF+pn592UdufnCVvy28wQj31rJB8sP4KjlXXr7EtMI9jOz4jFZukWIklQ1gVoATMq7Pwn40TXhuE5BC1Rg/bwNZzwXjChbbiYcWALR14E1oNKHt63blrti7mLl0ZVsPrnZDQG6XpY9C4uyYA1uCE6bzEbuYf4WM1Mv78yqx4fSu1U9Xvp1D/1eXMKD32xl0+Ha+eVrc/xpujWtg9mkPB2KEF6pItMYzAHWAh2UUglKqVuA6cBwpdQ+4OK8x14lP4EKCMxbRy3Ld7p3ap3f/g2OXGh7cZVPMbHzRML8wnhz05uui8uNsh3ZBFgCIDivWzvD9+ayMqJG4QF8dWsfXrm6G10uCOPHrUe56oM1jHhzBb/lzYdUG6Rk5LLjaCq9W9XzdChCeC1LeTtomvavUp4a5uJYXKoggQqqD5YASKs9f/x8ypH1sOlTfdHgVoOqfJpASyAjW43km73fMHX1VJ7p/wxKee835yx7lp5A5Y82TDsBEe08G5QA9FF642ObMT62GSfOZvPVusN8suoQd3yxieim4Qzu0JDhnSPp2iTc06G6zZZ4vdWtf5v6Ho5ECO9VbgLlqwpqTKxBEFRfuvC81ZJnwT8cRr0GpuqNaXi89+PYnXa+3/c9g5sNZmjzoS4K0vWy7dkEmAslUOmJng1IlKhReAAPX9KB2y9qzf/+PMiSPSd5a8k+3lqyj0Crmc4XhNEgxJ+mdQPp2iSc7s3r0KK+78+ZtOnwaSwmRbemdTwdihBey7AJVEENlCUQAutKEbk3Sj4AcSuhz13gX/1h0laTlSl9p7DhxAZmbJ/h/QmUJUAvnAfIlNnIvVlogJVHLu3AI5d2YF9iGr/vSuRgUgZHUjLZlnCGXwt17zUM9adesB+tGwTTrmEo0c3CCQ/0w2pWWEwmrGaFn8VEs7pBmLy0vmhL/Bk6NQ6Tte+EKINhE6iCpTIsgXrylLijnCNEjds4U591fMADLjul1WRlQqcJvLj+RVYmrGRg04EuO7crZTmy8pL7OoCSBMqHtIsMpd158yKlZdv4JzGNvw6mcCApnZOpOWw7cpZF20svHVBKnyIgIsQff4sJP4uJOkF+hAdaMCmFQu9ONCmFUmBS+mP9vv58/nOq0GOTggah/gT6mbGYTJhNCotJYTbrt3WD/PCzmHA4NRxODadW+BZy7A7WHkzmxn4t3Po6CuHrDJtAFdRAWQKk9ckbZaboCVQlZh2vqLFtx/Li+hf5ZMcnXptAFbRAmcx6C6kkUD4tNMBKzxb16NmiaNF1cnoOe0+kketwYndo2J1ObA6NM5m5JJzO4tCpDLJsDnLsTs5k2th7Ig27U0PTNJwaaJqGpoFT09DIu9UocZurDWpfc/P2CeGLDJ1AmZQJP5Mf9L4dVr+p/9Xx4sLiWmXrbLBlwsCHXX7qYGswV7W7iu/3fc+aY2vof0F/l1+jurLt2dQLyPuwDaovs5EbVP0Qf/q3rZlFePOTrly7k8TUbOx5rUp2h97CZHc6ybI5OJWei0mBWSlMJoVZKcymc/dNJgjysxDd1LhF8kK4gmETqPwiXaUUBOfVmeSkQUCYZwMTeiK7cSY06wuNurrlEg/HPszaY2t5deOrzB09F7PJu2o5ClqgQE+gpAVKVJNSCrPSJwZtGeH7hexCeLtqL+XirQqGiYPeRQLSlectDq2AlAMQe7PbLhHqF8oDPR9g3+l9LI5f7LbrVFW2I1uvgQIIqiejRIUQwscYNoHKthf6gCpIoGQyTa+w8lX9/6TzWLdeZniL4dT1r8tnOz5z63WqIsuepU9jABBYT96bQgjhYwybQGXZswolUHm1JpnyIeVxZ47oLVAdR1dp2ZbKsJgs3NbtNnYk72BJ/BK3XquyiiT4oY30iTTtuZ4NSgghRIUZN4FyFEqg8mugpFDX83blLZvY/74audz49uMJsYbwxqY3yLRl1sg1y6Np2rmlXAAi2oPmgJSDng1MCCFEhRk3gbIVqoEKb6rfnon3XEBCLx5f/aaeMNTQsiUBlgCe7v80h1MP8+TqJ2vkmuWxOW04Nee592fdvPl2UhM8F5QQQohKMWwCVaRI1xoIoY3h9CHPBlXbHVmnL5rb/YYanU7i0paXcnPXm1l8eDFbT26tseuWpsgs+XBuQeHjf3soIiGEEJVl2ASqSJEuQN2WcDrOU+EIpxN+fQICwqHXrTV++Rs73wjAN3u/qfFrny8/gfI3580PVKe5fuuQGighhPAVhk2gihTpgiRQnnZ4NRzbAhfeD341P0dN/cD6jG49mp8P/syM7TNq/PqFZdr1WqwgS5C+wWzVE0up0RNCCJ9h2ASqyDxQAHVbQeoxsGV7LqjabNMs/bbnTR4L4al+T3FB8AW8t+U9TmSUvkaZu+UXswdZg85tDIqATEmghBDCVxg2gSqxBQpNCsk9ITcD9i2GNsP0SSM9JMASwLQB07Brdh5b8RhOzemROHIcOUChLjzQR4pKC5QQQvgMQyZQTs1ZtIgcoF4r/VYKyWve399CTioMetzTkdCrUS8mdJrAlpNbeGbtMx6JITev1qlIAhXeVO/mFEII4RMMmUBl2/VuuqJdeC31W6mDqllOJ/z1PkRGQbPeno4GgMd6PcZ1Ha7jh30/eGRUXn4C5Wf2O7cxJBI0pyzpIoQQPsKYCZRDT6CKtEAFNwBrsCRQNW3bHDj1D/S7u0anLiiLSZm4O+Zu6gXU4/EVjxOfWrPduvldeEUSqBb99VtpIRVCCJ9gyAQqf5h4kWkMlNJboVLkA6pG7V2kz8EVfZ2nIymiTkAdXr7oZVKyU5i6ZmqNXjvXWUIXXt28LmZ5fwohhE8wZAKV34UXaA0s+kS9VtICVZPSEuGf36DLOK9pfSqsT+M+3NjlRjYlbuKl9S9xLP1YjVy3oAvPVKgFqqCLWRIoIYTwBYZMoApmejafl0DlzwWlaTUeU620aRY4bRB7i6cjKdWkLpPo3rA7X+7+klt+u6Wge82dSuzC8w/Ru5klwRdCCJ9g6ASqSBE56AmUPQvSPDcHUK3hsMOmz/SpCyLaejqaUoX5hfH5yM95rNdjJKQn8Oifj7r9miWOwgO9G0+68IQQwicYOoEqUkQO0ChKv01YX8MR1UJ7F0LaMejlva1PhU3sPJFLWlzCsiPL2Juy163XKnEUHkgXsxBC+BBDJlAlTmMAENlVv03eX8MR1TJOJyx5DsKbQbtLPR1NhT0U+xAAn+/63K3XyS8it5qsRZ+o2wrOJoDd/d2IQgghqseQCVSpLVD+IfqSGacPeyCqWmTbHEjeB8OeArPF09FUWJOQJlzb4VoWHFjA8fTjbrtOjiMHP5Mf6vzC+nqtkNnyhRDCNxgygSoYhXd+AgWyqHBN+OsDfc6tLuM8HUml3dDpBszKzCMrHnHbUi+5jtzi9U9wbiSe1EEJIYTXM2YCVdJEmvki2sGpfTUcUS2SlgiJ2yHqap9qfcrXMrwl93a/l7+T/uaznZ+55Rq5jtzi9U9wbi4oSfCFEMLrGTKByrTrq92X+C0/op1e3JyTVsNR1RJ/f6PfRv/Ls3FUw01db6Jf4368v/V9Dp456PLz5zhySk6gQhrmzZYvLVBCCOHtDJlAZduzsZqsWEwltIA06KTfHt1Us0HVBvYcWPsutLgQWvTzdDRVZlImnun/DP4Wf6asmlJQU+cqNoet5OQ+f7b85AMuvZ4QQgjXM2QClWXPKrn7DqB5X/326OaaC6i22P0TpCfCwIc9HUm1NQ5pzB3d7mBH8g6u+ekalyZRpbZAATTsBIk7XXYtIYQQ7mHIBCrbnl18CoN8QfWgTgs4vq1mg6oNdnyvj3JsPcTTkbjEDZ1uYGq/qcSlxvHw8ofRXDSDfY4zp+gyLoU17gapCZBxyiXXEkII4R6GTKCy7FkEWYJK3+GCGDi+tabCqR0cdji4HJr1AZMx3lZKKca3H8/1Ha9n5dGVrDy60iXntTlspbdARbTXb3+4zSXXEkII4R7G+KQ7T5ktUACNY/SRTlmnayok4zu8CmyZ0HmspyNxuQd6PkDDoIbcs+QelsUvq/b5chw5JddAAbQapN+eP8mmEEIIr2LIBKrMGiiAxtH67fG/ayag2mDvL2AJhM5jPB2JywVaAvl0xKdEBEYwdc1UjqUfq9b5Sp3GAMAvSF9yKPtMta4hhBDCvYyZQDmySv+GD3BBd/1WuvFc5/AaaNYLrGUkrj6sWWgzZlwyg1xHLpN/nVywnl1VlJlAAbS7BBI2Qm5mla8hhBDCvQyZQOXYc8ruwguqp98ue6FmAjK6tEQ48Tc0992pCyqidZ3W3Nv9Xo5nHOe+Zfdhd9qrdJ4yu/AAmvcHzQGHVlQxUiGEEO5myAQq11nKUhmFBdSBvCVfRDXtmq/ftujv0TBqwg2db+DumLtZfXR1lRcdznWW0wLV6iL9ds61cHJ3la4hhBDCvYyZQDlySx8mnm/Q4/ptWqL7AzIypwN+eUyv28kvgDa427vdTo+GPfhw24eczDxZ6ePLfX9a/M6tI7jrxypGKYQQwp2Mm0CV9Q0foGmsfhu/1v0BGdmehfpt+5H6TNq1gEmZ+G/f/2Jz2rhnyT2czq7caM5yu/AArpoJZn/IzahGpEIIIdzFkAlUmTM957ugB/iH6XMXiarb8b1+2+9uz8ZRw9rWbcvzFz7P7pTdDJs7jINnK75mXpnzQOUzmfRlXVJcvxafEEKI6jNkApXrqEANlNmit0IlbKiZoIzIYYO4VRA1HgLreDqaGndZ68uYcckMbE4b0/6aRnYFaursTjt2zV5+AgVQv42siyeEEF7KcAmUpmnlF+nma9YHTu6C7FT3B2ZEh/6EzFPQcZSnI/GYPo37cF2H69hwYgN3Lym/FS5/+oNyE3yAeq0heR84ndUNUwghhIsZLoGya3acmrP8InLQFxbWnLD/D/cHZkSH14Ay6/MW1WJT+k5hTJsxrD+xnhnbZ5S5r81pA6hYgt8oCpx22PmDK8IUQgjhQoZLoGwO/QOqQt/wWw7U60x+f9K9QRnVP7/r3aB+wZ6OxOOe6f8MzUKb8dbmt1h1dFWp++U4coAKJlCdr9BvpU5PCCG8juESqEp9QJnMejde6lFY866bIzOYtBOQuB3aDvd0JF7BYrLwxuA38DP5cc+Se9h6cmuJ++V34VWohdQaoE9OuuULF0YqhBDCFaqVQCml4pRS25VSW5VSG10VVHUUfEBVJIECuOR5/fbIX26KyKD2LtJvm/f1bBxepEO9Dvx4xY9YTVZu/u1mfj30a7F9KlUDBZCeN8/UoZWuClMIIYQLuKIFaoimaTGapsW64FzVVukPqJAG+u3un9wUkUHtXwKWAGhxoacj8SpNQ5syb+w82tdtz6MrHuWbPd8Ueb5SLaQAE+bqtweXuTJMIYQQ1VS7u/Dy5c+gLd/yK0bT4OhmfekWk+HeQtXWNLQpH13yEZ3qdWLaumlMXT0Vp6aPpMt1VrKFtH4b/f257n+Qk+aukIUQQlRSdT/9NOB3pdQmpdTtJe2glLpdKbVRKbUxKSmpmpcrX8EHVEVqTPINeky//Wy0fEhVxKZZkHYMul3n6Ui8VphfGF9e9iXXdriWefvncfeSu7E5bJVvIQXoexfkpsPf35S/rxBCiBpR3QRqgKZpPYCRwN1KqYvO30HTtI80TYvVNC22QYMG1bxc+ar0AdU45tz9pdNcG5ARrf8YguqfW69NlMjP7MeUPlO4t/u9rDq6imnrplW+Rg/0gQ4A2ySBEkIIb1GtBErTtKN5tyeBeUBvVwRVHVX6gPIPgf+e0u+v+xCyzrg+MKPIOqNPPtrnLn3RW1EmpRS3d7udka1G8sO+H3jur+eASraQBtWDgHBIWA9njrgpUiGEEJVR5QRKKRWslArNvw9cAuxwVWBVVaUaKACzFfr+n34/XkbklSp/0tFmHs+VfcrzFz7PqNajOJp+FAB/SyVaSAEuvF+/fbMrrH7LxdEJIYSorOq0QEUCq5RS24D1wEJN04qP265hVerCy9c7r4xr5asujMhgjqzXb5t6xaBLn2E1W5k+cDqzL5vNE72foFVYq8qdoG+hZWIWT4XMFNcGKIQQolKqnEBpmnZQ07TovH9dNE173pWBVVWlRzkVVi/vQy1hA8yWAukSxa/RZ3CX2cerJKpBFBM6TUApVbkDrQHw1BmIukZ//HIlEzAhhBAuZbgx6FXuwsvX7x799p9fICfdRVEZxMndcGI7tB7s6UhqJ6Xgqo/PPV73kediEUKIWs5wCVS1uvAALpmmTxAJsOgRF0VlEJs+1W9jrvdoGLVefpL/y6Ow7AXIW/9RCCFEzTFcAlXtFiil4L4t+v1tc+DpcBmVly9uFbQYAGEXeDqS2u3S589NIfHnS/BcBMwcobcOCiGEqBHGTaAqM0z8fGEXwKjXzj1e9Gg1ozKAEzsgcQd0GOnpSATA+E/h6pnnHsevhQ8HgNPhsZCEEKI2MVwCZcvrzqhyC1S+XrfCjQv0+9u/hc+vgOQD1TunL0vMm6Gi3XDPxiHO6XoVPLiz6LZn60HSP7DsRRmpJ4QQbmS4BCrXmYtFWTApF/xorQdBz8n6/YPL4J0eepeeplX/3L7m5C5QZqgro7+8SnhTePosPPzPuW3v9YI/p+sj9Z4OL37Mvj/07bkZNRenEEIYjOESKJvDhtVsdd0JL38LQhoV3Za4s+R9jcrphO3f66PvZPZx7xQaWbw1Kl/+8kSaBu/1ga+u0h+/cAFkJNdMfEIIYTCGS6Dsmh2Lsrj2pI/s1b/l37NRf/zhhTDnX/oHkj3XtdfyRodXQ2oCdLvW05GIsoQ3hX8f1Wuj/u8vyB+JuuIVvcXpmTqQtKfoMa+0rn5LVOoxsOeUv589B/76EBz26l1PCCG8gPESKKfdtS1QhUW0O3d/7yL9A2laA/3DKW6Vvn3Tp7D2PUg97p4YPOHgMr37ruNlno5ElMc/RK+NatgJ/nuy6Azm+YIbwpQT5x6/UMVRlfZc+PU/8HonmNaw6HMntkPWaf3+sS16q+3MEfDr4/BcfX1B6ookXUIUlpOm/719oSksedY7k/F9i/UYFz3m6UiEmymtBut5YmNjtY0bN7r1Gk+teYpVCatYcs0S91zAlg1vRUP6ifL3fTLJ9V1e9hyo7Dpq1TVrFNgy4PblNXtd4Rq//hv+eh8u6A43/KAvTgww7y7YNlu//+TJir2vslPho8EQ2hgOryp5n/Yj4J+8VZ3+e0qfZqE8o17TB24UlpsJaDLrvdCdTYA3uhTffv82qNsSbFl6rWaTnqWfIzMFfrhNf4/2vq3kfX5+CFoOgK5Xlh/TqX2w+k0Y9br++5N2Al7rcO75p87oU+OcPQrBDaQEwgcppTZpmlbi2mWGS6CmrJrCpsRN/HqVm5flSz0Or3csex+/EPjP0apfw5YNJrPexfJSi3Pbx30E+37X/2j0uQNCGpZ6ilLFrYJPR8Hg/8Dgx89t1zT9Fz6fwwYvNtOL6UdOr+pPIrxRxil4pc25xw/tgbDGZR/z0WC9RamwXrfBho9L3L2YNkPhwNKyn4//C