{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Identification of design margins in the design of a strut"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Defining the components of the margin analysis network\n",
"\n",
"The components of the margin analysis network are:\n",
"\n",
"* Input parameters\n",
" * Design parameters\n",
" * Fixed parameters\n",
" * Input specifications\n",
"* Intermediate parameters\n",
"* Decision nodes\n",
"* Output parameters\n",
" * Performance parameters\n",
" * Target thresholds\n",
" * Decided values"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Design parameters\n",
"\n",
"\n",
"
\n",
" Fig.1 Schematic of strut design example\n",
"
\n",
"\n",
"This example shows how to evaluate the design margins of a strut which is a part of the turbine rear frame of an aeroengine. In this example, we consider three design parameters of the strut the lean angle $\\theta$, the vane width $w$, and the vane height $h$"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from mvm import DesignParam\n",
"\n",
"# define design parameters\n",
"d1 = DesignParam(100.0, 'D1', universe=(70.0, 130.0), variable_type='FLOAT', description='vane length', symbol='w')\n",
"d2 = DesignParam(15.0, 'D2', universe=(5.0, 20.0), variable_type='FLOAT', description='vane height', symbol='h')\n",
"d3 = DesignParam(10.0, 'D3', universe=(0.0, 30.0), variable_type='FLOAT', description='lean angle', symbol='theta')\n",
"design_params = [d1,d2,d3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fixed parameters\n",
"\n",
"We also specify some constants for this example, such as elastic modulus $E$, coefficient of thermal expansion $\\alpha$, hub and shroud radii, $r_1$ and $r_2$, respectively, and ambient temperature $T_\\mathrm{sink}$"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from mvm import FixedParam\n",
"\n",
"# define fixed parameters\n",
"i1 = FixedParam(7.17E-06, 'I1', description='Coefficient of thermal expansion', symbol='alpha')\n",
"i2 = FixedParam(156.3E3, 'I2', description='Youngs modulus', symbol='E')\n",
"i3 = FixedParam(346.5, 'I3', description='Radius of the hub', symbol='r1')\n",
"i4 = FixedParam(536.5, 'I4', description='Radius of the shroud', symbol='r2')\n",
"i5 = FixedParam(25.0, 'I6', description='ambient temperature', symbol='T_sink')\n",
"\n",
"fixed_params = [i1, i2, i3, i4, i5]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Input specifications\n",
"\n",
"The strut experiences a compressive force $F$ and bending moment $M$ as a result of non-uniform expansion of the turbine rear frame during operation. This is attributed to two temperatures $T_1$ and $T_2$ that occur at the shroud and hub surfaces, respectively as shown below:\n",
"\n",
"\n",
"
\n",
" Fig.2 Temperature loads\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The temperatures $T_1$ and $T_2$ are defined as input specifications. The argument ``inc`` specifies the expected direction of change of these temperatures during operation and will be used later on to compute the ability of a design margin to absorb change of said temperatures\n",
"\n",
"* $T_1$ is expected to decrease by 1\\% of the nominal value\n",
"* $T_2$ is expected to increase by 1\\% of the nominal value\n",
"\n",
"If the absolute change is to be specified, then change the argument ``inc_type`` to ``'abs'``"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from mvm import InputSpec\n",
"\n",
"# define input specifications\n",
"s1 = InputSpec(450, 'S1', universe=[325, 550], variable_type='FLOAT', description='nacelle temperature',\n",
" symbol='T1', inc=-1e-0, inc_type='rel')\n",
"s2 = InputSpec(425, 'S2', universe=[325, 550], variable_type='FLOAT', description='gas surface temperature',\n",
" symbol='T2', inc=+1e-0, inc_type='rel')\n",
"input_specs = [s1, s2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Performance parameters\n",
"\n",
"we then calculate weight and cost as:\n",
"\n",
"$$\n",
"W = \\rho whL\\\\\n",
"\\mathrm{cost} = c\\rho whL,\n",
"$$\n",
"\n",
"where $c$ is the raw material cost per unit weight. and $L$ is the length of the strut given by $L = -r_1\\cos{\\theta} + \\sqrt{{r_2}^2 - {r_1}^2\\sin^2{\\theta}}$\n",
"\n",
"$W$ and $\\mathrm{cost}$ are *performance parameters*. Their calculation is given by a *behaviour* model ``b1``"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from mvm import Behaviour\n",
"import numpy as np\n",
"\n",
"# this is the weight and cost model\n",
"class B1(Behaviour):\n",
" def __call__(self, rho, w, h, theta, r1, r2, cost_coeff):\n",
" L = -r1 * np.cos(np.deg2rad(theta)) + np.sqrt(r2 ** 2 - (r1 * np.sin(np.deg2rad(theta))) ** 2)\n",
" weight = rho * w * h * L\n",
" cost = weight * cost_coeff\n",
" self.performance = [weight, cost]\n",
"\n",
"b1 = B1(n_i=0, n_p=2, n_dv=0, n_tt=0, key='B1')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We specify whether increasing this parameter is beneficial or detrimental to the design's performance using the ``direction`` argument"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from mvm import Performance\n",
"# Define performances\n",
"p1 = Performance('P1', direction='less_is_better')\n",
"p2 = Performance('P2', direction='less_is_better')\n",
"performances = [p1, p2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Target thresholds\n",
"\n",
"The strut must support the forces shown perviously on Figure 2:\n",
"\n",
"* A compressive stress $\\sigma_a$ due to the force $F$\n",
"* A bending stress $\\sigma_m$ due to the bending moment $M$\n",
"\n",
"$$\n",
"\\sigma_a = \\dfrac{E\\alpha}{L}\\left(T_2r_2 - T_1r_1 - T_\\text{sink}(r_2-r_1)\\right)\\cos{\\theta}\\\\\n",
"\\sigma_m =\\dfrac{3}{2}\\dfrac{Eh\\alpha}{L^2}\\left(T_2r_2 - T_1r_1 - T_\\text{sink}(r_2-r_1)\\right)\\sin{\\theta}\n",
"$$\n",
"\n",
"The maximum stress value becomes the *target threshold*.\n",
"\n",
"\n",
"$$\n",
"\\sigma_\\mathrm{max} = \\max{\\left(\\sigma_a,\\sigma_m\\right)}\n",
"$$\n",
"\n",
"This is defined by *behaviour* model ``b2``"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# this is the stress model\n",
"class B2(Behaviour):\n",
" def __call__(self, T1, T2, h, theta, alpha, E, r1, r2, T_sink):\n",
" L = -r1 * np.cos(np.deg2rad(theta)) + np.sqrt(r2 ** 2 - (r1 * np.sin(np.deg2rad(theta))) ** 2)\n",
" sigma_a = (E * alpha) * ((T2 * r2) - (T1 * r1) - (T_sink * (r2 - r1))) * np.cos(np.deg2rad(theta)) / L\n",
" sigma_m = (3 / 2) * ((E * h) / (L ** 2)) * (\n",
" alpha * ((T2 * r2) - (T1 * r1) - (T_sink * (r2 - r1))) * np.sin(np.deg2rad(theta)))\n",
" self.threshold = max([sigma_a, sigma_m])\n",
"\n",
"b2 = B2(n_i=0, n_p=0, n_dv=0, n_tt=1, key='B2')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Decided values (from Decision nodes)\n",
"\n",
"After calculating the target thresholds (what the design needs to do) we have to make decisions regarding certain 'off-the-shelf' components. The decided value in this case is given by the yield stress of the selected material. We define a decision node using the ``Design`` class and a corresponding ``Behaviour`` model to translate the selected material to a decided value. The material model also supplies two additional intermediate parameters that are required by the cost model in ``b1``. They are the density $\\rho$ and the cost density $c$"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from mvm import Decision\n",
"\n",
"class B3(Behaviour):\n",
" def __call__(self, material):\n",
" material_dict = {\n",
" 'dummy' : {\n",
" 'sigma_y' : 92, # MPa\n",
" 'rho' : 11.95e-06, # kg/mm3\n",
" 'cost' : 0.1 # USD/kg\n",
" },\n",
" 'Steel' : {\n",
" 'sigma_y' : 250, # MPa\n",
" 'rho' : 10.34e-06, # kg/mm3\n",
" 'cost' : 0.09478261, # USD/kg\n",
" },\n",
" 'Inconel' : {\n",
" 'sigma_y' : 460, # MPa\n",
" 'rho' : 8.19e-06, # kg/mm3\n",
" 'cost' : 0.46, # USD/kg\n",
" },\n",
" 'Titanium' : {\n",
" 'sigma_y' : 828, # MPa\n",
" 'rho' : 4.43e-06, # kg/mm3\n",
" 'cost' : 1.10 # USD/kg\n",
" },\n",
" }\n",
" chosen_mat = material_dict[material]\n",
"\n",
" self.intermediate = [chosen_mat['rho'], chosen_mat['cost']]\n",
" self.decided_value = chosen_mat['sigma_y']\n",
"\n",
" return self.decided_value\n",
"\n",
"b3 = B3(n_i=2, n_p=0, n_dv=1, n_tt=0, key='B3')\n",
"\n",
"# Define decision nodes and a model to convert to decided values\n",
"decision_1 = Decision(universe=['Steel', 'Inconel', 'Titanium'], variable_type='ENUM', key='decision_1',\n",
" direction='must_not_exceed', decided_value_model=b3, description='The type of material')\n",
"\n",
"decisions = [decision_1, ]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We concatenate all behaviour models into a list"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"behaviours = [b1,b2,b3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Excess margins\n",
"\n",
"The design must support the following loads\n",
"\n",
"* A maximum stress **less than** the yield stress $\\sigma_y$\n",
"\n",
"We have one design margin given by:\n",
"\n",
"$$\n",
"e_1 = \\sigma_y - \\sigma_\\mathrm{max}\\\\\n",
"$$\n",
"\n",
"It is of the ``must_not_exceed`` type"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"from mvm import MarginNode\n",
"\n",
