Single Sim w/ Multiple Variations Support

Helicity_Spectra_Single_Sim.ipynb

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      "cell_type": "code",
      "source": "import warnings\nwith warnings.catch_warnings():\n    warnings.simplefilter(\"ignore\")\n    %pylab notebook\n    %load_ext cython\n    \nimport sympy as smp\nimport sympy.utilities.autowrap as awrap\nsmp.init_printing()\n\nimport rebound\nimport numpy\nimport multiprocessing as mp\nimport os\n\nfrom tqdm import tqdm_notebook\nfrom pickle import dump,load\nfrom functools import partial\nfrom itertools import permutations as perm\nfrom itertools import combinations as comb\nfrom matplotlib.gridspec import GridSpec\n\nnumpy.seterr(all='raise')\nphaseSpaceParameters = ['x', 'y', 'vx', 'vy']\nrc('text', usetex=True)",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Populating the interactive namespace from numpy and matplotlib\n",
          "name": "stdout"
        }
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      "cell_type": "code",
      "source": "r, rc, re, q = smp.symbols('r rc re q', Real=True, Positive=True)\nphi = smp.symbols('phi', Real=True)\n\ndef derivX(f, order=1):\n    if order == 0:\n        return f\n    elif order == 1:\n        return smp.powdenest(smp.powsimp(smp.cos(phi) * smp.diff(f, r) - smp.sin(phi) * (smp.diff(f, phi) / r)))\n    elif order > 1:\n        return derivX(smp.powdenest(smp.powsimp(smp.cos(phi) * smp.diff(f, r) - smp.sin(phi) * (smp.diff(f, phi) / r))), order=order-1)\n\ndef derivY(f, order=1):\n    if order == 0:\n        return f\n    elif order == 1:\n        return smp.powdenest(smp.powsimp(smp.sin(phi) * smp.diff(f, r) + smp.cos(phi) * (smp.diff(f, phi) / r)))\n    elif order > 1:\n        return derivY(smp.powdenest(smp.powsimp(smp.sin(phi) * smp.diff(f, r) + smp.cos(phi) * (smp.diff(f, phi) / r))), order=order-1)\n\nUrphi = smp.log(rc**2.0 + r**2.0 * (q**-2.0 + 1.0) / 2.0 - r**2.0 * (q**-2.0 - 1.0) * smp.cos(2 * phi) / 2.0 - r**3.0 * smp.cos(2 * phi) / re) / 2.0\nU_evaluable = awrap.autowrap(Urphi, args=(r, phi, re, rc, q), backend=\"Cython\", language=\"C\")\n\ndU_dx = smp.simplify(-1 * derivX(Urphi))\ndU_dy = smp.simplify(-1 * derivY(Urphi))\n\nforce_x = awrap.autowrap(dU_dx, args=(r, phi, re, rc, q), backend=\"Cython\", language=\"C\")\nforce_y = awrap.autowrap(dU_dy, args=(r, phi, re, rc, q), backend=\"Cython\", language=\"C\")\n\ndU_dx_dx = derivX(dU_dx)\ndU_dx_dy = derivY(dU_dx)\ndU_dy_dx = derivX(dU_dy)\ndU_dy_dy = derivY(dU_dy)\n\ndfx_dx = awrap.autowrap(dU_dx_dx, args=(r, phi, re, rc, q), backend=\"Cython\", language=\"C\")\ndfx_dy = awrap.autowrap(dU_dx_dy, args=(r, phi, re, rc, q), backend=\"Cython\", language=\"C\")\ndfy_dx = awrap.autowrap(dU_dy_dx, args=(r, phi, re, rc, q), backend=\"Cython\", language=\"C\")\ndfy_dy = awrap.autowrap(dU_dy_dy, args=(r, phi, re, rc, q), backend=\"Cython\", language=\"C\")\n\ndU_dx_dx_dx = derivX(dU_dx_dx)\ndU_dx_dy_dx = derivX(dU_dx_dy)\ndU_dx_dy_dy = derivY(dU_dx_dy)\ndU_dx_dx_dy = derivY(dU_dx_dx)\n\ndU_dy_dx_dx = derivX(dU_dy_dx)\ndU_dy_dy_dx = derivX(dU_dy_dy)\ndU_dy_dy_dy = derivY(dU_dy_dy)\ndU_dy_dx_dy = derivY(dU_dy_dx)\n\ndfx_dx_dx = awrap.autowrap(dU_dx_dx_dx, args=(r, phi, re, rc, q), backend=\"Cython\", language=\"C\")\ndfx_dy_dx = awrap.autowrap(dU_dx_dy_dx, args=(r, phi, re, rc, q), backend=\"Cython\", language=\"C\")\ndfx_dy_dy = awrap.autowrap(dU_dx_dy_dy, args=(r, phi, re, rc, q), backend=\"Cython\", language=\"C\")\ndfx_dx_dy = awrap.autowrap(dU_dx_dx_dy, args=(r, phi, re, rc, q), backend=\"Cython\", language=\"C\")\n\ndfy_dx_dx = awrap.autowrap(dU_dy_dx_dx, args=(r, phi, re, rc, q), backend=\"Cython\", language=\"C\")\ndfy_dy_dx = awrap.autowrap(dU_dy_dy_dx, args=(r, phi, re, rc, q), backend=\"Cython\", language=\"C\")\ndfy_dy_dy = awrap.autowrap(dU_dy_dy_dy, args=(r, phi, re, rc, q), backend=\"Cython\", language=\"C\")\ndfy_dx_dy = awrap.autowrap(dU_dy_dx_dy, args=(r, phi, re, rc, q), backend=\"Cython\", language=\"C\")\n\nenergy = smp.Symbol('E', Real=True)\nvy = smp.sqrt(2*(energy - Urphi))\n\nvyinit_var_zeroth = awrap.autowrap(vy, args=(energy, r, phi, re, rc, q), language=\"C\", backend=\"Cython\")\nvyinit_var_first = awrap.autowrap(derivX(vy), args=(energy, r, phi, re, rc, q), language=\"C\", backend=\"Cython\")\nvyinit_var_second = awrap.autowrap(derivX(vy, 2), args=(energy, r, phi, re, rc, q), language=\"C\", backend=\"Cython\")",
      "execution_count": 3,
      "outputs": []
    },
    {
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        "ExecuteTime": {
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          "end_time": "2017-07-10T03:49:11.216490Z"
        },
        "collapsed": true
      },
      "cell_type": "code",
      "source": "%%cython\n\nimport rebound\nimport cython\nimport os\ncimport cython\nimport numpy\ncimport numpy\n\n\n@cython.boundscheck(False)\n@cython.wraparound(False)\n@cython.profile(False)\ndef bisect(sim,t1,t2):\n    tm = (t1+t2)/2.\n    for i in range(52):\n        if t2-t1<1e-4:\n            break\n        tm = (t1+t2)/2.\n        sim.integrate(tm)\n        if sim.particles[0].y>0.:\n            t2 = tm\n        else:\n            t1 = tm\n    return tm\n\ndef pearsonSpectraDistance(A, B, axis=0):\n    # Compute the mean of each spectra\n    AMean = numpy.mean(A, axis=axis)\n    BMean = numpy.mean(B, axis=axis)\n\n    # Compute the Pearson Correlation Coefficient for two sets of binned data\n    correlation = numpy.vdot(A - AMean, B - BMean, axis=axis)\n    correlation /= numpy.linalg.norm(A - AMean, axis=axis)\n    correlation /= numpy.linalg.norm(B - BMean, axis=axis)\n    return correlation\n\ndef euclideanSpectraDistance(A, B, normalised=0, axis=0):\n    return (numpy.linalg.norm(A - B, axis=axis)) / (1 if not normalised else A.shape[axis] * B.shape[axis])\n\ndef custModuloSpectraDistance(A, B, normalised=0, axis=0):\n    A_temp, B_temp = numpy.array((A, B))\n    initSum = numpy.linalg.norm(A_temp - B_temp, axis=axis)\n    rollCount = 0\n    for i in numpy.arange(0, A_temp.shape[axis]):\n        A_temp = numpy.roll(A_temp, 1, axis=axis)\n        if (numpy.linalg.norm(A_temp - B_temp, axis=axis) < initSum):\n            initSum = numpy.linalg.norm(A_temp - B_temp, axis=axis)\n            rollCount = i\n    A_temp = numpy.roll(A, rollCount, axis=axis)\n    return (numpy.linalg.norm(A_temp - B_temp, axis=axis)) / (1 if not normalised else A.shape[axis] * B.shape[axis])\n\ndef fsum(x):\n    \"\"\"\n    Compensated summation that can work with numpy arrays as well.\n    \"\"\"\n    temp_x = x[:]\n    interSum = c = 0\n    for num in temp_x:\n        y = num - c\n        t = interSum + y\n        c = (t - interSum) - y\n        interSum = t\n    return interSum\n\ndef ensure_dir(path): # Ensures that the path to a directory exists, and creates it if not\n    try:\n        os.makedirs(path)\n    except OSError:\n        if not os.path.isdir(path): # Raises the OSError if and only if the specified path is a file and not a directory\n            raise\n            \ndef runningMeanFast(x, window=100, axis=0):\n    assert(axis < x.ndim)\n    cumsum = numpy.cumsum(numpy.insert(x, 0, 0, axis=-1), axis=axis)\n    if window == 0:\n        N = x.shape[axis]\n        dim_array = numpy.ones((1, x.ndim), int).ravel()\n        dim_array[axis] = -1\n        cumsum = numpy.delete(cumsum, 0, axis=-1)\n        return (cumsum / numpy.reshape(numpy.arange(1, N+1), dim_array))\n    elif window == -1:\n        return x\n    else:\n        cumsum[window:] = cumsum[window:] - cumsum[:-window]\n        return cumsum[window - 1:] / window",
      "execution_count": 4,
      "outputs": []
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      "cell_type": "code",
      "source": "class PhaseSpaceCoordinates:\n    def __init__(self, **kwargs):\n        for key in kwargs.keys():\n            if key in phaseSpaceParameters:\n                self.__dict__.update(((key, kwargs[key]),))\n            else:\n                raise KeyError(\"Invalid key in arguments! {} not defined as a valid phaseSpaceParameter\".format(key))\n        for key in phaseSpaceParameters:\n            if key not in kwargs.keys():\n                self.__dict__.update(((key, 0.0),))\n    \n    def __getitem__(self, items):\n        if hasattr(items, '__len__') and not isinstance(items, str):\n            raise TypeError(\"Multiple arguments are not allowed\")\n        elif isinstance(items, int) and items >= len(phaseSpaceParameters):\n            raise IndexError(\"Index out of range for {} phaseSpaceParameters\".format(len(phaseSpaceParameters)))\n        elif isinstance(items, str) and items not in phaseSpaceParameters:\n            raise KeyError(\"{} not a phaseSpaceParameter\".format(items))\n        else:\n            if isinstance(items, int):\n                return self.__dict__[phaseSpaceParameters[items]]\n            elif isinstance(items, str):\n                return self.__dict__[items]\n            elif isinstance(items, slice):\n                return tuple(self.__dict__[attribute] for attribute in phaseSpaceParameters[items])\n            \n    def __iter__(self):\n        for parameter in phaseSpaceParameters:\n            yield [self.__dict__[parameter]]\n        \n    def as_dict(self):\n        return dict([(parameter, self.__dict__[parameter]) for parameter in phaseSpaceParameters])\n            \n    def as_numpy_array(self, returnType=\"double\", normalised=False):\n        if normalised:\n            if numpy.linalg.norm(numpy.array([self.__dict__[parameter] for parameter in phaseSpaceParameters], dtype=returnType)) != 0:\n                return numpy.array([self.__dict__[parameter] for parameter in phaseSpaceParameters], dtype=returnType) / numpy.linalg.norm(numpy.array([self.__dict__[parameter] for parameter in phaseSpaceParameters], dtype=returnType))\n            else:\n                return numpy.array([self.__dict__[parameter] for parameter in phaseSpaceParameters], dtype=returnType)\n        else:\n            return numpy.array([self.__dict__[parameter] for parameter in phaseSpaceParameters], dtype=returnType)\n        \n            \nclass VariationalSimulationResults:\n    def __init__(self, sampleTimes=[], positions=[], numberOfVariationalParticles=0, variationalPositions=None):\n        assert(len(sampleTimes) == len(positions))\n        assert(len(variationalPositions[0]) == numberOfVariationalParticles)\n        assert(len(variationalPositions[1]) == numberOfVariationalParticles)\n        self.RealParticle = [PhaseSpaceCoordinates() for _ in range(len(sampleTimes))]\n        for sampleIndex in range(len(self.RealParticle)):\n            for attributeIndex, attribute in enumerate(phaseSpaceParameters):\n                setattr(self.RealParticle[sampleIndex], attribute, positions[sampleIndex][attributeIndex])\n        \n        self.VariationalCount = numberOfVariationalParticles\n        self.VariationalParticles = []\n        if self.VariationalCount > 0:\n            for variationalIndex in range(numberOfVariationalParticles):\n                self.VariationalParticles.append({'1st Order': [PhaseSpaceCoordinates(**dict(((attribute, attributeValue) for attribute, attributeValue in zip(phaseSpaceParameters, variationalPositions[0][variationalIndex][sampleIndex])))) for sampleIndex in range(len(sampleTimes))], \n                                                  '2nd Order': [PhaseSpaceCoordinates(**dict(((attribute, attributeValue) for attribute, attributeValue in zip(phaseSpaceParameters, variationalPositions[1][variationalIndex][sampleIndex])))) for sampleIndex in range(len(sampleTimes))]})\n        self.SampleTimes = sampleTimes[:]\n        self.SampleCount = len(sampleTimes)\n        \n    def __getitem__(self, items):\n        if not hasattr(items, '__len__'):\n            sampleIndex = items\n            return [self.SampleTimes[sampleIndex], self.RealParticle[sampleIndex], [self.VariationalParticles[varIndex][varOrder][sampleIndex] for varOrder in [\"1st Order\", \"2nd Order\"] for varIndex in range(self.VariationalCount)]]\n        elif len(items) == 2:\n            sampleIndex = items[0]\n            whichAttribute = items[1]\n            if isinstance(whichAttribute, str) or isinstance(whichAttribute, int):\n                if isinstance(whichAttribute, str) and whichAttribute in self.__dict__:\n                    if whichAttribute == \"VariationalParticles\":\n                        return [self.__dict__[whichAttribute][varIndex][varType][sampleIndex] for varType in [\"1st Order\", \"2nd Order\"] for varIndex in range(self.VariationalCount)]\n                    else:\n                        return [self.__dict__[whichAttribute][varType][sampleIndex] for varType in [\"1st Order\", \"2nd Order\"]]\n                elif isinstance(whichAttribute, int) and whichAttribute < 3:\n                    if whichAttribute == 0:\n                        return self.__dict__['SampleTimes'][sampleIndex]\n                    elif whichAttribute == 1:\n                        return self.__dict__['RealParticle'][sampleIndex]\n                    elif whichAttribute == 2:\n                        return [self.__dict__['VariationalParticles'][varIndex][varType][sampleIndex] for varType in [\"1st Order\", \"2nd Order\"] for varIndex in range(self.VariationalCount)]\n                else:\n                    raise ValueError\n            else:\n                raise TypeError\n        elif len(items) == 3:\n            sampleIndex = items[0]\n            whichAttribute = items[1]\n            varParticle = items[2]\n            if (isinstance(whichAttribute, str) and whichAttribute == \"VariationalParticles\") or (isinstance(whichAttribute, int) and whichAttribute == 2 and varParticle < self.VariationalCount):\n                return [self.__dict__[\"VariationalParticles\"][varParticle][varType][sampleIndex] for varType in [\"1st Order\", \"2nd Order\"]]\n            elif not (isinstance(whichAttribute, str) or isinstance(whichAttribute, int)):\n                raise TypeError\n            else:\n                raise ValueError\n        elif len(items) == 4:\n            sampleIndex = items[0]\n            whichAttribute = items[1]\n            varParticle = items[2]\n            varOrder = items[3]\n            if (isinstance(whichAttribute, str) and whichAttribute == \"VariationalParticles\") or (isinstance(whichAttribute, int) and whichAttribute == 2):\n                if (isinstance(varOrder, str) and varOrder in [\"1st Order\", \"2nd Order\"]) or (isinstance(varOrder, int) and varOrder < 2 and varParticle < self.VariationalCount):\n                    return self.__dict__[\"VariationalParticles\"][varParticle][varOrder][sampleIndex]\n                elif not (isinstance(varOrder, str) or isinstance(varOrder, int)):\n                    raise TypeError\n                else:\n                    raise ValueError\n            elif not (isinstance(whichAttribute, str) or isinstance(whichAttribute, int)):\n                raise TypeError\n            else:\n                raise ValueError\n        else:\n            raise IndexError(\"Too many arguments\")\n            \n    def computeSpectra(self):\n        if \"VariationSpectra\" not in self.__dict__.keys():\n            finalSpectra = []\n            temporarySpectraArray = numpy.empty((self.SampleCount, 2, (len(phaseSpaceParameters) * (len(phaseSpaceParameters) - 1) // 2)), dtype=\"double\")\n            \n            for variationPairIndex in range(0, self.VariationalCount):\n                deviationIterable = tqdm_notebook(enumerate(zip(self.VariationalParticles[variationPairIndex][\"1st Order\"], \n                                                                self.VariationalParticles[variationPairIndex][\"2nd Order\"])), \n                                                  total=temporarySpectraArray.shape[0])\n                for sampleIndex, (dev1, ddev1) in deviationIterable:\n                    i = 0\n                    for (idev1, iidev1), (iddev1, iiddev1), in zip(comb(dev1, 2), comb(ddev1, 2)):\n                        temporarySpectraArray[sampleIndex][0][i] = numpy.arctan2(idev1, iidev1)\n                        temporarySpectraArray[sampleIndex][1][i] = numpy.arctan2(iddev1, iiddev1)\n                        i += 1\n                finalSpectra.append(numpy.copy(temporarySpectraArray))\n            \n            self.__dict__.update([(\"VariationSpectra\", [{\"1st Order\": numpy.ravel(finalSpectra[varIndex][:, 0]), \"2nd Order\": numpy.ravel(finalSpectra[varIndex][:, 1])} for varIndex in range(0, self.VariationalCount)])])\n            \n    def getVariationSpectra(self):\n        if \"VariationSpectra\" not in self.__dict__.keys():\n            self.computeSpectra()\n        return self.__dict__[\"VariationSpectra\"]\n    \n    def splitSpectra(self, parts=2):\n        if \"VariationSpectra\" not in self.__dict__.keys():\n            self.computeSpectra()\n        spectraStride = len(phaseSpaceParameters) * (len(phaseSpaceParameters) - 1) // 2\n        temporaryListOfSpectra = [[varSpectra[\"1st Order\"], varSpectra[\"2nd Order\"]] for varSpectra in self.__dict__[\"VariationSpectra\"]]\n        temporaryListOfSpectra = numpy.transpose(temporaryListOfSpectra, (2, 0, 1))\n        \n        retList = []\n        for partIndex in range(1, parts + 1):\n            retList.append(temporaryListOfSpectra[spectraStride * (self.SampleCount * (partIndex - 1)) // parts:spectraStride * (self.SampleCount * (partIndex)) // parts])\n        \n        retList = numpy.transpose(retList, (0, 2, 3, 1))\n        \n        return retList\n        ",
      "execution_count": 5,
      "outputs": []
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      "source": "qcur = .9\nrccur = 0.14\nrecur = 3.0\nE = -0.337\n\ndef af_with_second_order(sim):\n    # Particle whose trajectory is being integrated\n    p = sim.contents.particles[0]\n    # The position components\n    x = p.x\n    y = p.y\n\n    rcur = numpy.sqrt(x**2 + y**2)\n    phicur = numpy.arctan2(y, x)\n\n    # Acceleration along x-axis\n    p.ax = force_x(rcur, phicur, recur, rccur, qcur)\n    # Acceleration along y-axis\n    p.ay = force_y(rcur, phicur, recur, rccur, qcur)\n\n    \"\"\" NOTES TO SELF:\n    # #############################################################\n    # Generating the expressions using a symbolic algebra package is far\n    # more convenient than doing it all by hand...\n    \"\"\"\n    \n    # Compute the derivatives as they will be reused for each set of variational equations\n    fx_dx = dfx_dx(rcur, phicur, recur, rccur, qcur)\n    fx_dy = dfx_dy(rcur, phicur, recur, rccur, qcur)\n    fy_dx = dfy_dx(rcur, phicur, recur, rccur, qcur)\n    fy_dy = dfy_dy(rcur, phicur, recur, rccur, qcur)\n\n    fx_dx_dx = dfx_dx_dx(rcur, phicur, recur, rccur, qcur)\n    fx_dx_dy = dfx_dx_dy(rcur, phicur, recur, rccur, qcur)\n    fx_dy_dx = fx_dx_dy\n#     fx_dy_dx = dfx_dy_dx(rcur, phicur, recur, rccur, qcur)\n    fx_dy_dy = dfx_dy_dy(rcur, phicur, recur, rccur, qcur)\n\n    fy_dx_dx = dfy_dx_dx(rcur, phicur, recur, rccur, qcur) \n    fy_dx_dy = dfy_dx_dy(rcur, phicur, recur, rccur, qcur)\n    fy_dy_dx = fy_dx_dy\n#     fy_dy_dx = dfy_dy_dx(rcur, phicur, recur, rccur, qcur)\n    fy_dy_dy = dfy_dy_dy(rcur, phicur, recur, rccur, qcur)\n        \n    for i in range(1, len(sim.contents.particles), 2):\n        \"\"\"\n        For each pair of variational particles, we compute the relevant accelerations\n        \"\"\"\n        p1 = sim.contents.particles[i]\n        p2 = sim.contents.particles[i + 1]\n\n        p1.ax = (fx_dx * p1.x + fx_dy * p1.y)\n\n        p1.ay = (fy_dx * p1.x + fy_dy * p1.y)\n        p2.ax = (fx_dx * p2.x + fx_dy * p2.y)\n\n        p2.ay = (fy_dx * p2.x + fy_dy * p2.y)\n\n        p2.ax += (fx_dx_dx * p1.x * p1.x + \n                  (fx_dx_dy + fx_dy_dx) * p1.y * p1.x + \n                  fx_dy_dy * p1.y * p1.y)\n\n        p2.ay += (fy_dx_dx * p1.x * p1.x + \n                  (fy_dx_dy + fy_dy_dx) * p1.y * p1.x + \n                  fy_dy_dy * p1.y * p1.y)\n\ndef af_without_second_order(sim):\n    # Particle whose trajectory is being integrated\n    p = sim.contents.particles[0]\n    # The position components\n    x = p.x\n    y = p.y\n\n    rcur = numpy.sqrt(x**2 + y**2)\n    phicur = numpy.arctan2(y, x)\n\n    # Acceleration along x-axis\n    p.ax = force_x(rcur, phicur, recur, rccur, qcur)\n    # Acceleration along y-axis\n    p.ay = force_y(rcur, phicur, recur, rccur, qcur)\n\n    \"\"\" NOTES TO SELF:\n    # #############################################################\n    # Generating the expressions using a symbolic algebra package is far\n    # more convenient than doing it all by hand...\n    \"\"\"\n    \n    # Compute the derivatives as they will be reused for each set of variational equations\n    fx_dx = dfx_dx(rcur, phicur, recur, rccur, qcur)\n    fx_dy = dfx_dy(rcur, phicur, recur, rccur, qcur)\n    fy_dx = dfy_dx(rcur, phicur, recur, rccur, qcur)\n    fy_dy = dfy_dy(rcur, phicur, recur, rccur, qcur)\n        \n    for i in range(1, len(sim.contents.particles), 1):\n        \"\"\"\n        For each pair of variational particles, we compute the relevant accelerations\n        \"\"\"\n        p1 = sim.contents.particles[i]\n\n        p1.ax = (fx_dx * p1.x + fx_dy * p1.y)\n\n        p1.ay = (fy_dx * p1.x + fy_dy * p1.y)\n        \n\ndef getxtDeviations(xinit=0.03, yinit=0, vxinit=0, vyinit=0, tfin=1, with_second_order=True, initDeviation=({'x': 1, 'y': 0, 'vx': 0, 'vy': 0},),\n                   sampleCount=100, progress_bar_visible=True):\n    sim = rebound.Simulation()\n    \n    if with_second_order: \n        sim.additional_forces = af_with_second_order\n    else:\n        sim.additional_forces = af_without_second_order\n    sim.add(x=xinit, y=yinit, vx=vxinit, vy=vyinit)\n    \n    numVariationalParticles = len(initDeviation)   # Each dictionary in the tuple sets the initial conditions for ONE variational particle pair\n    initialDeviations = initDeviation[:]          # Copies the initDeviations argument to a local variable to ease modification and prevent an external variable from being modified\n    \n    # Initialises array to store the current deviations of each variational particle pair\n    curDeviation = numpy.empty((numVariationalParticles, sampleCount, len(phaseSpaceParameters)), dtype='double')\n    \n    # Initialises array to store the derivatives of the current deviations of each variational particle pair\n    derivDeviation = numpy.empty((numVariationalParticles, sampleCount, len(phaseSpaceParameters)), dtype='double')\n    \n    # Initialises array to store the position of the real particle\n    positions = numpy.empty((sampleCount, len(phaseSpaceParameters)), dtype='double')\n    \n    # Initialises array to store the simulation times at which the system is sampled\n    curTime = numpy.empty((sampleCount,), dtype='double')\n    \n    # Initialises each variational equation particle pair by first setting undefined attributes to 0\n    # And then adding the particle pairs\n    for varParticleIndex in range(numVariationalParticles):\n        for attribute in phaseSpaceParameters:\n            if attribute not in initialDeviations[varParticleIndex].keys():\n                initialDeviations[varParticleIndex].update(((attribute, 0.0),))\n        \n        norm_init_dev = numpy.linalg.norm(list(initialDeviations[varParticleIndex].values()))\n        initialDeviations[varParticleIndex] = {key: value / norm_init_dev for key, value in initialDeviations[varParticleIndex].items()}\n        \n        sim.add(**initialDeviations[varParticleIndex])\n        if with_second_order: \n            sim.add(x=0, y=0, vx=0, vy=0)\n    \n    # Hide progress bar if it isn't necessary\n    if progress_bar_visible:\n        integration_loop_iterable = tqdm_notebook(enumerate(numpy.linspace(0, tfin, sampleCount)), total=sampleCount)\n    else:\n        integration_loop_iterable = enumerate(numpy.linspace(0, tfin, sampleCount))\n    # Iterates over the sample points that are linearly spaced between the simulation start and end times\n    # And records the state of the system at each step\n    for sampleIndex, samplePoint in integration_loop_iterable:\n        try:\n            # Append the current simulation time to the curTime array\n            curTime[sampleIndex] = sim.t\n            \n            # Append the position of the real particles to the positions array\n            for attributeIndex, attribute in enumerate(phaseSpaceParameters):\n                positions[sampleIndex, attributeIndex] = getattr(sim.particles[0], attribute)\n            \n            # For each variational particle pair, append the current variational derivatives to the relevant arrays\n            for varParticleIndex in range(numVariationalParticles):\n                for attributeIndex, attribute in enumerate(phaseSpaceParameters):\n                    if with_second_order:\n                        curDeviation[varParticleIndex, sampleIndex, attributeIndex] = getattr(sim.particles[varParticleIndex * 2 + 1], attribute)\n                        derivDeviation[varParticleIndex, sampleIndex, attributeIndex] = getattr(sim.particles[varParticleIndex * 2 + 2], attribute)\n                    else:\n                        curDeviation[varParticleIndex, sampleIndex, attributeIndex] = getattr(sim.particles[varParticleIndex + 1], attribute)\n            try:\n                sim.integrate(samplePoint)\n            except KeyboardInterrupt as e:\n                raise e\n        except KeyboardInterrupt as e:\n            raise e\n            return\n\n    # Append the current simulation time to the curTime array\n    curTime[sampleIndex] = sim.t\n\n    # Append the position of the real particles to the positions array\n    for attributeIndex, attribute in enumerate(phaseSpaceParameters):\n        positions[sampleIndex, attributeIndex] = getattr(sim.particles[0], attribute)\n\n    # For each variational particle pair, append the current variational derivatives to the relevant arrays\n    for varParticleIndex in range(numVariationalParticles):\n        for attributeIndex, attribute in enumerate(phaseSpaceParameters):\n            if with_second_order:\n                curDeviation[varParticleIndex, sampleIndex, attributeIndex] = getattr(sim.particles[varParticleIndex * 2 + 1], attribute)\n                derivDeviation[varParticleIndex, sampleIndex, attributeIndex] = getattr(sim.particles[varParticleIndex * 2 + 2], attribute)\n            else:\n                curDeviation[varParticleIndex, sampleIndex, attributeIndex] = getattr(sim.particles[varParticleIndex + 1], attribute)\n            \n    return VariationalSimulationResults(curTime, positions, numVariationalParticles, (curDeviation, derivDeviation))",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "hide_input": false,
        "ExecuteTime": {
          "start_time": "2017-07-10T03:59:26.211963Z",
          "end_time": "2017-07-10T03:59:26.218144Z"
        },
        "collapsed": true
      },
      "cell_type": "code",
      "source": "initial_x = 0.62\norbit_type_options = [\"chaotic\", \"regular\", \"I'm very confused...\"]\norbit_type = orbit_type_options[1]\nfinal_time = 1000\ntotal_samples = 1000\nsavePath = \"./SingleSim/resultsDeviations_{:.2e}_{:.2e}_{}_/\".format(initial_x, final_time, total_samples)\nfilename = savePath + \"data.bin\"\nresim = False",
      "execution_count": 44,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-07-10T03:59:26.377700Z",
          "end_time": "2017-07-10T03:59:26.455762Z"
        },
        "trusted": true,
        "collapsed": true
      },
      "cell_type": "code",
      "source": "# numberOfVariations = 6\n\n# def get_MLE_first_order(a):\n#     time = a[:, 0]\n#     LCE1 = []\n\n#     for varIndex in range(numberOfVariations):\n#         LCE1.append(numpy.array([numpy.log(numpy.linalg.norm(i.as_numpy_array()))/t for t,i in zip(time[2:], a[2:, 2, varIndex, \"1st Order\"])]))\n        \n# def get_MLE_with_second_order(a):\n#     time = a[:, 0]\n#     LCE1 = []\n#     DLCE1 = []\n\n#     for varIndex in range(numberOfVariations):\n#         LCE1.append(numpy.array([numpy.log(numpy.linalg.norm(i.as_numpy_array()))/t for t,i in zip(time[2:], a[2:, 2, varIndex, \"1st Order\"])]))\n#         DLCE1.append(numpy.array([numpy.log(numpy.linalg.norm(i.as_numpy_array()))/t for t,i in zip(time[2:], a[2:, 2, varIndex, \"2nd Order\"])]))\n\n# test = partial(getxtDeviations, xinit=initial_x, vyinit=vyinit_var_zeroth(E, initial_x, 0, recur, rccur, qcur), tfin=final_time, sampleCount=total_samples, progress_bar_visible=False)",
      "execution_count": 45,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-07-10T03:59:26.527615Z",
          "end_time": "2017-07-10T03:59:26.645580Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# %%timeit -n 5\n# a = test(initDeviation=[{'x':1}, {'y':1}, {'x':1, 'y':1}, {'x':-1, 'y':1}, {'x':-1, 'y':-1}, {'x':1, 'y':-1}], with_second_order=False)\n# get_MLE_first_order(a)",
      "execution_count": 46,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2017-07-10T03:59:26.681680Z",
          "end_time": "2017-07-10T03:59:26.828715Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# %%timeit -n 5 -r 7\n# a = test(initDeviation=[{'x':1}, {'y':1}, {'x':1, 'y':1}, {'x':-1, 'y':1}, {'x':-1, 'y':-1}, {'x':1, 'y':-1}], with_second_order=True)\n# get_MLE_with_second_order(a)",
