From d7cf66e9b63994e703dda2a9347259995959e41c Mon Sep 17 00:00:00 2001 From: AndrewCurtis27 <89710614+AndrewCurtis27@users.noreply.github.com> Date: Tue, 11 Oct 2022 08:31:17 -0600 Subject: [PATCH 1/8] Add files via upload Initial scipy spline fit implementation --- optimization_initial_Spline.ipynb | 2135 +++++++++++++++++++++++++++++ 1 file changed, 2135 insertions(+) create mode 100644 optimization_initial_Spline.ipynb diff --git a/optimization_initial_Spline.ipynb b/optimization_initial_Spline.ipynb new file mode 100644 index 0000000..6e0bf90 --- /dev/null +++ b/optimization_initial_Spline.ipynb @@ -0,0 +1,2135 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iZN2u5WKRFqR", + "outputId": "6842718b-36d3-4c1c-b523-796de23e5470" + }, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "import os\n", + "import random\n", + "import pandas\n", + "import numpy\n", + "from scipy.optimize import curve_fit\n", + "from scipy.interpolate import interp1d\n", + "from scipy.interpolate import UnivariateSpline\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "\n", + "# seed = 7777\n", + "# random.seed(seed) \n", + "# torch.manual_seed(seed);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load Saved MP (Magnetic Parameter) Model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "B-TMPJtCKIM6" + }, + "outputs": [], + "source": [ + "# 12 input -> 42 output\n", + "class MpModel(torch.nn.Module):\n", + " def __init__(self):\n", + " super(MpModel, self).__init__()\n", + "\n", + " self.linear1 = torch.nn.Linear(12, 100, bias=True)\n", + " self.linear2 = torch.nn.Linear(100, 100, bias=True)\n", + " self.linear3 = torch.nn.Linear(100, 42, bias=True)\n", + "\n", + " def forward(self, x):\n", + " x = torch.atan(self.linear1(x))\n", + " x = torch.atan(self.linear2(x))\n", + " x = torch.atan(self.linear2(x))\n", + " x = self.linear3(x)\n", + " return x\n", + "\n", + "\n", + "mp_model = MpModel().to(\"cuda\")\n", + "mp_model.load_state_dict(torch.load(\"saved_model_state.pt\"))\n", + "\n", + "for param in mp_model.parameters():\n", + " param.requires_grad = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create GP (Geometry Paramter) Generator" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "z9cggAl1BGHo" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\lazy.py:178: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.\n", + " warnings.warn('Lazy modules are a new feature under heavy development '\n" + ] + } + ], + "source": [ + "class GpModel(torch.nn.Module):\n", + " def __init__(self, input_length: int):\n", + " super(GpModel, self).__init__()\n", + "\n", + " self.dense_layer1 = torch.nn.Linear(int(input_length), 128)\n", + " self.dense_layer2 = torch.nn.Linear(128, 256)\n", + " # self.dense_layer3 = torch.nn.Linear(256, 512)\n", + " # self.dense_layer4 = torch.nn.Linear(512, 256)\n", + " self.dense_layer5 = torch.nn.Linear(256, 128)\n", + " self.dense_layer6 = torch.nn.Linear(128, int(input_length))\n", + " \n", + " self.batch_norm1 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm2 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm3 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm4 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm5 = torch.nn.LazyBatchNorm1d()\n", + "\n", + " def forward(self, x):\n", + " x = self.batch_norm1(torch.atan(self.dense_layer1(x)))\n", + " x = self.batch_norm2(torch.atan(self.dense_layer2(x)))\n", + " # x = self.batch_norm3(torch.atan(self.dense_layer3(x)))\n", + " # x = self.batch_norm4(torch.atan(self.dense_layer4(x)))\n", + " x = self.batch_norm5(torch.atan(self.dense_layer5(x)))\n", + " x = torch.sigmoid(self.dense_layer6(x))\n", + " return x\n", + "\n", + "\n", + "gp_model = GpModel(10).to(\"cuda\")\n", + "optimizer = torch.optim.Adam(gp_model.parameters(), lr=3e-4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# System Constraints" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "Bse1wsKN1NxY" + }, + "outputs": [], + "source": [ + "# x-direction of the primary winding (mm)\n", + "lpx_min, lpx_max = (50.0, 650.0)\n", + "\n", + "# y-direction of the primary winding (mm)\n", + "lpy_min, lpy_max = (50.0, 2050.0)\n", + "\n", + "# width of the primary winding (mm)\n", + "wp_min, wp_max = (25.0, 325.0)\n", + "\n", + "# length of the edge of the primary core (mm)\n", + "a_min, a_max = (0.0, 200.0)\n", + "\n", + "# pitch of the adjacent cores (mm)\n", + "p_min, p_max = (0.0, 200.0)\n", + "\n", + "# length of the secondary winding (mm)\n", + "ls_min, ls_max = (50.0, 450.0)\n", + "\n", + "# width of the secondary winding (mm)\n", + "ws_min, ws_max = (25.0, 225.0)\n", + "\n", + "# input RMS current (A)\n", + "ip_min, ip_max = (50.0, 200.0)\n", + "\n", + "# turn number of the primary side\n", + "np_min, np_max = (4.0, 10.0)\n", + "\n", + "# turn number of the secondary side\n", + "ns_min, ns_max = (4.0, 10.0)\n", + "\n", + "# switching frequency (kHz)\n", + "f = 85 * (10 ** 3)\n", + "\n", + "# output power at the center (kW)\n", + "p_out = 50 * (10 ** 3)\n", + "\n", + "# input DC voltage (V)\n", + "v_dc = 400\n", + "\n", + "# output DC voltage (V)\n", + "v_bat = 400\n", + "\n", + "# length of the edge of the secondary core\n", + "b = 50\n", + "\n", + "# ???\n", + "w = 2 * numpy.pi * f # [rad/s]\n", + "\n", + "# ???\n", + "q_coil = 400" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "brlwMfzj1Nxh" + }, + "outputs": [], + "source": [ + "# Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive.\n", + "# Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch.\n", + "\n", + "@torch.jit.script\n", + "def linspace(start: torch.Tensor, stop: torch.Tensor, num: int):\n", + " # create a tensor of 'num' steps from 0 to 1\n", + " steps = torch.arange(num, dtype=torch.float32, device=start.device) / (num - 1)\n", + "\n", + " # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings\n", + " # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript\n", + " # \"cannot statically infer the expected size of a list in this contex\", hence the code below\n", + " for i in range(start.ndim):\n", + " steps = steps.unsqueeze(-1)\n", + "\n", + " # the output starts at 'start' and increments until 'stop' in each dimension\n", + " out = start[None] + steps * (stop - start)[None]\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "dMoXnTFV8U1P" + }, + "outputs": [], + "source": [ + "# Generate a pytorch tensor full of random floats in the range [-1, +1] with the given shape\n", + "\n", + "def generate_noise(shape):\n", + " return torch.distributions.uniform.Uniform(-1, +1).sample(shape).cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "xloGE25R8Jtm" + }, + "outputs": [], + "source": [ + "def stack_parameters(*params):\n", + " return torch.stack(params).transpose(0, 1)\n", + "\n", + "\n", + "# Scale an array to be between our specified [min, max] contraints\n", + "def scale(arr):\n", + " scaler_min = torch.tensor(\n", + " [a_min, lpx_min, lpy_min, ls_min, p_min, wp_min, ws_min, ip_min, np_min, ns_min],\n", + " device=\"cuda\",\n", + " )\n", + " scaler_max = torch.tensor(\n", + " [a_max, lpx_max, lpy_max, ls_max, p_max, wp_max, ws_max, ip_max, np_max, ns_max],\n", + " device=\"cuda\",\n", + " )\n", + "\n", + " return arr * (scaler_max - scaler_min) + scaler_min\n", + "\n", + "\n", + "def extract(arr):\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns = [\n", + " numpy.squeeze(x) for x in numpy.hsplit(arr, 10)\n", + " ]\n", + "\n", + " ys_min = (\n", + " 5 * wp +\n", + " 4 * a +\n", + " 3 * lpy +\n", + " 2 * p\n", + " )\n", + "\n", + " ys_max = (lpy + wp)\n", + " ys_max = ys_min + (ys_min - ys_max) / 2\n", + "\n", + " ys_min /= 2\n", + " ys_max /= 2\n", + "\n", + " ys = linspace(ys_min, ys_max, num=5)\n", + "\n", + " np = torch.round(np)\n", + " ns = torch.round(ns)\n", + "\n", + " # take all 15 tensors of shape (X) and convert to (15, X)\n", + " return a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys\n", + "\n", + "\n", + "def scale_and_extract(arr):\n", + " scaled_array = scale(arr)\n", + " return extract(scaled_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "44TlTSnclCKs", + "outputId": "c2d4a61b-35c8-42ad-99f9-98c99022a0b2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " GP Parameters Shape: torch.Size([256, 12])\n", + " Extra Parameters Shape: torch.Size([256, 3])\n", + " MP Model Output Shape: torch.Size([256, 42])\n", + " \n" + ] + } + ], + "source": [ + "# Enclose in a function so we don't leak variables\n", + "def test_models():\n", + " noise = generate_noise(shape=(256, 10))\n", + " gp = gp_model(noise)\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "\n", + " gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + " extra_parameters = stack_parameters(ip, np, ns)\n", + "\n", + " mp = mp_model(gp_parameters)\n", + "\n", + " print(f\"\"\"\n", + " GP Parameters Shape: {gp_parameters.shape}\n", + " Extra Parameters Shape: {extra_parameters.shape}\n", + " MP Model Output Shape: {mp.shape}\n", + " \"\"\")\n", + "test_models()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "eEb7J5bOFEEr" + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "Method to take two equally-sized lists and return just the elements which lie \n", + "on the Pareto frontier, sorted into order.\n", + "Default behaviour is to find the maximum for both X and Y, but the option is\n", + "available to specify maxX = False or maxY = False to find the minimum for either\n", + "or both of the parameters.\n", + "\"\"\"\n", + "\n", + "\n", + "def pareto_frontier(x, y, max_x=False):\n", + " if max_x:\n", + " return _pareto_frontier(y, x)\n", + " else:\n", + " return _pareto_frontier(x, y)\n", + "\n", + "\n", + "def _pareto_frontier(x, y):\n", + " # Combine inputs and sort them with smallest X values coming first\n", + " sorted_xy = sorted(zip(x, y))\n", + " p_front_x = torch.zeros(len(x), device=\"cuda\")\n", + " p_front_y = torch.zeros(len(y), device=\"cuda\")\n", + " \n", + " # Loop through the sorted list\n", + " # Look for lower values of Y…\n", + " # and add them to the Pareto frontier\n", + " min_y = sorted_xy[0][1]\n", + " for index in range(len(sorted_xy)):\n", + " x, y = sorted_xy[index]\n", + " if y < min_y:\n", + " min_y = y\n", + " p_front_x[index] = x\n", + " p_front_y[index] = y\n", + " \n", + " p_front_x = p_front_x[p_front_x.nonzero()]\n", + " p_front_y = p_front_y[p_front_y.nonzero()]\n", + "\n", + " return p_front_x, p_front_y" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def y_util(x, a, b):\n", + " return (1 / (x ** b)) ** (1 / a)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def log_util(x, a, b):\n", + " return ()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def lin_util(x, a, b):\n", + " return ((a * x) + b)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def prod_util(x, a):\n", + " return (1 / (x * a))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def prodb_util(x, a, b):\n", + " return ((1 / (x * a)) + b)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def one_param_util(x, a):\n", + " return ((1 - (x**a))**(1/a))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def one_param_b_util(x, a, b):\n", + " return (((1 - (x**a))**(1/a)) + b)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "#testing for creating total pareto f\n", + "\n", + "def oldgraph_pareto_frontier(Xs, Ys, maxX = False, maxY = False):\n", + "# Sort the list in either ascending or descending order of X\n", + " myList = sorted([[Xs[i], Ys[i]] for i in range(len(Xs))], reverse=maxX)\n", + "# Start the Pareto frontier with the first value in the sorted list\n", + " p_front = [myList[0]] \n", + "# Loop through the sorted list\n", + " for pair in myList[1:]:\n", + " if pair[1] <= p_front[-1][1]: # Look for lower values of Y…\n", + " p_front.append(pair) # … and add them to the Pareto frontier\n", + "# Turn resulting pairs back into a list of Xs and Ys\n", + " p_frontX = [pair[0] for pair in p_front]\n", + " p_frontY = [pair[1] for pair in p_front]\n", + " return p_frontX, p_frontY" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def findSpline(x, y, smoothing_factor = 2):\n", + " spl = UnivariateSpline(x, y)\n", + " spl.set_smoothing_factor(smoothing_factor)\n", + " return spl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load DWPT data" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "jkTrlAJMH5UO" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1010, 12)\n", + "(1010, 42)\n" + ] + } + ], + "source": [ + "def load_dwpt():\n", + " folder_name = \"./dwpt_v5\"\n", + " file_names = [\n", + " \"DWPT_v5_N10\",\n", + " \"DWPT_v5_N100\",\n", + " \"DWPT_v5_N200\",\n", + " \"DWPT_v5_N300\",\n", + " \"DWPT_v5_N400\",\n", + " ]\n", + "\n", + " df = pandas.DataFrame()\n", + "\n", + " for file_name in file_names:\n", + " new_df = pandas.read_csv(f\"{folder_name}/{file_name}_after.csv\", index_col=0)\n", + " df = pandas.concat([df, new_df])\n", + "\n", + " return df\n", + "\n", + "df = load_dwpt()\n", + "df_gp = df.loc[:, :\"ys4[mm]\"]\n", + "df_mp = df.loc[:, \"k0mm_ys0\":]\n", + "\n", + "print(df_gp.shape)\n", + "print(df_mp.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define Loss Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def kdiff_loss(k_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + "\n", + " kdiff = abs(k_0_0 - k_1_0)\n", + " kdiff = kdiff / torch.max(abs(k_0_0), abs(k_1_0))\n", + "\n", + " # their kdiff - why is there a difference?\n", + " # kdiff = abs(k00 - k10) / k00\n", + "\n", + " return kdiff\n", + "\n", + "\n", + "\n", + "def bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters):\n", + " # Unpack parameters\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " # Scale l_parameters\n", + " lp_0_0 = lp_0_0 * np ** 2\n", + " ls_0_0 = ls_0_0 * ns ** 2\n", + "\n", + " # Calculate Is\n", + " # n1 = (numpy.pi * w) * (lp_0_0 * 10 ** -9) * (ip * numpy.sqrt(2) / (4 * v_dc))\n", + " # t1 = (numpy.pi) ** 2 * w * (lp_0_0 * 10 ** -9 * ls_0_0 * 10 ** -9) ** 0.5\n", + " # t2 = p_out / (8 * k_0_0 * n1 * v_dc * v_bat)\n", + " # n2 = t1 * t2\n", + " # # \"Is\" is way too big?\n", + " # Is = 4 * n2 * v_bat / (numpy.pi * w * ls_0_0 * 10 ** -9) / numpy.sqrt(2)\n", + " \n", + " # Paper formula\n", + " n1 = (torch.pi * w * lp_0_0 * ip) / (2 * numpy.sqrt(2) * v_dc)\n", + " t1 = (torch.pi ** 2 * w * p_out * torch.sqrt(lp_0_0 * ls_0_0))\n", + " t2 = 8 * k_0_0 * n1 * v_dc * v_bat\n", + " n2 = t1 / t2\n", + " Is = (4 * v_bat * n2) / (torch.pi * w * ls_0_0)\n", + "\n", + " # Calculate B_0mm - unneeded since b100mm always bigger?\n", + " # bx_0 = (\n", + " # (bx_p_0_00 * ip * np + bx_s_0_00 * Is * ns) ** 2 +\n", + " # (bx_p_0_90 * ip * np + bx_s_0_90 * Is * ns) ** 2\n", + " # ) ** 0.5\n", + " # by_0 = (\n", + " # (by_p_0_00 * ip * np + by_s_0_00 * Is * ns) ** 2 +\n", + " # (by_p_0_90 * ip * np + by_s_0_90 * Is * ns) ** 2\n", + " # ) ** 0.5\n", + " # bz_0 = (\n", + " # (bz_p_0_00 * ip * np + bz_s_0_00 * Is * ns) ** 2 +\n", + " # (bz_p_0_90 * ip * np + bz_s_0_90 * Is * ns) ** 2\n", + " # ) ** 0.5\n", + " # b_0 = (bx_0**2 + by_0**2 + bz_0**2) ** 0.5\n", + "\n", + " # Calculate B_100mm\n", + " bx_100 = (\n", + " (bx_p_1_00 * ip * np + bx_s_1_00 * Is * ns) ** 2 +\n", + " (bx_p_1_90 * ip * np + bx_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " by_100 = (\n", + " (by_p_1_00 * ip * np + by_s_1_00 * Is * ns) ** 2 +\n", + " (by_p_1_90 * ip * np + by_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " bz_100 = (\n", + " (bz_p_1_00 * ip * np + bz_s_1_00 * Is * ns) ** 2 +\n", + " (bz_p_1_90 * ip * np + bz_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " b_100 = (bx_100**2 + by_100**2 + bz_100**2) ** 0.5\n", + "\n", + " # Bstray is the max between the two targets\n", + " # bstray = torch.max(b_0, b_100)\n", + " bstray = b_100\n", + "\n", + " return bstray" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# (\n", + "# # [0 thru 5]\n", + "# k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + "\n", + "# # [6 thru 17]\n", + "# lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + "# ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + "# lp_1_0, ls_1_0,\n", + "\n", + "# # [18 thru 29]\n", + "# bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + "# bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + "# bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + "# bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + "# # [30 thru 42]\n", + "# bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + "# bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + "# bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + "# bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + "# )" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def coil_loss_and_power_average(k_parameters, l_parameters, b_parameters, extra_parameters):\n", + " (k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " n1_i = math.pi*w*lp_0_0*10**(-9)*ip*math.sqrt(2)/(4*Vdc)\n", + " t1 = (math.pi)**2*w*(lp_0_0*10**(-9)*ls_0_0 *10**(-9))**0.5\n", + " t2 = Pout/(8*lp_0_0*n1_i*Vdc*Vbat)\n", + " n2_i = t1*t2\n", + " Is_i = 4*n2_i*Vbat/(math.pi*w*ls_0_0*10**(-9))/math.sqrt(2)\n", + " loss = w*lp_0_0*10**(-9)*ip**2/QCoil + w*ls_0_0 *10**(-9)*Is_i**2/QCoil\n", + "\n", + " a6 = lp_0_0.clone() * np.clone()**2 # LP0MM_YSO\n", + " a7 = lp_0_1.clone() * np.clone()**2 # LP0MM_YS1\n", + " a8 = lp_0_2.clone() * np.clone()**2 # LP0MM_YS2\n", + " a9 = lp_0_3.clone() * np.clone()**2 # LP0MM_YS3\n", + " a10 = lp_0_4.clone() * np.clone()**2 # LP0MM_YS4\n", + "\n", + " a11 = ls_0_0.clone() * ns.clone()**2 # LS0MM_YSO\n", + " a12 = ls_0_1.clone() * ns.clone()**2 # LS0MM_YS1\n", + " a13 = ls_0_2.clone() * ns.clone()**2 # LS0MM_YS2\n", + " a14 = ls_0_3.clone() * ns.clone()**2 # LS0MM_YS3\n", + " a15 = ls_0_4.clone() * ns.clone()**2 # LS0MM_YS4\n", + "\n", + "\n", + "\n", + " p0 = w*k_0_0*(a6*10**(-9))*(a11*10**(-9))**0.5*ip*Is_i\n", + " p1 = w*k_0_1*(a7*10**(-9))*(a12*10**(-9))**0.5*ip*Is_i \n", + " p2 = w*k_0_2*(a8*10**(-9))*(a13*10**(-9))**0.5*ip*Is_i \n", + " p3 = w*k_0_3*(a9*10**(-9))*(a14*10**(-9))**0.5*ip*Is_i\n", + " p4 = w*k_0_4*(a10*10**(-9))*(a15*10**(-9))**0.5*ip*Is_i \n", + " pave = (p0+(2*p1)+(2*p2)+(2*p3)+(2*abs(p4)))/8 \n", + " return loss,pave\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def power_average(k_parameters, l_parameters, b_parameters, extra_parameters, gp_parameters):\n", + " \n", + " return " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + " def core_losses(k_parameters, l_parameters, b_parameters, extra_parameters, gp_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " (\n", + " a, lpx, lpy, ls, p, wp, ws, *ys\n", + " ) = gp_parameters.transpose(0,1)\n", + "\n", + " V_PriCore = (lpy +2*wp+2*a)*(lpx+2*wp+2*a)*5/(10**3) # cm3\n", + " #print(V_PriCore)\n", + " V_SecCore = (ls+2*ws+2*b)**2*5/(10**3) # cm3\n", + " #print(V_SecCore)\n", + " V_PriWind = (2*(lpx+wp)+2*(lpy+wp))*6.6*6.6/(10**3)*np\n", + " V_SecWind = 4*(ls+ws)*6.6*6.6/(10**3)*ns\n", + "\n", + " V_PriWind_ave = V_PriWind/(lpy+2*wp+2*a+p)*10**3 # cm3/m\n", + " V_PriCore_ave = V_PriCore/(lpy+2*wp+2*a+p)*10**3 # cm3/m\n", + "\n", + " #print(V_PriWind_ave, V_PriCore_ave)\n", + " return V_PriCore, V_SecCore, V_PriWind, V_SecWind" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "noise = generate_noise(shape=(25_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "plt.scatter(bstray.detach().cpu(), 100 * kdiff.detach().cpu())\n", + "plt.title(\"Random Network: Bstray vs Kdiff\")\n", + "plt.xlabel(\"BStray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# if (chosen_curve_util == one_param_util) or (chosen_curve_util == prod_util):\n", + "# if normalize == True:\n", + "# (a), pcov = curve_fit(chosen_curve_util, normalized_pareto_kdiff_added_numpy[:,0], normalized_pareto_bstray_added_numpy[:,0], maxfev = 3000)\n", + "# else:\n", + "# (a), pcov = curve_fit(chosen_curve_util, pareto_kdiff_added_numpy[:,0], pareto_bstray_added_numpy[:,0], maxfev = 3000)\n", + " \n", + "# if (chosen_curve_util == y_util) or (chosen_curve_util == lin_util) or (chosen_curve_util == prodb_util) or (chosen_curve_util == one_param_b_util):\n", + "# if normalize == True:\n", + "# (a, b), pcov = curve_fit(chosen_curve_util, normalized_pareto_kdiff_added_numpy[:,0], normalized_pareto_bstray_added_numpy[:,0], maxfev = 3000)\n", + "# else:\n", + "# (a, b), pcov = curve_fit(lin_util, pareto_kdiff_added_numpy[:,0], pareto_bstray_added_numpy[:,0], maxfev = 3000)\n", + "# # (a, b), pcov = curve_fit(lin_util, pareto_kdiff_numpy[:,0], pareto_bstray_numpy[:,0], maxfev = 3000)\n", + " \n", + " \n", + "# #one param util\n", + "# if chosen_curve_util == one_param_util:\n", + "# if normalize == True:\n", + "# x_calc = torch.as_tensor((1 / (normalized_kdiff_numpy ** a)) ** (1 / a)).cuda()\n", + "# else:\n", + "# x_calc = torch.as_tensor((1 / (kdiff_numpy ** a)) ** (1 / a)).cuda()\n", + " \n", + "# #one param with intercept util\n", + "# if chosen_curve_util == one_param_b_util:\n", + "# if normalize == True:\n", + "# x_calc = torch.as_tensor(((1 / (normalized_kdiff_numpy ** a)) ** (1 / a)) + b).cuda()\n", + "# else:\n", + "# x_calc = torch.as_tensor(((1 / (kdiff_numpy ** a)) ** (1 / a)) + b).cuda()\n", + " \n", + "# #exponential utils\n", + "# if chosen_curve_util == y_util:\n", + " \n", + "# if normalize == True:\n", + "# x_calc = torch.as_tensor(((1 / normalized_kdiff_numpy ** b)) ** (1 / a)).cuda()\n", + "# else:\n", + "# x_calc = torch.as_tensor(((1 / kdiff_numpy ** b)) ** (1 / a)).cuda()\n", + " \n", + "# #prod util\n", + "# if chosen_curve_util == prod_util:\n", + "# if normalize == True:\n", + "# x_calc = torch.as_tensor(1 / (normalized_kdiff_numpy * a)).cuda()\n", + "# else:\n", + "# x_calc = torch.as_tensor(1 / (kdiff_numpy * a)).cuda()\n", + " \n", + "# #prodb util\n", + "# if chosen_curve_util == prodb_util:\n", + "# if normalize == True:\n", + "# x_calc = torch.as_tensor((1 / (normalized_kdiff_numpy * a)) + b).cuda()\n", + "# else:\n", + "# x_calc = torch.as_tensor((1 / (kdiff_numpy * a)) + b).cuda()\n", + " \n", + "# #linear util\n", + "# if chosen_curve_util == lin_util:\n", + "# if normalize == True:\n", + "# x_calc = torch.as_tensor((a * normalized_kdiff_numpy) + (b)).cuda()\n", + "# else:\n", + "# x_calc = torch.as_tensor((a * kdiff_numpy) + (b)).cuda() \n", + " \n", + "# x_calc_numpy = x_calc.cpu().data.numpy()\n", + "# #x_loss = torch.abs(x_calc - bstray)\n", + " \n", + "# if normalize == True:\n", + "# x_loss = x_calc - normalized_bstray\n", + "# else:\n", + "# x_loss = x_calc - bstray\n", + "# # x_loss_relu = torch.nn.functional.relu(-x_loss)\n", + " \n", + "# elif len(pareto_bstray_numpy) > 0:\n", + "# x_loss = pareto_bstray[:,0]\n", + "# print('single point')\n", + " \n", + "# if len(pareto_bstray_numpy) > 2:\n", + "# if (chosen_curve_util == one_param_util) or (chosen_curve_util == prod_util):\n", + "# if normalize == True:\n", + "# (a), pcov = curve_fit(chosen_curve_util, normalized_pareto_bstray_added_numpy[:,0], normalized_pareto_kdiff_added_numpy[:,0], maxfev = 3000)\n", + "# else:\n", + "# (a), pcov = curve_fit(chosen_curve_util, pareto_bstray_added_numpy[:,0], pareto_kdiff_added_numpy[:,0], maxfev = 3000)\n", + " \n", + "# if (chosen_curve_util == y_util) or (chosen_curve_util == lin_util) or (chosen_curve_util == prodb_util) or (chosen_curve_util == one_param_b_util):\n", + "# if normalize == True:\n", + "# (a, b), pcov = curve_fit(chosen_curve_util, normalized_pareto_bstray_added_numpy[:,0], normalized_pareto_kdiff_added_numpy[:,0], maxfev = 3000)\n", + "# else:\n", + "# (a, b), pcov = curve_fit(lin_util, pareto_bstray_added_numpy[:,0], pareto_kdiff_added_numpy[:,0], maxfev = 3000)\n", + "# # (a, b), pcov = curve_fit(lin_util, pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0], maxfev = 3000)\n", + " \n", + " \n", + "# #one param util\n", + "# if chosen_curve_util == one_param_util:\n", + "# if normalize == True:\n", + "# y_calc = torch.as_tensor((1 / (normalized_bstray_numpy ** a)) ** (1 / a)).cuda()\n", + "# else:\n", + "# y_calc = torch.as_tensor((1 / (bstray_numpy ** a)) ** (1 / a)).cuda()\n", + " \n", + "# #one param with intercept util\n", + "# if chosen_curve_util == one_param_b_util:\n", + "# if normalize == True:\n", + "# y_calc = torch.as_tensor(((1 / (normalized_bstray_numpy ** a)) ** (1 / a)) + b).cuda()\n", + "# else:\n", + "# y_calc = torch.as_tensor(((1 / (bstray_numpy ** a)) ** (1 / a)) + b).cuda()\n", + " \n", + "# #exponential utils\n", + "# if chosen_curve_util == y_util:\n", + "# if normalize == True:\n", + " \n", + "# y_calc = torch.as_tensor(((1 / normalized_bstray_numpy ** b)) ** (1 / a)).cuda()\n", + "# else:\n", + "# y_calc = torch.as_tensor(((1 / bstray_numpy ** b)) ** (1 / a)).cuda()\n", + " \n", + "# #prod util\n", + "# if chosen_curve_util == prod_util:\n", + "# if normalize == True:\n", + "# y_calc = torch.as_tensor(1 / (normalized_bstray_numpy * a)).cuda()\n", + "# else:\n", + "# y_calc = torch.as_tensor(1 / (bstray_numpy * a)).cuda()\n", + " \n", + "# #prodb util\n", + "# if chosen_curve_util == prodb_util:\n", + "# if normalize == True:\n", + "# y_calc = torch.as_tensor((1 / (normalized_bstray_numpy * a)) + b).cuda()\n", + "# else:\n", + "# y_calc = torch.as_tensor((1 / (bstray_numpy * a)) + b).cuda()\n", + " \n", + "# #linear util\n", + "# if chosen_curve_util == lin_util:\n", + "# if normalize == True:\n", + "# y_calc = torch.as_tensor(((a * normalized_bstray_numpy)) + (b)).cuda()\n", + "# else:\n", + "# y_calc = torch.as_tensor(((a * bstray_numpy)) + (b)).cuda()\n", + " \n", + "# y_calc_numpy = y_calc.cpu().data.numpy()\n", + " \n", + "# if normalize == True:\n", + "# y_loss = y_calc - normalized_kdiff \n", + "# else:\n", + "# y_loss = y_calc - kdiff\n", + "# y_loss_relu = torch.nn.functional.relu(-y_loss)\n", + " \n", + "# elif len(pareto_bstray_numpy) > 0:\n", + "# y_loss = pareto_kdiff[:,0]\n", + "# print('single point')\n", + "\n", + "# # loss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Network Training Loop" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "OXzpQf5O1Nxk", + "outputId": "1bc1a1b1-95ac-43d8-f1d8-5dd49e4bbb03" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(20645.8978, device='cuda:0', dtype=torch.float64,\n", + " grad_fn=)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0\n", + " Kdiff : 21.0943%\n", + " Bstray : 49.9969\n", + " Pareto Kdiff : 12.5770%\n", + " Pareto Bstray : 36.7439\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "tensor([0.], requires_grad=True)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 50\n", + " Kdiff : 66.7762%\n", + " Bstray : 40.6598\n", + " Pareto Kdiff : 7.6802%\n", + " Pareto Bstray : 19.8319\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "tensor([0.], requires_grad=True)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 100\n", + " Kdiff : 65.6406%\n", + " Bstray : 37.4112\n", + " Pareto Kdiff : 7.7133%\n", + " Pareto Bstray : 18.9657\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "tensor([0.], requires_grad=True)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 150\n", + " Kdiff : 64.0126%\n", + " Bstray : 36.5757\n", + " Pareto Kdiff : 7.7155%\n", + " Pareto Bstray : 17.4412\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n" + ] + } + ], + "source": [ + "p_front_x = []\n", + "p_front_y = []\n", + "pareto_bstray_points = []\n", + "pareto_kdiff_points = []\n", + "kdiffs = []\n", + "bstrays = []\n", + "combined_losses = []\n", + "\n", + "#testing for graphs\n", + "kdiff_designs = []\n", + "bstray_designs = []\n", + "pareto_bstray_points2 = []\n", + "pareto_kdiff_points2 = []\n", + "\n", + "x_calc_numpy =[]\n", + "y_calc_numpy=[]\n", + "\n", + "#training parameters\n", + "n_epochs = 200\n", + "n_noise = 1000\n", + "chosen_curve_util = prod_util\n", + "normalize = True\n", + "\n", + "for epoch in range(n_epochs):\n", + " \n", + " # Clear old gradients in the GP model\n", + " for param in gp_model.parameters():\n", + " param.grad = None\n", + "\n", + " # Create GP parameters from noise\n", + " noise = generate_noise(shape=(n_noise, 10))\n", + " gp = gp_model(noise)\n", + "\n", + " # Unpack and filter GP parameters\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + " gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + " extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + " # Scale GP parameters - why?\n", + " min_x = torch.min(gp_parameters)\n", + " max_x = torch.max(gp_parameters)\n", + " gp_parameters = (gp_parameters - min_x) / (max_x - min_x)\n", + "\n", + " # Push GP parameters through MP model\n", + " mp_parameters = mp_model(gp_parameters)\n", + "\n", + " # Scale MP parameters\n", + " y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + " min_y = torch.min(y, 1).values\n", + " max_y = torch.max(y, 1).values\n", + " mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + " k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + " l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + " b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + " # Calculate losses\n", + " kdiff = kdiff_loss(k_parameters)\n", + " bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " pareto_bstray, pareto_kdiff = pareto_frontier(bstray, kdiff)\n", + " pareto_kdiff_fory, pareto_bstray_fory = pareto_frontier(kdiff, bstray)\n", + " \n", + " max_kdiff_point = torch.max(kdiff)\n", + " max_bstray_point = torch.max(bstray)\n", + " \n", + " #scale bstray and kdiff\n", + " normalized_kdiff = kdiff / max_kdiff_point\n", + " normalized_bstray = bstray / max_bstray_point\n", + " normalized_pareto_kdiff = pareto_kdiff / max_kdiff_point\n", + " normalized_pareto_bstray = pareto_bstray / max_bstray_point\n", + "\n", + " \n", + " #add extrema points\n", + " if normalize == True:\n", + " zero_tensor = torch.tensor([[0.01]]).cuda()\n", + " max_tensor = torch.tensor([[1]]).cuda()\n", + " max_tensor_bstray = torch.tensor([[max_bstray_point.cpu().data.numpy() * 2]]).cuda()\n", + " max_tensor_kdiff = torch.tensor([[max_kdiff_point.cpu().data.numpy() * 2]]).cuda()\n", + " else:\n", + " zero_tensor = torch.tensor([[0.001]]).cuda()\n", + " max_tensor = torch.tensor([[100]]).cuda()\n", + " max_tensor_bstray = torch.tensor([[max_bstray_point.cpu().data.numpy() * 2]]).cuda()\n", + " max_tensor_kdiff = torch.tensor([[max_kdiff_point.cpu().data.numpy() * 2]]).cuda()\n", + " \n", + " normalized_pareto_bstray_added = torch.cat((zero_tensor, normalized_pareto_bstray, max_tensor),0)\n", + " normalized_pareto_kdiff_added = torch.cat((max_tensor, normalized_pareto_kdiff, zero_tensor),0)\n", + " \n", + " pareto_bstray_added = torch.cat((zero_tensor, pareto_bstray, max_tensor_bstray),0)\n", + " pareto_kdiff_added = torch.cat((max_tensor_kdiff, pareto_kdiff, zero_tensor),0)\n", + " \n", + " \n", + " #numpy conversions for curve fit and plotting\n", + " normalized_bstray_numpy = normalized_bstray.cpu().data.numpy()\n", + " normalized_kdiff_numpy = normalized_kdiff.cpu().data.numpy()\n", + " \n", + " normalized_pareto_bstray_numpy = normalized_pareto_bstray.cpu().data.numpy()\n", + " normalized_pareto_kdiff_numpy = normalized_pareto_kdiff.cpu().data.numpy()\n", + " \n", + " normalized_pareto_bstray_added_numpy = normalized_pareto_bstray_added.cpu().data.numpy()\n", + " normalized_pareto_kdiff_added_numpy = normalized_pareto_kdiff_added.cpu().data.numpy()\n", + " \n", + " \n", + " pareto_bstray_added_numpy = pareto_bstray_added.cpu().data.numpy()\n", + " pareto_kdiff_added_numpy = pareto_kdiff_added.cpu().data.numpy()\n", + " pareto_bstray_numpy = pareto_bstray.cpu().data.numpy()\n", + " pareto_kdiff_numpy = pareto_kdiff.cpu().data.numpy()\n", + " pareto_bstray_fory_numpy = pareto_bstray_fory.cpu().data.numpy()\n", + " pareto_kdiff_fory_numpy = pareto_kdiff_fory.cpu().data.numpy()\n", + "\n", + " bstray_numpy = bstray.cpu().data.numpy()\n", + " kdiff_numpy = kdiff.cpu().data.numpy()\n", + " \n", + "\n", + " #compute delta y loss from utility fit function \n", + " if len(pareto_kdiff_numpy) > 3:\n", + " # f = interp1d(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0])\n", + " # f2 = interp1d(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0], kind = 'cubic')\n", + " # plt.plot(pareto_bstray_numpy[:,0], f2(pareto_bstray_numpy[:,0]), '--', pareto_bstray_numpy, pareto_kdiff_numpy, 'o')\n", + " \n", + " \n", + "\n", + " spl_y = findSpline(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0])\n", + " y_calc = torch.as_tensor(spl_y(bstray_numpy)).cuda()\n", + " # plt.plot(pareto_bstray_numpy[:,0], spl_y(pareto_bstray_numpy[:,0]), '--', pareto_bstray_fory_numpy, pareto_kdiff_fory_numpy, 'o')\n", + " # plt.show()\n", + " y_loss = kdiff - y_calc\n", + " y_loss_relu = torch.nn.functional.relu(y_loss)\n", + "\n", + "\n", + " # pareto_kdiff_numpy_reverse = numpy.flip(pareto_kdiff_numpy[:, 0], 0) \n", + " # pareto_bstray_numpy_reverse = numpy.flip(pareto_bstray_numpy[:,0], 0)\n", + " # # print(pareto_bstray_numpy, pareto_bstray_numpy_reverse)\n", + " # x_calc_reverse = numpy.flip(spl_x(kdiff_numpy), 0)\n", + " \n", + " \n", + " spl_x = findSpline(pareto_kdiff_fory_numpy[:,0], pareto_bstray_fory_numpy[:,0])\n", + " # plt.plot(spl_x(pareto_kdiff_fory_numpy[:,0]), pareto_kdiff_fory_numpy[:,0], '--', pareto_bstray_fory_numpy, pareto_kdiff_fory_numpy, 'o')\n", + " # plt.show()\n", + " \n", + " \n", + " x_calc = torch.as_tensor(spl_x(kdiff_numpy)).cuda()\n", + " x_loss = bstray - x_calc\n", + " x_loss_relu = torch.nn.functional.relu(x_loss)\n", + " \n", + "\n", + " if len(pareto_bstray) > 3 and len(pareto_kdiff) > 3:\n", + " # combined_loss = torch.sum(y_loss_relu * x_loss_relu)\n", + " \n", + " combined_loss = torch.sum(x_loss_relu)\n", + " # combined_loss = torch.mean(bstray * kdiff)\n", + "\n", + " else: \n", + " combined_loss = torch.zeros(1, requires_grad=True)\n", + " print('No new pareto points found, used zero grad')\n", + "\n", + " # Push loss through the optimizer and update the GP model\n", + " try:\n", + " combined_loss.backward(retain_graph=True)\n", + " optimizer.step()\n", + " except Exception as e:\n", + " print(f\"Error on epoch {epoch} regarding\", e)\n", + " break\n", + "\n", + " \n", + " if epoch % 50 == 0:\n", + " \n", + " print(combined_loss)\n", + " plt.plot(pareto_bstray_numpy[:,0], spl_y(pareto_bstray_numpy[:,0]), '--', bstray_numpy, kdiff_numpy, 'o')\n", + " plt.plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + " plt.xlim([0,100])\n", + " plt.ylim([0,0.5])\n", + " plt.show()\n", + "# figure, axis = plt.subplots(2, 1, figsize=(10,10))\n", + "# plt.figure(figsize=(10, 5))\n", + " \n", + "# if normalize == True:\n", + "# axis[0].plot(normalized_pareto_bstray_numpy, normalized_pareto_kdiff_numpy)\n", + "# axis[0].scatter(x_calc.cpu().data.numpy(), normalized_kdiff_numpy)\n", + "# axis[0].scatter(normalized_bstray_numpy, normalized_kdiff_numpy)\n", + "# axis[0].set_xlim([0,1])\n", + "# axis[0].set_ylim([0,1])\n", + "# axis[0].set_xlabel(\"Bstray\")\n", + "# axis[0].set_ylabel(\"Kdiff %\")\n", + "# axis[0].set_title(f\"x_calc vs. designs, epoch: {epoch}\")\n", + " \n", + "# axis[1].plot(normalized_pareto_bstray_numpy, normalized_pareto_kdiff_numpy)\n", + "# axis[1].scatter(normalized_bstray_numpy, y_calc.cpu().data.numpy())\n", + "# axis[1].scatter(normalized_bstray_numpy, normalized_kdiff_numpy)\n", + "# axis[1].set_xlim([0,1])\n", + "# axis[1].set_ylim([0,1])\n", + "# axis[1].set_xlabel(\"Bstray\")\n", + "# axis[1].set_ylabel(\"Kdiff %\")\n", + "# axis[1].set_title(f\"y_calc vs. designs, epoch: {epoch}\")\n", + "# else:\n", + "# axis[0].plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# axis[0].scatter(x_calc.cpu().data.numpy(), kdiff_numpy)\n", + "# axis[0].scatter(bstray_numpy, kdiff_numpy)\n", + "# axis[0].set_xlim([0,100])\n", + "# axis[0].set_ylim([0,1])\n", + "# axis[0].set_xlabel(\"Bstray\")\n", + "# axis[0].set_ylabel(\"Kdiff %\")\n", + "# axis[0].set_title(f\"x_calc vs. designs, epoch: {epoch}\")\n", + " \n", + "# axis[1].plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# axis[1].scatter(bstray_numpy, y_calc.cpu().data.numpy())\n", + "# axis[1].scatter(bstray_numpy, kdiff_numpy)\n", + "# axis[1].set_xlim([0,100])\n", + "# axis[1].set_ylim([0,1])\n", + "# axis[1].set_xlabel(\"Bstray\")\n", + "# axis[1].set_ylabel(\"Kdiff %\")\n", + "# axis[1].set_title(f\"y_calc vs. designs, epoch: {epoch}\")\n", + " \n", + " \n", + " # plt.xlim([0,100])\n", + " # plt.ylim([0,0.5])\n", + " # plt.show()\n", + " #paretopoints graphing test\n", + " pareto_bstray_test, pareto_kdiff_test = oldgraph_pareto_frontier(bstray.cpu().detach().numpy(), kdiff.cpu().detach().numpy())\n", + " pareto_bstray_points2.append(pareto_bstray_test)\n", + " pareto_kdiff_points2.append(pareto_kdiff_test)\n", + "\n", + " # Store metrics for plotting or further logging\n", + " combined_losses.append(combined_loss.cpu().data.numpy())\n", + " kdiffs.append(torch.mean(kdiff).cpu().data.numpy())\n", + " bstrays.append(torch.mean(bstray).cpu().data.numpy())\n", + " kdiff_designs.append(kdiff.cpu().data.numpy())\n", + " bstray_designs.append(bstray.cpu().data.numpy())\n", + " \n", + " p_front_x.append(pareto_bstray)\n", + " p_front_y.append(pareto_kdiff)\n", + " pareto_bstray_points.append(pareto_bstray[:].cpu().detach().numpy())\n", + " pareto_kdiff_points.append(pareto_kdiff[:].cpu().detach().numpy())\n", + "\n", + "\n", + " print(f\"Epoch: {epoch:4d}\")\n", + " # print(f\" Combined Loss: {combined_losses[-1]:.10f}\")\n", + " print(f\" Kdiff : {100 * kdiffs[-1]:.4f}%\")\n", + " print(f\" Bstray : {bstrays[-1]:.4f}\")\n", + " print(f\" Pareto Kdiff : {100 * torch.mean(p_front_y[-1]).cpu().data.numpy():.4f}%\")\n", + " print(f\" Pareto Bstray : {torch.mean(p_front_x[-1]).cpu().data.numpy():.4f}\")\n", + "\n", + " # Save model for further training or testing\n", + " torch.save(gp_model, f\"./models/gp_model_{epoch:06}.pt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "4\n", + "7 7\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#scatter plot of final pareto points\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff Combined Final Frontier\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.5])\n", + "\n", + "print(len(pareto_bstray_points2[:]))\n", + "print(len(pareto_kdiff_points2[:]))\n", + "# print(pareto_bstray_points2[:])\n", + "final_x = []\n", + "for list in pareto_bstray_points2:\n", + " for item in list:\n", + " final_x.append(item)\n", + "\n", + "\n", + "final_y = []\n", + "for list in pareto_kdiff_points2:\n", + " for item in list:\n", + " final_y.append(item)\n", + "\n", + "final_x, final_y = oldgraph_pareto_frontier(final_x, final_y)\n", + "print(len(final_x),len(final_y))\n", + "\n", + "plt.scatter(final_x, final_y)\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "4\n", + "7 7\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#line plot of final pareto points\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff Combined Final Frontier\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points2[:]))\n", + "print(len(pareto_kdiff_points2[:]))\n", + "# print(pareto_bstray_points2[:])\n", + "final_x = []\n", + "for list in pareto_bstray_points2:\n", + " for item in list:\n", + " final_x.append(item)\n", + "\n", + "\n", + "final_y = []\n", + "for list in pareto_kdiff_points2:\n", + " for item in list:\n", + " final_y.append(item)\n", + "\n", + "final_x, final_y = oldgraph_pareto_frontier(final_x, final_y)\n", + "print(len(final_x),len(final_y))\n", + "\n", + "plt.plot(final_x, final_y)\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'\\nplt.figure(figsize=(10, 5))\\nplt.title(f\"Pareto Kdiff During Training\")\\nplt.xlabel(\"Epoch\")\\nplt.ylabel(\"Coupling %\")\\n\\nfor x in pareto_kdiff_points:\\n plt.plot(x)\\nplt.show()\\n'" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points), len(pareto_kdiff_points))\n", + "\n", + "while i < len(pareto_bstray_points):\n", + " plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()\n", + "\n", + "\n", + "'''\n", + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"Pareto Kdiff During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Coupling %\")\n", + "\n", + "for x in pareto_kdiff_points:\n", + " plt.plot(x)\n", + "plt.show()\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points), len(pareto_kdiff_points))\n", + "\n", + "while i < len(pareto_bstray_points):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "4 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "print(len(bstray_designs))\n", + "#colors = numpy.arange(len(bstray_designs))\n", + "colors = numpy.arange(1000)\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Bstray vs Kdiff During Training, Generated Designs\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(bstrays), len(kdiffs))\n", + "\n", + "while i < len(bstray_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " fig = plt.scatter(bstray_designs[i], kdiff_designs[i], label = f\"epoch: {i*100}\")\n", + " i+=1\n", + " \n", + "# plt.colorbar()\n", + "plt.legend()\n", + "# from matplotlib.patches import Rectangle\n", + "# someX, someY = 45, 0.5\n", + "# fig,ax = plt.subplots()\n", + "# currentAxis = plt.gca()\n", + "# currentAxis.add_patch(Rectangle((someX -0.1, someY-0.1), 0.2, 0.2, alpha=0.5, facecolor='red'))\n", + "#plt.savefig('Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(bstray_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " ax.scatter(bstray_designs[i], kdiff_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,100)\n", + "plt.ylim(0, 0.6)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"Bstray\", fontsize = 18)\n", + "plt.ylabel('Coupling %', fontsize = 18)\n", + "plt.suptitle(f\"Bstray vs Kdiff During Training, Generated Designs\" , fontsize = 20, y=1.01)\n", + "# plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle(( 0,0 ), 45, 0.15, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AvJmZXnfGSt316F4NDrMox0wQwACAhjjv5OU6NDCsnS/2hS6lKRDAAICGKOViSdKRITrgmSCAAQAN0VYP4OFy4EqaAwEMAGiItky9AyaAZ4IABgA0RCFHAM/GtAFsZl8ysz1mtnXMtuVmdquZba89HrewZQIAFru2bDVSOAc8MzPpgL8i6fIJ2z4k6TZ3P03SbbXfAQApVh+C7h+kA56JaQPY3e+StG/C5qsk3VB7foOkqxtbFgCg2XR25JWLI23bfSB0KU1hrueAV7n7bkmqPa5sXEkAgGZUymf0mtNW6Jatz8ndQ5ez6C34JCwz22xm3WbW3dPDGqEA0Mou37Baz/b266Gd+0OXsujNNYCfN7M1klR73DPVG919i7t3uXtXZ2fnHHcHAGgGl525SpnIdMvW50KXsujNNYC/K+kdtefvkPSdxpQDAGhmy4o5XXDq8bpl626Goacxk8uQbpR0t6TTzWynmV0n6eOS3mBm2yW9ofY7AADatGGNnnqhT9t2HwxdyqI2k1nQ17r7GnfPuvs6d/+iu7/g7pe6+2m1x4mzpAEAKXXZWasUmXTL1t2hS1nUWAkLANBQK9rzOv+U5ZwHngYBDABouCvOXqMdew5p+/MMQ0+FAAYANNwbz1otSXTBx0AAAwAabtWSNnWdfJy+/yvOA0+FAAYALIhNZ6/RI88d1BN7D4cuZVEigAEAC+LyDfVhaLrgyRDAAIAFsXZZQa84cZl+wHngSRHAAIAFs2nDaj20c7+e2dcXupRFhwAGACyYTbVh6B8+TBc8EQEMAFgwJx9f0plrljAbehIEMABgQV1x9mrd93Svdu/vD13KokIAAwAW1Kaz10iSfshkrHEIYADAgjq1s10vW9Wu7xPA4xDAAIAFt2nDGv3yyX3qOTgQupRFgwAGACy4TWevljuzoccigAEAC+70VR16yYoSi3KMQQADABacmenyDat19+MvaO8hhqElAhgAkJCrz1krSbruhm719g0GriY8AhgAkIiXrerQ599+nrbtPqC3bblHew4eCV1SUAQwACAxbzhzlb78B/9JT73Qp//8hXu0qze9i3MQwACARL36pSv0L3/429rV268v3PlY6HKCIYABAIk77+TjtHZZQS8cTu+5YAIYABBEKZ/R4YHh0GUEQwADAIIo5WMdHiiHLiMYAhgAEER7PqNDdMAAACSrlM/o8CABDABAojgHDABAAB0MQQMAkLxSPqMjQxUNlyuhSwmCAAYABFHKZyQptTOhCWAAQBDt+ViSdCilE7EIYABAEKMdMAEMAEBi6gGc1olYBDAAIIh2OmAAAJJXyhHAAAAkrqOtGsAHjxDAAAAkhklYAAAEUKpdhnR4kOuAAQBITD4TKxsbs6ABAEhamm/IQAADAIIp5dJ7QwYCGAAQTDsdMAAAySvlYzpgAACS1t6W1SHuhgQAQLLa8zFD0AAAJK2U4xzwnJjZX5jZVjN72Mze16CaAAApUcozC3rWzGyDpD+SdL6kV0i60sxOa1RhAIDWV58F7e6hS0ncfDrgl0u6x9373H1Y0p2S3tSYsgAAaVDKZ1Rx6chQJXQpiZtPAG+VdKGZHW9mRUlXSDqxMWUBANKgvbYe9MGBocCVJC8z1w+6+zYz+3tJt0o6JOlBSUcN5JvZZkmbJemkk06a6+4AAC1o9I5IZakjcDEJm9ckLHf/oruf6+4XStonafsk79ni7l3u3tXZ2Tmf3QEAWkx7im9JOOcOWJLMbKW77zGzkyS9WdIFjSkLAJAG9QBO40zoeQWwpG+a2fGShiS9291fbEBNAICUKNEBz427v7ZRhQAA0qeU4g6YlbAAAMG0j52ElTIEMAAgmFLtMqRDKbwMiQAGAARTytWHoOmAAQBITBSZirl03hGJAAYABFVfDzptCGAAQFDtKb0jEgEMAAiqRAcMAEDySvmYy5AAAEhaez6jg3TAAAAkiyFoAAACIIABAAiAWdAAAARQymU0MFzRcLkSupREEcAAgKDa29J5QwYCGAAQVHv9hgyD6RqGJoABAEGN3BP4CAEMAEBiRgI4ZROxCGAAQFDt+fo5YAIYAIDE1O8JTAADAJCgYq46CatvkFnQAAAkZiSAhwhgAAASU6gFcD+XIQEAkJxi7RwwQ9AAACQojkz5TKR+AhgAgGQVczEdMAAASSvmMgQwAABJK+Ri9Q8xCQsAgEQVczF3QwIAIGmFbMwkLAAAklbMxepjCBoAgGQxCQsAgAAKOYagAQBIHNcBAwAQAB0wAAABlHIZDZYrGi5XQpeSGAIYABBcGm9JSAADAIIbvSUhAQwAQGJGOmACGACA5BSy1XsCHx5Iz2IcBDAAILh6B9zPOWAAAJLDEDQAAAGMTsJiCBoAgMQUc9VzwHTAAAAkiCFoAAAC4DpgAAACKGbpgAEASFwmjpTLROobYhLWjJjZ+83sYTPbamY3mllbowoDAKRLMWV3RJpzAJvZWkl/LqnL3TdIiiW9rVGFAQDSpZhN1z2B5zsEnZFUMLOMpKKkXfMvCQCQRoVcrD6uA56euz8r6ZOSnpa0W9J+d//RxPeZ2WYz6zaz7p6enrlXCgBoacVchg54JszsOElXSTpF0gmSSmb29onvc/ct7t7l7l2dnZ1zrxQA0NKqHTABPBOvl/SEu/e4+5Ckb0l6VWPKAgCkDZOwZu5pSa80s6KZmaRLJW1rTFkAgLQpcg54Ztz955JuknSfpF/V/taWBtUFAEiZQjaTqg44M58Pu/tfS/rrBtUCAEixUj5WH/cDBgAgWUzCAgAggGI2o8HhisoVD11KIghgAMCiMHpLwnRMxCKAAQCLQtpuSUgAAwAWhXoHfJgABgAgOQxBAwAQQCFXvTKWIWgAABI02gETwAAAJKaQJYABAEhcvQPuH+IcMAAAiSnlq+eA6YABAEgQ1wEDABBAkXPAAAAkLxNHysURAQwAQNIKuVj9LMQBAECyirmYpSgBAEhatQMmgAEASFQxF7MWNAAASStmM0zCAgAgaYVcrP4hAhgAgESV8jEdMAAASStkM0zCAgAgaUzCAgAggGoA0wEDAJCoQi7WwHBF5YqHLmXBEcAAgEVj9J7Ard8FE8AAgEWjkKvdE3ig9c8DE8AAgEUjTbckJIABAItGfQiaAAYAIEGFkXPADEEDAJCYYv0cMB0wAADJYQgaAIAARi5DIoABAEgOQ9AAAARQGBmCZhIWAACJYQgaAIAAsnGkbGzqYylKAACSVcjGLEUJAEDSirkMk7AAAEhaMRczBA0AQNIKuZhJWAAAJK2Yi7kMCQCApBVzGTpgAACSVu2ACWAAABJVIIABAEheMRern1nQUzOz083sgTE/B8zsfQ2sDQCQQtXrgFt/ElZmrh90999I2ihJZhZLelbSzY0pCwCQVoVsrCNDFVUqriiy0OUsmEYNQV8q6TF3f6pBfw8AkFL1GzK0+mIcjQrgt0m6sUF/CwCQYrlMNZqGhiuBK1lY8w5gM8tJ+l1J/3eK1zebWbeZdff09Mx3dwCAFhfXhp3L7oErWViN6IA3SbrP3Z+f7EV33+LuXe7e1dnZ2YDdAQBaWWTVAK5UCODpXCuGnwEADUIHPANmVpT0Bknfakw5AIC0i2sdcLnFO+A5X4YkSe7eJ+n4BtUCAMDIpUeV1p6DxUpYAIDFJa4lE0PQAAAkKErJEDQBDABYVOqTsCp0wAAAJCctk7AIYADAolKfhEUAAwCQoHoHzBA0AAAJiumAAQBIXsQkLAAAkjc6CStwIQuMAAYALCq1BpghaAAAklQfgnaGoAEASA53QwIAIACWogQAIACWogQAIABmQQMAEEBUvx0hQ9AAACSHIWgAAALgbkgAAATAUpQAAARABwwAQADcDQkAgAAYggYAIACuAwYAIICR64DpgAEASE69A65wDhgAgOQwCQsAgADMmIQFAEDiWIoSAIAAmAUNAEAA9VnQdMAAACSIpSgBAAiAWdAAAARgZjJjCBoAgMTFZnTAAAAkLYqMpSgBAEhabMZSlAAAJC2OjOuAAQBIWsQkLAAAklftgAlgAAASFTMJCwCA5EVMwgIAIHkMQQMAEEBkDEEDAJC4KBJD0AAAJC02U4vnLwEMAFh8WIpyGma2zMxuMrNHzGybmV3QqMIAAOmVhqUoM/P8/Gck/cDd32pmOUnFBtQEAEi5NMyCnnMAm9kSSRdK+gNJcvdBSYONKQsAkGaRGUtRHsNLJPVI+rKZ3W9m/2RmpQbVBQBIsTR0wPMJ4IykcyV9zt3PkXRY0ocmvsnMNptZt5l19/T0zGN3AIC0qE7CCl3FwppPAO+UtNPdf177/SZVA3kcd9/i7l3u3tXZ2TmP3QEA0iI2rgOekrs/J+kZMzu9tulSSb9uSFUAgFRLwxD0fGdBv1fS12ozoB+X9M75lwQASLs0LEU5rwB29wckdTWmFAAAquLINDhcCV3GgmIlLADAosP9gAEACID7AQMAEAAdMAAAAURmKrf2KWACGACw+ERcBwwAQPLiiLWgAQBIHPcDBgAggDTcD5gABgAsOsyCBgAggOp1wKGrWFgEMABg0YkjtfzNGAhgAMCiwxA0AAABsBQlAAAB0AEDABBAdSlKAhgAgETFEUPQAAAkjiFoAAAC4DpgAAACiCPRAQMAkLSYSVgAACTPzCS19j2BCWAAwKITR9UAbuVhaAIYALDo1AO4QgADAJCcaGQIOnAhC4gABgAsOnEtnRiCBgAgQfUOuJVnQhPAAIBFZ+QcMAEMAEBymAUNAEAAEdcBAwCQPDpgAAACiJmEBQBA8qKI64ABAEgc1wEDABAA1wEDABAAa0EDABAAk7AAAAjACGAAAJLHEDQAAAGMzIKmAwYAIDkjS1G2bv4SwACAxYchaAAAAmAWNAAAAUTcDxgAgORxNyQAAAJgKUoAAAJgEhYAAAGMTsIKXMgCysznw2b2pKSDksqSht29qxFFAQDSLUrBQhzzCuCaS9x9bwP+DgAAkhiCBgAgCK4Dnp5L+pGZ3Wtmmyd7g5ltNrNuM+vu6emZ5+4AAGkQ0QFP69Xufq6kTZLebWYXTnyDu29x9y537+rs7Jzn7gAAaUAHPA1331V73CPpZknnN6IoAEC6jSzEQQAfzcxKZtZRfy7pMklbG1UYACC9ag1wSw9Bz2cW9CpJN1v1KGUk/au7/6AhVQEAUm20Aw5cyAKacwC7++OSXtHAWgAAkDR6DpgOGACABNVnQf/bL59R95P7FEWm2ExxZOOe138iM8WRatsjxVH1b0RmMlXXljaTirmMrtp4gkr58PEXvgIAACZYVsjqtaet0M4X+3Xf070qV1wV93GPwxVXpeIqu6tSqd45aSaTtu5+/AX9w7XnJPCvODYCGACw6GTiSF+97rfn9NlKPZzd5S65XBWvDmd/6WdP6NM/3q7Xv3ylrtq4tsFVzw4rYQEAWkoUmXKZSG3ZWIVcrGIuo/Z8RkvasnrPJS/VOSct00e/vVW7evvD1hl07wAAJCgTR/pf12zUUNn1gZseVCXgdcYEMAAgVdavKOmjV56p/7fjBd1w95PB6iCAAQCpc+35J+p1Z6zUx295RNufPxikBgIYAJA6ZqaPv+VslfIZvf8bD2hwOPkVPwhgAEAqrexo09+96WxtffaArr9te+L7J4ABAKl1+YbV+r3z1umzd+zQvU/tS3TfBDAAINU+9jtn6oRlBb3/3x7U4YHhxPZLAAMAUq2jLatPXbNRz7zYp7/93q8T2y8BDABIvfNPWa4/vvBUHRmqJHYPYpaiBABA0gffePrITSCSQAcMAICUaPhKBDAAAEEQwAAABEAAAwAQAAEMAEAABDAAAAEQwAAABEAAAwAQAAEMAEAABDAAAAEQwAAABEAAAwAQAAEMAEAABDAAAAEQwAAABEAAAwAQAAEMAEAABDAAAAGYuye3M7MeSU8ltsOFs0LS3tBFNDGO39xx7OaH4zc/HL/ZO9ndOyd7IdEAbhVm1u3uXaHraFYcv7nj2M0Px29+OH6NxRA0AAABEMAAAARAAM/NltAFNDmO39xx7OaH4zc/HL8G4hwwAAAB0AEDABAAATwJM3vSzH5lZg+YWXdt23Izu9XMttcejxvz/g+b2Q4z+42ZvTFc5WGY2ZfMbI+ZbR2zbdbHy8zOqx33HWZ2vZlZ0v+WEKY4fn9jZs/WvoMPmNkVY17j+NWY2YlmdruZbTOzh83sL2rb+f7NwDGOH9+/JLg7PxN+JD0pacWEbf9D0odqzz8k6e9rz8+U9KCkvKRTJD0mKQ79b0j4eF0o6VxJW+dzvCT9QtIFkkzSLZI2hf63BTx+fyPpLyd5L8dv/PFYI+nc2vMOSY/WjhHfv/kdP75/CfzQAc/cVZJuqD2/QdLVY7Z/3d0H3P0JSTsknZ98eeG4+12S9k3YPKvjZWZrJC1x97u9+r/mfx7zmZY2xfGbCsdvDHff7e731Z4flLRN0lrx/ZuRYxy/qXD8GogAnpxL+pGZ3Wtmm2vbVrn7bqn6pZW0srZ9raRnxnx2p479BU6L2R6vtbXnE7en2XvM7KHaEHV9CJXjNwUzWy/pHEk/F9+/WZtw/CS+fwuOAJ7cq939XEmbJL3bzC48xnsnO8/B1PKpTXW8OI7jfU7SqZI2Stot6X/WtnP8JmFm7ZK+Kel97n7gWG+dZBvH7+jjx/cvAQTwJNx9V+1xj6SbVR1Sfr42zKLa457a23dKOnHMx9dJ2pVctYvWbI/XztrzidtTyd2fd/eyu1ck/R+Nntbg+E1gZllVw+Nr7v6t2ma+fzM02fHj+5cMAngCMyuZWUf9uaTLJG2V9F1J76i97R2SvlN7/l1JbzOzvJmdIuk0VScjpN2sjldtmPCgmb2yNnvyv475TOrUw6PmTap+ByWO3zi1f+sXJW1z90+NeYnv3wxMdfz4/iUk9CywxfYj6SWqzvJ7UNLDkj5S2368pNskba89Lh/zmY+oOhvwN0rhzD9JN6o6TDWk6n8JXzeX4yWpS9X/oT8m6R9VWyim1X+mOH5flfQrSQ+p+n96azh+kx6716g61PmQpAdqP1fw/Zv38eP7l8APK2EBABAAQ9AAAARAAAMAEAABDABAAAQwAAABEMAAAARAAAMAEAABDABAAAQwAAAB/H/Sj8Z0HC2A+gAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "noise = generate_noise(shape=(10_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "pareto_bstray, pareto_kdiff = pareto_frontier(bstray, kdiff)\n", + "pareto_kdiff = pareto_kdiff * 100\n", + "\n", + "plt.plot(pareto_bstray.detach().cpu(), pareto_kdiff.detach().cpu())" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "noise = generate_noise(shape=(25_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "plt.scatter(bstray.detach().cpu(), kdiff.detach().cpu())\n", + "plt.title(\"Randomly generated points after GP reshaping\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Kdiff\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Noise 0\n", + "[ 0.2751342 -0.6740954 0.61911607 0.18069482 -0.686888 -0.11200416\n", + " -0.8588847 -0.19239724 0.9543762 0.41499412]\n", + "\n", + "Generated Parameters 0\n", + "[0.53727657 0.53903234 0.4000629 0.3960704 0.5231776 0.4970947\n", + " 0.40224743 0.54973644 0.5314659 0.63126224]\n", + "\n", + "Noise 1\n", + "[ 0.9397385 -0.40799916 -0.3760078 0.09982812 -0.9387262 -0.34254456\n", + " 0.6540235 0.9822897 -0.21698594 0.17750084]\n", + "\n", + "Generated Parameters 1\n", + "[0.05589174 0.9487347 0.00816341 0.47298834 0.07641255 0.02717948\n", + " 0.78230375 0.9008925 0.6321077 0.5526524 ]\n", + "\n", + "Noise 2\n", + "[-0.53828275 -0.82220745 -0.11271048 -0.9610523 0.555905 -0.06995106\n", + " -0.04305661 0.25442874 -0.44234514 -0.65141225]\n", + "\n", + "Generated Parameters 2\n", + "[0.9389827 0.04833657 0.9940819 0.6344562 0.9242728 0.9649931\n", + " 0.2515366 0.06269799 0.2831032 0.32247937]\n", + "\n" + ] + } + ], + "source": [ + "noise = generate_noise(shape=(3, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "noise = noise.cpu().numpy()\n", + "gp = gp.cpu().detach().numpy()\n", + "\n", + "for i in range(3):\n", + " print(f\"Noise {i}\")\n", + " print(noise[i])\n", + " print()\n", + " print(f\"Generated Parameters {i}\")\n", + " print(gp[i])\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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0HABAiqAIA5KorCKsyVOL1L1dS9113rCg4wAAUgjTkUAS/WTOUn2yda+evvo4tS3ICzoOACCF0AkDkuSfK7fqD++t1eUn9dWJAzoHHQcAkGKSWoSZ2Vlm9rGZrTSzW+sZc6qZzTezxWb2VjLzAM1l975KTZlWpP5dWuuWs4YGHQcAkIKSNh1pZiFJv5R0hqRiSR+a2Sx3XxIzpr2kX0k6y93XmVnXZOUBmtPds5bosz3lev66E1WQFwo6DgAgBSWzEzZG0kp3X+3uFZKelTSu1phLJM1w93WS5O6bk5gHaBavLP5Uz88r1vWnDtDI3u2DjgMASFHJLMJ6Slof87o4uizWYEkdzOxNM/vIzC5NYh4g6baWlOu2GQt1TI8jdOPYQUHHAQCksGReHVnXLcG9js//kqTTJbWU9J6Zve/uyw/YkNk1kq6RpD59+iQhKnD43F23z1yoPfuq9PTVI5Wfy3UvAID6JfOvRLGk3jGve0naWMeYl919r7tvlfS2pMLaG3L3x919lLuP6tKlS9ICA4dj5r836JXFn2nyVwdryJFtg44DAEhxySzCPpQ0yMz6mVm+pImSZtUa86KkU8ws18xaSTpO0tIkZgKSYuPOMn1/1mKN7ttBV53SP+g4AIA0kLTpSHevMrMbJL0iKSTpSXdfbGbXRtc/6u5LzexlSQskVUt6wt0XJSsTkAzV1a6bpy9QuNr1wPhChXL4cm4AQOOSesd8d58taXatZY/Wev2/kv43mTmAZHrqg7X6x8qt+tHXj9VRnVoHHQcAkCY4cxg4DJ9s3asfz16qrwzuokvGcNEIACB+FGHAIQpXuyZPna/8UI7uv3CEzJiGBADEjy/wBg7RY2+v0rx1O/XwxJE6sl1B0HEAAGmGThhwCJZu2q2fv7pcZw8/UucX9gg6DgAgDVGEAU1UXhXWd5+br3Yt8/XDC4YzDQkAOCRMRwJN9PBrK7Ts0z367bdHqWPr/KDjAADSFJ0woAk+WrtDj761ShNG9dLpR3cLOg4AII1RhAFxKq2o0k3TitS9XUvdee6woOMAANIc05FAnO6fs0yfbN2rp68+Tm0L8oKOAwBIc3TCgDj8Y8VW/eG9tbr8pL46cUDnoOMAADIARRjQiF1llZoyvUgDurTWLWcNDToOACBDMB0JNOLulxZr855yzbjuRBXkhYKOAwDIEHTCgAa8svhTzZi3Qd85dYAKe7cPOg4AIINQhAH12FpSrttmLNQxPY7QDWMHBR0HAJBhmI4E6uDuun3mQu3ZV6Wnrx6p/Fz+fwUAkFj8ZQHqMGPeBr2y+DPddOZgDTmybdBxAAAZiCIMqGXjzjL9YNZijenbUVee3D/oOACADEURBsSornbdPH2Bwu56YHyhQjl8OTcAIDkowoAYT32wVv9YuVW3n3O0+nRqFXQcAEAGowgDoj7Zulc/nr1UXxncRZeM6RN0HABAhqMIAyRVhas1eep8tcgN6f4LR8iMaUgAQHJxiwpA0mNvr9a8dTv18MSROrJdQdBxAABZgE4Yst6Sjbv10GvLdc7w7jq/sEfQcQAAWYIiDFmtvCqsSVPnq13LfN17wbFMQwIAmg3TkchqD7+2Qss+3aPffnuUOrbODzoOACCL0AlD1vpo7Q49+tYqTRjVS6cf3S3oOACALEMRhqxUWlGlyVPnq3u7lrrz3GFBxwEAZCGmI5GVfjJnmdZsK9UzVx+vtgV5QccBAGQhOmHIOv9YsVV/fG+trjipn04Y0CnoOACALEURhqyyq6xSU6YXaUCX1rr5rCFBxwEAZDGmI5FV7n5psTbvKdeM605UQV4o6DgAgCxGJwxZ4+VFn2rGvA36zmkDVdi7fdBxAABZjiIMWWFrSblun7lQx/Y8QjeOHRh0HAAAmI5E5nN33TZjofaUV+mZCSOVF+L/PQAAweOvETLejHkb9Lcln+mmrw7W4G5tg44DAIAkijBkuI07y/SDWYs1pm9HXXly/6DjAACwH0UYMlZ1tWvK9CKF3fXA+EKFcvhybgBA6qAIQ8b60/tr9c+V23THOcPUp1OroOMAAHAAijBkpNVbSnTfnKU6dUgXXTymd9BxAAA4CEUYMk5VuFqTpxWpRW5I9184QmZMQwIAUg+3qEDGeezt1fr3up16eOJIdTuiIOg4AADUiU4YMsqSjbv10GvLdc7w7jq/sEfQcQAAqBdFGDJGeVVYk6bOV/tW+br3gmOZhgQApDSmI5ExHnpthZZ9ukdPXjZKHVvnBx0HAIAG0QlDRvho7XY99tYqXTSqt8YO7RZ0HAAAGpXUIszMzjKzj81spZndWsf6U81sl5nNjz7uSmYeZKbSiipNnlqk7u1a6o5zjw46DgAAcUnadKSZhST9UtIZkoolfWhms9x9Sa2h77j7ucnKgcz3kznLtGZbqZ65+ni1LcgLOg4AAHFJZidsjKSV7r7a3SskPStpXBI/D1nonRVb9Mf31urKk/vphAGdgo4DAEDcklmE9ZS0PuZ1cXRZbSeYWZGZzTGzY5KYBxlmV1mlpkxboIFd22jKmUOCjgMAQJMk8+rIuu4P4LVez5N0lLuXmNnZkl6QNOigDZldI+kaSerTp0+CYyJd3T1rsbaUlOvxS7+kgrxQ0HEAAGiSZHbCiiXFfmlfL0kbYwe4+253L4k+ny0pz8w6196Quz/u7qPcfVSXLl2SGBnp4uVFn2rGvzfoO6cN1Ihe7YOOAwBAkyWzCPtQ0iAz62dm+ZImSpoVO8DMjrToHTXNbEw0z7YkZkIG2FpSrttnLtSxPY/QjWMHBh0HAIBDkrTpSHevMrMbJL0iKSTpSXdfbGbXRtc/Kumbkq4zsypJZZImunvtKUtgP3fX92Ys1J7yKj0zYaTyQtzqDgCQnpJ6x/zoFOPsWssejXn+C0m/SGYGZJbn523Qq0s+0+1nH63B3doGHQcAgENGGwFpY8POMt09a7HG9O2oK07uF3QcAAAOS9xFmJmdZ2YfRO9sf30yQwG1VVe7bp5epLC7HhhfqFAOX84NAEhv9RZhZlZYa9F/Sjpe0hclXZfMUEBtf3p/rf65cpvuOGeY+nRqFXQcAAAOW0PnhF0fvXLxLnf/VJEbr/5IUrVq3WoCSKbVW0p035ylOnVIF108pnfjbwAAIA3UW4S5+39Fu2GPmdlcSXdKOlFSK0n3NlM+ZLmqcLUmTS1Si9yQ7r9whKJ3NAEAIO01eE6Yuxe5+zhJ8xW5x1d3d5/l7uXNEQ547O3Vmr9+p+694Fh1O6Ig6DgAACRMQ+eEXWtm/zazeZJaSzpLUgcze8XMTmm2hMhaSzbu1kOvLdc5I7rr/MIeQccBACChGuqEXe/uX1DkZPwp7l7l7o8ocuf7rzdLOmSt8qqwJk2dr/at8vXDcccGHQcAgIRr6MT8DWZ2r6SWkpbVLHT3HZImJTsYsttDr63Qsk/36MnLRqlD6/yg4wAAkHANFWHjJJ0pqVLSq80TB5A+Wrtdj721ShNH99bYod2CjgMAQFI0dHVkhaSXmjELoNKKKk2aWqQe7VvqjnOHBR0HAICkSep3RwJNdd/sZVq3vVTPXH282rTg8AQAZC6+OxIp450VW/Sn99fqipP66fj+nYKOAwBAUjXaajCzjnUs3uPulUnIgyy1q7RSU6Yt0MCubTTlzCFBxwEAIOni6YTNk7RF0nJJK6LPPzGzeWb2pWSGQ/b4wUuLtaWkXA9OKFRBXijoOAAAJF08RdjLks52987u3knS1yRNlXS9pF8lMxyyw8uLNmnmvzfohtMGakSv9kHHAQCgWcRThI1y91dqXrj73yR92d3fl9QiacmQFbbsKddtMxdpeM92umHswKDjAADQbOK5/Gy7md0i6dno64sk7TCzkKTqpCVDxnN33TZzoUrKq/TghELlhbhOBACQPeL5q3eJpF6SXpD0oqQ+0WUhSROSlgwZ7/l5G/Tqks805atDNKhb26DjAADQrBrthLn7Vkk31rN6ZWLjIFts2Fmmu2ct1pi+HXXFyf2CjgMAQLOL5xYVgyXdJKlv7Hh3H5u8WMhk1dWuKdOKVO2uB8YXKpRjQUcCAKDZxXNO2DRJj0p6QlI4uXGQDf743hq9u2qb7vvGcPXp1CroOAAABCKeIqzK3X+d9CTICqu3lOgnLy/TqUO6aOLo3kHHAQAgMPGcmP+SmV1vZt3NrGPNI+nJkHGqwtWaNLVILXJDuv/CETJjGhIAkL3i6YR9O/rvlJhlLql/4uMgkz329mrNX79Tj1z8BXU7oiDoOAAABCqeqyO5dA2HbfHGXXroteU6Z0R3nV/YI+g4AAAErt4izMzGuvsbZvaNuta7+4zkxUImKa8Ka9JzRWrfKl8/HHds0HEAAEgJDXXCviLpDUnn1bHOJVGEIS4/f3WFPv5sj3532Wh1aJ0fdBwAAFJCvUWYu38/+u/lzRcHmeajtdv1+NurNHF0b502tGvQcQAASBkNTUdOauiN7v5g4uMgk+wtr9KkqUXq0b6l7jh3WNBxAABIKQ1NR/Jlfjgs981ZqnXbS/XM1cerTYt4LsQFACB7NDQdeXdzBkFmeXv5Fj31/jpddXI/Hd+/U9BxAABIOY3erNXM+pvZS2a2xcw2m9mLZsY9wlCvXaWVunn6Ag3s2kY3nTkk6DgAAKSkeO6Y/7SkqZK6S+qhyHdJPpPMUEhvP3hpsbaUlOvBCYUqyAsFHQcAgJQUTxFm7v4nd6+KPp5S5BYVwEFeXrRJM/+9QTecNlAjerUPOg4AACkrnrOl/25mt0p6VpHi6yJJf635/kh3357EfEgjW/aU67aZizS8ZzvdMHZg0HEAAEhp8RRhF0X//a9ay68Q3yGJKHfX92YsVEl5lR6cUKi8UDxNVgAAshffHYmEmP5RsV5b+pnuOOdoDerG3U0AAGhMo0WYmV1a13J3/2Pi4yAdbdhZpnteWqIx/TrqipOo2QEAiEc805GjY54XSDpd0jxJFGFQdbVryrQiVbvrZ+MLlZNjQUcCACAtxDMdeWPsazNrJ+lPSUuEtPLH99bo3VXbdN83hqt3x1ZBxwEAIG0cytnTpZIGJToI0s+qLSW6b84ynTqkiyaO7h10HAAA0ko854S9pM/vCxaSdLQiN29FFqsKV2vS1CK1zA/ppxeOkBnTkAAANEU854Q9EPO8StJady9OUh6kiUffWqWi9Tv1fxd/QV2PKAg6DgAAaafR6Uh3f0vSMkltJXWQVJHsUEhtizfu0sOvr9C5I7rrvMIeQccBACAtxfMF3hMk/UvSeEkTJH1gZt+MZ+NmdpaZfWxmK6N33a9v3GgzC8e7XQSnvCqsSc8VqX2rfN077tig4wAAkLbimY68XdJod98sSWbWRdJrkqY39CYzC0n6paQzJBVL+tDMZrn7kjrG3S/plabHR3N78NXl+vizPfrdZaPVoXV+0HEAAEhb8VwdmVNTgEVti/N9YyStdPfV7l6hyHdPjqtj3I2Snpe0uY51SCFz12zX42+v1sVjeuu0oV2DjgMAQFqLpxP2spm9IumZ6OuLJM2J4309Ja2PeV0s6bjYAWbWU9LXJY3VgTeFRYrZW16lydOK1KtDS91+zrCg4wAAkPbiuVnrFDP7hqSTJZmkx919ZhzbruueBV7r9UOSbnH3cEO3ODCzayRdI0l9+vSJ46ORaPfNWap120v17NXHq02LeGp3AADQkHr/mprZQEnd3P2f7j5D0ozo8i+b2QB3X9XItoslxd7Bs5ekjbXGjJL0bLQA6yzpbDOrcvcXYge5++OSHpekUaNG1S7kkGRvL9+ip95fp6tO7qfj+ncKOg4AABmhoXO7HpK0p47lpdF1jflQ0iAz62dm+ZImSpoVO8Dd+7l7X3fvq8iJ/tfXLsAQrF2llbp5+gIN7NpGN505JOg4AABkjIbmlfq6+4LaC919rpn1bWzD7l5lZjcoctVjSNKT7r7YzK6Nrn/0EDOjGX1/1iJtLSnXby4dpYK8UNBxAADIGA0VYQ3dBr1lPBt399mSZtdaVmfx5e6XxbNNNJ85Czfphfkb9T//MUjDe7ULOg4AABmloenID83s6toLzexKSR8lLxJSwZY95bpt5kIN79lO3zltYNBxAADIOA11wv5H0kwz+5Y+L7pGScpX5LYSyFDuru/NWKi9FWE9OKFQeaF4bgsHAACaot4izN0/k3SimZ0mqeb7af7q7m80SzIEZvpHxXpt6We645yjNahb26DjAACQkeK5T9jfJf29GbIgBRTvKNXdLy3RmH4ddcVJ/YKOAwBAxmKeCftVV7umTFsgd9fPxhcqJ6f+G+gCAIDDQxGG/f7w3hq9t3qb7jx3mHp3bBV0HAAAMhpFGCRJq7aU6Cdzlmns0K66aHTvxt8AAAAOC0UYVBWu1qSpRWqZH9JPvjFcDX2PJwAASAy+iRn69ZurVLR+p/7v4i+o6xEN3aMXAAAkCp2wLLdowy49/PoKnVfYQ+cV9gg6DgAAWYMiLIuVV4U1eWqROrbO173jjgk6DgAAWYXpyCz24KvL9fFne/S7y0arfav8oOMAAJBV6IRlqblrtuvxt1fr4jG9ddrQrkHHAQAg61CEZaG95VWaPK1IvTq01O3nDAs6DgAAWYnpyCz049lLtW57qZ69+ni1acEhAABAEOiEZZm3lm/Rnz9Yp6tO7qfj+ncKOg4AAFmLIiyL7Cqt1M3TizSoaxtN/uqQoOMAAJDVmIvKIt+ftUjbSir0xKWjVZAXCjoOAABZjU5Ylpi9cJNemL9RN4wdqOG92gUdBwCArEcRlgU279mn22cu1PCe7fSd0wYGHQcAAIgiLOO5u26bsVB7K8L6+UWFygvxKwcAIBXwFznDTfuoWK8t3aybzxyigV3bBh0HAABEUYRlsOIdpbrnpSU6rl9HXXFSv6DjAACAGBRhGaq62jVl2gK5ux4YX6icHAs6EgAAiEERlqF+/+4avbd6m+48d5h6d2wVdBwAAFALRVgGWrm5RPe/vExjh3bVRaN7Bx0HAADUgSIsw1SFqzV5WpFa5of0k28MlxnTkAAApCLumJ9hfv3mKhWt36lfXPIFdT2iIOg4AACgHnTCMsiiDbv08OsrdF5hD507okfQcQAAQAMowjLEvsqwJk8tUsfW+bp33DFBxwEAAI1gOjJD/PzV5fr4sz363WWj1b5VftBxAABAI+iEZYAP12zX4++s1sVj+ui0oV2DjgMAAOJAEZbm9pZXafLUIvXq0FK3n3N00HEAAECcmI5Mcz+evVTrd5TquWtOUJsW/DoBAEgXdMLS2FvLt+jPH6zTVSf305h+HYOOAwAAmoAiLE3tKq3UzdOLNKhrG03+6pCg4wAAgCZi/ipN3TVrkbaVVOiJS0erIC8UdBwAANBEdMLS0OyFm/Ti/I26cewgDe/VLug4AADgEFCEpZnNe/bp9pkLNaJXO11/2oCg4wAAgENEEZZG3F23zViovRVhPTihUHkhfn0AAKQr/oqnkWlzi/Xa0s26+cwhGti1bdBxAADAYaAISxPrt5fqnr8s0XH9OuqKk/oFHQcAABwmirA0UF3tmjK9SJL0wPhC5eRYwIkAAMDhoghLA79/d43eX71dd557tHp3bBV0HAAAkAAUYSlu5eYS3f/yMo0d2lUTRvUOOg4AAEiQpBZhZnaWmX1sZivN7NY61o8zswVmNt/M5prZycnMk26qwtWaPHW+WuaH9JNvDJcZ05AAAGSKpN0x38xCkn4p6QxJxZI+NLNZ7r4kZtjrkma5u5vZCElTJQ1NVqZ086s3V6moeJd+cckX1PWIgqDjAACABEpmJ2yMpJXuvtrdKyQ9K2lc7AB3L3F3j75sLckFSdKiDbv0yOsrdH5hD507okfQcQAAQIIlswjrKWl9zOvi6LIDmNnXzWyZpL9KuiKJedLGvsqwJk2dr46t83XPuGOCjgMAAJIgmUVYXScwHdTpcveZ7j5U0gWS7q1zQ2bXRM8Zm7tly5bEpkxBP391uZZ/VqL7vzlC7VvlBx0HAAAkQTKLsGJJsZfz9ZK0sb7B7v62pAFm1rmOdY+7+yh3H9WlS5fEJ00hH67ZrsffWa2Lx/TRaUO6Bh0HAAAkSTKLsA8lDTKzfmaWL2mipFmxA8xsoEUv+TOzL0rKl7QtiZlS2t7yKk2eWqReHVrq9nOODjoOAABIoqRdHenuVWZ2g6RXJIUkPenui83s2uj6RyVdKOlSM6uUVCbpopgT9bPOj2Yv1fodpXrumhPUpkXSfjUAACAFJPUvvbvPljS71rJHY57fL+n+ZGZIF29+vFlPf7BO13y5v8b06xh0HAAAkGTcMT8F7Cqt1C3PL9Dgbm006YzBQccBAADNgDmvFHDXrEXaVlKh3357tAryQkHHAQAAzYBOWMD+umCTXpy/UTeOHaRje7YLOg4AAGgmFGEB2rxnn+54YaEKe7XT9acNCDoOAABoRhRhAXF3fe/5hSqtCOtnE0YqL8SvAgCAbMJf/oBMm1us15dt1s1nDdXArm2CjgMAAJoZRVgA1m8v1T1/WaLj+3fU5Sf2DToOAAAIAEVYM6uudk2ZXiRJ+t9vFionp66v2AQAAJmOIqyZ/e7dNXp/9Xbdee7R6t2xVdBxAABAQCjCmtHKzSX66cvLdPrQrpowqnfjbwAAABmLIqyZVIWrNXnqfLXKD+m+C4cr+r3lAAAgS3HH/GbyqzdXqah4l355yRfVtW1B0HEAAEDA6IQ1g0UbdumR11fo/MIeOmdE96DjAACAFEARlmT7KsP67nPz1bF1vu4Zd0zQcQAAQIpgOjLJHnx1uVZsLtHvLx+t9q3yg44DAABSBJ2wJPrXJ9v1m3dW65Lj+ujUIV2DjgMAAFIIRViS7C2v0k3TitS7QyvdfvbRQccBAAAphunIJPnR7KVav6NUz11zglq3YDcDAIAD0QlLgr9/vFlPf7BOV5/SX2P6dQw6DgAASEEUYQm2s7RCt0xfoMHd2mjSGYODjgMAAFIU82QJdteLi7V9b4WevGy0CvJCQccBAAApik5YAv11wSbNKtqo/z59kI7t2S7oOAAAIIVRhCXI5t37dMcLC1XYq52uP3VA0HEAAECKowhLAHfXrTMWqrQirJ9NGKncELsVAAA0jGohAabOXa83lm3WzWcN1cCubYKOAwAA0gBF2GFav71U97y0RMf376jLT+wbdBwAAJAmKMIOQ3W166ZpRTIzPTC+UDk5FnQkAACQJijCDsPv3l2jDz7ZrrvOHaZeHVoFHQcAAKQRirBDtHLzHv305WU6fWhXjR/VK+g4AAAgzVCEHYLKcLUmTS1Sq/yQ7rtwuMyYhgQAAE3DHfMPwa/+vkoLinfpV9/6orq2LQg6DgAASEN0wppoYfEu/d8bKzRuZA+dPbx70HEAAECaoghrgn2VYU2aOl+d2uTrnvOPDToOAABIY0xHNsGDry7Xis0l+v3lo9WuVV7QcQAAQBqjExanD1Zv02/eWa1LjuujU4d0DToOAABIcxRhcSgpr9JN04vUu0Mr3X720UHHAQAAGYDpyDj86K9LVbyjTFP/6wS1bsEuAwAAh49OWCP+/vFmPfOvdbrmlP4a3bdj0HEAAECGoAhrwM7SCt0yfYEGd2uj754xOOg4AAAggzC31oA7X1ys7Xsr9ORlo1WQFwo6DgAAyCB0wurxlwUb9VLRRv336YN0bM92QccBAAAZhiKsDpt379MdLyxSYe/2uv7UAUHHAQAAGYgirBZ3160zFqqsIqyfjS9UbohdBAAAEo8Ko5b3V2/XG8s265azhmpg1zZBxwEAABmKE/NrOWFAJz1z9fE6rh+3owAAAMlDEVaHEwZ0CjoCAADIcEmdjjSzs8zsYzNbaWa31rH+W2a2IPp418wKk5kHAAAgVSStCDOzkKRfSvqapGGSLjazYbWGfSLpK+4+QtK9kh5PVh4AAIBUksxO2BhJK919tbtXSHpW0rjYAe7+rrvviL58X1KvJOYBAABIGckswnpKWh/zuji6rD5XSppT1wozu8bM5prZ3C1btiQwIgAAQDCSWYRZHcu8zoFmpylShN1S13p3f9zdR7n7qC5duiQwIgAAQDCSeXVksaTeMa97SdpYe5CZjZD0hKSvufu2JOYBAABIGcnshH0oaZCZ9TOzfEkTJc2KHWBmfSTNkPSf7r48iVkAAABSStI6Ye5eZWY3SHpFUkjSk+6+2Myuja5/VNJdkjpJ+pWZSVKVu49KViYAAIBUYe51nqaVskaNGuVz584NOgYAAECjzOyj+hpMfHckAABAANKuE2ZmWyStbYaP6ixpazN8TrZgfyYe+zSx2J+Jxz5NLPZn4jXHPj3K3eu8tUPaFWHNxczmcn5a4rA/E499mljsz8RjnyYW+zPxgt6nTEcCAAAEgCIMAAAgABRh9ePLxBOL/Zl47NPEYn8mHvs0sdifiRfoPuWcMAAAgADQCQMAAAhAVhdhZnaWmX1sZivN7NY61puZPRJdv8DMvhhEznQSxz491cx2mdn86OOuIHKmCzN70sw2m9mietZzjDZBHPuT47MJzKy3mf3dzJaa2WIz+391jOEYbYI49ynHaROYWYGZ/cvMiqL79O46xgRznLp7Vj4U+SqlVZL6S8qXVCRpWK0xZ0uaI8kkHS/pg6Bzp/Ijzn16qqS/BJ01XR6Svizpi5IW1bOeYzSx+5Pjs2n7s7ukL0aft5W0nP+ONss+5Tht2j41SW2iz/MkfSDp+FpjAjlOs7kTNkbSSndf7e4Vkp6VNK7WmHGS/ugR70tqb2bdmztoGolnn6IJ3P1tSdsbGMIx2gRx7E80gbtvcvd50ed7JC2V1LPWMI7RJohzn6IJosdeSfRlXvRR+4T4QI7TbC7CekpaH/O6WAcf6PGMwefi3V8nRNvCc8zsmOaJlrE4RhOP4/MQmFlfSV9QpMsQi2P0EDWwTyWO0yYxs5CZzZe0WdKr7p4Sx2lusj8ghVkdy2pXxvGMwefi2V/zFPkKhxIzO1vSC5IGJTtYBuMYTSyOz0NgZm0kPS/pf9x9d+3VdbyFY7QRjexTjtMmcvewpJFm1l7STDM71t1jzw0N5DjN5k5YsaTeMa97Sdp4CGPwuUb3l7vvrmkLu/tsSXlm1rn5ImYcjtEE4vhsOjPLU6RY+LO7z6hjCMdoEzW2TzlOD52775T0pqSzaq0K5DjN5iLsQ0mDzKyfmeVLmihpVq0xsyRdGr1q4nhJu9x9U3MHTSON7lMzO9LMLPp8jCLH4LZmT5o5OEYTiOOzaaL76reSlrr7g/UM4xhtgnj2Kcdp05hZl2gHTGbWUtJ/SFpWa1ggx2nWTke6e5WZ3SDpFUWu6nvS3Reb2bXR9Y9Kmq3IFRMrJZVKujyovOkgzn36TUnXmVmVpDJJEz16aQoOZmbPKHIlVGczK5b0fUVOKuUYPQRx7E+Oz6Y5SdJ/SloYPd9Gkm6T1EfiGD1E8exTjtOm6S7pD2YWUqRgneruf0mFv/fcMR8AACAA2TwdCQAAEBiKMAAAgABQhAEAAASAIgwAACAAFGEAAAABoAgDkFHMLGxm82MetyZw233NbFHjIwGgcVl7nzAAGavM3UcGHQIAGkMnDEBWMLM1Zna/mf0r+hgYXX6Umb1uZgui//aJLu9mZjOjX5JcZGYnRjcVMrPfmNliM/tb9A7cANBkFGEAMk3LWtORF8Ws2+3uYyT9QtJD0WW/kPRHdx8h6c+SHokuf0TSW+5eKOmLkhZHlw+S9Et3P0bSTkkXJvWnAZCxuGM+gIxiZiXu3qaO5WskjXX31dEvSP7U3TuZ2VZJ3d29Mrp8k7t3NrMtknq5e3nMNvpKetXdB0Vf3yIpz91/2Aw/GoAMQycMQDbxep7XN6Yu5THPw+LcWgCHiCIMQDa5KObf96LP35U0Mfr8W5L+EX3+uqTrJMnMQmZ2RHOFBJAd+D84AJmmpZnNj3n9srvX3KaihZl9oMj/gF4cXfbfkp40symStki6PLr8/0l63MyuVKTjdZ2kTckODyB7cE4YgKwQPSdslLtvDToLAEhMRwIAAASCThgAAEAA6IQBAAAEgCIMAAAgABRhAAAAAaAIAwAACABFGAAAQAAowgAAAALw/wONs2KncifNygAAAABJRU5ErkJggg==\n", 