"# Define margin nodes\n",
"e1 = MarginNode('E1', direction='must_not_exceed')\n",
"margin_nodes = [e1,]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Calculation of the value of excess margins in the strut\n",
"\n",
"### Constructing the margin analysis network\n",
"\n",
"We combine all the perviously defined parameters and behaviour models inside a ``MarginNetwork`` object. The ``forward`` method represents a single calculation pass of the margin analysis network\n",
"\n",
"The ``randomize`` method is optional and is used to randomly seed any probabilistic input specifications or behaviour models (see example in next section). The ``MAN`` object below mimics the MAN shown in the figure below:\n",
"\n",
"\n",
"
\n",
" Fig.3 Margin analysis network of the strut example\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from mvm import MarginNetwork\n",
"\n",
"# Define the MAN\n",
"class MAN(MarginNetwork):\n",
"\n",
" def randomize(self):\n",
" pass\n",
"\n",
" def forward(self, num_threads=1, recalculate_decisions=False, allocate_margin=False, strategy=['min_excess',], outputs=['dv',]):\n",
" # retrieve MAN components\n",
" w = self.design_params[0].value # w\n",
" h = self.design_params[1].value # h\n",
" theta = self.design_params[2].value # theta\n",
"\n",
" T1 = self.input_specs[0].value # T1 (could be stochastic)\n",
" T2 = self.input_specs[1].value # T2 (could be stochastic)\n",
"\n",
" alpha = self.fixed_params[0].value # alpha\n",
" E = self.fixed_params[1].value # E\n",
" r1 = self.fixed_params[2].value # r1\n",
" r2 = self.fixed_params[3].value # r2\n",
" T_sink = self.fixed_params[4].value # T_sink\n",
"\n",
" b1 = self.behaviours[0] # calculates weight and cost\n",
" b2 = self.behaviours[1] # calculates maximum of axial and bending stresses\n",
" b3 = self.behaviours[2] # calculates material properties\n",
"\n",
" decision_1 = self.decisions[0] # select a material based on maximum bending or axial stress\n",
"\n",
" e1 = self.margin_nodes[0] # margin against axial or bending failure (sigma_max,sigma_y)\n",
"\n",
" p1 = self.performances[0] # weight\n",
" p2 = self.performances[1] # cost\n",
"\n",
" # Execute behaviour models\n",
"\n",
" # T1, T2, h, theta, alpha, E, r1, r2, T_sink\n",
" b2(T1, T2, h, theta, alpha, E, r1, r2, T_sink)\n",
" sigma_max = b2.threshold # the applied load as the target threshold\n",
"\n",
" # Execute decision node and translation model\n",
" decision_1(sigma_max, recalculate_decisions, allocate_margin, strategy[0], num_threads, outputs[0])\n",
" sigma_prime = decision_1.output_value # this either the target threshold (sigma_max) or the decided value (sigma_y)\n",
" sigma_y = decision_1.decided_value # the yield stress of the chosen material\n",
"\n",
" # invert decided value: to get rho, and cost_coeff\n",
" b3.inv_call(sigma_prime) \n",
" rho = b3.intermediate[0]\n",
" cost_coeff = b3.intermediate[1]\n",
"\n",
" # Compute excesses\n",
" e1(sigma_max, sigma_y)\n",
"\n",
" # Compute performances\n",
" # rho, w, h, theta, r1, r2, cost_coeff\n",
" b1(rho, w, h, theta, r1, r2, cost_coeff)\n",
" p1(b1.performance[0])\n",
" p2(b1.performance[1])\n",
"\n",
"\n",
"man = MAN(design_params, input_specs, fixed_params,\n",
" behaviours, decisions, margin_nodes, performances, 'MAN_1')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculating the impact of excess margin on performance\n",
"\n",
"To calculate the level of overdesign w.r.t margin node $m$ and performance parameter $j$ given by\n",
"\n",
"$$\n",
"\\text{impact}_{mj} = \\dfrac{p_j - p^\\text{threshold}_{mj}}{p^\\text{threshold}_{mj}}\n",
"$$\n",
"\n",
"we need to calculate the threshold performance $p^\\text{threshold}_{mj}$, i.e., the weight of a hypothetical design where margin node $e_m = 0$, To do so, we construct a surrogate model associated with each behaviour model that is connected to a decision node to allow interpolation of this hypothetical design. In this example it is ``b3`` associated with ``decision_1``.\n",
"\n",
"This allows the MAN's performance parameters $\\mathbf{p}$ to be expressed as functions in terms of the margin values $\\mathbf{e}$ and input specifications $\\mathbf{s}$\n",
"\n",
"$$\n",
"\\hat{\\mathbf{p}} = f(\\mathbf{e},\\mathbf{s})\n",
"$$\n",
"\n",
"we substitute $e_m = 0$ while holding all the other components at their nominal values."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"___________________________________________________________________________\n",
" \n",
" LS\n",
"___________________________________________________________________________\n",
" \n",
" Problem size\n",
" \n",
" # training points. : 50\n",
" \n",
"___________________________________________________________________________\n",
" \n",
" Training\n",
" \n",
" Training ...\n",
" Training - done. Time (sec): 0.0009418\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/khalil/.local/share/virtualenvs/mvmlib-9r_YoSyI/lib/python3.10/site-packages/smt/surrogate_models/krg_based.py:211: UserWarning: Warning: multiple x input features have the same value (at least same row twice).\n",
" warnings.warn(\"Warning: multiple x input features have the same value (at least same row twice).\")\n"
]
}
],
"source": [
"# train material surrogate\n",
"variable_dict = {\n",
" 'material' : {'type' : 'ENUM', 'limits' : decision_1.universe},\n",
"}\n",
"b3.train_surrogate(variable_dict,n_samples=50,sm_type='KRG')\n",
"b3.train_inverse(sm_type='LS')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now calculate the impact using ``mvmlib`` using the Kriging surrogate model\n",
"\n",
"$$\n",
"\\text{impact}_{mj} = \\dfrac{p_j - \\hat{p}^\\text{threshold}_{mj}}{\\hat{p}^\\text{threshold}_{mj}}\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.08317867, 0.26427108]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"man.reset()\n",
"man.init_decisions()\n",
"man.allocate_margins()\n",
"man.forward()\n",
"\n",
"man.compute_impact()\n",
"man.impact_matrix.value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* The margin node (against yielding) has a positive impact on cost (i.e., its elimination will increase raw material cost). \n",
"* However, the margin node (against yielding) has a negative on weight (i.e., its elimination will reduce weight). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calculation of change absorption capability\n",
"\n",
"We now calculate the ability of the design to absorb deviation in the input specifications from their nominal values. We incrementally increase or decrease each specification $s_i$ until one of the margin nodes is equal to 0. The value of the specification at this point is called $s^\\text{max}_i$. The maximum allowable deterioration in the input specification is give by\n",
"\n",
"$$\n",
"\\text{deterioration}_i = \\dfrac{s^\\text{max}_i - s_i}{s_i}\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([414. , 403.75])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"man.compute_absorption()\n",
"man.spec_vector - man.deterioration_vector.value * man.spec_vector"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The value of the target thresholds when $s^\\text{max}_i$ is reached is $\\mathbf{t}^\\text{new}$. The value of the target thresholds at the nominal specifications $s_i$ is $\\mathbf{t}^\\text{nominal}$. The change absorption per unit deterioration is given by\n",
"\n",
"$$\n",
"\\text{absorption}_{mi} = \\dfrac{t^\\text{new}_{mi} - t^\\text{nominal}_{mi}}{t^\\text{nominal}_{mi}\\times\\text{deterioration}_i}\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[2.31557453, 3.38611472]])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"man.absorption_matrix.value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Aggregation of impact and absorption\n",
"\n",
"We average the absorption and impact across all input specifications and performance parameters for each margin node (assuming equal weighting)\n",
"\n",
"$$\n",
"{a}_m = \\dfrac{1}{n_\\text{specs}}\\sum_{i=0}^{n_\\text{specs}} \\text{absorption}_{mi} \\\\\n",
"{i}_m = \\dfrac{1}{n_\\text{perf}}\\sum_{j=0}^{n_\\text{perf}} \\text{impact}_{mj} \\\\\n",
"$$\n",
"\n",
"as follows:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.850844625951363 0.09054620735655292\n"
]
}
],
"source": [
"mean_absorption_node_1 = np.mean(man.absorption_matrix.value,axis=1)[0]\n",
"mean_impact_node_1 = np.mean(man.impact_matrix.value,axis=1)[0]\n",
"print(mean_absorption_node_1,mean_impact_node_1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively, we can aggregate across margin nodes instead\n",
"\n",
"$$\n",
"{a}_i = \\dfrac{1}{n_\\text{nodes}}\\sum_{m=0}^{n_\\text{nodes}} \\text{absorption}_{mi} \\\\\n",
"{i}_j = \\dfrac{1}{n_\\text{nodes}}\\sum_{m=0}^{n_\\text{nodes}} \\text{impact}_{mj} \\\\\n",
"$$\n",
"\n",
"as follows:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.3155745312790024 -0.0831786667397811\n",
"3.3861147206237234 0.2642710814528869\n"
]
}
],
"source": [
"mean_absorption_spec_1 = np.mean(man.absorption_matrix.value,axis=0)[0]\n",
"mean_absorption_spec_2 = np.mean(man.absorption_matrix.value,axis=0)[1]\n",
"\n",
"mean_impact_perf_1 = np.mean(man.impact_matrix.value,axis=0)[0]\n",