      "execution_count": 47,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "The ratio of the time it takes to do the second order calculations to the first order calculations including the MLE for both is $3.46\\pm0.175$"
    },
    {
      "metadata": {
        "trusted": true,
        "hide_input": false,
        "ExecuteTime": {
          "start_time": "2017-07-10T03:59:26.978778Z",
          "end_time": "2017-07-10T03:59:27.055244Z"
        }
      },
      "cell_type": "code",
      "source": "if not os.path.isfile(filename) or resim:\n    try:\n        test = partial(getxtDeviations, xinit=initial_x, vyinit=vyinit_var_zeroth(E, initial_x, 0, recur, rccur, qcur), tfin=final_time, sampleCount=total_samples)\n        a = test(initDeviation=[{'x':1}, {'y':1}, {'x':1, 'y':1}, {'x':-1, 'y':1}, {'x':-1, 'y':-1}, {'x':1, 'y':-1}])\n    except:\n        raise\n    else:\n        ensure_dir(savePath)\n        with open(filename, 'wb') as f:\n            dump(a, f)\nelse:\n    with open(filename, 'rb') as f:\n        a = load(f)\n        final_time = a[0, -1]\n        total_samples = len(a[0])",
      "execution_count": 48,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": false,
        "ExecuteTime": {
          "start_time": "2017-07-10T03:59:27.136333Z",
          "end_time": "2017-07-10T03:59:29.924942Z"
        }
      },
      "cell_type": "code",
      "source": "samplingSteps=1\nparts = 2 # The number of slices to split spectra data into\nbinCount = 100 # Automatically determine the number of bins to use, can be overrided with an integer\nfig = plt.figure(figsize=(6 * parts, 6))\n\naxisGrid = GridSpec(2, parts)\n\naxes = [fig.add_subplot(gridIndex) for gridIndex in axisGrid]\n\n# Plot the histograms for the first and second halves to the appropriate subplots\n\nspectraInParts = a.splitSpectra(parts=parts)\ntime = a[:, 0]\n\nplotLabels = [(varIndex, r\"$\\vec{{S}}_{}^{{\\left(0\\right)}}$\".format(varIndex), r\"$\\vec{{S}}_{}^{{\\left(1\\right)}}$\".format(varIndex)) for varIndex in range(spectraInParts.shape[1])]\n\nif binCount == 0:\n    for partIndex in range(parts):\n        for varIndex in range(spectraInParts.shape[0]):\n            for varOrder in range(2):\n                binCount = numpy.maximum(binCount, len(numpy.histogram(spectraInParts[partIndex, varIndex, varOrder], range=(-numpy.pi, numpy.pi), bins='fd')[0]) - 1)\n\nprint(\"Optimal bin count is {} bins\".format(binCount))\n\nfor varIndex, histLabel, derivHistLabel in plotLabels:\n    for partIndex in range(parts):\n        axes[partIndex].hist(spectraInParts[partIndex, varIndex, 0, ::samplingSteps], range=(-numpy.pi, numpy.pi), histtype='step', bins=binCount, normed=True, label=histLabel)\n        axes[partIndex + parts].hist(spectraInParts[partIndex, varIndex, 1, ::samplingSteps], range=(-numpy.pi, numpy.pi), histtype='step', bins=binCount, normed=True, label=derivHistLabel)\n        axes[partIndex].set_title(\"Spectra from {:.1e} to {:.1e} Orbits\".format(time[(len(time) - 1) * partIndex // parts], time[(len(time) - 1) * (partIndex + 1) // parts]))\n\n# Formatting fluff\nfor subplotAxis in axes:\n    subplotAxis.set_xlabel(\"Angle (Radians)\")\n    subplotAxis.legend(fontsize=12)\n    subplotAxis.relim()\n    subplotAxis.autoscale_view()\n    \nplt.tight_layout()\n\n# Savefile if a savePath is given\n\nif savePath is not None:\n    for extension in [\".png\", \".pdf\"]:\n        fig.savefig(savePath + 'Spectral_Comparison_Plots_{}{}'.format(time[-1], extension))",
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.Javascript object>",
            "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support.' +\n              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        this.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overriden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": "<img 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YjW92APAnEtcPJf4nPU1VBXz3Prat9MKP6t5bjdHRCooc/+EsY9NREv2VyYGBgIgAAJFE4D2tkeYNO0Nredd6wWhva2zyot5njTt7TQHxR+XVBbrMDdo76If66gABMIhAPM8HM5oBQTsCIj0B2xUsbHN/+VfuGwC8b/Nq5iFoGCziA13/i+vzBX5xYVZJkxjNsHZROODDXeuU5hKogx/JuoSZczmPP/VXOwYzsYX97dQbmJhZLMI4L6JlC0i/7bq80ZXwIvVtjxmFmt8cCoIzvHN/RYrAoJcec5tij9k8NiZ8VSbjUgFo+dsNltVuV4lvk53m+rKc52xqPS50N3vNMdUY+pUtzBm+Y8/3MZRuY1M7dJ68BfpUogHx4IPcQ9Y2ajmAzc/a5gf18v8+BnCzx3zPSXaUDHP+T7ktybEs4uvscubbjXP7eYI18Ux5ucex0/cb/wMsL79gd8+IAACIAACICAIQHvbz4Ukam9ovfxYqmi9oLQ390Z85+PvQvxv8aYn6zbzCu1S0d7iLWfWo8yVv3uK7992etnpSQztjd9RIJAAAqpmVgKGgi6CQCIJsCHFBhKbheLVLzb4zDkHeWDmv8aLzTjZKOJyXN58sJE00yRgOJ2L2FBUlLOWYUHDZfiv5OLgvnE93A6b+2y0838LGWU8BvHaq9jEj80svkb1eaMr4Lm/1vHwODjns3llatDmubUPbDwyU3MfxOuGdjn9VK63G6tdfO1uBh3z3Gtf7MZc6MZ0mmOqMS1UNwtacW/xvOXxW1dkl1I8+L4pdKjef1wH38cs/vmLDzPie5mfM+KPbdZ2VMxzNs7Fs0s8b8zPrkK5Va3PIb6e/zjF84f7yveY3T2fyF8K6DQIgAAIgECgBKC98/EmVXtD68mxVNF6QWlvoantbl7z29SlpL15rKyV+fseLzjjw6sehfYO9LGPykGgeAIqD9jiW0ENIAACxRJwMn6LrRfXlw8BHfO8fKhEN1LEIzr2aBkEQAAEQAAE3AhAe7sRwuduBKD13AiF+zniES5vtAYCJUUA5nlJhRODKWECEPAlHNyQhua0qU1IzaMZCwHEA1MCBEAABEAABOJLANo7vrFJSs+g9eIVKcQjXvFAb0AgUQRgnicqXOhsGROAgC/j4GPoIAACIAACIAACIAACoRKA9g4VNxoDARAAARAAgfgSgHke39igZyBgJgABj/kAAiAAAiAAAiAAAiAAAuEQgPYOhzNaAQEQAAEQAIHYE4B5HvsQoYMgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAJhE4B5HjZxtAcCIAACIAACIAACIAACIAACIAACIAACIAACIAACIBB7AjDPYx8idBAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQCBsAjDPwyaO9kAABEAABEAABEAABEAABEAABEAABEAABEAABEAABGJPAOZ57EOEDoIACIAACIAACIAACIAACIAACIAACIAACIAACIAACIRNoBzN8x5EdBIRfU5EO8MGjvZAAARAAARAAARAAATKgkAnItqPiF4nooayGLH9IKG9yzj4GDoIgAAIgAAIgAAIhEQgMO1djub5aCKaE1Lg0AwIgAAIgAAIgAAIgEB5EziHiJ4rYwTQ3mUcfAwdBEAABEAABEAABEIm4Lv2Lkfz/LtEtGjOnDl04IEHhhw/NAcCIAACIAACIAACIFAOBD799FM65xzW7nQ0Ef21HMbsMEZo7zIOPoYOAiAAAiAAAiAAAmEQCFJ7l6N5PpiIli5dupQGD+Z/4gABEAABEAABEAABEAABfwl88MEHdMQRR3Cl/H8f+Ft7omqD9k5UuNBZEAABEAABEAABEEgegSC1N8zz5M0H9BgEQAAEQAAEQAAEQCDmBIIU8DEfurV7MM8TFjB0FwRAAARAAARAAASSRiBI7Q3zPGmzAf0FARAAARAAARAAARCIPYEgBXzsBy93EOZ5wgKG7oIACIAACIAACIBA0ggEqb2DMs8nElEtETUS0UAiuiP3bzf24rqeRDSEiKYQ0RLTRV7rNbcLAe8WBXwOAiAAAiAAAiAAAiBQFIEgBXxRHQv/Ymjv8JmjRRAAARAAARAAARAoKwJBau8gzPORRDSciCblojSAiOYS0VCXqE3PGexsmPNxAxFNI6IeufNe67U2CwFfVrcPBgsCIAACIAACIAAC4RMIUsCHP5qiWoT2LgofLgYBEAABEAABEAABEHAjEKT2DsI8X5EzzheaBvZ+bhW5+Zx13Gyw82p1Nt75GEZEC3I/83Ve64V57jbD8DkIgAAIgAAIgAAIgICvBIIU8L52NPjKYJ4HzxgtgAAIgAAIgAAIgEBZEwhSe/ttnrP53ZBL1bLSFDU2wflnsRpdJaDmledc3q96IeBV6KMMCIAACIAACIAACICAZwJBCnjPnYrmQmjvaLijVRAAARAAARAAARAoGwJBam+/zXOxWpzznJvNc+uqcrfgsQnPq9U5bcsM0yr0YuvldiHg3ejjcxAAARAAARAAARAAgaIIBCngi+pY+BdDe4fPHC2CAAiAAAiAAAiAQFkRCFJ7+22ec15yNspFnnIRKM5nfqRC3nMuzyvORxMRp2oR+c+91tuXiPh/5uNAIpqzdOlSGjyYtTwOEAABEAABEAABEIieQCaToa1btxr/27lzJ6XT6eg7hR4YBCoqKqhTp07UrVs343+plLuEDlLAW8IyMZf6sDH39ucduf2C3KInrutJRENyunuJ6SKv9VrbhXnuFgl8DgIgAAIgAAIgEDoBaO/QkSs16EV3c8VBam935a80tN2FnExuNtR541C3TUPNrbHhztdwDnSv9f6CiG6xGwLMc73AojQIgAAIgAAIgEBwBFi8r1u3jhob2f8kqqqqUjJog+sRajYT4Pi0trYap2pra6lv376u8QlSwJv6xhqZtbJIjcjamXW3m+Zmnc2TTSxUMadL5PNe67WbODDPcTuBAAiAAAiAAAjEigC0d6zCIXXGi+7mCoLU3n6b536lbeFxs/jnTUJZ1PMqGM6brpu2BSvP43s/oGcgAAIgAAIgAAI5Ak1NTfTVV19R586dqX///sYqZxzxIsBvA6xZs4Z27NhBe+21F9XU1BTsYJAC3tQwa2U2zvmNTXFw6kPWz+Zz1r5aUyoKDc9GPF/ntV6Y5/GatugNCIAACIAACICADQFo73hPC13dnTTzXGwYyqtdzK99qmwYyiKeXzM1X5fJCfhRuQ1DvdRrnRFY/RLvewS9AwEQiJDAR/95HHVvWW/bgy0d+tAhP387wt6haRAoXQJsnLOIHzBgAIzzGIeZhfzKlSsN45wN9EJHCOa50N3WxSUqutvadfPKc/6swWbRipd6ua5Ea++HJ1xBLduzb4RYjw5daumymQ/GeMaiayAAAuVMYP7wQ6h6q30KuOZuFTRiwUfljAdjL3MC0N7xnwA6uptHE6T29nvlOffXbrWLWEE+zyE8nGdRXHdnroz4QsAbhvKKGi/12jWXaAEf/+mNHoIACCSZwOpbD6Q+6Y20vqKXNAxxbu9bPk3y8NB3EIgtATZk29ra6Jvf/GZs+5iUji1ZsoSGDGFpWfhg5vzHCt1j2bJlRlodt2uDFPC5Pvv1xidrbtbZ04iIdbdf9Qq0idbe9513PrW1bKHKDt2lqSLOXT37Kd0phPIgAAIgEAqBt48+mHo0ETVYXpQS545b9HEo/UAjIBBHAtDe/kUlSO2tqrt5NEFq7yDMc95ciF/55NXifPC3EpFyRUSHRTkL9BNNGxrxynNxDZfjejgfo1hNo1KvSvQTLeBVBogyIAACIOCVAJvnfFhNcqfzXtvBdSAAAjKB5cuXGycGDRoENIoEOD/84sWLadgwlpXZ484776SRI0e6Gtu6Zc1dUo1VkAI+1x+nPYFYPx+pkPecq+EV56Nzb3qK/OfF1FtyKRPZPOfDapI7nVecvigGAiAAAoETYPOcD6tJ7nQ+8A6hARCIEQFVPRejLkfelSi0t06cgtTeQZjnHFA2utn05hXn/F9Ox2J+35FF+Uwi2t90nle9TDXNBjbdWcSvNJ1zq1dlMsE8V6GEMiAAAmVJAOZ5WYYdg44BAR1hGIPuxqYLQ4cOpTfeeMPYxHPhwoVGSpWJE1kuZg/+efr06TRw4EBasWIFDR8+XDLbR40aRXPn8voN9UM1VkEK+FxvnUxuHhDraLdNQ82DZsOdr+EFMMXU+wsiusWO5tKlS2nwYJbhyTpgnicrXugtCIBAOwGY55gNIOBMQFXPgaFMIGztrROnILV3UOZ5nOcXzPM4Rwd9AwEQiJQAzPNI8aPxMiagIwzLCROvcGGDu76+3nbY/Joom+NsmLMxvmABv+zYfpgFPp+1/jxv3jyjbrPh7sZXNVZBCvhcH/1Mr8LGuUizyPsPibdGzYtYrJuM2qHCynO3CYTPQQAEQCAkAjDPQwKNZhJJQFXPJXJwRXQ6btpbJ05Bam+Y50VMKlwKAiAAAqVGAOZ5qUUU40kKAR1hmJQxBd1PNtWPOuoouuGGG4wV5lOmTJFWkfM5NssbGnjvy+zB17DJbjbL7Uz3Qn1XjVWQAj7XP7E/EK8wZ8NbHCobe7IRzm+Gmq/L5NK3cBpFhualXjt0iV64gpXnQd/JqB8EQCAoAjDPgyKLekuBgKqeK4Wx+jWGKLS3TpyC1N4wz/2aRagHBEAABEqAAMzzEggihpBIAjrCMJEDDKDT5s2JeAU5m+VspItjxowZNG3aNCNdizgmTeI96MlYrS4ONtjff5/3y1Q7VGMVpIA39ZQ7zmkOF5rOiRXk8xxGxLupiuvuzJURRjxvGMqQvNTrBBDmudrUQikQAAEQ8JUAzHNfcaKyEiOgqudKbNhFDScK7a0TpyC1N8zzoqYOLgYBEACB0iIA87y04onRJIeAjjBMzqjC6ylvFDpgwABjs1BxsHnOJrnZGGfznNO0mPOc88pzLsfXqxyqsQpSwJv6yQneOU85rxbngwchUq6IYpzeZRoRnWjaa4hXnotruBzXw39R4L2KOFWLSr0quLgMzHNVUigHAiAAAj4SgHnuI0xUVXIEVPVcyQ3cpwGFpb114hSk9oZ57tPEQTUgAAIgUAoEYJ6XQhQxhiQS0BGGSRxf0H1mU5xfJR02jH3i7GG38pzL1NXVSSvPE26eC+ObTW9ecc7/5XQsjSbm/BeFmUS0v+k8rzSfairDpjuvYDfnOGcDvVC9qmGFea5KCuVAAARAwEcCMM99hImqSo4AtHdxIQ1Le+vECeZ5cTG1Xp1oAe8vCtQGAiAAAjIBmOeYESAQDQEdYRhND6NtlVe3bN68eXcnevbsaaRo4TznnJrFbvULv1p64oknSjnP2ShnA92c8zzhaVuiDYxa67HS3g9PuIJatpv/ttA+iA5daumymQ9Ko0LOc7UgoxQIgEDwBOYPP4Sqt6ZtG2ruVkEjFnwkfQbzPPiYoIXkEoD2Lhy7uGhvnTjBPPf3foyVgPd3aKgNBEAABIojAPO8OH64GgS8EtARhl7bSOJ1jY2NhtnNBvmQIZyqO3ssXLjQMM451QqnYOGc5++9955RznwMHDjQSNtSW8sLrYmsP/M5mOeBz4xYaW82w9tatlBlh+7SwMW5q2c/JZ2HeR74/EADIAACigTYDO/RRNRQI18gzh236GPpA5jnimBRrCwJQHvbhz1u2lsnTjDP/b2VYyXg/R0aagMBEACB4gjAPC+OH64GAa8EdISh1zaSeB2vFGeT3JyORYyDP+NXRkWec/55wQJO991+8CaibKizQc4bh3IZc128Op3znZs3EHXjpBqrIAW8Wx9j9nmstLeuGa5bPmbs0R0QAIESIqBrhuuWLyFUGAoIuBJQ1XOuFZVYgbhpb504Bam9kfO8xCY6hgMCIAACxRCAeV4MPVwLAt4JuAnDMTMW0Zot2703EOKV/bt3oWcnHl10i2x8s+nd0NBgWxcb52yMi1Xl/LNYja7aOJcfPXq0tKrd7Vq3WInrg4qW8+YAACAASURBVBTwbn2M2ecwz2MWEHQHBEAgmQR0zXDd8smkgl6DgDcCbnouKdrbL93NFOOovd3iZI5+kNob5rm3+wxXgQAIgEBJEoB5XpJhxaASQMBNGB5/15v0VcN22qtHl1iPRvTxretPKLqfnIqFU7ZkMhnbuvhzsepcFGADXXUVOX9B4Do4d7rO4RYrmOd5NGGe60wwlAUBEAABBwK6ZrhueYAHgXIi4KbnkqC9/dTdHPs4am+3OME8D+6ujZWALzTMQn/p8vOvS8GhRs0gAAJJI1B25vnjpxJtWW0fpu57E437fdJCiP4mlICbMGQBz4cfpnSQiPzsJ5vbnKOcc51PnTo1zyi3Gwdfw6lYrKa6XVneCEnXOOd63GIF8xzmeZD3GOoGARAoXwK6Zrhu+fIli5GXIwE3Peenpg2Kr999jKP2dosTzPOgZhdRYsxzp790+f3XpeBQo2YQAIGkESg78/z+wURbviTqvo8cKnHuqg+SFkL0N6EE3ISh3+I4KEx+95NXwEyYMIF48yI+OF+5Oc95UOMoVK9brGCewzyPYl6iTRAAgdInoGuG65YvfYIYIQi0E3DTc35r2iDYB9HHuGlvtzjBPA9iZmXrTJR5zh22rjIL4gYJDjdqBgEQSBKBsjTPOUBWk5xNdbvzSQom+pooAm7CMCm/+4Pq58KFC43NQFnQi01Avawa92NSuMUK5jnMcz/mGeoAARAAASsBXTNctzyIg0A5EXDTc0FpWj8ZB9nHuGhvtzjBPPdzRsl1wTwPji1qBgEQSDgBmOe5AMI8T/hMTl733YRhkOLYT1ph9JNXnj///POOG4n6OR67utxiBfMc5nnQcxD1gwAIlCcBXTNct3x5UsWoy5WAm54LQ9MWyz6sPkapvd3iBPO82FnkfD3M8+DYomYQAIGEE4B5DvM84VM4sd13E4ZhieNiAfrVT7vNQEXfOKf50KFDHTcSLXYMbte7xQrmOcxztzmEz0EABEDACwFdM1y3vJc+4RoQSCoBNz3nl6YNko+ffYyr9naLE8zz4GYYzPPg2KJmEACBhBMoN/N83e0HUWs6Qz/d4xEpck9/fQlVVaSo702fJDyi6H5SCLgJQz/FcZBM/Ogn5ze/4447aNq0abZdZXE/ffp0I40LH5zGhX/mzUVXrFhBw4cPN3KjB3W4xQrmOczzoOYe6gUBEChvArpmuG758qaL0ZcbATc954emDZqpX32Ms/Z2ixPM8+BmGczz4NiiZhAAgYQTKDfznMfbls7Q2OrpUuSebJ5ElRUp2vuWTxMeUXQ/KQTchKFf4jhoHn7002qOW/vM5jgb60OGDDE+4lXob7zxBtXW1tr+7PeY3WIF8xzmud9zDvWBAAiAABPQNcN1y4MyCJQTATc954emDZqnX32Ms/Z2ixPM8+BmGczz4NiiZhAAgYQTKEfznENmNcmdOCQ8vOh+jAm4CUO/xHHQCPzoJ+dVXLx4MU2dOpVGjhwpdZk/Y7N84sSJxnledc4/NzQ07C43atQoY/W5KOP3mN1iBfMc5rnfcw71gQAIgADMc8wBEPCXgJue80PT+tvj/Nr86mOctbdbnGCeBzfLYJ4HxxY1gwAIJJwAzPNsAGGeJ3wiJ7D7bsLQL3EcNBo/+skCntOw8CoYkZpF9Js/EyvO+dyMGTOMVeicrsVchv/NdQRxuMVKtPnBBx/QEUccwT/y/30QRF8SUmestPd9551vYLt69lMSPr/OJyQm6CYIgEACCeiuJNctn0Ak6DIIeCbgpuf80LSeO6d4oV99jLP2douTGVWQ2julGJNSKhYrAV8IrNON4NcNUkpBxVhAAATUCYx/bTyt3bbW9oLW+lXUuzVDsy/5SPq8VM3kcvtjgfosQcmwCbgJw6T87g+jnw3rt1G6NWOE6InZj9ETT/+a/vjKO8bPFVUp+tebr6X6+nqaO3duIGF0i5VoNEgBH8jAgqs0VtrbL5PcqZ7gMKJmEACBciega4brli93vhh/eRFw03NhaNpiiUfRR164wgtU3n///d3dZ/M9KO3tFieY58XOIufrYyXgYZ4HF2jUDAIgYE9gxIsjaG3zWupX3S+vwJomNs+JXr/4Q5jnNulcMKdAICgCbsIwCnHsZaxh9HPzV83U1pqmyqoKwzx/8Ff30eJ3/3f3uUuuvpDq6uqw8txLAIO5JlbaG+Z5MEFGrSAAAsET0DXDdcsHPwK0AALxIVAK2jsM3W2NmN1bn5wyMSjt7RYnmOfB3VOxEvAwz4MLNGoGARBwNs/5k/lnzc8rcNKjhxrnYJ4faHDAhqG4i8Ii4CYMoxDHXsYeRj/ZPOej517VtGTJEjrxxBONnOfi/DnjziQW8ch57iWCgVwTK+0N8zyQGKNSEACBEAjomuG65UMYApoAgdgQKAXtHYbutgbMrL3FZ7zXUFDa2y1OMM+Du6ViJeBhngcXaNQMAiAA89xtDiBtixshfB4WATdhGIU49jL2MPppNs+5jwMHDjReHW3bVmV0+TvHHW78XFtb62UIrte4xUpUgLQtu1HGSnvDPHed4igAAiAQUwK6Zrhu+ZgOG90CgUAIuOm5MDRtsQOLqo9Cewutbf252HGZr3eLE8xzP2nLdcVKwBcaJnKeBzcJUDMIlDMBTtvCB1aeO28MWqo53st53sd97G7CkDXBVw3baa8eXWI9FNHHt64/IbB+Ws3zlStXGpuGHjjgEPr8i8/otLNOoWHDhgXWvlusYJ7noY+V9oZ5HtitgYpBAAQCJqBrhuuWD7j7qB4EYkXATc8lQXuHobvtgia099ChQ2nFihXEK8+D0t5ucYJ5HtxtFSsBX2iYvznhdKprbsj7osw3SH11DzrzzZeCo4SaQQAESpaAm3m+oYqof82+0vidNhJ1gvT8bT+nrZs22n7cbc9edPbN/xkLvlh5HoswoBNE5CYMx8xYRGu2bE8Eq/7du9CzE48OrK9W81w05HTe7464xQrmOcxzv+cc6gMBEAABJqBrhuuWB2UQKCcCbnouKdo7aN0d9ZxwixPM8+AilBjz/K2jjqOezZup8z57SzR2fLmaNlf3pOPfezs4SqgZBECgZAkUMs/Pe+QQ2lCVoqo62Tx32kjUCdKsKydQ06YNVLNnb6mIODf+gZmx4AvzPBZhQCcUzHNAaicA8zxxsyFW2hsrzxM3f9BhEACBHAFdM1y3PECDQDkR0DFly4lL3MaqE6cgUyam4gYmhP7ESsAXGi+b53xYTXKn8yGwQxMgAAIlQKCQee5kJjttJFrIPOfPrCY5m+p256PCCvM8KvJo10pARxiWOz2Y54mbAbHS3jDPEzd/0GEQAAGY55gDIOA7AWhv35EGUqFOnGCe+xuCWAl4mOf+Bhe1gQAIuBOAed7OCOa5+3xBiXAI6AjDcHoU31Zgnsc3Ng49i5X2hnmeuPmDDoMACMA8xxwAAd8JQHv7jjSQCnXiBPPc3xDESsDDPPc3uKgNBEDAnQDMc5jn7rMEJcImoCMMw+5b3NqDeR63iLj2J1baG+a5a7xQAARAIKYEdNOw6JaP6bDRLRAIhAC0dyBYfa9UJ04wz/3FHysBD/Pc3+CiNhAoZQKFNi2x2yjEqXxj3W1UVZmi98f9MQ/XsuMOovTXRFV135A+45znDd2ITl/wsRJip/QsSNuihA+FypCAjjBMIp6VG5tpV1taq+sdKytoQK/qvGtgnmthjEPhWGlvmOdxmBLoAwiAgBcCuma4bnkvfcI1IJBUAqWuvZMaF2u/deIE89zfqMdKwMM89ze4qA0ESpnA8Xe9SV81bKe9enSRhinOvXX9CdJ5p/Kbam+lqooU/X38m3m4PjnyIMpsI+qwj2ye71i9iupriI5bBPO8lOcYxhYdAR1hGF0vvbf8ybomamnNUIcqte12RNmD+tbAPPeOPS5Xxkp7wzyPy7RAP0AABHQJ6JrhuuV1+4PyIJBkAqWuvZMcG3PfdeIE89zfqMdKwMM89ze4qA0ESpkAm+F82JnkOucPn5U12Z3Mc/7soMWfSCidxLcTb6w8L+WZiLEFQUBHGAbRftB1snluPFtszHC7tguVx8rzoKPle/2x0t4wz32PLyoEARAIiYCuGa5bPqRhoBkQiAWBUtfesYDsQyd04gTzXAZeS0R1RFRPRI0eYhErAV+o/28ddZzx8fHvvS0VczrvgQUuAQEQSBABmOf+BwsbhvrPFDV6I6AjDL21EO1VYZrnX6xfRkOGDHEd8MqVK2nAgAGu5awFVGMVpIDX7nS0F8RKe8M8j3YyoHUQAAHvBHTNcN3y3nuGK0EgeQRU9VzyRhZ+j5csWRKY9taJU5DaW+3dWX32E4mITW42twcS0R2KRve0XFPim8wUIlqZO8ffgt43dYXrnkBE8zS7FysBX6jvMM81I4viIFDiBGCe+x9gmOf+M0WN3gjoCENvLUR7VRDmeWXXVlq8eDENGzbMGByvSH/gl/fSBeN/qmSK33nnnTRy5EilsmZ6qrEKUsBHG03t1mOlvWGea8cPF4AACMSEgK4Zrls+JsNEN0AgFAKqei6UziSkkcbGRkl7c7d19LROWYFEJ05Bau8gzPORRDSciCblBstG+FwiGuoyH9g4N5vs/DOb8PvnjHf+ZiQMeV51vsTj/IqVgId57jGKuAwEypDAETPPonRFg23O84p0D1o64UWJym9OOJ3qmvPLf9n0FW2qqaRRr/89jyLnPOcDaVsONDjsfcunZTjTMOQoCOgIwyj6V2ybX61qogoi4k1ArUdFVYp69OkqnV79+RdUkWmjDjbl061polQl9dn/GzR06FB64403qLa2ll6c8zKt/GwlXTh2/O66Pv/iM3pi9mO0/4ABtG7jaho+fPhus50LjRo1iubOZZmqfqjGKkgBr97bWJSMlfaGeR6LOYFOgAAIeCCga4brlvfQJVwCAokloKrnEjvAgDpu1t4LFy4kfpNz4kS2brMH/zx9+nQaOHAgrVixomjtrROnILV3EOb5ipxxvtAUK14xzqvIzeesoWywrCRno5zP8XV3ElF2WVHhOlSmR6wEfKEOY+W5SjhRBgTKhwDnKk9X1tM+NXtJg2YzvKKtLi+HOT9DejZvps777C2V3756lWGen7Dowzx4MM+zSJxWpJfPbMNIwyagIwzD7psf7a1l8zxDVFUlm+dtrWmqrKqgnntVS82s+WwlVaTbqPnr7TT+Zz+jxsYtuz/PZDLEAraqUyfi10RZoLNo/5fjf0AvPPM7qZ4f/OhYmjf7JarrWWe0YRb8XHDevHlUX18viX638arGKkgB79bHmH0eK+0N8zxmswPdAQEQUCaga4brllfuCAqCQAkQUNVzJTBUrSHw6nJeXML62O4wa29elLJgwQKpmFVrF6u9deIUpPb22zwXhjenahHpVhgk0+SfxWp0uxgIo3yG6cMMEfHPfB3M8xwY5DzXuvdRGARKhoDTRp9O552eFW8efajBBOa5s0kO87xkbpvEDERHGCZmUKaOsnnOR799a6TuO23+yeY5H/33z89Jvn5l9rPLp0yho446im644QZjlcuUKVOkVeR8jgX78g+/NMqzec5fBljom1fI2An/QoxVYxWkgA9xDhS71xB3FeZ5iAFDUyAAAqVLQNcM1y1fuuQwMhDIJ6Cq58CunQDraBXt3dDA9m72KFZ768QpSO3tt3nOBjcb5VbznN+HZfHN6VxUD7uV55z3XGwSymlgeFW67qahsRLwhWBg5bnqVEE5ECgPAjDP/Y8zcp77zxQ1eiOgIwy9tRDtVUGY5181Nu7enIhXkLNZzka6OGbMmEHTpk2jv72dTVHF5vmkSdl1HLxaXRxssL//vnlbncKsVGMVpIC39DDOew1xV2OlvbHyPNpnAVoHARDwTkDXDNct771nuBIEkkdAVc8lb2TB9di8MWgh7c3pWsRRrPbWiVOQ2ttv85zznbNR3sNiavM3lCMV8p6bo8zffqaacp6zcc51iJXpbNRzvWzUOx19iYj/Zz44ke2cpUuX0uDBrOXje8A8j29s0DMQiIKAk3k++ZIfUdcdKdrXks5l++qvqGNrmib+7lWpu1h53o4D5nkUMxlt2hHQEYZJJOhknm/6PLsypXrXRmlYTRUp4tcP+wz6Zt5wxcrzPgPaV6XzBkS9a7vTKT88eXf5J595hp58+ll6/aWXdudIZwHPr6Ga85zzynM20weY6ivEWDVWQQp4U//ivtcQdxXmeRJvWvQZBEAgdgR0zXDd8rEbMDoEAgESUNVzAXYh0VWz9mbtPHIkS9HswQtXWFObF6UUq7114hSk9g7LPGdDnb/huG0aKpjzqvPPiOhEl41B+XvVKE5Z6TDrfkFEt9h9BvM80fcpOg8CZUnAyTy/ZdwIqt6Rorre/SUuW9atoY6tGbr8xfnSeZjn7ThgnpflrRTLQesIw1gOwKVTBc3zTIaqWzZJNTQZqdFT1FvRPGdhPvz4Y+nYo4+miqoqoy42zx/81XRa9MeFlOINRgcMMF4draurk1aeJ9w8j/teQxwKmOdJvGnRZxAAgdgR0DXDdcvHbsDoEAgESKDUtXeA6IyqWXuzrh42TGTYzprn/NaneeV5sdpbJ05JMs/9StvCqV84JcsSl4DzcqXnC+RSx8rzoO8Y1A8CIBAagULmOXfi1sdlk/yhs0YYfYN57hwimOehTV805EJARxgmEabbyvM99+OXFtuPDcv/z/hBmOe8umXz5s3GuW2NjdSjtpZunzbNyHPOIp0/37NbNf34lBHUa9/9jHL8aumJJ55In+RSsrB5zkY5i3hzzvMEp21Jwl5DHAqY50m8adFnEACB2BHQNcN1y8duwOgQCARIoNS1d7HozNqb6+rZs6eRHtGsva0rz4X2Nuc8L1Z768QpSea5EPG8wtxsfKtsGCpiOy2XN31h7gTXyXnN2SifYFllzuc4jQsb7apHrAR8oU4jbYtqSFEOBMqDAMxz/+MM89x/pqjRGwEdYeithWiv8mqed9yzl2F2s0E+ZAhn8CPitC1vv/su/dd99xmvi3IKFs67+NYbC+nmqf+62zznsgMHDqRXXniButfUGCvP+Wd+lbS2luVl9kiwee7XohXGENReQ1x3rLQ3cp5H+yxA6yAAAt4J6JrhuuW99wxXgkDyCJS69vYakcbGxjztzXUtXLjQMM7N2vu9994zNLr5sGrtYrW3TpySZJ4zM95xic1sYX7zOX6llM85pVcRrHnDo5WWazn3+Z25OjhFizDlhcjnTUjNbbnNkVgJ+EKdhXnuFkp8DgLlRQDmuf/xhnnuP1PU6I2AjjD01kK0V3k1z3966c8MoW5+JVTkPD9v0iTjlVGRa/H4Y/8fzXt6tmSe8yaiv7jxRvr2oYfSxqYmY+W5uS5eIcO5Gc0biLqRUo1VkAI+18e47TXE3Yr9W59O5vk954wxsB75+QfSFFi8X3aPpGvnPCudd6rHbf7gcxAAARDwSsDJDL//nAuojXbtTlsm6k+3tlIldaSr5jwhNelUj9d+4ToQSCIBVT2XxLEV02fWylbtLerjz8zam39esIDXSrcfrL3ZUOfFKZy+pVjtrROnILW33znPmRgb4Gxos9HNB+c6Z5rmjT15pQz/eYJzmvOqcj743KRcWUGer2HjnVeXc/k7TOXZVOd2+H86R2Tm+fjXxtPabWtt+9qvaz+adfIs6TMn8/zNo79NRGm664Z98+qyq+eLC8ZRy1r7djv060ffeOJxHX55ZX977xLaWr/Tto5udZ3ojGuyK8VwgAAIqBF4/raf09ZN8uZ5fGXzmlXUuaWVTtyezecrjleridoqiK6a95p0vlDaFt4w4tafPJjXoenPXWqcO2jxJ9Jnj//0V9TSsY46753/3LG7z2ddyS8KEY1/YKZUz/1jx1ImnaGe+/8sr227epxY8MXd9uxFZ9/8n2pQHUqd98ghtKEqRVV18rha61dR79YMzb7ko6Lqx8UgoEpARxiq1hlFuZ2ffUaZlpa8prdUdaRMJk2VHYxk5ruPdEuaKFVBfQZkU62Ig9O2fL5qFZ38k5FkfvWTPxfm+c3TphniXKwiH/vTc+mKSy+ho/7fcVJddhuMigL85WD06NG7V7WrMFONVZACPtdPJ/M8qr2GuFux32+okHnOX4yGWszz9/cbbGxeC/Nc5e5AGRAAgSAJOJne/Me/TGYbVXboLjXf1rKFUqmuec8vmOdBRgl1J4WAqp5Lynj86Ccb32x6W7W3qJuNc7P25p/FanTV9nW1t06cgtTeQZjnzIwNdGF883/Npjd/zmKfHZX9TWY461K7w5wChg1zPrhONt110rWIuiMzz0e8OILWNq+lftX9pHGKc/PPkvMVO5vnhxJRG911A+NrP5zqWX7SydSyZg116C9vJijODXpdNtxUJ70o99RN/0PNm3dQdc/O0qXi3Pm3H6NbJcqDQFkTYOO5adMGqtmzt8Shcd0a6rKrlU76Wsbz+h5E6QrOba5mnv/p6EMNI+AXDuY5/2I40GKez7zwOdrVsY5qelcr3edO5jmbFum2DPUadJlyPXYsxDmrOa87cU569FDaUEXUv0Y2z9c0sXlO9PrFH+pWifIg4ImAjjD01EBIF+1Ytswwz1MdOkgtbqmsogy1UaXlfBuXpUrqM5DXWrQfbJ6//MqrdPGVV1ImI0tEYYa/s2TJ7lXnfOXGVZ/TdVN/Tk8+/YxUl5N5zl8QON0L52/UOVRjFaSAz/XXr7Qtfu01xN1K/Mpzq0kuVqTDPNe5S1AWBEAgCAKFzHNuT/X5BfM8iOigzqQRUNVzSRtXMf1lXczpEq3aW9TJn4s3PsU5NtBV3+D0or114hSk9g7KPC8mXkFfG6l5zoOzmuRsqtudL2yeE52wSDZ1nOph85wPq0nudF43AGye82E1yZ3O69aP8iBQbgScjOeHzsrey/kmudN5+w1D3/neYUY9x/7lH3lol3/n4Ozz4m8fS5+xec7HhF+Pls473eeFzHOu4OrZTxVVj1P9unOFzXM+rCa503nd+lEeBFQJuArDx08l2rJatbrIyqVbWijTtS9VXvqG1Id1KzgrH1Ffi0m+PnfezjznleffPXGYsSp86tSpu8W6kxnO5jlf88W6DZKwdyrPGyHpGuc8BtdY5UYepIDPNZGEvYa4q5Fpb7sbwS1ti6r5hLQtkT1m0DAIlC0BmOdlG3oMPAACrnouIdqbuu9NNO73vhBic5tzlFu1d6HK+RpOg2g11e2u8aK9XeNkaihI7Q3z3JcpplaJk7kN81yNH0qBQLkQgHneHmknFjDPy+VuKJ9xugrD+wcTbfmSqPs+sYaSaVxFma79qGKynPLIi3nOA337g7/ThAkTiDcv4oPzlY8+4wz68Y9+ZGwAaj7YPOej175yCphCaVu8wHSNVa7SIAW8qd9x32uIuwrz3MtEwzUgAAIgYCEA8xxTAgT8I+Cq55KgvcV3g6vk/VqKocSry63a25znvJi6vVzrGidTpUFqb5jnXqLn8RqY5x7B4TIQKDMCMM/bAw7zvMwmfxkP11UYsoDnw0dxHATu9N2HGNX6ZZ73HvRNo76FCxcaGxKxoOcVLv92ww10+zTeDqf9KFPzPO57DXGAYJ4HcbOhThAAgcQQmD/8EKremrbtb3O3ChqxQG2PHZjniQk5OpoAAiWhvQP8fiC093Nz5tAXq1YZ2vvySbxNZfuRqqii3vvl74nmZ/hd42RqDOa5n+QjFPBO5vmNV5xCnb8m6t9Vzkm+ffVX1LE1TRN/96pE4M2jD6U9m9qovmsv6fzKA86lnZ17UNf+8uTl3OZdWptozBMXSuWRtsXfiYXaQMAvAl7M84o00WEb1khd+Efv/kYu9PmnyudPf6k3tVRWURebzT9bVq+iLq2tdMHv5JQLTmlbfj3xt5Rpa6PjvuR9nduPhTVVRFWVdMmc30nn+TV33ryotk8f6Xzjpm2UqdyD/nim3Ndj/pCiqlQVTX30Jak8Vp77NdtQT1wIuArDAMWxnwy8mOecCz2VqpS6kcm0EVEldegs7/3Aha6ePIFenj+f1i16V7qmqUNHohRR74EHSOdLfOU5jzXOew1x/2Ce+3mToS4QAIHEEWDTu0cTUUON3HVx7rhFcrpEpwHCPE9c6NHhGBMoCe0dwvcD1tGTb/w5vTz/Ffp06dLdEWWtzvrd+iao3yF3jZOpQZjn/tKPTMA7mee3jz+FOm8jqustm+db1q2hjq0ZuvxFeSPReScdTj2b2qiirU4is/xbl9GOzvkb+m3d0EydW5voolk/kcs75ELXxY2c57rEUB4EChPQNc/vH/lDqkxn6PB1svH8975Z8/zVEfL5Eb/vT7uqqqh7X/mZw73i584era10iWXzUSfz/LHxLxC1ttJxax+TBrVgD6JUVRVdMu8P0vkHxl1G6dYm6lYnbzDcsH4dUWU1/XHkFqn8d15OU2VFJd00S64H5jnuolIj4CoMQxDHfjDVNs9XfkbEG4Ja3oVkgf7jEadQVac987q1ZPEf6aQzTqft/5T3bTDMcyLqPajszHM/QhdkHZFpb7tBIed5kKFG3SAAAnYEnExv3Y07YZ5jfoGAfwRKQnv7+P3AbjNQps3m+f9++CGddPrp0kaifi9OcYqsa5xgnvt3U1hqikzAFzLPuY9Wc+ihs+w3+3OqZ0ZuQ7+Jlg39DHOLCOZ5YFMKFYOAvwR0zfPbxp5ndODmJ2dLHZl+3knGz5Nmvy6dfyS38ajVIOdCTp8VNM9tni+PjDzFaNNqnjv9se3uc8cY5Sc/86zUV/7jot3zEea5v3MOtUVPwFUY+iiOgxytrnm+Y9kyozudD2g3vDm/+a3XTabbrptM3b51UF53Zz34AD055zl661155flpP/ohnX/OOTTqgnHSNX6Le9dY5VoPcvVLkDEMoO7ItLfdWGCeBxBhVAkCIFCQAMxzTBAQiB8BVz2XBO3tUx9Ze99xBLAVOAAAIABJREFUxx00zZISkaPGOvrlV16h5377WyOFojj4/MWXX05XXXutsSdRUIdrnEwNB6m9kfM8qAjb1AvzPETYaAoEEkwA5nl78GCeJ3gio+taBFyFoU/iWKtTHgr7YZ7zypdf3nMPvfToY7bm+fHf/z7ddMNkGnb6mUYPufx7771H0x95hGY+cD/Mcw9xC/gSmOcBA0b1IAAC8SYA8zze8UHvypNASWhvn74fsJaePn26ZI6bTfKzx46lex94gIYMGbJbe/9pwQKaPWcOzXvhBZjnJXoLRSbgRzx+BFG6leZvlfN63r60D1EqRTf9Ws5trrvy/L4x4ymd+Zq6966WQte0oZkqKrrSVc88Kp13ynk+ZsYiWrNlu234+3fvQs9OPFr6DGlbSvROwbAiI+Bknt87+mzKpCqpV6+sYSSOTZteNP65555nSec38/lUmq5+5nnpPK8uz6SJhn+dP0Qj3UoF6aVt8WHl+T3nZFeen1ZfL3Xq1WqidGWKrpwrPx+x8jyy6YmGAyJQEgKeiNzM8611nMu8/ei7bpfxw7q+2ZQrfNx0zU30yXv/oGsnXExjr75GKj9p0iT65j5703ljzqHKDh2kz04ccSrddP1kmOcBzdEiqo1Me9v1GSvPi4gkLgUBEPBEAOa5J2y4CAQCJVAS2tsn85z19eLFi2nq1Kk0cuRIifvYMWPo24ceSpNvvFE6zyvPTzrtNPrv++6DeR7oTI2u8sgE/IjHDsua59s6SaM3zHP+svj4a9J5XfOczadMpplq+/SV6mlcv44qUtV0zRw5HYKTeX78XW/SVw3baa8eXaR6xLm3rj9BOg/zPLrJjJZLk4CTMWwYzKkK6rXn6dLAnczzjZteohSl6Zpn5Xv/kZEnE6WJhjXn81vIf3tj83ye/DwKOm0Lj41fhfqxxTx/fQ8y8rZfbsnBDvO8NOd+OY+qJAS8j+b5jCn/Rr99bQG9+/Eneeb5vt26UjqVolTHdsOdCw0bcSrdPPk6+sk4eYN0pG2J/M6KTHvDPI889ugACIAAEcE8xzQAgfgRKAnt7aN5zivPeQW6OTULR23kKacY5rl1Y1CY5/Gb0373KDIBb5jnRDT/InmDq9vHneybec4VXWsxye/NrejUMc+5HqtJzqa63XmY535PUdRX7gQKmuc297hTzvO7x/zUQDn52aclpLMu+KHx8/gn5NXcfM7pszDMc7vn10O5/Owwz8v9rij98ZeEgFcwz/sOHCAF0y7nORdo+vgjo1zNwYfkBX/Hxx8a5zoffKj02ZBDvkX/fu01NGL8BOk8zPPI75/ItLfdyLHyPPL5gA6AQNkRgHlediHHgBNAoCS0t0/meaFwOelomOcJmORFdjEyAQ/zvMjI4XIQKBMCMM/bAw3zvEwmPYZJJSHgYZ5LMznITYsSdstEpr1hnidspqC7IFCiBGCel2hgMaxEEygJ7Q3zPDTtjQ1DQ7zdYZ6HCBtNgUCCCcA8h3me4OmLrnskUBICHuZ5aALe4zSL6rJYmedij40jP/9A4vH+foMpY/N2l0grNtRSfvF+PKz8Nz6jgox2QQAEvBN4eMIV1LK90baCDl1q6bKZD0qfzR9+CFVvTduWb+5WQSMWZN+eEodf5vn951xAbbSLKqqqpPrbWrZQKtU17w30bFrXbVTZobtUPt3aSpXUka6a84R3aLgSBBJOoCS09/2Dqa1lF319irzHmQhNupKo+wHym5obPl9FmXSrbfRSFVXUe799pc+w8jyLA+Z5iDf8L65/hLrs6kGZtl5Sq1X1MymTaqPrn5YnvFhxOf88eYPRtc1rqV91P5p/1nypHvFlAGlbQgwqmgKBAAjE1TxnU+G3x/67NOIz3/k34+cJvx4tnb/vnNGUoQra85tXSuebN++g6p6d6fzbj1F6fmHleQATDFXGkkBJCHgV87xa3pB8Rz0/WVLU2ZKeBWlbYjlNvXYqduY5fwFSNcN1zXavkHAdCIBAdAQ4nRMb0FaTWZy7evZTUufYDO/RRNRQI/dZnDtu0cd55fmE6nknErpmuK7ZHl0E0DIIhE+gJLT3/YMp3bKLdvzweWqTbUOqbCPjXDdLmkM2wzOZNkql5AvEObvc5hwd5DwPf45G3WJkAn7aVc/RHrvqaFulPEk7bJ5FmVSaJj8jb+rnZJ4zwH5d+9Gsk2cpmU/IeR71lEP7IKBHIK7mOY/iNxbz/Ix3/s34K6zVPOfnToY3Nx10ed7gu9V1ojOuGaL0/IJ5rjd3UDq5BEpCwHsxzzeniVJsnsurYnTM8yVLltDChQtpypQp9INjjqbR4y6kiRMn7p4MyHke+X0Rmfa2G7nTYhOnXOi6OdIjp40OgAAIaBPQvf91V5Lrli9knvNn1sVyuvU7PQe1weECEEgwgZLQ3jnzfNspz+eZ5FtzewTZmedOZrjqedbev3nuOfr3O++kYcOG0ahRoyTt7ee0cI2TqbEgUyZi5bmfUXWpi81zPqbcL6/QvPvcMcZ5J/PculGe7i9TmOchBhlNgYAPBGJpno+bZ4xswuMjpRE6bSTq9NzRfX7BPPdhQqGKRBBwFYac03DLl0Td94n1eDKNqyjTtR9VTJZfWV+3YqXR77wNQx02/9QxzwUQp41EYZ5HPmVgnkceAnQABECgEAGY55gfIFB+BEpCe2/5ktJ79KGwzXOeLX7ra6cZ6Bon04Uwz/29jyMT8DDP/Q0kagOBUiUA87w9sjDPS3WWY1xWAq7C8PFTibasjj24dEsLZbr2pcpL35D6CvOc5ATbsY+krx2MTHvbjQIrz32NLSoDgZIgAPO8JMKIQYCAFoFS0d6tHXrQ9hMeDHXlOcxzramW2MKRCXiY54mdM+g4CIRKAOY5zPNQJxwaiwUBVwEfi166d2LHsmVGoc4HHADz/IMP6IgjjmAO/H8wz5cupcGDs5tsRnn4aZ7bbcTHY7PbYDDKMaNtEACBwgT8NM/9yIXu1Fun55eXtC12ez9wu3YbnmL+gEApEigV7e2UnsVL2hY/cqH7PVd04oSV5/7Sj695/l15Y5GH3upjjDzuaVseGHcZpVubqFtdZylSW+t3UEVVDV35+MP+RhC1gUCJEwjFPE+30vgjN+WRnLV4T6KKKhr/xKvSZzMTkrZlzIxFtGaLvCGhGEj/7l3o2YlHS+N6afjB1GMrUf8aeVfxNU2rqKEb0ekL5OdyiU+9yIanG7fIOhpgwzrCMMBuFF11KOb5Pz8k3mb0izr5vv1G/SpjD4bO35Lzp/v9WqlqrIIU8EUHKtwKItPedsP0yzwvtBEfbzpo3WAwXORoDQRAQIeAX+b5/OGHUPXWtG3Tdqa0k+nt1Hc/zXNu48jP5b/rOm14qsMSZUEgKQRU9Vzcx+OXeb7h81WUSbfaDjdVUUW995N1t9/62omzTpyC1N7IeR7ineC68jyh5rnYnby2T9bsF0fj+vXGjuX48hDiJENTJUEgcPP8wlOJ2Dwfsi6P16wlfbPm+a9/L32WFPP8+LvepK8attNePbpI/Rfn3rr+BOk8f2mpayLqvLcsBnasXkX1NUTHLYJ5HsZNpRu3MPoUdhs6wjDsvum0F4Z5vv2fHxpdsjPP+XwXmOc6IQujbEma506ml5MJFwZotAECIOCNgF/muW7rUZnnTuPV7Y/ueFEeBOJEoFS0t1/muW5sYJ7rEkte+cgEvKt5/syzEk2nXL9OyJ3+Eh30hqG6YiN5UwY9BoFwCQRunl85wRjQ+Adm5g3Mqe0kmec8KKtJzuas3Xnd11zDnQnl05pTfJzOlyKZUhHwYZrnVpNcmOowz2N3h0Smve1I+LXyHOZ57OYZOgQCngnofp/1y2TWrcevlecwzz1PFVxYQgRKRXvDPG+flFh57u8NGpmAh3nubyBRGwiUKgGY5+2R1d0wVNeEhXkej7tIN27x6LW/vSgVAQ/zPBwB7+/sC7y2yLQ3zPPAY4sGQKAkCMA8z4ZR18wvieBjEGVLoFS0N8zzcLQ30raE+KiAeR4ibDQFAgkmAPMc5nmCp6+nrsM8JyoVAQ/zPBwB7+lGi+4imOfRsUfLIAACCgRgnsM8V5gmKFJiBEpFe8M8D0d7wzwP8QHgZJ6L169Oq6+XevP6HkTpCqLLj1+f38vuexONk3MSO73GJc4vPHuLVM/Uu7+kyooq+t6fl0rn//XiS2mPXVtpr1pLzuDG7fR1x270X4/+SiqvKzZCRI6mQCCRBB455zSi1jYa1iRv2PFyXZ2xSd61c+QUT7eNPc8Y581PzpbGe/eYnxo/T372aem8kznPhXTTttw3ZjylM19Tc8dKqY2uO7dQKlWd11engIjn1EtDJ0pFzlj8K8qkKqnXN6+Uzm/+7JeUqkjRVU8+KZ3/zQmnU11zg23O8/rqHnTmmy9J5bHyPB63CMzz8MzzlRubaVeb/WZmHSsraECv6qImxabln1KaUpSSHwnUlmbJWUl9Bw6Q6t/xcTZ/eeeD5U0+mz7+yDhfc/Ahef1xSs+CtC1FhS7Ii2GeB0kXdYMACBRNQPf7rF8rtHXrQdqWokONCkBgNwGY57Im150ayHmuSyx55SMT8IXMc/5K+WOLef5qddY8v/JYi3m+5Uui7vsQXSXvjq1rnl9/1+dUlaqkY//yDymKbMR12dVEdb3lDUDrN6yn7R1r8gw6XbGRvCmDHoNAuAQeGXkKZVpbafjXcru/q6szTsTJPOfnTibTTNs6dZc6W72rjSpSXenqZx9Vgudknp/+/gyiVAX1GnS5VM/G5Q9TRWUqb0Pit446jno2b6bO++wtG3RfrqbN1T3p+Pfels7DPFcKT+CFYJ6HZ55/sq6JWloz1KFKXj8hzh3Ut6aoeG9YvozSqRRVVvCf+tqPtnQVpVIV1GfAfvK9CfO8KN4JuTgy7W3HBznPEzJr0E0QCJGA7vdZXdPbaSi69cA8D3FSoKmSJwDzHOa5ziTHynMdWkWWdVt5bjXEbhk3wmjx1sfnyy3fz99BSNs8t9b/zvcOM6qxM8/5vHUVq9PqVl2xUSRGXA4CJU+AzXM+Lpn3B2msd587xvh5smVz4ShXnjuJeKcNRp2C5/bHP+vzy+m5w+Y5H1aT3Ok8zPN43E4wz8M1zznqVpOcTXW787ozhM1zPnoPOkC6dONn2bfreu2f/SOgOIJYef7xjl00ZMiQ3W04rYxZuXIlDRig/8VB9ctWkJsW6cYl4vIwzyMOAJoHARAoTED3+6yu6e3Uum49MM8xk0HAPwKqes6/FoOpKQ5pW5YsWSJpb6eRetHeOnEKUnvDPA9m/trWCvM8RNhoCgQSTADmeXvwdFcIwjxP5sSHeQ7z3Evalp3996bFixfTsGHDjInPaVvumfUYjbnscskUdzLP77zzTho5cqS2ga4q4oMU8Am70yMxz+cPP4Sqt+anKHp/v8G2KdD8Ms+c6klYzNBdECgrAoXu/7aWLVTZQX7DMt3aSpXUka6a80RRnJzM84cnXEEt2xvz6ua+pFJd895C1V0M4jReXTO/qMHjYhCImICqnou4m67NFzLPK9uI2iypFLd13MOo05pK0bUhImpsbNytvYW+fmLePGU97UV768QpSO0N81xlhvhUJgzz3C79i1Oe5D8dfSj1bGqj+q69pBH+795dKZVK0Q+b26TzL/eopQynT7DkHnZKn4AvDz5NnICqGf/aeFq7ba1t7f269qNZJ88KqGVU60YgaPP8/rFjKZPOUM/9f5bXFadc4k4ryXVXno+ZsYjWbNme1y6nZ+H0L7V9+kqfNa5fZ5s7nZ8v/AWito+cXmrX6lVElKGO3SqkerZvzVDHtlZ6+DTZSLlv5jpiPXHcoo+l8vjy4DZL/f1c1zz/7b1LaGv9TttOdKvrRGdc077q2N+eBlebjjAsphdOK8yTtPK8+eMPDfNzXd9OdOYJZ9ITv32CarrX0Mcv/ok+X72arrzl1t2IeIXLPdOm0X777ksbm5po+PDhu812LjRq1CiaO3euFlLVWAUp4LU6HH3hSMxzfo73aCJqsGQiWrxf9g1O1TeadE0m6N/oJxx6AAK6BJzuW10TW7ddJ70pdK6qaQ/zXJc8yoNAeAtXgmbtZJ5vWfYhVciWntGVrzvuYehoL+Y5Xz906FB64403aGd9Pb397rvUsH07TZzYvm8Za+/p06fTwIEDacWKFUVrb1XdzX0LUnvDPA96JpvqD8M85+asG4865Umee9Lhhnle2Sa/Qv2PfbKbheWZ57xZoU3uYSezDV8eQpxcHpoa8eIIWtu8lvpV95OuFufmn2VJF+ShDVzijUDQ5jnfm+m2DPUadFleB53+GOaXec4m6VcN2/M29DxzyWxj49HuveXNCrdsaKaK1B509bPyH3MeGHcZpVubqFtdZ2kMu1Z/QfyLrYPFPN+yvZI6trbSzFN2SeUfZPM8Q/T9v8I89zZb/blK1zx/6qb/oebNO6i6pxx/ce7824/xp2Mh1qIjDIvpVtLM83S/vQyDu960L0x6x3ZD9Ld2qKCP/v4R3XbPbXTOuHNo4ilj6XezZlI30+ajLPCfnTWLutfUUJ8BA3YL/traWgPjvHnzjLrNot+Nr2qsghTwbn2M2eeRmefMwfrHUd0V5jDPYzab0B0QCICA7vdWp8Ujul0rZJ5zXVfPfkqqUtck96t+3XGhPAgkgYCqnov7WJzMc6d+r1ux0vjIyTzn1eVW7W2ui1O0sDl++rBhdPbYsfTWn/8sNSXMdaG1rT/ram+dOAWpvWGeh3gnBG2eO+VDdjp/+KwTjNH/ffybEoVZV04wfh7/wEzpvJNIcCqvK0JCDAWaIiI2z/mwmuRO5wEtPAJhmOd2gpzPOd23fprn3M5b12efP+KYeeFzxj8n/Hq0dH5G7vxEy3k2T/mwmqSfHHmQcf6gxZ9I9Tx01snGz5e/+Jp0fvl3DjZ+HvQ3mOfhzfD8lryY53bxd5oXUY5NtW0dYahap125pJnnNQcfkjcM8SXhoptvpaOOOopuuOEG4lUu1116CT153z27zXM+x4L9k/ffN+pg85y/DPDqc7NZzj8vWLBAGatqrIIU8MqdjUdBmOfxiAN6AQIg4EBA93srzHNMJRBIPgFVPRf3kfptnhcaL+toob3/9qc/0e133kkvz29fdCm0d0NDw+5qitXeOnEKUnvDPA/xToB5HiJsNOVKAOa5K6LICsA8b0cP8zyyaRhqwzDPw3t1tJTM8//b3r4xKK9i+XjRX+jq8RftNs9nzJhB06ZNo7/kjHE2zydNmmTMbV4xIw422N/PGewqE19VxAcp4FX6GaMyMM9jFAx0BQRAIJ8AzPMsE6QtxN1RTgRU9VzcmYRpnps3Bn304Yfpi1Wr6PZp03YjEtqb07WIo1jtrROnILU3zPMQ7wSY5yHCRlOuBGCeuyKKrADMc5jnkU2+iBqGeQ7zXGfDULsvCbwBUb+OVXTGySdJ5jmb5PNzOc2Fec5pWsx5znnlOZcbMGCA0h2gKuKDFPBKHY1PIZjnEcfCKW8zd6tDl1q6bOaDEfcQzYNAtARgnsfXPHfa/Jl73NytgkYs+CjayYPWE0tAVc/FfYBhmudmFjdNmULf2Hdfuviy9lSwbJ6zpjYvSmHzvBjtrROnILU3zPMQ7wRfzfMtXxJ130fq/d1/zaYf+OPILdL5H8zL7g4++ZlnpfNOaVucNhPcuPwhokw6b4MlP9O2fHHBOGpZa7+JZYd+/egbTzweYsRKuymY59HH12nTw03/9wClKE1Xz8mmMhGHkYKprZl6WDbVrN+wnrZWVtNbg7Mpl8Rx6pJHjfzfk599Wjpf6AtCobQtGcrQ04fsKdXFG33yYd14zSnNi5NJymlbdnWsoxpLzvOmDc3UYVc9qaZtefe7B1Nbiui/bthf6ueI2dndUoJM2/L8bT+nrZs22k6sbnv2orNv/s/oJ11MewDzPHnm+cebVlA605o3o3ptyUrL3oMOkD7b+Fm98XO3lk3S+cyuXZSqJCrWPGdhfsp3jqQTvndMwZXn/OpoXV2dtPIc5nngDwaY54EjLtyA0+aDvPE2b0hozasccXfRPAiETgDmeRZ5HFeeO23+LDaEtu5rEfrkQYOJJaBjysZ5kFGZ52PHjKEfjxhBo84/fzceu5XnxWpvnTgl0TznrVZ5J6ZGIhpIRHfk/u0258R6f7H0ZwoRZbPZZw+v9ZrbjUTAcwd8M88fP5Voy+o8lv/914MoQylb8zxFKbrumWeka5zMc6fNBDeyoZdJ0zXPyYaen+b58pNOppY1a6hD//5SX8W5Qa/L+YrdJhQ+dyYA8zz62eG06SH/ocq41+bIf/C66/zxlGr7mnrsKW+qua5pB22tqqb/OfBsaVA/XvIoUSpFk5+ZLZ33ZJ5fOI94h77Zh/SU6vLLPH/8vEeopUMP6rz3vlL921evog67GujCpy+RzjvltmaBzTb5XRGY5/wsbNq0gWr27C31VZyz7iMR/QyMTw9gnifPPP9o46dEqVaiTJU0kZzM8w2f11Mqk2+eU9suSlUQdTrwUPm++Ti7kkzkPOeV5Zs3bzbO7dq8kepqa+mmu++hKVOmGKlZ7Fae86ulJ554opTznI1yFvHmnOdI2xL4syAS7a27UZ7uRqK69QdOuUADumOLsq9oGwSiIADzPEs9ruY5981qksexr1HMXbTpnYCOKeu9leCvDMo8N2tvHkXPnj2NfYaE9rZbeS60tznnebHaWydOSTPPRxLRcCLKJpUkYiN8LhENdZk2bJybTXb+mc1yXj7IJrzXeq3NRiLguRO+mecOIO8dc47xyTXPzpFKOImBQuY5V2BdhfLQWdkNJi9/sX1DAP7Zb/Oc67Sa5Gyq250P/lFUui3API8+tk4G8L3njMneyxbz3OkZ4jQSL1+Wna55bPwLRjMXzfqJ1JzThkm6K8/f+d5hRr3H/uUfUv1vHp011E5Y9KF0vpB5zgWtAvuR3IahlwS4YajTs9DpfPQzMD49gHmeUPOciA7pdaA0kTYsX2b8bF15vnZVduV5v33r5Im3Pve6dR95Y9CmnHme7reXYXazQT5kyBDjWv6S8OZf/odu/eUjRqoVTsHCOc/ffXU+3XbdtbtXnnPZgQMH0isvvEDda2qMDUP5Z36VtLaW13hkD5jngT8LItHeuua27u9M3foDp1ygAd2xRdlXtA0CURCAeZ6lHkdD2qlPcexrFHMXbXonoGPKem8l+Cv9Ns8bGxvztDePYuHChYZxLrQ35zz/4H//lx4y7SMktLdZaxervXXilDTznDPDs3G+0DRN3iciXkVuPmedRbwdK+ccmJf7gL/V8Dm+7k4i8lqvtZ1IBDx3Aua5+4PDySSHee7OTrcEzHNdYv6Xh3nezhTmuf/zK0k1wjyHeU4O5vlPrrzaEOrDhg3bPaXFl4SzrrzG2AB05EheX0H0g+8dQy/NmimZ5ytXrqRf3HgjffvQQ2ljUxPx6hdzXbxChnMzmjcQdbt3VEV8kALerY8x+zwS7a1rbusazLr1F8rby/EKMnev7thiNn/QnTInEEbOfpjn2UlWyJAOIw52Ux3meZk/AAIcvqqeK6YLW5Z9SBXZ7KF5R7qSqPsB8puXXtry2zxnrWzV3qJf/JnQ3utXrqSzx46lt/78Z6nbrL150QsvTuGNQ4vV3jpxClJ7+53zXBjenKrFnG5lQe5nsRrdbk4IozybQDd7ZIiIf2YDnT/3Uq+1rUgEPHcC5rn7owDmuTsjv0rAPPeLpPd6YJ63s4N57n0elcKVMM9hntuZ5599+SX9y+gxZH71k+e7+JIw+b4HDXEuVpFfdPYouvrii+jbJ/1Iui1Y3PPBK8+tB385GD169O5V7Sr3k6qID1LAW/rpNa1hGOkSuauRaG9dc1vXYNat3ylvLwMKOnev7thU7gOUAYGwCISRsx/meTaahczzMOJgN6dgnod1p5VfO6p6rhgyrFkr24jaKuVaxLluB8fLPGfjm01vq/YWvWfjXGhv1tfX33gj/eI//sNYja566GpvnTgFqb39Ns95WRAb5VaTm9O2sLHO6VxUD/PK8yU+1huJgOdBh2We/3aI/DcKI+8xEb085GKJ/abaW6mqIkV/H/+mdP6/zz2XeGNA68ajvOHerqoq2raHvEFY5x1V1Nqljab++lWpHl0RwhcvP/owonQrDfppJ6mu5U/vJKqookGL5JQOqpOplMo5bTLJY+xW14nOuCb7WrvbAfPcjZDz537FoJB5nklVUK9Bl0udaNzUTF93rKcp949W6rzj/gXLH6aKypTtBmGRpW05+lvG30uPHSPf+28/u9MYqzUNixO7x3/6K2rpWJeXO503YeWje9fsCtXdR2srdWmtpzFPXyqd9vIqKNK2KE1L20JnTx9MjZWtxu8kKTzpDNW2VdHzkz6QzjvF3+m8956Fd6WbMBz/2nhau81+Q22dXra07SpYvENlR6XqdrW1GOU6VnaQyre1tFDvTnvSv37/F9L5uu29jJ9t07ZwXZZ6ttZn6DevvU4XXHMdLavPpoIRR591O43NkF/76JPdq875M/6CctUtt9Jjz7PsbD+czHP+gsDpXjh/o87hFitRV5AC3tRfr2kNw0qXyF2NRHvrmtu6BrNu/YV+r3j5naMzZ3XHplM3yoJA0ATCmL+631ud0hbqsvDrOaJrMDuN18085/FZU7vqsvOLUdDPTd1+onzyCLjpOT+0d3pXVndXdJT1tdN5LxR7UTd66OCbpDcvC9WzbkV2UUnfgfmGN+tiTpeYyfA65vyDPxdvfAp9ffO0acpvcHrR3m5xMvcySO3tt3nOAp6/sfSwbBA6nYiOVMh7bh43f5OZmst5zqa8l3r78pywhJyTc85ZunQpDR7MWj68I27meWPdbVRVmaL3x/1RgnD3udl8y1bz/IwndtKuyipqqJGZtaWIWjq30X/8Ss7K4+UX6fLvHJw1z8/vKjWy/KltWfP8bx9nwbKUAAAgAElEQVSHF7CYtuS0yWTz5h1U3bMznX/7MUo9h3muhMm2kF8xcDL67jtnNGWogvb85pVS+2uaVtH2jg30i7vkzTOdRnL/2LGUSWeo5/4/k4ps/uyXlKpI0VVPPpl3aXTm+cFGX6zm+TvP7jReQVI1z2de+Bzt6lhHNb3lTVWdzPMdVTXUpaWeLnxM/oOEF0EO89z7PXXSo4fSxiqifmlZlqytyFCvVqLXL1bLeV/K5jk/s9c2r6V+1f28gyYiNs/5nrIKQHGuWPN8TfMa6tOpF93zg4ekfmbN8xT125clounY9H/Em4Zaj+b6DK388ks6/OQRdMjhh9CkqyfRD0//oVGsb848r7as1mHznFerL2vaJpnqTuY5b4Ska5xz+6oiPkgBb+LlNa1hWOkSuaswz11WdHr5naPzIAjDfNTpD8qCgA6BMOav7vdWmOftEdRlpxN7Lqv7hwHd+lG+fAm46Tk/tHfQ5jl/N+jTsSfNPfw+X8xzNrc5RznvMzR16lRJT1tnitDX24iI0yAKU73QjPKivd3iZG4vSO0dlnnOxjf/WcNt01Axbl51/hkRnchxyG0Wameeu9XLy55usQteKZrn9+VM76ufeVYastMvNCfzVJjnky31fHLkQUa9By3+RKp/xGPZjf7mXySvCvfyi9Qwz3nDUItJ7nS+HB/1fq24hHnuffb4FQOneh4ZeYrRuUvm/UHqJBuMfFiNRKeReDFzIzfPF8l/IHMSzE7s2DznY8KvZTN81gVZ0238E/IbMk9d8bxx/vwHz5YwejEyvPD2PgtL60qnue103q97ME4U3YSh0zNbdwzLNmY36Dygl7xBp9N5p/o/2vip8ZF1w9CTn8u+ZPjaaH4Rsf3Y/FWz8UPPveQ/bDnVv/2f2T+Y/OGfn9CECROINy/ig/OVXzjiR3TmySdRl2/Jr7o65XwslLZFlx+Xd4uVqDNIAZ9rIwnpErmrMM9hnnu51XANCBgEYJ63TwRdI9mvle1hxcFuyuuOGbcNCKgScNNzfmhvJ22qm6fcaUzcRzbo/TLPuR1eXW7V3uY9hkRf/NbXTmN0i5P5uiC1t9/muV9pW/gbF+c5Z+Pc+K7kMW1LWa08h3mu+phMdjm/TCOY597ngV8xgHneHoN3js6tPId57n1iJvhKmOfuhqwfAp6nSNLMc2GQL1y4kBYsWGAIel4V8+/XXkM3/vc90qwvQ/Pcqz62e1oElS6xpM1zkavcDHTxftk3W6+dIy9mQdqWBP+SQtcjJQDzPBjzvK1lC1V26C7FNt3aSpXUka6a80RezMOIA8zzSG+1smvczZT1Q3sn0TwXE8GqvTnXufmNTZjnxd0yQnjzCnNhfHONKhuGipY5ByOXFzlAuE4++PXSYuoV9Uey+oUbDzptC8zz4iZvUq72y7iFee494n7FAOY5zHPvs7C0roR5DvPcOqPFynPr6nIuN/7sUfTia69Rw5Ym6bIyNM/jli6R4xGbhSu6Ky51jaH5ww+h6q3pvIfx+/sNNlIjwTwvrd9TGE10BHTvTS891X1jOulpWx6ecAW1bM++0WU+2FBPpbrmPb+4TBhxsIsdVp57mdG4RoUAzHP1TT555fnzzz8vbSQK81xllhUu835u1bg5ATbnY+SV5PNcqp9IRJy93nwt5z6/k4iKqdfcbGTm+SPjn6PKNNEJXz0mYXi5ro54c8Advc+Uznfc+AKlU2m64elsGgK3o5B5zr8Ia/v0kapYs20N7diD6D8elFND7E7b8l05fQLnHee/RSNti1skgv3cL+MW5rn3OD0w7jJKtzZRt7rOUiVb63dQRVUNXfn4w9L552/7OW3dtDGvQafycUzbcv+5F1M6vS0vl3jj+nWUqqih1t6nSePruqOOOu6qz0ufcvxd2Q2K37r+BKl8oZXndU2UtwHo2/tMpFRlJV044wypHqRt8T6vo7wS5nl45nnjsg+pso3Imtucc6G3VRLVHiCnQtn52WeUacluDmo+eMPQ1kqiugPk9C9+pW155t57bFOzcB/+Mu95+v6o0XmbGcE83x0ht7SG1nD6lS6R641NysSgzXOnZ6aTqYaV51H+lkHbSSYQhmlbbua57vOLy4cRB7t+wTxP8t0b777DPJfNc/NmoNbIcU7zoUOHStob5nnx85sNcE54OSpXFUeEV5IPNFXNr5nyCnPOaS7+5MnnJuXKiqJ8DRvvM4hIpV6V3kdmnrOpU5EmOn6txTzv0Z0yqUra0ecnUv87bfgNZVJpsuYedxqkk3nuZPTVb1hDO7oS3TRLzTz/5Kkd1EaVdMjibL5UcSDnucq0868MzHP/WHqticWj3R+kGtevN15/tO5Cz7mwmzZtoJo9e0tNOpWPo3l+7zljKJ1ppto+8h7MTeu3UKqiG+3qfZI0ttomMszzcU9fKp3XNc9fGn4w9dhK1L9mX6met/tdRFRVRRfNkp+bMM+9zupor4N5Hp55zgZzVRtRqmNHKeiZXbsMM7ybZRPOHcuWGeZ5qkMHqTznV2ytTFHNwf6b55zf/PbrrqV/v+7avLzm3Ak21mfNnUtv/OV/jD6xoOdXS3duWEdL/vEhXXb9DUZudHH4Le7dvmyJdoPMu5hrw6+0LX6lS+Rulc3Kc6enJszzaH+foPXSIxCGaQvzPDtvCq2oDyMOdrMX5nnp3dNxGZGbniuntC2sve+44w7i1Cx2Bxvr06dPN1IoCu39m+eyi3w/WbmSeGW6WXv7GWO3OJnbClJ7+53zXPSbjW5hfPN/7zCZ5FyGXzOdSUT7m87zG452hzlVi1u9KjGK1DznDuZtZHflBKPf4x9gJO2H7i9xJ/PcyWy9fXx2U0JH89yyYehbRx1nlD/+vbelfsI8V5l2/pWBee4fS6816YpHp40kneqJq3nOvK6x5HBdftLJBsZBr78m4XQSurrmuZOp+tj4F4z2YJ57ncXxug7mebjmOUffapI7rdpm85yPzgccIE2apo+zf0gPwjxngf6re/6bfv/oTFvz/MTvHUO3X3sNfW/k2cZGokLs8xgam5po36O/TytWrKABA7IrakrYPE9CukQOQSTaGyvP3b9T6H7XiNdvDvSmXAjo6m4vXHTvhaSnbXFiBPPcy+zBNUkl4GbKlpN5bjXHrTEdPny4YawPGTJkt/a+dhKvfSbqVFdHPXr0kLS3n3PCLU7mtpJonvvJyu+6IhHwPAjHFZEwz3fHePl3spsGDvqbJWWMw3m/J0cS6oN5Hn2UdEU8zPP2mME8j37+xrEHMM9hnpvnJa9e+duf36HrL76Yzr3mWmnK8meH9etr5D3nfOjiNdKGhgaqXLvaKDv41NNpypQpNHEir7koafOch1dMWsMw0iUaISGipUuXLqXBg7ObaYZxwDxvp6yrW8KID9oAAVUCYcxfv8xzp1ziPNYOXWrpspkPSsPWfU7prsIulC7Kjj/Mc9VZiXKlQMDNlC0n85z19V/ffYeuu/hiOuNk+Y3yq265lQ4/7Ft01U23GmEX2vvTpUupe00N9RkwgAYOHChpbz/nh1uczG3BPPeTfEQCnofgl3nulD+Zcw9XVlbT1ZYV409d9SJRupXOH/SfEsnbl/YhSqXopl+/Kp3fnfM8RivPPz3yIGPzpUmjfyX19ZhPn6durc3Ut0bOPc2Fuu3Zi86+WR6z9lR6/FSiLdkv43lH972Jxv1eu8piL4B5XizB4q/XFfEwz9uZ/+aE06muuYH26tFFCsSO1auooYbo2EXyH85KeeX5FxeMo5a1a20nZId+/egbTzxe/GRNSA1O6XnWNK2ihm5Epy+Q54Vfz8E44XEThn4IeB6v0wpzp/Ni485KsX17DtrOLdl/WFew+5HznAX8fVddQb957XV665NPpTDxZwd3zqacEZuJsojnlTBiDDXfOsx4rVS8PlrCK88Zg0pawyjTJXIfYZ4TEXKex+mJi74kiYCu7vYyNr/Mc5HakdM4mg9O92iX2hHmuXu0dP9g4F4jSoBAlkAY2ltXd+vGhr8fcCrFuYffl6fJnepat4K3mSTqO7A95znr67uvvoJ+98rr9MaibFpEcYwfOYq+fdi3pPpZe+9Vm/1ywOZ5KpWStLfuOAqVd4uT+VqY536Sj0jA8xD8Ms+d8idv3dRMFRVd6crZj0rEnrri+ax5fgDvu9p+GOY5p215XE63EEfz/JMjDzL6ajXPj/9gJnVra6a63vJmqCK/tDUVjvZUun8w0ZYvibrvI18qzl31gXaVxV7gl2mEDUO9R0JXxMM8b2fN6Z96Nm+mzvvsLQVgLZukNSk67fV/SudL2TznlDcta9ZQh/79pTGLc9ZUON5nbPyv5C9GdhvD8h9V6muIjrP8UcWv52CcyLgJwyjN8xRlqKJWzvQXpHnOcRGmvTDIzbFy+oy/oNw36zF6++//2J2Tka8rcfOch+iW1jDKdIncP5jnMM/j9LhFXxJGQFd3exmen+Y5t2/d/8ipfpjn7tGCee7OCCW8EQhDeyfFPGeCun0V+vqJefMM3S3yoXuLhvNVbnEyXwnz3F/6kQh4HoKf5jnXZzWGDZOciM5/8GyJmNP528dlcxUnyTw/aPEn0thuG3ue8fPNT86WzjuZldpTic1zPqwmudN57Qb0L/DLNIJ5rs9eXKEr4mGet7PW3Tuh1M1zJmM1yZ3yyHufsfG/UveLkV/PwTiRcROGUZrnzMlqYjsJbD9WnnN7Xszzp+67h1585VVjxbr5KAPzPE5T2a4vkWhvXVNK93e7E3RsGBr36Yj+JY2AX/dmoXHDPM/SQdqWpN0d6G8xBMLQ3rqGtO54/Fp5zu3q9pX19cuvvEKv/elPNHfuXN2uK5d3i5O5IpjnyliVCkYi4LlnMM/d4+OU81ysPId5TuSXaQTz3H0+OpXQFfEwz9tJwjxvZ+FkksM8b2fkZH759Rz0/hTw/0o3YQjzvJ25nbHOr48+9cuH6LbrrjVeK125cmU5bBjq/0QMpsZItPf951xAbbSLKqqqpFE5pU/Q/d0O8zyYyYJaQcBKwK97M0nmuTCxj/xcfsu5RxMZaQ6tb+TpLkLQfX5x+TDiYNcvv8aGOwsErATC0N66hrRulKI0zxf87nf00h/+QA9Nn25026y9dcdRqLxbnMzXwjz3k3xEr47CPFcLIsxzd05+mUYwz91ZO5XQFY8wz9tJwjxvZwHzvJ2F7hcjv56D3p8C/l/pJgxhnrczt5rnbJw/99xzdPlZp1PDli20rGmbUXjkSM5WUhZpW/yfkP7WGIl5zuZTJrPNyDNsPew27tP93a5rPiHnub+TCrWVDwG/7s1CxOK28pyfX5wsbajFPOcxNHeroBELPpKGo6ujdJ9fXD6MONj1y6+xlc8dg5GqEghDe5eqec7a+7Hp0+nKSy+lqpoaYxNRs/ZWjYFKObc4meuAea5CVL1MJAKeu+dl5Xm6LUO9Bl0mjW7zZ780fu65/8+k880bm6m6Q4N+2pYj1kv13P3Xg42fJ9tsGOqYq7gb0WmndpLquS9Xj3UD00Kheve7B1NbiuiKCX2lYg/OXEeVGaLv/1XeNC7otC3jHz2M1lYQUa0l53njl9QvTTTr4n+ozzyfSvplGjma548fYeTIn7+1Mr/HQW6SGsPNWZ1Cpisenczze8ecS+n0Vuralpaa+rqCqGNrhuYec710vlOPK4yfX7/4Q6XZ5LQ/QqE9AZzGdu85Y4w2r5nzrNS2kwHMQreNcz5a7uV7Z240vgzcdcO+Uj1rm7L31PyL5HvKz7QtTTsrqaa3nF/caa+IQiYHOczV+/96AGWoknoOvDLvmZ2qSNFVTz6pxM6vlee/vXcJba3faTtXutV1ojOuGaI0j8IopPvFyK/nYBhjU23DTRjyM3tt81rqV91PtUrbcrypEB8VHbObborD6XwmVz5lKd/W0mJb//qdG6lPp1702ugF8n3wVbPxc8+9qpX6bxjkaSJru3yx0aeKbCqZxsZG6tGjR16dK1aswMpzJdKhFIpEexdKP2A3at3f7U7kkLYllDmFRsqIgF/3ZiFkcTTPub/XWnS30xh0dZTu84vLhxEHu375NbYyumUwVEUCYWhvXd2t2PXdxfi7QZ+OPYveMJQrVDX6VbS37jgKlXeLk/lamOd+kk/QyvP7x46lTPr/s3fucVJUV+I/1d0zvObF8Bx8IRAjERMFTdSNuiholo3RJQiiIgiCGB9g1kfM/tSouxofG8FgIggIgoKIruZBIhCNcaPE4CMboyYCIiKvgWEYhtdMd9fvc6qmpvtW16mqU32rq2fm1h/iVN+699xzH3XqW6fO0XMgOQXPof5zKC/ZC5f85FpBY2TM86u/BaDrcKdPeP4/wy+G6sa9cFT3LkL9mNANPx87e7wNnq87EUDTgAPPKeA2C+E55H6iFjY8H7XQhOc1FSI8p0Cf3KnqXJssaETC84Unm/D8gDierYlTw0qSWoTJWanx5BqPFDyfffk1kE4fgC7JBqGpZFoHPR2Dlf/078J5hOcInl/xCc9X3PtD2L+71rEb5T17wdi77s/5TRY8p16EzWqB5w/Z4DnuX04vpGTB8xVTR8D+pkRO8t/6nTsgHi/L2adc4TkxV2etOxHSEIdeg24Q9Fq74XGIxbWc5FFhe57jXtG45zCU9egsyGOdm3DfmYXYsny1wX0wkrUP+hKuQIW8DMMpr0yB7Qe25y0N14jnwnMUsE+nnvD0JeKLtj1MeH744w9AR3ieECE/1q8nm0CLAXQ+cYigD8roVzHP8542+Vag4LlKGJrvHFLXd2ANcO3uIKpS8NzUmop5HmT2qGvaqgYKYXtz7e4guuwF5TBn8J1G2EI/x46Nm4xifQcO8GVHF8q+pmT3Gqfs6xQ89zMD/JeJxIBH8bie5xRwI5NhEkksSXg+5V8Nrd254NeC9h653PQwtXuen/vwa8b5128dLpR/46yTjb/PflP0GJ3VUg8XnmNd9jhub5xhesOfva6wnucIz/Gwe8NS5/1Pw+AlZUEjV3ju0GcIO0lqESZnpUaJa8RTa3bhlBeMJiYv+K7QFLXWCjHvZMFzKgQT6VVNjL8seE7NX2qf8oTnOGK2F0lUXZROCwHPUUw7JKf2kOC7Uv5XKngOwDEM89G4X68Sqw0qcee2zZ8YRfr1/5IvcbjwvGmb+YVNab/cBwHqt0IZ937HKkwD3pfSi6dQJLa38jzPTACu3VI8U0dJojRQGI9nBc/NmabguVpxHUkDfu25fHTCtbuDtEW1QdWl4HkQLYPhxNjRjkgMeFSygufeU40CKAqeZ3Sn4Ln3PAq7BPchVMHzzIgoeO6tC1lhW2TtFWGvJ6xfwXMFz+3zTMHzQqy8grURie0dJTx3ilVMJfpz2wOpEVo18iQo2y+GfLPKOsVD5tot3Jnx+NQboflQveNlTvHlufWr8h1bA2HPX9RuEHjulFOBm5CYsn+4+xfXjqJmlFuuCG7fqDa4+4WsvsnaTzv2amxfve/I8BztlK5NB4UBjacAUnHI8WAvlHMKNbs44xSm44qC5wVc/wqeeytbwXNvHckCYsrz3FvXVAmuEa/guTcwpjzDled58Hkqa68ILoH/K7kPRm2pb361wDEM/dbpVI7rAaM8z3O16HeswjTg85kDEVzb4eA56vg0n4n+gsBz3DMtGJ89nhSg59ot3DmC9VtgLftaCrZx61flO7YGwp6/QeD57MsmQgqaIJZI5AwOJyFxscFzt35hRzl9c3uO4uwXXBuRu1q4+ym3flW+eDXg157LpwdcuztIW0E9z7vZ4Dm2nY4DVJ4QTVhEqu+ccQrT9lbwPMjsDHhNlPC8sbk7lPUSk3Tt3PgYpLUU/P67ZhIv6zhvZaXxvzLCtqT1WE78372fLwU9vR/KqmwxtQHg0NYtUJpKwrSXfyfIFJXn+YXzh4AOAEf2/lSQhxt7OuCUcbxMFjTqiPD8s4mToHm7c8zgkpoaOG7xIl9DxTXiFTzPqHXDGWZM/UFX+Iupj/B8VwJAT/USxub6/50OzaXVUNFb3NcadjVCaVMdTH1qnDiWRFgYt7AtmPD04duOz5kT05/YDD0bdOhdISY9/WV1tVHWnlRVhW3xXlbcByNZ+6C3ZIUr4WUY1m3bCqlk0lGgeCIB1f2O9iUs14hX8DxXrV5jZV0RpgHva7CLp1CHgudcD1YcJtdQYQ7jyN0zuXYLd+qEXT9XHlW+fWmgEPOLu265a5aqv9jgObdfONO4uuOOJ3e/487+sOvnyqPKF04Dfu25fCTi2t1B2uLCc24uIOV5bo6KgudBZmfAa6KC5y99fy7sb+4OUCUmvdy1YQ7oWhpeHbNP6BHCcw00+PdnnxXOc2Oez758LOgQhx4DbxLqweR5eroBqvr0ydHkvh3boDSZhBtefEX4LSp4bnm9OsFzFHC1z8SNAaeM42WyoFFHhOcYDqN52zYo6ddP0K11btBqcd5R48Y1+hQ8z2jSiIWO8HxCt1z1Vh4NMOlXwvkJc0+CnXENdmoiPJ/56lgDnnc5WgTYmMC4pKkOJj1znVg/E55bCU9/7ADPb33oU+jVANDZ1raC58F3Ou6Di6x9MLjE8q/0MuBrt2yGdDKZ4+Vmnet1bH9fQnGNeAXPc9XqNVbWFQqet+pOwXOP1ckFVtw9k2u3+NpMsgqFXT9XHlW+fWmgEPOLC4C5a1bB88yc5I4nd7/jzv6w6+fKo8oXTgN+7bl8JOLa3UHaUvA8o7UwbW8Fz4PMzoDXRAXPyUR5V04wejJz6RKhR9QNjQvPF0z8llHvlMW/9VU/Fpoz+kKjbLHBczskp0JJBJwarMtkQaOOCs9R2XZIzo0xzTX6FDzPTHEqkSi5CAjo/doZ5udkw9eZSQWtg3yYYcLzj0870ajyxPUf54hGvcx79DIz2bLyPGdtaUZh7oOLrH2QL2l4V3gZ8AjP8bBDcuo8JSnXiFfwPFeTXmNlXRGmAR/eTAylZgXPPdTKBXHcPZNrt3BnQdj1c+VR5duXBgoxvxQ8N+cMdy/Ca7i6444nd7/jzv6w6+fKo8oXTgN+7bl8JOLa3UHaUvA8o7UwbW8Fz4PMzoDXKHhuKs7tBqvguffkkgWNFDzP6FrB84wuqPVJgWFKdxQkV/Dce95x5yO1a8jaK7x3pfxLcB9c2lLf/GrHy4BX8DyjSSqZaKE+K/UaK0vSMA14v/OqSMq1GXjOicNL6ZYLkoIAK+6eyYVV3HkTdv1ceVT59qWBQswv7rrlQmY3z3On/AXv9D/FCB36/eXLfA2mrLjd3H55Pds7Cc8dT+5+50thWYXCrp8rjypfOA34tefykai9w3NdT4GmxXNUpMUS0Lu/+IV4UD1yxilM21vB86AjGOA6Bc9NpSl4HmDyZF0iCxopeJ5RKhdWco0+5XmepWsM24Le/29/5G8hKM9zf3pyKCVrrwgsAONC7oNLW+qbXzV4GYYKnmc0qeC531lVNOXaBDx/fOqN0Hyo3lFpTknyKO1yIRzWwwVW3D2Ta7dwZ07Y9XPlUeXblwYKMb+465a7Zqn6V408Ccr2p3MGbH1/3Db9w3OqHqyjsTwGo9b8zdek4PbL69neqVHueHL3O18dzSoUdv1ceVT5wmnAy/aWIUl7hue7Nm8BPZ2bj8kC6n0GDJChQuCMk4LnUlTeWkkkBjy23h7g+Rd7D8FR3bsII/KjF240guf/sy18guywLfhW3h5j+LdlcUhpMZjxghgnmYKV3KlEhWeJMmzLY5Ouh3SyAcqrOwvd2V93GGKJCrhp0eO+uqngeUZNYcPz2VddBXpahx7Hf08cs12N0DnZAJMXfFc4/+6934Teei0cXSWutVHdjgDEErBq8l99jXGQQsrz3NSa7LAt6M1oz/OAsfa7pHWY+OIqYai485Ea56IEzIu+DbBva47Ibyw7YnhYnbNOfKlCPdAUZd8cBmL8vHWwbd8hxyHqV9kFlk07o/U3L8MwKnje+NEHxtjs6Csm+e1WrwNoGvTr/yVfW82eL8zk5D2OEpP8UhdTgBzLe8HzT7Z+BEO/+pXWqnceMPdSuxG/adMmGBDAsPcaK6vhMA14X0ovnkKR2N4/aQml5ddzU5a6uBAO2+UCKy7s4cIqri7Crp8rjyrfvjRQiPnFXbfcNcutn1te1ohz+4XtcmXljid3v+PqIuz6ufKo8oXTgF97Lh+J2jM8z9bLu+++C0OHDjVOuSUkDWJ7c8YpTNtbeZ7nsxKY17Z1eE5BgLtb4Lk99rBMeP6LC74C3Rt0qKkQP/1Y3RUgHYvDDTb41J7hORocTiCufudOiJdU5sSwp6apgucZzXBhJdfow/LplA69Bl0vDIcBT5MNMH7x1cL5rfd8GfrqtZDoLs73UeUpE55Peo+5+/gvruC5qSuZ8Jx64YUJkrumAaav/LUwQNz5SI1uUQJm/JJg3+cAlWIC6z8sO2J0o73Bc8wV4vTS2Tr3+q3DW4fPyzCMCp6j0Y/wfKcdnu/DrPMa1PQf5GuDCQOeH+x6NKxfvx5GjBhhyICyzlqwEK4YewEMOC4zx3YewJfNWg48f+ihh2DMmDFsgO41VpZCwjTgfSm9eAopeO4xFlxgxYU9XLuFO3XCrp8rjyrfvjRQiPnFBcDcNcutn1te1ohz+4XtcmXljid3v+PqIuz6ufKo8oXTgF97Lh+J2hs8r6+vF2xv1I3dnnaD50Fsb844hWl7K3iez0pgXtvW4TnVXSpxn0x4PmrhyUbzdo/bOaNHGec7GjzHPvtN9EqNm4LnGc1wYSXX6KPKU+0iPMfj6Lv/LgwfNWbMrci1uILnpnpkwnMKYj8x5l+NtjocPMdOz3hfmIfcBLBF+WLAYWVRibadznsZhlHCc+xa+WAzSa91cBOVhgHPS/sNgWHDhsHvfvc7qKqqgpcXzIfNW7fCjLt/1Conerj85MEHof+xx0JtQwOMHDmyFbZjoUsvvRSef/551jbqNVZWZWEa8CyBoxnJYGgAACAASURBVC+s4DkAuIWFSSfNz55jiUTOaDmFjOHCHje7xSnOOwohI1QNF6pFP1X9SyArzI//FjtuSa7dHURT3LnKhczc+rnlg/TZ6Rq3fnmFmDlts2jbYf1OIWO44+m23znFi6fapXTE3U9l6TrKemSF+YmyDzLa9mvP5dNWe4PnqIts23vt2rWAtva0adNa1fT2738PTy9bBl8dNgw2btyYt+3NGacwbW8Fz/NZCcxrFTw3FeZmDFAJQxU8z0w2rsFBTVMFzzOaUfDce36phKEZHb1xhhm3/WxbiBFKRwqeZ+1CkmLYK3ie0SkXYnONeKo8t92g8By9yxFw19XVtXZabzZD4WglXQA/E507d65htA8/60wDoFcMPqm1LBr4yxYsgMqKCsPzPNvgx0IrV6406s42+r3MO79GfJgGvJeMRfa7guct9i8Fqil4bpW3O0xwYQ9lO1IAmGqXmleybNMim7eu4lhfguJXn9kHV3dtqc9RyVqI+cWF1R0RnlNJSa347HZ4boFt+1eF3PGkdE0BYKpdav5y99Oo1oHMdqmx5OpOpkxR1OXXnstHNq7dHaQtqg2qLjfPcLwGvcuzbe/0YdPujnU2wyBm297olLJmzRqhqa8NGQLPL10KJ5xi5m7I1/bmjFOYtreC50FmZ8BrZo2fAmn9IFT2FuN9NuzeBRU9e8OUx54UaqZCj5AhSYjP4Vs/kbd5+nFvXFS3led5wAkR8DIv7yF7XGVsprxnLxh71/1Ci8UGz3fcdyIk0zpc0fUJQc5nDk6HREyDvnd+HFBjmcsoSK7geUZHVIiZ2g1zQNPTcFEWwMKrMPRMSb9+MGj1K8L4bCASg1LnycH1CPNx53d/Jlx63wtmXHm7oQ5EPbPWnWjEbZ757DKhnkJ4nltfzjx/5q1C2/euuNuY8//0p9fzmvMkYJ7xIkA6CRMGiXuC0Vjl0QCTxBwSeQlhvzgqeE7EWg+7z4XwPN/+6SYjrEpDl545Q1Uaj8GAXqLNwTXio4Tnuq7Dp/Hjcvp1fOoz0DQNrphxD5x++ulw2223GV4v37/uOnh61k9a4TmeQ4P943feMepAeI4PA2joZ8NyJ8Pfbd77NeLDNOClrsvwK1Pw3MN5hII3lM3HhT1cMCirPLee8KeivBZkPUfJk6j91lQIXXPnakeF5042Nnef4o4nV9eyynPraUsrkHsPaUt948jq157j1Gkvy7W7g7QlG5679QHt6Gzb+/bbbxe+4ETbe+ipp8Lf33uvNVxivrY3Z5zCtL0VPA8yOwNeg4mLdL0Rqvr0zanBCW6y4TkTDnBvXFS3FTwPOCECXkaNGxVXmXo5U2zwHEOVpNI6XFU2V9DM043XQjym5YQwCaI+Bc+9tUYlN9294aeg6Sn4dl19TiUlNTVw3OJFwnlp8NwjwaRveE7UM+tPphd5VPAc4eDKs24TdHfX8jshEYvBuX/+g/eAuZQg4fmNK0x4fsJD4tVWLHLbi9a8hLBfHBU8Z75cltXnQsDzbQjPdYD9XUV43pzUoSShwYl9K4TucI34qOB58zYz1vqmmJj7ATszIL3FSFT+1x1NrcmJ0IP8w3Vvwcwpk1vh+bx58+DBBx+EN1s8YhCeX3vttYY+0FvdOhCwv9MC2P2MvV8jPkwD3o+cRVRGwXMFz4toOsoRRdZzlBxp2ncthdC1gufmHHIDxrJe8nHHkwuxZZXn1tOWVqGC5+Zo+bXn8hlbrt0dpK1CwvPsxKBoeyMsRycW60Db+/7/+i/402uvtcLzfG1vzjiFaXsreB5kdga8BuE5Ht9fLno4UtWx4TlTLu6Ni6pewXOm4vMsTo0bBcqoeVSM8BxVY4/zTcX/DqJGBc+9tSZr35EGzwmRqdAp1Hmq59R6KqTnuT1nw+unn2OIGyo8B4AJPx0rqoUA296zhlEiSniOYtpfDITc50LBc+xav+MHCAPx8Y4G4++2Cs9h59/M/vTJhGBp7aDDb5iAqG9pCVxy4QUCPEdIvqolprkFzzFMS3acc/Q8x3IDBog6pGa2XyM+TAOeseqKoaiC5wqeF8M8lCqDrOcoqUK108oKoWsFz83Jo+B5ZhEpeN5ON5Ssbvm15/LRRHuD59m6QNsbbecxY8a0nkZ4/vhjj8HqX/xCgOf52N6ccQrT9lbwPJ+VwLxWwXNTYSrmOXPi2IoreB5cfwqee+tOwXNTRwqee8+VQCUUPDfUJjNhKHqe49HR4Tl6tYz6+ulG3HMr5rmT5zl+OlpdXS14nit4Hmg1cy5S8FzBc858aRNlCwF024QiCiAkpWvr2dpvoko3Uak2ZMXV5sJ5bnlZw6DguYLnqIGc8JeyJliR1cOBskFFp+D5zo2fgg46Rg91PLRYAnr3z/360qkw1cauzVtAT5sJybMPXU+BpsVb4bZX36j60fZGu3rEiBGtVTh5nudre3PGScFzr9Hk/R6JAY8iKnhuDpSC57wJay+t4Hlw/Sl47q07Bc9NHSl47j1XApVQ8NxQm4LnPmaPzbscvVv27NljXnhgN/ToXgW33fffgLEWMTSLk+c5flp6/vnnCzHPEZSjEZ8d81yFbfExHvkVicT25trd+XUxc3UQqCorHAK3HqrPXHAXpM+y9B1VPR2xz8Wma1zjyJ2GbX5fEC1I0kO3uN1Wffb+N5bHYNSalq+kPJQja02FPQYKnmc0rDzPw55t0dfPgbJBpaXA846N6PxiQmz7IQtuY2JQqy57G37gvGV7N+2pNS6vOeFEI0RLtu1t9zxH2/u84cOFmOf52t6ccVLwPOhMdb4uEgMeReEa8bIgFtcw5t7cMWxLz4YU1HXrJTT14TGdAS2a615YLZwPCs+3x3SoSYuv5ka+Mhh0LQ69vnST0MaeT38GWkyDGU8/ndfsuWD+EOP61dd8INRDnX/p0Xdhf90RxzbLqzvBJTcPzUsevFgWPB+x4GsQ01Ow8ItmQaZpR8VgWyIOekoczz56LfRJ6bDkWn9GIlcXVz5xEuxKaJCoFt+wJuu2QO+kDkun+2vXTcG/P+NroEMKZk4V+zbryVrQIA7/vO4vvsaH+8BEladgPhWqhgq140ton4Vk7TtG2JZ0EgZN6Ca0vGHJAYBYAga9/ZFPiZyLyQzbkmreB/ZEu01bP4MuySRM+sWrOQJQbT/aEprrZltoLgyp1LjnMJT16CzUVfvJY0Yc+TUXirr4wbwkxHSAb/4pPx25xjx3CNsyZf7JsB2SxvjYj5pYJ1gwaX1eY2ZcXAB47qRrqP8cykv2wiU/MeNdtx6EPFNemQLbD2x37G9NtxpYcOEC4Tdqv/ti7yE4mAC4d9Z5Qvkg8Hznp58CBjdPdOot1JU8sgsAdOjT7bBwvimlQzMkoFu/E4Xz3M9Ho4p5boRtSTVDfeMhuHTq9+HB//d9GPrVr5h9STXD2j+uh9t//HPjc1EMwYJxF//3t7+Be//9+62e51h04MCB8JsXXoDKigrDwwb/xvjmVVVVrXpR8Dz/pe1RQyS2N9fulqUFro2A7XKhNzdeLde2l1WeW4+sMShEPUHGuRBytcc2vDzP7SFRg0BPN3iOOs3XE5e7FrjlZY27gucZTQaZR7LGIex6uPeQsOWJqn4OlA0qozs8B+g7MDdkIEJvPNBu9XOQ3u3Meqy26uvrDUcTdE4ZOnQoWPX/6YsdBjjPtr3//Oc/G+Wyj/7HHQerX34ZTjgFzT/TFs/H9uaMk4LnfmaM/zKRGPAoHteIlwWxKNXIMvpWXvA16NGQgliqWmjqw6M6A8QArnsxf3g+ZdFpsD2dC6VHrB4CoMWg16AbhLZrNzwOsbgGM5cu8T8zHEpy4TkFySyYM+G+M/OSBy+WBc+xb3FIwZNfpAWZftAnAdsTCditieOpxWuhdzL3RQLVIa4uUJ5dCYB+FSI839aA8Nx/u24KNuPzIzwXk/bOenIHAMRh+DrxJYmstdMh4fkZJ5vw/IpOgho3PHPEhOfr/prXWpAFz6lEu/t2bIMuyeac/QuF5sJzCqzWbpgDoKdh7QXivLt1XhLQByHfBzUuPB+16FQDnteIWwJYLy5XTc5vzIwBDxmeU7purG2EspK9vuO844uq7Y3boaasRpin1rlVo1cJ56n9rr72EBxIAPxgjgR4jt4jAFBig+fNR3aB5gDP9VQTNEMJlPYzXwJbR5uB57s/AUg1wcix18DtN0yBEefY7p/xUhg5/ntGAlAr1iKGbHl5wXwBnmMiox/9x3/AV4cMgdqGBkDvl+zPS9FDBuOdZycQ9dqc/BrxYRrwXjIW2e+R2N5cu1uWzoLY1wqey9J+4eoJMs6Fk659taTgeeHGU8HzjK4VPC/cvIuqJb/2XD7ytUV4jrYyQnLLXs7uA/6WbXvj32vWrBFU9Pbvfw9z5s6Fbw4fDhs3bszb9uaMU5i2t4p5ns9KYF7LNeLbCjynvGF/PvoCQ0My4Dmlakqnst7WB4HnKKsdklMQizmFjOIy4TnWZ/eqp2SidEGVJ8HdnW8Zl9h1xNV1EN1RyW2p81Qb3AemDgnPL7jQUN+g1a8IaqS87bnjKQueU/OU2r9QTi485+5fsox1Njx/cZQhqh0Mj1p4snm+DcBzcj+6cYW57/hMkkomVCZ0ROn6xzeYXy7IgudYl90LZeemjUYbfQYMFLrftM18KdNm4TkAIPhGr/C9e/c6Di0a7+jtYnmRXz12LMy8ZjJ87YJvCeXdPHjw4WDcuHGGZ43fw68RH6YB71fWIimn4LmKeV4kU1GeGFxbUF7LHa8mBc8LN+YKnmd0LcseL9zo+W9JeZ6buvJrz/nXbG7JtgbPnWzv7D7YbW/82/JGt3rv5TnPtb054xSm7a3geT4rgXmtguemwoKEbaFUreB5RjMUvKFewnBhOLe8gueZsVHwPKMLBc8zuqD2L1nGuoLnWftjO4Dnn4wdB+naWoiXlAi3xFSzGXrLfl5Pml9raQnx6490k3k+Vprf+VSypd2EKA/WXVJTA8ctXiTIueeLRuPvHkeV+baeMBQLfjaq6+hzn3vg75bXOf7a8NHfYMbd98BTK8yXJdZBGfH4gIB1YPxGzuHXiA/TgOfIWwRlFTwPCM+phIhUTOfZl02EFDRBLCGG38LwZPGSSt9fZHKdUDoiSI6qz49PvRGaD9WTy7qkSxVc/+RPi2DZyxMhCDyn4pSjVE6xylXYFnO8ZMJz7n7E3e+oGca1ozsiSI6yz1QSXmptyttJcmvysuc+mzgJmrc7h3H0Kxdld1P2O9Zr/oa2rzOutdv8sYpy6HXfj6B8sPi1qRfEpmxru+2dDc/ttjfWgQA9+wtOt3aD2N5e45TdjzBtbwXP/c56CeUUPDeVqOB5fpNJeZ4H15/yPPfWnawvXmQlZ6UkVp7n3mOp4HlGR0vaATz/+/DzIL1rF5QcdZQw+MUGz5u3bYOSfv1yvjoJAs/RwMY4iegVfscddwig3GkFIDz/9PPP4ZOGRqEsZcRjIiQuOMd2/RrxYRrw3jtAUZVQ8DwPeO6UEJGCDPisoesHDFBuPzhQVcFz7/UTFTzHdq2XIXYpuS9JvHtZHCW48NwNzlEvnhQ8N8daJjzn7kdUAlhqv6Nmp4Ln3us2SniObTu93AqS6Ne7p+4lvOw5fJ617NqgbQWH504tmkA9G56jfPGePaDm53OkwHMn25vynrckxGswDKLl0OIGz4PY3l7jlK2pMG1vBc+DroIA1wWB5w27d0FFTzE5mHVuymNPBpAic4kso4+KDTtqacpo7IYXxbANbQmevzxyMHTf7xyHe285wMVrxIR+XG/rIAPoBs+dEuVh8tRUch9U9e4jNHdg2xbo3JyE8w7lJgd08hqkdIGVOpXn6oLybP/Z6PMhDQlIOCQx7FpSClct+x/fapQFzx+5/Eojcd8vh14jtH3Ru/MBNA1ueXapcL6jep47GRsUWPM9iC0FZcJzp3Wz+5PZoOlpmPmc6MGKzRcibAvenM8eL3oGG12vPBpg0q98qYuK575/dyPEYt3gpqXzhXqGLToPkikdquruEs536n6j4ffwii1xsi8hbIWMpKQxAKg6Rvjl1oc+dcw7gAZ2dQNA56PFXAi/7nkMpPWDUNFLTEiLlZb37AVj77pfqP+xK6+BdPoAlPe0eT3v+xzKS5Mw9sm1QnmM/44x+1ftx+jzmWNUuXlfs59fsuGHRiz/CbNHC+Vnj58Caf0AdGkSw47ooEPndBqufinTrpdhiPAcjy+/JiaxLbawLdSLsyDwHPuLHi5Tp04FTF6EB8ZfzI61mK1whOfoqbOzr7h2yvea49jXZ+Ilr7ntNVbW9WEa8F4yFtnvCp7nAc9xLO0JEanx5T5rUPUoeO69gmQ9R3m3JJZwGxvuuHHbjqo8F567ySkrvwBXF9yx4ZbnykOVlw3Po9i/FDz3ng1Rw3OU0J7biTtu3r30LuFlz8n4YjpI2BZKcicojTIioJcFz51s7+FnngFXj70UJsz8vrdSASCIx7tbxV7jlH1tmLZ3W4TnVQCAmQzrAID+Zo3WfiQGPIrDNWhX3PtD2L+71rEnTnDA10zOKiTL6JvyyhTYfiD3c5b2AM8pcHN46xaoq8jd9LnAmDtmWJ4aNypR3u5NT4OmNUJ5dWehOUyI2LkpCRccFKWg4CalC6o8VxcUPJ8z+kJoSiSgqy2J4cEYGOemr/y1bzVKg+fjrzA+pPqVDZ5/+935BmS8ZdkzgkwdEZ67febm9LLF9yC2FJQFz92SeWp6Cm5evjxHtELAc8OotMPzfZ8DVB4DMON9X+qyvNOq+ogvzup37oB4vAxmPrtMqOdrC4ZDMq1Dz/q7hfMIz/Hwmx/BTTiMn47wvKbCDs+3YCARGL7u/4TLqZd2v6yuhrTeCFV9xOS/1MvlWZePh1TKofyubVDRKQVTFv9WaNeI847w/IAIYUl4/o/bTHhui6n+6GWXGfC8W3OTUP/BRCInIa2XYdhR4bmluLVr1xoJiRCmo4cLxjq3e42b8BxgZ99SQd8KnvvaMsIsFIntzbW7ZSkgiH1NQQNuH7jlqT5zwV2QPsvSd1T1RNVnBc8zIx5kvit47r5iFDzP6CcKmFuo/UzBc1PTXrZ3R4Xndtt7xbJnYfPnWx1tb6c5q+A5byVPQ7+yFriNGaweYIDuAQDwPAAMszWJmZzeyTqH4HwqOiXxRINIDHiUMcgNntk3VvGwjT6Enni0Zc9z7o2FC4xZA9ZSmPtAQ8lEjQ91k6B0QZXn6sINnjvNoyfG/KuhkajgObZth+SPjL/CkEnB8yAzm3eNLHhOtYrQE48o4DmZwHY23r6ABc+x+MylS4RuIkg2zjvAczz/lymvCeW5+Q7cRpJKPvr66ecYl5375z/4avvRy8w+3LxcfAFAhR2i+rxgoplY0hGeOyRJpRKJUmFhqHnklJDWy4Dv6PA8e2Kg5/mKFStyEonu//Bjo1j5V04U5tGOTZuMv5XnOW+flVg6Ets7KrubCqvhFlJDJjynwrxQ48mJ/0zVwX2mcIvbzQkvE2SOymqb2+cgsjpdU4zw3CsOu1M/OOMs2/PcKWTE+v6mjWX/ykMWQKX6QOkuqhA8VEgN1A035A13D3YL80KtH6d5xB0z7vO+W1gglNNpT5W1/mXFC+f2WZb8WE+Ubdv7gbZ38sB+OKo0N38Plt1+3Q3GJejVnX2k4wCVJ4jxxSkdyfY81/UUaFrmy9j6KeaX8FXzF0CfgccLYsiC2NgHzCn0P2vWCrb3vn98ADHzo1zhOFDa1fi770BEu/kfXs9I2S20Nc/zMQAwEuPGt3SCguF2LY4AgEtbTiJ8t3vF4+8WkEev83cDDkMkBjzKyr2BBOyf78vCNvoUPM8MBQWSfQ9WVkEFz01lKHgeZPZ4XyMr5rl3S/mVUPDcW3/kHq/geavyFDzPzCPKuKfO127ZbFzc69j+wmSUFbbFKSGR1RDGVRw2bFhOIlEFz733hQAl8v3iE5uMxPaOyu4OAmdlwnNU+Gmb/X2hxIVh1PzhPlMEecEQYO46XiKrbW6fZcqPddlfjOM57jOCTJmoOOxObXDBsEx4TsHHd/qfYnxVWmh4Ts1H1BvnBYOssQwChmWND5VglOobNY/ChudBXjDIGh9Z8cKjBNhRtm0fBwOeN+6HYxMlkBIjNRpFneB5PAVGWXtyTmqMZcLzXZu3gJ5OCk2Z8FyHyvkLcmA1F55Ttjf24f0PP4RzxowTbG88b+kjW6iDpV2N/VTBc++Vv7EFnGcHEEWP8dsBQAwq6lwXQvI1BDzHK/zU4SZlJAY8ChSVES/L0PUeerGEFW6jsm8/cYHv3AlarAJ6Dbo+p8raTx4DI1SCQ5xhp/YpncoyHrmbO9fbmqvTIIZxVJ7nT017CfRUCs75fJ7QTSpW8baGLXCocxLuf+J3QvlHx40FXYtDry/dJJzf3TJXvr13X44aqbAglEfvU1c8Ac2l3aGLLa4yVlxe3QkuuRk/fMkc1rw7fcsHwvk/H2u+gbYb3h0xbEuQuc25plDw/KI6MVY1yohhm2orAGZOFUOGTFxj/p1vbNr27HluhWHRU72E4a5u3AN1ZT2KxvP8P64bAZ0PxaFfhRhrvbElV8SJ25oF+Tee9D2MOgOTF5pfLFiHtVesHSvuUxjWDMNRZd8fjx9+IfQddAL0qqyAeCIB1f2OFuqiPM8tr+r93UXXDz3ZBAldhwG9Rc+YnRs/BYy5rtlcFHQdvRa0HK+VKOA5xjd/4IEHjM9DnQ407ufOnWuEccEDQ7pgKJfD23bAa2+9CVdMvUZIGNoBPM+L+YtPHKJIbO9is7vd7nGy4DnX/uWGsKD6wAXJ3PIc+8CrrKy2ZdXjJa/992L0POfOO1nlZa5xqi4uiJW1RrjzIsrysuA5V9dUu9x6uM/7QULbyBofrqxUu7LqCdKvKNu2y2vB86M6lTjCcCeHEK/kmfY2ZMJzJ32jjKnmZqicPz8veO5me2MfXnplNTz9698ItveHf3zDEOmNDz6EcePGtdreOza2fPGpPM9dlwh6pyBpwFAtpsbMA59u8G/LG92tEgXPg+xCAa4J2+ibd/H50BRP5ADJxvojoMXKofsxV+ZIXbthDoCezhs+cY0yWTcWBc8zmlw45QWAZBLO2b5QUC8Vq7hu1zY43DkJdy4SE8waxqwWg16DzM+mrAPnCiZ0vKgOP0TJHG4JKSkoOe/q56C5tBoqeovJBK1EkhPuO1Now8oGf5oNnq8/doij14qC5wE2KI9LooTnO/dtgd0VAHddpuA5d2St3Al7uonwHOs5UNUDRq0REwBTIWPCDtty36QLofPhBFT3tr383bENujQl4aQvDgld3/iV7xnfy01e5A+eX7z4CDTb7o8WPO/RrSvEEokcj24uPG9KNUGJDnBCr5MEWU2DVvzkEwvgZ6AA8RzDOwp4bofj9nk2cuRIA6wPHWq+2OzevTs8//zz8I1+R8P7H/4Nzrl0jPFZaVUVmqUA7RyeF/sXnzgECp57bJYKnpsKkmW/u6lb1vOPrHq491EFzzMaU/CcO3vCKa/geUavXHDPHRFZ4FlWPVz5sXyUbdvlVfA8oxE32xufBS6eMhUemfO4YHsvfuQhGH7WmfDJoSbji1DL9lbw3N/KsMC3HZ5jDHN8gsFwLl6HGzzHpyQrSSjGREdvdm7S0EgMeOy0zBu8lxL9/B620Rfk5sHVkfI89x7pqDzPDXgOAJMXfFcQkoJeCKvwcITnEry5sW43eI6/T3tqnCArpTtq3nHnIxXeYOs9XzbkOPruvwvyUPGWvWeB/xIqbIupq0fHt8Q8X5abMJQaH1n7V3v2POfeF6KE5077ERWObOEkc55Q8NzPZ+BWPL/K0oRRlz0cihc8t8fz/ketmTzTGZ7nfkpJGbpRwHOMab5+/Xq44447BA9y7A/+hgb6tGnobG0e6HU+YMAAwLAtHRCeF/sXnzhEkdje3D3Z/51SfkkFz02dKnjuPbcUPM/oSOYaV57n3nOPKqHgeUYzXDuXq3VZ4FlWPVz5sXyUbdvlVfA8oxE323vy2EvhlJO+AjfdfY9ge/c6ctD4W8HzICsBAL1fEJR3t0HtuRh+zyEJqFMrFDxHcI51WDEgsBzWi6Cec0RiwKOAMm/wnA5zb3SyDNcgNw+ujriwkqs37uauPM8zGlbwPKML5XnOXXne5UP3PFfwvHUQZCYM5d4XFDzPrIWOBs8xLAt6wVihWSxNoHFveZzbdwqE53f99yNQ2rOHEPKlHXuet4UvPnGYIrG9uTal950nvBLtGZ47xcKmYhW7PYNQseTTSTH+a/YoxbVSmLFssTBw1rywx4WnEkZG9RzllkhS07o5xrW3+uAU8z7MJIbcZ0dZ5YOscTe9xrRuMMyWL4DKC8DdDcJ2WuPKI7N8e4bnTsll3eYEtZdTseTf6/91SEGT43A47V9UXHjcB+NQCjOWi/sdNc5cxsGdL26x898/7uuQ1JqMryyzD2svt5/HMmHF/29r8NwpQSfGZde1GFTOnyskEkW9WclF+wzwTtyJ9jVle195wflwyle+khPaxnKw+c9FS4yhtMItKs9zfyuGgucI1HHE0Fvc66DgudN1GIcek4yuJCrFb+rF7+oB0KVz+XvvvQennGJm1S7UEeQGH6ZsYd/EuZAE+8rVkYLn3jNEeZ5ndKQ8z73ni/I8N3WkPM8zc0XB84wulOd5RhdhJwz13q3EEphEdMnjP4O/fPQhvLhqVWvIFizVjuF5W/jiE4dAwXOPCd1e4Tk3eaqXV7UTiEfggikc8KEw+9D1A4CQ2f7ljxV6zw5JqYSR1NCF/RyF9VP9TeilcMpnb+eIRsFzWQCYq4uwy3OfG1EeN70ifDx1c65eZbx4CHu+cO+ZMsu3V3juBoCpOeEGpZ1APK5Za6/yu39h+XhJpTCEuFc47XfUOIcNz7F+p/6iPFaf7X2g4Dk3wTBnbrc1eO6UoDMDz+fn5DNC/0kCwwAAIABJREFUXWixBPTuL+Zw4ugIy1Jfob7xwgp48Te/hQ8++9wIn9gaLlHFPPel4jCNeCcBML76CpdY6j8CgLudLlTw3DQe0ik9J3Fn7YbHIRbXHLO4+5oFLYWoTfmziZOgeft2x6owHrZTpnOqXQXPvUckCDy34oZn145JEusqAM5Z95HQKBV6ZOHVzxlPMpNPfUAo/+hbg423o/YY5js3Pga6loJbn8ElnTm4Y0zJgzW2zsnxnYQ2Zq0bBmn9IFTaYp7vrzsMsUQF3LTocWeZzhR18ZO3Bhvlvm87P+tP5vmZzy7zpbsrnzgJdiU0SFSLN7rtjduhpqwGVo1e5T3wAUsgPG/YvQsqevYWarDOTXnsyYA1y70MPc/R1+zh244XKr71oU8B/RjOts1TbusWPH9paG6qjikf3geHmhOQqD5OqLZ+5w7QtLK8czZ0xLAtK+79IezfXZszTGQiYcyFAAA3LxfXFDV/cWzi8bKcNbhg4reMeqYs/q3QNhVGyoLnq66MC+X/7ZcTjb+n2kI/cT4DDxy2pcVArUqJ3pfNqSZIxQGqThAThlLeILLCtvzj/JHG3t9jiRi/PpVMQzwRgx5HibkluGvTrTx6nm/6/HM4Z9ylsPKV1+GY48z9oergbuNfe2iboG1bYzVo0CDXKt5//3049dRTsQz+5/2g7blc1xa++ETxFTz3GPz2Cs+5c94LnmN9M5eaHm7WwdUdN/Qe1YewYWiQZIhBruGOkVN5WZ7kXF0HhedO80iGHtzqCHu+hC1/kL5xx4freCdrvnPbddMFF0pzXzzI2r+4cnLnl5tOuePG3V84srZFeI79Kx+cse3d2AdHF25lKXhuna/t1NUIqfjOO+8YYRSV57k/zVufj6KH+btZl8hIGIqgfKrNyxzPYRgXjH3udCjPc5dxm33VVaCndehx/PeEUns+/RloMQ1mPP20v1EnSlGbJi5wJziL1fyiutqoze4lQgnCBavcDnFvLO0hbAv1cgMh1t5ygIvX+ITnk54z/IAmn/pjQe2z3jwBdC0OPb90k3B+14Y5oGtpuMUGmLlj7AXP0TvpbBs8R+it641Q1Uf8UKV+507jzb79Qa1VJr/wfN2JgK+C/cJz9PTdlQDoV5H7lrimWw0suHABdyr7Lk9BTONG3bMXjL3rft91hVnwzTMGA6Y3fMAGz+946FNArHlWiPD88r88AAeaS6Cyj5hMct+uRohpXWHmMn/jQ83tjgjPKehNJRKmcidQ83f/7kaIxbrBTUvnC9NSFjy/5JcTDc/HqOA5tl1pg+d6UxMk46KBjZ0PHZ6fNxJ0HaDnUhGeY9uxhAbd+3STujWsXbsWMIGoruvQ+NHHRtsnjfoXuODbF8Mt/+9eo60OCM+j+uIT1V00tjcX3EidmMzKuACYqp4LGah2ufVwyweRnwtcuJCJO1/ChqHc/qJOg1zDnKqOxbnjL6s8d8zcdCRDD251hD1fwpY/SN+448OF2LLmO7ddN11w2YGC5xltyrofcdaCguf+tGWH55bt3fDhX40KEOYPHDjQyFOEoVsUPPenVyz1TgvMXpt1CSYzQsBNhVfJrp0K24J1YIgWC8pboB6TkGa35SVpJN4vKBT3BuLVkXx/Dzs8gxs8R9kHrX4lpwuPXG56E9oBKtVXLljl6ox7A2wP8JzSERW6gfQ8N+A5JtATk3A+MdpMDDr9RXH8qbHnjrEXPMe27d7z3Da4D2SzWuY1B56jnKuv+YA7ZTtM+Q1fN735B71te5lDnOcqhhozrIcCrk/i1xYOAJW7f3VUeI56sn/ZQHmAU/Cc0vWSG80vWib8dKxQhA/PRxnX3/Ci+PUHNfaF9DzvO1CMZ0h5iRQCnqOOTngV/SbCPzBZKBrqGKfx8D/+YTTY5ctfNj4fRSMeDxW2xXUcOOESvb74xIaK5qvPYrO73Uaho8FzKhyCW9xxLijD8eeGQ8AXkfZwLlZMYnscXm7cdm4ICG5/cX5xr+HKRM1hLgyn4s5T8Z/dQvCgTH6drtx0FPbdSsFzbw1zITZ3vlMScNt1C0dFxR7nguEg+5dTOBdLHns4IiqUEyUnNwRXITzPuTI5jX8QeL5z46egg54TIgWdN5wOvK8AaNBnoPi1dBDA7GTbR+F5btnej8y80egdwnNN01pt7yB9c9sh/H7xiXWE+dWnOZZyj2kAgEAbQTce+DSHT1DZiT3RUH8QAM63JRbF8pQRj+Ux/kN9S723tbSDbXEOBc9btKXgufe0UfA8oyMFzzO6UPDce+2EXULBc28Nkw9qxMucry0YblT6lymvCZUXIuY5dT9S8DwzFJgwNL1rF5QcdZQwPqnmZuPveEmJcD7ddMT4O1YqhqmiyrPrSba0mxDbbf7iC4j3qSkYPMc+YmJRNOSba2vhnb/+Fb41ejRMm4bmqHm0Y3hebF98orqV57n39pxToqPBcyoerlvccS4omz1+IqR0/4n4KKCLMqUdYgzjIDolsXOTk5N8kNtflId7DTUO3BjpQeC504sKKv4zlo05JEMM8oKMK2uA5ex4iYLn3prkQmzufKckCNKuUz4CrJ+KPc6F59z9i0okivI4JcJFWZ3itrvJyUn+XAh4juPPkclp/P3Ac3vEBNNeRlLuF6WaZe12OmV3u60UJ9veks/JMdV71fkr4QTt0fb+aN2bRgX/99nnxleglu2t4Lk/vVql8IkFYTl6i+O/2dAby6ArEAbOxdcvFgwfCgDooorwHP8fvcnRy9wOzPF6rBOvo8K1uEmr4HmLdhQ8957UCp5ndKTgeUYXCp57r52wSyh47q1hBc8zOmoPnuefjB0H6dpa38Z3VPA8nUxDrHdfGLRcjEvsPWPzL2F5nnc+4QShsnYMz7Gfxf7FJ8oYie0dBKzlPwuD1dAR4Tlqyu+XgFhWFiijRogbPoFbD/eZIkh/uddwZeL2mSrP/eJT1vpwm0fBVq7/qxQ899ZVEIiNtfrNg0BJIKtdrJ/zxaHM+ShrjXAhP3fPCbKXc9cO5wWZFzx3CmfLdjYhnFxkwXPUaUlNDRy3eJH3IgtYwivmeXYMdmxCwfOAii7CyyIx4N0206h0FHZiwDYVtmXRtwH2bc0ZijeWHTHeK9qNe+ybU+zsJRt+iEFdYcLs0UJdT017CfRUCs75HEP0iwd3s+PcELAlbsJQaj5ScbjveORziMcScNb/vidcunDScuPvxSf1Ec5f9paZQPS6F1cL59lhWy4fD6nUAYjHxfi5ehogAaVw4/LFOV3hGhbcBynLYFo7dp/Q9ogVZkb0UzebccGso2dDCvZUlMI/r/uLcF6mp29U+0vY7SI8b24EKDlajAvfvHULlJTlhnPhymOEbdF1mHnGxzmXLljf01jn9iSTMsO24L5zz3d/KrT9zMHpkIhp0PfOXJmc+icLnv9w+vnQ5XDCMQY/Nw4+tQYL4Xne2NwdynqJySr3bHwMNEjBjGfFRMV0wlB5YVvQs293RSbxaJdpN0HV0NOhuqwb6BpAn4EiAN65aZMxzH0GiOFZuIk+XcO2aAAlncRkwckju0HX0w6fp6ZAiyWgz/HiZ6h7vmg05AwzMSi1njsoPC/2Lz5xuCKxvRU8977zcUEJVSPXNuXaY9huEEjjrYFMCa7Nx9UFF1QH6S/3Gq5M3D5T5RU89553nLkbdVlZa0cWxObWwy3vtt8peG7OxkJ5nmNb9pcnnPuRFzx3Wltcu5trv7utZwpih70HKHhuatjvtwZhj0ch64/EgMcOFpsRH3ZiwDYFz2efArDvc4DKY4S5+Idl5mfvTvDcOG9LPLnkH7eZ8NwWV3fhlBcAkkk4Z/tCof4gn9lwbgjYmCx4fuUTJ8GuhAaJahFW3vrwZkhocTj7TREMhw3PH58wEZqTR4xEnNkHAnX8PO3m5cty9hXuwxrXGHSD5yjlKTZ4DpCC3RWlcOlqBc+5N4HPzvsKNDfqAFW2pKr1CM81OO7VD7lVCuVbY55/Q4ypjoUWvNvXhOdP/Uq4RhY8//0ZQ4yXdj+ywfOnG6+FeEyDo+/+u6++yYLnCJI7H05AdW8xQWrD7l1Q0bN3TpxyN+GigucvfX8u7G/uDlAl7vG1G+ZATEvnJPMNG57/ZuRg6LYfdwBneI467D2osPB858aNGMERSjqL8Lz58C78EBk0LSMrymd8hKrFoc/xxwlDruB5Rh1hxl20rbNi/uITRY3E9i42uzvI3sjtA9dGVPA8Mypcm48aT1kAm1sPysO9RsFzX+aUlEJc71kpjRaoEllrRxbE5tbDLa/gufe+qeB5RkcKnue/EbXnmOf5ayfcGiIx4LFLXAM4XDWEX3ubg+eokhnvC4qhEvdR56nQAAY8x+SZC74r1B8kwQP3wUgWPN96z5cN2e3g7o2zTjbOU/B88qLLhD7/fPQFxt/5ep4DvvBwGDO3RIKFguf2xEWPXmbq4Oblpje+dRQixnT4Kz2iFojxp+YFV0q3dUZ5ScuC59SaotYg1TeZ8BzbuHORmOSX0oObrqOC59S8oBLDhg3PneSxDMMKSBsqLDQ837XBTLZpb5c6T0FyBc8zK6CA8Jy7xRW6fCS2d1uyu7n2CZUojUpiSQ24W7tOyTaxnjiUgj35nFuiT6e2vdqNl5hf7GUfVN+44It7z8R55BSfG+txihlM3XupmMRUgkEuCEd5uNdw4bmseSfT89wpSSLqwikevZuOwt4UcWzyjc8ctoxB6+fCcypRraxY+7jW0tBkvOR3OuzJf6k1yN0rsDy1X1B94z7XB93LnfZUpzXidS86bbPISqi9vxjhudO86zRtJlSd+nXo17nESHrp55DpeY73lq5NBx2bTccBKk8QZYrS8zyeAkiJfjRgnVNhW/zMnLZZJhID3tpM8V9ORvC2qWJTagXPM6On4HlGFwqeZ3Sh4HkeO5yC557KU/A8S0XEfFHwPKMjBc89l1RrAb8eMAqet6osEtu7PcNzCsShxilo6DTDKVudgk8WULcDFLdEn9x2U9AEdrhl1cGBPf5XtFnSCwDa+8yFYTgfnUAvlWCQC8Ld+sCF5G5fJFCJEjnzThY8d0uSiLDQHs7BTUfc+cItT7144K5ZbruFKO+1duzsA+eXU/JclNXphRTVB7d2nV7+IahEoG7fX6g1yG0Xy1OJh6m+hQ3PuWvEDZ47vUSk9v5ihOdO8w7heeXQr0NN15IcUE2Nv0x4jm10c4DnFJSOCp7v+8cHEEs5a8QJ8quY54XYeQvTRiQGfPZmquD5hcZIO2UEpuJeU1ODa3yRU4wAK8rzPKMx5Xme0QU3YajyPA9hc1fw3FOpCp5nqUjB81ZlUAatgueeS6q1gILn/nXVUjIS27u9w3PUrRMc5IwOF6pyIRklC7ddtz6F7XnOhdhcHXGfZWRCKe44yAJ9svocJOyQrD5w1ll7L8ud88W2Zrn3Crc5xJ1f3PKy9lTuvsZ99pW5T3FDHnH65teey9a7LHhOhXPBtrgxxottj1HwvNhGJLg8kRjwCp5nBswtVIlMeJ5O6dBr0PU5M6W8uhNccvNQ8XwAeO6U0G