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7uf6RhexV+RIREUk5Kl4p5ILhfbn/ipG8s34H1z+6iH01Kl8iIiKpRMUrxVw0oi+/vKyA4rXbmPJoMftr6oKOJCIiIjFS8UpBEwrz+cVlBcz/eCtTf1dM1QGVLxERkVSg4pWiLh3Zj7u/PIK3P6zknx9frPIlIiKSAlS8UthXi/rzs0tP5s33K7h51jtU16p8iYiIJDMVrxR3xZgB3HnpcF5fVc43nlxCTW19028SERGRQKh4tQFXjT2Gn0wYxqvvbeFbs5dwoE7lS0REJBmpeLUR15w6kDsuPomXVmzm20+VUKvyJSIiknTSgg4greeG04+lrt6584WVhEPGry4vJByyoGOJiIhIlIpXGzP1jEHU1jt3vbSKtJBxz1cLVL5ERESShIpXG3TTWcdRV1/Pva+8Tyhk3P3lEYRUvkRERAKn4tVGfeOcIdTWO/f9+QPSQsZPLz1Z5UtERCRgKl5t2C1fGEJdvfPr19cQChl3ThyOmcqXiIhIUFS82jAz47vnHc+BOufBNz8kLWT8ePwwlS8REZGAqHi1cWbGbRcMpa6+nof++jHhkHHHxSepfImIiARAxasdMDP+7cITqa13Hnl7LWmhyGuVLxERkcRS8WonzCIjXXX1Hh35CnHbBUNVvkRERBJIxasdMYuc41VX/49zvr53/vEqXyIiIgmi4tXOmBn/MWE4dfXOA39ZQ1rY+Pa5xwcdS0REpF1Q8WqHQtH7ejW8z9c3zhkSdCwREZE2T8WrnQqFjLu+PIK6eufeV94nHApx01nHBR1LRESkTVPxasfCIePerxZQ1+C3HaeeMSjoWCIiIm2Wilc7Fw4Zv7wsUr7ufGEl4ZBxw+nHBh1LRESkTVLxEtLCIe67opC6eucnz71HWti45tSBQccSERFpc0JBB5DkkB4Ocf+kkZx7Ym/umLuCWQvWBR1JRESkzVHxkk9kpIX4zVUjOeeEXvzgmeX8ftH6oCOJiIi0KXEvXmYWNrMlZvZcg2nfNLPVZrbCzO6OdwaJXWZamN9eNYozj8/l+396lz8Ubwg6koiISJuRiBGvW4CVB1+Y2dnABGCEuw8D7k1ABmmGrPQw/3P1KZw+OIdb/7iMZ5ZsDDqSiIhImxDX4mVm/YCLgBkNJt8E/NzdqwHcvTyeGaRlstLDTL+6iFMH9eR7Ty9lbklp0JFERERSXrxHvO4DbgXqG0w7Hvi8mS0wszfNbHScM0gLdcgIM+PaIkYP7MF3fl/C88s2BR1JREQkpcWteJnZxUC5uy8+ZFYa0B0YB/wr8LQ18ivNZjbNzIrNrLiioiJeMaUJHTPSmHndaE45pjvfemoJLy1X+RIREWmpeI54nQaMN7O1wFPAOWb2BLAR+JNHLCQyGpZz6Jvdfbq7F7l7UW5ubhxjSlM6ZabxyPVjKOjXlW88uYRXVmwOOpKIiEhKilvxcvfb3b2fuw8ErgBed/fJwBzgHAAzOx7IACrjlUNaR+fMNB69YQzD8rvy9Sff4bWVW4KOJCIiknKCuI/XTGCQmS0nMhJ2rbt7ADmkmbpkpfO7G8ZwQp8u3PTEO7yxWtdFiIiINEdCipe7v+HuF0ef17j7ZHcf7u6j3P31RGSQ1tG1QzqPTxnDkN6dmfb4Yv76gc6/ExERiZXuXC/N1q1jBk9MGcugnE7c+Fgxf1ujI8UiIiKxUPGSFuneKYNZN47lmJ4dmfJYMfM/2hp0JBERkaSn4iUt1rNzJrNuHEd+9w7c8OgiFq3dFnQkERGRpKbiJUclNzuTJ6eOpU/XLK6buZDF67YHHUlERCRpqXjJUeuVncXsqePo1SWLa2cuZMl6lS8REZHGqHhJq+jdJYsnp46lR6cMrpm5kGUbdwQdSUREJOmoeEmr6du1A7OnjaNrh3Sufnghy0t3Bh1JREQkqah4SavK79aB2VPH0TkzjckPL+C9sl1BRxIREUkaKl7S6vr36MiTU8fSIT3M5IcXsHrz7qAjiYiIJAUVL4mLY3p24smp40gPG1c+NJ8Ptqh8iYiIqHhJ3BybEylfoZAx6aEFfFixJ+hIIiIigVLxkrg6Lrczs6eOBZxJ0+fzceXeoCOJiIgERsVL4m5wr2xm3TiO2vpI+Vq3VeVLRETaJxUvSYihfbKZdeNYqmrrmDR9Phu27Qs6koiISMKpeEnCnNi3C09MGcvemjomPTSfjdtVvkREpH1R8ZKEGp7flSemjGXn/gNc+dACNu3cH3QkERGRhFHxkoQ7uV9XHp8ylu17a5g0fT5bdlUFHUlERCQhVLwkEIX9u/HoDWOo2F3NpOnzKVf5EhGRdkDFSwJzyjHdefSGMWzeVcWVMxZQsbs66EgiIiJxpeIlgRo9sAePXDea0u37uWrGfLbuUfkSEZG2S8VLAjd2UE8evq6I9dv2cdWMBWzbWxN0JBERkbhQ8ZKk8LnjcphxzWg+rtzL5BkL2LFP5UtERNoeFS9JGqcPyWH6NUWsKd/D1Q8vZOf+A0FHEhERaVUqXpJUzjw+lwevHsWqzbu4ZuZCdlWpfImISNuh4iVJ55wTevPbq05hRelOrp25kN0qXyIi0kaoeElSOu+k3jxw5SiWbdzJ9Y8sYm91bdCRREREjpqKlyStC4b34f4rRrJkww6uf3QR+2pUvkREJLWpeElSu2hEX351eSHFa7cx5dFi9tfUBR1JRESkxeJevMwsbGZLzOy5Q6b/i5m5meXEO4OktvEFefzisgLmf7yVqb8rpuqAypeIiKSmRIx43QKsbDjBzPoD5wHrE/D50gZcOrIf93ylgLc/rOSfH1+s8iUiIikprsXLzPoBFwEzDpn1K+BWwOP5+dK2fOWUfvz8n07mzfcruHnWO1TXqnyJiEhqial4mdm9ZjasBeu/j0jBqm+wrvFAqbsvbcH6pJ27fPQAfnrpyby+qpyvz1pCTW19028SERFJErGOeK0CppvZAjP7mpl1beoNZnYxUO7uixtM6wj8ALgjhvdPM7NiMyuuqKiIMaa0B1eOHcB/TBjGn1du4Vuzl3CgTuVLRERSQ0zFy91nuPtpwDXAQGCZmT1pZmcf4W2nAePNbC3wFHAO8DhwLLA0Or0f8I6Z9WnkM6e7e5G7F+Xm5jbjK0l7cPWpA/nhJSfx0orNfPupEmpVvkREJAWkxbqgmYWBE6KPSmAp8F0z+2d3v+LQ5d39duD26HvPAv7F3b98yDrXAkXuXtnC/NKOXX/asdTVO//5/EpCIeNXlxWQFtYdUkREJHnFVLzM7JfAeOA14KfuvjA66y4zWx2vcCJNufHzg6itd37+4irSQsa9Xy0gHLKgY4mIiDQq1hGv5cD/c/d9jcwb09Sb3f0N4I1Gpg+M8fNFDutrZx5HXb1zz8urCYeMu788gpDKl4iIJKGYipe7zzSz7mY2HMhqMP3/3H1n3NKJxOjrZw+mts751Z/fJy1k/PTSk1W+REQk6cR6qPFGIjdC7QeUAOOAvxM5YV4kKdxy7hDq6uu5//U1hELGnROHY6byJSIiySPWQ423AKOB+e5+tpmdAPw4frFEWuY75x3PgXrnv9/4kLSQ8ePxw1S+REQkacRavKrcvcrMMLNMd19lZkPjmkykBcyMW784lLp6Z/r/fUQ4ZNxx8UkqXyIikhRiLV4bzawbMAd41cy2A2XxCiVyNMyM2790ArV1zsy3PyZsxg8uOlHlS0REAhfryfWXRp/+yMz+AnQFXopbKpGjZGb8+8UnUldfz4y3PiYtHOK2C4aqfImISKCaLF5mFgKWuftwAHd/M+6pRFqBmfGj8cOoc+fBNyPnfH3v/ONVvkREJDBNFi93rzezpWY2wN3XJyKUSGsxM34yfji1dc4Df1lDWtj49rnHBx1LRETaqVjP8eoLrDCzhcDegxPdfXxcUom0olD0vl619c59f/6AsBnf/MKQoGOJiEg7FGvx0q0jJKWFQsZdXx5Bfb3zi1ffJxw2bj5rcNCxRESknYm1eF3o7rc1nGBmdwE630tSRjhk3PPVAurcuful1aSHQkw9Y1DQsUREpB0JxbjceY1M+1JrBhFJhHDI+MVXC7hoRF/ufGElM9/6OOhIIiLSjhxxxMvMbgJuBgaZ2bIGs7KBt+MZTCRe0sIh7ru8kPp65yfPvUda2Ljm1IFBxxIRkXagqUONTwIvAj8Dvt9g+m533xa3VCJxlh4Ocf+kkdw86x3umLuCkBmTxx0TdCwREWnjmjrUeAAodfdJ7r4OyAL+CTgr3sFE4i09HOI3V47iCyf04v/NWc5TC3W3FBERia+mitdLwEAAMxsM/B0YBHzdzH4e32gi8ZeRFuK3k0dx1tBcbn/mXf5QvCHoSCIi0oY1Vby6u/sH0efXArPd/ZtETqy/KK7JRBIkMy3Mg5NP4fTBOdz6x2U8s2Rj0JFERKSNaqp4eYPn5wCvArh7DVAfr1AiiZaVHuaha4o4dVBPvvf0UuaWlAYdSURE2qCmitcyM7vXzL4DDAZeATCzbvEOJpJoWelhHr52NKMH9uA7vy/huWVlQUcSEZE2pqniNRWoJHKe1/nuvi86/STg3jjmEglEh4wwM68bzSnHdOeWp0p48d1NQUcSEZE2xNy96aUavsEsB9jqzX3jUSgqKvLi4uJEfZwIe6pruebhBSzbuJPfXjWK84f1CTqSiIikCDNb7O5Fjc074oiXmY0zszfM7E9mNtLMlgPLgS1mdkE8wookg86ZaTx2wxiG53fl60++w2srtwQdSURE2oCmDjU+APwUmA28Dtzo7n2AM4jcVFWkzcrOSuexG8ZwYt8u3PTEO7yxujzoSCIikuKaKl5p7v6Ku/8B2Ozu8wHcfVX8o4kEr2uHdB6/YSxDendm2uOL+esHFUFHEhGRFNZU8Wp4y4j9h8xL2DleIkHq2jGdJ6aM5bjcztz4WDF/W1MZdCQREUlRTRWvAjPbZWa7gRHR5wdfn5yAfCJJoXunDJ6YMoaBPTtxw2OLmP/R1qAjiYhICjpi8XL3sLt3cfdsd0+LPj/4Oj1RIUWSQc/OmcyaOpb+3Ttyw6OLWLRWvxMvIiLN09SIl4g0kBMtX326ZnHdzIUsXrc96EgiIpJC4l68zCxsZkvM7Lno63vMbJWZLTOzZ3QXfEk1vbKzmD11HL26ZHHtzIUsWa/yJSIisUnEiNctwMoGr18Fhrv7COB94PYEZBBpVb27ZPHk1LH06JTBNTMXsmzjjqAjiYhICohr8TKzfsBFwIyD06K3p6iNvpwP9ItnBpF46du1A7OnjaNrh3Qmz1jA8tKdQUcSEZEkF+8Rr/uAW/n0bSkaugF4Mc4ZROImv1sHZk8dR3ZWOpMfXsB7ZbuCjiQiIkksbsXLzC4Gyt198WHm/wCoBWYdZv40Mys2s+KKCt20UpJX/x4dmT11HB3Sw0x+eAGrN+8OOpKIiCSpeI54nQaMN7O1wFPAOWb2BICZXQtcDFx1uB/bdvfp7l7k7kW5ublxjCly9Ab0jJSv9LBx5UPz+WCLypeIiHxW3IqXu9/u7v3cfSBwBfC6u0+O/rj2bcB4d98Xr88XSbSBOZ2YPXUcoZAx6aEFrCnfE3QkERFJMkHcx+sBIBt41cxKzOzBADKIxMWg3M7MnjoOcK58aD4fV+4NOpKIiCSRhBQvd3/D3S+OPh/s7v3dvTD6+FoiMogkyuBenXly6jhq651J0+ezbqvKl4iIROjO9SJxcHzvbGbdOJbq2jomTZ/Phm06qi4iIipeInFzYt8uPHHjWPbW1DHpofls3K7yJSLS3ql4icTRsLyuzLpxLLv2H+DKhxZQtmN/0JFERCRAKl4icTY8vyuPTxnL9r01XPnQfDbvrAo6koiIBETFSyQBCvp347EpY6jcEylf5btUvkRE2iMVL5EEGTWgO49eP5rNu6qY9NB8KnZXBx1JREQSTMVLJIGKBvbgketGU7ajiqtmzGfrHpUvEZH2RMVLJMHGDurJzOtGs37bPq6asYBte2uCjiQiIgmi4iUSgFOP68nD147m48q9TJ6xgB37VL5ERNoDFS+RgJw2OIfp1xSxpnwPkx9ewM59B4KOJCIicabiJRKgM4/P5X+uPoXVm3dzzcwF7KpS+RIRactUvEQCdvYJvfjvq07hvU27uHbmQnarfImItFkqXiJJ4NyTevPrSaN4d+NOrn9kEXura4OOJCIicaDiJZIkLhjeh/snjWTJhh1c/+gi9tWofImItDUqXiJJ5MKT+3Lf5YUUr93GlEeL2V9TF3QkERFpRSpeIknmkoI8fnlZIfM/3srU3xVTdUDlS0SkrVDxEklCE0fmc89XCnj7w0r++fHFKl8iIm2EipdIkvrKKf24659G8Ob7Fdw86x2qa1W+RERSnYqXSBK7bHR/fnrpyby+qpybnniHNeV7go4kIiJHIS3oACJyZFeOHUCdOz+cu5zXV5UzPL8LEwryuaQgjz5ds4KOJyIizWDuHnSGJhUVFXlxcXHQMUQCVb6rinlLy5i3tIxlG3diBuOO7cnEkXlcMLwvXTukBx1RREQAM1vs7kWNzlPxEkk9H1bsYV5JGXNLSlm7dR8Z4RBnn5DLxMJ8zj6hF1np4aAjioi0WypeIm2Uu7Ns407mlJTy7NJNVO6pJjszjQuG92FCYT6nHteTcMiCjiki0q6oeIm0A7V19fz9o63MWVLGyys2s6e6ltzsTC4ZkcfEkXmcnN8VM5UwEZF4U/ESaWeqDtTx2spy5paU8sbqCmrq6hmU04nxhXlMKMzn2JxOQUcUEWmzVLxE2rGd+w7w4vJNzCkpZcHH23CHgn5dmVCYz8UFfemVrSsjRURak4qXiACwaed+nl1axtySMlaU7SJkcNrgHMYX5HHB8D5kZ+nKSBGRo6XiJSKfsaZ8N3OWlDF3aSkbtu0nMy3EuSf2ZnxhHmcNzSUzTVdGioi0RKDFy8zCQDFQ6u4Xm1kP4PfAQGAtcJm7bz/SOlS8ROLH3Xln/Q7mlZTy3LJNbN1bQ5esNC48uS8TCvMZe2wPQroyUkQkZkEXr+8CRUCXaPG6G9jm7j83s+8D3d39tiOtQ8VLJDEO1NXz9ppK5pZErozcV1NHny5ZjC/MY3xBHsPyuujKSBGRJgRWvMysH/AYcCfw3WjxWg2c5e6bzKwv8Ia7Dz3SelS8RBJvf00dr67cwrzolZG19c7gXp2ZUBC5MnJAz45BRxQRSUpBFq//BX4GZAP/Ei1eO9y9W4Nltrt790beOw2YBjBgwIBT1q1bF7ecInJk2/fW8MLyTcxdUsbCtdsAGDWgGxMK87loRF9yOmcGnFBEJHkEUrzM7GLgQne/2czOopnFqyGNeIkkj43b9/Hs0k3MLSll1ebdhEPG6YNzmDgyj/NO6kPnzLSgI4qIBCqo4vUz4GqgFsgCugB/AkajQ40ibcKqzbuYW1LGvJIySnfsJys9xHkn9WFCQR5nHJ9LRloo6IgiIgkX+O0kDhnxugfY2uDk+h7ufuuR3q/iJZLc6uudxeu3M7eklOeXbWL7vgN065jOhSf3ZWJhPkXHdNeVkSLSbiRb8eoJPA0MANYDX3X3bUd6v4qXSOqoqa3nrTUVzFlSxqvvbWH/gTryu3XgkoLIb0ae0KdL0BFFROIq8OJ1tFS8RFLT3upaXn1vC3NKSvnrB5XU1TtDe2czYWTk9hT9uuvKSBFpe1S8RCRwW/dU8/y7m5hbUsbidZF7Jo8e2J3xhflcdHJfenTKCDihiEjrUPESkaSyYds+5i0tY86SUj4o30NayDjj+FwmFOZx3km96ZihKyNFJHWpeIlIUnJ3Vm7azdySUuYtLWPTzio6ZoQ5/6TeTBiZz+mDc0gP68pIEUktKl4ikvTq652Fa7d9cmXkrqpaenbK4KIRfZlQmMeoAd31c0UikhJUvEQkpVTX1vHm6grmLi3jz+9tobq2nn7dOzChMI+JhfkM6Z0ddEQRkcNS8RKRlLW76gCvrIhcGfn2mkrqHU7s24WJhXlcUpBHXrcOQUcUEfkUFS8RaRPKd1fx/LJNzCkpY+mGHZjBmIE9mDgyny8N70O3jroyUkSCp+IlIm3O2sq9zC0pY25JKR9V7iU9bJw1tBcTCvM498TeZKWHg44oIu2UipeItFnuzvLSXZ9cGVm+u5rOmWmcP6w3Ewvz+dxxPUnTlZEikkAqXiLSLtTVOws+2sqcklJeXL6Z3VW15HTO5OLolZGF/bvpykgRiTsVLxFpd6oO1PHG6nLmlpTx2qpyamrrGdizI+ML85lQmMdxuZ2DjigibZSKl4i0azv3H+Dl5ZuZu7SUv324FXc4Ob8rE6JXRvbukhV0RBFpQ1S8RESituyq4tmlZcwtKePd0p2YwamDejKxMJ8LTu5Dl6z0oCOKSIpT8RIRacSHFXs+uTJy3dZ9ZKSFOGdoLyaOzOOsob10ZaSItIiKl4jIEbg7SzfuZG5JKc8u3UTlnmqys9L40vA+TCjMZ9ygnoRDOilfRGKj4iUiEqPaunr+/tFW5iwp4+UVm9lTXUuv7EwuKYj8XNHw/C66MlJEjkjFS0SkBaoO1PHaynLmlJTyxupyDtQ5g3I7MaEgcmXkwJxOQUcUkSSk4iUicpR27jvAC8s3MbeklAUfb8MdCvp3Y2JhHheN6EuvbF0ZKSIRKl4iIq2obMd+nltWxpwlZby3aRchg9MG5zChMJ8vDutNtq6MFGnXVLxEROLkgy27I1dGLi1lw7b9ZKaFOPfE3kwozOPMoblkpunKSJH2RsVLRCTO3J131u9gbkkpzy3bxLa9NXTtkM6FJ0eujBwzsAchXRkp0i6oeImIJNCBunreWlPJvJLIlZH7auro2zWL8QV5jC/M46S+ujJSpC1T8RIRCci+mlpefW8L80rKePP9CmrrnSG9OjOhMI8Jhfn079Ex6Igi0spUvEREksC2vTW88G7kyshFa7cDMGpANyaOzOeik/vSs3NmwAlFpDWoeImIJJmN2/cxb2kZ80rKWLV5N+GQ8fkhOUwszOe8k3rTKTMt6Igi0kIqXiIiSWzV5l3MLYmUsNId++mQHua8kyJXRp5xfC7p4VDQEUWkGVS8RERSQH29s3j9duYsKeX5dzexY98BundM58KT+zJxZD6nDOiuKyNFUkAgxcvMsoD/AzKBNOB/3f2HZlYIPAhkAbXAze6+8EjrUvESkfampraev35QwZySMl59bzNVB+rJ79aB8YV5TCjM44Q+XYKOKCKHEVTxMqCTu+8xs3TgLeAW4CfAr9z9RTO7ELjV3c860rpUvESkPdtbXcsr721mbkkZf/2gkrp654Q+2YwvzGN8QR79uuvKSJFkcqTiFbezNz3S6PZEX6ZHHx59HPxfta5AWbwyiIi0BZ0y07h0ZD8uHdmPyj3VvPDuJuYsKeXul1Zz90urGT2wOxMKI1dGdu+UEXRcETmCuJ7jZWZhYDEwGPiNu99mZicCLwMGhIDPufu6I61HI14iIp+1fus+5i0tZU5JGWvK95AWMs48PpcJI/M578TedMjQzxWJBCHwk+vNrBvwDPBNYBrwprv/0cwuA6a5+7mNvGdadFkGDBhwyrp1R+xmIiLtlrvz3qZ/XBm5eVcVHTPCfHFYH8YX5vH5wTmk6cpIkYQJvHhFQ/wQ2Av8O9DN3T16HthOdz/iWaIa8RIRiU19vbPg423MW1rK88s2sauqlp6dMrh4RF/GF+YzakA3/VyRSJwFco6XmeUCB9x9h5l1AM4F7iJyTteZwBvAOcAH8cogItLehELGqcf15NTjevKj8cN4c3UFc0vKeGrRBh77+zr69+jAhIJ8Jo7MY3Cv7KDjirQ78bw1cl/gseh5XiHgaXd/zsx2AP9lZmlAFdHDiSIi0roy08KcP6wP5w/rw+6qA7y8YgtzS0r57RtreOAvazipbxcmjszjkoI8+nbtEHRckXZBN1AVEWlnyndX8fyyTcwpKWPphh2YwdhjezCxMJ8vDe9L147pQUcUSWlJcY7X0VDxEhGJj48r9zKvpIy5JaV8VLmXjHCIs4bmMqEwny+c2IusdF0ZKdJcKl4iInJE7s7y0l3MKSnl2aVllO+upnNmGl8c1oeJI/M4dVBPXRkpEiMVLxERiVldvTP/o63MLSnlxXc3s7u6lpzOmVxS0JcJhfkU9OuqKyNFjkDFS0REWqTqQB1/WVXO3JIyXl9VTk1dPcfmdGJ8QeQ3Iwfldg46okjSUfESEZGjtnP/AV5evpk5JaX8/aOtuMOIfl0ZXxD5zcheXbKCjiiSFFS8RESkVW3ZVcWzS8uYW1LGu6U7CRmMHtiDQbmdyOmcSU7nTHKzM6PPM8jNzqRzZpoOUUq7oOIlIiJxs6Z8D/OWlvHayi1s2VXF1r01NPZPS2ZaKFLEsjPJ7ZxJbnbGIQVNJU3aBhUvERFJmNq6erbtq6Fydw2Ve6qp3FNNxe7q6POaT71uqqQdLGWNlbTI8wyVNEk6gfxkkIiItE9p4RC9srPold30OV9NlbSK3dVs3L6Pkg3bm1XScqMjayppkmxUvEREJDBHU9L+UdAOPq+JqaR9etTs0yWt4XlpKmkSDypeIiKSElpa0ir2VFN5mJK2ZP12tu1rvKRlpYcanHsWKWW5nTM+OUctRyVNWkDFS0RE2pzWKGkND3k2p6R9csizkZKWm51Jp4ywSlo7puIlIiLtWktK2sFRs8ZK2vqt+3hnXfNL2ieHOVXS2jQVLxERkRgdbUn79GHP2Evap85LO6SkHRxRU0lLDSpeIiIicdDskrY3eqizkZJWsae6ZSXt4HlpKmlJQ8VLREQkYGnhEL26ZMX0s0uHlrRPDnM2s6R9+sa1ny5pDUfUVNJal4qXiIhICmmtklYRvcqzqZLWIT1MTvQGtoc9L00lLWYqXiIiIm1US0vaJ+elNVLSFq/bzvYYStqnruY8pKTlZmfSKbN9VpD2+a1FRETkU1qjpDW8qe26rfsobklJa3heWhssaW3nm4iIiEhCtKSklR/m9zor91SzduteitdtZ9vemkbXcbCk5Xb+7C03Dj0vLdlLWnKnExERkZTWnJJ24OBI2iG/13mwoLW0pOV+8mcGIwd0p3cMWeJFxUtERESSQno4RO8uWTEVo6ZKWsXuxkvaA1eO5OIRefH8Gkek4iUiIiIpp6UlrV/3DglId3gqXiIiItKmNaekxVso6AAiIiIi7YWKl4iIiEiCqHiJiIiIJIiKl4iIiEiCxK14mVmWmS00s6VmtsLMftxg3jfNbHV0+t3xyiAiIiKSTOJ5VWM1cI677zGzdOAtM3sR6ABMAEa4e7WZ9YpjBhEREZGkEbfi5e4O7Im+TI8+HLgJ+Lm7V0eXK49XBhEREZFkEtdzvMwsbGYlQDnwqrsvAI4HPm9mC8zsTTMbHc8MIiIiIskirsXL3evcvRDoB4wxs+FERtm6A+OAfwWeNjM79L1mNs3Mis2suKKiIp4xRURERBIiIVc1uvsO4A3gAmAj8CePWAjUAzmNvGe6uxe5e1Fubm4iYoqIiIjElUVOxYrDis1ygQPuvsPMOgCvAHcRGf3Kc/c7zOx44DVggB8hiJlVAOviEvQfcoDKOH9Ge6Nt2rq0PVuftmnr0vZsfdqmrS8R2/QYd2901CieVzX2BR4zszCRkbWn3f05M8sAZprZcqAGuPZIpQvgcOFbk5kVu3tRvD+nPdE2bV3anq1P27R1aXu2Pm3T1hf0No3nVY3LgJGNTK8BJsfrc0VERESSle5cLyIiIpIgKl7/MD3oAG2Qtmnr0vZsfdqmrUvbs/Vpm7a+QLdp3E6uFxEREZFP04iXiIiISIK0u+JlZhdEf6B7jZl9v5H5Zmb3R+cvM7NRQeRMFTFsz7PMbKeZlUQfdwSRM5WY2UwzK49e+dvYfO2jzRDD9tQ+2gxm1t/M/mJmK81shZnd0sgy2kebIcZtqv00RmaWZWYLzWxpdHv+uJFlgttH3b3dPIAw8CEwCMgAlgInHbLMhcCLgBG5u/6CoHMn6yPG7XkW8FzQWVPpAZwBjAKWH2a+9tHW3Z7aR5u3PfsCo6LPs4H39d/RhGxT7aexb08DOkefpwMLgHGHLBPYPtreRrzGAGvc/SOP3NbiKWDCIctMAH7nEfOBbmbWN9FBU0Qs21Oayd3/D9h2hEW0jzZDDNtTmsHdN7n7O9Hnu4GVQP4hi2kfbYYYt6nEKLrf7Ym+TI8+Dj2hPbB9tL0Vr3xgQ4PXG/nszh3LMhIR67Y6NTrk+6KZDUtMtDZN+2jr0z7aAmY2kMj9GhccMkv7aAsdYZuC9tOYmVnYzEqAcuBVd0+afTSed65PRp/5MW4+24JjWUYiYtlW7xD56YQ9ZnYhMAcYEu9gbZz20dalfbQFzKwz8Efg2+6+69DZjbxF+2gTmtim2k+bwd3rgEIz6wY8Y2bD3b3heZ6B7aPtbcRrI9C/wet+QFkLlpGIJreVu+86OOTr7i8A6Wb2mR9Fl2bRPtqKtI82n5mlEykIs9z9T40son20mZraptpPW8bddwBvABccMiuwfbS9Fa9FwBAzOzb6m5FXAPMOWWYecE30iodxwE5335TooCmiye1pZn3MzKLPxxDZ57YmPGnbon20FWkfbZ7otnoYWOnuvzzMYtpHmyGWbar9NHZmlhsd6cLMOgDnAqsOWSywfbRdHWp091oz+wbwMpEr8ma6+woz+1p0/oPAC0SudlgD7AOuDypvsotxe34FuMnMaoH9wBUevaREGmdms4lcwZRjZhuBHxI5OVT7aAvEsD21jzbPacDVwLvRc2gA/g0YANpHWyiWbar9NHZ9gcfMLEykoD7t7s8ly7/1unO9iIiISIK0t0ONIiIiIoFR8RIRERFJEBUvERERkQRR8RIRERFJEBUvERERkQRR8RKRlGdmdWZW0uDx/VZc90AzW970kiIiTWtX9/ESkTZrv7sXBh1CRKQpGvESkTbLzNaa2V1mtjD6GBydfoyZvWZmy6J/DohO721mz0R/iHipmX0uuqqwmT1kZivM7JXo3bBFRJpNxUtE2oIOhxxqvLzBvF3uPgZ4ALgvOu0B4HfuPgKYBdwfnX4/8Ka7FwCjgBXR6UOA37j7MGAH8OW4fhsRabN053oRSXlmtsfdOzcyfS1wjrt/FP0R4s3u3tPMKoG+7n4gOn2Tu+eYWQXQz92rG6xjIPCquw+Jvr4NSHf3/0zAVxORNkYjXiLS1vlhnh9umcZUN3heh86PFZEWUvESkbbu8gZ//j36/G/AFdHnVwFvRZ+/BtwEYGZhM+uSqJAi0j7o/9pEpC3oYGYlDV6/5O4HbymRaWYLiPyP5qTotG8BM83sX4EK4Pro9FuA6WY2hcjI1k3ApniHF5H2Q+d4iUibFT3Hq8jdK4POIiICOtQoIiIikjAa8RIRERFJEI14iYiIiCSIipeIiIhIgqh4iYiIiCSIipeIiIhIgqh4iYiIiCSIipeIiIhIgvx/JwIhRIqG73UAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"KDiff Error During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Coupling %\")\n", + "\n", + "plt.plot(numpy.arange(len(kdiffs)), kdiffs)\n", + "plt.show()\n", + "\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"BStray Error During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"BStray\")\n", + "\n", + "plt.plot(numpy.arange(len(bstrays)), bstrays)\n", + "plt.show()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "optimization_date4_22_22.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 11a793b6d39bb5b85d93c270f2fdf432832835e8 Mon Sep 17 00:00:00 2001 From: AndrewCurtis27 <89710614+AndrewCurtis27@users.noreply.github.com> Date: Tue, 18 Oct 2022 10:34:22 -0600 Subject: [PATCH 2/8] Add files via upload spline fit and plotting with calculated x values --- optimization spline_10_18.ipynb | 1996 +++++++++++++++++++++++++++++++ 1 file changed, 1996 insertions(+) create mode 100644 optimization spline_10_18.ipynb diff --git a/optimization spline_10_18.ipynb b/optimization spline_10_18.ipynb new file mode 100644 index 0000000..51f8152 --- /dev/null +++ b/optimization spline_10_18.ipynb @@ -0,0 +1,1996 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iZN2u5WKRFqR", + "outputId": "6842718b-36d3-4c1c-b523-796de23e5470" + }, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "import os\n", + "import random\n", + "import pandas\n", + "import numpy\n", + "from scipy.optimize import curve_fit\n", + "from scipy.interpolate import interp1d\n", + "from scipy.interpolate import UnivariateSpline\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "\n", + "# seed = 7777\n", + "# random.seed(seed) \n", + "# torch.manual_seed(seed);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load Saved MP (Magnetic Parameter) Model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "B-TMPJtCKIM6" + }, + "outputs": [], + "source": [ + "# 12 input -> 42 output\n", + "class MpModel(torch.nn.Module):\n", + " def __init__(self):\n", + " super(MpModel, self).__init__()\n", + "\n", + " self.linear1 = torch.nn.Linear(12, 100, bias=True)\n", + " self.linear2 = torch.nn.Linear(100, 100, bias=True)\n", + " self.linear3 = torch.nn.Linear(100, 42, bias=True)\n", + "\n", + " def forward(self, x):\n", + " x = torch.atan(self.linear1(x))\n", + " x = torch.atan(self.linear2(x))\n", + " x = torch.atan(self.linear2(x))\n", + " x = self.linear3(x)\n", + " return x\n", + "\n", + "\n", + "mp_model = MpModel().to(\"cuda\")\n", + "mp_model.load_state_dict(torch.load(\"saved_model_state.pt\"))\n", + "\n", + "for param in mp_model.parameters():\n", + " param.requires_grad = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create GP (Geometry Paramter) Generator" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "z9cggAl1BGHo" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\lazy.py:178: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.\n", + " warnings.warn('Lazy modules are a new feature under heavy development '\n" + ] + } + ], + "source": [ + "class GpModel(torch.nn.Module):\n", + " def __init__(self, input_length: int):\n", + " super(GpModel, self).__init__()\n", + "\n", + " self.dense_layer1 = torch.nn.Linear(int(input_length), 128)\n", + " self.dense_layer2 = torch.nn.Linear(128, 256)\n", + " # self.dense_layer3 = torch.nn.Linear(256, 512)\n", + " # self.dense_layer4 = torch.nn.Linear(512, 256)\n", + " self.dense_layer5 = torch.nn.Linear(256, 128)\n", + " self.dense_layer6 = torch.nn.Linear(128, int(input_length))\n", + " \n", + " self.batch_norm1 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm2 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm3 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm4 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm5 = torch.nn.LazyBatchNorm1d()\n", + "\n", + " def forward(self, x):\n", + " x = self.batch_norm1(torch.atan(self.dense_layer1(x)))\n", + " x = self.batch_norm2(torch.atan(self.dense_layer2(x)))\n", + " # x = self.batch_norm3(torch.atan(self.dense_layer3(x)))\n", + " # x = self.batch_norm4(torch.atan(self.dense_layer4(x)))\n", + " x = self.batch_norm5(torch.atan(self.dense_layer5(x)))\n", + " x = torch.sigmoid(self.dense_layer6(x))\n", + " return x\n", + "\n", + "\n", + "gp_model = GpModel(10).to(\"cuda\")\n", + "optimizer = torch.optim.Adam(gp_model.parameters(), lr=3e-4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# System Constraints" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "Bse1wsKN1NxY" + }, + "outputs": [], + "source": [ + "# x-direction of the primary winding (mm)\n", + "lpx_min, lpx_max = (50.0, 650.0)\n", + "\n", + "# y-direction of the primary winding (mm)\n", + "lpy_min, lpy_max = (50.0, 2050.0)\n", + "\n", + "# width of the primary winding (mm)\n", + "wp_min, wp_max = (25.0, 325.0)\n", + "\n", + "# length of the edge of the primary core (mm)\n", + "a_min, a_max = (0.0, 200.0)\n", + "\n", + "# pitch of the adjacent cores (mm)\n", + "p_min, p_max = (0.0, 200.0)\n", + "\n", + "# length of the secondary winding (mm)\n", + "ls_min, ls_max = (50.0, 450.0)\n", + "\n", + "# width of the secondary winding (mm)\n", + "ws_min, ws_max = (25.0, 225.0)\n", + "\n", + "# input RMS current (A)\n", + "ip_min, ip_max = (50.0, 200.0)\n", + "\n", + "# turn number of the primary side\n", + "np_min, np_max = (4.0, 10.0)\n", + "\n", + "# turn number of the secondary side\n", + "ns_min, ns_max = (4.0, 10.0)\n", + "\n", + "# switching frequency (kHz)\n", + "f = 85 * (10 ** 3)\n", + "\n", + "# output power at the center (kW)\n", + "p_out = 50 * (10 ** 3)\n", + "\n", + "# input DC voltage (V)\n", + "v_dc = 400\n", + "\n", + "# output DC voltage (V)\n", + "v_bat = 400\n", + "\n", + "# length of the edge of the secondary core\n", + "b = 50\n", + "\n", + "# ???\n", + "w = 2 * numpy.pi * f # [rad/s]\n", + "\n", + "# ???\n", + "q_coil = 400" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "brlwMfzj1Nxh" + }, + "outputs": [], + "source": [ + "# Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive.\n", + "# Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch.\n", + "\n", + "@torch.jit.script\n", + "def linspace(start: torch.Tensor, stop: torch.Tensor, num: int):\n", + " # create a tensor of 'num' steps from 0 to 1\n", + " steps = torch.arange(num, dtype=torch.float32, device=start.device) / (num - 1)\n", + "\n", + " # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings\n", + " # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript\n", + " # \"cannot statically infer the expected size of a list in this contex\", hence the code below\n", + " for i in range(start.ndim):\n", + " steps = steps.unsqueeze(-1)\n", + "\n", + " # the output starts at 'start' and increments until 'stop' in each dimension\n", + " out = start[None] + steps * (stop - start)[None]\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "dMoXnTFV8U1P" + }, + "outputs": [], + "source": [ + "# Generate a pytorch tensor full of random floats in the range [-1, +1] with the given shape\n", + "\n", + "def generate_noise(shape):\n", + " return torch.distributions.uniform.Uniform(-1, +1).sample(shape).cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "xloGE25R8Jtm" + }, + "outputs": [], + "source": [ + "def stack_parameters(*params):\n", + " return torch.stack(params).transpose(0, 1)\n", + "\n", + "\n", + "# Scale an array to be between our specified [min, max] contraints\n", + "def scale(arr):\n", + " scaler_min = torch.tensor(\n", + " [a_min, lpx_min, lpy_min, ls_min, p_min, wp_min, ws_min, ip_min, np_min, ns_min],\n", + " device=\"cuda\",\n", + " )\n", + " scaler_max = torch.tensor(\n", + " [a_max, lpx_max, lpy_max, ls_max, p_max, wp_max, ws_max, ip_max, np_max, ns_max],\n", + " device=\"cuda\",\n", + " )\n", + "\n", + " return arr * (scaler_max - scaler_min) + scaler_min\n", + "\n", + "\n", + "def extract(arr):\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns = [\n", + " numpy.squeeze(x) for x in numpy.hsplit(arr, 10)\n", + " ]\n", + "\n", + " ys_min = (\n", + " 5 * wp +\n", + " 4 * a +\n", + " 3 * lpy +\n", + " 2 * p\n", + " )\n", + "\n", + " ys_max = (lpy + wp)\n", + " ys_max = ys_min + (ys_min - ys_max) / 2\n", + "\n", + " ys_min /= 2\n", + " ys_max /= 2\n", + "\n", + " ys = linspace(ys_min, ys_max, num=5)\n", + "\n", + " np = torch.round(np)\n", + " ns = torch.round(ns)\n", + "\n", + " # take all 15 tensors of shape (X) and convert to (15, X)\n", + " return a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys\n", + "\n", + "\n", + "def scale_and_extract(arr):\n", + " scaled_array = scale(arr)\n", + " return extract(scaled_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "44TlTSnclCKs", + "outputId": "c2d4a61b-35c8-42ad-99f9-98c99022a0b2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " GP Parameters Shape: torch.Size([256, 12])\n", + " Extra Parameters Shape: torch.Size([256, 3])\n", + " MP Model Output Shape: torch.Size([256, 42])\n", + " \n" + ] + } + ], + "source": [ + "# Enclose in a function so we don't leak variables\n", + "def test_models():\n", + " noise = generate_noise(shape=(256, 10))\n", + " gp = gp_model(noise)\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "\n", + " gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + " extra_parameters = stack_parameters(ip, np, ns)\n", + "\n", + " mp = mp_model(gp_parameters)\n", + "\n", + " print(f\"\"\"\n", + " GP Parameters Shape: {gp_parameters.shape}\n", + " Extra Parameters Shape: {extra_parameters.shape}\n", + " MP Model Output Shape: {mp.shape}\n", + " \"\"\")\n", + "test_models()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "eEb7J5bOFEEr" + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "Method to take two equally-sized lists and return just the elements which lie \n", + "on the Pareto frontier, sorted into order.\n", + "Default behaviour is to find the maximum for both X and Y, but the option is\n", + "available to specify maxX = False or maxY = False to find the minimum for either\n", + "or both of the parameters.\n", + "\"\"\"\n", + "\n", + "\n", + "def pareto_frontier(x, y, max_x=False):\n", + " if max_x:\n", + " return _pareto_frontier(y, x)\n", + " else:\n", + " return _pareto_frontier(x, y)\n", + "\n", + "\n", + "def _pareto_frontier(x, y):\n", + " # Combine inputs and sort them with smallest X values coming first\n", + " sorted_xy = sorted(zip(x, y))\n", + " p_front_x = torch.zeros(len(x), device=\"cuda\")\n", + " p_front_y = torch.zeros(len(y), device=\"cuda\")\n", + " \n", + " # Loop through the sorted list\n", + " # Look for lower values of Y…\n", + " # and add them to the Pareto frontier\n", + " min_y = sorted_xy[0][1]\n", + " for index in range(len(sorted_xy)):\n", + " x, y = sorted_xy[index]\n", + " if y < min_y:\n", + " min_y = y\n", + " p_front_x[index] = x\n", + " p_front_y[index] = y\n", + " \n", + " p_front_x = p_front_x[p_front_x.nonzero()]\n", + " p_front_y = p_front_y[p_front_y.nonzero()]\n", + "\n", + " return p_front_x, p_front_y" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def y_util(x, a, b):\n", + " return (1 / (x ** b)) ** (1 / a)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def log_util(x, a, b):\n", + " return ()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def lin_util(x, a, b):\n", + " return ((a * x) + b)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def prod_util(x, a):\n", + " return (1 / (x * a))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def prodb_util(x, a, b):\n", + " return ((1 / (x * a)) + b)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def one_param_util(x, a):\n", + " return ((1 - (x**a))**(1/a))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def one_param_b_util(x, a, b):\n", + " return (((1 - (x**a))**(1/a)) + b)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "#testing for creating total pareto f\n", + "\n", + "def oldgraph_pareto_frontier(Xs, Ys, maxX = False, maxY = False):\n", + "# Sort the list in either ascending or descending order of X\n", + " myList = sorted([[Xs[i], Ys[i]] for i in range(len(Xs))], reverse=maxX)\n", + "# Start the Pareto frontier with the first value in the sorted list\n", + " p_front = [myList[0]] \n", + "# Loop through the sorted list\n", + " for pair in myList[1:]:\n", + " if pair[1] <= p_front[-1][1]: # Look for lower values of Y…\n", + " p_front.append(pair) # … and add them to the Pareto frontier\n", + "# Turn resulting pairs back into a list of Xs and Ys\n", + " p_frontX = [pair[0] for pair in p_front]\n", + " p_frontY = [pair[1] for pair in p_front]\n", + " return p_frontX, p_frontY" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def findSpline(x, y, smoothing_factor = 2):\n", + " spl = UnivariateSpline(x, y)\n", + " spl.set_smoothing_factor(smoothing_factor)\n", + " return spl" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load DWPT data" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "jkTrlAJMH5UO" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1010, 12)\n", + "(1010, 42)\n" + ] + } + ], + "source": [ + "def load_dwpt():\n", + " folder_name = \"./dwpt_v5\"\n", + " file_names = [\n", + " \"DWPT_v5_N10\",\n", + " \"DWPT_v5_N100\",\n", + " \"DWPT_v5_N200\",\n", + " \"DWPT_v5_N300\",\n", + " \"DWPT_v5_N400\",\n", + " ]\n", + "\n", + " df = pandas.DataFrame()\n", + "\n", + " for file_name in file_names:\n", + " new_df = pandas.read_csv(f\"{folder_name}/{file_name}_after.csv\", index_col=0)\n", + " df = pandas.concat([df, new_df])\n", + "\n", + " return df\n", + "\n", + "df = load_dwpt()\n", + "df_gp = df.loc[:, :\"ys4[mm]\"]\n", + "df_mp = df.loc[:, \"k0mm_ys0\":]\n", + "\n", + "print(df_gp.shape)\n", + "print(df_mp.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define Loss Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def kdiff_loss(k_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + "\n", + " kdiff = abs(k_0_0 - k_1_0)\n", + " kdiff = kdiff / torch.max(abs(k_0_0), abs(k_1_0))\n", + "\n", + " # their kdiff - why is there a difference?\n", + " # kdiff = abs(k00 - k10) / k00\n", + "\n", + " return kdiff\n", + "\n", + "\n", + "\n", + "def bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters):\n", + " # Unpack parameters\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " # Scale l_parameters\n", + " lp_0_0 = lp_0_0 * np ** 2\n", + " ls_0_0 = ls_0_0 * ns ** 2\n", + "\n", + " # Calculate Is\n", + " # n1 = (numpy.pi * w) * (lp_0_0 * 10 ** -9) * (ip * numpy.sqrt(2) / (4 * v_dc))\n", + " # t1 = (numpy.pi) ** 2 * w * (lp_0_0 * 10 ** -9 * ls_0_0 * 10 ** -9) ** 0.5\n", + " # t2 = p_out / (8 * k_0_0 * n1 * v_dc * v_bat)\n", + " # n2 = t1 * t2\n", + " # # \"Is\" is way too big?\n", + " # Is = 4 * n2 * v_bat / (numpy.pi * w * ls_0_0 * 10 ** -9) / numpy.sqrt(2)\n", + " \n", + " # Paper formula\n", + " n1 = (torch.pi * w * lp_0_0 * ip) / (2 * numpy.sqrt(2) * v_dc)\n", + " t1 = (torch.pi ** 2 * w * p_out * torch.sqrt(lp_0_0 * ls_0_0))\n", + " t2 = 8 * k_0_0 * n1 * v_dc * v_bat\n", + " n2 = t1 / t2\n", + " Is = (4 * v_bat * n2) / (torch.pi * w * ls_0_0)\n", + "\n", + " # Calculate B_0mm - unneeded since b100mm always bigger?\n", + " # bx_0 = (\n", + " # (bx_p_0_00 * ip * np + bx_s_0_00 * Is * ns) ** 2 +\n", + " # (bx_p_0_90 * ip * np + bx_s_0_90 * Is * ns) ** 2\n", + " # ) ** 0.5\n", + " # by_0 = (\n", + " # (by_p_0_00 * ip * np + by_s_0_00 * Is * ns) ** 2 +\n", + " # (by_p_0_90 * ip * np + by_s_0_90 * Is * ns) ** 2\n", + " # ) ** 0.5\n", + " # bz_0 = (\n", + " # (bz_p_0_00 * ip * np + bz_s_0_00 * Is * ns) ** 2 +\n", + " # (bz_p_0_90 * ip * np + bz_s_0_90 * Is * ns) ** 2\n", + " # ) ** 0.5\n", + " # b_0 = (bx_0**2 + by_0**2 + bz_0**2) ** 0.5\n", + "\n", + " # Calculate B_100mm\n", + " bx_100 = (\n", + " (bx_p_1_00 * ip * np + bx_s_1_00 * Is * ns) ** 2 +\n", + " (bx_p_1_90 * ip * np + bx_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " by_100 = (\n", + " (by_p_1_00 * ip * np + by_s_1_00 * Is * ns) ** 2 +\n", + " (by_p_1_90 * ip * np + by_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " bz_100 = (\n", + " (bz_p_1_00 * ip * np + bz_s_1_00 * Is * ns) ** 2 +\n", + " (bz_p_1_90 * ip * np + bz_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " b_100 = (bx_100**2 + by_100**2 + bz_100**2) ** 0.5\n", + "\n", + " # Bstray is the max between the two targets\n", + " # bstray = torch.max(b_0, b_100)\n", + " bstray = b_100\n", + "\n", + " return bstray" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# (\n", + "# # [0 thru 5]\n", + "# k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + "\n", + "# # [6 thru 17]\n", + "# lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + "# ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + "# lp_1_0, ls_1_0,\n", + "\n", + "# # [18 thru 29]\n", + "# bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + "# bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + "# bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + "# bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + "# # [30 thru 42]\n", + "# bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + "# bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + "# bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + "# bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + "# )" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def coil_loss_and_power_average(k_parameters, l_parameters, b_parameters, extra_parameters):\n", + " (k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " n1_i = math.pi*w*lp_0_0*10**(-9)*ip*math.sqrt(2)/(4*Vdc)\n", + " t1 = (math.pi)**2*w*(lp_0_0*10**(-9)*ls_0_0 *10**(-9))**0.5\n", + " t2 = Pout/(8*lp_0_0*n1_i*Vdc*Vbat)\n", + " n2_i = t1*t2\n", + " Is_i = 4*n2_i*Vbat/(math.pi*w*ls_0_0*10**(-9))/math.sqrt(2)\n", + " loss = w*lp_0_0*10**(-9)*ip**2/QCoil + w*ls_0_0 *10**(-9)*Is_i**2/QCoil\n", + "\n", + " a6 = lp_0_0.clone() * np.clone()**2 # LP0MM_YSO\n", + " a7 = lp_0_1.clone() * np.clone()**2 # LP0MM_YS1\n", + " a8 = lp_0_2.clone() * np.clone()**2 # LP0MM_YS2\n", + " a9 = lp_0_3.clone() * np.clone()**2 # LP0MM_YS3\n", + " a10 = lp_0_4.clone() * np.clone()**2 # LP0MM_YS4\n", + "\n", + " a11 = ls_0_0.clone() * ns.clone()**2 # LS0MM_YSO\n", + " a12 = ls_0_1.clone() * ns.clone()**2 # LS0MM_YS1\n", + " a13 = ls_0_2.clone() * ns.clone()**2 # LS0MM_YS2\n", + " a14 = ls_0_3.clone() * ns.clone()**2 # LS0MM_YS3\n", + " a15 = ls_0_4.clone() * ns.clone()**2 # LS0MM_YS4\n", + "\n", + "\n", + "\n", + " p0 = w*k_0_0*(a6*10**(-9))*(a11*10**(-9))**0.5*ip*Is_i\n", + " p1 = w*k_0_1*(a7*10**(-9))*(a12*10**(-9))**0.5*ip*Is_i \n", + " p2 = w*k_0_2*(a8*10**(-9))*(a13*10**(-9))**0.5*ip*Is_i \n", + " p3 = w*k_0_3*(a9*10**(-9))*(a14*10**(-9))**0.5*ip*Is_i\n", + " p4 = w*k_0_4*(a10*10**(-9))*(a15*10**(-9))**0.5*ip*Is_i \n", + " pave = (p0+(2*p1)+(2*p2)+(2*p3)+(2*abs(p4)))/8 \n", + " return loss,pave\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def power_average(k_parameters, l_parameters, b_parameters, extra_parameters, gp_parameters):\n", + " \n", + " return " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + " def core_losses(k_parameters, l_parameters, b_parameters, extra_parameters, gp_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " (\n", + " a, lpx, lpy, ls, p, wp, ws, *ys\n", + " ) = gp_parameters.transpose(0,1)\n", + "\n", + " V_PriCore = (lpy +2*wp+2*a)*(lpx+2*wp+2*a)*5/(10**3) # cm3\n", + " #print(V_PriCore)\n", + " V_SecCore = (ls+2*ws+2*b)**2*5/(10**3) # cm3\n", + " #print(V_SecCore)\n", + " V_PriWind = (2*(lpx+wp)+2*(lpy+wp))*6.6*6.6/(10**3)*np\n", + " V_SecWind = 4*(ls+ws)*6.6*6.6/(10**3)*ns\n", + "\n", + " V_PriWind_ave = V_PriWind/(lpy+2*wp+2*a+p)*10**3 # cm3/m\n", + " V_PriCore_ave = V_PriCore/(lpy+2*wp+2*a+p)*10**3 # cm3/m\n", + "\n", + " #print(V_PriWind_ave, V_PriCore_ave)\n", + " return V_PriCore, V_SecCore, V_PriWind, V_SecWind" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "noise = generate_noise(shape=(25_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "plt.scatter(bstray.detach().cpu(), 100 * kdiff.detach().cpu())\n", + "plt.title(\"Random Network: Bstray vs Kdiff\")\n", + "plt.xlabel(\"BStray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# if (chosen_curve_util == one_param_util) or (chosen_curve_util == prod_util):\n", + "# if normalize == True:\n", + "# (a), pcov = curve_fit(chosen_curve_util, normalized_pareto_kdiff_added_numpy[:,0], normalized_pareto_bstray_added_numpy[:,0], maxfev = 3000)\n", + "# else:\n", + "# (a), pcov = curve_fit(chosen_curve_util, pareto_kdiff_added_numpy[:,0], pareto_bstray_added_numpy[:,0], maxfev = 3000)\n", + " \n", + "# if (chosen_curve_util == y_util) or (chosen_curve_util == lin_util) or (chosen_curve_util == prodb_util) or (chosen_curve_util == one_param_b_util):\n", + "# if normalize == True:\n", + "# (a, b), pcov = curve_fit(chosen_curve_util, normalized_pareto_kdiff_added_numpy[:,0], normalized_pareto_bstray_added_numpy[:,0], maxfev = 3000)\n", + "# else:\n", + "# (a, b), pcov = curve_fit(lin_util, pareto_kdiff_added_numpy[:,0], pareto_bstray_added_numpy[:,0], maxfev = 3000)\n", + "# # (a, b), pcov = curve_fit(lin_util, pareto_kdiff_numpy[:,0], pareto_bstray_numpy[:,0], maxfev = 3000)\n", + " \n", + " \n", + "# #one param util\n", + "# if chosen_curve_util == one_param_util:\n", + "# if normalize == True:\n", + "# x_calc = torch.as_tensor((1 / (normalized_kdiff_numpy ** a)) ** (1 / a)).cuda()\n", + "# else:\n", + "# x_calc = torch.as_tensor((1 / (kdiff_numpy ** a)) ** (1 / a)).cuda()\n", + " \n", + "# #one param with intercept util\n", + "# if chosen_curve_util == one_param_b_util:\n", + "# if normalize == True:\n", + "# x_calc = torch.as_tensor(((1 / (normalized_kdiff_numpy ** a)) ** (1 / a)) + b).cuda()\n", + "# else:\n", + "# x_calc = torch.as_tensor(((1 / (kdiff_numpy ** a)) ** (1 / a)) + b).cuda()\n", + " \n", + "# #exponential utils\n", + "# if chosen_curve_util == y_util:\n", + " \n", + "# if normalize == True:\n", + "# x_calc = torch.as_tensor(((1 / normalized_kdiff_numpy ** b)) ** (1 / a)).cuda()\n", + "# else:\n", + "# x_calc = torch.as_tensor(((1 / kdiff_numpy ** b)) ** (1 / a)).cuda()\n", + " \n", + "# #prod util\n", + "# if chosen_curve_util == prod_util:\n", + "# if normalize == True:\n", + "# x_calc = torch.as_tensor(1 / (normalized_kdiff_numpy * a)).cuda()\n", + "# else:\n", + "# x_calc = torch.as_tensor(1 / (kdiff_numpy * a)).cuda()\n", + " \n", + "# #prodb util\n", + "# if chosen_curve_util == prodb_util:\n", + "# if normalize == True:\n", + "# x_calc = torch.as_tensor((1 / (normalized_kdiff_numpy * a)) + b).cuda()\n", + "# else:\n", + "# x_calc = torch.as_tensor((1 / (kdiff_numpy * a)) + b).cuda()\n", + " \n", + "# #linear util\n", + "# if chosen_curve_util == lin_util:\n", + "# if normalize == True:\n", + "# x_calc = torch.as_tensor((a * normalized_kdiff_numpy) + (b)).cuda()\n", + "# else:\n", + "# x_calc = torch.as_tensor((a * kdiff_numpy) + (b)).cuda() \n", + " \n", + "# x_calc_numpy = x_calc.cpu().data.numpy()\n", + "# #x_loss = torch.abs(x_calc - bstray)\n", + " \n", + "# if normalize == True:\n", + "# x_loss = x_calc - normalized_bstray\n", + "# else:\n", + "# x_loss = x_calc - bstray\n", + "# # x_loss_relu = torch.nn.functional.relu(-x_loss)\n", + " \n", + "# elif len(pareto_bstray_numpy) > 0:\n", + "# x_loss = pareto_bstray[:,0]\n", + "# print('single point')\n", + " \n", + "# if len(pareto_bstray_numpy) > 2:\n", + "# if (chosen_curve_util == one_param_util) or (chosen_curve_util == prod_util):\n", + "# if normalize == True:\n", + "# (a), pcov = curve_fit(chosen_curve_util, normalized_pareto_bstray_added_numpy[:,0], normalized_pareto_kdiff_added_numpy[:,0], maxfev = 3000)\n", + "# else:\n", + "# (a), pcov = curve_fit(chosen_curve_util, pareto_bstray_added_numpy[:,0], pareto_kdiff_added_numpy[:,0], maxfev = 3000)\n", + " \n", + "# if (chosen_curve_util == y_util) or (chosen_curve_util == lin_util) or (chosen_curve_util == prodb_util) or (chosen_curve_util == one_param_b_util):\n", + "# if normalize == True:\n", + "# (a, b), pcov = curve_fit(chosen_curve_util, normalized_pareto_bstray_added_numpy[:,0], normalized_pareto_kdiff_added_numpy[:,0], maxfev = 3000)\n", + "# else:\n", + "# (a, b), pcov = curve_fit(lin_util, pareto_bstray_added_numpy[:,0], pareto_kdiff_added_numpy[:,0], maxfev = 3000)\n", + "# # (a, b), pcov = curve_fit(lin_util, pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0], maxfev = 3000)\n", + " \n", + " \n", + "# #one param util\n", + "# if chosen_curve_util == one_param_util:\n", + "# if normalize == True:\n", + "# y_calc = torch.as_tensor((1 / (normalized_bstray_numpy ** a)) ** (1 / a)).cuda()\n", + "# else:\n", + "# y_calc = torch.as_tensor((1 / (bstray_numpy ** a)) ** (1 / a)).cuda()\n", + " \n", + "# #one param with intercept util\n", + "# if chosen_curve_util == one_param_b_util:\n", + "# if normalize == True:\n", + "# y_calc = torch.as_tensor(((1 / (normalized_bstray_numpy ** a)) ** (1 / a)) + b).cuda()\n", + "# else:\n", + "# y_calc = torch.as_tensor(((1 / (bstray_numpy ** a)) ** (1 / a)) + b).cuda()\n", + " \n", + "# #exponential utils\n", + "# if chosen_curve_util == y_util:\n", + "# if normalize == True:\n", + " \n", + "# y_calc = torch.as_tensor(((1 / normalized_bstray_numpy ** b)) ** (1 / a)).cuda()\n", + "# else:\n", + "# y_calc = torch.as_tensor(((1 / bstray_numpy ** b)) ** (1 / a)).cuda()\n", + " \n", + "# #prod util\n", + "# if chosen_curve_util == prod_util:\n", + "# if normalize == True:\n", + "# y_calc = torch.as_tensor(1 / (normalized_bstray_numpy * a)).cuda()\n", + "# else:\n", + "# y_calc = torch.as_tensor(1 / (bstray_numpy * a)).cuda()\n", + " \n", + "# #prodb util\n", + "# if chosen_curve_util == prodb_util:\n", + "# if normalize == True:\n", + "# y_calc = torch.as_tensor((1 / (normalized_bstray_numpy * a)) + b).cuda()\n", + "# else:\n", + "# y_calc = torch.as_tensor((1 / (bstray_numpy * a)) + b).cuda()\n", + " \n", + "# #linear util\n", + "# if chosen_curve_util == lin_util:\n", + "# if normalize == True:\n", + "# y_calc = torch.as_tensor(((a * normalized_bstray_numpy)) + (b)).cuda()\n", + "# else:\n", + "# y_calc = torch.as_tensor(((a * bstray_numpy)) + (b)).cuda()\n", + " \n", + "# y_calc_numpy = y_calc.cpu().data.numpy()\n", + " \n", + "# if normalize == True:\n", + "# y_loss = y_calc - normalized_kdiff \n", + "# else:\n", + "# y_loss = y_calc - kdiff\n", + "# y_loss_relu = torch.nn.functional.relu(-y_loss)\n", + " \n", + "# elif len(pareto_bstray_numpy) > 0:\n", + "# y_loss = pareto_kdiff[:,0]\n", + "# print('single point')\n", + "\n", + "# # loss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Network Training Loop" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "OXzpQf5O1Nxk", + "outputId": "1bc1a1b1-95ac-43d8-f1d8-5dd49e4bbb03" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(7052.8287, device='cuda:0', dtype=torch.float64, grad_fn=)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0\n", + " Kdiff : 19.6274%\n", + " Bstray : 51.4964\n", + " Pareto Kdiff : 10.7884%\n", + " Pareto Bstray : 41.2743\n", + "tensor(108305.9238, device='cuda:0', dtype=torch.float64,\n", + " grad_fn=)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 50\n", + " Kdiff : 18.7811%\n", + " Bstray : 51.3963\n", + " Pareto Kdiff : 14.5136%\n", + " Pareto Bstray : 46.4303\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "tensor(1323.9358, device='cuda:0', dtype=torch.float64, grad_fn=)\n" + ] + }, + { + "data": { + "image/png": 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0ApKvnvocfJVmkApQ0JeKW/LFLxR9vG/7mYwHw0baEnEqXnhqIk2TW2u/UN5epRmkAsy9NgNl3d3d3tvbW5P3luortkgLoOupvuBGJo+dm9ZoNi1tQS4f8n/fTOqn2GNK78SWme129+7pPl85fakDWRcemWD2va3FZ8U0uuwFWYXy9tfuDe4/fHOQ0kkuDWb5KODLDCjoS3UsWADHjhV4MGcGz5pN6f1lm1zqAAX3Bs7k7VWaQcpMOX2piq5Hf1H08bz0Txzy1pZQ3l6qTkFf6s+eHSU2Fm8SPlq4sujQCc3Hl4pQ0Jf6khnIjUOVzUxdobAyDYNHtBBLKkJBX+pL2IKkpmSw4pLiH3DaI1cqQEFfqmZ8WmYxsVl45ND7jdIfcLE5H1ItCvpSN/pWdmkAM5fOh5SZgr7UlzhtmViK9siVClDQl/oyaa9YSu861azaF2rlrVSEFmdJ/clekHTr6voss1xpI3EYzJZaiHQZZWaXmtnTZrbPzD4R8vh/NLM96X8/NbO15e+qNIPixdc8f4piXAcyNXNHKqRk0DezBHA7cBmwCrjKzFblNHseuNDd1wCfBbaVu6PSHJIbNhRv8MB1OU9o4oFMSxR/PK4feFJRUa701wH73P05dx8CtgMbsxu4+0/d/cX03UeAJv5LlYrKnbO+4pLa9KOS2tqh+wNw2hLACgf/9tOr2i2JhyhB/ywgO6l6MH2skA8AD4Q9YGZXm1mvmfUePnw4ei8lvp59qNY9KA9LML5nwNJ10HvXRH39QtVETx3TilwpuyhBP6wMYGgRfjN7K0HQvy7scXff5u7d7t69aNGi6L2U+MjdRKVZUhw+ClduC2rqPP8jIm34nl1+WaRMogT9g8CyrPtLgUO5jcxsDfB1YKO7/7E83ZPYueyWyfebKac/nZpClfzQK7RjlzS1KEH/UWCFmS03s1nAZuC+7AZm9ipgJ/BX7v5M+bspsZE7L33FJRSsOd9oplNTqFIfepnCdpkUU+qACrzFRMmg7+4jwDXAg0AfsMPdnzCzrWa2Nd3seuAVwB1m9piZaR9ECdV3/joipTYgCECPfyd6+0aXmDX5fiVX5IYVttM00ViItDjL3e8H7s85dmfW7b8F/ra8XZOmdOwYha/cc47HpuImMGserNkcDFxntkZccUlwDnZePb2tEvfsKLzVYqG0UbOMoUhBWpEr9StOAWjoRPCtJrMh+gPXBVU4MzLpF4gW+HM3mM99fnJp+ErnZhpDkVAxLWwiDSFuAWh4EHZ+MPgXNuA7PJi/eK2QUumbsMJ2KvAWCwr6Uj8sJ72jipv5Bo9EG2wtlb6ZVNguvX5ABd5iQekdqQsOrOp7cvLBTAB6+OZ0KsKYNKibmAVjo4UXNzWrh28uHZyjpG+yC9tJbOhKX+pG359ekH9wzSa4di/cmILu90+ULLAEtLTFL+BDtLEOpW+kAF3pS10wwF8oUJpj18dg993gYxPHfBSGT1Slb2VjLZN/h+mKMtYx6VtSyOwdiS0FfakrfWvfQNfjj00c2PWxybNYGlVLW1BWYaamcrWu9I2EUHpH6oYBnDrFvssvnzi4+5s16k0ZJZfBO+6Y2A1sWjTYKuWhoC9VVXwTlcDwvl+nV+7S+Dn79oUTaZXpzkZKLoMbjwZjG2s2qWaOzIiCvlRVyU1UMo4do29lV+mNRurd4JFg3v2uj8FvH4Hhlycea4mQXc1O5+zZAbcsD15PNXNkmhT0peo6rtocuW3fPa+sYE+qqPcb6bGJrCmnYyP59XayZadzMitsCy3aUs0ciUhBX6qu84Yb8hdiFdG3/cyJO5aAM1ZWoFc1MjpU4AGbSOdA6TpEcSpZITOioC810TW+ECtKBU1j3w/PCFIdb3ofpH5TwZ7VidxpmaWCetxKVsi0KehLzXQ91Ue0WvnG8LG24Ep39zebr/JmS9vk+2HTMksF9WbcS1gqQkFfaioI/NH0/e8zG382T5jZC0rXwCk186dZ9hKWilPQl5qLFvgNvEl20Mo1eCR81Wz21MyHb4a17yn8GsrpS0RakSt1oeOqzRy9Z3utu1FDPrnmPeTXw++9C9rmhZefUE5fIlLQl5rqv+mmmAf7HMOD8L2tBdJYHgT83JIO5SqkVmynLWkaCvpSE09fcCFjAwNTeIaDFZjp074QRgabZ4C31LjF7AXBz8yc/dYy7DlQaqctaRrK6UtVPX3BhfSt7JpiwA90vfv3+Qfb2uGyWyZvCNK+sPiip0Y3eCT4kMu+P9NVudooPTZ0pS9V0Xfuahid7swbp33xqfzDlpg80yX7inTPjiJpkgZnicIBOveqPDdls+KSyZuvZ1I42ig9NhT0paL6zl8Hx45N+/kO2JxRzr4opPyAjxVOPazZBL/6Z3j+R9N+77qUmFV4FW9ugA5L2RTabF0bpceG0jtSEfu3bAkKps0w4P/LefDMXxUoulYsIO3Z0XwBH2DW/MIlmnPPxwPXlR7nyHxD0E5bsaGgL2XX17WKwZ89MqPX6Hqqj9fs/RXf+ovZPLJ0/dQCUuYKtxkNvhgtQO/ZEV6cLUzqoDZKjxGld6Ss+lZ2zej57W9Zz9l33w3AnNY5/JuFK3hhdlsQgKJOJyxVnKyRJZdG2wpxKgOwmW8I2mkrFhT0pWz6zl09redlB/pc577iXB7c/xBfb/8k773mcea0RaivH5abbgbZV/OlAvRUBmBVtydWlN6R8pnG7JwlX/xCwYAP8PozXs/x4WN87qGf0PP4oWgv2ugbrxSSWbi162Ol205lAFZ1e2JFQV+qb/Zsup7qo+upvpI7aa0+I/j2sPTMw3ztJ88xMjpW+vWbcZpmho8GM3BKBf6pbM2oaZmxoqAv1XfqFP033RSp6Tkd5zAnMYfVr3mJZ/5wnG/9LEIt/RltQN4gSm0YnxmYbV84ccwK/LlrWmasKOhLTRy9Zzt9K7vG/z19wYWh7VpbWlm5cCXH/Xne+rpF/ONDT/P71MuhbcddfH1zr8iF/G8zhTZLz1656yHfkjQtM3Y0kCt1YWxgYNLMn0RHB6/89KdIbtjA6jNWc+8z97Lj8pXc2PMUQyNRUjxRduRqYNnjFoXq5rS2h89iskTwAaCiarFkXqM/ju7ubu/t7a3Je0tl7N+yZcbz88Nk/odmV9MvNuOHW1c37wyejO4PwKvWp6dtTuN3TS5TwG9QZrbb3bun/XwFfSmnSgX+XE74Routc0dYvOYYybObdJ4+wPIL4Y3vnXx1Px1t7VqA1YAU9KUuzazA2kw54CxZn2rO4J8ZqC55hW/QNqf4B0NyGVy7t2xdk8qbadCPNJBrZpea2dNmts/MPhHy+Eoz+5mZnTKzf5huZ6R5dD2xl7bXnlOjdzeghUOPdJDan562GHX6YiNIHYiY0vGs0gqFXkvTNeOmZNA3swRwO3AZsAq4ysxW5TQ7AnwY+FLZeygN67W7do3Px++4ajNYtfe4Nfp3nzZRR6ZZF20VklwWpG6u3Ru9SJs0vShX+uuAfe7+nLsPAduBjdkN3H3A3R8FhsNeQKTzhhvo6nty/ENgyRe/gLVX/urbh9OBfufVzb1oK1fuVMywxVqJWTB0In+apzS1KEH/LCD7u+TB9LEpM7OrzazXzHoPHz48nZeQJpDq6WHg1tvwwUFIpINyolJX4ekNxyk2dlXtbyCVVKBCZm4VzfaFwbTWwSNM2pRdgb/pRZmnH/YXMa3RX3ffBmyDYCB3Oq8hjSHV08MfPvd5Ro8eDQ60tMBYyPz6zGBvhQZ9E7NKzek36H5/ejepBp/mWWpQNrtI262r80svF9p9S5pKlKB/EMhOCC4FIla+kjjpv+kmjt6zPfzBsIBfcc4rz3upwGM2eXHSnh2w84NV7d2MtLTBWFY2NTudk7tFYth8fG2PGFtR0juPAivMbLmZzQI2A/dVtlvSaPZv2VI44FeRp/9ZeztLLmoJn7KZXAY3Hg2uirP3182uU1PPZs2Dd9wRvuFJZnVuJqVVKG1TaABXA7tNr2TQd/cR4BrgQaAP2OHuT5jZVjPbCmBmZ5rZQeBjwH8zs4NmdlolOy71I9XTU5UFWcU4cDIxi3uu7OJDN5/J6365m+TWG6e249ZltzTG1M6hkxOzcm48Gvw+D1wHNyaDbyuFNk3Ppu0RYytS7R13vx+4P+fYnVm3f0+Q9pEYGrj1tuq8UXs7S26+KbQcs7vzqe/t5d5n7mXO4E72v7Sf5VF2mMoW1r4e8/zZV+N7dsD3/25yqidMbtpmqudGmoZW5MqM9XWtKmuBs46rNtN5ww2hj+UOEFtHB8nLLuX4j37MSH8/J+fP4UcrBrn4mTFmHW+h9Ywk89922fjjNmcO/vLLQX8TCTo2/YeC7wXALcuj7zVbDdmlE/bsCDZViTIVVStvm4bKMEjNPXvRxYwcKt/YfuuSJaz414fzjqd6euj/1Kfx4eJXtbl1eQrV6clof8t6hn/zW0b6+2nt7KTt1a9i8BePBjOKWoy2eacYPt42/kIdrzlB5/nBAHFqfzsDexYwcjJB69xR5ne+zPH+OeP3K1IHKLks2OLw8e9Eq72jGjtNRUFfai7V00P/Z64PrqDLwYyuvifzDpf7wyWj1IdC2MdIxzknmLt4lP5fLMBHWwq2tcQYneenOHm4jaPPzQv94JgeI9LMaVXTbDozDfqqpy8zlsmx919/Q7DgaoZaOztDj4/098/4tcOUXpqV28I48tw8Dg7A/NH8x7L5aAv9vUl8xCYeczj663kc/fU8rG2Mzje9NI1vAyUCfmIWbLxdwV7yaOcsKYvkhg2s/NUvgxo7mdW1ZtjcuWBG65IldFy1GevoKPo6NmcOi6/9aOhjhT4MasEc5h2LtpJ3bKSFsA8OMHw4waGfJycKw0WQ2t/Os/ctpm97J8/etzj8uQr4UoDSO1J1mTIMI/39JJJJxgBPpWjt7GTxtR8tuFl61Jx+lITN1FvmMMdOT+JHSqdoHMdKvMvovFFG3/Ui88acOSRYMDrE3LEx2nL6mNrfTv+jyUkppUwKafzbggZtm5rSO9Jwkhs2FAzspZ4HFJ290/qK05i/OMXx/SOMnGwtOXtn7rrzeflXj5UYj8jP6Y++/ky+1HYRH0ndy5zR4YJtLTEGeSmgfHYiwXuXnJl3vNWdeWNjzBtz5voYH/+h0TGan0Ia2LMgCPqaay8lKOhLQ5nuB0Yx2d88xmfv/PwX+NgY3tLC8dPmc1oqlY7nRsfbzufkZ77ClYde4vSnzmX4q3eMP3f+6iUc/2kvI8ed1rljLF7zEidfOp2j+ePSk80b5Y4XTnCiewuDS97AieETnBw5ycDxFKOP3c3Q2MucbGkheawt9OkjJxNB6ei171FaR4pS0JfYi/JBkjo5zDMDx+jqPI35s4M/m3MWzYe174R3v7P46wOM1yUKSSjZGEvf3Mq5b/tSeMCe/+rxrRGfnbuYkZP5f7atc0eD+fqPfyfYO1eBXwrQQK5IBMm5bZx/9sLxgD9VnTfcEOwj8PfvJDEbMrNvbH47S77wJZJ3PFl8pXC6LPLiNceClFGWoVbntLXHgjthJRdEsmggV6TBpG7/NAN372TkuDM2b4yvvjXBuxcd4U8GM+MSFtTkkaakxVkicXXrakgd4EhLCwuzS1dr9k5Tq8rG6CJSh9KVMicFfM3ekRIU9EUaVe4WiGHbJIrk0OwdkUaWvQWiSAS60hcRiREFfRGRGFHQFxGJEQV9EZEYUdAXEYkRBX0RkRhR0BcRiREFfRGRGFHQFxGJEQV9EZEYUdAXEYkRBX0RkRhR0BcRiREFfRGRGFHQFxGJEQV9EZEYUdAXEYkRBX0RkRhR0BcRiREFfRGRGIkU9M3sUjN72sz2mdknQh43M/ty+vE9ZnZe+bsqIiIzVTLom1kCuB24DFgFXGVmq3KaXQasSP+7GvhKmfspIiJlEOVKfx2wz92fc/chYDuwMafNRuB/euARoMPMOsvcVxERmaHWCG3OAg5k3T8IvDlCm7OA/uxGZnY1wTcBgFNmtndKvW1eZwAv1LoTdULnYoLOxQSdiwmvm8mTowR9Cznm02iDu28DtgGYWa+7d0d4/6anczFB52KCzsUEnYsJZtY7k+dHSe8cBJZl3V8KHJpGGxERqbEoQf9RYIWZLTezWcBm4L6cNvcBf52exbMeSLl7f+4LiYhIbZVM77j7iJldAzwIJIC73P0JM9uafvxO4H7g7cA+4CSwJcJ7b5t2r5uPzsUEnYsJOhcTdC4mzOhcmHte6l1ERJqUVuSKiMSIgr6ISIzUJOiXKuvQzMxsmZn9XzPrM7MnzOwj6eMLzez/mNmz6Z+n17qv1WBmCTP7lZntSt+P63noMLN7zeyp9P+Nt8T4XFyb/tvYa2b3mNmcOJ0LM7vLzAay1zEV+/3N7JPpWPq0mf1FqdevetCPWNahmY0A/8Xdu4D1wIfSv/8ngIfdfQXwcPp+HHwE6Mu6H9fz8E/Av7j7SmAtwTmJ3bkws7OADwPd7r6aYPLIZuJ1Lr4JXJpzLPT3T8eOzcC56efckY6xBdXiSj9KWYem5e797v7L9O1jBH/cZxGcg2+lm30LeEdNOlhFZrYU+Evg61mH43geTgMuAL4B4O5D7n6UGJ6LtFag3cxagbkEa35icy7c/cfAkZzDhX7/jcB2dz/l7s8TzKBcV+z1axH0C5VsiB0zOxt4I/Bz4JWZtQ3pn4tr2LVquQ34ODCWdSyO5+E1wGHg7nSq6+tmNo8Yngt3/x3wJeC3BGVcUu7+EDE8FzkK/f5Tjqe1CPqRSjY0OzObD3wX+Ki7v1Tr/lSbmV0ODLj77lr3pQ60AucBX3H3NwInaO70RUHpXPVGYDmwBJhnZu+tba/q2pTjaS2CfuxLNphZG0HA/1/uvjN9+A+ZyqTpnwO16l+V/Anw781sP0GK7yIz+2fidx4g+Js46O4/T9+/l+BDII7n4s+B5939sLsPAzuBf0s8z0W2Qr//lONpLYJ+lLIOTcvMjCB32+fu/5j10H3A36Rv/w3wg2r3rZrc/ZPuvtTdzyb4P/Cv7v5eYnYeANz998ABM8tUT7wYeJIYnguCtM56M5ub/lu5mGDcK47nIluh3/8+YLOZzTaz5QR7mvyi6Cu5e9X/EZRseAb4NfDpWvShVv+APyX4+rUHeCz97+3AKwhG5Z9N/1xY675W8Zz8GbArfTuW5wF4A9Cb/n/xfeD0GJ+Lm4CngL3At4HZcToXwD0E4xnDBFfyHyj2+wOfTsfSp4HLSr2+yjCIiMSIVuSKiMSIgr6ISIwo6IuIxIiCvohIjCjoi4jEiIK+iEiMKOiLiMTI/wc/lyJfNAR4bgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 100\n", + " Kdiff : 19.4590%\n", + " Bstray : 50.6138\n", + " Pareto Kdiff : 11.5232%\n", + " Pareto Bstray : 48.3914\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "No new pareto points found, used zero grad\n", + "tensor(64466.3871, device='cuda:0', dtype=torch.float64,\n", + " grad_fn=)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 150\n", + " Kdiff : 24.5446%\n", + " Bstray : 48.2143\n", + " Pareto Kdiff : 13.1438%\n", + " Pareto Bstray : 40.3695\n", + "No new pareto points found, used zero grad\n" + ] + } + ], + "source": [ + "p_front_x = []\n", + "p_front_y = []\n", + "pareto_bstray_points = []\n", + "pareto_kdiff_points = []\n", + "kdiffs = []\n", + "bstrays = []\n", + "combined_losses = []\n", + "\n", + "#testing for graphs\n", + "kdiff_designs = []\n", + "bstray_designs = []\n", + "pareto_bstray_points2 = []\n", + "pareto_kdiff_points2 = []\n", + "\n", + "x_calc_numpy =[]\n", + "y_calc_numpy=[]\n", + "\n", + "#training parameters\n", + "n_epochs = 200\n", + "n_noise = 1000\n", + "chosen_curve_util = prod_util\n", + "normalize = False\n", + "\n", + "for epoch in range(n_epochs):\n", + " \n", + " # Clear old gradients in the GP model\n", + " for param in gp_model.parameters():\n", + " param.grad = None\n", + "\n", + " # Create GP parameters from noise\n", + " noise = generate_noise(shape=(n_noise, 10))\n", + " gp = gp_model(noise)\n", + "\n", + " # Unpack and filter GP parameters\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + " gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + " extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + " # Scale GP parameters - why?