"mean_impact_perf_2 = np.mean(man.impact_matrix.value,axis=0)[1]\n",
"print(mean_absorption_spec_1,mean_impact_perf_1)\n",
"print(mean_absorption_spec_2,mean_impact_perf_2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Effect of uncertainty in the nominal values of the input specifications $T_1$ and $T_2$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This caused by unequal temperatures $T_1$, and $T_2$. Their exact values are not known and are expected to vary. We model this uncertainty using a joint probability density function:\n",
"\n",
"$$\n",
"T_1,T_2 \\sim \\mathcal{N}(\\boldsymbol{\\mu},\\boldsymbol{\\Sigma})\n",
"$$\n",
"\n",
"where $\\boldsymbol{\\mu}$ and $\\boldsymbol{\\Sigma}$ are the means and covariances, respectively of multivariate normal distribution. For more examples of different probability density functions supported by the library, see the examples [here](https://sed-group.github.io/mvmlib/notebooks/PDF_examples.html)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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uvvrqK0F3Pc8TTzyBF198EfX19QgKCsLRo0eh0+mwadMm/L//9/+wbt06DB8+HD/99BPuu+8+5OfnQ6/Xm9dnOX+nTp0C0Cp2hWJH161bZxakn332Gbp3705dmghCBA1n66ciCIIgfIannnoKRqPRLtP+u+++w7333ovDhw873X3JZDJh8uTJqK2txVdffQU/P9faMvi+9JMmTRK12paVlcFgMFhZTy2pqqpCr1698Prrr2PatGn461//il27duG3335jGoOa56+xsRE9e/bE888/j9mzZzu1LYJoq5CFlCAIwgdpbm5Gc3MzWlpa0NzcjIaGBuj1enOFg507d2LixImqtAJ98sknUVJSgu+//97lYhQAU9JXdHS05PcGgwF/+9vf8MYbb2Dq1Kk4evSorHhcNc/funXroNfrJev5EkR7hyykBEEQPsjChQuxaNEiq8/WrVuHxx9/XNX9nDlzBj179kRgYKBVOa9t27Zh+PDhqu7LVXAch/DwcDzzzDN47bXXPD0cgiAEIEFKEARBtGl+//139O7dG5988gkefvhhTw+HIAgBSJASBEEQBEEQHoXqkBIEQRAEQRAehQQpQRAEQRAE4VF8MsveZDLhwoULCAsLEyyKTBAEQRAEQXgWjuNQU1ODzp07Q6uVtoH6pCC9cOGCuQc1QRAEQRAE4b2cO3cOXbt2lVzGJwVpWFgYgNYD5HtREwRBEARBEN5DdXU1unXrZtZtUvikIOXd9OHh4SRICYIgCIIgvBiW8EpKaiIIgiAIgiA8CglSgiAIgiAIwqOQICUIgiAIgiA8ik/GkBIEQRCEL9Ji4nCgqAJlNQ2IDgvE4Lgo6LRUvpAgSJASBEEQhBvYXlCCRd8WoqSqwfxZrCEQL41LQFpirAdHRhCeh1z2BEEQBOFitheU4On1eVZiFABKqxrw9Po8bC8o8dDICMI7IEFKEARBEC6kxcRh0beF4AS+4z9b9G0hWkxCSxBE+4AEKUEQBEG4kANFFXaWUUs4ACVVDThQVCF72y0mDjmnL+Ob/PPIOX2ZRC3hs1AMKUEQBEG4kLIacTGqZDkeikkl2hJkISUIgiAIFxIdFqjqcgDFpBJtDxKkBEEQBOFCBsdFIdYQCLHiThq0WjYHx0UxbY9iUom2CAlSgiAIgnAhOq0GL41LAAA7Ucr//dK4BOZ6pK6MSSUIT0GClCAIgiBcTFpiLN59OBlGg7Vb3mgIxLsPJ8uK+XRVTCpBeBJKaiIIgiAIN5CWGIsxCUanOzW5IiaVIDwNCVKCIAiCcBM6rQYpvTs4tQ0+JrW0qkEwjlSDVssra0wqQXgD5LInCIIgCB9C7ZhUgvAGSJASBEEQhI+hZkwqQXgD5LInCIIgCB9ErZhUgvAGSJASBEEQhI+iRkwqQXgD5LInCIIgCIIgPApZSAmCIAjCAS0mjlzjBOFCSJASBEEQhATbC0qw6NtCq+5IsYZAvDQugZKHCEIlyGVPEARBECJsLyjB0+vz7Fp1llY14On1edheUOKhkRFE24IEKUEQBEEI0GLisOjbQsHi8/xni74tRItJaAmCIORALnuCIAgJvC120NvG4wq85RgPFFXYWUYt4QCUVDXgQFEFZboThJOQICUIghDB22IHvWE8rhaL3nCMPGU14mJUyXIEQYhDgpQgCEIAPnbQ1hnLxw66uxuON4zH1WLRG47RkuiwQMcLyViOIAhxKIaUIAjCBm+IHWwxccg5fRnf5J/HnpPlWLjZs+NxdXKPN5xzWwbHRSHW4FhsVtY2umE06mD5u8o5fZniXwmvgSykBEEQNng6dlDIEimFq8fjSCxq0CoWxyQYFbvv9/1+2eviNXVaDRakJ2DmhjzJ5TK3HsddibFeH8vrTeEQBGELWUgJgiBs8GTsoJgl0lPjAeQJdCVsLyhBxqfSoo9H6TEqtQxGhvg7XMaZY3cXvlS+iqy47ROykBIEQdjgqdhBKUukJ8bD40qBLhY3KoaSY3TGMtgWEpvcYeFWC7Litl/IQkoQBGEDHzso9mrWoPUlOTguStX9OrJEiqEBYAwPgInjXGJVcpVAlyPAlZ5zVsugmFWuLSQ2udrCrRa+ZMUl1IcspARBEDbotBq8NC4BT6/PgwawEky8SH1pXILq1iQlVjZ+fA3NJkx5f7/5czWtSrxAL61qEBSPGgBGBWJRrgCXe85ZLYMmE4fMrccFrXJjEowuOXZ34gtWXl+y4hKugSykBEEQAqQlxuLdh5NhtMmyNhoCXVZ+SImVzRCsBwBcqWuy+lxNqxIv0AHYWY2dEeisAigiWK/onLNaBmduOCRqlcsqLBU9dn4bC9L7OTx2T8ZF+oKV11esuITrIAspQRCECGmJsRiTYHRb1yAWS2RMeAD+8VASyq82omNIAP7rs8MAmuyWVduqxAt02/g+oxOWWFYBtHJSMobGd5S9fWcsfpbnb/e8UYLHzpO59Ti0Wo3oOfB0XKSj3xUARAbrPWrl9QUrLuFaSJASBEFIoNNq3FpmyFGowMLxN2Fon1ZxlnP6Mkqr3VcqSW2BzhoKMIRx7LZdpDqGBigaF4/l+UtLjIXJBMESUFKF+11R7F9utyz+d/XUevFKBpV1TcgqLPVY4pAvWHEJ1+KUy37JkiXQaDSYM2eO3Xccx+Huu++GRqPB119/bfXd2bNnkZ6ejuDgYERHR+O5555Dc3OzM0MhCIJoE8gJFfCEVYkX6BOSuiCldwenLK9qhgJsLyjBsKU7MGntPszelI9Ja/fhv/4vHxHBetHkNFbKahrQYuKQubVQ8Huxwv2uKPYvdJzDlu5wGJoxJsGIiOvhHULw1mBPlVjyVCIh4T0otpDm5uZi9erVGDBggOD3y5cvh0Zj/9NqaWlBeno6jEYj9u7di5KSEjz66KPQ6/V47bXXlA6HIAiizcBqifQmq5LSHvdqhAKIWSEvVjeaPxOyOMspNaWkWYLaDRacsbYeKKqwizN2Zixq46lEQsJ7UCRIr169iilTpmDt2rV45ZVX7L7Pz8/HP/7xD/zyyy+IjbW+OX744QcUFhYiOzsbMTExSEpKQmZmJubNm4eFCxfC399xEWKCIIi2DkuogKuy3+XibIykM6EALFbI0AA/hAb4WYU3GA2BWJCegMythUznb8uRCw7HAlhbo9W0YDubhe4LMZquiFMmfAdFgjQjIwPp6elITU21E6R1dXWYPHkyVq5cCaPRaLduTk4O+vfvj5iYGPNnd911F55++mkcO3YMN998s5IhEQRBtDu8waqkVoyk0lhdltJRVxubodcBc1Pj0bNjiJXg1WrBdP6UWKPVtGA7a231Jmu6FO5OJCS8B9kxpJs2bUJeXh4WL14s+P3cuXNx++23Y8KECYLfl5aWWolRAOa/S0tLBddpbGxEdXW11T+CIAjCM+WpeFwRIykXVoteZV0zlmefRICf1ir2lfX8KYlxVDMu0lkLpy/FaKoZp0z4DrIspOfOncPs2bORlZWFwED7WdTmzZuxY8cOHDp0SLUBAsDixYuxaNEiVbdJEATRVvCUVUntGEkhHMWmyrXoCbm1Wc6flDUa1/+eeGs3q8/UtGA7a+H0Bms6QUghy0J68OBBlJWVITk5GX5+fvDz88NPP/2Et99+G35+fsjKysLp06cRERFh/h4AHnjgAYwcORIAYDQacfHiRavt8n8LufgBYP78+aiqqjL/O3funNzjJAiCIFTG1XGJLBnljix/lvACed/py3bfsVjlxKypPMuyT9qNTy0LthoWTk9a0wnCERqO45h9KTU1NThz5ozVZ1OnTkXfvn0xb948dOzYEeXl5Vbf9+/fH2+99RbGjRuHuLg4bNu2Dffeey9KSkoQHR0NAFizZg2ee+45lJWVISDAcd246upqGAwGVFVVITw8nHX4BEEQbRJPFV7POX0Zk9buc7jcxulDZFtIxWJTeUFmKaDElhUjIkiPJQ/0V3xuWkwcVuw4hWXZv9l9JzQ+fh1nLdj8cQLCFk5WUanGWAjn8MQ18MQ+5eg1WYJUiJEjRyIpKQnLly8X3oFGg6+++gr33XcfgNayT0lJSejcuTNef/11lJaW4pFHHsFf//pX5rJPJEgJgiBakSPc1KbFxGHY0h0Os9R3zxsluwf9sKU7RMMBhLa7vaAEf/+qABW115j2oYG8c2P5Muc7ZIk1JVB63CyITT4WpCcgMsTfZWKDRKx6eGIC6alJqxy95vZOTTqdDlu2bMHTTz+NlJQUhISE4LHHHsPLL7/s7qEQBEF4HGde9M6WAnIWV8UlKolNTUuMxai+MRiyOBsVteL1Ni1hPTdCL3MpXFnTUyjetbK2EZlbXSc2PN36tC3his5d3rhPJTgtSP/9739Lfi9kgO3Rowe+++47Z3dNEATh0zj7ondHUpEjxGpHRobo8cqEREUvOqWxqf5+Wrx2f38m973UubGcJBSX12JZ9kkZoxcfn1pYlsjaXlCCjA2HXCY2fEXM+AKemEB6etIqB6dahxIEQRDK4F/0toKSf9E7agUJeE+x87TEWCxIT0BUyB+NTSpqm5C59bjocbSYOOScvoxv8s8j5/Rlq9JQzmSU8wI5Iki8TaYltufGNpFKqRgVG5+auLrsljeU9WpLyJlA+vI+leJ2lz1BEIQ34MmYOLWsFqyCp7ymES0mzmXH12qlY7eiObIMO9uBKi0xFmGBekx5f7/DsXcMDTD/FrIKS/HhnmLHB8xAVIi/opqecn6XrraQK92+nGNoT7GpnphAesuklQUSpARBtDs8HROnlpBwJNx4Mrcex/u7i5w+PiHxAECWuGZ1ATsbmzqkVwemczPz04PQaDSSfd6VcJvCLHo5v0tXiw0l25dzDErvQ18VsZ7oluUrHboActkTBNHOUMNV7ixqCQk+qQiAwzqczh6fWE3QFTtOMYtrOS5gZ2tmsp6bqvpm1cUoAPTuFCJreSW/S1eLDdb1isvrAMg7BqX3IUttWm/FE92yfKlDFwlSgiDaDd4SE6emkHBUrJ3HmeOTEg9CtTiFKKtpkB3PlpYYi93zRmHj9CF4a2ISNk4fgt3zRjFbeflzExPufutPSq+OzMu2mDg8/+VR0d8lB+DvXx3FtWaT1XfOig2pOF6W7fMsz/4N3x0pYb63lN6H3jCZdAapSZKrumV5Yp9KIUFKEES7wVsC/Fle9FEhepRWNwgKBVt44bYgvZ/kckqOj0U8sBAdFshsGc4uLDX/39m+5mmJsfjHgwNlreMsEcF6DJERs7lix0mHVtqK2iYMWfyjlehyRmywWBr57bNc5wXfFDDfW0ruQ2+ZTDqLJ7pl+UqHLoohJQii3eAtAf6O+qIDrQJk7r/yAbDF1em0GnQMc9zpDpB3fI7EAwtRIXoM6hGJg2cqmZb/7OB/MO/ufvD306oSL1he26hk2IpZ8qf+smrJrmNMpKqovWaXJCZWdsso8ZuRU8opLTEWc1PjJasNcAAuMzYkkPPbs1zWG0qcqYVQLVlXx8F6Yp9yIUFKEES7wRsC/HmB1dhswpzUG7DxwFnRjj88rDUfO4awCVJHy1mKwJMXrzJtE4CkuL7jjZ3m0lCOuilVNzRjyOJsPDioKzYfLnE6+UzN6yl2jDyRwXqMSTAyb+9AUQWu1MuLYbWtwCBHbCip8NCzo7x4WCnkXAvLZb1lMqkWlrVk2/I+5UCClCCIdoOz5YSUwgu87MJSfJV/3qqTkDE8AHNT49E9KhiZW48LijVboQBAWHywGjsklpPblYhnbmo8NuWeE12vpKoBMzfkIaFzGFN7z4raJqzeVWT3uZKC7KzVCFhwtH5lXZMsK51cAeWJUk6sIjIqRI/K2iame0vufegNk0nCtZAgJQii3eCqVpdSOBJ4F6sbsTz7JOakxksKNV4orNhx0k74tfYy74fCkmqmMZVfFXZhi7lypeDFw6xR8Xh6ZB8MWfyj5HEUXqiRsXV7lHSXYQmRUBM5IlOpgMoqLEVK7w5oMXFYseMU1u0psrK0ilmSlVgaWSdyC9L7IWPDIaZ7S+596KnJJOE+KKmJIIh2hTsD/MWygi3hX66scYTLsk/aba/V+ngIK3aeZtqGkAiScuVKwQEYPzAWOq0GB89UMlk/nUVJchZrNQI16BjKFjoBsGey2/LhnmIs/q4Qg17JwrLs3+zc/mKZ56xjs1yONXnqngGdme8tufehL2WLE8ogCylBEO0Olpg7Z5Np5Ag8DpAdR6gEKSuSM8lLa3YVYWDXSGYLrVo4svbZXsMxCUar615cXofl18tWqWo1lbExnVaDBen9MHPDIdm7EQppsByCoCWZdWw2y6UlxmLl5GS88E2B1aTDNnlKTjyr3EQbJQlcvlpEvz1CgpQgiHaJVIC/Gp2clAi8iCA9quqFY/CcxZEVyZlkEA7ArI15cHfFHSl3N+s1vNEYqihmVgo5Wf3bC0qQufW4avu2RCgelHVse38vxxCLMlut4yy0EqNRIXosSO8naM0UurfExKGcWFhLEVtaVY+K2muICg2AIcjfrj2upzuyEfIgQUoQhM+jphVETkkcKZQIvKlD48wWO0vUiHuUsiIBzieDuFOMOooXlFvWaEyCER/tKVJNGLKeSyUxu0qw/C2yjm3lztP4dP9ZLPlTfwAQHGdlbRMyNhzCu1qN+XyK3YtqikOdVoOq+mt4/fsTottT4z4m66p7IUFKEIRPo+aLTklJHDHkCDxeYMVHh8IQrLcrkh4RrMfjt8cxd0WyZdadfTB3zA2SY1YzE10JrKLbkaWX9RqGBehRXttoFhqsNVylrNi2QllK0CiN2VWC5W9xUI9IRIXorSo9iHGlrglPrc9DsL+O6Z7IKiwVvBfHD4zFml1FTk/yeByJzZWTb0bm1uNO3cdkXXU/lNREEIRX4qitIaB+K0HWkjgf7Sly2BFGbrLK+IGxyNiQJ9ixp7KuCfHRoYgI1jNuzZqhfTo6FNCsvd9dQWSwHjHh1oIw1hCIJ0fEIVZm8hnrNZzywX6rDkXF5bVMY506tKfk97xQdtQJSY2GAyxYtg7dXlCCO97YySRGLam71iL6nWX1B7F7cbWAGOXXBeR1WGLp2PSCjK5RQvh6i1JfhSykBEF4HdsLSrBwc6FVwXhjeCAWjk+wcg2qZc3kYXWzZ249jvd3F0laSyxLDUnBl2wSs+gArcfy8pZjTGOzXU/MtS1kvRNLGnE1lXVN+PSvt0Gr0dhZE/+W1k+W21RJqERJVQOWZZ9ERLAeVXXS1s9Zo+JRd60Za38usgpT0GqA6cPjmN3FjTZ96V2BBtYC2ZXhAev2FCtqLyu3rirLhINVcO85VW73u3LFc4VggwQpQRBexfaCEjwlIOJKqxvw1Po8rJp8M+4Z0NklrQTluNlZ3I1pibGYMSJOULyMvLETpg/vjcFxUUzHUlqtrP0lL0gsBWhxee31DlF/bNPSHcknjWQXluIDxnJUzlJ+tRETkrqY/+Yt5LxguHdAZ6YqCM7EwjZdF4lStTGzCksF3c8c11ptILFLBOZ/edShoHnzwYGKx8lCZLAei//UH2mJsW4JD3C2SkRpVb3V9RabeKjZiWnFzlPm//O/f0OQf5tpUeprkCAlCMJraDFxeP7Lo5LLZGw8hJXQoMnEZmEqrapn3r+cOEoWa8n2ghJR8bLz10t46JZu0Gk1Lml3GBqgw5sPDjRb7BxZPW0FdkrvDhgcF4UvDp0XDCNgQQMgkjFe0VJIOorfE/o+KkSP+5O6YFTfGBjDA3GxWn4sbO21Ftw7IBYHz1QKlhUak2DEsKU7JC2B/2/jIcn98oIGnHS3Ila0GuuEsoggPaYO7YlZo+LNv0lXhgdoAMG4Z7nYdikTi9dk7xrlj8raa8znlv/9OwrJ4PGVFqW+BAlSgiC8hn2/X3b4YuM4YOaGPMwZHc+0zcytxxHkr2NKRJDb0UfKWsIS68aLWVe0O7za2Br3x+qqFRLYB4oqnBYar0xIRObW45LCKzJYbxXnKGghvy4YZoyIExT5FbVN+GBPMT7YU4yIID/FIm/3yXIc+J9UHDxTibKahtYC8VxruaSP9hQ5FHas+y2vbcT4gbGStUQdoQGwYlIyIkP8JS2LrhJP/F6mOpFwx2PbUEHMA8HeNSoBGRvYO3Pxv/