/0X5ZCOnUAKnqVC/U37GqEWKwbzHh2vnC+IGFbZrwIkE7ChEH3C23Ped1M5HnDi2IoBmppPXfTz6EpVSHE6MWyzQc1KGmqg0nPXCdcasU89xu2xbopf6euTqjnl9XVhpdAzksnl7AtuhaDXoNuyOnKng3zQU/vh4o+4ufA9Tt3gKaVwffPFONbz/rTYKOOmc+K8dNdDQgtBk09pghtl+5ZAJqecgzbkorXQTxVLZTX4rXQOwmw+poPgu907f1KIk9Ba+4CWwgmrjpkhm0ZP28dbNt3KEeEi98xEwjb5zaGbUnqKXj41v7CNcm6LdA7qcPS6X/z1R0uPH/+gq8BJrE9puIoof7flgGkYwA3rYwwbMupOwWZHn3LXJtOuQ2clDNh7kmwM67BTq2X8POE3/U1Psf/d9sa54ZtmTV+CqT1g1DZW0xI2rq32HMwOMzfDYNvADhqGFR0MpMg76tqFmQt39fF+LsqlRTO74snjD2y70AxkShleHvB8309xDwSlXvMD5/t4VwwPEsqmYZ4IibIY52LKmGo3twMWkmJIFN9PGH8bdeRr4XkUMjvw5byPG9VXiS2NxeIBJ0PMq7z+lTefp8oNuDC1TUX2rrpuNhAHBckygLJqCMONMLy3HGQNe9k9VnBcxm7T/51cOd8sa1Z7v6l4HlmznATpAbZpxQ8z+g7Ks9z7i6h4DlXY8VbPhIDHtXB3ZiLV4X+JIsybMvsq64CPa1Dj+O/JwjbuOcwlPXoDBPuO1PsBBOeUwn9Lnl3LqTTB6CqT1/xwX3nDohpZTmgpyDw/MYVJjw/4SFBJi48X3DDSjiSrISu8T1CPQeSPaC0qQ6mPjVOOB8EniOyucgGz39RbYJlv/B81rixoGtx6Pmlm3ImKiYH1NMNOeOzD19uaF1h5hnvCNfMWneikZDULzx/dNw4QHDf1HOqUE+n3fNA09Mw87kVwvkznhoDh9KiPrFAH70W+qR0WHKtP0jqb0W2s1KLvg2wb6tzpyqPBpgkJvPk9l4mPD/34dfgi72H4KjuJvy0Dgqev/nNUyGVTsIDt4jJLbc1IDz3/1KFC8/xSxuE512OFpOwru5qwnP7i7ZCxDy/d9KFBty+Mween2jkPPcLzy+YPwR2JQD0VC48x/G4JU94bn6O30jvLcsWiFPQYf76geeoi0obPKfAcNjwfO/OA5BOOkcUjSU06N7HfAlQyOPIp58CwnP7Qb1gCCqbgudszUVie7clu1vBc3NOBYFqQa5xmsFcQMMFt1zPTS4Ixz5xr1HwnL2XqQscNKDgeUYp3BdM3PLUBJS1lrn3IgXPMyNC2d2Uh7nyPPe/nfq1u7HGMB1XRPci//K35ZKRGPCosLZkxMsY4CjhOQV1qOSZVDI5KmwLldDv0fEtiSGXiYkhqSSWBYPnADDhp2OFYZ0z2gyf49fznNIdlSQxCDxHeeyQnPwagXjh8URLv6Y7eNRTa3De1c8ZuphmewFAxUOm6nlizL8a9Uxf+WtB124yOa41KiSJjIWp6vClAdnwHBt9/dbhQtvUPKL2BQTAePj9IiEIPMf6h68Tv3ig9opCwPO7J40y+nzPolWC7twSAzsNMKU7an/hep5TY8lJIptJGGp5en9J6ArlxbFj0yajXN8BcjzP/SYM9bWQiqRQVB4wYRrwRaJav2JEYnu3JbubCyyKDbhwdc0FPW4TTcHzjHaKDZ5TiSGpOMmy5JfpGex3k+vI5RQ8916D1Pwotr2cey9qz/B81+YtoKfFrz1xHHUdg3/HfX/xqeB5/rujguf56zBoDZEY8Cgs17AM2sFiuU7B88xIKHie0QWVMJRaHwqeF8uK7lhyKHieGW8Fz7N1YcL8G14UYb6C58W9Pyh4Hvn4RGJ7tyW7mwssig24cHWt4HlmTcoKYYI1yoLPXK96aofBepwSQ67vj1tCrsOMLPkVPC/snq/guYLnTklhUSvnrBNDogbZp7hfBXH2ETcoi9AbQbmmxYUFpev47asGfQYeL5xXnucZdURld6MEYTquKM/zAt5buIZlAUULpSmZ8PylR9+F/XVHcuTEMBwYDmPY5veF3/6v37GOMXq5nudPXfEENJd2zwljYMXObiw7ILTbpTEGWrwsJ35ue4Dnj026HtLJBiiv7iz0uTXkiS0sgeVx+T8XLRbKj1pqpmq2e7xz4fmU+SfDdgy1WyWGtqDqxza5nufWuNlDyVBx2N09z3WYfu6u3LXmFGJEeZ6HsidxKpUJz8fOPQXq40lIxMRb7ojVQ0DXD0C8RIzBryeTkNA6w43LnhJE5nqe//fll4MOOrw6Zp9Qz4gVlQBaDKq6jhaNwWQSYsm9cPUz04XzxQvPdbj5zI99ramXRw6G7vsB+lWIIWmosFAPXzEWND0OfQaK4Z9qP3nMuOfc/Jz5tYp1cOG50z3t6G8koE//CqjuUgIa6NBn4EChDcsQre/WUzhfdXC38bfd87x2w99BR/O+tFQonzLCmuR6zHAThnLWU6HLvvvuuzB0aCa3CWXEb9q0CQbYPPb9yOrXAyZMA96PnEVURsFzj8FQ8NxUUBAv8iDXOA0HF9BwAXNHDNvC1REHernNFwXPC7v7K3ie0Tf3xSa3PDWy3BeS3LXG3b/c9mVu29y9mVO/FzxHfffx+WVnR4fn2ba3GzwPYnv7tbtxvMK0vRU8L+C9RcFzU9lBEoYi9LbilWcPmQkyUjDss78KI/lBXxOe2wEtF55jOI/m0mqosCWBs2JnHyxLC+12OVQBerwr3LpEjG/bHuA53ohSzfugqo+ZaNQ6qIR4CM8x+MBLIcHzUQtNeF5TUXh4TgE3z7At54pJD8nklgqeF3Bndm5KJjxH6F2bAKhJi7fcC1cPhRQ0QSxhJjO0DlxnMa1bTjxvLjy3vtqww/ORz5UBaHGo7DZGaPdQogLiyTqYvkDMX1C88BzgZluSX2pNoRFf3QDQ2RbPnXoRhrrT9Bj0tiUexr0f9HROeCkuPHe6p7XC864lgJunPbklF54jDE9rGsRtyTNTzWnQtBj0GSAmpG2L8Ly+vh7Wr18PI0aMaJ3LDz30EIwZM0aA4pQR71TWz+bj14gP04D3I2cRlVHw3GMwFDw3FRQEhAe5xmk4uICGC4a58IkDgKz+cK+RBdyo6c3VkSz5FTwv7O6v4HlG31wYzi3PXWvcNci9F7XnsC1UuBUKkncUeO7H9naD50Fsb792N66PMG1vBc8LeG9R8NxUdlB4jtfaE31SMXcp2BMEnmO79ljYFAx/cIbpjXj7bBE+tRd4jn2buXSJsGq40IgaG67nOcJzPFZNFl+cuMVyD+p5bk9KSIWSIeE5EQudirVPni/gftXRm5INz1Gf9ljl1F5I7RdB4bk9GSa1Ru6fsdIY9h/OFqF6UcPz5cvEqUq8eOI+DFBrnAs/qJjnTvciyzAs0/CTGhqe50B1IuY5BcNrP60z6u91vJmM2TraIjxH2YcNGwa/+93voKqqCtauXQvo0TJt2rTWfuHfP3nwQTju2GNhd0MDjBw5UoDtl156KTz//POsLc+vER+mAc8SOPrCCp57jAF3jyo24IJ7Iz5U2r8ExW43lsdg1BoxAToX2hr23siToGy/6LSC562wIE7hAThTP0p47qQ7KrSJTI9Ot3HghFuh9MwFdwqec2Zs8ZRtD/Dc6UtQ1HBJlyq4/smfCsoO8nLm8ak3QvOh+pxBQ4cZ/ALV/nzNHV3unspda272r5Pu0skkxKEUZiwXv0DHfnHb5u7NlsOf/cteJ5mU5zl3ppnlvWzvP732e1iyfBl8ddgw2LhxY962t1+7G2UL0/ZW8DzYfAl0lYLnptoUPM9Mn7aUMJRrGFHQSMHzrO2D8jBXnueB9liZFyl4ntGmgucZXSh4LnOV+asLPVwQcNfVmdDffuBnonPnzjWAOYLxNWvWCEXQwH92/gKorKgwvPmzDX4suHLlSqPubODuJZlfIz5MA95LxiL7XcFzjwFpD/Acu3iaLYwiBba5oAfrpuJn429OgJ67BriAhguGve4fdt1RSTULAc+5iT4pXXN1xIVq3PpRTlkvnrjzqz2X5z4jFtvXIrMvm0h+CeoEtoPAcwro4rxwAvTc+cLdU7lrjdq/3HSnad1yvtR0W4Pc9Uz1we1FhV0mBc+dZ1q+tvdXhwyB55cshS+faua3yNf29mt3Y1th2t4KnnN3pjzKK3huKk/B88wkUvA8owvleZ61uSh4nsdOK+dSBc8zelTw3Huf4n6BozzP5axThOqnn3463HbbbYbH+e233y54keM5NNg/Wv+O0SDCc7wGIXs2LHeC7m4S+jXiwzTg5WiwYLUoeO6h6rYOz7kghgt6UH2ygBs1FFHBc5ngUdY4UDriPsvKgmHc+RIEbhZsN2yHDcmcwxz1yFqzsuapGxgO+6WNrDXCvRdxyxcCnnP2LwXPOSvOLOvH9h566qnw8bvvtYagzNf29mt3o3xh2t4KnvPnS+AruAZH4IaK5EJqMw0Cz6lklQ27d0FFz94w5bEnhV4j7DmSSMDBclEZGJMc49jqmvjZZywNkIBS+NrGT4ULNnzlekjFJIZtscXn3fDMEYBYAgatE0OPuA0hdfP9bOIkaN6+PefSP/SbjLkB4eqF/uIYcx8quNBIluf5hfOHGDHVj+wVP6W79K0fG114/swf5HTl4nfmGefsGcExtj0eTuF5dC0GvWxxj3dtmGPMIXs4DAzbcjAGUNm3n9D2vh3boGsaYPrKX4syKc/zItmtcsWQCc+pZJXN27ZBSb9+MGj1K4IAGLYF5509oWcqnTTmvP3GfeDwatD1/VDRW0w8unfnDoB4Wc48VWFbMur2emn3y1Mz4T/wKmoP4e6DhYTn+3qIM6aqvoehACpsS4UtKkKDGUUGeg86oejWa3ZyIvQgR1iOIN065s2bBw8++CD8cbXpjY7w/NprrzX+H73VrQMB+zvvmIDdz+HXiA/TgPcjZxGVUfDcYzC4AEIWiJEFXGRBWzdAruC5OYkK4XlOTVfus6wsKClrnmK/ZK2dItpfIxdFwfPMEHCBvqzBk7VGuPcibnm3NShrv+DsXwqe82egH9v7/v/8L1j32mut8Dxf29uv3Y29CdP2VvCcP18CX8E1OAI3VCQXyoTn1qdO9mSV2NXynr1g7F33C71+8jvnG/B8b3cxEV/nw2UGPLcfWuqAkaBv6MYvhJ/+PuR6I/GoPYFesJjnOtx85sdC/RuWHDDh+dsf+R416qaMLyUsGJdd2R9qJhu0bfKi9gXPrfjPYcLzWePGgq7FoeeXbhLGZ+fGx0DXUnDrMyuE84tHj4JDMc0AotkHjkuXtA4TX1wljrOC577nfaELyoTnuBc6JavEPpXU1MBxixcJ3Zt12WWgg5aT0BPhudOx//AvQE83QFWfvsJaUHSgAAAgAElEQVTPe3c3AsS7wS1L5gvnFTzPqEPB84wurJjnbQmeZ09sTEA0YMAAI1modSA8R0j+6xVmTHMLnmOYluw45+h5juXwej+HXyM+TAPej5xFVEbBc4/B4AIIWQBQFnBR8DwzwFyQyC2v4HlG11zYhlfKWjtFtL9GLorMOczpDBdUh73fuc2vsOedrL5x70Xc8m464q5nrk6dbH4FzzkrLrcsZXvPeewxWP3yLwR4no/t7dfuRgnDtL0VPM9vvrCuVvDcVFcQz3Pu5vjxaScabZ24XoTV1IBRY/PQzS8Yl9z26HeFS4PBcwB74skNXx9s1CsLnht12bxYF04yvarbKzy3J2GUmTD0idEXGrqb/qLoGXzfJPP8nYvE89TcJue8guesPbSQhWXDc5TdbzIzKhHyuQ+/Zqjg9VuHC6rg7l8KnmfU5wXP7V+XUHt/MXue2z3Gd7ckDO1pSxhau+kfhmJ6DRA9zKnzhVyPftpCrxb8LHTEiBGtxZ08z7FMdXW14Hmu4LkfDedVRsFzD/UhNHBK0EjFvebaxVTzsoCLgucZDXNBolt5p0SibglSZY0D93mJO7+igp4op6y1k9eO2M4utpzc7AkaMRmmU9xrWV+RRDWP3OYQVyZZU0HWXs6F4W7lqSSsVJJUBc8zs2H/Rx8Yf5QPHiJMEeq8rHnErYeyve2e5/na3gqec0dGXvlIDHgUX8FzcxAVPM9MZgXPg0Msy/NcwXN5m6OqyfvhF0tQcJtKkst9SFDwPDMOd08aZfxxzyLxqw0KYgPxQor7MPDI5eONdhU8BygWeI7eLXv27GmdHD169DBCtGCccwzN4uT9gp+Wnn/++ULMcwTlaMRnxzxXYVtC3/0jsb3bkt1NJWhc399MtmUPNycLAMoCLrKgrQrbkmsX2xOJYgkqQaqscaB2BO6akgXDZM1T7JestRP6rtmGGqASNKaTSYhDKcxYvljoDdcuplTBBdWy5pGC55kRoXRKJRK1rnRKkiprv+DsX8rz3H2jCWp7nzd8uBDzPF/bW8Hz6G4IkRjwCp5nAeMLTK9du4c0nqOABdfQUZ7nGX0rz/PcBxG/Mc+V53l0G3XULSvP88wIqIShGV0oz/PCr8z6+noDdiMgHzp0aKsAa9euNcA5hlrBECwY8/zPf/6zUS77GDhwIKxa+QJUVlQYn4/i3xjfvKqqqrWYguehj2sktjcX9IWuhQANcOEQt4mwYRK3fgXPMyPIffZxA8PcceDAJ7c5JwuGceUPAje5a0eV99YAd9y8axRLcPdHrjzc+t3WYJD1zNGHrL5xnU247RZiv+DsXwqeO2srX9u7/7HHwSsvvwxfPtV0AMjX9lbwnLMbyC0biQGPXWgPRjxnKHAzTQHAw7cdL1x2xyOfQzyWgLP+972c6toSPHdK6HcoUQHxZJ3vGOkyPc/f/OapgDGRH7jlGEGv//bLidBcUg2dUw3C+X0HVhrJB3vZ4nljofLqTnDJzRlI4WYMUPN61vgpkNYPQmXvMrHdHdugNJmEG2yhULzCJ9gT93XqfqNRL+l5fu7OnPn1k7fMMDkUPH/pItEzYtRSnMGQI6tb2BanuPNUYkjDS3bf5wCV4pi1npvxPmfJqbISNYBGbjqlQ69B1+fUuufTn4EW02DG008Lv7Ulz3MqobIe7wa3+oyRTnnIBzGMZ191FehpHXoc/z3hcswvAKlGOFQmZrHs2hgDTSuDtWP3ic3Vfw41aYAF14hJmL0eBl4e5pwY9Dt1dUL9v6yuNv62h+AKI2xLfbeeQttVB3Ybf/ctOySc33Ggi/F3SafewvnkYXMP7DNwoHDeLWxLStcgXlIilE81N0Nc03PCuUhcbq5VobcKQvLscCzWBfgbfjJqxTnHv9esMZODWgcmEb37P/4DvjpkCOxuaAAsk10XeqdjvPPsBKJeffNrxIcZd9FLxiL7PRLbuz3Y3UHgDWfsueCDKw+3fgXPM6MXBLYpz3NTfwqec3aB8Mpy1z9XkrD3I279bnMvyHrm6IOra+5eQd1Pue269UnWyzaqDRXz3P+Mytf2/tNrv4c58+bC2cOHw8aNG/O2vf3a3djDMG1vFfPc/xzKu2R7MOI5SvjjNwZDSgP4sQ2e3/rwZkhocTj7TRFuYN1tBZ5TCf10/DwtWQeTnrlOUBXlrSgTnr9x1smQ1FPw8K39hbYvefki0GLVxguL7KP+wErjTzs8b9xzGMp6dIYJ950plHeLyYgF7UAa57uuN+YkMTy0dQuUppIw7eXfCfUHgee4gb1yjRkTzDpavWQjgOefTZwEzdu3Oy4Tp8SQsOjbAPu2Oi+ryqMBJv2Ks+RUWYkaoGAuNlG74XGIxTWYuXSJ0GJbgedPXnw+HInnJlTu0hgDiJflhCophOc59bKidtNiSOv74FAX8eVftwMVENO6wupLxfW2vcGE56sm5w/PcX+5yAmeaxrcvOxZX/sXNSeW3PmWcX32PmsZhmWamdTaDs//b+EHcLAhCfEYvvbMHKm0acppWlw4r+vmyz87DE83m+djJWL5dHOTeb1mMw11s71YSanvFeb0Atb3xVkFEXyjV/jevXsdL0dwjp7mlhc5/m15o2dfsGPjJuNP9Dy3H1h+3Lhxgle7l6x+jfgwDXgvGYvsdwXPAw5IEHjDaYoLPrjycOtX8DwzekFgGxeIccNncJ9lZcEw7jxS8JyzC4RXljtuXEnC3o+49aP8Qa7h9tupPFfX3L2io8Lz15fvgCOHRLtYbzLtZa1UtIup8+iE4mSPU+exLLcNWXY3ti3D9nazu7ENru3t1+7GusO0vRU8l7Fb+ayDa3D4rLZoi1GhUxDy4tGW4TnlcUnduAoFz530OmqhqW87TKJiCTtBHTdjgJrX3JusFzy3xx6m+hUkYeiTk8wXCVMXjRHWE1UX5XletItRCcbWgJtXNWVwthV4Tr20e2T8FYaebln2jK91INPznNLpj2941ZDlB3POE2RaOMVM5jx5gZjMmdoXvDzPnV7+Yf1+4wzL9Dwvb4HnfWygd9EP/mDA8/Iepqe5dXCNcgqeQ7IFnidskJw6T6wq6gUsexECGKFYMGSL3gLw7XXg75bXufUbAnS7FzllxOMDAtaBsdM5h18jPkwDniNvEZRV8DzgIIQNYoIAFyvZm7gP7QNMFmh/qcytv6PCc45O3aYSF4gpeB5wYarLfGmAu/59VZpViLs/ojxOiZmpJLzc+lG0INdw++1Unqtr7l7h9lzP0albX2W9bKPaCOJ5/pt5W+FQQ8pwLLQOLtjm2unYDqcNmXY3ti3D9naD50Fsb792N8ofpu2t4LmM3cpnHQqem4pS8DwzYWR7nmPN9pcSCp5n9E0CLgXPfe5iHaeYgueZsS6U5zm2aAcvCp5nxgHhOR6TfnyOsBC3bf7E+Ltf/y8J53du3Gj87TdsS9M28yue0n5DhHqo89RuQL2ADbJ7oIGNcRIx1vkdd9yRA8qd6sRrMBRLNlSnjHhMhMQF59imXyM+TAM+iD4jvEbB84DKDxvEcIELlRgQu8dJAMdtF+vngl6uyrm65oIeygbl6tStX1wgxtUp91mWqyNZ8ivPc+7sD6d8kHXOkYS7ZqnEzNimUxJebv1YT5BrOH2mynJ1zV1r1Nrn6tStr7L2C6qNoPAcv+DM/lJ0/0emvVw+WLSXqfM7N5lfP/YZIH79SJ3Hspw2ZNrd2LYM29sNngexvf3a3Sh/mLa3gucydiufdXANDp/VFm0x5XmeGRrleZ7RBdcDlArlozzPi3bptwvBFDzPDKOC5xldcMNXBQnb4uZ5jpJ0JHiO/UUPmKlTpwImL8ID45Vnxzn3s+F4fT7qp47sMn6N+DANeK7MEZdX8DzgAIQNYrjAhdsNbv0d0fOcq1O38lwgpuC5TO2ruuwa4K5/rgbD3h+D1B/kGm6/ncpzdc3dKwrBsRQ8z4xslPBchu0dld2t4LmM3USsIxIDHkUoxKYjX13Ba7Tg+bXjfi5U8qMXbgR8a/PP68RY1VhIdsxze9uPHroTeuu1OZ1a8dFQIz73QVtSui6HKgDi3eAWWwI9btgWa+ztye3ueOhTwKizZ637SJDp7unXQ/ygLRFeS4muRxoA4hVwy7NLhWveOOMr+JEPnD2+k3B+VLcjALFE6GFbFo/cIbQ7cU1f42972INCwPNYGuDkXdtyxnn9sUMAE732GnSD8FvDrkYoOVIPz5wkJui79K0fQ1MiAZVdzBjB1lF3JA6HOyfhzkWvBF8g6sqi1kCbg+daDMo7/Zug06ZOlWYC468+IJzfsOSAsScMelvcd6iwLY+OGwu6Fs/Jj0AlTsXGqPj/h7dugboKgHNse16kYVsc9oTaDXMA9HRkYVt0SOXEMMdPR/H4p9trhPHsVq8bccopz/PKJjHBaGPnfsb1PY83k59aB3qYl0AzaHFbDMdUEzRDSY5HOrWAZXvAWO2sXbvWSAaKMB29YjDWuV+v8aiMeAXPW2dJJLZ3e7C7wwYxXODCvXFz61fwnKthsTwXiCl4np++1dXuGuCuf64+w94fg9Qf5Bpuv53Kc3XN3SsKcT9V8DwzslHD83xt76jsbpQ7TNtbeZ7L2K181lGITcenKAUp9vfTTgRMM2YH2He3wPPhIcJzqu1nDk6Hvnot7NB6CTpY+fE3Ia0fhAPdxKR0mEBPi5fDvz8rJoeTBc9vfehTwDSeZ9tA0r1XXQmdjzTA4U4VOWPVpVk34PmtS8WXEm+cMdgomwPPy1MmPJ/0nlCX7JjnxQLPf3rpv4CWSsPXdjjA82MGO0LAw1u/gJIj++CpU8XPqa743/+EpngCupSLW+U2TYeDnVPwwBNrC7KWVCOF10BbguePXjYWdIhDeWcx/ndMT5kJjL8+V1DghmfMF2qD1olJNSl4bty7HAGzc+JUbGzDBRdC87ZtUNLPBLXWsa1hC+wtB7h4jQjuo4Ln1IuB2k8eA01Pwc3PrRDkL4jneSwOgDG+bRYaCc/3YVENavoPEmTdaSTJ1MEJnusaQK/+Ijw/sO1jKIEklMbFhptSOjRDArr1O9HXQgwLnmc3jp7nK1asIBOJ2gWNyogP04D3NRjFU0jB84BjgXuOrHjYMoALtxvcGMNUeWyXikvMlYkqz4VelKzr++N0z3UeKcRzoBsQc4pL/P5xX4ek1gSxBD6NiIdTGB5uH2TBMFlgEHvoFtJF1lxS9Zga4K5/rt64azZI/dz9N+w9m+pDkDXi1Ld0MglxKIUZyxcLTXHXPlfX1nzBf50cbKhx0LRucNrm93OacwrDo8K2BBmVzDUc2zsquxulDdP2VvA8vznEuroQmw5LoJALU/G8XzvDjA8VJjwnY4nPNg1amCFuslTyOeqmHBSe272wW4G3AzxHMe96WvQux3NUDGCqrlEvjjK6vGr0KmHEZcFzKiQNdT5sz3M36En9hqAPj0GrRU9yaq5+bcFwo/xfprwW8ipS1UelgbYEz6kEttRao+a7KzxnJM/EMaPauGC+uf+vvkb88igqeE6FpKHOFwKeH51OGzrqfMIJwvR/+vbXjb+vevBc4Xztls3G372O7S+c39ESY7GvLcbi9i3mS+KaY8WXsx/vMM+f2NffeWptyoLnTslArTYxpvmwYcPIRKJ22aIy4sM04KPaGwO2q+B5QMXJjIftJAIXuHC7wY2H61Ye23YCIlyZqPJcEEfJ+k7/UwznIb+JqGXJj/VQfaBkRdCv6weMZK/ZhwWq7HlIuM+yCp7LHN22Vxd3/XN7yF2z3PqD7L9BruHKJWMvp+TEtY9AOor9i9ovKFkR9Cf0Ujjls7cFlVAvWhU8955psmzvqOxu7GGYtreC595zSFoJrsEhreGIKlLwPKN4auwVPPfWETfmuYLnES34dtasgufea9PNe0vBc1N/QWKeK3gORnzzBx54wAjN4nSgcT937lwjjIv9wEREAwYM8JUwNOi2pWKeszWn4DlbZYW5IGx4XpheyGlFFoijbP5CPAdyvaq5feb2QcFzOXNT1eKsAe78bc96lLWXR7l/ccNIcfus4Ln7CpBpeyt43n52m0gMeFQf1+Bo6yqPHJ6nkzBoQjdBjZ+9uB+aD5UCVB0rnP9DzWSARAImLxDDHgTxPMc3UvbQKT95ywyp4hT/G6Npz5xqxge3jkve6GL875+/kzsLvvn6ZACIwQ9njxZ+DOJ5XnZYg+reYliF/XWHIZaogJsWPS7UT+miUJ7nr44RY8Bvb/gcatKQE8tdNjxH76F7vvtTQRe7q+6BRExTnudtfZNykb8Y4fkPrrkOSg/tg0Q8JkjepWmvYwx+yqj84zfOhWRah7vG3iPU8+135xuRQm5Z9oxwnrp34Z6QTunQa9D1OZps3roFI4ZAiS3k0faYDgdL98KPHp7ua3/BL21S6TSsPh6zQ2SOK/+2BzAszaAPxX1Ki9c6hoWhdCHd8/xMMRzNk+/fAU2l1VDRu0yQv3HPYSjr0Rkm3Hdm63kLyEYJz5uTOpQkRL8K65zdI51aPjI8z93gOLY7cuRIA6wPHTpUEAM90i+99FLjtzFjxrT+FpURH6b3SxvbfiOxvTua3R1kTnDhQ5A22so1skBclPBJwXNztrnpgaujtjJ/O6KcstZse9CdrL08yv1LwfPMTIwi5rlM2zsquxs1GKbtrTzPC7hbdjQjPlJ4fsbJAAjPrxCTZ2Ks3+ZGgJKjbfD8mGmgxeNw9bxLfAEdz7AttqSdFDz/4zcGQ0oDuNEBnmsawNsX5U7Qb74+EQDi8MPZGTCApbjw/Jbp/wLdDmtwbMVRQiP1O3can2/aP9WMCp7/9+Xjjc9f7fAc6k14vuAaMW6zTHj++zOGGG3/yAbP66vvhURcg3cmvVrAHUQ1VUgNFCM8/69JE6D08D44VCqG1dBitQY8/68nfieoiDJCXz/9HEim03DvZfcJ5S96d76ReNKejJi6d82+6irQ0zr0OP57OUPT/PkWI952SbkI+uuT3eFgaR3cPnucr732kRmvQTKlw2+OE82V8R/thXg6DSf87TGhnh4Hah0TkkYFzxe9fS00l1ZDZ9s9B4Uur+4El9ycgb9Rw/NNtY3QlDJDxtiP0ngMBvQSXwBQ61EGPMe4iuvXr4c77rhDgODYJv6GIVumTZuWI8K8efMMb/Rx48YpeF7IDdO7LQXPvXUUSQlZwCUS4SU3KgvERQmfuGCY22fus6zyPJc8SVV1vmxH7jpoD2qVtZdHuX8peJ6ZiVHAc5m2t4Ln7WFXMfsQiQGPDXMNjrau8kjhORHDmgolQD3scz3PqRjZ1Nh/fJqZgO3E9R8Lw+0G7u6fsdIomy88p+J2cw3psD3PZ10+3ujvzGeXiUuCiF8vE56/cdbJRptnvykCeiqOfFtfs0r+jAaKEZ5TMo1aaM7TVZPFeeoGz7H8uX/+g6+HEGr/ctMRtf9T+xe171B7M3We+/Ag3fN8ubhPueX4sK+3qOG5rPUvC55jWBb0grGHZkHj3u5xjrJjuJbbbrvN8DxX8FzWaEqrJxLbu6PZ3UFGK+yEfkFkiuoarv1LyYnzDuOIY9zg7MM6Z/8KVWZ/udCQ22fumlLwXOboqrrsGogqOWcxjgTX/i3G/UvB88yoRAXPZdneCp4X4y4RTKZIDHgUlWtwBOte8Vyl4HlmLBQ8z+iCmzBUwfPiWdMdSRIFz733LwXPvXWk4Hlhdo21a9dCdXW1AdUVPC+MzpmtRGJ7dzS7mzkmRvGwE/oFkSmqa7ggmZJz9viJkNKbHH+Oa6UwY9ni0Lqo4LmpWhW2JbQpVlQVR5Wcs6iU0CKMLHge5f6l4Hm08Jw7r91sbwXPedrEb2mrAKAeAAYCwAMt/++nlgEA8DwADHMonE+9VnWRGPDYeEcz4hU89wYryvPcW0cKnvvZNlUZ2RpQ8Nx7bSp47q0jBc9lr8zc+jDB0YoVK1rDuCh4Hr7OA7QQie3d0ezuAOOiLsnSgCx4HqVSFTxX8DzK+afajk4DsuB5dD0AUPC87cBzL9tbwXP/KwkDMY/EsJQtl7jB8OxaRwDApS0nEJLb47EHrdcueSQGfEeF5837MeatOJSHGwFqKwAenpbImVXnrR0CkGqE7n3EBJpUHG5qWlLhWdzCtlgJ3LLrrN3wOMTiWk78bwoaSQvbcvW3jZjtU4buyOnig5vuMM7ZYwZbN5w7v/sz4RqMz41HVd1dwnkq6SX34QHDtuhaDKo6idlN64/8AjQ9DTfbwhgUwvO8YfcuqOjZO0d31vkpjz0p/IbzonnbNijpJyZPPbx1C9RWavDwrf2F8tsbt0NNWQ2sGr3K/86oSrYpDeAap+YR7kdarCInUWbDrkbofLgOzt2+UOgrzqO6CoBz1onJJCmFUPuLW9gWTMRZkxb32lvnJQHTbNrbxZjneKiwLQAYtuVIIgEHuyaF4eh6MAGdkkm44cVXhPPU/khBuqDwXG9uBq2kRGh7xYLtxt9XPXiucL52y2ZIJ5MQS4j31FRzM4AWBz3RUygf0wFwqtQcK8bOl7VAZYRt4ciCcc7RiLcO/OQUPdCzQ7dEZcSHmbSIo6MiKBuJ7a3geRGMfBsSgWv/FmPXFDw3R0V5nhfj7FQyhakBBc8z2uWwBitk4qBBg3KGZ+emTfCbeVshXlICE+47s/V3TkgVvAjrwaPPAMSimYM6jyU4bRTa7kb5vGzvqOxulC1M2zuMhKEbW8D52qy58Q6yPgDIPkftHwjR1zjA83zrtdqLxIDHxjuaEf/ZBadCc/3h3I1IA9hdATB3fC48H/7a10FLHYTuPcXEZPvrDkMsUQE3LXrc132HC89fevRd2F93JKfuPZ/+DLSYBjOeflr4LXR4PvFbJjw/bXeOTA9tusNIYpkvPKeSXnIfHmZdNhZ0iENlZzHZ6r7DL4EGKZi5fIXQB84NzTCAmTHPV9z7Q9i/u5acJ+U9e8HYu+4Xfv9s4iRo3m6Cqexj18Fdxlx94pqanN9qutXAggsX+JqPqlDb04DbPGqsPwJarBy6H3Ol0LED27YY8PybtS8J57c1bIG95QAXrwkHnk9ZdBpsT+fuXz+YlwQEpd/8k9iugueZ4Xny4vPhSDwBe20cuXsDQKdUEqa+LCZhLQQ8PyYeB4Tn9gPhuaZpMOHH5ssP66jbthVSSRH+42+pJCb/RHjePXcBahrUHFMeysKMwojP7ggmE7UnGo3KiA/TgLcNXj5fZob9xSeKGont3dHs7lAWdAeqlGv/FqNqFDw3R0XB82KcnUqmMDWg4HlGuxzWoOC5nFlpt72jsruxN2Ha3rLhOYZq2dsSqsV8xWIeCMPxb8sb3W2UnOC5jHqtNiMx4LFxZcSbQ3Duw68Z/75+6/CcefDjG141zv1gznnCb9yHcS48pyYk1wNUmuc5wnMAmLL4tzmiPTjjOeMcBc/tXqZUckvqPPfhgasjzg0N+8mF53JuAaoWpQG+Bi6YP8S4aPU1HwgXU+dl7TtUPRQkV/A8ozHuvaIQ8NzJ+wUlxvug0xdS/Jka7hWWjNleOuG2aNaO3ufoCfPAAw/AaaedBphYdMwY/GgRICojPkwDPkunQb/MLNQXnyhqJLa3srsLsfLaTxvtIfkgdY+iYkOnmvdBvKQy5wtbN13EtG4wbPP7OQPfWB6DUWv+JpyXmTAUX2o7vejGc/ZnHwXP28+6VD3xp4H2kPyZ2i+o3BzWnmBf/5Qu3ul/CqT1A8aeZx2DL7oIjh58IvSqqILe/Y8VlG15nh9qSEFZj86tv+lNZk4LrbRUKE+dN74EdTnQs91+cNqIyu52s72jsrtRpjBtb9nw3ALfGOc8G55jDHME4BjOxetwgucy6rXajcSAx8aVEW8OgYLnmSVAxjxX8LxVSQqee22Z6vdi0YCC55mRoHJe3D9jpVHoh7NNqGkd1IMu9eKUOs/1vGlL8Jz6QqpY5n+2HOXVneCSm4cWjWhRGfFhGvBZys33y8ywv/hEUSOxvZXdXTRLsE0I0h6SD7o5wFig3D4YJV2q4PonfyqcpnSBIcLiUAqnbn5bKO8GsbCgX7hNyc9NbKvgeZtYckpIiRrgrhGJTUurys2Gd3p5hg07vbSjdPFe/69DCpqEMIcGPD/xy9CzvNwxrMrry3fAkUMiMuWAbZQx1Yxfh2LcAKdDg3hJbjQGbhvK7s7oNkzbWzY8x6dhBOX4jXAmACXAXAA4jUgCap9FTka8jHqtdiIx4LFxZcSbQ6DgeWbKK3ie0QW1PhQ8l2aTqIpC1oCC5xkFK3hu6iJIzHPK8zzk6duuq2/H8FzGl5lhf/GJcysS21vZ3e16WavOOWiA+/WoLCVyX1yHLaeC57JGVtWjNFA4DXD3ERmSYdiWIwcPQs+yMt8xyTnxyIPKWIg2gsrm57qo7G6UrT3AcwTqGFNxmA9lc+C5V72YdVLMPAnwZQBY/t5778Epp6AtX7hDGfGmrr3gebckQFWvLsLAcD9H4XoTUrOAShpIJZ5EUILvFe/5rujBcfE780DXG6HKlgi1aetn0DXZDBPPx2hHmeOJP/QG0DUY0Zgr2WtHTYZUDGD6gnHCj9QNR4VtKdwaVy11bA0gPN+VAOhXIX72hzHPeydzw7m47Tv4mz2xLRUiiaoHw7NUN+6BurIeQhHrHCdhKL5pv6iuTqhnbUUCIBGH6ct/kSPC4u+cD4cSCSg5WtTF3t2NoMe7wa1L5gvXKM9zALe4ix17ZeXf+6iM+DAN+BatyPgyM+wvPlFUBc/zn8aqBqUBTw2EDaUpAbjQK2w5FTz3nCqqgNJA0WmAu4/I6ICC5zK0mFtHVHY3ShKm7S3b87zYjPgfAcDdTlNCwfNwFoqfWt3g+V0zX4WuSYCjuovwHOvlfI4iC567JQ10Sjz5+xZ4/iMbPP+3d5dCWj8Ilb3FRKj7dmyDLslmuO7cXYLqnni9D+hpgJEHczX6es1kSMcApj6l4LmhnRm5cRf9zCHAwWYAACAASURBVENVRmlAtgaufOIk2JXQIFEtAuNkHcJzHZZOF2OBUu1z8whQ9awa+W/QrX6P488HqnrAqDX/I/zmFc/7OzZ4vqYrgJZIwPSVv85p44nRF8LBRAIq+/YTftu7cwdAvAxueXaZr7ZV2BbZs7Rj1heVER+mAd8ykjK+zOQ4rXC+JM2ebAqed8ylp3pdYA2EDaWp7nChV9jx5RU8L/DEU80pDUjQAHcfkdCk4bgSxPM8ngJIxUUJrHPlg80cWPkcyvNc1B7HwShM21s2PLc+H0UP83ezuiwrYSi3XuV5ns+qDelaN3ju9htHHFnwnNMmln3jrJONS85+86/CpfOuNpN8TrMB7zmjRxnnb3hxlVD+iTH/avztBKWebKlLwfOWL0cUPOdOU1U+JA1svQc/bAI4+u6/Cy1Q5ykxZMFzbjeph81HLh9vVGUH3m77FMJzYw978RVBDKou5XmuPM+585VTvgPCc68vM7PVx4HnfuotGttbffHJWSWqbHvQQFuB52HHl1fwvD3MZtWHjqaBtgLP9/3jA4ilnEcnHQeoPEHB86jsbhyVtgTPUd53AOB2AFibNaUwmRGeMzOFuR9U4qJ867VajcT7BRtXRrw5BAqeZxaAgucZXaiY515bo/q92DWg4HlmhBQ8N3WhYp4Xx6qNyogP04Bv0WyxffGJYhXNV5/K7i6O9aekKJwG2go8D1sjCp6HrWFVv9KAfA20FXguv+e5NSrPc1En7dXzHHs5DQBGAsClLV3GWOfoeT4wSwVo7D8IAOfbEotiEQqe+6nXz1xW8NyPlkIsUyh43rxtG5T0E0MGWOcGrRa9IWV1183zvLm0GvaXiN/3lO5+EjQ9DTc/Z3qmW0d79zzHT17OHt9J6PNP3hrsGBe+fucOiMe6wcwzPhaHad/nAJXHqLAtsiavqidvDciE51ZehWyhqFwLeQveUoGX5/mrIz4Qmhr1K3N/vcHmXY7nfj76AuO35WfeIVxz0XvzjL/bY9gWp7jwh7Zugd0VcRi+TtSd05hxDENZY95R6mnH8LzYvvjEKaU8zzvKwlL9LDoNKHhuDomC50U3NZVASgOeGlDwPKMiBc/F6cJ5RgrTcUV22Barlwi6EZajxzn++4ANkmOMxifh/7P3LlByVOe97zcjIZAAPQ0WD0OQ8CVZ8XWwZNaxTxKzOEjJCTgPcySEkvBYy2EkHzvh4hsky46N34rkJBx88Q0S5l6MY0tICrEdm8RoWJj4ha9e2HFWgo8lH/M2NtJIgMRDM3PX19M1U9XT1bV39d61q6p/tZZspns/vv37dnf/5z+7vxI5L/b4IhHRIs5qnut/68l1Lf0S75s1buaLMtRNizQwTsCMpacI8/yn114nrz79dNv9cMIZZ8i5n7vLZK9Yt0kzz+/6o9vllWlz5ODJpyXGnPbcnaLFzd+7NVn/t+7muUJ4W4t5/j8eXty2Lvzzv3hB+vtPlj+7aPfkfMw6W+S6r1rniQ4Q8EHAlXlue68FV2vBPJ8gmVb6y7Yu/ONHnmyY58vv/35mmmyEYeZgNEgQqLF5ruvs9puZvr/xqTEGObiC7uaNoNcIYJ6PZRzzvNd2PuutAwHM84ksYp4nd7TN70hVNM/L/PoNIuAVCCJ+bFsUYZ6H2oBp5nna47dcNVZL+MYeMs/Tyhik1YVPu2FgqBwzLwTSCLgyz0MRTjXPV/5RI6Q/3/KFRGi3Neuadzp5/q5770/0qWvN87R1/dqdlzTW//13PpiZVhthmDkYDRIEam6em3wzM+Q3PjUXQbQ3ups3gl4jgHk+lnHM817b+ay3DgQwzyeyiHme3NE2vyNhnrt9Nwgi4HUJiPixRGKeT2xozPMJFpjnbt/oGK14ApjnE8yjsi2Y55jnxb8SRfbu3SuLFumXGMeuNPP8wIEDsmCBVhe0u0xFvE8B3xJx1jczQ37jU0MNor3R3Xb7mtbVJ4B5PpZDzPPq72VW0HsEMM8ncl5F8zyuvTsdWsmjvU11txL0qb19lW0p86s9iIBXIIj4sW2BeT7x8sA8n2CBeV7mt01iMyGAeT5BCfN8jAUnz01eOfnbDA0Nye7du2XJEj1YPXZt3LhRli1bljDF00R8u7Ym0ZiKeJ8C3iTOErUJor3R3SXaAYRSCAHM8zHMmOeFbDcmgYBTApjnEzjLbJ6baO9O5nke7W2qu5WgT+2Nee70Jd95MET8GB81z588dEzOmjN9ErDo8YduGjutV7VLy7McHx2WT930S4nQb/rU/5KpfVPkN7/zr4nH1Twf7euX085/T+LxX/zPTzd+fs3r/2wSgiPPviAnvXRQLn76/0k899ITj8nBmSJve/jfE49fdu9l8vQLT8sZp5yReDx67L4r7ks8biu87/yz68Xm5oadyrboTVVnnn5KIp4XnntJTpl3klz9sbdWbTsQb48RqIN5PjI8Kqed/+5E5p798W0y2qFsy31/nLwRsna+7O+GG2O0PvdfdsxqPG5zw9DoPSAeVNr7gq3w1trmNjeXzqwLv+xwgp3WPO8fnkvZFo/vBYsXL5YHHnhAZs+eLYODg6InWgYG9CD22KU//82GDXLuOefIL44ckaVLlybM9uXLl8v27dutIjQV8T4FvFXA4RtjnofPARH0AAFbDe8Kie1nr6t508bBPPdNmPEh4J5AiPcR1XMvHz0qrznlFHltyzcRf3bgQGORrY+7X/nkEctsnmu0Wdr7ew9+Qz6/dYu8cfFi2b9/f9fa21R3a2w+tTfmeRG7vzkH5vkYiJWbH5anDh9LJX/mrOmyZeAtBWbG3VTf+Y03yfDIcVn/569LDLrurx6XKf1T5T9/a1/i8f9x1QoZlf5JJnkn8/zFpx5rmOe/8fMvJcZ66shjcuhUkd/fmTTP3/n1d8rTL7a/eeoZJ58hd/72ncmY/vjqxs//x9993uhx25sbppnn/+8f3S6vTpsj088+Z1JCTp17ovzBjRNfwXeXMUaCgDsCVTfPb73mGhkdGZV55/33BJSf/fg2GekTWfPF9jXPbc3zPumT//OLXzR6f/nSLXvl+YMvt01Su/cFW+Fte3NpW/Nc/yDcPzJH9l1/b+ZGsxGGmYPVqIGecFGD++DBg21XpV8T3bRpU8MwV2N8586diXYq8L/42Ttl1syZMn/hgoTg14Y7duxojB033LPwmebKp4DPirFkz2OelywhhFNPApjnY3nFPK/n/mZV9SZgq+Fd0MA8b0+xW+39xje8QbZ//u/kgjep/Eua7Xm0t6nu1rF9am/McxevOsMxMM8NQVW4mZ5i1Ov8+7+eWEXa47cvu7zRbvWOryXapz2ujX7rs29otL3/T36Y6JP2uC1O38I7zTxPe9w2ftpDIBSBqpvn+i0Svd756TsSCD96zR83fv7Q3X+XfJ9q3jB09b3J9zttlFa2xff7i2/hbXtT1U5lylr3qY0wDLXHyzavmuoXXXSRrFmzpnHCfO3atYlT5PqYmuf/vntPI3Q1z7WPmuxxs7yd6d5praa58ingy5aLjHgwzyuWMMKtJgHfn7FpVPSzd84RkUMzky2ix1q/FeubrnIYfvWwTDlh7Ntu8St6vPWQkO+YGB8CEOhMwLeGbzd7ZJ7PO3m69PUlv0k7OjrceCzUyfMpwyLDLV/ujR479VfG/KAQl4n2XvSmN8l/7N3X0N16dau9TXW3zuVTe2OeF7jjMM8LhB1oKszzbPCY59mMaFFNApjnE3nDPB9jgXnu97UcvzmRniBXs1yN9OjavHmzbNiwQb59/9hpdBXxq1atavy3nlaPLjXY9+wZM9hNLlMR71PAm8RZojaY5yVKBqHUl0Ao8/y+pb8qpzw/0hbsC6f2y2U7/61Q6J+5/k/l1WNDqXOeMH22vPuO/6vQmJgMAhDoTCCUef7K0WMy75ST2wbX1z9VTv+lyd+K953Lwz/6ofSPVcCcdI1MEZn1v4Uzz0209yc//gl5+MEHx83zbrW3qe5WWD61N+a5750fGx/zvEDYgabCPM8Gj3mezYgW1SSAeT6RN8zzMRaY58W9lvUGRAsWLGjcLDS61DxXk/xr28ZqmkfmuZZpidc515Pn2k77m1ymIt6ngDeJs0RtMM9LlAxCqS+BUOZ5fYmyMghAoCgCocxzXd/5559f1DJrNU+a9r7t05+W+7/8lYR53o32NtXdCten9sY8L3D7Yp4XCDvQVEWZ51NlWO578cTEKi87+WU5LlMmlXOxRRF91XH2a1+b6Dr0s581vv7Y7dccMc9tM0L7qhBQ83x4ZFSuOWXiRKvGfvcLq2RKf5+cffOjpV5Kp7Itx4dH5KELx8q6RNdV310vKiJWX/zspHXd/tDpjZuMvuve+xPP+f7FXoX33CMiJ7XcOyHthsq2CelUtkXX+9VFf5IY0uYm2DbC0DbuXmivp1r0a6FLliwZX267k+faZu7cuYmT55jn3ncI5rl3xEwAgfRa351qgMMNAhCAQBkIYJ6XIQt2MaRp79aT591qb5vfkTDP7XKY1TqIgNegMM+zUlP954swzy/97K/JFBmW+1+cmgD2Wycfl2GZIg/8yfe7Avnp694tI8ePyKlzT0qM8/zBl6R/6kz5s7s+09X4mOdd4aNziQk887FfluMjo/JHM25PRPmFo6tlan+fzP/gf5Q4epE08/wT113d+KPAA//7OxPxr/juX0qfjMq72pjnf/vQ6SLSJ+9qqYfu2zz/8tJfkTnPi5w5M/kVy7QbKtsmJP2GoX8sMjoq/9hinuv4pjfBthGGtnHXob2ebnnuuefGlzJv3rxGiRatc66lWdqdftGvll566aWJmudqlKuIj9c8p2yL9x0SRHuju73nlQlKRiCt1jd1vkuWKMKBAAQmEQhx7wS0d+eNmFd7/5dLLknUPO9We9vkCfPc7ZtLEAGPee42iWUdrQjz/NfuvKSx/O+/88EEhrTHbVl9/oPfbXS5+mNvTXRNe9x2fMxzW2K0rwyBW8fuKC43PJIMOe3xki0szTxPe/y2Ky5rrOA99943aSVpz/k2z6t8Q2UbYViyreM1nKGhoYbZrQb5okWLxucaHBxsGOdaakVLsGjN8127djXaxa+FCxfKfTv+XmbNnNn4+qj+rPXNZ8+ePd4M89xrCnXwINob89x7XpmgZAQ61fqmznfJkkU4EIBAgkCIeyegvdtvwm619y+dc658/ctflgveNPa7cbfa2yZPmOdu31iCCHhdAiLebSLLOBrmeXZWMM+zGdGiogQwz8cTh3luv4dthKH96NXtoadV1CSPl2OJVqPP6VdGozrn+vPOnWM3B40uvYnozR/4gLzxDW+QXxw5ItomPpaeTtd65/EbiGbRMs2VTwGfFWPJng+ivdHdJdsFhAMBCEAAAhAoEQFTPVeikAsJpVvt/b0HvyG3bd4kv3nJJbJ///6utbdNnnxqb2qeF7L9xiZBxBcIO9BUmOfZ4DHPsxnRoqIEMM/HE4d5br+Hs4Thto++X57/xc/tBw7Q49TXnCZXfuiTXc+sxreeCj906FDbsdQ415Pm0Sly/Tk6jR7v8Mz+A40f9eR566XtV6xYkTjVnhV4Vq6i/j4FfFaMJXse87xkCSEcCEAAAhCAQK8TyNJzVdHernS37gcX2ruT7tY5bLV3Vp7i+9in9sY8L/AdA/O8QNiBpspjno8ePy5LjyYD3jlDpG/qVFm942uTVqLlWUamHJTXzTwr8dzjR56U/uG5k8q52KLQ8iwvPPeSnDIvWfM8eqy1nIvt+GPm+bB8as15ia43bfyJiEyRSx7+oe2QtIdAOQioeX74cZFZr0vGEz3WWs6lHFGPR5GnbMsrU/tk1vwzJ63k8DNPybTjo5NKumjZlpHhUTnt/Hcn+vz8x5+R/il9Xd+QWMu2PDu1fc3z04+Lkxsq+4o/Sxhqfo784lmZ+RqtJ1/eK4rxnZ++o+sgtRSLlmwZHdXbsU6+9Pno1Hn0rBrorafI00S8/oKgY2jtdJsrK1fRWD4FvE28JWiLeV6CJBACBCAAAQhAAAITBLL0XBW0t0vdrWRcaO9O5nke7Z2Vp/ie9qm9Mc8LfPfAPC8QdqCprM3zq35P5PiwLDlyPBHx4MypIlOnyOqtX5m0kjfdcYWM9B+Ss+ZMTzz35KFj0j8yR/Zdf29Xq//SLXvl+YMvtx3j1Lknyh/cOFFzNs9ED77ljSIyIp9ak7yh300bHxORfrnk4R/kGZY+EAhP4K63ixx+on0cs84Wue6r4WPsEIGteb759/6rvDK1X6afnfxDnk5x7IknZdrxERn4yj8nZrz1mmtkdGRU5p333xOPP/eT/1v6+vvkhrvv7orRH9/+q/Ls1D6ZOjf5/nL84GNy+vFR+bvV/9bV+D7jzxKGafnpakEeOruMUwW21knUWufr1q2bZJS3C1/7aCmWuKmeJuL1Rki2xrnOmZWrKC6fAt5D6nwOiXnuky5jQwACEIAABCBgTSBLz7nUtNbBGXZwHaML7d3JPM+jvbPyFEflU3tjnhtuShfNMM9dUCz3GLbmua1Zpau/+FNjNwp96KaxG4dGV9rjZSP20EVva4R08a5/SYSW9njZ4iceCNSVgO37UafXbNpztnPYsn7iIxc0upx986OJrmmP247vM/4sYehaHNuu3bS96zj1BMz1118vevMivbReebzOuUlcWV8fNRkj3iYrV1FbnwLeNubA7THPAyeA6SEAAQhAAAIQSBLI0nOuNa0P/j5i7FZ7h9Ldyten9sY897GDU8bEPC8QdqCpMM+zwWOeZzOiBQRCELA1hjHPJ7LkQrjWQcArERcs2u3/wcHBxs1AVdDrqRitdW56ajyUiPcp4EO8R3QxJ+Z5F/DoCgEIQAACEICAewJ10N6+dLfSzqu9Q+luzHP3r5EgAl6XgXnuPpllG1HN81efekpOODNZAzh67Pz7v54I2das0s56wlxLtLQr26KPtZ5ILxsjNdyOj4zIR6/6WCK0D239oEzt7590Ir1s8RMPBOpKIK2uX1otPcxzzPN2rwWfIj6aT0+eb9u2LfVGoq1xhRLxmOfjmQiivdHddf20Yl0QgAAEIACB7glgnpsztNHeoXQ35rl5Pk1bBhHwmOem6al2u59ee528+vTTbRdxwhlnyLmfu6tr83zl5oflqcPH2s5x5qzpsmXgLaWG+O3/dLEcHxmVD135kUScH912s0zt75Nf/95DpY6f4CBQVwKd7ijf7i7umOeY5z7N83Y3A43m05rmixcvTr2RKOZ56d6lgmhvzPPS7QMCggAEIAABCJSGAOZ5MhWutDfmeWm2eNeBBBHwmOdd562WA+Q5eV51ELalbaq+XuKHQF0JYJ5jnvsyz7W++fr16xulWdpdKu43bdrUKOOi19q1a0VvQKTX7NmzGzcXjZd0CSXiOXk+nr0g2hvzvK6fPqwLAhCAAAQg0D0BzPMJhi61dyjdravxqb2ped79a854BES8MaqeaYh5PpHqNFO9ZzYDC4VAxQhgnmOe+zLPW83x1nmWLl3aMNYXLVrUeEqN84GBATl48KAsWLBgUlihRLxPAV+xtwvM84oljHAhAAEIQAACdSeAeT6RYZfaO5Tuxjx3/4oNIuB1GZjn7pNZ9RExzzHPq76Hib93CWCeY577Ms+1ruLu3bsbJ8iXLVuWmEaf05ItapZHl5rnnW4eGkrEY56PpyiI9kZ39+7nEyuHAAQgAAEIZBHAPJ8g5FJ7h9LdmOdZO97++SACHvPcPlG90APzHPO8F/Y5a6wngbzmeXQD0jiVtJuS2pJ74iMXyPzRn8vUOeckuh4/9Jg803eanH3zo7ZDJtr7fM+ug4BXWC5uGKoCXsuy6CmYqDRLlAh9LjpxHjfP9b+1ZMuePXtk+fLlsmTJkvHchRLxmOeY51294dAZAhCAAAQgAAFvBOqgvV3obgXsUnuH0t2Y5+5fKpjn7pkyYk4CPo2YnCF570bNc++ImQAChRDIY57b3pTUdiF7P/obcvroz+Xs2dMTXZ8YOibP9p0miz70LdshMc8tibkS8TbT6g1E44b6nDlz5Cc/+UnDTNcrlIjHPMc8t9nHtIUABCAAAQhAoDgCmOf5WXfS3qF0N+Z5/nym9cQ8d8+UEXMSwDyfAEfN85ybiG4QCEQgj3nuO9SLP/VgY4qHbrokMVXa47bx+HzProOAV54hzPPWPC5cuLBxE9GotEsoEY95jnlu+x5DewhAAAIQgAAEiiFQB+1dBt2t2Ypr71C6G/Pc/esG89w9U0bMScCnEZMzJO/dOHnuHTETQKAQApjnE5hdCNc6CPgQ5vmBAwcaNdAPHTo0nhD9Wb9+inleyFuBySRBtDc1z01SQxsIQAACEIBAbxKog/Z28TuIbfaztDfmuS3R8rYPIuAVByK+vJsiVGSY5xPkOXkeahcyLwTyEcA8xzxvt3OKFvFDQ0ONU+ZaI10v/fm8886jbEu+l7WvXkG0N7rbVzoZFwIQgAAEIFB9Apjn+XKYpb0xz/NxLWOvIAIe87yMWyF8TJjnmOfhdyERQCAfgbKa508eOiZnzUnWPI8eay3nYrtyn+/ZJgK+3c1Wbdfgu72rm7/axDk4OChaezG6Yaia6QsWLBgfIpSIp2zLeAqCaG/Mc5tXEW0hAAEIQAACvUWgDto7hO7WXdJJe4fS3RqXT+3d11svj8Zqgwh4zPMe3GkGS/ZpxBhMH6QJZVuCYGdSCDgnUEbzfOXmh+Wpw8farvXMWdNly8BbuuLg8z07S8B3utlqV4vy0PnU15wmV37okx5GzjdkKBHvU8DnIxGsVxDtjXkeLN9MDAEIQAACECg9gbpob3T3xFbzqb0xzwt8SSPiC4Rdkal8GjFlRYB5XtbMEBcE7AiU0Ty3W4F9a5/v2VkC3j5aekQEMM+D7wXM8+ApIAAIQAACEIAABOIE0N5+9kMo3a2rwTx3m9MgAl6XgHnuNpF1GM2nEVNWPpjnZc0McUHAjgDm+QQvF3W+EfB2+8+mdSgR71PA26y/BG2DaG90dwkyTwgQgAAEIACBkhJAe/tJTCjdjXnuPp9BBDzmuftE1mFEzPOJLHLD0DrsaNbQSwQwzzHPq7LfQ4l4zPPxHRJEe2OeV+UVSpwQgAAEIACB4glgnvthHkp3Y567z2cQAY957j6RdRhRzfN2N4ALdeOHIphy8rwIyswBAf8E1Dyf+8JzcvCUeZMmix6/eNe/+A+kwBl8/sETAe8vkaFEPOY55rm/Xc3IEIAABCAAAQh0QwDt3Q299L6hdDfm+eSczBaRuSJyUESGcqQb8zwHNLr4IdDpBnBlu/GDKwKY565IMg4EwhK4b+k75OSh51KDeHH2PLls5z+EDdLx7JjnjoEWNFwoEY95jnle0BZnGghAAAIQgAAELAlgnlsCM2weSndX1TwfEBE1udXcXigi6w2N7k79FonInli+dOzrRWSHYQ6jZpjnlsBoDgGXBDDPXdJkLAhAoEgCmOdF0nY3VygRXxPzvNtDK5rIINqbsi3uXkOMBAEIQAACEKgbAcxzPxkNpburaJ4vE5GlIrKqmYoFIrJdRBZnpCar35KYIa+nzvfmTHUQAa+xIuJzZoxutSKAeV6rdLIYCPQUAczzaqY7lIgv0Dwv86EVzPNqvmyIGgIQgAAEIFBrApjnftIbSndX0Tzf3zTOB2Op0BPja0Uk/lhrprL6qXmuV6cxTLKPeW5CiTYQ8ERAzfNXn3pKTjjzzMQM0WPn3/91TzMzLAQgAIHuCPi8T8WBAwdkeHhYXv/613cXJL0nEXAt4n/0ox/J1KlTZcECPR+SfhVknmcdPkkLMKufq0MrOn8Q7c2hFd4MIAABCEAAAhBII4D29rM3QuluXY1P7d3nGJd+tfNQs1TLgdjYO0VEf45Oo7dOa9IP89xxshgOAiEI/PTa6+TVp59uO/UJZ5wh537urhBhMScEIACBTAI+71Px5JNPypEjRxqG7IknnpgZCw3MCbgU8S+//LLoL1szZ86Us846q2MQPgV8bOKswydpMWb1c6W7Mc/NtyotIQABCEAAAhAoiADa2w/oULq7aua5Cm01yrXOedw817ItapBrOZd2l0k/baN1z6ObhGoZGD3NbnvT0CCnX3TRnIDx8+JkVAhAAAIQgEDVCahxriL+pJNOkjPPPBMD3WFCXYl4Nc6feuopeemllxrGuRrona4CzHOTwyftQjTph3nucA8yFAQgAAEIQAAC5SKA9vaTj1C6u2rmuX4FVI3yOS2m9iYReXOHuucm/dQ41zE2N1Osol7HVaM+7ZovIvovfl0gIlv37dsnF16oPnpxF+Z5cayZCQIQgAAEIFAlAqOjo/LMM8/I0NDYmYApU6ZIf39/lZZQ2lhffvFoI7YTT56RO8aRkZFGWR29Zs+eLfPnz5e+vs5f4CzAPDc5fNJuzSb9XB1a0fmDHFxBd+fe7nSEAAQgAAEI1J4A2ttPikPpbl2NT+3tumxLmgmuhroWhky7aWjefqMislxEdqSk/cMicnO75zDP/bxQGBUCEIAABCAAgXwEVMQ///zzjX96ylkNW67uCezfvbsxyMI36xmMfJf+IUPL6Zx66qmNf1nGuW8B31yFyeGTdgs26Zfn0IrOVZqDK5jn+fY6vSAAAQhAAAK9QgDt7T7ToXS3b+3t2jw3OcnSLjt5+2l99W0daqmXRsDrohHx7l+YjAgBCEAAAhCAAAQ6EQilv3yefskwz0MdWtGwSnNwJVTeeTVCAAIQgAAEIACBXiUQUn/51N6uzfOohqKeMN8b2yymNwzt1E+N8utbTpnrY1rGRWufm15BvjqKeW6aHtpBAAIQgAAEIAABdwRCiXifAr5JJ+/hk7z9sg6taFilObgSKu/udi4jQQACEIAABCAAgWoRCKm/fGpv1+a5ZnVP08wejKV4f/OxtPIqJv10DC3REpnykVGvNyGNz5W1szDPswjxPAQgAAEIQAACEKgJilTh/wAAIABJREFUgVAi3qeAb6amCodWNNQg2jtU3mvysmEZEIAABCAAAQhAwJpASP3lU3v7MM8HREQNbTW69dJa53ryPH5jTz3xskFELo3dWDSrn7ZfH2u/pjmPzmVzBRHwGmDITWQDiLYQgAAEIAABCECgLgRC6S+fAj6Wm7IfWsE8r8sLiXVAAAIQgAAEIACBDAKhdLeG5VN7+zDPNWY1wtUs19Pi+v9x01uf1xsV3SEi58XMcJN+apjrpWMOWZZriVKMec7LHQIQgAAEIAABCPQIgVAi3qeAj6Uu6/CJNg15aAXzvEdeZywTAhCAAAQgAAEIhNLdVTXPy7xjMM/LnB1igwAEIAABCEAAAg4JhBLxBZnnSqrMh1Ywzx3uZYaCAAQgAAEIQAACZSYQSndjnrvfFZjn7pkyIgQgAAEIQAACECglgVAivkDzvJTcY0EF0d6h8l72ZBAfBCAAAQhAAAIQ8EUgpP7yqb19lW3xlQcX4wYR8Bp4yE3kAhxjQAACEIAABCAAgaoRCKW/fAr4iuUgiPYOlfeK5YZwIQABCEAAAhCAgDMCIfWXT+2Nee5si2QPFHITZUdHCwhAAAIQgAAEIFA/AqH0l08BX7EsYZ5XLGGECwEIQAACEIAABPIQCKW7NVaf2hvzPM9uyNkn5CbKGTLdIAABCEAAAhCAQKUJhNJfPgV8xRKCeV6xhBEuBCAAAQhAAAIQyEMglO7GPM+Trc59ggh4DSnkJnKPkREhAAEIQAACEIBA+QmE0l+Y5+N7I4j2DpX38r8iiBACEIAABCAAAQj4IRBSf/nU3pw897Nf2o4achMVuEymggAEIAABCEAAAqUhEEp/+RTwpYFrFgjmuRknWkEAAhCAAAQgAIFKEwiluxWaT+2NeV7gtgy5iQpcJlNBAAIQgAAEIACB0hAIpb98CvjSwDULBPPcjBOtIAABCEAAAhCAQKUJhNLdmOfut00QAa/LCLmJ3GNkRAhAAAIQgAAEIFB+AqH0F+b5+N4Ior1D5b38rwgihAAEIAABCEAAAn4IhNRfPrU3J8/97Je2o4bcRAUuk6kgAAEIQAACEIBAaQiE0l8+BXxp4JoFgnluxolWEIAABCAAAQhAoNIEQuluheZTe2OeF7gtQ26iApfJVBCAAAQgAAEIQKA0BELpL58CvjRwzQLBPDfjRCsIQAACEIAABCBQaQKhdDfmufttE0TA6zJCbiL3GBkRAhCAAAQgAAEIlJ9AKP2FeT6+N4Jo71B5L/8rggghAAEIQAACEICAHwIh9ZdP7c3Jcz/7pe2oITdRgctkKghAAAIQgAAEIFAaAqH0l08BXxq4ZoFgnptxohUEIAABCEAAAhCoNIFQuluh+dTemOcFbsuQm6jAZTIVBCAAAQhAAAIQKA2BUPrLp4AvDVyzQDDPzTjRCgIQgAAEIAABCFSaQCjdjXnuftsEEfC6jJCbyD1GRoQABCAAAQhAAALlJxBKf2Gej++NINo7VN7L/4ogQghAAAIQgAAEIOCHQEj95VN7c/Lcz35pO2rITVTgMpkKAhCAAAQgAAEIlIZAKP3lU8CXBq5ZIJjnZpxoBQEIQAACEIAABCpNIJTuVmg+tTfmeYHbMuQmKnCZTAUBCEAAAhCAAARKQyCU/vIp4EsD1ywQzHMzTrSCAAQgAAEIQAAClSYQSndjnrvfNkEEvC4j5CZyj5ERIQABCEAAAhCAQPkJhNJfmOfjeyOI9g6V9/K/IogQAhCAAAQgAAEI+CEQUn/51N6cPPezX9qOGnITFbhMpoIABCAAAQhAAAKlIRBKf/kU8KWBaxYI5rkZJ1pBAAIQgAAEIACBShMIpbsVmk/tjXle4LYMuYkKXCZTQQACEIAABCAAgdIQCKW/fAr40sA1CwTz3IwTrSAAAQhAAAIQgEClCYTS3Zjn7rdNEAGvywi5idxjZEQIQAACEIAABCBQfgKh9Bfm+fjeCKK9Q+W9/K8IIoQABCAAAQhAAAJ+CITUXz61NyfP/eyXtqOG3EQFLpOpIAABCEAAAhCAQGkIhNJfPgV8aeCaBYJ5bsaJVhCAAAQgAAEIQKDSBELpboXmU3tjnhe4LUNuogKXyVQQgAAEIAABCECgNARC6S+fAr40cM0CwTw340QrCEAAAhCAAAQgUGkCoXQ35rn7bRNEwOsyQm4i9xgZEQIQgAAEIAABCJSfQCj9hXk+vjeCaO9QeS//K4IIIQABCEAAAhCAgB8CIfWXT+3NyXM/+6XtqCE3UYHLZCoIQAACEIAABCBQGgKh9JdPAV8auGaBYJ6bcaIVBCAAAQhAAAIQqDSBULpbofnU3pjnBW7LkJuowGUyFQQgAAEIQAACECgNgVD6y6eALw1cs0Awz8040QoCEIAABCAAAQhUmkAo3Y157n7bBBHwuoyQm8g9RkaEAAQgAAEIQAAC5ScQSn9hno/vjSDaO1Tey/+KIEIIQAACEIAABCDgh0BI/eVTe3Py3M9+aTtqyE1U4DKZCgIQgAAEIAABCJSGQCj95VPAlwauWSCY52acaAUBCEAAAhCAAAQqTSCU7lZoPrU35nmB2zLkJipwmUwFAQhAAAIQgAAESkMglP7yKeBLA9csEMxzM060ggAEIAABCEAAApUmEEp3Y5673zZBBLwuI+Qmco+RESEAAQhAAAIQgED5CYTSX5jn43sjiPYOlffyvyKIEAIQgAAEIAABCPghEFJ/+dTenDz3s1/ajhpyExW4TKaCAAQgAAEIQAACpSEQSn/5FPClgWsWCOa5GSdaQQACEIAABCAAgUoTCKW7FZpP7Y15XuC2DLmJClwmU0EAAhCAAAQgAIHSEAilv3wK+NLANQsE89yME60gAAEIQAACEIBApQmE0t2Y5+63TRABr8sIuYncY2RECEAAAhCAAAQgUH4CofQX5vn43giivUPlvfyvCCKEAAQgAAEIQAACfgiE1F8+tbevk+cDIjJbRIZEZKGIrG/+d1Z2svplPZ81vj4fRMBjnpukhjYQgAAEIAABCEDALYFQIt6ngG8hlFcfZ/XLet40UUG0d6i8m0KhHQQgAAEIQAACEKgbgZD6y6f29mGeLxORpSKyqrkJFojIdhFZnLEpsvplPW+654IIeMxz0/TQDgIQgAAEIAABCLgjEErE+xTwMTp59XFWv6znbRIURHuHyrsNGNpCAAIQgAAEIACBOhEIqb98am8f5vn+pnE+GNsAe0RkrYjEH2vdH1n9sp433W9BBDzmuWl6aAcBCEAAAhCAAATcEQgl4n0K+BidvPo4q1/W8zYJCqK9Q+XdBgxtIQABCEAAAhCAQJ0IhNRfPrW3a/NcS7UcapZqORDbADtFRH+OTqO37o2sfmq85xm33R4MIuAxz+v0dsBaIAABCEAAAhCoCoFQIt6ngG+yz9LPZdDdGmoQ7R0q71V5XRAnBCAAAQhAAAIQcE0gpP7yqb1dm+dLRESNcq1zHjfPtWyLCnwt59Luyuq3Iee4mOeuXwmMBwEIQAACEIAABCpEIJSI9yngm/iz9HMZdDfmeYVeK4QKAQhAAAIQgAAEuiEQSndrzD61t2vzXOsjqlE+p+UGoZtE5M0d6p5n9dMbjuYZd76I6L/49QYR+fzWrVvlggsu6GZPWPf9/Nr3NfpcveEvrfvSAQIQgAAEIAABCEDAnkAo/fXoo4/KVVddpQG/RUS+Zx95Zo8s/Zx2v6Gsfnl1twZcGu0dKu+ZWaMBBCAAAQhAAAIQqCmBkPrLp/YuyjxX41tvHGor4qN+aSI+a9wPi8jNNd2TLAsCEIAABCAAAQhAoPwE1EG/x0OYaSZ4lj7O6pdXd+sS0d4eEs2QEIAABCAAAQhAAALGBJxrb9fmedm+Ptru9MtMEfllEfm+iLxsjD5cQz0ev1VENPmPhgujp2cmB+HTTw7IQXgCYSPgNRCWv85ODsiBLYETReSXROT+5r17bPtntS+b7tZ40d5ZWeP5LAK812YR8v88OfDPOGsGcpBFyP/z5MA/46wZyEEWIb/PV5G/N+3t2jyPblykJ8z3xvJoesPQtH7RDUNtx/W7lYoZvXGTJRF5k5bwKWZKZmkhQA7CbwlyQA7CEwgbAa+BsPx1dnJADsITSEaA7vaTEV7rfriajgp/U1L+2pEDf2xNRyYHpqT8tSMH/tiajkwOTEn5aQf/GFfX5rkOvUdE1OwejM2zv/nYjg45zeqX9byf7RJ+VDYsOQhPIHwEvA7IQXgCYSPgNRCWP+Z5eP7koH0O8urjrH5Zz5djR/iJgvdbP1xNR4W/KSl/7ciBP7amI5MDU1L+2pEDf2xNRyYHpqT8tIO/Z/N8QESWisjy5jxa61xPni+MzatfM90gIpfGbiya1S/reT/bJfyobFhyEJ5A+Ah4HZCD8ATCRsBrICx/jNvw/MlB+xyY6GN0t93+5f3Wjpfr1vB3TdR+PHJgz8x1D3Lgmqj9eOTAnpnrHuTANVG78eDv2TzX4VXIq1muJ871//XGQ0OxefVGRXeIyHktj2f1y3rebitUozUbNnyeyAE5CE8gfAS8DsLmAP5h+WPchudPDtJzkKWP0d12+5f3WzterlvD3zVR+/HIgT0z1z3IgWui9uORA3tmrnuQA9dE7caDfwHmuV1KaN2JgN54abWI3C4iz4AqCAFyEAR7YlJyQA7CEwgbAa+BsPx1dnJADsITIIIiCPBaL4Jy+hzwD8ufz7vw/MkBOSgHgfBR8HkQNgfwxzwPuwOZHQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABMpNwMcNQ8u9YqKDAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCGQQwDxni0AAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEWghgnrMlIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAKY5+wBCEAAAhCAQKUILBCR7SKyuFJREywEIAABCEAAAhCAAASqRwDtXb2cETEEvBLg5LlXvAwOAQh4JICo8Qg3NvSAiMwWkSERWSgi65v/XczsvT3LEhFZ3kSgeeAzO9x+2NCcWt939ForIgfChdOTM0fvRfNEZFEzB3t7kgSLhgAEIFA8AXR3cczR3sWxbp0J7R2OfXxmdHf4PKC7W3LAL+LhN6VtBGxiW2Lu2/Nm7p6pzYiIGhta3bVdJiJLRWRVzDjkBHR3TPP01j2/E/M8DzonffQ9P/5HI/1ZP4vP4w9JTviaDLKpyVr/aKHXGhHRPMwhByb4aNMlAbR3lwAddEd7O4CYcwh0d05wObuhvXOCc9wN7e0YqMVw6G4LWJ6aorvbgMU897TbPA3LJvYE1mJY3swtYHluiqjxDFhE9jeN88HYVHuaJz7jj/mPpLdnYK+Hzf8hEbleRHY0w9BvYuhjauRuDBtaz8yuf7RT7vrHPL2i14T+zHtRz2yDIAtFewfBnpgU7R0+B/H3XfwDv/lAe/vlazo62tuUlPt26G73TG1HRHdjntvumdK1ZxOHTwlv5uFzEEWAqPGbi8gg1FIt8fIUegJaf45Oo/uNgtH5hTX8HoiM8s2xUEZFRH/mdRAmP5w8D8O9F2dFe4fPOto7fA7QIsXkAO1dDGeTWfg904SSnzbobj9cuxkV3c1XwLvZP6XoyyYuPg28mRfPPG1GRI3fXER8W83zViPBbxSMzi+s5dsDnDwPmxPlr9+A0dOo8T9ohI2K2XuFANq7+EyjvYtn3m5GdLf/PKC9/TM2nYH9bkrKfzt0t3/GnWZAdzfp8LWrsBuxm9nZxN3Qc9eXN3N3LG1HQtTYErNrrzUX1ShvrSmsX2F/s4gsthuO1l0QYK93Ac9DVzXP1lHz3APZ7CGV/YpmqZao/nl2L1pAwA0BtLcbjt2OgvbulmC+/miRfNxseqG9bWj5bct+98vXZnR0tw0tt23R3TGemOduN1dRo7GJiyKdPQ9v5tmMfLVA1PgiOzZumoBXQ30B5rlf+C2js9cLxd1xMjVtfiIil4rI3vKE1XOR6B/x9H0oqoHecwBYcOEE0N6FI0+dEO0dJhdoEf/c0d7+GZvOwH43JeW3HbrbL1/T0dHdlG0x3SulbccmDpsa3sy7568i0aRmsNbZbr0xH6Kme/6dRuCro3752ozOXreh5betvhfpiWeMc7+cs0ZX41xvqsZNW7NI8bxrAmhv10TtxkN72/FqbY3u7o6f795ob9+EzcdHe5uz8tkS3e2TrvnY6G7Mc/Pd4qFlN+IlCodN3F1ius0Bb+bd8e+2N6KmW4Kd+0dfi9byLHGjkBuG+uXebnT2evHM282oNbZ1/w82n9TXyFA5Qqt9FPqNl/Ut70V601bNBafPa59+ZwvsVvdpIGjv7tLRbQ7Q3t3x76Y3WqQbemZ90d5mnIpoxX4vgnLnOdDd4XKA7m7DnrIt4TZknpnZxHmo+enDm7kfrjajImpsaOVrqzfl05OdkVmoo0SnPXfkG5JeOQiw13NAc9xlQEQOtLwWtHRA6zdiHE/LcCKyqHmD0Pgp88hg0BuGmnx7CZAQyEsA7Z2XnPt+aG/3TG1GRIvY0MrfFu2dn53Lnux3lzTtx0J32zNz1QPdnUIS89zVFvM/DpvYP2PTGXgzNyXltx2ixi9fHV33up7qXN6cSk/c6amvhf6nZoamaag3R9S9rp8B+kcM/RaAnsDlxHNxW0T5q0Grez+69DWgf0hS85bLPwE1MKP3oei9SctnaB70jxpcEPBBAO3tg2q+MdHe+bi57IXudkkzfSy0dzGc02bR9320d9gcoLvD8tfZ0d1tcoB5Hn5j2kTAJrah5actb+Z+uNqMiqixodV9WxXxkVGo/49x2z1TRqgWAS0P0u5qLWlUrVVVK1o9ab4uFrL+IU9PomOcVyuPVYwW7R0+a2jvsDlAdxfPH+1dPHNmLA8BdHf4XKC7Mc/D78IuI2ATdwnQQXfezB1AZAgIQAACEIAABCBQAQJo7/BJQnuHzwERQAACEIAABHqaACfPezr9LB4CEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAATaEcA8Z19AAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABFoIYJ6zJSAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACmOfsAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAZwKcPGeHQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAARaCGCesyUgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAApjn7AEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQGcCnDxnh0AAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEWghgnrMlIAABCEBACSwTkQMisrdHcAyIyDYRGeqR9bJMCEAAAhCAAAQgAIFyEEB3lyMPRAEBCEDAiADmuREmGkEAAhCoNYElIrJBRBbXepXJxS0Ske0isrCH1sxSIQABCEAAAhCAAATCEkB3h+XP7BCAAASsCWCeWyOjAwQgAIFMAiqKd4rIZhFZldk6fwM1f/Vann+IRs9DInJpy6lzPZm9qWVcPaU9KCLrHZxQ1znXNhnpNMprbsEGfrQ+nznqMjV0hwAEIAABCEAAAhDoQADdnb090N3ZjGgBAQhAIJUA5jmbAwIQgIB7AmpqL2j+m+N++PERXZjna0RkafNfPNTIPNf4o9Ims0VEH9dT6tpHjfS8V6uI13H10j84FHVpjvaLSHyNRc3NPBCAAAQgAAEIQAAC3RNAd2czRHdnM6IFBCAAgVQCmOdsDghAAALuCYw2y4GoMaunwne4n6IxogvzXMX09W1ibGeeR8tQ81xrNXZT8qRVxHtClDnsHhG5R0Q2ZrakAQQgAAEIQAACEIBA2Qigu7Mzgu7OZkQLCEAAApjn7AEIQAACBRFQ01kNcz2ZraVI9NL/9nF1a55HJ6/VBNebhcavTua5nlZXA72bP8CWRcTrWlYUXC7Gx15gTAhAAAIQgAAEINBrBNDdZhlHd5txohUEIACBtgS6MT5ACgEIQAACkwnoSWatCa6nzfV0thrcrWVB9LGDzZIh65pDaLkSrQEeXVoi5Y7mGGps63ha01ENb72xpz7WzjxXUzsqgbIto+Z6VIKlXWmZNPM8ejxezz268ZHGppeuv/Ukt8aqPDRujVH/xWuet64la0wThmqMaz1zjUvn1RrnrXFFOeLzkFczBCAAAQhAAAIQqBYBdDe6u1o7lmghAIFKEsAsqGTaCBoCECgpgegkd/y9VU96tJrJkZGsRq4aumrgqpmspvje5tq0jRroajDruPqznmBXEzg6Jd5qOOtYb26WYdFh1HzvVJJE51STWudtvSKTPF7vPGqjRn78JqU6jpaoUbNeY9VfZNS0juqXa5w6j/bR2PW5yNiOt9Hxo3FNxlRuaQwXicgDzRuh6pwal7Jprake3WSKz8OSvqgICwIQgAAEIAABCLQhgO5Gd/PCgAAEIFAIAcyCQjAzCQQg0CME1PBVw1vN4eiKDO24QR2Z5/H3YK3XGK+Prqa7/hzdlFMNaR0rbv7GzfN2N7/UWLRfWm1yHU/7tSsrE5nn0Sn3uU2DPG6Kp6U1Mv513Ciu1huM6npbDfa4ed46dnxMfS6LoRrr+seD82I3PG0Xb6fSNT2ybVkmBCAAAQhAAAIQqBwBdPdYytDdldu6BAwBCFSNAOZ51TJGvBCAQJkJ6OnrqHRJa5zxuuIqcqPyK1G7NDNZb+YZneaOn0yPxHJkOEflR9rxSXuvbzWk433blW0xrQ8eP9GeVhbF1jxvPSVvwjDKh57mTzuBr39g0D9UtLIt8z4jNghAAAIQgAAEINDrBNDdYzsA3d3rrwTWDwEIeCeAee4dMRNAAAI9QiAqE9KufriKey11EtU0b2dat5rJ0alwLTWi9dG1r44Rv+Inz6OT1u3mT0uBSdmW1nrt7W44pAa01m6ParLrz2pYqyGd1zzvNGb0hwNtEz8138pQ2+n82uZKEdnd5pQ9J8975AXKMiEAAQhAAAIQqA0BdDe6uzabmYVAAALlJ4B5Xv4cESEEIFANAmp2a33w+E0/o8ijm3hGxraJea6Gu5rPUc3xdhTi5nmeE9R5bxiq64mXQ1HTOrr5p8YbN+Xzlm3pNKaNeR5x01+ytIRN6+de9HjrHwmqseuIEgIQgAAEIAABCPQeAXT3WClHdHfv7X1WDAEIBCCAeR4AOlNCAAK1JKBmb1rpj8igjep+m5jn2kZvdLmzefJc/7vVSG+9YWhk0kc35tR59UR4uxuCahI63SyzXdmWKHFq7Gstdq1ZHpn2UX326OamGms0r65BH4/i0lrkeiI8rea5yZhZDHX8i5r8opuU6ppaT+Z3+gNCLTcqi4IABCAAAQhAAAIVJ4DuHtPi6O6Kb2TChwAEqkEA87waeSJKCECg3ARMSqZouRMVuWogZxm/utrohpjxlashHb+JaKt5rm21Lrma0iqmtXTK+jblXqIxO51W72SeR6Z79McCNe11Xo1PS6Po/8druus8kWGuMekpdTXUO90wNGvMLIY6v46hf0CIWGj9eJ0/frVjWO7dRnQQgAAEIAABCECgdwmgu9Hdvbv7WTkEIBCEAOZ5EOxMCgEIQKAjATWu1WCOl22JaoC3Oz3dDc7ohHu7cjPdjFuVvvpHDTXVW+vJVyV+4oQABCAAAQhAAAIQyE8A3Z2fnW1PdLctMdpDAAKlIIB5Xoo0EAQEIACBBIHoRI2aunpaXU9yRyVY4ie6XWDTU+RqoNvcaNTFvGUYQ39Z0tPvaWVtyhAjMUAAAhCAAAQgAAEI+COA7vbHNj4yursYzswCAQh4IIB57gEqQ0IAAhBwQCCqCf7mZl1xrdutp6O1DEunm4jmmVrN810isjFP5wr30drtap7rHyi4IAABCEAAAhCAAAR6kwC623/e0d3+GTMDBCDgiQDmuSewDAsBCECgQgS0JMwDzfIlrTXBK7QMq1B79Q8GVpBoDAEIQAACEIAABCDglAC62ylOBoMABCDgnwDmuX/GzAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIVI4B5XrGEES4EIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQj4J4B57p8xM0AAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgEDFCGCeVyxhhAsBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAL+CWCe+2fMDBCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIFAxApjnFUsY4UIAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgIB/Apjn/hkzAwQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCFSMAOZ5xRJGuBCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIOCfAOa5f8bMAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhUjgHlesYQRLgQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCPgngHnunzEzQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAQMUIYJ5XLGGECwEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAv4JYJ77Z8wMEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgUDECmOcVSxjhQgACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAgH8CmOf+GTMDBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIVIwA5nnFEka4EIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg4J8A5rl/xswAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACFSOAeV6xhBEuBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEI+CeAee6fMTNAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIBAxQhgnlcsYYSN76TqAAAgAElEQVQLAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAAC/glgnvtnzAwQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBQMQKY5xVLGOFCAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEICAfwK9aJ7PEZHfEpH/JSIv+0fMDBCAAAQgAAEIQAACPUjgRBH5JRG5X0QO9eD6oyWjvXs4+SwdAhCAAAQgAAEIFETAm/buRfN8hYhsLShxTAMBCEAAAhCAAAQg0NsErhKRe3oYAdq7h5PP0iEAAQhAAAIQgEDBBJxr7140z/+TiDy8detWueCCCwrOH9NBAAIQgAAEIAABCPQCgUcffVSuukq1u7xFRL7XC2tOWSPau4eTz9IhAAEIQAACEIBAEQR8au9eNM8vFJF9+/btkwsv1P/kggAEIAABCEAAAhCAgFsCjzzyiLzpTW/SQfV/HnE7eqVGQ3tXKl0ECwEIQAACEIAABKpHwKf2xjyv3n4gYghAAAIQgAAEIACBkhPwKeBLvvTW8DDPK5YwwoUABCAAAQhAAAJVI+BTe2OeV203EC8EIAABCEAAAhCAQOkJ+BTwpV98MkDM84oljHAhAAEIQAACEIBA1Qj41N6Y51XbDcQLAQhAAAIQgAAEIFB6Aj4FfOkXj3lesRQRLgQgAAEIQAACEKg2AZ/aG/O82nuD6CEAAQhAAAIQgAAESkjAp4Av4XI7hcTJ84oljHAhAAEIQAACEIBA1Qj41N6Y51XbDcQLAQhAAAIQgAAEIFB6Aj4FfOkXnwwQ87xiCSNcCEAAAhCAAAQgUDUCPrU35nnVdgPxQgACEIAABCAAAQiUnoBPAV/6xWOeVyxFhAsBCEAAAhCAAASqTcCn9sY8r/beIHoIQAACEIAABGpCYHR0VJ5//vnGv5dffllGRkZqsrLqL6O/v19OPPFEOfXUUxv/+vqyJbRPAd9CdEBEZovIkIgsFJH1zf/OAh/1mycii0RkrYjsjXXKO27rvJw8z8oEz0MAAhCAAAQgUDgBtHfhyI0mzKO7dWCf2jtb+RstrVKNEPCVShfBQgACEIAABOpPQMX7M888I0ND6n+KTJ061cigrT+ZcqxQ83P8+PFGMLNnz5b58+dn5sengI9RWSYiS0VkVfOxBSKyXUQWZ5Db1DTY1TDXa42IbBCROc3H847bblq0dzm2MVFAAAIQgAAEINAkgPYu71bIo7t1NT61N+Z5efcLkUEAAhCAAAQg0CMEjhw5Ik8++aScdNJJcuaZZzZOOXOVi4B+G+Cpp56Sl156Sc466yyZOXNmxwB9CvjYxPubxvlg7LE9zVPk8cdaY1WDXU+rq/Gu1xIR2dn8WfvlHRfzvFzblmggAAEIQAACEGhDAO1d7m1hq7sxz93ns/KnX+5e+Q45+uorbcnMOGGaXLPlH9xTY0QIQAACEIAABLwRUONcRfyCBQswzr1R7n5gFfIHDhxoGOdqoHe6CjDP1fw+1CzVciAWi5rg+nN0Gt1k4fGT59re1bg6VuW1twlA2kAAAhBII3DrymtleLT97+9T+qbJDVs+BzwIQKBgAmjvgoHnmM5Gd2Oe5wCc0aXyAv72ZZfL0X6RGS2lUKPHVu/4mntqjAgBCEAAAhCAgDcCasgODw/L61//em9z9MrAe/fulUWLtIR350uZ6x8rbK8f/ehHjbI6WX0LMM+j0+Ja5zxunreeKs9aoprwelpdy7Zsjp1C73bcaN7Ka+8sgDwPAQhAoBOBv7lqpYyOvih9fScnmkWPvXfrFgBCAAIFE0B7uwPuU3ub6m5djU/tTdkWd/ulsJHUPNer1SRPe7ywwJgIAhCAAAQgAIFcBH784x83+p1//vm5+vdiJ60Pv3v3blmyRD3ksWvjxo2ybNmyTGPbtm2cr2mufAr4Zjxal1yN8qhOeRSm1jN/s0Hdc22vJ85XiIiWaonqn3cz7nwR0X/x6wIR2bpv3z658EL10bkgAAEI9BYBNc/1ajXJ0x7vLTqsFgJhCJjquTDRlXPWENrbJk8+tTfmeTn3ZMeoMM8rmDRChgAEIAABCHQgYCMMATlBYPHixfLAAw80buI5ODjYKKkyMDAwCdHy5ctl1apVCaNdG+nj27er/2x+mebKp4BvRptmcuuC9Eh91k1D44tWw137aA30bsb9sIjc3I4m5rn5HqMlBCBQLwKY5/XKJ6upBwFTPVeP1bpbRdHa2yZPPrU35rm7PVTYSJjnhaFmIghAAAIQgEAhBGyEYSEBlWQSPeGiBvfBgwfbRqRfE920aVPDMF+6dKns3KnlvieuHTt2yK5du2Tz5s0Nkzx+Sl1b6fM6djvDPQ2Baa58CvhmbK7KtuhwapzrTUL19Pne5s1D85Rt4eR5SV47hAEBCJSHAOZ5eXJBJBCICJjquV4jVjbtbZMnn9ob87yCrwTM8womjZAhAAEIQAACHQjYCENAjhFQU/2iiy6SNWvWNE6cr127NvUUuZ6S2bBhwyTzXMdpZ7p3YmyaK58CvhlfdMNQPWGuhnd0mdwwVE+nr2/pN9os37K8ecPQPOO2Q0fNc160EIBATxPAPO/p9LP4khIw1XMlDT9IWHHt/f994yH52IYNcsdtt02Kpa+vTy5bvsyJ9rbJk0/tjXkeZMt1NynmeXf86A0BCEAAAhAoGwEbYVi22EPFE785kZ4gVwNdjfR2VyfzXJ/bs0fvl2l2mebKp4CPRaqB62lxrVkeXdEJ8h0pK9K7qUb9NjbbREa83jB0Vex5m3HTAGKem20tWkEAAjUlgHle08SyrEoTMNVzlV6k4+Dj2vuzt31Gfvr4T+XdA6tbZhkWkSly+ZXLU81zG+1tkyef2hvz3PFmKmI4zPMiKDMHBCAAAQhAoDgCNsKwuKiqM5PeKHTBggWNm4W2u7JOnmvpF+1vcpnmyqeAj8WpBd61TrmeFtdLF6Enz7XkSnRpeZcNInKpiAw1H9ST51EffUjH0brnUakWk3FNcGkbzHNTUrSDAARqSQDzvJZpZVEVJ2Cq5yq+TG/hf3DtWjn3defIn7zn3Yk5ntl/oPFzJ/Ncv/Vpqr1t8uRTe2Oee9tK/gbGPPfHlpEhAAEIQAACIQjYCMMQ8ZV9Tr0ZqH6VtLWmeRR3jc3zyPhW01tPnOv/azmWyCTX5/UvCneIyHmxx/Wk+bpYXtV01xPsY7/xjF1qoHca13RbYJ6bkqIdBCBQSwKY57VMK4uqOAG0d3cJvHrlSvnd37lMrrzm6sRAmOfdcS1T78oLeMzzMm0nYoEABCAAAQh0TwAB35mhnix/7rnnxhvNmzevUaJF65xrLfNuTp7bfHVUAzDNlc/TL93vuEJHqLz2LpQWk0EAArUjgHleu5SyoBoQMNVzNVhqriVkae9uTp7baG+bPPnU3r5OnutJFT3Roqde2p2ASUte1G+eiGg9Rj0BE78BUt5x4/NVXsBjnud67dMJAhCAAAQgUFoCNsKwtIvwENjQ0FDjRLka5IsWqTQcuwYHBxvGuZZa2b59u2jN8127djXatbtqXPPcA3XnQ1ZeezsnwoAQgEBPEcA876l0s9iKEEB7t0+UqfbWmueP/OsP5LZNWvVv4jI5eY55PsZLvxqqtRf1ZkN66ddAta7i4ozXkBJXs10Nc730jk/6G9Cc5uN5x22dtvICHvO8Iu/GhAkBCEAAAhAwJICAbw9KayKqSd6uHIs+p+Vaojrn+vPOnVrue+LSGxtFRruOoUb8wICexRi79Hmtuaj/TC/TXPk8/WIaa0naVV57l4QjYUAAAhUlgHle0cQRdq0JmOq5WkNoszhT7a0m+Yprr5GHvvWtxCj3f/kr8s3vfFs+vnFjQ793q71t8uRTe/s4ea71FtU4H4wR3NM0xeOPtaZJDXY9ra7Gu156cyP9DUh/1n55x22dp/ICHvO8196+WC8EIAABCNSdgI0wrDuLaH0HDhwQPZly6NChtktW41xPms+erfJRGkZ6dBrdlJG2X7FiReJUe1Zf01z5FPBZMZbs+cpr75LxJBwIQKBiBDDPK5Ywwu0JAqZ6ridgNBdpo73VPL/pLz4gH/nEJxrfBI2u6OT5/IUTj8UZ2mpvmzz51N6uzXP97UV/w9FSLfEbDqkJrj9Hp9FN9l/85Lm2dzVu5QU85rnJ9qENBCAAAQhAoDoEsoThys0Py1OHj1ViQWfOmi5bBt7SdaxaikVPq4yOjrYdS5+PTp1HDdRANz1Frr8g6BhaO93myspVNJZPAW8TbwnaVl57l4AhIUAAAhUmgHle4eQRem0JZOm5qmhvV7pbE22jvSOT/OaNGxLau5N5nkd7Z+UpvkF9am/X5nl0WrzVPG89VZ71AlQTXk+ra9mWzbFT6N2Oq/NWXsBjnmdtH56HAAQgAAEIVItAljC8+FMPypOHjslZc6aXemFRjA/ddEnXcarAXrhwYeNU+Lp16yYZ5e0m0D5aiqXVVG/XVm+EZGuc6zhZucI8n0S78tq7683MABCAQE8TwDzv6fSz+JISyNJzVdDeLnW3pslGe0cm+dG+sTKIkfbuZJ7n0d5Zeaqqea51ydUoj+qUR+vQQpJvNqh7ru31+M+KZqmWqP553nHni4j+i18XiMjWffv2yYUXqpav3oV5Xr2cETEEIAABCECgE4EsYagCXi8XprTPTLiOU0/AXH/99aI3L9JLayfG65z7XEva2Fm5ivr5PP0SYt1dzIl53gU8ukIAAtUngHle/RyygvoRyNJzrjWtD4I+YjTV3mkmeVbZFlsOWXmKj+dTe7s+eZ5mcquhrgVvsm4aGl+3Gu7aR2ue5x33wyJyc7vkYJ7bblnaQwACEIAABCDgi0CWMPQhjn2sxVecetNPvRmoCno9FaO1zvOcGnex5qxcYZ5Poox57mLjMQYEIFBZApjnlU0dgdeYQJae86VpXSL1GWOW9sY87y6Trsq2aBRqnOtNQvX0+d7mzUNty7Zw8ry7fNIbAhCAAAQgAIECCNRBwCsmnyI+SoOePN+2bVvqjUR9pysrV5jnmOe+9yDjQwAC1SKAeV6tfBFtbxDI0nNFaNpuSRcVYzvtjXneXfaiG4bqCXM1vKPL5Iahejp9fUs/vUPUoIgsb94wNM+4rSuq/OkXyrZ0t0npDQEIQAACECgbgToIeJfmebubgUY507qKixcvTr2RqO/cZuUK8xzz3PceZHwIQKBaBDDPq5Uvou0NAll6rihjuhvaLmO01d6Y591kbqyv3uhTT4ur6R1d0QnyHSnDL4r129hsExnxesPQVTnHbTcd5nn3OWYECEAAAhCAAAQcEqiDgFccLkS81jdfv359ozRLu0vF/aZNmxplXPTSMi76s95cdP/+/bJ06dJGbXRfV1auonl91l30tTZP4wbR3reuvFaGR19pu6QpfdPkhi2f87RchoUABCCQJIB5zo6AQPkIZOk5F5rW96pdxZhHe//Nhg1y7jnnyC+OHElob2qem2d9oFmnXE+L66XlV/S3Gy25El36G43+RnSpiIzdAWrsRqNRH/1Zx9G651GpFpNxTaIMIuBNAjNtw8lzU1K0gwAEIAABCFSDQB0EvJJ2IeJbzfHWDKo5rsb6okV69kIap9AfeOABmT1bz11M/tn1DsjKVTQf5vk4+SDaW82q0dEXpa/v5MQWiB5779YtrrcG40EAAhBoSwDznI0BgfIRyNJzLjSt71W7ijGP9v7iZ++UWTNnyvyFCxJaHPPcLutqdKvprSfO9f+1HEtkkutIegPQO0TkvNjj+hvPutg0arrrCfYDsceyxjWJMoiANwnMtA3muSkp2kEAAhCAAASqQaAOAl5JuxDxWldx9+7dsm7dOlm2TCXjxKXPqVk+MKCScOzUuf586NCh8UbLly9vnICJ2rjeAVm5iubDPB8nH0R7Y1a53vmMBwEI5CXA+1FecvSDgD8CWXrOhab1F/3YyK5izKO9/323Fh2Rhnke196Y576zXtz4QQS8y+VhnrukyVgQ6D0Cd698hxx9tf1X2ZXGjBOmyTVb/qH3wLBiCAQkUAcB70rEq4DXMix6CiYqzRKlRp+LTpzrY5s3b26cQtdyLfE2+t86ho8rK1fRnJjn4/SDaG/MKh+7nzEhAIE8BHg/ykONPhDwSyBLz7kypn2uwlWMebT3t+8fK5+o5rn2j7Q35rnPjBc7dhAB73KJmOcuaTIWBHqPgL6HHO0XmTEyee3R46t3fK33wLBiCAQkUAcBr/hciXjTVKh5rib5nj1jp1/0UgF/8OBB2b5dKwK6v7JyFc2IeT7OPoj2xqxyv/cZEQIQyEeA96N83OgFAZ8EsvRc0Zo2z1pDxBhp769tG9PZkXkeaW/M8zyZLGefIALeJQrMc5c0GQsCvUcg7T1ESXR6rvdIsWIIFEegDgJeaRUt4tudPNevjs6dO5eT58Vt36yZgmhvzKqstPA8BCBQFAHej4oizTwQMCdQB+1dtO5WupH2jp88j2tvzHPzPVj2lkEEvEsomOcuaTIWBHqPAOZ57+WcFZefQB0EfAjzfO/evXLppZcmap5rvXMV8dQ8L82+D6K9MatKk38CgUDPE+D9qOe3AABKSKAO2juEeR5p73jN87j2xjwv4WbPGVIQAZ8z1rbdMM9d0mQsCPQeAczz3ss5Ky4/gToI+BDmuc65cOHCRtmW2bP13vOTf3ad/axcRfNRtmWcfBDtjVnleuczHgQgkJcA70d5ydEPAv4IZOm5EMa07WpDxaja+74dfy+zZs5slG2Ja3HMc9sslrd9EAHfEcddbxc5/ET7JrPOFrnuq4nnbr/itxs/r774Z8nHH3rt2OP3fr289IkMAhBwTuDWldfJ8OjLbced0nei3LDlrsRzt6y4Ukb7psjx1/7BpD5Tf/Yl6Rsdlhvv2eY8TgaEAATSCZgI+CcPHZOz5kwvNcYoxoduuqSwOA8cONC4aejixYsbNw7V0y9LlizxNn9WrqKJMc/HUxBEe2NWeXsJMDAEIGBJgPcjS2A0h0ABBLL0nBrTZdfeIXS3pka1980f+IC88Q1vkF8cOZLQ3pjnBWzegqYIIuA7ru3WC0UOPy4y63XJZtFjNzySeHzMPB+V1Rc/m3z8odNFpA/zvKCNxDQQKAsBFeSjoy9KX9/JiZCix967dUvi8YaA7+uX46f/3qQlTH32KyKjI9LapyxrJQ4I1JVAloBfuflheerwsUos/8xZ02XLwFsqEWueILNyhXk+iWoQ7d3JrGr3malRT+mbJjds+VyebUEfCEAAAqkEMM/ZHBAoH4EsPVcV7R1Kd6eZ5Jjn5dvreSMKIuAzzXNt0GKSi5rqbR4fP3necsI87fG8oOgHAQhUg4CtIE9rr6vt9Fw1aBAlBKpJIEvAV3NV9YzaNFecPB/PfxDtnfZ5duvKa2V49JVJmzPtD8713MWsCgIQKJKArVYvMjbmgkCvEjDVc73KJ2vdmOdZhKr/fBABj3le/Y3DCiBQVgK2ghzzvKyZJK5eJoCAr072TXOFeV5O8zxtp/HH4+q8BokUAlUjYKvVq7Y+4oVAFQmY6rkqrq2ImDHPi6Acdg7M87D8mR0CEHBMwFaQm5jnX148MCnKUF8Jc4yL4SBQSgII+FKmpW1QprnCPMc8r86uJlIIQMAnAVut7jMWxoYABMYImOo5eLUngHle/52BeV7/HLNCCPQUAVtBnsc8D3Uzkp5KJIvtaQII+Oqk3zRXmOeY59XZ1UQKAQj4JGCr1X3GwtgQgADmuYs9gHnugmK5x8A8L3d+iA4CELAkYCvITczz1huG6t3G9Xropksso6M5BCBgQsDUkDUZizZ+CZjmCvMc89zvTmR0CECgKgRstXpV1kWcEKgyAVM9V+U1+owd89wn3XKMjXlejjwQBQQg4IiArSDHPHcEnmEg4JAAAt4hTM9DmeYK8xzz3PNWZHgIQKAiBGy1ekWWRZgQqDQBUz1X6UV6DB7z3CPckgyNeV6SRBAGBCDghkAkyFvrlP/+ns2NCVpPkRuZ52/990RwTwwdk2f7TpNFH/qWm6AZBQIQSBBAwFdnQ5jmCvMc87w6u5pIIQABnwQwz33SZWwI5CNgqufyjV7/Xpjn9c8x5nn9c8wKIdBTBIowz48fekye6TtNzr750Z5iy2IhUBQBBHxRpLufxzRXmOeY593vNkaAAATqQADzvA5ZZA11I2Cq5+q2blfrwTx3RbK842Celzc3RAYBCOQgYCvIjU6eb92SiOSJj1zQ+BnzPEeC6AIBAwIIeANIhk327t0rixYtymx94MABWbBgQWa71gamucI8xzy33lx0gAAEaknAVqvXEgKLgkDJCJjquZKFXZpw4uZ5XHunmeoaeB7tbZMnn9q7rzTkiwsE87w41swEAQgUQMBWkGOeF5AUpoCAJQEbYWg5dG2bDw0Nye7du2XJkiXja9y4caMsW7bMyBS3aRuHaJornwK+YkkNor07fda142fbvmI5IFwIQCAgAVutHjBUpoZAzxAw1XM9A8RgoXHtHZnkd//9joT27mSe59HeNnnyqb0xzw02iPcmt+rvFCJywyPJqVIev/2K3260W33v1xPt0x73Hj8TQAACQQnYCnLM86DpYnIItCVgIwxBOEFg8eLF8sADD8js2bNlcHCwcaJlYGBgEqLly5fLqlWrEka7NtLHt2/fboXUNFc+BbxVwOEbY56HzwERQAACAQnYavWAoTI1BHqGgKme6xkghguNtPdLzx2Uf/n2t2XopWMJ7R2Z53/6vrVOtLdNnnxqb8xzww3itRnmuVe8DA6BuhOwFeSZ5nlfvxyff0UC2+jIcRk+4ZC8/69W1x0n64NAEAI2wjBIgIEm1RMuanAfPHiwbQT6NdFNmzY1RPvSpUtl586diXY7duyQXbt2yebNmxsmefyUujbU53XsdoZ72pJNc+VTwBeYjtkiMldENAFDOefFPM8Jjm4QgEA9CNhq9XqsmlVAoNwETPVcuVfhPjpT7f17ly6RFddeIw9961uJID5722fkkX/9gXxh2zYn2tsmTz61N+a5+71mPyLmuT0zekAAAuMEbAV5J/P8lhVXymjfFDl+xrIE4ekvzZSj0w7K2ltXQB4CEPBAwEYYepi+kkOqqX7RRRfJmjVrGifO165dm3qKXE/JbNiwYZJ5rgtvZ7p3AmKaK58CviU+PWqvJrea2wtFZL2h0b2hOU5U+H2tlqNsPqZF4/fE5tGxr9e/N+TYLJjnOaDRBQIQqA8BW61en5WzEgiUl4CpnivvCoqPLK69v/fgN+TjGzfKP/7TfYlAopPnl1+53In2tsmTT+2NeV78fps8I+Z5GbJADBCoLAFbQd7JPL/tissaHN5zb/JDcMMN9zQexzyv7DYh8JITsBGGJV9KYeHFb06kJ8jVQFcjvd3VyTzX5/bsifvEnZdgmiufAj4Wof6lc6mIrGo+pka41qFZnJEINc7jJrv+rCb8eU3jXQvJR4a8njrf20ViMc+7gEdXCECg+gRstXr1V8wKIFB+AqZ6rvwrKS7CuPbWE+Y/ffwx+diG6CzGWBwm5rmN9rbJk0/tjXle3D5LnwnzvAxZIAYIVJaArSDHPK9sqgm8xgRshGGNMeRemt6AaMGCBY0bFtma53ryXEu/aH+TyzRXPgV8LM79TeN8MPaY/iVAT5HHH2td2qGWk+RqlOtj2m+jiER3Ye00hgkubYN5bkqKdhCAQC0J2Gr1WkJgURAoGQFTPVeysEsTzgfXrpVzX3eO/Ml73p2IycQ8t9HeNnnyqb0xz0uw9Z752C/L8ZFR+aMZtyei+cLR1TK1v0/mf/A/Eo//7RW/1fh561vXJR6/6rt6gEjkXffeX4JVEQIEIFAUAVtBHrX/8uLJN9Vb9p2N8uoJ/TJjJBn90akzpa9/htz4xTuLWhbzQKCnCNgIw54CY7hYvRmofpW0taZ51D2rbEtFzfPI8NZSLVG5FV2yFn7Xn6PT6O0oRkb55tiToyKiP2s/zHPDvUczCEAAAlkEbLV61ng8DwEIdE8A7d0dw6tXrpTf/Z3L5Mprrk4MhHneHdcy9Q5y+qUTgCc+coEMj4zKNadsSjS7+4VVMqW/T86++dHE45jnZdpOxAKB8ARsBXkn8/zK7/y1jPaNNP5wF79enNIvfX2nyHu3bgm/YCKAQA0JIOA7J1VPlj/33HPjjebNm9co0aJ1zrWWeTcnz22+OqoBmObK5+mXJgg1uNUobzXPtWyLGutazsX0anfyXOueRzcJ1TIweio9z01Dg2jvTt+yagfFtr0pWNpBAAIQsNXqEIMABPwTMNVz/iMp5wxZ2rubk+c22tsmTz61NyfPS7BP1TzXq9UkT3s8Ms9bT5inPV6CJRICBCDgkYCtIM9jEOTp43HJDA2B2hGwEYa1W3yHBQ0NDTVOlKtBvmiRerlj1+DgYMM411Ir27dvF615vmvXrka7dldNa55rjRo1yue0mNp6GuPNBnXP46i0WLx+pTGqea6wdYzoZLoa9TquGvWdrvkiov/ilwrdrfv27ZMLL1QfvZjL9nPLtn0xq2AWCECgDgRstXod1swaIFB2Amjv9hky1d5a8/yRf/2B3LYpeQjY5OQ55nnZXx1j8QU5/dIJDeZ5NTYOUUKgrARsBXkegyBPn7LyIi4IlJEAAr59VrQmoprk7cqx6HNariWqc64/79ypB7EnLr2xUWS06xhqxA8MTJSs0ue1ZIv+M71Mc+Xz9Esz1jTzXA11LeCeddPQaMl66vwnInJpxo1BtazLchHZ0YHVh0Xk5nbPY56b7jDaQQACdSNgq9Xrtn7WA4EyEjDVc2WM3WdMptpbTfIV114jD33rW4lw7v/yV+Sb3/m2fHzjxoZ+71Z72+TJp/bm5LnPXWc4Nua5ISiaQQACbQnYCvI8RniePqQLAhAwJ2AjDM1HrXbLAwcOiJ5MOXRIy3NPvtQ415Pms2er9ysNIz06jW66cm2/YsWKxDTpW9sAACAASURBVKn2rL6mufIp4Jsxuirbon9x0JIsezPWronYllFLnZPnWRuI5yEAgZ4jYKvVew4QC4ZAAAKmei5AaMGmtNHeap7f9BcfkI984hONb4JGV3TyfP7CicfiC7LV3jZ58qm9Mc+DbcuJiT/557fLlFfnSF//1EQ0oyPHZfiEQ/L+v1qdeJyyLSVIGiFAoEQExgX5W/89EdXffPdXGj+31inPY4Tn6WOD6O6V75Cjr77StsuME6bJNVv+wWY42kKgcgQyheFdbxc5/EQ11jXrbJHrvtp1rFqKRU+rjI7qgefJlz4fnTqPnlUD3fQUuf6CoGNo7XSbKzNXzcF8CvjmFFGdcj1hHje+TW4YGi1Z69xo+8HYmFrXXI3y61tOmetjWsZFjXabK8i3Pm0/t2zb2wCgLQQg0NsEMM97O/+svpwEMvVcVbS3I92tWbLR3pFJfvPGDQnt3ck8z6O9M/MU214+tTfmeQlexxtuuEdmvDJXjp10JBHN9JdmytFpB2XtrSsSj2OelyBphACBEhGog3l++7LL5Wi/yIyRJNjosdU7vlYi4oQCAfcEMoXhrReKHH5cZNbr3E/ucsQoxhse6XpUFdgLFy5snApft27dJKO83QTaR0uxtJrq7drqjZBsjXMdJzNXzcl8CvjYevY0zezI/Nan9jcf61ReRdtp/ZoDMeNcH9O/JGxsjqElWiJTPjLq9Sak8blM8ox5bkKJNhCAQG0JYJ7XNrUsrMIEMvVcFbS3Q92tqbTR3pFJfrRPEtq7k3meR3tn5im2B31q7yqa5yre54rIwZabI5m+bIMI+E7BqXmuV6tJnvY45rlpqmkHgd4gYCvI85yuy9PHhr6a53q1muRpj9uMTVsIVIFApjBUAa+XA1PaKw/HceoJmOuvv1705kV6ae3EeJ1zr2tJGTwzV81+PgV8LDQ1wNXQVqNbL/2OrJ4kj9/YU8u76AlzrWk+BlJEH1vVbBsNp33UeNfT5dp+fay9muo6j/6zvYJob9vPLW0/Ovqi9PWdPGl9U/qmyQ1bPme7btpDAAIQaBCw1epggwAE/BPI1HOONa2XFXmIsVV7v+3Xf12uvmqlvP13fqdlCcMiMkVay7NklW2x5ZCZp9iAPrW3L/Nchbya3CrQVYjHxXcnVirUI+Gv/69fC9UTMXotEhE9XRNdOnbr10lN8hBEwHdcNOa5Sd5oAwEIpBCwFeS2hkIn0e8qKZjnrkgyTlUJZApDD+LYCytPcepNP/VmoCro9VSM1jrPc2rcxZozc9WcxKeAb1mH6u7I+G6nu/XGoneIyHkxM7x9LZyxm4xGp82jejY6pupu23ItUZhBtLftZ92tK6+V4dHJ5cMiQ721BJqLvcQYEIBAbxCw1eq9QYVVQiAsgUw950nTOl21xxgj7X3Plq3y08cfk79Ys0bePaDnLiauvr4+ee0ClZcTF+a5eYZVoOuplIiqnoDZLmNivNPVesJFf9ZfBiKhrydkIkNeT51n3dQoba4gAr7jwjHPzXcXLSEAgUkEbAW5raGgE+bpY5OqUOb5ys0Py1OHj7UN9cxZ02XLwFtslkFbCOQmUAsBr6v3KOIjuHryfNu2bak3Es2dBMOOmblqjlOgeW4YebBmQbS3q88tV+MEo8/EEIBAcAK2Wj14wAQAgR4gkKnnCtC0XWMuIMboxqBf/ed/NtLemOfmWdWve6pxHq+H2K4eY+uIrTcmimor6ikXrb2o5rletnUWW+cJIuA74aNsi/nmoiUEIDCZgK0gz2ME5Oljk6tQ5vnFn3pQnjx0TM6aMz0RbvTYQzddYrMM2kIgN4FaCHhdvSMR3+5moBFcrWm+ePHi1BuJ5k6CYcfMXDXHwTwfBxpEe7v63HI1juH2ohkEIFBDArZavYYIWBIESkcgU8850rReF+4wxjTtrWb4D374Q/ntP/h9I+2NeW6W8cjw1q93RuVWtKfWXtSfk2f8k2Oqea5GudZZjC79Sqn+rP0wz5tUqHluthlpBYFeIWAryPMYAXn62PAPaZ5rnK0muZrq7R63WRNtIWBDoBYCXhfsQMRrffP169c3SrO0u1Tcb9q0qVHGJX4tX768UQ9d66L7vDJz1Zwc83w8C5jnPjckY0MAAqUnYKvVS78gAoRADQhk6jkHmtY7JkcxdtLeaoZ/9Z/+Se758peMtDfmuVnW9beV6CZFcfNcy7aosW5zk6F2J8+17nl0oyMtA6Nme/SzWYQiQQR8p+A4eW6aOtpBAALtCNgK8jxGeJ4+NtnCPLehRds6EqiFgHdknqeZ41Hely5d2jDWFy1SWSiNOui7du2SzZs3y/bt2zHPy/cCCaK9XX1uuRqnfGkhIghAoCgCtlq9qLiYBwK9TKAW2tuRed5Je6sZvuLaa+SWT3/aSHtjnpu9qrTeuRrlc1pM7U0i8maDuufxWfQmRetiNc/1NyQdIzqZrka9jqun3NOu+SKi/+LXBSKydd++fXLhharlw1+Y5+FzQAQQqDIBW0GexwjI08eGKea5DS3a1pFALQS8JsaBiNfT47t375Z169bJsmUqLScufU5LtgwM6G1xkpc+rqY6J89L9wrBPC9dSggIAhAokoCtVi8yNuaCQK8SqIX2dqC7Nf+dtPfVK1fKG9/wBrnpAx8w0t6Y52avqDTzXA11vXFo1k1Do1n01PlPROTSjBuDalmX5XroKCW8D4vIze2eK5N5fssfvlNGR47KjONHEqEenTpT+vpnyI1fvDPx+C0rrpTRvily/IzkL5RTn94hfaPDcuM928yyRSsIQKAWBGwFeR4j3LbPX67ZLP0vzzLmO/VnX2r7/pVmqhsPnNEwrTwLZVtcEWYcUwK1EPC6WAciXgW8lmXRUzCtpVn0uejEeStbzHPT3VZ4O8zzwpEzIQQgUCYCtlq9TLETCwTqSqAW2tuB7o7M8zTt/d8uu7xhns9fqJZu8mqnvTHPzV4xrsq2aOkXLcmyN2NarZOuTnFaLfVKnDzXD9PR0Rfk5OGRxHJfnNIvfX2nyHu3bkk83vjw7euX4/OvSDw+9Zl7RUZHJrU3Sx2tIACBqhKwFeS2Rrhyse2z8YZ7ZPorc+XYtINGWKc++5W271+Y50b4aFQDArUQ8JoHRyI+T0oxz/NQK6QP5nkhmJkEAhAoKwFbrV7WdRAXBOpEoBbauwDd3ckMxzzP/4qI6pTrCfO48W1yw9BoVr07lLYfbD6gY2pdczXKr285Za6PaRkXNdpNryACvlNwth+mtu1NwdAOAhCoJgHb9wRbIzyvea791ty6wghqWkyY50b4aFQDArUQ8JjniZ3IDUPHcQTR3nk+69q9lbgapwZvUywBAhDIScBWq+echm4QgIAFgVpob8zzwrR3n8XeMm26p2lmR+a39tvffCytvEo0thaw1BuNxvtq7fONzTG0REtkykdGvd6ENN4+K84gAh7zPCstPA8BCOQlYCvI8xgBtn305Dnmed6M0q8XCdRCwGOeFybgK/YaCaK9bT+30pi6GqdiOSNcCEDAIQFbre5waoaCAARSCNRCe2OeF6a9fZjnaoCroa1Gt15aGEdPksdv7KnlXfSEudY011PleuljWn5F20aX9lHjXU+Xa/v1sfZqqus8+s/mCiLgOwVo+2Fq294GDm0hAIHqEbB9T8hjBNj2UfNcb0px39mnGwH9/T1j94JuLVPl++T53o/+hpw++nM5e/b0RJxPDB2TZ/tOk0Uf+pZR/DSCQLcEaiHgFUIBIj6NNWVbut2F3voH0d62n1tpq3c1jje6DAwBCJSegK1WL/2CCBACNSBQC+1dgO6mbMvYZvdhnuu4aqBHxrf+f9z01uf1Tpd3iMh5MTNcfZZ2V7wEjBrmeumYarrblGuJxg4i4Du9t9h+mNq2r8H7GkuAAAQ6ELB9T8hjBNj22dA8eV528/yJj1wg80d/LlPnnJMgfPzQY/JM32ly9s2PsvcgUAiBWgh4JVWAiG9NyN69e2VwcFDWrl0rS5YskeXLl8vAgEpRP1dmrprTUrZlnH8Q7W37uZW2W1yN42c3MioEIFAFArZavQprIkYIVJ1App4LoGmtmRYQYzvzvJP25oah1lksbYcgAh7zvLT7gcAgUHkCtoI8jxFg2ycyz9eWvOa5mud6tZrkaY9XfrOwgNISMBLwhx8XmfW60q6hEVgU4w2PlDvOLqLLzBXmeSvdINrb9nML87yLFwVdIQCBjgRstTo4IQAB/wQy9Zwa02XX3gXoblsz3LZ9VqYz8xQbwOfBFV8nz7PWH/L5IAIe8zxkypkbAvUmYCvI8xgKtn0wz+u951idewKZwvCut4scfsL9xD5GnHW2yHVf9TFyKcbMzBXmOeZ5KXYqQUAAAmUhYKvVyxI3cUCgzgQy9VxVtLdn3W1rhtu2z9pjmXnCPM9CmPt5zPPc6OgIAQiUkYCtILc1wnXNtn2qYp5/8s9vlymvzpG+/qmJ1I6OHJfhEw7J+/9qdRlTTkw1JGAjDGu4/EotyTRXPk+/VAqYSBDtbfu5lcbU1TgVyxnhQgACDgnYanWHUzMUBCCQQsBUz/U6QFsz3LZ9Fl+bPPnU3pw8z8pUAc/bfpjati9gCUwBAQgEJGD7npDHCLDtUxXzXOOc8cpcOXbSkUQGp780U45OOyimZWcCpp+pa0LARhjWZMmVXYZprnwK+IrBwzyvWMIIFwIQcEvAVqu7nZ3RIACBdgRM9Vyv07M1w23bZ/G1yZNP7Y15npWpAp63/TC1bV/AEpgCAhAISMD2PcHWCNel2fapknmu62s1yW3jD5h+pq4JARthWJMlV3YZprnyKeArBg/zvGIJI1wIQMAtAVut7nZ2RoMABDDP8+8BWzPctn1WZKa6W8fxqb0xz7MyVcDzth+mtu0LWAJTQAACAQnYvifYGuGY5wGTy9Q9Q8BGGPYMlJIu1DRXPgV8SdGkhYV5XrGEES4EIOCWgK1Wdzs7o0EAApjn+feArRlu2z4rMlPdjXmeRdL++SACvlOYth+mtu3tEdEDAhCoEgHb9wTM84nspp0w5+R5lV4B9YjVRhjWY8XVXYVprjDPx3McRHvn+axrtytdjVPdHU/kEIBAtwRstXq389EfAhDIJmCq57JHqncLWzPctn0WPZs8+dTenDzPylQBz9t+mNq2L2AJTAEBCAQkYPuekMcIsO1jaz6njX/7sssbZFfv+JoXwpjnXrAyaA4CNsIwx/B0cUjANFc+BbzD5RQxFOZ5EZSZAwIQKC0BW61e2oUQGARqRMBUz9VoybmWYmuG27bPCsomTz61N+Z5VqYKeN72w9S2fQFLYAoIQCAgAdv3BFsjXJdm2wfzPOCGYOpKErARhpVcYI2CNs2VTwFfMZyY5xVLGOFCAAJuCdhqdbezMxoEINCOgKme63V6tma4bfssvjZ58qm9Mc+zMlXA87YfprbtC1gCU0AAAgEJ2L4n2BrhmOcBk8vUPUPARhj2DJSSLtQ0Vz4FfEnRpIWFeV6xhBEuBCDgloCtVnc7O6NBAAKY5/n3gK0Zbts+KzJT3a3j+NTemOdZmSrgedsPU9v2BSyBKSAAgYAEbN8TMM8nkkXZloAbl6kTBGyEIeg6E9i7d68sWrQoE9OBAwdkwYIFme1aG5jmyqeAtw46bAfM87D8mR0CEAhMwFarBw6X6SHQEwRM9VxPwOiwSBMzPK69O7XPo71t8uRTe2OeF/hKuHvlO+Toq69MmvHFKf3S13eKvHfrlsRzth+yeQyxApfPVBCAQJcEPrjmbpn+8oxJo5z07D+IjI50/R7SKTzb9xfKtnSZbLr3HAEbYdhzcFIWPDQ0JLt375YlS5aMt9i4caMsW7bMyBS3aRsPwTRXPgV8xfYA5rmI3LryWhkenfx7QJTLKX3T5IYtn6tYagkXAhAwIdDp9/rR0Relr+9kk2EabXivMEZFwxQCnT6Peml/meq5Xt9IcTPcRHt3Ms/zaG+bPPnU3pjnBb4S9MZ3R/tFZowkJz06dab09c+QG794J+Z5gflgKghUjcAnb9ghp7wyS16YdjgR+kk/+3vpGx2WG+/Z1tV7COb5BAFb879qe4l4y0fARhiWL/pwES1evFgeeOABmT17tgwODoqeaBkYGJgU0PLly2XVqlUJo10b6ePbt2+3WoBprnwKeKuAwzfGPG/eOyTNJIsebz1IEz51RAABCLggkGaeZ/1RrXVu3itcZIMxdD+2+zzqtf1lqud6fce0muFZ2jtq/6fvW+tEe9vkyaf2xjwv8JWg5rleq3d8LTFrmknDyfMCk8NUEKgAATXP9Xr/rcsS0d5+xW+Pvbfc+/XE47bvIZ0QcPK8AhuEECtNwEYYVnqhlsHrCRc1uA8ePNi2p35NdNOmTQ3DfOnSpbJz585Eux07dsiuXbtk8+bNDZM8fkpdG+rzOnY7wz0tVNNc+RTwlhhDN8c8z7jxtu1nbOiEMj8EIGBHwNVr3NU4dtHTum4EXP6OWGU2pnquymvME3ur9n715Zcbw5xw4omN/8/S3p+97TPyyL/+QL6wbZsT7W2TJ5/aG/M8z27K2QfzPCc4ukEAAg0CmOfuNwI1z90zZcR8BGyEYb4Z6tdLTfWLLrpI1qxZ0zhxvnbt2tRT5HpKZsOGDZPMc6XSznTvRMs0Vz4FfMWyiXmOeV6xLUu4EHBLwJXp7Woct6tjtKoRwDwfy5ipnqtafl3HGz95bqK9o/aXX7ncifa2yZNP7Y157npndRgP87xA2EwFgRoSwDx3n1TMc/dMGTEfARthmG+G+vWK35xIT5Crga5Gerurk3muz+3Zs8cYkGmufAp442DL0bCW5rltzdhOpheGmPuNapsf9xEwom8CVcqxq9e4q3F854bxy00A8xzz3GaHxs1zE+1tYp7baG9T3a1r8qm9Mc9tdk2XbW+74rLGCNvfelNipMueeLbx89pbVyQet31T8/1h+pd/8UXpf3Hsqxqt18jJL8v7Pv6HXRKiOwQg0IlAlczzW/7wnTI6clRmHD9ilNS0Gyen/dHRaFCDRmlxpt2LwmBImkAgFwEbYZhrgtCdfvE/RYZTbpY4ZZrIa15vHOHTTz4nfSP9ifaf+dtPy7nnnCuX/+7vyhlnzZs0VtbJcy39smDBAqMYTHPlU8AbBVqeRrU0z21rxmKeF7shbfNTbHTM5oJAlXLs6vd0V+O44M8Y1SVg6zNVd6WdIzfVc3Vdv+m60m4Aqjf/VO28bFmypKyJea7f+jTV3jZ58qm9Mc9Nd4yDdmqej46Oyo7/nDwVpea5JmJNyc3zjTf+vUx/aaYcOylphkWPrbnlvzmgxBAQgEAagSqZ52O/0LwgJw+33CE5ZXFpZrVv8zwtzjQzn90JAV8EbIShrxi8jvuzfxMZflVkygnJaaLHXvurxtM/8/gh6RuZIqP9w+N9bnrfjfJ7l71DfvNtb5P5r5uDeW5Ms5CGtTXPlV7rTT7zmBIYYu73YZ48uI+CEX0SqFKOXb3GXY3jMy+MXX4CVXrt+KRZe+3tCF6aeb5q1arGfYla7yeEee4IfAmGCSLgdd3RyfP33HtfAsPGG+5p/FwF87wRZ4tJrqZ6u8dLkGtCgECtCFTNPG9nKqQlJK18ShHmuY35UasNxWJKRaD2Al7Nc71aTfK0x1uyo6dbnnvuucajLx55SebMmSsf++TNjTrnWstcn58783R5++W/b22e23x1VOc3zZXP0y+l2rzZwQTR3q5MJluTwba94nMVa3YqeqdFnjz0Dp16rLRKOXb1Gnc1Tj12AKvIS6BKr528azTpZ6rnTMaqY5tIe784NNRY3jkLFzbKI8a1d96T5zba2yZPPrU3J88L3OWY5wXCZioI1JAA5rn7pCIe3TNlxHwEbIRhvhkC98ppng8NDTVOtahBvmjRosYi9OT5v3zzG7Lhrz/e+Lro9u3bRWuef+OBb8pfvP/DmOeBU91mesxzbhha+K7k871w5IVPWKUcuzK9XY1TeLKYsFQEqvTa8Qmu9to7J7xW7R2dJP/hTw40jPO49t61a1dDo8cvk5PnmOc5k1NwtyACXtd4y4orZbRvihw/I1kTqFH2ZNpBfyfP73q7yOEn2mOedbbIdV81SkHaCXNOnhvhoxEEuiYwXp/7lYOJsY5OnSozjh+X1fd+PfG4S2FkK9Zt2+vJc/1r7h/O/VBiDf/4/XNFpE/e1bK2rmE2B3DJyFVMjNObBGov4HOa51oTUYV6/Cuhzzw29h549dWXy6qrr5Rlv/tbjZ8vXvZu2fqFe+XwtKnjm+jffvCIfPeb35C//vjNjTHUiB8YGBh/Xm98pDUX9Z/pZZorn6dfTGMtSbsg2tv2cyiNle3nhG17nddVrCXJdynCsM1DlW4+WQrAJQjCNschQ3b1Grcdh30dMuvlnbtKrx2fFE31nM8Yyjh2q/aOl23R57RcS1TnXH/euXNnYhn3f/kr8s3vfFs+vnGjE+1tkyef2puT5wXu1sabVF+/HJ9/RXLWkeMyMu2QvO9Tq7syvlI/TG+9UOTw4yKzXpecN3rshkf+f/beBUyu4joXXd3zEBoJPUEPLLCZEcG+zs3hSPg7JifXXNvI3GBiHpaQhWOZ83EkxBdyZEgYIUxC4gdCQ65l5drHEjbnYmyjpwUkmByQsEPi17kgwE782cQwxDZGD4NGkqURzKP7fmv37Oneu3ftqlV71X70rP19fGh2r1q16l+1q//+u3qVEQoinhvBJEaCgDME8Bn36ogPNx+61wUVWLkn+MbFSYyoZJ1q75dt+WhIPH/0R2+FKgDcuOcJJ7hyYuQkQHE6YRDQEcPrH78eDpw8UAg85k+ZD/ddel8wVgvxvL+/H3BnysDAQMCXL57fecca2HjHzTBj+jTv9Y/dcg/8yY3/DWYsXBiwHx6pQkd7Cd4+r2bXeKEwv3z58vFd7SYA63Ll+3BJ4ENx4rcBMwAAf1fbAwAbxv6tG46/Vcg/KXUdAPQ3NLL1G+5XxHPZea6bi+yvU9/faxzrJJRKUwKx+PfCte3ZAxaHZASoOSZ3wNiAyotVXVP9yLxmTGILuSrSs+MSdh2fKwr3juTdlsBFce9G8RyFc9xpPmMG0k7whHR/N7rfpapGuv86lXvr8tQ4VJfcW8Rzy0ll0yxW3EaHIRGbuqhR/QOK6hH9qsYm4rlN1qWNIMCHgA1hxt5NDzOLi5Srb+r68qWraztKRTznm0fiKZ8I6IjhZXsugwMnDsD8qfPzOYCxqPwYH7s6eL4LWIjnWIoFd4rjYeuNly+ef/f/+/b4zhd8/cAvj0Dv+lvga9+4P2D/s4O1g87D4jl+QMA+sH4j5dLlyvflksA3xIs/Z1yCn1/G7qEQvgsAFmvGhMJ5o8iOf6NYfu6Y8G7rN6pbEc9FPKc8Yiy2XJ+jqPyHJXhxYoQANcdGTh0Zcc0jqp8iYeQIenEbgYDMixooOj5XBO6t5N2WMz+KezeK4fi6v+vc7wIF9MZfcMaJ5zbcW5enxqG65N4inltOKptmVHGbuqhR/Yt4bpNFaSMIZIdAloSZq28Veqov50Q8z26+Sc/pIqAjhkjg8WoSpdMNU9ubMk4L8RwJdk9Pj7crfP369eNk3RfP550zKxAPiue/+OW/wysH+wPEXiWe40FIVOHc5MOWH5RLAt8w8JfGhPN9Dff2AwDuIm+8F84dbudfBQC7x17ALUR4D9v1AYCt36g5IuK5iOfatYPbgOtzFJX/cI9D/KkRoOY4Syy55hHVT5EwyjI/E61vmRe1jLcC9+b+fBDFvXU7ybENlkH0RfU4exvurctT4/PrknuLeJ7iSjm+SF3002CvivIp1EXNtXi+Ye1ur3zC4wtmB+K/9JXXvVrF6zcHa7mnCK10JQhMCASyJMxcfasSheK5d/7DabUdov7VfmA3lKqjcPPv/1vg/t2vrIHy0CyAcluTy8qUN+G2z1xrNCe062x4vfa9Es6LMApEjCY8AjpiyE2OXQF+6Q7cBA3wtQv/e6CLKpQBSm1QaW8unQKlEZh/9pzIkHCHy6pVqwAPL8IL65Yvv/qjcPkHPwRR4jnazA+J6irx3BYDXa58vy4J/FgfvuCNpVoay61gDS/829+NHjVUXyi/t+FFpHn4Nwro+LqN36i+RDwX8dz2cbNup31/374t4Jtqbx2YNGRDII2ccdUMp/JoFUjoJ6q8ENq3lTph7bavFnZec2HNNsFa2FEaz04R4NPxuSJwbxcxhrn3e/7zf4aPfWQF/Neb/sQorTqx3chJg5EuT43+XHJvEc+pmUtgrxTP0WeEEENd1FyL53etrW1OihLP8f7tIp4nmB3SVBDQI0Al3tQ1JC4Crr5Vfdx9x4NQPjmp6eX2g3sAqhW4JSRi9/Wvh8lDs5rEdl+A7930YT2gMWJG7HpNPC/CKBAxmvAI6IihC3LsAnSVeF7BzkptUG2v1Uj0r3K1HSoonocE73Bs+/bt8w4kQkKPO1zuWH8nfPquvwqY4c5zvCaQeH4JAKBQHha5sWwLAl37JsPsatx5/iyjX+xdxHMRz81mIaMVlQNR7RlDFVeWCKSRM5VYTa2FT+XRKkhUArMqnjQwskxfUzMurLniaWU/RZoXLvPQCtzb5ecDn3vv2LYdfvGrX3q1zk1+sSniuctZm67vTAg8DpH6pkld1NISz8MiuS+qi3ie7kSW3iYeAq7XkDhEufqmZk3Vb9/aHZ6r3s3LAy5V5V9U/VLXWc8P8bwI6pjFfmIi0AoEHjPni+ePLw8eYKwi0irBO24WfOyj18HfP/owHD1W243uXxNQPMef/KFQPjN0QOhWALjQoO55I3xY+H39WM1zFOVt/c4DAPyv8TofALY/99xzcMEFY+ftpPCYU9+3uN4nbN5XuGJNAdbCdEHNA9W+MEC0cKBp5IyrD9fPODVO1/HYTDvqGGz6kDY1BATrGg6twL1diuf+84Ic/tY7PgmP/s//CQMD+MPE+EvEGR7NTgAAIABJREFUcx1CxXldxHM/V0QBSCWSi3henMkvkRYbASrR5SRGXH1TMyDiORUxsS8qAq1A4BF7LvE86kAiP7dPfOs7cOnl72s6SFTE8/HZj8I3HhyqOzTUb4C7zl8GgPcDAO46V4nyJn7x5wB3Rj2HIp6v8GAJH+IdJ2QUdT3LQ9xUDkS1z8MYJ3oMaeSMqw8qj6bmlhqn63io8cetg3mM1WZ8eWpDnS95ip0zllbg3pziuYp7oxj+43/9V7j0yiuauHdUPkQ855yl2foS8VzE82xnoPQuCFgiQCWPnMSIq2/q0EU8pyIm9kVFoBUIPJd4jvXNN2zY4P08NOr6ypfuh689eD889c//6L2MhxThT0uPHx2EH/3oOVh78596tdH9q4VrnnOVbcGfCWCdcxTO8UriV3aefyRaJI97H6W+xxZ1nUszbioHotqnORbpKxqBNHLG1YfrZ5wap+t4bOYsdQw2fUibGgKCdQ2HVuDeXOJ5HPdGMfzRf/gH2PHIw14JxUbujf9++umn4YYbbhjn3iKet85KM+HE8weuej8MltoByu3BLFZGoKs6AisfetIou5uuvR6qlUHoGgke6DfYPg1K5S64+cH7jPyIkSAgCNghQCW6nMSIq2/qyHXi+bWz/jLg8htHPuX9/diC5sMHz5o+GbatfnfA3goj4q92qGMW+4mJQCsQeMwcx85z3PmydevWcYIenhEX/x//J9x+253w1sWL4fixo3Dv//M5+PM7PgUzB494pVzevug/wv/69j542znn1JriMZgAMGfheSyTS5crvxOXhxaN9eHXKccd5r7wjS+ZHBjqh4nfUKD9vgaf+E/8XW4Sv41YZ8K9qe9bqslBfZ+g2mO/XLGyTPAWcULNA9W+RWAq9DA4c8ZVS5y6jnAlgIpFHtcc6hi4sJuIfgTrWtZ1fI5LmHY5x7hijOPeKIYv//hK2PS3fwuLFi2CsNCOf8+cORNeeukl6O7uBhHPaRlfPXZQERajxEOMNoRqMaq8+VuM8KemeOEumP4GY1u/mRN4G2JMXdRU9luuvhQG29uhyzutq34NlgG6RkZgzZ7HjbKL/qvVEzBlNOjoZFsZSqWpkT9DNXIsRoKAIGCEAJXoUteQuCC4+jYaaIORqt+NYzXPPxoSzx888ilPJwuL578eOAVvmTkZnrr1vYEQrDAS8ZyaRrE3QKAVCDwOk0M8x90rzzzzDKxfvx6WLsUKIvULX1t47tvhj6/9OBzrbIef/Ph5WHrpxfC/fvYLOKd9BABG4d3vfT/cdMMqWPmRj3gNkbWUqy0pnuPw9o/xZV/8xnsvjd2rnfSuvpBXI89ubIu1z/sS+g33KOK5HBhqsArymlDf36n2vNGKNxsEOHNW+5x7EkqlKU2htJU6Ye22rxrxR9U4qDyaigcVC9fxUONHe+oYbPqQNjUEBOsaDq3AvbnE8zju/bEVK+D3fvd34dZPftLDDX/xuXjxYq/++YwZuI8DoKenB9atWwerV68W8Zyw0OCnnCUAcMNYGxTCsUairu4iCueNIjv+jaT+3DHh3dZvLgh83CJFfZOlLnYonuMVFslV97niIcwZMRUEBAEDBKhEl7pWxIXA1bfBMI0+nKjOWlAdJHrxPd/x/Ip4Ts2A2KeFQCsQeMSKSzzHnee4C8b/eaifByT38894m/fn/HNmjZN43Anj73SZv7DHa+eXbjn84s89uxbceY7DQq6MvHtZA+/GneS4ecW/sAwL8mqsae6fsor3kKs3nuyKbVB4v9fQr+njIeK5iOemc4XNjsqBqPZsgYojawQ4c+aa51L9U0GhYuE6Hmr8aE8dg00f0qaGgGBdw6EVuDeneK7i3h++7IOeeD6vx9/jXBPQkXv7V6lUGufesvPcfKVB0o1kvHEXS9SumLBH/HnoKgDwd8n4P0XF3ee4A8bWb7ifTAh83CKlgpa6qMXtPMc+RDw3n8RiKQjkEQEq0aWuIXFj5uqbiquqXxHPqUiKfd4RaAUCjxhziOe6XKkOBkWy/sV7t8IPn302ILq3uHiOcKGA7gvfUb/4xA0oX27YkIJtxorZNKHdWKpF51eXKv/1TLg39X3LNR+Pi4crVtOETAQ7Kgei2k8EDPM+Rs6cUZ9B1/ZU7KlYUOOnxmNjTx2DTR/SpoaAYF3DoRW4N5d4Hvds6MTwvr4+j3f7G1509tTnUJenRn8uSyaWqIFr7H3BG4l7Y7kVk9qLKJ6jUI67XfwLiT3+jffxdRu/4ZAzIfBxi5Rrss6+8/yinwZC/twP3uH9fcv2bczTSdwJAsVC4IEVV8Hg8FBk0F0dnbBy20OJBkQlupzESOVLNWauck4inieaMtK4QAjoiGEa5JgDrizF86984YvwyGPfgi9v+XpwKMNHoQRVmNvTuBnbfrS6XPmeXRJ4++gzaZkJ96a+Z7rm4yKepzv3qByIap/uaKS3KAQ4c0ZdL1zbUzNOxYIaPzUeG3vqGGz6kDY1BATrGg46PlcE7p1GjHFiOP5KdMeOHbBrFxYbqV0inputNPgTUP+noo3iOSKJwjr+rNT0atx5jocgcfnNhMDHLVKuybqI56ZTTuwEgWQIbFn6QfDOEog6X6ACsGb3txJ1QCW6nMRI5Us5ZqaDhEU8TzRlpHGBEGgFAo9wZyWe489H/8fWrXBH7zqAjhnwi1/+O7z1nFp5Fxg56u2zbvy5aZKpocuV71vE83GUM+He1PdM13xcxPMkTx29LZUDUe3pEUkLbgQ4c0ZdL9DeZY10KlZULKjjpcZjY08dg00fRWmjOsAW44+qwU8dl2BdQ0zH59IQpqm5C9unEaNKDEfujcL5xo21oyv7+/vlwFBCQvFnoSiUzwwdELoVAC40qHve2BUeWLR+7CemKMrb+J0HAPhf43U+AGx/7rnn4IILkMund1HfpKiLmlLckprn6SVZeprQCKCQjFdYJFfdp4Lleg2JiydOPI8as3+g57rNy6nDDNiLeJ4IPmlcIARagcAj3FmI5z55/68fuQYGjh6FX76OP1aE8cNGs9oBI+L5+AMo4rnUPE99Neb6HEXlXqkPdAJ3SM2xDc9VtVGJm76gHv5Ftut5RMXCdTw205I6Bps+itJG9eWMan5RxyVY1xBrBe6dlXjuc+/169fDkSNHvBroPvfOindj/y65N3fZFpV4jsI3VpfXHRrqP/e46/zlsYONMAu2fv8KAO6MWkxEPAeQA0OpbzNiLwjEIyDieR0fEc/laREEaAiYEPgDJw7A/KnzaY5Ttn71xKswd9KZ8PjyxnMo1T/hVNUvjwu7sc3Ro0dh5kzcsxG8XnrpJW/3C15ZkXiXBD7ltCbtTsRzEc+TziFye6o4RLUnByQN2BHgzBmXmMwZEwUwar9c46XEqLOljkHnr8ivu8bCtf+iYN8K3Nv/bPDY1Y85gz3Mo3XcOyvejQC45N7c4jlX2Rb8xIV1zmtfXwDY+pWd56AWyUU8d7a+iOMJioCI5/XEi3g+QR8CGbY1AjoCf/3j18OBkwes/afVcHR4GOZMOgO+dmXwHBQVkU4qnjeO6/BLP/f+nNNzXmC4WZF4lwQ+rXwy9SPiuYjnTFPJ3A1VHKLam0cilq4Q4MwZl5jMGRMFN2q/XOOlxKizpY5B56/Ir7vGwrX/omDfKtx7/pT5cN+l9zmDncqjqfa6wHV5amzvkntzi+d+nXLcYe4L3zgWkwND/TFjwRy03zd2A33ihb/BTeLX958JgcfOqW9StZ/rnIApo8ECyqqD+FT+Ny2/BqqlNhiZjxv461f7gd1Qqo7CzTt26uar93rsIlsqw8i8q5v8VKa8Cbd95loj/2I0MRC4+44HoXxyUuRgiz5fJqR47peFuvhQIKd9/euxxDBwlW15ZPHqgP8rn/s6VCuD0DVyPHB/sH0alMtd8IkHgwTi4nu+49k9det7A/ZW5HHzWMmvtc8X8qH9yV3vgenDwXz5AznWMRfeefs/GY0r7oBcdMBxSK5RIC1iRCGGeR4yVcAW8TzP2WSLLRPuTeXdqtFS3yeo9jafEdgy08KOqHmg2rcwdIUZGmfOslovuMCmYsE1Xq74tVoDAIRL4XD2nTdf1HxS43ftnxpPVvatwr1d40cVw6n2uvgpeSqSeI7j3j+2a9wXv/HeS2P3dmuAQXUEDxptbIu1z/sS+m3sNhMCb0OMN117vVIcKpW74OaQOEQVt9sP7gGoVozfiKji/OQ3psGp045D76YP654HeX0CIdB38zfBnxuNw26F+TJxxfMqrLn4cGAWb+zHIyvciedX7L+X9OWiiOf19Lzy1+fD3Mpv4FD5zEDO/HsL7nzBaEVSHRaLjf2Dc5MekmsUSIsYUYhhnocs4nk9Oy4JfJ7nQERsmXBvLnGIKjJQ7W0+IxQs/5mES80D1T6TQUmnAQQ4c5bVesGVUioWXOPlij9uHcxjrJzjjvJFzSc1Htf+qfFkZd8q3Ns1flQxnGqvi5+SJ5fcm3vnOY4bBfAlALBsDAQsOIk7yXsaQMEyLLjD/P0NB4vivRvGbH1TbIPC+72GfnW44+uZEHgbYqwqe6C6T10EqW9EKvsvXf0BD/cb9zwRwB9FUrxEPDeZlhPHRjUvWmG+TFzxHGDNnscDk/iutbXvSm/fHPzFC3Wmc61rIp7XkUfxHK+wSK66r8pZ3EG4XIfkUudLke0pxDDP4xTxvJ4dlwQ+z3MgIrZMuDeV56ow5XofiouHK9aCzQun4aaRN6cDEOdaBKg5jnPI9QxyxqQFoMGA2i/XeCkx6mypY9D5K/LrrrFw7b8o2LcK93aNN1UMp9rr4qfkySX3diGe49hRQPeFb/z/hgaRHF9HNeXLAHBuw338hX/U1ViqRedXhzu+ngmBx46pb1IinpukU2yKhoCI5/YZo64hnMRI5Ut1doKI5/Z5dt1SxHPXCNv5pxBDux7SaSXieR1nlwQ+nWyy9ZIJ96a+Z6pGS30vpdrbfEZgy0wLO6LmgWrfwtAVZmicOctqveACm4oF13i54o9bB/MYK+e4o3xR80mNx7V/ajxZ2bcK93aNH1UMp9rr4qfkySX3diWe68af5euZEHgbYoziOSaotxu/e6hfqlrCukUwXDMYyx7gZVo/THaeZzltW6dvEc/tc0klj7o1wfTZj1u//F+ebL+oVqbFvy595XXvn5nsPI84g2GkUoWTnYPw2XtWBuK0wqjgNc8nonjOVefd/unVt6QQQ7237CwOv1g7tPPI1LmBIGacfM37e14P/iCxftnUPD/U/+9QrVagDKMBX3hCTFkODM0u+eqeM+He1PdMVfjU9wmqfdx7bB6TWZSYqHmg2hcFh1aOkzNnWa0XXPmhYsE1Xq7449bBPMbKOe4oX9R8UuNx7Z8aT1b2rcK9XeNHFcOp9rr4KXkS8VyHJu31TAi8DTEe33keEs9VtYR1i6CI57SJItZuEBDx3B5XKnnUrQmuxXP88m99ymVbVAckn/bGNDjReaxJzLfCSMRzbxIXqWwLV513+6dX35JCDPXesrNA8bwKVRiYOi8QBKd4XiPlo55QHr7KpRKc0b0wcDsrEu+SwGeXYaueM+He1PdM1cio7xNUe/8zQrV6EkqlKU1htJU6Ye22r1oBn9dGm1d8HEarQ5HhcY2XmgeqfdwYVLhzjS2veU07LmrO4uLLar3gwoyKBdd4ueKP00ow1qKvj9Q1j5pPah5c+6fGk5V9q3BvKn6H+l+GajW68EepVIK53VggpH5ReTTVXhc/JU8uubfsPNdlivF16puUquyB6j51EaTGo7KXmueMk2QCuBLx3D7JXM8s1U8coXX9/FPXNWoZGap/L3sinnswFE08x5iT1nm3f3r1LSnEUO8tOwt/5/mchecZEW+bnedUUk6116FnmiuXBF4XY85eF/FcU75RJaz4ghHly+6c5T4yHJUYxjle6vu7jb1K0IsaNOfYipDjNGKk5iwuJhtuHOWPMyYKhtR+ucZLiVFnq4qpFdZH6ppHzacO2/Drrv1T48nK3pTPZRWfq379TSgAbaEu8BedbU2/EqXyaKq9bpyUPLnk3iKe6zLF+Dr1TUrEc0bwxVVuEBDx3D4V1DWEkxhl9eUZdQwinuvn10Qs28I1Zj269hYUYmjfi/uWIp7XMXZJ4N1nkrUHEc8tzj7CDFDf91mz5tAZ9b3dJhRqH1z2qlhbNZc2ueFqQ81ZXL9c+eGMiYITtV+u8VJi1NlSY6La6/p3+TpXfrjGTI3HJTZZ+m4V7k3FUCVuU++r+hXxnJqR/NpnQuBtCLCI5/mdRBKZPQIinttjRyVMnMRIxPOGvMnOcw8M2Xlu/yxHtWwVAp8H8fzZZ5+FRYsWjcOsIvH9/f3Q3R2swW6SVdNciXg+jmYm3Jv6nkkVPanvsTbx2LQxmcNZ21Cxs4mX2geXPXUe2YxN2tQQoOYsDjeuZ40zJkqeqf1yjZcSo86WGhPVXte/y9e58sM1Zmo8LrHJ0rcpn8syRhd9U0VyEzG8kXvH2dtwb0qeXHJv2XnuYjYqfFIXOxTPpw5NhzdOOx7wGFu7N+KgvPaDewCqlaaDQVXxPLDiKhgcbq5DeLKtDKXS1CY/WLYBJ9Kaiw8H4sSDTQFK0Lv5mhRRlq7yjkDf2p0AUI08CLfo80UlJsaJjJR8UdeQcfuLfhro5nM/eIf3N+Vn4FmK51E/i1b9/Lm287zatB7ZnhURiZGI5978KZJ4ftefb4G24ZlQKrcHnoVqZQRGOwbg9r9ZQ3kUndhSiKFRAK/9HGA0uqYwtHUCnBEsq2Lk08AoTfH8tNmz4JlnnoFLLrlkPLK+vj5YunRpQBRXkfgoW4MhgmmuXBJ4kzhzZCPiueUucur7fo5yHhtKGsINtQ8ue9XAWzWXWc45as7iYuXKD2dMFGyp/XKNlxKjzpYaE9Ve17/L17nywzVmajwuscnStymfyzJGF30nEc+PHj2q5d5x4rkN96bkySX3FvHcxWxU+KQudp+89QGYMtQF7eVgmkYqVTjZOQifvWdloCfVQXntB3ZDqToKN+9A0bJ+qeJBQWSwDNBVCQ5ksH0alMpdcPOD9wVe+NKYWHVjpHgO0Lt5eYooS1d5R6Bv7Q4vxN7QQbi1L1uKPV9EPK/PPtUvDKjzc/OK62C0+mZks7bSJFi77f7Aa+NlWy4+FLiP8wuPRVkXWo+syKOI5x62RRLP8QDurqFZcCr0ZfTkN6bBYOeRpnlBnacc9hRiaNTfoZ8AjA4DtHUEzf17c99p5IZqlKZ4Pq+nGxYvXgxPPvkkzJgxA/bt2we4o2X16tWBsJHEr7rpJlj7Z7cEhHY0WrZsGezatYs0TNNcuSTwpICzNy68eE75EhffVyj2cemhfnbIPtVmEVi995q5Hrei9sFlrwqzVXNJTAurOTVnaTxrnDFRwKL2m8f5SI2Jak/Bk9uWKz9cY6bGw41HXvyZ8rm8xMsVR7x4Xqt7HvrwEKiFruPevv8/vW0d3HDDDYm5NyVPLrm3iOdcM9DAD3Wxu/ie73hen7r1vQHvqvuqg/tU9+PEc+xwze5vBfpF8QGvsPj0hasv8+7ftOexgP24SCriucHsmDgmqnnRCvMlt+L59m2BCUZdi7Cxqo3rA0OpT4YqB6r1y4o8injupaVo4nnU+5dqXlDnHYc9hRga9YfiOV5hkVx138ip3ohbPMcdLihwHzlyZLzz4TdrX6h1TJoE+DPRrVu3eoL5kiVLYO/evYEgd+/eDf+4dy98fft22P3NbzYReHwdfYcF97iRmubKJYHXZyJXFoUWz1WH1SHCbaVOWLvtqwGwqfZxmbJ5v85V5hXBWL33EgdG7YPLXhVmq+aSmBZWc2rO0njWOGOigEXtN4/zkRoT1Z6CJ7ctV364xkyNhxuPvPgz5XN5iZcrDpV4fqj/ZahWq3Ds+HFY/ac3wcDRo+NdlqAE7ZM6vb913PsrX/giPP8vP4Zv7NzpbVBp/IUotqdyb0qeXHJvEc+5ZqCBH+piJ+K5AahiUjgERDy3Txl1DeEkRiKeN+RNxHMPDBHP7Z/lqJYUYmjUc4uI51FjbdzR8q53vQt6e3u9Hefr1q2L3EWO9h+44kPwuc9/vonAo/8o0T0OY9NcuSTwRnMgP0aFFs+zhJH6vp9lrJS+OfmJql9qH1z21HgouIltEAFqzuLw43rWOGOi5JvaL9d4KTHqbKkxUe11/bt8nSs/XGOmxuMSmyx9m/K5LGN00bdJDXNVv7ihRce9ff8fvGYZbNy4MTH3puTJJfcW8dzFbFT4pC52RRLP8Ruq3b/fGxj5Za8c9mqhS9mWFCdZAbpC8RzLZzy2YE7LzZc87jyvVk/ApGH8+VX9erOjLfL8Au2HiqgzFQ7s9pqNzF8aaI7lMLBERu+mD6c6K1l3npfK8Macq5rib4PR3NTJtgH3/ivfB6fK7VAqB8t5VCvDMLkyAtc9/G0jt0USzzddez1UK4PQNRI8Q0RVjswIAGYjCjE06joj8fzQSy9BFd/9O2cGwxwewOMIAEutNF4HflnbUT7/nFlGw0Ijn5S/euzo+MGguIsFBXQU0sOXTjzHn5/u37/fuH/TXLkk8MbB5sNQxHPLPFA/O1h2k3qzNIQbah9c9iowWzWXqU+ehg6pOdPyXOJ5QFH+OGOiYEvtN4/zkRoT1Z6CJ7ctV364xkyNhxuPvPgz5XN5iZcrjiTieePBoCrubSKeU7g3JU8uubeI51wz0MAPdbErkniOw9910a0BFFA8xytc5sUAKjFpYQT8MglR4nnR50vexHNfMOwcOhaYUUOd0yPPL4ibdnFnKmC7sHiO9ypT3oTbPnNtqrOZSzz3x/vG3Gbx//Sh6bmpk20DLpbaOdXe0XyuRRlg8sgw3LjnCSO3RRLP8f0Xv0iaMho8zEN1ELYRAMxGFGJo1HVG4rlHmJFddoTE86EBKEEV5vb0BMJPIp43CvF4AFF3d7d3WGj40onnuPMcS79ge5PLNFcuCbxJnDmyEfHcMhnUzw6W3aTezEa4oZbDofbBZa8Cs1VzmfrkaeiQmrO4WLnyU+MbJ6FUmhLoTnXQPRd+VCxUcWI8UeWouOLkzAFXzrIcm03eMN5bQiU5qWOI6zdq/mY5L6hjU9lHvYe888orYME7/jc44/TTYW73uVxd5d5PEvG8cXAq7m0inlO4tynvxthccm8Rz1Oc2tQFvijiOVWsShFy6SqHCKhqDOep9rAtbHkTz1WHdtoc5kk9U8EWw6TtqOuRal2OE4aLPle5clk08Tzqwwb1fTnp/IxrTyGGRnFkKZ5D8w7zwy/+mxf2nIW/EwifSzzHA4nwp6ThuorYmYjnRjPGpZGI55bo5mmNshxCZDOqYIROqKIktQ8uexVOrZpLznlB9UXNWZx/rvxQv+Shjpk6v1TjUsXpWuTnzAFXzrhyYDM26hzmGnOR5gVXfqLeQ2ri+Ttg9tTTm34ZydVvHv1wiecq7i3ieR6zbhdTJgTeJ31RH95VwxDx3C7B0irfCIh4bp8fKmES8byONfXAUBHP9fNUxHM9RhQLEc/jy7bg7pbXX3/dg/Tk0aMwc8YM+PTGjV6dc6ynmGTnOeWno9i/aa5c7n6hzK0c2GbCvanvmTnAqSmEVhhDFK5UwSjucxTVl2t7qriZx3lXlJioubQRNycaFlmuOdS+qfZZ5pI6V6n21LFRsaPaU+NJwz5qDMjn3jw5CLOnThXxPCIJjdwbX549e7ZXHlHHvU3Ecwr3NuXdGKNL7i07z9N4Usf6oC46KJ7/euAUvGXm5ECU/r2nbn1v4D51N6EfzyOLVwf8LPvBPd7fN+15LHBfJT5Rd3re3XsvlN+crkS+MukY3NYXjCnFNE2cru6/HODYK9Hjnb4A4LpHnWDRyuJ5XGmTUnUUbt6xMxGm1DUExXO/9nhjxzb1yKnrS6KBJmhMXY9UmKKfwTI0lTbB0PJUJzsOqi9d/UdYnb3JxK95Hy61035gN1DmqWq+Y4dUXwlSbtTU9YcQoyA0RhRiaNLfay++ABWsn1IKUb1qFcpQhTMWnm/ihmwzvptl6qlA28O/rdXYp+48P3r0qLejHAXyRYsWeT6wj3/63vdg4+bPe6VWdu3aBVh38emnn/bswpdu5zmFwKNv01y5JPDkxGTbQMRzS/yp7/uW3aTezGZNprbJyl4FZqvmMvXJ09AhNcdxsRY9P1xYZIkDtW+qfZHmKlc+udajImFNGbOI59HlCqO4N+K6b98+TzjXcW8Rz7NcbXj7zoTA4xCoi86Ke38Irx4Lfvj0oThr+mTYtvrdAWSo4pZKPF/6/T4olUrOxHM8MHLy0Cw41Vk7JKzx8u/LIaO8kz7S2+YLAI79CmD62cGX/Xtrn3cSRCuL594zFXWo5sE9ANWKs/p0qkTdfceDUD45KfJlaj1y6vriZPIYOOUSzx9YcRUMDg9F9pinOtlxkHzh6stgqL0EnSN4RG/98mret02H4TlLAvfbifNUNd/RKdWXQWoTmbj+EJIouLHGpoKsaV9YJqVSKkE51ACrvper1SYR29Svzo5bPMeaiEjUG8ux+H18bM0NgD8Z9euco+3evXsDIeLBRg/t2AGf6evzfKAQv3p1/Qt6fB3rneN/ppdprkQ8H0c0E+5N5d2m+U/TrhXGEIWXzZpMbZOVvWp+tGou03wewn1RcxwXa9Hzw4VFljhQ+6baF2mucuWTaz0qEtaUMYt4Hi2eR3FvH1d8Tce9n3jk7+Cfv/89Nu5tyrsxRpfcW3aep7iKul50qOKWKh4UXPBytfMcxXO8ogTyuNdSTNXE6ArFc7zCIrnqPhMqLS+eRxziwvXsc/mxSSV1fbHpg6MNl3jeCh+wVGu5ap2lzq84e6ovjtzb5CxPceqI4S8+fh0MHzhgDNXo2Jc/bR2dgTaq+8aONYajw8NQPvNMOG9n7b3evw6/9HPvn3N6zgvcj6t53t/fD7grfGBgINDGF8/v7NvoGRfNAAAgAElEQVTo7TSfMWOG9zqSeX9HTGODuNqOaL98+fLxXe0mOOhy5ftwSeBN4syRjYjnlsngWqOyqsOsGraNMBTXhnJAI7Vvqj11zJZTQ5rFbEyzeW5s2uQpCa0wT6k5oNpnmS9qfqj21DWeih3VPi4ezEMWh9JGjUEnnlO5d1ZzrGP+fHjrV+837j6OF6u4t+8cubaOe+tqqlO5tynvxhhdcm8Rz42nWHJD6qJD7ZEqbol4TkW4xexFPGdPKJXoUANwvYbExUNdX6hj47IX8byOpIjndSxcP5sc81dHDF/8wKUw/Oqr0HHWWUbdZSWeD//611CeMwfO/863A3HaiOdYigV3ilerwV9P+KT8u889O77rvJHUh3eRq0g8fkDAPrB+I+XS5cr35ZLAU+LNga2I55ZJ4HrfRz8UgdkyXONmNmuyqg2XaESNiZobqr0xmBPYkJqzOKiKnh8uLLLEgdo31T7LR4WaHxt7yhpPxc7GPioezEFWh9LaiOdU7p3FHPM/Gyx84nHj7uPEbRX39p3j6/4vPlXcWyfOU7m3Ke/GeFxybxHPjadYckPqokPtkSpuiXhORbjF7EU8Z08olehQA3C9hsTFQ11fqGPjshfxvI6kiOd1LFw/mxzzV0cMkcDjZUqOsWwLXuEa46r7HGNAHy+8932eKw7xHMXtnp4eb1f4+vXrx8m6jpRjKZZGYq+yx4OQqMI5jk2XKx9LlwSeK18p+RHx3BJorvf9vK2BNvFkhYVNrFHp5orfciq1ZDOu3CA4Rc8PFxZZ4kDtm2qf5UNAzU9W9iqMqFjH2VN9ceXNVjyncG+uWCl+qJ8P0LeOR0dx77iYkK83cu84/zbc25R3Y4wuubeI55SZmdDW9UJBFbdEPE+Y0II3v/vWLVAemglQbg+OpDIClc4BuO2eNU5GuOna66FaGYSukeMB/0U5hBGD/sld74Hpw4ea8Nn509phdrds3xZ4TfWsUWuSu15D4hKO68up9o6mAzTxUM3JI8Nw454nnMwXqlPVQZ+q+WWDqU0b6jg47NMSz695x7PGzwLHuGx8UD+E2PSRtI2OGFLJcR7Fc6/eegioCrRBqVSGud1vi4QQd6esWrUK8PAivLBu+fIrroTL//APYV5PdK3GsCPdz0epudPlyvfnksBTY87YXsRzywRwvd/kbQ20iScrLGxijUo3V/yWU6klm3HlBsEpen64sMgSB2rfVPssHwJqfrKyV2FExVrE8/RmG/XzAUam48VR3LuxzrludDr/uvbh1015N7Zzyb1FPKdmLoE9ddGhdiXiORWxiW0/fnDraUERe/Ib07zDXF0d2orPQbV6AqaMooxSv4pyCCNG/Mpfnw9zK7+BQ+UzA2Ogiud9N38TPLyjcnDacejd9OGAf9drSNwTcf+V74NT5XYolTsCZtXKMEyujMB1DwdLNGT1dKkO+lTNLxtMbdpkgYeI53XUqR9CssiXjhhSyXHexPPX+l+ESqj8CuJceydo0wrh+/bt8w4DRUKPO1zu6O2FT2/caJSqrEi8SwJvNPD8GIl4bpkLrvebvK2BNvFkhYVNrFHp5orfciq1ZDOu3CA4Rc8PFxZZ4kDtm2qf5UNAzU9W9iqMqFiLeJ7ebKN+PsDITHlxmHtjrXOTX2ya+jdFSfcZqdGPS+4t4rlpxhjsqIsOtUsRz6mITWx71aGBrg9tpZKBPGYJxXO8Ftz5gpG4rRoziud4hUVy1X3Xa0gc1qoxq+7nLW+c8y7LPFBwTUs8D//SIo8fQjnzT8kBxVZHDKnkOG/iuQoLG4L9sRUr4O8fewyOHjtmBLFNH3GOdbny27ok8EYDz4+RiOeWueB6v8nbGmgTT1ZY2MQalW6VH2rNdsupFNksy745xhGXG1W9Zew36rBCrvnFMS4bH67nqU1M1DbUHFDtqfFw2lPzk5W9asxUrG3Ec+p6RLWPikl3YCiVe3POGVNfNjHa8GLceb5z504YGBjQhmbjn4N3ow+X3FvEc23q+Qyoiw61ZxHPqYhNbHsRz+3zL+J5HTsRz+3nkeuWIp7XEaZ+CHGdmyj/OkGWSo6LLp5HHUjk4/bEI38Hl155RdNBoqq8ZUXiXRL4FOfoDACYBQBHAKBWO4d+iXhOx8xrwfXZIW9roE08WWFhE2tUuqlCbxqH6mFMlEMGLaexs2Y2X0iocOWaX84Gq3Hsep6mMS5qDqj2aYxB1Qc1P1nZU+O3sedaC6nrl4jn9WypeHEc98aa5osXLzbi3lnxbhyhS+4t4nmKq6jrBZ5ak1gVj0pw2bh2h4fWus3LA6hRawyjaFsFgMcWzGlC/7JXDgNOyqQlQ1S1pDsP/QNUR4/D5NGTTX13dXTCym0PpTgjAOD+ywGOvRLd5/QFANc96iweEc/r0FJrj4t4XsdOxHNnj2hix5uWXwPVUhuMzF8a8KUqzUR9j7LZVZJ4UBoHaZTtcTUGEc/r9cuxvvmGDRsAfx4adX3lC1+Er23fBk9997vey/izUizlgheWdlm+fLnRgaG2udTlyvfrksCHYl8NAChyo7jdAwAbCEI3Ar8LABaHfOIhHvsb7qHvVQCw2wI3Ec8tQMMm1HVZ1Q1ViLEM17iZTTxZYWETaxQQVD9c441LCjUm4wSnZGiDUdHH7PoZt8GUK93Uvqn2XHHa+KHOu6zsqfPLxj6rsYl4Xs9WlLit494orG/dutXj2TruLeK5zSqRzzaZEHhOAqyClVqTmEs8p4oVvgivEs9xfGGBnjqVVLWk2w/ugWrleFO9bTz0sKsCsGb3t6hdJbPffAHAsV8BTD876Me/t/b5ZP5jWot4XgeHWntcxPM6diKeO3tEEzv21vhSGUbmXR30pTgUmPohJI/iOfXLXOqYEyclxoFOkJ1IO8/DBD0M28V/8AfwyVt74QNXfMh7aebMmbBr1y7vMFF/Zwz+rHTGDNSTzWs7muZXlyvfT0riOX47tgQAbhjrVyWGh4d3CQAsG7uJ4nt4Qw2+7gvyuOu8+WRgU8AAMuHeeXq+zaEKWnKNgSpW2MZr2s4mnqywsIk1CgeqH67xxuWEGpNpftOys8Go6GNWYcs1LhtMufJN7ZtqzxWnjR9qfrKyp84vG/usxibieT1bUeK2jnsvWbLE29SyaBHurYjn3iKe26wS+WyTCYFHKFwv8NSaxFziOXXRVO1gRz9xr1GmE7VmNAoueGUinmPHYZEcRfWo+xQQNLYintcBotYeF/G8jp2I54wPJbMr5XuOYn2hvkflVTyPWstV7y3UMTOnKOBOJ8hOJPEc6yo+88wzsH79+sAOcgQMX1t4zjnwsY+sGD9kFHedd3fXdq5PQPH8pTHhfF/DhMId4+twU77BnEWRHLcRRYnn2NzEh66bTLh3np5vHUBULs1R6zWNzybUcaXxvuJauOEaM8apqtsdVbPbZo5RsbDpg9KGa17H9Zm3MVPwSWNccfMO++eae1Fjoa7ZVHsurOPmqQoj6rzLyp66ftnYpzE203JUUvO8/qtPHffGki2rV+Nei9oVx71FPOdabbL3kwmBT4Oginhen1winusfNBHP9fNFNY9EPK9jJ+K5/lnLykLE8zryIp7XsVDVQueapy+8932eq/O/820jl1EEGwk8/jQUd8H4Pw/1neFrZ02v7Sif11Mn/f7r69ahZgyBki9ZkfgUdp4jEHhyE5ZqqdWtqV0ohuPf/m70uFyIeG40U7MxihMZTMWBuM8gWYlPVPGE83MUtW+qvWqmUP2oBDrOWujUmFw/BSrhlrNOed7GzIUp17jihGHOuRc1bup6RLXnxFr1xRZ1rlLz5tqeun7Z2LseA+VLOBHPg+J5HPf2d5xH5TzMvbPi3RibS+4tNc+5VlEDP64XeBHP60kQ8Vw/IUU8188XEc/rGFHXF/0MTNeCStTionO9lnMhI+J5HUkRz+tYFEE81z0DUaQcd5zv2LHD23mOJVz8ki3oKysS75LAj2HkC99h8RxrmKOwjuVcdFeceI6/zfUPCcWa6PjNhM2hoZlsXCnKWm3zfkN9T6Pa6yZN0tdt4uHKJ7Vvqj1VTKKOi2rPOb+S5l3Xnoq1DRbUPnQx5+X1NMZlgzcFH6p/qj0lFpvnBttQ85A3e+r6ZWPvesyUPIt43rwJhYKfintnxbsxdpfcu4jiOX4YmAUAWH+xMAQ+bjGlTNA4WxS3RitVWDl1a8DsgRM3QFu5BAvufCFwX7VwUQ8MpS6am669HqqVQegaOd7UdLB9GpTKXXDzg/cFXyMerFkU8fzgp98OI5UqfLRrS2C83xhcA+3lEsz7i59xTY8mP5+/9nqoROQBc1Aud8EnwjlgioT6hsl5qOpf9D4Ak9/sahrJ1KHpcLLzGKzfHDxYUTWP7vrzLdA2PBNK5faAL6ypD9UKPLK4/pMmNLhi/72e3S3btwXsqeVisiKJGDR1fWGaLmxuyPMupufM8kBcB0U8rydxIovnR6bOC8zmWScOen/PWfg7gfsHfv06lCrlyJlfLVdg/ltmGz2PHDvPsaND/S9DtYrHi0ddo/jj8cid5/gzUvxp6f79+8dLuWRF4l0S+DFU8E0LhfKZIU6MRPDCiENAo8BUieconKOP2hsYANqhXxTqqZeI51TExuyp711c9pbhGjejxomOud57qX1T7VUguPZjDH6DIVdMNn1HtaHGYzMnqH1wjc21nzTGZYM3ZdxU/1R7SixxtnH9UvOQN3vq+mVj73rMlDyLeJ5MPPexDnPvrHg3xuOSe7sSz1E58g8ZQpK9gSB0qw46QhKPNRz9C4XzVQCwm/KAQEaHFnGSPtV4qUJsVuI59lutnmg6tBPHdbKtDKXS1CaREYgHaxZFPM9SkFTlQZkD4oNGfTONFfqYDlW9a+1uQKH8ROexpvBOdg7CZ+9ZGbivmkcoxHUNzYJTpwW/AGo/sBtK1VF4+MIbA35aQTynri9M04XNDZWo2ZJmtoCjHBHXQRHP6yCKeF7HQiWeH/zVAJQqbVAtozBdv/x7885GfVZ/cYnnNeJdE8mjrhKUYG7PubBv3z7AQ4wahfaenh6vVjoebIRXViTeJYEfw0QlnqOgjnwad4vrLpV4HtUOv83AQ0bjuDd+WxP8xgbgfADY/txzz8EFF4yd6aKLiuH1rIQVhtDHXVDfu7jsOccQ5YsaJ/rgyie1b6q9CjvXfmxyxhWTTd8c88JmTuRtzFlhZ9OvDd6Ufqj+qfaUWOJsRTzXI5klRvro6hYintuJ5zrunRXvxsy65N4uxHMk8vgzUb/OokoMD89rJO9IyPFC8T3q4CJfkMdd589SHowG20x2v3CSPuW4VYdMEg+Hc73z3GYx9cRzvAwP1iySeI7DCv8qII060lmRR3K/xNzHrQsonuN1e2iH+cX3fMe7/9St7w00jxPP0XDd5uUB+y1XX+r9vWbP44H7qjEXaec59Rm0XJ+dNSPPu5hIsiLr1ByIeF5P4kQWz8M7zFVlW1A8xysskqvuqx4RXvE8uq750Kv/6nXfedbvegcWoUiOdRr9q1QqeaVbUEDHKysS75LAj43VZdmWqBTjJNmpqaX+VwBwZ1RjEc/pb3HU9y4ue0rNWBwV1Z4aJ/bB9d5L7Ztqr8qyaz/02UUvMWHTB6VNHEaUGv9xfXLlgTKuNGzTGJeqD+rzT31GuOy58mCjZVDzk5U9FWubsxlcj42SZxHP7cRzHffOindj7l1ybxfi+UtjpHpfw8TFHeNYJ7Hxnmpeuz64SMTzMeRVC5eI55QlN5ltlnWkqW9cyUZab03uV8TzcfC4Pjha5ZIxD1b9J2xEnncx/WWWB2IORDyvJ7FVxPPhV1+FjrPOMnoaRoeHPLu2js6AvfL+SG3HeVt7cKf3qOK+KojhX/8aynPmJDowFH3HEe9G8Rxt8WBRJPJ4Pf30095O9NWr6+WzsiLxLgn8GP7+gaG4w7xxUwnHgaEolId/4Yn3sIxL7VTW6Et2nhs9oWZG1PcuTnuKWIn9Uu0RgXA5OxtRygxJex5KxVQVj2s/VBzQnismm76j2rgWZ/M4ZtfYcXJW119uUGOl2rvG2mZ+UZ9B1/Y265fq8NS2Uies3fbVJpdZjSFqbCbiOYV7c80xih8/voVPBDfvxfng4MVx3JvDf2P8mCe8Fi5cqIXGJffmFs99Eh8+uIiDxKOojpeJAB8HqojnY+h4C1epDCNzPhTAq/3Qw17piZt34Oai+qUSH2wWWWwTJsxxbzgPXPV+GCy1A4RqTJ9qmwLQdjqcOvMPA2GoalirFustSz/otV+z+1vaBzLO4O47HoTyyUmRJpUpb8Jtn7k28FqceB5Vv95vfNb0ybBt9bsTxepjYVqfO1FnDY2pb5jU3bYPrLgKBsdEo3DMqpr6/++Vl8Ab5TJgGYDGa7BzlleD/+H/+MeB+5e9ctj7O3rneRXWXFx73b8+94N3eP8MY33lc1+v1f8fwh/T1C/sN6rufFYk0YuMWDKEa75w+SHPu5iOM8uDrXh+0U+Do/HLIIV+yaNaE7Bx1JqjEzm88lwRz2IXVGDlHqQF+ku1pnYe+geojh6HyaMng89OGaCr0ryWt4J4/ouPXwfDBw7oQRuzGB0erv2rFKpjXq0o7pcASlVoKwXLtoxWa2J6WFRXBYL9ls88E87bucMoVo9g49LbESoLMzwAUAWYN/VUk5/q6BAMQ4e389zkyorEuyTwDeOO2qCCG1lQ4DYpbajatII+8Behvijvc3z8dSmVi2fCvTNbq00mpaEN9b2rVe0RLq58usZIlVpqv1Q/hlMqYMYVk03fUW24chwXT97G7Bo7Tkyp2FH7dm3vGuu4dYoLO9d+qOsONWdpYETJs048p3JvSt+cth3z58Nbv3q/sUtuXhzumNt/q4rnLn8+ir6x7rl/SCjussEPBtRDQzMh8JykT/lUEIWVTcuvgWqpDUbmXhlw2X7477xDD8PidpbiOZbDGGxv90SRxsurz12eBm/MuaoJlqga1q7FcyzDMfmNaU21sP17vZs+HIhTJZ6r6ktj418PnIK3zJzcVGLEeLUcM2xV8Ry/CBkcE9DCmKjquX/p6g/AqfYO6BoZCc6vjk6vBn9Y9EbxHLWeXlXZlosPBT+cKMRzrIUeJTCeHOuXsiOLmn+yPfGwSrJ/xw2oZNPmg5fjIdS+wMDLsHzV+JjD4jn6mL4A4LpHg/MUv1CN+JJHtebEEebxg6HDXwzhOj4y0lTaSIWdak3Fw3mrleORZ2d0dXTCym0PBVy2gnhOnV9KUXqoVp4FOkNidbUK1dIIzO/8TaCrg2/O9/6edw6e1a6/qIT50EsvQRVX1HA8QwNQgirMnfpGU6dDo1UYhnaYctbb9QG1dtkWHD9usUdB2y99iL/BxW+nGg/2RA6NBeDfH8GbVeI52jeeWdQ71g/2Rb0y4d42H+qpA3NtT33valV7xJkrn64xUs0Jar9UPzZzkSsmm76j2nDlOC6evI3ZNXacmFKxo/bt2t411nHrFBd2rv1Q1x1qztLAiJJnnXhO8VUkWypXp46N23+riueqg4uwEOWFCQ8uQuEcfeDPRfFCso9+Gz8cmOQ1EwLPSfqUgyQKK6rd1qpFMGvxHMdtWktaVcM6DfEc4wyL5Kra1sra5qpcAoBqbCaTv9GG+uZL9c/25ss0r+OeQRTP8bpxzxOBsFUY9a2t7ahsEs8Vv2CgYk2158pNK/vhxNSGKLJgS3wWqHGq7KnrKY5Vdb6A6lwAFT7U8ytUfiaseI6id0+wnqGq5jkc+kkNvrnvDMB48Je1X8a4Es9V8SjjBICfHawd1vz2edOMHq2sSHxKO88RAxTQkQ/jbnH8f6Poja8jP/8yAJzbIJ4jr8aDO/zNKbibHHeZhwVzbI8+cbNKXLmWuFxkwr2pa6DRZErZiPre1ar2cRyOmhLXGLHxX4UjznlNxYKKNdWec2yu80Adm2v7NHJJ7YOaT9f2XDmIi5MLo6z8pPHcuB4bJc8intvVPNdhnBXvxrhccm/usi0q8XwXAGBmcLe47lLtgIlqVx3baaP6WWpu6i5ykj4lgERhRcTzOpJcZVuoB0CKeF7PgZKIMM3ruGdQxHPdslz816lELW7EVHLPhh7xWaDGKeI5W6asHFF2VZh0oCKuIp6boBdvY5orlwQ++ShS9SDiuSXc1PeuVrXn/ByFGLmuzx6VbmpuqCKWzRTjismmbwpGXP7j5hGVM3HGxOGLOq9t+qTOFyqmNvaqetuq8anqcFPw4BbPXa5HXPOCmnubz1HUPqjzJSqmOPH8UP/LUK2i3Nh84cH0c7txP0IxL25xO4wCt39T3o1xuOTe3OK5y7ItUTMTf3uMhblvUEzbvwKAO6Nee+655+CCC8Z+Ap/SnOd4wGNDJQorRRLPfXFz+0XrAxBg2Qu8wuUtvnjV5VAtVaC9HJziftmOa97ReK4WwN//6K1YGBZu3GN+0EJULvrW4nSsQm83btyqX339GHcJejdfE7i/+SMfh1GoHeoWvlRv7kXZea6qPa4qnSLieX0GUMlDSktYobupkccTTWU+VPMxbrBefvC8iHlXN5lFnW3ABdzdt26B8tDMprMfoDIClc4BuO2eNYGuqO853OI5nj3xxmm1HcL+1X5gd+SZGqr1As8pKJenwCce/IrR2Fbc+0N49VhzjWzVOQVUjKjnWlByTyGGjX77f3MChkZDNc0AYMbJ1zwz1c7zI1Nxf0H9OuPEAah4BamC75uVUhuUSmWY2/02o+FQCfO4mH/6WI32sV4O/7bD+1c4Trw3PFKFjvaS7Dw3ykgujEQ8t0wDlQ+0qj3CR12vVZBvXvFxGK2ac28qpqp+XfuxmWJcMdn0HdWGK8dx8eRtzFzYUee1Tb9U7Kj5pNrHjTlqfL5IHXXuGgUPTvGcmjdqDqj+Xa9fcWs5dWzU+RI1tjjxvMZn8Ryg2tk/9at2L8yvKXMoa1sqV6fGy+2f8hmpSOK5f5gQ7jBvVCc5DgxFoXxV6PAjvIfqqepnpLLzHGe6QlRvZfH8C1dfBkPtpaYa6TUhpguWnv/dwBrw6I/eiueSNZXtoC4U4+U8IsXz5jIfNt8GF0U8V9UeVx3aKeJ5fbZRyQN1nk5E+/Ea3CNBMVc1H+MwGj8vYj7+2Kp+qc424MIb15fJQ7Oiz1ToPNJURohKKjnF80/e+gBMGepq+gITa5VHnamhWi+oX7bh+ujXaG/EXXVOARUj6rkWlNxTiGGjXyxh4ovJjfep4vmsEwehUipBObTJpuJp6eYfEqiE2UY8x4g628rQfeZUI4ipMemcmubKJYHXxZiz10U8t0wIlQ+0qj3CR12vLSFvakbFVNWvaz824+WKyabvqDZp5DhvY+bCLg0/VOyo+aTaU8fM5Z9TPOcaA9fYXK9fcWu56/kVNTa9eN68CYWbU1LnAIe96zFw+zfl3YiNS+7NvfMc490/JmZj7UT/whqMKHCryqs0zgFV2Rb0gYch+aK8L9TjwUWNfenmUyYEPhXSNwF2npvWpEbxHK+b9jwWmA+qureqsh26yRR+XVULW3Wf+iaB/RVJPMd41+z+llEORDyvw2QzL6hzdaLZq5596lkOiJtqvVCVbeLCmmt9oRJjm5rn1DbUL3OpQj91DVZhRC3NRck9hRg2+lXV/6aWbVGVc6ESYKr94Rd/7g1nzsLzAnCp7lMw9W2pMen6MM2VSwKvizFnr2fCvV2LCWlgTOUDrWqfyucoRUKpmFLfY6nzlGofN0+5xsb1LHCOzXUeuMZcJD/U+ULNJ9Weih2XfxHP9Z9ZOdcd6ryjzAsRz6XmOWW+uBDP8dAiFLRR6MYLM4I7zxsP9kSBfCMAvL/h4CI/bpV4jvbhQ4ywH/yPcmVC4FMhfSKej88DEc/1j4TLNyLsXSWGkQ/uY5rXcc+g1DzXz5eiW4h4rs8gVZCO+/Ag4rke77CFqSAbbifiuR5rEc/1GDm2yIR7cwkljrGJdU/lanm056jnm8rnKEUmEFPKGFQJ5fSDfSQtPRGHaVbPThr9Up+RLJ//vPVNxU4153FcUeVJXeefGk/cs6x6BqkYUXPs2j91zDY5o46Bak/BVMRzEc8p88WFeI79o4COYjnuFsf/N4re+Dr+1v3LAIBV9o+OBbwIAJYDAIrn+G/cTY67zMOCOZqjT2ynKtcSh0EmBD4V0sckMqoWKOoOTZuFTtWGKm6KeK5fBmzyo/datxDxvI4FFWuqPSUvE9VWxHN95kU812MkO8/1GFGFatl5rse0BSwy4d42H+rzhjWVD+TNnlpv12ZHp+ucUcegiofLD+e8ps4X11hzji0NEdA1HnnzT50vqjmvqj3uOv/UeKhzKE7v4RobNQdcc4izX6ovqj1lzCKei3hOmS+uxHNKDGnbZkLgUxPPj/0KYPrZQUz9e2ufD9yn1pjNUjxX1Rger5970U8DY/vSU3O8v//oP/wicP/BI5/yapuv24zf09QvFOfxYVhz8eHm+Th9AcB1jxrNU66yCnFvsEUp2xJXF3qw80hTDpRjjvtSKGK+b3lqrperNRGHv+q+nIk6kNY7ZHI4eLDUYOcsr4+ucP3sMnh19sOlanRv+o8sxu8b65fqIFwu4mU0mVvMCNcvfMavnfWXRmtC3PBV6wUeDIzry2MLauuPf13z/f878gDjuD66Ojph5baHAia4vkT556rnrZpf1PrlGLSq9rhqbvtfeO666NamZ4Fy0KsqVq8ePaUu/P2XAxx7pSlFtcOfwfhQaMpjZLvz/NVf/QZK1bbQMZ8AMHIUz6+OPDDUq20eCg6PHC1XqzBn4e8EXqGK4VR7FM+rUIWB0AGmM08chBKUmsq5mGD67LPPwqJFuA+jdqli6u/vh+5u+gcH01xJ2ZbxFGTCvVvhPVPHH8K7j/Nmb/K8NtrkUTynjsG1Pee8ps6XIo1NFWvexuwaU07/XNhx+eEaG/WZslmnqH3kbf5y5ozqi2pPmRcintM5sAvD/1IAACAASURBVArfRu4d91nAhnub8m6MzSX3FvGc8nQltOVaNJVhKD7se/YRAvADK66CwZAoiKaqA/SyFM897EplGJl3dWD47Qd2Q6k6Cjf//r8F7qvE828c+ZRn1yyeXwqoMtwYFs8VXzyociDieR0ZVc6qlREY7RiA2/9mTQBGsniumO9bvC9OSizi+ZXPfR2qlUHoGjoSiFUlnqNRlOipe9MX8Tzh4mrQ3F+/PhoSz1VrQpzLL10dvV6oxPOl3++D4Y5y0wHGqj4GFV/C+GMIi/Monketa9T3HJX9XWt3w9Sh6fDGacHDVsfX3x07m4ay4t4fwqvHTjXdjxPPq9Uq7P793kCb8Wcw4qDXcnkKfOLBrwTsVbFCZQQqnQNw2z2EdSfiy7l48bz5UGiDqTluQiGGjX4P/PIIlKvtUC2NBLsbPgolqMLcnsaqeQCvvfgCVFBqL4UoYLUKZajCGQvPD/ihiuFUe3/n+ZGptS8+/WvWiUPeP8O10Bttjh49Cs888wxccgn+aLF29fX1wdKlSwOiuCqmKFuTnJnmyiWBN4kzRzYinlsmQ8cfRDy3BLbAzajv7XFDpc4v17Bxjk0Va97G7BpTTv9c2HH54Robdd6JeF5HnoodtqTmn2pPmRcintPFcxPuHfdZwIZ7m/JuzL1L7i3iOeXpSmhrs7gk7NKqOVd5A5uFjtpGVRpEdV/1k3tVmRdQ7XpWICviuf7N9JW/rgkzC+58IYAiWTxX5GCLJ2zSdp6r2nCVaKDOa6q91YM+wRpx5RJho5aFUq4vqjm89IO1OWx42C75HAFFv3HiOTa5fTNWXKtfNgctq/pQYYRieFTfqnyq7FVrOXXdUc6jtTu8OHtDv2qiPGYUYtjoF8VzvOafU/tFjH8dfmnsIM6e4EGccOgnNZO57wyGp7hPFcOp9qo4lfGHQF28eDE8+eSTMGPGDNi3bx/gjpbVq4O/5sGYVt10E6z9s1sCQju6WrZsGezatYuSKjDNlUsCTwo4e2MRzy1zQOUDebOnDttGlKL2UXR7zs+T1PniGjvOsalizduYXWPK6Z8LOy4/XGOjzjubdYraR97mL2fOqL6o9pR5IeI5XTxHfHXc2/8s8Ke3rYMbbrghMfc25d0Ym0vuLeI55elKaMu1aCYMQ9tcxPMGiEQ8184X6pu7iOd1xKhkoChriPWkcdhQxHM9uCKe69d+Ec/184hbPO+cfaYncB85EvwFkB8J/kx069atnmC+ZMkS2LsXz6ivX7t374Z/3LsXvr59O+z+5jebCDy+jr7DgnvcSE1JvEsCr89ErixEPLdMBxdP4PTDcXgmlTuivXCgGmqcOFDnheU0Nm7GOTbqHEujb2MgcmrINV+4/HDBhPFErWvon3qwqeuxufYf99xwrf3UMVDtKfNCxPNo8Rx3lyfh3l/5whfh+X/5MXxj505vg0rjL0QxP1Tubcq70bdL7i3iOeXpSmhblDdlamkATnJCXRxtdp57tW8jyg9gXd1SaWrTcErlLjh15h823T81aRA+3bcycF+183zTtdd75T86h44F7N/saPP6jPrZbVSdX2w8UqlCqVqGP3nIrA773Xc8COWTk5riH68Xv31b4LUagWiu841GXVCBlXuCwoQ2/6F69CMDv4SDpTONd57HHbBULk2CT2y7PxCCbud5FK5emYyRkaZSL1yCqzevo8oOHdwDUK1E5h8HZfpz7IRL04RozpVLBEu1S9p/zpvK/LS3w+SREbgxog5/FPjUw3bZd56HntmNY3W+o86KwPhv3POE8RxSrfHa3fzdeHZ4/aqVTylB7+ZrAvdd7zxXreVDndMB3ytufvC+IBaEcmo6Ytj/mxMwNIqVyYPX9KFauZYi7zz36q2HxuXfmxPeOd9gh8T+Xe96F/T29no7ztetWxe5ixwF/Q9c8SH43Oc/30Tg0V2U6B43qXW58tu6JPDGD10+DEU8t8wDlRe7tuc69FLLHUPcFO2L8jnKMtXGzThxoM4X4yAtDTnHRp1jafRtCUtumnHNFy4/XMBQDxKNmyuux+bavwpTzrWfOgaqPWVeiHhO33luwr39jTQfvGYZbNy4MTH3NuXdmHuX3FvEc8rTldC2KG/K1EPpOMkJdXGkiucqIbnj0GOeuB2+fEH9jbkfDryE9X9PdB5rKmOgEs99QXrS8GjAj0pw+fy110MFa22H6vxiYxR6O0eqcNOex4xmJIqGqi8MvHrxoXrFcQJglMCszX9IiHvl6Ck4XDoTFv3ldwNN43JP+aY7TjwfH1sY18oIdFVHYOVDTwZi4hJclQfe+jX7QzmgPgdGE2GCG3HlEmFUCb3qL56qcFplBK57+NtGWcibeO7Xcs9KPEei0hspnjeXSXEtnqvWctUXoV65GMODvHXE8GcHj8PwSBU62oPUDcVzvDOvoGVbXut/ESpVPAq3+SqXSnBG90Llc9N4OBHuYkEBHYX08KUTz/Hnp/v37zd6PtFIlyvfkUsCbxxsPgxFPLfMA5UPuLa3HIZxMxtRyth5ixhyfp6kzhfXEHKOTfv5JGLzELYJb1xxPeYi+eeaL1x+XGNnE6dNG8o4XPunxGJrSx0D1Z4Sl4jndPHchHubiOcU7m3KuzH3Lrm3iOeUpyuhbRqEIGGIXnNq3W5OckJdHKniORUflRCrrMOrqHurGpcN1tT6ySrRUFWvWDW2OFE6ClfVmC++5zue+VO3vjfQjJp7lX1cnFRhjUtwVcWkuk/FgjqvJ6I9Vy4RuzjxPOqDl6pUkSoPmYvnoQ+Uqp3tadQ8V/WtWjupz7jyfVlRsov8bKpKf0Xc1xFDFM/xevu8aYGpc3Cs5nlRxXOu9QgPIOru7vYOCw1fOvEcd55j6Rdsb3LpcuX7cEngTeLMkY2I55bJoK45ru0th2HcTMRzPVScnyep80UfXTILzrFxfj5NNqrWac01X7j8uEbWJk6bNpRxuPZPicXWljoGqj0lLhHPzXivClMV9zYRzync25R3Y5wuubeI55SnK6FtGoQgYYhecxtBN6pfm4WO2kbEc33GRTyvY0QV1rgEVxHP9fPUtQVXLjFOEc9r2RLxvD5rqSJ81AGmOmIo4nn8KoEHEuFPScN1FbGViOeuV1itfxHPtRBFG1B5sWt7y2EYNxPxXA8V5+dJ6nzRR5fMgnNsqkjyNuZkiKXbmgs7Lj+uR28Tp00byjhc+6fEYmtLHQPVnhKXiOfJxHMV9xbxnDIL822bCYFHSNIgBBzQi3heR1F2nuuxoJJT2Xmux9QlSeBYI4roQ8RzfdZU8052nutFchHP6xhRDwzVz8yaBe5uef3118fNZ8+e7ZVowTrnWE8xyc5zyk9HMQDdFx1+kC53v5jilhO7TLh3UXh3XI5wDJTSdVT+QLV3PZ9EPNcjzDmv4/KvOkBRFWHUwYr60QQtOMdG/XySRt9UPPJmT12Pip4Dm/XRpg0lz679U2KxtaWOgWpPiasunk/G42FDTbHcbhvM6wkKzK54rknch/pfhqqi1GGpVIK53eeauPE2leAVHlu4sS33NhHPKdzblHdj/C65t+w8N5pePEZFeVMeF88Na8xyvjH6GD2yeHXA7RX77/X+Dtehw53n3mGPoTPU/Htrdn8rUfJQPB9sb2/231772Xy4Jvng2H3Tg0Ftvqiglm1RHnDXXoLJI8NNh/3h7mys6f5GxKGqUTXSdfkP5/LXA6fgLTMnZ1K2ZVwEDM3t8ZrEa58PDEdVL96vId+7KVgLX4VF7UuYKqy5+HDAZMtTc7xDD9eEDpJ0SRISPRAFbsyVS4SAuvP8rj/fAu3DMyGqqvNoxwCsPH1TANm//9FbvXkRPmBUJWKzHhgacbAtzvfBziPgsua56lwAv+/HFuCzUr8ue+WwV+c7XAt9/HBTw2dc9awd/PTbvcOZP9q1xei9SPXe9V++92k4VW6HUrkj+PREnLPgE8Mpk2dCqRI+PhOgNHwMqtXmA0MRiBKUYW732wJ9HH7p597fTQduHvoJwOgwQFsoJv/e3HcG/JgSbL8R1V63rBw9etTbUY4C+aJFi8bN9+3b5wnnWGpl165dgDXPn376ac8ufOl2nlMIPPo2JfEuCbwOt5y9LuK5ZUKoh7RR+QPV3nIYxs1EPNdDxfl5UuUrbt5FRegL7UnrhXOOTff5JBxrGn3rs5tvC+p6VPQc2KyPNm0oWXftnxKLrS11DFR7SlyeeD44CLOnTI1sFiVIc/NcSry1vmuifvCKFvpVvnVjSMq9RTynZDXftpkQeISkKG/KfWt3ekJf9AFtJejdfI1Rhm0WOqp4/sCKq2BweCgynq6OTli57SGjWFVGD1z1fhgstQOU2wMmvkhuKp6rDgZNQzyv7RI4AeHDSttgBLoqI/Dx0CGGn7z1AZgy1AXt5eB3a+0H9wBUK8YH6ahyiUCeNX0ybFv97gCm1Pmiso+reY4iY5Tg5gUyfQHAdY8GYlIdMItGlSlvwm2fudZofo3HdPGhgP2Wp+Z6f4t4bgRjIiOuXGIQVPEc513X0CxPgG68/HsfnfWXgfuP/uitntB+454nAvddi+cqAbtaGQEU+W//mzWBeDjLtnjPc4RwD5URGOkYgMfnnh/oG8VzvNaFRHKleK54xlXrCNapH61UYeXUrYF+VV/kqta7j/xgA5xq74j+gndkJPDs+4Ls1EmzoVRpg2o5eMA0DA0oCbMnnvf0BGJViuev/RxgNPp9E9o6Ac44L+BHR7DDDybVXvdgY01EFMmjyrHga/iTUb/OOf69d+/egEs82OihHTvgM319ng8U4levrn9Bj69jvXP8z/QS8dwUqXG7TLh3UXg3Gc2YBlxcKivsRDzXzwbO3HD5ypufOBSpz4g+I2JBRaAoObCJ06YNBT/X/imx2NpSx0C1p8RlyucafXLzXEq8qr6pMensk3LvJx75O/jn73+PjXtT8uRy44rsPKfM1oS2XMQiYRja5lzlDWwWOps22gElMVAc9qaqnU093C4t8RwhaNoNohgbtayKCl7qfKfm3lY8x3jDO2iTTBGTttTa/FQsTGIQGz4EqOI5dU1VidKuxXPVPFUdeMounhPWKdUarDzXQJH+OPEcmyy484VAS+qzqcIo6ku+RvEcO5139sxA34df/Dfv7zkLfyd4X7HDXCmeEx8FHcEOu6Pax4XT398PuCt8YAC/OGi+UDjHneYzZszwXsS//d3ojdZxMaH98uXLA7vadRCZkniXBF4XY85eF/E8pYRQ1yiqvethiHiuR5jKr+M8cvnKmx+bMXONQZ9BscjbuqPKiE2cNm0oM8K1f0ostrbUMVDtKXGZ8jlTTknp28Y2DfGcg3vrPgtQuTclTy65t4jnNrPWsk1R3pSpQk+R3nDIqRPxfBwy6vzlsqe+Yep2nuOARDwnPwnSoAEBEc9rYIh4Xp8UqnWKUzzf+dnvweBvR6CtozPwPI4OD3t/t3UEy7Co7lMfZqoftO+a1g7X3P771K6a7LEUC+4UV9V3xNf9Xed+YxTQw7vIVSQePyCgD6ydTrlMSbxLAk+JNwe2Ip6nlARci1zWSHc9DBHP9QhT+TV6VJXb4Cy3oqqRTqmFbjM2PWJBC+rnCqp/sdcjUJQcUNdTHLnrsdnEpM9IuhZUjFyOWcfnHt70LPz2yJtGvJuKos+vqe2iOP+kyVV4z/IzI8q5AFBLz3Bw7zjx3IZ76/LUiKFL7i3iuc1stWyTBiGwDC3QLBfi+UU/DcT0uR+8w/s7aS09Mj4onh/7FcD0swNN/dIAUXV40TAszuIO88lDs+BUqGyDf6938/JgDtbu8P4Ol87Be18aq5MdroesGtv4vAthqqrzjTvP/brkjT5V5QpW3PtDePXYqabuVfbaOLdvC+YeyzlE5N4fV7imOpZJwCtc8gLvqXbukucFsYHtznPT2v/EcMQ8IQKx4rmiZvip045DuEa+qg57+4HdXoQj85cGIlXVHvfndXg9oj6DqjMkqpWaODsKwfJVQ4pzE+Lg1RJmw3XK9c7z+698X2St8pNtZSiVpja9F6Uhnt9/21MweHwUTp+NhxrVr7yJ5799/ZQnnl9393sSPmkASLB7enq8XeHr169vEsqjOsA2WIqlUVRXkXg8CIkqnGOfpiTeJYFPDG66DkQ8Twlvak1i7Zoc4mSuhyHiuR5hm8+TKvEJe6OI26rouMR5m7HpEQta5G3OU+NvBfui5IC6nmJuXI/NJqa8zRkqRi7HrONzX/uLH8CJ19+AqbNPG4eRuqlEhT+XeI7xTZ7WBv/XqgURXdEPPeXg3nHiuQ331uWpceAuubeI5ymuJmkQAo7hiHjegOL9lwMce6UJ1r7+9V5NYuUhdiEx/O7ee6H85vTI9FQmHYPb+oIHpKrqzqMDPGQyqh6yKvdK8RwbRNT5porhVLFdG6eI5+PES8RzjhWN34dKPFfVDMcIomrkq+qwq8RzVe1x/HIuaj2iiueqMyRU4rnq3IQ4xLWEOSyeK9Yp1+I57hiPrFXePg3K5SnwiQe/EhhmWuI5dnrd3RcH+laVZ8mqbMv9t/2TFx+HeI5+cAfMqlWrAA8vwgvrljfWOTd5wnU/HzXx0WhjSuJdEnhqzBnbi3iecQK4uJfrYYh4rkfY5vOkTRt9JHoLar9Ue30EzRZaHpLyF0Y2Yyh6m1bOQSuPjWve5QkjHZ9D8Ryvj336ovHhc3FKLj9RMfrB2pZ5Scq9ucbmj0OXp8a56ZJ7i3jOtQoY+EmDEBiEoTXJhXhuKKBqB+PIwKZWOSUUVQ7QB7VUAte8U/lxXSOd+gYbh0/Rdp6Hf2nBlUvKXBTbZgRU4rnqPhVD1RxW1R5XrUds80VRvgpU92MGTH2eVa7SEM+x7/AvWFRrs4jn9Uxxi+e+53379nmHgSKhx10xWOvcdNd4ViTeJYGnrisZ24t4nnECVN1zrclcwxPxXI+kzXu7TRt9JHoLar9Ue30EzRZ5m/M2Yyh6m1bOQSuPjWve5QkjnSg7UcXzpNw7K96Ncbvk3iKec60CBn7SIAQGYWhNRDzXQgQintcxEvFcP19sy7aIeK7HNgsLEc/HUBfxfHz6iXjuXjxvfNZx5/nOnTuVB4mG14WsSLxLAp/F2pegTxHPE4DnsmmeRAwcp4jn+mzbfJ60aaOPRG9B7Zdqr49AxHMbjFy3ydu6wzneVh4bF055wkjE827jtFK4d1a8W8Rz43QaG2ZC4HWE0Dj6FAxFPNeDLOK5iOf6WVK3EPGcglb+bUU8F/E8XL9exHN+8TzqMFC/F6xpvnjxYuVBoiKe524dzYR7pyHE5Q5pYkB5EjH8z0qqgye5DrckQpQ787h5zVV7nGvQqlizjDNvc54L6yL5aeUctPLYuOZYnjDiFM8P9b+s5KXUQzspWHOWbeHi3iKeUzKYb9tMCHzRxHM8mA4PuGu8/Hth0UCVblwYq9UTMGW0EjBRHbg2jlHEgXvtB/cAVCvpHxiqGNz4AaBRGHUegfABoNRHQnWQIPrBesil6ijcvGOnkVuuD4+en4jcdBx6DGD0tzB59KRxnqMCp76RquLx8Xn4whuburnslcPevfCBrkZAJjBSHcQ4WAboqgCs2f2tgHcqFglCk6YWCKB4jodlYu4aL8xn50gVbtrzmIXXehNV2RbVIZaD7dO8xl0jwTU7bq0lBag4OFl16HCcb665jWVbpg5NhzdCa/Bpb0yDE53H4PbNwcNW496n8LXwrzxUOYgt2xL13qVYr7dcfakX0po9j4+H5hP4qZNme/fmnT0zEDYeGIpXNjXPawcOha8SVKEEwQfhsS8fGIvT/sBQrG++YcMGrzRL1IXkfuvWrV4Zl/CFBxF1d3cbHRhKeg4ajHUftnxT2Xk+Dlom3JuL/9jOkyK041qTucYadzAc9sFxuCVXrFn50e3OV335kAV2cfMrqzjzNuezmkdZ9tvKOWjlsXHNmTxhpONzlLItNcE4ii/TD+2kYM0lnnNybxHPKRnMt20mBB4hKQqJVx1ih2OIOvROle5N114P1cpgk6CDQk+p3AU3P3hfU1PVgXtUwdj1FLz71i1QHpoJUG4PdlUZgUrnANx2z5pEIcTlgPpFAte8U+bm4B6oVo43fUmCeS6Xu+ATEXmOAof6RhoXD37REj5sE/tE8RxrVSX9coOaXNVBjOinq6MTVm57KOCSigU1HrFPhsAXr7ocqqUKtJeDlc9GKlUoVcvwJw89mqgDlXAbd4ilN5dC4jn1GVQGrTg42bOPOHQ4bvBcc/uTtz4AU4a6InNwsnMQPnvPSqMcUHeMq8Rz3XoUFueLJJ4feullqHpH0oYunP5VgLL3QaF+cYjnceI49rRkyRJPWF+0aFGgb9yRvmzZMu+1pUvrX6BkReJFPB9PTybcm4v/GC0mBTXiWpMLOvxChq0Tz3FQ4fecrAaax/mVx5iyyk9W/bZyDlp5bFzzJU8Y8YvnAPN6gqVQbA/tNMWbSzzn5N5Z8W7EzCX3lprnprOSwW6ikXjV4YxxhzZSy1swpMXOBeMBetQAqPOIaq+KRyXoqfyrStuo/FPfSFVzJW681JioueGyp2LB1a/4MUNAVedfdd/Ma90qTjxHq6ZDLNfu8BqHvxTK43znmttcOeASz6PEcMyJyn+RxPOhV//Vm1+dZ/1uYCof/NWA93fTDvn13/XuX7fhD6hTf9we6yo+88wzsH79+oAIjgb4GpZsWb16dZP/e++919uNvnz5chHPrdF30lDEcyewJnfKtSYnj0Q8mCIg4rkpUtF2MueT4cfRupVz0Mpj48h9HDfm0iwocYp4Xhf6Obm3iOeUWZhv20wIfNxCkW+47KMT8dweu7iW1DcWqr2qbxHP3eQzyqsQr/SwtumJS7ilPmvKUiIino9DSf0CQ8Rz/ROQlXiOZVlwF0y4NAuS+/COcxwFlmvp7e31dp6LeK7Pa8oWmXBvLv6TMlapdocYRZXPkPriqaaB1JmI5yS4moyFYyfDj6N1K687rTw2jtyLeF5HkUtg5tp5jvyai3tzjc1HS/clR+PclJ3nXE9qzU8mBD5uoeAdXn68+eL5YwvmBIKKqztNrQ2d2WgZawBTx0D9MEi1jxP0TrV3NNV5Hq+rfNFPA037+td7f5uWSKGSWdVc8eIpT4OReVc3DcWr289Qk56aM6o9FQuqf7FPhgAKtL8eOAVvmTk54Mi/99St703UAYrk+LOwy//DLwJ+Hv3RW73iGVE7zycPzYo+pyJn851rbnPlQBWPqgyL6uwP3c7zcBmpj/xgg5fbxlyO1zzvnOW9Nm9SrXa4f92/6TXvn1E1z7HqeDk06/x7c3rOSzQfUTyvVqvwcttbA36mD414fx/rDJYv++HmH9fiTLDznBrwvn37YNasWZ6oLuI5Fb1U7DPh3lz8JxWEMuokrsZ4FjWyM4KhUN2KeJ4sXVw8JFkUE7t1K687rTw2rlmbp2dQJ8rSa54Xt2wLNb9x3FvEcyqa+bXPhMAjHBONxGPJABR6osRzVd1pam3ozKYZYw1g6hio84hqr4on7rBCr7b5f9ofaFoTz0vQu/kaoyFS30hVc+VU2xQotZ0OQ3Mva+6XqSa90YASGFGxSNCVNLVAYMW9P4RXj52KbHnW9MmwbfW7LbzWm3zJO0yyCn8UEs///kcoXpbgxoZDJrGV6zMYEg0m1JhrbnPlIDaeiANAcThRZ39kKZ6/1v8iVKoRNclRUC+V4IzuhYlSOIziOQD0l88J+MmLeI4HHO3cuXO8jIuI54nS7apxJtybi/+4AkX8CgI2CIh4boNavQ0XD0kWhbQWBCYuAnl6BkU8D9ZnN52VOu4t4rkpkvm3y4TAIywTjcSr6u3msQ5v/qetnvSpxsA171756/O9Lhbc+UKgK1V5HtXBetQ4yfGr6tFjx3Gv5WgS5IlU5AiWCRMK+eyHDM9goCYlb3ObKx6deB4+vC2qBE/TzvNzajvQ/ev+2/7J++d1d7+HCnsy+0M/qbWf+86AH5c1zykBY51zJPH+hT85xR3ojaVbsiLxLn86SsEoB7aZcG8yf8gBUBKCIKBDQMRzHULxr3O97yeLQloLAhMXgTw9gyKe24nnOu6dFe/Gp8ol95YDQ1NctyYaiRfx3M3kos4jqr0qahHP3eQzymueSEV6o5aefAREPE9vLnA9ayKe13N2P8OBoUlmAB4mGj5oNCsS75LAhzDC01NnAAB+i9ADAFgXqP6NQjyg+MlpFwAsjjBL4rfRnYjnSSa1tBUEGhAQ8TzZdOB6308WhbQWBCYuAnl6BkU8txPPw7M3zL2z4t0invOvK5kQeBwGl4jJD4kbjyiee3V4O48EOvDvmdbCdhNdcb2Oz6NQjfEH/nE2DJbaAcrB+rPjNcm3b0s0aBTP51Z+A4fKZwb8PHjkU95P+tdtXh64LzvP7eH2chxRMqL94B6AagXCu1jte5KWeUSAfPZDhmcwUPHLE2H235er1RMwZRQrhNcv6roZK55HPcsHdoPX7/DQeKcLl/4xnPXO34NZU0+HUqkMc7vfFogJd54PHh+B02cHa+1Tc0C2Hx0CwLIwpeB+i9FqG0C1BFAKlowZPDYMXdM7Uq15jmPC3ee4E2bDhg1w4YUXAh58tHTpUm+4WZH4lMRzHOQSALhhLLdxYnhj+i8BgGVjN1AkD2+osfUbNcUy4d4TjXeTn21pUEgERDxPlra88ZBko5HWgkDxEMjTM2ginp94/Q2YOvu0caBHh4djQW/r6Ai87tub3qdm1I/vY5++qKlpjf+OAkBb6LXavXk9ycRzFffOinfjIF1yb9l5Tp2dCewnGom/u/deKL85PRKxyqRjcFsfflaTi4qASjzf8tQcGIw40HOwfRqUyl1w84P3UbsK2P/krvfA9OFDTT6+ceRT3j0RzxPBG2isOqyw/cBuKFVH4eYdO/k6E0+5Q4B89kOGZzBQwcsTYcbYN117PVQrg9A1cjwwFOq6qRLPzI9G6wAAIABJREFUVc9yx+G9UB09Bl1D9S+X6+J5VySh3fnZ78Hgb0chTL6pOSDbj+KHhOaa6p54rri6pnXANev/E7krVw2yIvEuCXwDVi+NCef7Gu7hISTrAKDxngpeFNH3RojnSf029ifiuavJLX4nHAIinidLed54SLLRSGtBoHgI5OkZ1InnD296Fn575M0AyHkTzzG402dNgitvXtQ0GQ71vwxVxblIpVIJ5naf62QCZcW7cTAuubeI506mS7TTiSaepwjthOpKNY9U4o2qJjkXaKod5rLz3B5hVS5V9+17kpaCQLoI5Ikw48hV6yN13aQ+s3et3e0Bf/vm2s5ovMZrnpfK3t/h3SCHX/y5d3/OwvPSTVoL9JYViXdJ4MfSgqVaBsZKteD2Iv9CMRz/9nejx2UxSjzn8NvYp4jnLfAcyRDygYCI58nykDcekmw00loQKB4CeXoGdeI5B7oqDsrNTTli5fLBPTZKnlxybxHPuWaIgR8Rzw1AEhMtAiKeayBqgQNDqUKcdtKIgSCQEwTyRJgREhHPczIxHIeRFYl3SeDHIPOFb6xz3iieYw1zFMCxnIvuihLPOfyKeK5DXl4XBCwQEPHcArSGJnnjIclGI60FgeIhkKdnkCLK2iIt4rktcvV2lDy55N6uxPMkBwy5Prgok90vmHoRz5M/OOJBPY9k5/nY7BDxXB4TQSC3COSJMIt4nttpwh5YC4vn+NMFFMpnhg4I3QoAFyoOAQ3jGyWec/gV8Zx9JotDQSD+82TePmvm7T0/7vN43rCTuS4ItCoCeVoXKKKsbT5EPLdFbmKI57YHDKV1cJGI58nnr3jIEAF8w6lWT0KpNCUQBR4+V4bJTfWwqeUHqEPLZdmWY78CmH5281D8+2ufpw4zVXv8ImSwvR26RkYC/fr31ux5PNV4pDNBgAuB2vqV/IBOrnhk5zkXkvn2MwHFcxTUcTPKYoPMUMRzE7/zsOpQqN/zAWD7c889BxdcgDQ8nUvEsHRwll7SRUD1OQCj8D8f5OVg+TyJZH6W8hhTujNIehMEskUgT8+giOdu5kJWvBtHU7Sd50kPGHJ9cJGI526eEfGaEgKfX3EdVKrBgyu8rktlKJdOg0+EDgadcOJ53OGJiNP0BQDXPZpStuy6eeDqJTAItbrH4asLKrByD5azlUsQKB4CXAd0co1cxHMuJPPtJysS75LAjyHOUV6Fu2zLXwHAnVEzQsTzfD8nEl0xENi84uMwWh1SBttW6oS1276ai8HkSSTzAcljTLlIlgQhCKSEQJ6eQRHP3SQ9K96No3HJvbnLtnAcMOT64CIRz908I+I1JQQuvuc7Xk9P3freQI99a3d4f/duXh64P+HE85TyIN0IAoIAHQEusZrec3QLrnio5xTIgaFcGTTzkxWJd0ngx0bu827cYf5sAxpcB4ba+JWd52bTUqwEgZZHIE8imYjnLT/dZIAFQSBP64KI524mTVa8u2jieR53wIRnhIjnbp4R8ZoSAiKepwS0dCMICALsCHCJ1VyBccUj4jlXRtz4yYrEpyCeI2D7AWAdAOxrQA9/BYr3dhsgqvrFZ1K/jV1nwr2lbItB9sVEEHCIQJ5EMhHPHSZaXAsCBATytC6IeE5IHME0K95dNPGc44AhSu1FyoFIfrozIfDYuZB4whMnpkoE4sTzKgA8tmBOoO1lrxz2/l4X2pHOBTHWPJ/8xjQ4ddrxgEv/Xu+mDxt15ddCnjQ8GrB/s6MNSqWpkJf6jUaDESNBQBCIRADF6q6hWZHrxWDnEWfrlCodWYrnU4emwxsN6+bvXTwHzu45A2ZPmQRQBZjXgyWr69fhF3/u/TFn4Xkyu4gIZEXiUxLPVwPAEgBYNgYLThzced7TABNy640A8P7QwaJoohLPTfyaZiIT7i282zQ9YicIuEEgTyKZP0L12VG186Tk84abuSBeBYE8PoMinruZl1nxbhyNS+7NXbZFJZ6bHDDkZ44inuv85uanozg4IfFuHs6J5lUlnvsiUJR4jg96uJwLF2533/EglE9OinRXmfIm3PaZa4268mshdw4dC9gPdU6HUrkLbg7VcjdyKkaCgCCQKwTu+vMt0DY8E0rl9kBc1coIjHYMwO1/sybVeLMSzz956wMwZagL2st1GjYunndNghJUYW5Po/YJIOK5+dR49tlnYdGiReMNVCS+v78furuDX1KY9GL6YcslgQ/FiUI3ThjccY7/3xASyZGffxkAzm24jwBhnTfk3fhv3LmOpV8a2+r8msCFNiKemyIldoJACyGQR/E8rmZ8nurFt9A0kKEIAgEE8vQMmvK5JClUcVBugTlJjBxtG7l33NhsuDclTy65N7d4nreyLbk5tEjEc45HUnwgAjrxPLzDXFULPW9oquIsSvx5w1PiEQTyiMArf32+F9aCO18IhKe673oMWYnnUeu4TwynlfA3RABzeoI7zEU8b54NR48ehWeeeQYuuQTpZ+3q6+uDpUuXBkRxFYmPsjWZc6Yk3iWBN4kzRzYinucoGRKKIJAWAnkUz9Mau/QjCAgC+UfAlM8lGUmriecm3DtOPLfh3pQ8ueTe3OJ53g4ukp3nSZ50aZtLBEQ8z2VaJChBQBAwQEDE8xpIIp4bTBZDk8WLF8OTTz4JM2bMgH379gHuaFm9GjdM1y8k8atuugnW/tktAaEdLZYtWwa7duEPGc0vUxLvksCbR5sLSxHPc5EGCUIQSBcBEc/TxVt6EwQEARoCpnyO5jVo3WriOY5Ox739Mf/pbevghhtuSMy9KXlyyb25xXPEMukBQ64PLsqEwCMwUrYlybIjbX0ERDyXuSAICAJFRUDE81rmRDw3n8G4wwUF7iNHjkQ2wp+Jbt261RPMlyxZAnv3Yrnv+rV79274x7174evbt8Pub36zicDj6+g7LLjHRWhK4l0SeHMEc2GZCfcW3p2L3EsQExgBEc8ncPJl6IJAARAw5XNJhlJE8Twp9/7KF74Iz//Lj+EbO3d6G1QafyGKWFK5NyVPLrm3C/Hc5IChLA8uyoTAi3ieZMmRto0IoOjy64FT8JaZkwPA4MGgXm3zbixXWr/6+td7f7iqec6VHSnbwoWk+BEE8osAiuejlSqsnIrnfdevB07cAG3lUlM5F9cj4TrAdMvVlwKe8rnm4toBzf615Sk8wLkEa/Y8HrgftY7/l/99Mlxw9gw4a1IbVEoA5Vr1lvHLvycHhtYxQVH9Xe96F/T29no7ztetWxe5ixw/uHzgig/B5z7/+SYCj96iRPe4uWdK4l0SeNfPBrP/TLi3iOfMWRR3ggARARHPiYCJuSAgCKSKgCmfSxJUTTwfBYC2kJvavXk99LN3ksSTtK0J9/a/MPjgNctg48aNibk3JU8uubcL8RzzoTtgKMuDizIh8AiKkPikj6q0RwRW3PtDePXYqSYw4sXzEvRuvibXAIp4nuv0SHCCAAsCBz/9dhipVOGjXVsC/r4xuMY7PHPeX/yMpR9TJ1wHmNbEc4A1Fx8KdL3lqbm1+yHxPGodbxTP4+IX8byOTuPhRLiLBQV0FNLDl048x5+f7t+PP5w0u0xJvEsCbxZpbqwy4d7Cu3OTfwlkgiIg4vkETbwMWxAoCAKmfC7JcA71vwzVamhHzJjDUqkEc7vxLPfiXCbc20Q8p3BvSp5ccm9X4nmes58JgUdAhMTneVoUP7a+m7/pDaJ304cDg1Hdz9uIRTzPW0YkHkHAAQKb8S0YANY+H3Suuu8ghEaXXGVktiz9oOd2ze5vBSJW3Y8aFoUYOoalkO7xAKLu7m7vsNDwpRPPcec5ln7B9iaXaa5cEniTOHNkkwn3Ft6doxkgoUxIBEQ8n5Bpl0ELAoVBwJTPFWZAKQeq4t4m4jmFe1Py5JJ7i3ie4gQTEp8i2BOwKxHPJ2DSZciCQNEQEPFcmTEKMSxa2tOIFw8kwp+ShusqYt8inqeRgdg+RDzPPAUSgCCQPgIinqePufQoCAgC5ggI9zbHKspSxb1FPE+Ga55aZ0LgEQARz/M0DVovFhTPJ78xDU6ddjwwOP9eeEd63hAY33le0JrtecNT4hEEcokAiufHfgUw/exgeP698I50x4PgqsGOO8wHywBdlWDA/r3wjvSoYQmBj0827m55/fXXx41mz57tlWjBOudYTzHJznPKT0cxANNcudz94vjR4HafCfcW3s2dRvEnCNAQEPGchpdYCwKCQLoImPK5dKPKT2+23NtEPKdwb0qeXHJv2Xme4twUEp8i2BOwq7vveBDKJydFjrwy5U247TPX5hqVvrU7vQP3og88zX/N9lyDK8EJAnlB4P7LAY69Eh3N9AUA1z2aaqRcNdgfWHEVDA4PRcbe1dEJK7c9pB0XhRhqnbWQwdGjR70d5SiQL1q0aHxk+/bt84RzLLWya9cuwJrnTz/9tGcXvnQ7zykEXsRzq8kl4rkVbNJIECg2AiKeFzt/Er0g0OoICPeOznBS7i3iees8OZkQeIRPxPPWmUQyEn4Eil52hh8R8SgICALOEchRGRkh8NHZxpqIKJJHlWPB1/Ano36dc/x77969AUd4sNFDO3bAZ/r6PB8oxK9ejefa1y58Heud43+ml2muXO5+MY01J3aZcG/h3TnJvoQxYREQ8XzCpl4GLggUAgFTPleIwTAGmZR7P/HI38E/f/97bNybkieX3Ft2njNOMp0rIfE6hOT1iYyAiOcTOfsydkEgIwREPM8IeLNu+/v7AXeFDwwMRDZA4Rx3ms+YMcN7Hf/2d6M3NvB3wMzraT4QFO2XL18e2NWui86UxLsk8LoYc/a6iOc5S4iEIwikgYCI52mgLH0IAoKALQKmfM7WfxHbcXDvON6NmFC5NyVPLrm3iOcpzmgRz1MEW7oqHAJFr9leOMAlYEFAEADIUQ12HTHc+anb4bev/aYQWTv9jDPhmr+8K3GsWIoFd4pXq9VIX/i6v+vcN0ABPbyLXEXi8QMC+sDa6ZRLlyvfl0sCT4k3B7YinucgCRKCIJA2AiKep4249CcICAIUBHR8rijcm4t3I3Yc3DtOPLfh3ro8NebcJfcW8ZzydCW0FfE8IYDSvKURKHrN9pZOjgxOEGhVBHJUg11HDO/7b6vg+GuHYdoZc3KdDT/G6//2y4njRILd09Pj7Qpfv359k1Ae1QG2wVIsjaK6isTjQUhU4Rz71OVKxPOmzIh4nvhpEAeCQPEQEPG8eDmTiAWBiYSAjs8VgXtz8m7MPQf3jhPPbbi3Lk8inrt7ajMh8DgcEc/dJVU8CwKCgCAgCAgCRUZARwyRwOPFIUq7xIk7TtwBs2rVKsDDi/DCuuWNdc5NxqL7+aiJj0YbXa5EPBfxnDqnxF4QaEUERDxvxazKmASB1kFAx+e4Oa0L5FzEmJR7Z8W7EV/Zec47y0Q858VTvAkCgoAgIAgIAoJAQgRagcAjBC5IPPrdt2+fdxgoEnrcFYO1zk13jWdF4l0S+ITTLe3mmXBv2bSSdpqlP0EgiICI5zIjBAFBIM8ItAL3dsW7k3DvrHi3iOf8T1smBB6HISSeP5niURAQBAQBQUAQaAUEWoHAuxTPG3OMO8937typPEg0PB+yIvEino9nIhPuLby7FVZGGUORERDxvMjZk9gFgdZHoBW4t0vx3JZ7Z8W7RTznf2YzIfAinvMnUjwKAoKAICAICAKtgkArEHhO8TzqMFA/11jTfPHixcqDREU8z91TkQn3FvE8d/NAAppgCIh4PsESLsMVBAqGQCtwb07xnIt7i3hesAchJtxMCLyI560zgWQkgoAgIAgIAoIANwKtQOC5xHOsb75hwwavNEvUheR+69atXhkXvNatWwd4ABFeM2bM8A4XbSzpkhWJl53n49nLhHuLeM69Sok/QYCGgIjnNLzEWhAQBNJFoBW4N5d4zsm9s+LdOHtccu9SutMzF71lQuBFPM9F7iUIQUAQEAQEAUEglwi0AoHnEs/D4ng4YUuWLPGE9UWLFnkvoXC+evVqOHLkCHR3dzflNysS75LA53ISq4PKhHuLeF6wWSLhthwCIp63XEplQIJASyHQCtybSzzn5N5Z8W4Rz/kfz0wIvIjn/IkUj4KAICAICAKCQKsg0AoEnks8x5rmzzzzjLeDfOnSpYEU42tYsgXFcv9C8Tzu8NCsSLyI5+MpyoR7i3jeKqujjKOoCIh4XtTMSdyCwMRAoBW4N5d4zsm9s+LdIp7zP7eZEHgRz/kTKR4FAUFAEBAEBIFWQaAVCDyneI5lWXAXjF+axc8zknt/x3mjeI7/xpIt+/fvh2XLlsEll1wyPjWyIvEinot43irrk4xDELBBQMRzG9SkjSAgCKSFQCtwb07xnIt7Z8W7RTznf3JEPOfHVDwKAoKAICAICAKCQAIEWoHAc4nnVBjxANFGQX3mzJnw8ssve2I6XlmReBHPRTynzmWxFwRaCQERz1spmzIWQaD1EGgF7s0lnlOzG8e9s+LdIp5Ts6i3F/Fcj5FYCAKCgCAgCAgCgkCKCLQCgc9KPA+nqaenxztE1C/tkhWJF/FcxPMUlxDpShDIHQIonlerJ6FUmhKIzb93y/ZtuYtZAhIEBIGJg0ArcO+sxPM47p0V7xbxnP/ZFfGcH1PxKAgIAoKAICAICAIJEGgFAp+FeN7f3+/VQB8YGBhHH//G8i4inieYkLxNM+HeUvOcN4niTRCgIrB5xcdhtDoU2ayt1Alrt32V6lLsBQFBQBBgQ6AVuHcW4rmOe4t4zjZFM3eUCYHHUQuJzzz3EoAgIAgIAoKAIJBLBFqBwGchnh89etTbZY51GvHCv8/9/9u7n59ZrvJO4A+wMD80ls2YKAgJ5foKyGIWGduRsgf/Bzhhk1WEmb8AD7OYRFnEMctZBVhm44D/gvHNHzAabJQloGskJMRosOIrRvzwAjx6rrtuyp3urqruOvVUdX9asuz7vlWnTn3O6b5fP+95T925Y9uWdc3ykuwtd69rEugNAQIECBBYk8A1ZO+K4vlQ9lY8X9Msv6wvJQFe8fyyQXM2AQIECBC4ZoExAf6Xb//fePypP1g1Q9fHv/of31msn/fu3Yvce7F7YGgW059++ulH168K8bZteTQEJdlb8Xyxt6ALESBAgACBzQlcQ/auyN050Keyd1Xuzn61zN4f2twMv7zDJQFe8fzygdMCAQIECBC4VoGhAP/dv/1v8f/e/sUmbv8/PPWp+PP//ner6WtViG8Z4FeDO64jJdlb8Xzc4DiKAAECBAjcosC1ZG+5+99mb8vsrXi+4KeEEL8gtksRIECAAIENCQwF+A3dyuq6qnhePiSK5+VDoAMECBAgQIBAX0D2bjMfqnJ33o3i+bxjWhLg8xYUz+cdSK0RIECAAIFrERDg241kVYhvGeDbaTVpuSR7y91NxlKjBAgQIEDgKgRk7zbDWJW7Fc/nH8+SAK94Pv9AapEAAQIECFyLgADfbiSrQrzi+aMxLcneiuft3lNaJkCAAAECWxeQvduMYFXuVjyffzxLArzi+fwDqUUCBAgQIHAtAgJ8u5GsCvGK54rn7Wa1lgkQIECAAIFLBGTvS/SOn1uVuxXP5x9PxfP5TbVIgAABAgQIXCAgwF+AN3BqVYhXPFc8bzertUyAAAECBAhcIiB7X6KneN5Gb12tKp6vazz0hgABAgQI3LyAAN9uCiieX2T7RER8MiL+NSIenNlSSfa2bcuZo+U0AgQIECBwAwKyd5tBrsrdeTctF658qA3XqlstCfApIsSvel7oHAECBAgQKBMQ4NvRV4X4lgF+T+vFiMgidxa370bEyyML3afOeyYi3uhdJ9v+akS8dsZIlWRvufuMkXIKAQIECBC4EQHZu81AV+VuxfN/P56XroApCfCK523emFolQIAAAQLXIHD//v34/e9/H5/73Oeu4XZWdQ9zh/gf//jH8eEPfzju3s069fHXQsXzL0fE8xHxtV1Pno6I70XEswODMHTel3oF+Vx1/uYFg1qSvRXPLxgxpxIgQIAAgSsXkL3bDHBV7t5q8XzNK2BKArzieZs3plYJECBAgMA1CPz0pz+NX/3qV/H5z38+PvKRj1zDLa3mHuYM8b/73e/iRz/6UXziE5+Iz372s2sont/fFc7v9TqTK8Zfioj+1/b7OnReFs/zdaqNsWNckr0Vz8cOj+MIECBAgMDtCcjebca8KndvsXg+tJLl2AgNnTfXCpiSAK943uaNqVUCBAgQIHANAm+//Xb84he/iM985jPx+OOPX8MtreYe5gzxv/zlL+NnP/tZfOpTn4qnnnqquniev435zm6rlrd6nXk9IvLP3Wr0/X6OOU/xfDUzWEcIECBAgACBuQVk77lF32+vKndvsXg+tJLl2AgNnTdXiFc8b/Me0SoBAgQIECBwpsC7774bP/nJTx5uB5IF9I9//OPxoQ/d4qNpzgQ8cdocIf69996LX//61w8L57m9zp07d+Kxxx6rLp5nNs5Cee4f0y+e57YtWSDP7VwOvcacl8fkvufdQ0JzG5hczX7OQ0NLsreV5/O/l7RIgAABAgSuRUD2bjOSVbl7a8XzMStZDo3QmPMUz9vMba0SIECAAAECKxB48OBB/PznP3/Yk9y6JQvpXpcLvPurXz9s5LFPfPzsxrJgnlu25OvTn/50PPFERtfTrwX2PM/f2sxC+ZN7Re1vRcRzJ/Y9H3NeFs6zjW/v7jJzeLZ7eqP3iD+M9//pv74QEa/+4Ac/iD/5k6yjL/NSPF/G2VUIECBAgMBWBWTv+UeuKnfnnbTM3nMvaRqzkuXQ6Iw5b64VMCWrX/Kmhfj535haJECAAAEC1yTwm9/8Jt5555347W9/+3CFs9flAve///2Hjdx9LmvB573yBxkf/ehH48knn4yPfexjoxppGeB3HThWBM+Cej449NhDQ889772IeCEiXjsB8DcR8deHvq94PmraOIgAAQIECBBYUED2nhe7KnfnXbTM3nMXz8esZDk0MmPOO2cFzGpWv+RNK57P+6bUGgECBAgQIEBgSKAqf7UM8Lt7HrP45BDPuefl/urfPbGXel5rNdm7atyH5qPvEyBAgAABAgSuVaAyf7XM3ksVz6tWwKxm9Yvi+bV+NLgvAgQIECBAYM0CVSG+ZYDfeXfbHuYK8zd7YzD2gaGnzstC+Vf3Vpnn13Ibl9z7fMqr5Lc+q8Z9CoxjCRAgQIAAAQLXJFCZv1pm77mL5+euZDn3vKEVMKtZ/aJ4fk0fB+6FAAECBAgQ2IpAVYhvGeB79m/sitn3el+7v/vaqe1Vhs7LNnKLlq4o3xXq8yGk/WuNmQaK52OUHEOAAAECBAgQ2LhAVe5OtpbZe+7i+RZWwJQEeMXzjX8C6D4BAgQIECCwSYGqEN8ywPcG4sWIyIJ2FrrzlXud58rz/oM9c5HKKxHxxd6DRYfOy+Nf7h3/9d118lpTXyXZu2rcp+I4ngABAgQIECBwLQKV+atl9p67eJ7jPbSS5dicGDpvrhUwJQFe8fxaPgrcBwECBAgQILAlgaoQ3zLA7/lnITyL5ZmV89/9oncems8W+k5E3OkVw/PrQ+dlwTxf2eaDM7Zr6bpZkr2rxn1L7w19JUCAAAECBAjMKVCZv1pm7xbF86GVLDkulStgSgK84vmcb0dtESBAgAABAgTGCVSF+JYBftydr+aokuxdNe6rUdcRAgQIECBAgMDCApX5q2X2blE8z6EZWslSuQKmJMArni/8jnU5AgQIECBAgEBEVIX4lgF+YwNbkr2rxn1jY6O7BAgQIECAAIHZBCrzV8vs3ap4Pht8g4ZKArzieYOR1CQBAgQIECBAYECgKsS3DPAbG/SS7F017hsbG90lQIAAAQIECMwmUJm/WmZvxfPZpshwQ5WTaLh3jiBAgAABAgQIXJ9AVf5qGeA3NkqK5xsbMN0lQIAAAQIECJwjUJW7s68ts7fi+Tmz4cxzKifRmV12GgECBAgQIEBg0wJV+atlgN/YgCieb2zAdJcAAQIECBAgcI5AVe5WPD9ntE6fUxLgs0uVk2h+Ri0SIECAAAECBNYvUJW/FM8fzY2S7F017ut/R+ghAQIECBAgQKCNQGX+apm9rTxvM18Otlo5iRa8TZciQIAAAQIECKxGoCp/tQzwq8Ed1xHF83FOjiJAgAABAgQIbFqgKncnWsvsrXi+4LSsnEQL3qZLESBAgAABAgRWI1CVv1oG+NXgjuuI4vk4J0cRIECAAAECBDYtUJW7Fc/nnzYlAT5vo3ISzc+oRQIECBAgQIDA+gWq8pfi+aO5UZK9q8Z9/e8IPSRAgAABAgQItBGozF8ts7eV523my8FWKyfRgrfpUgQIECBAgACB1QhU5a+WAX41uOM6ong+zslRBAgQIECAAIFNC1Tl7kRrmb0VzxeclpWTaMHbdCkCBAgQIECAwGoEqvJXywC/GtxxHVE8H+fkKAIECBAgQIDApgWqcrfi+fzTpiTA521UTqL5GbVIgAABAgQIEFi/QFX+Ujx/NDdKsnfVuK//HaGHBAgQIECAAIE2ApX5q2X2tvK8zXw52GrlJFrwNl2KAAECBAgQILAagar81TLArwZ3XEcUz8c5OYoAAQIECBAgsGmBqtydaC2zt+L5gtOychIteJsuRYAAAQIECBBYjUBV/moZ4FeDO64jiufjnBxFgAABAgQIENi0QFXuVjyff9qUBPi8jcpJND+jFgkQIECAAAEC6xeoyl+K54/mRkn2rhr39b8j9JAAAQIECBAg0EagMn+1zN5WnreZLwdbrZxEC96mSxEgQIAAAQIEViNQlb9aBvjV4I7riOL5OCdHESBAgAABAgQ2LVCVuxOtZfZWPF9wWlZOogVv06UIECBAgAABAqsRqMpfLQP8anDHdUTxfJyTowgQIECAAAECmxaoyt2K5/NPm5IAn7dROYnmZ9QiAQIECBAgQGD9AlX5S/H80dwoyd5V477+d4QeEiBAgAA2lSjxAAAZvklEQVQBAgTaCFTmr5bZ28rzNvPlYKuVk2jB23QpAgQIECBAgMBqBKryV8sAvxrccR1RPB/n5CgCBAgQIECAwKYFqnJ3orXM3ornC07Lykm04G26FAECBAgQIEBgNQJV+atlgF8N7riOKJ6Pc3IUAQIECBAgQGDTAlW5W/F8/mlTEuDzNion0fyMWiRAgAABAgQIrF+gKn8pnj+aGyXZu2rc1/+O0EMCBAgQIECAQBuByvzVMntbed5mvhxstXISLXibLkWAAAECBAgQWI1AVf5qGeBXgzuuI4rn45wcRYAAAQIECBDYtEBV7k60ltlb8XzBaVk5iRa8TZciQIAAAQIECKxGoCp/tQzwq8Ed1xHF83FOjiJAgAABAgQIbFqgKncrns8/bUoCfN5G5SSan1GLBAgQIECAAIH1C1TlL8XzR3OjJHtXjfv63xF6SIAAAQIECBBoI1CZv1pmbyvP28yXg61WTqIFb9OlCBAgQIAAAQKrEajKXy0D/Gpwx3VE8Xyck6MIECBAgAABApsWqMrdidYyeyueLzgtKyfRgrfpUgQIECBAgACB1QhU5a+WAX41uOM6ong+zslRBAgQIECAAIFNC1TlbsXz+adNSYDP26icRPMzapEAAQIECBAgsH6BqvyleP5obpRk76pxX/87Qg8JECBAgAABAm0EKvNXy+xt5Xmb+XKw1cpJtOBtuhQBAgQIECBAYDUCVfmrZYBfDe64jiiej3NyFAECBAgQIEBg0wJVuTvRWmZvxfMFp2XlJFrwNl2KAAECBAgQILAagar81TLArwZ3XEcUz8c5OYoAAQIECBAgsGmBqtyteD7/tCkJ8HkblZNofkYtEiBAgAABAgTWL1CVvxTPH82NkuxdNe7rf0foIQECBAgQIECgjUBl/mqZva08bzNfDrZaOYkWvE2XIkCAAAECBAisRqAqf7UM8KvBHdcRxfNxTo4iQIAAAQIECGxaoCp3J1rL7K14vuC0rJxEC96mSxEgQIAAAQIEViNQlb9aBvjV4I7riOL5OCdHESBAgAABAgQ2LVCVu7daPH8xIp6IiAcRcTciXt7999AkGDpv6PtD7ef3SwJ8XrhyEo2BcQwBAgQIECBA4NoEqvLXgsXzc/Px0HlD3x87VUqyd9W4j0VxHAECBAgQIEDg2gQq81fL7N1i5fmXI+L5iPjabhI8HRHfi4hnBybF0HlD3x8750oCvOL52OFxHAECBAgQIEBgPoGqEN8ywPd0zs3HQ+cNfX/KAJVk76pxnwLjWAIECBAgQIDANQlU5q+W2btF8fz+rnB+rzcB3oiIlyKi/7X9+TF03tD3x863kgCveD52eBxHgAABAgQIEJhPoCrEtwzwPZ1z8/HQeUPfnzJAJdm7atynwDiWAAECBAgQIHBNApX5q2X2nrt4nlu1vLPbquWt3gR4PSLyz91q9P25MXReFt7PaffQHCwJ8Irn1/Rx4F4IECBAgACBrQhUhfiWAX5nP5Sf15C7s6sl2btq3LfyvtBPAgQIECBAgMDcApX5q2X2nrt4/qWIyEJ57nPeL57nti0Z8HM7l0OvofNeObNdxfO53wnaI0CAAAECBAhsSKAqxLcM8Dv+ofy8htyteL6h94quEiBAgAABAgQuEajK3dnnltl77uJ57o+YhfIn9x4Q+q2IeO7EvudD5+UDR89p9w8jIv/pv/5TRPzjq6++Gl/4whcumROTz/3Hl/7rw3P+8pW/n3yuEwgQIECAAAECBKYLVOWvH/7wh/GVr3wlO/xnEfG/pvd88Iyh/HzseUND552bu7PDq8neVeM+OGoOIECAAAECBAhcqUBl/mqZvZcqnmfhOx8cOjXEd+cdC/FD7f5NRPz1lc5Jt0WAAAECBAgQILB+gayg/1ODbh4rgg/l46Hzzs3deYuyd4OB1iQBAgQIECBAgMBogdmz99zF87X9+uih1S+PR8QfR8S/RMS7o+nrDszl8a9GRA7+D+u6cdNXNgb1w28MjEG9QG0PvAdq/fPqxsAYTBV4LCL+KCL+5+7ZPVPPHzp+bbk7+yt7D42a7w8J+KwdEmr/fWPQ3njoCsZgSKj9941Be+OhKxiDIaG239+if7PsPXfxvHtwUa4wf7M3jmMfGHrsvO6BoVPbbTuVlmn94UOWIuI/5xY+y1zSVfYEjEH9lDAGxqBeoLYH3gO1/nl1Y2AM6gU+2AO5u82IeK+3cR3bKv+xUu2OMwbtbMe2bAzGSrU7zhi0sx3bsjEYK9XmOP4917mL59n0GxGRxe57vevc333ttRNjOnTe0PfbTJf6Vk1YY1AvUN8D7wNjUC9Q2wPvgVp/xfN6f2NweAzOzcdD5w19fx0zok0vfN62cR3bKv+xUu2OMwbtbMe2bAzGSrU7zhi0sx3bsjEYK9XmOP6Ni+cvRsTzEfHC7jq513muPL/bu27+mukrEfHF3oNFh84b+n6b6VLfqglrDOoF6nvgfWAM6gVqe+A9UOuvcFvvbwwOj8GYfCx3T5u/Pm+nec19NP+5Rae3Zwymm819hjGYW3R6e8ZgutncZxiDuUWntce/cfE8m88gn8XyXHGe/84HDz3oXTcfVPSdiLiz9/Wh84a+P20qbONoE7Z+nIyBMagXqO+B90HtGPCv9Ve4rfc3BsfHYCgfy93T5q/P22lecx/Nf27R6e0Zg+lmc59hDOYWnd6eMZhuNvcZxmBu0Wnt8V+geD5tSBx9SiAfvPRfIuIfIuL/oCoRMAYl7B+4qDEwBvUCtT3wHqj1z6sbA2NQL6AHSwh4ry+hfPwa/Gv9/X1X728MjME6BOp74e+D2jHgr3heOwNdnQABAgQIECBAgAABAgQIECBAgAABAgTWLdDigaHrvmO9I0CAAAECBAgQIECAAAECBAgQIECAAAECAwKK56YIAQIECBAgQIAAAQIECBAgQIAAAQIECBDYE1A8NyUIECBAgAABAgQIECBAgAABAgQIECBAgIDiuTlAgAABAgQ2JfB0RHwvIp7dVK91lgABAgQIECBAgMD2BGTv7Y2ZHhNoKmDleVNejRMg0FBAqGmI22v6xYh4IiIeRMTdiHh599/LXP22r/KliHhhR5Dj4O/suvnwyu7S+bmTr5ci4q267tzklbvPov8YEc/sxuDNm5Rw0wQIEFheQO5ezlz2Xs56/0qyd519/8pyd/04yN17Y+B/xOsn5dQemMRTxeY/3of5/KZTWhRqpmhdduyXI+L5iPhar3BoBfRlpuecnXP+dcXzc+hmOSc/8/s/NMo/59/Fd/wgaRbfMY18a2edP7TI19cjIsfhSWMwhs8xFwrI3hcCznC67D0D4plNyN1nwp15mux9JtzMp8neM4NOaE7unoDV6FC5+wCs4nmj2daoWZO4EeyEZn2YT8BqfKhQ0xg4Iu7vCuf3epd6Y7fis/+19j257SuY67Xj/05EfDUiXtt1I38TI7+Whdxv1nbtZq6eP7RL9/xhXr6690T+2WfRzUyDkhuVvUvYP3BR2bt+DPqfu+oHbcdD9m7rO7Z12Xus1PzHyd3zm05tUe5WPJ86Z1Z3vElcPyQ+zOvHoOuBUNN2LLoCYW7V0t+eIldA55+71ehte6F1/8NaPwe6Qvm3e115LyLyz94HNeNj5XmN+y1eVfauH3XZu34MZJFlxkD2XsZ5zFX8f+YYpTbHyN1tXC9pVe72K+CXzJ9VnGsSLz8MPsyXNz92RaGm7Vh0vvvF8/1CQtteaN3/sK5vDlh5Xjsm6Z+/AZOrUfs/0KjtlavfioDsvfxIy97Lmx+6otzdfhxk7/bGY69gvo+Van+c3N3e+NQV5O6djl+7qp2Il1zdJL5Eb75zfZjPZzm1JaFmqti043PPxSyU7+8pnL/C/lxEPDutOUdfIGCuX4DX4NQsnn3DnucNZIebTPu/2G3V0u1/PnyWIwjMIyB7z+N4aSuy96WC550vi5znNuUs2XuKVttjzfe2vlNal7unaM17rNzd81Q8n3dyLdWaSbyU9PB1fJgPG7U6QqhpJft+u8cCfBbUn1Y8b4u/17q5vij3yYtl0eYnEfHFiHhzPd26uZ7kD/Hyc6jbA/3mANzw4gKy9+LkRy8oe9eMhSzS3l32bm889grm+1iptsfJ3W19x7Yud9u2ZexcWe1xJnHt0Pgwv9w/Q+KYPYNzn+39B/MJNZf7n2rBr4629Z3Surk+RavtsflZlCueFc7bOg+1noXzfKiah7YOSfn+3AKy99yi09qTvad57R8td1/m1/ps2bu18Pj2Ze/xVi2PlLtb6o5vW+5WPB8/WxoceUl46bpjEl82MJeOgQ/zy/wvPVuouVTw9Pndr0Xn9iz9QqEHhrZ1P9S6ub68+aEr5h7bOf/v7b6Z75EH6+ja1fcif+Pl5b3Ponxoa46F1edXP/yz3eCluS87IntfNhyXjoHsfZn/JWfLIpfojTtX9h7ntMRR5vsSyqevIXfXjYHcfcDeti11E/KcK5vE56i1OceHeRvXKa0KNVO0zjs2H8qXKzu7YmG20q32fO28Jp11hoC5fgbazKe8GBFv7b0XcuuA/d+ImfmymouIZ3YPCO2vMu8KDPnA0DG/vQSSwLkCsve5cvOfJ3vPbzqlRVlkitb5x8re59vNeab5Pqfm9Lbk7ulmc50hdx+RVDyfa4q1b8ckbm889go+zMdKtT1OqGnrm63nXM9VnS/sLpUr7nLV1932l3aFXdEwH46Ycz3/DsgfYuRvAeQKXCuel5si6Z8F2pz73SvfA/mDpCzeerUXyAJm9znUfTbl9hk5DvlDDS8CLQRk7xaq57Upe5/nNudZcvecmsfbkr2XcT52lfzcl71rx0DurvXPq8vdB8ZA8bx+Yk7pgUk8RavNsT7M27hOaVWomaJ1+bEZ4rtCYf5b4fZyUy1sSyC3Bzn02t/SaFt3ta3e5krzb/S6nD/Iy5XoCufbGsct9lb2rh812bt2DOTu5f1l7+XNXXE9AnJ3/VjI3Yrn9bPwwh6YxBcCznC6D/MZEDVBgAABAgQIENiAgOxdP0iyd/0Y6AEBAgQIELhpASvPb3r43TwBAgQIECBAgAABAgQIECBAgAABAgQIHBJQPDcvCBAgQIAAAQIECBAgQIAAAQIECBAgQIDAnoDiuSlBgAABAgQIECBAgAABAgQIECBAgAABAgQUz80BAgQIECBAgAABAgQIECBAgAABAgQIECBwWsDKczOEAAECBAgQIECAAAECBAgQIECAAAECBAjsCSiemxIECBAgQIAAAQIECBAgQIAAAQIECBAgQEDx3BwgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKnBaw8N0MIECBAgAABAgQIECBAgAABAgQIECBAgMCegOK5KUGAAAECKfDliHgrIt68EY4XI+K7EfHgRu7XbRIgQIAAAQIECKxDQO5exzjoBQECBEYJKJ6PYnIQAQIErlrgSxHxSkQ8e9V3+cGbeyYivhcRd2/ont0qAQIECBAgQIBArYDcXevv6gQIEJgsoHg+mcwJBAgQGBTIUPx6RHw7Ir42ePT5B2TxN18vnN/EwzPfiYgv7q06z5XZ39prN1dp34uIl2dYoZ7XfGlnlJdJr08uXMDv7q/lGF04NE4nQIAAAQIECBA4ISB3D08PuXvYyBEECBA4KqB4bnIQIEBgfoEsaj+9++fJ+Zt/1OIcxfOvR8Tzu3/6Xe2K59n/bmuTJyIiv56r1POcLKSf+9oP8dluvvIHDku9cozuR0T/Hpe6tusQIECAAAECBAhcLiB3DxvK3cNGjiBAgMBRAcVzk4MAAQLzC7y32w4kC7O5Kvy1+S/xsMU5iucZpr96oI+HiufdbWTxPPdqvGTLk/0Q34ho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width=\"1199.5966002008581\">",
            "text/plain": "<IPython.core.display.HTML object>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e6207b1db799463eb5faff8eb5b274f8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6dac1b954e964c05bb4558e4a45111a2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "58af9c1dca62425fa85e854ff51b918f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "670044ada3fc4391bfeb637c2ab419e0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6bf7074fa37f44128e715c50cba2d25e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4847f0e532ca4a9fb62dba2762a537c5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "\nOptimal bin count is 100 bins\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": false,
        "ExecuteTime": {
          "start_time": "2017-07-10T03:59:38.797623Z",
          "end_time": "2017-07-10T03:59:43.356150Z"
        }
      },
      "cell_type": "code",
      "source": "distanceMeasureSamples = 50\ndSlice = a.SampleCount // distanceMeasureSamples\nstride = (len(phaseSpaceParameters) * (len(phaseSpaceParameters) - 1)) // 2\n\nDistanceMethods = {\"cumulative\": 1, \"small_windows\": 0}\n\nmethodOfDistances = \"cumulative\"\n\nvariationSpectra = a.getVariationSpectra()\nnumberOfVariations = len(variationSpectra)\n\nvariationIndexCombinations = list(comb(list(range(numberOfVariations)), 2))\n\npearson = numpy.empty((distanceMeasureSamples, len(variationIndexCombinations), 2), dtype=\"d\")\nmodDist = numpy.empty((distanceMeasureSamples, len(variationIndexCombinations), 2), dtype=\"d\")\neuclid  = numpy.empty((distanceMeasureSamples, len(variationIndexCombinations), 2), dtype=\"d\")\n\ndistMethodType = DistanceMethods[methodOfDistances.lower().strip()]\n\nfor finalTimeIndex, timeIndex in tqdm_notebook(enumerate(range(dSlice, time.shape[0] + dSlice, dSlice)), total=distanceMeasureSamples):\n    if timeIndex == 0:\n        startTimeIndex = 0\n    else:\n        startTimeIndex = timeIndex - dSlice if distMethodType == 0 else 0\n    \n    startTimeIndex *= stride\n    endTimeIndex = timeIndex * stride\n    \n    for varCombIndex, (varIndex1, varIndex2) in enumerate(variationIndexCombinations):\n        # Compute spectra for the deviations\n        baseSpectra11, _ = numpy.histogram(variationSpectra[varIndex1][\"1st Order\"][startTimeIndex:endTimeIndex], range=(-numpy.pi, numpy.pi), bins=binCount)\n        baseSpectra12, _ = numpy.histogram(variationSpectra[varIndex2][\"1st Order\"][startTimeIndex:endTimeIndex], range=(-numpy.pi, numpy.pi), bins=binCount)\n\n        # Compute spectra for the derivative deviations\n        baseDSpectra11, _ = numpy.histogram(variationSpectra[varIndex1][\"2nd Order\"][startTimeIndex:endTimeIndex], range=(-numpy.pi, numpy.pi), bins=binCount)\n        baseDSpectra12, _ = numpy.histogram(variationSpectra[varIndex2][\"2nd Order\"][startTimeIndex:endTimeIndex], range=(-numpy.pi, numpy.pi), bins=binCount)\n\n        baseSpectra11  = baseSpectra11.astype(\"d\")  / fsum(baseSpectra11  * 2 * numpy.pi / binCount)\n        baseSpectra12  = baseSpectra12.astype(\"d\")  / fsum(baseSpectra12  * 2 * numpy.pi / binCount)\n        baseDSpectra11 = baseDSpectra11.astype(\"d\") / fsum(baseDSpectra11 * 2 * numpy.pi / binCount)\n        baseDSpectra12 = baseDSpectra12.astype(\"d\") / fsum(baseDSpectra12 * 2 * numpy.pi / binCount)\n\n        # Similarity measures between spectra using the Pearson Correlation Coefficient\n        pearson[finalTimeIndex, varCombIndex, 0] = pearsonSpectraDistance(baseSpectra11, baseSpectra12)\n        pearson[finalTimeIndex, varCombIndex, 1] = pearsonSpectraDistance(baseDSpectra11, baseDSpectra12)\n\n        # Similarity measures between spectra using the Pearson Correlation Coefficient\n        modDist[finalTimeIndex, varCombIndex, 0] = custModuloSpectraDistance(baseSpectra11, baseSpectra12)\n        modDist[finalTimeIndex, varCombIndex, 1] = custModuloSpectraDistance(baseDSpectra11, baseDSpectra12)\n\n        # Similarity measures using the Euclidean Distance\n        euclid[finalTimeIndex, varCombIndex, 0] = euclideanSpectraDistance(baseSpectra11, baseSpectra12)\n        euclid[finalTimeIndex, varCombIndex, 1] = euclideanSpectraDistance(baseDSpectra11, baseDSpectra12)",
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "88b1c0f4eb4741388f30826e536ffedd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "scrolled": false,
        "ExecuteTime": {
          "start_time": "2017-07-10T03:59:43.357398Z",
          "end_time": "2017-07-10T03:59:47.059953Z"
        }
      },
      "cell_type": "code",
      "source": "fig = plt.figure(figsize=(18,14))\nax1 = []\nax2 = []\nax3 = []\ngs1 = matplotlib.gridspec.GridSpec(3, 2)\nax1.append(fig.add_subplot(gs1[0]))\nax2.append(fig.add_subplot(gs1[2], sharex=ax1[0], sharey=ax1[0]))\nax3.append(fig.add_subplot(gs1[4], sharex=ax2[0]))\nax1.append(fig.add_subplot(gs1[1], sharey=ax1[0]))\nax2.append(fig.add_subplot(gs1[3], sharex=ax1[1], sharey=ax2[0]))\nax3.append(fig.add_subplot(gs1[5], sharex=ax2[1], sharey=ax3[0]))\n\n\ntitles = [[\"modDist Helicity\", \"modDist Derivative Helicity\"],\n          [\"PCC Helicity\", \"PCC Derivative Helicity\"],\n          [\"Euclidean Dist. Helicity\", \"Euclidean Dist. Derivative Helicity\"]]\n\nprint(time[0:time.shape[0] + dSlice:dSlice].shape)\n\nfor varCombIndex in range(len(variationIndexCombinations)):\n    varIndex1 = variationIndexCombinations[varCombIndex][0]\n    varIndex2 = variationIndexCombinations[varCombIndex][1]\n    comp1 = a[0, 2, varIndex1, \"1st Order\"].as_numpy_array()\n    comp2 = a[0, 2, varIndex2, \"1st Order\"].as_numpy_array()\n    if numpy.vdot(comp1, comp2) == 0:\n        for i in range(0, pearson.shape[-1]):\n            ax1[i].plot(time[0:time.shape[0] + dSlice:dSlice], modDist[:, varCombIndex, i], label=r\"$mE^{{\\left({}\\right)}}_{{{}{}}}$\".format(i + 1,*variationIndexCombinations[varCombIndex]), alpha=0.8)\n            ax2[i].plot(time[0:time.shape[0] + dSlice:dSlice],  euclid[:, varCombIndex, i], label=r\"$E^{{\\left({}\\right)}}_{{{}{}}}$\".format(i + 1,*variationIndexCombinations[varCombIndex]),  alpha=0.8)\n            ax3[i].plot(time[0:time.shape[0] + dSlice:dSlice], pearson[:, varCombIndex, i], label=r\"$P^{{\\left({}\\right)}}_{{{}{}}}$\".format(i + 1,*variationIndexCombinations[varCombIndex]),  alpha=0.8)\n\nfor i in range(0, 2):\n    ax1[i].legend(fontsize=12)\n    ax2[i].legend(fontsize=12)\n    ax3[i].legend(fontsize=12)\n    ax1[i].grid(which='both')\n    ax2[i].grid(which='both')\n    ax3[i].grid(which='both')\n    ax3[i].set_xlabel('Orbits')\n    plt.setp(ax1[i].get_xticklabels(), visible=False)\n    plt.setp(ax2[i].get_xticklabels(), visible=False)\n\nax1[0].set_ylabel('Minimised Euclid. Distance')\nax2[0].set_ylabel('Euclid. Distance')\nax3[0].set_ylabel('PCC')\nplt.setp(ax1[1].get_yticklabels(), visible=False)\nplt.setp(ax2[1].get_yticklabels(), visible=False)\nplt.setp(ax3[1].get_yticklabels(), visible=False)\n\nfig.suptitle(r\"$x_{initial} = \" + r\"{:.3f} - {}\".format(initial_x, orbit_type.title()) + r\"$\", fontsize=16)\ngs1.tight_layout(fig, rect=[0, 0.03, 1, 0.95])\n\nif savePath is not None:\n    for extension in [\".png\", \".pdf\"]:\n        fig.savefig(savePath + 'Spectral_Distances_Over_{}_Yrs{}'.format(time[-1], extension), dpi=200)",
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.Javascript object>",
            "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support.' +\n              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        this.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overriden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n"
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        },
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width=\"1799.798261632895\">",
            "text/plain": "<IPython.core.display.HTML object>"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": "(50,)\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "ExecuteTime": {