\n", + " min_x = torch.min(gp_parameters)\n", + " max_x = torch.max(gp_parameters)\n", + " gp_parameters = (gp_parameters - min_x) / (max_x - min_x)\n", + "\n", + " # Push GP parameters through MP model\n", + " mp_parameters = mp_model(gp_parameters)\n", + "\n", + " # Scale MP parameters\n", + " y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + " min_y = torch.min(y, 1).values\n", + " max_y = torch.max(y, 1).values\n", + " mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + " k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + " l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + " b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + " # Calculate losses\n", + " kdiff = kdiff_loss(k_parameters)\n", + " bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " pareto_bstray, pareto_kdiff = pareto_frontier(bstray, kdiff)\n", + " pareto_kdiff_forx, pareto_bstray_forx = pareto_frontier(kdiff, bstray)\n", + " \n", + " max_kdiff_point = torch.max(kdiff)\n", + " max_bstray_point = torch.max(bstray)\n", + " \n", + " #scale bstray and kdiff\n", + " normalized_kdiff = kdiff / max_kdiff_point\n", + " normalized_bstray = bstray / max_bstray_point\n", + " normalized_pareto_kdiff = pareto_kdiff / max_kdiff_point\n", + " normalized_pareto_bstray = pareto_bstray / max_bstray_point\n", + "\n", + " \n", + " #add extrema points\n", + " if normalize == True:\n", + " zero_tensor = torch.tensor([[0.01]]).cuda()\n", + " max_tensor = torch.tensor([[1]]).cuda()\n", + " max_tensor_bstray = torch.tensor([[max_bstray_point.cpu().data.numpy() * 2]]).cuda()\n", + " max_tensor_kdiff = torch.tensor([[max_kdiff_point.cpu().data.numpy() * 2]]).cuda()\n", + " else:\n", + " zero_tensor = torch.tensor([[0.001]]).cuda()\n", + " max_tensor = torch.tensor([[100]]).cuda()\n", + " max_tensor_bstray = torch.tensor([[max_bstray_point.cpu().data.numpy() * 2]]).cuda()\n", + " max_tensor_kdiff = torch.tensor([[max_kdiff_point.cpu().data.numpy() * 2]]).cuda()\n", + " \n", + " normalized_pareto_bstray_added = torch.cat((zero_tensor, normalized_pareto_bstray, max_tensor),0)\n", + " normalized_pareto_kdiff_added = torch.cat((max_tensor, normalized_pareto_kdiff, zero_tensor),0)\n", + " \n", + " pareto_bstray_added = torch.cat((zero_tensor, pareto_bstray, max_tensor_bstray),0)\n", + " pareto_kdiff_added = torch.cat((max_tensor_kdiff, pareto_kdiff, zero_tensor),0)\n", + " \n", + " \n", + " #numpy conversions for curve fit and plotting\n", + " normalized_bstray_numpy = normalized_bstray.cpu().data.numpy()\n", + " normalized_kdiff_numpy = normalized_kdiff.cpu().data.numpy()\n", + " \n", + " normalized_pareto_bstray_numpy = normalized_pareto_bstray.cpu().data.numpy()\n", + " normalized_pareto_kdiff_numpy = normalized_pareto_kdiff.cpu().data.numpy()\n", + " \n", + " normalized_pareto_bstray_added_numpy = normalized_pareto_bstray_added.cpu().data.numpy()\n", + " normalized_pareto_kdiff_added_numpy = normalized_pareto_kdiff_added.cpu().data.numpy()\n", + " \n", + " \n", + " pareto_bstray_added_numpy = pareto_bstray_added.cpu().data.numpy()\n", + " pareto_kdiff_added_numpy = pareto_kdiff_added.cpu().data.numpy()\n", + " pareto_bstray_numpy = pareto_bstray.cpu().data.numpy()\n", + " pareto_kdiff_numpy = pareto_kdiff.cpu().data.numpy()\n", + " pareto_bstray_forx_numpy = pareto_bstray_forx.cpu().data.numpy()\n", + " pareto_kdiff_forx_numpy = pareto_kdiff_forx.cpu().data.numpy()\n", + "\n", + " bstray_numpy = bstray.cpu().data.numpy()\n", + " kdiff_numpy = kdiff.cpu().data.numpy()\n", + " \n", + "\n", + " #compute delta y loss from utility fit function \n", + " if len(pareto_kdiff_numpy) > 3:\n", + " # f = interp1d(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0])\n", + " # f2 = interp1d(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0], kind = 'cubic')\n", + " # plt.plot(pareto_bstray_numpy[:,0], f2(pareto_bstray_numpy[:,0]), '--', pareto_bstray_numpy, pareto_kdiff_numpy, 'o')\n", + " \n", + " \n", + "\n", + " spl_y = findSpline(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0])\n", + " y_calc = torch.as_tensor(spl_y(bstray_numpy)).cuda()\n", + " # plt.plot(pareto_bstray_numpy[:,0], spl_y(pareto_bstray_numpy[:,0]), '--', pareto_bstray_fory_numpy, pareto_kdiff_fory_numpy, 'o')\n", + " # plt.show()\n", + " y_loss = kdiff - y_calc\n", + " y_loss_relu = torch.nn.functional.relu(y_loss)\n", + "\n", + "\n", + " # pareto_kdiff_numpy_reverse = numpy.flip(pareto_kdiff_numpy[:, 0], 0) \n", + " # pareto_bstray_numpy_reverse = numpy.flip(pareto_bstray_numpy[:,0], 0)\n", + " # # print(pareto_bstray_numpy, pareto_bstray_numpy_reverse)\n", + " # x_calc_reverse = numpy.flip(spl_x(kdiff_numpy), 0)\n", + " \n", + " \n", + " spl_x = findSpline(pareto_kdiff_forx_numpy[:,0], pareto_bstray_forx_numpy[:,0])\n", + " # plt.plot(spl_x(pareto_kdiff_fory_numpy[:,0]), pareto_kdiff_fory_numpy[:,0], '--', pareto_bstray_fory_numpy, pareto_kdiff_fory_numpy, 'o')\n", + " # plt.show()\n", + " \n", + " \n", + " x_calc = torch.as_tensor(spl_x(kdiff_numpy)).cuda()\n", + " x_loss = bstray - x_calc\n", + " x_loss_relu = torch.nn.functional.relu(x_loss)\n", + " \n", + "\n", + " if len(pareto_bstray) > 3 and len(pareto_kdiff) > 3:\n", + " combined_loss = torch.sum(y_loss_relu * x_loss_relu)\n", + " \n", + " # combined_loss = torch.sum(y_loss_relu)\n", + " # combined_loss = torch.mean(bstray * kdiff)\n", + " # elif len(pareto_bstray) > 0 and len(pareto_kdiff) > 0:\n", + " # combined_loss = torch.sum(pareto_bstray * pareto_kdiff)\n", + " # print('3 or less pareto points, grad using pareto points')\n", + " else: \n", + " combined_loss = torch.zeros(1, requires_grad=True)\n", + " print('No new pareto points found, used zero grad')\n", + "\n", + " # Push loss through the optimizer and update the GP model\n", + " try:\n", + " combined_loss.backward(retain_graph=True)\n", + " optimizer.step()\n", + " except Exception as e:\n", + " print(f\"Error on epoch {epoch} regarding\", e)\n", + " break\n", + "\n", + " \n", + " if epoch % 50 == 0:\n", + " \n", + " print(combined_loss)\n", + " plt.plot(pareto_bstray_numpy[:,0], spl_y(pareto_bstray_numpy[:,0]), '--', bstray_numpy, kdiff_numpy, 'o')\n", + " plt.plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + " plt.plot(x_calc.cpu().data.numpy(), kdiff_numpy, 'o')\n", + " plt.xlim([0,100])\n", + " plt.ylim([0,0.5])\n", + " plt.show()\n", + "# figure, axis = plt.subplots(2, 1, figsize=(10,10))\n", + "# plt.figure(figsize=(10, 5))\n", + " \n", + "# if normalize == True:\n", + "# axis[0].plot(normalized_pareto_bstray_numpy, normalized_pareto_kdiff_numpy)\n", + "# axis[0].scatter(x_calc.cpu().data.numpy(), normalized_kdiff_numpy)\n", + "# axis[0].scatter(normalized_bstray_numpy, normalized_kdiff_numpy)\n", + "# axis[0].set_xlim([0,1])\n", + "# axis[0].set_ylim([0,1])\n", + "# axis[0].set_xlabel(\"Bstray\")\n", + "# axis[0].set_ylabel(\"Kdiff %\")\n", + "# axis[0].set_title(f\"x_calc vs. designs, epoch: {epoch}\")\n", + " \n", + "# axis[1].plot(normalized_pareto_bstray_numpy, normalized_pareto_kdiff_numpy)\n", + "# axis[1].scatter(normalized_bstray_numpy, y_calc.cpu().data.numpy())\n", + "# axis[1].scatter(normalized_bstray_numpy, normalized_kdiff_numpy)\n", + "# axis[1].set_xlim([0,1])\n", + "# axis[1].set_ylim([0,1])\n", + "# axis[1].set_xlabel(\"Bstray\")\n", + "# axis[1].set_ylabel(\"Kdiff %\")\n", + "# axis[1].set_title(f\"y_calc vs. designs, epoch: {epoch}\")\n", + "# else:\n", + "# axis[0].plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# axis[0].scatter(x_calc.cpu().data.numpy(), kdiff_numpy)\n", + "# axis[0].scatter(bstray_numpy, kdiff_numpy)\n", + "# axis[0].set_xlim([0,100])\n", + "# axis[0].set_ylim([0,1])\n", + "# axis[0].set_xlabel(\"Bstray\")\n", + "# axis[0].set_ylabel(\"Kdiff %\")\n", + "# axis[0].set_title(f\"x_calc vs. designs, epoch: {epoch}\")\n", + " \n", + "# axis[1].plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# axis[1].scatter(bstray_numpy, y_calc.cpu().data.numpy())\n", + "# axis[1].scatter(bstray_numpy, kdiff_numpy)\n", + "# axis[1].set_xlim([0,100])\n", + "# axis[1].set_ylim([0,1])\n", + "# axis[1].set_xlabel(\"Bstray\")\n", + "# axis[1].set_ylabel(\"Kdiff %\")\n", + "# axis[1].set_title(f\"y_calc vs. designs, epoch: {epoch}\")\n", + " \n", + " \n", + " # plt.xlim([0,100])\n", + " # plt.ylim([0,0.5])\n", + " # plt.show()\n", + " #paretopoints graphing test\n", + " pareto_bstray_test, pareto_kdiff_test = oldgraph_pareto_frontier(bstray.cpu().detach().numpy(), kdiff.cpu().detach().numpy())\n", + " pareto_bstray_points2.append(pareto_bstray_test)\n", + " pareto_kdiff_points2.append(pareto_kdiff_test)\n", + "\n", + " # Store metrics for plotting or further logging\n", + " combined_losses.append(combined_loss.cpu().data.numpy())\n", + " kdiffs.append(torch.mean(kdiff).cpu().data.numpy())\n", + " bstrays.append(torch.mean(bstray).cpu().data.numpy())\n", + " kdiff_designs.append(kdiff.cpu().data.numpy())\n", + " bstray_designs.append(bstray.cpu().data.numpy())\n", + " \n", + " p_front_x.append(pareto_bstray)\n", + " p_front_y.append(pareto_kdiff)\n", + " pareto_bstray_points.append(pareto_bstray[:].cpu().detach().numpy())\n", + " pareto_kdiff_points.append(pareto_kdiff[:].cpu().detach().numpy())\n", + "\n", + "\n", + " print(f\"Epoch: {epoch:4d}\")\n", + " # print(f\" Combined Loss: {combined_losses[-1]:.10f}\")\n", + " print(f\" Kdiff : {100 * kdiffs[-1]:.4f}%\")\n", + " print(f\" Bstray : {bstrays[-1]:.4f}\")\n", + " print(f\" Pareto Kdiff : {100 * torch.mean(p_front_y[-1]).cpu().data.numpy():.4f}%\")\n", + " print(f\" Pareto Bstray : {torch.mean(p_front_x[-1]).cpu().data.numpy():.4f}\")\n", + "\n", + " # Save model for further training or testing\n", + " torch.save(gp_model, f\"./models/gp_model_{epoch:06}.pt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "4\n", + "13 13\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#scatter plot of final pareto points\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff Combined Final Frontier\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.5])\n", + "\n", + "print(len(pareto_bstray_points2[:]))\n", + "print(len(pareto_kdiff_points2[:]))\n", + "# print(pareto_bstray_points2[:])\n", + "final_x = []\n", + "for list in pareto_bstray_points2:\n", + " for item in list:\n", + " final_x.append(item)\n", + "\n", + "\n", + "final_y = []\n", + "for list in pareto_kdiff_points2:\n", + " for item in list:\n", + " final_y.append(item)\n", + "\n", + "final_x, final_y = oldgraph_pareto_frontier(final_x, final_y)\n", + "print(len(final_x),len(final_y))\n", + "\n", + "plt.scatter(final_x, final_y)\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "4\n", + "13 13\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#line plot of final pareto points\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff Combined Final Frontier\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points2[:]))\n", + "print(len(pareto_kdiff_points2[:]))\n", + "# print(pareto_bstray_points2[:])\n", + "final_x = []\n", + "for list in pareto_bstray_points2:\n", + " for item in list:\n", + " final_x.append(item)\n", + "\n", + "\n", + "final_y = []\n", + "for list in pareto_kdiff_points2:\n", + " for item in list:\n", + " final_y.append(item)\n", + "\n", + "final_x, final_y = oldgraph_pareto_frontier(final_x, final_y)\n", + "print(len(final_x),len(final_y))\n", + "\n", + "plt.plot(final_x, final_y)\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'\\nplt.figure(figsize=(10, 5))\\nplt.title(f\"Pareto Kdiff During Training\")\\nplt.xlabel(\"Epoch\")\\nplt.ylabel(\"Coupling %\")\\n\\nfor x in pareto_kdiff_points:\\n plt.plot(x)\\nplt.show()\\n'" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points), len(pareto_kdiff_points))\n", + "\n", + "while i < len(pareto_bstray_points):\n", + " plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()\n", + "\n", + "\n", + "'''\n", + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"Pareto Kdiff During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Coupling %\")\n", + "\n", + "for x in pareto_kdiff_points:\n", + " plt.plot(x)\n", + "plt.show()\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 4\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points), len(pareto_kdiff_points))\n", + "\n", + "while i < len(pareto_bstray_points):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "4 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "print(len(bstray_designs))\n", + "#colors = numpy.arange(len(bstray_designs))\n", + "colors = numpy.arange(1000)\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Bstray vs Kdiff During Training, Generated Designs\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(bstrays), len(kdiffs))\n", + "\n", + "while i < len(bstray_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " fig = plt.scatter(bstray_designs[i], kdiff_designs[i], label = f\"epoch: {i*100}\")\n", + " i+=1\n", + " \n", + "# plt.colorbar()\n", + "plt.legend()\n", + "# from matplotlib.patches import Rectangle\n", + "# someX, someY = 45, 0.5\n", + "# fig,ax = plt.subplots()\n", + "# currentAxis = plt.gca()\n", + "# currentAxis.add_patch(Rectangle((someX -0.1, someY-0.1), 0.2, 0.2, alpha=0.5, facecolor='red'))\n", + "#plt.savefig('Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(bstray_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " ax.scatter(bstray_designs[i], kdiff_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,200)\n", + "plt.ylim(0, 0.2)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"Bstray\", fontsize = 18)\n", + "plt.ylabel('Coupling %', fontsize = 18)\n", + "plt.suptitle(f\"Bstray vs Kdiff During Training, Generated Designs\" , fontsize = 20, y=1.01)\n", + "# plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle(( 0,0 ), 45, 0.15, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "noise = generate_noise(shape=(10_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "pareto_bstray, pareto_kdiff = pareto_frontier(bstray, kdiff)\n", + "pareto_kdiff = pareto_kdiff * 100\n", + "\n", + "plt.plot(pareto_bstray.detach().cpu(), pareto_kdiff.detach().cpu())" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ISZwo/tkkgqviJM60Nds4gTg2Ikk2QWk1F79b6+OnAVGafMduZv369erw4cOdHgYhJAK6Gto5J5tK8+7Gsf3aDUkhn8PB0c21x2GCqd1zYhp3VgTzSjUtPFu9jl66N5JERI4opdb7n6cmTghJnG7XrqNg4xqw0Xrb3VnMNG63cI07xsMvnsOBE2etv6dWr6OTHdZ6AQpxQkhb6JXUJRvXgI1gMgnV4nQJK0b3tqQZ6zZLQVXwvGOM2l611WyCuHP5+w1GpxNCSARsosVtBFNQtH2zHcSCUvl04zZ9tpewKm+tZhPEmY3Qj1CIE0JIBGxSt2wEk41QLZXn8PFHj2LF6F5sHNvfUqlV/7izoitsqydIK241H5357K1BczohhEQkzDVgkybnjxMwhRj7fdbe9/oJswB4x60LKBM0auJAsFbcarxDL8VLdAIKcUIIiRlbweQVqqbocS9hAV+tpvJtWrUMjx8pRs7RbzXeoVfiJTpBokJcRN4L4LcBZAH8gVJqTHPMTwLYA8AB8A9KqZ9IckyEENIOogomU9tOP2Gm7SiFcnRjXH/tUmrFKSIxIS4iWQC/C+DdAF4C8IyIPKmU+prnmDyA/wLgvUqpUyLy+qTGQwgh3YxfM86IaPuWJ2nads9BoZ0ektTE3w7gOaXUtwBARD4P4IMAvuY55iMAnlBKnQIApdTLCY6HEEK6mjCftU6r1qWUeYvOkN4myej0AoDTnscvVZ/z8mYAQyLylyJyRER+JsHxEEJIarCJgo/SHY70Jklq4rr8Bb9taADADQDeBSAH4G9F5JBS6u/rTiTyUQAfBYDly5cnMFRCCOk+wkzbrHZGktTEXwJwjefxGwGc0RzzP5VSF5RS/wDgrwGs9Z9IKfVZpdR6pdT6ZcuWJTZgQghJE6x2RpIU4s8AeJOIrBCRBQA+BOBJ3zH/HcCPi8iAiAwCuBHA1xMcEyGE9AysdkYSE+JKqVkAvwxgHyqC+VGl1HER+ZiIfKx6zNcB/E8AzwL4CippaH+X1JgIIaSXYLUzwlakhBCSYtiLuz9gK1JCCOlBmNfd31CIE0JIjFAzJu2EQpwQQmLCX6DFpmkJIa3AVqSEEBITQXnbhCQBhTghhMQE87ZJu6EQJ4SQmGDeNmk3FOKEkL5jYrKIjWP7sWJ0LzaO7Y+t1jjztkm7YWAbIaSvSDL4LI5WoIREgUKcENJXJN00hHnbpJ3QnE4I6SsYfEZ6CWrihJC+4up8DkWNwGbwGQvVpBFq4oSQvoLBZ3rcWIHidAkKV2IF4gr6I8lAIU4I6StGhgt44NY1KORzEACFfA4P3Lqm7zVOFqpJJzSnE0L6DgafNcJYgXRCTZwQQggL1aQUCnFCCCGMFUgpNKcTQghhoZqUQiFOCCEEAGMF0gjN6YQQQkhKoRAnhBBCUgqFOCGEEJJS6BMnhJAehCVU+wMKcUII6TGSbLdKugua0wkhpMdgCdX+gUKcEEJ6DJZQ7R8oxAkhpMdgCdX+gUKcEEIiMDFZxMax/Vgxuhcbx/Z3ZatOllDtHxjYRgghlqQlYIwlVPsHCnFCCLEkKGCs2wQkS6j2BzSnE0KIJQwYI90GhTghhFjCgDHSbVCIE0KIJQwYI90GfeKEEGJJtwSMsaQqcaEQJ4SQCHQ6YKyVCHkK/96D5nRCCEkRzZZUdYV/cboEhSvCvxvz3Ik9FOKEEJIimo2QZz313oTmdEJIaulH8/DV+RyKGoEdFiHP9LjehJo4ISSV9Kt5uNkIeabH9SYU4oSQVNKv5uGR4QIeuHUNCvkcBEAhn8MDt64JtUAwPa43oTmdEJJK0mQejtvs32yE/CInU9v45HMOdt6yuufdD70OhTghJJU06xtuN93QNMU/BgC4NDtv/d5+iztIEzSnE0JSSVrMw91g9mdaWu9CIU4ISSXN+obbTTeY/ZmW1rvQnE4ISS2drp5mQzeY/ZmW1rtQEyeEkBiZmCxi49h+rBjdi41j+7Fp1bKOm/2Zlta7UIgTQkhM6HzIjx8p4rYbCh01+zMtrXehOZ0QQmLC5EM+cOIsDo5u7tCoKjTjeuiWrm3EDIU4IYTERC/6kNMQd9DP0JxOCCExQR8yaTcU4oQQEhP0IZN2Q3M6IYTEBH3IpN1QiBNCSIzQh0zaCYU4IYR0GaxXTmyhECeEkC4iroYp3Aj0BwxsI4SQLiKOeuVsXNI/UBMnhFhBza49xJFrHrQR4HfWW1ATJ4SEQs2ufcSRa96LRWeIHgpxQkgobEnZPuLINWfRmf6BQpwQEkoaNTt/N7G0WA3i6JPOojP9Q6I+cRF5L4DfBpAF8AdKqTHf6z8J4L8DeL761BNKqU8mOSZCSHS6oSd2FOKK8O4Ureaas+hM/5CYEBeRLIDfBfBuAC8BeEZEnlRKfc136P9WSn0gqXEQQlpn+5aVdUIR6G7NjoFdLDrTLySpib8dwHNKqW8BgIh8HsAHAfiFOCGky0mbZpdG838zMGOAJCnECwBOex6/BOBGzXHvEJGjAM4A+HdKqeMJjokQ0iRp0uyaNf/7heKmVctw4MRZnJkuYUnOgQgwPVPuCoFp6zKIIujdY4vTJWRFMKcUCl1wrcRMkkJcNM8p3+OvArhWKfWqiLwfwASANzWcSOSjAD4KAMuXL495mISQXmPTqmV46NAp7fN+JiaL2PnkcUyXynXPF6dLdefwvm7jY09KS/YKWj9+l8HEZBHbv3AU5XlVG/f2LxzVjtu/KZhTV94TNZ6AFoL2kaQQfwnANZ7Hb0RF266hlPpHz99fEpH/IiKvU0r9g++4zwL4LACsX7/evxEghHQBQVpskgu5TmAcOHFWe6z/eb+Qi4JXYHoFa0YA/+mCBOHEZBG7njqO8zOVTUI+52DnLau1c+UXtDq8LoOdTx5vuLbyvMLOJ4/XCXrTpkB3rWGkPagwbSQpxJ8B8CYRWQGgCOBDAD7iPUBEfgjAd5VSSkTejkrK2/cSHBMhJAF0C7dXi01qITcJDJOQ8wuq3ftONiXAvefzj8F0ulJ5DjufPN6w0Rl/5jTKc1feNF0qG7VlXcCeH6/LwG9d8D9vsylwsY0nYFBhe0lMiCulZkXklwHsQyXF7I+UUsdF5GPV1z8D4HYAvyQiswBKAD6klKKmTUjKsBEucSzkfq175vKsVmC4/lwdd/3+3+KF75Vwplp9rhUEwK6njlsJQaAiPF0BWpwu4eFDp7RjKM8rfPzRRkEeJkgFepeBCZvvzcU2nbBfggq7hUTzxJVSXwLwJd9zn/H8/TsAfifJMRBC4sPk67RdoFtZyHVat4k5pSBoDMIBgIPfPNf0GPwooGYGb/b9JuaUwj3jU7hnfKoWXGYK2POe7/EjRay/dilGhgsYGnS048tIZT5tv48o6YRpqymQdlixjRBiRVD9dNsFupWFPIrWWMjnWtayuwl3rjetWtZQic2PtxzujptXw8k2xhjPK+DeJ44hP+gYz5OVyvuiVoxjtbj2QiFOCLEiyNepW7j9RFnIdSVTo2qNhSY3DKLLq2mRfM7BUIDAtKFUnsOBE2fxwK1rQs9VnC5h49h+AMDu29fWBLL/fEpBK3D3bF2Hbz7wfrwwdhMOjm6O5AKJo2wssUfS5oJev369Onz4cKeHQUjfsWJ0r1a7FQDPj90UW3S6Ltgq52SxcCCjDdTK5xwsXjjQ8LlBZmc/hXwOB0c3B15nM+RzDj6w9g218fhN/Dkni9tuKGDvs9+2NsubTOQ6ck4WD9y6BtvGp4zf3YNb1zEdLAWIyBGl1Hr/8+wnTgixwuTrVACuG90bW1EQk8a/yMkg52QbhLs3HStKtLWLAHUWgjC/sy1uqph3PKr6eQqom6/7R9ZgYrJoFLbesUbxwbuWkiA/dZxFfJgf3n5oTieEWBFmMo+rx7jJbD49U66ZaYGKz9YVUu5nmvzmOnOyi0J9BPj2LSu1laqicuHyrDZy3RXgfjP1yHABd21YHvjZzVgIzkyXtN+dkxVcuDTb0OWt2e5v7DnfGSjECSFWuL7OIIFo22M8SFAE9cIeGS7UBJK/oliQ33y+Wj5Uh//5keFCLOb08pwyas2mcd4/sgYPbl3XtD9fhztvXj/10KADqErKm1fg3jdxrGlBzJ7znYFCnBBizchwwZh/7RIWgBamsYVFNwcJi6ANQJSo6VaD0MIIitIfGS7g4Ojm2AS5e33ueZ8fuwkAGorclMpzeOTp000LYuaHdwYKcUKINROTxVBTc1gaWZjGFhbdHCQsggR1lKjpuOJ98zmn6XSrOISf7ruamCwaLQSmDZrNWII2UCQ5GNhGCLFm976TgaZmGwFlo7EFBVuFBWm549QFV4UFcbmBWaZypX6crACqUasFrgTdBY0nCNN1ZkUwrxQyAVXpXBTQUDBm55PRG0XaCOK09ZzvFSjECSFG/NHGQVHb+Wqrzm3jU7XccZ2waqail3cc+UEHTkbqBKdXWDQbbd1MZLu35rmf224o1G0edJ8XJNy3b1mJ7Y8drfsMJyvY+rZrjClrQbgdzMJqxevS4GwEcdp6zvcKFOKEEC26MqdBQuOVapCUe6yp4UlUjc0/Dr8peGjQwY6b69PMbASJ/7gLlxrrsLeCqZOa7pp083X4xXMNm4TynMLnnj5Va7IS1epv0+zFjZ5vRhCnqed8r8BiL4QQLRvH9ms15ijan5tK1WwhmInJIj7+6NFAs7Fb0AQAPvHEs5gpz9e97mQEVy0awPRMGUuq1oJW6p1HwVuYxZ23oOYs3vm6Z3yqLWM0jYF0F6ZiLxTihPQ4YZqp6fWgymUFy4IobkUwv1kYaNSgdeO2NW8POhmU51RLbUW7Bdu5TQJ3Q0RtuvtgxTZC+pAws23Q6ybftaup2ZQnzYjgE088q/Udn58pB/YYj9LwxK99pxVBcHe2sPe2soVxK8y1KsBZta29MMWMkB4mLJ0ralMTr+/aJmJ5TqlAARuUg9yP+cWtCGHXl91sfvmFy7MtfHoFVm1rP9TECelhwtK5gl4PizbWBag1O0ad9hZXDfN+ojhdQqbJmrHlOYXd+07WLDTNaNNBm8Ju0MZ70UpAnzghPYwpOA2oaG3nL1zSasq2wU02gWdhLF6QxbxCQ7T6bTcU8PiRYqwR4yScfM7BhcuzdS4QW195WKe7TmLqjpeWGACTT5zmdEJ6mKCmJcXpklaAO1mxLtAxMlzAp+9cG9pLPIgLl+e02pvbOzufS7YEKqlnulRuiGGwLb3azVXberW2O4U4IT2Mt9SoLYsXDETSTGwaozSDa9JfvDDY6xfvp3YXQ4NOrUTsxuuXtnQud56aNbfbxChEqU/fbnq1tjuFOCE9jtv4wnbtfsWy5Kj/M+Zjds1dnc9hYrIY6BdvNSK72xlcMIAHt67DwdHNePgX34E9W9dZWyYygro68Q9uXYcXxm7Ctx64KdJ5XGy06Sj16dtNN1sJWoGBbYT0CbaBYrpFzSYgKKzWt1sVzaYuuQDYtGpZLQXNRDcJ8LANxaCTwX+49S3Y9uiUdYMVf0rgyHDBurb7R25cjvtH1hhfX7xwAK+UylY12P3adND90K1V23q1tjs1cUL6hCD/uItuUbNNGzKZUj9951o8P3YTDo5uxs5bVlv5zxUqZUtbCWqLajXOOZk6DTIKTkaQCbBTZwT4D7e+BSPDBTx457qGOXCyYtSM/X5bG/PvxuuXGgW4//vUCXAnI3WmfK82rbsf7hmfwrpdX+7qVLJuthK0AjVxQvoEXcqYTflTm7QhVzMrledqZUULmvP5x2DSAt3a3a2QczKRisDcdsMb6wTf9fd+KVBDlarqfXU+h5nLs4GlXJfknIZmKFGq5HnnIsiiYlOwxVREx2sxCUq9Mr1/uhRcvKcb6FYrQStQiBPSRzSziIUFBPlTd+aUquvhHTQGU9rP9i0rsXvfyZbyxEvleeRzjnVbUX/Dkg0/PISD3zynPdbJCnbfvrZ2HStG9waee9on4E3fg02Ht+1bVmLb+JRW2C9eGB6UaPo+55WySgML2lx1U054v0BzOiEkkLCAoFZSd4JMnDbm/7Bx25rvgUbh9ML39MIqK/UCfGKyiExIZL5t8JRNdPfIcMHoe7exXrQa4BV2XNqjvdMGhTghJJAwwdJq6o4bPe/6zb1mZ6+AN6WwiVQ0Y934/Cl2QaLWL5yCNFa/JSGsy1qUvHsbv63JZ28jiFtNAwvbXKU92jtt0JxOCAkkrPyqjQm4lc8ONVkrYPfta62ipScmi9j11PEG/7VOiNlcV1iTlqFBB0oB28anavXow0zNNi6PViKtw75P2/fbziNJFpZdJYS0RLvKWZpKyGZF8Ok710b6LG+KlNtjfHqmXCfQbK4rqMzog1vXJTov3VAHvBvG0C+wnzghJDHasZgH9RdvVjiGCer7Jo7hkadPY04pZEXw4RuvqYtgN20sXHN3UCtXCsAKnAc72E+ckB6nk4thO1J33PPrGq6UynP4+KNH644Lw9S8xRuU9/iRYu31OaXw+JEi1l+7NLCTm2tS3jY+pf1ct2tbUJ932/GnXfjFMQ/9DgPbCOkB+qWPc1B51zmlsG18CteN7sXGsf2B1x4WkHZmumQVdT8yXMBtNxRqQXdZEdx2Q2VDExQF3mozjl75vnu1KUk7oRAnpAfop8UwP2iu+e2K5DChFhaQtiTnWOXHr9v1ZTx06FSDtj4xWTRGgW9atcyY/16cLmHj2H6sCNmI9Mr33atNSdoJzemE9AD9shhOTBbx6sVZq2NL5TnsfPJ4Q1W5M1XtNQiR4Oj0+yaO4eFDp7TncYWp24/dXyHv8SNmbVlwxY8eZFrule87ycyGfoFCnJAeoNnFMG1+1d37TqI8bx+MO10q17TZ7V84av3e6Zkydty8Wuvv3rRqmVGAu7jC1B8rsHFsv9ECoGugYqqA1ivCr1ebkrQTmtMJ6QGaKeCRRr9qM5rm7n0nsfPJ45GE/9X5nLbwym03FPDI06dDNXmTMA0af5QqbN3ctzsKvdqUpJ1QEyckRZg0Z9sCHt7365qPtLP2dTNWANt2ql5szOdenIzUhKGuznvUtp1eTOMPSknTbQhaLdjSTfRiU5J2QiFOSEoIS8cJWwx1jUp0tMOv2mxqkcn8etsNBXzu6VPQKds2/bK9XLVI30QkLBgOqFRo23GzvovYxGQRM5cb/fmu0D/84rkGM33QhoDCjwAU4oSkBpuWoFHfr6MdftVmr8XUTvXxI0WtAAfMmxUT/o5jLkGbGwFw14bloT28/deczzn4wNo3YOeTxxu6rQlQS1cjxASFOCEpodWIZJvj2uVXbeVaogSLNYMphc1kCndLrDbTwxuoFJTRvabQ2B6VED8MbCMkJSTdQnJo0GlbUFGr1+KlGfN/IZ/D3RuWN3Q/A4BXL85qg/u2b1kJJ9N4/IDmHLZjnC6VAzcgaUsZI+2HmjghKaHVdBzd+71cLM/HMk4/ugC2qNeia1hyfqaMrEikoDXgSu1yAPgfR7/dYMYuzyvco+k6NjJc0HbuKs+pUDdAMwF57vsICYJCnJCUEFcLyd37TmoFir84io6oEeWmALYHbl2DB25doz2X+xnF6RKy1aA0bw61V+hG9Xf7NwqvlPT+b+9YgStz14y/HDBvwBY5mYZNgWmshOhgFzNC+hBTC00A2GPw7+qqlLnCtVAVwgDwiSeexUyIVu/6kYH6Tcl1P5DD33zzXGTtOoisCOaV0m46TF3IvHg196CuZWGdyXSvAdBaR4Ki3El/wlakhMRA2iqcmcYbJLy8Qst7nm3jU4HC1ckIZueVtQDOAMhmBeW5ZNegxQuyUErVNhb5nIOdt6wGAK153I+74TBZMNzWpUCjQPa+FkW4d/M9RToDhTghLRLWezrpz/b7hKdnyoGL/sRksaHUqJMRbH37NfjiV4u4cNkcUFXI5+qEikmA9QNDgw4ulue1sQTe1DLTxkj3fhvhTogXCnFCWiTMlBqFKNqXThh78W8kvD7lZvDX8HYyEqlkaS+hq2fux/3+g1wUOvI5B5dm9cKdgpz4MQlxBrYRYklcnaOiVisLq/vtbUFpYx4Ow/9J/SrAgXABDlz5/qNGoPuj4oH2lr0lvQGFOCGWxNUpbObybKRqZbrF3k9xuoR7xqdCjyPhFPI5XLg0azXvwJXv3xSBvnAgY30ugLnhJBos9kKIJbrOUQJg06plxvfoOoWZNGX/4j0xWcTGsf2tDptEIJ9zcHB0s7XQ9aaBmTpy7bxlNcLLwVyBueEkCtTECbFkZLjQ0KRCoVI2c/21S5tumuHiXbx16VwkWZyMYOctqzExWbT2hftjGUxNSWytJILKRm/j2H4GuRErKMQJicCBE2cbFvcgU3gU0+iFS1fKfVKAt5eCL/0uaO4FwPNjN1md13WlmBgadDC4YADF6VLdxsG2qxshFOKERCBqcFt+0NGaz3Wa3nSpjHufOIZFToYCvI34i9uEbbxszd2mzmUuOSdbK+iiy3wolee05V8J8UKfOCERiNK4Y2KyaCzraRLSpfJcy9HlpDWChHSUUqi7njpuFOCuv9wVzEEbB1cr1zVlIYRCnJAI6ILbTAv77n0njT2uSfdwz/gUNo7tx8RkEROTRcxcntUeF6XL28RkMXAzdma6hN37TtYEc5h2700jJMQLzemERCBKExKmCqWH4nQJ2x87Cih9XryTlUi1zMMErpup4Pq9wzrMAbyfiB4KcUIiYopA9rMk50TKDyadJaiGu027US+2AtfVsN2Kf0GV9ph6RnTQnE5IQkiU5GDS9UTRhKMIXPe8I8MFHBzdjD1b11m7bAihECepxS2GsmJ0b82n2U2Yek8D1dQi58rPb9DJwMnUS30nI8hm9DuBbEaw8fqlkYqIkNZYknOs7zdTYSAdfoFvKhrD6HSig+Z0kkqi1h/vBKYyrVmp9696G5ZkRTCnVC1v+fCL5/C5p0/VBcgNDTq46S1vwONHiqlORfNe6+CCDL7x8oVI7/f2Mk+6w5qTEVy4fKUUq+5+85fXve2GAg6cOFt7vGnVMjx+pNhQllWnYdu6bAhJVBMXkfeKyEkReU5ERgOOe5uIzInI7UmOh/QOukpo3RbBq9PGAGBOqVrKkLcsq/uad2F//EixToC7ucUHTpy1rgQXBxuvXxqre6CQz+GbD7wfL4zdhIOjmzFzeT7yOVwBvn3LSuRzTmxjcy8zW73gQj6HqxYNNPjMvfebrrzu40eK2L5lJZ6vXuP9I2uoYZPYSawVqYhkAfw9gHcDeAnAMwA+rJT6mua4PwdwEcAfKaUeCzovW5ESAMa2j1GqabWDickiPv7oUcxpfmeFqhnV1N406LU4Nc+ck0GpHCxEC/kczl+4hJmQ42xwsoLdt6+tE15R23h6yTlZ3HZDAZ87dAq60eWbCDD0t5cNut8e3Lou8DuO2qaWEB2mVqRJauJvB/CcUupbSqnLAD4P4IOa4/4VgMcBvJzgWEiPEaXoSicZGS5g3rBRPjNdCqwAF1fr0zCWLl6IocFgTbY4XYpFgAPQVrpp5Xsrledw4MRZ/NbWdVqNvJkMAf8cm8anAGwbn9IKcN15CImbJIV4AcBpz+OXqs/VEJECgJ8C8JkEx0F6kChFVzpN0IajmddsNVadKV+Hq9UbYugAXDEtN/u6l/K8anB7tPq9nZkuYWS4gKkd76lZMWzIOfol0D/3JtcIEPx9RAmGI6QZkhTiul+1/37fA+BXlFKBzj0R+aiIHBaRw2fPno1rfCTFpCmCN2jDEfaaP2LdBu982Aq08zNlZDNSFzHvPZ9J0wQqtce/+cD7IwlPv4Y6Mlxoya+tgJqQjKL9lsrzDQuVaTO4cCDacukGw3n95CyfSuImSSH+EoBrPI/fCOCM75j1AD4vIi8AuB3AfxGREf+JlFKfVUqtV0qtX7bM3LuZ9BduXq0bONSNAhwI33As8gjOfM5X2rOJYLIHt64DUDHzXrikLyGqozynMLR4IfZsXVdnXg/SNIcGHYwMFzAxWYz0WTorw85bVodaDxZkzRPiCsklETcDClemWbcZdIPWopjlsyKhwXCExEGSKWbPAHiTiKwAUATwIQAf8R6glFrh/i0ifwzgfyilJhIcEyEdQZcypOtydWn2it95976TgVXETGwbn6oJ3ulSGRmBdQ13V4u9aOH/diPlw7p16TClVQFXqpb5O73lnCwuzgZ/Rqk8h0VOBjknG2k8bqS7LggtSk94d5wP3LoG2ww9xIvTJawY3RtYsrff8afrcZ7MJKaJK6VmAfwygH0Avg7gUaXUcRH5mIh8LKnPJSQthKXJNRsU5ZfX86qiMXstAaZAtqvzOdz7xLOhQsursUYVcvmcY1yQXevKC2M34cGt6xqsFzbJNNMz5TrLx9CgY7XQtRJIqNPkg4L1mjWvd3uBozjQpevRDWEmsRSzpGCKGekVwtLkdD2mW+EFT+qdTnt2sgIoBZsgdO+5oqSHuVpqFK3Kq5XZfE4+52DxwoE6LQ6AMQ3MJSuCT9+5tmFspu8hK4J5pYya4sRkEdsfO2plTSlYaJu676yZ+Qyj01qwab77PV3PlGLGim2EdAhTRTdXg7PpbGWLP3rc340t52QipZBdN7q3JnhsG72IIFDgTEwWsfPJ47VzeSvTRZmD6VK5rrLaPeNTDaZ5HXNKYfsXjtYeu3OTr2ry3tlxMoLdd6ytHXfP+FRtk+AVyN7rCSKs4qCp3oBruYlLyHZDJcR2pVb2CtTECYkZW00mSLMC0FCK1f2/WQr5HM5Ml7Ak50CkYnZ2y4E+fOhUU8VW/MItiMULspi5PNcwJxOTRex66nhg/+124mSAgWywT93JCra+7RrjBsPrF48yrzptMyzmIM4CR92gBXfDGLoRkyZOIU6IhmZNiiYz9eIFA3ilVA4VYINOBgoIraAWFRtNtN0MRtT+u42wTVVQ1T0TbgU47703c3k2cIMTp3DrhkqI7XIbJEGSrgia0wmxpBWToi7IqzynGsy7u546jh03rwZQHwmelFDrNgEOJHet7SLMKlKcLmkDCIM2VEtyTsO9F0TcBY7CXDztwO/qSUt0eqdcERTihPgIihoP+zHa+u3Oz5Rx7xPHsMjJtLWRCWkfAjRo0Pmcg523VDZvOm1TBNb3Q1Ykdu1UF4fh3Si0K+gtjV3cWlk3WoFCnBAftoE1ugXNpMnoKJXnKMBTjkmrNj2/eOFA3YLuDZ5TqlHoB32uP5I+DgEbpAV3Q9BbN9OpgDwKcUJ8mASxW9rT1Up0C9ptNxQiR1OT9OLtZ+76yIO6pnkXdFfbbKZYjgIaBHhcAtakBXdK00wLnXJFJNpPnJA0EtTswl0cdz11XLugHThxFg/cuiZSQxCSXlyftwD4oSWLcPeG5XVV9/xcnc81FGzZ+WTjvRSGv059WOGgOGDqVzCdaspEIU6ID2+tcx2l8pzR7Ol20zK1HyXtoZDP4e4NyxP/nFcv1jc4efjQqUCBfP7CJWx/7Gjde4LyyIcGnYYmODrBEIeADasGl5b2v52iU02ZaE4nRINrUoxSjQy4sqBF8Y3ryOccXLg821Tt9H4nK4Iz0yX8j6PfTvyzyr6i9GHfVpSIfDd1zMbX3aop18YcHxb0RjoTkEdNnJAAomgZ3gUtyCQfRiGfw9SO92D37Wtplm+COaWggEhdx7oN773k7da3fctK7N53skFb1t1vTlZw4dKsVZ11G3N8mtr/9hPUxAkJwKb0qQANGpL7v23ZTRf/4g00piKR5Ol0cRydcLTRlr3R7q9enK2rTxAU6GZrjk9j6levQ02ckABc7cOkERfyOWM/85HhAqZ2vAd7fN24gvAv3n7/vDuOIA2dunvrDGQ6N4uFfC5ydLiffyzNNpj6gwLdTBanjEhPd0zrBaiJE4LgHFuTRmzrD/RrL0G1oXWLt6kX+fYvHG1YqN2a3gdOnI21A5qJODXWTmu/Xvzz2i6C7qkgbdmvpZuqyZnOYbI4uecpTpdqzWGoiXcX1MRJ32PTvzhOf2BQKoptv+iR4QJ237EW+dyVsp5Dgw52374W94+swcHRzdizdV3gD9zJAG96/eLI4/fOwTuvXxqb5m8SmwLEEmnejRaKQSdjfU8FRYfb9nQ3ncN/f+sMPeV5hZ1PHg/9DNJeqImTvse2iEVc/kBTVSxAX0DG+x7/ecLGk80K5g0R7gPZLP7lpjdF8tt7m224m5+kddb8oIOHD52K5TwXy/NdFV8wU57H1wzNS/zWoU2rljUUEnI3f9vGp0I/K8xy5L2frhvdqz0mzcGCvQqFOOl7OlHEQieAN47tj7Ui1u59JwNT1Nxzv2K5MDsZwczlSrTz1fkcLlyajV0g+k3qOScLpeIxs7u5/d7Kat+/NIu5DpnOXSYmi1ZBbA8dOoXFC7LI55yGjnhu29ogvFp+u2qgk+ShECd9Tzd0bgLi30zYvK84XQosE+ri5q27gjCqvz0jQJiszAiwcCBT14Z14UAmdu3P9fNenp3DooEMLlzurGZ+z/gUfvWLx+BkMzXhPHNZv0G6cHkOTmYeD25dVyvZaoqx8OKNt7CJch8adLQFjXRd2UhnoU+c9D1Jl0u09XPHXRHL9n0XLs82VAVzcTKCPVvXYfHCgZYKz8wrYOP1S0OP8fdRDxPgCweaX8JmyvMdF+AuFy7PYbpUrsVkBDVCcX3T3liOMM68UsJ1AWVeS+U53DM+Vbs/d9y8Gk62/p5wslJrn0u6Bwpx0vckWcTCJmjOJe7NhG3BmfKcwlWLBhrS2PI5B1ctGsC28alYIt0PfvNcy+fwE1SnvJeZLpWtg9kAwA1WDyvzWpwuYdv4FA6/eA67b19b95vYfftamty7EFEpq/G8fv16dfjw4U4PgxArgtLJDmoCmnS+SkDfGtKE9xxLcg5EgOmZMvIGE6mLt2jN4RfP4eFDp7om5Yu0FwFqJnsvcfnS6ZOPjogcUUqt9z9PnzghljSz8ETxc5sEeJSIdb+/c7pURs7J1hbkIP+paynY/thR1mzvcxSAXU8dT6TdKfuSxwvN6YRYEMUs7sXWz206v8l/aaq8FVbVy8bETgFOgEo0/8RksRbTcc/4VCztTtvRNrWfCBTiIrKiXQMhpJtpduGx9XObzm/yX0aNZHefDysjS4iXXU8dDw2ei5o9wb7k8RJmTn8MwA0i8hdKqXe1Y0CEdCPNLjymwi5+s2HUwLEgDV93rvygg41j+2tjMJXlJMRLUAyFS9TsiW5J6ewVwoR4RkR2AHiziPxb/4tKqd9KZliEdBetLDzewi6u33vb+FSd39tUN3xIU2UsKGLdVAP7/Ey5Lse7m+qUk/TSTPYE+5LHS5hP/EMALqIi7F+j+UdIXxBH+leQ31snUAXAjptXR0p/Gxku4LYbwoODKMCJDd7a/H6aTcVkX/J4CdPE36uU+g0RWaiU+mRbRkRIF2JrFg/C5Pc25foq3+fpUn683DdxDI88fbopU7mtZk4NPt24JWdtyOcc7LxltVZrblXosi95fIQJ8X8B4LcBjACgECd9TasLT9TAHcEVX3lYGs59E8fwUAtNQiiYuw8R4EeWLcZzL1+I5fsRAN984P2VNrYhaYQ5J4udt6yOZfPqwtzwZAgT4l8XkRcALBORZz3PCwCllHpLYiMjJMXoFiyTX33xgiwuz8439LD2L7Fuaczd+042LICPPH06ictooFVhYlNDPY0IgOfHbjJ2/wKAF8ZuworRvcY5fGHsJu3z/nup2ep5bvyGTjBvWrUMB06c1QrYOLRm5oYnR6AQV0p9WER+CMA+ALe0Z0iEpA9/lbQLl2drmo67YN12QwGPfOV0Q9esC5fnkDXULtfhnu/wi+ew99lvW0UQdwtvWFJp7hHnmIcGHQwuGIilNGyzuAKyYBCybklbkxAuBARI+oWoTcMTP/74jXabs23b/ZLohBZ7UUp9Rym1Vin1ov9fOwZISLfjD1ibLpUbTJWl8hz2PvttY9vLqO0wS+U5PHToVKoEOBDe3CMqblOOJHOMw7ZXTlZqAjIsAFL3ugDYtGpZ3XNBTXNsgyndUgDdEDjG3PDkCNTEReRRpdSdInIM9ZY0mtMJqWLbiCJtAjcVKODwi+eQiRCwpcOkQbu8MHaTUQNevGCgzvQMmH3II8OFhrr0CsDjR4pYf+3SWnvRINPzyHABu546rr2fTDX5Ow1zw5MjzCf+b6r/fyDpgRCSVqhNdI7yvGopoM9l06plxoYvrqAxfc+v+KrqhZmqD5w4q413cE3LNqbnHTfro8a7NdeaueHJEeYT/3b1f5rOSV9i6gjm1bBsgo1yThYLBzKh/bFJZzhw4izu2rC8QZB7BU1c2mSYadnG9BwlarwbosLjjHIn9YSZ07+PgIBUpdRrYx8RIR3Cv9htWrUMjx8p1nUEc/GaOHVahpMRXLVooCbwN61ahr3Pfru9F0SsOTNdwv0ja7D+2qVGQROXNhm2GbDdLNgEpyUdFR5lg8Dc8GQI08RfAwAi8kkA3wHwp6j4w+8CK7aRHkK32IX10y6V53DvE8/i67/+vgY/p5su9uDWdezN3UFsU9q86VdBQghoXZsM2wzEaXpOMiqcaWPdgSiLYBAReVopdWPYc+1g/fr16vDhw+3+WNLjNJO24/KDr1mAs9+/jPmYx0TaQ87J4rYbCsY86SQI02DjMoGb8tLdvPZWMP1mujW4Lu2IyBGl1Hr/82GBbS5zInIXgM+jYl7/MIDwcFxCOohpIdQ930qO8Xe/fznGUZN2I1AY/8rpmvWkHRplmGnZfd3bMEdX5CeMJKPCmTbWHYTmiVf5CIA7AXy3+u8OVAQ5IV2JqdnIu3/rL3HP+FTd89vGpzo8WtJJZsqN1fJsesUnjeke9uaMhxFH4x4TQe1wSfsIC2x7o1LqJaXUCwA+6HvtZgCMWiddhau56LSPUnkO33j5QsPz9FUTHWemS1Zm7aSiv+PwZycZFc60se4gzJz+FyKypSrEa4jIvwBwH4CnkhoY6W1sF74oiyj7ZJM4WZJzQgO3kgzuistc3UxUuM3vjmlj3UGYEN8G4M9F5P1KqW8AgIjci4p5/SeSHhzpTWwXPn+3peJ0CdsfO1p3nP9cFOAkDnJOFiII1YSTjP7uVJWzKBsTpo11nkCfuFLqSwA+BuDPROSfisgeVKq3/TOl1EttGB/pEby1oD/+6FHjwudl11PHG2qQl+cUdj11vPbYtuQpIUE4WUE+50Bwpdb4tKFMrqsJT0wWjQGRcQR3JenPDiJoY0K6j9DodKXUX4jIzwH4SwB/A+BdSqmLCY+L9BD+nb2pxrV/4TPVGvc+38nOVSSdZEXw4RuvCU0pM8VWXJ3P1e5pE3Foy50yVzPqPF3YVmwTAAsBvAvAyyLiNkBhxTYSiq223MzCl22x8QXpL6LkhAcFbgXd03Fqy50wV7NZSboIM6e/Rin12ur/C5RSiz2PKcCJFTY7eN3Cl8852mO9z1OAE1sK+RzeunwJHj50yipta2S4gAduXYNCPldnZh8ZLgTe051u+9kqnTLjk+awLfZCSNOYdvZZEcwrZdSGdt6yGtu/cLQuh9fJCHbesrr2OKyFJCFAxZS4fctKbBufCuwg5sekCZvu6UI+1xEBHmeaG6PO0wWFOEkck1kyTGOxWUy2b1nZIOgJ8XN1Pofd+04asxei+nu7KUf6voljdbX540hzY9R5eqAQJ4nTys7epjwlAK2GRQhwRbgGVeaL6u/tFm11YrKoba4TV5ob6X4oxElbSHJnPzJciFw6NedkUCqzZUmaEAB3bVheaxdanC7VAhvznl7v+UEHSgGvlOr7vu966rgx48GkQQeZqbtBW43TuhBEN/QkJ3ooxElPYPJR6th4/VI8/IvvaEh9I93Ng1vX1QnQqJhiIHNOxlgtsJtabeoEaZCgjiuavNvmgdRj2wCFkK5m+5aVkJBjCvkc9mxdh4d/8R0AGqOPSXQGnfQsIa+U9Fr4RYNFJumiJ94CSBvH9gc2NtE1Q9k2PoXBBVnt8W4gXxyw+Et3Q02c9AQjwwUcfvFcg3/QNcHeP7LG+D5Xm2ilp3i/8rVffx+ARm0NqGQSzAOYiyno8OOP1pfcjUrU/Ocki55E1W53PXW8QZAqABcuz8HJSl1lQ/eej0tLZvGX7iY922hCQrh/ZA0e3LquLq/3wa3rjALcjy4/lgRz38Sxmpm3VJ5DVio2jUI+h913rMWn71iLocEref35nIO7Nyyv+472bF2HPdXvLYg5pSK34vRiyn/etGqZViNOstVmFO12YrJo9OUDwOIFA7X5HBp0sCTn4OFDp0K1e/fcYdaAbmk5GsVy0U+ISlmxjPXr16vDhw93ehikR5mYLOIe9hePRDYjRm3b7SpXsAyGmpgshmYaFPI5HBzd3NRY/X7lTauW4fEjRW36I4CmUiNtuG50r/Z5AfD82E11z4VZiNz36KwhQeO1PT7qeZOgG8bQaUTkiFJqvf95auKEeBgZLoRqhCaGBp06DXPj9Uv7wtceZC735y6HaU8jwwXctWF54Ly1YsYdGS7g4OhmPD92Ew6ObsaBE2cDu5CZKra1wsRk0Xh9Ou027Hrd90T1Xdsen9Q8RIF+eTP0iZOepdm0GF0hjzByThY7bl7d0CI1XXauRjICxFVHxzZ3+f6RNVh/7VJse3RKG1G+xFCOtxlMArI4XcKK0b2JpFOZ0sJMwWhBmRfeAjNRfddRnu90Oh398maoiZOeRBfNG8WfunDA7qchqPh5FzkZ3DM+hevv/RKuM7RbTSO/dee6Op92q9guuiPDBWPtfGnSvKHzqQb5dZu5b2wwzYGCPqjNFKuRzzl1GnFU33W3+LptSNNY202iQlxE3isiJ0XkOREZ1bz+QRF5VkSmROSwiPxYkuMh/UOz5jdX+E8b0pG8uIFzFy7P1gKP3IYsvdCYRVCZx+mZcs09oCObsZeq3kU3LFDJ1M/b9HzQ+Uybuk2rloUGM5bKc/j4o0djC6gyCR6TG0dnzt6zdR2mdrynoQRxlMYlaWp0kqaxtpvEzOkikgXwuwDeDeAlAM+IyJNKqa95DvsLAE8qpZSIvAXAowBWJTUm0j+Emd9MpnbbtqnuArLrqeN16T3tZmjQCYxcbgVX2KH6/5npEt70+sX41tmZuk3KaxYOQMTc/93Fay62SbGKkhIWdj7Tpu7AibN44NY12Pnk8cCNm3u9unHeN3EMjzx9GnNK1XqVB2VENFN33cacHbUUbLeUjrUhTWNtN0n6xN8O4Dml1LcAQEQ+D+CDAGpCXCn1quf4xUDqXYikSwgSAEELvo25NytSM2N2MpL97moJ0naNQQH4xssXsHhBFpdn52tNZ6ZLZeScLPZsXQcA+NUvHsOFy40boZyTwbbxKezedxIzl2cDLSVuWdXGc+iFXZDlJah16JnpUk3I21hf/Oe9b+IYHjp0qvbanFK1x0G1Cdwxxy2QovquO+3rjkKaxtpOkhTiBQCnPY9fAnCj/yAR+SkADwB4PYCb/K8T0gxB2k7Qgm9TvnVeqY4sJqZ0rX/76FRo8Jn73jjQCWh3/tzUr+2PHW2wUMxUK6MFza+7odJZQwTAbTfoF/Iwy0uYVh81QMo9/nNPn9K+/sjTpwO1cQokEhdJ+sR1jrKGdUQp9UWl1CoAIwB+XXsikY9WfeaHz549G+8oSU8SlBYTtODbFHzJ+4qXtAuFihWgOF3C7n0na77Zj9y4PPB9hXwO7zT4s+PEndfd+0625GIwuTMUgAMn9L//sMCnMJ9q1AAp16Jj2jz1QkwESQdJCvGXAFzjefxGAGdMByul/hrA9SLyOs1rn1VKrVdKrV+2bFn8IyU9gT+wCUBdTnBYFK9CRQDddkNwrri7Pk9MFpuOlG4Wv292YrKI+0fWaIPOXBP3wdHNeOF7yafiNKvVRqE4XdIGlm1ataxBa/AK6bBc5yjV+rwWHRPZdt8YpG9JUog/A+BNIrJCRBYA+BCAJ70HiMiPiFTudhF5K4AFAL6X4JhIjxIlpSxowS5Ol/D4kWJgkNF0qVz7PJugsnzuShGYfM6JLWXL60N++BffgT1b19VZBhZ5mpMknU/bilYbFf/3OjFZxONHig018/2md3+hF/9rD9y6xih8syLWFh0A+PCN1xhfIyROEvOJK6VmReSXAewDkAXwR0qp4yLyserrnwFwG4CfEZEygBKArSptdWBJVxAW2OTFG1ik85O673N7VfvJilhHsQOV7llTO97T8HwcDVf8guTS7JWOXOdnyrWAvSitWm0RqVgl/D56m2I5TkYAQVNmd//3qvsugkzvJtzz2Zb3NM1pzslY1+snpFUSzRNXSn1JKfVmpdT1SqlPVZ/7TFWAQyn1G0qp1UqpdUqpdyil/k+S4yG9S9SKTq5WZjJ6npkuGf2ac0pF0mxNmqlN+9Qo5zZtZO4Zn8KFS7OR8rm9CCo92P3+f6WuaOB+Aee1Auianuy+Yy1237626Taw3vmPs5pXlBKjJj/7A7e+JfLnEtIsLLtKegKTVqRQ0XhNKTxLco42tUjhiqbpx/WX22i2Qfm/pvaptvjPHTSe6VIZGQEWZAWXLbRfbzS7AvDVU6/UCWYXv1asa1RxaXYe669dqtVOw9rAmqwh3s1L1BajYdhGjjN3mXQDLLtKeoIwP7fOPz4xWcSFy7PGc+oEuCs4bQKhvPnkftwgvIcPnUJ+0EFOIyBN5wQaNUSbKmLzCli8cCDUJ58VadhUlMpzRv+/V+NttlKeSavd8MNDgQFrQe9tRzWvID87Ie2AmjjpCWz93N5F1jYVKiuCeaW0mpbp85yMYPcda61aQJ6fqRRLuXvD8lrlLz9B7Tfd89lwfqaMPVvXGX3WOScbuea7V+NtxrTt70c+pxQKnjahNgFrADVi0p9QiJOewTWDrhjdqzVP+wWJrc90Tins2bpOGyA3MlzAxGSxsbiJR330l3g1VSs7cOIsPn3n2sglOaME2bnjdt9XnC7VCU43dco2CM4/tqimbf+GZk6pwKI8poA1Fk8h/QqFOOk5bAVJlIjt7Y8dBaDvMqXT6MtzqmZC9pd4NeGWAAWAXU8dr5mvFw5kcPjFc0ZNM0oAlxucFib0gqLLgywTUeuCB5nf2X6SkHDoEyc9h62PNEqBj/Kcwq6njmtfCxI2UbRk7ybjYvlKqth0qYyHDp0y5sDbBnBlUAnWC+vG5UZom5hXyugDjhLdDQTPHdtPEhIONXHSc+h8pJtWLcPufSexbXyqQYN0j1uSc1Cem9fWBgcq/uThT34Z0zPlunPkDZ3EMtUSqTZ4Nxk2gt/r4zdpv7fdUMCBE2dr1+ZtmepuBA6/eK52jPea3KYgzUR9RzFtB1lNmun2RUi/QSFOehKvIAlrU+kXONeN7jWeVycEX72oj3APq5/t90VHNY+7x9kEdm0c29+QSlcqz9Wlt/nnpR1CNOgzGLBGSDgU4qTniVLNDaj4jW3aUpbKc8Zochu8QVzecdj66r0acZj2a9oY6FLJ3HlphxAN+wwGrBESDIU4SQX+CO8owiRqgNTOW1Zj+xeO1vplB9FqtyrdZmL7lpXaVp5eTBqxd57ygw6UqpR9zRiKpujwzks7hCgFNSHNw8A20vVEaW6iI2qA1MhwAbvvWGvViSqOblX+zcTIcAGLF5j316YiMv55Oj9TxnSpDAX9ZsM0cgaOEZIeKMRJ19NsFTCXZip6jQwX8Ok714bW9dYJRycrlQYfluiE5isB5vx5pYypbmEBcd5uXHdtWN6xSmeEkHigOZ10Pa3mCzfr2x0ZLuCe8alIY82KoDynMFQ1ZYf51k1CM8gvbtKUbebDTQ9zWX/t0oZ5ASqBcAwmI6T7oRAnXU8cDS6a9bsWIrbwdDVzt5RqELoSoi7bt6zU+uWdrBg1ZZuAOP+c+eclLJKfENJd0JxOup5ONrho5TPcWuAmgnpeu355b/vPoUEHW992DXbvO6kt2BJWvMZmzlp1XRBC2gs1cdL1dDJfeGS4UFcCNSpuGpnJVx1kAo+iJQNXBLCbfz7kiU63nTOWOiUkXVCIk1TQyTSkHTevDqwlHoRbyOXjjx4N7YsdhklL3vnkcVyanW9oIrLj5tWR5yzu3tyEkGShOZ2QEHT1wG3af3sLuXz6zrUtuwRM2vB0qRybCbyTrgtCSHSoiRNigd8SsCKgNKsA2spjQGsuAVONdhPNmMBZ6pSQdEEhTkgTmMzOhXwOB0c3a9/TqkvAVHBNRP9aftBpKlWMFdQISQ8U4oQgelnXTauW4aFDp7TPJzUGUwEYpdAQPOdkBa9ebOxaBjSXKtZK2VtCSHLQJ076nmbKuppSw0zPxzGG/KCjfe/QoNPgs1+8YKAhx7xZP3mrZW8JIclBIU76nmZyo+NOxbIZg8mcfrF6nFdLNmnt3vFNTBaxcWy/Nuc86tgIIZ2B5nTS9wQJZJMZOe5ULJtNgUkwl8rztbG4WrIpCM4dX5TKbMwdJ0HQ1dJZqImTvsckeJfkHKMZOe5ULJtOa0tyenO6n1J5ruYnN40vinYdtQvcfRPHcP29X8J1o3tx/b1fwn0Tx7THkfRDV0vnoRAnfY9JIIvAKOh0ueO69qCtjsEVuhOTRVy4PGt9vldK5dr4gEpjFnfsE5PFSNp1lA3LfRPH8NChU7XCNnNK4aFDpyjIexS6WjoPzemk7zHlRm8zdDBzBV2cqVhh+dm7951Eec7gFNeQEcG28SksyTlwslJ7r6spLck52g5rOu06Su74I0+f1o7nkadP4/6RNdbjJ+mArpbOQyFOCPQC2VQzPakSpEGbgqiLoqsJ6wR1qTyHRU6mIS0tyB1gu2HRlZYNep60Rqf90SzT23loTidEw8RkEa9ebDRfB7UCTZK4F8XzM2Us8tSOzeecltwBLqaubUHd3EhzdIM/mmV6Ow+FOCEadu872ZBnDQCLFwx0JPJWt1i2IhYFqLMyXJqdb+FsV/jwjddEep40Tzf4o+OODSHRoTmdEA0m87UpzSsJ/KbS224o4MCJs7XHm1Ytw+NHinULuaDSpzwI3THegL1WcP3ejzx9GnNKISuCD994Df3hCdAt/miW6e0sFOKEaGi3r88vsP0CujhdwuNHig1azvprlwa+DwCcjOCqRQOYnikbrwuIb/G/f2QNhXYboD+aABTihGjZvmVlQw/xZnx9NoFHusIrDx86ZaUt67Qgv2D3f+bGsf2pWPw7HbTV7cR1j5J0Q584IRri8PXZBh7pfJsmk3gc2nIagpG6IWir26E/mgDUxAkx0qqvb9dTxwOLxbhEEcxh2rJNOdWgvO9u0X6DgrYopK5AfzShECckASYmi9occ6BRaJt8m/4ANBtt2Vb46Rb/KPXUk6ZbgrZaoVs2RKS3oTmd9CW2HbyafX9Qmo9fm960allDuljOyeKuDcsjm0pbEX7dkLLkErVee7dBdwBpF9TESd8RpHEC4eVFbTTWIKHp1aYnJot4/EixTuMWALfdUGgqwruViOVu0n7THrRFdwBpFxTiJFXEYaI0LbC7njqOi+X5UHPyzif1vu5dTx2vHWcSpvmcU3cuU1DbgRNnG95rc+2tCL9uSlmKUq89LuI0f3fThoj0NhTiJDXY+mzDFmPTQqrzYfu1p4nJorYeufv+60b3olDN1x7/yumGqm8ilXOEaez+522vvRXh123abzuDtuKOB+imDVE/0Y9xCBTiJDXYmChtFuOggic6vALVxj9cnC5h/CunoStken6mXDce28U+inm2WeEXt/abpgU1bvN3t22I+oFuCsxsJwxsI6nBRmu1Cc4y5UnnHP3PwStQbc2h5XmFOU3tdf94bHO222WeHRku4ODoZjw/dhMOjm5uSYCnKbAr7vllDnf76abAzHZCTZykBhut1WYx1mmcm1Ytw/gzjb2wnUx917KoWrwJb09y/1h0GmvazLNpC+xKYn6Zw91e+jUOgUKcpAYbE6XtYuxfYDeO7Ud5rlFzvmpRfdcy3RiawTsem8U+qnk2KVO27XnTtqDS/J1+0rbRjQua00lq8JsohwYdLBzIYNv4VC1Xu9mSoibhMu0LdvOOAbjSJ9uf5+1kBE5W3yy0GeEQxTyrM2XfMz6Fdbu+3JI5O4qJPG153jR/p580lBNOAlEqrHFhd7F+/Xp1+PDhTg+DdBh/EAtQ+cE+cGsltzqqFmpqClLI53BwdLPVePyf6Y6jOF1CVgRzSqHQhgAv07UAV+aomc+PMkdB3083CsY0BeERM738PYrIEaXU+obnKcRJGmlV6PpJUujo2ox6+4LHvdCsGN0b2FO8mTmamCzinvEp7WsC4Pmxm7TvScOCmrYNB+lPTEKcPnGSSpKIJgbiLy6iS3t56NCp2utJpMGEBd9FnSP3GoI+T0e7Arta3Sy0EoSXlo0K6V0oxEkqSUs0sU5A+Ik7ajss+C7qHAVdQ6d9jnHkBje7IezXvOR20G7rVZqhECepJC3RxLZab5xR2+7ituup4w1V6JqZo6Cxddrk3KwWfd/EMTzy9GnMBbgTwzY7ps++Z3wKh188F6n2fVJCyz1vu+MyWsHGerVtfAr3jE91/bW0Awpxkko6UVu7GWzzyuOO2natCnGYe03XUMjnWhIsUcY0MVls2JQMDTrW7V69n7nIyaBU1tXTu0IrGQ0AakLHRpBHFVqbVi3D3me/Xbv2fM7BzltWN5Qe9s+Xu2GJYjGw+a7CNiBRNyQ21it369VOgd6trhMGthESQis/Xl3QlJ92B1FFvZ44A7+aOdfEZBHbHzuqzeM34Q3es/kOvGRF8OEbrwkVwEFZAO55Pn3n2tC5DjuPDU5GsPuOtbWNm831hgU4hn1X900cw+eePgVDYUIjAuCuDcuN8xsWmBlEUr+lbgh+ZHQ66Uta3T3H8eOdmCzi448e1Zpu3YW+U40+ALvriUsLaSarIKqQc4WEq/1lqmbkKAgq2l6QdmcjLN3zuOjmuhWh5cWdQ9v5MmUVuJjOMzTo4Eff8Boc/Oa5pscqAB7cuk47r61uaprNUAki7myYZjAJcRZ7IT1LHPW746jHPDJcwLxBiMwr1VaTXLPXE1dN9WaCyKLGCygAjx8p1r73qALcPQcQfM+4BWJszuOim+u4XCnuPNnOV9jnBnX7a0WAA5V5Md1zuqItUShOl2Kv0d/NFQgpxEnPEocAbiVyeePYfqwY3YuNY/uRH3S0x0VZwP3nbGahMo07jnrwNjRTyS2qkMuKtFwW10vQPTMyXMDdG5ZHOp/3O5iYLGLm8mxL43Nx58lmvgQI9fknXV3PdM/pqufdvWF5rTqiDXE32+nmCoQMbCM9Sxy756ipbLqAouJ0qVaG1evXjRIpHjWdyWT+Nl2PoL7PeZx4x7Ik5yAjqPOj+pvM+Nm+ZaW1TzznZK0EeAbQtoo14Re8/rl94shLmAkJlnNx7x2TOT6fc3Bpdi40+M6Ldw5t6vvftWG5VYCaN8AuboKEsi7dc/21S61jG9qRttkt2TDUxEnPEsfuOUo9ZndR1kVMl+cVFi8YaLo2dxSrQpAbYfuWlQ113oFg82Yr+McyXSo3BkKFKFgjwwXsvn0thjTWjJyTwdCgUzenhZDvVxBNgAPAklzls01zayvAgcqG4L6JY8Yo7MULB3Ax5HwZz5zlc04tqA1orO/vP3bP1nUNQWW663r8SBGDhva8gWMD6r4TE1HdHP7rCtPL407b7Nba+tTESc8Sx+45SipbWGrMK6Uypna8x+pz/VqRyfSoW6iCBP7B0c3G8qlJ+Pds0oXKcypUa/KmzHm/04q2Kg1BUkEaWzNBZBcuz9a+E93cZiMEzykgUMMtTpdQCElNVAp4ISAoLWrhItN15XMOFOrdE05GAIHWMqILBAwKCvNiEzzpvS5vDryOpNI2uw1q4qRniWP3HCUqO0wI2i4qOq3IpHXozhnmRjBpR0n49+IudmNjkbAJODORc7JYvKAxqMrdaJjGOadUqGZoiwDYtGpZYHBX3N+V6bpeKZUbfkO771iL3bevrXtuz9Z1eMEQ9GhjzWomCNUNttyzdV1fdi9zSVQTF5H3AvhtAFkAf6CUGvO9fheAX6k+fBXALymljiY5JtJftLJ7juqHDtKYoywqOkFl0vE2rVpmPQ534W/GQtFsilncxW5s4xxGhguBWpoOAXDbDQU8bNCSw6wiCo0pZc2gABw4cRYP3LoGO588julS61X3wgi6Z0y/IdvflY01q5X69Wkp/JQUiQlxEckC+F0A7wbwEoBnRORJpdTXPIc9D+AnlFLnReR9AD4L4MakxkTaRyeqG8X9mVEXFlNAka6iVhBRBM+BE2cbntu0ahkePnSqIT/ZXfijLnqtBNXlBx04GUE5oCJIFKEUtkHxmlijasau8DR9hgJw3Q/kcKaqLZrO4Vo6Won4dyuRXZ3P4QNr35B43XDdvetkBDOXZ7FidG/Lnxu2mW41CLVbTd3tIElN/O0AnlNKfQsAROTzAD4IoCbElVJ/4zn+EIA3Jjge0iY60Rgiic+MurDEpRFE8a/qyos+fqRYJ2RcDdM7Dv9YXXO0ra/ftJnxfw/nZ8pwsoJ8zsErpXLLdcGDrAj+z25GIz4zXcKDW9cZfeo2+dHuOaJWmfPjDTBLoiWud97998OSnIMLl2drQZpJ/4aTaGjULyQpxAsATnsev4RgLfvnAfxZguMhbaIV01g3fWYzC0scGkGUqF3/WEymeL/GHmXTE2Uzo/v88pzC4oUD1kF9QQRtlDaO7W85P9yN0r/thkLT6VW1mgCGrzErgg0/PISvnnqlbelSNt+3997dOLa/wYyf5G+4m1O4up0khbgpk6XxQJFNqAjxHzO8/lEAHwWA5cujFVYg7acT1Y3i/syJySIuXGoswtGOhcUUmawr4ekfi+08RNn0RNnMtOO7N22U4vqM4nQJ48+cDjwmKB9dqcr86lwI/pru/g5jJlq9tqib3CjZEHHQ737tVkhSiL8E4BrP4zcCOOM/SETeAuAPALxPKfU93YmUUp9FxV+O9evXp6vYex/SCdNYnJ9pKsIxNOhgx832vu1mMWklt91QCDVD285DFGEbRUvqpFk0LIjOvwlyMoKrFg3o8/oDzOBZETxw6xpjqt4rpTJeKTWeE6ifX91mxJSO1er8Rfm+JyaLxgC9JL/HfvZrt0KSQvwZAG8SkRUAigA+BOAj3gNEZDmAJwD8tFLq7xMcC2kjnTCNxfmZptzmwQUDtVzlJDWGVrQS23kwCbz8oIONY/u1n2sznk6aRYMqlQVtgqI2IJlTlXSzfM5pMDkDVwRdVGHcqvUn6L6Msrnave+kdj5sSrWS9pNoFzMReT+APaikmP2RUupTIvIxAFBKfUZE/gDAbQBerL5lVtelxQu7mKWDNEenBy3quoW73S0Jwwjr7+wuxA3RyFkBFOrMwM1cWyf7LutM1GF9ppvtmhU0XwCMgW268US1/ui+48ePFI3d6aJ0rwu6/4MKzPjp1v7baYWtSEnPkPTiYFrUg3KAhwYdTP5a64FbcTIxWTTmGbuCxjuPFy7NajXLdrZb7ARR+437cTcL+ZwDEWB6poz8oINXZsrG8q5OVrB4wUAtYn/GEwnuxT/3utr8Qeh88GG/mzjabnZD/+1ewyTEWXaVpIp2pK/pzLJhRTzOz5RjayASxyYlSDB5S7B6z7tidK/2XN3QbjGMVubMPc7U8z2MOaXgZAT/ePFKXfgwIVueU7UNU5AVwN94JepmI8wH79/oDToZiKYxic6kHzTnnchQ6VcoxEmqSHpxcDUd72cMDTpWmk8rY/AXKfH3swbQYH6NWgHLi64rl0l8xRHMlKT1RLex2zY+hXvGp0JN6S4jwwVjoJoNQcVsWmFJzsF9E8fwyNOnm9pgmFrgAsB9E8ca0uh0jVwE9aVtdeZ5/33azf23ew0KcZIqklwcJiaLWj/mqxdnrQS5TY9xvyAD0GAe9S/V/k2KjTXCto57mHYXR1BaFOtJmE/bXw1OKWhdAGGbIO9nuefqRkrluZbagb56cbZmIfK3hNXNmw7vXN4zPoWdTx6HCAI30yze0j4oxEmqSHJx2L3vpDYQqTyvcNHChBkWeewXZNu/cNTYDcqPVyjbWCNs67gHaey2WmwQE5NFrZlaZz3xz5H7HlcQH37xXF3wlq1f2OazbM/Vbi7NRm2aWk95XtU0aO/12gpwHUHvde9TFm9pHxTiJFHiNqNGXRzi6kJWCunv7GQF5y5cwnVVv7I/qlhbySyCCda7QTCNszhdwsax/di+ZWXl3xeONnyGf1ymcwmAg6ObMTFZNKacheEKSpMZ2KYAjUurGmlxuoQVo3uxpBp81q1COwncsrqtVrOzwb1PWbylfVCIk8QIM6M2I+CjLA5xdiELYvGCLEqX5+o06vMz5Zrpcectq1sy9/s3KUHjdK/xthsKDTUTnaw0pCsFWTZaDSIMExy2BWjiQqE1DbTd5JwsLpbnjLEKhYCMAi9X53Nt8UX771MWb2kP7CdOEiPI7NtM/2CXkeECtm9ZWVucdu87ifsmjmHj2H6sGN2LjWP7axuEsN7TXrZvWVnJ/dWg6zENVDTb/OACYyrRdKmMe584Ftnn6o4iK1I3Z+44g3pNl8pzeOTp0w1mercntpegXs9R589PkOAwFaAhV1jkZPDO65dqX7t7w3IcHN1srAznZdOqZU3PrZOx6wU3NOgwfaxDUIiTxAgKQmtFQOg2AA8dOlX3eNv4VKC26gp6LyPDBey+fa1WYF+enW8Q8Dknix03h2vZpfIcpmfKyFouiPmcg7s2LEfOydb5he8Zn8K6XV8GUOlKltWkArnYmrBHhgt44NY1KORzEFS0O3cxbjWI0CQ4RCoCatv4VN33ELY56Qe83+j5mTK+euoVbLx+ae27zorg7g3Lcf/Imsq8WdxSB06cbWpuC/kcdt+xFkMWG1C3mqEtrpvGu+kmzUFzOkmMIFNtKwLCxr8X5m02mYZHhgvYve8kLlyuH0d5vlLMY/HCgQYzvhtNHTaeuRAfuDeQzNSRa7pUrgXEBaUcmRpq6ASryezZShDhxGQRM5cbS4i6Vc5MLS4Pv3iuoRd6pwmrEZDk55TKc7X2p954Bncja5N1VqxumqN2Ztu0alnt3vBmDZg+w4/JXdaJVsW9DCu2kcQIqtpkWhCyIvj0nWvr0qn8C8G28anYFlVXcHo/I0ggF6obEP+ipAsii4KTFWx92zW18qitXJ9bJ9xfhtMVEu41H37xXC3/WAAMLshi5vJcYGlWAMgI8JEbr2iDcX4fJDmcjES6R52sYPfta+sEa1A1wwe3rjOmQQLhv31T1UOWb63AsqukI9juxv0MDTq46S1v0NaDXjiQiTVAyb+4RdG8RCqtJ9ulrdniZICQgHpCrPDn6Zs2bd6yrM3Wo3ddBS4s33oFll0lHcFkqg0rdXl+pqw1q5bKc1jkZAL7Oetwha0Ov3YSRRi75+wmAQ5QgJP4KE6XsP2xowCCK9t5XWHNRsM/fOgU1l+7lOVbI8DANtIxRoYLmA+wBJlemZ4p14KxAKvYHivfISFET3lOYddTxwHAGOjmjZVoNhpeAXXBrSzfGg6FOOkozfzYr87nMDJcwMHRzSjkc12nBRPSi7hNfl69qA9Y9KYMtpJp4BXQpvWB6YhXoBAnHSXsx+7Xsv35xdyRE9I+du87qQ2OW+xLMfOmLkbFK6CD6hiQCvSJk8QJii51/zf1vb7thgIOnDhba4jhzyXPGFKpCCHxIjBvmnVFZ7zpabrgtLcuX4K/+ea5OkuaruobwPKtQTA6nSRKlOhSv7DftGoZ9j77bW2daycj1s1DCCFmhgYdDC4YqHVzC6orXzCkYHoj03UEZalQQNvBFDPSEUypJjY/el1bUEJIOCZhq2OPJ78bAIY/+WWtIHdTzZjy1RlMQpw+cZIoQR23gkoumtqCEkKCEegrqOnI55wG4bvj5tVGP3RQmV7SGegTJ7ETVqLRxdv4BKgvuciANdIuNl6/tFbatNvIORnMzqtIG1oFc9ldPztvWd3wXJgfmt3Jugua00mshFViM5EVwbxStQXDZhNASKu49fC79V67e8NyrL92qbHAShBhVQTzOQdTOxrLnJLuhOZ00jRROg7ZNCfRMaeUVQcyQuIi52Rb7vWeNAdOnMXIcKGpVC2FKymaulRNnRZO0geFOAkkat/vOBbEdNmGSBwMOhnr3tVx4O1/HVY4xGZUQccEtYwNw93MmvKl7662rDXhNrx5cOs6+rF7FApxEkjUvt+spNRZnKxgz9Z1nR6GNYKKyfhrv/4+7L5jbaRSuq3g7X+tE5DuuPZsXYe8RT/tuzYs15YjzTlZfPrOtU1p0sCVDYApoOz+kTWhRVXOTJdqFQ6fH7sJB0c3U4D3EBTiJJCotYtbKbdIopNzMhgadGoLu9s6slmh0W4UgC9+teKu2Vb1++7Zug4Pbl3XkgYbRnG6VLMm6QTkg1vXYf21S3HvE8cC86aBSmvW9dcuxY6bVzcI8oUDlSW22Qpjc0rV3Fnu/Ny1YTlmLs/invEpXDe6FzufPI7tW1Yav3NurHsbBraRQJrJ8/ZGp9tGycZFlPzYtOLP69URNc9eBLh6Sc6q4EfSuHnHAAL7tLd6bwkqAnH9tUvrKgYODTrYcfNq7HrquPU85HMOLs3Oa+NB3M/53NOnoLsUd6uiuxIRYCAjod+jkxFsffs12ta9NJ33Biz2Qpoijn6+K0b3tsXP7d1YXDe61+o9g04GM9W+nc30BHc/c92uL7fc43zxgiwuXDYHBbrCwNtv2cVb+So/6EApYLpUrmvBGnZ9SfVEX7wgi3mFyAGP+ZyD71+axZxBiKcJAfAjr1+Mb7x8IdHPGap+96+UyqyA1mOwnzhpijhqF1/dpHYcRag4mfouSkOW2uRMeR75nAORSpemqILMdSt8YO0btP3Po3B5dh5OVq91LV6QhZPN4OFDp3DgxNm678C/0fJet1KVudl9x9pamctPPPFsbePiJSlReeHyHIYGnchCvNVNUTehAHzr7Ezin+N+9/mcU7tH7ps4hkeePo05pZAVwYdvvAbrr13Kcqc9AjVxkjim3HGvFtwK+ZyDnbesrluEWtGMXUFuY641laIEKr7SqEqkd0MRhNcaYnJ5+M8bx0aDpAe3yYiukI3u3tT9jkj3wDxx0jF0gUN7tq7D1379fS0HYDlZ0S48uq5KtrhpOZ++c21gkJ5bitKUG9+MFfiVUhmDC8INZN4MARsrx3SpnIgA10VkEz1JBurpKJXnjJXodPfmdKlclz4apT4E6Rw0p5PIxNl5aPuWldg2PtW0cCnPKWx7dApAfdnWZk34Lm5azuEXz2mFn1draaaalon8oGM97uJ0ydr3DyRjLr/pLd2l3S9ekMVMeQ7daGBsKQjPE9uQJN7Node6ZCqP7KILZi3QTN8WaE4nkWgm0M30Hm+v8FZxMoKrFg1geqZca2Pqj9SNSiGfw4VLs0azfKH6OXEKMScjxmhs0r8MDTr40Te8pi013gXmTbAuKyWo1DKj4+OD0ekkFppJOTO9xx9EFmd0dNybBEL6hUK+kmpo+i26Qt7VssNiMsLaDhM76BMnsRC1+MvEZNH4A/cvEm4wWRyUynM4cOIsrvsBFrogJArbt6wMLBDjL78cVmq5m2vT9wIU4iQSph+37nnXzBaFOaViq/hWnC51bYtJQroRt7+4TeVF13++JBcc3MiKcclCIU4iYWrEoCsr2WxHs0VOBvnqwtDuiF5C+hXBlf7i/owSE2emSwj6iZrWBhIfjE4nkYhS/KVZM5o3R7qdJVsJ6Rd08Sh3bVhe9zseGS7UHpv83ldX/ecmGNSWPAxs60K8KVxLAop/uNHRpuCtN71+Mc5+/3JPVb4ihLSGN+jTNk00KCvFTS3zw4C2eGHZ1S5lYrIY2GghSAAXp0t46NAp4+tJ12kmhHQ/OSeDRU62ln7ZTO52mAVOJ+BpRm8PFOIdJGqnKUJIf+AWStE1tTHhFllJqtiK17zufx5orb8CaR4K8Q6ye99JCnBCSANuxUCdINQ1NNF1tmsnprGS5GF0egdh/iQh6aPZev/5nIO7Nyyv6yFgqj0flJZ1/8gafPrOtSjkc5hXCgdOnGVd8z6GmngHabW+NyGkddwAraDYFJegrnWuQDYFoeqCvEwBY0H+ZP97wuqak96GmngH2b5lJZws86AJicqbXr84lvMU8jncdkMBO58MF+AAMHN5FgC0Xfkmf+092HHzaus6CoC+w19YWpau/oK3cQnpL6iJdxD3h+rXALzRpEEpZoT0K8+1mHkhAJ4fu6kSXPqFo9ZNZ87PVNp1PnDrGq1m3UyQV1R/ctTSx6S3oRDvMFF+wFHaThLSK7htOL0FSsJErhuhbSIjUqvHELVrnKv1+n+3/ha9D25dZ/3bjtLe1+SGY3nT/oTm9BTRbEANIWlmySIHhXwuUoe7hQMSWC50Tinc+8SxpmNS/O9z/dTFavcvb4MQ9/WNY/uxYnQvNo7trwtEC3uvnyilj0nvQ028w5jSRdydeXG6FKpVENLLTJfKkasOzpTnQ48Jq+vvbppNbXQnJot15vMgP3VQIFrQe5mXTcJg2dUOct/EMW3FtQVZwWXmjxPSMbIZwWsWDoRuHtxo9W3jU0ZLgWkT7kasrxjdq32v67cnBGDZVS1R/FBJnOORp09rn6cAJ6SzzM0rK+2/OF3CPeNTwecyKEquhk8fN2mFvtXETfmZ/sYAboMR3eP8oINXL85qA2PcHfrhF8/h4UOnIvnzCCH9wd0blmP9tUuNzUWSCIwj6cSkifetEB/+5Je1aVv+Fn2EENIJFg5kcGm24tt31yVdPfSgDmMU5L2DSYj3ZXT6xGTRmHdNAU4I6QZcAQ5cWZd0kess/tLf9KUQ581NCEkrfgHN4i/9TV8Kcd7chJA0413DTAFwDIzrD/pSiJtu7kEn01BEgRBCug3vGsbiL/1NXwpx003/H259S0Mzgo3XLw2s/EQIIe3EL6CbaaJCeoe+jU6PkpLhPdZtSDI9U0Z+0IFSwCulcu0cAPBvH51CWDlmRsETQqKii04n/QFTzCIQJuC9r/sF+czlWXYcIyQlRNlMewXoxGQRO588blUQJp9zsPOW1ZEEL/O+iZ+OCHEReS+A3waQBfAHSqkx3+urAPxXAG8F8KtKqd8MO2fSQjws51L3OiH9wJtevxjfiNgCdCAjmI3YJczFyQq2vu0a7H3227WNsUkgbhzbb6xx7nYTsxWMUQUoBS5pB20X4iKSBfD3AN4N4CUAzwD4sFLqa55jXg/gWgAjAM53gxA3LQZunWPT64T0MndvWI77R9ZEbodr0nSdDPD61+rLjbrsidDK01R/HABeYP1x0gN0otjL2wE8p5T6llLqMoDPA/ig9wCl1MtKqWcAdI39OSznkulpJG1kpRKaOTTowMlEC9N0soI9W9fh/pE1deeyxSRYy/PAwdHNxva6Q4NOJG3WlHHSTPveoLahhHQbSQrxAgBvh4+Xqs91NWE5l8y9JO3m7g3LIwtfL3NKIedksePm1dh9x9paFLNJHmcEtSjn3bevrROmH77xmqbHoWP7lpVwso0DefXibCThGVeaVdTe3oR0miSFuG6JaMp2LyIfFZHDInL47NmzLQ4rmLDFQPe6l77M2SOJkRXBw4dO4apFA8jnnKbP4+1PfXB0M54fuwkmT9q8qrTAPDi6uUEbvn9kDe7esLymkYdp5osX6H8rQ4OVaxkZLmDxgsZmiuV5FamyYlxpVixhStJGkq1IXwLg3ba/EcCZZk6klPosgM8CFZ9460Mz4/7oTYEq/td1aWaHXzyHR54+bWxBSPobbzOLsPgK9x6KI+MhLlfQ/SNrauZ1wBxHMjToYMfNq7H9saMoe9rrOlnBjptX1x6/YojwjjrekeFCywFlLGFK0kaSQvwZAG8SkRUAigA+BOAjCX5ebIQtBjavexc5ALjr9/8WB795LrYxku7HjaTWbQjdiOa4EZj7U+cH6zX5fM7RpkgFafy6SOztW1ZqMzp23Lw6dFOMgPF2wnXVTWMhxIakU8zeD2APKilmf6SU+pSIfAwAlFKfEZEfAnAYwGsBzAN4FcCPKqX+0XTOduSJJ0lY9Htc53NhUZnOIaiYpf2EpSm28p1lRawsQIsXZPFTby1g/CunUfakgDkZwe471hpTr0zpl4BeUNvUXNj11PEGS4NNK80kUrvY1pN0Kyz20iXEvUhMTBax/QtH6xZiWzKC0MpyLrbCgVzBtDHrljTFjAAfuXE5Dpw4a5U7/fFHj2rvAdN1NltzIedk8MCtbwkV4EkJW+Z9k27EJMSTNKcTDTbmxSiLiPu8t3rUUNVPrzOVerW8JTkHr14sozzfcFgDH77xGjxx5CXM2BxM6oIhu9WVMq+AR585jbn5yj1x5pUSPvHEs9g2PtWgTd/7xDHjJs7kLw4KEhsZLmhfB4CLFvdY2LnDCPqNxeFbJ6RdUIh3gKBFwq9huCku7vtsz7fCUJTDuwxHCZZ66NAp62MJsHAgg23jU9j+hSmrTVKnuOwJOFMKtU2a974zCVsXv6/dpdmaC6r6mUGCtJUAtCi/MWrlpNthRlSXEVeKCwNxOst0qQwFdLUAD6NUnsM941Ohpn+Tl6WVmgvF6VJgwZVWemjb/saYM07SAIV4FzExWTQumFFTXDatWsYWqqQtmFLEdDUVBFcEdNg96hWe28ancJ1HoLdS3MX2N8accZIGKMS7BHfXb8JWs56YLGL4k1/GQ4dO1ZnOBZVAJh2LF2Qp8EnTmO5NbwEWF/eeLE6XMP6V03jn9UutPsP7vu1fOAoATRV3mZgsGu91/3UwZ5ykAfrEu4Qgv6OthhGUuqSgN3tmBHCyGSiwKxtpjvMXLmH4k1/G9Eyl4NGmVctw4MRZFKdLtawGXdpceV7h+JnvI+dkUIrgdyjPK9z7xLNYunhhZF/17n0ntel7AjT8xpgzTtIAhXiXELS712kYuoCbsAAkHfPKbA61pZkWlaR3mCnP1wXEeYMg3Yh2U3KiTT9uHaXyfE3A2gR/ugQF0/nfaypiE7UeOyFJQiHeJZh2/YV8TivAddG1zfY4N322LRTgpNP408tMUeVBvzM/NumgtjDKnSQFhXiXEGXXbwq4abYgy3U/kMO5C5eb3gQQ0g24WvZ9E8fwsCcmxA2Mu2d8CvmcAycrdbXcvb8znbA1FbKxFcrNpI0SYguFeJcQtuv3LhomMe22nIwqjA9+8xze9PrFeO7lCyzRSlLL1fkcJiaLdQLcxX08XSrDyQiGBp2aD99f1CZM2EYVyq0WpiEkCArxLsJUBCas1raXhQMZLHIykbte0SRO0o4bFxK2ES3PKwwuGMDkr72n7nlbYRtVKDPKnSQJhXgX42rfUfzVzQYKEZJm3FxZW8GoO85W2EYVyoxyJ0nCPPEuxVstihASzDwqGrKtYNQdZ1sFLmq1uFYK0xASBoV4l9JMuhgh/cyZ6ZJWYPoxCVBbYRtVKHuL3kQpTEOIDTSndyFB5VcJIXqu9qRjerv6ecnnHOy8ZbVWgNqmlDWTesbOaCQp2E+8y4gSxEZIL7FwIIM71r8Rjx8pRr7/db3EmZtNegn2E+9C4qq6Rki7WJAV3Pm2a2plVUWulPNdvCCLy7PzKM9fUQzccqv5nAMRNJRm1QnY9dcuNWrSOrIiuO2GRk2X2i/pB6iJJ4CNBqDTuJvJ8XbJ5xzMXJ6t6w9NSCvcvWG5UdCaiFP7jZKdodPECeklTJo4hXjMmISzf4HZOLZfuzg1U3Utg0p0LiE69mxdh5HhgvGe05HPOZja8Z7wAy1pRbjbupgK+Zy2uhohvYBJiDM6PWZsexCbckqbKZtKAd4/uBHOQ4NOxUQNIOeYf8be2vu2OdQ5J4udt6yOYbQVvOmSbn/we584honJotX7/dHdJlg8hfQj9InHjG0hiFabjpD+I0jT9NcLBxrTnkz33NCgg8EFA4kFgMVRdtTr3zZZFFg8hfQj1MRjYGKyiI1j+7FidC8yotcV/AuMTT4r6R2CNEjb9xenS9g4tl+rwd4/sgYPbl0XmItsym/ecfNqHBzdjOfHbsLB0c2x+5XjLjvK4imEXIGaeIv4/XU6c7hugfHmmlIj720qUdtzKLfg9/B25DI12wiLxo6ztWYU4i472qnrIKQboRBvEZuUsIUDZoPHy/9IAd7rXLgcb8pgKx2wOpF2FaXNri1MHyOkAoW4JaboWhuT4HSp3KA9TUwWcc/4VJJDJj1MmoK4qDkTkhwU4hYE9Q+2DVDza087nzye3IBJz5O2IC5qzoQkAwPbLAiKro0SoObVntgylDQLg7gIIS4U4hYERde6OaxZQ1S6l7RpT6Qz5HOO8TV2wCKEeKE53YKw6Fp3QQ2qKuXXnoYGHZyfoTZOriAA7tqwHOuvXWpV9Y8QQqiJW2CTl+qvKpXPORgadIw5uztuXg0n22r2MOlWnAxq3//QoIMFvu968YIs7t6wvC6v+8Gt63D/yBr2nyaEWMPa6ZYk0dbQe878oIOL5TmUqsnEbvcnEh23Y9b5mXJdl60wNl6/FA//4juSHRwhhDQBG6AQQgghKYUNUAghhJAeg0KcEEIISSkU4oQQQkhKoRAnhBBCUgqFOCGEEJJSKMQJIYSQlEIhTgghhKQUCnFCCCEkpVCIE0IIISmFQpwQQghJKRTihBBCSEqhECeEEEJSCoU4IYQQklIoxAkhhJCUQiFOCCGEpJTU9RMXkbMAXuzgEF4H4B86+PndDucnGM5PMJyfYDg/4fTqHF2rlFrmfzJ1QrzTiMhhXWN2UoHzEwznJxjOTzCcn3D6bY5oTieEEEJSCoU4IYQQklIoxKPz2U4PoMvh/ATD+QmG8xMM5yecvpoj+sQJIYSQlEJNnBBCCEkpFOI+RCQvIo+JyAkR+bqIvENElorIn4vIN6r/D3mOv1dEnhORkyKypZNjbwcisk1EjovI34nIIyKyqN/nR0T+SEReFpG/8zwXeU5E5AYROVZ97T+JiLT7WpLAMD+7q7+xZ0XkiyKS97zW9/Pjee3fiYgSkdd5nuP8VJ7/V9U5OC4i/9HzfF/ND5RS/Of5B+C/AfiF6t8LAOQB/EcAo9XnRgH8RvXvHwVwFMBCACsAfBNAttPXkODcFAA8DyBXffwogJ/r9/kB8M8AvBXA33meizwnAL4C4B0ABMCfAXhfp68twfl5D4CB6t+/wfmpn5/q89cA2IdKXYzXcX7q7p9NAP4XgIXVx6/v1/mhJu5BRF6Lyg3zhwCglLqslJoG8EFUhDuq/49U//4ggM8rpS4ppZ4H8ByAt7dzzB1gAEBORAYADAI4gz6fH6XUXwM453s60pyIyBsAvFYp9beqsuL8iec9qUY3P0qpLyulZqsPDwF4Y/Vvzs8VHgTw7wF4A5c4PxV+CcCYUupS9ZiXq8/33fxQiNfzwwDOAvivIjIpIn8gIosB/KBS6tsAUP3/9dXjCwBOe97/UvW5nkQpVQTwmwBOAfg2gFeUUl8G50dH1DkpVP/2P98P/F+oaEYA5wcAICK3ACgqpY76XuL8VHgzgB8XkadF5K9E5G3V5/tufijE6xlAxWzze0qpYQAXUDGFmtD5VHo23L/q1/0gKmaqqwEsFpG7g96iea5n58cS05z05VyJyK8CmAXwsPuU5rC+mh8RGQTwqwB+Tfey5rm+mp8qAwCGAGwAsB3Ao1Ufd9/ND4V4PS8BeEkp9XT18WOoCPXvVs0xqP7/suf4azzvfyMq5uVe5Z8DeF4pdVYpVQbwBIB3gvOjI+qcvIQrJmXv8z2LiPwsgA8AuKtq4gQ4PwBwPSob5aMi8gIq1/pVEfkhcH5cXgLwhKrwFQDzqNRM77v5oRD3oJT6DoDTIrKy+tS7AHwNwJMAfrb63M8C+O/Vv58E8CERWSgiKwC8CZXgiV7lFIANIjJY3fW+C8DXwfnREWlOqib374vIhurc/oznPT2HiLwXwK8AuEUpNeN5qe/nRyl1TCn1eqXUdUqp61ARQG+trk99Pz9VJgBsBgAReTMqQcj/gH6cn05H1nXbPwDrABwG8CwqN8oQgB8A8BcAvlH9f6nn+F9FJQLyJHok2jFkfnYBOAHg7wD8KSpRoH09PwAeQSVGoIzKgvvzzcwJgPXVef0mgN9BtRhT2v8Z5uc5VHyXU9V/n+H8XJkf3+svoBqdzvmp3T8LADxUvd6vAtjcr/PDim2EEEJISqE5nRBCCEkpFOKEEEJISqEQJ4QQQlIKhTghhBCSUijECSGEkJRCIU5IHyEicyIyJSJHReSrIvLOkOM/0a6xEUKiwxQzQvoIEXlVKXVV9e8tAD6hlPoJm+N9zwsq68d8cqMlhIRBTZyQ/uW1AM4DldKwIvLXVS3970Tkx0VkDJWOdVMi8rCIXCciXxeR/4JKgY1rROT3RORwtafzruq53iUiX3Q/RETeLSJPdOICCel1qIkT0keIyByAYwAWAXgDKpWujojIxwEsUkp9SkSyAAaVUt/3ae7XAfgWgHcqpQ5Vn1uqlDpXfc9fAPjX1fN/HcCPK6XOisjnADyilHqqzZdLSM9DTZyQ/qKklFqnlFoF4L0A/qRqGn8GwL8QkZ0A1iilvm94/4uuAK9yp4h8FcAkgNUAflRVNIM/BXC3iOQBvANXWo0SQmKEQpyQPkUp9beodH5appT6awD/DEARwJ+KyM8Y3nbB/aPaYOLfAXiXUuotAPaiouEDwH8FcDeADwP4glJqNpmrIKS/oRAnpE8RkVUAsgC+JyLXAnhZKfX7AP4QlRa8AFAWEcdwiteiItRfEZEfBPA+9wWl1BlUWj3eB+CPk7kCQshApwdACGkrORGZqv4tAH5WKTUnIj8JYLuIlAG8ikqrRgD4LIBnqybzX/WeSCl1VEQmARxHxVd+0PdZD6Oi5X8tiQshhDCwjRCSECLyOwAmlVJ/2OmxENKrUIgTQmJHRI6gYmp/t1LqUqfHQ0ivQiFOCCGEpBQGthFCCCEphUKcEEIISSkU4oQQQkhKoRAnhBBCUgqFOCGEEJJSKMQJIYSQlPL/A4XZvmFTZ2N7AAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "noise = generate_noise(shape=(25_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "plt.scatter(bstray.detach().cpu(), kdiff.detach().cpu())\n", + "plt.title(\"Randomly generated points after GP reshaping\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Kdiff\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Noise 0\n", + "[-0.10264802 0.50808656 -0.39786887 0.9097794 -0.48243952 0.29933286\n", + " -0.0572449 0.64627016 0.41877627 0.9662769 ]\n", + "\n", + "Generated Parameters 0\n", + "[0.45530102 0.3497225 0.6713627 0.3571434 0.05714112 0.18239929\n", + " 0.50713223 0.33716813 0.46310726 0.7092013 ]\n", + "\n", + "Noise 1\n", + "[-0.49542105 -0.50206625 0.4502871 -0.1443069 -0.48620474 -0.8488914\n", + " -0.06545985 -0.4735378 0.91595364 0.48849785]\n", + "\n", + "Generated Parameters 1\n", + "[0.63112944 0.6489526 0.45887542 0.52681893 0.74852484 0.787571\n", + " 0.57375085 0.7188927 0.42086276 0.5000445 ]\n", + "\n", + "Noise 2\n", + "[-0.73528636 0.14241159 0.33068836 -0.6645824 -0.9654193 -0.47818792\n", + " 0.7225338 0.92474973 -0.6418421 -0.71778727]\n", + "\n", + "Generated Parameters 2\n", + "[0.4136317 0.5374126 0.39793462 0.6257991 0.8283704 0.5639596\n", + " 0.4159876 0.40423587 0.55960333 0.29775935]\n", + "\n" + ] + } + ], + "source": [ + "noise = generate_noise(shape=(3, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "noise = noise.cpu().numpy()\n", + "gp = gp.cpu().detach().numpy()\n", + "\n", + "for i in range(3):\n", + " print(f\"Noise {i}\")\n", + " print(noise[i])\n", + " print()\n", + " print(f\"Generated Parameters {i}\")\n", + " print(gp[i])\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"KDiff Error During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Coupling %\")\n", + "\n", + "plt.plot(numpy.arange(len(kdiffs)), kdiffs)\n", + "plt.show()\n", + "\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"BStray Error During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"BStray\")\n", + "\n", + "plt.plot(numpy.arange(len(bstrays)), bstrays)\n", + "plt.show()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "optimization_date4_22_22.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 82ddf08463e2c37f6f20b0b5f46852a3023f665e Mon Sep 17 00:00:00 2001 From: AndrewCurtis27 <89710614+AndrewCurtis27@users.noreply.github.com> Date: Tue, 18 Oct 2022 13:34:46 -0600 Subject: [PATCH 3/8] Add files via upload Abstracted spline fit and added num inv and coil loss calculations and some graphics --- optimization spline_numinv_coilloss.ipynb | 1955 +++++++++++++++++++++ 1 file changed, 1955 insertions(+) create mode 100644 optimization spline_numinv_coilloss.ipynb diff --git a/optimization spline_numinv_coilloss.ipynb b/optimization spline_numinv_coilloss.ipynb new file mode 100644 index 0000000..25ef989 --- /dev/null +++ b/optimization spline_numinv_coilloss.ipynb @@ -0,0 +1,1955 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iZN2u5WKRFqR", + "outputId": "6842718b-36d3-4c1c-b523-796de23e5470" + }, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "import os\n", + "import random\n", + "import pandas\n", + "import numpy\n", + "import math\n", + "from scipy.optimize import curve_fit\n", + "from scipy.interpolate import interp1d\n", + "from scipy.interpolate import UnivariateSpline\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "\n", + "# seed = 7777\n", + "# random.seed(seed) \n", + "# torch.manual_seed(seed);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load Saved MP (Magnetic Parameter) Model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "B-TMPJtCKIM6" + }, + "outputs": [], + "source": [ + "# 12 input -> 42 output\n", + "class MpModel(torch.nn.Module):\n", + " def __init__(self):\n", + " super(MpModel, self).__init__()\n", + "\n", + " self.linear1 = torch.nn.Linear(12, 100, bias=True)\n", + " self.linear2 = torch.nn.Linear(100, 100, bias=True)\n", + " self.linear3 = torch.nn.Linear(100, 42, bias=True)\n", + "\n", + " def forward(self, x):\n", + " x = torch.atan(self.linear1(x))\n", + " x = torch.atan(self.linear2(x))\n", + " x = torch.atan(self.linear2(x))\n", + " x = self.linear3(x)\n", + " return x\n", + "\n", + "\n", + "mp_model = MpModel().to(\"cuda\")\n", + "mp_model.load_state_dict(torch.load(\"saved_model_state.pt\"))\n", + "\n", + "for param in mp_model.parameters():\n", + " param.requires_grad = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create GP (Geometry Paramter) Generator" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "z9cggAl1BGHo" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\lazy.py:178: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.\n", + " warnings.warn('Lazy modules are a new feature under heavy development '\n" + ] + } + ], + "source": [ + "class GpModel(torch.nn.Module):\n", + " def __init__(self, input_length: int):\n", + " super(GpModel, self).__init__()\n", + "\n", + " self.dense_layer1 = torch.nn.Linear(int(input_length), 128)\n", + " self.dense_layer2 = torch.nn.Linear(128, 256)\n", + " # self.dense_layer3 = torch.nn.Linear(256, 512)\n", + " # self.dense_layer4 = torch.nn.Linear(512, 256)\n", + " self.dense_layer5 = torch.nn.Linear(256, 128)\n", + " self.dense_layer6 = torch.nn.Linear(128, int(input_length))\n", + " \n", + " self.batch_norm1 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm2 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm3 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm4 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm5 = torch.nn.LazyBatchNorm1d()\n", + "\n", + " def forward(self, x):\n", + " x = self.batch_norm1(torch.atan(self.dense_layer1(x)))\n", + " x = self.batch_norm2(torch.atan(self.dense_layer2(x)))\n", + " # x = self.batch_norm3(torch.atan(self.dense_layer3(x)))\n", + " # x = self.batch_norm4(torch.atan(self.dense_layer4(x)))\n", + " x = self.batch_norm5(torch.atan(self.dense_layer5(x)))\n", + " x = torch.sigmoid(self.dense_layer6(x))\n", + " return x\n", + "\n", + "\n", + "gp_model = GpModel(10).to(\"cuda\")\n", + "optimizer = torch.optim.Adam(gp_model.parameters(), lr=3e-4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# System Constraints" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "Bse1wsKN1NxY" + }, + "outputs": [], + "source": [ + "# x-direction of the primary winding (mm)\n", + "lpx_min, lpx_max = (50.0, 650.0)\n", + "\n", + "# y-direction of the primary winding (mm)\n", + "lpy_min, lpy_max = (50.0, 2050.0)\n", + "\n", + "# width of the primary winding (mm)\n", + "wp_min, wp_max = (25.0, 325.0)\n", + "\n", + "# length of the edge of the primary core (mm)\n", + "a_min, a_max = (0.0, 200.0)\n", + "\n", + "# pitch of the adjacent cores (mm)\n", + "p_min, p_max = (0.0, 200.0)\n", + "\n", + "# length of the secondary winding (mm)\n", + "ls_min, ls_max = (50.0, 450.0)\n", + "\n", + "# width of the secondary winding (mm)\n", + "ws_min, ws_max = (25.0, 225.0)\n", + "\n", + "# input RMS current (A)\n", + "ip_min, ip_max = (50.0, 200.0)\n", + "\n", + "# turn number of the primary side\n", + "np_min, np_max = (4.0, 10.0)\n", + "\n", + "# turn number of the secondary side\n", + "ns_min, ns_max = (4.0, 10.0)\n", + "\n", + "# switching frequency (kHz)\n", + "f = 85 * (10 ** 3)\n", + "\n", + "# output power at the center (kW)\n", + "p_out = 50 * (10 ** 3)\n", + "\n", + "# input DC voltage (V)\n", + "v_dc = 400\n", + "\n", + "# output DC voltage (V)\n", + "v_bat = 400\n", + "\n", + "# length of the edge of the secondary core\n", + "b = 50\n", + "\n", + "# ???\n", + "w = 2 * numpy.pi * f # [rad/s]\n", + "\n", + "# ???\n", + "q_coil = 400" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "brlwMfzj1Nxh" + }, + "outputs": [], + "source": [ + "# Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive.\n", + "# Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch.\n", + "\n", + "@torch.jit.script\n", + "def linspace(start: torch.Tensor, stop: torch.Tensor, num: int):\n", + " # create a tensor of 'num' steps from 0 to 1\n", + " steps = torch.arange(num, dtype=torch.float32, device=start.device) / (num - 1)\n", + "\n", + " # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings\n", + " # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript\n", + " # \"cannot statically infer the expected size of a list in this contex\", hence the code below\n", + " for i in range(start.ndim):\n", + " steps = steps.unsqueeze(-1)\n", + "\n", + " # the output starts at 'start' and increments until 'stop' in each dimension\n", + " out = start[None] + steps * (stop - start)[None]\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "dMoXnTFV8U1P" + }, + "outputs": [], + "source": [ + "# Generate a pytorch tensor full of random floats in the range [-1, +1] with the given shape\n", + "\n", + "def generate_noise(shape):\n", + " return torch.distributions.uniform.Uniform(-1, +1).sample(shape).cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "xloGE25R8Jtm" + }, + "outputs": [], + "source": [ + "def stack_parameters(*params):\n", + " return torch.stack(params).transpose(0, 1)\n", + "\n", + "\n", + "# Scale an array to be between our specified [min, max] contraints\n", + "def scale(arr):\n", + " scaler_min = torch.tensor(\n", + " [a_min, lpx_min, lpy_min, ls_min, p_min, wp_min, ws_min, ip_min, np_min, ns_min],\n", + " device=\"cuda\",\n", + " )\n", + " scaler_max = torch.tensor(\n", + " [a_max, lpx_max, lpy_max, ls_max, p_max, wp_max, ws_max, ip_max, np_max, ns_max],\n", + " device=\"cuda\",\n", + " )\n", + "\n", + " return arr * (scaler_max - scaler_min) + scaler_min\n", + "\n", + "\n", + "def extract(arr):\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns = [\n", + " numpy.squeeze(x) for x in numpy.hsplit(arr, 10)\n", + " ]\n", + "\n", + " ys_min = (\n", + " 5 * wp +\n", + " 4 * a +\n", + " 3 * lpy +\n", + " 2 * p\n", + " )\n", + "\n", + " ys_max = (lpy + wp)\n", + " ys_max = ys_min + (ys_min - ys_max) / 2\n", + "\n", + " ys_min /= 2\n", + " ys_max /= 2\n", + "\n", + " ys = linspace(ys_min, ys_max, num=5)\n", + "\n", + " np = torch.round(np)\n", + " ns = torch.round(ns)\n", + "\n", + " # take all 15 tensors of shape (X) and convert to (15, X)\n", + " return a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys\n", + "\n", + "\n", + "def scale_and_extract(arr):\n", + " scaled_array = scale(arr)\n", + " return extract(scaled_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "44TlTSnclCKs", + "outputId": "c2d4a61b-35c8-42ad-99f9-98c99022a0b2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " GP Parameters Shape: torch.Size([256, 12])\n", + " Extra Parameters Shape: torch.Size([256, 3])\n", + " MP Model Output Shape: torch.Size([256, 42])\n", + " \n" + ] + } + ], + "source": [ + "# Enclose in a function so we don't leak variables\n", + "def test_models():\n", + " noise = generate_noise(shape=(256, 10))\n", + " gp = gp_model(noise)\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "\n", + " gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + " extra_parameters = stack_parameters(ip, np, ns)\n", + "\n", + " mp = mp_model(gp_parameters)\n", + "\n", + " print(f\"\"\"\n", + " GP Parameters Shape: {gp_parameters.shape}\n", + " Extra Parameters Shape: {extra_parameters.shape}\n", + " MP Model Output Shape: {mp.shape}\n", + " \"\"\")\n", + "test_models()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "eEb7J5bOFEEr" + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "Method to take two equally-sized lists and return just the elements which lie \n", + "on the Pareto frontier, sorted into order.\n", + "Default behaviour is to find the maximum for both X and Y, but the option is\n", + "available to specify maxX = False or maxY = False to find the minimum for either\n", + "or both of the parameters.\n", + "\"\"\"\n", + "\n", + "\n", + "def pareto_frontier(x, y, max_x=False):\n", + " if max_x:\n", + " return _pareto_frontier(y, x)\n", + " else:\n", + " return _pareto_frontier(x, y)\n", + "\n", + "\n", + "def _pareto_frontier(x, y):\n", + " # Combine inputs and sort them with smallest X values coming first\n", + " sorted_xy = sorted(zip(x, y))\n", + " p_front_x = torch.zeros(len(x), device=\"cuda\")\n", + " p_front_y = torch.zeros(len(y), device=\"cuda\")\n", + " \n", + " # Loop through the sorted list\n", + " # Look for lower values of Y…\n", + " # and add them to the Pareto frontier\n", + " min_y = sorted_xy[0][1]\n", + " for index in range(len(sorted_xy)):\n", + " x, y = sorted_xy[index]\n", + " if y < min_y:\n", + " min_y = y\n", + " p_front_x[index] = x\n", + " p_front_y[index] = y\n", + " \n", + " p_front_x = p_front_x[p_front_x.nonzero()]\n", + " p_front_y = p_front_y[p_front_y.nonzero()]\n", + "\n", + " return p_front_x, p_front_y" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def y_util(x, a, b):\n", + " return (1 / (x ** b)) ** (1 / a)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def log_util(x, a, b):\n", + " return ()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def lin_util(x, a, b):\n", + " return ((a * x) + b)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def prod_util(x, a):\n", + " return (1 / (x * a))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def prodb_util(x, a, b):\n", + " return ((1 / (x * a)) + b)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def one_param_util(x, a):\n", + " return ((1 - (x**a))**(1/a))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def one_param_b_util(x, a, b):\n", + " return (((1 - (x**a))**(1/a)) + b)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "#testing for creating total pareto f\n", + "\n", + "def oldgraph_pareto_frontier(Xs, Ys, maxX = False, maxY = False):\n", + "# Sort the list in either ascending or descending order of X\n", + " myList = sorted([[Xs[i], Ys[i]] for i in range(len(Xs))], reverse=maxX)\n", + "# Start the Pareto frontier with the first value in the sorted list\n", + " p_front = [myList[0]] \n", + "# Loop through the sorted list\n", + " for pair in myList[1:]:\n", + " if pair[1] <= p_front[-1][1]: # Look for lower values of Y…\n", + " p_front.append(pair) # … and add them to the Pareto frontier\n", + "# Turn resulting pairs back into a list of Xs and Ys\n", + " p_frontX = [pair[0] for pair in p_front]\n", + " p_frontY = [pair[1] for pair in p_front]\n", + " return p_frontX, p_frontY" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def findSpline(x, y, smoothing_factor = 2):\n", + " spl = UnivariateSpline(x, y)\n", + " spl.set_smoothing_factor(smoothing_factor)\n", + " return spl" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def pair_loss_calc(x, y, x_numpy, y_numpy, pareto_x_numpy, pareto_y_numpy, pareto_x_forx_numpy, pareto_y_forx_numpy):\n", + " if len(pareto_y_numpy) > 3:\n", + " # f = interp1d(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0])\n", + " # f2 = interp1d(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0], kind = 'cubic')\n", + " # plt.plot(pareto_bstray_numpy[:,0], f2(pareto_bstray_numpy[:,0]), '--', pareto_bstray_numpy, pareto_kdiff_numpy, 'o')\n", + "\n", + "\n", + " spl_y = findSpline(pareto_x_numpy[:,0], pareto_y_numpy[:,0])\n", + " y_calc = torch.as_tensor(spl_y(x_numpy)).cuda()\n", + " # plt.plot(pareto_bstray_numpy[:,0], spl_y(pareto_bstray_numpy[:,0]), '--', pareto_bstray_fory_numpy, pareto_kdiff_fory_numpy, 'o')\n", + " # plt.show()\n", + " y_loss = y - y_calc\n", + " y_loss_relu = torch.nn.functional.relu(y_loss)\n", + "\n", + "\n", + " spl_x = findSpline(pareto_y_forx_numpy[:,0], pareto_x_forx_numpy[:,0])\n", + " # plt.plot(spl_x(pareto_kdiff_fory_numpy[:,0]), pareto_kdiff_fory_numpy[:,0], '--', pareto_bstray_fory_numpy, pareto_kdiff_fory_numpy, 'o')\n", + " # plt.show()\n", + "\n", + "\n", + " x_calc = torch.as_tensor(spl_x(y_numpy)).cuda()\n", + " x_loss = x - x_calc\n", + " x_loss_relu = torch.nn.functional.relu(x_loss)\n", + " \n", + "\n", + " if len(pareto_x_numpy) > 3 and len(pareto_y_numpy) > 3:\n", + " combined_loss = torch.sum(y_loss_relu * x_loss_relu)\n", + " else: \n", + " combined_loss = torch.zeros(1, requires_grad=True)\n", + " print('No new pareto points found, used zero grad')\n", + " \n", + " \n", + " return combined_loss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load DWPT data" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "jkTrlAJMH5UO" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1010, 12)\n", + "(1010, 42)\n" + ] + } + ], + "source": [ + "def load_dwpt():\n", + " folder_name = \"./dwpt_v5\"\n", + " file_names = [\n", + " \"DWPT_v5_N10\",\n", + " \"DWPT_v5_N100\",\n", + " \"DWPT_v5_N200\",\n", + " \"DWPT_v5_N300\",\n", + " \"DWPT_v5_N400\",\n", + " ]\n", + "\n", + " df = pandas.DataFrame()\n", + "\n", + " for file_name in file_names:\n", + " new_df = pandas.read_csv(f\"{folder_name}/{file_name}_after.csv\", index_col=0)\n", + " df = pandas.concat([df, new_df])\n", + "\n", + " return df\n", + "\n", + "df = load_dwpt()\n", + "df_gp = df.loc[:, :\"ys4[mm]\"]\n", + "df_mp = df.loc[:, \"k0mm_ys0\":]\n", + "\n", + "print(df_gp.shape)\n", + "print(df_mp.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define Loss Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def kdiff_loss(k_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + "\n", + " kdiff = abs(k_0_0 - k_1_0)\n", + " kdiff = kdiff / torch.max(abs(k_0_0), abs(k_1_0))\n", + "\n", + " # their kdiff - why is there a difference?\n", + " # kdiff = abs(k00 - k10) / k00\n", + "\n", + " return kdiff\n", + "\n", + "\n", + "\n", + "def bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters):\n", + " # Unpack parameters\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " # Scale l_parameters\n", + " lp_0_0 = lp_0_0 * np ** 2\n", + " ls_0_0 = ls_0_0 * ns ** 2\n", + " \n", + " # Paper formula\n", + " n1 = (torch.pi * w * lp_0_0 * ip) / (2 * numpy.sqrt(2) * v_dc)\n", + " t1 = (torch.pi ** 2 * w * p_out * torch.sqrt(lp_0_0 * ls_0_0))\n", + " t2 = 8 * k_0_0 * n1 * v_dc * v_bat\n", + " n2 = t1 / t2\n", + " Is = (4 * v_bat * n2) / (torch.pi * w * ls_0_0)\n", + "\n", + " # Calculate B_100mm\n", + " bx_100 = (\n", + " (bx_p_1_00 * ip * np + bx_s_1_00 * Is * ns) ** 2 +\n", + " (bx_p_1_90 * ip * np + bx_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " by_100 = (\n", + " (by_p_1_00 * ip * np + by_s_1_00 * Is * ns) ** 2 +\n", + " (by_p_1_90 * ip * np + by_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " bz_100 = (\n", + " (bz_p_1_00 * ip * np + bz_s_1_00 * Is * ns) ** 2 +\n", + " (bz_p_1_90 * ip * np + bz_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " b_100 = (bx_100**2 + by_100**2 + bz_100**2) ** 0.5\n", + "\n", + " # Bstray is the max between the two targets\n", + " # bstray = torch.max(b_0, b_100)\n", + " bstray = b_100\n", + "\n", + " return bstray" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# (\n", + "# # [0 thru 5]\n", + "# k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + "\n", + "# # [6 thru 17]\n", + "# lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + "# ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + "# lp_1_0, ls_1_0,\n", + "\n", + "# # [18 thru 29]\n", + "# bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + "# bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + "# bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + "# bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + "# # [30 thru 42]\n", + "# bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + "# bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + "# bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + "# bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + "# )" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def power_average(k_parameters, l_parameters, b_parameters, extra_parameters, gp_parameters):\n", + " \n", + " return " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + " def core_losses(k_parameters, l_parameters, b_parameters, extra_parameters, gp_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " (\n", + " a, lpx, lpy, ls, p, wp, ws, *ys\n", + " ) = gp_parameters.transpose(0,1)\n", + "\n", + " V_PriCore = (lpy +2*wp+2*a)*(lpx+2*wp+2*a)*5/(10**3) # cm3\n", + " #print(V_PriCore)\n", + " V_SecCore = (ls+2*ws+2*b)**2*5/(10**3) # cm3\n", + " #print(V_SecCore)\n", + " V_PriWind = (2*(lpx+wp)+2*(lpy+wp))*6.6*6.6/(10**3)*np\n", + " V_SecWind = 4*(ls+ws)*6.6*6.6/(10**3)*ns\n", + "\n", + " V_PriWind_ave = V_PriWind/(lpy+2*wp+2*a+p)*10**3 # cm3/m\n", + " V_PriCore_ave = V_PriCore/(lpy+2*wp+2*a+p)*10**3 # cm3/m\n", + " \n", + "\n", + " #print(V_PriWind_ave, V_PriCore_ave)\n", + " return V_PriCore, V_SecCore, V_PriWind, V_SecWind" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def num_inverters(k_parameters, l_parameters, b_parameters, extra_parameters, gp_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " (\n", + " a, lpx, lpy, ls, p, wp, ws, *ys\n", + " ) = gp_parameters.transpose(0,1)\n", + " \n", + " num_inverter = 1/((lpy + 2*wp + 2*a + p)*10**3) # 1/m\n", + " \n", + " return num_inverter\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def coil_loss_and_power_average(k_parameters, l_parameters, b_parameters, extra_parameters):\n", + "\n", + " # Unpack parameters\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + " \n", + " f = 85*10**3 #[Hz]\n", + " w =2*math.pi*f #[rad/s]\n", + " Pout=50000 \n", + " Vdc = 400 \n", + " Vbat = 400 \n", + " QCoil = 400\n", + "\n", + " n1_i = math.pi*w*lp_0_0*10**(-9)*ip*math.sqrt(2)/(4*Vdc)\n", + " t1 = (math.pi)**2*w*(lp_0_0*10**(-9)*ls_0_0 *10**(-9))**0.5\n", + " t2 = Pout/(8*lp_0_0*n1_i*Vdc*Vbat)\n", + " n2_i = t1*t2\n", + " Is_i = 4*n2_i*Vbat/(math.pi*w*ls_0_0*10**(-9))/math.sqrt(2)\n", + " loss = w*lp_0_0*10**(-9)*ip**2/QCoil + w*ls_0_0 *10**(-9)*Is_i**2/QCoil\n", + "\n", + " a6 = lp_0_0.clone() * np.clone()**2 # LP0MM_YSO\n", + " a7 = lp_0_1.clone() * np.clone()**2 # LP0MM_YS1\n", + " a8 = lp_0_2.clone() * np.clone()**2 # LP0MM_YS2\n", + " a9 = lp_0_3.clone() * np.clone()**2 # LP0MM_YS3\n", + " a10 = lp_0_4.clone() * np.clone()**2 # LP0MM_YS4\n", + "\n", + " a11 = ls_0_0.clone() * ns.clone()**2 # LS0MM_YSO\n", + " a12 = ls_0_1.clone() * ns.clone()**2 # LS0MM_YS1\n", + " a13 = ls_0_2.clone() * ns.clone()**2 # LS0MM_YS2\n", + " a14 = ls_0_3.clone() * ns.clone()**2 # LS0MM_YS3\n", + " a15 = ls_0_4.clone() * ns.clone()**2 # LS0MM_YS4\n", + "\n", + "\n", + "\n", + " p0 = w*k_0_0*(a6*10**(-9))*(a11*10**(-9))**0.5*ip*Is_i\n", + " p1 = w*k_0_1*(a7*10**(-9))*(a12*10**(-9))**0.5*ip*Is_i \n", + " p2 = w*k_0_2*(a8*10**(-9))*(a13*10**(-9))**0.5*ip*Is_i \n", + " p3 = w*k_0_3*(a9*10**(-9))*(a14*10**(-9))**0.5*ip*Is_i\n", + " p4 = w*k_0_4*(a10*10**(-9))*(a15*10**(-9))**0.5*ip*Is_i \n", + " pave = (p0+(2*p1)+(2*p2)+(2*p3)+(2*abs(p4)))/8 \n", + " return loss,pave\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Network Training Loop" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "OXzpQf5O1Nxk", + "outputId": "1bc1a1b1-95ac-43d8-f1d8-5dd49e4bbb03" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(4848.8742, device='cuda:0', dtype=torch.float64, grad_fn=)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0\n", + " Kdiff : 22.0486%\n", + " Bstray : 51.5666\n", + " Pareto Kdiff : 13.2616%\n", + " Pareto Bstray : 45.9091\n", + " Coilloss : 50.4148\n", + " num inv : 0.0029\n", + "tensor(14306.0101, device='cuda:0', dtype=torch.float64,\n", + " grad_fn=)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 50\n", + " Kdiff : 35.2908%\n", + " Bstray : 48.7584\n", + " Pareto Kdiff : 20.2053%\n", + " Pareto Bstray : 45.5377\n", + " Coilloss : 46.0012\n", + " num inv : 0.0033\n", + "tensor(1474.3780, device='cuda:0', dtype=torch.float64, grad_fn=)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 100\n", + " Kdiff : 52.5443%\n", + " Bstray : 47.8593\n", + " Pareto Kdiff : 47.5650%\n", + " Pareto Bstray : 41.5482\n", + " Coilloss : 48.6483\n", + " num inv : 0.0041\n", + "tensor(1164.7019, device='cuda:0', dtype=torch.float64, grad_fn=)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 150\n", + " Kdiff : 35.3156%\n", + " Bstray : 48.6385\n", + " Pareto Kdiff : 28.5708%\n", + " Pareto Bstray : 46.4949\n", + " Coilloss : 51.5042\n", + " num inv : 0.0036\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\scipy\\interpolate\\fitpack2.py:280: UserWarning: \n", + "The maximal number of iterations maxit (set to 20 by the program)\n", + "allowed for finding a smoothing spline with fp=s has been reached: s\n", + "too small.\n", + "There is an approximation returned but the corresponding weighted sum\n", + "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n", + " warnings.warn(message)\n" + ] + } + ], + "source": [ + "p_front_x = []\n", + "p_front_y = []\n", + "pareto_bstray_points = []\n", + "pareto_kdiff_points = []\n", + "pareto_numinv_points = []\n", + "pareto_coilloss_points = []\n", + "\n", + "kdiffs = []\n", + "bstrays = []\n", + "numinvs = []\n", + "coillosses = []\n", + "combined_losses = []\n", + "\n", + "#testing for graphs\n", + "kdiff_designs = []\n", + "bstray_designs = []\n", + "numinv_designs = []\n", + "coilloss_designs = []\n", + "\n", + "pareto_bstray_points2 = []\n", + "pareto_kdiff_points2 = []\n", + "\n", + "x_calc_numpy =[]\n", + "y_calc_numpy=[]\n", + "\n", + "#training parameters\n", + "n_epochs = 200\n", + "n_noise = 1000\n", + "chosen_curve_util = prod_util\n", + "normalize = False\n", + "\n", + "for epoch in range(n_epochs):\n", + " \n", + " # Clear old gradients in the GP model\n", + " for param in gp_model.parameters():\n", + " param.grad = None\n", + "\n", + " # Create GP parameters from noise\n", + " noise = generate_noise(shape=(n_noise, 10))\n", + " gp = gp_model(noise)\n", + "\n", + " # Unpack and filter GP parameters\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + " gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + " extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + " # Scale GP parameters - why?\n", + " min_x = torch.min(gp_parameters)\n", + " max_x = torch.max(gp_parameters)\n", + " gp_parameters = (gp_parameters - min_x) / (max_x - min_x)\n", + "\n", + " # Push GP parameters through MP model\n", + " mp_parameters = mp_model(gp_parameters)\n", + "\n", + " # Scale MP parameters\n", + " y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + " min_y = torch.min(y, 1).values\n", + " max_y = torch.max(y, 1).values\n", + " mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + " k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + " l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + " b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + " # Calculate losses\n", + " kdiff = kdiff_loss(k_parameters)\n", + " bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " coilloss, pave = coil_loss_and_power_average(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " numinv = num_inverters(k_parameters, l_parameters, b_parameters, extra_parameters, gp_parameters)\n", + " \n", + " # compute pareto points for pariwise\n", + " pareto_bstray, pareto_kdiff = pareto_frontier(bstray, kdiff)\n", + " pareto_kdiff_forx, pareto_bstray_forx = pareto_frontier(kdiff, bstray)\n", + " \n", + " pareto_numinv, pareto_coilloss = pareto_frontier(numinv, coilloss)\n", + " pareto_coilloss_forx, pareto_numinv_forx = pareto_frontier(coilloss, numinv)\n", + " \n", + "\n", + " \n", + " # converting to numpy for spline and graphs\n", + " pareto_bstray_numpy = pareto_bstray.cpu().data.numpy()\n", + " pareto_kdiff_numpy = pareto_kdiff.cpu().data.numpy()\n", + " \n", + " pareto_numinv_numpy = pareto_numinv.cpu().data.numpy()\n", + " pareto_coilloss_numpy = pareto_numinv.cpu().data.numpy()\n", + " \n", + " pareto_bstray_forx_numpy = pareto_bstray_forx.cpu().data.numpy()\n", + " pareto_kdiff_forx_numpy = pareto_kdiff_forx.cpu().data.numpy()\n", + " \n", + " pareto_numinv_forx_numpy = pareto_numinv_forx.cpu().data.numpy()\n", + " pareto_coilloss_forx_numpy = pareto_coilloss_forx.cpu().data.numpy()\n", + "\n", + " bstray_numpy = bstray.cpu().data.numpy()\n", + " kdiff_numpy = kdiff.cpu().data.numpy()\n", + " numinv_numpy = numinv.cpu().data.numpy()\n", + " coilloss_numpy = coilloss.cpu().data.numpy()\n", + " \n", + " bstray_kdiff_loss = pair_loss_calc(bstray, kdiff, bstray_numpy, kdiff_numpy, pareto_bstray_numpy, pareto_kdiff_numpy, pareto_bstray_forx_numpy, pareto_kdiff_forx_numpy)\n", + " numinv_coilloss_loss = pair_loss_calc(numinv, coilloss, numinv_numpy, coilloss_numpy, pareto_numinv_numpy, pareto_coilloss_numpy, pareto_numinv_forx_numpy, pareto_coilloss_forx_numpy)\n", + " \n", + " \n", + " combined_loss = (bstray_kdiff_loss + numinv_coilloss_loss)\n", + "\n", + " # Push loss through the optimizer and update the GP model\n", + " try:\n", + " combined_loss.backward(retain_graph=True)\n", + " optimizer.step()\n", + " except Exception as e:\n", + " print(f\"Error on epoch {epoch} regarding\", e)\n", + " break\n", + "\n", + " \n", + " if epoch % 50 == 0:\n", + " \n", + " print(combined_loss)\n", + " # plt.plot(pareto_bstray_numpy[:,0], spl_y(pareto_bstray_numpy[:,0]), '--', bstray_numpy, kdiff_numpy, 'o')\n", + " plt.plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + " # plt.plot(x_calc.cpu().data.numpy(), kdiff_numpy, 'o')\n", + " plt.xlim([0,100])\n", + " plt.ylim([0,0.5])\n", + " plt.show()\n", + "# figure, axis = plt.subplots(2, 1, figsize=(10,10))\n", + "# plt.figure(figsize=(10, 5))\n", + " \n", + "# if normalize == True:\n", + "# axis[0].plot(normalized_pareto_bstray_numpy, normalized_pareto_kdiff_numpy)\n", + "# axis[0].scatter(x_calc.cpu().data.numpy(), normalized_kdiff_numpy)\n", + "# axis[0].scatter(normalized_bstray_numpy, normalized_kdiff_numpy)\n", + "# axis[0].set_xlim([0,1])\n", + "# axis[0].set_ylim([0,1])\n", + "# axis[0].set_xlabel(\"Bstray\")\n", + "# axis[0].set_ylabel(\"Kdiff %\")\n", + "# axis[0].set_title(f\"x_calc vs. designs, epoch: {epoch}\")\n", + " \n", + "# axis[1].plot(normalized_pareto_bstray_numpy, normalized_pareto_kdiff_numpy)\n", + "# axis[1].scatter(normalized_bstray_numpy, y_calc.cpu().data.numpy())\n", + "# axis[1].scatter(normalized_bstray_numpy, normalized_kdiff_numpy)\n", + "# axis[1].set_xlim([0,1])\n", + "# axis[1].set_ylim([0,1])\n", + "# axis[1].set_xlabel(\"Bstray\")\n", + "# axis[1].set_ylabel(\"Kdiff %\")\n", + "# axis[1].set_title(f\"y_calc vs. designs, epoch: {epoch}\")\n", + "# else:\n", + "# axis[0].plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# axis[0].scatter(x_calc.cpu().data.numpy(), kdiff_numpy)\n", + "# axis[0].scatter(bstray_numpy, kdiff_numpy)\n", + "# axis[0].set_xlim([0,100])\n", + "# axis[0].set_ylim([0,1])\n", + "# axis[0].set_xlabel(\"Bstray\")\n", + "# axis[0].set_ylabel(\"Kdiff %\")\n", + "# axis[0].set_title(f\"x_calc vs. designs, epoch: {epoch}\")\n", + " \n", + "# axis[1].plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# axis[1].scatter(bstray_numpy, y_calc.cpu().data.numpy())\n", + "# axis[1].scatter(bstray_numpy, kdiff_numpy)\n", + "# axis[1].set_xlim([0,100])\n", + "# axis[1].set_ylim([0,1])\n", + "# axis[1].set_xlabel(\"Bstray\")\n", + "# axis[1].set_ylabel(\"Kdiff %\")\n", + "# axis[1].set_title(f\"y_calc vs. designs, epoch: {epoch}\")\n", + " \n", + " \n", + " # plt.xlim([0,100])\n", + " # plt.ylim([0,0.5])\n", + " # plt.show()\n", + " #paretopoints graphing test\n", + " pareto_bstray_test, pareto_kdiff_test = oldgraph_pareto_frontier(bstray.cpu().detach().numpy(), kdiff.cpu().detach().numpy())\n", + " pareto_bstray_points2.append(pareto_bstray_test)\n", + " pareto_kdiff_points2.append(pareto_kdiff_test)\n", + "\n", + " # Store metrics for plotting or further logging\n", + " combined_losses.append(combined_loss.cpu().data.numpy())\n", + " kdiffs.append(torch.mean(kdiff).cpu().data.numpy())\n", + " bstrays.append(torch.mean(bstray).cpu().data.numpy())\n", + " numinvs.append(torch.mean(numinv).cpu().data.numpy())\n", + " coillosses.append(torch.mean(coilloss).cpu().data.numpy())\n", + " kdiff_designs.append(kdiff.cpu().data.numpy())\n", + " bstray_designs.append(bstray.cpu().data.numpy())\n", + " numinv_designs.append(numinv.cpu().data.numpy())\n", + " coilloss_designs.append(coilloss.cpu().data.numpy())\n", + " \n", + " p_front_x.append(pareto_bstray)\n", + " p_front_y.append(pareto_kdiff)\n", + " pareto_bstray_points.append(pareto_bstray[:].cpu().detach().numpy())\n", + " pareto_kdiff_points.append(pareto_kdiff[:].cpu().detach().numpy())\n", + " pareto_numinv_points.append(pareto_numinv[:].cpu().detach().numpy())\n", + " pareto_coilloss_points.append(pareto_coilloss[:].cpu().detach().numpy())\n", + "\n", + "\n", + " print(f\"Epoch: {epoch:4d}\")\n", + " # print(f\" Combined Loss: {combined_losses[-1]:.10f}\")\n", + " print(f\" Kdiff : {100 * kdiffs[-1]:.4f}%\")\n", + " print(f\" Bstray : {bstrays[-1]:.4f}\")\n", + " print(f\" Pareto Kdiff : {100 * torch.mean(p_front_y[-1]).cpu().data.numpy():.4f}%\")\n", + " print(f\" Pareto Bstray : {torch.mean(p_front_x[-1]).cpu().data.numpy():.4f}\")\n", + " print(f\" Coilloss : {coillosses[-1]:.4f}\")\n", + " print(f\" num inv : {numinvs[-1]:.4f}\")\n", + " # print(f\" Pareto Kdiff : {100 * torch.mean(p_front_y[-1]).cpu().data.numpy():.4f}%\")\n", + " # print(f\" Pareto Bstray : {torch.mean(p_front_x[-1]).cpu().data.numpy():.4f}\")\n", + " \n", + "\n", + " # Save model for further training or testing\n", + " torch.save(gp_model, f\"./models/gp_model_{epoch:06}.pt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "4\n", + "28 28\n" + ] + }, + { + "data": { + "image/png": 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SPIQmUVvfFh0G3PqfflX9GLgZuG+SOwMLe5e37w+bYN8PpamBehlNbdF7gTPGfkG3jgGWAvcG7gu8rt328cBJwLOAe9LM7Xl6u2wv4MvAF9t47gN8pWefT2vXXQCcAbyr3W4xcDzwsKraqz3uxZNfoWkZf81+CfyY267LVsvZ+podBlxUVddPsLyfic5vPvA54D+BuwF/Dny0Pe/xXg1cBuxLU3P6WqDaBOlzbQyLgCcAr0yydJJ4puNvgEcAD6GpWTyC9l5PEsu071eSE9pEr+9rgm32AN4JPKnd/6OA7/as8nDgImAf4A3AyvR0A5jCU4CHtef6rDZ2kixrz++o9nz/m6ZGdStVdVNV7dl+fHBV3Xvc8i8Cfwd8om0WffA045JmjYmatI2S7A18GHhTVV3bFu8JXDtu1WuBvdpljFs+tqyflwLvraqz29q7DwI30fzCHvOuqrq0qq4B3gY8py0/Bjitqs6rqpuAE4FHJjmI5pfgz6rqH6vqV1V1fVWd3bPP/6mqL1TVlvb8xn6JbQFuD9w/yfyqurhNRHfUZNes3/JrgT3bWpaptu1novN7RLu/k6vq5qo6C/g8t13TXptpEuADq2pzVf13NRMmPwzYt6re3O7jIppa06MniWc6jgHeXFVXVNWVwJtomtcni2Xa96uqTq6qBRO9Jonr18ADkoy0tcy9zYxXAO9oY/oEsA74g2me78lVtamqfgp8lSZBheaPlpOq6gdVdQtNsvWQfrVq0q7GRE3aBklGaGpOvl1VJ/UsugHYe9zqewPXt8sYt3xsWT8HAq8eV7OxP00t2JhLe95f0rNsYfsZgLYZ9mqaWp79aWqsJvKznvc3AndIsltVrQdeSdM0eUWS09PTnLoDJrtm/ZbvDdzQJiNTbdtP3/OjuWaXVtWve5ZfQnPNxltBU4v6n23T3wlt+YHAwnH37LU0NV07Yqv7ydb3um8sA7xftPv/JfBs4Djg8iRn9jRXA2xo71G/mKcy/h6N/ZFzIPBPPdf2GiD0v0fSLsVETZqmtulxFbCBrTu1Q9Nx+cE9696Lplbjh1X1C+Dy3uXt+4k6O18KvG1c7cYdq6q3qWf/nvcH0HTap/23d+TkHjTNpxva/W7VFDRdVfWxqnp0u+8C/n579jPO+Gu2RxvfBf2Ws/U1uwC4V27rAzh++bbYCOw/1r+rdQDNNdtKWwv56qq6F/BU4FVtH6xLgZ+Mu2d7VdWTtyOe8bH11hrdeq8niWXa9yvJa9u+Wn1fEwVVVaur6ok0NXoX0tQejlnU27eMrb+f2+tS4GXjru9IVX1zO/ZVU68idYeJmjQNbT+mTwOjwAvG1b4AfBR4apLHtAnHm4GVPX2oPgS8Lsmd29qHlwIfmOBw/wocl+ThaeyR5A/GJSV/lmS/tu/Pa7lttOTHgBcneUibWP4dcHZVXUzTnHePJK9sO13vlXGPGJng3BcneXy7v1+112BLu+xxSSb8xZdktzQDHeYB85KM1WIBfJam+ewP23VeD3yvqi7suWavSrKorRF69dg1q6of0vSLekO7z2fQjAz9zFTn08fZwC9pRuLOT/O8rafS9u0bdz5PSXKfNhG5rr0OW4DvANel6cQ/kmRekgckedh0rlNrfnsud+i5Th+n+d7sm2aU7OuBj0wWy2T3a7yq+ru2r1bfV79tktw9ydPa7/lNNLWbvfu/G/CK9lr+EXA/4AtTnPtUTgFOTDO6miR3ave9PX4OHDQuMZc6yy+qND2Pounj9fvApp5ah8cAtH10jqNJ2K6g6Sv18p7t30DT7HgJ8F/AirZj82+oqjU0idy7gF/QNG+9aNxqH6Pp/H5R+3pru+1XaEacfoamFu/etP2k2qTxiTRJyM+AH9E8KmQqtwdOBq5qt7sbTXIITc3etybZ9nU0icIJwPPa969r47mSZpTm29rzfDhb9+l6L00z8/nA/wFntmVjjgaWtNueDDyz3ec2qaqbaQYaPKk9x/fQJOMX9ln9EJoBGTfQnPd7quprbb+3p9L0qfpJu5/3AXdqt5vqOkGTzIz2vN5Ic1/XAN+juQ7ntWUTxsLk92sm3I4mad5I0wT5WLb+rp/dxnYVzb19ZlX1G/U7bVX1WZpawdOTXEfzfXjSdu7uU+2/Vyc5b0fiknaGbN2VQFLXJbkYeElVfbkDsbwP+FRVrZ7tWLpsrlynJC+i+W4+erZjkXYVu029iiT1V1Uvme0YhoHXSdL2GmjTZ5Ij0zxscX3P6Kje5Y9L81DE77av1w8yHkmSpGEysKbPJPOAH9L0ibmMZvqd51TV93vWeRzwV1X1lIEEIUmSNMQGWaN2BLC+mql3bqYZQfX0AR5PkiRplzLIRG0RWz+U8zL6P5zwkUn+N8l/jA29liRJ0mAHE6RP2fh21vNopj+5IcmTaR4meshv7Cg5FjgWYI899vitQw89dPwq0qy78GfXs3nL+Merwfx5t+PQe0w2s5EkaVd17rnnXlVV+27v9oNM1C5j66en78e4p1NX1XU977+Q5D1J9qmqq8atdypwKsCSJUtqzZo1g4ta2k6r1m7gxJXnM7r5tmd/jsyfx0lHPZBlhzvTjSTNRUkumXqtiQ2y6fMc4JAkByfZnebhlGf0rpDkHmNTjSQ5oo1nhx6MKM2WZYcv4qSjHsiiBSPNJIQLRkzSJEk7ZGA1alV1S5LjgdU008ecVlUXJDmuXX4K8EzgT5PcQvMk7qPLJ/BqiC07fJGJmSRpxgzdzAQ2fUqSpGGR5NyqWrK92zvXpyRJUkeZqEmSJHWUiZokSVJHmahJkiR1lImaJElSR5moSZIkdZSJmiRJUkeZqEmSJHWUiZokSVJHmahJkiR1lImaJElSR5moSZIkdZSJmiRJUkeZqEmSJHWUiZokSVJHmahJkiR1lImaJElSR5moSZIkdZSJmiRJUkeZqEmSJHWUiZokSVJHmahJkiR1lImaJElSR5moSZIkddRusx2AtCtZtXYDK1avY+OmURYuGGH50sUsO3zRbIclSRpSJmrSDFm1dgMnrjyf0c1bANiwaZQTV54PYLImSdouNn1KM2TF6nW3JmljRjdvYcXqdbMUkSRp2JmoSTNk46bRbSqXJGkqJmrSDFm4YGSbyiVJmoqJmjRDli9dzMj8eVuVjcyfx/Kli2cpIknSsHMwgTRDxgYMOOpTkjRTTNSkGbTs8EUmZpKkGWPTpyRJUkeZqEmSJHWUiZokSVJHmahJkiR1lImaJElSRznqUxoAJ2eXJM0EEzVphjk5uyRpptj0Kc0wJ2eXJM0UEzVphjk5uyRpppioSTPMydklSTPFRE2aYU7OLkmaKQ4mkGaYk7NLkmaKiZo0AE7OLkmaCTZ9SpIkddRAE7UkRyZZl2R9khMmWe9hSbYkeeYg45EkSRomA0vUkswD3g08Cbg/8Jwk959gvb8HVg8qFkmSpGE0yBq1I4D1VXVRVd0MnA48vc96fw58BrhigLFIkiQNnUEmaouAS3s+X9aW3SrJIuAZwCmT7SjJsUnWJFlz5ZVXznigkiRJXTTIRC19ymrc53cAf11VW/qse9tGVadW1ZKqWrLvvvvOVHySJEmdNsjHc1wG7N/zeT9g47h1lgCnJwHYB3hykluqatUA45IkSRoKg0zUzgEOSXIwsAE4Gnhu7wpVdfDY+yQfAD5vkqa5ZtXaDT4cV5LU18AStaq6JcnxNKM55wGnVdUFSY5rl0/aL02aC1at3cCJK89ndHPT+r9h0ygnrjwfwGRNkjTYmQmq6gvAF8aV9U3QqupFg4xF6qIVq9fdmqSNGd28hRWr15moSZKcmUCaTRs3jW5TuSRpbjFRk2bRwgUj21QuSZpbTNSkWbR86WJG5s/bqmxk/jyWL108SxFJkrpkoH3UJE1urB+aoz4lSf2YqEmzbNnhi0zMJEl92fQpSZLUUSZqkiRJHWWiJkmS1FEmapIkSR1loiZJktRRJmqSJEkdZaImSZLUUSZqkiRJHWWiJkmS1FEmapIkSR1loiZJktRRJmqSJEkdZaImSZLUUSZqkiRJHWWiJkmS1FEmapIkSR1loiZJktRRu812AJIaq9ZuYMXqdWzcNMrCBSMsX7qYZYcvmu2wJEmzyERN6oBVazdw4srzGd28BYANm0Y5ceX5ACZrkjSH2fQpdcCK1etuTdLGjG7eworV62YpIklSF5ioSR2wcdPoNpVLkuYGEzWpAxYuGNmmcknS3GCiJnXA8qWLGZk/b6uykfnzWL508SxFJEnqAgcTSB0wNmDAUZ+SpF4malJHLDt8kYmZJGkrNn1KkiR1lImaJElSR5moSZIkdZSJmiRJUkeZqEmSJHWUiZokSVJHmahJkiR1lImaJElSR5moSZIkdZSJmiRJUkeZqEmSJHWUiZokSVJHmahJkiR1lImaJElSR5moSZIkddRAE7UkRyZZl2R9khP6LH96ku8l+W6SNUkePch4JEmShslug9pxknnAu4EnApcB5yQ5o6q+37PaV4AzqqqSPAj4JHDooGKSJEkaJoOsUTsCWF9VF1XVzcDpwNN7V6iqG6qq2o97AIUkSZKAwSZqi4BLez5f1pZtJckzklwInAn88QDjkSRJGirTTtSSPDXJ2W1/spdPZ5M+Zb9RY1ZVn62qQ4FlwFsmOPaxbR+2NVdeeeV0Q5YkSRpqEyZqSR48ruj5wCOAhwJ/Oo19Xwbs3/N5P2DjRCtX1deBeyfZp8+yU6tqSVUt2XfffadxaEmSpOE32WCClycJ8Pqq+hlNM+bbgF8zScLV4xzgkCQHAxuAo4Hn9q6Q5D7Aj9vBBA8Fdgeu3vbTkCRJ2vVMmKhV1cvaWrX3JlkD/C3wKOCOTNBEOW77W5IcD6wG5gGnVdUFSY5rl58C/CHwgiSbgVHg2T2DCyRJkua0TCcvSvJU4C+AD1bVhwce1SSWLFlSa9asmc0QJEmSpiXJuVW1ZHu3n6yP2nFJ1iY5j+bRGUcCd06yOsljtveAkiRJmp7JRn2+vKoOpxlAsLyqbqmqd9L0NXvGTolOkiRpDptsMMGGJG8BRoALxwqr6hfAqwYdmCRJ0lw3WaL2dGApsBn40s4JR9L2WLV2AytWr2PjplEWLhhh+dLFLDv8N54vLUkaMpON+rwZ+NxOjEXSdli1dgMnrjyf0c1bANiwaZQTV54PYLImSUNukFNISdoJVqxed2uSNmZ08xZWrF43SxFJkmaKiZo05DZuGt2mcknS8JisjxoASe7Sp/j6qto8gHgkbaOFC0bY0CcpW7hgZBaikSTNpOnUqJ0HXAn8EPhR+/4nSc5L8luDDE7S1JYvXczI/HlblY3Mn8fypYtnKSJJ0kyZTqL2ReDJVbVPVd0VeBLwSeDlwHsGGZykqS07fBEnHfVAFi0YIcCiBSOcdNQDHUggSbuAKaeQSrJm/NQHY2VJvltVDxlkgOM5hZQkSRoWOzqF1JR91IBrkvw1cHr7+dnAL5LMA369vQeWJEnS5KbT9PlcYD9gFfDvwAFt2TzgWQOLTJIkaY6bskatqq4C/nyCxetnNhxJkiSNmc7jOe4L/BVwUO/6VfX4wYUlSZKk6fRR+xRwCvA+YMsU60rqEOcAlaThNp1E7Zaq+peBRyJpRjkHqCQNv+kMJvhckpcnuWeSu4y9Bh6ZpB3iHKCSNPymU6P2wvbf5T1lBdxr5sORNFOcA1SSht90Rn0evDMCkTSznANUkobfhIlaksdX1VlJjuq3vKpWDi4sSTtq+dLFW/VRA+cAlaRhM1mN2mOBs4Cn9llWgIma1GFjAwYc9SlJw2vKuT67xrk+JUnSsBjYXJ9JXjXZhlX19u09qCRJkqY2WdPnXjstCkmSJP2GCRO1qnrTzgxEkiRJW5vygbdJ7pXkc0muTHJFkn9P4jPUJEmSBmw6MxN8DPgkcE9gIc3cnx8fZFCSJEmaXqKWqvpwVd3Svj5C83gOSZIkDdB0ppD6apITgNNpErRnA2eOzfdZVdcMMD5JkqQ5azqJ2rPbf182rvyPcc5PSZKkgXGuT2kOWLV2gzMUSNIQmjJRS/KCfuVV9aGZD0fSTFu1dsNWc35u2DTKiSvPBzBZk6SOm85ggof1vB4DvBF42gBjkjSDVqxet9XE7ACjm7ewYvW6WYpIkjRd02n6/PPez0nuBHx4YBFJmlEbN41uU7kkqTumU6M23o3AITMdiKTBWLhgZJvKJUndMZ2ZCT6X5Iz2dSawDvj3wYcmaSYsX7qYkfnztiobmT+P5UsXz1JEkqTpms7jOf6h5/0twCVVddmA4pE0w8YGDDjqU5KGz3T6qP1XkrvTDCYA+NFgQ5I005YdvsjETJKG0HSaPp8FfAf4I+BZwNlJnjnowCRJkua66TR9/g3wsKq6AiDJvsCXgU8PMjBJkqS5bjqjPm83lqS1rp7mdpIkSdoB06lR+2KS1cDH28/PBv5jcCFJkiQJpjeYYHmSo4BHAwFOrarPDjwySTudc4JKUrdMmKgluQ9w96r6RlWtBFa25b+T5N5V9eOdFaSkwXNOUEnqnsn6mr0DuL5P+Y3tsiklOTLJuiTrk5zQZ/kxSb7Xvr6Z5MHT2a+kmeecoJLUPZMlagdV1ffGF1bVGuCgqXacZB7wbuBJwP2B5yS5/7jVfgI8tqoeBLwFOHWacUuaYc4JKkndM1midodJlk1nksAjgPVVdVFV3QycDjy9d4Wq+mZV/aL9+G1gv2nsV9IAOCeoJHXPZInaOUleOr4wyZ8A505j34uAS3s+X9aWTeRPcDSpNGucE1SSumeyUZ+vBD6b5BhuS8yWALsDz5jGvtOnrPqumPwuTaL26AmWHwscC3DAAQdM49CStpVzgkpS90yYqFXVz4FHtUnUA9riM6vqrGnu+zJg/57P+wEbx6+U5EHA+4AnVdXVE8RyKm3/tSVLlvRN9iTtOOcElaRumc5z1L4KfHU79n0OcEiSg4ENwNHAc3tXSHIAzWM/nl9VP9yOY0iSJO2ypjMzwXapqluSHA+sBuYBp1XVBUmOa5efArweuCvwniQAt1TVkkHFJEmSNExSNVwtiUuWLKk1a9bMdhiSJElTSnLujlRCObm6JElSR5moSZIkdZSJmiRJUkeZqEmSJHWUiZokSVJHmahJkiR1lImaJElSR5moSZIkdZSJmiRJUkeZqEmSJHXUwOb6lDT3rFq7gRWr17Fx0ygLF4ywfOlilh2+aLbDkqShZaImaUasWruBE1eez+jmLQBs2DTKiSvPBzBZk6TtZKImaUasWL3u1iRtzOjmLaxYve7W5da0SdK2MVGTNCM2bhrtWz5Ws2ZNmyRtOwcTSJoRCxeM9C2fl0xa0yZJmpiJmqQZsXzpYkbmz9uqbGT+PLZU9V1/w6ZRfvvks1i1dsPOCE+ShpJNn5JmxFgz5vi+aCtWr2PDFM2iay65hq9eeKV92CRpHBM1STNm2eGL+iZYvX3UxhvdvIWPfvunjNW72YdNkm5j06ekgVp2+CJOOuqBLJqgDxvA+MZR+7BJUsNETdLALTt8Ed844fGTJmvjTTSKVJLmEhM1STtNvwEHmWDdiUaRStJcYqImaafpbQYNsGjBCMc84oC+o0WXL108O0FKUoc4mEDSTtVvwMGSA+/izAWS1IeJmqRZN9FoUUma62z6lCRJ6igTNUmSpI4yUZMkSeooEzVJkqSOMlGTJEnqKEd9SuqMVWs3+JgOSephoiapE1at3bDV5O1Ozi5JNn1K6ogVq9fdmqSNcXJ2SXOdiZqkTphoEnYnZ5c0l5moSeqEiSZhd3J2SXOZiZqkTli+dLGTs0vSOA4mkNQJYwMGHPUpSbcxUZPUGU7OLklbs+lTkiSpo0zUJEmSOspETZIkqaNM1CRJkjrKRE2SJKmjTNQkSZI6ykRNkiSpo0zUJEmSOspETZIkqaMGmqglOTLJuiTrk5zQZ/mhSb6V5KYkfzXIWCRJkobNwKaQSjIPeDfwROAy4JwkZ1TV93tWuwZ4BbBsUHFIkiQNq0HWqB0BrK+qi6rqZuB04Om9K1TVFVV1DrB5gHFIkiQNpUEmaouAS3s+X9aWbbMkxyZZk2TNlVdeOSPBSZIkdd0gE7X0Kavt2VFVnVpVS6pqyb777ruDYUmSJA2HQSZqlwH793zeD9g4wONJkiTtUgaZqJ0DHJLk4CS7A0cDZwzweJIkSbuUgY36rKpbkhwPrAbmAadV1QVJjmuXn5LkHsAaYG/g10leCdy/qq4bVFySum/V2g2sWL2OjZtGWbhghOVLF7Ps8O3q4ipJQ21giRpAVX0B+MK4slN63v+MpklUkoAmSTtx5fmMbt4CwIZNo5y48nwAkzVJc44zE0jqlBWr192apI0Z3byFFavXzVJEkjR7TNQkdcrGTaPbVC5Ju7KBNn1K0rZauGCEDX2SsoULRuy7JmnOsUZNUqcsX7qYkfnztiobmT+P3z10X05ceT4bNo1S3NZ3bdXaDbMTqCTtBCZqkjpl2eGLOOmoB7JowQgBFi0Y4aSjHshXL7xyIH3XVq3dwG+ffBYHn3Amv33yWSZ+kjrFpk9JnbPs8EW/0aT5l5/4bt91d6TvmiNMJXWdNWqShsLCBSPbVD4djjCV1HUmapKGwkR915YvXbzd+3SEqaSuM1GTNBQm6ru2I02Ug6ilk6SZZB81SUOjX9+1HbF86eKt+qjBjtfSSdJMMlGTNGeNJX29z2b73UP3ZcXqdfzlJ77rs9om4PPspJ3HRE3SnNZbS+co0KnN1WtkcqrZYh81SWo5CnRqc/EajSWnPmxZs8FETZJajgKd2ly8RnMxOVV3mKhJUstRoFObi9doLian6g4TNUlqDeJZbbuauXiN5mJyqu4wUZOk1iCe1barmYvXaC4mp+qOVNVsx7BNlixZUmvWrJntMCRJc4ijPrW9kpxbVUu2d3sfzyFJ0hRm+mHL0nTZ9ClJktRRJmqSJEkdZaImSZLUUSZqkiRJHWWiJkmS1FEmapIkSR1loiZJktRRJmqSJEkdZaImSZLUUSZqkiRJHWWiJkmS1FEmapIkSR1loiZJktRRJmqSJEkdZaImSZLUUSZqkiRJHWWiJkmS1FEmapIkSR1loiZJktRRJmqSJEkdZaImSZLUUSZqkiRJHWWiJkmS1FEmapIkSR1loiZJktRRJmqSJEkdNdBELcmRSdYlWZ/khD7Lk+Sd7fLvJXnoIOORJEkaJgNL1JLMA94NPAm4P/CcJPcft9qTgEPa17HAvwwqHkmSpGEzyBq1I4D1VXVRVd0MnA48fdw6Twc+VI1vAwuS3HOAMUmSJA2NQSZqi4BLez5f1pZt6zqSJElz0m4D3Hf6lNV2rEOSY2maRgFuSvJ/OxibZs8+wFWzHYS2i/duuHn/hpv3b3gt3pGNB5moXQbs3/N5P2DjdqxDVZ0KnAqQZE1VLZnZULWzeP+Gl/duuHn/hpv3b3glWbMj2w+y6fMc4JAkByfZHTgaOGPcOmcAL2hHfz4CuLaqLh9gTJIkSUNjYDVqVXVLkuOB1cA84LSquiDJce3yU4AvAE8G1gM3Ai8eVDySJEnDZpBNn1TVF2iSsd6yU3reF/Bn27jbU2cgNM0e79/w8t4NN+/fcPP+Da8dundpciVJkiR1jVNISZIkddRQJWpTTUml7kiyf5KvJvlBkguS/EVbfpckX0ryo/bfO892rOovybwka5N8vv3svRsSSRYk+XSSC9ufwUd6/4ZHkr9s/9/8vyQfT3IH7193JTktyRW9jw6b7H4lObHNY9YlWTrV/ocmUZvmlFTqjluAV1fV/YBHAH/W3q8TgK9U1SHAV9rP6qa/AH7Q89l7Nzz+CfhiVR0KPJjmPnr/hkCSRcArgCVV9QCawXhH4/3rsg8AR44r63u/2t+DRwOHtdu8p81vJjQ0iRrTm5JKHVFVl1fVee3762l+USyiuWcfbFf7ILBsVgLUpJLsB/wB8L6eYu/dEEiyN/A7wL8BVNXNVbUJ798w2Q0YSbIbcEea54t6/zqqqr4OXDOueKL79XTg9Kq6qap+QvPUiyMm2/8wJWpONzWkkhwEHA6cDdx97Fl57b93m8XQNLF3AK8Bft1T5r0bDvcCrgTe3zZdvy/JHnj/hkJVbQD+AfgpcDnN80X/E+/fsJnofm1zLjNMidq0pptStyTZE/gM8Mqqum6249HUkjwFuKKqzp3tWLRddgMeCvxLVR0O/BKbyYZG25fp6cDBwEJgjyTPm92oNIO2OZcZpkRtWtNNqTuSzKdJ0j5aVSvb4p8nuWe7/J7AFbMVnyb028DTklxM08Xg8Uk+gvduWFwGXFZVZ7efP02TuHn/hsPvAT+pqiurajOwEngU3r9hM9H92uZcZpgStelMSaWOSBKaPjI/qKq39yw6A3hh+/6FwL/v7Ng0uao6sar2q6qDaH7Ozqqq5+G9GwpV9TPg0iRjE0E/Afg+3r9h8VPgEUnu2P4/+gSaPr7ev+Ey0f06Azg6ye2THAwcAnxnsh0N1QNvkzyZpu/M2JRUb5vdiDSRJI8G/hs4n9v6Ob2Wpp/aJ4EDaP5D+qOqGt8JUx2R5HHAX1XVU5LcFe/dUEjyEJqBILsDF9FMz3c7vH9DIcmbgGfTjJ5fC7wE2BPvXycl+TjwOGAf4OfAG4BVTHC/kvwN8Mc09/eVVfUfk+5/mBI1SZKkuWSYmj4lSZLmFBM1SZKkjjJRkyRJ6igTNUmSpI4yUZMkSeooEzVJu4wkW5J8N8n/JjkvyaOmWP+1Oys2SdoePp5D0i4jyQ1VtWf7finw2qp67HTWH1cemv8ff91nM0naaaxRk7Sr2hv4BTRTuCT5elvb9n9JHpPkZGCkLftokoOS/CDJe4DzgP2T/EuSNUkuaB9CSpInJPns2EGSPDHJyn4BSNKOskZN0i4jyRaa2TDuANwTeHxVnZvk1cAdquptSeYBd6yq68fVwB1E8xT/R1XVt9uyu1TVNe02XwFe0e7/B8BjqurKJB8DPl5Vn9vJpytpDrBGTdKuZLSqHlJVhwJHAh9qmzHPAV6c5I3AA6vq+gm2v2QsSWs9K8l5NNP4HAbcv5q/bj8MPC/JAuCRwKRTwEjS9jJRk7RLqqpv0cy9t29VfR34HWAD8OEkL5hgs1+OvWknTP4r4AlV9SDgTJqaOoD3A88DngN8qqpuGcxZSJrrTNQk7ZKSHArMA65OciBwRVX9K/BvwEPb1TYnmT/BLvamSdyuTXJ34EljC6pqI7AReB3wgcGcgSTBbrMdgCTNoJEk323fB3hhVW1J8jhgeZLNwA3AWI3aqcD32ubNv+ndUVX9b5K1wAU0fde+Me5YH6Wprfv+IE5EksDBBJK0XZK8C1hbVf8227FI2nWZqEnSNkpyLk2z6BOr6qbZjkfSrstETZIkqaMcTCBJktRRJmqSJEkdZaImSZLUUSZqkiRJHWWiJkmS1FEmapIkSR31/wO0PFOON13/ggAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#scatter plot of final pareto points\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff Combined Final Frontier\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.5])\n", + "\n", + "print(len(pareto_bstray_points2[:]))\n", + "print(len(pareto_kdiff_points2[:]))\n", + "# print(pareto_bstray_points2[:])\n", + "final_x = []\n", + "for list in pareto_bstray_points2:\n", + " for item in list:\n", + " final_x.append(item)\n", + "\n", + "\n", + "final_y = []\n", + "for list in pareto_kdiff_points2:\n", + " for item in list:\n", + " final_y.append(item)\n", + "\n", + "final_x, final_y = oldgraph_pareto_frontier(final_x, final_y)\n", + "print(len(final_x),len(final_y))\n", + "\n", + "plt.scatter(final_x, final_y)\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "4\n", + "28 28\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#line plot of final pareto points\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff Combined Final Frontier\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points2[:]))\n", + "print(len(pareto_kdiff_points2[:]))\n", + "# print(pareto_bstray_points2[:])\n", + "final_x = []\n", + "for list in pareto_bstray_points2:\n", + " for item in list:\n", + " final_x.append(item)\n", + "\n", + "\n", + "final_y = []\n", + "for list in pareto_kdiff_points2:\n", + " for item in list:\n", + " final_y.append(item)\n", + "\n", + "final_x, final_y = oldgraph_pareto_frontier(final_x, final_y)\n", + "print(len(final_x),len(final_y))\n", + "\n", + "plt.plot(final_x, final_y)\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'\\nplt.figure(figsize=(10, 5))\\nplt.title(f\"Pareto Kdiff During Training\")\\nplt.xlabel(\"Epoch\")\\nplt.ylabel(\"Coupling %\")\\n\\nfor x in pareto_kdiff_points:\\n plt.plot(x)\\nplt.show()\\n'" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points), len(pareto_kdiff_points))\n", + "\n", + "while i < len(pareto_bstray_points):\n", + " plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()\n", + "\n", + "\n", + "'''\n", + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"Pareto Kdiff During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Coupling %\")\n", + "\n", + "for x in pareto_kdiff_points:\n", + " plt.plot(x)\n", + "plt.show()\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'\\nplt.figure(figsize=(10, 5))\\nplt.title(f\"Pareto Kdiff During Training\")\\nplt.xlabel(\"Epoch\")\\nplt.ylabel(\"Coupling %\")\\n\\nfor x in pareto_kdiff_points:\\n plt.plot(x)\\nplt.show()\\n'" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Number of Inverters vs Coilloss During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Num Inverters\")\n", + "plt.ylabel(\"Coil loss\")\n", + "# plt.xlim([0,1])\n", + "plt.ylim([0, 5000])\n", + "\n", + "print(len(pareto_numinv_points), len(pareto_coilloss_points))\n", + "\n", + "while i < len(pareto_numinv_points):\n", + " plt.plot(pareto_numinv_points[i], pareto_coilloss_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "# plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()\n", + "\n", + "\n", + "'''\n", + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"Pareto Kdiff During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Coupling %\")\n", + "\n", + "for x in pareto_kdiff_points:\n", + " plt.plot(x)\n", + "plt.show()\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 4\n" + ] + }, + { + "data": { + "image/png": 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AzG42s/OGOLwRzkkLIcieTgjGc+kAOtx9T8L75fp4nKlAFyGQ2IsQKP+oDLWyPyC8RloI5+VLZjavUFmADxMCnOmE9/yZhC/ZXcTzszXP3815HjPUe38opwELCO+dq4F3mNmeMe86wmvz2rjvVUB/PMZrgfnALn2h3P3b8Xn+Jr6OvpC1/YOEQCf9OvtKjnLdTghQAP4OWA+8JeP+7TmO+3fx5pEx33QtfhPhGkwlBFlfj5/d+XyI8Nptic/30qztbyG8No8nvJc/QmhNmUFooUl//swEvkkI1loI139ageMOqchzJyVQoFZj4gfU1cAF7t4Tk3cn/LLK1APsEbeRtT29LZd/Ar7l7r919x3ufhXhl+zRGft8zd03uPuTwBcJH6QQArgr3X2Nuz9PqHl7o5kdQKil2+zu/+Hufe7+jLv/NiPPX7n7Tzz0Ybia0OQI4Yv8pcBMM5vg7o/GQHSkCp2zXNt7gN1jP7WhHptLvud3dMzvYnd/wd1vJdSanZYjj+2EAGN/d9/uoU+NA68H9nX3ZTGP9YTg6NQC5cl2qbt3x2vaCbwmphe6pmkXufuT7p7+ct8OfNHdtxOCun0IAcIz7n4/odZwFoC73+Pud7l7v7s/Svhh8BaKZGaHEGoI3ufuGwivs0fd/TsxzzWEmsb3xi/19wBL3b3X3f9I+GIf4O4nufvFWYdZkxGMXOruD7v7z939eXd/HPhqgTJvB15pZvu4+7PufldMPx34SXxNvOjuPyfUdr2j2Oee41xMJ9TsfDa+x+4DriB8CRcqy3bCF/Qr43v+Hnd/Otcx4vmZnOfvpDxFG+q9P5RL4+fNNnd/DFhD+HEAMBd4zt3vMrNXACcCi+L13QIsp7T3QSluZ3BgdlHG/beQI1ArYDuwLL6vf0Koicr1Yy3tanf/Y/yBuQRYmO6oH7XHc7CN8B7+qruvd/dnCe/hU2Oz6HuBm939jvj+XkKoDZMEUaBWQ8xsIuFL9C53vyhj07PAnlm770loZns24372tlz2Bz6d+UuZ8Eu7JWOfDRm3H8vY1hLvAxA/FJ4g/EqcTqixymdzxu3ngAYzq3f3h4FFhBqLLWZ2nZm15Hh8qQqds1zb9wSejYHRUI/NJefzI5yzDe6e+eH4GOGcZbuEUIv6MzNbn1Hzsz/QknXNzifUuhUru3zpAL/QNU3LfD0APOE7Ow2ng7e/Zmzfls7fzA6JtTSbzexpQjP+PsUU2MwmEZp9lrj7L2Py/sAbss7FBwg1FvsC9ez6+h3K7Ixg5GwLg3uuM7NNsczfL1DmjxFqnR80s7vNLB3M7A/8fVY5jyUE4sPVAjyZVdOb+VrKV5argVuA6ywMWPpKrOktl6He+0PJfn1dy84fMu9nZ23a/sAEIJVxTr9FqFmthN8QWi1eQfhh8z1geuz/ehSh2bFYT7h7f8b9zPdgLtmv4QkMfg1mbh/0Ho636wmfDy2Z+8bA74kSyi2jQIFajYhNjzcS+l18PGvz/eysoSE2K70U+LO7P0Xo83Bkxv5HxsfksoFQG5L5S/ll7v6DjH2mZ9zej9Bpn/g/c+RkI+GX+qaY70FFPNVduPu17n5szNuBLw8nnyzZ56wxlu/+XNsZfM7uB2bEJp1c20vRTfhwz3wv7kc4Z4PEmohPu/sMoBX4VGzW2gA8knXN9nD3YdfOZJUv3zUdKNoI8v8m8CBwsIcmufMJTYsFxfN1LfALd/9WxqYNwO1Z52J3d/8Eof9eP7u+fkt1EeE5z4plPj1fmd19nbufRggWvkwYdNIYy3l1Vjkbc9TmlaIb2CvrdTnwWspXlliLc4G7zwSOIdSAfSjXAczspxkjYLP/fpqnXIXe+73AyzLuN+XYJ/v19UPgODObBryLnYHaBkLt/z4Z53RPdy/UJaGQgq9rd38OuAdoA/7o7i8Q+hx+Cvg/d//bMI9bjOzX8HZCf8mB4mXcHvQejvv3E348pTLzMrOXEd7fQxnquo3kM0GyKFCrAfHX7UpCTcSHsmpfIHRmbrUwD1AjsAy4IeOX9feAz5vZy83sMELz5nfzHO4/gTPN7A0WNJrZgqwP/382s2kWpgA5n52jJa8FPmpmr4mB5ZeA38YmrZuBJjNbZGG6kD0sa4qRPM/9UDObG/Pri+dgR9x2nJnl/UAws3ozawDqgDozS9diAfwYeJWZvSfusxRY6+4PZpyzT5nZ1FiD9+n0OXP3PxMGW3wh5vkuQlPej4Z6Pjn8lvChd66ZTTCz4whBWHY/MMzsJDN7ZWx+fTqehx2ETsVPm9lnzWyimdWZ2avM7PXFnKchFLqm5bAH4bk8G1+bnyjycV8kDABpy0q/mVDL8cF4PieY2evN7PBYy3cD0G5mL7PQP+fDwyzzs8BWC33Nzsm3o5mdbmb7xvfs1pi8g1AL12pmx8fr1RCv07T4uHYzu22Icrw0Pq4hvoY3EQKFi2LaLEIt2jWFymJmbzWzV1toOnua8KWfcxoFdz/Rd46Azf47MU85C7337yP0OdvLzJoItecFxebm2wgDmR5x9wdieorQ1/M/zGxPM3uJmR1kZkU3pWf5K6FPVyG3E/rQpps5b8u6P9x8h3K6mc2MgdUywgCLfFNf/ABYbGYHWpgp4EvAf8UavJXASWZ2rJntFvMqJi64j8LXrRzPUSIFarUh/St3PuHLIf0L9s0AHvr9nEn4QN5C+CLJ7JD9BULTw2OED5BL3P1/ch3I3VcTArmvEUZ1PkzsWJ7hWsIH4vr492/xsasIfRx+RPildhCxf0gMGt9OCEI2E0YJvrWI5/5S4GLCr8XNhNqA8+O26YTmh3w+TwjsziPUemyLaekP+/cQvvCfInSGz+zL8i1CM/MfgD8SOn5n1tycCsyJj70YeG/MsyTxV/g7CX1r/kaYVuVDGQFjpoMJnbKfJTzvb7j7bfEDupXQ/PJIzOcKQudkGPo8FSpf3mtaJp8hNF89Q/iR8F+Fdx9wGqF/31MZ74cPxNfZ/FjGbsJr5suE1xGEL9HdY/p3CV/2A2KN0fkUdgEwm9AvsYsQ/OVzAnC/mT1L6Mx/auyntQE4mfBafpxQG3QOOz+TpwO/HqIczxJe0+m/uYTzcgDhuf8Y+IKH/m95y0KoDVlJCNIeIHxGlG0C6iHe+1cTBsE8SvhMKfb6X0sYWXltVvqHCAOt0qPSVzL85uSLCD9wt5rZZ/Lsczvh8/aOPPdzaQeuivkuHGbZria8fjcTBu6cXWDfK+P+dxA+H/oIg5bS3x3/TDiPKcI5K2by3aGuWzHnTopkocuNSHHM7FHCsOr/TUBZrgB+6O63VLssSabzVHvM7D7CNDTqLySDxJrW77v7FdUui4wOTYYnNcvdtQRJEXSeao+7v6baZRCRZKho06eZnWBmD5nZw5ZjbqLYJ6PHzO6Lf0srWR4REREZmuUfODJU1wAps4o1fcaOqX8m9E3YSJjt+jR3/1PGPscBn/H88++IiIiIjFuVrFE7Cng4TrL3AmEU28kVPJ6IiIjImFLJQG0qgyfd20juSTzfaGa/j9Wsw53vRkRERGTMqeRgglwTQGa3s64hLIfzrJm9gzCh68G7ZGR2BmERbRobG1932GGHlbmoIiP3/J//jL+wfZd0220CLz3kkCqUSEREqu2ee+75m7sPex3fSgZqGxk8e/I0ds5gD4BnrCfn7j8xs29YWIfub1n7XQ5cDjBnzhxfvXp15UotMkw9nZ2klizF+/oG0qyhgeYLlzGptbWKJRMRkWoxs2KWqsurkoHa3cDBZnYgYcbsUwkTWw6IMxr/1d3dzI4iNMVq3iCpSelgbMvyFfSnUtQ3NzNl8SIFaSIiMmwVC9Tcvd/MziIs9lsHXOnu95vZmXH7ZcB7gU+YWT9hZu1TXTPwSg2b1NqqwExERMqm5lYmUNOniIiI1Aozu8fd5wz38VqZQERERKQI27dvZ+PGjfRl9EVOa2hoYNq0aUyYMKGsx1SgJiIiIlKEjRs3sscee3DAAQdgtnNyC3fniSeeYOPGjRx44IFlPWZFl5ASERERGSv6+vrYe++9BwVpAGbG3nvvnbOmbaQUqImIiIgUKTtIGyp9pBSoiSRQ1/ou5q+cz6yrZjF/5Xy61ndVu0giIlIF6qMmkjBd67tov7Odvh2hCj3Vm6L9znYAFsxYUMWSiYjIaFONmkjCdKzpGAjS0vp29NGxpqNKJRIRkbR805pVarozBWoiCbO5d3NJ6SIiMjoaGhp44okndgnK0qM+Gxoayn5MNX2KJExTYxOp3lTOdBERqZ5p06axceNGHn/88V22pedRKzcFaiIJ0za7bVAfNYCGugbaZrdVsVQiIjJhwoSyz5M2FAVqIgmTHjDQsaaDzb2baWpsom12mwYSiIiMQwrURBJowYwFCsxERESDCURERESSSoGaiIiISEIpUBMRERFJKAVqIiIiIgmlQE1EREQkoRSoiYiIiCSUAjWRMurp7GTd3Hk8cPhM1s2dR09nZ1nz71rfxfyV85l11Szmr5xP1/qusuYvIiLJonnURMqkp7OT1JKleF9YUaC/u5vUkqUATGptHXH+Xeu7Bq1YkOpN0X5nO4DmXBMRGaNUoyZSJluWrxgI0tK8r48ty1eUJf+ONR2DlpUC6NvRR8eajrLkLyIiyaNATaRM+lO7LqReKL1Um3s3l5QuIiK1T4GaSJnUNzeXlF6qpsamktJFRKT2KVATKZMpixdhDQ2D0qyhgSmLF5Ul/7bZbTTUDc6/oa6BttltZclfRESSR4MJRMpkUmsr/OUutnznBvqfdep3N6Z89B1lGUgAOwcMdKzpYHPvZpoam2ib3aaBBCIiY5gCNZFyWXs9k7Z+m0knbduZtvXbsPZImLWwLIdYMGOBAjMRkXFETZ8i5bJqGWzfNjht+7aQLiIiMgwK1ETKpWdjaekiIiJDUKAmUi6TppWWLiIiMgQFaiLlMm8pTJg4OG3CxJAuIiIyDArURMpl1kJovRQmTQcs/G+9tGwDCUREZPzRqE+Rcpq1UIGZiIiUjWrURERERBJKgZqIiIhIQilQExEREUkoBWoiIiIiCaVATURERCShFKiJVEBPZyfr5s7jgcNnsm7uPHo6O6tdJBERqUGankOkzHo6O0ktWYr39QHQ391NakmY9HZSa2s1iyYiIjVGNWoiZbZl+YqBIC3N+/rYsnxFdQokIiI1S4GaSJn1p1IlpYuIiOSjQE2kzOqbm0tKFxERyUeBmkiZTVm8CGtoGJRmDQ1MWbyoOgUSEZGapcEEImWWHjCwZfkK+lMp6pubmbJ4kQYSiIhIyRSoiVTApNZWBWYiIjJiavoUERERSaiKBmpmdoKZPWRmD5vZeQX2e72Z7TCz91ayPCIiIiK1pGKBmpnVAV8HTgRmAqeZ2cw8+30ZuKVSZRERERGpRZWsUTsKeNjd17v7C8B1wMk59vsX4EfAlgqWRURERKTmVDJQmwpsyLi/MaYNMLOpwLuAywplZGZnmNlqM1v9+OOPl72gImNR1/ou5q+cz6yrZjF/5Xy61ndVu0giIlKiSgZqliPNs+6vAD7r7jsKZeTul7v7HHefs++++5arfCJjVtf6LtrvbCfVm8JxUr0p2u9sV7AmIlJjKhmobQSmZ9yfBnRn7TMHuM7MHgXeC3zDzE6pYJlExoWONR307Ri83mjfjj461nRUqUQiIjIclQzU7gYONrMDzWw34FTgpswd3P1Adz/A3Q8AVgKfdPcbK1gmkcTp6exk3dx5PHD4TNbNnUdPZ+eI89zcu7mkdBERSaaKTXjr7v1mdhZhNGcdcKW7329mZ8btBfuliYwHPZ2dpJYsxftC7Vd/dzepJUsBRjRhblNjE6neXReBb2psGnaeIiIy+io6j5q7/8TdD3H3g9z9izHtslxBmrt/xN1XVrI8IkmzZfmKgSAtzfv62LJ8xYjybZvdRkPd4PVGG+oaaJvdNqJ8RURkdGkJKZEq6k/tWutVKL1YC2YsAEJftc29m2lqbKJtdttAuoiI1AYFaiJVVN/cTH939hibkD5SC2YsUGAmIlLjtNanSBVNWbwIaxjcRGkNDUxZvKjsx9K8aiIitUc1aiJVlB4wsGX5CvpTKeqbm5myeNGIBhLkkp5XLT1lR3peNUC1biIiCWbu2XPQJtucOXN89erV1S6GSE2Zv3J+zlGgzY3N/Oy9P6tCiURExgczu8fd5wz38Wr6FBkHNK+aiEhtUqAmMg7kmz9N86qJiCSbAjWRcUDzqomI1CYNJhAZBzSvmohIbVKgJjJOaF41EZHao6ZPERERkYRSoCYiIiKSUArURERERBJKgZpIAvV0drJu7jweOHwm6+bOo6ezs9pFEhGRKtBgApGE6ensJLVkKd4Xlnvq7+4mtWQpQNmXlhIRkWRTjZpIwmxZvmIgSEvzvj62LF9RnQKJiEjVKFATSZj+1K5rchZKFxGRsUuBmkjC1Dc3l5QuIiJjlwI1kYSZsngR1jB4uSdraGDK4kXVKZCIiFSNBhOIJEx6wMCW5SvoT6Wob25myuJFGkggIjIOKVATSaBJra0KzERERE2fIiIiIkmlQE0kKdZeD8tfBe2Tw/+111e7RCIiUmVq+hRJgrXXQ+fZsH1buN+zIdwHmLWweuUSEZGqUo2aSBKsWrYzSEvbvi2ki4jIuKVATSQJejaWli4iIuOCAjWRJJg0rbR0EREZFxSoiSTBvKUwYeLgtAkTQ7qIiIxbCtREkmDWQmi9FCZNByz8b71UAwlERMY5jfoUSYpZCxWYiYjIIKpRExEREUkoBWoiIiIiCaVATWQc6lrfxfyV85l11Szmr5xP1/quahdJRERyUB81kXGma30X7Xe207ejD4BUb4r2O9sBWDBjQRVLJiIi2VSjJjKG9XR2sm7uPB44fCbr5s6jp7OTjjUdA0FaWt+OPjrWdFSplCIiko8CNZExqqezk9SSpfR3d4M7/d3dpJYs5aDfbcq5/+bezaNcQhERGYoCNZExasvyFXjf4Joz7+vj9Nst5/5NjU1581KfNhGR6lAfNZExqj+Vypn+8p4dNNTtPqj5s6GugbbZbQP3ezo72bJ8Bf2pFNv3ncQtx2wjdfgOQH3aRERGk2rURMao+ubmnOlmxiXPt9Lc2IxhNDc2035M+0DQld1kOmHLVj568/O86f4dA3moT5uIyOhQjZrIGDVl8SK6zzl31w3uTL3mdn5266qcj8vVZNrQD++/zfn1ETvT1KdNRKTyVKMmMkZNam3Nuy1fs2ihbXs/Pfh+oT5tIiJSHgrURMaw+paW3Ol5mkULbTMYaP7M7tMmIiKVoUBNZAybsngR1tAwKM0aGpiyeFHBx+RihObP7D5tIiJSOebu1S5DSebMmeOrV6+udjFEakbmCM765mamLF5UsFkU4IHDDs+9wYzDH/hTBUopIjI2mdk97j5nuI/XYAKRMW5Sa+uQgVm2+paWMOozO71Ak2kuN967iUtueYjurdtomTyRc44/lFNeO7WkPERExrOKNn2a2Qlm9pCZPWxm5+XYfrKZrTWz+8xstZkdW8nyiEhxhtNkmj0p7gW3Xs3nbvgDm7Zuw4FNW7fxuRv+wI335l4ZQUREdlWxQM3M6oCvAycCM4HTzGxm1m6rgCPd/TXAPwBXVKo8IlK8Sa2tNF+4LAxGMKO+pYXmC5flrZlLL/Se6k3hOKneFCsfW872iYO7KWzbvoNLbnloNJ6CiMiYUMmmz6OAh919PYCZXQecDAx0cHH3ZzP2bwRqq8OcyBhWSpNproXeecl2XrrvLfQ//dpByd1bt5WriCIiY14lmz6nAhsy7m+MaYOY2bvM7EGgi1CrJiI1Jt/ktzZh6y5pLZMnDrqvdURFRPIrOlAzs1Yz+23sT/bJYh6SI22XGjN3/7G7HwacAlyY59hnxD5sqx9//PFiiywioyTv5Lf9kwfdnTihjnOOP3Tgfq4m0/Y72xWsiYhEeQM1MzsyK+mDwNHAbOATReS9EZiecX8asOswssjd7wAOMrN9cmy73N3nuPucfffdt4hDi8hoapvdRkPd4MEHDXUN/P2MM5g6eSIGTJ08kYve/epBoz5zNZlqHVERkZ0K9VH7pJkZsNTdNxOaMb8IvEiBgCvD3cDBZnYgsAk4FXh/5g5m9krg/9zdzWw2sBvwROlPQ0SqKT35bceaDjb3bqapsYm22W0smLGAL8zN/7h8TaZaR1REJMgbqLn7x2Ot2rfMbDWwBDgGeBl5miizHt9vZmcBtwB1wJXufr+ZnRm3Xwa8B/iQmW0HtgHv81qbgVdEgBCslbpaQVNjE6neXdcW1TqiIiJBwT5q7v57dz8ZuA+4CWh295vc/fliMnf3n7j7Ie5+kLt/MaZdFoM03P3L7n6Eu7/G3d/o7r8a2dMRkVqSr8lU64iKiASF+qidaWb3mtkawtQZJwAvN7NbzOzNo1ZCERmzFsxYQPsx7TQ3NmOY1hEVEcmSd61PM1vr7rPMbDfgN+7+upj+cmCJu39qFMs5QGt9ioiISK2o5Fqfm8zsQmAi8GA60d2fAqoSpImIiIiMJ4UCtZOB44HtwM9HpzgiMixrr4dVy6BnI0yaBvOWwqyF1S6ViIiMUKFRny8AnaNYFhEZjrXXQ+fZsD0uzdSzIdwHBWsiIjWukktIichoWLVsZ5CWtn1bSBcRkZqmQE2k1vVsLC1dRERqRqE+agCY2V45kp9x9+0VKI+IlGrStNDcmStdRERqWjE1amuAx4E/A+vi7UfMbI2Zva6ShRORIsxbChMmDk6bMDGki4hITSsmUPsf4B3uvo+77w2cCFwPfBL4RiULJyJFmLUQWi+FSdMBC/9bL9VAAhGRMSDvhLcDO5itzp6oLZ1mZve5+2sqWcBsmvBWREREakUlJ7xNe9LMPgtcF++/D3jKzOqAF4d7YBEREREprJimz/cD04Abgf8G9otpdYDaVkREREQqZMgaNXf/G/AveTY/XN7iiIiIiEhaMdNzHAJ8Bjggc393n1u5YomIiIhIMX3UfghcBlwB7KhscUSkrLQGqIhITSsmUOt3929WvCQiUl5aA1REpOYVM5ig08w+aWbNZrZX+q/iJRORkdEaoCIiNa+YGrUPx//nZKQ5MKP8xRGRstEaoCIiNa+YUZ8HjkZBRKTMtAaoiEjNyxuomdlcd7/VzN6da7u731C5YonIiM1bOriPGmgNUBGRGlOoRu0twK1Aa45tDihQE0my9IABjfoUEalZQ671mTRa61MkWXo6O9myfAX9qRT1zc1MWbyISa25ft+JiIw/FVvr08w+VeiB7v7V4R5URMaGns5OUkuW4n19APR3d5NaEppWFayJiIxcoek59hjiT0TGuS3LVwwEaWne18eW5SuqUyARkTEmb42au18wmgURkdrTn0qVlF5I1/ouOtZ0sLl3M02NTbTNbmPBjAUjLaKISE0bcsJbM5thZp1m9riZbTGz/zYzzaEmItQ3N5eUnk/X+i7a72wn1ZvCcVK9KdrvbKdrfVc5iikiUrOKWZngWuB6oBloIaz9+YNKFkpEasOUxYuwhoZBadbQwJTFi0rKp2NNB307Bjeh9u3oo2NNx0iLKCJS04oJ1Mzdr3b3/vj3fcL0HCIyzk1qbaX5wmXUt7SAGfUtLTRfuKzkgQSbezeXlC4iMl4Us4TUL8zsPOA6QoD2PqArvd6nuz9ZwfKJSMJNam0d8QjPpsYmUr279mtramwaUb4iIrWumBq19wEfB34B3AZ8AvgH4B5AE5qJyIi1zW6joW5wE2pDXQNts9uqVCIRkWTQWp8i48Ha6xO9QkF6dKdGfYqIDDZkoGZmH8qV7u7fK39xRKTs1l4/eM3Png3hPiQuWKtUYKapP0SkVhXT9Pn6jL83A+3AOytYJhEpp1XLBi/MDuH+qmXVKU8Z9XR2sm7uPB44fCbr5s6jp7Nzl3009YeI1LJimj7/JfO+mU0Crq5YiUSkvHo2lpZeI4pdvqrQ1B+qVRORpCumRi3bc8DB5S6IiFTIpGmlpdeIYpev0tQfIlLLilmZoNPMbop/XcBDwH9XvmgiUhbzlsKEiYPTJkwM6TWs2OWr8k3xoak/RKQWFDOP2r9n3O4HHnP32m4zERlP0gMGEjzqczjqm5vp7+7OmZ6pbXYb7Xe2D2r+1NQfIlIriumjdruZvYIwmABgXWWLJCJlN2thzQdm2aYsXjSojxrkXr6q2Kk/ejo72bJ8Bf2pFPXNzUxZvGjEE/mKiIyUuRdeDcrMFgKXECa7NcLIz3PcfWXFS5fDnDlzfPVqzbMrIuULrrIHJkAI+oazHJaISCYzu8fd5wz78UUEar8H3u7uW+L9fYH/dfcjh3vQkVCgJiLltm7uvNzNqC0tHHzrqiqUSETGipEGasWM+nxJOkiLnijycSIiNaHYgQkiIqOtmMEE/2NmtwA/iPffB/y0ckUSESm/QqsTFDswQURktA1ZM+bu5wDfAmYBRwKXu/u5lS6YiFTB2uth+augfXL4v/b6apeoLIZanWDK4kVYw+BF4XMNTBARGW15AzUze6WZvQnA3W9w90+5+2LgCTM7aNRKKCKjI70maM8GwHeuCToGgrVCqxNAWMmg+cJl1Le0gBn1LS0aSCAiiVCoRm0F8EyO9OfitiGZ2Qlm9pCZPWxm5+XY/gEzWxv/7jSzqgxQEBHG9JqgxaxOMKm1lYNvXcXhD/yJg29dpSBNRBKhUKB2gLuvzU5099XAAUNlbGZ1wNeBE4GZwGlmNjNrt0eAt7j7LOBC4PIiyy0i5TZG1wQFrU4gIrWrUKDWUGDbxALb0o4CHnb39e7+AnAdcHLmDu5+p7s/Fe/eBdT24oMitWyMrgkKYXWChrrBH2lanUBEakGhQO1uM/un7EQz+xhwTxF5TwU2ZNzfGNPy+RgaTSpSPWN0TVAIqxO0H9NOc2MzhtHc2Ez7Me27rE4gIpI0habnWAT82Mw+wM7AbA6wG/CuIvK2HGk5Z9c1s7cSArVj82w/AzgDYL/99ivi0CJSsjG6JmjaghkLFJiJSM3JG6i5+1+BY2IQ9aqY3OXutxaZ90Zgesb9acAuExWZ2SzgCuBEd38iT1kuJ/ZfmzNnTuGlFERk+MbgmqAiIrWsmEXZfwH8Yhh53w0cbGYHApuAU4H3Z+5gZvsBNwAfdPc/D+MYIiJlpcXZRSRJilmZYFjcvd/MzgJuAeqAK939fjM7M26/DFgK7A18w8wA+keyHpaIyEhkL87e391Naknoo6dgTUSqYchF2ZNGi7KLSKVocXYRKbfRWJRdRGRc0OLsIpI0CtRERKJ8i7AXWpy9a30X81fOZ9ZVs5i/cv7A+qEiIuWgQE1EJCp1cfahFnsXERkpBWoiIlGpi7MPtdi7iMhIVWzUp4hILZrU2lr0CM9iFnsXERkJ1aiJiAyTFnsXkUpToCYiMkxa7F1EKk1NnyIiw5ReO7RjTQebezfT1NhE2+w2rSkqImWjQE1EZAS02LuIVJKaPkVEykjzqolIOalGTUTKZ+31sGoZ9GyESdNg3lKYtbDapRo16XnV0lN2pOdVA1TrJiLDoho1ESmPtddD59nQswHw8L/z7JA+xvR0drJu7jweOHwm6+bOo6ezExj+vGr58hMRUaAmIuWxahls3zY4bfu2kL72elj+KmifHP7XcPDW09lJasnSsHi7O/3d3aSWLKWns3NY86rlyq/7nHP589FvVMAmIgrURKRMejbmSd8wpmratixfgfcNrjXzvj62LF8xrHnVcuUHsGPr1oEAUETGLwVqIlIek6blTre6/DVtNag/lcqbPpx51fLlBzsDQBEZvxSoiUh5zFsKEyYOTpswEXxHzt1f3LqRN118Kzfeu2kUClc+9c3NedMXzFhA+zHtNDc2YxjNjc20H9NecCBBvvzSCgVyIjL2adSniJRHenRn9qjPVctis+dg3b43m7Zu43M3/IHVjz3JLx58nO6t22iZPJFzjj+UU147dZSfQHGmLF5EasnSQc2V1tDAlMWLgNLnVcuVX6ahAjkRGdsUqIlI+cxamHs6js6zBzV/Pue78ZX+sN+27Tu45q6/4HFbOngDEhmspRds37J8Bf2pFPXNzUxZvKjohdzz5Zf64pfwrVsHb6yvHwgAIQw8KNdxRaQ2KFATkcrKqGl7cetGun1vvtK/kJtePHZgF896yLbtO7jklocSGahBCK7KGSANBGvn/yu+fftAupkN3E6PDk3XvKVHm2Y+XkTGHvVRE5HKm7UQFv+RN0+8gWNfuHRQkJZP99ZtQ+4zlmxZvmJQkAbg27cPDCYoNNo0H83PJlL7FKiJyKg55/hDmTihblCa5dm3ZfLEPFvGpkKjSYvZnq3QfG8iUjsUqInIqDnltVO56N2vZurkiRgwdfJEPnD0frsEbxMn1HHO8YfuTBhDE+bmU2g0aTHbsw2nBk5Ekkd91ERkVJ3y2qm79D2bs/9eXHLLQ7lHfaaXpkoPRkhPmAtjah3RoUaTDrU9W6k1cCKSTArURKTqcgVvAwotTTXcQC2Bi8cPNZq01NGm9c3NodkzR7qI1A4FaiKSbHmXpsqTPpRRqKHrWt9Fx5oONvdupqmxibbZbUXNrTbUaNJSRpuWWgMnIsmkPmoikmz5lqbKlz6UQjV0ZdC1vov2O9tJ9aZwnFRvivY72+la31WW/Is1qbWV5guXUd/SAmbY5Mm8pKGB7nM/qxGgIjVEgZqIJFu+panmLR1efuWuocvSsaaDvh2DO/H37eijY01HWfIvxaTWVg6+dRUtX/ky9PWxY+tWjQAVqTEK1EQk2WYthNZLYdJ0wML/1kuH30xZ7hq6LJt7N5eUPho0AlSkdqmPmogkX76lqYZj3tJdlrQaUQ1dlqbGJlK9u46sbGpsKkv+w1HKCFAtUyWSLKpRE5HEuPHeTbzp4ls58Lwu3nTxrdx476byH6TcNXRZ2ma30VDXMCitoa6BttltZcl/OIqdg02T5Iokj2rURCQRbrx3E5+74Q9s274DqPDi7OWsocuSHt05nFGflVLsCNBCTaSqVROpDgVqIpIIl9zy0ECQlpb0xdnzWTBjQVUDs2zFzsGmSXJFkkeBmogkQr5F2Mfb4uyVUswcbJokVyR51EdNRBIh3yLs421x9mqasngR1jC4f10pk+T2dHaybu48Hjh8puZqEykTBWoikgjnHH/o0IuzV8I4WPC9WNmT5Na3tNB84bKi+qdpIIJIZZi7V7sMJZkzZ46vXr262sUQkQq48d5N+Rdnr4Ts5aQgTNVRxlGg48W6ufNyN5u2tHDwrauqUCKRZDCze9x9znAfrz5qIpIYBRdnr4RKLPg+TuUdiJAjeBOR4qnpU0TGrwovJzWe5B1wYKbmT5ERUKAmIuNXhZeTGk/yDjhw11JVIiOgQE1Exq9cC74DvNA7rgcVDEehAQeah01k+BSoicj4lV5OauJeg9O3PRkGGShYK0l9S0vudM3DJjJsCtREZHybtRB2a9w1PT2oQIo20nnYRGRXGvUpIjKGBxV0re8atXVHi12qSkSKp0BNRGTSNOjZkGODh0lw5y2tyek6utZ30X5nO307wkLrqd4U7Xe2A1Q0WFNgJlI+avoUEck3qABCAFej/dU61nQMBGlpfTv66FjTMaJ8u9Z3MX/lfGZdNYv5K+fTtb5rRPmJSH4K1ERE0oMKJk3Pvb1G+6tt7t1cUnox0rV0qd4Ujg/U0ilYE6mMigZqZnaCmT1kZg+b2Xk5th9mZr8xs+fN7DOVLIuISEGzFsLiPwKWe3sN9ldramwqKb0YlaqlE5HcKhaomVkd8HXgRGAmcJqZzcza7UngbODfK1UOEZGSjKFJcNtmt9FQN3gUZkNdA22z24adZ6m1dD2dnaybO48HDp/JurnztEqBSIkqWaN2FPCwu6939xeA64CTM3dw9y3ufjewvYLlEBEpXq7+ahMmhvQas2DGAtqPaae5sRnDaG5spv2Y9hENJCillq6ns5PUkqVhvU93+ru7SS1ZqmBNpASVHPU5FcgcRrUReMNwMjKzM4AzAPbbb7+Rl0xEJJ/06M5Vy0Jz56RpNTvqE0KwVs4Rnm2z2waNJIVda+nSU4J8/isb2HdwKyne18eW5Ss0MlSkSJUM1HJ19PDhZOTulwOXA8yZM2dYeYiIFG3WwtyB2drrx0wAN1zpoC/f3GyZU4Ls/XTuPLSklEjxKhmobQQyh1BNA7oreDwRkcpZe32YpmP7tnA/PW0HjMtgLV8tXeZggyf2hH1zBGtaUkqkeJXso3Y3cLCZHWhmuwGnAjdV8HgiIpWzatnOIC2tRqftqKTMQQXXHmf0ZVUHVHpJKc3xJmNNxWrU3L3fzM4CbgHqgCvd/X4zOzNuv8zMmoDVwJ7Ai2a2CJjp7nkqzEVkPLjx3k1ccstDdG/dRsvkiZxz/KGc8tqp1S1UqctMFWomHcNNqE2NTaR6Q9Pmr4+oA3bw/tucfZ4Gq6sb6KMGlL2fWjVWYhCpNHOvrS5fc+bM8dWrV1e7GCJSITfeu4nP3fAHtm3fMZA2cUIdF7371dUN1pa/KvcyU5Omx/nXMmQ3k0IYOdp6abidb9sY6BeXHSwBvPWBOj7+0xd5yfM7B/hbQwPNFy4ra7A2f+X8gSAxU3NjMz9778/KdhyRUpjZPe4+Z7iP18oEIpIol9zy0KAgDWDb9h1ccstDVSpRVMq0HYWaSUtpQk0HfD0bAK+J5axyTQnyj3dOHBSkAYNq1tJGOudaJVZiEKk2LcouIonSvXVbSemjppRpO0ptJs23rVBQl+BatezBBg8syZ7rPMgc/Zmec837Qk1ces41KL6JNLPZNTtdpFapRk1EEqVlcu7F0VsmT+TGezfxpotv5cDzunjTxbdy472bRrdw6WWm2reG//mCpUKrG5Sy8kGuptZC6QmVb5RnZvqW5SsGgrS0XLVuhVRiJQaRalOgJiKJcs7xhzJxQt2gtIkT6njrYfvyuRv+wKat23Bg09ZtfO6GP4x+sFaMQs2kpTShWt2uaYXSE2rK4kVYw+AAKnv0Z7651UqZc60SKzGIVJuaPkUkUdIDBrJHfRbquzaSQQYVGWFaTDNpMU2ovmPXtELpCZVuutyyfAX9qRT1zc1MWbxoUJNmfXNzWGoqS6lzrpV7JYZy6OnsLPjcRQrRqE8RqQkHnteVc2kTAx65eHhfzIkdYZpWykjTGpfdRw0qMzJ0tI3V5yXF06hPERkXCvVdG67EjjBNK6WZdO31IbBrnxz+J3hkaC6TWltpvnAZ9S0tYEZ9S8uYCGbK0fdurBnp6N7xRk2fIlITzjn+0Jy1X+ccf+iw80zsCNO0YkeajpHlrSa1ttZ8YJatHH3vxpJyjO4dbxSoiUhNyNd3bSRNlC2TJ7IpR1A2klq6ssu3QHymGp3Go9K61nflXTx+tJSr791YUaiGUYFabgrURKRmnPLaqWXtO1aJWrqqGM68bWNcUpaTmrJ4Uc4+apVc7zTJVMNYOvVRE5Fx65TXTuWid7+aqZMnYsDUyRN5z+umcsktD1VvrrbhKGVutnKogf5wHWs6Bi1jBdC3o4+ONR2jWo6x2vduuIqZU08GU42aiIxrmbV02aNA03O1pfdLrHlLc68fmmvQwUjVSH+4ci8nNZJm1LHY9264VMNYOtWoiYhEiR8Fms+shWFR90nTAQv/8y3yPlKlrFVaRfmWjRrOclLpZtRUbwrHB5pRu9Z3jbSY445qGEunGjURkSjxo0ALKWbQQTnUSH+4ttltg/qowfCXkyrUjJq0yXVrgWoYS6MaNRGRqBJztY05o90fbpjKuZxUuZtRRUqhGjURkWjMjAKtpNHsDzdC5VpOqqmxiVTvrqMSh9OMKlIq1aiJiES5RoEmZjmppBjN/nAJ0Ta7jYa6wYvKD7cZVaRUWutTRERkCEmYPFdq00jX+lTTp4iIyBDK1YwqUio1fYqIiIgklAI1ERERkYRSoCYiIiKSUArURERERBJKgZqIiIhIQilQExEREUkoBWoiIiIiCaVATURERCShFKiJiIiIJJQCNREREZGEUqAmIiIiklAK1EREREQSSoGaiIiISEIpUBMRERFJKAVqIiIiIgmlQE1EREQkoRSoiYiIiCSUAjURERGRhFKgJiIiIpJQCtREREREEkqBmoiIiEhCKVATERERSSgFaiIiIiIJpUBNREREJKEUqImIiIgklAI1ERERkYSqaKBmZieY2UNm9rCZnZdju5nZpXH7WjObXcnyiIiIiNSSigVqZlYHfB04EZgJnGZmM7N2OxE4OP6dAXyzUuURERERqTWVrFE7CnjY3de7+wvAdcDJWfucDHzPg7uAyWbWXMEyiYiIiNSMSgZqU4ENGfc3xrRS9xEREREZl+ormLflSPNh7IOZnUFoGgV43sz+OMKySfXsA/yt2oWQYdG1q226frVN1692HTqSB1cyUNsITM+4Pw3oHsY+uPvlwOUAZrba3eeUt6gyWnT9apeuXW3T9attun61y8xWj+TxlWz6vBs42MwONLPdgFOBm7L2uQn4UBz9eTTQ4+6pCpZJREREpGZUrEbN3fvN7CzgFqAOuNLd7zezM+P2y4CfAO8AHgaeAz5aqfKIiIiI1JpKNn3i7j8hBGOZaZdl3Hbgn0vM9vIyFE2qR9evduna1TZdv9qm61e7RnTtLMRKIiIiIpI0WkJKREREJKFqKlAbakkqSQ4zm25mvzCzB8zsfjNri+l7mdnPzWxd/P/yapdVcjOzOjO718xujvd17WqEmU02s5Vm9mB8D75R1692mNni+Ln5RzP7gZk16Poll5ldaWZbMqcOK3S9zOxzMY55yMyOHyr/mgnUilySSpKjH/i0ux8OHA38c7xe5wGr3P1gYFW8L8nUBjyQcV/XrnZ0AP/j7ocBRxKuo65fDTCzqcDZwBx3fxVhMN6p6Pol2XeBE7LScl6v+D14KnBEfMw3YnyTV80EahS3JJUkhLun3H1NvP0M4YtiKuGaXRV3uwo4pSoFlILMbBqwALgiI1nXrgaY2Z7A3wHfBnD3F9x9K7p+taQemGhm9cDLCPOL6vollLvfATyZlZzvep0MXOfuz7v7I4RZL44qlH8tBWpabqpGmdkBwGuB3wKvSM+VF/9PqWLRJL8VwLnAixlpuna1YQbwOPCd2HR9hZk1outXE9x9E/DvwF+AFGF+0Z+h61dr8l2vkmOZWgrUilpuSpLFzHYHfgQscvenq10eGZqZnQRscfd7ql0WGZZ6YDbwTXd/LdCLmslqRuzLdDJwINACNJrZ6dUtlZRRybFMLQVqRS03JclhZhMIQdo17n5DTP6rmTXH7c3AlmqVT/J6E/BOM3uU0MVgrpl9H127WrER2Ojuv433VxICN12/2vA24BF3f9zdtwM3AMeg61dr8l2vkmOZWgrUilmSShLCzIzQR+YBd/9qxqabgA/H2x8G/nu0yyaFufvn3H2aux9AeJ/d6u6no2tXE9x9M7DBzNILQc8D/oSuX634C3C0mb0sfo7OI/Tx1fWrLfmu103AqWb2UjM7EDgY+F2hjGpqwlszeweh70x6SaovVrdEko+ZHQv8EvgDO/s5nU/op3Y9sB/hA+nv3T27E6YkhJkdB3zG3U8ys73RtasJZvYawkCQ3YD1hOX5XoKuX00wswuA9xFGz98L/COwO7p+iWRmPwCOA/YB/gp8AbiRPNfLzP4V+AfC9V3k7j8tmH8tBWoiIiIi40ktNX2KiIiIjCsK1EREREQSSoGaiIiISEIpUBMRERFJKAVqIiIiIgmlQE1Exgwz22Fm95nZ781sjZkdM8T+549W2UREhkPTc4jImGFmz7r77vH28cD57v6WYvbPSjfC5+OLOR4mIjJqVKMmImPVnsBTEJZwMbM7Ym3bH83szWZ2MTAxpl1jZgeY2QNm9g1gDTDdzL5pZqvN7P44CSlmNs/Mfpw+iJm93cxuyFUAEZGRUo2aiIwZZraDsBpGA9AMzHX3e8zs00CDu3/RzOqAl7n7M1k1cAcQZvE/xt3viml7ufuT8TGrgLNj/g8Ab3b3x83sWuAH7t45yk9XRMYB1aiJyFiyzd1f4+6HAScA34vNmHcDHzWzduDV7v5Mnsc/lg7SooVmtoawjM8RwEwPv26vBk43s8nAG4GCS8CIiAyXAjURGZPc/TeEtff2dfc7gL8DNgFXm9mH8jysN30jLpj8GWCeu88Cugg1dQDfAU4HTgN+6O79lXkWIjLeKVATkTHJzA4D6oAnzGx/YIu7/yfwbWB23G27mU3Ik8WehMCtx8xeAZyY3uDu3UA38Hngu5V5BiIiUF/tAoiIlNFEM7sv3jbgw+6+w8yOA84xs+3As0C6Ru1yYG1s3vzXzIzc/fdmdi9wP6Hv2q+zjnUNobbuT5V4IiIioMEEIiLDYmZfA+51929XuywiMnYpUBMRKZGZ3UNoFn27uz9f7fKIyNilQE1EREQkoTSYQERERCShFKiJiIiIJJQCNREREZGEUqAmIiIiklAK1EREREQSSoGaiIiISEL9f/a1NbNXDLbAAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points), len(pareto_kdiff_points))\n", + "\n", + "while