9v8i8wjZdalKoPJTURBOE15Jy+zLzsx/vOMC1XUXvNYSKCZQKVIcgfKyffLKujj9ALn8UqxSeDOEqA0qC1531UsD/zmHhxKeY+FsI22UOOddkWrQZ4Z2ISIkMCcHeiUXIMlXVNeH37cUkLOb/+2p+FE2QsuVLfDAAI9tfJHveV+iYcPFOJlN4dEOCnxX9/dticAKVmvdDi8lqscUKMGsMDsHLyzQ7FKOA68WQI1mNO6g14emRvRd2mpBBLeGLvGiW/MxcH4HLtNUSF6CXvxViJkmOEcshCShCEF8Fu15LbolLMtS7mHl6QnoCSqnomESL0wme1Si3LPokbjWGSvb05ANUNzZLZzraYXcMKKKtpwPaCEiz4Rn4iFY+JA1789hhzgsnqXUU4X1kvaZHl0GohZ4U1c9sW/vhdlQQUEaTHxgNnFW97buoNiI8OxctbpBP/eNQu6cX/Jq/UNWFZ9m/YlHvWXNpJLP52xog4bD5cYhdmIfX7EPNAsHZssu0EdfJiDVN73fuTuuDDPcWSscSU0KQ+ZCElCMJrSOnV0SXbFSvzIlXeJWNDHmINQQ4tl2LWEjlWqYWbj2FMglHQosOXepIjRp2luLwOT6/Pw9XGZqe2I7e80JajpU7tz5bGZhNC/LWyLXcdQwJcmgSU2i9aUZJaZLAe7z2cjBuNoZi5Ic9KjAJ/JP7ZegMsrYpqIFQ9YM2uIswYESfam37+PQnYPW8UNk4fgrcmJmHj9CFYcO9NTPsTmtylJcbabW/3vFGiXaMmJHXB0D6dmPaXKnIvOio5RjgHWUgJgvAahvTugAgZCRJRIXpU1rK32rR8sbGUd8ncWogF6f2QseGQbGsJb5VisVKWVjdixY5TmJ0ab2XR6RgagGf/lc94dM6jARATHuCU9c7baOH+uJ6OjomPP4RGuXXZESEBOtwWF4XP887LXreyrgkmE4e/f10gudz8L4/aeQPSEmMxJ/UGp2I9bROoePiP/pX7H7wz6WZoNRqU1zZaxd/mnL6MwXFRVpZO1hAdscmd3LajLJbiDiH+GNQjEv5+Wqt7kTo1uR6ykBIE4TXotBpzq0IpeMvkKxMSZW3f8sXGWjYqMiRAkbVErlVqWfZv2F5QYmXR0Wo0uFijrNyTXPjX7KTB3e0sb75MQ5MJc1PjHcYSWk4wyq+67pzXNrbgxc3KQiE0AP7n6wKHE7bKuibs+91e7PXsGCx7n7Pu7I23JiZhQXo/h+1hr9Q34ZEPD+C/Pz+MwgtVVvG3to0BAMfNI9SO12Rp/nC59hrueGOn3b2Y0rsDiVEXQ4KUIDwMS0ciZ9Z1ZvueYEyCEXNT4xGsF348WScudG4Vi+HSbR+FXmysMZ5lNQ2S7kGp85uWGIu5qTcw7Qdodd1fazaZt7fnVDnzus7CC+yeHUPctk930bNjiNX1m5t6A4zh4hMMV2dQ1zcpK4rPoVVssvD5L+fs7nUlxzW0TydMSOrC3FoVaJ3Ird5lX5HAttMRa4KSmkKQjz+VmqBQRybPQC57gvAgzvRLZlnX1/oxC43X1tUqlriwYscpQXek2IuN9eXMLyfkHmQ5v7NG9cHGA2eYYgZLqxuRnPmDuWSTKwkN0GH68N7o2THYyh0pp9KBrxAdFmh3/WaN6iPqjlU7CcgTfJV/AfuKKqx+i3KPy3ISp4ZIFyotxpqgpCZpibEY1TcGQxZnC8Y5q9mRSayBA2GPhuPk5Cx6B9XV1TAYDKiqqkJ4eLinh0MQihDL4uUfVVLuYJZ1ASjevieQOiYOwBNDe2JMglHygS5HgLeYOAxbukPy5RwRrMfBF8aIFr1nPb9itTU9ySdPDMbwG+yTPFjOiy8RGazH/r//UVeUVRS4utWmOxD7LfLtbB0dW0SwHkssuj2p+bvYOH2I1QTB3cIt5/RlTFq7T/Y45eBrBgFXIEevkSAlCA/AP9zFYhj55Ird80bZPZRZ1+U4TtQqJ7V9T+DM+RDaFuuLjUUovicg3OWOt8XEYcCi71HrBssnC5HBevwiIrQBeaLFF7BNlJPjhVi4+Zjitq1qowFgCPIz11llXcf23mHp3MWvC/whaNWcWL01McmqVay7+Sb/PGZvyne4nNJxOmNwaEvI0WsUQ0oQHkBOH3al60q9RKW27wmcOR+2yElEGJNgRIiD4unzvzxqF4snd7wHiiq8RowCQFK3CBwoqhCNJ05LjMXKya0dgNoCtklArDGCaYmx2PP8aKY4YL48l1yiQtjWM4euaORNIIXuHcuY6GV/SUJksHD0nm1x+jEJRsXHaYunOx3JDdmRA2uXNm+P53c3FENKEB5ATkKN0nXVHIcrsLRknrxYw7QO63htraSDekQKumz3/X4ZtQ7qe/IZy0P7/FEjlXUce05dwuC4KKc6HrmCnScuYeeJS6KWwu0FJcjcWii7+YCvYBkjOKpvDHKLKpDzezmA1snMkF5/TGR0Wg1mp8bjRmMonv/yqGCGe0SwHq/d19/cNam1XNchXKyRPn+xhkDs+K+RGLp0h8NzbTQEYuKt3bAs+6SiY7b9zfITt5zTl1FZJ25xtRW0zraSBbyj0xFrC1Il45QzYVUaDtAWIUFKEB7Amdm5mpYFT1kpWF2Gtjgab4uJw4odp7BuTxGu1P/x4rStn8h3tCk4X820389+OWeupxgdFthaX5GBFTtPY/3+s2hu8U5LiFC/8LYQO8kCLwqSM7OsGgCs2HkKwf46PDmiN2aN6mMWpmmJsTCZOMzccMhuW1V1TcjY0HoeeffuogmJDt3b9w6IRf65K0zC/80/D0R5rfLQAbF7x5nJsVK8odMRn+Ev1h0NUD5OT5zTtgAJUoLwAM7MzlnX5TgOF6sbVZ/9O4tSwRMRrIfJxKHFxIkmGYlZsGw9Y3xHm7sTY5j2/XX+BXydf8H8tzE8ABHBelTVOS7Kr4ZFyVXYZhPj+v/buhi1RKgbVd21FizL/g3r9hZZJfWItZG1dMOO6huDg2cq0djcWv909a7TqLsmXOZp7c9F+P3SVaZx8pMhuTi6113puhZibmq818ROuirD31XntK1n7DsVQ7pkyRJoNBrMmTPH/NmTTz6J3r17IygoCJ06dcKECRPw66+/Wq139uxZpKenIzg4GNHR0XjuuefQ3OxcizqC8CWcqb/Huu7C8Tcp2r4rkYqtcsSVuiZM+WC/XXFt4I/kJLnib8evZQpG0lqe6QqDGPUFLN2HjlyNahIaoMOKiUmShdE9zZW6JnMrTpZzU1LVgCGLszFp7T7M3pSPZdkn0eCg5uiPv15iGgsfeiLnlmW51x0Vpwf+cLGzLCtFrCEQs0bFK1zbNbC2IJWDKwr+by8owbClO8y/LaFGA76OYkGam5uL1atXY8CAAVafDxo0COvWrcPx48fx/fffg+M4jB07Fi0trXFaLS0tSE9Px7Vr17B3717885//xEcffYQXX3zRuSMhCB9DrEAzS79kqXVXTk5GWIAeBeerkJZon4TgyX7Magge24QUXuQqobG5LUhKdSiraXCrCzE0wA9jE2Mx8dbuXi/sF31biJIrbHHAtnUtWfJWtBrxzkHAH+Ll4JlKpu3xRIbo8cTQnjAE+Ysm0LB0FBs/MBY6rca8LEsLVtu/NfAOV70Qandk0mk1GD8wVvI8yTkXvFfJUaMBX0eRy/7q1auYMmUK1q5di1deecXquxkzZpj/37NnT7zyyisYOHAgiouL0bt3b/zwww8oLCxEdnY2YmJikJSUhMzMTMybNw8LFy6Ev3/byOokCBb4ou5K3DBC61bWNuLvX9u7rUMD/PDQLV0d1vF0NayCJ+PO3vh031mrOFAeWzezO616bZnosEC3Vl0orW4ULUzubZRUNeDQuUqXbd+RyOQFoZwJg0bTKo4/2FOMD/YUS5a6SkuMxYwRcVi9q0hwW2t2FeHm7pFMk9gnR8Rh8+EStxW590a2F5Rgjci5BIAZI+KYz4WjjH21Cvh7A4ospBkZGUhPT0dqaqrkcrW1tVi3bh3i4uLQrVs3AEBOTg769++PmJg/YrfuuusuVFdX49gxZf19CcKXcWZ2brluVf01zNxwSNBtfbWxGR/uKUZV/TWPPrRYY6aigv0FxSiPpZuZEgPUYcevF7FcoNOVK3GlGJVZHckhB8+4TpACQGIX8RqNa3YVYXtBiayYQ9sK41LWtBYTh82Hpa1si74txLVmk6Q3QgNg8+ES/PTcnaq6wH0JlrCkzYdLmEs+qVkSz9uRLUg3bdqEvLw8LF68WHSZVatWITQ0FKGhodi2bRuysrLMls/S0lIrMQrA/Hdpaang9hobG1FdXW31jyA8hTf2hm8xcVi42fGEzh2176TOz+C4KLse4pbwsVVRjPUv95y6hI4h7D22CXE+2F3k9a5zOajd8qWwhK00mVIcVXxY9G0hBvWIVBzDKVX/klX0fJJTzLTcwTOVqrrAfQnWWGNWAdmeMvZluezPnTuH2bNnIysrC4GB4i+VKVOmYMyYMSgpKcGbb76Jhx56CHv27JFcR4rFixdj0aJFitYlCDXx1lZwB4oqmLrJuKr2HZ/9mVVYiq/zL1iVsbE8P1mFpWhoFq77aZmAYQhiE6Qrdp7G5wfPIzRA55b+720ZL5hXeSUaAAF6rcPkJFdiKfTEShXJ2Y7tM4BVzJypqGNari2II6WoLSDdXQXBk8gSpAcPHkRZWRmSk5PNn7W0tGDXrl1YsWIFGhsbodPpYDAYYDAYEB8fjyFDhiAyMhJfffUVJk2aBKPRiAMHDlht9+LFiwAAo9EouN/58+fj2WefNf9dXV1tDgEgCHchVq5IqJajqxAr+yHnBaD2y8JRTVH+/MwYEYc1u8StcCHX41wNQf5I6hZhVztUjIvVbaPnOuGdcABMXqLWy2oaMCGpi2CpIjmUVtUj5/Rl83OEta5uj6hgpuXagjhSitoC0pUF/L0NWYJ09OjROHr0qNVnU6dORd++fTFv3jzodPYt+DiOA8dxaGxstd6kpKTg1VdfRVlZGaKjowEAWVlZCA8PR0KCcKZfQEAAAgLILUd4Dm8ILBcSfsbwQEwa3B2NMsqmqfmyYKkpyp+ftT9Lu4T5ONcP9xQjKsSf2WLnHVJBGg2Aj58YjH+fKMOmX855VRtRQpyIID2mDu2puDuS2vD3Lp/Q+NGeItHaqFJkbj1u5cVwVFeXFz2PpPTE+7uL2oU4UoraAtKVBfy9DVkxpGFhYUhMTLT6FxISgg4dOiAxMRG///47Fi9ejIMHD+Ls2bPYu3cvHnzwQQQFBeGee+4BAIwdOxYJCQl45JFHcPjwYXz//fd44YUXkJGRQaKT8Fo8HVguWvajugHLsn/Dqn//zrQdvnyMbZzntWaT7LhYOTVFOchzCbe1lpUcAD+dFgvG3YRDC8YiNMB+8k54HyunJKNrJJtV0NWEBvjBZOLM9ygAPD40TlFMqe39ddFBXV0OwIL0BPj7aRXXT24vOFNjWgxnSgT6Eqp2agoMDMTPP/+M5cuXo7KyEjExMRgxYgT27t1rtobqdDps2bIFTz/9NFJSUhASEoLHHnsML7/8sppDIQhV8WRguTPF5G15aVwCsgpL7Syttu5xlrhYKrckj5Ir9Wgxcfgkp5jiXb0c3oo1pFcHLNxc4OnhAGj1IEz5YL/5b/4edSamlIdfT2obmVsLodW6rrtRW8IV58iZEoG+gobj1M5FdD3V1dUwGAyoqqpCeLh4qQyCUIuc05cxae0+h8ttnD5E9YQh1n1LERmsx+I/9QcAprad/CNOavb9Tf55zN6U79S42hOBei0C9TqvbiVK/MHc1Bswa1QfLNxcgE/2nfX0cOywvEcBOBVTKnd/fCvVtiyO1IDOkTy9Rr3sCYIBTwaWO2t1/Z97+uGJYXEAgGFLdzC72B3FxbbnxAUlNDSZPJqpTchjWfZv2JR7FkN7d1R92xFBeqs6u0oz5vl7dPe8UWbr2baCEnycc0bF0drvj38mWE6++TCg9iy+bLE9R4Q0JEgJggFPBpY7K/yiwwOg02qQc/qyLAuKWIkYHkciXS2ccUUS6hHo4dJHnqC0qgGf5/1H9e2unJwMrVaD0uoGVFxtRG5xBbYfuyh7O7b3KH+fukKQCu2Px1vL4RG+heJe9gTRFpBT5N5TgeW88FMqdXlBq9TS+nFOkeB5YemBrQYkRr2D9iZGAfbf3piEaOb7M9YQiCG9O6Cq/hpe3/4rMrceVyRGLeHLOH2Tfx4mjoMx3LUJwpbPkvbSZ51wPRRDSrRblM7q5cQFqbUs/9AH2F+SfBjB7nmjoNNqsOdUOaa8v9/hekJEBOux5E/9Bc/L9oISLNx8jKkwv7cSFeJvV8x//MBYybqpBAEA04fH4X/SE/BW9kksY2i9Ojf1BtxoDGWK5WbF9vdrCPJDVT17KTi58LHyLSYOw5buEPW82D6DiPYHxZAShAOcKXLPGhckR/AKLRsVosf9SV2QmmDEmASjomLYVmEETrz9rtQ14an1eXhP4Lzw2Z8rdpwSfCF7u8s92F+Ht/6SBK1Wg/KrjVaTgZu7R+L5L45axft5kvT+Rvz7t0tUx9SLGNW3tfV1z45s5aG6RwWpVjWDx7aMkyvFaESw3hwrL6ccHsVSEo4glz3R7nBU5B5wvue7HDeW2LIVtU34YE8xJq3dh2FLdwAAds8bhY3Th+CtiUmYmxov6pqLFQgjKK913oIpdl50Wg1mp8bjvYeTESsQ0vDn5C5O79tV1F1rwSMfHsB//d9hFJfXoaym9QXaYuKQlhiLdybd7OkhAgCeHBGHlVMG4ZUJiZ4eCmHBwm+PYcHXRxERoGdavqL2mk+XS5t6e5x5ktue+qwTrocspES7w9WzejldnXD9/46kr5jldtao+NY+9lX1qKi9hqjQABjDhUMD1MiKd3RexGrltZg4fHnovFf3S+ebDPDwbvuvD13w4KhamXp7D8y/pzVe12gI8vBofJ8APy0am02qWO9PlNbgRGkNPtl3Fv5+WjQ1myQrcUQE+yvaT1SIHhW1nrXURwTrMWtUH/Pf7anPOuF6SJAS7Q5Xz+rldnVisZaIlWGSU1ZErax4/ryIxbwKjUmn1WD68Dis3lXkxJ7dS0lVg9eMd+xNf0xCBvWIhEYD+F70v/cwc2Qf3GgMVb1257Xm1sQvIaHLAbh3QCxe/U5+q08AWHDvTThTXovlPzrfxlQDwHC9VSg/NhaW/Km/1US3ksHrEtvOW4kS7JAgJdodrp7VswrZrMJS9O9iYN6us5ZbqdJVcogOC1SUEMZb+Nb+XOTVllJvwxgeYPVCzy2uIDHqBLyVT6fVYEyCEftOX0bGhjxV44Q7hepRdtV+e2t/Vj7BiQ4LwIKvne8axcvJJdcbZbCIcqF7u8XEIXOrY3G9IL19txIl2CFBSrQ7XF3knlXIfrinGKEB8m9BZ+KxxFraRQTrHXYQ4s9LZe01ZGwQTwibk3oDenYMFqwqMP+eBPzX2L74JKcYRZdr8U3+BdQ0uC4Bw93cFBuG38vrUN+kXtLRpMHdraotPP/FUdW23R6xtPLptBpotRrVk9ZujAlH2dXLqm0v1hCIA0UVuNrIfq/wVtBAPx1Kq8XbV7YmJJ7Esmxxy+uC9ASMSTBaFb43cRyTdTkyRFmIAtH+IEFKtDtcXeSeF7wsD2s5LxgeZ+OxxOI8swpL8fyXRwWFKX8mFqT3Q+ZW6YQw2zhMW8uKv58W04b3Qs7py1jvhS0ZnSEi2B/1TTWqbrN7hxAArWL0qeulvwj5hPjr8I+HBtpZ8F2RcPPzafXEKADUX2vG+z//Lnu9JX/qz9T/fFPuOdFtaAD8z9dH8fIW69JuEUFsSVyU0ESwQoKU8Arc3fNXzFJoaz1QAi941RYPSiy3cuI8/yjfdBLr9hRbWY2iQvyROSERkSH+smLupMpotcUX1R6VhQgAZG45hgCdBn9XwV3rbbizJNjjQ3sK3te+kHBzRWYZp6gQf7wyIdF8vFIhPiwx75UCk1RWq7IvnF/COyBBSngcT7WdE7MUqiGE0xJjMW1oT3ywp9j5gUKZ5VbJeW0t33QD4qND8T9fF5hfRJdrr+HlLceQ3l/e9eDFxsLNx6ySsa41m/BLcYWsbbVXKmqbMHPDIU8PwyW4MxT2dpGe9CzJft5eS9eWitpryNxaCK0W5ntdbHLqqomhs6FPRPuDOjURHkWsQD0vuVzZktPV5Jy+jElr96myLbkC3dF5XTn5ZkSGBIh2hXKFa3hu6g2YnRqPxd8VMic2hQf64aVxN6FzRBAqaxuRufW4lcAOCdBheJ+OTrdeJNo2oQF+OPzSWNHJHGsntIggPYL0OpRUe7913/IZCtgnL/HPFEOQv2rPKaF9++rzm1AHOXqNBCnhMdp62zn++JSWWVqQ3g9RoQGouNqIqBB/GA1BTBZcR+cVALQaWAlC/uU0JsGIQa9kSSY4aa6bi5Qc05iEaGQVljEvb9uytMXEYd/py8j5vRzA9bADDpjygbKWqET7YNXkZNwzQFoYCXkUbPE1S6kGrfeQkMvdcnKaufW4U+XgIoL0Vi58d3i4CN+ABCnhE7BaEPm+ya7GFXGszvSgb00gOi47lMEZy+wDyV3wRd55Reu6AltLi5BoYKkQQPg2tr3a5XDvgFi8NfFm873dMSQA0MCuTSzwx4Rn+ie/oO6ae9qz2k4O3ckfz5oEZGyQ95yy5NNpt0F73f3vjhwAwnegXvaET+BNbedcFccqljwVed1qIZblP35gLDI2HBItrSTlCnPmfHmTGAWsGwKYTBAsN0VitO1jMnEI9tfJFonhgTrck2iU9BhEhejxyoRE3DOgM3RaDX45U+EyMRp7faJpGS4zqEckDp6pRGl1AzK3HBPtxiRWxskZ+NrGkSH+gs+pWEMg6ptaUFXXJFkib0jvDiRACachQUp4DG9pOycWb8ki/ljgk6dsXc1VddfsLKC8tUKqtJJQxyZL2lpWK//SfOGbAp9ylxLsRATp8eAtXfD+7mLBov9K64S2mOAwIYxPGnvyP1fwt7R+WKdSIqIlEcF6rJyULCrceA9QkF4r6FGxLGZvmYhZXtPIVJzeEWU1DZiQ1EW0HJyrSuQRhCUkSAmP4eoC9SzI6TvvzEM3q7DUyvqwYuep69aSBESG+Fu9AOS0HrUNZWgxcTBxnF1MV1tAqcuW8H5qrzVj7c/FLtguu6Vz9a4iBOn9XHLfXKlrgvZ6uTUpWMvR8fd9i4nD+7uLnG4HzE9ixcrBuapEHkFYQoKU8BiuLlDPgjPijxUpC2zGhlYL7ISkLubPlYYysCRlEIQ30tTiHbbvd3865bJtO7qv+Rj2xmYT3vzzQNE4V0tYnqF8z3pnJv2uLJFHEDwkSAmP4unZt6vjWJVYYJWEMoiJXls8mUBBEN5OY7Prbg7b+9oyibK4vBYbD5y16oTEx7A7mgg7eoYCUGXSL2Q9JQg1IUFKeBxPzr5dHceqxAIrN5RBSvTy8DFsVfVNgolBBOFqwgL9UNMgv1VuWyDWxgrJ4s2QE8Pu6BlKLnfCFyBBSngFOq3GHD9ZVtMq0NwhSl0dx6rEAsvihluQnmCV2ODITc/HsN0zIBbvapOxcLN1X2pb+ON+888DUVbTgMytx1FZe42ELKGY+5I645N9Zz09DI9gaYVk9WbIjWGXsmCSy53wBUiQEl6BmmWX5NQTdXUcq1ILrJQbbvzAWGRulR8ryoveP3rWn8Ky7N/slvtD9PaDVquBVqvBYyk9sCz7pKz9EW2D+5I6o19sGBZvO+HUdnp2CFFpRL7FtKE9rRo7OPJmWOIohl3us45c7oQ3Q4KU8Dhqll1SImxdGcfqjAU2LTEWo/rG4JOcYpypqEOPqGDEhAXgmU35iiyVlqK3tWd9PG40hkqIXuuSVKEBfrja2D5dru2ZO/tG494BnfHR3jOKsrn533hMWCA0GgiWdVJKSIAOtY3imfT8JNMVzRNCA3S4KrFvntQEo/n/jkJ4xBDytDh61rmi0YcU7t4f0fYgQUp4FDXLLjkjbF3l0nLGAiv0wtFq5HdScSR6bY+7/GojntloX7uRxGj7pLi8VvJ37AgOrY0entlk3+jBGTQAwgP1WPPwLSivbURxed31xCD7SaXlb7zoUi0+3ndGdhkxDVo7Rr2Q3g9nK+ocegyE7julyZG2HhRHz7oZI+Kw+XCJ6o0+xHBVYxGifUGtQwmPolb7UEf92/mXw+55ozwya5f7wP7uyAWHBb1Z+KNfdbJdvVOh8/DdkQuYtfEQZeITZozhAdjz/GjotBpFpcXmjI7Hv34557JyZJbPBkdWOv770uoGlNc0YMXO06hiqDtq2cJ2TIJ05yfLdWwnwXLb+go9txw961iOQU2RKCaOXbU/wreg1qGEz6BW2SV31BN1BjkW2O+OlGCWgIVSCWIxp0JieHtBidMiWEl7R29HA+CB5C743MvaqrqL0upG833D/47f/vEk3v7xJJPFs9nEubQ2rm1CoNj9LSSmI4L1TPuwDN/JOX2Z6XjmpN5gJ8QchfBYwluiJ97aDVuOXGBunCGGmo0+eNzVWIRoH5AgJTyKWmWXXF1P1BlsrTb3Xu+ZLUSrKMxzan8L0vuhY1gAosMCUVnbiIwN9q5S2zAG/sXiLG1NjEYE+WHJAwNQf62l3QpSwPq+ySosxVs/sie4/aeyjmk5Q5CeyVppi1R9T9v2l7b3QdX1uFLb2FYNgHsHGJGaYLSbPLI+Q3p2DLb7TE7og+G6WLYMDYg1BOLuRKPYKg5Re2Lu7YYAwrcgQUp4FLXKLrmqnqizgfpyXPVqiMKIYD0eHxoHnVZjdu2xWC/kWl2iQvSoqG1brUmFWDllEIb26YgPfv7d00MxIzeGUw34+6bFxGHh5mPM62kAfJ1/gWnZVZOTodVqUFrdgMwtxxz+voSeDUL3mzE8EA3NLaL3ASCcaLXlSCnSB3S2E1LOPmvEkihjDYGYeGt39OwYjOLyOizP/k1wIvnhnmKm/Uuh1sTcmw0BhO9BgpTwKGqVXXJFPVFnA/XlJlkpdcVZcqWuCVmFpUhLjJVlvcgqLGXeR6whED89dydyiyuQ8WmeS3p/Owt/nQ6eqcTan4sUb6f8amut1qjQALWGBgC4b2BnfH2YTajZwv+e3ClM+fOwYscpyfq1trCOj3ed8y5plskOB7b6npZJTqxIuZtZnzWDekQi5/RlwcmsVAgPy0RS42THNaWNPpRuR639EW0bEqSEx3FUdmlMglH0wc6jdj1RZ0tRKYmtypYhCsWw3C6rVaK0qp7ZigW0FuU/eKYSOacve6UYnZt6A2aN6gMATlucO14XosZwdV+oXaPs3blymDa0J74rKHVpbKYlz2w8hILzV7B6l3JxL8WVuiZM+WA/jOEBuLl7BNM6o/t2UlzfkwUxdzPLs2b8wFjc8cZOycmsWLwry0SSt+gK7V/qHFhOzNUo0+TqxiJE+4IEKeEViFkMsgpL7TJKxayUatUTVSNQX25sVYuJw1f5zscoWm6X1SpRUXuNqQSORgP8dVicoqL87mRT7lnc0iMSOb+XOz/O6z8C/sWrxnFrNcBtcVFYsVP5NlITjPh7eoJocwNX4IylmZXS6kZsK7jItOyPv17C9oISJm+AMwhN7Bw1rlizq0jxZJZ1Iik0KbHcPyAsVife2g2vbS3EV/nnrSzRSso0ubqxCNG+cEqQLlmyBPPnz8fs2bOxfPlyVFRU4KWXXsIPP/yAs2fPolOnTrjvvvuQmZkJg8FgXu/s2bN4+umnsXPnToSGhuKxxx7D4sWL4edH+rg9Y2sxUGKlVKOeqBqB+nJjq1jdlKyU1TTg3gGdHVovIkP8se/3y0zb1Gs1bhElzlJS1YApH+xXZVvlta3uacsXr7NWOBMHaDUaRQLX1uK0Kdd9rThZXcRqF7+Xgp8YquFdEEMqFtT2WTOoRyTueGOnU5NZ1okkPykRetbd3D3STiwLJUlZoqQRCeDaxiJE+0KxAszNzcXq1asxYMAA82cXLlzAhQsX8OabbyIhIQFnzpzBU089hQsXLuDzzz8HALS0tCA9PR1GoxF79+5FSUkJHn30Uej1erz22mvOHxHRJnDGSulsizw1AvXlxlaVVtUzLc9KdFigw4xeDq3W0azjZUzbvNbS/oqTWl5H/sX7/JdHne76k/N7ORJiwxRZ9XiLE2v5IXfz/0bFy8rCd4aSqga88+NJfKBCoo8tLO5m22eNo2vCMpmV4wYXe9bZimWxJCnbsSkt0+SqxiJE+0KrZKWrV69iypQpWLt2LSIjI82fJyYm4osvvsC4cePQu3dvjBo1Cq+++iq+/fZbNDe3dnn54YcfUFhYiPXr1yMpKQl33303MjMzsXLlSly7Jq9zBtF2kWOlVBs1AvX5l4rY41iDVhfZ4LgobC8oQebW4/IH6mC7ADAmwYg5qfEwBLHVWyT+IFZAjKQlxuLA31Px5+Qu0Dnxrl2x8zR+/PWS7PFYWq+8MXP5z8ldkHFnH8nfvtqwit/IYD2M4ezJabZJUyyoMZnlJ5IA7M6hHDc4L1bvHdAZm3LPMln2nXmu8vubkNQFKb07kBglZKNIkGZkZCA9PR2pqakOl+Wr8/Pu+JycHPTv3x8xMTHmZe666y5UV1fj2DH2ciJE28aT5UQciUlAWKxYotNqsCC9n6iFA2h9qfD1EeW2MRTC9mW1vaAEw5buwLLsk+bko2B/HQL9FN32biU0QIdlf0nCrDt7e2wMQi/97QUlGPH6Tnyedx7uNBiHBvjhp+futHJ/qpG5PG1oT3w67Tb8vzt7qyIgP887jzve2InxA93npmW9DA/d0hV7nh+NT/96GwxBjp2DEcF6jLHoQ8+CWlnnvDXeaLBezmgzKWFBSXytN052iLaPbJf9pk2bkJeXh9zcXIfLlpeXIzMzEzNmzDB/VlpaaiVGAZj/Li0VjgNqbGxEY+MfpUaqq6vlDpvwMTxZTsTS1S3G+IGxkhYAKaunZfUAsfIuSogK8cer9yciLTFWNP7WVwrXv/ngQHNXnBU7T7t9/9OG9rR66beYOLcmENlytbEZb/94EkP7dDS7Qp1JtIo1BGJBej9EhgSgrKYBKX06Id4YjmdU6BBWUtWA1buKcO+AWOw5VY5KJ8Mb1GLz4RL8La0ftBoNquqbHS5/pa5JdkF3lk5MkcF6pqxztdzgSsQllWkiPIEsQXru3DnMnj0bWVlZCAyU/sFWV1cjPT0dCQkJWLhwoTNjxOLFi7Fo0SKntkH4Fp4uJ5KWGIsZI+JEy9ys2VWEm7tHCloqxMQgz4J0eS0IWXkhvZ9V1yVvi/iMCvGHycShqr5JcmzTh8eZz+ugHpEIDfDD1UbHAkJNOkcE4VqzCbnFFVi/7wx+OlGGuiaTW8dgy4qdp7Bi56nrYjIBkSH+uCfRKCt+cnTfTvjr8N6orL0m2E52+vA4vP+zfYa4ErYcKUF4oPckqvKuaDkCTa6Y4yezT0lMZistagWzbM/ZDkdyxCWVaSI8iaynxcGDB1FWVobk5GTzZy0tLdi1axdWrFiBxsZG6HQ61NTUIC0tDWFhYfjqq6+g1/8Rv2Y0GnHgwAGr7V68eNH8nRDz58/Hs88+a/67uroa3bp1kzN0wsfwZDmRFhOHfb9fxr9y/yO5nFDwvyMxqAGQubUQdyWy1wllxWgIAqBOgX21sLx2FbXXEBGsdyh23v+5CIN6tMamL9x8zO1iFAAytx7HK1uPe52oB1qFldL2sjt+vYQ+0aGiZYnUrqJQ3eCaaxcRrFeUWMZbGllRYikck2CUHJ+7+7uzWG0toTJNhKeQJUhHjx6No0ePWn02depU9O3bF/PmzYNOp0N1dTXuuusuBAQEYPPmzXaW1JSUFLz66qsoKytDdHQ0ACArKwvh4eFISEgQ3G9AQAACAtTtlEJ4P2rWFWV1ewl1ZxJCLFtWTjKWmm4xy5hWb4r/Yu0dbsv8L4963NXrjWJUDdaKWEDlHG+AnxaNzepZjA2Bfpg6NA6bcs8JdlaKCvHHo0N6IK5TCKLDAmEycYpKe/H3vzE8wGHHKUdx4mIcKKqQFMvu7u/uqNoGj5I6pAShJrIEaVhYGBITE60+CwkJQYcOHZCYmIjq6mqMHTsWdXV1WL9+Paqrq83xnp06dYJOp8PYsWORkJCARx55BK+//jpKS0vxwgsvICMjg0RnG0KNLiCA83FUctp/OnK1C2Er/ljF4LaCEtx1kxHG8ABcrG50WvxMvLU7thy5gOiwQHN3IU8i9uKT6h1uuYynxWhbxbLLjzNoVDagVTU047ZeHXBDTChe+KbAriZvRe01/OuXc3hpXIK5kYQcqx/wh8DUaTVYOP4mSbc6oNxS6I393cUm9x1C/DEhqTPGJBipTBPhcVQN8MnLy8P+/a2z1j59+lh9V1RUhJ49e0Kn02HLli14+umnkZKSgpCQEDz22GN4+eWX1RwK4UGc7QFvi9I4KjmF9ZXGXZbXNKLFxJkf5KxWz49zzuDjnDNmF7bSvuQhATrodVqrZBtjeIBil6YzBPppEeCnRVVDs8esi+MGGPHtEdcVSSdaKyBcbVQ/OS6rsBTr9hSL/nZKbO5bFqufJQmxYThQVMFk9Qz21zlcRmzS7YqETDUm+FQrlPB2NBznrp4a6lFdXQ2DwWAuKUV4D2IikH/kyS1ZopQWE2fXctR2PEZDIHbPG2UuMj5p7T5F+7IU2/x+WS03/MuUVUAG+mmR2DkcXSKD0aNDMN7ecUp0m0rxhJhVi7cmJiHAT4uFm485dMkSypg2tKdLCtGzJK/Z3resITaWGMMD0NBsYvqNvyfyvJKadPPVMxwlZPLH4Ai1J/gE4U7k6DXvL0hI+AyOuisBrcH8Law9CJ1AbmF9Z9xnvMV1e0GJZFFrsXFoAATpdfh02m14YmhPyXUbmk345ewVfHP4AlbstBejltuMCNYjIlheQXzLsbw1MQmzR8fLWt/TRIcFIi0xFnueH425qTd4ejgeIUwis10DQKlBLNYQiPceTkaqzNqcrLAkr9net2mJsdg9bxQWpPdj3k9pdSPzhEvoecVPum2fL/xzIKuwVJXC9iz72l5QwnQcBOELkCAlVMOd3ZVaTBxyTl/GN/nnkXP6st1LQ24clzMJRrZiW6yotdT6JVUN0Go1eHHcTXiPcV0pXc+htY7iyknJmJsajwjGTk2WY5mQ1AVzx9yAuam+IUpDA/zM7lidVoMbjaEIYXC9tiViDYFY+qcB0EBcDE0fHierCP6jKT2wcfoQ7J43CmmJsUyNI1yN5f2t02rQMcw1cdO2zytHk24OwPwvj6C+yYQ5qfGIsekMJaewvTdN8AnCHXhPkTjC53FXMD+LC0tuHJfc0ii22GbOWsZrbSsowcc5Zxxugz8vluuWVtUjc+txxZ2cymsbMTv1BswaFY8VO05i9U+/o67JcfxfaXUDck5fRllNA27pEQVjeKBg9rM3YbKIPlKSoNYW4O+Bd7XS1Slu7h6Jv39VwPS7ujsx1iqGm6XWpquxvb9dWcjd8nnFUlKtsq4Zc/+VDwAwhgdibuoN6NkxWHbMppwJvjuy9QnC1ZAgJVTDHd2VWBOV5BbWd1T3lFXY2Fpu+BcFiyC1PC/8ujmnLzvVVpTfZlZhKZZnn2Q+jswtx6wynVlaLboCR+WhLKm71mJOWvHGxgCuxrK7lKMElrTEWIzqG4Mhi7PtMtot0WqAShXa2qqFWOF2ZyeUUljel3In0xerG7A8+ze8+3CybNHojdn6BOFKyGVPqIYjV54G7LX9hFzyclxYUrGcYnFcUv2jWd3WHUPsXYfOnBelLxvLbSqpIGArUlhaLboCuSmXe05dwj6VO2D5CraxnfykZkJSF7MYsryndFoNXru/v6Tr3cQBGRvy8N2RC+Z195wsx8LNhaqPPzSALbxCKP5Sbuw2C0L3pdzJtDOudU+2TyYIT0AWUkI11OquJOaSn3hrd1kuLCWF9cUsSwBai3Y7sMD812eHsXC89badOS9KXja221S7Rak3s2LnaXyy76ynh+F2pCZ6LSYOK3acxLo9xbhS32S1zkvjErBy8s2YtfGQaEwyB0h+rxaOSkk5yiwXu9+F0AAwXE/4E0pw4u8hy/q+g+OizJNLOfeTUte6p9snE4S7obJPhOo4U6ZEqmwU6w/1rYlJmJDUxfy3WkX6+bFBYixS5a2UnBeWMlJajXWCk+02v8k/j9mb8h0cnTr1JcMCdahpUL9GJSHNkyPiMP8e+0532wtK8PyXRyVF15zUG6zq2HoTf07ugqHxnWAMZ79v+fs9q7AUH+4pFp0EvvtwMkwmDs99cQS1Nr97vr6v5Xnj7ysAiuJnbZ9LLIg9c9xdRo8glCJHr5EgJVyCEhHoqHYoKxunD3FZkP/2ghKHNS6l6gwqOS+OXkorJycjMsRfdJusNVZnjuyNVf8+7XA5R8iJ+ySkYZ2IxQr83lgSu3hLoTfWnZVbr1MIqUkgAFmJb5Yi8ERpDZZln5Q1FqXPJapDSvgycvQauewJl6CkuxJLBqsU7nBhpSXGIixQjynvi/fRlnLRKTkvSkIPLGF1/Q3t01EVQSpXjEaF+FslbkWF6CUTbdoTrKfS9vfGGjfMlwfzRvj7aFnWbxjap6PdRItlcicVgjNs6Q5ZcdV8fd9F3xbip+fuxMYD55grT3QI8Vf8XKIOS0R7gQQpoQpquMXlJPA4E6PqLOVX2ToAqZn9alUKqroBFVcbERXiD0OQv1XrUiFYY1iH9Org1vJOvBD+6bk7cfBMpfm3M6hHJO54Y6dLMqadxTY8wpuQW57IkoggParqm7zufAPAip2nsGLnKRjDAzFpcHf07BiM4vJabDxw1spTIWY1FJoEKo2r5kXywTOVWDievfTVhKTOTj2XlLZPJghfggQp4TRquZRYE3jmpsZjU+45RdZCNfBU9qtOq0FV/TUs3Xbc6kVsDA/AwvE3SR47q5V10uDubokntBTC/n5au5ctL6C9BV7IB+p1qLvmnTGyzpQneuz2HnjrR+HOX95CaXWD5G/TtvSbEPzEeZuTHY7KahpaG0cwxt+OcVF3K4JoS5AgJZyCtS4oC6yu5Vmj4jFrVLzHXFismbaVtdKWVLlW5e0FJYIWmdLqRjy1Ps9hAW4W11/PjsGSY1YLRxOItMRYzBgRh9W7itwyHkdEBOtRWdckW4xGBuvx6n2JyNx63KUW36gQvVPlif6V+x8E+8sT2xoAMeEBmDasF1797ris/fFEhehRWauOZdbSpT4mwWj3+1fS914M/vzOGtUHGw+ckYwpZy11RxDtHapDSihG7dZ2cmqH2tZYdGc8lU6rwYJ0+4xmWzK3Hhc99u0FJRi2dAcmrd2H2ZvyMWntPgxbukO0N3WLicPzXxyV3N+y7N8cbsvReXNHTcPIYD+8/sAANDabBNu+Aq3Hu/mw6/t0B+ula19GBOnxyRODEeAn/1H55+Qu+OWFMbhnQGfVa2Tacn9SF6trKbe9Z2l1g2wxCgALx9+EJ4bFKWolGmsIxCsTEq225yxi7YnFesLLxbY2qU6rwcLxN4m2atXAPWFEBNEWIEFKKMYVveulitN7U4mTyBB/h8uIHbvYy5G3KgsJybd//M2qhqQjpLYlBUsR/w4h/vjHnwcgKsRfkZCorGvGIx8ekBTPzia4sfLkHb0kj+G1+/vDT6eVtICJMfyGTladkYR+12ohVBTflSI4JjwAc1Lj0dhswoGiCixI78e8L0uhds+Azi45L5YhC0oaQwihpKGGNz2zCMLbIZc9oRhXtbbzhaxSpcfuyKos5HLcXlAiO77PkftSDJYEqFfvT0RaYixCAv0El5OLbXhHi4nDnlPlTmzRMXz4R3x0qGTZo8ythbgnUVn8n6212fZ33TEkAP/12WFcrJYOUeE4DherG2UXR5dTKJ6VWXf2gV6nxcYDZ63KHsUaAjFjRBw2Hy6x2leEQPF523ANy/Oy59QlrNjpfKUHy3Mvd3ITawjE+IGxdseipKGGNz2zCMLbIUFKKMaVyT3enlWq9NjlWJVTencwC1glKO0Qw5oApZbgsRTPJhOHzK3HXWod5SXC+IGxyNhwSFJMl1Y14IM9xbK3LyYSbX/XE5JiJeNkLetlKqksYSmUthWU4OOcM7KOxXJfRkMgEmLDkbFBOGZ8za4iwZq4ABwKNf68DI6Lwhd55xXH2wqde9bJ46MpPXB3Yqx5fH9L6ydLYHr7M4sgvB0SpIRi2nNrO6XHLteyqobrWshKq7R+o6PlOoYG4Nl/5eNijTwXNy+eZ244JGs9uYk4QKvV7tX7+iNzK1udTqC13BPHsVuCWVvkrpEQozNGxJnFv5DwNwTrMfX2OMEMbqFrDECRIOWPYkF6P9Fzxk8qMrcWChayZxVqUhZ61nHannvWyePdibFW4ySBSRDuhQQpoRi1etf7IpbHLobQscu1rKpRy9Ryn3JKdLG+kG2XWzThJoctVtVCSRmmAD8tDEF6WUJfTu3ROak3OIwbZIlr3Hy4BH9L6wedVmMW/pY96a/UNWFZ9m/YlHvW6vqJXeMF6f0cTqIigvUI8LOOmeUt44Ygf1nWfaUotbyLudTb88SZIHwJEqSEUzjbRcgdqNXL3ha+NNHan4usBItWA0wfHid47HJfjs5kvdtuS80SXVK4InZRjIraa4i8XpKJldLqRuT8Lj9G9Y4bOuGn3y45XO5ybSNyTl+W/J2xWL5txV1WYSmWZ5+UvH6AcDvM0qoGZGw4hBkj4rBmV5HoBHLxn/qLWsa/yT/v8NgBdSZRtpZ3sUL4E2/tLlnqDGjfE2eC8CVIkBJO480B/XIsgkrqgq7ZVWT38uc4YM2uItzcPVLQ6ijn5ehIwIphuy2WZKr/+aoA9ddaYDQEMV0/qfMlN4HHGRqbTQrWkv/bHBHfkUmQfpxzBh/nnJFsDiE3dIPl+i3cfAyARnKZzYdLsHJyMjK3Sk8ghSyc7m4IYWt5d6b2sC9MnAmivUOClFAFb4y3kmMRlNttSkm2PI+clyNLTN304T2x5Uip5LZYkqku117D3P877PDYAeHzFRnsh8dSeiKuU6hZMFj2VhfrAuVslr6Szkm3xUXhizw2oc9bmh9J6Yn3dxcxTw6kLM9yxR3L9XNUmop3qUeG+GP3vFGyxZ2nXd/OPmO8eeJMEAQJUqKNIscieLaiHsuzf5PlypabLW+LnJejmIC1FI3P350guS25btQSgVJM/PZ/v1SLt348abdOZV0zlluUp+LHB0DSfW80BCIt0Yh1MrPZnUGrYUuesW1xKifhRmpiwmL51mqAytprANRxg/OU1TQoEnfe4vp2JgTHGyfOBEG0QoKUcCmuit90hFyLoNgyYoJCjRqsrC/HFhMHQ5A//pbWFxVXGxEV4m/nVne0LSVuVA7OlWIqqWoQbHVqydzUP9rAulOQltc2YkJSF4exrs6WuhKbmLAkxZk4IGNDHt7VJqvaQcuZbXna9S3Xk9Ge8NSzliDUggQp4TI8+fJQy6IkJijcFU8ndQ7lvGyUxqIqKcXEigbAptxzmDUq3jw+VtEbIVHMngX+ugiVrQLXKlhZSl2x1vYU+j2mJcZi5eSbMWvjIcks/kXfFuKn5+506C6PCQ8AoHFYaJ93qSsVMJ5yfbsrKc8XIaFOtAVIkBIuwdMvD7V7stsKikE9IqHVSJcD0mpal1OKmufQmfqOrsJW7FtaDG3dwRxarak9O4YgOiwQJhOHKR/sl71PoThHpe5rfh0WQSr2e4wMCZD8DfHn6OCZSofu8oXjbwLAVkTfWQHjbte3MzHbbR1PP2sJQi2olz2hOo5eHkDry6NFTnFHmTjqyS4XW0Fx8Eylw9qUJg74JKcY3+SfR87py7KO1xXn0NX91JXCi32pnuDvPZyM2ak3YEJSF6T07oAhvTvIvr6uiHN09DvToFXoiSX6yAn9YOmZzrIML2BsrdG8gNleUMI0JnciJ2a7PeENz1qCUAuykBKq42zCjxqoZRF0tuNS5tbj5v/LsUC56hzy7taP9hRZjc2TWIp9VnewowQbDvZufVfEOTqb6CM39IPl/Egt46uWRjVittsi3vCsJQi1IEFKqI63vDzUKtDuTMclS+S40FjPzZ5Tl2TH7+m0Gjw+NA7v7y5yeeF6R0QE6+3EPqs72FGCjbviHKXGMfHW7mhsNokWyldSSonl/Igt46sCxt01UH0Fb3nWEoQakCAlVMebXh6W1qIfjpXio73FzNbSDiH+ePX+REUdl4SQY4FiPTcrdp7GF3nnZVv+LC17YuOfPjwOnx38j1PJQ464UteErMJSprELJeE4shi6S1SJdRayrLsaFaLH/UldkJpgNI/R3aWUfFXAeLoGqrfiTc9agnAWiiElVMfZuDq10Wk1qKq/JkuMRoXokTN/tKhQ4oUEIK/nD2usm5wYWKWxf3yWd1SI3urzDiH+WDU5GYN6RLpUjAJ/CHRHMW7bC0owbOkOTFq7D7M35WPS2n0YtnQHtheUmK2BfHypp1zN/DgC/LRYnn3SrlB9RW0TPthTbDV2QDp2Vu2EFE8JmBYTh5zTlxXFUwPS91t7bv/pbc9agnAGEqSE6njby0Mqbs4WzfV/r93fH/5+0reHM0lCjixQcgSv0uSF7QUlyNx6HBW1f4jOqBB/ZE5IxF2JRiz6tpBpOwF+yq8ji0D3pSQc1t+a7djTEmOxe94obJw+BG9NTMLG6UOwe94o1bOjPSFgpCYTckhLjMWMEXHQ2AxeowFmjIhrl5nk3vasJQhnIEFKuAR3Wn0c4ShuzhK54+OFxKw7+8gaE4sFSo7gtRV2jixSYiKvsvYaMjbkYcWOU0znbEF6P3z4+GCHyzlCTKD7WhbxvtOXmQvmA9Zjd4el19FEh4O6AkbNycT2ghKs2VVkV93CxAFrdhV51cTEnXjTs5YgnIFiSAmX4S29o1nj4Wbd2Qdzx9wge3w6rQYpvTpgxc5TjheGPAsUfw6XZf3GtP2ymgaHNSZZMq3X7S1iGl/HsAAM6dVBUdF9S8QEui8l4WwvKMHzXxxlXp4f+77Tl6HVatx2j/AC5vkvj9qFZEQE60XWko+aGf0slmdvrA7gLrzlWUsQzuCUhXTJkiXQaDSYM2eO+bM1a9Zg5MiRCA8Ph0ajwZUrV+zWq6iowJQpUxAeHo6IiAhMmzYNV69edWYohJfiDfF9rPFwQ/t0VDS+7QUl+K/PxFuQWqKBfAuUTqvB0D4dmZYtLq9zaJFiEXmssaPRYYFMbkMx1KzT6Ul4S+CVevkxtxkb8px2ZyuhSuAaV9U1qRYGoWbtUKpD6hhveNYShDMoFqS5ublYvXo1BgwYYPV5XV0d0tLS8Pe//1103SlTpuDYsWPIysrCli1bsGvXLsyYMUPpUAhCElfGzfFCpLTasSByJtaN5RiM4QHYeOCsQ/c2y1gBICJIz3zOpNyGT46IM8fm2m4DULdOpyeQE6MshK2IdXVsrLvCINScTPjKxIQgCOUoctlfvXoVU6ZMwdq1a/HKK69YfcdbS//9738Lrnv8+HFs374dubm5uOWWWwAA77zzDu655x68+eab6Ny5s5IhEe0Apb23XVVaR64Q4a7Hut3cPVK2KGU5hkmDu2NZ9knx/aPVilRxtVF0GUuGxXfE1iMlovtbkJ5gdT3GJBhF3YY3d48UrRcqdS4Gx0XBGB5gl7FuORZPl/uRE6PMgqsL1LsrDELNyYQvTEwIgnAORYI0IyMD6enpSE1NtROkjsjJyUFERIRZjAJAamoqtFot9u/fj/vvv1/JkIg2jrO9tx0VUVditVQqRJQKDUfH0NhsYtpOVIi/pMjjOXimEisn34zMrcft9jd+YCwyt7JfD6UxblmFpWgQOS5vySJ2hVXOlbGx7rI2qlk7lOqQEkTbR7Yg3bRpE/Ly8pCbm6toh6WlpYiOjrYehJ8foqKiUFpaKrhOY2MjGhv/eHlWV1cr2rdSlFrmCHXg3eK2LyI5nY8A9QP/lbywnRUaUseQc/oy0zaMhiCH1lRcH2dkSAB2zxtltb/K2kZkbDgk+3qwdmDiEbvuPIZgPZb8qb/Hs4iVWOUigvRM8aauELvusjaq6ZlwdwMBgiDcjyxBeu7cOcyePRtZWVkIDHSfa2Tx4sVYtGiR2/ZnibOWOcI51O69LVcUSeHMC9sZoSF2DHKsSHKsZJb7azFxGLZ0h8t7obOEQwTpdRiTYFS8D7VgOe8x4QH4x0NJKL/aiOiwQJg4DlPe3+9w265wQbvT2qimZ8IVXg6CILwHWYL04MGDKCsrQ3JysvmzlpYW7Nq1CytWrEBjYyN0Op3kNoxGI8rKyqw+a25uRkVFBYxG4ZfL/Pnz8eyzz5r/rq6uRrdu3eQMXRFqWeYI5Xhz2R8l7UN5XCE05FiRlFrJ3HU9WMIhvKXcE8t5Xzj+JqtKCS0mDrGGQMljdFWHHXdbG9X0TFB5I4Jou8jKsh89ejSOHj2K/Px8879bbrkFU6ZMQX5+vkMxCgApKSm4cuUKDh48aP5sx44dMJlMuO222wTXCQgIQHh4uNU/V9Ni4vD8l0d9piB3W8Wbs2uVtA91dSs/1iLZSisPuOt6ePN1t6XFxMEQ5I+pQ3siMsTf6jux4uQ6rQbjB0pPZscPjHWZ0HJ3MXU1SxJReSOCaJvIspCGhYUhMTHR6rOQkBB06NDB/HlpaSlKS0tx6lRrEe+jR48iLCwM3bt3R1RUFPr164e0tDRMnz4d7733HpqamjBr1ixMnDjRqzLsV+w4KVmL0ZsKcrdlvD27VsyNKIS7Yt1YrEhKrWTuuh7eft15hEJ6okL0uD+pC1ITjKLWuxYTh82Hpcs6bT5cgr+l9XOpKCVrI0EQ3oLqnZree+89q3jPESNGAADWrVuHxx9/HADw6aefYtasWRg9ejS0Wi0eeOABvP3222oPRTEtJg7r9hQzLesNFpq2jC9k1wq92CtrGwWz090V68YSK6skJs9d18MXrrtYSE9lbRM+3FOMWyXEnbeEJKgZU00QBOEMGo7jfM7nXF1dDYPBgKqqKpe473NOX8aktfuYlt04fQg90EVQqzoB/+IHhC153hrL6yvVGeSO013Xw5uvO5/cJSYqecG8e94owXP5Tf55zN6U73A/b01MwoSkLk6OliAIwjPI0WvUy14AVqtnRLCe6t6JoGZ1Al/NrvUV65OccVrGS36dfwEVtdfM36l9Pbz5ujub3OUrIQkEQRDuggSpAKwvgam3x3mlxcvTuKI6AcW7udfiKrSvrMJSRfGSzuAN113oXDibdOULIQkEQRDuhASpACzlfCKC9Zg1qo9bx+ULqF031BJfsTi6AnfWwxXaV0SwXjDJjyVe0lk8ed3FzvvEW9nKzolNbqnQO0EQhDWyyj61F1jK+Sz5U396WQggx5VJsMFbnG3PK29x3l4gna2txr7EKk605RJoUud9WfZJaBzc/loNMKhHpOj37i69RBAE4c2QhVQEsfg16tIkjS/Vj/QFXGlxlrMvKdpiCTRH5x0AHKWDmjjg4JlKyXPiDSEJBEEQ3gAJUgnoZSEfStZQF3d2qmIpRSRFW5pkOHsueFjOSXsORSEIguAhQeoAelnIQ61kDV8pmaQ2tsddWu0+i7Oz22hLkwy1xHVbOicEQRCuhAQpoSpqJGu4M4HHmxDr+sOCGsLHmW10CPFHUrcI5Jy+3CYmEc6eT8qSt6e9TjIJgmCDCuMTLkGpqBQrGeUNxdBdidhxO8JRAXY58MXepapLSKHVtMZN8vjyJMLRudAAMATrUXU92ctR4f72Lsba6ySTINo7cvQaCVLCZch9CTvb/cZXcXTcPGIWZzVFulR3JA7i5Z+E8PVJBEunKAAOhVZ7F2PtdZJJEAQJUsJHYW3Z6op2rZ60YLEed1SIv1VnJHfWIeX3NapvDIYszkZFLbso9eVJBIuYlPrttHcx1l4nmQRBtEKtQwmfxFMlozxtwWI9ngXp/WA0BLlcNEtVl8g5fZlZjAK+XxKKpdKGWOKjO0t2eSvurBJBEIRvQ4KU8Bo8UTLKFW1O5cJ6PEZDEAbHRZnF0YGiCpeJUjGRpXQy4MsloZRW2iAxRnWJCYJghwQp4TW4u7+3t1iwWI+7svaanfvT3bGISicD7bH8EYkxqktMEAQ71DqU8BqkWra6or+3t7Q5ZTnu8QNjkbHBPe1DpeDFM+sV0KBVNLfH8kckxhz/Xtrz74MgCGtIkBJehTv7e3uTBUvquFdOvhmbD5dItrF0Vy95KfFsiysmEb4EiTH3TzIJgvBdyGVPeB3uatnqbRYsseP2tlhEXjzbJoLZ1iE1tqPSRkKo0SSiLSD2e2nvvw+CIKwhQUp4Je5o2erumFUWhI7bmyy5PELieVCPSBw8U9lui78LQWKsFXdNMgmC8F1IkBLtFl+xYHmbJZdHSDy31WxxZyAx1oo7JpkEQfguFENKtGvcGbOqFIpF9H14MTYhqQtSendod2KUIAjCEWQhJdo93m7B8hVLLkEQBEEohVqHEoSP4OmOUgRBEAQhB2odShBtEG+35BIEQRCEUkiQEoQPQYkhBEEQRFuEkpoIgiAIgiAIj0KClCAIgiAIgvAoJEgJgiAIgiAIj0KClCAIgiAIgvAoJEgJgiAIgiAIj0KClCAIgiAIgvAoJEgJgiAIgiAIj0KClCAIgiAIgvAoJEgJgiAIgiAIj+KUIF2yZAk0Gg3mzJlj/qyhoQEZGRno0KEDQkND8cADD+DixYtW6509exbp6ekIDg5GdHQ0nnvuOTQ3NzszFIIgCIIgCMJHUSxIc3NzsXr1agwYMMDq87lz5+Lbb7/FZ599hp9++gkXLlzAn/70J/P3LS0tSE9Px7Vr17B3717885//xEcffYQXX3xR+VEQhAtpMXHIOX0Z3+SfR87py2gxcZ4eEkEQBEG0KTQcx8l+u169ehXJyclYtWoVXnnlFSQlJWH58uWoqqpCp06dsGHDBvz5z38GAPz666/o168fcnJyMGTIEGzbtg333nsvLly4gJiYGADAe++9h3nz5uHSpUvw9/d3uP/q6moYDAZUVVUhPDxc7vAJgpntBSVY9G0hSqoazJ/FGgLx0rgEpCXGenBkBEEQBOHdyNFriiykGRkZSE9PR2pqqtXnBw8eRFNTk9Xnffv2Rffu3ZGTkwMAyMnJQf/+/c1iFADuuusuVFdX49ixY0qGQxAuYXtBCZ5en2clRgGgtKoBT6/Pw/aCEg+NjCAIgiDaFn5yV9i0aRPy8vKQm5tr911paSn8/f0RERFh9XlMTAxKS0vNy1iKUf57/jshGhsb0djYaP67urpa7rAJQhYtJg6Lvi2EkPuAA6ABsOjbQoxJMEKn1bh5dARBEATRtpBlIT137hxmz56NTz/9FIGBga4akx2LFy+GwWAw/+vWrZvb9k20Tw4UVdhZRi3hAJRUNeBAUYX7BkUQBEEQbRRZgvTgwYMoKytDcnIy/Pz84Ofnh59++glvv/02/Pz8EBMTg2vXruHKlStW6128eBFGoxEAYDQa7bLu+b/5ZWyZP38+qqqqzP/OnTsnZ9gEIZuyGnExqmQ5giAIgiDEkSVIR48ejaNHjyI/P9/875ZbbsGUKVPM/9fr9fjxxx/N65w4cQJnz55FSkoKACAlJQVHjx5FWVmZeZmsrCyEh4cjISFBcL8BAQEIDw+3+kcQriQ6jM0DwLocQRAEQRDiyIohDQsLQ2JiotVnISEh6NChg/nzadOm4dlnn0VUVBTCw8PxzDPPICUlBUOGDAEAjB07FgkJCXjkkUfw+uuvo7S0FC+88AIyMjIQEBCg0mERhHMMjotCrCEQpVUNgnGkGgBGQyAGx0W5e2gEQRAE0eZQvVPTsmXLcO+99+KBBx7AiBEjYDQa8eWXX5q/1+l02LJlC3Q6HVJSUvDwww/j0Ucfxcsvv6z2UAhCMTqtBi+Na7XY26Ys8X+/NC6BEpoIgiAIQgUU1SH1NFSHlHAXVIeUIAiCIJQhR6/JLvtEEO2JtMRYjEkw4kBRBcpqGhAd1uqmJ8soQRAEQagHCVKCcIBOq0FK7w6eHgZBEARBtFlUjyElCIIgCIIgCDmQICUIgiAIgiA8ik+67Pk8LGohShAEQRAE4Z3wOo0lf94nBWlNTQ0AUAtRgiAIgiAIL6empgYGg0FyGZ8s+2QymXDhwgWEhYVBo6FsZzWorq5Gt27dcO7cOSql1caga9t2oWvbdqFr23ZpT9eW4zjU1NSgc+fO0Gqlo0R90kKq1WrRtWtXTw+jTUKtWdsudG3bLnRt2y50bdsu7eXaOrKM8lBSE0EQBEEQBOFRSJASBEEQBEEQHoUEKQEACAgIwEsvvYSAgABPD4VQGbq2bRe6tm0XurZtF7q2wvhkUhNBEARBEATRdiALKUEQBEEQBOFRSJASBEEQBEEQHoUEKUEQBEEQBOFRSJASBEEQBEEQHoUEaTtiyZIl0Gg0mDNnjvmzJ598Er1790ZQUBA6deqECRMm4Ndff7Va7+zZs0hPT0dwcDCio6Px3HPPobm52c2jJ6QQurY8HMfh7rvvhkajwddff231HV1b70bouo4cORIajcbq31NPPWW1Hl1X70fsns3JycGoUaMQEhKC8PBwjBgxAvX19ebvKyoqMGXKFISHhyMiIgLTpk3D1atX3Tx6Qgrba1tcXGx3z/L/PvvsM/N67f2+9clOTYR8cnNzsXr1agwYMMDq80GDBmHKlCno3r07KioqsHDhQowdOxZFRUXQ6XRoaWlBeno6jEYj9u7di5KSEjz66KPQ6/V47bXXPHQ0hCVi15Zn+fLlgi126dp6N1LXdfr06Xj55ZfNfwcHB5v/T9fV+xG7tjk5OUhLS8P8+fPxzjvvwM/PD4cPH7ZquThlyhSUlJQgKysLTU1NmDp1KmbMmIENGza4+zAIAYSubbdu3VBSUmK13Jo1a/DGG2/g7rvvBkD3LQCAI9o8NTU1XHx8PJeVlcXdcccd3OzZs0WXPXz4MAeAO3XqFMdxHPfdd99xWq2WKy0tNS/z7rvvcuHh4VxjY6Orh044wNG1PXToENelSxeupKSEA8B99dVX5u/o2novUtfV0T1M19W7kbq2t912G/fCCy+IrltYWMgB4HJzc82fbdu2jdNoNNz58+ddOWyCATnv2qSkJO6JJ54w/033LceRy74dkJGRgfT0dKSmpkouV1tbi3Xr1iEuLg7dunUD0Dpj79+/P2JiYszL3XXXXaiursaxY8dcOm7CMVLXtq6uDpMnT8bKlSthNBrtvqdr6704umc//fRTdOzYEYmJiZg/fz7q6urM39F19W7Erm1ZWRn279+P6Oho3H777YiJicEdd9yB3bt3m5fJyclBREQEbrnlFvNnqamp0Gq12L9/v9uOgRCG9V178OBB5OfnY9q0aebP6L4ll32bZ9OmTcjLy0Nubq7oMqtWrcLf/vY31NbW4sYbb0RWVhb8/f0BAKWlpVY3CADz36Wlpa4bOOEQR9d27ty5uP322zFhwgTB7+naeieOruvkyZPRo0cPdO7cGUeOHMG8efNw4sQJfPnllwDounozUtf2999/BwAsXLgQb775JpKSkvDxxx9j9OjRKCgoQHx8PEpLSxEdHW21np+fH6KioujaehiWdy3PBx98gH79+uH22283f0b3LQnSNs25c+cwe/ZsZGVlITAwUHS5KVOmYMyYMSgpKcGbb76Jhx56CHv27JFch/Asjq7t5s2bsWPHDhw6dMgDoyOUwnLPzpgxw/z//v37IzY2FqNHj8bp06fRu3dvdw2VkImja2symQC0JppOnToVAHDzzTfjxx9/xIcffojFixe7dbwEO6zvWgCor6/Hhg0bsGDBAjeNzncgl30b5uDBgygrK0NycjL8/Pzg5+eHn376CW+//Tb8/PzQ0tICADAYDIiPj8eIESPw+eef49dff8VXX30FADAajbh48aLVdvm/hdzAhHtwdG2zsrJw+vRpREREmL8HgAceeAAjR44EQNfWG2G9Zy257bbbAACnTp0CQNfVW3F0bXlrWEJCgtV6/fr1w9mzZwG0Xr+ysjKr75ubm1FRUUHX1oPIuW8///xz1NXV4dFHH7XaBt23ZCFt04wePRpHjx61+mzq1Kno27cv5s2bB51OZ7cOx3HgOA6NjY0AgJSUFLz66qsoKyszu4qysrIQHh5u9+Ak3Ieja9uxY0c8+eSTVt/3798fy5Ytw7hx4wDQtfVGlNyz+fn5AIDY2FgAdF29FUfXtlevXujcuTNOnDhhtcxvv/1mzsROSUnBlStXcPDgQQwaNAgAsGPHDphMJvPEhHA/cu7bDz74AOPHj0enTp2slqf7FpRl396wzPw7ffo099prr3G//PILd+bMGW7Pnj3cuHHjuKioKO7ixYscx3Fcc3Mzl5iYyI0dO5bLz8/ntm/fznXq1ImbP3++B4+CEMJRVidssuzp2voGltf11KlT3Msvv8z98ssvXFFREffNN99wvXr14kaMGGFenq6r72B7zy5btowLDw/nPvvsM+7kyZPcCy+8wAUGBpqrnnAcx6WlpXE333wzt3//fm737t1cfHw8N2nSJA+MnpBC6Hl88uRJTqPRcNu2bbNbnu5bjiMLaTsmMDAQP//8M5YvX47KykrExMRgxIgR2Lt3r3mGptPpsGXLFjz99NNISUlBSEgIHnvsMasaiIRvQtfW9/D390d2djaWL1+O2tpadOvWDQ888ABeeOEF8zJ0XX2XOXPmoKGhAXPnzkVFRQUGDhyIrKwsq9jgTz/9FLNmzcLo0aOh1WrxwAMP4O233/bgqAlWPvzwQ3Tt2hVjx461+47uW0DDcRzn6UEQBEEQBEEQ7RdKaiIIgiAIgiA8CglSgiAIgiAIwqOQICUIgiAIgiA8CglSgiAIgiAIwqOQICUIgiAIgiA8CglSgiAIgiAIwqOQICUIgiAIgiA8CglSgiAIgiAIwqOQICUIgiAIgiA8CglSgiAIgiAIwqOQICUIgiAIgiA8CglSgiAIgiAIwqP8f1uyJM05t859AAAAAElFTkSuQmCC",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from mvm import GaussianFunc, UniformFunc\n",
"import numpy as np\n",
"\n",
"# T1,T2 distribution (Gaussian)\n",
"center = np.array([450, 425])\n",
"Sigma = np.array([\n",
" [50, 12.5],\n",
" [37.5, 50],\n",
"])\n",
"Requirement = GaussianFunc(center, Sigma, 'temp')\n",
"\n",
"Requirement.random(1000)\n",
"Requirement.view(xlabel='Thermal loads $T_1,T_2 \\\n",
" \\sim \\mathcal{N}(\\mu,\\Sigma)$')\n",
"Requirement.reset()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We define the random input specifications as follows by supplying the arguments ``cov_index`` and ``distribution``"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"from mvm import InputSpec\n",
"\n",
"# define input specifications\n",
"s1 = InputSpec(center[0], 'S1', universe=[325, 550], variable_type='FLOAT', cov_index=0,\n",
" description='nacelle temperature', distribution=Requirement,\n",
" symbol='T1', inc=-1e-0, inc_type='rel')\n",
"s2 = InputSpec(center[1], 'S2', universe=[325, 550], variable_type='FLOAT', cov_index=1,\n",
" description='gas surface temperature', distribution=Requirement,\n",
" symbol='T2', inc=+1e-0, inc_type='rel')\n",
"input_specs = [s1, s2]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Instead of redifining the MAN from scratch, we can subclass the original deterministic ``MAN`` and extend it by defining the ``randomize`` method to allow the MAN to draw random samples on input specs $T_1$ and $T_2$. The new stochastic version of the previous MAN is defined by ``StoMAN``"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# Define the stochastic MAN\n",
"class StoMAN(MAN):\n",
"\n",
" def randomize(self):\n",
" s1 = self.input_specs[0]\n",
" s2 = self.input_specs[1]\n",
" s1.random()\n",
" s2.random()\n",
"\n",
"sto_man = StoMAN(design_params,input_specs,fixed_params,\n",
" behaviours,decisions,margin_nodes,performances,'MAN_2')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"We use a Monte-Carlo simulation to obtain the distribution of impact and absorption in the design"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Progress: 12% \r"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Progress: 99% \r"
]
}
],
"source": [
"import sys\n",
"\n",
"# Perform Monte-Carlo simulation\n",
"n_epochs = 1000\n",
"for n in range(n_epochs):\n",
" \n",
" sys.stdout.write(\"Progress: %d%% \\r\" %((n/n_epochs)* 100)) # display progress\n",
" sys.stdout.flush()\n",
"\n",
" sto_man.randomize()\n",
" sto_man.init_decisions()\n",
" sto_man.allocate_margins()\n",
" sto_man.forward()\n",
" sto_man.compute_impact()\n",
" sto_man.compute_absorption()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We view the distribution of excess"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# View distribution of excess\n",
"sto_man.margin_nodes[0].excess.view()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"we view the distribution of impact on performance"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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JO3bssG11AAAAMJO2tn50ZNsHW0dcx6HAO336dFVXV6uurk79+/e3tT/++OPq06eP04oDAAAAOsuhwCtJnp6ercKuJIWFhXW2HgAAAMCpHAq8VVVVWrp0qfLz83Xu3Dl997srmpubnVIcAABwPzfDl0zAXBwKvPPnz1dZWZl+9atfafDgwbJYLM6uCwAAAHAKhwLvRx99pL/85S+KiIhwcjkAAACAczn0WrLQ0NDrtjEAAAAA7sihwLtu3TqlpaWptLTUyeVcLywsTBaL5bpj0aJFbfbPzs6+rq+vr2+X1wkAAAD35NCWhocffliXL1/WiBEj1KdPH/Xq1avV+YsXLzqlOEn661//2uohuKNHj+qf/umfNHfuXLtj/Pz89Pnnn9s+s8cYAADg5uVQ4F23bp2Ty7AvMDCw1edVq1ZpxIgRmjZtmt0xFotFwcHBXV0aAACmxtsYYBYOBd7k5GRn13FDmpqa9MYbbyg1NbXdVdv6+nr94Ac/UEtLiyZNmqTf/va3Gj9+vN3+jY2NamxstH2uq6tzat0AAABwHYf28EpSSUmJli9frsTERJ07d06S9P777+vYsWNOK+67cnJyVFNTo/nz59vtM2bMGG3atEnvvPOO3njjDbW0tGjKlCmqqKiwOyYzM1P+/v62IzQ0tAuqBwAAgCs4FHj37Nmj8PBwHThwQNu2bVN9fb0k6ZNPPlFGRoZTC/y2P/7xj4qPj1dISIjdPtHR0UpKSlJERISmTZumbdu2KTAwUK+88ordMenp6aqtrbUd5eXlXVE+AAAAXMChwJuWlqbnn39eeXl58vb2trX/6Ec/0v79+51W3LedOXNGH3zwgR577LEOjevVq5cmTpyoU6dO2e3j4+MjPz+/VgcAAADMwaHAe+TIEc2ZM+e69kGDBqm6urrTRbVl8+bNGjRokGbOnNmhcc3NzTpy5IgGDx7cJXUBAADAvTn00FpAQIAqKys1bNiwVu2HDx/WkCFDnFLYt7W0tGjz5s1KTk6Wl1frkpOSkjRkyBBlZmZKkp599lndfffdGjlypGpqarR69WqdOXOmwyvDAAAA38femyxKV3VsgQ5dy6EV3kceeUTPPPOMrFarLBaLWlpa9PHHH2vp0qVKSkpydo364IMPVFZWpn/913+97lxZWZkqKyttn7/66istXLhQt99+u3784x+rrq5O+/bt07hx45xeFwAAANyfxXDgO4Kbmpq0aNEiZWdnq7m5WV5eXrp69armzZun7OxseXp6dkWt3aaurk7+/v6qra1lPy8AwPR4367ztbXCy2qwc3Ukrzm0pcHb21uvvfaaVqxYoSNHjqi+vl4TJ07UqFGjHCoYAAAA6Co3HHhTU1PbPf/ttzO89NJLjlcEAAAAONENB97Dhw+3+lxUVKSrV69qzJgxkqQvvvhCnp6eioyMdG6FAAAAQCfccODdtWuX7c8vvfSS+vXrp9dff139+/eX9M3DYikpKZo6darzqwQAAAAc5NBbGtasWaPMzExb2JWk/v376/nnn9eaNWucVhwAAADQWQ4F3rq6Op0/f/669vPnz+vSpUudLgoAAABwFocC75w5c5SSkqJt27apoqJCFRUV+p//+R8tWLBADzzwgLNrBAAAABzm0GvJNm7cqKVLl+rRRx/VlStXvrmQl5cWLFig1atXO7VAAAAAoDMcCrx9+vTRyy+/rNWrV6ukpESSNGLECPXt29epxQEAAACd5VDgvaZv37668847nVULAACAKfDtde7FoT28AAAAQE9B4AUAAICpdWpLAwAAcE/2fqVeumpmN1cCuB4rvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAU3PrwLty5UpZLJZWx9ixY9sd8/bbb2vs2LHy9fVVeHi4du7c2U3VAgAAwB25deCVpPHjx6uystJ2fPTRR3b77tu3T4mJiVqwYIEOHz6shIQEJSQk6OjRo91YMQAAANyJ2wdeLy8vBQcH246BAwfa7fu73/1OcXFxevrpp3X77bfrueee06RJk7R+/fpurBgAAADuxO0D78mTJxUSEqLhw4dr3rx5Kisrs9u3oKBAMTExrdpiY2NVUFDQ7s9obGxUXV1dqwMAAADm4NaBNyoqStnZ2crNzdWGDRt0+vRpTZ06VZcuXWqzv9VqVVBQUKu2oKAgWa3Wdn9OZmam/P39bUdoaKjT7gEAAACu5daBNz4+XnPnztWdd96p2NhY7dy5UzU1Ndq6datTf056erpqa2ttR3l5uVOvDwAAANfxcnUBHREQEKDRo0fr1KlTbZ4PDg5WVVVVq7aqqioFBwe3e10fHx/5+Pg4rU4AAAC4D7de4f2u+vp6lZSUaPDgwW2ej46OVn5+fqu2vLw8RUdHd0d5AAAAcENuHXiXLl2qPXv2qLS0VPv27dOcOXPk6empxMRESVJSUpLS09Nt/X/+858rNzdXa9as0YkTJ7Ry5UoVFhZq8eLFrroFAAAAuJhbb2moqKhQYmKiLly4oMDAQN1zzz3av3+/AgMDJUllZWXy8Ph7Zp8yZYrefPNNLV++XMuWLdOoUaOUk5OjO+64w1W3AAAAABdz68C7ZcuWds/v3r37ura5c+dq7ty5XVQRAAAAehq33tIAAAAAdBaBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmJpbv6UBAADgZhWW9t51baWrZrqgkp6PFV4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApubl6gIAAED3CUt7z9UloAvYm9fSVTO7uRL3xAovAAAATI3ACwAAAFMj8AIAAMDU3DrwZmZm6oc//KH69eunQYMGKSEhQZ9//nm7Y7Kzs2WxWFodvr6+3VQxAAAA3I1bB949e/Zo0aJF2r9/v/Ly8nTlyhXdd999amhoaHecn5+fKisrbceZM2e6qWIAAAC4G7d+S0Nubm6rz9nZ2Ro0aJAOHTqkf/zHf7Q7zmKxKDg4uKvLAwAA6DTenNH13HqF97tqa2slSbfeemu7/err6/WDH/xAoaGhmj17to4dO9Zu/8bGRtXV1bU6AAAAYA49JvC2tLRoyZIl+od/+AfdcccddvuNGTNGmzZt0jvvvKM33nhDLS0tmjJliioqKuyOyczMlL+/v+0IDQ3tilsAAACAC/SYwLto0SIdPXpUW7ZsabdfdHS0kpKSFBERoWnTpmnbtm0KDAzUK6+8YndMenq6amtrbUd5ebmzywcAAICLuPUe3msWL16sd999V3v37tXQoUM7NLZXr16aOHGiTp06ZbePj4+PfHx8OlsmAAAA3JBbr/AahqHFixdr+/bt+vDDDzVs2LAOX6O5uVlHjhzR4MGDu6BCAAAAuDu3XuFdtGiR3nzzTb3zzjvq16+frFarJMnf31+9e/eWJCUlJWnIkCHKzMyUJD377LO6++67NXLkSNXU1Gj16tU6c+aMHnvsMZfdBwAAXYUn/IHv59aBd8OGDZKk6dOnt2rfvHmz5s+fL0kqKyuTh8ffF6q/+uorLVy4UFarVf3791dkZKT27duncePGdVfZAAAAcCNuHXgNw/jePrt37271ee3atVq7dm0XVQQAAICexq0DLwAAAP6OLSyOceuH1gAAAIDOIvACAADA1NjSAACAm7H3a+vSVTO7uRLAHFjhBQAAgKkReAEAAGBqbGkAAKCH4Al9dFRH/p8x85YZVngBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgarylAQCAbtDW0/JmfioecCes8AIAAMDUCLwAAAAwNQIvAAAATI09vAAAuAjfnIaewAz7z1nhBQAAgKkReAEAAGBqbGlwE/Z+rdXTfmUAAADMr6flFlZ4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqfWIwJuVlaWwsDD5+voqKipKBw8ebLf/22+/rbFjx8rX11fh4eHauXNnN1UKAAAAd+P2b2l46623lJqaqo0bNyoqKkrr1q1TbGysPv/8cw0aNOi6/vv27VNiYqIyMzP1z//8z3rzzTeVkJCgoqIi3XHHHS64g+7T056YBICeji+OgJmY+f9nt1/hfemll7Rw4UKlpKRo3Lhx2rhxo/r06aNNmza12f93v/ud4uLi9PTTT+v222/Xc889p0mTJmn9+vXdXDkAAADcgVuv8DY1NenQoUNKT0+3tXl4eCgmJkYFBQVtjikoKFBqamqrttjYWOXk5Nj9OY2NjWpsbLR9rq2tlSTV1dV1ovqOaWm83GZ7R2pwxjUAADfO3r+7wM2qOzPHtZ9lGMb39nXrwFtdXa3m5mYFBQW1ag8KCtKJEyfaHGO1Wtvsb7Va7f6czMxM/frXv76uPTQ01IGqnct/nXtcAwAA4Pu4InNcunRJ/v7+7fZx68DbXdLT01utCre0tOjixYsaMGCALBaLCyvDNXV1dQoNDVV5ebn8/PxcXQ66EHN9c2G+bx7M9c2lO+bbMAxdunRJISEh39vXrQPvwIED5enpqaqqqlbtVVVVCg4ObnNMcHBwh/pLko+Pj3x8fFq1BQQEOFY0upSfnx//UN4kmOubC/N982Cuby5dPd/ft7J7jVs/tObt7