          "start_time": "2017-07-10T03:59:47.062852Z",
          "end_time": "2017-07-10T03:59:48.381292Z"
        }
      },
      "cell_type": "code",
      "source": "# Computing average distance between 1st order deviations\n\nAvg_Deviation_Separation = []\nAvg_Deviation_Sep_Derivative = []\nwindowWidth = 0\n\nfor varIndex1, varIndex2 in tqdm_notebook(variationIndexCombinations, total=len(variationIndexCombinations)):\n    deviationIterable = tqdm_notebook(zip(a[:, 2, varIndex1, \"1st Order\"], a[:, 2, varIndex2, \"1st Order\"], \n                                          a[:, 2, varIndex1, \"2nd Order\"], a[:, 2, varIndex2, \"2nd Order\"]), total=len(a[:, 2, varIndex1, \"1st Order\"]))\n    Avg_Deviation_Separation.append([])\n    Avg_Deviation_Sep_Derivative.append([])\n    for dev1, dev2, ddev1, ddev2 in deviationIterable:\n        dev1, dev2, ddev1, ddev2 = dev1.as_numpy_array(), dev2.as_numpy_array(), ddev1.as_numpy_array(), ddev2.as_numpy_array()\n        \n        curDev1Norm = numpy.linalg.norm(dev1)\n        curDev2Norm = numpy.linalg.norm(dev2)\n        curDDev1Norm = numpy.linalg.norm(ddev1)\n        curDDev2Norm = numpy.linalg.norm(ddev2)\n        \n        zerArray = numpy.zeros(ddev2.shape, dtype='d')\n        ddev1, ddev2 = zerArray if curDDev1Norm == 0 else ddev1 / curDDev1Norm, zerArray if curDDev2Norm == 0 else ddev2 / curDDev2Norm\n        \n        Avg_Deviation_Separation[-1].append(numpy.array((0 if curDev1Norm == 0 else dev1 / curDev1Norm, 0 if curDev2Norm == 0 else dev2 / curDev2Norm)))\n        \n        Avg_Deviation_Sep_Derivative[-1].append(numpy.array((ddev1, ddev2)))\n#         Avg_Deviation_Sep_Derivative[-1].append(numpy.array((zerArray if curDev1Norm == 0 else (ddev1 - dev1 / curDev1Norm), zerArray if curDev2Norm == 0 else (ddev2 - dev2 / curDev2Norm))))\n    \nAvg_Deviation_Separation = numpy.array(Avg_Deviation_Separation)\nAvg_Deviation_Sep_Derivative = numpy.array(Avg_Deviation_Sep_Derivative)\n\nAvg_Deviation_Separation = runningMeanFast(Avg_Deviation_Separation, windowWidth, axis=1)\nAvg_Deviation_Sep_Derivative = runningMeanFast(Avg_Deviation_Sep_Derivative, windowWidth, axis=1)",
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      "metadata": {
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        "scrolled": false,
        "ExecuteTime": {
          "start_time": "2017-07-10T03:59:48.385654Z",
          "end_time": "2017-07-10T03:59:51.635055Z"
        }
      },
      "cell_type": "code",
      "source": "fig = plt.figure(figsize=(14,10))\nax2 = fig.add_subplot(212)\nax1 = fig.add_subplot(211, sharex=ax2) \nif windowWidth < 0:\n    plotTime = numpy.concatenate((time[:-windowWidth//2+1], time[windowWidth//2:]))\nelif 0 < windowWidth <= 2:\n    plotTime = time[windowWidth - 1:]\nelse:\n    plotTime = time\n\nfor varCombIndex in range(len(variationIndexCombinations)):\n    # Deviation Plots - Magnitude\n    comp1 = r\"\\left<\\frac{{d \\mathbf{{x}}}}{{d \\alpha}}\\right>_{arg2}\".format(arg2=variationIndexCombinations[varCombIndex][0])\n    comp2 = r\"\\left<\\frac{{d \\mathbf{{x}}}}{{d \\alpha}}\\right>_{arg2}\".format(arg2=variationIndexCombinations[varCombIndex][1])\n    varIndex1 = variationIndexCombinations[varCombIndex][0]\n    varIndex2 = variationIndexCombinations[varCombIndex][1]\n    if numpy.vdot(a[0, 2, variationIndexCombinations[varCombIndex][0], \"1st Order\"].as_numpy_array(), a[0, 2, variationIndexCombinations[varCombIndex][1], \"1st Order\"].as_numpy_array()) == 0:\n        ax1.plot(plotTime, numpy.linalg.norm(Avg_Deviation_Separation[varIndex1, :, 0] - Avg_Deviation_Separation[varIndex2, :, 0], axis=1), alpha=1, \n             label=r\"${} - {}$\".format(comp1, comp2))\n\n        # Derivative Deviation Plots - Magnitude\n        ax2.plot(plotTime, numpy.linalg.norm(Avg_Deviation_Sep_Derivative[varIndex1, :, 1] - Avg_Deviation_Sep_Derivative[varIndex2, :, 1], axis=1), alpha=1, \n                 label=r\"$\\left||\\partial _{{\\alpha}} {arg1} - \\partial _{{\\alpha}} {arg2} \\right||$\".format(arg1=comp1, arg2=comp2))\n\n        if len(variationIndexCombinations) <= 2:\n            for i in range(Avg_Deviation_Separation.shape[2]):\n                comp1 = r\"\\left<\\frac{{d \\mathbf{{x}}_{arg1}}}{{d \\alpha}}\\right>_{arg2}\".format(arg1=i, arg2=variationIndexCombinations[varCombIndex][0])\n                comp2 = r\"\\left<\\frac{{d \\mathbf{{x}}_{arg1}}}{{d \\alpha}}\\right>_{arg2}\".format(arg1=i, arg2=variationIndexCombinations[varCombIndex][1])\n                # Deviation Plots - Compenentwise\n                ax1.plot(plotTime, numpy.abs(Avg_Deviation_Separation[varIndex1, :, 0] - Avg_Deviation_Separation[varIndex2, :, 0])[:, i], alpha=0.85, \n                         label=r\"${} - {}$\".format(comp1, comp2))\n\n                # Derivative Deviation Plots - Componentwise\n                ax2.plot(plotTime, numpy.abs(Avg_Deviation_Sep_Derivative[varIndex1, :, 0] - Avg_Deviation_Sep_Derivative[varIndex2, :, 0])[:, i], alpha=0.85, \n                         label=r\"$\\partial _{{\\alpha}} {arg1} - \\partial _{{\\alpha}} {arg2}$\".format(arg1=comp1, arg2=comp2))\n\n# Titles\nax1.set_title(r\"$x_{{initial}} = {}$ - {}\".format(initial_x, orbit_type.title()))\n# ax2.set_title(r\"Average Derivative Deviation\")\nax2.set_xlabel(r\"Number of Orbits\")\nax1.set_ylabel(r\"$\\left| \\ Avg.\\ 1^{st}\\ Order\\ Deviations\\ \\right|$\")\nax2.set_ylabel(r\"$\\left| \\ Derivative\\ of\\ Avg.\\ 1^{st}\\ Order\\ Deviations\\ \\right|$\")\n\n# Formatting\nax2.set_xscale('log')\nax1.set_yscale('log')\nax2.set_yscale('log')\nax1.grid(which='major')\nax2.grid(which='major')\nax1.legend(fontsize=6)\nax2.legend(fontsize=6)\n\nplt.tight_layout()\n\nif savePath is not None:\n    for extension in [\".png\", \".pdf\"]:\n        fig.savefig(savePath + 'Average_Deviation_Plots_Over_{}_Yrs{}'.format(time[-1], extension))",
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<IPython.core.display.Javascript object>",
            "application/javascript": "/* Put everything inside the global mpl namespace */\nwindow.mpl = {};\n\n\nmpl.get_websocket_type = function() {\n    if (typeof(WebSocket) !== 'undefined') {\n        return WebSocket;\n    } else if (typeof(MozWebSocket) !== 'undefined') {\n        return MozWebSocket;\n    } else {\n        alert('Your browser does not have WebSocket support.' +\n              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n              'Firefox 4 and 5 are also supported but you ' +\n              'have to enable WebSockets in about:config.');\n    };\n}\n\nmpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n    this.id = figure_id;\n\n    this.ws = websocket;\n\n    this.supports_binary = (this.ws.binaryType != undefined);\n\n    if (!this.supports_binary) {\n        var warnings = document.getElementById(\"mpl-warnings\");\n        if (warnings) {\n            warnings.style.display = 'block';\n            warnings.textContent = (\n                \"This browser does not support binary websocket messages. \" +\n                    \"Performance may be slow.\");\n        }\n    }\n\n    this.imageObj = new Image();\n\n    this.context = undefined;\n    this.message = undefined;\n    this.canvas = undefined;\n    this.rubberband_canvas = undefined;\n    this.rubberband_context = undefined;\n    this.format_dropdown = undefined;\n\n    this.image_mode = 'full';\n\n    this.root = $('<div/>');\n    this._root_extra_style(this.root)\n    this.root.attr('style', 'display: inline-block');\n\n    $(parent_element).append(this.root);\n\n    this._init_header(this);\n    this._init_canvas(this);\n    this._init_toolbar(this);\n\n    var fig = this;\n\n    this.waiting = false;\n\n    this.ws.onopen =  function () {\n            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n            fig.send_message(\"send_image_mode\", {});\n            if (mpl.ratio != 1) {\n                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n            }\n            fig.send_message(\"refresh\", {});\n        }\n\n    this.imageObj.onload = function() {\n            if (fig.image_mode == 'full') {\n                // Full images could contain transparency (where diff images\n                // almost always do), so we need to clear the canvas so that\n                // there is no ghosting.\n                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n            }\n            fig.context.drawImage(fig.imageObj, 0, 0);\n        };\n\n    this.imageObj.onunload = function() {\n        this.ws.close();\n    }\n\n    this.ws.onmessage = this._make_on_message_function(this);\n\n    this.ondownload = ondownload;\n}\n\nmpl.figure.prototype._init_header = function() {\n    var titlebar = $(\n        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n        'ui-helper-clearfix\"/>');\n    var titletext = $(\n        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n        'text-align: center; padding: 3px;\"/>');\n    titlebar.append(titletext)\n    this.root.append(titlebar);\n    this.header = titletext[0];\n}\n\n\n\nmpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n\n}\n\n\nmpl.figure.prototype._root_extra_style = function(canvas_div) {\n\n}\n\nmpl.figure.prototype._init_canvas = function() {\n    var fig = this;\n\n    var canvas_div = $('<div/>');\n\n    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n\n    function canvas_keyboard_event(event) {\n        return fig.key_event(event, event['data']);\n    }\n\n    canvas_div.keydown('key_press', canvas_keyboard_event);\n    canvas_div.keyup('key_release', canvas_keyboard_event);\n    this.canvas_div = canvas_div\n    this._canvas_extra_style(canvas_div)\n    this.root.append(canvas_div);\n\n    var canvas = $('<canvas/>');\n    canvas.addClass('mpl-canvas');\n    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n\n    this.canvas = canvas[0];\n    this.context = canvas[0].getContext(\"2d\");\n\n    var backingStore = this.context.backingStorePixelRatio ||\n\tthis.context.webkitBackingStorePixelRatio ||\n\tthis.context.mozBackingStorePixelRatio ||\n\tthis.context.msBackingStorePixelRatio ||\n\tthis.context.oBackingStorePixelRatio ||\n\tthis.context.backingStorePixelRatio || 1;\n\n    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n\n    var rubberband = $('<canvas/>');\n    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n\n    var pass_mouse_events = true;\n\n    canvas_div.resizable({\n        start: function(event, ui) {\n            pass_mouse_events = false;\n        },\n        resize: function(event, ui) {\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n        stop: function(event, ui) {\n            pass_mouse_events = true;\n            fig.request_resize(ui.size.width, ui.size.height);\n        },\n    });\n\n    function mouse_event_fn(event) {\n        if (pass_mouse_events)\n            return fig.mouse_event(event, event['data']);\n    }\n\n    rubberband.mousedown('button_press', mouse_event_fn);\n    rubberband.mouseup('button_release', mouse_event_fn);\n    // Throttle sequential mouse events to 1 every 20ms.\n    rubberband.mousemove('motion_notify', mouse_event_fn);\n\n    rubberband.mouseenter('figure_enter', mouse_event_fn);\n    rubberband.mouseleave('figure_leave', mouse_event_fn);\n\n    canvas_div.on(\"wheel\", function (event) {\n        event = event.originalEvent;\n        event['data'] = 'scroll'\n        if (event.deltaY < 0) {\n            event.step = 1;\n        } else {\n            event.step = -1;\n        }\n        mouse_event_fn(event);\n    });\n\n    canvas_div.append(canvas);\n    canvas_div.append(rubberband);\n\n    this.rubberband = rubberband;\n    this.rubberband_canvas = rubberband[0];\n    this.rubberband_context = rubberband[0].getContext(\"2d\");\n    this.rubberband_context.strokeStyle = \"#000000\";\n\n    this._resize_canvas = function(width, height) {\n        // Keep the size of the canvas, canvas container, and rubber band\n        // canvas in synch.\n        canvas_div.css('width', width)\n        canvas_div.css('height', height)\n\n        canvas.attr('width', width * mpl.ratio);\n        canvas.attr('height', height * mpl.ratio);\n        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n\n        rubberband.attr('width', width);\n        rubberband.attr('height', height);\n    }\n\n    // Set the figure to an initial 600x600px, this will subsequently be updated\n    // upon first draw.\n    this._resize_canvas(600, 600);\n\n    // Disable right mouse context menu.\n    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n        return false;\n    });\n\n    function set_focus () {\n        canvas.focus();\n        canvas_div.focus();\n    }\n\n    window.setTimeout(set_focus, 100);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items) {\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) {\n            // put a spacer in here.\n            continue;\n        }\n        var button = $('<button/>');\n        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n                        'ui-button-icon-only');\n        button.attr('role', 'button');\n        button.attr('aria-disabled', 'false');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n\n        var icon_img = $('<span/>');\n        icon_img.addClass('ui-button-icon-primary ui-icon');\n        icon_img.addClass(image);\n        icon_img.addClass('ui-corner-all');\n\n        var tooltip_span = $('<span/>');\n        tooltip_span.addClass('ui-button-text');\n        tooltip_span.html(tooltip);\n\n        button.append(icon_img);\n        button.append(tooltip_span);\n\n        nav_element.append(button);\n    }\n\n    var fmt_picker_span = $('<span/>');\n\n    var fmt_picker = $('<select/>');\n    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n    fmt_picker_span.append(fmt_picker);\n    nav_element.append(fmt_picker_span);\n    this.format_dropdown = fmt_picker[0];\n\n    for (var ind in mpl.extensions) {\n        var fmt = mpl.extensions[ind];\n        var option = $(\n            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n        fmt_picker.append(option)\n    }\n\n    // Add hover states to the ui-buttons\n    $( \".ui-button\" ).hover(\n        function() { $(this).addClass(\"ui-state-hover\");},\n        function() { $(this).removeClass(\"ui-state-hover\");}\n    );\n\n    var status_bar = $('<span class=\"mpl-message\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n}\n\nmpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n    // which will in turn request a refresh of the image.\n    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n}\n\nmpl.figure.prototype.send_message = function(type, properties) {\n    properties['type'] = type;\n    properties['figure_id'] = this.id;\n    this.ws.send(JSON.stringify(properties));\n}\n\nmpl.figure.prototype.send_draw_message = function() {\n    if (!this.waiting) {\n        this.waiting = true;\n        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n    }\n}\n\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    var format_dropdown = fig.format_dropdown;\n    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n    fig.ondownload(fig, format);\n}\n\n\nmpl.figure.prototype.handle_resize = function(fig, msg) {\n    var size = msg['size'];\n    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n        fig._resize_canvas(size[0], size[1]);\n        fig.send_message(\"refresh\", {});\n    };\n}\n\nmpl.figure.prototype.handle_rubberband = function(fig, msg) {\n    var x0 = msg['x0'] / mpl.ratio;\n    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n    var x1 = msg['x1'] / mpl.ratio;\n    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n    x0 = Math.floor(x0) + 0.5;\n    y0 = Math.floor(y0) + 0.5;\n    x1 = Math.floor(x1) + 0.5;\n    y1 = Math.floor(y1) + 0.5;\n    var min_x = Math.min(x0, x1);\n    var min_y = Math.min(y0, y1);\n    var width = Math.abs(x1 - x0);\n    var height = Math.abs(y1 - y0);\n\n    fig.rubberband_context.clearRect(\n        0, 0, fig.canvas.width, fig.canvas.height);\n\n    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n}\n\nmpl.figure.prototype.handle_figure_label = function(fig, msg) {\n    // Updates the figure title.\n    fig.header.textContent = msg['label'];\n}\n\nmpl.figure.prototype.handle_cursor = function(fig, msg) {\n    var cursor = msg['cursor'];\n    switch(cursor)\n    {\n    case 0:\n        cursor = 'pointer';\n        break;\n    case 1:\n        cursor = 'default';\n        break;\n    case 2:\n        cursor = 'crosshair';\n        break;\n    case 3:\n        cursor = 'move';\n        break;\n    }\n    fig.rubberband_canvas.style.cursor = cursor;\n}\n\nmpl.figure.prototype.handle_message = function(fig, msg) {\n    fig.message.textContent = msg['message'];\n}\n\nmpl.figure.prototype.handle_draw = function(fig, msg) {\n    // Request the server to send over a new figure.\n    fig.send_draw_message();\n}\n\nmpl.figure.prototype.handle_image_mode = function(fig, msg) {\n    fig.image_mode = msg['mode'];\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Called whenever the canvas gets updated.\n    this.send_message(\"ack\", {});\n}\n\n// A function to construct a web socket function for onmessage handling.\n// Called in the figure constructor.\nmpl.figure.prototype._make_on_message_function = function(fig) {\n    return function socket_on_message(evt) {\n        if (evt.data instanceof Blob) {\n            /* FIXME: We get \"Resource interpreted as Image but\n             * transferred with MIME type text/plain:\" errors on\n             * Chrome.  But how to set the MIME type?  It doesn't seem\n             * to be part of the websocket stream */\n            evt.data.type = \"image/png\";\n\n            /* Free the memory for the previous frames */\n            if (fig.imageObj.src) {\n                (window.URL || window.webkitURL).revokeObjectURL(\n                    fig.imageObj.src);\n            }\n\n            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n                evt.data);\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n            fig.imageObj.src = evt.data;\n            fig.updated_canvas_event();\n            fig.waiting = false;\n            return;\n        }\n\n        var msg = JSON.parse(evt.data);\n        var msg_type = msg['type'];\n\n        // Call the  \"handle_{type}\" callback, which takes\n        // the figure and JSON message as its only arguments.\n        try {\n            var callback = fig[\"handle_\" + msg_type];\n        } catch (e) {\n            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n            return;\n        }\n\n        if (callback) {\n            try {\n                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n                callback(fig, msg);\n            } catch (e) {\n                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n            }\n        }\n    };\n}\n\n// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\nmpl.findpos = function(e) {\n    //this section is from http://www.quirksmode.org/js/events_properties.html\n    var targ;\n    if (!e)\n        e = window.event;\n    if (e.target)\n        targ = e.target;\n    else if (e.srcElement)\n        targ = e.srcElement;\n    if (targ.nodeType == 3) // defeat Safari bug\n        targ = targ.parentNode;\n\n    // jQuery normalizes the pageX and pageY\n    // pageX,Y are the mouse positions relative to the document\n    // offset() returns the position of the element relative to the document\n    var x = e.pageX - $(targ).offset().left;\n    var y = e.pageY - $(targ).offset().top;\n\n    return {\"x\": x, \"y\": y};\n};\n\n/*\n * return a copy of an object with only non-object keys\n * we need this to avoid circular references\n * http://stackoverflow.com/a/24161582/3208463\n */\nfunction simpleKeys (original) {\n  return Object.keys(original).reduce(function (obj, key) {\n    if (typeof original[key] !== 'object')\n        obj[key] = original[key]\n    return obj;\n  }, {});\n}\n\nmpl.figure.prototype.mouse_event = function(event, name) {\n    var canvas_pos = mpl.findpos(event)\n\n    if (name === 'button_press')\n    {\n        this.canvas.focus();\n        this.canvas_div.focus();\n    }\n\n    var x = canvas_pos.x * mpl.ratio;\n    var y = canvas_pos.y * mpl.ratio;\n\n    this.send_message(name, {x: x, y: y, button: event.button,\n                             step: event.step,\n                             guiEvent: simpleKeys(event)});\n\n    /* This prevents the web browser from automatically changing to\n     * the text insertion cursor when the button is pressed.  We want\n     * to control all of the cursor setting manually through the\n     * 'cursor' event from matplotlib */\n    event.preventDefault();\n    return false;\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    // Handle any extra behaviour associated with a key event\n}\n\nmpl.figure.prototype.key_event = function(event, name) {\n\n    // Prevent repeat events\n    if (name == 'key_press')\n    {\n        if (event.which === this._key)\n            return;\n        else\n            this._key = event.which;\n    }\n    if (name == 'key_release')\n        this._key = null;\n\n    var value = '';\n    if (event.ctrlKey && event.which != 17)\n        value += \"ctrl+\";\n    if (event.altKey && event.which != 18)\n        value += \"alt+\";\n    if (event.shiftKey && event.which != 16)\n        value += \"shift+\";\n\n    value += 'k';\n    value += event.which.toString();\n\n    this._key_event_extra(event, name);\n\n    this.send_message(name, {key: value,\n                             guiEvent: simpleKeys(event)});\n    return false;\n}\n\nmpl.figure.prototype.toolbar_button_onclick = function(name) {\n    if (name == 'download') {\n        this.handle_save(this, null);\n    } else {\n        this.send_message(\"toolbar_button\", {name: name});\n    }\n};\n\nmpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n    this.message.textContent = tooltip;\n};\nmpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n\nmpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n\nmpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n    // Create a \"websocket\"-like object which calls the given IPython comm\n    // object with the appropriate methods. Currently this is a non binary\n    // socket, so there is still some room for performance tuning.\n    var ws = {};\n\n    ws.close = function() {\n        comm.close()\n    };\n    ws.send = function(m) {\n        //console.log('sending', m);\n        comm.send(m);\n    };\n    // Register the callback with on_msg.\n    comm.on_msg(function(msg) {\n        //console.log('receiving', msg['content']['data'], msg);\n        // Pass the mpl event to the overriden (by mpl) onmessage function.\n        ws.onmessage(msg['content']['data'])\n    });\n    return ws;\n}\n\nmpl.mpl_figure_comm = function(comm, msg) {\n    // This is the function which gets called when the mpl process\n    // starts-up an IPython Comm through the \"matplotlib\" channel.\n\n    var id = msg.content.data.id;\n    // Get hold of the div created by the display call when the Comm\n    // socket was opened in Python.\n    var element = $(\"#\" + id);\n    var ws_proxy = comm_websocket_adapter(comm)\n\n    function ondownload(figure, format) {\n        window.open(figure.imageObj.src);\n    }\n\n    var fig = new mpl.figure(id, ws_proxy,\n                           ondownload,\n                           element.get(0));\n\n    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n    // web socket which is closed, not our websocket->open comm proxy.\n    ws_proxy.onopen();\n\n    fig.parent_element = element.get(0);\n    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n    if (!fig.cell_info) {\n        console.error(\"Failed to find cell for figure\", id, fig);\n        return;\n    }\n\n    var output_index = fig.cell_info[2]\n    var cell = fig.cell_info[0];\n\n};\n\nmpl.figure.prototype.handle_close = function(fig, msg) {\n    var width = fig.canvas.width/mpl.ratio\n    fig.root.unbind('remove')\n\n    // Update the output cell to use the data from the current canvas.\n    fig.push_to_output();\n    var dataURL = fig.canvas.toDataURL();\n    // Re-enable the keyboard manager in IPython - without this line, in FF,\n    // the notebook keyboard shortcuts fail.\n    IPython.keyboard_manager.enable()\n    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n    fig.close_ws(fig, msg);\n}\n\nmpl.figure.prototype.close_ws = function(fig, msg){\n    fig.send_message('closing', msg);\n    // fig.ws.close()\n}\n\nmpl.figure.prototype.push_to_output = function(remove_interactive) {\n    // Turn the data on the canvas into data in the output cell.\n    var width = this.canvas.width/mpl.ratio\n    var dataURL = this.canvas.toDataURL();\n    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n}\n\nmpl.figure.prototype.updated_canvas_event = function() {\n    // Tell IPython that the notebook contents must change.\n    IPython.notebook.set_dirty(true);\n    this.send_message(\"ack\", {});\n    var fig = this;\n    // Wait a second, then push the new image to the DOM so\n    // that it is saved nicely (might be nice to debounce this).\n    setTimeout(function () { fig.push_to_output() }, 1000);\n}\n\nmpl.figure.prototype._init_toolbar = function() {\n    var fig = this;\n\n    var nav_element = $('<div/>')\n    nav_element.attr('style', 'width: 100%');\n    this.root.append(nav_element);\n\n    // Define a callback function for later on.\n    function toolbar_event(event) {\n        return fig.toolbar_button_onclick(event['data']);\n    }\n    function toolbar_mouse_event(event) {\n        return fig.toolbar_button_onmouseover(event['data']);\n    }\n\n    for(var toolbar_ind in mpl.toolbar_items){\n        var name = mpl.toolbar_items[toolbar_ind][0];\n        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n        var image = mpl.toolbar_items[toolbar_ind][2];\n        var method_name = mpl.toolbar_items[toolbar_ind][3];\n\n        if (!name) { continue; };\n\n        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n        button.click(method_name, toolbar_event);\n        button.mouseover(tooltip, toolbar_mouse_event);\n        nav_element.append(button);\n    }\n\n    // Add the status bar.\n    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n    nav_element.append(status_bar);\n    this.message = status_bar[0];\n\n    // Add the close button to the window.\n    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n    button.click(function (evt) { fig.handle_close(fig, {}); } );\n    button.mouseover('Stop Interaction', toolbar_mouse_event);\n    buttongrp.append(button);\n    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n    titlebar.prepend(buttongrp);\n}\n\nmpl.figure.prototype._root_extra_style = function(el){\n    var fig = this\n    el.on(\"remove\", function(){\n\tfig.close_ws(fig, {});\n    });\n}\n\nmpl.figure.prototype._canvas_extra_style = function(el){\n    // this is important to make the div 'focusable\n    el.attr('tabindex', 0)\n    // reach out to IPython and tell the keyboard manager to turn it's self\n    // off when our div gets focus\n\n    // location in version 3\n    if (IPython.notebook.keyboard_manager) {\n        IPython.notebook.keyboard_manager.register_events(el);\n    }\n    else {\n        // location in version 2\n        IPython.keyboard_manager.register_events(el);\n    }\n\n}\n\nmpl.figure.prototype._key_event_extra = function(event, name) {\n    var manager = IPython.notebook.keyboard_manager;\n    if (!manager)\n        manager = IPython.keyboard_manager;\n\n    // Check for shift+enter\n    if (event.shiftKey && event.which == 13) {\n        this.canvas_div.blur();\n        // select the cell after this one\n        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n        IPython.notebook.select(index + 1);\n    }\n}\n\nmpl.figure.prototype.handle_save = function(fig, msg) {\n    fig.ondownload(fig, null);\n}\n\n\nmpl.find_output_cell = function(html_output) {\n    // Return the cell and output element which can be found *uniquely* in the notebook.\n    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n    // IPython event is triggered only after the cells have been serialised, which for\n    // our purposes (turning an active figure into a static one), is too late.\n    var cells = IPython.notebook.get_cells();\n    var ncells = cells.length;\n    for (var i=0; i<ncells; i++) {\n        var cell = cells[i];\n        if (cell.cell_type === 'code'){\n            for (var j=0; j<cell.output_area.outputs.length; j++) {\n                var data = cell.output_area.outputs[j];\n                if (data.data) {\n                    // IPython >= 3 moved mimebundle to data attribute of output\n                    data = data.data;\n                }\n                if (data['text/html'] == html_output) {\n                    return [cell, data, j];\n                }\n            }\n        }\n    }\n}\n\n// Register the function which deals with the matplotlib target/channel.\n// The kernel may be null if the page has been refreshed.\nif (IPython.notebook.kernel != null) {\n    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n}\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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

Single_Sim_Results.ipynb