i < len(pareto_bstray_points):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "4 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "print(len(bstray_designs))\n", + "#colors = numpy.arange(len(bstray_designs))\n", + "colors = numpy.arange(1000)\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Bstray vs Kdiff During Training, Generated Designs\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(bstrays), len(kdiffs))\n", + "\n", + "while i < len(bstray_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " fig = plt.scatter(bstray_designs[i], kdiff_designs[i], label = f\"epoch: {i*100}\")\n", + " i+=1\n", + " \n", + "# plt.colorbar()\n", + "plt.legend()\n", + "# from matplotlib.patches import Rectangle\n", + "# someX, someY = 45, 0.5\n", + "# fig,ax = plt.subplots()\n", + "# currentAxis = plt.gca()\n", + "# currentAxis.add_patch(Rectangle((someX -0.1, someY-0.1), 0.2, 0.2, alpha=0.5, facecolor='red'))\n", + "#plt.savefig('Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(bstray_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " ax.scatter(bstray_designs[i], kdiff_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,200)\n", + "plt.ylim(0, 0.2)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"Bstray\", fontsize = 18)\n", + "plt.ylabel('Coupling %', fontsize = 18)\n", + "plt.suptitle(f\"Bstray vs Kdiff During Training, Generated Designs\" , fontsize = 20, y=1.01)\n", + "# plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle(( 0,0 ), 45, 0.15, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(numinv_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " ax.scatter(numinv_designs[i], coilloss_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "# plt.xlim(0,3)\n", + "plt.ylim(0, 5000)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"Num Inverters\", fontsize = 18)\n", + "plt.ylabel('Coil Loss', fontsize = 18)\n", + "plt.suptitle(f\"Number of Inverters vs Coil Loss During Training, Generated Designs\" , fontsize = 20, y=1.01)\n", + "# plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle(( 0,0 ), 1000, 2000, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "noise = generate_noise(shape=(10_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "pareto_bstray, pareto_kdiff = pareto_frontier(bstray, kdiff)\n", + "pareto_kdiff = pareto_kdiff * 100\n", + "\n", + "plt.plot(pareto_bstray.detach().cpu(), pareto_kdiff.detach().cpu())" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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xaWy/dhKQy2ZwcHSL1flRiyeEJAkROaKU2uBfTk28hzEJM53PujhfO9kzRbmHaf+Npos1Yo6nFk8I6SYoxHuUIGEWxTdtWjfMp96Iz70Rc3w9KXeEENKpMDq9RwkSZlHywdvReKSRFLh2NnEhhJBmQyHeowQJM9t88GbUTY9aEhZoLAWunpQ7QgjpVGhO71GCTNLDQzns2nMc04VizfdpEcwr1ZSAsEb80/Wa4+tNuSOEkE6EmniPEmaS3nXTamPDkWfHtuHg6JaGfcjtKPrSaCEbQgjpJKiJ9yhxR5Db0C7/NGuwE0K6BQrxHibOCHIbmtFohRBCehma00nbaLTRCiGE9DrUxEnbYI9wQghpDApx0lbonyaEkPqhOZ0QQghJKNTESc/AxieEkG6DQpzURdIEIhufEEK6EZrTSWRcgZifLkDhikC0KZnaLtpRWIYQQuKGQpxEJokCkY1PCCHdCIU4iUwSBSIbnxBCuhEKcRKZJApEFpYhhHQjFOIkMkkTiG4QXqE4h7QIADY+IYR0B4xOJ5FJUqU1f1T6nFKVCUcnjpcQQqJAIU7qIimV1oKC8JIwfkIICYLmdNLVJDEIjxBCbKEQJ11NEoPwCCHEFprTe5xOrbzWrHGNbF1Z5RMHOjsIjxBCokAh3sN0ainSZo4rSUF4hBASFQrxHqZTg76aPa6kBOERQkhU6BPvYTo16KtTx0UIIZ0GNfEeZlk2g7xGMEYJ+orDp96McRFCSC9ATbyHabTyWlzdzJJWEY4QQtoFNfEeptGgr7h86vWMq1Oj7AkhJE4oxHucRoK+4vRdRxlXp0bZE0JI3FCIEyt0mm6n+K47NcqeEELihkKchGLSdG9Zn8NjR/JtL6TSbIsATfOEkKTAwDYSiknT/fLRF3HfzWuQy2YguNLeEwA2je3HitG92DS2v+FAtzCaWVo1rmA9QgiJA2ripAqdFmrSaKcLRQClaHJ3m117juPi5VkU5xSA1vinm1lalaZ5QkiSEKVUPDsW+TMA7wfwklLqn2i+FwB/COB9AGYAfFQp9e2w/W7YsEEdPny42cMlqDWbAyVhuNBJ4fxMUbvN4ICDS8X5GsHnJ5fN4ODolqaO14t38pEdcKAUcKFQjGwOXzG6F7onQgA8O7atqWMmhBBbROSIUmqDf3mc5vQ/B/CegO/fC+BN5X8fB/AnMY6FWGDSQoPmeedniqECHCj5pycm87GZ2YeHcjg4ugX3b1+HS8V5TBeKdZnD2fWMEJIkYhPiSqm/BXAuYJUPAPgLVeIQgKyIvCGu8ZBwTGbzC4UiBgechvadHXBa4msOMofbwEIzhJAk0c7AthyA057PL5SXkTZh0jYVAKUAJy1Vy520QLRb1DKt0dijCFdbGo1UHx7KaYP16A8nhHQi7Qxs073/tYZbEfk4SiZ3LF++PM4x9TS6ADGX6UIRTkowOOBgeqaI7ICDly/N6n8wDab1dHnmtsSVu86uZ4SQpNBOTfwFAFd7Pl8F4IxuRaXU55RSG5RSG5YuXdqSwfUiXi1UR3FeYaC/D8+ObcNAfx+K840HRQpQl0ndlAq2edVSmsMJIT1DO4X4HgC/ICU2AriglHqxjeMhuBIgZjKTu2bpZrUFVUDFpB4l8M3k+z5w4izN4YSQniE2c7qIPAzgXQBeJyIvANgJwAEApdRnAXwFpfSyp1FKMfuluMZComMySyuUCrlkBxxj2pkXEQRGtwNXItej1D8P8n3THE4I6RViE+JKqVtDvlcAfj2u45PG2LxqKR48dEr7XX66ACclcNJSKeqiI+OktaVZ/SzLZiIXWemUuu2EENJOWHaVaDlw4mzg98V5hUX9fVVm69s3Lq8xY987vAa3rM8ZzfOuvzpqVDlTwQghhGVXiQEbn/eFQhFTO28IXe/AibPa6PS0SMVfvXvfyUiadaO90AkhpBugECdaTOZq/zo2mCYE80pVhG499c/p+yaE9DoU4kRLUM64y8zl2UoEeZBGbOO/pmZNCCHRia0BSlywAUrr8DcVuVScQ6E4X7WOkxZAoSpnPOOkq9K6TI1VmPpFCCF2mBqgUBPvAXSVzWyEp99cvWlsf41GrYtO90eVN6Jl1zt2QgjpBSjEu5yo+ddBRCnw4l+3Hv91M8duezxOGAghSYIpZl1Oo129vETJwW5GvnYzxx6GqYxrs7usEUJIM6EQ73Ia7erlxTYHu1n52s0cexitnDAQQkizoBDvckwacT2a8vBQDtmMvq94WqTptcqbOfYwWjlhIISQZkEh3uU0u7LZ+9e+oab6WsZJ49MfXotnx7bh4OiWpvmRW1mVrZUTBkIIaRYU4l2Ot71oo5ryxGQejx3JV1VfEwC3rI+n6Eozxx4Gy7gSQpIIo9N7gGZVNtP5jRXC66zXSyujxVlshhCSRCjEiTWt9Bu3Or3M3S+FNiEkSdCcTqxppd+Y0eKEEBIOhTixphV+44nJvLYynAujxQkh5Ao0pxNrmuE3DvJz62qs+2G0OCGEXIGaOInE8FAOB0e34P7t6wAAO8ansGlsv1Vls7CqaDoTuhdGixNCSDXUxElk7p44hocOnaqkmuWnC7hjfAr3PHEcO29cbdTMg/zcw0O5wP7luSZGi7NGenKvQVLHTUhcUIiTCjYvyInJfJUA93J+phgYQR4W3Z4WwZymNW5aBAdHt0Q8Gz26CUjcUe+dRjsi/5tBUsdNSJzQnE4A6E3dO8ancPfEsar1du87qRXgLq5m7QaorRjdWzG3h0W36wR40PKomCYgvRb1ntTI/6SOm5A4oRAnAMyFXB46dKrK320THe5qSH7f9+ZVSwOj23MGIW9aHpWgCUgvRb0ntU58UsdNSJxQiBMA5hehAqo0HdvocJ3G9PCTp3HL+pyxjGrcKWxBL/teinpPap34pI6bkDihECcAgl+E+elCRRvXCVpb5pTCY0fyGNm6UtssJe5a6aZzFNi3We0GklonPqnjJiROGNhGAJRekDvGp4zmZn8AkRsAtzjjQASYnikiZQhM8+KNRtcRZ+nTka0ra/LQBcBtG5f3VGBUUuvEJ3XchMSJqCYFDbWKDRs2qMOHD7d7GF2JP3LbTy6bCYwSXzG6NzDozUUAPDu2rZ4hNsTEZB73PHEc52eKAIBsxsGum8wpcYQQ0imIyBGl1Ab/cprTSYV7h9dUirjoCAsgyg44VsexXa+ZuNH3rgAHgFdm51s+DkIIaSY0p5Mqhody2L3vpLbwSpDffGIyj5cvzVod4+VLs5iYzAdqwI0W9fBvP3N5NrDQDCGEJBFq4qSGegKIdu87ieK8nWumOK8Cc3vDyrOGodveq4F7YXoSISTJUIiTGoaHcrhlfQ5pEQCACCBQgXXSowrDoPUbLeoRVoPdS5T0JF0BG0IIaScU4qSGick8HjuSr0SaKwXMFOerKrld6xNkUXN1g9ZvtKiH7XpR0pMatQ4QQkgcUIiTGsI0WX/d8YnJfOT88fx0wajNNlrUw7ReNuNoc9BtNGyW/CSEdCIMbCM1RDGNu4LMTT3btec4pgsl/3NKgCA3uamBhS6fO4rWbNpel05m21SDJT8JIZ0INXFSQ1TTuFeQedO2bOLcdNpso5Xbomxvq2Gz5CchpBOhJk5q0GmyQbiCLEpAmZf8dAErRvdWpZI1WrnNdntbDbtR60BU2DebEGIDhTipwVRa9fxMEQJUVWXzCrJGTMveYDHvGJqJTjAuy2ascuJbWfKTfbMJIbaw7CqJRJCGuGlsv1YgRiWsvGs9Y/MLRqA0AbllfQ6PHcnXLG9m45WomK5jvdeFEJJ8TGVXqYmTGoKEYZCZ2mSGTwHwFjj1a/N+6tHow7RXk+/7wImzuO/mNR1lumYQHSHEFgpxUkUjptzhoRwOP3+upolKOi14TX8fLhSKVULSpHHWEywWFKA2PJQLFIxxdk6rB1sTPyGEUIiTKsKEYRgHTpyt0bKLcwqLFvRhaucNVcvrDRbTWQrCtNe4BaM7pvx0AelyS9ZcnVp9q4PoCCHJhSlmpAqTMAwqzmKzvW55PalkpsppizP6zmiukK6nHrwt3jEBqFS6q7eqW6MpdvXAkrKEJBNq4qQKk8YKXBFKh58/hwMnzmp9yFE13qimbJOlYKGTQsZJG7VX2+jyelK7glLr6u2U5r0u7ph2jE/F4rNnNDwhyYXR6aQKXRR3GG6gWi6bweZVS5sW7a0TqDvGp7RBcQLg/u3rGm5fqjNjh419xejewEA9AHhubJv1OJoxpigwGp6QzofR6cQKr8Zqmy7mraX+2JE8blmfM2rqtui0w5EvHTWGti/LZqy0+iBNu954gCDrBVAaclj/dBONxijYwGh4QpILhTipwRWG9eR9u2lbrgZnMgWHma11wsvUr9zWtx1mNrYRZrpxj2xdiTvGp4zHVeXzqUfotkLAMhqekOTCwLYuRReoFDV4qd6grzPTBUxM5rHunq/ijvGpqiC0O8ancNvnvxHa1tNWSAmAhU4qsNe5S1id9LD66KagOqDUIS2IeoVuK2q2xxn0RwiJF2riXcbEZL6qkxhQbYouzlVHTrv4tcvDz5/DQ0+eqmsMizNOoF/94DPnapYVinPYted4aICcH4VSOVggPCArTKsNS+0KmgTsuml14DnXK3SjppvVE5jXypKyhJDmwsC2LqKeoLRsxsErs/NV26RTgjmbFmQanJTgVQv7KoI1KrdvXI4DJ842VL51cMDBQH9fjUAyuQeyGaeSwx4kBE0BbALg2bFt2gkUADhpwSJNsRtb/GPavGqpNuagFUFwhJD2YApsoxDvIppVu7ydhJVkrQdXkAHAyJeO1vjWnbRg9wfXhgo62yhur9DNDjh4+dJs1TEbEaxBgtoUjNhIlDm7qRHSGZiEOH3iXUQ3RBOHCfC0SNX/bSgU53DnI0exY3yqUojFS3FO1fQP12HjO/YLPaVqA/J0/cptCTLpNzsIzhQDwEIwhHQOFOJdRHbAHFyVspd5Hc2cUnDSohXGYdspACYvgY2gC6ukphN6ftN6lONF2c6dNOio1x8fFghICGk/DGzrIkxyTQR4zULHKFA6BRtTesoTnKfbfqGTQqE4r/0+CJ2gs/VFuwRVbrM5nu12pnQw2yA4WxN50ISBZnZCOgMK8S7igklIq4DvOgS32tuDh8wR8U5ajAIcKE0AFjppABIpuC/jpLF51VJsGttfJbC9lefy04Wqseki4W21aycldadvBQlqmyjzKCVWTROG7IBTV5lWCn5Cmg+FeBcRVrSjU4PevIFXe5960RjZvqi/L9SacH6miMEBBwv6UrhQKCIletN7WgTzShkFtr+dqg5/5TTbtDg04NoIE9RhVeuiVIAzTRiUQuQqcqzPTkg80CfeRQQFXum+6wQE1UVldt642rjudKGIjBN+y56fKeKV2Xncv30dPv3htdpr8ukPr8WzY9twcHQLDpw4WyOUbD3uXu1786qlVtvYBtKZGB7K4eDolsr4owjBervMAaWJT6E4V5efn/51QuKBmngXYWNO9X/nLmuXln7bxuU1ZVhFzP59W3+3KyBcDT/omjQS1e/1bR84cdZ6u3ZlEtTTZQ6AVf2BID8/67MTEg8U4l1GkDnV9J2pUEjcDA44uHd4Te2xm5Qo7gqIMBOzSbCFBdr5g8aiCKR21SWPWgEOsAvY8+5D5/tmfXZC4oHmdAJAnz61qD9e87vr+44S1R2FxSH1zF1MbojbNi6vuh63b1yOQU8a34K+6sfHViC1qy65K1wLxblKnr0/TU5H0OTEn2qnS7PbMT6Fa1+bYX12QmKAmjip4Gqs7sv+4uX4tfI4q8xdvDxr1QLUtnb4xGQejx25UuhkulCsCs7Sabl+0iJtKYPqt3bMKVUT1W7CpEXrKsHpJmQKwNefOYfbyiV1GZ1OSPNg2VVSRTvM6nFiqqNeT7qTTdlVd7/56UKNOT6uOuY252JbMta0f9ua7Kb68rbHIoToMZVdpSZOqojLtN0uzs8Ua7qcHX7+XE1KmU26k01wltf/bhKuUZcHYZu6ZbJ22FhBonQ5C0qzYxAbIc0nViEuIu8B8IcA0gD+VCk15vt+MYAHASwvj+X3lVL/Oc4xkWCS+KJNSUkztDH/F4pzePjJ0zW547o8Z79QXZzRV70LiuzWmeR1Qvfw8+cw/s3TlTrrlfaxCJ5Y2OZ9pwPy5W0ICw50Gdm6EjvGp7TaOIPYCGk+sQW2iUgawB8BeC+AtwC4VUTe4lvt1wF8Rym1FsC7AHxaRPrjGhMJJ4kvWl3xkSBMdde9ExhdgNbFy7NwfEXoowZnmYTuQ4dO1TRKKc4r7NpzPHB/tqlbpnOOWoM+jOGhHG7buLymng2D2AiJhzij098O4Gml1PeVUpcBfBHAB3zrKACvFhEB8CoA5wDMxjgmEkKnFoUJwtTYxKRjmrRP7wRGJ2yLcwqvWthnbIAClIT/prH9WDG6F5vG9td0/DIJXZMoDatQZ9v0JGdYz7S8Ee4dXoP7t68LvE6EkOYQpxDPATjt+fxCeZmXzwD4cQBnABwD8K+UUtG7V5Cm4U81y2YcOOlktkBTgDat6dbrrw5NdzIJW1NJWMCudWc9lg7ThACwa48aZb1m0UhVOUKIPXEKcd2b369wbAUwBWAZgHUAPiMir6nZkcjHReSwiBw+e9a+KhapD+8LeGrnDdj9wbU1+dJ+gdCJYt6toe79fN/Na3Dv8JrAlqJAsLA1CWmb0qImYRqUkx/UyzusPWrU9QghySLOwLYXAFzt+XwVShq3l18CMKZKeW5Pi8izAFYB+KZ3JaXU5wB8DiilmMU2YqLFFNTkBoilRbDxjYP49qkLHRPZ7qQFL1+arfIzX/KUbA0L1ArrqObiDSIzae/5cutO7zF15W9HHj0a2KXN1GTENujMdj1CSHKIU4h/C8CbRGQFgDyAnwPwEd86pwC8G8D/EpEfA7ASwPdjHBNB4y0h3aInblDUnFL49qkLuGV9DgdOnG1LHfYUgMUDDqZniliWzeDiK7M1/mSbTlvudUlZRm0DV0zvQelV3rSvIGHqHt8kypOYPUAIiY/YzOlKqVkAvwFgH4DvAnhEKXVcRH5VRH61vNrvAniniBwD8DUAv6WU+oe4xkTs/LZh29/5yFGt2fjhJ09jZOtKPDe2LYaRBzMPYNtb31DxwZoCwlyt2I//ukSJ2nZN70FdzGw6dnndGKaAsyRmDxBC4iPWPHGl1FcAfMW37LOev88AuCHOMZBqovST9uMKuqB0JVfjHBxwAoPA4uDBQ6fw5aMvYtdNq4150QC0xVDqLXLjBodNTOYx/s3TgetGsVDU06gkjEYtMEmj186X9Cas2NZjhOUVB734bASdOyFoVzVft555kCbtn7RMTOYjCVi3nGrOc302je2vyfP2Y1tYBYhWJc0G28pu3UKvnS/pXSjEuxRvDW9XK81lM8gaNOTsgKN98e0Yn8Lh58/h3uE11v7YZvttM046kpZss647xrsnjuGhgAA299p5r6FOmNqcc9TCKs0MRItqgUm6FtuIxYmQJEEh3oXoOlYBJaHspATplGDOpzW+fGkW9zxxXNuB6sFDp/DgoVOBJmovbgvQsEIltsQR8b4sm8HEZB4PHToV2DM8SHADV4SdjXiOo7CKLbaV3YDu0GKjnC8hSYb9xLuQILN3cV5hXmP2Lc6rUB+2rSZ58fIsinOdW7NHUPI52wpfU/CfNxgujGYWVgmrCqfDtrIbYJfv3ulEOV9CkgyFeBcSpm3E7a4uzqmW9CL3FnKJggIqrgZbCsU5/M5fHasSnrv21FoudDgpYKGTwo7xKWuha6Le7IIoFdu6QYttdYU6QtoFzeldSFC+MmDuaJU0XpmtX9vX9fsO4+LlOVy8XKhsb0txHjXtUIFa07SNH7peX28z2okmSYttdmAgIZ2KqIS9zDds2KAOHz7c7mF0NH6fppeMk8Yt63NV/bTjIJtx8MrsfMdUcOs0ctkMDo5uqXzW/Wa6KPgVo3u1Ew8B8GyT8vN1Y8k4aZZpJaSNiMgRpdQG/3Jq4l2IVwvxR6e7wmDDNUuqtJSZy7NNy+tOpwQXCsXYzfZJxm+a1mnY7vXzau+t0JKpxRKSHKiJEwAl7WvkS0dDc539DA442Hnj6soLf6A/3RJ/eCeRzTi4UChiccaxjsj3a+ImDdu/jakIDLVkQrobauI9QiP5vfX4yXfeuLoqn/m6u74SskX3sWhBH6Z2lgoPunnnQVdSF2AVFscAlDRyV2MPy1snhPQGjE7vInSRy3eMT2HdPV8NjV7etec4IirhAGqDs7ohYC4qXtP4vcNrcP/2dTWtW8NagI5sXRnat11wJaBuTqnKZIACnJDehZp4F2HKD3dLkQJXhK6uols93Pb5b+C5HxYqmn9KUNdkoNU4KYnsOjDh90fXU2lteCiHXXuOG83xukh6ViAjhFCIdxFB5ljvC99U0a0eDj5zzur4ncarFvZBqcaryjkp0eYe1+PWuBAwFrYmJYTooBDvEmwKiJwpt+G885GjPWn29nJ+pgj7diRm5lFyRewYn6oI68PPn6vyi5tyw/2C3hQY55Zr1U2SUiKYmMxTGyekR2F0epewaWx/qCbM3O34CXIneCPSdbnYTloAhSozvxt5DsCY+w+UfttdN62mMCekS2F0epcTZlbNOGmI2DcTSYpvu9MIumbe30gXv1CcUxgccDDQ32c0w5usKLq4B0JI90Mh3iUEpSilRXDfzWuwY3wqdD9uRbcDJ84myscdhgja1uPcxRsAZ5p0Tc8UMfmJG7TfDQ/lAn/DegLdkt5ylJBehylmXcLI1pVwUrVeXict+PSH12J4KBda1SuXzVRKsnaTAAcQf9cXC7wBcPV22Qr7PkqgW73NVAghnQOFeJcwPJTD7g+tRbbcyxsoVVPb/cG1Fc1Kl4vspAUPbF+HB7avw8zlWTx46FRX+szb3bxjwElVMgPc+AX/lMumy5auO5eXKOfZDS1HCel1aE7vIkz5yd6c8BoUcPj5cxj/1mkU5zpAXY2BjJPG5lVL8YUnT7XFz++kBf/25rfWBLMp6JuchLHQSRmb20RptdkNLUcJ6XUoxLucoI5mQCkS+qEnT7XdXxwXaREUinN48NCpth3ftYZsGtuvbXLir6NuwrbTmS3d0HKUkF6H5vQux1TFzUs3CnAp26rD8uEbyRUPMmu7zJePH5QCaKv5mjqdpUVwplxXPYo/W2eaj6rNE0LaCzXxLqdXTaO2E5N3XrcE3z51IXIcwOCAg21vfQMefvJ04ERBBKHd4Ww034lJc7Che3xvURkgvJUoW44SknwoxLuc7IDTtD7h3cjBZ85hwIlmkMpmSu1X73r8WKimP6+uaOM6gjRfbyyDrcWgUJzDPU8cx6XilaI+popx7mcKbUKSC4V4l9ONpvJmM1Ocj7T+dKGIOyxy7m1YaJhA6ILgbNFN2pLcLIW57ISYoRDvAoJeckFNNbqRrKH+eKft0+X8THWltcBMggYJc610orD0T2aCrAqE9CIU4glmYjKPe544XqV55acLGHn0KHbtOY4LhSJSDbQZTSIXX2m+sJ3aeQOuu+srsV1Hb252UCaBH10t/IyTxoK+lHbSEeR7txGW7RDyQbnsFOKEsAFKYglLHSPNIxdQ0tYGJy1Y1N+HC4Wi0SwuCC6dW7PPlGD3h9YCqA1MA2onA05K8KqFfZieKWoFsCl63k1/a3Z6WxhhFgkB8OzYtqYdj5BOhw1Qugyb1DEvbmDUsmwGm1ctxYETZysv/mtfm8HXnznXCZVJO5JGBLi/u5hJWC7LZqJlEpR/0KDANFe4L844uHh5tmKx0WnZpmPnpwvYNLYf5y6+goIvdiCs1Wq92ExQmctOSAlq4gllxejeyEL3OYPmYtPGlNSH/5rrBJTbbjSqLzwtgnmlQk3bYVp20DpRsC1aE0bYWNzrFVSdsJP8+oQ0A5MmzmIvCaUeTcSt271idC82je2vFAbp1VzyuMlpfqPhoRzuu3kNctkMpLyOK5BMTWxMzCll1bjEprxqWE12G5p1HwXtx3u9/LChC+lFaE5PKCNbV0byiS/oS1WlRbkBcEA0XyyxIyj/OzA3u84SckHBXjblVb2FX+q9F2yL1ng1Zb9rZ2TrSuN4wzR9BsGRXoSaeELRaXRBvDJbmwtdnFO454njTdHCyBW82qLJ+qFj976TDTWhMWmwtuVVh4dyODi6JfRe0mFTrlWnKT946FSN5rx51dK6ysGyoQvpRSjEE4z70n12bFvgyzdIuTs/U6xMCNLSSCVx4rJ51dKKAI9i3m1U2Cz2tKH1EmTC1xF1Uhe2PxebYMxCcQ4HTpyNNF6Xenu0E5JkaE7vInQm9oyTtja5v3phX2xFTZJEStBQy9KHnzyNe4fXRDbvNurWCJqDRSmv6q63a8/x0PshSjCb7STlzHShrnKwpvufDV1IN0NNvIswaVxB5tGMk8LqT/x33DE+VfPCHnBSGBxwIGis21cUOsEWMK8aG4dbFCYsbctvYt+8amkDRy1ZVZoVxDU8lMOiBeFz/Px0wcpVANhrxPVqzlEtDoR0A0wx6wEmJvMYefSo1t8apHV6C334K8ORYNIiWNAn2rrsbpEUl3pTzEz4c9PrpZ40xsGBUnOYoOjxIMtQUPoYIb0MU8x6mOGhHHZ/cC0W9df6OYPMxmemC5UXLwV4NOaUshLgwBUTe7MCsKYLRewYn8LdE8fCVw6gHo3YrQWv08rDYi/SIhTghESEmngXYip4UU9Bj3SP1V6Pk0bLt0ZFANy/fR2AWv+2jbbeSGnfIF+5ScNnKVVCzLDsapdjqjXtzQevR9OjAG8OaREcHN3S0up4CsA9TxzHy5dmUfSZXKYLRYx8qXRfBAnyhU6qLiEedK/Z5K0TQuygOb0L8KYy6XDzwbvpJZlCyf+aFOaUwsRkHjOXZ1t63PMzxRoB7lKcV7jzkaNa03ejbpSUiDHQzTZv3TsW21x7QnoNmtO7AFvt7oHt6zDypaPGl3qS8AZAJaGjmwBYGCHdr5XoOpw1I8iuGTXOg2rN03dOegmTOZ1CvAuwjSJ+YPs6Y5R6p7GgL6WtMufHjYYGgB2PTKFTb+dF/WlcvNxaAR7UWzxsu2ZNNhptimLTvIWQXoA+8S7GpkhINuM0XNazmThpCRyLjQAHSubiO8ankHFSSHVoEN6CvlTLBbgAuGV9DhuuWRLZ+tJMa4GbE19vVzGWUiUkGPrEu4CwMplOSrDrptXWLz4nLcgaSng2i2ZPJgrFecx1qJvAdkLSTBSAAyfOAoBV0Za4EKChrmIspUpIMBTiXYC/UtXggINsxqlUrdr9obUYHspZv/h2f3AtpnbeELsgJ/HiCk2vOd1JS6R2p41gyom/Y3zKOkAtahAcIb0GfeI9hG0AWC6bwZmy9kQ6l2zGwYVC0fg7mXL8B5wUXplVdbke0iKYL2+n29o9pk19AdsANdsgOEK6GQa2EQDmfHIXnfZEiItbkOXuiWN46NCpmvKxt6zP4bEjeWu/OgPUCLGDgW0EwJVuViatnAKcBLEsm8HEZB6PHclX3StuIN2BE2cjBcY1O0CNWjvpNSjEexD3RdeJOcukcxGgkkOum/wdOHE2slB2JwWudcg1w+fqEMD+iakbEwAEV6UjJMlQiPcYSSiMQjoThZIwvGN8Svu9q/1GKRJz7WszVfej60d3BfDh589VJgdezVqncd/zxHFj/3YAkTV0avUkCdAnnkAaebm0snY36S1SAnzk+uWRfOJRMfndg1rquttFqfrGSnGk02BgW5fQ6Mulnh7RpHtpdiBjSoB3vHEJDj5zrol7rSZqZz3T+rqgurDAT+82zXIDEGIDA9u6BJ0/slCcw52PHMWO8alQzTyquZN0Lw+U25TuGJ9qmiCfV8DXYxTgQPTOeqb1Xf+9VxiHTWq82wS5AQD64UlrYLGXhGEKHJpTyqoqVlh1N9IbCK5kKvzj1y9q6r47zdJj6nbnBtV5OwCGjd0tmBQUGOr1wxMSNxTiCcOm6lrQS8Rb3Y30Lq6wmpjM4+mXLrZ1LHGjFIxV36JkabglZG3iSljbnbQKCvGEYatJ+18i3p7Mu/edZNlKghWje5tqSu9ULhSKuGV9Dmm5Um7Wnejaupa8ZnbX7B4Ea7uTVkGfeMJw/WxudLqpc5f3JTIxma9qQZqfLmDk0aMYcFKYKba+OQfpDLpdeLsszjh47Ei+5jmxEeAZJ42FTgrnZ6rbuSqYgwJZ2520EmriCWR4KIeDo1vw7Ng2fPrDawMbRExM5vGbj0zVdA0rzimICJx0a5phEBI3KYG2ucv/vVSsK+Utl83gvpvXYHpG349dldcBUNHy3W0Y1EZaBTXxhOPXzP0FMe56/Jgxf/bi5TkMOKmO6TFOSCMoBez+8Frs2nO8qnNbPR1qnxvbVvnbZHZn3XfSCVCIdwFulLEfm6AdmtNJt5AdcLB738kqAV4P/qDPka0rtbUZaDInnUCgOV1EVjSycxF5j4icFJGnRWTUsM67RGRKRI6LyN80cjxSDSNkSS9xfqbYlBoI1742UwkC3TS2HwAqGR0CmsxJZxGmiT8KYL2IfE0p9e4oOxaRNIA/AvAzAF4A8C0R2aOU+o5nnSyAPwbwHqXUKRF5faTRk0BY2IWQK/h7nZsquXmrzbl1F+67eQ1N56QjCRPiKRHZCeDNIvKb/i+VUn8QsO3bATytlPo+AIjIFwF8AMB3POt8BMDjSqlT5f29FGXwJBidGRAAsplS8YtGzY6EJAVdaeIVo3utti0U53DH+FQlNZMaOOkkwqLTfw7AJZSE/as1/4LIATjt+fxCeZmXNwMYFJG/FpEjIvILtgMn4XgLu7hmwAe2r8PUzhuw66bVxnxzU4Url9s3Lg/NkyWkUzCZv6PmcodVQySkHYRp4u9RSv2eiCxQSn0y4r5173m/7aoPwHoA7waQAfANETmklPo/VTsS+TiAjwPA8uXLIw6jtzEFvXmj2v01o/05sV4GBxw8/OTpnskxJsklrDHQyNaVxraqJtwiMdTGSacQpon/Uvn/w3Xs+wUAV3s+XwXgjGad/66UuqiU+gcAfwtgrX9HSqnPKaU2KKU2LF26tI6h9B7eCm2bxvZrtQc33zyXzVgL5ZcvzUZuQEGIl1bU7s9mnNDgs+GhXKjVSQcDRkknEaaJf1dEngOwVESe8iwXAEop9daAbb8F4E3lCPc8Sqb5j/jW+a8APiMifQD6AVwP4P4I4yca/B2WTJ2Vwtou6ijWk3RLmkLUFpydiqYeSyQEwDuvW4LnfljAmekCFmcciADTM8XQLn5+dt64Whs3EtTNjCVVSScRKMSVUreKyD8CsA/ATVF2rJSaFZHfKG+bBvBnSqnjIvKr5e8/q5T6roj8dwBPAZgH8KdKqb+r50TIFUztSr1mQF1fctLZdIMAB0pFhhrh/nILVbfJz6IFfXUHnLnb3PPE8So3kkK5+pugqhgS88NJpxFa7EUp9QNoTNw2KKW+AuArvmWf9X3eDWB3PfsnekzmPu/yKN2bCOk0bCxNwBVrk7+aoZfhoRx27ztZEwtSnFfIOCnMz6OSknbLen2MCSHtIqzYyyPl/x8Tkac8/475zOukgzCZ+7zLg/x6uWwGAw7L6reStAieG9tWVe6T1DJYrspmsjR58fYKVwiOLjc9D4XifMUCMqcUHjuSZ3Q66SjC3tT/qvz/9wO40fPP/Uw6EF27Ur8Z0CTo0yI4M11gOVYfbm59XHhN5Z3c691JC7IZp5KyGPd18bPzxtVWliYg2K3kx9bPbdqekHYRKMSVUi+W//+87l9rhkiiossP90fqmvqSzynF9DENcRfG8QruzauWdmwefnFOYdGCPjw7tg0HR7cE1huoh7QIFvWb6xcMD+WsLE2AnVvJxfQ82G5PSLsI9ImLyI8Q0HZYKfWapo+INAVTfrj3+8PPn8PDT56uO2BqcMAJzCnvBUz9pqMgQFXr2MeO5Dt6IpWfLmDT2P5KZHhQtLmTEuuMBrcrmC7oMuOksfPG1QDsG5KYyg7rJgG6boAzl2e1vyuj00knERad/moAEJFPAvgBgL9E6Z1zG8IrtpEOxhUWtgLcTW/K+YKDbvv8N6pqTfcaAtXwREYBuGN8Crv2HEdxbr4jAg5zAUJMgIpw9FsonLRgUX8fLhSupHsdfv4cHjx0KvSYroYb1F7X5nuXqN3H/BNf02SC0emkkxBl8RIXkSeVUteHLWsFGzZsUIcPH271YbuOTWP7rfLD/T2T/dG+F1+ZbaqpOeOkUKA/PlactGD7T1yNx47ktROGjJPGLetz+MKhU6jnlxgccDD5iRuqlnnvGxF9j+/BAQcD/X2BgjkqNtHpQevYbE9IKxCRI0qpDTXLLYX411HqSPZFlBSHWwH8ulLqnc0eaBgU4uHYvHhWjO61MtlmMw523bQaw0M55pYnFJNwvHviWI07xbW0RC0C5OeB7euMwu7uiWN46NCp0PvPnUwcOHG2aULU/2xsXrW0ZjITVq6VkHbQqBC/FsAfAtiEkhA/COBftSO4jUI8GJMJ0P9SMmniKY2W5G7f6IudtA+vG2RiMl9T3ASoNoU36pP3W3Bcok4E/ZXTGhGwumObKrOZxk9Iu6hLiIvIVUqpFwzf3aiUeqKJY7SCQjwYk3DWmcV1wn5BX0prHs9mHGuzecZJY3ZuDrSKdxbplGCuRWVzBcCzmpx3WzdOEFEEbD2lhQHz+AlpFyYhHlax7WsislUp9ZxvZ78E4G4ALRfiJBhT+os3otg1S3q167QICsU5o4Y0XShqtXQdSTC3d0sd8ii0SoADQEoEK0b3VkzWrkm8GSOwTfFqxP3jRqDTJ046nTBN/H0omdHfp5T6XnnZXSg1MnmvSUuPE2riwZg0Hb/ZUADctnE5NlyzpGf93EFNLkjnkhbBvFIVoQroI9VttX6TyR6AlWuKkFZQt09cRN4N4D+i1I70YwB+AsD7lVLnYxhnKBTiwUTx+wmAbI/meucMOcQkefjdBK6g3TE+1VDwnK1ripBWUK85HUqpr4nIRwH8NYCvA3i3UupS00dImoIuh9YkrBTQkwIcAM5dfKXdQyC44tYYHHCgVHBlPJMLxO8mcEujBt377v6CtGqbim80t5N2Y1uxTQAsAPBuAC+JiNtPnBXbOhB/0YpmBBN1G8xFbz9ejdYVhkFCPEoMw5npAu7fvs7oKrIxi4dVfPNbvYK6qRESF2G101+tlHpN+f/9SqlFns8U4B3MxGQem8b2Y8XoXlx8Zda4XobdykibcAWkt9tYs1iWzVT1EABKmjeg7yWgI6yRUJQGK4TERag5nSQPv4Zgiix3635TKyXtQHBFA29mYKVX0Ib1EAgirLxrlAYrhMQFhXgXonspzqvayl2bVy21qmntYpNi5o0c3rxqqVVlLtKbKFwRkI3gpASvWtiH6Zli5b7bve8kdoxP1e2n9vu679dUoIvSYIWQuKAQ70JML8XpmWKlprVb+jIK8yo8LWtOKTxXLpKxaWw/BTgJJCj40jWD677zp5l5a5036qe23UfUBiuExAGFeBdiE5BTr4bsRjmatnX9joD+5UuIF1cIBwnDKLnaYX5qm0jyoH1417ftpkZInFCIdyG6lyJQEqpDn/wqlGqsyEmYJj4xmceuPccj7zfjpHuy6Ew3IeX/2AaSb1611EoY2gjKicm8ceLoatN+7frw8+dqcsSj+Lob8bkT0gysGqB0Eiz2YoepyUUzCDJzNsKAk0JuMIPvvXSxqfsl9THgpHBpdt6q1G69BBVOiZKDHVZi1ZRjrqvWZuofwCIvpJ3UXeyFJJPhoRx27zsZixDPTxcwOOA0fb8zxXkK8A6iOBf/BN9UOGVxxsHFy7OVMYT5toMi3IMsPP4zLBTnsNBJ1WxDXzfpVJgk3MXEmerSq5Xeksai/nT4SgaK8ypWLRyoLZySLzdJmS4UayYRphzsIDM6ANyyPlexHtkwPVOs5JcL7PPKCWkH1MS7mLCyk6T7uXi5c2MMMk4am1ctjVRR0D8xdYV/EAdOnNXGiZgCNN1CMSa/OwPZSCdBTbyLofmPdCppEdyyPofHjgRr0X78Odg2hWLOTBeqqre52vVtG5cHVmTz47cWuCb+icm89fgJaTYU4l3M8FDO6LsW7VJC4kcA3Hr91Xj4ydORsxE2r1pa9dnGZeQK/uGhHA6ObsH929cBAB46dAoL+lIYHHCszOYss0o6EQrxLmfnjau12sZtG5cjRUlO2oAC8OChU5EamrgcOHG28vfEZB4pCb6J/Zq1zvc+PVPEbRuX4+DolkDTOMuskk6EPvEux5SDCwAPPRmtYhsh7SY/XcDEZB6Hnz8XWrBocMDBzhtX1+Sb+7Vpd1Lx4KFTyAX4uVlmlXQiFOI9gD9Ix9VGElYigBAAwMijR63S3y55Gvu4AWlh/vegVDaWWSWdCIV4D9LsrlGEtBLb/HWvvzqoEIxpO78QZ5lV0olQiPcgTDsjvcKZ6UJdk1bXbK8T5BTapJOgEO9y/Hmtm1ctDe1ERki3sCybqTvwLGr3M0LaAaPTuxhdXiv7e5NewfVX1xt4xvQxkgQoxLsYUyQuId2GkxZsum5JpRWuW0xmeCiHka0ra9IsbWH6GOl0KMS7mKgvIKaNk3ZT7z1YnFP4+jPnKrnnc0rhsSP5SjW1hc6VV1024+D2jcurqrdlM/qiSEwfI50OfeJdTNTa6QqlhhnzCoxeJy0lLYJPf3htxf9898QxPHgoWh0DXUeyXXuO45XZ+ar7+ZXZeWy4ZgnuHV5TWaZrZcr0MZIEqIl3MTozYsZJI+OYf/aLl+fwtuWL4x4aIVXMKVVVh/ze4TXYdN2Shvc7XShalUrV1VZn5zKSBKiJdzGmvNYd41OB2x185lwLRkdINYXiHO4Yn8LufScxsnUlnvthfP5oUx/zZdkM7t++jsKbJAZRCSvbtWHDBnX48OF2DyPRRGn9SEg7yDjpWF06uWwGB0e3aM3oTlqwqL8PFwpFFnQhHYOIHFFKbfAvpzm9B2kkWpeQVtBMAe4PlvP6unUZHMU5helCke1GSSKgEO9BXP9fkG+ckE7ESQkW9UebgHptjdmMU+XrtsngqDdffGIyj01j+7FidC82je3nRIDEAt/iPcrwUA4LqY2ThLH7Q2tx/JPvwe0bl1dywqPwyux81WfbFLKo6Zq6QkvU6EkcUIj3KBOTeZyfKbZ7GIRE4vDz53D3xDE8VGc/cr9WbetaipovrjPTswIciQNGp/cYti0ZCelEbHLHw3oDeLVqfwZHdsDBy5dmUZy/sod68sVNmjsrwJFmQyHeJfjTZHQRtbd9/htMHyNdTVoEc0ohl81g5vKs1trk16r9nclsnqUwTIWWWAGONBummHUBrnnR9EumRfDGpQP43ksXWzouQtqJSSPfdN0SHD/zI0wXSgJ+cMDBzhtXNzWNzFQBjgVkSL2YUswoxBPOxGQed4QUbyGEhNNsYd4MjZ4QF5MQpzm9w4j64O/ac7yFoyOkezk/U2xqD3G/mZ6QOKAQbzNeoe0PqnHTUgD9S2ViMl8xCRJCGseNIKfwJUmBQryN+P1muiAc70vFK/AXZxxcvDzb6iET0pE4KaA4H76eDYwgJ0mCeeJtRJdLquPMdKGmeMR0oYjiXLLiGQiJg2zGwe4PrYOTqi7+4qSkqm+4bXEYRpCTJEFNvI3YzviXZTPWAp+QJLGoP411Vy+uO/Ux46Sx66bVxo59/tQxf8S4bn/sIU6SBIV4GzHlknoRgIVZSFfipIBP/Wwp5Sqos97ggINtb30DDpw4i/x0oSoX3CuowwLJdIJ+86qlOHDiLCPISWJhilkbCWqDOF0ohlaeIqRbGNRUStOt0+x8bkKSAlPMOpAgEyB7fpNewqaO//mZIkYePQrAnALmLSvs1diDNG7mc5MkQ028Q1kxujdQC3dfUIT0GrlsBgdHt9Qsn5jMY+RLRwO1eRe3ehqAplZWM00iODEgjUJNPGGY/OXZjINXZucZ5EZ6lvx0AdeO7q18Tgkwr8Ibn3jxdhQzdRuLqqn73WPuJDus3gMhjUAh3qGMbF2pjaS9cKkIKuCEXMFVvKM+FkHZIe53fsEcJJCDMkjaVUSmXa4CuihaB4V4h+Le8Pc8cbzKX0gBTkhzWJbN4OIrs9qqh26uuKkv+B3jU9i972SVcApLGXXrPdgKt0ZN81EmIEH7iCqMm3FcYg+LvXQww0M5DPRznkVIHGxetVRb9dBJSSVXPEgwu8JpYjIPILxIzOKMU1Wwyb+9F29xJ6DWNK/bxo9pAuK6EcLwF5iyPXajxyXRoBDvcFgCkpDmk804OHDirLbq4asW9lU0xjDB7BVOm1ctNa4nAETM/nc/Nqb5MEzvDtt3Sr3CuNHjkmjEKsRF5D0iclJEnhaR0YD1fkJE5kTkg3GOJ4mwBCQh9qQsKqsKgPevfYNRqEx73FcjW1fWlHP1k58uYNPYfjx46JRxHeXbr277FaN7sWlsPyYm81am+TBM745l2QwmJvM1x7Q9Rtixg45Lmk9sQlxE0gD+CMB7AbwFwK0i8hbDer8HYF9cY0kyLAFJiB3ZjIOPXL8c2YxTWaYTvwrAY0fyyA44mm+rhc3wUA79fcGvSZuqimkRLM7oj+du7zVZm9bVjdHFL5g3r1qKjJOuWifjpLF51VIrM3m9wnhk60rtcfkui4c4NfG3A3haKfV9pdRlAF8E8AHNev8CwGMAXopxLImFgSCE2DFdKOLBQ6eqAtVMcaCF4hyUgpWwuXjZnM5pm9Y2pxQuXp7VavX+7QvFOYjUji1ojDr/9WNH8rhlfa7SACabcbDQSeHBQ6eszOT1CuPhoRzuu3kNBj2TpAUhEyFSP3Fe2RyA057PL5SXVRCRHICfBfDZGMeReLIhs3JCSHRKpY0VFvVfEVRRhU2UZJHinMKrFvZZPc/TM0Xcsj5XY0kQALesr60Rb/JfHzhxFgdHt+D+7evwyux8YGU8v5ncFcbuJCCXzVgVwZmYzGPXnuqsmulC0TogL25sXAlJIs7QZ5Mly8sDAH5LKTUnAW0CReTjAD4OAMuXL2/W+BLDrptWW1eiIoTYM+NrQu4KG+CKFSybcbRpaPUwPVPEsmwmdH8KwMNPnq55YSoAB06crVnf5KfOTxdw3V1fwUInFVogyvWV+1PK/NXxgtLOgjrF1ZMr3+x8825Mf4tTE38BwNWez1cBOONbZwOAL4rIcwA+COCPRWTYvyOl1OeUUhuUUhuWLjVHgHYrw0M57P7QWuQYGEJI7PhNy7tuWt20F+WybMY6SttUVlm3fZCfumTKDxbgtr7ysLSzsJbJUSLU601xC6Ib09/iFOLfAvAmEVkhIv0Afg7AHu8KSqkVSqlrlVLXAngUwD9XSk3EOKbEMjyUw8HRLVrzBiGkueSnC7h74hg2je3HjvEpvV0xIq4/udEobd32Ov+1La6Z/MCJs6ECLkwI1hu5riMOgduN6W+xmdOVUrMi8hsoRZ2nAfyZUuq4iPxq+Xv6wevApgc5IaRxvCljjVZK9Fda2zE+VVebYW9gmd/UfMv6XKXnuu2+vD7uHeNT2vW8Ai7IbA8Ev5+iRqjHIXBN40ty+lusIYNKqa8opd6slLpOKfWp8rLP6gS4UuqjSqlH4xxPN9DIjJsQ0nrcrmuusBweyuG2jcvrUu5doWuKRh/ZuhLpgPgi75j8QWo2KWWmdQTlLnKG99PggBO5M1wc+ebdmP7GuP+EoYsYHXD4MxLSiZgExL3Da3D/9nVVaVhh5LKZihAMMjXfev3Vus0rCFA1qXCxEXAjW1caI5bdoDX/++mB7esw+YkbIgeOxSFw642472TYTzzhTEzmcYfBDEYIqUYA3L99He585KgxcKxZ2DQr2TS238r87Td9rxjdqzXHC4Bnx7bh7oljxgpypn7sgF00uLcNrO7YUfYVBruhXYH9xLsQ16RGCLFjmUebHXn0qLZ2ejNwtd0wbPy7uslAmG93wzVLMP6t0zXn523uomN4qDYHXTeeML9ys1K5bMbT69AOm2DC0jkISRpR/MRRfcpeU+zwUA67P7i2ypw94KSQzTgVM+vtG5fXndZp67cNW8/vT3cJMzXv3ncytLlLvdiYubsxlatToSaeYJKcFkG6G9typF5ck/GuPcetiqtE2X8242DXTaurBJitlmcyHwNAxknhUnG+aixuLfRNY/tDzb8jW1cai6ME+X+9vnGdqdmmuUu9hB076Ph8ZzUfCvGEMjGZR0okdr8eIfXgvSudFOArjKbF9fmaUp28ZDMOFi3oM/qTsxkHFwrFpvhR0wHP2X03vxVASaC5Y3HXtDEhewVifrpQOZaNPz1oEtKsVCqTTzpsAmQ6vgKsJjfEHgrxBOL6myjASRKwEeBe03hYLYSMk8aum1YDMOdb/+jSLO7fvq4pgiLoOfPuXzcWm1Kjcfh9dRp+1MjuRvzaQRaGbih12knQJ55A6Asn3YabogTofa6ukPemBA0P5Ywm9TmlmtZww+QX9y7fve+kcSztMCE3I5WqEb+29/g66B9vHtTEEwj9SqQbce9rG5+riylSGmheww0brTbomWxXNbBGNfxG/dru8U3pcHyPNQcK8Q5G90IBQF846Uq8ws5WAAWZbYH6Gm74zcf33bwG9928JnBSYXIBSHmMSaRZfvVuLHXaSVCIdyi6F8rIo0cBFeyjIySJuF20No3trwjKzauW4sCJs4HauPvZVLzFJCh0E+Qg87EuzcuLbjIhAG7buDyxft9m+NWbuR+ihxXbOhTbSk6EJBkBKgL7sSP50FgPXaoYoO9j7aQFi/r7aqLUdetmnLTx2P5KZCbqrS7WyVXJmjW2Tj7HpGCq2EYh3qGY/EiEdBPPlYVjlEmrvwSpi1dQZAccvHxpFsV5VbOdNx3MiymVLKhMaaOYJhRJr+dNmo9JiDM6vUOhv4h0O9nMlWppUXzXpsjm4aEcDo5uwbNj2zDQ31clwL3bmY41p1TLO1yxshlpFArxDoUtR0k3kxJUcr0BIBuhmxcQLvSDIqtNE2Q3DasVHa4mJvOB1ocz04XKOitG92LT2P6mpMuR7oOBbR2Kv5ITId3Egr4Udu87iTvGp+oq0RpmqQqKiA4KtAqLim9WZ66giHqgNKlpRgORRqAfOxlQE+9ghodyjOAkXUmhOF9TptQWGxN3UJOOeguhuMI3P12AwhXBGlVDDivWlHHSUAptNbM361xJ/FAT72DYapSQatIiuGV9eA55WMGYegqhBPmvo+wryBXg1kw31Y9vVYGUZp0riR8K8Q6G5VUJqWZOKTx2JI8N1yyxEuTNFDjN6syVHXBwXtNNbHDAqUTBm9xoizNOVS6938QdxQQetC67kCUHmtM7GPrCCamlXdHbJj98lEySick8Xr40q/3u5UuzFXO1zh3gpAQXL88aTdxRTOBh6zbjXElroBDvYNIi4SsR0oM0WyO0iQQP8rPbsnvfyZrUN5fivKpMTnR++1ct7ENxTp825+7b1o8etm4zzpW0BprTOxiWVyW9jAhgegSaqRHattyM0pjF3a9/3SipcX53wIrRvYHbRDGBh60b9VxJ+6AQ72CCOjQR0s0IzAIcADavWhq6D68QXZxxIAJMzxRrBFKUIC5bP7tpYrA442C6UOsPdwmanIQ1EonSaMRm3Tj6nJPmQ3N6B8OCL6QXsckbf+xIPjDdye/znS4UcX6mqPX/xhHEZZoYiMD4TIeZq8NM3FFM4DSXdw8U4h2M1y8WhcEBBw9sX4cHtq+r+NQGHP7UJBnYOJHCgtvCMju828cRxGWaAEzPFKueaTfuxSZXPSy/PUr+e7258qTzYAOUhDD0ya9q01L85Ay+K3ZFI51EPVXadPswdRezbSD03Ni2WJqQmJ63oGYqrJBGgmADlISz88bVcNLh0eq6tJN193yVApx0DCkp9dnWmXO9TVHC8GrK/ujyxRb7kfJ2cWilUc3VrJBG6oWaeIJwZ+r56YKVJjM44ODCTBHzrRgc6VmCenH7WdSfhpNO4UKhiOyAA6VQ1e8bQG1f8JQAgqr0Kq+mbOolDgVjOpdL3G1GbTXrejR30luYNHFGpycI9wUw8qWjoS8nAFbmd0IawXXfeIXV5lVLceDEWeSnC5Ue3bny8seO5CvR2ednisg4ady/fV2NcPMLP92yoOjy4pzC4ICDgf4+nClrtzrirEAWJbqbFdJIvVCIJ4ygYhGExIlf4/Z3/nI1z4cOnUJ2wIGTksq9mp8u4KFDp2qEqS6VyyT8TAIxKIhs8hM3ADBrulGrrTXbZ+3u0/REs0IaCYNCPGFwZk7agUnj3r3vJHaMT2FxxsHFy7MVk7fOChSXNmyT8xzUftQG24IwUQhrScqUL2IDA9sSBmfmpNXohMnM5VmMf/N0VR62vySoLVG1YX95VJsgskaD16KUNLUlKA2OKV/EFgrxhDGydaX2R3PSgk3XLWn5eEh7cdKC2zcuD11vcEAfrR2W75BxUrjv5jUAUBU9fX6mWJdbx388QUmrNdUr92KK4AZgJaCHh3I4OLoFz45tw8HRLU1pH9qIFcG0rQCRx0d6F5rTOwhbn1s6LZj3aT3FOYVvfP9cq4baNTQjX7ldDDgp/Nub34rhoVwlkEyHawrXmZNvWZ/D3qdeNAZBXiqWchua0RbXPZ47Vu+1tzFPB2nDcQu9KCVNm7FP5owTWyjEOwSdz+2O8SncMT6FrKfuc6oc7auD8W7RSfIlG1y0oBJQdvEVfXtLoFRn/PDz5zRCWOHJ7/8wMItBAdgxPlXXdXLSgkX9fVUpZK4g0gWameqVe1MrdbQiTqRRn3qUfW5etbTp/nfSvVCIdwhBmo63YQI7mxGX/HQB147uDbUmfOHQKW2tgEJxHt976WLoceq540yVA11szdNhwV9Aa+JE4ujqZdpnlIYshFCIdwiMOif1EiZkW1nsx7ZcqcmUnB1wsGlsf0WoXXxlNlCAtzKCO46uXrp97hif0q7LdwTRwcC2DoFR5yTpRImo1kWUO2nBy5dmqwLXgtp2Ro3g1kW2dyJxNGQh3QuFeIcwsnVlaKQwIZ2KWx7UVqDqUr4W9fdZR7xHPV6SapOzTSiJAoV4hzA8lMNtG5dbC/JsxmFKGbEi7oe8XgEzPJTDyNaVWJbN4EyI1t3o8eLI844LtgklUaBPvIO4d3gNNlyzpBKJmzZEojspwa6bVlce6onJPO585CiD3tpIWgS3Xn81HjuSbygVa3DAabjm/aL+NLID/VWV1R4/8gJmitXe8WzGwdJX99cEtzkpwfa3X40DJ87izHQBiz3ZEd7a6I0GeNkErenOp57jJa02eRz+d9KdUIh3GLqHNyxn1P3b5oXoJ+Ok8MrsvDY9LZ0SzDFvLRRvMNeGa5Zg157j1lqlF2/Hqnr7v2ecND71s2uqJnil+2K+ah2vZteunGTb3HMnnWq4k1cced6EdAIU4gnAZlbuT1cJyid3cV/mAKoEz+CAg503rsbh58/hwUOnmnAG7cfVLnVNOOpFgKouW25UdUqiRzf4TcRRNMRc2RztFcBBudWF4hzuGJ/C7n0nqxqYtBrbc7xQx4TITxx53oR0AhTiXYT3ZbxidK9xPb/w2b3vJC4UijW5vfX6C103gD9/OUrf6WbgHt/bvMMkwDddtwTfPnWh5iWfEuDiZf2YFYAfXLiELx0+VbVtVLeGLqfapDnqtvVqqROTeay756tWloB2FxGxPcdmaMtx5HkT0gkwsK1LMb34ctlMpXY0gJqI3TvGpzD0ya9iYjJft79wXik8N7YN929fVxOckzOMKy3S9Oh8Vd7vmelCYMUvAHjoV95RE0x0y/ocLs8GZ1nPKYWDz+iqodnhN6G76U+bVy2tiVD249ckXdN5FFN+O4O7dFHYfpqpLTdSO52QToWaeBeg82namA9NPsnzM0Xc9fgxDPSnjVpoEGG1n3Xjcn20K0b3NrUUqqsV+2t1e3EnFn6z8qax/U3r3e6kBBBUdfpyfw9dyd3HjuQrdca9AWpBAWX11jdvJLirEX+6TjtuRtAc646TXoJCPOGY+hzfd/Ma3HfzmsCXWdDLu1Ccq0sztq39bBpXvRMHG0ziePOqpdrlzYpcdmMMgCvnnR1woFSpOpcufqFQnMOBE2eNAV2uoNoxPlW5hvUEwgH1m6ub0WO72f74OPp+Jw1OYnoLCvGE00hnpzCfpK0O6vrAvb7noNrPQS/umZgEeBAHTpyt/O19AdoEB9ow0N9XOV836MwraEzHME0iTIJKBDAN1+0g5k+Ba8Rc3Yk1vjtxTK2Ek5jeg0I84TSS/6ozuXsx5al7EQDP3Pe+qmWN1H62DXYCrnTJqiedy0t+uoAVo3uRHXDw8qXZigldd+6mtLtN1y3BwWf0rWDPTBfqmhyYNGSToArCmwLXLC2tE3OvO3FMrYSTmN6zQlCIJwz/TZo1FAexMZG6N7cur9mkudkcp5Gc3M2rllqlgWUzTqXgzd0Tx6xS4YK6fSnAWGQlLYJ5pSo+24e/ebpKkKdTgg9tWI7nfljQnvfijGOleXsJ0pCjCqRcNlNlCWjWS60Tc687cUytpJcnMb1qhWB0eoLQ1X9++dIsnHS19zqKiXR4KIepnTfgAU0k+b3Da6oiyv0+ctNxRraurBmTk5bQMU1M5vHYkXygAE+L4IHt6zC184aKafrhJ0+Hnmcum8FtG5eHRkPrmFeqEtF84MTZGk18bl5Vcq51Na9FwjVl4EqEfliZTZNAymacltbcbneNb11Dk3aPqd2Y7g0FdHTTl2aQpNK6zYSaeILQ3aTFeYVsxsGiBX0NmZBMGpp3eSRTlV8SW7iWd+05Htp20l9p7K7Hj1mZ/N0AMa852dbbnRLBtaN7A90LZ6YLWsvGQidlVUZVANx6/dW4d3hN6LqmzINdN1UHz8VtTmxn7nUjAZ3dTJCLrNs10161QlCIJwjTzXihUMTUzhtaPBozu/edrEnNKpa1VdPLY2IyH+jbHvREc7tar21KVUoEE5P5mqA629KmruAOmix4NaBXPLnl52eKgWZ8FwXgsSN5bLhmSeTqfH5B1coXdLuqvTUS0NnNeO8NU7U+3XPYDb7kXnWlUIgniHpvUtMDGuXB1Wk+d4xPYdee41XNWADzZCNIYAaZvLIZB5eK8zVal21O9JxSWg1k86qlWl/6gJNCoThvHYDmNdfqhIutxh8lAKnXG2T0qtZlg3tvmGou+K9Rt/iSe7W0Ln3iCaIef5+pj/LdE8ci9Vc2ab3ThWLNdqZJhZTHoyPo5avzKUctalIozuHOR45WHd+bWuZlcNECPDu2DfMWAtzvv25UiHSbENL5rZuB6R5rpdYV17k161i216hbfMm92sKVQjxB+G/SwQEHC/pS2DE+ZXywTQ/ow0+ejvTghhWG8W43snWltlCMglnjDhL8jbbmdJlTCjvGp3D3REnLCNPmwgSCWzLVX/NcR9qyKUo3mf5ME8hmCLtWBbCZhGec56YbQz3Hsr1G3WTV6MXSuhTiCcO9Se/fvg6XivOYLhQDH2zTgxi1wEiYcPFuNzyUM5qQ8+WcaT+mOtrNboSqADx06BQmJvOhmsrmVUuNVeuCIvN1L85br7/aKjLeVD2ulTRLw4xTw2uF1hUkPFupvdZ7LNtr1AlWDVI/9IknFNuiDiY/uinS2vTghhWG8W+XCyjaovO3+YO1gOYLcBcF4J4njmurm3nrmZvS3XRdx1yCgs42XLMEdz5yNNDP/vCTp/HQoVNtCy5qpn80bg0v7riAoGesldprI8eyuUa96kvuFijEE4rtg216QKOW4BweyuHw8+fwhSdPwV+wTLddkNB3/dPeut/+yPFrA1qpNgOdid6tcT48lMOmsf3GCcvmVUsDX4xB6XpAbQMYL96GLa0ILvIHN85cnm1axa+kRwsHPWOtPLe4j8U2rcmGQjyh2D7YYZqhriGHm8LlfYhdzdQvwL2V09z13H0uzjhWwsoU5d5qvDXOg7ScBw+dwt6nXsT0TDHwhecXkG6HLtugvLjLZeq0bhP1aJhJ1/CCnrFWnlszj2XKSAnS2Lsh/ayboRBPKFEe7LBCLjYmVFN0+qIFfZV93PPE8SoNd7pglyPtruses114BVVYDXf3PE0as+6a2pSGDRpTs4nSujQlghWjeyO9xJOu4QU9Y3Gem05oNqOATT2ukm5JP+tmRDWhS5Nx5yLvAfCHANIA/lQpNeb7/jYAv1X++DKAX1NKHQ3a54YNG9Thw4fjGG7i0D3sQPQXi6noyeCAg8lPlIrImHJOBcD929dFytsOwi3xWm9bzWYc372OO8anrP3ybjEat2BNSlBjtdDh1mU35aS7EfBxUG/vdn/lvG6m1VqoX2gC0a530HhNz3nQPVbPNiQeROSIUmqDf3lsmriIpAH8EYCfAfACgG+JyB6l1Hc8qz0L4KeVUudF5L0APgfg+rjG1G34Nex6Z80mbe/8TLFS6SzItBhFowsjSjnUOPCW77xt43Jr7dnvY7cR4KX1SnXZTS/vOE3Ppt/UW8bX1Ou8V7pi6Z6xTWP7Iwt128lAI13I7p44VtU8yP/81xMg103pZ91KnOb0twN4Win1fQAQkS8C+ACAihBXSn3ds/4hAFfFOJ5EUY8GYPsC8O97ccYxljzdtee4sYQjcKXrWLNYWK6W1k4KxTncUW6nuqg/jYsx9jh3YxjiMs8G3UdBNdjDAgz990O3+E2DziNMSAbt03ZyXa/QnJjMa7v/eZ//egLkkh6c2AvEKcRzALztpV5AsJb9ywD+W4zjSQzN1qjz0wVsGttf0er8+/Z3HPMyXSgG1jR/+MnTTdWc2y3A/cQpwP2atm3KlG0Z3c2rllZlIPjvI5uJgykV0Vu8ZmIyj5EvHa3Uy89PFzDypaOV47SKRicSQc8dgFAhaSKKdl2v0Ny976TxOXTfC/UEyCU9OLEXiFOIm4p21a4oshklIf6Thu8/DuDjALB8+fJmja9jqVejNvUWB8ov1kePYnZO1fwIxbn6xbBNbXEbBCU/cgNDiXSsVpnsTb5xbzpbFEyC5vDz52oEtkno3OHLQAgag+n39S7ftee4tuHNrj3HQzXUZgRruZYi7+9aTwBWWFGVMCFpImhy7bqrTOcB2AnNoDHUY+3x/jbZcmXIC4XgbAzSHuIU4i8AuNrz+SoAZ/wrichbAfwpgPcqpX6o25FS6nMo+cuxYcOGdrpMW4KNSU33MndSAictRqHciLCOm/u3r6uYsJtJCoBXt3dz5L989MVAC0MzcNKC3R9ci8PPn8PDT57GnFJIi9S0HPVH9rtpe0DtCzeojK5f4Ab92rZCLqj9qiuATNcx6Po2I+rZv496tGQv9Zqyw7TkoEyHkUeP4q7Hn6qyQClcmWgGFRayOYYAka09/ut6fqaIjJPG/dvXUXh3IHEK8W8BeJOIrACQB/BzAD7iXUFElgN4HMDPK6X+T4xjSRQ2JrWw3uKNRnfnshlMz1yO1ZzscvvG5RgeysUixNNpwWv6+2q0iAMnzsYqxF1NGyi1GPW2M/W2HJ2YzGPk0aNVE6zpQhG/OT6FtGdCFta5rR6LiI2QC9qvGy9hg1/rvvhK40VlbAIqowRghT13NkLSxV8vwTS5Ls4p7XJXgNtGgJs68v3j1y+yEtpxFfwh8RObEFdKzYrIbwDYh1KK2Z8ppY6LyK+Wv/8sgE8AeC2AP5aSj21WF0Lfa9j4oUwvp+lCEYsW9GEwwLQehgA4OLoF6+75KoB4hfjtG5dXtFIRaEuhNoL7glyWzeDMdKEidJoRXbuoP43sQH+gaVJX+c37Qty976T2JT4PYN63vFCcC/RR65aHuQ7CrkNQ+dyweInBAQdAfEVlbNaN0qZXJ2y9z53/mRQAt5UnoH7z88uXZisuhulCEU7KrgGO6fxMhYO8kyIdT790sWIxMZ1/nAV/SPzEWuxFKfUVAF/xLfus5++PAfhYnGNIIja+qyATnY1pPQj3xXchoqZq62vWmZQB4Lbr7VO6ouAVNq5GGxSRb8vM5Tkc/2SwphRmoo36YpxTChknXSVMnLTASQlmitVX33UdHDhx1nivhAm5ka0rI+XLe8fkWiKipCBGiXoOK8gjqA7q9AsyvwDz3w+6uAVTQKHf/OynOK8CXROm89MVUfIXDgq6Bm7nQF3go8k9EzQe0nmwYluHEua7CmtIUpxXGHBS2kC2IFICXHxlFitG9xoLkOjIaaKhdTyg8at5Xy6toFCcw0InZZx02E5GbLQ80zVcnHEq+4ji+sh5Xr5erW/GF9WvK4dbT5SxWzNfFyRnIiUlC0hUq0eY0PUT9AzYBLmFCbBLvmtqeiZtBeGcUnBSUhMEqCPjpLF51dKmFFE6Uw6g08Ug2O6bEemdS6wV2+KAFduu4I1o7RQGnFSNQHFxq7sFpUG1iiBBbSPEnbRgUX8fpgvFioaVC9DOdNvv/uBaAKjxiZvQVe6KUlGrkWhwnd/Uxl2TcdJY6KSM67rX2n/NbQO77p44Zm298V8Tm4p1/kmT7rrZVr4bcFIozut94F5cC0Cznu2gKogm64C34A8j0juDlldsI/HjagamF3mziGIGNAlwoPRS9gosUxpUKwiyMgQFMgGll+zLl2Yr5ldd57Ew7czVVF2h8jt/dSw0iPCW9bWaoGmM9Vg1goS8rnKZTSBioTiHBX2pGheAi4L+/rJNFfvy0RftTg6118TGCuLXWN2GPfc8cbxiare1ppieDbf0rv+a74gY6LmoP42Zy3Pa9DTTvnTuGX/BH9LZpNo9ANI4I1tXIuOkq5ZlnDSyZZNto7ipUc3Ar4W0yw5kEuDuS2/zqqU1hQ4yThoPbF+Hgf4+o0k0rN+0F3ed4aEcsgP9oesfOHG26vPEZF5bjAGoNfXfPXEMO8ankJ8ulbV1hdPEZL6yr7sePxb4/aax/VgxuhebxvYDgPX9daFQxH03rzF+HzZB9OZq+4kS1+C/JiNbVxqvn0taRDv5OD9TrFyfka0r6wpcc3FL77oTOvc6pyI8cxknjU/97Brcv30dctkMBCUN3LXcmFw/7jq6bUgyoCbeBZgC4YDg3tVRSIlqSSGWduK+sHftOY6Ll2c1EwxlFeQV1G/ai/fFaiP03f2FuVH8aU82JTnDCp3o/Km3rM9ZmbKXZTOVY0Qx6XppNF5C59MNS2s0WQ9c3OszsnWlvrSVJSkRXDu6t8alYGv9GnBS+LcewasTwGEd2Si0kwuFeAdSj+/S9CB6C400QodVQ40F9xqZtDvbkrApkVBfv5OWKqFiI/QFJY06LIZAoTaAK6zaWFAUvUnAHzhx1iqVcfOqpQDMguSW9bnQc6onMjpXTisMeoZMKXRpkUr7z7C0K1OaoC3ufWfaQ9gkZ3DRgtCqa8uymUqmAv3c3QWFeBsxtRJtRv/eick8du05HntVMlKLW9DllvU54wRqUX9f1e85snVlaICbAqwmZDmfwLMpyRlU6CRIwN+2cXloXMOXj76IDdcsqUwGdIGA7vdRy46aGtQs6k9bFUoxTSy8JuUga1bQ9THhTi5ssz90fmsvuuProtEfO5KnqbwLoU+8Bfj9iROTeaMP8rcffyrQrGl7vLseP0YB3kZcTXXe8JLW5uBbKHM2L31X83UxabFes7sprmJk60qzFizA+LfCG+BMF4qVe909B68pFyhNUA+ObsFzY9uMfl0dTlr/CjMt9zM8lAv0Cbvf6/z/oddHgxsh/+zYNuO94cfkl3fRHT/MPUK6B2riMWPKz1zQl9I+ZCaiRJ83s783qZ/8dMGcwjNQLRR27ztplT9sk/42/q3TlbKugF7b9FYbA8ILDOm0UaXs6/FHKeMZxUdrKkjkLrdxTYUdz/0+aF+2sSfeCZZtVHvYxM3dp3d89TZrIcmDQjxmTDPiqEJWcKXhRBidlDfe65hewC9fmq36PW1frgP9acyr4Amfm75mK6BdTMLMXXbnI0eb1rUOaI5ACXID6Pp/3zE+hbsefwqXivOR/cJh18dfWU3HgRNncffEsabEqXj3GVaXwKWZVde6pYd80mGxl5i5dnRv0/Zl2xDhuru+0tSXLYkH7+9pm+vvLZgTVnL02bFtkcYT9lK2LWrixVsUR4epmEvQWLzfLXRSNQGHbrCcTQ0CJyXY/varq7raLehL4fLsfCWHfeMbB/GdF39kFNDe8qy6MqlxI7DT6nXFgupFN2lwrTv+csqkOZiKvVCIx8jEZN6YkjQ44OBScb7mIQj6NXQvZt3LLo5uYPVy+8Z46qF3It4qY7YC2f09bTUp73GC7pcoHbBMx/e+lCcm85E1cbf06x8d+B6+99LF0PU3XbcED/3KO4xjUSg9NxdmigjKE3jT6xfhmbMXtX3c48JJC95+7SAOPnOudQctE6UYU8ZJ4b6b39qwIDdNOt1JJjXy5kMh3gbCbnSg2sQZlpbkfzHrXnb1Nj1pNgLgndctwXM/LEQy7w84KQwuWtDxLgFXQ3vuh4VKBywRYHqmiKxlBzn395yYzFtVbIvCov40nHSqpgWrF9uyvf1pweUOuKdIc0gB+IOyoPVbDjJOCgudNKZnzPcNEGyVGRxwMNB/pWTrta/N4OvfP1fVodBf25+EQyHeBoJu9OcMpk6TOS5K3ex24wq4b5+60NEBdm7TFjd3dnHGwcXLs9aTIFc7zEbcDrjye37p8Km2aG+ERMHv9mjGu8dJCXZ/qNQ/wNsO1p0M089ejUmIM8UsRoJKHZoYHsph8hM34AGLNJtOjTSdUwpff+Zc0wV4LpvBA9vX1aRC+XnT6xdZlYkd2bqyqvjFrptWY/cH11qXE3VF9nShGNn6USjO4Y7xKQpwkgj8ZXib0dGsOK9wzxPHq1JtpwtFnJ8pakv/Ej3UxGPE1P6xWcElnaqJx0nUnsyEkGSjcyP2YlQ8zeltIs4bzraTFCGEJJlsxgktXhXUtrakUD1Vk8nwptcvwq9vflND7+hWTSooxFtIK37UVqeyiAAJu1UIIT2Gm9LoDegEgN8cnwrMaPASxVoat7XVC4V4iwhK1XHrQ/trpQcJfJv66iZSABY6qcAe37aEpU2lBC1N6SGEkDCcVMn9FvXdZJuiaXJpRk3xtIFCvEUE+amdlFSV1nRSAkh16UrvLG5iMh/aFKNTGBxwMF0OSCGEkCRjWyzJlIFUT7Gl0DFRiJvxaru6FAcAVvm0hBBCuo+0CG69/upK4SNXXojBApkWwac/vLapJnUKcR+2hS6ctAAKVs0pCCGEdC8pAJISzFk2K2pmGVrmiXvwtgENozinKMAJIYRgHrAS4ECpjsRDh07Fnufek0KcrToJIYTEjQJi7+Hek0K8UyudEUII6S7iljc9KcSb2VOXEEIIMRG3vOlJIT6ydWVo/W1CCCGkETJOuil15oPoi3XvHYob9u9Gp7v1uFmXmxBCiNuhMOx7f6nXdtR179kUMx1BrUMJIYTYMTjgYNtb31DVJdAv7O585GgkpWnTdUtq2hsHlThthkD1piK7Sl5QjfY4YZ64Bb3YFYy0Hps69GFWoTe9fhFmLs/XvFw2r1qKx47kQ7MvNl23BF9/5lzopDVMI7GhGfvoVAacFBY4aWNxqGaXI04B1jXA/dg0EWmEqKVGo1SkvL2cb92rHcwAsxDvSXO6iZGtK61qkhNSLw9sX1d56djUXTatM3N5XvvC3DS23+r+PfT981AInyx4vwkSxu4EYvybp2tKCyexzkJaBBvfOIhD3z8feH0KxXkMLlqAnTeurhImfsFy7ejepoyrHgHupAS7P7QWu/edjE2I1+P7da9RWCOnbMapFEwZHsr1jNC2pScD20wMD+Vw381rkMtmICiZhJyUtHtYpM2kRfDA9nXIZpyG9pPLZqpeQLoAS//L0JSe4l8+MZmPZElyBdOcUrC9wxVQs27GSeOB7etwcHQL7h1eg90fWlt5fnLZDHZ/aC0GBxq7bq0ml83gmfveh4d+5R2YDzGZKAD56QLuevxY7EU96qb8o5nuJUHpHo+CkxZkM07ld663a9fwUA6Tn7gBz41twwPb12mfh103rY68316CmrgP/0zPb77ZvGppxc/j1llvVTtQEo0BJ4VCcd6oPXq7y+3ac9yopcwpVXNf3D1xDA8eOmU9Fp2m4g2w9JsH3fvONHZv2oquc14UoujJbjCPyZype35evjSr3ZfXRLpjfMp6HDbm+UX9acwrVF0TJyV41cI+TM8UsTjAtOwVdstCuve5FIpz2L3vpFGQDQ44kd4TgwMO/m9htimBtsU5hd37ThrPxX2v2d7PaRHs/mBz64IDwc8DMUMhHkIj5puJyTzuGJ9q7oACSAnwBx9eF3uzFjdoxfTQ63xjYbXq3YYBQOPNZp7zdA8KEnAKwGNH8thwzRIsWtBnfKnnNHmeG65Zgr/6dh4XL4cLzqBAGN39FSaU/ROCVlYgNPk9Tb7K3ftOGs3p7rUPmqz4fcApAJn+dOh1n1cK99381kCBYLJceCdIUVxsQUU9dt64usb/66QF23/i6poYhoyTxs4bV+Pw8+ciTRTDxnb/9nXa3tfe6/Lwk6crmTob3zgYKZCsGdBcHh0K8ZhwX8TNxJ3N6wKjnHT17LhR377JVyooWR4OnDiLH3t1P/7+R5dr1rn2tbVCz304TRkAc0rhrseP4b6b11SEhF8wXPvaTKiP0hW43m2zAw4W9KW0QtrVoIJewH4NOormK0DkvsJBQjmbcbDrpmr/a9DYcz7rUaqBNEpB6bfdNLY/sL+9a14OG1uhOBcaoSy+yLB5wGriVCiWRH/QtdcJaP8ESacdXnxlVnsvBRX1CNIy3YmMbgIUhttdK+z3XeZx5ZgmNvcOr6lp1hFnIFkvB6k1E0anx0SQfzLjpHHL+hy+fPTFmpfBgr4U5udrm65sum4JHvqVd1Q+hz0AfiF2oVC0jpL1alteDTpKlLFJ+wzz2wZpejaC84Ht6wDUTmIyTtq4rcBsNh0ccDD5iRuqxhElNSabcbBoQZ/RXH5muoCFZbO/De69403dMQkV18vpP66t6XpRfxozl+fCI9gN0fbuhCrMqhJX9LqN1liPILl74hgeOnSqasxxaKhhKa/+iTugf07i1p7rISnj7CSYYtZigh5AN0LZdCO/bfnimvSfRm7woU9+1dofZypiUE/6nW7MYcJYADzrMYe72Bw/m3EwtfMG47qmdJ/BAQdKoUYQ+lsJRvU9OykBBFUmVFcI+6O4o1Cv0Atzg3hx0oJF/WYXgw0CaE24rSQtgnmlqqwG3mho91oODji4VJwLnUw5KQAiVb9p1JaTQbnH/vEFIeXB63Kw/fvPluN33FQ4r3UmaPISl7Zsekb9k2ZyBaaYtRiTZpfzmbX8L7dCca6S/uNf7g+csXnAJibzkQJq3OPmpwv4zbI/f3goV1cRf92Y3b9N2qzJJBkmwL1RrKaxzquScPL7JV++NKsVqF6fuWvetBVGaSkFUfmvfaE417Cfs95p9/mZov2xNZOaqLgdnExWp1bg3mP56QJGHj2KuXlVNZFz/7R9RkoyvvoXUAAOnDirXV/nEvJO0KvG96Wj0VpdllfLTxewY3wKh58/h3uHS5Pmw8+fw0OHTlX27732+elC1X3gdX8ETbhN69WD6Xk+P1PExGSe2ngEKMRjQudvc1KCmcuzWDG6NzDq1WSq9Qon0wN2+PlzVdHzFy6Fv5xy2Qxe+r8F+JWQeQB3jE/hzkeO1i04dALVfcnoBMrmVUtrlk1M5kNzlL3m4iC/76L+virztskU7eKdiESZyMwpleishWblduenC3jsSB733VzSUnc8MhVa6CYubIqK1Ivu3tA9o0GT0Uauudu7esM1S4Dy31H2VijOYdee41XC06RkBEXh2xJUn6AZ++8lKMRjwh9Esjjj4OLl2cqLPaqPGajWUk0PmPfhtdF8Mk46NL2kkTSXlIh2Zm3SXNzlXg0mJaK9Tq651mtGvOvxY4HjvVAoYmrnFXPdCosiHO4L2jbdiFTjvvgPjm4JTOVLMtkBpybgr5VZA0B17+p6ntjpQrUWbFujoB6CnlG2io4GhXiMeNMlNo3tr3l5RXnQbIuARNlnzvOyaZRMyVlY89KaUwojXzqK3378KcyUVf2gnNkz04WacoymB14BoZqDH7+53kYwu9uMbF0ZKZ+ZXMG9Xy90oQB3XTLeCXorU0u9NCoAvVpwUF55o+QCnju2io4GhXiLaOTh0kV6BxWriEozZr6XivO4f/s6ra+76Iu2DzIzKwC/+ciUVSS9P3877Dz87oyRrStDrRBOSiqTJ6+vkYI8GiLAunu+2tbr5o+HaBZxmukjI+VgvTqH5ArWick8Zi7XFumxKa/qz4xRqjR588btjGxdqa2b7n3e6iWoQFc3prIxOr1FNNJcxV+8pJnFXJy0YHZeNeyndKOAo+ym0dQiN9rca1Fotrlbl8YTtVpbPbS6aciAk0Jhdr5t/upW4KRQE/dBask4KczOqxoB66YSBhUvsilU5GasTEzmqyLx3RoIQP1V22wySJKaysYUszZw98SxSgUkKc+QvRqmzYs6LYJn7nsfgMbLa3Yi7OFOSPLQpdVFqaGQzTh4/9o3YO9TL1YJ8fevfYO2gp2N0I1y/Kgd1zoBCvEWE6atuTm7Nm0jXU08qjbvtkk8P1OsS1jaTDIaabXYzS0qCekF3JoXugI4zcRUNMklqoJjqkfRyTBPvMU8/OTpwO8vFeex4ZollZKLJuHs9ftG9V0PLlpQNduMMglwTWZhhScamQNSgBOSbO554jiA6CltUZkuFCsxQLp89aiZAN0UPMdWpDERpvV68y0Pjm4xtuHzBnlEvfH8Ql/X+tLEwdEtGB7KBQrwXDbTVQ8DISQa52eKgQ1s4sJ9f7pEUXDq6X3eyVCIx4RNf17vjefvZa7r0asTwkFH8QtY7zGawcjWlZEmBi65bMZ6DFHWbRZReysT0ss0O5jU9unzt4zVkRbB7RuXB75Xkw7N6TFx6/VXh0Yw64Rs0M2l60K0edVSjH/rtHWqhnuMoDap2YxT9bculS3jpGpytL1jMvn6vbPgMB+W9xxaFdAnAJ6573247fPfwMFnzsV+PEJINe+8bgme+2Gh8j6Z8RTJ8qJQchG6ykSvNlShEI8JN2rTjU73U69JRyfoN1yzRJuqETYh0JU+dVJSSfMAgF03rcbIl45W5Xk7KcF9N781dEymJg/edU0+94yTwn03v7VmohB3xbRl2QwmJvP45nPnYz0OIb2MG4n++JEXKkWgXL596kKV8A0KWnP94/fdvAb33bymJ1ubMjq9RXRq71zbJipxjd0UbBeWAmITjTo44GCgv89a8LtlXOudLPjH3EhtAEK6EdtnxL9eWH2MJKaMRYXR6W0mzFTeLmzGFefY663P7HUtmB7s6ZkiJj9xA661qI8OXCnjuqPOkpn+MbMGNCHV2D4j/uXuO8jU4rmXnzUGtpG2YgpIsYl6dyP7TYFv7j4GBxzt937c/dQbca+ryx4HjLsjScX2GWnW8l6AQpy0FV10e9R4gbB97LxxNZx0sOTzrj+ydWXo+kHbe8fVTHkrAG7fuBzP3ret5RH7pHdp1j2sC7aN+vw3433RbVCIk7Zik1rX6D6Gh3LY/cG1Vd8HpZ246wdp8E5akM04gWMeHsrhto3La16CGSeN2zcuD9x/CqgZ4/3b11UCJutJ7Qsi46Qw4HT+6yDi3IpYkk5duZ+zGQeDA1fu7ds2Ltemtr7p9Ysq6ZgpcTsZ6oV+xklh94fWap+RKM9/M94X3QYD2wixoJHgvriCB/3doi4V51DwtHt9yxtejW98/1xVWVy3FO/0TDH0OEHdoHTdqQBU9Qv3lv11m2e4eEvuDjgp9PelMV24Uh7Y/39dZoO3N4FLWgS3Xn81gCuZIe6ye4fXaM/psSMvVK5bSoCPXL+8sm5YxUIdGSeFW9ZfVVUX3F0+N69wOYauZyLAbYZxZ5wUBKhEgS/qT2Neqap7ZeeNwdksUe7PTg3iTTqsnU46Cj7ohBBiD6PTScfgTw/T1UImhBASTuc7wUjXoWtW4K+FTAghJBwKcdJy6s0NJ4QQUg2FOGk5zPUkhJDmEKsQF5H3iMhJEXlaREY134uI/Pvy90+JyNviHA/pDJjrSQghzSG2wDYRSQP4IwA/A+AFAN8SkT1Kqe94VnsvgDeV/10P4E/K/yddjK4bG6PTCSEkOnFGp78dwNNKqe8DgIh8EcAHAHiF+AcA/IUq5bkdEpGsiLxBKfVijOMiHUCn1pInhJAkEac5PQfgtOfzC+VlUdchhBBCiIY4hbiu+p6/sozNOhCRj4vIYRE5fPbs2aYMjhBCCEk6cQrxFwBc7fl8FYAzdawDpdTnlFIblFIbli5d2vSBEkIIIUkkTiH+LQBvEpEVItIP4OcA7PGtswfAL5Sj1DcCuEB/OCGEEGJHbIFtSqlZEfkNAPsApAH8mVLquIj8avn7zwL4CoD3AXgawAyAX4prPIQQQki3EWvtdKXUV1AS1N5ln/X8rQD8epxjIIQQQroVVmwjhBBCEgqFOCGEEJJQKMQJIYSQhEIhTgghhCQUCnFCCCEkoVCIE0IIIQmFQpwQQghJKBTihBBCSEKhECeEEEISipSKpiUHETkL4PkGdvE6AP/QpOF0MjzP7qJXzhPonXPleXYXcZ/nNUqpmg5giRPijSIih5VSG9o9jrjheXYXvXKeQO+cK8+zu2jXedKcTgghhCQUCnFCCCEkofSiEP9cuwfQInie3UWvnCfQO+fK8+wu2nKePecTJ4QQQrqFXtTECSGEkK6g64S4iKRFZFJEvlz+vERE/oeIfK/8/0HPuneJyNMiclJEtrZv1NERkayIPCoiJ0TkuyLyjm48VxHZISLHReTvRORhEVnYLecpIn8mIi+JyN95lkU+NxFZLyLHyt/9exGRVp9LEIbz3F2+d58Skb8Skaznu645T893/1pElIi8zrMskecJmM9VRP5F+XyOi8i/8yxP5Lka7t11InJIRKZE5LCIvN3zXevPUynVVf8A/CaALwD4cvnzvwMwWv57FMDvlf9+C4CjABYAWAHgGQDpdo8/wnn+FwAfK//dDyDbbecKIAfgWQCZ8udHAHy0W84TwD8F8DYAf+dZFvncAHwTwDsACID/BuC97T43i/O8AUBf+e/f69bzLC+/GsA+lOpbvC7p5xnwm24G8D8BLCh/fn3Sz9Vwnl91xwngfQD+up3n2VWauIhcBWAbgD/1LP4ASgIP5f8Pe5Z/USn1ilLqWQBPA3g7EoCIvAalm+s/AYBS6rJSahpdeK4A+gBkRKQPwACAM+iS81RK/S2Ac77Fkc5NRN4A4DVKqW+o0tviLzzbdAS681RKfVUpNVv+eAjAVeW/u+o8y9wP4N8A8AYgJfY8AeO5/hqAMaXUK+V1XiovT+y5Gs5TAXhN+e/FKL2TgDadZ1cJcQAPoPSwzHuW/ZhS6kUAKP//9eXlOQCnPeu9UF6WBN4I4CyA/ywl18GfisgidNm5KqXyAH4fwCkALwK4oJT6KrrsPH1EPbdc+W//8iTx/6CknQBddp4ichOAvFLqqO+rrjrPMm8G8FMi8qSI/I2I/ER5ebed6x0AdovIaZTeT3eVl7flPLtGiIvI+wG8pJQ6YruJZllSQvX7UDLx/IlSagjARZRMryYSea5lf/AHUDJNLQOwSERuD9pEs6zjz9MS07kl+pxF5HcAzAJ4yF2kWS2R5ykiAwB+B8AndF9rliXyPD30ARgEsBHACIBHyr7fbjvXXwOwQyl1NYAdKFtE0abz7BohDmATgJtE5DkAXwSwRUQeBPD3ZXMGyv93TTwvoOSrcrkKV8winc4LAF5QSj1Z/vwoSkK92871nwF4Vil1VilVBPA4gHei+87TS9RzewFXTNHe5R2PiPwigPcDuK1sZgS66zyvQ2kCerT8XroKwLdF5B+hu87T5QUAj6sS30TJIvo6dN+5/iJK7yIA+BKuuOzacp5dI8SVUncppa5SSl0L4OcA7FdK3Q5gD0oXHeX//9fy33sA/JyILBCRFQDehFLwQcejlPoBgNMisrK86N0AvoPuO9dTADaKyEB5Rv9uAN9F952nl0jnVja5/0hENpav0S94tulYROQ9AH4LwE1KqRnPV11znkqpY0qp1yulri2/l14A8Lby89s15+lhAsAWABCRN6MUcPsP6L5zPQPgp8t/bwHwvfLf7TnPuKL62vkPwLtwJTr9tQC+Vr7QXwOwxLPe76AUQXgSHRYVaXGO6wAcBvAUSg/PYDeeK4B7AJwA8HcA/hKlyM+uOE8AD6Pk6y+i9IL/5XrODcCG8vV5BsBnUC7i1Cn/DOf5NEr+w6nyv89243n6vn8O5ej0JJ9nwG/aD+DB8ti/DWBL0s/VcJ4/CeAISpHoTwJY387zZMU2QgghJKF0jTmdEEII6TUoxAkhhJCEQiFOCCGEJBQKcUIIISShUIgTQgghCYVCnJAeQkTmyt2XjorIt0XknSHr/3arxkYIiQ5TzAjpIUTkZaXUq8p/bwXw20qpn7ZZ37dcUHp/zGs2I4S0CGrihPQurwFwHiiVeBWRvy1r6X8nIj8lImModZCbEpGHRORaKfWu/2OUinlcLSJ/Uu6pfFxE7inv690i8lfuQUTkZ0Tkcd0ACCGNQU2ckB5CROYAHAOwEMAbUKqqdURE7gSwUCn1KRFJAxhQSv3Ip7lfC+D7AN6plDpUXrZEKXWuvM3XAPzL8v6/C+CnlFJnReQLAB5WSj3R4tMlpOuhJk5Ib1FQSq1TSq0C8B4Af1E2jX8LwC+JyC4Aa5RSPzJs/7wrwMt8WES+DWASwGoAb1ElzeAvAdwuIlkA78CVVqOEkCZCIU5Ij6KU+gZKXaaWKqX+FsA/BZAH8Jci8guGzS66f5SbPPxrAO9WSr0VwF6UNHwA+M8AbgdwK4AvKaVm4zkLQnobCnFCehQRWQUgDeCHInINgJeUUp9HqT/y28qrFUXEMeziNSgJ9Qsi8mMA3ut+oZQ6g1K3p7sB/Hk8Z0AI6Wv3AAghLSUjIlPlvwXALyql5kTkXQBGRKQI4GWU2iUCwOcAPFU2mf+Od0dKqaMiMgngOEq+8oO+Yz2Ekpb/nThOhBDCwDZCSEyIyGcATCql/lO7x0JIt0IhTghpOiJyBCVT+88opV5p93gI6VYoxAkhhJCEwsA2QgghJKFQiBNCCCEJhUKcEEIISSgU4oQQQkhCoRAnhBBCEgqFOCGEEJJQ/n8tUuNIifUzewAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "noise = generate_noise(shape=(25_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "plt.scatter(bstray.detach().cpu(), kdiff.detach().cpu())\n", + "plt.title(\"Randomly generated points after GP reshaping\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Kdiff\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Noise 0\n", + "[ 0.24565518 -0.6735562 -0.6820123 -0.30239606 0.13808942 0.06237304\n", + " 0.47562933 -0.26713276 -0.30655646 0.39100182]\n", + "\n", + "Generated Parameters 0\n", + "[0.702335 0.34781444 0.57425386 0.5266448 0.26035246 0.40736917\n", + " 0.4116256 0.69801396 0.40880558 0.53860617]\n", + "\n", + "Noise 1\n", + "[ 0.4715091 0.41461003 -0.7330948 0.81700337 -0.51812625 0.9896945\n", + " -0.46604025 0.47938716 0.75696623 0.45069325]\n", + "\n", + "Generated Parameters 1\n", + "[0.36198774 0.49668956 0.35663775 0.69322485 0.69104296 0.67214054\n", + " 0.869215 0.38502902 0.56286055 0.55255723]\n", + "\n", + "Noise 2\n", + "[-0.4618963 -0.45698416 -0.7908933 0.876065 0.41900563 0.4613037\n", + " -0.12351763 -0.23291111 -0.09601641 0.28420484]\n", + "\n", + "Generated Parameters 2\n", + "[0.41703537 0.670122 0.5969795 0.30887535 0.5411988 0.46432352\n", + " 0.22302881 0.4069521 0.55901086 0.39946315]\n", + "\n" + ] + } + ], + "source": [ + "noise = generate_noise(shape=(3, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "noise = noise.cpu().numpy()\n", + "gp = gp.cpu().detach().numpy()\n", + "\n", + "for i in range(3):\n", + " print(f\"Noise {i}\")\n", + " print(noise[i])\n", + " print()\n", + " print(f\"Generated Parameters {i}\")\n", + " print(gp[i])\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"KDiff Error During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Coupling %\")\n", + "\n", + "plt.plot(numpy.arange(len(kdiffs)), kdiffs)\n", + "plt.show()\n", + "\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"BStray Error During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"BStray\")\n", + "\n", + "plt.plot(numpy.arange(len(bstrays)), bstrays)\n", + "plt.show()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "optimization_date4_22_22.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 2a5ad38f42b77c65290ce3542ba8e77ebf26a6a6 Mon Sep 17 00:00:00 2001 From: AndrewCurtis27 <89710614+AndrewCurtis27@users.noreply.github.com> Date: Tue, 25 Oct 2022 08:37:25 -0600 Subject: [PATCH 4/8] Add files via upload --- optimization 10_20_22.ipynb | 2104 +++++++++++++++++++++++++++++++++++ 1 file changed, 2104 insertions(+) create mode 100644 optimization 10_20_22.ipynb diff --git a/optimization 10_20_22.ipynb b/optimization 10_20_22.ipynb new file mode 100644 index 0000000..c9326b0 --- /dev/null +++ b/optimization 10_20_22.ipynb @@ -0,0 +1,2104 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iZN2u5WKRFqR", + "outputId": "6842718b-36d3-4c1c-b523-796de23e5470" + }, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "import os\n", + "import random\n", + "import pandas\n", + "import numpy\n", + "import math\n", + "from scipy.optimize import curve_fit\n", + "from scipy.interpolate import interp1d\n", + "from scipy.interpolate import UnivariateSpline\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "\n", + "# seed = 7777\n", + "# random.seed(seed) \n", + "# torch.manual_seed(seed);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load Saved MP (Magnetic Parameter) Model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "B-TMPJtCKIM6" + }, + "outputs": [], + "source": [ + "# 12 input -> 42 output\n", + "class MpModel(torch.nn.Module):\n", + " def __init__(self):\n", + " super(MpModel, self).__init__()\n", + "\n", + " self.linear1 = torch.nn.Linear(12, 100, bias=True)\n", + " self.linear2 = torch.nn.Linear(100, 100, bias=True)\n", + " self.linear3 = torch.nn.Linear(100, 42, bias=True)\n", + "\n", + " def forward(self, x):\n", + " x = torch.atan(self.linear1(x))\n", + " x = torch.atan(self.linear2(x))\n", + " x = torch.atan(self.linear2(x))\n", + " x = self.linear3(x)\n", + " return x\n", + "\n", + "\n", + "mp_model = MpModel().to(\"cuda\")\n", + "mp_model.load_state_dict(torch.load(\"saved_model_state.pt\"))\n", + "\n", + "for param in mp_model.parameters():\n", + " param.requires_grad = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create GP (Geometry Paramter) Generator" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "z9cggAl1BGHo" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\lazy.py:178: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.\n", + " warnings.warn('Lazy modules are a new feature under heavy development '\n" + ] + } + ], + "source": [ + "class GpModel(torch.nn.Module):\n", + " def __init__(self, input_length: int):\n", + " super(GpModel, self).__init__()\n", + "\n", + " self.dense_layer1 = torch.nn.Linear(int(input_length), 128)\n", + " self.dense_layer2 = torch.nn.Linear(128, 256)\n", + " # self.dense_layer3 = torch.nn.Linear(256, 512)\n", + " # self.dense_layer4 = torch.nn.Linear(512, 256)\n", + " self.dense_layer5 = torch.nn.Linear(256, 128)\n", + " self.dense_layer6 = torch.nn.Linear(128, int(input_length))\n", + " \n", + " self.batch_norm1 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm2 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm3 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm4 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm5 = torch.nn.LazyBatchNorm1d()\n", + "\n", + " def forward(self, x):\n", + " x = self.batch_norm1(torch.atan(self.dense_layer1(x)))\n", + " x = self.batch_norm2(torch.atan(self.dense_layer2(x)))\n", + " # x = self.batch_norm3(torch.atan(self.dense_layer3(x)))\n", + " # x = self.batch_norm4(torch.atan(self.dense_layer4(x)))\n", + " x = self.batch_norm5(torch.atan(self.dense_layer5(x)))\n", + " x = torch.sigmoid(self.dense_layer6(x))\n", + " return x\n", + "\n", + "\n", + "gp_model = GpModel(10).to(\"cuda\")\n", + "optimizer = torch.optim.Adam(gp_model.parameters(), lr=3e-4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# System Constraints" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "Bse1wsKN1NxY" + }, + "outputs": [], + "source": [ + "# x-direction of the primary winding (mm)\n", + "lpx_min, lpx_max = (50.0, 650.0)\n", + "\n", + "# y-direction of the primary winding (mm)\n", + "lpy_min, lpy_max = (50.0, 2050.0)\n", + "\n", + "# width of the primary winding (mm)\n", + "wp_min, wp_max = (25.0, 325.0)\n", + "\n", + "# length of the edge of the primary core (mm)\n", + "a_min, a_max = (0.0, 200.0)\n", + "\n", + "# pitch of the adjacent cores (mm)\n", + "p_min, p_max = (0.0, 200.0)\n", + "\n", + "# length of the secondary winding (mm)\n", + "ls_min, ls_max = (50.0, 450.0)\n", + "\n", + "# width of the secondary winding (mm)\n", + "ws_min, ws_max = (25.0, 225.0)\n", + "\n", + "# input RMS current (A)\n", + "ip_min, ip_max = (50.0, 200.0)\n", + "\n", + "# turn number of the primary side\n", + "np_min, np_max = (4.0, 10.0)\n", + "\n", + "# turn number of the secondary side\n", + "ns_min, ns_max = (4.0, 10.0)\n", + "\n", + "# switching frequency (kHz)\n", + "f = 85 * (10 ** 3)\n", + "\n", + "# output power at the center (kW)\n", + "p_out = 50 * (10 ** 3)\n", + "\n", + "# input DC voltage (V)\n", + "v_dc = 400\n", + "\n", + "# output DC voltage (V)\n", + "v_bat = 400\n", + "\n", + "# length of the edge of the secondary core\n", + "b = 50\n", + "\n", + "# ???\n", + "w = 2 * numpy.pi * f # [rad/s]\n", + "\n", + "# ???\n", + "q_coil = 400" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "brlwMfzj1Nxh" + }, + "outputs": [], + "source": [ + "# Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive.\n", + "# Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch.\n", + "\n", + "@torch.jit.script\n", + "def linspace(start: torch.Tensor, stop: torch.Tensor, num: int):\n", + " # create a tensor of 'num' steps from 0 to 1\n", + " steps = torch.arange(num, dtype=torch.float32, device=start.device) / (num - 1)\n", + "\n", + " # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings\n", + " # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript\n", + " # \"cannot statically infer the expected size of a list in this contex\", hence the code below\n", + " for i in range(start.ndim):\n", + " steps = steps.unsqueeze(-1)\n", + "\n", + " # the output starts at 'start' and increments until 'stop' in each dimension\n", + " out = start[None] + steps * (stop - start)[None]\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "dMoXnTFV8U1P" + }, + "outputs": [], + "source": [ + "# Generate a pytorch tensor full of random floats in the range [-1, +1] with the given shape\n", + "\n", + "def generate_noise(shape):\n", + " return torch.distributions.uniform.Uniform(-1, +1).sample(shape).cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "xloGE25R8Jtm" + }, + "outputs": [], + "source": [ + "def stack_parameters(*params):\n", + " return torch.stack(params).transpose(0, 1)\n", + "\n", + "\n", + "# Scale an array to be between our specified [min, max] contraints\n", + "def scale(arr):\n", + " scaler_min = torch.tensor(\n", + " [a_min, lpx_min, lpy_min, ls_min, p_min, wp_min, ws_min, ip_min, np_min, ns_min],\n", + " device=\"cuda\",\n", + " )\n", + " scaler_max = torch.tensor(\n", + " [a_max, lpx_max, lpy_max, ls_max, p_max, wp_max, ws_max, ip_max, np_max, ns_max],\n", + " device=\"cuda\",\n", + " )\n", + "\n", + " return arr * (scaler_max - scaler_min) + scaler_min\n", + "\n", + "\n", + "def extract(arr):\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns = [\n", + " numpy.squeeze(x) for x in numpy.hsplit(arr, 10)\n", + " ]\n", + "\n", + " ys_min = (\n", + " 5 * wp +\n", + " 4 * a +\n", + " 3 * lpy +\n", + " 2 * p\n", + " )\n", + "\n", + " ys_max = (lpy + wp)\n", + " ys_max = ys_min + (ys_min - ys_max) / 2\n", + "\n", + " ys_min /= 2\n", + " ys_max /= 2\n", + "\n", + " ys = linspace(ys_min, ys_max, num=5)\n", + "\n", + " np = torch.round(np)\n", + " ns = torch.round(ns)\n", + "\n", + " # take all 15 tensors of shape (X) and convert to (15, X)\n", + " return a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys\n", + "\n", + "\n", + "def scale_and_extract(arr):\n", + " scaled_array = scale(arr)\n", + " return extract(scaled_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "44TlTSnclCKs", + "outputId": "c2d4a61b-35c8-42ad-99f9-98c99022a0b2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " GP Parameters Shape: torch.Size([256, 12])\n", + " Extra Parameters Shape: torch.Size([256, 3])\n", + " MP Model Output Shape: torch.Size([256, 42])\n", + " \n" + ] + } + ], + "source": [ + "# Enclose in a function so we don't leak variables\n", + "def test_models():\n", + " noise = generate_noise(shape=(256, 10))\n", + " gp = gp_model(noise)\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "\n", + " gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + " extra_parameters = stack_parameters(ip, np, ns)\n", + "\n", + " mp = mp_model(gp_parameters)\n", + "\n", + " print(f\"\"\"\n", + " GP Parameters Shape: {gp_parameters.shape}\n", + " Extra Parameters Shape: {extra_parameters.shape}\n", + " MP Model Output Shape: {mp.shape}\n", + " \"\"\")\n", + "test_models()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "eEb7J5bOFEEr" + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "Method to take two equally-sized lists and return just the elements which lie \n", + "on the Pareto frontier, sorted into order.\n", + "Default behaviour is to find the maximum for both X and Y, but the option is\n", + "available to specify maxX = False or maxY = False to find the minimum for either\n", + "or both of the parameters.