a3IyEjl5+fb2lpaWpSfn6/o6Og2x0RHR7fqL0l5eXl2+wMAAMDc3HqFV5JSU1OVnJysu+66S5MnT9a6devU0NCglJQUSVJSUpKGDBmizMxMSdLPf/5zTZs2TWvWrNHMmTO1ZcsWFRYW6tVXX3XlbQAAAMBF3D7wPvzwwzp//rxWrFghq9WqiIgI5ebm2h5MKysrk4fH3xeqp0yZojfffFPLly/XsmXLNGrUKOXk5Jj+Hbxm5+Pjo4yMjOu2nsB8mOubC/N982Cuby7uNt8W40be5QAAAAD0UG69hxcAAADoLAIvAAAATI3ACwAAAFMj8AIAAMDUCLxwSxcvXtS8efPk5+engIAALViwQPX19Tc01jAMxcfHy2KxKCcnp2sLhVM4Mt8//elPNWLECPXu3VuBgYGaPXu23a8ch/vo6FxfvHhRTz75pMaMGaPevXvrtttu089+9jPV1tZ2Y9VwlCN/t1999VVNnz5dfn5+slgsqqmp6Z5i0SFZWVkKCwuTr6+voqKidPDgwXb7v/322xo7dqx8fX0VHh6unTt3dlOl3yDwwi3NmzdPx44dU15ent59913t3btXjz/++A2NXbduHV8J3cM4Mt+RkZHavHmzPvvsM/35z3+WYRi677771Nzc3E1VwxEdneuzZ8/q7NmzevHFF3X06FFlZ2crNzdXCxYs6Maq4ShH/m5fvnxZcXFxWrZsWTdViY566623lJqaqoyMDBUVFWnChAmKjY3VuXPn2uy/b98+JSYmasGCBTp8+LASEhKUkJCgo0ePdl/RBuBmjh8/bkgy/vrXv9ra3n//fcNisRhffvllu2MPHz5sDBkyxKisrDQkGdu3b+/iatFZnZnvb/vkk08MScapU6e6okw4gbPmeuvWrYa3t7dx5cqVrigTTtLZ+d61a5chyfjqq6+6sEo4YvLkycaiRYtsn5ubm42QkBAjMzOzzf4PPfSQMXPmzFZtUVFRxk9/+tMurfPbWOGF2ykoKFBAQIDuuusuW1tMTIw8PDx04MABu+MuX76sRx99VFlZWQoODu6OUuEEjs73tzU0NGjz5s0aNmyYQkNDu6pUdJIz5lqSamtr5efnJy8vt//upJuas+Yb7qWpqUmHDh1STEyMrc3Dw0MxMTEqKChoc0xBQUGr/pIUGxtrt39XIPDC7VitVg0aNKhVm5eXl2699VZZrVa7437xi19oypQpmj17dleXCCdydL4l6eWXX9Ytt9yiW265Re+//77y8vLk7e3dleWiEzoz19dUV1frueeeu+EtTnAdZ8w33E91dbWam5tt33h7TVBQkN15tVqtHerfFQi86DZpaWmyWCztHo4+dLRjxw59+OGHWrdunXOLhsO6cr6vmTdvng4fPqw9e/Zo9OjReuihh/T111876Q5wo7pjriWprq5OM2fO1Lhx47Ry5crOFw6HdNd8A87E74PQbZ566inNnz+/3T7Dhw9XcHDwdRvfr169qosXL9rdqvDhhx+qpKREAQEBrdoffPBBTZ06Vbt37+5E5XBEV873Nf7+/vL399eoUaN09913q3///tq+fbsSExM7Wz46oDvm+tKlS4qLi1O/fv20fft29erVq7Nlw0HdMd9wXwMHDpSnp6eqqqpatVdVVdmd1+Dg4A717woEXnSbwMBABQYGfm+/6Oho1dTU6NChQ4qMjJT0TaBtaWlRVFRUm2PS0tL02GOPtWoLDw/X2rVrNWvWrM4Xjw7ryvlui2EYMgxDjY2NDtcMx3T1XNfV1Sk2NlY+Pj7asWOHfH19nVY7Oq67/27DvXh7eysyMlL5+flKSEiQJLW0tCg/P1+LFy9uc0x0dLTy8/O1ZMkSW1teXp6io6O7oeL/r9sejwM6IC4uzpg4caJx4MAB46OPPjJGjRplJCYm2s5XVFQYY8aMMQ4cOGD3GuItDT1GR+e7pKTE+O1vf2sUFhYaZ86cMT7++GNj1qxZxq233mpUVVW56jZwAzo617W1tUZUVJQRHh5unDp1yqisrLQdV69eddVt4AY58m95ZWWlcfjwYeO1114zJBl79+41Dh8+bFy4cMEVt4A2bNmyxfDx8TGys7ON48ePG48//rgREBBgWK1WwzAM4yc/+YmRlpZm6//xxx8bXl5exosvvmh89tlnRkZGhtGrVy/jyJEj3VYzgRdu6cKFC0ZiYqJxyy23GH5+fkZKSopx6dIl2/nTp08bkoxdu3bZvQaBt+fo6Hx/+eWXRnx8vDFo0CCjV69extChQ41HH33UOHHihIvuADeqo3N97dVUbR2nT592zU3ghjnyb3lGRkab87158+buvwHY9Yc//MG47bbbDG9vb2Py5MnG/v37beemTZtmJCcnt+q/detWY/To0Ya3t7cxfvx447333uvWei2GYRjdt54MAAAAdC/e0gAAAABTI/ACAADA1Ai8AAAAMDUCLwAAAEyNwAsAAABTI/ACAADA1Ai8AAAAMDUCLwAAAEyNwAsAbmz+/PmyWCyyWCzq1auXhg0bpl/+8pf6+uuvbX1+85vfaMqUKerTp48CAgJu+NqnTp1SSkqKhg4dKh8fHw0bNkyJiYkqLCzs9LUBwJ0QeAHAzcXFxamyslJ/+9vftHbtWr3yyivKyMiwnW9qatLcuXP1xBNP3PA1CwsLFRkZqS+++EKvvPKKjh8/ru3bt2vs2LF66qmnOnVtAHA3fLUwALix+fPnq6amRjk5Oba2Bx98UKdPn1ZRUVGrvtnZ2VqyZIlqamravaZhGAoPD5evr68OHjwoD4/Wax81NTXXrebe6LUBwB2xwgsAPcjRo0e1b98+eXt7O3yN4uJiHTt2TE899dR1YVcSWxcAmI6XqwsAALTv3Xff1S233KKrV6+qsbFRHh4eWr9+vcPXO3nypCRp7NixzioRANwagRcA3NyMGTO0YcMGNTQ0aO3atfLy8tKDDz7o8PXYyQbgZsOWBgBwc3379tXIkSM1YcIEbdq0SQcOHNAf//hHh683evRoSdKJEyecVSIAuDUCLwD0IB4eHlq2bJmWL1+u//u//3PoGhERERo3bpzWrFmjlpaW687zYBoAsyHwAkAPM3fuXHl6eiorK0uSVFZWpuLiYpWVlam5uVnFxcUqLi5WfX19m+MtFos2b96sL774QlOnTtXOnTv1t7/9TZ9++ql+85vfaPbs2ba+Hb02ALgjAi8A9DBeXl5avHixXnjhBTU0NGjFihWaOHGiMjIyVF9fr4kTJ2rixImtvkAiLCxMK1eutH2ePHmyCgsLNXLkSC1cuFC333677r//fh07dkzr1q2z9buRawOAu+M9vABgcpcvX9aAAQP0/vvva/r06a4uBwC6HSu8AGByu3bt0o9+9CPCLoCbFiu8AAAAMDVWeAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACY2v8DyxCYCzwbkDwAAAAASUVORK5CYII=",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# View distribution of Impact on Performance\n",
"sto_man.impact_matrix.view(0,0)\n",
"sto_man.impact_matrix.view(0,1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We view the distribution of change absorption capability of margin node 1"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sto_man.absorption_matrix.view(0,0)\n",
"sto_man.absorption_matrix.view(0,1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we view the margin value plot"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"0.011306362998459859"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# display the margin value plot\n",
"sto_man.compute_mvp('scatter')\n",
"sto_man.compute_mvp('density')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The effect of design parameters on change absorption and impact on performance\n",
"\n",
"Let us look at an alternative design given by different values of $w$, $h$, and $\\theta$"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Progress: 99% \r"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from mvm import Design\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Effect of alternative designs\n",
"n_designs = 100\n",
"n_epochs = 10\n",
"lb = np.array(man.universe_d)[:, 0]\n",
"ub = np.array(man.universe_d)[:, 1]\n",
"design_doe = Design(lb, ub, n_designs, 'LHS')\n",
"\n",
"# create empty figure\n",
"fig, ax = plt.subplots(figsize=(7, 8))\n",
"ax.set_xlabel('Impact on performance')\n",
"ax.set_ylabel('Change absoption capability')\n",
"\n",
"X = np.empty((1,len(man.margin_nodes)))\n",
"Y = np.empty((1,len(man.margin_nodes)))\n",
"D = np.empty((1,len(man.design_params)))\n",
"\n",
"for d,design in enumerate(design_doe.unscale()):\n",
" sto_man.nominal_design_vector = design\n",
" sto_man.reset()\n",
" sto_man.reset_outputs()\n",
"\n",
" # Perform Monte-Carlo simulation\n",
" for n in range(n_epochs):\n",
" \n",
" sys.stdout.write(\"Progress: %d%% \\r\" % ((d * n_epochs + n) / (n_designs * n_epochs) * 100))\n",
" sys.stdout.flush()\n",
"\n",
" sto_man.randomize()\n",
" sto_man.init_decisions()\n",
" sto_man.allocate_margins()\n",
" sto_man.forward()\n",
" sto_man.compute_impact()\n",
" sto_man.compute_absorption()\n",
" \n",
" # Extract x and y\n",
" x = np.mean(sto_man.impact_matrix.values,axis=(1,2)).ravel() # average along performance parameters (assumes equal weighting)\n",
" y = np.mean(sto_man.absorption_matrix.values,axis=(1,2)).ravel() # average along input specs (assumes equal weighting)\n",
"\n",
" if not all(np.isnan(y)):\n",
" X = np.vstack((X,x))\n",
" Y = np.vstack((Y,y))\n",
" D = np.vstack((D,design))\n",
"\n",
" # plot the results\n",
" color = np.random.random((1,3))\n",
" ax.scatter(x,y,c=color)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we calculate the distance to the neutral line to rank different designs"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"---------------------------------\n",
"The best design:\n",
"w = 85.258\n",
"h = 15.242\n",
"theta = 3.865\n",
"---------------------------------\n"
]
}
],
"source": [
"from mvm import nearest\n",
"\n",
"# Calculate distance metric\n",
"p1 = np.array([X.min(),Y.min()])\n",
"p2 = np.array([X.max(),Y.max()])\n",
"p1 = p1 - 0.1*abs(p2 - p1)\n",
"p2 = p2 + 0.1*abs(p2 - p1)\n",
"\n",
"distances = np.empty(0)\n",
"for i,(x,y) in enumerate(zip(X,Y)):\n",
"\n",
" dist = 0\n",
" for node in range(len(x)):\n",
" s = np.array([x[node],y[node]])\n",
" pn,d = nearest(p1,p2,s)\n",
" dist += d\n",
"\n",
" distances = np.append(distances,dist)\n",
"\n",
"# create empty figure\n",
"fig, ax = plt.subplots(figsize=(7, 8))\n",
"ax.set_xlabel('Overall distance from the neutral line')\n",
"ax.set_ylabel('Frequency')\n",
"ax.hist(distances,bins=len(distances))\n",
"\n",
"plt.show()\n",
"\n",
"best_i = np.argmax(distances)\n",
"best_design = D[best_i,:]\n",
"\n",
"print('---------------------------------')\n",
"result = 'The best design:\\n'\n",
"for value,d_object in zip(best_design,sto_man.design_params):\n",
" result += d_object.symbol + ' = ' + '%.3f'%value + '\\n'\n",
"\n",
"result += '---------------------------------'\n",
"print(result)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us visualize the distance calculation for three different designs"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"\n",
"# display distances only for three designs\n",
"X_plot = X[0:3,:]\n",
"Y_plot = Y[0:3,:]\n",
"\n",
"# create empty figure\n",
"colors = ['#FF0000','#27BE1E','#0000FF']\n",
"fig, ax = plt.subplots(figsize=(7, 8))\n",
"ax.set_xlabel('Impact on performance')\n",
"ax.set_ylabel('Change absoption capability')\n",
"ax.set_xlim(p1[0],p2[0])\n",
"ax.set_ylim(p1[1],p2[1])\n",
"\n",
"p = ax.plot([0, 1], [0, 1], transform=ax.transAxes, color='k',linestyle=(5,(10,5)))\n",
"\n",
"distances = np.empty(0)\n",
"for i,(x,y) in enumerate(zip(X_plot,Y_plot)):\n",
"\n",
" ax.scatter(x,y,c=colors[i])\n",
"\n",
" dist = 0\n",
" for node in range(len(x)):\n",
" s = np.array([x[node],y[node]])\n",
" pn,d = nearest(p1,p2,s)\n",
" dist += d\n",
"\n",
" x_d = [s[0],pn[0]]\n",
" y_d = [s[1],pn[1]]\n",
" ax.plot(x_d,y_d,marker='.',linestyle='--',color=colors[i])\n",
"\n",
" distances = np.append(distances,dist)\n",
"\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".env",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
},
"vscode": {
"interpreter": {
"hash": "d2c59efba7b009353695b5a6cbe310397ac58aaa3c8acf134630ccd9465f821a"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
![](\"../images/strut_example/strut_overview.png\"\n",)
\n",
" ![](\"../images/strut_example/simplified_strut_model.png\"\n",)
\n",
" ![](\"../images/strut_example/MAN.png\"\n",)
\n",
"