2yZ57YN/jcMfqL0Y1a+BjETILRR6fsI36Zpeot/aXrfAev/p9+v0wLOHIYR06FxNMwaqZdfrHoTtn557pgrP4Zu1xQ9T+FBF12vhrHv6l3U7/SApr3hhu8hIEx/fud8mDvp3P5DnoRl04qe74oPYP5d5x6HRFbwBxYVMvpNt/eM1KoE6rE/H2NXyi5+HvezW69TxJH18Ene8P6hT+ozRT+f98c8ZgIE1Yc1b+cFeEhvAbBlgckCJXU3Oh36cPSKuqC7fp1Fj8BVn0DU1XnnceofRGY/+N9FMGyq/mY7uQfe73Pu+Dot4Nov4X8D9ceFWwOOboKPh8LVsyr2jUz4nvNH6jXoqH+bPr4Nnjiif2Bomt41MXt80X3vWAmNu8FbMXD6UOnXuG6O3v0SnVdH98/v+nvzbILe5Z12rOwYL3sVWg2CBu2Lbv9tCqx9N+/nOFvujyp8UMImWPqcXkoAeqtm/t/NkkaZultQBGSeKnufuzfoo2ELazkQ6rcpeX9RNT0mQZMebr1ErUqgHlr+EIfOHmLe2HluvU4xCZsgoi0E5P1C56TDi00qdmxII71e5Il40BxVr0nJNzWv9uSbCXqtVmEPbIc3oypwjhS99euvD/W6lQd3QXgFfx7hWxJ3wgf9K75/l3HQ7hJo2qtoXWC+zBSwBsK6D+GPp+Hm36F5n+L7FfZuLzhVaCqGmAn6F5PkfUX3u3s97F6gt8queqPoc/dulg8oX7V/CZyOg4UPndvWeSxc83nRJOnO1dCo67nHKQfh7e7nHkd2PTdnXUlib9a72/58GZa/UPI+jx7QY5kxrPy4zf7wZGLR1rGbfoEW/YvG/ci+qvUUCI+rVQnUvUvu5UTmCeZePtet16mQ72+F7XlxTF4En1ayqfHqWZCwEf56D8b9D6Kv07cf2wL1WustVS9XY16mqaf1UVkf9Ct9n8goyEqBh3ZV/TrC++WkwYtNK7bv3euhQYfy93MFew58eRXErSx7P2XWfz+ueL9m4hJVd3QzbJwJXa7QVzhIOQCbPy//uMgouKuUurvC8j/T8pOaJ5P0Lj0omvCnJerX3btQ/5saEqnXU1kDz+1zcjds+ASG/AcC6sCzdc89N/Y96H6Dfn/Nu/rf+lsWS52TwdSqBOrOP+7kbPZZ5oye49brVMmBZfDFFfr90W/Czw9Ax9Gwp4TuxhsX6BN5VlRuhr4I8vldf2Pfh+4T9Pl+Xml9bnvh7o7EnXpXSssBet1V4f0AulwJ42dVPBZhDLkZevfauv/p3Xq9bvFsN25J3TW9boNLnoNZl0H6SXhwR9EaPlGzNE2vmzy2GZr1Kfp/sfBh2DCjaud9aLd3rMFpzwVlArOLp8oRXqtWJVC3/nYruc5cPh9ZgW80npC/vEZ+HVT+t53CRZJTT4OpmjNMvBmlD1e/rdBoxLQT8OlouGoGXBBT+rGpx/Sm8U9H6Y8f/kefqFEIT0rcCV9fr3fBtD2ve2X9x3oN4D15Xemi5mWnwvRmxbdPTdG7XedOLv3YyCh9kez8L432HHinJ5w9UrSuU4gaVlYCZbg02ua0YTF58Y8VVKiFqHBTsVKuLYJ9YHvxbaGN4N4KJLBhF+j/njgCTnvRmIXwlMguehdLSZrl1Vgd2ywJlCf8eDds+bLk585vFb/oMYi9SW+pOrhcr3c7v0XH4g+3LdW70CrTEi9EDfLiTKNq7E47AZYAT4dhDPnDdYXwdg066nNTbfmi+NB04V4n9xRPnsa8A+0uhdcKjZps2Bn+b23R/XpOolQhDaXwWng1wyVQXt8CJYRwPYuf3pKx6nW9m1xaTWvOP7/ot5e/DT1uBM2pj+AFfcqB36ZAo6hzBddCGITxlnLR7FhNblrKRQjhvdoN1z+89//h6Uhqj/1/6FNVgN6apNS55An0+Zouexl6TJTifmE4hkugbA5pgRKiVmrSE+q3hXl36qNKhXtpmj7FBOgtTELUMoZLoOxOaYESolYyW/UZzzWHPvWCcJ+c9HOjhqOvh9uWeTQcITzBcAmU1EAJUYs1aK/PW7b+Y33aDuEef31w7v6lz5e8JJUQBme4BEpaoISo5YZMAXs2rH7L05EY18pX9dtbFkvBvqi1DJdASQuUELVcRFtocSFsna0vdyRcy5atJ6gAzXp7NhYhPMhwCZTdaZcESojarsdEyD4DexZ6OhLj2febfjv0Sc/GIYSHGS6Bsjlt0oUnRG3X9Wqo0xwW3AMphzwdjXHYc+HbG8ESCP3v93Q0QniUoRIoTdOkBUoIoS8N8q+v9eVCylqDTVTOgaX6be/b9MlLhajFDJVAOTQHGpq0QAkh9LXzYm+G41sh/i9PR+P7NA3mXKvfH/iwZ2MRwgsYKoGyO+0A0gIlhNANmQIBdWDNO56OxPftW3zufmAdj4UhhLcwVAJlc9oApAVKCKHzC4Jet8Cen2HLV56OxrfNv0u/fWS/Z+MQwksYKoGSFighRDEXPQbN+8OPd8PBPz0djW/KTIHMU9C0F4Q08HQ0QngFQyZQ0gIlhChgDYBrvwA0+PXfemG5qJyl0/Tb7hM9G4cQXsRQCZR04QkhShQcAaPfgJM7YfFTno7G92z8RL+N/pdn4xDCixgqgZIuPCFEqWJvhlaD4K/3ZILNysgfwRgzQaYuEKIQQyVQ0gIlhCjT1TOhXhtY+Ii+JIko395f9NuRL3k2DiG8jKESKKmBEkKUKTgCLnsZ0o7Bl1eBw+7piLzf6jchsiv4h3o6EiG8iqESqPwWKOnCE0KUqu3FenfU4VXw1dVgz/F0RN7ryAb9tl5rz8YhhBcyVAIlNVBCiAoZ8y4MeAgOLoN3esLGWdKlV5KDy/XbEdM9GoYQ3shQCZTUQAkhKsRkgouf0tfLM5nh5wdgznX6ciVCdzYBluVNXxDexLOxCOGFDJlASQuUEKJCOoyE+7bqS74cXAazRkLcKk9H5R12/6zfNu/n2TiE8FKGSqCkiFwIUWlK6YvjXvw0JO6ET0fBL49DboanI/Osk7sgIBwmL/J0JEJ4JUMlUNICJYSoEpMZBjwID++BqGtg3Yfwcmv4bAzsnA9Op6cjrHlH1kOTnnp3pxCiGEP9ZkgLlBCiWvyC4aqP4YYfIGo8HNsCcyfBax1gwye1p0YqLRGSdkPLAZ6ORAivZagESlqghBAu0XYYjH0XHtwBl70KZissfAje6AI/3a/PZG7kUXsJ6/Xblhd5Ng4hvFi1Mg2l1AjgLcAMzNA0zaNjXQtaoMzSAiWEcIGAcOh9G/SYBJs/g39+hU2f6v8A6rSAkEio0xwuiIGmvaBZH72uypcdWQdmf2jczdORCOG1qpxAKaXMwHvAcCAB2KCUWqBp2i5XBVdZBS1QSlqghBAuZPHTE6net0F6EhxYAkl74HQcnD4Mu+bDju/0fZUJAutB/bbQoINeRxRYV2/FMlnBbNGTk3qtIbiBd9YYJWzUkyeLv6cjEcJrVSfT6A3s1zTtIIBS6mtgLOCxBEpaoIQQbhfSAKKvK7rNYYPUo/oUCKf2QdoJSDkA277WW65KYwnUl5ex+OtJVWBdCKyjJ2FK5d2aAFXKNnVuG4WeC2kI1iAwWfQCeZPl3L/Auvr1NIdeHK85wOk4d2vLhPi10O8e972GQhhAdRKoJsCRQo8TgD7VC6d6bA6pgRJCeIDZCnVb6v8Kc9j1RMqRqydZTrt+m5UCZ+Ih+YCesNhzwJalJ2FZKXqxuuYE8m4153nbtBK25e2Xmwn2rOr/TO0vrf45hDAwt2caSqnbgdsBmjdv7tZrXdLyEjrU60CQJcit1xFCiAoxW/RuvJqkaXpS5rTrLUpOR959u56kZZzMa6ky661TypR3az536xcE4U1rNm4hfEx1EqijQLNCj5vmbStC07SPgI8AYmNj3ToGuFFwIxoFN3LnJYQQwrsppU/HUJoG7WsuFiEMrDrVixuAdkqpVkopP+A6YIFrwhJCCCGE8F5VboHSNM2ulLoH+A19GoOZmqbtdFlkQgghhBBeqlo1UJqmLQJkoSQhhBBC1CpeOAGJEEIIIYR3kwRKCCGEEKKSJIESQgghhKgkSaCEEEIIISpJEighhBBCiEqSBEoIIYQQopIkgRJCCCGEqCRJoIQQQgghKkkSKCGEEEKISpIESgghhBCikiSBEkIIIYSoJEmghBBCCCEqSWmaVnMXUyoJOOzmy0QAp9x8jdpGXlPXktfT9eQ1dS15PV1PXlPXq4nXtIWmaQ1KeqJGE6iaoJTaqGlarKfjMBJ5TV1LXk/Xk9fUteT1dD15TV3P06+pdOEJIYQQQlSSJFBCCCGEEJVkxATqI08HYEDymrqWvJ6uJ6+pa8nr6XrymrqeR19Tw9VACSGEEEK4mxFboIQQQggh3EoSKCGEEEKISjJUAqWUGqGU2quU2q+UesLT8fgypVQzpdQypdQupdROpdT9no7JKJRSZqXUFqXUz56OxQiUUnWUUt8ppfYopXYrpfp5OiZfppR6MO93fodSao5SKsDTMfkapdRMpdRJpdSOQtvqKaUWK6X25d3W9WSMvqSU1/OVvN/5v5VS85RSdWo6LsMkUEopM/AeMBLoDPxLKdXZs1H5NDvwsKZpnYG+wN3yerrM/cBuTwdhIG8Bv2qa1hGIRl7bKlNKNQHuA2I1TesKmIHrPBuVT/oUGHHetieAJZqmtQOW5D0WFfMpxV/PxUBXTdO6Af8A/67poAyTQAG9gf2aph3UNC0X+BoY6+GYfJamacc1Tducdz8N/UOpiWej8n1KqabAKGCGp2MxAqVUOHAR8AmApmm5mqad8WhQvs8CBCqlLEAQcMzD8fgcTdNWACnnbR4LfJZ3/zPgipqMyZeV9Hpqmva7pmn2vId/AU1rOi4jJVBNgCOFHicgH/guoZRqCXQH1nk4FCN4E3gMcHo4DqNoBSQBs/K6RWcopYI9HZSv0jTtKPAqEA8cB85qmva7Z6MyjEhN047n3T8BRHoyGIO5Gfilpi9qpARKuIFSKgT4HnhA07RUT8fjy5RSo4GTmqZt8nQsBmIBegAfaJrWHchAukaqLK8uZyx6YnoBEKyUusGzURmPps8fJHMIuYBSagp6yclXNX1tIyVQR4FmhR43zdsmqkgpZUVPnr7SNO0HT8djABcCY5RScehdzEOVUl96NiSflwAkaJqW3zr6HXpCJarmYuCQpmlJmqbZgB+A/h6OySgSlVKNAfJuT3o4Hp+nlJoMjAYmaB6Y1NJICdQGoJ1SqpVSyg+98HGBh2PyWUophV5XslvTtNc9HY8RaJr2b03Tmmqa1hL9/blU0zT5dl8NmqadAI4opTrkbRoG7PJgSL4uHuirlArK+xswDCnKd5UFwKS8+5OAHz0Yi89TSo1AL4cYo2lapidiMEwClVdMdg/wG/ov/Leapu30bFQ+7UJgInoryda8f5d5OighSnAv8JVS6m8gBnjBs+H4rryWvO+AzcB29M8IWYKkkpRSc4C1QAelVIJS6hZgOjBcKbUPvaVvuidj9CWlvJ7vAqHA4rzPpw9rPC5ZykUIIYQQonIM0wIlhBBCCFFTJIESQgghhKgkSaCEEEIIISpJEighhBBCiEqSBEoIIYQQopIkgRJCCCGEqCRJoIQQNU4pVb/Q/GInlFJH8+6nK6Xed8P1PlVKHVJK3VnGPgOVUruUUjtcfX0hhPHIPFBCCI9SSj0NpGua9qobr/Ep8LOmad+Vs1/LvP26uisWIYQxSAuUEMJrKKUGK6V+zrv/tFLqM6XUSqXUYaXUlUqpl5VS25VSv+at1YhSqqdS6k+l1Cal1G/5642Vc53xSqkdSqltSqkV7v65hBDGIwmUEMKbtQGGAmOAL4FlmqZFAVnAqLwk6h3gak3TegIzgecrcN6pwKWapkXnnVsIISrF4ukAhBCiDL9ommZTSm0HzMCvedu3Ay2BDkBX9PWwyNvneAXOuxr4VCn1LfCDq4MWQhifJFBCCG+WA6BpmlMpZdPOFW060f9+KWCnpmn9KnNSTdPuVEr1AUYBm5RSPTVNS3Zl4EIIY5MuPCGEL9sLNFBK9QNQSlmVUl3KO0gp1UbTtHWapk0FkoBmbo5TCGEw0gIlhPBZmqblKqWuBt5WSoWj/017E9hZzqGvKKXaobdgLQG2uTVQIYThyDQGQgjDk2kMhBCuJl14Qoja4CzwXHkTaQI/AadqLCohhM+SFighhBBCiEqSFighhBBCiEqSBEoIIYQQopIkgRJCCCGEqCRJoIQQQgghKun/AQrLE/ngLKq2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(NITER):\n",
    "    sim.newRun()\n",
    "    sim.comp.molA.Conc = 31.4e-6\n",
    "    sim.comp.molB.Conc = 22.3e-6\n",
    "\n",
    "    sim.run(2.0)\n",
    "\n",
    "    sim.comp.r1['fwd'].Active = False\n",
    "    sim.run(4.0)\n",
    "    sim.comp.r1['fwd'].Active = True\n",
    "    sim.run(6.0)\n",
    "    sim.comp.r1['bkw'].Active = False\n",
    "    sim.run(8.0)\n",
    "    sim.comp.r1['bkw'].Active = True\n",
    "    sim.run(10.0)\n",
    "    sim.comp.r1['fwd'].Active = False\n",
    "    sim.comp.r1['bkw'].Active = False\n",
    "    sim.run(12.0)\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(saver.time[-1], np.mean(saver.data[-NITER:], axis=0))\n",
    "plt.legend(saver.labels)\n",
    "plt.xlabel('Time [s]')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Active` property is a boolean that is available for reactions and basically turns them on or off.\n",
    "Since the time points are different for these last runs, we need to take care of using `saver.time[-1]` (which returns the time points of the last run) instead of `saver.time[0]`. The data is accessed with `saver.data[-NITER:]` which returns the last `NITER` runs."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}