\n", + "\"\"\"\n", + "\n", + "\n", + "def pareto_frontier(x, y, max_x=False):\n", + " if max_x:\n", + " return _pareto_frontier(y, x)\n", + " else:\n", + " return _pareto_frontier(x, y)\n", + "\n", + "\n", + "def _pareto_frontier(x, y):\n", + " # Combine inputs and sort them with smallest X values coming first\n", + " sorted_xy = sorted(zip(x, y))\n", + " p_front_x = torch.zeros(len(x), device=\"cuda\")\n", + " p_front_y = torch.zeros(len(y), device=\"cuda\")\n", + " \n", + " # Loop through the sorted list\n", + " # Look for lower values of Y…\n", + " # and add them to the Pareto frontier\n", + " min_y = sorted_xy[0][1]\n", + " for index in range(len(sorted_xy)):\n", + " x, y = sorted_xy[index]\n", + " if y < min_y:\n", + " min_y = y\n", + " p_front_x[index] = x\n", + " p_front_y[index] = y\n", + " \n", + " p_front_x = p_front_x[p_front_x.nonzero()]\n", + " p_front_y = p_front_y[p_front_y.nonzero()]\n", + "\n", + " return p_front_x, p_front_y" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "#testing for creating total pareto f\n", + "\n", + "def oldgraph_pareto_frontier(Xs, Ys, maxX = False, maxY = False):\n", + "# Sort the list in either ascending or descending order of X\n", + " myList = sorted([[Xs[i], Ys[i]] for i in range(len(Xs))], reverse=maxX)\n", + "# Start the Pareto frontier with the first value in the sorted list\n", + " p_front = [myList[0]] \n", + "# Loop through the sorted list\n", + " for pair in myList[1:]:\n", + " if pair[1] <= p_front[-1][1]: # Look for lower values of Y…\n", + " p_front.append(pair) # … and add them to the Pareto frontier\n", + "# Turn resulting pairs back into a list of Xs and Ys\n", + " p_frontX = [pair[0] for pair in p_front]\n", + " p_frontY = [pair[1] for pair in p_front]\n", + " return p_frontX, p_frontY" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "def findSpline(x, y, smoothing_factor = 2):\n", + " spl = UnivariateSpline(x, y)\n", + " spl.set_smoothing_factor(smoothing_factor)\n", + " return spl" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def pair_loss_calc(x, y, x_numpy, y_numpy, pareto_x_numpy, pareto_y_numpy, pareto_x_forx_numpy, pareto_y_forx_numpy):\n", + " if len(pareto_y_numpy) > 3:\n", + " # f = interp1d(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0])\n", + " # f2 = interp1d(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0], kind = 'cubic')\n", + " # plt.plot(pareto_bstray_numpy[:,0], f2(pareto_bstray_numpy[:,0]), '--', pareto_bstray_numpy, pareto_kdiff_numpy, 'o')\n", + "\n", + "\n", + " spl_y = findSpline(pareto_x_numpy[:,0], pareto_y_numpy[:,0])\n", + " y_calc = torch.as_tensor(spl_y(x_numpy)).cuda()\n", + " # plt.plot(pareto_bstray_numpy[:,0], spl_y(pareto_bstray_numpy[:,0]), '--', pareto_bstray_fory_numpy, pareto_kdiff_fory_numpy, 'o')\n", + " # plt.show()\n", + " y_loss = y - y_calc\n", + " y_loss_relu = torch.nn.functional.relu(y_loss)\n", + "\n", + "\n", + " spl_x = findSpline(pareto_y_forx_numpy[:,0], pareto_x_forx_numpy[:,0])\n", + " # plt.plot(spl_x(pareto_kdiff_fory_numpy[:,0]), pareto_kdiff_fory_numpy[:,0], '--', pareto_bstray_fory_numpy, pareto_kdiff_fory_numpy, 'o')\n", + " # plt.show()\n", + "\n", + "\n", + " x_calc = torch.as_tensor(spl_x(y_numpy)).cuda()\n", + " x_loss = x - x_calc\n", + " x_loss_relu = torch.nn.functional.relu(x_loss)\n", + " \n", + "\n", + " if len(pareto_x_numpy) > 3 and len(pareto_y_numpy) > 3:\n", + " combined_loss = torch.sum(y_loss_relu * x_loss_relu)\n", + " else: \n", + " combined_loss = torch.zeros(1, requires_grad=True, device = \"cuda\")\n", + " # print('No new pareto points found, used zero grad')\n", + " \n", + " \n", + " return combined_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_pairloss_and_pareto(x, y):\n", + " \n", + " pareto_x, pareto_y = pareto_frontier(x, y)\n", + " pareto_y_forx, pareto_x_forx = pareto_frontier(y, x)\n", + " \n", + " pareto_x_numpy = pareto_x.cpu().data.numpy()\n", + " pareto_y_numpy = pareto_y.cpu().data.numpy()\n", + " \n", + " pareto_x_forx_numpy = pareto_x_forx.cpu().data.numpy()\n", + " pareto_y_forx_numpy = pareto_y_forx.cpu().data.numpy()\n", + " \n", + " x_numpy = x.cpu().data.numpy()\n", + " y_numpy = y.cpu().data.numpy()\n", + " \n", + " x_y_loss = pair_loss_calc(x, y, x_numpy, y_numpy, pareto_x_numpy, pareto_y_numpy, pareto_x_forx_numpy, pareto_y_forx_numpy)\n", + " \n", + " return x_y_loss, pareto_x, pareto_y, x_numpy, y_numpy, pareto_x_numpy, pareto_y_numpy, pareto_x_forx_numpy, pareto_y_forx_numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def pave_tomax(pavemin, pave_max_value=500):\n", + " pave_max = torch.sub(pave_max_value, pavemin)\n", + " return pave_max" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load DWPT data" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "jkTrlAJMH5UO" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1010, 12)\n", + "(1010, 42)\n" + ] + } + ], + "source": [ + "def load_dwpt():\n", + " folder_name = \"./dwpt_v5\"\n", + " file_names = [\n", + " \"DWPT_v5_N10\",\n", + " \"DWPT_v5_N100\",\n", + " \"DWPT_v5_N200\",\n", + " \"DWPT_v5_N300\",\n", + " \"DWPT_v5_N400\",\n", + " ]\n", + "\n", + " df = pandas.DataFrame()\n", + "\n", + " for file_name in file_names:\n", + " new_df = pandas.read_csv(f\"{folder_name}/{file_name}_after.csv\", index_col=0)\n", + " df = pandas.concat([df, new_df])\n", + "\n", + " return df\n", + "\n", + "df = load_dwpt()\n", + "df_gp = df.loc[:, :\"ys4[mm]\"]\n", + "df_mp = df.loc[:, \"k0mm_ys0\":]\n", + "\n", + "print(df_gp.shape)\n", + "print(df_mp.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define Loss Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "f = 85*10**3 #[Hz]\n", + "w =2*math.pi*f #[rad/s]\n", + "Pout= 50000 #W\n", + "Vdc = 400 #800 # V\n", + "Vbat = 400 #V\n", + "QCoil = 400\n", + "b = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def kdiff_loss(k_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + "\n", + " kdiff = abs(k_0_0 - k_1_0)\n", + " kdiff = kdiff / torch.max(abs(k_0_0), abs(k_1_0))\n", + "\n", + " return kdiff\n", + "\n", + "\n", + "def calculate_Is(l_parameters, k_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + "\n", + " a6 = lp_0_0 * np ** 2\n", + " a11 = ls_0_0 * ns ** 2\n", + " # Paper formula\n", + " n1 = (torch.pi * w * a6 * ip) / (2 * numpy.sqrt(2) * v_dc)\n", + " t1 = (torch.pi ** 2 * w * p_out * torch.sqrt(a6 * a11))\n", + " t2 = 8 * k_0_0 * n1 * v_dc * v_bat\n", + " n2 = t1 / t2\n", + " Is = (4 * v_bat * n2) / (torch.pi * w * a11)\n", + "\n", + " return Is\n", + " \n", + "def bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters):\n", + " # Unpack parameters\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " Is = calculate_Is(l_parameters,k_parameters)\n", + "\n", + " bx_100 = (\n", + " (bx_p_1_00 * ip * np + bx_s_1_00 * Is * ns) ** 2 +\n", + " (bx_p_1_90 * ip * np + bx_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " by_100 = (\n", + " (by_p_1_00 * ip * np + by_s_1_00 * Is * ns) ** 2 +\n", + " (by_p_1_90 * ip * np + by_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " bz_100 = (\n", + " (bz_p_1_00 * ip * np + bz_s_1_00 * Is * ns) ** 2 +\n", + " (bz_p_1_90 * ip * np + bz_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " b_100 = (bx_100**2 + by_100**2 + bz_100**2) ** 0.5\n", + "\n", + " bstray = b_100\n", + "\n", + " return bstray" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def power_average(k_parameters, l_parameters, b_parameters, extra_parameters, gp_parameters):\n", + " \n", + " return " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def number_of_inverters(gp_parameters):\n", + " (\n", + " a, lpx, lpy, ls, p, wp, ws, *ys\n", + " ) = gp_parameters.clone().transpose(0,1)\n", + " \n", + " number_of_inverters = 1/(lpy+2*wp+2*a+p)*10**3\n", + "\n", + " return number_of_inverters" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "def core_losses(extra_parameters, gp_parameters):\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " (\n", + " a, lpx, lpy, ls, p, wp, ws, *ys\n", + " ) = gp_parameters.transpose(0,1)\n", + " \n", + " V_PriCore = ((lpy +2*wp+2*a)*(lpx+2*wp+2*a)*5)/(10**3) # cm3\n", + " V_SecCore = (((ls+2*ws+2*b)**2)*5)/(10**3)\n", + " V_PriWind = (2*(lpx+wp)+2*(lpy+wp))*6.6*6.6/(10**3)*np\n", + " V_SecWind = 4*(ls+ws)*6.6*6.6/(10**3)*ns\n", + "\n", + " return V_PriCore, V_SecCore, V_PriWind, V_SecWind" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "def coil_loss_and_power_average(k_parameters, l_parameters, b_parameters, extra_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + "\n", + " a6 = lp_0_0 * np**2 # LP0MM_YSO\n", + " a7 = lp_0_1 * np**2 # LP0MM_YS1\n", + " a8 = lp_0_2 * np**2 # LP0MM_YS2\n", + " a9 = lp_0_3 * np**2 # LP0MM_YS3\n", + " a10 = lp_0_4 * np**2 # LP0MM_YS4\n", + "\n", + " a11 = ls_0_0 * ns**2 # LS0MM_YSO\n", + " a12 = ls_0_1 * ns**2 # LS0MM_YS1\n", + " a13 = ls_0_2 * ns**2 # LS0MM_YS2\n", + " a14 = ls_0_3 * ns**2 # LS0MM_YS3\n", + " a15 = ls_0_4 * ns**2 # LS0MM_YS4\n", + "\n", + " Is = calculate_Is(l_parameters,k_parameters)\n", + " \n", + " loss = (w*a6*ip**2/QCoil + w*a11*Is**2/QCoil) * 10 ** (-9)\n", + "\n", + " p0 = w*k_0_0*(a6*a11)**0.5*ip*Is\n", + " p1 = w*k_0_1*(a7*a12)**0.5*ip*Is\n", + " p2 = w*k_0_2*(a8*a13)**0.5*ip*Is\n", + " p3 = w*k_0_3*(a9*a14)**0.5*ip*Is\n", + " p4 = 2*w*k_0_4*(a10*a15)**0.5*ip*Is\n", + " pave = (p0+(2*p1)+(2*p2)+(2*p3)+p4)/8\n", + " pave_kW = pave / 1000\n", + "\n", + " return loss,pave_kW" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Network Training Loop" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "OXzpQf5O1Nxk", + "outputId": "1bc1a1b1-95ac-43d8-f1d8-5dd49e4bbb03" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(9.0761e+14, device='cuda:0', dtype=torch.float64,\n", + " grad_fn=)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0\n", + " Combined Loss: 907607329013455.3750000000\n", + " Kdiff : 22.3747%\n", + " Bstray : 50.1190\n", + " Pareto Kdiff : 6.3454%\n", + " Pareto Bstray : 54.2013\n", + " Coilloss : 2322.2939\n", + " num inv : 0.5983\n", + " Pareto Coilloss : 654.2814\n", + " Pareto num inv : 0.4552\n", + " V_SecCore : 1907.3503\n", + " pave : 47.3425\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\scipy\\interpolate\\fitpack2.py:280: UserWarning: \n", + "The maximal number of iterations maxit (set to 20 by the program)\n", + "allowed for finding a smoothing spline with fp=s has been reached: s\n", + "too small.\n", + "There is an approximation returned but the corresponding weighted sum\n", + "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n", + " warnings.warn(message)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(9.1657e+08, device='cuda:0', dtype=torch.float64,\n", + " grad_fn=)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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H3PCiM+tUS8auOWANwygjjSH0oxqRDx1yUTLVlMB0w4sm8A3DKBuNIfQ7V0LbtInjfiPy9IyJ+yaLRz832TMwDKOOaQybPsDAvvDxanLegquhA87kdKBvbEEa2GehmYZhFE3jCP3ILNZqQ8fnCgQzga1ypmEYRdIY5h0orLBZNWKVMw3DKILGEfqlSLxKtRZ/jVJQTY5nwzBqisYR+qUQlJmh4q9RCixxyzCMAmkcoV83glIsccswjIJpHKEfVplyHBKxXW2oOXENwyiYxone8QXlptUTa+O3pMdnwa6dXtGp5UX7/NzHGIZhRNA4mj44ob56B1x2N7TPRxH6MjN56w9vG689V60pyEw7hmEUR2MJfZ/OlXDDizzzpy/z/qE7+Pcp543t6+2GocOTN7dYzLRjGEZxNKbQ9/jtudNpa07x01c8c09vt0t+Kro1Ypkw045hGEXS0EK/tTnF6fOnjwn9zeuqp9pmNqmmZKad3m64fYnzS9y+JL7tomEYDUdOoS8i80XkByLyMxHZKiKf8cbXisirIvKc9/pg4JybRGS7iPxCRC4IjC8VkRe8fXeIyKSHyZy1qIOtu9/i8ODR6i7TkBlxi1K2EA8K+VsXwcbr8uu3axhGQ5Ekeuco8Beq+oyITAO2iMj3vH23q+qXggeLyCnA5cCpwEnAv4rIe1R1BLgTWAU8BTwGrAA2leZWCqNrYQcjme3s+tF9vBdhQtP0auLALthwjXsFm7/4TydhZqlc/XYNw2gocmr6qrpHVZ/xtg8CPwPmxpxyMfCgqg6q6g5gO3CWiMwBjlPVJ1VVgfuBS4q9gWI5c8F0UgInbfkiVS3wszmwyxVmS2KOOtBnZh/DMIA8bfoishA4A/iJN/RpEekVkXtExC9KPxcI2kn6vLG53nb2eNj3rBKRHhHp2bt3bz5TzJtpx7RwyknHcezga2X9nkklPcOZeYJmnw2rktfutwXDMOqGxEJfRI4FvgV8VlXfwplq3gWcDuwB/t4/NOR0jRmfOKi6XlW7VLVr1qxZSadYML+zsIM9enzZv2dS8LOQJzwRqKvdn0uA+xFNUX4CWxAMo6ZIJPRFpAUn8L+hqhsAVPU1VR1R1QxwN3CWd3gfEIwtnAfs9sbnhYxPOhen/o00R2rJuJMMaXKZxlENZNDcZZrDIpp8P0GuBcEwjKojSfSOAF8HfqaqtwXG5wQOuxR40dt+BLhcRNpEZBGwGHhaVfcAB0VkmXfNK4GNJbqPwunt5rRn19Ahh6q64k7epFrg0rucAzcuwzhXxFJUddIDffELgmEYVUmS6J33AR8FXhCR57yxvwT+REROx5loXgH+DEBVt4pIN/ASLvLnOi9yB+BTwL1AGhe1M6mROwBsXoccrdLY/GIIRsMuX+MifnLR2+0E9oG+sdaMUR3H2ufFLwiGYVQlOYW+qj5BuD3+sZhzbgZuDhnvAZbkM8GyU68Cym/6Dsk0b99U42vuvqnmtCvg2X901/NpanULwuZ10QuCYRhVSUNn5AK1K6Bap+YoFc2Y4I4z4UiTe48y1Wz53+MFPsDIUfceVq66JW1F4QyjijGhHyK4asKh29TmnLS56vHkiuPXERd1E7UwaCZkMONKVHeuDMxB3HuwRLVhGFVH49TTjyJQZ18H+hGqu4XKKAP9Y/b3YjmwC/LNRvazfztXmpA3jBrCNH1wQqt1am0I+yB+qGQUvulm4o6QsahUCsMw6gkT+j715tCVJlj6sYk291QL0QuFjplqIhcMj3RH8XM0DKPimND3qVWHbhR+lGy2zb1tWvQ57fPhhhdh7X4X49/UGn5cqgkuvLX4OVo2r2FUHBP6Pjkbp9cgW+4d7RLG2v3uPTI7l/FRN50rofXY8OPa2ou341s2r2FMCib0fTpXwmlXoPVk1x7NifPo7QaJ+JOnOyYK8qgFYqC/eM3csnkNY1Kw6J0g2x5HwuzdkoJU88R49Vrg1kXuPa4FZMqz36+dPpaJ65dviArl9DVzKEzrt2xew5gUTNMPEiVwVOHir4zZxmvJiTnQn7vnb2bEOyar7HKuJKtiNPMoH0q9+VYMo8owoR8kThD5tvHL1kM91uoZh7oGLUnq9RSqmVs2r2FMCib0g4QIokFp4+i5/21soJqbp08GcZp5XHSOZfMaxqRgNv0gvsDxMl0PH3MiN711Kct6dvEnP1iCHOijRoo0VIY4zTyqgBuM/c6WzWsYFUdcu9rqpaurS3t6eibt+//lG//Aub/8G6ZIDTpxy03X1bDtcWfiSXvdMgf2Oe1/6HC4L8HPBTAMo6yIyBZV7coeN00/Bx98/W7EBP5E0h3w/ANjmnxQwMdV9bToHMOYVMymnwOJFVLiShw3GpF9dxNg0TmGMamY0M9FhJB6q+1ERtbsq63wzXzwa+9kv/sO17jM3igsOscwJh0z7+Ri+ZrxDklcRM9/PXgZr/6vJ/nmgV21mcPb1AYjg9H7F74frnoken9U16wg6Q73JBRsv5iP4zasfaM5fg2jKEzo5yIroof2ebQuX8N5I+/jyY13ojq+HW3NECfwAXb82AndKCEbshiOoyXtirIVKqSTRP8YhpE3Fr1TBEf//lSaDyZwTEqT05x3/JiaCvnMFWkT1MSzo3fCtPJ8NPeobl4W/WMYiSg4ekdE5gP3AycCGWC9qv6DiHQA/wdYCLwCrFTVfd45NwFXAyPA9ar6XW98KXAvkMY1Vv+MVvuqE0PzwVeTHagjsONH5Z1MOcgVaZNPnH2+mrvV5jGMspDEkXsU+AtV/S1gGXCdiJwC3AhsVtXFwGbvM96+y4FTgRXAV0VGO3LcCawCFnuvFSW8l8pT75EoxdxfdjbuptXhVTU3rQ7P2rXaPIZRFnIKfVXdo6rPeNsHgZ8Bc4GLgfu8w+4DLvG2LwYeVNVBVd0BbAfOEpE5wHGq+qSn3d8fOKc2qcca/EHe7o8unxxXYiGsVn5U0beB/vHHbbjGVQZdfL7V5jGMMpBXyKaILATOAH4CzFbVPeAWBuAE77C5QNAY2+eNzfW2s8fDvmeViPSISM/evXvzmWJlGVc/pg4ZPuyE8KOfGxvr7XZCecM10Q1Qiq1PNNDvEr9Ou8Jq8xhGiUkcvSMixwLfAj6rqm9JdMhKPl23Q+35qroeWA/OkZt0jpOCb9de2z7ZMykfPV+HBcvc9sPXQmZ44jF+meXOlblDOZMwPOBKPJjT1jBKSiKhLyItOIH/DVXd4A2/JiJzVHWPZ7p53RvvA4Kq7zxgtzc+L2S8PpCmiZ2q6olNq917mMD3OdDnaftCziillqmuRLVm4q9nGEZJyWneEafSfx34mareFtj1CHCVt30VsDEwfrmItInIIpzD9mnPBHRQRJZ517wycE7tU88CH5I1Y5GUtzgkeDgbfjte4EPlnLbWoN1oIJJo+u8DPgq8ICLPeWN/CdwCdIvI1cBO4CMAqrpVRLqBl3CRP9epjkrETzEWsrnJe9UH7fNLY9aYVBJo6HHoSO6FYezg+N2VctpaEpjRYFhyVqno7YaHP+laD9YsMULfrzGUWKgXM40muPSuMaFbznIMlgRm1ClRyVlWcK1UdK6Etlp35kYI/FSLK6lw4a3Q1FqBaWTGC/zs8M9gpFCxWBKY0WCY0C8lEZUnlZoqvpCFQHOra5a+eR2c8dGxMEqJ+OfTMnV8qGXX1eM/5yJoyw8L/yymIXvcdyUZN4waxwqulZL2eaGmghFN0Sw5nJZVi7ouWODurece6Pq4C+F8+NpwZ+zIkcLq6vgEbfml0sSjTERhheMsCcyoY0zTLyUhGbranKapZgV+GOoE/6bV0eGbmZGxEM8wlq8hPG0D9/SwYdVYFE2hmngwIufWRW6BCjMRWYN2o8EwoV9KQgSIfPgOpO4ydjW3Qzduf+dK97QQJvg1wzjBXEg5hmw/wED/xAUqaCLqXOmctmv3u3cT+EYdY+adUhNVeXLDKmrZsl9yLrrNmYh8k4ukJuY6DA/Alnth6cfGGrAnid5JWgbCnLVGA2JCvxJ0rnS1auoJScUnV6U7wu3oEG5bjypjoSOuDES6Ay5bPybsH/2cWxB0xIV4Lv2YW0gguTA3Z63RgJjQrxR1kbzlI7mzaU/87YlJTw9f69qMjQyNjfmJULnKWAz0u6elnU+5zz1fH9vnLwzgBH+EQ30c5qw1GhSz6VeKOOdlzaG5G8Lv+NFEE0tmeEzg+/i29URlLNQJ9557wndvude9h5W8bmr15mzOWqOxMU2/UlTKxJPL7FIqSpmZe2BXnvOO8I34C0dIX+OyNlW3Bu5GDWFCv5JEmniKrHnjX2PtfieAatF/UIqFarRBG/m1ckxKlI/CavcYNYSZdypJmNmhJe3CF4MCqxB8p2TnSpcB24gs/Vj5rh1VDiKqDWSpMoYNo8SY0K8kfhx/umOsNENz2oUuXnpXEReWMa2ztxuef7D4uVY7LVPHFkppcgudH71TjlLJUeUgosxcdeO0N+oNM+9MBkcHxly6A/1OYzztisLt8V0fdwtKbzdsvG6is7TuEDjt8jEhHyTfUslJ7fH5xvQX++RmGGXChH6lidAYtecepBi7fm83PPTJ+m/mAoC6HroLlk0U0FEa+abVxdnjk4SBjptiI/wdjFrEzDuVJkJjLErg93zdK37WQILGF+TZRGnkA/3j7fEbroGHr0tuj48KA42i7kpvGPWCafqVJl+NMSlxvWvrlYF+V0xtYJ/7XRefH17OIYpMhBksbOEICwMdOhxh0xdL/DKqFtP0K02Yxlg3SVtF0tQKi87O75yBfka1956vl+ZpJ6o8Q3Zhtoj+CaAWrmlULSb0K01YKd+uj9tCAKAKx7+7Mt25oki1JNfSI8s+m2nHqF7MvDMZhCUOLVjG0ONraT64m8Gpc0i/vTvmAqVI5qpCMsPja+pMBm3Tkmvpi88Pn+/i80s7J8MoITk1fRG5R0ReF5EXA2NrReRVEXnOe30wsO8mEdkuIr8QkQsC40tF5AVv3x0i0oCqbAydK3njE1t45+A32HjOd6O1xfb5rtqk/6RglJZIk00I2x7Pb9wwqoAk5p17gRUh47er6une6zEAETkFuBw41TvnqyKjAct3AquAxd4r7JoNzfEvb+SJ1uv548c6nZMw1TL+AL8yZNC2bKaE0pKekfxYa6pu1CA5hb6q/hhIWl3rYuBBVR1U1R3AduAsEZkDHKeqT6qqAvcDlxQ45/qkt5vWTZ9lXuoNF7450O/KEOeqDFlX1TurgMGDyTN4oxYIq9NvVDHFOHI/LSK9nvnH/9c/FwjGI/Z5Y3O97ezxUERklYj0iEjP3r17i5hiDbF5HZIdMz4yBEcOxJ/XuZK6tO9PFplhF5aZq5RDbzcMHZp4fpwjON/yEOUoJ2E0PIUK/TuBdwGnA3uAv/fGw1ROjRkPRVXXq2qXqnbNmjWrwCnWGFEmAR1hQjPvbMzEU1r85K2wRuo+m9eFl7uIChmNKtgWJcjzPd4wElKQ0FfV11R1RFUzwN3AWd6uPiAogeYBu73xeSHjhk8Sk0A+2aIVoYbNSi1p8pp/9m8fuUhnwoVzVHmIqGqc+R5vGAkpSOh7NnqfSwE/sucR4HIRaRORRTiH7dOqugc4KCLLvKidK4GNRcy7/kgouPVAHz/+5V4ymcCDUnbsf6WKfS36QGW+p1jSHa4qZ/DzaVeQt1ksKOjjFukw4Zyv09ecxEaZSBKy+c/Ak8DJItInIlcDX/TCL3uBc4EbAFR1K9ANvAR8B7hOdfR591PA13DO3V8Bm0p9MzVNQsG9h+O58p6nWX7bj/j6Ezs4MDA8dr4f0VOJzlkAfU9X5nuKoWUqDOyH4cNjYwP9heUDBAV9rkX6wK4xbb+325WHyHXNYsYNIyHigmmql66uLu3p6ZnsaVSe7BLBAC1phv/jl/kX/X3uf/IVntm5n3RLE5eccRIfXbaQU046Lr7apjS5BcGvU7Pt8YklhddOJ5EGXKm2jFVDCi77X+Ojp3JWNvWT6CKS6VrS0b16I/7+1tvXSIqIbFHVrgnjJvSrmBy13l989QD/+OSv2fj8qxwZzvDZE57l04f/B80jRyZeK5eA8b9HpMGEeZ60zx//dwgTzsVcL8ijn3PN3nXELdhLPxbeQ8AwQjChX8cceHuY/7tlFxd9/3xO1JAQV2lynbmSapRGPC1p5xPwn5TSM4pvFJ/ugAtvjV9MTNM38iBK6FvBtTqgfUoLn/j9dzJb3wjdr5ohs+Qj4SeHRYkY8QwPQM89Y+GUxQp8GOug5vsBJit6x3ID6h4T+nWERDj5Xs0cz9lf+gF3/vBXvHlocPxOiwYpkDI8IfuNYW5fEt1zodi/V5xQt9yAhsCEfj0RElGiLWleO+vznNSe5tbv/Jz/8Lff5zMPPstPX+lHVcsXDZLuKM91S8Jk5Bd435krnHa0w1cEhf69ertdw5m4pDPLDWgIrLRyPRHS3UmWr2Fp50r+D7DttYN84yc7+daWPjY+t5uTZ0/jy9OW8d4Du0orBlMt4SUKqoYK+7GCztpifCh+wb18iftOX6h3rrTcgAbBhH69EVar32Px7Gms/fCpfH7FyTzy3G7+6Se/ZtrOH0SGkCcm3QGtUxO0EaxB0h0weAAyhXbkEpc/4eP/bTatzu83kqbxWnc+ztxcfhtfqEe18rTcgLrCzDsNyJTWZi4/awHf/vT7mZt6szQXXb4mQRvBGmSgvwiBT7jA7FwJq3fkYQKTsVyAA7tgwyoXzpmUXJq6P8ewhLNCny6MqsWEfgMjIpHO37zIjjyZbM2wfX51FKHLJTATL47Z5ih10UNJHaxxf4/gHMNaeWaHiFp0T81jcfqNTiE2ZmmKzkJtn++yfZ9/YHJCQf1YdoCN14VXwqwUftayr9EP7BufZBcXpZOE9vnjTUcQntAH0X/j7PyAOCx3oKawOH0jHE+7yyT9p5BqiSk7gBNizz/gkpeCGmMiCnQnSxMTNNPOldB6bGHXKxV+ZvNAv2e/z4qYKdZskm22iQq5hIAGD+N+5+yntDgmM7rHnjBKhgl9AzpXsv19X+JtbR0/nmpxDlqfdAdc8tXcQnx4wGWr+gXgbngxoeDX/M0yLWmXbex/T1DjrFbfQjBippjQ1myzTZxQ9gvytc9ngrkoqeCerOgeyx8oKRa9YwCw4PipvE0raYacHpjrsT+X6SRbECxfk9uM5JsrerudszJXaGVwjvd9GHb8aGzforOjo1GqAf/3ufDWwkI4w/wFSYRyPoI721QUVW6i3D6cXIuZkRem6RvQ280xmz5Lhxwae/A/mkMI5fIF+YLAfyzfsAqa0wHNNsSUM3TYHd+5Ero+Hn5MNmECH9znkaO5z58s/N8n23maRPOP6pecpBxz0pLNYdr10CH39BckSXRPsaYZyx8oKebINSIdigfb5tD9+5tobU7R1pSitdm9zt10Hum3oxufaXOagRW309wktDz22fG9f/1iZVsfio5Tb50KF30Zdj7lolTiNP61B2Bte7L7rBZSLdA2baJjt7cbHr7W9ekNo+vq+CqbSRytSZ2xUU7mUae097drmQrNbRPvJW5O/nWSOpCj5hLmyDZGiXLkmnnHiNSYph75DX/96EsTxl9u2x2qhKvCqzqTL769kke+eRxPtF7PvNTEx3LtuQeJE+RDh535qPVYJq3pe7pjTJCVMtmsZaoT6v71gs7WzeuiBX66I7fA980gfnRVWNnmkKztUU399iVjY1FmsYH+8bH8w4fHGtQE7yX4PWGmK9+BHDw2ijDTYEvaRYkF5xxVotoYhwl9IyYTcy7Pf/J8BkdGGDqaca+RDEf/aS6th16dcPjbU+bwb+c9zu8czdB5NMPczeGJX7EC32dkKLegLdQJOppBHCHYghpkb7fLni0Fo1ry4fHjvn06zlwR55TO1qZ1ZMzsEiYEs7O2s8/P5QeJ8z9k29rj7impXT5socoOCw5bcIxQTOgbkZpU6g++QPuUFiDLjnv+2tDjp164jj/uXDA21lNmR+qplxZ+blT8etBGXepeA3GLWC4NO85ZWqyjs9TltbN7Ccf9G0hql89eqG5fYs7dAjFHrpEsE7OQ40P7yCaPxdd0R3wf2ucfcII52PQ8CQP9znwE8feRrzDMdx5B0jPc75XtKAVoanX7ohyixTo6S+0QzaeXcKGRP+bcLRjT9A1HTKG2go9P8lgewZA2c/cx13DNB95J6w//JlxbHB5wpYILqRg3MuTMNqt3RN9HPgKkJe0cmsOHcx8bxkC/uxdgXE9d3+EJE00wvjmj2EJppej85ZMdzRNXYK4Yu7wVhyuYnP9bROQeEXldRF4MjHWIyPdEZJv3PiOw7yYR2S4ivxCRCwLjS0XkBW/fHSIyGUXNjUrjJwX5yVMX3TZRu+66erx9Pt3Bc0tv5ku/OY2v/b+XyeSKMIvr6RuX7JVL0CUVIP4TQsmSwdRp95fdPbYoxZlwkhZKC3tS6O2GwYMTp9DUCqnWiePg/lbBENN0B7FPfH6BucvuHv93P+0KpwAUknRlxeEKJmfIpoh8ADgE3K+qS7yxLwL9qnqLiNwIzFDV1SJyCvDPwFnAScC/Au9R1REReRr4DPAU8Bhwh6puyjVBC9lsXJ56+E46n13DFCmwfo7vkI0L6Vx7IHpfEpt+0OlbbC2dbNIdTliCE9RRDvDL7nbv2RE5uUInW9IudyJs8fOjjMIS8PIJt4yj2FDMsDpDZs8fpeCQTVX9sYgszBq+GDjH274P+CGw2ht/UFUHgR0ish04S0ReAY5T1Se9ydwPXALkFPpG47Jsx1egUIEf1PrSHeGCLVf0T87a9+LMEz5hDvGmVhd6Woj5ZKB/LFktziH67eudhh0nKKOeFKIWtDgzVT7hlnEUa5fP1yRpAIU7cmer6h4A7/0Eb3wuEPyX2eeNzfW2s8dDEZFVItIjIj179+4tcIpGzRPxnz/y2TSs8Bo4rTTbQZpqGbOV5yKyC5iOOZMh3MF98Vectl5oqWe/Jk6cQzRJ7ZxSOzhLUWgtaXZwqWnw4m2lduSG2ek1ZjwUVV0PrAdn3inN1IyaI0K77c8cS1qGssw+kn9CUtJwxrgaQ9lhglHaZ5LaQ2H4wtq/5qizN+I4CDd7RD0ppDtcyY1x8xJomZLbKV3oQjI6v12Mc1pD+e3yYTkJDRbfX6jQf01E5qjqHhGZA7zujfcBQZVmHrDbG58XMm4Y0UTkDwyf/bf8+Fdv8rs7/icn6hsgkPIFR9R/4kJNAUkEW5Jjxi08u8Zq7eciPWN8dEukqWpGwEYeEKT+7+E7TbNt+hfeGlLuQp09P9USnSEM4Rp5Ljv7BN9CQOCHLdilZtPqho/vL9S88whwlbd9FbAxMH65iLSJyCJgMfC0ZwI6KCLLvKidKwPnGEY4EfkAJ77/Si696gaO/6+/5MiUkyb+Iy5ljfckpoak5ojRSKYD8IV9LmopLm+hqdVF1gSjWwYPuvEgfiP6UU0+pHTytsfdbxn0YzR75qJtj088Z2TI+56I+UVFB2UXaXv4Wrh10ZgpJUzogvueXAK/WLNMb3e0b6WB4vtzavoi8s84p+1MEekDvgDcAnSLyNXATuAjAKq6VUS6gZeAo8B1qqMdNz4F3AukcQ5cc+IauYnR0Nuam2BgT/h5pfpPvHxNfBnpOHNELq33ottgwbKxY9Je5HNczZ/MMKOhkvnUBvJ/j2D1VN8hm68zV5rCQzPDnMXZdYYi0XhtuxRmmThFoIHi+5NE7/xJxK7lEcffDNwcMt4DLMlrdoaRiwhb9eH0iRSRHztGWASPb5qJM0ckFVJxZqfIUFN1wvuy9e7ctdNz30f7vOgInrj2l6Ffnwmfc7ELbdyiUIqa+nHzCy7cSUNBazRk1DJyjdomxO4/KG3cdOBS/uhbX+EDO+8s/j9lIf6AJEIql9CIE8bBa+Wqb+M/jWxYFb5fRyba70fLP+fRNKXYpjXSFL2vFGUX4pzZUaWnoxbrGnYIW+0do7YJsfvLh+/gt09qp6t37eS12MslpJK0AMylffvXiqtxJE1jC4RvPsom3QHZCfIirqBdPlmvuers5CL7foM2/MhSG+p8BsVk8QZDd5P2AZ7MfsFFYpq+UftkaeKtwCd+eCqSndg1PMDhTWt4fOT3SImQEqEpJaQERIQmEVKpwLb3eexYty/l7Rdh3DVSKe8cgbnT5tJ8cKLgHzluLocGhpn2r39FKteTQPv8eM052H0LcpceTrU452zQP+ELwWyfxcjQmPM3qQkjex7pGc7BPOH7UuH+gqCTObuhTNwCGCygF6dlJwndjVysd7nFxz+nhgu+Wecsoz6JKFuQUeGdg98o+9d/OPUEt7R8bVwuwdvayo3Dn+CRzPt5ue0KUiGBMRkV3j30DZpSwodS/8bNqfWkQ7KSB2jjvzd9kn9tPnvc4uQvRg8c+gSzdWJi49tNxzHSMpVjj/yGwSlzeHPZjZz0/c9E9DgQVzMpCVGmqrDxqAznYNmJWxfln8Vcik5aSUppxJWvqKJuXtY5y2gsIuy3meNO4vtXnk1GQVUZUSWTgYyq9/K2MxO3R7xjVJUR75zg9ugrAxnt5Pm+BXT+/B+YcuQ3HD7mRJ55959z+okfpFOVw0/MYdrgxMijQ8fM5trfezcZVUZ0Ed/dO5Nz+u6ifeg3ZEiRIsP+ltl898Q/Y3j6H/L+4Dz9+WeUE7a9EfqzHHP0IO88fJf7cAR4DJ5oPZ55qYnH72+dzTd+sJ1Zx7Yxc1orx09tY+a0No6f2soxLQH7ey77drb2HZVgFhSihZStyNbGk/pOgiRJohsecEK/JR3di6GKMU3fqE+S9oKdLMo9vwiNVdvn89afPcsbhwd54+AgbxwaYtq2DfyHretoyRwZPe4Ibfy3zDX836HfC738tLZmZk5rY+axrdz1xlUcf/T1CccMpE/iyYt/SFtzE23NKffekmLxXe9Awsw10gRf8IR9sX2P/d8S8v+dg4tEZOEAcdFT5YjeKVFUUJSmb0LfqF+qPaSunPPLd1GJmMvA0AhvHBr0XkO8cWiQN73tvd72A7svHMuIDhBlStvRdsUEvzG4Hstdzd+krTnFY4NXMZ2JJZ+PcAzNDNNM7hDTw+k5AEwNy+VIaoapdFP2EioDJvQNo9Go1KIXIRiHjp3LS3/87wwOjzB4NOO9Rjh303mhgnh/64l86ZRvMjicYcmb3+WK39xKC0dH9w/TzN8dcz3DIxmuH/4a0zk0qoiHLSIZdeUownwnivBv/2k7p81vZ9oxId3KfEr9RJbrb1LCRcaEvmEY5aGQp4okxydctPT2JUiIoDw6bR6gNB98dcK+vsxM3j90ByJw8uxpnLFgBmcumM6Z75jBO2dOZVyPp1ItnknuO7JvQh5Odf8ME/qGYZSNfAVjKZ9C4oQphO57+4Lb+elxf8Azv97HMzv38dyu/Rw84p4qpk9p4Yz50zlzwQzOfMcMTps/nWPbShDzkkSLr4Cmb9E7hmEUTzl6LOdzLYhfRLL2TelcydnA2e+ZBbiIp+17D40uAs/s3M8PfuFCXlMC75k9jTPfMcMtBAumsyj7aSAJSWL7IyrLljIqyDR9wzCMEA68Pcyzu9wC8OzOfTy3cz8HB93TwIwpLWMmoQXuaWBqrqeBpFp8maN3TNM3DMMIoX1KC+ecfALnnOwaA2YyyrbXD7knAe+J4Ps/d6GqKYGTTzxudBE48x0zWHj8lLGngd5uVw01mzAtvsxtIE3TNwzDKJD9bw/x7K79PPtr90Tw3K79HPKeBjqmtnLG/On88TFPsXzbzTSNZCV8larBfASm6RuGYZSY6VNaOffkEzjXexoYySjbXj/IM7/e7/kG9nHKgS/TlArJ8G2dOil5Iyb0DcMwSkRTSnjvicfx3hOP44rfXQCArn0z/OBJKs5mpZUNwzDKiMT1H5gETOgbhmGUk6g6/pNUnM2EvmEYRjkJafQzmYX/zKZvGIZRbsochpkPRWn6IvKKiLwgIs+JSI831iEi3xORbd77jMDxN4nIdhH5hYhcUOzkDcMwjPwohXnnXFU9PRAPeiOwWVUXA5u9z4jIKcDlwKnACuCrInGdkA3DMIxSUw6b/sXAfd72fcAlgfEHVXVQVXcA24GzyvD9hmEYRgTFCn0FHheRLSKyyhubrap7ALz3E7zxuUCw8ESfNzYBEVklIj0i0rN378Q+n4ZhGEZhFOvIfZ+q7haRE4DvicjPY44NK0kXWgNCVdcD68GVYShyjoZhGIZHUUJfVXd776+LyEM4c81rIjJHVfeIyBzAb57ZB8wPnD4P2J3rO7Zs2fKGiPy6mHnmwUwgvKN0fdEI99kI9wiNcZ+NcI9Q+vt8R9hgwQXXRGQqkFLVg97294B1wHLgTVW9RURuBDpU9fMicirwAG5hOAnn5F2sGtYheXIQkZ6wAkX1RiPcZyPcIzTGfTbCPULl7rMYTX828JBXOrQZeEBVvyMiPwW6ReRqYCfwEQBV3Soi3cBLwFHgumoS+IZhGI1AwUJfVV8GTgsZfxOn7YedczNwc6HfaRiGYRSHlWEYz/rJnkCFaIT7bIR7hMa4z0a4R6jQfVZ9ExXDMAyjdJimbxiG0UCY0DcMw2ggGlLoi8gKr+jbdi+sNOq43xGRERH5o0rOrxQkuUcROccrlrdVRH5U6TmWglz3KSLtIvJtEXneu88/nYx5FoOI3CMir4vIixH7RUTu8H6DXhE5s9JzLJYE9/ifvHvrFZF/F5EJQSS1QK77DBxXPtmjqg31ApqAXwHvBFqB54FTIo77PvAY8EeTPe9S3yMwHRc+u8D7fMJkz7tM9/mXwK3e9iygH2id7LnneZ8fAM4EXozY/0FgEy7rfRnwk8mecxnu8feAGd72hbV4j0nu0zumrLKnETX9s4Dtqvqyqg4BD+KKwWXz58C3GMsoriWS3OMVwAZV3Qkuq7rCcywFSe5TgWniEkqOxQn9o5WdZnGo6o9x847iYuB+dTwFTPey4WuGXPeoqv+uqvu8j0/hMvprjgR/Syiz7GlEoZ+z8JuIzAUuBe6q4LxKSZLidu8BZojID72CeVdWbHalI8l9/k/gt3AlP14APqOqmcpMr2IkLmZYJ1yNe7KpOyohexqxc1aSwm9fBlar6oiXcVxrJLnHZmApLpEuDTwpIk+p6i/LPbkSkuQ+LwCeA84D3oUrDPj/VPWtMs+tkiQuZljriMi5OKH//smeS5n4MmWWPY0o9JMUfusCHvR+9JnAB0XkqKo+XJEZFk+Se+wD3lDVw8BhEfkxLsO6loR+kvv8U+AWdcbS7SKyA3gv8HRlplgRCipmWGuISCfwNeBCdZn/9UjZZU8jmnd+CiwWkUUi0orr5vVI8ABVXaSqC1V1IfBN4NoaEviQ4B6BjcDvi0iziEwBfhf4WYXnWSxJ7nMnXlkQEZkNnAy8XNFZlp9HgCu9KJ5lwAH1elrUCyKyANgAfLTGnkbzohKyp+E0fVU9KiKfBr6L85Lfo64Y3Ce9/bVqxx8lyT2q6s9E5DtAL5ABvqaqsWFk1UbCv+VfA/eKyAs4M8hqVa2pMr0i8s/AOcBMEekDvgC0wOg9PoaL4NkOvI17uqkpEtzjGuB4XJtVgKNag5U3E9xn+efghQgZhmEYDUAjmncMwzAaFhP6hmEYDYQJfcMwjAbChL5hGEYDYULfMAyjgTChbxiG0UCY0DcMw2gg/j9kzhicTvSuogAAAABJRU5ErkJggg==\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 50\n", + " Combined Loss: 916573049.8952329159\n", + " Kdiff : 40.9268%\n", + " Bstray : 50.1190\n", + " Pareto Kdiff : 6.2375%\n", + " Pareto Bstray : 65.0233\n", + " Coilloss : 1970.5956\n", + " num inv : 0.5983\n", + " Pareto Coilloss : 636.1221\n", + " Pareto num inv : 0.3630\n", + " V_SecCore : 1873.7225\n", + " pave : 57.9137\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\scipy\\interpolate\\fitpack2.py:280: UserWarning: \n", + "The maximal number of iterations maxit (set to 20 by the program)\n", + "allowed for finding a smoothing spline with fp=s has been reached: s\n", + "too small.\n", + "There is an approximation returned but the corresponding weighted sum\n", + "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n", + " warnings.warn(message)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(36971338.8153, device='cuda:0', dtype=torch.float64,\n", + " grad_fn=)\n" + ] + }, + { + "data": { + "image/png": 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ikw30B4et5Jrn5vGHDwc9/0sbug0eDN5pKPBLilQS9MNWyCbN5N39e+5+KvBHwJ+HPZC7b3H3LnfvWrp06bQGKhlVSWOz1vYgr7/2hrmb8Y8Nw/Ark2/XYeiSMpUE/X6geFtkO/BU1MXufh/wOjNbMsuxiUydNy8+orCzG9bvDap55iL4R/X1H9gXfrtIAlUS9HcCK81shZk1A5cA24svMLNTzMzyn78BaAZ+U+3BSgaFNl/Lv/lsWQyNLbBt3fiB5X09wcy7lq0SLFe7nyUyS1NW77j7iJldCdwD5ICt7v6wmV2Rv/9G4I+B95vZMDAIvKdoYVdk5gqNzXZsYmygn4Gm4zn2wv8a3HbHVUF6BYLZ9raPEFneOZeSdpSjSBkWV2zu6ury3t7eWH62pNP5X/4xJ7bOZ+sH3xTM7JOSVrEcfFZN2KQ2zGyXu3fN9Pu1I1dS46TW+Tx1qDCzT9DGKM30JUUU9CU1Tmqbz1mHfhDM8pO0FSSJ7Z9FIkyZ0xdJiu5nr+N0vw0G4h5JkeLqIRhfSB7oDyqPjraJEEkGBX1Jh74efufp27AktbK3HLSvCoL8tnXB7uGhl2B0KLh/YN94mwgFfkkIpXckHXZsSt7RJT4Ke3+UX1D2oEy0EPALtHlLEkZBX9IhSQu305XmsUvdUdCXdEhzR8s0j13qjoK+pEO5nblJVrrQKxIzBX1Jh85uuPB6DrecxJgbQ8csg4u2JLgFggWlnBder0VcSRQFfUmPzm4OfeSfee2Rb/Ots/82CKZJ3BjV2gEbD8HVuxXwJXEU9CVVlrW1cPJxC/jHXx7I97FPWorHlM6RRFOdvqTOFYsf5G1PfAXfeyBxIZ8Vb5s8u9eGLUkQBX1Jl74eLn7qv9NoIQeaJMEz/y/fDC4f4FeeCw/dMrEbqDZsSYyU3pF02bGJxtGEBnwINmgVNmsN7IPereMBv0AbtiRGCvqSLqnb6BTRGC51v4fUCwV9SZdyG51aFpO8hd0I2rAlMVHQl3QJ26TV1BKci7tmc9D0LC4W9d+p9IXIgly/SAwU9CVd8pu0xha1M+bGQPOJwQYoCBZIZ3M2btflsxubhxzV2NQSVPRMCPweLO729czu54nMgIK+pE9nNw3XPMxly+7mkoU3BS8EOzZNXjA9qsKUT+/NVRvi0Z97xmVw8Akm5fa1mCsxUdCX1HrryqU8+vQL7H/xyBQLo3GdsuXw+L3RY9NirsRAQV9S65xTlgDwk18eSO7C6MC+6LEldcxS1xT0JbVOX9ZKa0sT9z9+IKILZxLkF23DFp/VrkFioKAvqZVrMN7yuuO4f88B/HfeHSzotnYAFpRvRlbT1JLDrq8Huf3C2NR9U2KkNgySauesXMLdu5/hl/sPc0pndxBI+3rg+x8Lr6aJg48G1ToK9JIASZgKiczYW09ZCsA/7DkwfuPd6yefVRs3VetIQijoS6otP24Byxcv4MePFwX92dTqzyVV60gCKL0jqfex4x7krU98Bd/4GyzJFTFJHptkhoK+pFtfD39c3Gp5YF+84yln8WvjHoGI0juScklvtVxs74/UekFip6Av6VYuT14okWxamJDyTYJFZpEYJeR/gsgMRe527QgOJr9oCzCWnPLNwYOwsS04XUuzfomBgr6kW1Sr5cJu17KN2OKSP1Vr25/C5hUK/lJTCvqSblGtlguboJJeJjl4MGgJrcAvNaKgL+mXb7X87hPv4gOtWyfuek1DmaQ2bkkNKehL3fjwop381XPvx4tz5oltxFYi6e9IpG5UFPTN7Dwze8zM9pjZtSH3/4mZ9eX//cTMzqj+UEXK6OvhvCf+G+12ACvkzO+4KrivtBFb08JYhxoqDe9IpC5MGfTNLAfcAKwBTgMuNbPTSi7bC/yeu3cCfw5sqfZARcrasYncaMmCbSFt0tk9XsnTvBCGXw4+xiHsBUdtlqWGKpnprwL2uPsT7j4E3AqsLb7A3X/i7s/nv3wA0LRFaisiPeKF2/t6gpn/wD7AYehw7cZW7DNPBYe4q82yxKSSNgzLgOK97f3Am8tcfzlwd9gdZrYOWAewfPnyCocoUoHW9tAWDAcalvIvvzpI193r4y/dtFzw4lNoAS0Sg0pm+mGnSoceOmpm/4Yg6IduO3T3Le7e5e5dS5curXyUIlMJWbAdaZjPdX4p39zyl3gSOm/6qMozJXaVzPT7gY6ir9uBp0ovMrNO4CZgjbv/pjrDE6lQYea8Y1OQ6mltp3H1Bj5z6kWM/I/XY0fiHd5RxesMIjGoJOjvBFaa2QrgSeAS4LLiC8xsObANeJ+7/6LqoxSpREjaZAHAkWdn/pgNjeAezNKrZWBf0IqhtT14h6IXAKmhKYO+u4+Y2ZXAPUAO2OruD5vZFfn7bwQ2AMcBXzEzgBF375q7YYtMQ0S+vyJjI9Udy1ElZaUK/FIj5h6anp9zXV1d3tvbG8vPlozp64HbPwpjw3GPJFxrRzDjL0pNTegdVHybXhwyz8x2zWZSraAv2dDXw4vfu4Zj/MXQyoTYNbVMrC5qaAKziWf9NrWovFNmHfTVhkGyobObv+i8m67cbUGdfJJaM1hucjnp2PDkw93Vo0eqQEFfsqGvh089ejE7Ry7Gd2yCMy4b3yAVp6aW6S0Sq0ePzJKCvtS//G7c1qFnaDCwgX3w0C1BjvyimDuGtK/Kv/hUSD16ZJYU9KX+hR2kMjwId3wiOMgkTnvvg5XnBjn8CRog1zzxJvXokSpQ0Jf6F9WXZzim/jsTODz8vWDRtliuEc56n3r0SNVVsjlLJN0i6vQTU8UT1iJidAgevzfoDipSRZrpS/1Ly0EqpbRoK3NAQV/qX/4c3QmpEivzp9+yuGZDK8/HTwATqRKldyQbSvvy3HkN9N48+bquy2H52bBtHRHNZOeIhf88tWqQKtNMX7Lpgi8GAd5yODDiDRw580PB7QBNC2o8IA82aYXRpiypIgV9ya4LvgifPciDH9rLKUf+hh+89lPjJ2zFUdlTbpOW8vtSJQr6knlndhxL24ImfvjY/vCa/iQo3pTV1xPk+je2Kecv06acvmRersF468ql/PCx/fhIf3JKOY+y8U1ZR9+J5F+YlPOXadJMXwR438KfcvvQR6jt4m2lfOLJYGG7i5Xzlwpppi/S18Ob+jZiDQlM60CwwHvnNcFmrajDYJTzlwop6Ivs2ISNJDTgQ7DAG1ZeWkyN2KRCSu+IJGmWHFW2WY4asck0KOiLJGmWPN0D2NWITaZJQV8krb15WjuChmwK+DINCvoipb15WhaH9LdPmIYmpXRkRrSQKwKTe/P09QRlkAP9QXO26aZd5lrSxiOpoaAvEqb4RaB0Q1QS+Bjc/tHgc6V3ZBqU3hGZSiH9k5iWy3ljw3D3+rhHISmjoC9Sic5uWL8XLvoatHYwhrE/d3z8LwRhp26JlKGgLzIdnd1w9W6+cPYDnD34ZV5e/fn4K3/UcE2mQUFfZAZW//YJjI45P2h8W1HlT0zUd0emQQu5IjNwZkcbf9LyAG+982oYeXZmO2mrJUk7iiXxFPRFZiC3+//wWf4XzSNHghumU0LZ0AS55uod1NJybNBXf6A/2F28eoMqeiSSgr7ITOzYRLMfqeza5oUw/ErwwmANQblltQK+5WDopfEFXfXXlykopy8yExWmVEYbmhgbHhp/J+Bj1d1Y5aMwOjTxNvXXlzIU9EVmYoombQ486Us4NDKPBh+uzZiKDexTVY+EUtAXmYlyTdqaWrCLvsaS//wLFjfEcMB6wR1XVRb4deZupijoi8zEhCZtjFfvFLU6nteYw2rStjniVN9K0jyFFhMD+wAfXxNQ4K9bFS3kmtl5wJeBHHCTu/9Fyf2nAn8NvAH4jLt/odoDFUmc0iZtYVZvmNS3x4kM09OXaw6qgaIWhqdaeyh35q4WguvSlDN9M8sBNwBrgNOAS83stJLLDgJXAQr2IsVK3hFMDvizfLM9OlS+Esgayqdtol4UVPtftyr5i1sF7HH3J9x9CLgVWFt8gbs/5+47gRhWrEQSLt+6gdaOkBn+2Nz+bB/laNpm27rggPViUemnJJ0mJlVVSdBfBuwr+ro/f9u0mdk6M+s1s979+/fP5CFE0iv22bND79aJM/6wBWmduVvXKgn6YelHn8kPc/ct7t7l7l1Lly6dyUOIpFciZs8+cXG39NQwnblb9yoJ+v1AcTepduCpuRmOSB1bvYEqLuHOXOzvOCROlQT9ncBKM1thZs3AJcD2uR2WSB3q7GaGb5KryxrGUzwq2cycKUs23X3EzK4E7iEo2dzq7g+b2RX5+280sxOBXmARMGZmnwBOc/cX5m7oIinU2pEPsFXWvDDf06eCIx19dLw/j0o2M6eiejF3v8vdf8vdX+fun8vfdqO735j//Bl3b3f3Re7elv9cAV+k1FwtkObmTczNtywOavijFAL7bEo2tZM3ldRlU6SWOruDc21Djjl0DJtp+mfw4OTNYn09+cAe8c6i0Io57P7iReejj1PUuhkmbjpTd8/UUBsGkVpbs3lSmeQg8/j22B8wuODVM3xQmzzTLtofEKrlWBgK2dhVXLIZlfO/e310WkgSTUFfpNZCyiSHz7+O2064mtOe/wJbfv9B/KKvTfPQdY8OuGG1+LlmOPLi5HccLYsnlmxG5fyjDmRXZVDiKb0jEoeSVMwi4NazRvlkz0N8/q6fs2jlr3lP8wJs8Pn8wSsV9OCPCrjFAbyQohk6HB64mxdOTM9MO4h7kN/X6V2JpaAvkhDzm3L8z0vP4jy/j9WPb8YsfzhKxYeulAm4pfn+jW3hD1Ea5KNy/i2LYWQwvFpI+f1EU3pHJEEaGowL99/EAhuafGclh69XWmdfac+dqDYNazZPbC1dSvn9xFLQF0maiJSK+1iQ6486vKUgLOCWlleuPLeynjud3XDGZeMvOJYLvi68c7h6N5G7jJXfTyQFfZGkiZiFPzl2HBf/QzuPrfocHjXDLigOuGEVOA/dEgTvqXru9PUE1x4943c0+Lr4nYQ6daaKgr5I0oSkVLyphV+d+UmefH6QP9xxItdzKaO5MjP+4oAbVYGz6+vBz9p4KJixh+Xfy+3YLTNedepMLi3kiiRNSLWNrd7AOZ3d/HB4lP+9cx/n3nsVOSJaLuSag+qcjW3RC7EwsR1D1IJrJTt2w6qDVL2TWOYeTwOorq4u7+3tjeVni6Sdb2wL3b3rgFsTDV58npFRttFba0c+Nx/iS6dH7Ngt8z0yp8xsl7t3zfT7ld4RSaGoA9dHvaEk4MOUp/KWW3BV6qbuKOiLpFFEMM5Z1PGLHl3yWW7BVYes1B3l9EXSKCKPblEN1lo7gheK4iZpUNmsvXRjl6Sagr5IWkUF46jArgVXQUFfpL5MFdg1a888BX2ReqPALmVoIVdEJEMU9EVEMkRBX0QkQxT0RUQyREFfRCRDFPRFRDJEQV9EJEMU9EVEMkRBX0QkQxT0RUQyREFfRCRDFPRFRDJEQV9EJEMU9EVEMkRBX0QkQxT0RUQyREFfRCRDFPRFRDKkoqBvZueZ2WNmtsfMrg2538zs+vz9fWb2huoPVUREZmvKoG9mOeAGYA1wGnCpmZ1WctkaYGX+3zrgq1Uep4iIVEElM/1VwB53f8Ldh4BbgbUl16wFvumBB4A2MzupymMVEZFZaqzgmmXAvqKv+4E3V3DNMuDp4ovMbB3BOwGAI2a2e1qjrV9LgANxDyIh9FyM03MxTs/FuH81m2+uJOhbyG0+g2tw9y3AFgAz63X3rgp+ft3TczFOz8U4PRfj9FyMM7Pe2Xx/JemdfqCj6Ot24KkZXCMiIjGrJOjvBFaa2QozawYuAbaXXLMdeH++iudsYMDdny59IBERideU6R13HzGzK4F7gByw1d0fNrMr8vffCNwFnA/sAV4GPlTBz94y41HXHz0X4/RcjNNzMU7PxbhZPRfmPin1LiIidUo7ckVEMkRBX0QkQ2IJ+lO1dahnZtZhZn9vZo+a2cNm9vH87YvN7Adm9nj+47Fxj7UWzCxnZv9sZnfmv87q89BmZreZ2c/zfxu/m+Hn4ur8/43dZvYdM5ufpefCzLaa2XPF+5jK/f5m9ul8LH3MzP5wqsevedCvsK1DPRsBPunuvw2cDXws//tfC+xw95XAjvzXWfBx4NGir7P6PHwZ+L/ufipwBsFzkrnnwsyWAVcBXe5+OkHxyCVk67n4OnBeyW2hv38+dlwCvD7/PV/Jx9hIccz0K2nrULfc/Wl3fzD/+YsE/7mXETwH38hf9g3gj2IZYA2ZWTvwb4Gbim7O4vOwCHgbcDOAuw+5+yEy+FzkNQItZtYILCDY85OZ58Ld7wMOltwc9fuvBW519yPuvpeggnJVucePI+hHtWzIHDM7GTgL+ClwQmFvQ/7j8TEOrVauAz4FjBXdlsXn4bXAfuCv86mum8xsIRl8Ltz9SeALwK8J2rgMuPu9ZPC5KBH1+087nsYR9Ctq2VDvzOwY4LvAJ9z9hbjHU2tmdgHwnLvvinssCdAIvAH4qrufBRymvtMXkfK56rXACuDVwEIze2+8o0q0acfTOIJ+5ls2mFkTQcD/trtvy9/8bKEzaf7jc3GNr0b+NfBOM/sVQYrv983sb8je8wDB/4l+d/9p/uvbCF4Esvhc/AGw1933u/swsA14C9l8LopF/f7TjqdxBP1K2jrULTMzgtzto+7+xaK7tgMfyH/+AeD7tR5bLbn7p9293d1PJvgb+Dt3fy8Zex4A3P0ZYJ+ZFbonrgYeIYPPBUFa52wzW5D/v7KaYN0ri89FsajffztwiZnNM7MVBGea/FPZR3L3mv8jaNnwC+CXwGfiGENc/4BzCN5+9QE/y/87HziOYFX+8fzHxXGPtYbPyduBO/OfZ/J5AM4EevN/F7cDx2b4ufgvwM+B3cC3gHlZei6A7xCsZwwTzOQvL/f7A5/Jx9LHgDVTPb7aMIiIZIh25IqIZIiCvohIhijoi4hkiIK+iEiGKOiLiGSIgr6ISIYo6IuIZMj/B4fd+GI3CB4XAAAAAElFTkSuQmCC\n", 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\n", 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RBmc5q5WogzyJvQyV9q835SIGi40Vn4gTHK0pIk7SZtS6XsWd59zjy7Mx+yfitYn0o7jDnkPcCTwJklI+xVQj1pCheEakmICI9ADnAzcB17ieuhX4PPAD17GLgPuMMUPAFhHZDCwEfpWIxCkyZ6o1Wb302n66F93g78OM0pjG6/1RyHdN7N7kfI7TbWzw9QruZUiapmjzn+Sy6etNou6R5KyKs1G71Y3st96T7xr/rYvHl7tkgzNGRgaj7/ZuavGOCQzvHy+RnQVilIiJTDViDRmKZ0QNDH8Fa7I/zDkgIhcC240xz4gU3MWzgMddf2+zjxUgIsuB5QCzZ8+OJXRazD3cUgJbdu/nlL6QiT5scDrPRenc5NCStzKKHIqDae7zhNUXygpjIzAcoSiaUxcma4rNt7dvDMyYld8P0YP4ZtTK4llyR/A482q+UlyWpBin/g5MHJ+De5Jp2lLJ4nLl4reASrIyaTU+IyKhSkBEFgM7jTHrROR0+9ghwPWA1w4Sr3XdhJnIGHMHcAdYO4aji5wePVPyNDcJL+22V35JrEKKS1j74VUYK9TdUxOXNRrO969Gu8aWSVbweWAb4dcwgWvs3PiLb4GN90dfFJhR/wm5oNhgEWEKwJ15tvrGifLEbQVZPOHPO7uwDlbWuoEFWfm19BkRiRITeD9woYi8BNwHnAn8KzAXeMY+3gM8KSJHYK383Q7zHsAv0b6maM410TMlz0uvJZSeGDqJi5Xnv2LA28+aRfdISYT4g/zq0PuFmvJd5QVGR/ZbE9WKveENgMql+MY/92bLFRMVLz/y+n74wZXxrSavSahct4VXAHTtXen5w6Nk5FQq1lDtz4hIrNpBtiXwOWPM4qLjLwF9xpjdInICcC9WHGAmsBqYZ4y/IzKrtYMmsL6f3T+4nq7RXTSVasK6V0VBq8goJXGj1HPJd1nWRtrF53x90WIFLZ/8jnetfHcOursJOgRXoYSJ7oawTlvFVLrPgd9vfPPc+PsyOnrHv+vgHst3H5fi6wvRKoyCv3snVs2hGP07SqEOq5Zmsp+AMWajiPQDzwEHgCuDFEDNYA+gqaNFJuzLj1tZK1F8m1HLT0fdDBbmHnHHEJwbVJrS6Xm74GMepbBtBbD4FqssRbHL4Kl/LQxMDu6xNjFBoSsuKC7jJk78BSq8IU38x0rsjXkyPtGWEzMZ3GMpyvv/0lK8Hb0TXTcw0WIoHtdu904ca7XS/vCgjJwaVQJJoFVEoxJ1RVO8snCvkCJNwHYwN2pzjKDsIK/3V7IPQhArBqyG5uvutq6BNIE0w5g9yRevQoOud77L6rTlNfEXfEZuvM8zwLcv9G6ekhZeyn59v9WbOCvxHKeXRdBCJ8hagNLum0rgW7W0whZIBcmkJVC3RF3RuFcWXpkZgRR1J3NWUxA9C8mtFBwfa3HqIMRfFZdDR68l1zP3jl8DMwamaJX/gyvHZQy63u6CaO7rVFxG2YyO/z37VLvdZ4bw+o6rbyS6AqhC9tfIoKUAgizToLjBkjvCFx2Sq6wCcO4Jv2vV4P2IVQlEJU7evXNTRN2s5bdiGhm0Jmu3T784k6LYEhh6c9y3HpR10TrJmkhjdQkrYdJpyVsuhfsvD/8cd5vC/JToSsppROLHurvtQngZWV07eE0+QcpvyZ0T2ylWo8tb2AIoKN2xuA2kF2assgogzGWaQkZOltDaQVGJUvbWwbm5o1gPziD0e+3gHn8/ZnHmxeCeicFVd9bF+n4r6Ljyk+M3pJM3HtaVytkpGnfztzRZQd+oE5VTsG7ozXifE4QZTTGTSmDuB5mQneSefNwZK35lsDt6rYnSGYcHLSqP69qS9/5MP/nCCFsph+2Qd3YTRymnknQ9naCFWIoZOVlClUBUopS9hcLB73fzSI4JaWFxTVLH3RPF0nAm1h9e5b26ds7hp+SaWsa/U9xV5/B+76wfP5yCdXHeE4mYVsCSOxNq8GPg0gcst4hXOmCxIve6vrlW6zqu6PTpK0DhmDrpEtjzO0K/c0veCswHLQCirJSjpjuGKYtK1NPxVf5SvfIWGUfdQXEIKnsLE4Ob88723gHqDlY6+G0eac77tP7rib66dSbWIIUxuMeS33OCca0WK7lrtykHb+0JzsrJtcLoAaCCNY2cVTdYcYoo5ZT9cBSJ38ZCv99FcpabJD8FBveOjwE/JWzGLEstcuBfLGWx+Bbrn3tzmeNiipqcEPT9il/z8uOFgfuTLil0GSWdvZOhnblZRS2BUnCtfMYQ9jRPt1aO126J1o3J67jfaurcm/1XT1EGcpi76SDi74N3fPUQzy0Wl7HR8LTMd/83yHdW5vNhohvjon+K18C9mDDLye93cSZ1IJLCc8ZC5KKBxgrUO6vsgwXgBqxOcn4bFA+6azrgC13W/1HdNhOSA0YLZahEPZ1KVRqtI9QSKBV75bPs9l+BQP/89058TdxBHbSa8ssOKl715Vqh9dDCNFEISU+NEPB1ZC7Oza92sHXTI9Z3qxTFbozi3yRuw/Uw96FfAPxgum+E4Lh7UoszYQatsr02f4F3tltQAkJYirRbhkqs2qMUc2xwVAmUyczOdta97DMpJTWo3W6o1TdaeeSrrrWeGxks3FXbeiiccPF4Xveqa2F4n78CyHdFm2hExqtHuifGKJNiUw7GxkhEYQxsjS5zKfil2zqTh+cGPUeJFinTKCvO0eLe1XGQcWWx8pNWvKCcRkcOfpu/mgMKz3kplKgp0o4Mftc2bpP7YipRabSOUHdQmczozPPqwNuMjnncfFFM0ajZEF6ZQAf9xC53weAeKw7hfp2XT1tyVl2i1oiN382YtVu3WL5I7qEmCienMhqcVLI1hXvV7hekhIluuyV3WO4Tv+CvH+v7/cs7ONaOnysq32V93oHB8HhBEF4LEj/ffJjiLVYokVOkbRnmL7ViBAXjo8htpSSOWgJlMrMzz8ioYfe+IaZPbi98MswUDdpuXzx5JNEgxo3jj41zzrERa7W5cvnE7+K3D0ByHpk+pvT8djOakDuoaNXe1DKegdPRYz32C1K6feVu6yyuqyGoYJozMZ5788TgdK7VOh5lTARdZz9LpVQffLFCiZMi7eC1n0NLO1QUtQTKZGaHNfG/stfnZgzquBSnu1DSee6SK02pmFEmpO/NXwoX3+5tEfhms5S4wamjd9wFUjK2AnCsinwXMGavdO3v5rfqdf8OXtbCyuXRg6VBv6l7Ymw9dPxxvssKVoftqgZA/H+XfJe/peLnrvSszmrjpVDipEg7ZKjZSqOgSqBMZnZaN8Ure9+O/+Y4Az7JlDb3ZqMJBPRILsatsJzspnIyadzkWieWVHYmmgMl+NEPur1cFoCzUe7AkJWZFAX37+C5Ei8q+xGkCPx+05ZJ1rlXdFhKxa2Q3P0nwsaEs2N3gvvKI5PNjV+P4hMuLtwr4yjRuPsCLr7dvw1lJZvHK56oEiiTmR3WIN8xUMKqOs6ALyc1s6nFnpxdq6+wHsnFPWz9GNhamGYYNcYQxugwtB020ccOwWmkfZfZu2VdTH0n5NrsPzxcDVGrhRavdsNWp2E18r1+06YWy312MNju4xrxe7+XrEHWqBdBvnmIlkrq0OySL8j6cNCUzqqjMYEymZxvZlJrju1+7qAg4nQXKo4vBKUQNuWgrSO4miiE90iOWmTOHccIzBSSeKWsB1+3VqwQ3CnLzbp/KQyUA+z+TbTPC8KryFmU+kZhisKdceNYUVHPWVyXp5RNXn6U65v32rQWpYuepnRWHVUCZSIizOzMe8cEwvqoxh3wfqlupfRrjVKLP2oaaMGK12fPgVM2eX1/9OYujkUUp/x1sQKIgjvFNui8pUxEftae3yQZJ5sGKpf+WK5v3i/eteraaGMzqXGuhKJKIAFmdObZMVAUE4ia+ZPETVzqOaK+L0pv34Olq33qtbtdE1GUgNsiSjozqvhzTrrEv7OZg9dkHpal5FRQvfXEiROX3yQZljVVLddIuXtcggoiOskEcYiTSafEQmMCCTCrs32iJRAn8yfrRCmeF1jLyMS7UYsDjZXKDHFcPItvgY983fX9Aip+FsgZMCE6hdyeude7IJpvWeVR//7IUateJlGJc97ZRL4OXgRdm1LugXq6nzKGKoEEmNGRZ/e+Yd4eca3g6i3VzQku9l3m/fy8swMC3UXKwy+DqGWSd6Axyuoz7iayXKuVpeJ2fzkBz6ibvvyCmEvutM616RF/l0jQ9/DbjBYlqJtEJU6nxk/xBj93sbfi1xcrnSBlUco9UG/3U4ZQJZAATproq26XUL2mugUVxYua2XHuzROzj3KtcMFXvM+96AZ8dxl39FoTpF8+vCcynmvvRZxsmqDslyCXiB9mNH42j5skVsx+qa9ev33Qzmo/ZV/KPVCv91MGUCWQAAc3jLnTROs11c13RbbVmjxOuiR8Fe1U53S/zpmUH7xmvDql82/1jTD3AwS6J6K4rBxyLeGvCcOrP8PgHqtJu7NZrJRNbVH3aDgyFK/Ak1gxxzlHkNIJqoAbl3q9nzKABoYTwHPDWKmpblnPgAhqszmw1XIjRPFbewWlH7zGu//CwFZ4a7fVAMWv4XlBtcqQ4Kq7jWWp+AWrnSyjga1Wzn+utbDkQ1CPCHcAPQy/QKlf2mqcFXOcoHCQwkgy3VNTRytGZCUgIjlgLbDdGLNYRL4MXAAMAy8CHzfG7LVfex1wGTAKXGWMeThpwbPEEX6lI+Jm7dRCBkRYplA5dV7W3e3/XFDD86jVKt2U60uO8v6xEcsl0jopuCSzw9wPxKs75LUCb85biibK3hM/4uxfCVMYSaawajXQihDHHXQ18Lzr70eBE40x84HfAtcBiMjxwDLgBOAc4Ou2Aqlb2ltyTD20tbRdw25qIQMiitul1Ak2tAHLVu9sl6DuXH6U60uO+v7B1yf69z135ALb1ljfLUp2j2+84fVorR4dvD7Lq9RE3OC4kxqbVK9gpWJEsgREpAc4H7gJuAbAGOOOEj0O/Kn9+CLgPmPMELBFRDYDC4FfJSV0FpnZmWd7KfWD3NRKBoQzkfltIit1go1SWdTLOgrqzrXkzuir2jhE2TsB/tfCb0fuqmsLN435WYNBK/CoK+YwyzPKObzcNPPOLqxQW3zerLs8G4yolsBXgM/j3+fuE8Aq+/EswD06t9nHChCR5SKyVkTW7tq1K6IY2WVGRzs7Sikd4SbNDIhScsuDgnXF53vwmvDzL/hYNFmLraOg61a8qs13WS6TlcvLW6FGKZjnpWyc6+IXVxncE80aTCJQmpTlWZzN5Jcau/rGyjSTL4Uk9lLUCaFKQEQWAzuNMet8nr8eOADc4xzyeNmEbaTGmDuMMX3GmL7u7u4YImcTp3SEMWV0z0orA6LUG9PPbQATz+dudON3/sW3WPsQongP3av/sOvmTFIFTViKSj87GUk3z7X+RZkc5i+1ahstuTNaZc2C6xyTYmvHSwk1F12DuOcMO57EebPg8syKIsoIUdxB7wcuFJHzgHZgsoj8mzHmoyJyKbAYWGTGZ79tgNth3AO8kqTQWWRWZ579w6O88fYBOvIlpiCmlQERdGOWkuVz64nhbhL3jV/8fRffUniuMJeT33U7+P5t/o1i3GWloTCzJmpgPqrrpJzyF37Wjrso2+CeeIkElejpG3betFyecXodl3PuGnRvhVoCxpjrjDE9xpg5WAHfn9oK4BzgWuBCY8xbrrc8ACwTkTYRmQvMA9ZUQPZMMaPDSRMt0yVUzkahUkn6xoz6PmeSDVqRRbWOiq8bTDx3KX2Jk1ylBl2XIOvHzxosZ1Xt19oyqXiJ32+WhsuzeOUf1uu4nHPXoFVRzj6BrwFtwKMiAvC4MeZyY8xGEekHnsNyE11pTKltpGqHmZ3jaaLHzZicsjQxSXpFGLSXwI1XdzMvCyE/xc6tDymN7SbJonNJrVJ9r3Nv8Gc4LrbiQnSlKm+/qqz5LmuDVxIFDcF/dVyJQH0QcXsdl3vuGmuHGUsJGGMeAx6zHx8T8LqbsDKJGoaDG8aKq4nWAnHywks93wQkYEW2tfD9g3vsmjx3TLyx/EzxJN0LSa1Sg66zX6+EfNfEvg7lbgzzmxRbJ1U+pz8Nl2cpvY7LPXfWMvoC0B3DCdF9aBstOSnfHZQGSd+YBefbysQeA+La/esx8QVZCF6BVq9URL9V98HNW15yeeA3OVSih0Oxgsi1wtCb3iWuy9kYlvbEVe1NX35jQXJWGnE5471ScZUqokogIZqahOmTE0gTTQuvG7OcgJf7fH7n8XJLFE9qboonqSBT3G/V7XZ3uLuVOXsUDnb3CnA9lbOzO84KeXh/cBxj8HXLOor7G9XBxBULv7EQpbxJqeeuoZpGqgQSxEoTrUF3kBdJlrCI6xrwc40UT1Ll1q0pdUVaLT9wWCA7zsYwN3UwccWiki6oOqhppEogQWZ15vn1SyVkoGSRak10fpNYlEmqmnVr3ARVUi0VL6Ub5K4qZ9Kug4krNpV0QdV4TSNVAgkyo6OdVwfeZnTMkGvyqX9fK6TpN446SaW1ovXNfpLSWieCfw1/L0UQJYsnSn/rGp64lORQJZAgMzvzHBgz7Hpz6GBl0Zolbb9xlEkqTFlUahPPohusncYTVukG7r/cei7u5wW15sx3xUuPrYVqtEpmUCWQIAf3CgwM1r4SqBW/sZ+yiDIRlqok5i+FlZ/0fs5Je4078QbtrTgw6J0e60cd5K7HpsZ37aaJdhZLkPHmMjWaIeQmTjnhNPErBBa2m7bcnZ5ROoDF2W286AarCY3fee6/PLpscV15tV5MrQ527aaJKoEEcZTAjnrJEEqjhEUcgm7+sImw3EJmXqURgj4vjPlLoe0w/+fNaPSJLU5phnqYQLNQlK6GUSWQIJPbWzi0rZnt9WAJ1AJBN3/YRBiU4RO1gqjbUvKr/RMnhjL4evDzUSe2ONVo62ECTXvzW42jSiBhZna2l99hLItk0WUQdPOHTYSBk3PEFbHbUrr49vLLgEdRGFEmtjiuvHqYQNPsw1EHqBJImBkddbRhzCGrLoM4zWSKJ8Io7pw4K+IkYihRZHK+c5hSjurK87uG0pT+7xuVtPpw1AmaHZQwMzvzbNg+kLYYyZLVbJOwDKagNNPi9FK/TVlxVsTl5t7PXwovPw7r7vYurufu2pZUCqhfsT8nBlHKOatNI25+SxBVAgkzs6Od1/YP8/bIKO0tETpkuUkqzS3pdLmsugzKvfndk3bS/ZJLYX2/1Zu3QAHYm8U6ese/m1fTnlKVsvP6+y9PttFKtdHNbyWjSiBhDmYIDbzN3KmTor8xqdVdJTYKpb1xLIhSb/5iRVncHB2q71Lw2zXc0TveKAeSV8rzl9qb3xI8p1IzaEwgYcbTRGMGh5PK0qhEtke9+Vy9YhzP3AsnXZLuvoiok3slAqHlxgaymDigREItgYRxdg3HThNNanVXCddNvflc/RTlpkcKV9zVJqrFVYnd3OXEBrRMRU2jlkDCOOUidsTtMJbU6q5S6XJZ3zgWh6zGOOL0U056N7dzTq/9DmGWZD3sNWhgVAkkTFtzjqmHtsUvHZGUy6XeXDeVIKt55XEm90oo5flLrU5bXgQpyKwqVSUS6g6qALM62+O7g5JyudSb66YSZLk4XtpZLqUkAWQ5cUAJRZVABZjZmWfTzn3x35jUBJD2RJJ1VFH6U4qCzLJSVUKJrAREJAesBbYbYxaLSBfwXWAO8BKw1Bjzuv3a64DLgFHgKmPMwwnLnWlmdOT5+W93YYxBpMaby9QDfvsmdNKfSCkKUpVqTRPHErgaeB6YbP/918BqY8yXROSv7b+vFZHjgWXACcBM4CcicqwxXlsg65OZne28NTzKG4MH6DjEpzywUh00cyU+pShIVao1S6TAsIj0AOcD/+w6fBHwbfvxt4GPuI7fZ4wZMsZsATYDCxORtkZw9gpoNdEM0OiZK0nl7+s+gLolqiXwFeDzgLvg+XRjzA4AY8wOEZlmH58FPO563Tb7WAEishxYDjB79ux4Umccd3OZ42dODnm1UlEaNXNlfT+suhYG94wfy9IudCUzhFoCIrIY2GmMWRfxnF5O8AnVuYwxdxhj+owxfd3d3RFPXRvMPLhXQC2B1MlqOmglcSZttwJwKLaCoqzwG92aqnOiuIPeD1woIi8B9wFnisi/AX8QkRkA9v877ddvA9y993qAVxKTuAaYemgbLTlhe72VlK5F5p3NhHVJUOZKPbg9PGsQuXCsoKglwhvVmmoQQpWAMeY6Y0yPMWYOVsD3p8aYjwIPAJfaL7sU+IH9+AFgmYi0ichcYB6wJnHJM0xTkzCjI6+WQNo4VTkLDFGxagQFNafPWt+EuIRNzo4VFHWF34jWVANRzo7hLwEfEpFNwIfsvzHGbAT6geeAHwNXNlJmkMOMjvb6aDifVUp1Y2CsGkFeRJ0Us24tBE3Obiso6gpfd6HXNbGUgDHmMWPMYvvxa8aYRcaYefb/e1yvu8kYc7Qx5h3GmFVJC10LzOqsww5jWaFSbowox2vBWvDrUJbvKixDEXWFX4laRUpm0NpBFeKMkcfoH/wkJqurxVqmUm6MKMdrIUjqNWkvuROu3VI4ccdZ4ddTAUGlAC0bUQnW93Pu7/43zWJbArWcUpd0l7IkiOPGiFPOIMrrayVIGmXzlu70VVAlUBlW30jzWJErqJZa9TlkNT88asEyr0lu3tnW3yuXT5z0okyK9VYsrRF2+mZxIZMhxBifBttVpK+vz6xduzZtMZJjRSdejcsNwo//5HlOf8c08q0x+w+ngW/f3d50m68UKyewVuxhfupS35f0OZRwkuy3Xce/l4isM8b0lXMOjQlUAp9V4Q4O54p7nmTBFx/lynuf5EfP7mBwOMOJU1l1fZQaqEzCn1/JIGnWs46qRZLB91qI4aSMuoMqgY9vefr5f8e9h/4RDz27g4c3vspD63eQb8lx5juncd67ZnDGO7s5pDVDP0lc10c1ze5S3BhJKbVKuFCy6npLg6CJO63fvI7J0IxTR/j4lnPzl/I+4H3HTOXGi07kiS2v8aNnd/DjDa/y0LM7aG9pOqgQznzntPQVQpzAai1MYln25yc58dU6SU7cWf7NM4LGBDLA6JhhzZY9/OjZHaza8Cq79w3R3tLEGe8YVwiT2lJSCFFX91mLH3jJDdn1D/vEkUCstMxGIsmxpDGB8HOoEsgWo2OGX780rhB2vTlEW3MTp7+jm/PeNYNFx03n0LQUQhBZmsSCbnzIZqZI1pRomiQ9cddxdpAqgTpndMyw1qUQdtoK4YPHdnP+/IwphCxNYlmSJSp1vmKNTR1P3EmiSqCBGBszrHv5dR5av4NVG3bwhzeGaHUUwrtmsOi4aRzWnmIXsyxNYlmySvzwc1d5TXxJT4hpT7Bpf34doUqgQRkbMzz58us89OwOVj37Kq++8TatzU18YF43588/grOOm56OQsjKzZ11SyCOwqyEayRNZZ3259cZqgQUxsYMT219nYfWv8qqDTvYMfA2rbkmPnDsVM571wzOOn46k9O0ENIg6xNNHCWVtEJLW0Gm/fl1RhJKICMOZaVUmpqEBUd2seDILv7m/ON4auteK4bw7A5+8vxOWnNNnDZvXCF05G2FkJVVeyXIek2cOCmQSee5p503n/bnKxNQJVBHWAphCguOnML15x3H09v28qP1VlB59W920pITTpvXzV9OWcfCZ1cgBzKc018uWa6JEyd3Pek897Tz5v0+X5qsWE7WFHYDoGUj6pSmJuE9s6fwN4uP55fXnsH9n3ofH3vfHF549U1mrfvyuAJwqNet9FksxRCnhHPSDV0W3QC51sJjudbqNYjx63VgRslsf4Y6R5VAAyAivHv2FK4/31IIs5pe835hvZnkWW0AE6f+UCVqFRXHAasZFyz+PuJRSLFeFyQZRQPDjYhPcM509CCf3ZiCQBVCg5ATydo1qYV03gyjVUSV0vAwyd8yrXxN/pw33h5JSagKoEHIifhek63pWEjaxD51VAk0Ih4uho0L/hdf3Xkyy775ODvfrJPeyDrBTCTou6fhKtMm9qmjSqBRKeoZe8qFl/PPl/bx0mv7+ZNv/Cdbdu9PW8Ly0QlmIn6BWZjoi69GUF2b2KdOaExARNqBXwBtWCml3zPG/K2InAzcDrQDB4BPGWPW2O+5DrgMGAWuMsY8HPQZGhPIDs9s3cvH7/41AP/ysVM4qbczXYGCiLLXoZ73Q5TK+n5Y+UmfJ21ffNY33ClAlXYMi4gAk4wx+0SkBfglcDVwI3CrMWaViJwHfN4Yc7qIHA/8O7AQmAn8BDjWGOPbQkuVQLbYsns///2uJ3ht3zDf+OgCPnhsd9oiTUQnqfIICxBnLYCseFKVwLCx2Gf/2WL/M/a/yfbxDuAV+/FFwH3GmCFjzBZgM5ZCUGqEuVMn8R9XvI8jD5/EZXf/mvufymAgVdsGlkeYq0yD6g1DpJiAiORE5GlgJ/CoMeYJ4DPAl0VkK/B/gOvsl88C3EuIbfax4nMuF5G1IrJ2165dpX8DpSJMO6yd7/7lqZwyp4vPfvcZ7vzF79IWqRCdpMojzBevQfWGIZISMMaMGmNOBnqAhSJyInAF8FljTC/wWeBb9svF6xQe57zDGNNnjOnr7s6gu0FhcnsLd3/iFM6fP4ObfvQ8X3zwOcbG0t9XAugklQRFyQEFbjQNqjcMsbKDjDF7gceAc4BLgZX2U/+XcZfPNqDX9bYexl1FSo3R1pzjH5e9m0vfeyT//MstXNP/NMMHxtIWSyepSqNZOw1DaAE5EekGRowxe0UkD5wF3Iw1sX8QSymcCWyy3/IAcK+I3IIVGJ4HrEledKVaNDUJKy48gWmT2/nywy/w2v5hbv/ogvT6HkP2K4XWA1kuwqckRpS7eAbwbRHJYVkO/caYB0VkL/BVEWkG3gaWAxhjNopIP/AcVurolUGZQUptICJcecYxdB/WxnUrn+XP7nycuz52ClMPbUtPKJ2kFKVstHaQEpvVz/+BK+99kiMmt/OdT/wRsw8/JG2RFKUh0dpBSiosOm469/zFqewdHGHJN/6TDdsH0hZJUZQSUSWglMSCI6fwvcvfS2tOWHbH4/zn5t1pi6QoSgmoElBK5phph7HyU+9nVmeeS/9lDT98RpPAFKXWUCWglMURHe30/+V7eXfvFK667ynu/n9b0hapMchixzSlJlEloJRNxyEtfOeyhXzouOms+OFz/P2Pf0MWEg7qlrQ7pqkCqitUCSiJ0N6S4xsfXcAlfzSbrz/2Iv/ze+sZGc3AprJ6JM26SWkrICVxVAkoiZFrEm76yIl85qx5fG/dNpZ/Zy1vDR9IW6z6I826SVq4r+5QJaAkiojwmbOO5YsfOZGf/3YXl9z5BK/vH05brPoizbpJWriv7lAloFSEj556JF//8wU8t+MN/um2/82BfzhBfchJkWbdJC3cV3eoElAqxjknHsGPztjB/xj6Gs1vbkN9yAmRZnE3LdxXd6gSUCrKMetvIU+RO0h9yOWRZstMrS5ad6RYBlJpCNSHnCzFbTUdywqqqwh00q8b1BJQKov6kJNFs3OUhFEloFQWDx/ySFO7+pBLRS0rJWFUCSiVpciHvDs3jRVmOW+9c0naktUmalkpCaNKQKk8rl62L/23J7hn8FTuefzltKXKPl7lGTQ7R0kYVQJKVemb08X7jzmcb/7iRQaHteGcL37lGUCzc5RE0ewgpepcvehYln7zV9zzxO/5i9OOSlucbBIUAP7shmxM+mmmqiqJoZaAUnUWzu3ivUcdzjd/8TveHlFrwJOsB4C1kFzdoEpASYWrz5rHrjeHuPeJBo8N+JVlznoAWFNV6wZVAkoqnHrU4fzR3C5u//mLjWsNBK2msx4AzrqlokQmVAmISLuIrBGRZ0Rko4h8wfXcp0XkBfv437uOXycim+3nPlwp4ZXa5uqz5rHzzSG+++utaYuSDkGr6ayXZ8i6paJEJkpgeAg40xizT0RagF+KyCogD1wEzDfGDInINAAROR5YBpwAzAR+IiLHGmMadLmn+PHeow5n4ZwuvvHYiyxb2Etbcy5tkapL2Go6y+UZFt1QWL4CsmWpKJEJtQSMxT77zxb7nwGuAL5kjBmyX7fTfs1FwH3GmCFjzBZgM7AwccmVmkdEuPqsebz6xtv0N6I1UMur6axbKkpkIsUERCQnIk8DO4FHjTFPAMcCp4nIEyLycxE5xX75LMB9R2+zjxWfc7mIrBWRtbt27SrrSyi1y/uOPpy+I6fw9cdeZOhAgxmLWff7h+HaBJiZtFUlNpGUgDFm1BhzMtADLBSRE7FcSVOAU4H/CfSLiADidQqPc95hjOkzxvR1d3eXKr9S4zjWwI6Bt/m/axssqKiraSUDxNosZozZKyKPAedgrfBXGmMMsEZExoCp9vFe19t6gFeSEVepR/74mKm8Z3Yn33jsRZb29dLa3EBJa1n2+ysNQZTsoG4R6bQf54GzgN8A3wfOtI8fC7QCu4EHgGUi0iYic4F5wJpKCK/UB5Y1cCzb9w7yvXUNZg0oSspEsQRmAN8WkRyW0ug3xjwoIq3AXSKyARgGLrWtgo0i0g88BxwArtTMICWMD8ybysm9nfzTzzbzpwt6GssaUJQUEWveTpe+vj6zdu3atMVQUuZnL+zk4//ya7605F0sWzg7bXEUJfOIyDpjTF8559DllpIZTj+2m5N6OvjazzYzMjqWtjiK0hCoElAyg5MptO31Qe5/cnva4ihKQ6BKQMkUZ7xjGu+apdaAolQLVQJKphARrlo0j5f3vMX3n1JrQFEqjSoBJXOcddw0Tpg5ma/9bDMH1BpQlIqiSkDJHI418PvX3uIHT+s+Q0WpJKoElExy9vHTOW6GWgOKUmlUCSiZRES4etExbNm9nx+uV2tAUSqFKgEls5x9/BG884jD+MefbmZ0LP1NjYpSj6gSUDJLU5MVG/jdrv08qNaAolQEVQJKpjnnhCN4x/TDuG31JrUGFKUCqBJQMk1Tk/DpRcfw4q79PPTsjrTFUZS6Q5WAknnOO3EG86Ydyj+u3sSYWgOKkiiqBJTMY1kD89i0cx8/2qDWgKIkiSoBpSY4/10zOLp7ErepNaAoiaJKQKkJcnam0G//sI8fb3w1bXEUpW5QJaDUDIvnz+QotQYUJVFUCSg1Q65J+PSZx/CbV9/kkefUGlCUJFAloNQUF8yfydypk/jq6s1qDShKAqgSUGqK5lwTf3XGMTy/4w0eff4PaYujKDWPKgGl5rjo5JnMOfwQblu9CWPUGlCUcghVAiLSLiJrROQZEdkoIl8oev5zImJEZKrr2HUisllEXhCRD1dCcKVxac41ceUZx7DxlTf4yfM70xZHUWqaKJbAEHCmMeYk4GTgHBE5FUBEeoEPAS87LxaR44FlwAnAOcDXRSSXsNxKg3Pxu2cxu+sQvrr6t2oNKEoZhCoBY7HP/rPF/ufcdbcCn3f9DXARcJ8xZsgYswXYDCxMTmRFGY8NbNj+Bj/9jVoDilIqkWICIpITkaeBncCjxpgnRORCYLsx5pmil88Ctrr+3mYfKz7nchFZKyJrd+3aVZr0SkNz8Xtm0duV56saG1CUkomkBIwxo8aYk4EeYKGIzAeuB27weLl4ncLjnHcYY/qMMX3d3d0xRFYUi5ZcE1eefgzrtw3w2Au6kFCUUoiVHWSM2Qs8huXymQs8IyIvYSmHJ0XkCKyVf6/rbT2AdgRRKsKS9/QwqzPPV9QaUJSSiJId1C0infbjPHAW8JQxZpoxZo4xZg7WxP8eY8yrwAPAMhFpE5G5wDxgTaW+gNLYtDZbmULPbN3Lz3+r1oCixCWKJTAD+JmIrAd+jRUTeNDvxcaYjUA/8BzwY+BKY8xoEsIqihd/usCyBjQ2oCjxaQ57gTFmPfDukNfMKfr7JuCmsiRTlIi0NjdxxelH8zff38AvN+/mtHkaY1KUqOiOYaUu+C99PczoaOerP1FrQFHioEpAqQvamnN86vSjWfv71/nPF19LWxxFqRlUCSh1w9JTejlisloDihIHVQJK3dDWnOOK049mzUt7+NXv1BpQlCioElDqiv96Si/TJ7fx1Z9sSlsURakJVAkodUV7S47LP3g003//AENfPg5WdMKtJ8L6/rRFU5RMEpoiqii1xp8f8gTLWr9F2/4h68DAVvjhVdbj+UvTE0xRMohaAkrd0frYF8kzVHhwZBBW35iOQIqSYVQJKPXHwLZ4xxWlgVEloNQfHT3xjitKA6NKQKk/Ft0ALfnCYy1567iiKAWoElDqj/lL4YLboKMXEOv/C27ToLCieKDZQUp9Mn+pTvqKEgG1BBRFURoYVQKKoigNjCoBRVGUBkaVgKIoSgOjSkBRFKWBkSzUXReRXcDv05bDh6nA7rSFiIDKmSwqZ7KonMniyHmkMaasfqqZUAJZRkTWGmP60pYjDJUzWVTOZFE5kyVJOdUdpCiK0sCoElAURWlgVAmEc0faAkRE5UwWlTNZVM5kSUxOjQkoiqI0MGoJKIqiNDCqBBRFURqYhlMCInKXiOwUkQ2uY18Wkd+IyHoRuV9EOu3jc0RkUESetv/d7nrPAhF5VkQ2i8htIiJVkHOFiGx3yXOe67nrbFleEJEPpyznd10yviQiT9vH07yevSLyMxF5XkQ2isjV9vEuEXlURDbZ/09xvafq1zRAzkyN0QA5MzVGA+TM1BgVkXYRWSMiz9hyfsE+XvnxaYxpqH/AB4D3ABtcx84Gmu3HNwM324/nuF9XdJ41wHsBAVYB51ZBzhXA5zxeezzwDNAGzAVeBHJpyVn0/D8AN2Tges4A3mM/Pgz4rX3d/h74a/v4X7t++1SuaYCcmRqjAXJmaoz6yZm1MWqf81D7cQvwBHBqNcZnw1kCxphfAHuKjj1ijDlg//k4ENiHUERmAJONMb8y1lX/DvCRSssZwEXAfcaYIWPMFmAzsDBtOe0VyFLg34POUSU5dxhjnrQfvwk8D8zCunbftl/2bdfnpnJN/eTM2hgNuJ5+ZOp6Os9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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 100\n", + " Combined Loss: 36971338.8153418675\n", + " Kdiff : 39.1135%\n", + " Bstray : 50.1190\n", + " Pareto Kdiff : 5.9626%\n", + " Pareto Bstray : 64.6855\n", + " Coilloss : 2018.8070\n", + " num inv : 0.5983\n", + " Pareto Coilloss : 534.3835\n", + " Pareto num inv : 0.3699\n", + " V_SecCore : 1873.1886\n", + " pave : 57.0063\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\scipy\\interpolate\\fitpack2.py:280: UserWarning: \n", + "The maximal number of iterations maxit (set to 20 by the program)\n", + "allowed for finding a smoothing spline with fp=s has been reached: s\n", + "too small.\n", + "There is an approximation returned but the corresponding weighted sum\n", + "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n", + " warnings.warn(message)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(16221746.8537, device='cuda:0', dtype=torch.float64,\n", + " grad_fn=)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 150\n", + " Combined Loss: 16221746.8536623884\n", + " Kdiff : 45.7391%\n", + " Bstray : 50.1190\n", + " Pareto Kdiff : 6.1351%\n", + " Pareto Bstray : 68.5420\n", + " Coilloss : 1953.4524\n", + " num inv : 0.5983\n", + " Pareto Coilloss : 501.5187\n", + " Pareto num inv : 0.3564\n", + " V_SecCore : 1861.9526\n", + " pave : 60.7718\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\scipy\\interpolate\\fitpack2.py:280: UserWarning: \n", + "The maximal number of iterations maxit (set to 20 by the program)\n", + "allowed for finding a smoothing spline with fp=s has been reached: s\n", + "too small.\n", + "There is an approximation returned but the corresponding weighted sum\n", + "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n", + " warnings.warn(message)\n", + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\scipy\\interpolate\\fitpack2.py:280: UserWarning: \n", + "A theoretically impossible result was found during the iteration\n", + "process for finding a smoothing spline with fp = s: s too small.\n", + "There is an approximation returned but the corresponding weighted sum\n", + "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n", + " warnings.warn(message)\n" + ] + } + ], + "source": [ + "pareto_bstray_points = []\n", + "pareto_kdiff_points = []\n", + "pareto_numinv_points = []\n", + "pareto_coilloss_points = []\n", + "pareto_V_SecCore_points = []\n", + "pareto_pave_post_points = []\n", + "\n", + "kdiffs = []\n", + "bstrays = []\n", + "numinvs = []\n", + "coillosses = []\n", + "combined_losses = []\n", + "\n", + "#testing for graphs\n", + "kdiff_designs = []\n", + "bstray_designs = []\n", + "numinv_designs = []\n", + "coilloss_designs = []\n", + "V_SecCore_designs = []\n", + "pave_designs = []\n", + "\n", + "pareto_bstray_points2 = []\n", + "pareto_kdiff_points2 = []\n", + "\n", + "x_calc_numpy =[]\n", + "y_calc_numpy=[]\n", + "\n", + "#training parameters\n", + "n_epochs = 200\n", + "n_noise = 1000\n", + "\n", + "for epoch in range(n_epochs):\n", + " \n", + " # Clear old gradients in the GP model\n", + " for param in gp_model.parameters():\n", + " param.grad = None\n", + "\n", + " # Create GP parameters from noise\n", + " noise = generate_noise(shape=(n_noise, 10))\n", + " gp = gp_model(noise)\n", + "\n", + " # Unpack and filter GP parameters\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + " gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + " extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + " V_PriCore, V_SecCore, V_PriWind,V_SecWind = core_losses(extra_parameters,gp_parameters)\n", + " numinv = number_of_inverters(gp_parameters)\n", + " \n", + " # Scale GP parameters - why?\n", + " min_x = torch.min(gp_parameters)\n", + " max_x = torch.max(gp_parameters)\n", + " gp_parameters = (gp_parameters - min_x) / (max_x - min_x)\n", + " \n", + " # Push GP parameters through MP model\n", + " mp_parameters = mp_model(gp_parameters)\n", + "\n", + " # Scale MP parameters\n", + " y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + " min_y = torch.min(y, 1).values\n", + " max_y = torch.max(y, 1).values\n", + " mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + " k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + " l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + " b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + " # Calculate losses\n", + " kdiff = kdiff_loss(k_parameters)\n", + " bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " coilloss,pave = coil_loss_and_power_average(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " \n", + " #pmax - pave_kW, to get tensor to minimize, pavemin is tensor to minimize\n", + " pavemin = torch.sub(500, pave)\n", + "\n", + " # Call pairwise pareto and loss\n", + " bstray_kdiff_loss, pareto_bstray, pareto_kdiff, bstray_numpy, kdiff_numpy, pareto_bstray_numpy, pareto_kdiff_numpy, pareto_bstray_forx_numpy, pareto_kdiff_forx_numpy = compute_pairloss_and_pareto(bstray, kdiff)\n", + " numinv_coilloss_loss, pareto_numinv, pareto_coilloss, numinv_numpy, coilloss_numpy, pareto_numinv_numpy, pareto_coilloss_numpy, pareto_numinv_forx_numpy, pareto_coilloss_forx_numpy = compute_pairloss_and_pareto(numinv, coilloss)\n", + " V_SecCore_pavemin_loss, pareto_V_SecCore, pareto_pavemin, V_SecCore_numpy, pavemin_numpy, pareto_V_SecCore_numpy, pareto_pavemin_numpy, pareto_V_SecCore_forx_numpy, pareto_pavemin_forx_numpy = compute_pairloss_and_pareto(V_SecCore, pavemin)\n", + " \n", + " #get pave points for plotting\n", + " pareto_pave_post = pave_tomax(pareto_pavemin)\n", + " \n", + " #combine pairwise losses\n", + " combined_loss = (bstray_kdiff_loss + numinv_coilloss_loss + V_SecCore_pavemin_loss)\n", + "\n", + " # Push loss through the optimizer and update the GP model\n", + " try:\n", + " combined_loss.backward(retain_graph=True)\n", + " optimizer.step()\n", + " except Exception as e:\n", + " print(f\"Error on epoch {epoch} regarding\", e)\n", + " break\n", + "\n", + " \n", + " if epoch % 50 == 0:\n", + " \n", + " # plt.plot(pareto_bstray_numpy[:,0], spl_y(pareto_bstray_numpy[:,0]), '--', bstray_numpy, kdiff_numpy, 'o')\n", + " plt.plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + " plt.plot(bstray_numpy, kdiff_numpy, 'o')\n", + " # plt.plot(x_calc.cpu().data.numpy(), kdiff_numpy, 'o')\n", + " plt.xlim([0,100])\n", + " plt.ylim([0,0.5])\n", + " plt.show()\n", + " \n", + " plt.plot(pareto_numinv_numpy, pareto_coilloss_numpy)\n", + " plt.plot(numinv_numpy, coilloss_numpy, 'o')\n", + " plt.show()\n", + " \n", + " plt.plot(pareto_V_SecCore_numpy, pareto_pavemin_numpy)\n", + " plt.plot(V_SecCore_numpy, pavemin_numpy, 'o')\n", + " plt.show()\n", + "\n", + " #paretopoints graphing test\n", + " pareto_bstray_test, pareto_kdiff_test = oldgraph_pareto_frontier(bstray.cpu().detach().numpy(), kdiff.cpu().detach().numpy())\n", + " pareto_bstray_points2.append(pareto_bstray_test)\n", + " pareto_kdiff_points2.append(pareto_kdiff_test)\n", + "\n", + " # Store metrics for plotting or further logging\n", + " combined_losses.append(combined_loss.cpu().data.numpy())\n", + " kdiffs.append(torch.mean(kdiff).cpu().data.numpy())\n", + " bstrays.append(torch.mean(bstray).cpu().data.numpy())\n", + " numinvs.append(torch.mean(numinv).cpu().data.numpy())\n", + " coillosses.append(torch.mean(coilloss).cpu().data.numpy())\n", + " \n", + " kdiff_designs.append(kdiff.cpu().data.numpy())\n", + " bstray_designs.append(bstray.cpu().data.numpy())\n", + " numinv_designs.append(numinv.cpu().data.numpy())\n", + " coilloss_designs.append(coilloss.cpu().data.numpy())\n", + " V_SecCore_designs.append(V_SecCore_numpy)\n", + " pave_designs.append(pave.cpu().data.numpy())\n", + " \n", + " \n", + " pareto_bstray_points.append(pareto_bstray[:].cpu().detach().numpy())\n", + " pareto_kdiff_points.append(pareto_kdiff[:].cpu().detach().numpy())\n", + " pareto_numinv_points.append(pareto_numinv[:].cpu().detach().numpy())\n", + " pareto_coilloss_points.append(pareto_coilloss[:].cpu().detach().numpy())\n", + " pareto_V_SecCore_points.append(pareto_V_SecCore[:].cpu().detach().numpy())\n", + " pareto_pave_post_points.append(pareto_pave_post[:].cpu().detach().numpy())\n", + "\n", + "\n", + " print(f\"Epoch: {epoch:4d}\")\n", + " print(f\" Combined Loss: {combined_losses[-1]:.10f}\")\n", + " print(f\" Kdiff : {100 * kdiffs[-1]:.4f}%\")\n", + " print(f\" Bstray : {bstrays[0]:.4f}\")\n", + " print(f\" Pareto Kdiff : {100 * torch.mean(pareto_kdiff[-1]):.4f}%\")\n", + " print(f\" Pareto Bstray : {torch.mean(pareto_bstray[-1]):.4f}\")\n", + " print(f\" Coilloss : {coillosses[-1]:.4f}\")\n", + " print(f\" num inv : {numinvs[0]:.4f}\")\n", + " print(f\" Pareto Coilloss : {torch.mean(pareto_coilloss[-1]):.4f}\")\n", + " print(f\" Pareto num inv : {torch.mean(pareto_numinv[0]):.4f}\") \n", + " print(f\" V_SecCore : {torch.mean(V_SecCore).cpu().data.numpy():.4f}\")\n", + " print(f\" pave : {torch.mean(pave).cpu().data.numpy():.4f}\")\n", + " \n", + "\n", + " # Save model for further training or testing\n", + " torch.save(gp_model, f\"./models/gp_model_{epoch:06}.pt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "#use inside every so many epoch printing \n", + "\n", + "\n", + "# figure, axis = plt.subplots(2, 1, figsize=(10,10))\n", + "# plt.figure(figsize=(10, 5))\n", + " \n", + "# if normalize == True:\n", + "# axis[0].plot(normalized_pareto_bstray_numpy, normalized_pareto_kdiff_numpy)\n", + "# axis[0].scatter(x_calc.cpu().data.numpy(), normalized_kdiff_numpy)\n", + "# axis[0].scatter(normalized_bstray_numpy, normalized_kdiff_numpy)\n", + "# axis[0].set_xlim([0,1])\n", + "# axis[0].set_ylim([0,1])\n", + "# axis[0].set_xlabel(\"Bstray\")\n", + "# axis[0].set_ylabel(\"Kdiff %\")\n", + "# axis[0].set_title(f\"x_calc vs. designs, epoch: {epoch}\")\n", + " \n", + "# axis[1].plot(normalized_pareto_bstray_numpy, normalized_pareto_kdiff_numpy)\n", + "# axis[1].scatter(normalized_bstray_numpy, y_calc.cpu().data.numpy())\n", + "# axis[1].scatter(normalized_bstray_numpy, normalized_kdiff_numpy)\n", + "# axis[1].set_xlim([0,1])\n", + "# axis[1].set_ylim([0,1])\n", + "# axis[1].set_xlabel(\"Bstray\")\n", + "# axis[1].set_ylabel(\"Kdiff %\")\n", + "# axis[1].set_title(f\"y_calc vs. designs, epoch: {epoch}\")\n", + "# else:\n", + "# axis[0].plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# axis[0].scatter(x_calc.cpu().data.numpy(), kdiff_numpy)\n", + "# axis[0].scatter(bstray_numpy, kdiff_numpy)\n", + "# axis[0].set_xlim([0,100])\n", + "# axis[0].set_ylim([0,1])\n", + "# axis[0].set_xlabel(\"Bstray\")\n", + "# axis[0].set_ylabel(\"Kdiff %\")\n", + "# axis[0].set_title(f\"x_calc vs. designs, epoch: {epoch}\")\n", + " \n", + "# axis[1].plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# axis[1].scatter(bstray_numpy, y_calc.cpu().data.numpy())\n", + "# axis[1].scatter(bstray_numpy, kdiff_numpy)\n", + "# axis[1].set_xlim([0,100])\n", + "# axis[1].set_ylim([0,1])\n", + "# axis[1].set_xlabel(\"Bstray\")\n", + "# axis[1].set_ylabel(\"Kdiff %\")\n", + "# axis[1].set_title(f\"y_calc vs. designs, epoch: {epoch}\")\n", + " \n", + " \n", + " # plt.xlim([0,100])\n", + " # plt.ylim([0,0.5])\n", + " # plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "4\n", + "17 17\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#scatter plot of final pareto points\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff Combined Final Frontier\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.5])\n", + "\n", + "print(len(pareto_bstray_points2[:]))\n", + "print(len(pareto_kdiff_points2[:]))\n", + "# print(pareto_bstray_points2[:])\n", + "final_x = []\n", + "for list in pareto_bstray_points2:\n", + " for item in list:\n", + " final_x.append(item)\n", + "\n", + "\n", + "final_y = []\n", + "for list in pareto_kdiff_points2:\n", + " for item in list:\n", + " final_y.append(item)\n", + "\n", + "final_x, final_y = oldgraph_pareto_frontier(final_x, final_y)\n", + "print(len(final_x),len(final_y))\n", + "\n", + "plt.scatter(final_x, final_y)\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "4\n", + "17 17\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#line plot of final pareto points\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff Combined Final Frontier\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points2[:]))\n", + "print(len(pareto_kdiff_points2[:]))\n", + "# print(pareto_bstray_points2[:])\n", + "final_x = []\n", + "for list in pareto_bstray_points2:\n", + " for item in list:\n", + " final_x.append(item)\n", + "\n", + "\n", + "final_y = []\n", + "for list in pareto_kdiff_points2:\n", + " for item in list:\n", + " final_y.append(item)\n", + "\n", + "final_x, final_y = oldgraph_pareto_frontier(final_x, final_y)\n", + "print(len(final_x),len(final_y))\n", + "\n", + "plt.plot(final_x, final_y)\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'\\nplt.figure(figsize=(10, 5))\\nplt.title(f\"Pareto Kdiff During Training\")\\nplt.xlabel(\"Epoch\")\\nplt.ylabel(\"Coupling %\")\\n\\nfor x in pareto_kdiff_points:\\n plt.plot(x)\\nplt.show()\\n'" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points), len(pareto_kdiff_points))\n", + "\n", + "while i < len(pareto_bstray_points):\n", + " plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()\n", + "\n", + "\n", + "'''\n", + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"Pareto Kdiff During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Coupling %\")\n", + "\n", + "for x in pareto_kdiff_points:\n", + " plt.plot(x)\n", + "plt.show()\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 4\n" + ] + }, + { + "data": { + "image/png": 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Z5LDyeWb2T/N6tpfXtG1/3g/DPjszu9zMVvi/6/uq9NBeambL/Lo3wnt3q7RXHhAEvjCzVX75WjM71f/H1E3AheYN037RkO0iEmlK7EQixMyG4iV2K/2iI4HKPxLOuVVACdDfzNoD3cPr/ekja1j3MLwerql4vVEPAi9X/EH3TQbOAA4D+gO/85c9BbgD+D7QDVgHPO3XJQNvA6/78fQD5oStc7w/bzvgZeBef7kjgJ8DI5xzyf5219beQvVStc3ygVXsbZd96tm3zY4EVjvncmuor05N+xcNvAK8CaQBvwCe9Pe7quuALKAzXs/sTYDzE6pX/Bh6AGOBa8zsjFriqY/fAqOAoXg9lyPxv+taYqn392VmN/iJYbWvGpZJxHv26Th//ccBi8NmOQZYDXQCfg+8YGGnJdThbGCEv6/f92PHzM719+88f38/wOux3Ydzrtg5l+R/HOKcO6xK/evAn4BZ/jDtkHrGJdIsKLETiQAzSwFmALc453b7xUnA7iqz7gaS/Tqq1FfUVednwIPOuU/83sHpQDHeH/gK9zrnNjjndgK3Az/wyycDjzrnPnPOFQM3AseaWW+8P5qbnXN/c84VOedynXOfhK3zQ+fcq865cn//Kv7olQOxwCAzi3bOrfUT10NVW5tVV78bSPJ7cepatjo17d8of313OudKnHPvAP9lb5uGK8VLmDOcc6XOuQ+c91DuEUBn59yt/jpW4/XKTqolnvqYDNzqnNvqnNsG3II33F9bLPX+vpxzdzrn2tX0qiWuEHCUmcX7vdjhw55bgbv9mGYB3wDfref+3umcy3HOrQfexUtowftHzh3OuWXOuTK85Gxodb12Iq2ZEjuRBmZm8Xg9Mx875+4Iq8oDUqrMngLk+nVUqa+oq04GcF2VnpOeeL1sFTaETa8Lq+vufwbAHxbegdeL1BOvR6wmm8OmC4A4M4tyzq0ErsEbKt1qZk9b2PDuIaitzaqrTwHy/OSlrmWrU+3+4bXZBudcKKx+HV6bVfUXvF7aN/2hyBv88gyge5Xv7Ca8nrRDsc/3yb7fdbWxRPD7wl9/PnAhcDmwycz+FzZ8DpDtf0fVxVyXqt9RxT+KMoB/hLXtTsCo/jsSabWU2Ik0IH8odDaQzb4n8YN3ovaQsHn74vWafOuc2wVsCq/3p2s6uXsDcHuV3pME51z40FPPsOleeBcp4L+HX1maiDecm+2vd5+hqfpyzs10zo321+2Auw5mPVVUbbNEP76l1dWzb5stBfra3nMYq9YfiI1Az4rz03y98NpsH34v53XOub7AOcD/+eeQbQDWVPnOkp1zZx1EPFVjC++Vqvyua4ml3t+Xmd3kn2tW7aumoJxzbzjnTsPrMVyO1ztZoUf4uXHs+/s8WBuAqVXaN945N/8g1uXqnkWkeVJiJ9JA/POwngMKgYur9O4APAmcY2Yn+AnKrcALYeeAPQ78zsza+70bPwP+U8PmHgYuN7NjzJNoZt+tksRcZWbp/rlLN7H3atKZwI/NbKifiP4J+MQ5txZveLGrmV3jn2SebFVu2VLDvh9hZqf46yvy26DcrzvJzGr8Q2lmUeZd2BEEgmZW0UsG8CLecN73/HmmAV8655aHtdn/mVkPv8fpuoo2c859i3de1+/9dU7Eu3L2+br2pxqfAPl4VypHm3e/s3Pwz02ssj9nm1k/P3HZ47dDObAA2GPeRQvxZhY0s6PMbER92skX7e9LXFg7PYX3u+ls3lXE04Anaoultu+rKufcn/xzzap9VbeMmXUxs/H+77wYr/c0fP1pwNV+W14ADARerWPf6/Iv4Ebzrj7HzFL9dR+MLUDvKom8SIugH61IwzkO7xy104GcsF6NEwD8c4wux0vwtuKd63Vl2PK/xxsGXQe8B/zFP5F7P865hXiJ373ALrzhtkuqzDYT72T/1f7rNn/ZOXhX5D6P10t4GP55Xn6SeRpe0rIZWIF365W6xAJ3Atv95dLwkknweg4/qmXZ3+ElFjcAF/nTv/Pj2YZ3Fevt/n4ew77npD2IN+y9BPgK+J9fVmESkOkveydwvr/OA+KcK8G7sGKcv4/34yXvy6uZ/XC8C1Dy8Pb7fufcXP+8vXPwzglb46/n30Cqv1xd7QRe8lMY9voD3ve6EPgSrx0+88tqjIXav6+GEMBLsjfiDYmOYd/f+id+bNvxvtvznXPVXRVdb865F/F6HZ82sz14v4dxB7m6Z/33HWb22aHEJdLYbN/THESkNTCztcBPnXNvN4NY/g0865x7o6ljac7aSjuZ2SV4v83RTR2LSGsUVfcsIiIHzzn306aOoSVQO4lIQ4joUKx5N3xcYt5NVBf6ZR3Mu/npCv+9fdj8N5p309RvLOzeTmY23F/PSjO7p8pJtyIiIiJChIdi/eGgTOfc9rCyPwM7nXN3mnfpfXvn3G/MbBDeScAj8S57fxvo75wrN7MFwC+Bj/HOMbnHOfdaxAIXERERaYGa4uKJCXjP0MR/Pzes/Gnn3RV8Dd7J4CPNrBuQ4pz7yL/v0eNhy4iIiIiIL9KJncO7MeYiM7vML+vinNsE4L+n+eU92PeGqll+WQ9/umq5iIiIiISJ9MUTxzvnNppZGvCWmVV3a4AK1Z0352op338FXvJ4GUBiYuLwAQMGVDebiIiISLOyaNGi7c65zoe6nogmds65irufbzWzF/HOn9tiZt2cc5v8Ydat/uxZ7Hun/HS8eyBl+dNVy6vb3kPAQwCZmZlu4cKFDbk7IiIiIhFhZuvqnqtuERuK9e+En1wxjXfT1q+Al4Ep/mxTgJf86ZeBSf7d7vvg3bxygT9cm2tmo/yrYS8OW0ZEREREfJHssesCvOjfmSQKmOmce93MPgWeMbOfAOuBC8C7K7+ZPQN8DZQBV/l3age4Au8xQfHAa/5LRERERMK02idPaChWREREWgozW+ScyzzU9ejJEyIiIm1caWkpWVlZFBUVNXUorV5cXBzp6elER0dHZP1K7ERERNq4rKwskpOT6d27N3q4U+Q459ixYwdZWVn06dMnIttoihsUi4iISDNSVFREx44dldRFmJnRsWPHiPaMKrETERERJXWNJNLtrMROREREWrW5c+dy9tlnH9Ayzjmuvvpq+vXrx+DBg/nss88iFF3DUmInIiIiUsVrr73GihUrWLFiBQ899BBXXHFFU4dUL0rsREREpMk98cQTjBw5kqFDhzJ16lTKy71b2SYlJXHdddcxbNgwxo4dy7Zt2wBYvHgxo0aNYvDgwUycOJFdu3YBsHLlSk499VSGDBnCsGHDWLVqFQB5eXmcf/75DBgwgMmTJ1PX7d5eeuklLr74YsyMUaNGkZOTw6ZNmyLYAg1DiZ2IiIg0qWXLljFr1izmzZvH4sWLCQaDPPnkkwDk5+czbNgwPvvsM8aMGcMtt9wCwMUXX8xdd93Fl19+ydFHH11ZPnnyZK666iq++OIL5s+fT7du3QD4/PPPufvuu/n6669ZvXo18+bNA2DatGm8/PLL+8WUnZ1Nz557n3Sanp5OdnZ2RNuhIeh2JyIiIlLplleW8vXGPQ26zkHdU/j9OUfWWD9nzhwWLVrEiBEjACgsLCQtLQ2AQCDAhRdeCMBFF13Eeeedx+7du8nJyWHMmDEATJkyhQsuuIDc3Fyys7OZOHEi4N0zrsLIkSNJT/cePT906FDWrl3L6NGjufXWW6uNqboevZZwgYkSOxEREWlSzjmmTJnCHXfcUee8tSVXtQ2vxsbGVk4Hg0HKyspq3U56ejobNmyo/JyVlUX37t3rjK+pKbETERGRSrX1rEXK2LFjmTBhAtdeey1paWns3LmT3NxcMjIyCIVCPPfcc0yaNImZM2cyevRoUlNTad++PR988AEnnHACM2bMYMyYMaSkpJCens7s2bM599xzKS4urjxX70CNHz+ee++9l0mTJvHJJ5+QmppaOazbnCmxExERkSY1aNAgbrvtNk4//XRCoRDR0dHcd999ZGRkkJiYyNKlSxk+fDipqanMmjULgOnTp3P55ZdTUFBA3759eeyxxwCYMWMGU6dOZdq0aURHR/Pss8/Wuu1p06aRmZnJ+PHj9yk/66yzePXVV+nXrx8JCQmV62/urK6rQlqqzMxMt3DhwqYOQ0REpNlbtmwZAwcObOowqpWUlEReXl5Th9GgqmtvM1vknMs81HXrqlgRERGRVkKJnYiIiDRbra23LtKU2ImIiIi0EkrsRERERFoJJXYiIiIirYQSOxEREZFWQomdiIiItGpz587l7LPPPuBlUlNTGTp0KEOHDt3n0WOvv/46RxxxBP369ePOO+9s6HAPiW5QLCIiIlKNE044gf/+97/7lJWXl3PVVVfx1ltvkZ6ezogRIxg/fjyDBg1qoij3pR47ERERaXJPPPEEI0eOZOjQoUydOrXyUWBJSUlcd911DBs2jLFjx7Jt2zYAFi9ezKhRoxg8eDATJ05k165dAKxcuZJTTz2VIUOGMGzYMFatWgV4t005//zzGTBgAJMnT671ubK1WbBgAf369aNv377ExMQwadIkXnrppQZogYahxE5ERESa1LJly5g1axbz5s1j8eLFBINBnnzySQDy8/MZNmwYn332GWPGjOGWW24B4OKLL+auu+7iyy+/5Oijj64snzx5MldddRVffPEF8+fPr3y+6+eff87dd9/N119/zerVq5k3bx7gPVLs5Zdfrjaujz76iCFDhjBu3DiWLl0KQHZ2Nj179qycJz09nezs7Mg0zEHQUKyIiIjs9doNsHlJw66z69EwruZz0ebMmcOiRYsYMWIEAIWFhaSlpQEQCAS48MILAbjooos477zz2L17Nzk5OYwZMwaAKVOmcMEFF5Cbm0t2djYTJ04EIC4urnIbI0eOJD09HYChQ4eydu1aRo8evc+5c+GGDRvGunXrSEpK4tVXX+Xcc89lxYoV1fb0mdmBtkjEKLETERGRJuWcY8qUKdxxxx11zltbElXb8GpsbGzldDAYpKysrNbtpKSkVE6fddZZXHnllWzfvp309HQ2bNhQWZeVlUX37t3rjLuxKLETERGRvWrpWYuUsWPHMmHCBK699lrS0tLYuXMnubm5ZGRkEAqFeO6555g0aRIzZ85k9OjRpKam0r59ez744ANOOOEEZsyYwZgxY0hJSSE9PZ3Zs2dz7rnnUlxcXHmu3oHavHkzXbp0wcxYsGABoVCIjh070q5dO1asWMGaNWvo0aMHTz/9NDNnzmzgFjl4SuxERESkSQ0aNIjbbruN008/nVAoRHR0NPfddx8ZGRkkJiaydOlShg8fTmpqKrNmzQJg+vTpXH755RQUFNC3b18ee+wxAGbMmMHUqVOZNm0a0dHRPPvss7Vue9q0aWRmZjJ+/Ph9yp977jkeeOABoqKiiI+P5+mnn8bMiIqK4t577+WMM86gvLycSy+9lCOPPDIyDXMQ7GCvCmnuMjMz3cKFC5s6DBERkWZv2bJlDBw4sKnDqFZSUhJ5eXlNHUaDqq69zWyRcy7zUNetq2JFREREWgkldiIiItJstbbeukhTYiciIiLSSiixExEREWkllNiJiIiItBJK7ERERERaCSV2IiIi0qrNnTuXs88++4CWWb58OcceeyyxsbH89a9/3afu9ddf54gjjqBfv37ceefeGzrv3LmT0047jcMPP5zTTjuNXbt2NUj8B0KJnYiIiEgVHTp04J577uFXv/rVPuXl5eVcddVVvPbaa3z99dc89dRTfP311wDceeedjB07lhUrVjB27Nh9kr7GosROIqpo+XKK/B+8iIhITZ544glGjhzJ0KFDmTp1auWjwJKSkrjuuusYNmwYY8eOZdu2bQAsXryYUaNGMXjwYCZOnFjZO7Zy5UpOPfVUhgwZwrBhw1i1ahXg3Tbl/PPPZ8CAAUyePLnW58oCpKWlMWLECKKjo/cpX7BgAf369aNv377ExMQwadIkXnrpJQBeeuklpkyZAsCUKVOYPXt2g7VPfSmxk4ja9v/uZs1536Pwiy+aOhQREWmmli1bxqxZs5g3bx6LFy8mGAzy5JNPApCfn8+wYcP47LPPGDNmDLfccgsAF198MXfddRdffvklRx99dGX55MmTueqqq/jiiy+YP38+3bp1A+Dzzz/n7rvv5uuvv2b16tXMmzcP8B4p9vLLL9c71uzsbHr27Fn5OT09nezsbAC2bNlSub1u3bqxdevWQ2yZA6dnxUpEpf3qOvLee4+iZcuJHzKkqcMREZE63LXgLpbvXN6g6xzQYQC/GfmbGuvnzJnDokWLGDFiBACFhYWkpaUBEAgEuPDCCwG46KKLOO+889i9ezc5OTmMGTMG8HrHLrjgAnJzc8nOzmbixIkAxMXFVW5j5MiRpKenAzB06FDWrl3L6NGjufXWWw9oX6rr6TOzA1pHJCmxk4iKzsgAoHzXziaOREREmivnHFOmTOGOO+6oc97akqjahldjY2Mrp4PBIGVlZQcWpC89PZ0NGzZUfs7KyqJ79+4AdOnShU2bNtGtWzc2bdpUmZw2JiV2ElGBmBgCSUmU7Wz8K4NEROTA1dazFiljx45lwoQJXHvttaSlpbFz505yc3PJyMggFArx3HPPMWnSJGbOnMno0aNJTU2lffv2fPDBB5xwwgnMmDGDMWPGkJKSQnp6OrNnz+bcc8+luLi48ly9hjJixAhWrFjBmjVr6NGjB08//TQzZ84EYPz48UyfPp0bbriB6dOnM2HChAbddn0osZOIC3boQPlO9diJiEj1Bg0axG233cbpp59OKBQiOjqa++67j4yMDBITE1m6dCnDhw8nNTWVWbNmATB9+nQuv/xyCgoK6Nu3L4899hgAM2bMYOrUqUybNo3o6GieffbZWrc9bdo0MjMzGT9+/D7lmzdvJjMzkz179hAIBCrPz0tJSeHee+/ljDPOoLy8nEsvvZQjjzwSgBtuuIHvf//7PPLII/Tq1avObUeC1XVVSEuVmZnpFi5c2NRhCLD2wklYQjwZ/v90IiLSvCxbtoyBAwc2dRjVSkpKIi8vr6nDaFDVtbeZLXLOZR7qunVVrESc12OnoVgREZFIU2InERfsqKFYERE5OK2tty7SlNhJxEW170BZTk6dN4MUERGRQxPxxM7Mgmb2uZn91//cwczeMrMV/nv7sHlvNLOVZvaNmZ0RVj7czJb4dfdYc7phjNQp2KEDlJYSys1t6lBERERatcbosfslsCzs8w3AHOfc4cAc/zNmNgiYBBwJnAncb2ZBf5kHgMuAw/3XmY0QtzSQqA5e7q7hWBERkciKaGJnZunAd4F/hxVPAKb709OBc8PKn3bOFTvn1gArgZFm1g1Icc595LyxvMfDlpEWINihAwBlSuxEREQiKtI9dncD1wOhsLIuzrlNAP57xW2ZewAbwubL8st6+NNVy/djZpeZ2UIzW1jxkGBpehWJnXrsRESkKcydO5ezzz77gJZZvnw5xx57LLGxsfz1r3/dp653794cffTRDB06lMzMvXco2blzJ6eddhqHH344p512Grt2Nf4dISKW2JnZ2cBW59yi+i5STZmrpXz/Qucecs5lOucyO3fuXM/NSqRFqcdORERamA4dOnDPPffwq1/9qtr6d999l8WLFxN+z9w777yTsWPHsmLFCsaOHcudd97ZWOFWimSP3fHAeDNbCzwNnGJmTwBb/OFV/Pet/vxZQM+w5dOBjX55ejXl0kLs7bHTvexERKR6TzzxBCNHjmTo0KFMnTq18lFgSUlJXHfddQwbNoyxY8dSMSK3ePFiRo0axeDBg5k4cWJl79jKlSs59dRTGTJkCMOGDWPVqlWAd9uU888/nwEDBjB58uQ679SQlpbGiBEjiI6Orvc+vPTSS0yZMgWAKVOmMHv27ANthkMWscTOOXejcy7dOdcb76KId5xzFwEvA1P82aYAL/nTLwOTzCzWzPrgXSSxwB+uzTWzUf7VsBeHLSMtQCA2lkBCAuW71GMnIiL7W7ZsGbNmzWLevHksXryYYDDIk08+CUB+fj7Dhg3js88+Y8yYMdxyyy0AXHzxxdx11118+eWXHH300ZXlkydP5qqrruKLL75g/vz5dOvWDYDPP/+88rFgq1evZt68eYD3SLGXX375gOI1M04//XSGDx/OQw89VFm+ZcuWyu1169aNrVu31rSKiGmKZ8XeCTxjZj8B1gMXADjnlprZM8DXQBlwlXOu4sm9VwD/AeKB1/yXtCDBDh0oU4+diEizt/lPf6J42fIGXWfswAF0vemmGuvnzJnDokWLGDFiBACFhYWkpXmn4AcCAS688EIALrroIs477zx2795NTk4OY8aMAbzesQsuuIDc3Fyys7OZOHEiAHFxcZXbGDlyJOnp3gDg0KFDWbt2LaNHj+bWW2894P2ZN28e3bt3Z+vWrZx22mkMGDCAE0888YDXEwmNktg55+YCc/3pHcDYGua7Hbi9mvKFwFGRi1AiLdihA+U7djR1GCIi0gw555gyZQp33HFHnfPWdivb2oZXY2NjK6eDwSBlZWUHFmSY7t27A95w7cSJE1mwYAEnnngiXbp0YdOmTXTr1o1NmzZVJqeNqSl67KQNiurQgdItW5o6DBERqUNtPWuRMnbsWCZMmMC1115LWloaO3fuJDc3l4yMDEKhEM899xyTJk1i5syZjB49mtTUVNq3b88HH3zACSecwIwZMxgzZgwpKSmkp6cze/Zszj33XIqLiyvP1Wso+fn5hEIhkpOTyc/P580332TatGkAjB8/nunTp3PDDTcwffp0JkyY0KDbrg8ldtIogu3aUfTNN00dhoiINEODBg3itttu4/TTTycUChEdHc19991HRkYGiYmJLF26lOHDh5OamsqsWbMAmD59OpdffjkFBQX07duXxx57DIAZM2YwdepUpk2bRnR0NM8++2yt2542bRqZmZmMHz9+n/LNmzeTmZnJnj17CAQClefnbd++vXKot6ysjB/+8Ieceab33IQbbriB73//+zzyyCP06tWrzm1HgrXW53dmZma68EuQpWlt/tOf2P3Cixyx8NOmDkVERKpYtmwZAwcObOowqpWUlEReXl5Th9GgqmtvM1vknMusYZF6a4xHiokQTE4hlJeHa+AucREREdlLiZ00imBKMgChVvavLhERiazW1lsXaUrspFEEklMAKM/V/6AiIiKRosROGkUgOQmAUO6eJo5ERESq01rPuW9uIt3OSuykUQQreuz25DZxJCIiUlVcXBw7duxQchdhzjl27Nixz42TG5pudyKNovIcO/XYiYg0O+np6WRlZVU+h1UiJy4urvIJGJGgxE4aRSBFPXYiIs1VdHQ0ffr0aeowpAFoKFYaRTBZPXYiIiKRpsROGkUgybt4QlfFioiIRI4SO2kUFgwSSExUj52IiEgEKbGTRhNISdE5diIiIhGkxE4aTTA5mXL12ImIiESMEjtpNIGUZELqsRMREYkYJXbSaILJKZTnKrETERGJFCV20miCKcnk79zC/I3zmzoUERGRVkmJnTSaQHIKJXtyeGrZU00dioiISKukxE4aTSA5ibiiEKt2rmjqUERERFolJXbSaILJKQQc7NiVTWFZYVOHIyIi0uoosZNGE0zxHisWX+RYu3tt0wYjIiLSCimxk0YTSE4BILEYVuasbOJoREREWh8ldtJoKnrsUoqDrN69uomjERERaX2U2Emjqeixywh0ZlXOqiaORkREpPVRYieNJpicBECvQEdW7NKVsSIiIg1NiZ00mmD79gD0L+tIVl4W3+z8pokjEhERaV2U2EmjCaakEHv44fRZkUd0IJoXVrzQ1CGJiIi0KkrspFElHnccpZ9/weldT+K/q/9LcXlxU4ckIiLSaiixk0aVOPp4XEkJ5xUMYE/JHt5d/25ThyQiItJqKLGTRpWQmYlFR9Nr+S66J3bnuRXPEXKhpg5LRESkVVBiJ40qEB9P/PDhFMybz8TDJ/LJpk8Y/dRofvrGT7nns3vIL81v6hBFRERaLCV20jhe/TW8/QcAEo8/juJvv+WSrhO47fjbGNdnHDuLd/LwkoeZv3F+08YpIiLSgimxk8axczWsnANA0vHHA1D88QIm9JvAzcfezD9O/gcABaUFTRaiiIhIS6fEThpHpyNg+woIhYgdMIBghw7kz9/bOxcfFQ9AYVlhU0UoIiLS4imxk8bR+QgoK4Td67FAgMRjjyVv/nycc4ASOxERkYagxE4aR+cjvPdt3wLe/ezKt22n+Fvv0WJxwThAiZ2IiMihUGInjaNTf+9923LAu4ACIH/ePACCgSCxwViKyoqaJDwREZHWQImdNI6EDpCYBtu958NGd+1KTL/DKhM7gLioOArKdPGEiIjIwVJiJ42n8xGw7ZvKj4nHHUfBwoWEir3HisVHxWsoVkRE5BAosZPG0/kI7xw7/4KJpOOPxxUXU/j5YsBL7DQUKyIicvCU2Enj6XQEFO+GvC0AxPTuDUDZls2AdwGFeuxEREQOnhI7aTyVV8Z6F1AEUlIAKN+TC2goVkRE5FApsZPGU+WWJ8GkJADKc/cAEB+toVgREZFDocROGk9SF4hNreyxs+hoLD6eUEWPXVA9diIiIociYomdmcWZ2QIz+8LMlprZLX55BzN7y8xW+O/tw5a50cxWmtk3ZnZGWPlwM1vi191jZhapuCWCzLxeu+3fVhYFk5P39thpKFZEROSQRLLHrhg4xTk3BBgKnGlmo4AbgDnOucOBOf5nzGwQMAk4EjgTuN/Mgv66HgAuAw73X2dGMG6JpM7997nlSVSnTpRt3QYosRMRETlUEUvsnCfP/xjtvxwwAZjul08HzvWnJwBPO+eKnXNrgJXASDPrBqQ45z5y3oNFHw9bRlqajv0gfysUe8Ov0T16ULpxIwBpCWnsKt5FQaluUiwiInIwInqOnZkFzWwxsBV4yzn3CdDFObcJwH9P82fvAWwIWzzLL+vhT1ctl5Yo0f+687cDfmKXnY1zjj6pfQBYu2dtEwUnIiLSskU0sXPOlTvnhgLpeL1vR9Uye3XnzblayvdfgdllZrbQzBZu27btgOOVRpDYyXsv2AF4iZ0rKqJ85076pvYFYPXu1U0VnYiISIvWKFfFOudygLl458Zt8YdX8d+3+rNlAT3DFksHNvrl6dWUV7edh5xzmc65zM6dOzfkLkhDSfATu7AeO4DSjRvJSMkgaEHW7F7TVNGJiIi0aJG8KrazmbXzp+OBU4HlwMvAFH+2KcBL/vTLwCQzizWzPngXSSzwh2tzzWyUfzXsxWHLSEuT2NF7L9g3sStZvZroYDQ9k3sqsRMRETlIURFcdzdgun9lawB4xjn3XzP7CHjGzH4CrAcuAHDOLTWzZ4CvgTLgKudcub+uK4D/APHAa/5LWqJEvyfV77GL6Z1BVPdubL7lVgKpqfRJ7cPqHA3FioiIHIyIJXbOuS+B71RTvgMYW8MytwO3V1O+EKjt/DxpKWISISoe8r1zIAOxsfR+6imyrriSrCuv4qQfjuSPvdZSFiojKhDJf3eIiIi0PnryhDS+xE6VF08ARHfpQsYTM0geO5aBT3zMj18tYf0uDceKiIgcKCV20vgSOlYOxVYIJCTQ4x93E7roXE5b7Nhz1a8o3727iQIUERFpmZTYSeNL7FR58UQ4CwTIuP633Ht2gOglK1l74SRK1q5t/PhERERaqDoTOzM73swS/emLzOzvZpYR+dCk1UroBPk7qq1Kikli+ciuvPWr0ZTv3s2GK69q5OBERERarvr02D0AFJjZEOB6YB3eY71EDk5KN8jdBIW7qq3u064Pn3TeQ7vvnUdpdnYjByciItJy1SexK/Of0ToB+Idz7h9AcmTDklbtqO9BqBQ+f6La6r6pfVmzZw0Wn4ArLsaVlTVygCIiIi1TfRK7XDO7EbgI+J9/X7royIYlrVrXo6HXcbDgIQiV71c9qOMg8kvzWVO6CYBQQUFjRygiItIi1SexuxAoBn7inNsM9AD+EtGopPUb8RPIWQ9Zn+5X9d0+36Vval9e3fwOoMRORESkvurVY4c3BPuBmfUHhgJPRTQqaf1S/ccCl+TvVxUdjOa3x/yWjS4HQLc9ERERqaf6JHbvA7Fm1gOYA/wY7/FeIgfPzHt3rtrqkd1GkjHsRAA2fvJeY0UlIiLSotUnsTPnXAFwHvBP59xE4MjIhiWtnvk/vdL9e+wq/PTsP7AnwVj89kxcDQmgiIiI7FWvxM7MjgUmA//zy4KRC0nahNSeEJMEz/8UXrsBCnbuN0uXxC6UHd2fjt9s4d0N7zZBkCIiIi1LfRK7a4AbgRedc0vNrC+gv7JyaJK7wFWfwOALYcGD8N9rq52t/ykTSNsND755O4VlhY0cpIiISMtSZ2LnnHvPOTceuN/Mkpxzq51zVzdCbNLapabDhHth6GRY/S6EQvvNkpQ5EoD2327mjx/9kfJqbo8iIiIinvo8UuxoM/sc+Ar42swWmZnOsZOG03s0FO2GrUv3q4obMIBAYiLnFx7JK6tf4YYPbqA0VNoEQYqIiDR/9RmKfRD4P+dchnOuF3Ad8HBkw5I2JeN4733tvP2qLBgkftgw+qwp4Lrh1/H62tf5v3f/j+Ly4kYOUkREpPmrT2KX6JyrPKfOOTcXSIxYRNL2tOsJ7XrBug+rrU7IzKRk5Sou6jGB3x3zO+ZmzeXqd64m5PYfuhUREWnL6pPYrTazm82st//6HbAm0oFJG5MxGtbNr/a+dgmZwwEoXLSICwdcyC++8wvmb5xPdm52Y0cpIiLSrNUnsbsU6Ay8ALzoT/84kkFJG5RxHBTsgG3L96uKO/poLCaGgoWLABjexUv01uWua9QQRUREmruoumZwzu0CdBWsRFbvivPsPoS0gftUBWJiiB88mIKFCwHISMkAYN2edYzuMbpRwxQREWnOakzszOwVoMbb/fu3QBFpGO37QHJ3WDcPRv5sv+r4EZnseOhhyvPy6ZjYkcToRDbkbmiCQEVERJqv2nrs/tpoUYiYeb12a973zrOreJasL2F4JjvK/0Xh4sUkjT6eTvGd2Fm4/9MqRERE2rIaEzvnnJ68Lo0r43hY8izsWAWd+u1TFT90KASDFCz8lKTRx9Muth07i5XYiYiIhKvPxRMijaO3f75cNbc9CSYlEjdoEIX+BRTtY9uTU5TTiMGJiIg0f0rspPno2A8S06q9UTFAwvDhFH75JaGSEtrFtWNX8a5GDlBERKR5U2InzYeZd9uTdfOqv5/diExcSQlFS5ZU9ti5auYTERFpq3RVrDQvvUfD17Nh+7fQ+Yh9quKHDQMgf948Uk9OpSRUQmFZIQnRCU0QqIiISPOjq2KleRl4Drx+A3z2OJxx+z5VUe3bk3DMMWx/4F/02TyKYD9Haai0iQIVERFpfnRVrDQvyV3hiHGweCaccjNEx+1T3fOB+9lyx53w7LPc0h1KxmTBYalNFKyIiEjzUuM5dmb2jP++xMy+DHstMbMvGy9EaXMyL4XCnbDslf2qAgkJdPvjraz99fn02AE7Lvop5Xl5TRCkiIhI81PbxRO/9N/PBs4Je1V8FomMPidB+96w6LEaZ8kdfTS3TQriduWw7R/34EKhxopORESk2aoxsXPObfLf1wFFwNH+q9AvE4mMQACOPA/WfwxlxdXOErQgq7obMd87h10zZpB19dWU5+WRX1zWyMGKiIg0H3Xe7sTMvg8sAC4Avg98YmbnRzowaeO6HAmuHHasrLY6YN5PN/aGq+ly043kvTuXleedz5TfP8MzC/UMWRERaZvqcx+73wIjnHNTnHMXAyOBmyMblrR5nQd471uXVVsdDAQByCvNo8PFF9PrsUex/Dx+97+/8OwDz/LRqh2NFamIiEizUZ/ELuCc2xr2eUc9lxM5eJ0OBwvCtuXVVg/sMJDYYCyXvXkZ76x/h8SRI+nz/HMkdE3jxyve5vInFrF6my6qEBGRtqU+CdrrZvaGmV1iZpcA/wNei2xY0uZFxUKHvjX22B3e/nCeOfsZuiZ25Zfv/pI/zP8DpR1TSDnhePoUbCNo8JPpC8kpKGnkwEVERJpOnYmdc+7XwIPAYGAI8JBz7vpIByZCz2Pg2zdg5Zxqq/u268uTZz3JpUddygsrXuCCVy5ge+dYyMvj4bP7kL2rkKkzFlFSpitmRUSkbajtPnb9zOx4AOfcC865/3POXQvsMLPDGi1CabvO/JN3rt2siyBrYbWzRAejuXb4tTxyxiOUhkq5c+uTAPQr2cafzx/MJ2t28tsXl+iZsiIi0ibU1mN3N5BbTXmBXycSWXGpcNHzkNQFnjwftlZ/vh3AiK4jeG78c/QfejIA/3jh15D8KT8/pS/PLsri9a82N1bUIiIiTaa2xK63c26/J0w45xYCvSMWkUi45C7woxchGAMzJkLO+hpnTYlJ4ebxdxOKj6XnTuPmeTdT3u4VkuOieH/F9kYMWkREpGnUltjF1VIX39CBiNSoQx+46AUoyfeSu9wtNc5qZiQcdjhj3QBG9xjNe1lzycxoz8K1OxsxYBERkaZRW2L3qZn9rGqhmf0EWBS5kESq0fUo+OEs2J0NDxwLS1+scdaYvn0oXr2aUd1GsSF3AwPTYcXWPHbl6wpZERFp3WpL7K4Bfmxmc83sb/7rPeCn7H2OrEjjyTgWLnsX2vWCZy+BZ38M+fvfiDi2b1/KNm0iM/lIABJSvOHbhet2NWa0IiIija62Z8Vucc4dB9wCrPVftzjnjnXO6Ux0aRppA+Enb8MpN8OyV+D+Y7z3MDF9+wKQsSeGhKgEdpQvIyYY0HCsiIi0evW5j927zrl/+q93GiMokVoFo+DEX8FlcyG5m3c7lOd/BgVe4hbrJ3bla9YxNG0oX2z/nKPTU/lUiZ2IiLRyEXs0mJn1NLN3zWyZmS01s1/65R3M7C0zW+G/tw9b5kYzW2lm35jZGWHlw81siV93j5lZpOKWFqTrUfCzd+Ckm2DpC3D/KFj1DtG9ekEwSPHq1QxLG8aKXSsY3CuaJdm7KSotb+qoRUREIiaSz3wtA65zzg0ERgFXmdkg4AZgjnPucGCO/xm/bhJwJHAmcL+ZBf11PQBcBhzuv86MYNzSkgSj4aTfwM/ehfj2MOtiAoXbiElPp2T1GoZ3GQ5AVPJSSssdX2zIadp4RUREIihiiZ1zbpNz7jN/OhdYBvQAJgDT/dmmA+f60xOAp51zxc65NcBKYKSZdQNSnHMfOe/xAY+HLSPi6TYYfvAUhMrgv9cSc1hfSlav4jtp32Fw58H8N+shLCpHF1CIiEirFskeu0pm1hv4DvAJ0MU5twm85A9I82frAWwIWyzLL+vhT1ctF9lXh74w9mZY8QaxSaWUrF1HIOS4c/SdlLsyOvR+gU/W6EbFIiLSekU8sTOzJOB54Brn3J7aZq2mzNVSXt22LjOzhWa2cNu2bQcerLR8x1wO6SOI2fkurrSU0uxseqb05MaRN1IS/S2f57xESVmoqaMUERGJiIgmdmYWjZfUPemce8Ev3uIPr+K/b/XLs4CeYYunAxv98vRqyvfjnHvIOZfpnMvs3Llzw+2ItByBIJz1V2LjcgAoXrUagHP7ncuQDqNxHV7jXx990IQBioiIRE4kr4o14BFgmXPu72FVLwNT/OkpwEth5ZPMLNbM+uBdJLHAH67NNbNR/jovDltGZH/dhhCT3gWAvA/exzmHmXHPqbcT5ZJ49NvbyC8pbOIgRUREGl4ke+yOB34EnGJmi/3XWcCdwGlmtgI4zf+Mc24p8AzwNfA6cJVzruLeFFcA/8a7oGIV8FoE45aWzozgESfSrn85OU89zda//BXnHB3iOzCl/28oj9rM/739p6aOUkREpMFFRWrFzrkPqf78OICxNSxzO3B7NeULgaMaLjpp9XqOpOt3ZmJHX8bORx8llJdH199P4+pjz+apJW8wf9tsXl9zPGf20Z1zRESk9WiUq2JFGl3PYzCDLud/h45Tp5LzzDNs/PX1BMrLuHbYtZQVZHDDBzexYNOCpo5URESkwSixk9ap8wCITcGWzCJt8pmk/eo69rz6Klm/uJrzjuxG+z1TCZZ34up3r2bZjmVNHa2IiEiDUGInrVMgACN/Bqvfg/uPoWPJo3T94XHkvfcem6+8kisyD2PnqkuIDSRyxdtXsCF3Q93rFBERaeaU2EnrNXYaXLccxv0ZYpNpH3qO7sfspGDBJxx/3030sgS6FPyC0lApl791OTsKdzR1xCIiIodEiZ20bsld4Zip8JM34JqvSL3st6SfmEvJN9/y1/kPsuLLQq7/zl/YWrCVK+dcSWl5aVNHLCIictCU2Enb0a4nHPcLks+aQM+Tc0navplH37qD9Gn387cNoylespQnvnqckNOTKUREpGUy56p9OleLl5mZ6RYuXNjUYUhztPFzeOgkdmRczkPP7uSMkizis9YCkB8L6/om4S4+j1PGTaVDXIemjVVERNoEM1vknMs81PVE7D52Is1W9+9A/3F0WPskzwz+C52/ewyXDEwh/5MFLH/rGTI+WMSOvz/O2NxnOKnnSUw8fCLHdT+OqID+dxERkeZNQ7HSNp36ByjN5/KoV8grLiOqUydSv3sWx9z9H/peewO9tsFlyeNYtGURV825ijOeO4N/fPYP1u9Z39SRi4iI1EiJnbRNaQOwPicyOvg1eUVl+1SlnHE6BAKcv6Ercy6Yw/876f8xoOMAHv3qUb774ne59t1rKQ3pIgsREWl+lNhJ29XlKA6zbAqKi/cpjurcmYSRI9nzv1eJCkRxasap3Df2Pt46/y1+dvTPeHv92/x5wZ+bKGgREZGaKbGTtittIHGUEFvNzYlTzhpHybp1FC/b+1SKtIQ0rh52NZcceQlPf/M0L6x4oTGjFRERqZMSO2m70gYC0DF/xX5VyaedBlFR7Hn11f3qrhl2Dcd1P44/fvxHFm9dHOkoRURE6k2JnbRdnQcQwkjZs5Kqt/2Jat+exOOOZc+rr+1XFwwE+fOJf6ZbYjeuefcaZi6byeb8zY0ZuYiISLWU2EnbFZNIfmIvzit6gawHzoWP7oNNX0LIu0FxyllnUbpxI0VffLHfoqmxqfzzlH/SPq49dyy4g9OeO41fzPlFI++AiIjIvpTYSZuW9P1/8WnSyYS2LIc3boIHT4C/HAZZi0geOxaLiWF3NcOxAIe1O4wXJ7zIS+e+xOkZpzM3ay4FpQWNvAciIiJ7KbGTNs0yjqPfTx5lnPsHv+z2BO6ce6BwJ2QtIJicTOKJJ5D72uu48vIa19E3tS+nZZwGQFZeVmOFLiIish8ldtLm9eyQwA3jBvDSmgDPlY8BDAp3AZB61lmUbdtGwaJFta6jR1IPALJzsyMdroiISI2U2IkAFx2TwTF9OnDrq98Qik2Fgp0AJJ10EhYfz/YHHqAkq+akrWdyTwzjwS8fZOFmPaNYRESahhI7ESAQMO763mBKy0NsK0/E+T12gYQEOl99NYULF7HqzDPZ+NvfUrJu3X7Lt4trx+2jb2drwVZ+/MaPufyty1m6Y2lj74aIiLRxVvVWDq1FZmamW7hQPSdyYB75cA3D3/we3bp0octVr1WWl27Zwo5HHiFn1jO4sjJSzz6bjlOnEtu3zz7LF5UV8fTyp/n3V/9md/FuTss4jZ8P/Tl92/Vt7F0REZEWxMwWOecyD3k9SuxE9ioPORbfeSpxJTvpfN1HpCXH7VNftm0bOx59jF1PP40rLiZl3Dg6XXE5sf367TNfXkkej3/9ONOXTqeovIhrhl3DJUdegpk15u6IiEgL0VCJnYZiRcIEA0b/jF50c9t4cNbs/W9c3LkzXX5zPf3efouOP7mU3HffZfU548m65lqKvvmmcr6kmCSuHHolr3/vdU7tdSp/X/R3bv34VkpDpY29SyIi0oaox06kqm/fpGTWFGLKC9jccRRpZ15PoN8pUE1vW9muXeycPp1dM54glJ9P0tixxB99FIGERGIyehE/PBNLjOfez+/l4SUPc2y3Y/nbSX8jOSa5CXZMRESaKw3F1kGJnRyKsvxdPHn/LZyZ9yJdLIctcX0pHnoJ6WMuIRCfut/85bt3s3PGE+yaMYPy3bv3VgSDxB99NAnHjuLznmXcnDOD7h16c9+p91XeIkVERESJXR2U2MmhKigp4+0vN7DjkycZufVZjrS1FBDH8k6n0f7Ey+kzeHS1y4VKSgjl51O8fDn5H39C/scfUbTkKwiFcDHRfJ3u+LZvLOdccBNHjZ6ABYONvGciItLcKLGrgxI7aUi5hSUs+ugdoj5/jOF73iHeStjT4WhSRl8GR30PYhJrXb48N5eCTxeS//FH5Mz/ALdyLQChxHjSLrmUzr/4eSPshYiINFdK7OqgxE4iZffObfzj/93G5Unvk1a4GmJTYPCFkPlj6HJkvdaxLXslD//nlwx5azXpRYkM/uhTXTErItKG6apYkSaS2qEzL8WczT39p8Olb8AR4+Czx+GB4+CR0+GLp6G0sNZ1dO7Rj+tueIFdxwwgJief2979ra6YFRGRQ6bETuQgxEUHKSx10GsUnPcQXLccTr8d8rfDi1PhbwPg9Rth6/Ia1xEbjOWcky4D4JOFL3PF21ewp2RPY+2CiIi0QkrsRA5CfEyQotLyvQUJHeC4n8MvFsHFL8NhJ8OCh+H+Y+DRM2HxU9X24sX27g3AH9/rSvrzn3DlvWfw9wV/Y92e/R9bJiIiUhedYydyEM7+5wekJcfx6CUjap4pbxt8MRMWTYedqyAuFQZPguFTKs/Fc6EQ2//1L/LenkPR118DsDsBFvc18r5zOIO/+yNOPnoC0cHoxtgtERFpIrp4og5K7CSSLvjXfMyMZ6YeW/fMzsHaD2HRf2DZy1BeAukjYNgUOOq8yitqy7ZvJ3/ePHbMfZu8Dz8kOreIELC+RxR7hh1O7nePpX33vnRJ7EKXhC50SexCcnSyLroQEWkFlNjVQYmdRNKNLyzhv19s5PNppxEVPIAzGvJ3wJdPe71427/xrqg9+nwYfgl0G1I5mysvJ3/JEr557Sn2fPA+XVfnkJMAD54V4PPDjFDAS+bio+Irk7wuCd6ra2JX0pPTGdVtFAHT2RYiIi2BErs6KLGTSHr5i41c/dTnvHTV8Qzp2e7AV+AcrP/Y68X7ejaUFcGJ18Mpv6129h2P/YcdjzxC+fbtuM4d2H1aJmtPPIz1CYVsKdjivfK3sK1wGyEXAmBwp8H8dtRvGdRx0EHvp4iINA4ldnVQYieRtDW3iJG3z+GGcQO4fMxhh7aywl0w60ewewP88osaZ3OlpeS99x67Zj1D/ocfghmxA44gkJBAIDYOi42FmGhKgrC1fBeLcpbw7hGlHHPqj/h15q81ZCsi0ozpPnYiTSgtOY5+aUl8tGrHoa8svj30PwN2rYXcLTXOZtHRJJ96Kr0efojD3nqLjlMvI6pjJywQJJSXR+nGjZR88y3u8yV0XLiasZ+HuPXxMty9/2Fx1oJDj1NERJq9qKYOQKSlOrZvR57/LIvS8hDRB3KeXXV6jvLeN3wMgybUOXtMeg/SfvnLWucpz8sn+647GP/s8+RefDWFdz9E/JAhtS4jIiItm3rsRA7SsYd1pKCknC+zdh/6yroNgag477y7BhJMSqTXH2/j1atHUFqQx9of/JBNN09jx2P/oXjlygbbjoiINB9K7EQO0qi+HQH4eHUDDMdGxUCP4Q2a2FU4+qyLuPZSo2TcaHbPns3Wu+5i9Tnjyb7+ekrWr2/w7YmISNNRYidykDokxjCga3LDnGcH0PMY2PwllBQ0zPp8J6SfgCUl8uL3unHEl1/Q7/336HDpj8l98y1WjTuLTTdPo3TTpgbdpoiINA2dYydyCEb17cjTn66nuKyc2Kjgoa2s17Hw4d/h7T9A+wwIxkBUrPde8Qr/HFVRHrvvdDB673xmxEfFc3Kvk3l7/dv89pjfEp2WRpdf/5oOU6aw48GH2PXMM+yePZt2P5hEp8suI6pTpwZpGxERaXxK7EQOwbGHdeQ/89fyxYbdjOzT4dBW1usYSOgECx5smOAAAl6SNy6tN/+L3sNHSx7nxMGXQCBIdFoaXW/+HR0v/THbHniAXU/OZPdzz9PtT38i5cwzGi4GERFpNLqPncgh2F1QytA/vsnVpxzOtaf1P/QVhkLezYrLS/a+yorDpkugvDhsurp5ivddvrSI0uyFjAlsolN5OecXhzgluR/pgVjAwAwsQElOiI1vF1O4bBUdL59K56uvxgI6W0NEpDE01H3s1GMncghSE6IZ2bsDsxdnc82phx/6TYADAYhJABIaJL4K0cDvv3meBxffz1+KtvIXNnBEWRRjQzGcEoqhvwsQU7CRXt/JZ3Ov77LjXw9SvPwbuv/lzwSTkxs0FhERiRz9c1zkEF04oifrdhTw8eqdTR1Krc444nu8cOEcXp34Kr/K/BWJXQfzQFQh58fkMK5jPH8+bjKfd+5GWqdX6HLlJPI+/JC137+Qoq+/pjw3F1dW1tS7ICIidYjYUKyZPQqcDWx1zh3ll3UAZgG9gbXA951zu/y6G4GfAOXA1c65N/zy4cB/gHjgVeCXrh5BayhWGkthSTkj//Q2R3VP5aqT+zG0VzuSYltGZ/j2wu28t+E95qyfw8ebPqY0VEoHZ4zJL2BcygV0fOB9yneGJazR0QTi471XXByWkEAgPp74IUNI+/WvNHQrInKQmv2zYs3sRCAPeDwssfszsNM5d6eZ3QC0d879xswGAU8BI4HuwNtAf+dcuZktAH4JfIyX2N3jnHutru0rsZPGdN+7K/nrm9/gHAQM+ndJZlhGe4b1as+wXu3o0ymx2T+rNb80nw+yP+Cd1a/zwfo55BmMcb34uTuTziQTKizAFRYRKiz0pwsJFRZRvmsXhYsX0+V3v6PDRZObejdERFqkZp/YAZhZb+C/YYndN8BJzrlNZtYNmOucO8LvrcM5d4c/3xvAH/B69d51zg3wy3/gLz+1rm0rsZPGtqeolMXrc1i0bhefrd/F4vU55BZ7w5cdEmP4Ts92DMtoz9iBaQzomtLE0dauNG8LLz49nnsCeeQFo5g08Aecf/j5xEXFERuMJSYYU/luGBumTqVgwaf0eeEFYvv2aerwRURanJaa2OU459qF1e9yzrU3s3uBj51zT/jljwCv4SV2dzrnTvXLTwB+45w7u4btXQZcBtCrV6/h69ati9SuidQpFHKs3JbnJXp+srdqWz4AYwekcdUp/RjWq30TR1mLwhxynjiXe4vW8UxKEjUdKaID0aQVRPPHf+WyvWM0/7q8J9Ex+yeA4e81lfVO6c3IbiMbdTdFRJqD1nZVbHVjVK6W8mo55x4CHgKvx65hQhM5OIGA0b9LMv27JPODkb0A2JFXzJOfrOfReWs47/75HN+vIz8/+XBG9e3Q/IZq49vR7kcv87uP7mXSkpmsLN5BcUwCxT2GU9LjOxQnpVFSXkJxeTHF5cV8dckKRj0wjwsWRLHgzO6VdbkluZXzVJRVvodK9tvsuD7juGnkTbSLa9f4+ywi0sJpKFakCeQVlzHzk3U89P4atucVMzyjPT8/pR8n9e/c/BI88O6vt+Y9WPwkLHvFu9de2iAYOhkGXwhJnQHIvu5X7HnjDfq++AKxhx9e92pdiNJQqZf4lRXz3IrneOiLh2gf154nznqC7kndI71nIiLNQkP12DX2JWwvA1P86SnAS2Hlk8ws1sz6AIcDC5xzm4BcMxtl3l+7i8OWEWmxkmKjuOzEw/jwNydz64Qj2by7iB8/9inn3Pshr3+1iVComXU4BwJw2MnwvX/Ddd/A2f8PohPgzd/C3wfAi5dDaSFp118PZWXkf/RR/VZrAWKDsaTEpNA5oTNXDLmCJ7/7JHmledz+ye201huoi4hESsQSOzN7CvgIOMLMsszsJ8CdwGlmtgI4zf+Mc24p8AzwNfA6cJVzrtxf1RXAv4GVwCq8c+9EWoW46CAXH9ubd391En/+3mDyisq4/InPOOPu95n9eTZl5aGmDnF/8e0g81L42Ry48hMYeRl88TTMOI+olBgCiYmUbMg66NUP6jiInw/9Oe9nvc8b695ouLhFRNoAPVJMpBkpDzn+t2QT972zkm+25NKrQwJXnHQY5w3rQWxUsKnDq9lXz8MLU6HzAFa/2o7oHj3p+a8HDnp1ZaEyfvi/H7J2z1oGdhhIn9Q+9E7p7b2n9qZHUg+iAs3lFGERkUPXIq6KbUpK7KQlC4Ucby/bwr3vruTLrN10S43jshP7MmlEL+JjmmmCt/JtmPUjNnzYgZKybvR6fAaB5BQCiQkHdd7ghj0beHTpo6zOWc3aPWvZWbT3RslRgSgykjPondp7v6QvJaZ530pGRKQ6SuzqoMROWgPnHB+s2M6976xkwdqddEyMYVD3FFLjo2mXEE37hBh/OoZ2fpn38sqjg418Gu2GT9l67Q/Y8VX03rJAgEByMsGkJAIpKXvfk5O98or3lGQCSf57cgrB5L3zW3Q0u4t3s2b3GtbsXsPaPWsr3zfs2UCZ2/u4s45xHSuTvD4pfSqnuyd2JxhopkmxiLR5SuzqoMROWpsFa3Yy/aO1ZO8qZHdhKTkFJewuLKW26yySYqMqk8CKhK8yAYyPITUhmnbx0bRP9MrT2yccco9g+ZpF5P91MuWFJYR6nUZ5cYhQQTHlBSWECoooLyj234sI5RcRKiiqc50WF0MwMYFAYgLB5EQCiYkEkxIJJCVCUgJ5MbAjuoStwQKyg7msI4fV5dvYHJVPXjyURhkxgRgGtu/Pid2PY0yPE+nfvj8WCIIFIBCE5ng1soi0GUrs6qDETtqCUMiRW1xGTkEJOQWl5IQlfDkFpewqKGF3WHlOYWnl5/JqMsKYYIDhGe0ZfXgnzjiyC/3Skg8usF3r4InzYMfKOmd1IQiVGaHSAOUl/nupESoJUF4aIFRqlJf4ZaUBv3zf+V2o5qTMBRz53cpY19sxp3+QD7vGAtCtrIwTCwoZU1DIyKIiYh1ekmcBsODe6Yqk70DqouMhsbP/6hQ2Hf65E0TFHlz7ikiro8SuDkrsRGrmnJcQ7i4o9RPCEnYVlPJV9m4+WLGdZZv2EDC4+NjeXHtaf1Ljo+teaVXlpZC7ycvcXMi7F17FtCsPm66rrhycq7HOhcpxJaWE8gsozysgVFBIeV4hofxCyvMLKdm8k7zFqynZtAuAYJcUtg5IYX6fcl7sspO8QDnxFmRUXFfGxHXjxNgudA7E7rudyjiqxumqryvJg/zt/msblBdX30axqfsmetUmgP4rvr132xkRaZWU2NVBiZ3IwduaW8S976zkiY/X0SExhhvHDeS8YT2a582T66lkwwby5r5H3nvvUfDJJ7jSUiwxkaJhR7CkfwzPd1nPKrcVgCM7HsmY9DGM6TmGgR0GHtp+OwfFuV6CV5HoFWzf9/M+dTu85LAqC0BClSQwvh1ExXk9f8FY773ic1TVz3HVzBMHUTF765Q4ijQZJXZ1UGIncui+yt7NzS99xefrcxjRuz23jD+KQd1b/lWnofx88j/+uDLRK9u6FUuIh3PP4OOTuvB23kK+3PYlDkdafBon9jyRMeljOKbbMcRHxUc4uHIo3BWW8NWQAOZvg8IcKC+BsmIIlR76toMxtSd/4eWBaG8oOhC1d1g6EAWxydD9O9AjE1J7HHpMIm2EErs6KLETaRihkOO5RVnc+fpycgpKuPjY3vzf6f1JiTuI4dlmyDlH0dKv2Tl9Onv+9z8sOpp2F1xA8EffY37pN7yX9R7zN84nvzSf2GAsx3Q7hjHpYzgx/US6JnZt6vD3CpV7CV5ZkfdeXrzv58r3asqqnbfEfw8rK/fLSou8RLJiCDpU5k2HyrzeyYokM7kb9BgO6SMgPdNL+GISm7adRJopJXZ1UGIn0rByCkr425vf8sQn6+iYGMtNZw1g4nda9vBsVSVr17L94YfZ/dLLYEa7iRPpeNnPsG5dWLhlIe9nvc/cDXPJyvOerDGgwwBOTD+RXsm9iA5EEx2MJiYQUzkdHfBeUYEoYoIxlZ8r6irKAtaKhkDLimHzEshaCNkLIetT2LXWq7OA94zh9EyvRy89EzodoSFgEZTY1UmJnUhkLMnyhmcXb8hhZO8O3HrukQzo2vKHZ8OVZGWz498Ps/v5F3Dl5SSfeiodLv4R8cOHA7Bm9xrey3qPuRvmsnjbYkLVnRN3AIIW3C/pqzq9T2Lo10UForxE0p8vPiqe5JhkkqOTSYpJIjkmmaToJJJikkiJSSEpOomE6ITGTyTzt0P2Ii/Zy/oUsj+D4t1eXUwy9Bi2b7KXlNa48Yk0A0rs6qDETiRyQiHHs4s2cOdry9lTVMaUY3tzzWmHt5rh2QqlW7aw64kn2PXMs4R27yaqezcCsXFepd9TGcIRcuU4wOH2ecd/D1WUOEfI8N8dzrkqy4UIgV/u/RfCURZtrD08heVHpbKpSzQlrpTS8lJKQ3tfJeUlFJQVUBYq228/whlWmexVJH7JMV4imBTtJ4D+dHh9YnQiccE4YqNiiQ3GEhcVR0wg5uB6bEMh71Y4FT16WQthy1LvSmOAdr32Jnk9MqHbEIiOO/DtiLQgSuzqoMROJPJyCkr4yxvfMHPBejolxfLbswYyYWj3VjU8CxAqKGD3y69QsGAB4CVkAFQcPis/V3mvSO/qO78LTwf3fi7P2U3RkiXgHNE9epAwcqT3hI7ExLBXEpaYQHlcDEWxRn5MiIKoEHnR5eQGS8grzSevNI/cklxyS3Irp/NK88gr8ctLc8kryaO8IsGqg2HEBmNJjknm2O7HMrbXWI7rfhxxUQeRhJUUwKYvwpK9RbDHG/ImEAVdj9432et4mG4qLa2KErs6KLETaTxfZuVw8+yv+CJrNyP7dOCPE47iiK4HeXNjqVbZ9u3kvvsuuW+/TfHXywjl5xMqKKjfwmYEEhIqk8CYjAwSRo4kYeRI4gYOwIJ7nzbinKOwrHBvwlfqJYL5pfkUlxdTVFZEcXnxPtNbC7byQfYH5JbkEh8Vz+geozm116mcmH4iSTFJB7/TuZvDhm8XeUO4pfleXXx778KMymRvOCR0OPhtiTQxJXZ1UGIn0rhCIceshRu46/XlXH/GAH54TK+mDqnVc6EQoYJCL8kLfxXk71dWnpfnTeflU/zNN5SsXQtAIDmZhOHDvUTvmJHEDdg30auv0lApn27+lHfWv8Oc9XPYXrid6EA0o7qNYmyvsZzc62Q6xB1i4hUqh23L9w7fZi+Crcuo7ArtcJiX5PU/E44679C2JdLIlNjVQYmdSNPIKSghOS6aYEDDZM1Z6ZatFHz6KQULFlCwYMG+iV5mJnFHH0UgPgGLjsZiorHoGG+64hVTw+eYaFxUkGV7VjB384e8vfE91hdlY4Egw9KGcWrGqYztNbbhbhVTnAsbP987fJu9EPqfAeP/2TDrF2kkSuzqoMRORKT+SrdsoWBBWKK3bl2Drj8UMMqCUBJ0lAXAoqOJiUsgIT6FuHYdiTvqKOKHDCZ+yBCie/Y8+PM0nfNuuaKLLaSFUWJXByV2IiIHL1RUhCst9V4lJXunS0txJaW40hL/vbTm+aop2523g+xda9mcs4Hcgl1ElUOX4jh6bSwlqti7ojfYvj3xgwcTP3QIcYMHEz9kKMEk3dhYWreGSuyiGiIYERFpXQJxcRDX8L1eXYD+/vTm/M3MWT+H59fP4fNNC+mxNciIHamM2tWR9LUryXvvPQCiunSh70uzCbZr1+DxiLQ26rETEZEmt7NoJ+9teI+317/NRxs/ojRUSrprz/m5R3DsA/NJPf979Lj11qYOUyRiNBRbByV2IiItU15JHh9kf8Db697mg+wPOP+NPL67wNHliUfplHlsU4cnEhFK7OqgxE5EpOUrKivi45Xv0u7HN5MQiCOmVy/vdixRUf57EAt60xYdBcH9yyuno4IQ9Kejo/ZOh5dHBb11RAW9Zf15K6fDy6OCxA0ahEXprCY5dDrHTkREWr24qDhOGjCOgn92ZsfD//Yu2igr9y7GKCzElZfjysuhrAxXVrZ3Ory8yjTl9XuyRn30X/AJwZTW9axkadmU2ImISLOXkJlJQuYhd2YA3o2d8ZM9V1YO5X7yV1ZWWR6eKFaW7zOvNx2Ij2+QmEQaihI7ERFpUywQgEAAi45u6lBEGlygqQMQERERkYahxE5ERESklVBiJyIiItJKKLETERERaSWU2ImIiIi0EkrsRERERFoJJXYiIiIirYQSOxEREZFWQomdiIiISCuhxE5ERESklVBiJyIiItJKKLETERERaSWU2ImIiIi0EkrsRERERFoJJXYiIiIirYQSOxEREZFWQomdiIiISCuhxE5ERESklVBiJyIiItJKKLETERERaSWU2ImIiIi0Ei0msTOzM83sGzNbaWY3NHU8IiIiIs1Ni0jszCwI3AeMAwYBPzCzQU0blYiIiEjz0iISO2AksNI5t9o5VwI8DUxo4phEREREmpWWktj1ADaEfc7yy0RERETEF9XUAdSTVVPm9pvJ7DLgMv9jsZl9FdGoWp5OwPamDqIZUrtUT+1SPbXL/tQm1VO7VE/tUr0jGmIlLSWxywJ6hn1OBzZWnck59xDwEICZLXTOZTZOeC2D2qR6apfqqV2qp3bZn9qkemqX6qldqmdmCxtiPS1lKPZT4HAz62NmMcAk4OUmjklERESkWWkRPXbOuTIz+znwBhAEHnXOLW3isERERESalRaR2AE4514FXj2ARR6KVCwtmNqkemqX6qldqqd22Z/apHpql+qpXarXIO1izu13DYKIiIiItEAt5Rw7EREREalDi0vs6nq0mHnu8eu/NLNh9V22JatHu0z22+NLM5tvZkPC6taa2RIzW9xQV+U0F/Vol5PMbLe/74vNbFp9l22p6tEmvw5rj6/MrNzMOvh1rfm38qiZba3pNklt8dhSjzZpq8eVutqlzR1XoF7t0uaOLWbW08zeNbNlZrbUzH5ZzTwNe2xxzrWYF96FE6uAvkAM8AUwqMo8ZwGv4d37bhTwSX2XbamverbLcUB7f3pcRbv4n9cCnZp6P5qoXU4C/nswy7bE14HuF3AO8E5r/634+3YiMAz4qob6tnhsqatN2txxpZ7t0qaOK/VtlyrztoljC9ANGOZPJwPfRjpvaWk9dvV5tNgE4HHn+RhoZ2bd6rlsS1Xnvjnn5jvndvkfP8a7F2BrdyjfeWv9vRzofv0AeKpRImtizrn3gZ21zNLmji11tUkbPa7U57dSk1b7W4EDbpc2cWxxzm1yzn3mT+cCy9j/yVkNemxpaYldfR4tVtM8rfmxZAe6bz/B+9dBBQe8aWaLzHt6R2tR33Y51sy+MLPXzOzIA1y2pan3fplZAnAm8HxYcWv9rdRHWzy2HIi2clypr7Z0XDkgbfXYYma9ge8An1SpatBjS4u53YmvPo8Wq2meej2WrIWq976Z2cl4B+DRYcXHO+c2mlka8JaZLff/5dXS1addPgMynHN5ZnYWMBs4vJ7LtkQHsl/nAPOcc+H/Am+tv5X6aIvHlnppY8eV+mhrx5UD1eaOLWaWhJfIXuOc21O1uppFDvrY0tJ67OrzaLGa5qnXY8laqHrtm5kNBv4NTHDO7agod85t9N+3Ai/idf+2BnW2i3Nuj3Muz59+FYg2s071WbaFOpD9mkSVoZJW/Fupj7Z4bKlTGzyu1KkNHlcOVJs6tphZNF5S96Rz7oVqZmnQY0tLS+zq82ixl4GL/atMRgG7nXOb6rlsS1XnvplZL+AF4EfOuW/DyhPNLLliGjgdqPaKphaoPu3S1czMnx6J9//Ejvos20LVa7/MLBUYA7wUVtaafyv10RaPLbVqo8eVOrXB40q9tbVji/87eARY5pz7ew2zNeixpUUNxboaHi1mZpf79f/CezrFWcBKoAD4cW3LNsFuNLh6tss0oCNwv3+8KXPeQ5i7AC/6ZVHATOfc602wGw2unu1yPnCFmZUBhcAk512O1Cp/L/VsE4CJwJvOufywxVvtbwXAzJ7Cu5qxk5llAb8HoqHtHlvq0SZt7rgC9WqXNnVcqVCPdoG2d2w5HvgRsMTMFvtlNwG9IDLHFj15QkRERKSVaGlDsSIiIiJSAyV2IiIiIq2EEjsRERGRVkKJnYiIiEgrocROREREpJVQYicizZ6ZOTP7W9jnX5nZHyKwnUvM7N6GXm+VbbQzsysjuQ0RabuU2IlIS1AMnOffvb/FMrMg0A44oMTOv3GpjtciUicdKESkJSgDHgKurVphZv8xs/PDPuf57yeZ2Xtm9oyZfWtmd5rZZDNbYGZLzOyw2jbor/ceM5tvZqsrtmFms8x7/mf4fN8zs6CZ/cXMPjWzL81salgc75rZTGAJcCdwmJktNrO/+PP8Omy5W/yy3ma2zMzux3v2aE9/W1/58e/XFiIiLerJEyLSpt0HfGlmfz6AZYYAA4GdwGrg3865kWb2S+AXwDV1LN8N78H2A/Ae5fMc8DRwIfCq/5ifscAVwE/wHgU0wsxigXlm9qa/npHAUc65NWbW258eCmBmp+M9IH4k3kO/XzazE4H1wBHAj51zV5rZcKCHc+4of7l2B9AOItJGqMdORFoE59we4HHg6gNY7FPn3CbnXDGwCqhItJYAveux/GznXMg59zXeY48AXgNO8ZO3ccD7zrlCvOdbXuw/NugTvEdtHe4vs8A5t6aGbZzuvz7H65kbELbcOufcx/70aqCvmf3TzM4E9tQjfhFpY9RjJyItyd14yc9jYWVl+P9I9R+4HRNWVxw2HQr7HKJ+x7/w5Q3AOVdkZnOBM/B67p4Kq/+Fc+6N8BWY2UlA+HMxqzLgDufcg1WW6x2+nHNul5kN8bd7FfB94NJ67IOItCHqsRORFsM5txN4Bm/Ys8JaYLg/PQH/oeMR9jTeg7pPwHtAN/77FWYWDWBm/c0ssZplc4HksM9vAJeaWZK/XA8zS6u6kH/hSMA59zxwMzCsoXZGRFoP9diJSEvzN+DnYZ8fBl4yswXAHGrvHWsob+INC7/snCvxy/6NN7z7md9zuA04t+qCzrkdZjbPzL4CXnPO/drMBgIfeYuRB1wElFdZtAfwWNjVsTc27C6JSGtgzrmmjkFEREREGoCGYkVERERaCSV2IiIiIq2EEjsRERGRVkKJnYiIiEgrocROREREpJVQYiciIiLSSiixExEREWkllNiJiIiItBL/HwtGwTyJlQGsAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Number of Inverters vs Coilloss During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Num Inverters\")\n", + "plt.ylabel(\"Coil loss\")\n", + "plt.xlim([0,2])\n", + "plt.ylim([0, 5000])\n", + "\n", + "print(len(pareto_numinv_points), len(pareto_coilloss_points))\n", + "\n", + "while i < len(pareto_numinv_points):\n", + " plt.plot(pareto_numinv_points[i], pareto_coilloss_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "# plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto V_SecCore vs Power Avg During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"V_SecCore\")\n", + "plt.ylabel(\"Pave\")\n", + "plt.xlim([0,3000])\n", + "plt.ylim([0, 200])\n", + "\n", + "while i < len(pareto_V_SecCore_points):\n", + " plt.plot(pareto_V_SecCore_points[i], pareto_pave_post_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "# plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points), len(pareto_kdiff_points))\n", + "\n", + "while i < len(pareto_bstray_points):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "print(len(bstray_designs))\n", + "#colors = numpy.arange(len(bstray_designs))\n", + "colors = numpy.arange(1000)\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Bstray vs Kdiff During Training, Generated Designs\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "while i < len(bstray_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " fig = plt.scatter(bstray_designs[i], kdiff_designs[i], label = f\"epoch: {i*100}\")\n", + " i+=1\n", + " \n", + "# plt.colorbar()\n", + "plt.legend()\n", + "# from matplotlib.patches import Rectangle\n", + "# someX, someY = 45, 0.5\n", + "# fig,ax = plt.subplots()\n", + "# currentAxis = plt.gca()\n", + "# currentAxis.add_patch(Rectangle((someX -0.1, someY-0.1), 0.2, 0.2, alpha=0.5, facecolor='red'))\n", + "#plt.savefig('Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(bstray_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " ax.scatter(bstray_designs[i], kdiff_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,200)\n", + "plt.ylim(0, 0.2)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"Bstray\", fontsize = 18)\n", + "plt.ylabel('Coupling %', fontsize = 18)\n", + "plt.suptitle(f\"Bstray vs Kdiff During Training, \\n Generated Designs\" , fontsize = 20, y=1.01)\n", + "# plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle(( 0,0 ), 45, 0.15, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(numinv_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " ax.scatter(numinv_designs[i], coilloss_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,2)\n", + "plt.ylim(0, 5000)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"Num Inverters\", fontsize = 18)\n", + "plt.ylabel('Coil Loss', fontsize = 18)\n", + "plt.suptitle(f\"Number of Inverters vs Coil Loss During Training, \\n Generated Designs\" , fontsize = 20, y=1.01)\n", + "\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle(( 0,2 ), 1, 2000, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(V_SecCore_designs):\n", + "\n", + " ax.scatter(V_SecCore_designs[i], pave_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,5000)\n", + "plt.ylim(0, 200)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"V_SecCore\", fontsize = 18)\n", + "plt.ylabel('Pave', fontsize = 18)\n", + "plt.suptitle(f\"V_SecCore vs Power Average During Training \\n Generated Designs\" , fontsize = 20, y=1.01)\n", + "\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle((0,30), 1500, 200, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "noise = generate_noise(shape=(10_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "pareto_bstray, pareto_kdiff = pareto_frontier(bstray, kdiff)\n", + "pareto_kdiff = pareto_kdiff * 100\n", + "\n", + "plt.plot(pareto_bstray.detach().cpu(), pareto_kdiff.detach().cpu())" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "noise = generate_noise(shape=(25_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "plt.scatter(bstray.detach().cpu(), kdiff.detach().cpu())\n", + "plt.title(\"Randomly generated points after GP reshaping\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Kdiff\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Noise 0\n", + "[ 0.7958461 0.18297267 0.49575043 0.2926432 0.37254918 0.69575644\n", + " 0.63518155 0.1229732 -0.9885057 0.9297763 ]\n", + "\n", + "Generated Parameters 0\n", + "[0.24295673 0.2889659 0.10189038 0.4350825 0.5535919 0.10842346\n", + " 0.23845567 0.30237943 0.41842827 0.38261238]\n", + "\n", + "Noise 1\n", + "[ 0.91317 -0.49649215 0.5561123 0.722435 0.51169753 0.57239664\n", + " -0.19461226 0.5508702 0.43709326 0.42120826]\n", + "\n", + "Generated Parameters 1\n", + "[0.85569173 0.8398273 0.9502481 0.7533455 0.48661894 0.8524261\n", + " 0.686167 0.6596291 0.57808053 0.4556794 ]\n", + "\n", + "Noise 2\n", + "[ 0.16362631 0.23143137 0.39028585 0.7493527 -0.48393202 -0.6908225\n", + " 0.42792177 0.6304339 -0.48207605 -0.7222413 ]\n", + "\n", + "Generated Parameters 2\n", + "[0.39839154 0.32989618 0.3020975 0.33692053 0.51863885 0.600775\n", + " 0.62405336 0.4492834 0.5311451 0.69901896]\n", + "\n" + ] + } + ], + "source": [ + "noise = generate_noise(shape=(3, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "noise = noise.cpu().numpy()\n", + "gp = gp.cpu().detach().numpy()\n", + "\n", + "for i in range(3):\n", + " print(f\"Noise {i}\")\n", + " print(noise[i])\n", + " print()\n", + " print(f\"Generated Parameters {i}\")\n", + " print(gp[i])\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"KDiff Error During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Coupling %\")\n", + "\n", + "plt.plot(numpy.arange(len(kdiffs)), kdiffs)\n", + "plt.show()\n", + "\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"BStray Error During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"BStray\")\n", + "\n", + "plt.plot(numpy.arange(len(bstrays)), bstrays)\n", + "plt.show()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "optimization_date4_22_22.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From daedc3ccbeaddd29fdc8433b0b28c2258f1c095c Mon Sep 17 00:00:00 2001 From: AndrewCurtis27 <89710614+AndrewCurtis27@users.noreply.github.com> Date: Tue, 25 Oct 2022 08:37:49 -0600 Subject: [PATCH 5/8] Add files via upload --- optimization 10_23_22.ipynb | 2069 +++++++++++++++++++++++++++++++++++ 1 file changed, 2069 insertions(+) create mode 100644 optimization 10_23_22.ipynb diff --git a/optimization 10_23_22.ipynb b/optimization 10_23_22.ipynb new file mode 100644 index 0000000..38bad17 --- /dev/null +++ b/optimization 10_23_22.ipynb @@ -0,0 +1,2069 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iZN2u5WKRFqR", + "outputId": "6842718b-36d3-4c1c-b523-796de23e5470" + }, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "import os\n", + "import random\n", + "import pandas\n", + "import numpy\n", + "import math\n", + "from scipy.optimize import curve_fit\n", + "from scipy.interpolate import interp1d\n", + "from scipy.interpolate import UnivariateSpline\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "\n", + "# seed = 7777\n", + "# random.seed(seed) \n", + "# torch.manual_seed(seed);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load Saved MP (Magnetic Parameter) Model" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "B-TMPJtCKIM6" + }, + "outputs": [], + "source": [ + "# 12 input -> 42 output\n", + "class MpModel(torch.nn.Module):\n", + " def __init__(self):\n", + " super(MpModel, self).__init__()\n", + "\n", + " self.linear1 = torch.nn.Linear(12, 100, bias=True)\n", + " self.linear2 = torch.nn.Linear(100, 100, bias=True)\n", + " self.linear3 = torch.nn.Linear(100, 42, bias=True)\n", + "\n", + " def forward(self, x):\n", + " x = torch.atan(self.linear1(x))\n", + " x = torch.atan(self.linear2(x))\n", + " x = torch.atan(self.linear2(x))\n", + " x = self.linear3(x)\n", + " return x\n", + "\n", + "\n", + "mp_model = MpModel().to(\"cuda\")\n", + "mp_model.load_state_dict(torch.load(\"saved_model_state.pt\"))\n", + "\n", + "for param in mp_model.parameters():\n", + " param.requires_grad = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create GP (Geometry Paramter) Generator" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "z9cggAl1BGHo" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\lazy.py:178: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.\n", + " warnings.warn('Lazy modules are a new feature under heavy development '\n" + ] + } + ], + "source": [ + "class GpModel(torch.nn.Module):\n", + " def __init__(self, input_length: int):\n", + " super(GpModel, self).__init__()\n", + "\n", + " self.dense_layer1 = torch.nn.Linear(int(input_length), 128)\n", + " self.dense_layer2 = torch.nn.Linear(128, 256)\n", + " # self.dense_layer3 = torch.nn.Linear(256, 512)\n", + " # self.dense_layer4 = torch.nn.Linear(512, 256)\n", + " self.dense_layer5 = torch.nn.Linear(256, 128)\n", + " self.dense_layer6 = torch.nn.Linear(128, int(input_length))\n", + " \n", + " self.batch_norm1 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm2 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm3 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm4 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm5 = torch.nn.LazyBatchNorm1d()\n", + "\n", + " def forward(self, x):\n", + " x = self.batch_norm1(torch.atan(self.dense_layer1(x)))\n", + " x = self.batch_norm2(torch.atan(self.dense_layer2(x)))\n", + " # x = self.batch_norm3(torch.atan(self.dense_layer3(x)))\n", + " # x = self.batch_norm4(torch.atan(self.dense_layer4(x)))\n", + " x = self.batch_norm5(torch.atan(self.dense_layer5(x)))\n", + " x = torch.sigmoid(self.dense_layer6(x))\n", + " return x\n", + "\n", + "\n", + "gp_model = GpModel(10).to(\"cuda\")\n", + "optimizer = torch.optim.Adam(gp_model.parameters(), lr=3e-4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# System Constraints" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "Bse1wsKN1NxY" + }, + "outputs": [], + "source": [ + "# x-direction of the primary winding (mm)\n", + "lpx_min, lpx_max = (50.0, 650.0)\n", + "\n", + "# y-direction of the primary winding (mm)\n", + "lpy_min, lpy_max = (50.0, 2050.0)\n", + "\n", + "# width of the primary winding (mm)\n", + "wp_min, wp_max = (25.0, 325.0)\n", + "\n", + "# length of the edge of the primary core (mm)\n", + "a_min, a_max = (0.0, 200.0)\n", + "\n", + "# pitch of the adjacent cores (mm)\n", + "p_min, p_max = (0.0, 200.0)\n", + "\n", + "# length of the secondary winding (mm)\n", + "ls_min, ls_max = (50.0, 450.0)\n", + "\n", + "# width of the secondary winding (mm)\n", + "ws_min, ws_max = (25.0, 225.0)\n", + "\n", + "# input RMS current (A)\n", + "ip_min, ip_max = (50.0, 200.0)\n", + "\n", + "# turn number of the primary side\n", + "np_min, np_max = (4.0, 10.0)\n", + "\n", + "# turn number of the secondary side\n", + "ns_min, ns_max = (4.0, 10.0)\n", + "\n", + "# switching frequency (kHz)\n", + "f = 85 * (10 ** 3)\n", + "\n", + "# output power at the center (kW)\n", + "p_out = 50 * (10 ** 3)\n", + "\n", + "# input DC voltage (V)\n", + "v_dc = 400\n", + "\n", + "# output DC voltage (V)\n", + "v_bat = 400\n", + "\n", + "# length of the edge of the secondary core\n", + "b = 50\n", + "\n", + "# ???\n", + "w = 2 * numpy.pi * f # [rad/s]\n", + "\n", + "# ???\n", + "q_coil = 400" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "brlwMfzj1Nxh" + }, + "outputs": [], + "source": [ + "# Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive.\n", + "# Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch.\n", + "\n", + "@torch.jit.script\n", + "def linspace(start: torch.Tensor, stop: torch.Tensor, num: int):\n", + " # create a tensor of 'num' steps from 0 to 1\n", + " steps = torch.arange(num, dtype=torch.float32, device=start.device) / (num - 1)\n", + "\n", + " # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings\n", + " # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript\n", + " # \"cannot statically infer the expected size of a list in this contex\", hence the code below\n", + " for i in range(start.ndim):\n", + " steps = steps.unsqueeze(-1)\n", + "\n", + " # the output starts at 'start' and increments until 'stop' in each dimension\n", + " out = start[None] + steps * (stop - start)[None]\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "dMoXnTFV8U1P" + }, + "outputs": [], + "source": [ + "# Generate a pytorch tensor full of random floats in the range [-1, +1] with the given shape\n", + "\n", + "def generate_noise(shape):\n", + " return torch.distributions.uniform.Uniform(-1, +1).sample(shape).cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "xloGE25R8Jtm" + }, + "outputs": [], + "source": [ + "def stack_parameters(*params):\n", + " return torch.stack(params).transpose(0, 1)\n", + "\n", + "\n", + "# Scale an array to be between our specified [min, max] contraints\n", + "def scale(arr):\n", + " scaler_min = torch.tensor(\n", + " [a_min, lpx_min, lpy_min, ls_min, p_min, wp_min, ws_min, ip_min, np_min, ns_min],\n", + " device=\"cuda\",\n", + " )\n", + " scaler_max = torch.tensor(\n", + " [a_max, lpx_max, lpy_max, ls_max, p_max, wp_max, ws_max, ip_max, np_max, ns_max],\n", + " device=\"cuda\",\n", + " )\n", + "\n", + " return arr * (scaler_max - scaler_min) + scaler_min\n", + "\n", + "\n", + "def extract(arr):\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns = [\n", + " numpy.squeeze(x) for x in numpy.hsplit(arr, 10)\n", + " ]\n", + "\n", + " ys_min = (\n", + " 5 * wp +\n", + " 4 * a +\n", + " 3 * lpy +\n", + " 2 * p\n", + " )\n", + "\n", + " ys_max = (lpy + wp)\n", + " ys_max = ys_min + (ys_min - ys_max) / 2\n", + "\n", + " ys_min /= 2\n", + " ys_max /= 2\n", + "\n", + " ys = linspace(ys_min, ys_max, num=5)\n", + "\n", + " np = torch.round(np)\n", + " ns = torch.round(ns)\n", + "\n", + " # take all 15 tensors of shape (X) and convert to (15, X)\n", + " return a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys\n", + "\n", + "\n", + "def scale_and_extract(arr):\n", + " scaled_array = scale(arr)\n", + " return extract(scaled_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "44TlTSnclCKs", + "outputId": "c2d4a61b-35c8-42ad-99f9-98c99022a0b2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " GP Parameters Shape: torch.Size([256, 12])\n", + " Extra Parameters Shape: torch.Size([256, 3])\n", + " MP Model Output Shape: torch.Size([256, 42])\n", + " \n" + ] + } + ], + "source": [ + "# Enclose in a function so we don't leak variables\n", + "def test_models():\n", + " noise = generate_noise(shape=(256, 10))\n", + " gp = gp_model(noise)\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "\n", + " gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + " extra_parameters = stack_parameters(ip, np, ns)\n", + "\n", + " mp = mp_model(gp_parameters)\n", + "\n", + " print(f\"\"\"\n", + " GP Parameters Shape: {gp_parameters.shape}\n", + " Extra Parameters Shape: {extra_parameters.shape}\n", + " MP Model Output Shape: {mp.shape}\n", + " \"\"\")\n", + "test_models()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "eEb7J5bOFEEr" + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "Method to take two equally-sized lists and return just the elements which lie \n", + "on the Pareto frontier, sorted into order.\n", + "Default behaviour is to find the maximum for both X and Y, but the option is\n", + "available to specify maxX = False or maxY = False to find the minimum for either\n", + "or both of the parameters.\n", + "\"\"\"\n", + "\n", + "\n", + "def pareto_frontier(x, y, max_x=False):\n", + " if max_x:\n", + " return _pareto_frontier(y, x)\n", + " else:\n", + " return _pareto_frontier(x, y)\n", + "\n", + "\n", + "def _pareto_frontier(x, y):\n", + " # Combine inputs and sort them with smallest X values coming first\n", + " sorted_xy = sorted(zip(x, y))\n", + " p_front_x = torch.zeros(len(x), device=\"cuda\")\n", + " p_front_y = torch.zeros(len(y), device=\"cuda\")\n", + " \n", + " # Loop through the sorted list\n", + " # Look for lower values of Y…\n", + " # and add them to the Pareto frontier\n", + " min_y = sorted_xy[0][1]\n", + " for index in range(len(sorted_xy)):\n", + " x, y = sorted_xy[index]\n", + " if y < min_y:\n", + " min_y = y\n", + " p_front_x[index] = x\n", + " p_front_y[index] = y\n", + " \n", + " p_front_x = p_front_x[p_front_x.nonzero()]\n", + " p_front_y = p_front_y[p_front_y.nonzero()]\n", + "\n", + " return p_front_x, p_front_y" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "#testing for creating total pareto f\n", + "\n", + "def oldgraph_pareto_frontier(Xs, Ys, maxX = False, maxY = False):\n", + "# Sort the list in either ascending or descending order of X\n", + " myList = sorted([[Xs[i], Ys[i]] for i in range(len(Xs))], reverse=maxX)\n", + "# Start the Pareto frontier with the first value in the sorted list\n", + " p_front = [myList[0]] \n", + "# Loop through the sorted list\n", + " for pair in myList[1:]:\n", + " if pair[1] <= p_front[-1][1]: # Look for lower values of Y…\n", + " p_front.append(pair) # … and add them to the Pareto frontier\n", + "# Turn resulting pairs back into a list of Xs and Ys\n", + " p_frontX = [pair[0] for pair in p_front]\n", + " p_frontY = [pair[1] for pair in p_front]\n", + " return p_frontX, p_frontY" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def findSpline(x, y, smoothing_factor = 2):\n", + " spl = UnivariateSpline(x, y)\n", + " spl.set_smoothing_factor(smoothing_factor)\n", + " return spl" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def pair_loss_calc(x, y, x_numpy, y_numpy, pareto_x_numpy, pareto_y_numpy, pareto_x_forx_numpy, pareto_y_forx_numpy):\n", + " if len(pareto_y_numpy) > 3:\n", + " # f = interp1d(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0])\n", + " # f2 = interp1d(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0], kind = 'cubic')\n", + " # plt.plot(pareto_bstray_numpy[:,0], f2(pareto_bstray_numpy[:,0]), '--', pareto_bstray_numpy, pareto_kdiff_numpy, 'o')\n", + "\n", + "\n", + " spl_y = findSpline(pareto_x_numpy[:,0], pareto_y_numpy[:,0])\n", + " y_calc = torch.as_tensor(spl_y(x_numpy)).cuda()\n", + " # plt.plot(pareto_bstray_numpy[:,0], spl_y(pareto_bstray_numpy[:,0]), '--', pareto_bstray_fory_numpy, pareto_kdiff_fory_numpy, 'o')\n", + " # plt.show()\n", + " y_loss = y - y_calc\n", + " y_loss_relu = torch.nn.functional.relu(y_loss)\n", + "\n", + "\n", + " spl_x = findSpline(pareto_y_forx_numpy[:,0], pareto_x_forx_numpy[:,0])\n", + " # plt.plot(spl_x(pareto_kdiff_fory_numpy[:,0]), pareto_kdiff_fory_numpy[:,0], '--', pareto_bstray_fory_numpy, pareto_kdiff_fory_numpy, 'o')\n", + " # plt.show()\n", + "\n", + "\n", + " x_calc = torch.as_tensor(spl_x(y_numpy)).cuda()\n", + " x_loss = x - x_calc\n", + " x_loss_relu = torch.nn.functional.relu(x_loss)\n", + " \n", + "\n", + " if len(pareto_x_numpy) > 3 and len(pareto_y_numpy) > 3:\n", + " combined_loss = torch.sum(y_loss_relu * x_loss_relu)\n", + " else: \n", + " combined_loss = torch.zeros(1, requires_grad=True, device = \"cuda\")\n", + " # print('No new pareto points found, used zero grad')\n", + " \n", + " \n", + " return combined_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_pairloss_and_pareto(x, y):\n", + " \n", + " pareto_x, pareto_y = pareto_frontier(x, y)\n", + " pareto_y_forx, pareto_x_forx = pareto_frontier(y, x)\n", + " \n", + " pareto_x_numpy = pareto_x.cpu().data.numpy()\n", + " pareto_y_numpy = pareto_y.cpu().data.numpy()\n", + " \n", + " pareto_x_forx_numpy = pareto_x_forx.cpu().data.numpy()\n", + " pareto_y_forx_numpy = pareto_y_forx.cpu().data.numpy()\n", + " \n", + " x_numpy = x.cpu().data.numpy()\n", + " y_numpy = y.cpu().data.numpy()\n", + " \n", + " x_y_loss = pair_loss_calc(x, y, x_numpy, y_numpy, pareto_x_numpy, pareto_y_numpy, pareto_x_forx_numpy, pareto_y_forx_numpy)\n", + " \n", + " return x_y_loss, pareto_x, pareto_y, x_numpy, y_numpy, pareto_x_numpy, pareto_y_numpy, pareto_x_forx_numpy, pareto_y_forx_numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def pave_tomax(pavemin, pave_max_value=500):\n", + " pave_max = torch.sub(pave_max_value, pavemin)\n", + " return pave_max" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load DWPT data" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "jkTrlAJMH5UO" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1010, 12)\n", + "(1010, 42)\n" + ] + } + ], + "source": [ + "def load_dwpt():\n", + " folder_name = \"./dwpt_v5\"\n", + " file_names = [\n", + " \"DWPT_v5_N10\",\n", + " \"DWPT_v5_N100\",\n", + " \"DWPT_v5_N200\",\n", + " \"DWPT_v5_N300\",\n", + " \"DWPT_v5_N400\",\n", + " ]\n", + "\n", + " df = pandas.DataFrame()\n", + "\n", + " for file_name in file_names:\n", + " new_df = pandas.read_csv(f\"{folder_name}/{file_name}_after.csv\", index_col=0)\n", + " df = pandas.concat([df, new_df])\n", + "\n", + " return df\n", + "\n", + "df = load_dwpt()\n", + "df_gp = df.loc[:, :\"ys4[mm]\"]\n", + "df_mp = df.loc[:, \"k0mm_ys0\":]\n", + "\n", + "print(df_gp.shape)\n", + "print(df_mp.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define Loss Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "f = 85*10**3 #[Hz]\n", + "w =2*math.pi*f #[rad/s]\n", + "Pout= 50000 #W\n", + "Vdc = 400 #800 # V\n", + "Vbat = 400 #V\n", + "QCoil = 400\n", + "b = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def kdiff_loss(k_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + "\n", + " kdiff = abs(k_0_0 - k_1_0)\n", + " kdiff = kdiff / torch.max(abs(k_0_0), abs(k_1_0))\n", + "\n", + " return kdiff\n", + "\n", + "def calculate_Is(l_parameters, k_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + "\n", + " a6 = lp_0_0 * np ** 2\n", + " a11 = ls_0_0 * ns ** 2\n", + " # Paper formula\n", + " n1 = (torch.pi * w * a6 * ip) / (2*numpy.sqrt(2) * v_dc)\n", + " t1 = (torch.pi ** 2 * w * p_out * torch.sqrt(a6 * a11))\n", + " t2 = 8 * k_0_0 * n1 * v_dc * v_bat\n", + " n2 = t1 / t2\n", + " Is = (4 * v_bat * n2) / (torch.pi * w * a11)\n", + "\n", + " return Is\n", + " \n", + "def bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters):\n", + " # Unpack parameters\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " Is = calculate_Is(l_parameters,k_parameters)\n", + "\n", + " bx_100 = (\n", + " (bx_p_1_00 * ip * np + bx_s_1_00 * Is * ns) ** 2 +\n", + " (bx_p_1_90 * ip * np + bx_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " by_100 = (\n", + " (by_p_1_00 * ip * np + by_s_1_00 * Is * ns) ** 2 +\n", + " (by_p_1_90 * ip * np + by_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " bz_100 = (\n", + " (bz_p_1_00 * ip * np + bz_s_1_00 * Is * ns) ** 2 +\n", + " (bz_p_1_90 * ip * np + bz_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " b_100 = (bx_100**2 + by_100**2 + bz_100**2) ** 0.5\n", + "\n", + " bstray = b_100\n", + "\n", + " return bstray" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "def power_average(k_parameters, l_parameters, b_parameters, extra_parameters, gp_parameters):\n", + " \n", + " return " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def number_of_inverters(gp_parameters):\n", + " (\n", + " a, lpx, lpy, ls, p, wp, ws, *ys\n", + " ) = gp_parameters.clone().transpose(0,1)\n", + " \n", + " number_of_inverters = 1/(lpy+2*wp+2*a+p)*10**3\n", + "\n", + " return number_of_inverters" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def core_losses(extra_parameters, gp_parameters):\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " (\n", + " a, lpx, lpy, ls, p, wp, ws, *ys\n", + " ) = gp_parameters.transpose(0,1)\n", + " \n", + " V_PriCore = ((lpy +2*wp+2*a)*(lpx+2*wp+2*a)*5)/(10**3) # cm3\n", + " V_SecCore = (((ls+2*ws+2*b)**2)*5)/(10**3)\n", + " V_PriWind = (2*(lpx+wp)+2*(lpy+wp))*6.6*6.6/(10**3)*np\n", + " V_SecWind = 4*(ls+ws)*6.6*6.6/(10**3)*ns\n", + "\n", + " return V_PriCore, V_SecCore, V_PriWind, V_SecWind" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_coilloss_pave(k_parameters, l_parameters, b_parameters, extra_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + "\n", + " a6 = lp_0_0 * np**2 # LP0MM_YSO\n", + " a7 = lp_0_1 * np**2 # LP0MM_YS1\n", + " a8 = lp_0_2 * np**2 # LP0MM_YS2\n", + " a9 = lp_0_3 * np**2 # LP0MM_YS3\n", + " a10 = lp_0_4 * np**2 # LP0MM_YS4\n", + "\n", + " a11 = ls_0_0 * ns**2 # LS0MM_YSO\n", + " a12 = ls_0_1 * ns**2 # LS0MM_YS1\n", + " a13 = ls_0_2 * ns**2 # LS0MM_YS2\n", + " a14 = ls_0_3 * ns**2 # LS0MM_YS3\n", + " a15 = ls_0_4 * ns**2 # LS0MM_YS4\n", + "\n", + " Is = calculate_Is_test_2(l_parameters,k_parameters)\n", + " \n", + " loss = (w*a6*10**(-9)*ip**2/QCoil + w*a11*10**(-9)*Is**2/QCoil) \n", + "\n", + " p0 = w*k_0_0*(a6 * a11)**0.5*ip*Is\n", + " p1 = w*k_0_1*(a7 * a12)**0.5*ip*Is\n", + " p2 = w*k_0_2*(a8 * a13)**0.5*ip*Is\n", + " p3 = w*k_0_3*(a9 * a14)**0.5*ip*Is\n", + " p4 = 2*w*k_0_4*(a10 * a15)**0.5*ip*Is\n", + " pave = (p0+(2*p1)+(2*p2)+(2*p3)+ p4)/8\n", + " pave_kW = pave / 1000\n", + "\n", + " return loss,pave_kW" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Network Training Loop" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "OXzpQf5O1Nxk", + "outputId": "1bc1a1b1-95ac-43d8-f1d8-5dd49e4bbb03" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0\n", + " Kdiff : 23.5755%\n", + " Bstray : 51.4322\n", + " Pareto Kdiff : 5.9767%\n", + " Pareto Bstray : 52.9604\n", + " Coilloss : 2427.9536\n", + " num inv : 0.6104\n", + " Pareto Coilloss : 877.9530\n", + " Pareto num inv : 0.4307\n", + " V_SecCore : 1838.8491\n", + " pave : 48.0044\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\scipy\\interpolate\\fitpack2.py:280: UserWarning: \n", + "The maximal number of iterations maxit (set to 20 by the program)\n", + "allowed for finding a smoothing spline with fp=s has been reached: s\n", + "too small.\n", + "There is an approximation returned but the corresponding weighted sum\n", + "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n", + " warnings.warn(message)\n" + ] + 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 50\n", + " Kdiff : 62.5420%\n", + " Bstray : 51.4322\n", + " Pareto Kdiff : 5.6302%\n", + " Pareto Bstray : 57.6606\n", + " Coilloss : 1881.5154\n", + " num inv : 0.6104\n", + " Pareto Coilloss : 500.3254\n", + " Pareto num inv : 0.3253\n", + " V_SecCore : 1824.6876\n", + " pave : -22.1071\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\scipy\\interpolate\\fitpack2.py:280: UserWarning: \n", + "The maximal number of iterations maxit (set to 20 by the program)\n", + "allowed for finding a smoothing spline with fp=s has been reached: s\n", + "too small.\n", + "There is an approximation returned but the corresponding weighted sum\n", + "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n", + " warnings.warn(message)\n", + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\scipy\\interpolate\\fitpack2.py:280: UserWarning: \n", + "A theoretically impossible result was found during the iteration\n", + "process for finding a smoothing spline with fp = s: s too small.\n", + "There is an approximation returned but the corresponding weighted sum\n", + "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n", + " warnings.warn(message)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 100\n", + " Kdiff : 51.1690%\n", + " Bstray : 51.4322\n", + " Pareto Kdiff : 5.6244%\n", + " Pareto Bstray : 43.0439\n", + " Coilloss : 1683.3733\n", + " num inv : 0.6104\n", + " Pareto Coilloss : 622.4586\n", + " Pareto num inv : 0.3167\n", + " V_SecCore : 1886.3834\n", + " pave : -0.0129\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\scipy\\interpolate\\fitpack2.py:280: UserWarning: \n", + "The maximal number of iterations maxit (set to 20 by the program)\n", + "allowed for finding a smoothing spline with fp=s has been reached: s\n", + "too small.\n", + "There is an approximation returned but the corresponding weighted sum\n", + "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n", + " warnings.warn(message)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 150\n", + " Kdiff : 51.8642%\n", + " Bstray : 51.4322\n", + " Pareto Kdiff : 5.5912%\n", + " Pareto Bstray : 44.1153\n", + " Coilloss : 1664.8450\n", + " num inv : 0.6104\n", + " Pareto Coilloss : 612.3871\n", + " Pareto num inv : 0.3132\n", + " V_SecCore : 1894.5220\n", + " pave : 6.2520\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\scipy\\interpolate\\fitpack2.py:280: UserWarning: \n", + "The maximal number of iterations maxit (set to 20 by the program)\n", + "allowed for finding a smoothing spline with fp=s has been reached: s\n", + "too small.\n", + "There is an approximation returned but the corresponding weighted sum\n", + "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n", + " warnings.warn(message)\n" + ] + } + ], + "source": [ + "pareto_bstray_points = []\n", + "pareto_kdiff_points = []\n", + "pareto_numinv_points = []\n", + "pareto_coilloss_points = []\n", + "pareto_V_SecCore_points = []\n", + "pareto_pave_post_points = []\n", + "\n", + "kdiffs = []\n", + "bstrays = []\n", + "numinvs = []\n", + "coillosses = []\n", + "combined_losses = []\n", + "\n", + "#testing for graphs\n", + "kdiff_designs = []\n", + "bstray_designs = []\n", + "numinv_designs = []\n", + "coilloss_designs = []\n", + "V_SecCore_designs = []\n", + "pave_designs = []\n", + "\n", + "pareto_bstray_points2 = []\n", + "pareto_kdiff_points2 = []\n", + "\n", + "x_calc_numpy =[]\n", + "y_calc_numpy=[]\n", + "\n", + "#training parameters\n", + "n_epochs = 200\n", + "n_noise = 1000\n", + "\n", + "for epoch in range(n_epochs):\n", + " \n", + " # Clear old gradients in the GP model\n", + " for param in gp_model.parameters():\n", + " param.grad = None\n", + "\n", + " # Create GP parameters from noise\n", + " noise = generate_noise(shape=(n_noise, 10))\n", + " gp = gp_model(noise)\n", + "\n", + " # Unpack and filter GP parameters\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + " gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + " extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + " V_PriCore, V_SecCore, V_PriWind,V_SecWind = core_losses(extra_parameters,gp_parameters)\n", + " numinv = number_of_inverters(gp_parameters)\n", + " \n", + " # Scale GP parameters - why?\n", + " min_x = torch.min(gp_parameters)\n", + " max_x = torch.max(gp_parameters)\n", + " gp_parameters = (gp_parameters - min_x) / (max_x - min_x)\n", + " \n", + " # Push GP parameters through MP model\n", + " mp_parameters = mp_model(gp_parameters)\n", + "\n", + " # Scale MP parameters\n", + " y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + " min_y = torch.min(y, 1).values\n", + " max_y = torch.max(y, 1).values\n", + " mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + " k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + " l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + " b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + " # Calculate losses\n", + " kdiff = kdiff_loss(k_parameters)\n", + " bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " # coilloss,pave = coil_loss_and_power_average(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " # print(pave)\n", + " coilloss,pave = coilloss_pave_test_2(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " \n", + " #pmax - pave_kW, to get tensor to minimize, pavemin is tensor to minimize\n", + " pavemin = torch.sub(500, pave)\n", + "\n", + " # Call pairwise pareto and loss\n", + " bstray_kdiff_loss, pareto_bstray, pareto_kdiff, bstray_numpy, kdiff_numpy, pareto_bstray_numpy, pareto_kdiff_numpy, pareto_bstray_forx_numpy, pareto_kdiff_forx_numpy = compute_pairloss_and_pareto(bstray, kdiff)\n", + " numinv_coilloss_loss, pareto_numinv, pareto_coilloss, numinv_numpy, coilloss_numpy, pareto_numinv_numpy, pareto_coilloss_numpy, pareto_numinv_forx_numpy, pareto_coilloss_forx_numpy = compute_pairloss_and_pareto(numinv, coilloss)\n", + " V_SecCore_pavemin_loss, pareto_V_SecCore, pareto_pavemin, V_SecCore_numpy, pavemin_numpy, pareto_V_SecCore_numpy, pareto_pavemin_numpy, pareto_V_SecCore_forx_numpy, pareto_pavemin_forx_numpy = compute_pairloss_and_pareto(V_SecCore, pavemin)\n", + " \n", + " #get pave points for plotting\n", + " pareto_pave_post = pave_tomax(pareto_pavemin)\n", + " \n", + " #combine pairwise losses\n", + " combined_loss = (bstray_kdiff_loss + numinv_coilloss_loss + V_SecCore_pavemin_loss)\n", + "\n", + " # Push loss through the optimizer and update the GP model\n", + " try:\n", + " combined_loss.backward(retain_graph=True)\n", + " optimizer.step()\n", + " except Exception as e:\n", + " print(f\"Error on epoch {epoch} regarding\", e)\n", + " break\n", + "\n", + " \n", + " if epoch % 50 == 0:\n", + " \n", + " # plt.plot(pareto_bstray_numpy[:,0], spl_y(pareto_bstray_numpy[:,0]), '--', bstray_numpy, kdiff_numpy, 'o')\n", + " plt.plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + " plt.plot(bstray_numpy, kdiff_numpy, 'o')\n", + " # plt.plot(x_calc.cpu().data.numpy(), kdiff_numpy, 'o')\n", + " plt.xlim([0,100])\n", + " plt.ylim([0,0.5])\n", + " plt.show()\n", + " \n", + " plt.plot(pareto_numinv_numpy, pareto_coilloss_numpy)\n", + " plt.plot(numinv_numpy, coilloss_numpy, 'o')\n", + " plt.show()\n", + " \n", + " plt.plot(pareto_V_SecCore_numpy, pareto_pavemin_numpy)\n", + " plt.plot(V_SecCore_numpy, pavemin_numpy, 'o')\n", + " plt.show()\n", + "\n", + " #paretopoints graphing test\n", + " pareto_bstray_test, pareto_kdiff_test = oldgraph_pareto_frontier(bstray.cpu().detach().numpy(), kdiff.cpu().detach().numpy())\n", + " pareto_bstray_points2.append(pareto_bstray_test)\n", + " pareto_kdiff_points2.append(pareto_kdiff_test)\n", + "\n", + " # Store metrics for plotting or further logging\n", + " combined_losses.append(combined_loss.cpu().data.numpy())\n", + " kdiffs.append(torch.mean(kdiff).cpu().data.numpy())\n", + " bstrays.append(torch.mean(bstray).cpu().data.numpy())\n", + " numinvs.append(torch.mean(numinv).cpu().data.numpy())\n", + " coillosses.append(torch.mean(coilloss).cpu().data.numpy())\n", + " \n", + " kdiff_designs.append(kdiff.cpu().data.numpy())\n", + " bstray_designs.append(bstray.cpu().data.numpy())\n", + " numinv_designs.append(numinv.cpu().data.numpy())\n", + " coilloss_designs.append(coilloss.cpu().data.numpy())\n", + " V_SecCore_designs.append(V_SecCore_numpy)\n", + " pave_designs.append(pave.cpu().data.numpy())\n", + " \n", + " \n", + " pareto_bstray_points.append(pareto_bstray[:].cpu().detach().numpy())\n", + " pareto_kdiff_points.append(pareto_kdiff[:].cpu().detach().numpy())\n", + " pareto_numinv_points.append(pareto_numinv[:].cpu().detach().numpy())\n", + " pareto_coilloss_points.append(pareto_coilloss[:].cpu().detach().numpy())\n", + " pareto_V_SecCore_points.append(pareto_V_SecCore[:].cpu().detach().numpy())\n", + " pareto_pave_post_points.append(pareto_pave_post[:].cpu().detach().numpy())\n", + "\n", + "\n", + " print(f\"Epoch: {epoch:4d}\")\n", + " # print(f\" Combined Loss: {combined_losses[-1]:.10f}\")\n", + " print(f\" Kdiff : {100 * kdiffs[-1]:.4f}%\")\n", + " print(f\" Bstray : {bstrays[0]:.4f}\")\n", + " print(f\" Pareto Kdiff : {100 * torch.mean(pareto_kdiff[-1]):.4f}%\")\n", + " print(f\" Pareto Bstray : {torch.mean(pareto_bstray[-1]):.4f}\")\n", + " print(f\" Coilloss : {coillosses[-1]:.4f}\")\n", + " print(f\" num inv : {numinvs[0]:.4f}\")\n", + " print(f\" Pareto Coilloss : {torch.mean(pareto_coilloss[-1]):.4f}\")\n", + " print(f\" Pareto num inv : {torch.mean(pareto_numinv[0]):.4f}\") \n", + " print(f\" V_SecCore : {torch.mean(V_SecCore).cpu().data.numpy():.4f}\")\n", + " print(f\" pave : {torch.mean(pave).cpu().data.numpy():.4f}\")\n", + " \n", + "\n", + " # Save model for further training or testing\n", + " torch.save(gp_model, f\"./models/gp_model_{epoch:06}.pt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "#use inside every so many epoch printing \n", + "\n", + "\n", + "# figure, axis = plt.subplots(2, 1, figsize=(10,10))\n", + "# plt.figure(figsize=(10, 5))\n", + " \n", + "# if normalize == True:\n", + "# axis[0].plot(normalized_pareto_bstray_numpy, normalized_pareto_kdiff_numpy)\n", + "# axis[0].scatter(x_calc.cpu().data.numpy(), normalized_kdiff_numpy)\n", + "# axis[0].scatter(normalized_bstray_numpy, normalized_kdiff_numpy)\n", + "# axis[0].set_xlim([0,1])\n", + "# axis[0].set_ylim([0,1])\n", + "# axis[0].set_xlabel(\"Bstray\")\n", + "# axis[0].set_ylabel(\"Kdiff %\")\n", + "# axis[0].set_title(f\"x_calc vs. designs, epoch: {epoch}\")\n", + " \n", + "# axis[1].plot(normalized_pareto_bstray_numpy, normalized_pareto_kdiff_numpy)\n", + "# axis[1].scatter(normalized_bstray_numpy, y_calc.cpu().data.numpy())\n", + "# axis[1].scatter(normalized_bstray_numpy, normalized_kdiff_numpy)\n", + "# axis[1].set_xlim([0,1])\n", + "# axis[1].set_ylim([0,1])\n", + "# axis[1].set_xlabel(\"Bstray\")\n", + "# axis[1].set_ylabel(\"Kdiff %\")\n", + "# axis[1].set_title(f\"y_calc vs. designs, epoch: {epoch}\")\n", + "# else:\n", + "# axis[0].plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# axis[0].scatter(x_calc.cpu().data.numpy(), kdiff_numpy)\n", + "# axis[0].scatter(bstray_numpy, kdiff_numpy)\n", + "# axis[0].set_xlim([0,100])\n", + "# axis[0].set_ylim([0,1])\n", + "# axis[0].set_xlabel(\"Bstray\")\n", + "# axis[0].set_ylabel(\"Kdiff %\")\n", + "# axis[0].set_title(f\"x_calc vs. designs, epoch: {epoch}\")\n", + " \n", + "# axis[1].plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# axis[1].scatter(bstray_numpy, y_calc.cpu().data.numpy())\n", + "# axis[1].scatter(bstray_numpy, kdiff_numpy)\n", + "# axis[1].set_xlim([0,100])\n", + "# axis[1].set_ylim([0,1])\n", + "# axis[1].set_xlabel(\"Bstray\")\n", + "# axis[1].set_ylabel(\"Kdiff %\")\n", + "# axis[1].set_title(f\"y_calc vs. designs, epoch: {epoch}\")\n", + " \n", + " \n", + " # plt.xlim([0,100])\n", + " # plt.ylim([0,0.5])\n", + " # plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "4\n", + "12 12\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#scatter plot of final pareto points\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff Combined Final Frontier\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.5])\n", + "\n", + "print(len(pareto_bstray_points2[:]))\n", + "print(len(pareto_kdiff_points2[:]))\n", + "# print(pareto_bstray_points2[:])\n", + "final_x = []\n", + "for list in pareto_bstray_points2:\n", + " for item in list:\n", + " final_x.append(item)\n", + "\n", + "\n", + "final_y = []\n", + "for list in pareto_kdiff_points2:\n", + " for item in list:\n", + " final_y.append(item)\n", + "\n", + "final_x, final_y = oldgraph_pareto_frontier(final_x, final_y)\n", + "print(len(final_x),len(final_y))\n", + "\n", + "plt.scatter(final_x, final_y)\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "4\n", + "12 12\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#line plot of final pareto points\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff Combined Final Frontier\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points2[:]))\n", + "print(len(pareto_kdiff_points2[:]))\n", + "# print(pareto_bstray_points2[:])\n", + "final_x = []\n", + "for list in pareto_bstray_points2:\n", + " for item in list:\n", + " final_x.append(item)\n", + "\n", + "\n", + "final_y = []\n", + "for list in pareto_kdiff_points2:\n", + " for item in list:\n", + " final_y.append(item)\n", + "\n", + "final_x, final_y = oldgraph_pareto_frontier(final_x, final_y)\n", + "print(len(final_x),len(final_y))\n", + "\n", + "plt.plot(final_x, final_y)\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "'\\nplt.figure(figsize=(10, 5))\\nplt.title(f\"Pareto Kdiff During Training\")\\nplt.xlabel(\"Epoch\")\\nplt.ylabel(\"Coupling %\")\\n\\nfor x in pareto_kdiff_points:\\n plt.plot(x)\\nplt.show()\\n'" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points), len(pareto_kdiff_points))\n", + "\n", + "while i < len(pareto_bstray_points):\n", + " plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()\n", + "\n", + "\n", + "'''\n", + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"Pareto Kdiff During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Coupling %\")\n", + "\n", + "for x in pareto_kdiff_points:\n", + " plt.plot(x)\n", + "plt.show()\n", + "'''" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Number of Inverters vs Coilloss During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Num Inverters\")\n", + "plt.ylabel(\"Coil loss\")\n", + "plt.xlim([0,2])\n", + "plt.ylim([0, 5000])\n", + "\n", + "print(len(pareto_numinv_points), len(pareto_coilloss_points))\n", + "\n", + "while i < len(pareto_numinv_points):\n", + " plt.plot(pareto_numinv_points[i], pareto_coilloss_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "# plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto V_SecCore vs Power Avg During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"V_SecCore\")\n", + "plt.ylabel(\"Pave\")\n", + "plt.xlim([0,3000])\n", + "plt.ylim([0, 200])\n", + "\n", + "while i < len(pareto_V_SecCore_points):\n", + " plt.plot(pareto_V_SecCore_points[i], pareto_pave_post_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "# plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points), len(pareto_kdiff_points))\n", + "\n", + "while i < len(pareto_bstray_points):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "print(len(bstray_designs))\n", + "#colors = numpy.arange(len(bstray_designs))\n", + "colors = numpy.arange(1000)\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Bstray vs Kdiff During Training, Generated Designs\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "while i < len(bstray_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " fig = plt.scatter(bstray_designs[i], kdiff_designs[i], label = f\"epoch: {i*100}\")\n", + " i+=1\n", + " \n", + "# plt.colorbar()\n", + "plt.legend()\n", + "# from matplotlib.patches import Rectangle\n", + "# someX, someY = 45, 0.5\n", + "# fig,ax = plt.subplots()\n", + "# currentAxis = plt.gca()\n", + "# currentAxis.add_patch(Rectangle((someX -0.1, someY-0.1), 0.2, 0.2, alpha=0.5, facecolor='red'))\n", + "#plt.savefig('Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(bstray_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " ax.scatter(bstray_designs[i], kdiff_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,200)\n", + "plt.ylim(0, 0.2)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"Bstray\", fontsize = 18)\n", + "plt.ylabel('Coupling %', fontsize = 18)\n", + "plt.suptitle(f\"Bstray vs Kdiff During Training, \\n Generated Designs\" , fontsize = 20, y=1.01)\n", + "# plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle(( 0,0 ), 45, 0.15, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Ekiu9ksGext6QrhSR07w2FpGjRCTrCoEicorPX3Wx93QuVQszXjcR2UdEvIYZ9sA2D2ebvPhT7O/fjSJynddzEJE9ReRxWnJfFmD/MjxGRM5NWvdcbKvAJ+S/zz6df2LzH+5wB50i0htbuyS2Tmx5Vq+fiBwpIl5/Ue7mXq8tRKS7iNyDHfFRjW1BcYvlKvwwabuTaAl8OkssgfFWd6u2Ezz/ynuTvFsJHCAise6p2PDdW7DJ2AXNycmaim3t/6X7MREZih3EkEJERks7zAmzQ+RwxBhjNovIbfiM83eSZO7GvhkXi8hT2Od4MraPuSOrZX6ITRpz1+HYDzt2Ozb6BWPMJueD9SngLRF5GTtsMYq9wR2FbW4tbesJGWO2i8j3gH8Dr4nIv7HJTcOx/fzrsEOpCtHD2IDz19i8Gb+/0n+LHd51BTBeRF7BJvj2xXYVjMIGrMkJuOm8jM3ReFJEnsXegL8wxjzivOfOwSb8zRaRN7AJmHXYvxqPwAZy/cj+BvI40CAi87EfcIL9q/gIbHP4SzmcezbXbSjwlIgswhb+WosdyXCms83vPLZJYYz5yEl+fAL4PXC1836OlTYfir3+JrZPY4wRkUuw/eFTReRp7B8OB2H/wt8GXGy8a920CxF5MM3DP8Y+l1Ox12Op8x7ogn1P9MWOFnMHRNm+fhOwgepr2KHVW7CfEeOxwxPvyvGpXCN2YkWhpbT5cdhr/wnwbZNay+gBbIvUjc4N5wNs8vWp2M+kb+Z4Du3pYeACbH7L+yIyE/t+/CY2SfIgWrrRO8qd2DpNi0XkCexn+yhssDEL+9oVuonAicAvROTr2KTcftj8yGexv3fJ1zXWONG2ectMHsbatvULVx0Oj8dKsAVmYmOPQ0mPi3NBP8P2tX2JDVC6kKHSqMexRuMzJhlXhcqk5XOd5cmVRldgo+ASn+e1N7awzXJsQtJW7AfvI8BZSes+SBbj7tNc3yOwHyYbXNfoL0D/XK5BFq+hyeaaJV+7NPt8ydm+GdgtzXqC/YvuZWyLThM26JgP3ATske05OesEsaMJVjjH9hpX3xc758/72MBiu/NaTscW2nLXb5iER50C1+NXOK/PClqqDy7GFvbp1orXO+11w/bt34ZtbVjnvF9XYxNLT23F8boC12KLma13jluDvdnejkftGOzN4xFsEmaz8/1feFfa9bx+Xq9LNu/RDF+VzrqlznvnfWzAuc15P13Y2tcPW+TpL8BSZ516bODxAB71U9I8j5VJ59xMS8XSR7CJwJ4Vjp3tB2NvNNuc9+1cbHfapfjX4ViZZn85vz74fKY51/03tHyOrsQW3hrgrD8j1/dn0jVLPt5cMtRmca7LEmyy6EbntT40l+ftt67z2N6kubckLRtNms9nv9fKuX4PYe8B7kqjsbpX1yStf7Wz/Aetud6xL3F2ppRSSu0QRORkbFXMycaYGzOtr7IjIv+LDaxPMbY2Umz5k9h6HvuaNsw6vKPlcCillNpFuHMlXMt60TKD+FMde0Y7B5/reih2GPdmXHWknByVY4HftyXYgB0sh0MppdQu5Q9Obskb2Ob/gdj8kp7AfcaYXIsfKmuhiHyK7SKsxea3jcM2QlxhbGVsID5EuF2q02rAoZRSqlA9iR21Mx5b1qABm1T/T+ycLKp17sMmh16InTSxGpv4/ntjzNx8HVRzOJRSSimVd5rDoZRSSqm804BDKaWUUnmnAYdSquCIyCSnsuHozj6XtthZnodS7UEDDqUcIlIuIj8RkedFpEpEGkVku4h8IiKPiciF7TxHyw6lPUob54vrxh77iorIVhH5QkSeFZEbRGRAZ5+nUrsyHaWiFCAiR2PnGBiInaL5FWAVNijfEztJ1wXAz7CVWlVheg1bkRFsSe9+2NLTpwK/FpFJxpjJPtvmw5+wpc4LcX4ipTqUBhxql+dMXPY89gb1P3gUuHEm7TobuLLjz1DlYK6xU4XHOYWLzgHuB24XEToq6DDGbMSWv1Zql6ddKkrZv0K7Ycsk3+ZVTc8YEzHGTMdOBJhCRMY6Tfcbna6Yz0TkDhGp9Fh3pfPVxVnnS2ebT52mf/E5xtdFZLqIrBORJhFZJSL3+VQNnOt0LRSLyM0i8rFzjAedxytE5HoReUVEVjv72yAiMyVpJmERuVREYuPnj0/qupjU2nN01h/udGFtc7pAXhKRo7zWbS1jPYGdJwLszMz9PM4ll9dwiNPNttJZd4OIvCMid7m73dLlcIjIRc429SKyXkQeEZH+sdcuad3YbJ2TRGSYiMwWkWoRqROR15wWuuT9dxORX4nI+8613eY8p6kiMjznC6lUG2kLh9qlich+2AmQ6vGZhdjNGJMyW6KI3IydjXUz8Ax2wrIhwM+B00TkKGPM1qTNirBzQfTHTpIWxhbimYydsOrXScf4LvA37ARWM7HdPQcAP8DOijvSGOPVbP8EtgvoOWCGc24AB2MnwZqHncF4C7br6AzgVBEZb4x53ll3iXM+twBfYCfaipnb2nN0bpIvAcXYAk+fAsOcfb7i8VzaxBjzqtgZXI/Btnjc6zqXrF9DERkC/Ac7mdVM7MRi3YH9sbPL/hI7gZovEbke+37bgp1EqwYbzC5wfvYzAjsJ3JvYwld7YmdPfVlEhhljPnb2L9hWu6Nd64axMxiPBl7HTqanVMdpy8xv+qVfO/oXcDH2xvF6K7c/wdn+DZyZRV2PXeo8dmfS8pXO8meBMtfyvtiKf9VAkWv5gdgZbz8FBiTt60QgAjyVtHyuc4x3gd4e513hs3wgdjr5Dz0eSzfbZ07niJ3R9yNnn2cmrR+bmdJ3Rl2P408ii1mNgd866z3U2tcQ+D+v83Ye6wEEPM5rtGvZvtiAZAOJMxcL8FjsuSftd7Trmlya9NjlzvI/u5Yd6ix7yuMcA0CPfPw+6Zd+pfvSLhW1q9vd+b7G60ERucZpxnZ/7e1a5Srn+w+NMdXubY0xD2JbBy7yOfZVxph61/rrgaexwcBBrvV+hG0RudoYk3CexphXsH9ljxeRbh7H+JWxeQQJjDE1PstXA9OBQSKyp895e8n1HI/GPsd5xpink/b1J+CzHI6di9i5ueeGaO1rWJ+8wBizxRgTzXAOE7Cty380xqxybWuAidjgzM8C55zc/oltvTgyy3OMGmO2ZDhHpdqddqmoXV0sX8Kvxv81wF5Jy+ZiWykAjsL+tXqeiJznsX0x0EdEehljNrmW1xhjPvVYP3YD6uFaFstpOF5EvEbI9AWC2FaG5GZy38mtRGQUtjXhKGcfxUmrDCD70RW5nuPhzvLXklc0xkScro/9sjx2Lrxe71xfw6nY6zZDRKZju4UWGGOyDZIOc77PT37AGPOFiKwC9vbZdqHHNs0i8hWJ75kPsIHShSKyFzaQnQ8sNG2c8VOp1tKAQ+3qqpzvnjUajDF7x34WkX+R+pduL+zv0S0ZjtMVcAcc1T7rxXJEgknHALg+i2MkW+e1ooicjW3JaABexLYo1AJRbPP98UBJhuO55XqOFc73r3zW8zzvdhBLXt3gWpbTa2iMeVtEjsWOaDoX+A6AiHwM/NoY81iG/WR67l/hH3BU+ywP43rPOEHbicDNzjn+znlom4g8BNxojNme4TyValcacKhd3QLn+wgR6W5SkzszqcH22fds5/NKPgZARa7n5zTTe/ktNudihDHmQ/cDInIfNuDI5znG1t/N5/HdfZa31QnO9/8knUtOr6Ex5k3gdBEpAYYDpwA/BaaIyAZjzEtpNo9dn92wM58m87smOXG6Ta4FrhWR/bGv6eXAT7Azr36nPY6jVLY0h0Pt0pxm8LlAGZn/OvfyFtBDbC2PfHnL+X5sO+5zf+ADj2AjgB3F4SVKYsuLW67n+I7zPSWwEVvzxO8cWs35i38UNq/hKddDrX4NjTGNxpg3jDE305ILcmaGzRY731Oeo9P9sUeu55GJMeZTY8w/sNd7O5nPUal2pwGHUvYv0+3ATWLrYKSUL3duxN09tr3T+f43r1oTYsulj0xenqM/YXMM7hSRAz2OUew08ediJXCA+5ydoZS3AIf4bLMJ/5thruf4BvAxcJyIJN/8fkI75m+IdQ7wb2fRLcYYd5dNTq+hiBwrIhXJ69HSMlGX4ZSmYLtAfioi8evpXP/b8Q/qsiYi+/gEUD2wXWUpyaRK5Zt2qahdnjHmfREZi70hTQauEZFXsQmTQWzz/gnYPI/PaUnsxBjzsohMxN4olovIs846XbHJpsdjk/VOacP5fSQi38OORlgmIs8Dn2BHheyJbVXYAAzKYbd3An8FFovIE9hgYRQ22JgFjPfY5mXgAhGZhU38DGNHmczL9RyNMUZEvo/NH3lCRGJ1OIYC38DWkGjNNRstLcXIyrA5G6OAfbD1QW4wxtzh3qAVr+F1wBix88qswAarg7Hl07dgK5r6MsZ85tT9uA1YKiJTaanD0RNYiq0B0hZDgadEZBHwPnaocx9sy0YRLTkdSnWczh6Xq1/6VShf2NLmP8UW5FqHzXGoxd4Ip2LnUin22fYYYBr2g70Je3NdAvwBmyfhXnclsNJnP5PwqT+Bra3wILb4ViO2SNX7wH3AiUnrziWploPH/i51zrEWW377KecYnueAHWkyBZvUGMGj7kUu5+isPxwbXGxzvl7CjhrxvQ4ZrlvsK+rs7wtsvZMbSKoP0trXEBgDPIAdCVLjXL+PgXuAvXJ4Pb+D7V5pcI71L2yA9D5QnbTuaK/r7feewtZTuQ2bo7TOeS1WYwvAndrZv2v6tWt+iTF+OWVKKaU6koh0xwZ0S4wx7VriXanOpjkcSinVwUSkT3KukIiEsFVMS0lMalVqp6AtHEop1cFE5ArgN9gupFXY3I3jsIXRlgBHG1cVWqV2Bpo0qpRSHe8/2ETU42gpmvY5dkK932mwoXZG2sKhlFJKqbzTHA6llFJK5Z0GHEoppZTKOw04lFJKKZV3GnAopZRSKu804FBKKaVU3mnAoZRSSqm804BDKaWUUnmnAYdSSiml8k4DDqWUUkrlnQYcSimllMo7DTiUUkoplXcacCillFIq7zTgUEoppVTeacChlFJKqbzTgEMppZRSeacBh1JKKaXyTgMOpZRSSuWdBhxKFQARmSQi/+rs8yhEIvJXEflVZ5/HzkBE5orID5yfLxKRF/J0nLNFZJWIbBeRw0RkmYiMzsex1I5DAw61QxCRviLymIisFZEaEVkgIl9PWmeCiHwhIrUiMkNEeroeKxGRf4rIVhFZJyI/6/hn0fFE5CcislBEGkXkQY/HTxKRj0SkTkReFZG9XI+JiPxORDY5X/9PRMT1+N7ONnXOPr6Rj+dgjLnCGPPbfOw7RkRW5uv8C5Ux5lFjzJg87f73wE+MMV2NMYuNMYONMXNBg+tdmQYcakfRFfgvMBzoCTwEzBaRrgAiMhi4D/gOsBtQB/zZtf0k4ABgL+AE4BcickpHnXwnWgvcCvwz+QER6Q08CfwKe00XAlNdq1wGnAUMBYYApwOXux5/DFgM9AL+B5guIn3a/RmoHdFewLLOPglVYIwx+qVfO+QXsBUY7vx8GzDF9dh+QBPQzfn/GmCM6/HfAo+n2ffpwBKgGngDGOJ6bCVwI/ABsAV4ACh1Pf5D4FNgMzAT6O96bDDwovPYV8BNzvJJwDTgYWAb9sN6hGu7G5znsA34GDgpx2t1K/Bg0rLLgDdc/y8H6oFBzv/fAC5zPf594C3n5wOBxtj1dZa9Dlzhc/xMz+9gYK5zvZcBZ7geexC41fm5N/CMs95m55gB57H+wBPABuBz4Kocrs9K4Bsey0uAu7CB21rn55IszqVNr1cW53spsMLZ/+fARa7lC4A/AjXAR+5jO9f4B65157seM8AVwHLs+/peQFyPfw/40HlsDrCXz/Xa7uyrFvjMfX2BU7C/l83Oeks74rNCvwrjS1s41A5JRIYBxdgbO9gb+dLY48aYz7AfbAeKSA/szWipaxdLnW289n04tkXgcuxf7/cBM0WkxLXaRcBYbGBzIPBLZ9sTgduB84F+wBfA485j3YCXgOed89kfeNm1zzOcdSuxgcqfnO0OAn4CHGGM6eYcd2X6K5SV5GtWC3xGy3VJeJzEazYYWGGM2ebzuBe/51cEzAJeAPoCPwUedZ53suuA1UAfbEvWTYARkYCzj6XAAOAk4BoRGZvmfLLxP8BIYBi2pedInNc6zblk/XqJyEQRqfb78tmmHLgHONXZ/9HY4Djm69hgpDdwC/Cku3sxg9OBI5zner5z7ojIWc7zO8d5vq9jW7gSGGMajTFdnf8ONcbsl/T489g/DqYa290yNMvzUjsBDTjUDkdEugOPAL82xtQ4i7ti/6JzqwG6OY+R9HjsMS8/BO4zxvzHGBMxxjyE/Wt+pGudPxljVhljNgP/C1zoLL8I+Kcx5h1jTCO2JeQoEdkb+2G+zhjzf8aYBmPMNmPMf1z7nG+MedYYE3GeX+zDOIL9y/EQESkyxqx0Aqq2SnfNvB6vAbo6eRyZtvXi9/xGOvubbIxpMsa8gm05uNBjH83YQG4vY0yzMeZ1Y4zB3iT7GGN+4+xjBfA34II055ONi4DfGGPWG2M2AL/GdtulO5esXy9jzGRjTKXfV5rzigJfE5EyY0yVMcbdfbEeuMs5p6nYFpZxWT7fycaYamPMl8Cr2EALbPB9uzHmQ2NMGBs0DHPn/CiViQYcaociImXYv2TfMsbc7npoO9A9afXu2Cbn7a7/Jz/mZS/guqS/NPfAtkrErHL9/IXrsf7O/wEwxmwHNmH/6t4D24LgZ53r5zqgVERCxphPgWuw3RLrReRxEenvsX2u0l0zr8e7A9udm2qmbb14Pj/sNVtljIm6Hv8Ce82S3YFt1XpBRFaIyERn+V5A/6TX7CZsy0NbJLyeJL7WnueSx9cLZ/+1wLew3R9VIjJbRAa5VlnjvEZe55xJ8msUC9b3Au52XdvNgOD9GinlSQMOtcNwujRmYPvGL096eBktfzEjIvti/8r8xBizBahyP+787JfUtgr436S/NrsYY9xNyHu4ft4T27+P89090qMc2y2zxtlvQhNztowxU4wxxzj7NsDvWrOfJMnXrNw5v2Vej5N4zZYB+zrdRF6P52ItsIfTLRKzJ/aaJXBaha4zxuwLjAd+JiInYa/t50mvWTdjzGmtOJ/kc3P/FR9/rdOcS9avl4jc5Awd9fzyOyljzBxjzMnYFpaPsK05MQPco4lIfH+21irg8qTrW2aMeaMV+zKZV1E7Iw041A7B6eefjk1qvDjpr2GAR4HxInKsc+P8DfCkK8fgYeCXItLD+Wvwh9hkRC9/A64Qka87Q0PLRWRc0s31ShEZ6PSN30TL6I4pwHdFZJgTIN0G/McYsxLbTbC7iFwjdphuN0ka2uvz3A8SkROd/TU41yDiPDZaRHw/wEUkJCKlQBAIikisVQHgKWyz/DeddW4G3jXGfOS6Zj8TkQHOX+jXxa6ZMeYTbN7ALc4+z8aOZHki0/Px8B9sguEvRKRIbL2G8Ti5L0nP53QR2d+5oW51rkMEeBvYKiI3iEiZiARF5GsickQ218lR5DyXUtd1egz7vukjdlTPzcC/0p1LutcrmTHmNieXwfPLaxsR2U1EznDe543Y1ib3/vsCVznX8jxsQu6zGZ57Jn8FbhQ7GgwRqXD23RpfAXsnBZhqF6AvuNpRHI3NgRgDVLv+CjwWwOnDvgIbeKzH5hL82LX9LdjujC+A14A7nAS2FMaYhdiA5E/YjPxPsRn9blOwSY4rnK9bnW1fxg4zfQLbqrIfTh6BE/ycjL2ZrsOOBjghi+deAkwGNjrb9cUGOWBbWt5Ms+0vsTe8icC3nZ9/6ZzPBuCb2ByULdhkQ3fOw33Y7qv3gPeB2c6ymAuAEc62k4FznX3mxBjThE0oPdV5jn/GBpUfeax+ADbxdjv2ef/ZGDPXyQsZj805+NzZz9+BCme7TNcJ7E253vU1Cfu6LgTexV6Hd5xlvudC+terPQSwwd9abNfG8SS+1//jnNtG7Gt7rjFmU1sOaIx5CttK87iIbMW+H05t5e7+7XzfJCLvtOW81I5FErv6lFKZiMhK7NDClwrgXP4O/NsYM6ezz6WQ7SrXSUQuxb43j+nsc1EqWSjzKkqpQmWM+UFnn8OOQK+TUp2v07pUxJaZXi8i77uW9RSRF0VkufO9h+uxG0XkUxH5WFxj60VkuIi85zx2TyxZyukjn+os/4/YYYlKKaWU6gSdmcPxILbqnNtE4GVjzAHYgkgTAUTkEGx/8WBnmz+LSNDZ5i/YiokHOF+xfX4f2GKM2R+4k/bJ6lcKY8zehdCdolQyY8yD2p2iClWnBRzGmHnYhCe3M7FzZOB8P8u1/HFjq9h9jk3iO1JE+gHdjTFvOuPOH07aJrav6cBJSUPFlFJKKdVBCm2Uym7GmCoA53tfZ/kAEgstrXaWDXB+Tl6esI1TGa8GWw9BKaWUUh1sR0ka9WqZMGmWp9smdecil2G7ZSgvLx8+aNAgr9WUUkqpnc6iRYs2GmPyPtNzoQUcX4lIP2NMldNdst5ZvprEyo4DsWPQVzs/Jy93b7PaKeBTQWoXDgDGmPuB+wFGjBhhFi5c2E5PRymllCpsIvJF5rXartC6VGYClzg/XwI87Vp+gTPyZB9scujbTrfLNhEZ6eRnXJy0TWxf5wKvGC06opRSSnWKTmvhEJHHgNFAbxFZja0EORmYJiLfB74EzgNbRVJEpgEfAGHgSqeyIMCPsCNeyoDnnC+AfwCPiMin2JaNts4aqZRSSqlW0kqjSbRLRSml1K5ERBYZY0bk+ziFlsOhlFJqJ9bc3Mzq1atpaGjo7FPZ5ZSWljJw4ECKioo65fgacCillOowq1evplu3buy9995oaaSOY4xh06ZNrF69mn322adTzqHQkkaVUkrtxBoaGujVq5cGGx1MROjVq1entixpwKGUUqpDabDROTr7umvAoZRSqtNs2rSJYcOGMWzYMHbffXcGDBgQ/39TU1PabRcuXMhVV12V8RhHH310u5zr3LlzOf300+M/v/HGG+2yX4CVK1cyZcqU+P+zfW47Es3hUEop1Wl69erFkiVLAJg0aRJdu3bl5z//efzxcDhMKOR9qxoxYgQjRmQeXNGegUHM3Llz6dq1a07BTLrnEgs4JkyYAGT/3HYk2sKhlFKqoFx66aX87Gc/44QTTuCGG27g7bff5uijj+awww7j6KOP5uOPPwYSWxwmTZrE9773PUaPHs2+++7LPffcE99f165d4+uPHj2ac889l0GDBnHRRRcRKw3x7LPPMmjQII455hiuuuqq+H69rFy5kr/+9a/ceeedDBs2jNdff50NGzbwzW9+kyOOOIIjjjiCBQsWxM/rsssuY8yYMVx88cWsXLmSY489lsMPP5zDDz88HgxNnDiR119/nWHDhnHnnXcmPLfNmzdz1llnMWTIEEaOHMm7776b8TkXIm3hUEopVXA++eQTXnrpJYLBIFu3bmXevHmEQiFeeuklbrrpJp544omUbT766CNeffVVtm3bxkEHHcSPfvSjlCGgixcvZtmyZfTv359Ro0axYMECRowYweWXX868efPYZ599uPDCC9Oe2957780VV1yR0BozYcIErr32Wo455hi+/PJLxo4dy4cffgjAokWLmD9/PmVlZdTV1fHiiy9SWlrK8uXLufDCC1m4cCGTJ0/m97//Pc888wxgg6OYW265hcMOO4wZM2bwyiuvcPHFF8dbhfye82mnncbf//53+vfv39qXoN1pwKGUUqrgnHfeeQSDQQBqamq45JJLWL58OSJCc3Oz5zbjxo2jpKSEkpIS+vbty1dffcXAgQMT1jnyyCPjy4YNG8bKlSvp2rUr++67b3y46IUXXsj999+f0/m+9NJLfPDBB/H/b926lW3btgFwxhlnUFZWBtg6JD/5yU9YsmQJwWCQTz75JOO+58+fHw+wTjzxRDZt2kRNTU3a5/zss8/mdP4dQQMOpZRSBae8vDz+869+9StOOOEEnnrqKVauXMno0aM9tykpKYn/HAwGCYfDWa3THhW3o9Eob775ZjywcHM/lzvvvJPddtuNpUuXEo1GKS0tzbhvr/OLjTjJ5jkXCs3hUEopVdBqamoYMGAAAA8++GC773/QoEGsWLGClStXAjB16tSM23Tr1i3eggEwZswY/vSnP8X/H+vySFZTU0O/fv0IBAI88sgjRCIRz/25HXfccTz66KOA7Wrp3bs33bt3z+apFRQNOJRSShW0X/ziF9x4442MGjUqfoNuT2VlZfz5z3/mlFNO4ZhjjmG33XajoqIi7Tbjx4/nqaeeiieN3nPPPSxcuJAhQ4ZwyCGH8Ne//tVzux//+Mc89NBDjBw5kk8++STe+jFkyBBCoRBDhw7lzjvvTNhm0qRJ8X1PnDiRhx56KONzOu2001i7dm2WV6Bj6ORtSXTyNqWUyp8PP/yQgw8+uLNPI8X27dvp2rUrxhiuvPJKDjjgAK699trOPq1253X9O2ryNm3hUEoptcv729/+xrBhwxg8eDA1NTVcfvnlnX1KOx1NGlVKKbXLu/baa3fKFo1Coi0cSimllMo7DTiUUkoplXcacCillFIq7zTgUEoppVTeacChlFJKtTP35GvZMsZw1VVXsf/++zNkyBDeeeedPJ1d59CAQymllCoAzz33HMuXL2f58uXcf//9/OhHP+rsU2pXGnAopZQqeDMWr2HU5FfYZ+JsRk1+hRmL17R5n//617848sgjGTZsGJdffnm8imnXrl257rrrOPzwwznppJPYsGEDYMuVjxw5kiFDhnD22WezZcsWAD799FO+8Y1vMHToUA4//HA+++wzwBYTO/fccxk0aBAXXXRRxjlbnn76aS6++GJEhJEjR1JdXU1VVVWbn2eh0IBDKaVUQZuxeA03Pvkea6rrMcCa6npufPK9NgUdH374IVOnTmXBggXxmVtj85XU1tZy+OGH884773D88cfz61//GoCLL76Y3/3ud7z77rsceuih8eUXXXQRV155JUuXLuWNN96gX79+ACxevJi77rqLDz74gBUrVrBgwQIAbr75ZmbOnJlyTmvWrGGPPfaI/3/gwIGsWdP2wKpQaOEvpZRSBe2OOR9T35w4h0p9c4Q75nzMWYcNaNU+X375ZRYtWsQRRxxh91dfT9++fQEIBAJ861vfAuDb3/4255xzDjU1NVRXV3P88ccDcMkll3Deeeexbds21qxZw9lnnw2QMPvrkUceycCBAwEYNmwYK1eu5JhjjuE3v/mN5zmlmxV2Z6ABh1JKqYK2tro+p+XZMMZwySWXcPvtt2dcN91NP103Sa5Txw8cOJBVq1bF/7969Wr69++f8fx2FNqlopRSqqD1ryzLaXk2TjrpJKZPn8769esB2Lx5M1988QUA0WiU6dOnAzBlyhSOOeYYKioq6NGjB6+//joAjzzyCMcffzzdu3dn4MCBzJgxA4DGxkbq6upadU5nnHEGDz/8MMYY3nrrLSoqKuLdMzsDbeFQSilV0K4fexA3PvleQrdKWVGQ68ce1Op9HnLIIdx6662MGTOGaDRKUVER9957L3vttRfl5eUsW7aM4cOHU1FRwdSpUwF46KGHuOKKK6irq2PfffflgQceAGzwcfnll3PzzTdTVFTEv//977THvvnmmxkxYgRnnHFGwvLTTjuNZ599lv33358uXbrE97+z0Onpk+j09EoplT+tnZ5+xuI13DHnY9ZW19O/sozrxx7U6vyNTLp27cr27dvzsu/O1pnT02sLh1JKqYJ31mED8hZgqI6hORxKKaWUy87autHZNOBQSimlVN5pwKGUUkqpvNOAQymllFJ5pwGHUkoppfJOAw6llFKqnbVmevq5c+dSUVHBsGHDGDZsWEIJ9Oeff56DDjqI/fffn8mTJ7f36XYIHRarlFJKFYhjjz2WZ555JmFZJBLhyiuv5MUXX2TgwIEcccQRnHHGGRxyyCGddJatoy0cSimlCt+70+DOr8GkSvv93Wlt3mWhTU/v5+2332b//fdn3333pbi4mAsuuICnn366zc+/o2nAoZRSqrC9Ow1mXQU1qwBjv8+6qk1BRyFOTw/w5ptvMnToUE499VSWLVsG7DzT1muXilJKqcL28m+gOWlm2OZ6u3zI+a3bZQFOT3/44YfzxRdf0LVrV5599lnOOussli9fvtNMW68Bh1JKqcJWszq35VkoxOnpu3fvHv/5tNNO48c//jEbN27caaat1y4VpZRSha1iYG7Ls1CI09OvW7cuHsC8/fbbRKNRevXqxRFHHMHy5cv5/PPPaWpq4vHHH0+ZaXZHoC0cSimlCttJN9ucDXe3SlGZXd5KhTg9/fTp0/nLX/5CKBSirKyMxx9/HBEhFArxpz/9ibFjxxKJRPje977H4MGDW/3cO4tOT59Ep6dXSqn8ae309Lw7zeZs1Ky2LRsn3dzq/I1MdHr6/NAWDqWUUoVvyPl5CzBUx9AcDqWUUsplZ23d6GwacCillFIq7zTgUEoppVTeacChlFJKqbzTgEMppZRSeacBh1JKKdXOWjM9/UcffcRRRx1FSUkJv//97xMe85uefvPmzZx88skccMABnHzyyfEJ5QqRBhxKKaVUAejZsyf33HMPP//5zxOWx6anf+655/jggw947LHH+OCDDwCYPHkyJ510EsuXL+ekk05KCEYKjQYcSimlCt7sFbMZM30MQx4awpjpY5i9Ynab91lo09P37duXI444gqKiooTl6aanf/rpp7nkkksAO6FcrMR6IdKAQymlVEGbvWI2k96YRFVtFQZDVW0Vk96Y1Kago1Cnp/eSbnr6r776Kn68fv36xeeGKURaaVQppVRBu/udu2mINCQsa4g0cPc7dzNu33Gt2mchTk/vR6enV0oppTrAutp1OS3PRiFOT+8n3fT0u+22G1VVVfTr14+qqqp40FSItEtFKaVUQdu9fPeclmejEKen95NuevozzjiDhx56CLCz2Z555pnteuz2pC0cSimlCtrVh1/NpDcmJXSrlAZLufrwq1u9z0Kcnn7dunWMGDGCrVu3EggE4vkf3bt3952efuLEiZx//vn84x//YM8998x47M6k09Mn0enplVIqf1o7Pf3sFbO5+527WVe7jt3Ld+fqw69udf5GJjo9fX5oC4dSSqmCN27fcXkLMFTH0BwOpZRSymVnbd3obBpwKKWUUirvNOBQSimlVN5pwKGUUkqpvNOAQymllFJ5pwGHUkop1c7ae3r6vffem0MPPZRhw4YxYkTLCFadnl4ppZRSOfGbnj7m1VdfZcmSJbhrRen09EoppVQ7qpk1i+UnnsSHBx/C8hNPombWrDbvc0eZnj4dnZ5eKaWUaic1s2ZR9aubCa9dC8YQXruWql/d3KagY0eanh7sBHJjxoxh+PDh3H///fHlOj29Ukop1U7W33kXpiFxenrT0MD6O++iYvz4Vu1zR5qeHmDBggX079+f9evXc/LJJzNo0CCOO+64Vj33zqIBh1JKqYIWrqrKaXk2dqTp6YH4dPR9+/bl7LPP5u233+a4447T6emVUkqp9hJyugyyXZ6NHWl6+traWrZt2xb/+YUXXuBrX/saoNPTK6WUUu2m77XXUPWrmxO6VaS0lL7XXtPqfe5I09Nv3Lgx3mUTDoeZMGECp5xyCqDT0+/QdHp6pZTKn9ZOT18zaxbr77yLcFUVoX796HvtNa3O38hEp6fPD23hUEopVfAqxo/PW4ChOobmcCillFIuO2vrRmfTgEMppZRSeacBh1JKqQ6luYOdo7OvuwYcSimlOkxpaSmbNm3q9JvfrsYYw6ZNmxIKk3W0gkwaFZFrgR8ABngP+C7QBZgK7A2sBM43xmxx1r8R+D4QAa4yxsxxlg8HHgTKgGeBq42+y5VSqtMMHDiQ1atXx+cnUR2ntLQ0Xvm0MxRcwCEiA4CrgEOMMfUiMg24ADgEeNkYM1lEJgITgRtE5BDn8cFAf+AlETnQGBMB/gJcBryFDThOAZ7r8CellFIKgKKiIvbZZ5/OPg3VCQq1SyUElIlICNuysRY4E3jIefwh4Czn5zOBx40xjcaYz4FPgSNFpB/Q3RjzptOq8bBrG6WUUkp1oIILOIwxa4DfA18CVUCNMeYFYDdjTJWzThUQKxg/AFjl2sVqZ9kA5+fk5UoppZTqYAUXcIhID2yrxT7YLpJyEfl2uk08lpk0y72OeZmILBSRhdqvqJRSSrW/ggs4gG8AnxtjNhhjmoEngaOBr5xuEpzv6531VwN7uLYfiO2CWe38nLw8hTHmfmPMCGPMiD59+rTrk1FKKaVUYQYcXwIjRaSL2DmBTwI+BGYClzjrXAI87fw8E7hAREpEZB/gAOBtp9tlm4iMdPZzsWsbpZRSSnWgghulYoz5j4hMB94BwsBi4H6gKzBNRL6PDUrOc9Zf5oxk+cBZ/0pnhArAj2gZFvscOkJFKaWU6hQ6W2wSnS1WKaXUrqSjZostxC4VpZRSSu1kNOBQSimlVN5pwKGUUkqpvNOAQymllFJ5pwGHUkoppfJOAw6llFJK5Z0GHEoppZTKOw04lFJKKZV3GnAopZRSKu804FBKKaVU3mnAoZRSSqm804BDKaWUUnmnAYdSSiml8k4DDqWUUkrlnQYcSimllMo7DTiUUkoplXcacCillFIq7zTgUEoppVTeacChlFJKqbzTgEMppZRSeacBh1JKKaXyTgMOpZRSSuWdBhxKKaWUyjsNOJRSSimVdxpwKKWUUirvNOBQSimlVN5pwKGUUkqpvNOAQymllFJ5pwGHUkoppfJOAw6llFJK5Z0GHEoppZTKOw04lFJKKZV3GnAopZRSKu804FBKKaVU3mnAoZRSSqm804BDKaWUUnmnAYdSSiml8k4DDqWUUkrlnQYcSimllMo7DTiUUkoplXcacCillFIq7zTgUEoppVTeacChlFJKqbzTgEMppZRSeacBh1JKKaXyTgMOpZRSSuWdBhxKKaWUyjsNOJRSSimVdxpwKKWUUirvNOBQSimlVN5pwKGUUkqpvNOAQymllFJ5pwGHUkoppfJOAw6llFJK5Z0GHEoppZTKOw04lFJKKZV3GnAopZRSKu804FBKKaVU3mnAoZRSSqm804BDKaWUUnmnAYdSSiml8k4DDqWUUkrlnQYcSimllMo7DTiUUkoplXcacCillFIq7zTgUEoppVTeacChlFJKqbzTgEMppZRSeacBh1JKKaXyTgMOpZRSSuWdBhxKKaWUyjsNOJRSSimVdxpwKKWUUirvNOBQSimlVN5pwKGUUkqpvNOAQymllFJ5pwGHUkoppfJOAw6llFJK5V1BBhwiUiki00XkIxH5UESOEpGeIvKiiCx3vvdwrX+jiHwqIh+LyFjX8uEi8p7z2D0iIp3zjJRSSqldW0EGHMDdwPPGmEHAUOBDYCLwsjHmAOBl5/+IyCHABcBg4BTgzyISdPbzF+Ay4ADn65SOfBJKKaWUsgou4BCR7sBxwD8AjDFNxphq4EzgIWe1h4CznJ/PBB43xjQaYz4HPgWOFJF+QHdjzJvGGAM87NpGKaWUUh2o4AIOYF9gA/CAiCwWkb+LSDmwmzGmCsD53tdZfwCwyrX9amfZAOfn5OVKKaWU6mCFGHCEgMOBvxhjDgNqcbpPfHjlZZg0y1N3IHKZiCwUkYUbNmzI9XyVUkoplUEhBhyrgdXGmP84/5+ODUC+crpJcL6vd62/h2v7gcBaZ/lAj+UpjDH3G2NGGGNG9OnTp92eiFJKKaWsggs4jDHrgFUicpCz6CTgA2AmcImz7BLgaefnmcAFIlIiIvtgk0PfdrpdtonISGd0ysWubZRSSinVgUKdfQI+fgo8KiLFwArgu9jgaJqIfB/4EjgPwBizTESmYYOSMHClMSbi7OdHwINAGfCc86WUUkqpDiZ2AIeKGTFihFm4cGFnn4ZSSinVIURkkTFmRL6PU3BdKkoppZTa+WQdcIjIkSLyw6RlZzqVPNeIyG3tf3pKKaWU2hnk0sJxC3BG7D8isifwGLA7UAPcICLfbd/TU0oppdTOIJeAYyiwwPX/C7C1LoYZYw4BXsCWEVdKKaWUSpBLwNELWOf6/1hgnjFmjfP/mdghqUoppZRSCXIJOKqB3QBEpAQYCcxzPW6ww0+VUkoppRLkUodjCfADEXkJOBsoBea4Ht8H+Kr9Tk0ppZRSO4tcAo7fYvM03sbmbrxojHEXrDgd+I/XhkoppZTatWUdcBhj3hCRw7G5GzXA47HHRKQXNhh5qt3PUCmllFI7vJxKmxtjPgE+8Vi+Cbi2vU5KKaWUUjuXrAMOEQkCJcaYOteySuD7QE/gMWPM++1+hkoppZTa4eXSwnEfdmTK1wBEpAiYDxziPP4zETnKGLOkXc9QKaWUUju8XIbFHoOttRFzLjbYuBI4GjtCZWL7nZpSSimldha5tHD0Az53/X8csMwY8xcAEbkfuLwdz00ppZRSO4lcWjgECLr+Pxp41fX/KqBvO5yTUkoppXYyuQQcn2OHxCIio7AtHu6Aoz92uKxSSimlVIJculQeAP4gIu8DA4D1JFYa/TrwUTuem1JKKaV2Elm3cBhj7sJOUd8ILAbOjg2RdQp/jQSezcM5KqWUUmoHl2vhr99iS5wnL9+E5m8opZRSykcuORwJRKS3iPRuz5NRSiml1M4pp4BDRPqLyEMiUo2tu/GViGwRkQdFZEBezlAppZRSO7xcSpvvCbwF7I6dqn6Z89AhwMXAySIy0hizqr1PUimllFI7tlynp+8BnG6MSUgOFZFTgSeddS5tt7NTSiml1E4hl4BjDPDn5GADwBjznIj8BZjQbmemVJ7NWLyGO+Z8zNrqevpXlnH92IM46zDtGVRKqXzIJeDoASxP8/hyoLJNZ6NUB5mxeA03Pvke9c0RANZU13Pjk+8BZB10aMCilFLZyyVpdDW2nLmf45x1lCp4d8z5OB5sxNQ3R7hjzsdZbR8LWNZU12NoCVhmLF6Th7NVSqkdXy4Bx7+B80TkdhGpiC0Uke4ichtwPjC1vU9QqXxYW12f0/JkbQ1YlFJqV5NLwPFb4E3gBmCjiHwhIl8Am7DT0r8B3Nr+p6hU++tfWZbT8mRtDViUUmpXk0tp8zrgeOwU9C8AtUAddj6Vy4ATjDH6aat2CNePPYiyomDCsrKiINePPSir7dsasCil1K4mp8JfxpiIMeZvxphxxphDjDEHG2NON8b8HdhfRM7P03kq1a7OOmwAt59zKAMqyxBgQGUZt59zaNZJn20NWJRSaleT01wqGXwT+A0wrR33qRSQnxEhZx02oNX7iG2no1SUUio77RlwKJUX7TGENR/aErAopdSuptWTtynVUXREiFJK7fg04FAFT0eEKKXUjk8DDlXwdESIUkrt+DTgUAVPR4QopdSOL23SqIhsA0yW+ypq++kolUpHhCil1I4v0yiVRWQfcCiVNzoiRCmldmxpAw5jzOgOOg+lCtKMxWuYNHMZ1fXNAPToUsQt4wdr8KOUUjnSOhxK+ZixeA3X/3spzdGWRr4tdc1cP30p0Lk1QJRSakejSaNK+bhjzscJwUZMc8RoDRCllMqRBhxK+UhX50NrgCilVG404FDKR7o6H1oDRCmlcqMBh1I+rh97EEUBSVleFBStAaKUUjnSpFGlfMSSQnWUilJKtZ0Yo2U23EaMGGEWLlzY2aehCtCMxWu0+JhSaqcjIouMMSPyfRxt4VAqCzMWr+HGJ9+Lz1q7prqeG598D9DhsUoplQ3fHA4RiYpIJMevcEeevFId5Y45H8eDjZj65ogOj1VKqSyla+F4GC1rrhTgPwxWh8cqpVR2fAMOY8ylHXgeSnWoXPMx+leWscYjuNDhsUoplR0dFqt2ObF8jDXV9Rha8jFmLF7ju831Yw+irCiYsKysKKjDY5VSKkuaNKp2OenyMfxaOWLLvVpFdPSKUkpl5htwiMjnQBQYZIxpFpEVWezPGGP2a7ezUyoH2d74W5uPcdZhA1L2p6NXlFIqO+laOL7AJo3GEke/RJNIVYHK5cbfnvkYrWktUUqpXVG6pNHR6f6vVGdzt2gERIgkFbHzu/FfP/aghOAEWp+PoaNXlFIqO5rDoXZIyS0aycFGjNeNP10+Rq509IpSSmUn54BDRLoD3wD2dRatAF40xmxrzxNTKh2vrgwvfjd+r3yMbCTniZwwqA9PLFrTLq0lSim1M8sp4BCRHwD/B3QFYtNoGmC7iPzMGPOPdj4/pTxl02XR3jd+rzyRJxat4ZvDB/DqRxt0lIpSSqWRdcAhImcA92NbNG4G3nceGgz8FLhfRNYbY2a1+1kqlcSvKyMoQtQYKrsUYQxcO3UJd8z5uF2CAL8E0Vc/2sCCiSe2ad9KKbWzy6WF4xfAh8DXjTHbXctfFpEHgLeAGwANOFTe+SV+3n7OoQB5GaramgRRrdGhlFJWLpVGhwIPJgUbADj5Gw856yiVd2cdNoDbzzmUAZVlCDCgsozbzzmUsw4bkLeJ1iq7FHku98sTaU1FU6WU2lnlmjQqaR7TGh2qQ/klfvq1OKyprmfU5Fda1dowY/EatjekToZcFBTfPBGt0aGUUi1yCTiWApeIyL3GmFr3AyLSFbjUWUepTuWX3yEQXx5rbVj4xeasEj7vmPMxzdHUmLq8OOQbPGiNDqWUapFLl8rvgYOBd0TkShE5wfn6CbAIGATckY+TVCoXXhOtCalNcPXNER5968usujz8goSa+mbf8/DratEaHUqpXVHWAYcxZgbwE6A/8EfgJefrHmfZT4wxT+fhHJXKiVd+h19/n1cQMmnmspT1cg0eZixeQ11TaheM1uhQSu2qcsrhMMb8WUSmACcD+2D/cPwMW/irJg/np1TW0o0IGTX5Fc9uFi/V9c3MWLwmoaskl3LoyfU6YirLiph0xmDN31BK7ZJyrjRqjKkG/t3+p6JU62WavM0rYPDqZolJTuzMpRy6XxXU8hL/fA+llNrZpQ04RCQI/C+w0hjz1zTr/QjYA/gfY3wmtVAqjzKNCEkOGCq7FNHQHKG+Oeq5P785WLymp08OQjRZVCmlUmXK4fg2cD3w3wzrvY0t+nVhe5yU2vXMWLyGUZNfYZ+Jsxk1+ZWca1WkGwob29dZhw1gwcQTufNbw2hojvoGG5BdYqdfnY1c63UopdSuIFOXyvnAS8aYRelWMsYsEpE52IBjSnudnNo1ZOoOyYbfUFggZV+ZJn5Ll9jpbtEIiKTMUlvfHKEkFKCsKKgTuimllEumFo7h2JEo2XgVGNG201G7ovaoDOo1FNZvX+m6NtwVS5PNWLyG6/+9NN6ikRxsxNTUN/tWQVVKqV1VphaOnsD6LPe1wVlfqZy0R85D7GZ+zdQlno/HulbOOmyAb2vIgMqylEnY3C0aCGSTodS/ssy3CqpSSu2qMrVwbAN6Z7mvXkDKPCtKZeKX2xAQySmX46zDBjAgTZ5ErKjX3r2811m/tT4hhyQ5RyObYEO7TpRSylumgGMZMCbLfZ3srK9UTvy6QyLG5DzZWTZdK2+t2OL5eHOUePLnNVOX8LNpS9Lmerhp14lSSqWXqUvlSeD/ROTMdFVEReQMbMDxs/Y8ObVriN2gr5u21DMJM5fJzjJ1rax1Wiuy4TF1iqceXYpYfHO2cblSSu2aMrVw3Ad8CkwTkf8Vkb3dD4rI3iJyKzAN+MRZX6mcnXXYAKI+fRa51q9I17XSv7KMoKSb9Dg3RUHhlvGDU5a3dZivUkrtbNIGHMaYemAc8DlwI/CZiFSLyJcisgVb1vwm5/HTjTEN+T5htfNqz8nOvLpWYvkVF359j1adH9gAo7KsKN6Fcse5Qz2LgXnV59CgQym1K8tY2twY86mIDAN+CJwLDAZ2B7YCrwNPAH93ghOlWs2r/HhRQKhrCrPPxNkp5cTTzZ2SqRT5k4tWU+cU/hLny68MWFCEqDFpy5m7Zap6qpRSuyIp1ErkTln1hcAaY8zpItITmArsDawEzjfGbHHWvRH4PhABrjLGzHGWDwceBMqAZ4GrM5VeHzFihFm4cGE+npLKgjuIqCgrorYpTHOk5SUrKwpy+zmHAnhOppYpadNrYrWyoiDfHD6AZ5ZWUZ003bz7eNnMowKwz8TZnnkiAnw+eVymS6CUUh1KRBYZY/JeRyvr6ek7wdXAh67/TwReNsYcALzs/B8ROQS4ANvycgrwZydYAfgLcBlwgPN1SsecumqtWPnxzyePo7wklBBsQEtLQWuLhflt9+pHG1hyyxi+PXLPeI5HUITD96xg0sxlXDN1SdZdJO3ZNeRHc0SUUjuaggw4RGQgNnfk767FZwIPOT8/BJzlWv64MabRGPM5Nsn1SBHpB3Q3xrzptGo87NpG7QDSFQTLVCzM74acbrsZi9fwxKI18ZEyEWNY8NnmlFYPSB/cpMsf8ZNLAKE5IkqpHVFBBhzAXcAvSOxW380YUwXgfO/rLB8ArHKtt9pZNsD5OXm52kGkaylI95jXDfnaqUv45Yz30m6XaY6VZGuq6z2Dg7MOG5BTafNcA4j2KAWvlFIdreACDhE5HVifacI49yYey0ya5V7HvExEForIwg0bNmR5WJVv6VoK/Ap8Vdc1MWnmspQbsgEefetLThjUx3efrZk+3i84cHcNLZh4Ytq8klwDiPYoBa+UUh2t4AIOYBRwhoisBB4HThSRfwFfOd0kON9jc7ysBtzjHAcCa53lAz2WpzDG3G+MGWGMGdGnT5/2fC6qDdK1FJx12AC+OTz1Jl7bFPHsAgEbdPzrrS8pCQXo0aUoZZ+tzbFIDg5yza/INYDoiBwRpZRqbwUXcBhjbjTGDDTG7I1NBn3FGPNtYCZwibPaJUCs8ulM4AIRKRGRfbDJoW873S7bRGSkiAhwsWsbtYNwtxRcP/Yg7pjzcfxG/szSqlbts7q+meq65pTmrnRl0TNx547kml+RawDRmhwRpZTqbBnrcBSQydiKp98HvgTOAzDGLBORacAHQBi40hgTa5/+ES3DYp9zvtQOZsbiNUyauSyh5cJrttdcxIKNNdX1XD99KZNmLqOmvpnKLkWUhALU1DfTv7KMEwb14dWPNsSHw26ubaS+ObViR0VZEdC6Ghxe9UfSBRCZaowopVQhKtg6HJ1F63AUFq+6GdkoLw5S1xTJet4Ut3T1PA77zQtsqUvtsulSFKBHeYlvIJSpBke6ImZKKZVPHVWHY0dq4VCF4N1p8PJvoGY1VAyEk26GIee32+6Tb7y1jeGcgw2Asw8fwIi9enLTk+/GK4pmK12LRLVHsAFQ1xylLk2rS6b8ilheilJK7awKLodDFbB3p8Gsq6BmFWDs91lX2eXtwCv/wS8BNKZHlyK6FKW+jZ9YZHMmvLo/spFrwmY6AppfoZTa5WnAobL38m+gOelG3Fxvl7eDXOtglBUFuWX8YHqUl6Q8FmulaG2HYWWXIs+RJq1JLDWQ0HqhVUKVUrsi7VJR2atZndvyHOVSR6JHlyLGDenHHXM+9s2baG1iaVFQ2N4QjudqxEaaQGLCZrb7H+BqFUnOSfHat1JK7Yy0hUNlr2Jgbstz5Ndd0aNLUUItjru+NYxbxg/miUVr0t70Y3Oi5Kq8OERzNHUOl+umLWXG4jXxobrZ7r2uKRxvxdAqoUqpXZW2cKjsnXSzzdlwd6sUldnl7cBveOgt4wen/PU/avIrabtfyoqCrUo27dGlyHMUCti5VdytEf0ry7Jq5dhS1xzfTquEKqV2VdrCobI35HwYfw9U7AGI/T7+nuxGqbw7De78GkyqtN89Ek1zmYMk3Q06tl1rWji2N4TTPu5ujfArwNWjS5HndtdMXULA55y0SqhSamenLRwqN7HgIjY0NpYwmi7oiI1uibWMxEa3eGyXbnioe8hsQCQ+q6tbjy5FLJh4IgDXTF2S9dMCKCsKZDWqJRbs+BXgujbNcb3OWauEKqV2BRpwqNx4BQ9P/hBmXQPNdd61OdKNbsmyhkdysqXXjRtsC0Usz2JAll0esQTUf731ZVbn4m6N8AqQck0o1SJfSqldgQYcKjdewQNAc6397tV60crRLTMWr+HXs5b55lR4aY4afjZtCddOXUJllyKKApKSABojwJ3fGgYQz7HIRIATBrVM8OdVITTWypFpSG6sZUODDaXUrkBzOFRualZlXie5NkcrRrfMWLyG66cvzSnYiIkaW/tiS10z6YaSGGxrRLr6H8m/IAZbVGzG4jW+E7XF1stER6copXYl2sKhciNBMFmM/nC3XrRidMsdcz6mOdL2eX6aI4agT74HZDE6REiJHtyBgt8Q12y7c3R0ilJqV6EtHCo32QQbkNh6MeR8GDrBBitgvw+dkDZ/I5ubdVEwu1EoEWN8Gzr6V5alHSHi0xvD2ur6tENcs61IqqNTlFK7Cg04VG7KemZeJ7n14t1psHRKS7BiIvb/aeZgyWZIa1Egu4BjQGUZF43cMyXoiOVQtKZcebpApX9lWcoQ38qyIs8AyV0ULEZLnyuldkY6PX0SnZ7eR3yW2Aw5HBV7pI5SufNr3ttV7AHXvu+5m70nzm7DybZwTzXvleAJLaNK0nW9uMWSTRd+sZlH3/oyoccl3dT2MxavYdLMZSkT0iWfY3Lxs6KA0LU0xJa65vg5+o1u0WnulVK50unpVeFIHgrrJ9aykdxV0opRKtnmQKRTVhRIuPnHhrDGbsrXTF2SkKIR63rJFHLEHn9i0ZqEdQX45vABKcFNRVkRInZqe6/CX/XNESbNXMZZhw3wTGBtjpp48mwsIPKag0XnaVFKFTJt4UiiLRwe/FoovEgASiuhfktLTQ6/lhEJwtl/9czl8PpLv7UGuFozvFoYUk6LzEGHX2tIjy5F3DJ+cKvOvbKsKOO5JRtQWRYvdDZq8iueQZp7HaWUSqYtHKpw5DIbrIlC/WZnO6cmx9AJNmcjuYXERNJWHIXcimj5WVNdz/XTl4LBtyZHwmnR0sLiF3z4db1sqWtm0sxlrQqUcg02IHGUS3vPmquUUu1Jk0ZVZm2ZDba5Hpa/YOdcEY/EzOZ6W6nUY36Vsw4b0KqETs/TiJisgg1oaRFYOXkcd35rmOfcKOm0JnBoLXfiql+ibWtnzVVKqfakAYfK7KSbIVjc+u1rVtkWDJNmnpJYa0hS0JGuKFc+eM1r0pDF/CqdIflc/VpdskmEVUqpfNMuFZXZkPPhuRtaukpyJjaQqBiYPhekuZ51T97EUVPK4yMsOrIwVlCEbw63iZvXTl1C/8oyahvDOQc86aa4b49z9Bul4pdoO0BrfSilCoAGHCo79VvasLGxiaNeFUeT9DUbE8qEV+SQSFlZVkR5SajVOQsRYxImcGvNfoqCwi3jB2c1U21AwBio7FLE9oZw2i6fbBI/rx97UEqyqs5Eq5QqFNqlorLTljwOsImnQ863uRwVe/iuttb0iv9c3xxBJLuKomVFQU4f2q9t59gecui9MAY+nzyOxTeP4Y7zhsZbIvwKlGWSXGxsQGWZb00QpZTqaDosNokOi/WRbS0OP8lFvjz2V2eKmdj8A2ZGj4kvE8jYyjGgsowTBvXhiUVrOjTfI9351DWFM3ar+LVaaPEupVRH0mGxqrDEhq22JpfDa6K22P5e/g3UrGYdvbmt+byEYAPsKIx0eRyxv/47Ork0nbXV9dz5rWFcP32p7wR0RUGhtjHMPhNnpwQVsQJlSim1M9EuFZW9NJOtpRWbrj557pQh59tWj0nVvHXma7wYPD7h4VgwkW6Cs9jsrIU062pAhIVfbKa8uCWe71IUiA+vFbHDdKvrmxPyVXTOFKXUzky7VJJol0oGkypav21Rmc3h8Alc/LoSMlUdFWziZT5GhmRTdTTb/Ry9X0/e/nxLxuRQvzle/OZPUUqptuioLhUNOJJowJGBT8BRs7KM9e92I1wXJNQlQt8h26jY26PVIZbLEZ8MbnVLCfQ0LSgzFq/humlLPWtKDHCGr+ar4FZxUGjy6RrJh6Kg+FZFTTc5nFJKtUZHBRzapaKy5zOdfM3KMqr+W0G4LgQI4boQVf+toGalR1dIzeqWhNGaVYDxLfrlnqb9jjkfc+HX90ipOhrrdqnJY3XPpojJaqRMe0lXFTXWhaSUUjsaDThU9mZd47l4/bvdMJHEt5KJBFj/brfUlSsG2paN5NEusTwPR6wbZU11fTzP4YlFa/jm8AGewz4rcyw/niu/5M/OoHOjKKV2RDpKRWWvudZzcbjOe66T1OViu06evMx7/64qpF6jTuqbI7z60QbPoaS7Us+gYAMy7VZRSu1ItIVDtVmoi3cyZ+pyY/M00hURe+ZnAL6jTvyW57NLpdAY0G4VpdQORwMOlR2f/A2AvkO2IcHECc4kGKXvkG2JK5b1tPtp8m4pAWDhP+Hdab5DYXNdvrMqpGHASimVDQ04VHZc+RXJKvaup98RNYS6hAFDqEuYfkfUpI5SaaiBJy/PUDjMUPfczdQ1hVMeiSWIupNJR01+hRmL13D92IMoCux407AXBYXKsqJ4Tkq2drUASym149McDpWdmtVpH67Yu957GKybya4SaGndOrY0JnaRVJYVMemMwQAJNTliRbNuP+dQupaG8jZLaz4ERfjWEXtw61mHxpcd9psXMj4HnZBNKbUj0oBDZSfT1PLtyD2BW0x5SYizDhvAqMmveCaTZjM7a6GJGMMTi9YwYq+e8QTQTMmvXsW/dO4VpdSOQAMOldkzP8vYwtFe6kwx/y+cWgAslrOws+Uu1DdHuG7aUsDOoZIu+bWyrCg+b8y1U5fQ32PSuliLT2x/SilVKDTgUOk98zNY+I+OOVbFHvy25mxmRkemPlRm62z0ryzb6epQRIyJBwnpnl91fTPX/3tpvCjYmup6Hn3ry5TS67HiYF4Bh7aGKKU6iyaNqvQWPdgxxxnxfbj2fZ4PHBtfdEZgPvOLr2JFyQSeifyISbfewgmD+qRUG+0sPboUJRQhu+tbw1g5eRzfHrknuaavxoKE68celHbb5Aqkfj0wXi1BXsXUdNI4pVRH0RYOlV6aRM9086fMLu/C3T0qWRcKsns4wtVbqhlXW+d/nEUPwp4jqa4rB2ywcUfRfZSIPf5A2ciNzX/kf94J883hF/DqRxtyaukYvWoRl37wHH3qq9lQVsmDh5zK3D2GZ719MgFuGT84pXXglzPe82x1yEYsSOhSHKS2KbsEWz9eo1j8iqn5tYYopVR70hYOlZ54tyakmz9ldnkXJvXuSVVRCCNCVVGIiX16ccweA5hd3sX7OCYCT17G78se5ozAfO4s+ks82IgpkQg38mC82uhd3xqW1Rwno1ct4uol09mtvpoAsFt9NVcvmc7oVYtyvBiu08XewN2tAzMWr2l1sAE20LjxyfdyDjaSr4DfKJZci6kppVR70oBDpTf8Us/FfvOnLP2gBzf16UVDIOmtJUJNKMik3j09g46alWUsn9mHQx5byk+fn872L0o9j9tTtrOmuj5e2ru8OHMj3aUfPEdpJDEZszTSzKUfPJdx23TWVNdz/fSlDPv1C+wzcTbXTVvapqnsa5siKS0QmZQVBblo5J6e88sk06JpSqnOpF0qKr3T/2C/JyWO+s2f0msrRMW/1aEhEODuHpUJ3Sux1pJYABNrLQF8a3vEkiyzKWnep746p+W5aI4Yqp1ziHTwhC5lRYGcpqq/fuxBCTVM7D60podSqmNowKEyO/0PsOdIePKH8UWhLhGnOyXRpu4tP49aFmHCXEOvrXb5lNE2EJkwFz7c2i+e95FuttnkgGMLXQE4OfIaR8y4ks9KNrLW9Ob/hc9nZvQYz9PfUFbJbh7BxYayyiyefOFqaI5mXsklFpgkj1IBGDX5FR25opTKKzG70jSbWRgxYoRZuHBhZ59GQZp9R794Iui4dyN8e44hEGlpzWgIwX2nCQsGBxm1LMLlzxpKXRXKmwQkAEWuXgMJRjERITUTAcBw8AVVLf8zsN2U8GT0WM4LzqOLNMUfixp4JPINbgl/L2UvsRwOd7dKQ7CIu4ed26bE0UIwoLLMc/bcbMVGriS3emTbcqLDbJXa8YnIImPMiHwfR3M4VFZmr5idkAj6zNAQfzwtwIbuEAU2dE8MNn4yKzHYACg2icEG2JYMv3GgybPNikC3QCPfCb6UEGwABAS+E3yJMwLzU/Yzd4/h3D3sXL4qqyQKfFVWuVMEG9D2hM90I1cy0WG2SqlcaJeKysrd79ydkgi64GtBFnzN+Y8xIBJv2Qjm0HBmjGCCia0lnrPNOgLiPyT3F6FpzGxK7VqZu8fwHTLAqCwriueIeElO+HS3OFR2KcIYm+fi1/rQlpErOsxWKZULDThUVtbVrgO88zIWDA7Gg42fzMot2ACICrxyqDB6eYSi2kBKTQ+3mpVlVC3qjmkOEGsaCdeFWLmwB1tKijl+942cEZjvm8+RDyKZ50BprSW3jAG863skJ3wmd4+4J4HzK3nuV9k0m5ErOsxWKZUL7VJRWdm9fPd460WfrfaN02crXDXT8Pc7w9w0JcxPZ6YPNpoEwh7vuKCBY9+Hx44PcvAFVRxwxvqEYGN2eRfGDOzPj2oHsHJhD0xzkOR+mOIwBBaXc+yeAxhd+S/PrpV8yVew4Z6ufsRePePl3cFWOU3Os/BqcXDz6iq5fuxBKZVbsx25osNslVK50IBDZeXqw69mwtzUvAwBujfA0C/Sv5kiAn8ZL9x7uhDxyNkoDcOpHjGCu4jYha8ZisOp68T02go1oSC39+nOiMonsnlaBasoIPGbfqzlwt21Ul3XzMIvNidsk03LQvI6Zx02gNvPOTSrOh7J2hKsKKV2PdqlorIybnstH2z1fzxdvU8D1JbYnxcMDvLTmd5RQ2+P/d/dozKeO9IrzfGhZUhuQyDAv3oGYXP69QtZc9Rw45Pvcu3UJQREUmp8GODRt75MmNo+m4ntvFofzjpsQKtyLvyG2Wr+hlLKiwYcKjsv/4ZweYSi2twnTou1glz+rAEibOpuu2OSbe5uWzTG1dbFk0LvrAMjYQLG5nr4lfJsCLXU+QBYF7LneUZgPr8ITaO/ZK7XUWjqnTobfgXFYuXVYzd4r8JebuKs055aG6wopXY9GnCo7NSsYq9Dy/hyYSWhcK5zoVqlYZgw1zBltKTU6DBAz63Q8x+VLAt1JxABTMB20zj326CxP7qPboBtZfDAyU7yqmP3cITrQv/kguCrCRPA3VF0HzTTKUFHjy42B8OdzNlWsS6S2OiUdDkcBjQ4UEp1Gs3hUNmRIBV71/PgKcLWstSGhmzzJntttd0q950mbOhut4tig4jYVyAcAOP91hRsPkis9sfzh0NjEfx0puHee8OMWhahNBrl6i3VXBx8yXMCuFtCD+fwxNtPdV0zt4wfnJL30Bb9K8sS6mGkExRhn4mzGTX5Fa2VoZTqcNrCobLjTFP/wteCvHCoJAyP3V4KXRohlEXUEcuzWDA4yILBcO+9Yc/ulXTEwAU3hlKqmfbZClc8a6iu2c7ofnW+iSU9ZXtuB2wn/SvLEvIe1lTXE/TIz8hWLEEzU8tGTOw4fkNklVIqnzTgUNmp2IPZ4U1Aai2Okubsgg13nsWoZRG++6KhWytKNgjw+OQwYlJjipIwDPhvCZxh80Fipdh3D0e4ekt1wqRxYMueX/rBc/Spr2ZDWSUPHnJq3gqEnTCoT0JhrgFOkuV105ZmFXSUFQUoLQpSXZdYyOvaqUtyPhe/Al1aqlwplS86l0oSnUsl1ewVs7n7rdupaqrmuy9EOOWd1DyKTFkdBtv9MeoD6NZgl7UuEyQzg+Hz79cw56tKzp1HPDCafhyM3a2ao7YHOLzxfkavWsTPlkyjKNLSOhAJCl1GNLP33uvbPcm0rChAQ3M0pXhXLlPSC3DRyD259axD48tGTX4lY3eKn7u+NSweULR1XhWl1I5J51JRBWH2itlMemMSVc01jPogyth3UgOFbAOHU96xo1X8pmpzM0lfuYiKsM8/Krn8GRKKlH33eXi9qpJJzRcD8OMPZiQEGwDBiKHqwxDD9tmD7+1Z3K5FxOqTgg27LEJQvK+G1/LYcFh3DoZXPYyYsqJgPFnVi3vuk7bMq6KUUplowKHSuvudu2mI2CaJCXNNq98w2QQZ0BJguJNIc2Gwo1mE1Dd3aRhOm98yQqVbfV3y5gBUbgUjQlVRqEOKiPl1p6QbDnvdtKXxBFCA2885lMqyxMCissxWIx03pJ/vsd0BhZYqV0rlk+ZwqLRic6hA5sJb7SEqpJRHzyXoyLRu760wv/gq/l/4fEJdIoTrUn8FYomt0LlFxAICUZ/mHXcC6LVTl3h2a9U2hbnpyXepc+p5+IkFFG2ZV0UppTLRFg6V1u7luwM2yTNfORcxTWJHoHhpr0yjuq5RBgY2cnfRn+k9ZBsSTLwZJxcQg5YiYh3NL9hIZpK+xzRHTMZgA1oCCi1VrpTKJw04VFpXH341pcFSJsw1eQk43HkaDaV2iK0Xca3fWg0heOI4+5YXgR5717P7ETWEuoQBw+bucN9piQXEAIrDXdLu94zAfOYXX8WKkgnML76qQyeOayt3QNGWeVWUUioTHaWSREeppJq9YjZ7n/bzDolOm4NgolCc5m0ZJftIORbMbOpuWy4WHBKgX9IQ2divwCPlu3Fnn1LCgZaDl0aj/HRDMwurv+k5WuWMwHwmF/2dLtIUX1ZnipnY/IOCL6EeFOH/zh+qAYVSu7iOGqWiAUcSDTi8zTvy4JwLdLWW+x3p1apigI3dbU5JcmJpk9iWkq71riDDabFw1w/Z3B0ih9Uyul8NAGETIECUf5Xvxn09y9kWak6o3eEXRMwvvoqBgY0p57g62ptjmu5p03XIp6KgcMe5GmwopTou4NCkUZXR7BWz+Xh/Uupv5EumY0SlJZBILkLmDjDckquS9t4KjQvKmTsKavdvZn5VJafOhxFbYVh5LXsdupWKvVsSKLtIE78ITWNmU0vAMXrVIuo/DPBhXT9CXSL0HbItvk1/2eR7/umSQTtCjy5FjBvSjzvmfMy1U5dogS+lVIfQgEOlNXvFbH614Ffc9WnHBBvZCJqWmWdjJdJTGGMTNRwT5iZOFge2Kmnf18rZ9h+4sBmKnBIURbVB1rxVSdWi7vQbvtUziBi9ahFXL5lOOGJ/hcJ1Iar+WwFAxd71rDW9fM/fGFtwK9sKo20RFCFqTDyoWPjFZqb850v+9daX8XW01LlSqiNo0qhK6+537qY52twhQ2JzEZt5NpNRyyLce2+Y3j7nL9hiZEWR5OWCaQ5S9d8KalbaURzuIOLSD56jNJI466uJBFj/bjfqTDH/L3y+7znF5lT5v/OHtstEbukKe0WN4fPJ41gw8UQWfrGZf731pWfrSn1zhGumLtGJ3ZRSeaMtHCotdx2OQuMOIr47J8yYxRAwtsvlhWHwyR6S0I3SGiYSYOkHPbhydG+CkWJCXy0GoE99tef64bog/44cF8/18Jqr5axvfR9oaU1oS0tHALhl/GB+PWuZ57T3la5g5LH/rMq4P23tUErli7ZwqLRidTgKpTvFy3fnhDnlnZYKo0EDpyy2U9a3JdiI6bUVECESambM5in8c8ajvtcj1CXCSYElQEu3y2711QSA3eqruWbJdE5Y/U58/bMOG0C0Ld0qYvdxy/jBBAOpZ7W9IRxvscg2qNFy5kqpfNCAQ6V19eFXd/YpZDRmsff8Ln5v7lxv77HKo6OWRfjh84Y+W70DMAlG6TtkWzzXw6vbpSTSzPo770pY1pZKnlFjJ1379axlRDz6SpqjJh48eMQjvrScuVKqvWnAodIat+84AgX6NjEC994bJpBjBGHEDp91axKoL0oNRtyVR70ST8Fus6E79DuihvmDhZP3GEDXQRN9u12a1q5NmXyttUTsBGxe3Skxa6rrmbF4DcEcAo6ASHyuFs3pUEq1B83hUBlFyVweu6MZ7F/3rakNEjBQRGJJ8JcPgwfGhjh9aZjvzQ3TXBdiY9IwW7/EWQPcehlcvUWY1LsnDYEAgm0Z8Tq/DWWVXP/vpYDtDjnrsAHM+P0/UnI95u4xPONzKQsFqMtievsbn3yPLKqcx7nnatGcDqVUe9CAQ2U0alnmG1pnaMubV5J+PmkxrOwf5ph+1RxwRh2zy7vwqz69aHYNrfULIDZ3h59uruEPPW2wETNldGrSakOwiAcPOTWhq2Penx/h6iXT490vu9VXc/WS6QBpg47ioGQ1VwqQMu18LmI5HRpwKKXaQgMOlVG+5lEpJMUGfvBqhCHjbbnzWNnzyb16UO0EETOOhe/OMYTCLVejIQQPDjmayNK9uGX5Y/TaGk0qQNZSmMzdchHqvpjqXnP45dJq7l0SpTSSGDiURpq59IPnPAOOyrIiapvCNEXat4ZHUVBo9tmn5nQopdpKAw6V1q1v3co5BVaDIx2vadqzFaoN8rNt/Xnp0GC8rPnrX9r8hRlduvL7wZXUBQwTXnNVNj2uCKpxWijsfvpshatmGo5fGua2CSFeP6gLRIuRompM84uUbF9FaeV/iQbsBr23ebdSeOWA9OhShDH4BgZtUV4correOxdEp6hXSrWVBhwqrakfT+UssUNNC4lfUNGWlhgBvjMH6oJRFgwOMal3TwAWlxQztXs3EGHB12DB19xbGf70p7dSWigEGPoF3PRomAHVW12l1zex4JC3iLpONF2uR7Itdc2Eui+mvP8cJ4CppHHDWMJbD2vDM7czw6ZrxdAp6pVSbaUBh8pICizYyKfSMFz2nGHBYGgIBLi9Zw9qgoGEMunJ/FooBBj6ZUsQFGv5uGqmTeqICrxwWPpcj2TF3RdT3O9JJGBbIqS4mtJ+T9IArQ46BBtQ3DHnY9Z4BB09uhRp/oZSqs0Kc7yjKhijlkV2+vyNZKXNtpgYEA82YiXSH789zL33hhMSaau7++/Lqz5I7Cto7IR4B6423HeasLE7RLFDbP86toT5g1N/PYv6zIkHG/F9Bpop6TOHb4/ckwGVZQi2xeKubw1j5eRxDMjQHXLRyD0567ABXD/2oJRS62VFQW4Z7zVZjVJK5abgWjhEZA/gYWB37Ofv/caYu0WkJzAV2BtYCZxvjNnibHMj8H0gAlxljJnjLB8OPAiUAc8CVxuT59mydjIT5hZ2ldF8EGwxsQfG2v8nzzTbZ6utYnrg6jD/GhOkqhJ6+BQDy/ZYj5wsvH1wgOZ4da4GSqOpLRdSVO25n0BRNbeedajnY9ePPYjr/72UZo/CYN8euWd8u1grxh1zPmZtdb3OIquUalcFF3AAYeA6Y8w7ItINWCQiLwKXAi8bYyaLyERgInCDiBwCXAAMBvoDL4nIgcaYCPAX4DLgLWzAcQrwXIc/ox1Y7627ZnwWMDDq/Qjffs3Q0yOYCGBbJwLRCId82bagLGCgqzFUBxNbNGItF+6AwzRXIsXVCeuNWhbhwleFDyYfTG1lbx47dBxP9To0HjAAlJckJoT26FLELeMHpwQTsbogSinV3gou4DDGVAFVzs/bRORDYABwJjDaWe0hYC5wg7P8cWNMI/C5iHwKHCkiK4Huxpg3AUTkYeAsNODI2uwVs+nmk9C4szPAT56JEjT+oYQA31ja9hYgEUNNwHvWWHeLxuhVi/jui3X0rg3Hh94CTuuLDQy7Vm/kO29MYfOwc5nLcFtgTPIzqkUppXJRcAGHm4jsDRwG/AfYzQlGMMZUiUhfZ7UB2BaMmNXOsmbn5+TlKkt3v3M3+44Wrpq589fhSCZAIE2wEZNtWXW/4boG6LFvLd0j3agJpQYdJlLG6FWLuOK9p+neVJeQgHr5s4bGICnl1t01PJqjxnPG2rl7DOfaqUu4ZuoSBmjXiVKqAxRswCEiXYEngGuMMVvFf5SA3+e433KvY12G7Xphj9370fjS/NxPeCc04L9r2W+VIdTYOQFHW2pq5HNfuezXuNaJkpilHQUaBzbTs08jwz7qynaP6OWQzxs5beFUSkxqpdCujdDV57j9Grfw9S8+YsjGTznzs9cpMTYqGdC4hZ+/OY0D1lfxbu/97cpfwBPL3qXimL05er/emZ5y/lR0p+SIIZ13fKVUXhVkwCEiRdhg41FjzJPO4q9EpJ/TutEPWO8sXw3s4dp8ILDWWT7QY3kKY8z9wP0Aww8cZEp6aJEjgC49u3LSM19RpK3x7aYxCE+OgqX7Bji5to4f1GwDYEN5N8+ht99YFqE8bfVy75BncxdhaOhPnP9pag2VkGlk7JfzeXPAoPiyWuCR99dxwog9aItt8+axZcoUIhs3Euzdmx4TJtDtuOOy2rZxi+27q5k1i/V33kW4qopQv370vfYaKsaPb9N5KaU6X8EFHGKbMv4BfGiM+YProZnAJcBk5/vTruVTROQP2KTRA4C3jTEREdkmIiOxXTIXA3/soKexU7hg0IVU1N6V12Pkq+WhUJVE4JRF8Nle0Xiw8XpZKcM+izL2HaiohZpyeH44LN0vSEVt+v0FiqJEIwLRlraTxiB8uKfhvAX+Bdt6NW7jeFnCa2ZYfNmGbY1tChi2zZvHxr/8FZqaAIhs2Gj/D1nvo2bWLKp+dTOmoQGA8Nq1VP3qZgANOpTawUmhjRIVkWOA14H3ID5N6U3YoGEasCfwJXCeMWazs83/AN/DjnC5xhjznLN8BC3DYp8DfpppWOzwAweZRY892s7Pasf1zsXn0iPDTW9H1VnBjgGqy6FHrSFcFmXhHkEO+8wGI+51akvsD12b0u3JCToMEA6wpTzA88MN4/9ju1z8BEvD9Dl2O38KnxUPOk6s+ZSzl70QDxgAKC6m94+uyCpg+PKKK4hs2Jh6rD692fOvf824feOWer687RbCa1MbIkP9+3PAKy9n3IdSKncissgYMyLfxym4Fg5jzHz87wMn+Wzzv8D/eixfCHwtdQuVjb+/93dK94CjP9q1WiHyzYATxAmh+iBf/8S7QFjXRggHoFnw6dayW0WbgxigrhieH25Yul+QC+almR02EKXHAXWUSTMXh17kteZhAIz/fEFisAHQ1MSWKVOyCjgiG1ODjXTLvYSrqnJarpTacWilUeXrxS9eYujn+Q82OqONra2tG609Z0PqL1268whFobE48/EEKG+Cc1+HX07xDzYM0Hvwdrr1s80ffagBICAQqtniuU1kw0ZY/mL8/9vmzePLK67g83PP5csrrmDbvHkABHt7J5zWlAd4fXV2idihfv1yWq6U2nEUXAuHKiSG8jTN8u17pI5tRcnmWLGbvNe6uZxrbD9RyX4YrVuXxlgXTOZ1iwwUpXnNIkXReLABsIEKAE752u4E3+/t2SVCKMKXk+4l0vBXAkXi5IzYJ+LO0+gxYUJCDgfYfJJnD4/y4bv3AXDswGPS5on0vfaahBwOACktpe+112R+8kqpgqYtHKrTxeYWKTQGqC1u+37cc6f4STcQJZZEGm7jb2tUDLsP2h7/f70p4uHwyezZo5Qfj96fHhMmQHHSE5YoRAJEGkKAEG0mHmzEubpdev/oCmq6BogCW8qdETn7BWmKNvH4R4/FE0sjGzaCaQlYts2bByteo+LTG+k3rIpQV0Bs7ka/3/5GE0aV2gloC4fyNeLzAHZ6ml2PAZb3g3cOgHMWJCZ0toVXPY6IGJoDEj+GO/hqDNpgA6C1+d0G27ryeb/+dN39S8pNExuo4JXVh3LCioV8s3EuX863LQ2bvnUCoSfn0K0WtpXbhNVgc+ZwMJan0e2447h965/xCiE3Nmxiy/Qp3nkiD/2T3YZvgT22UbE3VOxdD0VlMP7XMESDDaV2BhpwKF9nLS1BaM684k5IgAOr4IAqqCuxN+2SSPu0xAj2r/+KWrvvkiah1BXQRJ11ql3DY2+YFml1PZRY68oe677izxWns2i3gxn+1Ydc+PHLlERtQbDIho2s//OfeXWUsPD8loqntz+Q3TECXVtKkPUu7cXGhtSumd6RqH9iafV2iCQFIs318PJvYMj52Z2EUqqgaZeK8hXasj3zSjmI0jkJoq0l2F+Qro3pg42o8xUR+/wikv65VpfD784PctN3gzSHUkegBHCGxALfmge/fDRCZTsMTS6JRjl3+VyGf/Uh3/loTjzYiB+3OcxJ/00MMKvLs9u3u0vogkEXUhxI7Jopjka5oKaGYKlfU5Fh+1ce/Vc1q1OXKaV2SNrCofwFAhBNW+Yyt93RfgFHuoTOfPA7jgGmHmdbIWKGfhbx7YYxQFmjXSddYa/yRhAnt9O/DkfuysONXPTRC775JMnn8/xwuGBe5utstrcEp8cOPAaAxxfdw8DPo5z6DnSvNYRKyyjr3cj21V089iBUf96FXl/flnRCAz3WtRML3v3O3ayrXcfu5btz9eFXM27fcRnOUinVmTTgUP7aMdiIaa8AIbafzq5UarCtEKcsisS7P05Z5J/zIdjJ1s5ZABChxmf0SWueUzZBmABFaZJBapJaNJbuF2TP9RGO+ih9c2gU+Pzccwn27k3Z4Yez1zvv8PMNsYtg02YjDSG2r42FnalnGW1MOkJRGZx0c8p6s1fMZtIbk2iI2JEsVbVVTHpjEoAGHUoVMA04lK9wj67t3q3S3gTbhSEGmkJQEm79zTrTdsnJnu6aGj1qW4KITOXIoaXE+fPD2y8pta2BlztB1W3WUUG+7BvhlEVQWZtaS8TQMgInsmEj2+e84H9G0YB9sTxinkC3rlCxh+1GqRhogw2P/I2737k7HmzENEQauGn+TQnrZNP6ofO2KNVxNOBQvp4/PMC4lwtzyKqbGNutcc6C1p+rkL6FIAq8OQgOWWW7HYykDnMticD4/9jHsuk7igUmzUEobqeE1NaKSMsQ1qGf2eAieV6XpfvZdd2Pe12HjAwQiCbM/0JxMZWjhwFvZdx8Xe06z+VRE+VXC36FMYawMztuVW0Vc+7/H/Z441aKNtQkBBU6b4tSHUsDDuVr/p51nEbH3whz7SapKyZtN4bX/vE4RqauiFlHBZl1lP3/bQ94H6y8Mftzbwq175Db1jLAtGNbgg33OblbbmJ5Kkv3DbB0P/ss/a5DOsHSCGW9m9i+psxGLIEAXQ/fj661z4MzoR01q2DWVfbnpFaO3ct3p6rWljoftSzChLmGXlthU3eYMjrCgsEt+TSjlkX47rNhisI2IcYdVKy/866EAmMApqGB9Xfe1S4Bh7aeKJVIR6koX71Le/HmoDYkeoZyiGedER7VXVtxPElNdsywes5BVG0x3DAtwm0PRLhhWoSmoPd6uey3JJw52DDkf2RPbXFL0qtX4Bbr/sEYukYilLpyQJJzPjIKRG2wsbbUaQoColG2//dDtifP2RYbFpvk6sOvpjRYyqhlES5/1tBnq/0g67MVLn/WMGpZyxOYMNdQmjgYJx5U+M7bsnYtHx58CMtPPImaWbNyfIJWrPUkvHYtGBMPdFq7P6V2BhpwKF8XDLqQL/u23Bdy1fWkEyk5NMu58wxs7RrgucPTjwjxUt7Y+nPMRrPYRM8etfYXpkdtx+RcNAbh8eOyH5raGmGBWSNtN8kN0/yH31bWwh2PN3HX4q00SMuZPz/cnqdb8utknH+DpWF6D95O/cbixO4UgAhUf+4xesU1LPbWt25l6MNDmfj6RBojjZ7BRGnYBhkxvbZ6P59wVRXNfSq8H4R4kLD2+l9Q9etf21OZNYvlJ56UVTCSrvWkNWavmM2Y6WMY8tAQxkwfw+wVs1u1n7bK5RoolUy7VJSvYwceQ9d37mrV/B8A2+e8YBMBs1SxPco5C+xf3F5DQf26WmKFrfIxYsVgJ09Lnuo9n91Msenrnx9uuy8wUc55I7sgJ/kaZLomDUX2e6auHQEiDSE2vteNX38QpTlk53ipKYeFB7TkttSUwwd7JP5/zuFwY1lLwa+N73XzPEbKKBWID4t98A/f57jH3+DseNeJ+AYT7uWbutuWjxSBAKH11SmJwF6qH3scgJqnZmSd79Ges94WyqgczXlRbSWmtfWSd1LDDxxkFj32aGefRsFY8c1zOzyHo8FjtIkBPqocyL5b16UUrHKLOiNWcp1czW/97SX2xpqvpsDkYzeLDXBiN/NYwub4NyOM/NhO/pbNaJpYpdIPB8Lg1bYOhngNRQXfobnZagy2JJx6EWPsLLWRKBds3cZec8qcuVmSzqW0mP0ucCWEFpXB+Huo+aKMz2/8BSWul70hZHNgujek7IaN3eHGHwWpCUi82yW5JcQt1m3Vmq62UP/+HPDKyynLl594ku1OyXL9dMZMHxPPWXHrV96PF859wWOL/GjP56QKi4gsMsaMyPdxtEtFpdXYCW1gXkNbBehbX82zQ08hXNnDt3vFL9jwWz9dsBEOwKyv55anYIDtznTy2VQxaQzZMudRZzsR25oS67o5ZwGMfzPCiOW2FSebG2IAe86139jGRXttYNs3tlFT7r1lTTltrmJaErHFwW6YFmHop0nNJMZgRECEjaEg91d2Z/0hzfaFcq8GRBqF5TP7UrOyDCQIQyfAkPP56n9vSwg2wHadIDbwcGsIwaPHC10ikXhCaUm4pfprxOMyCLC9zAYqufJrseh77TVIaWnicVox6+3sFbM9gw3wH62TL9m02miXi0pHAw6VVnGavww7Wo/G7ZQecww3HnsUW7rk9reoYDAeYYd/vohhhvNXezZ5CrFlbwyCWy8KcuN3g0w9Ln3CpwEW7e8qc14EoaQopSQCIz/OPWekotZwbL398//x7t14zuM5xOputEeOiOAESG/YfJCWBxKvcFMgwJtlZSnBWKx1IVwXouq/FdR8XgxLp1Bz7/8Qqa72PGbXerjvNGFDdxtMbOhu/79gcID9PpZ4Qmmsy60phG/3YLd6WLh/7gm6oX79PJdXjB9Pv9/+hlD//iCSMuttNjkZsa4UP7uX757j2baN33ON5cJooqzKRHM4VFrt1TXhJ7kPvTEQojkQpGu4MXXdyh68uXYBgZ4vM2eESck7aAraL89S4AKSQ2apACfU1nPStDI7yVoxCXkLK/rDESsjRJrtXby22CZfursVbNXRiG93hQAjlsOXfdOXOW9NDk3INWfJxmCAjfsJkFg/o9gZfbK+u23laI+us9iIlljNDi+jlkAgzWthIgHWv9uNir3Xs/6BJ33X29QdFgwOsmBw8g4ME17DM6E01uWWTIBRH4ApK0bqs6sl35oWC0ifk3HMsmh8BE1Fd2H48SZhmK9bVW0VY6aP6ZCy7rNXzGb+0Q2cPyPxujaE4IGj6xm7YjYH+iTKvn/rjVQPDmgVWKUBh/K3bd687G9CgQDdTv4G2198ybskereu0NiUMDV5Y7Al4bCyFiKVPZg+4Os0R03CTKYAFBez+yXfoWHTwwQCYefG3nIDre4izD7wUILly/nmW/UJgUg4ECAUzbGJIGjotaQk3i/Stcme79Tj4IN9Aly2pYa99mngyt36sDHkM0aWzJVE3Tdov1yKaI7FtQyGSEOQz1/sDQZuLI/y7HDi1yy5zkZ7BRsxmYYoZ9OFE64LMru8C/ts9+8iK2mydTaSb8hlEqTnVu8LHity6rXPbg2w/vpv0vMPj1GU6e0SDNLvt79h/uAAd08fk1LVNF2C5d2Nf/SslDr/n7ex3zN18W161RgufxYg9TnGdEQCaTxAOqCBLadJUt0TYcHBEcr/eRv7rt3suX1lTYRrtPS8QgMOlcaWKVOyW7G4mN4/uoJuxx1H6aBBbPzLXxMCC4qL6f297wGw7uF/EtyyPWHq9TmjirlsyOUcO/AYRn20nkfe+oLHgTNXvkFFwzZCvXvTY8IEuh13HIFZ98Z3665+aQzUrTwROJGmAz9k/IoF9GjczpaSrszadxTjVyygV2OWZdrF2K9I4od8SQROWwTH9qhhVH0DCHx3WT3Bj8vo7qrK+f4+gogQFonf5E9dZLs5vBI3Yzdor+AkFpSNWJ59t0r8GKZl/+e9DuP/E/EsTNbeScEJOS/GgEhiddIsjmmAWet7MKG78RxlItiE0SueNbhvyKFolOJohO2l3gmlmYrA3lYxn33H2Ztq760t27g1B+Hv40O8uvkmeL1lufvm7/fX/vo772LdJRs8j33qC5sxSeccG+ab0orj0hBp4O537k57M29LETJ3KXmvFqVRyyKc/6x3sAH2+g1/t5aborb0fDZBh07Ot3PSgEP5imzc6P+g88kd7NMSDADx71umTCGycSPB3qmPv756Po9/9BgbGzbRu7QXFwy6MD7D6AmD+nLCoL7AEcAP44d79aP1PPLgf4lWdCNQlDSjKGDCLUMtF+12MIt2Ozjh8bJQkLOXvUCxq9Uk9cZnCBRF6Tmo1nfoZkWtYZgTbGytKqHXspZWkB61NnmSeVEQWLy/MG1UgDV7wfYe2+jlNzqjLAImkNJq4x6l4p7LpDUBQsikDu3NB6/5WIZ+GuG8Ban5KekIwoTXDFNGS9pRJiVhuGqmYcLcMB/uA0d9GiFYG2zVRWrqWsK62nVUuW6qo5ZF+O4Lhm5OILCtDB442f5V7yV287/bJ8Gyee1ajM/Hbu8shvn6nc8bg/0TSNsynLVm1ix++f9WJbZoJLW2eNVDcRNiQVM0q9aYbLuc0gVOWuW1MGnAoXwFe/cmssEn6HCCjT3/+teExdvmzfMNNmKOHXhMPMDIxqsfredPr35KYzhKMDqSkt6vIgFX4BAN0bRlpO/2oQC81ftA6g+KJLR8bDvwa+y58l0C22oJlkbocUAd3fo1sq2qxP9kXDey6uVdUgpYSexfA4cvNxxXv50+h9gmjG0HGDYu62b7SBzNQXhiRDD+l7e71cZt6X5Blu5ruP7dWnq+U1ZQ89vE4gh3gBQnwvi3cws2Ynptxbm5ReItDn51WPpshT5LDeAcO00zhgHCQRK6TZqDsM8tv2X3xj+mlE3v2mBHsCTfbEcti/DdFw3d6hP3v61sFVJRifFIdvUbCVMaLCXcN0TR+tRtNnVvOd6PZhmKXc+tez38eLahR3FLgJx8s43U1Xm2tqy9/hesv/OuhJuxe9tgRQWR7dvp4/yqxSq5Jnfx+AVKbrGgKRaQgf8Ee36T8yV3OSUHTvFzX7vWJis7JR8yBVj5aE3RgMebBhzKV48JE9h49z2+jye3gGybNy+hOyWyYaP9P6QEHbl45K0vaAzbO1akdhCNQHGPt5DQNky4G01bRhKpHeS/AwNhk9rycbwsYeTuaymT5pbnUFXCxmVd8f0T2fVhH2nwz92whO1ryuIBR2j3KG+FD+bQFZ/TpbGBmnLhOfcN2ul+8NM7EmVE7+08cUAZw5anztiaPItrR6krtiNz/JS3smUldqONNePfe2/Yu4hXXPZh2J/HpeYivLnll0SNfZ8l1+9IvtmOWhbhx7ONZ65H93qINm1FQiEItwTGDSF7HC8lwRLqjjiQitlvJiw32NEzYFsJij0CqaIInPZiNUMOGMK45d2YMLOGUJM9sfDatemHfrtuxkBCS4jX6KDkLp7Tl3eHwBaIpk8y2uQKtKpqq/jl/F8mTLD3y/m/BGzLh99wX68uJ3f1Vve5k1Rfym+OnHwUVevoAmk7UveTBhzKV7fjjksbcAR79074/5YpUxJzNwCamtgyZUqbAo4N2xLvWJHaQdSnCzCShH0+Cy8OvZgQbABs8Wi1cAs6oz/qTRG1JaWUN2a4mxqIGmEDFTwcPpnXdhsGu0HlHvfTHLLb+s3OmrgfwwVbbVdS30O28eTu3TjpncRtoHMmg+uS3aCOnBhSb85Tjhd++myUgFcxjRz4jW4Z9X5zPAjxmgXXfbOdMNc72IiRSJRgSRSprCC8aSsbuhnP7oiYmqYamuenzpQrwIhP4YGx/mXaAXrWRDEEOPWFzYSSXo+M+TKum3ZyS4iX3lth8rGTOWZZlKpnbsZkCDaag6mvZSzYcP//9v/czrh9xyVMzpd8XC/hqirPUvJe6yXza03JlBOTTr4nBXQrlCq02dKAQ7VajwkTEv7vl/ORNhckC326lbB+W+qNPSgQaUOh3D5UpyxL22oRMFQeUMdXppKHwyezfd+S1NE0yfsT4ezib6e0wDQFGxHIanbWmMe7d+NPPSroHYmye5dmfrdfiUeLSEuuh3sIaGtaPrId5pypMJpfqfp06kPw3RcNV82013ZbKXx4TCP/PQH2fbvEt3slWfJz8GtlSKlI6vO+it300938YyKNhoPPX0lN5ffZ8NCT/NTJNfEKPEYti9CrxvugsRvt9jLbeuIl1oLgd16ZXstcSq5LMMi+465nbSAAkczRbX0R8VahlBEurutQ01QD2Mn53DdRSN/lFOrXL6vz96oj4tea4rc8m9aE9ixrn0k+AqZ80oBDpRXo1pXoNo/RHSUlKa0WfjkfyS0hufrOyL3iORzxw4cCnHRwX17+cH3C8lxsoJLdkoKOYGnEM7EzIvDIQaewqNfBEGsU2c1+G79iAT2dETDJ5djfOghKer9KIyQEHSbcDSnalnZ21uRcjtjw242hIBuDAc/uF68ckKGfRbhgXvpJ8fyGnmYzwVxykmiyWSPh3NehKOvg0FAagUC45ejdG2D4yyXce7rwf1cGsypZbvdkg5WuDf5Jj5A58TEmdmP3naMlSdUbRdR88WRKHsSBq8OM+NQGCNtLoazZ/1rHU358rp/B7ufee8O+Qcm2MujZoz/Na9d6Hqe5q2FbIEBPn6DHfSyJBRlZBBtgr32mLiq3cfuOY/H6xfz7k38TNVECEuDM/c9kr+uHJnabYAPIaUc3cOG8Cs9gxC1SV0fNrFkJrQyx1pTkYOi5MT1Tts+2NSHUr593CXifwmltkWvA1Nm00qhKq+f3vpc6zXwoRO8rLk9Zt8eECVBcnLiwuDilJSRXJwzqy09O2J++3UpsifNuJfzkhP358ej948tb4+HwydSbooRlXQ9oIBxI/LVoDIR4ZNDYlJEvYPNCJh31A64afQ3z+h1KRJwy3WKrjs46KogEwhT3SGwub9oyEhMN+dasSFjuldvhl+vhMTfS0v2C1Ga4RM1J9+BowPD2ganVSRMOhfccKkM/tTPP3vaA/Q4w/VjvsuJ++/UqDBYyMOE1+/wWDA4mVBn1u00GsNVy/3iGcOWVId8ujWxaLJAoPRujPH57mJLmbKqSCps/60pS7wGlYRj7jr3pBrDBVNruGedAXX16DAS7nz5bobQh9bVsCMGDJwf47g8buecMSSkHHw4Y6puhsiaadgqA2LFyZcS2VmWa2Tdm9orZPP3p0xz1fjP33htmym1NHHfVo9zw+g3cM6aZLZWhhMqyzxywlb8fXU+0pChlXwnnUV2dUPm0ZtYs/vD/NjH19jBXzTTx16PPVvjW05uZ+4/fxLetmTWLygtv4MFbt3PvvWFGLbMvmDsJNnbu/zy6IeUat6ZIXDZl4v2qzXZ0FdpsaQuHSivTMNfWrpurluGyqcsB/vDiJzmXpX7NDIOwzeXoQ43Ns+h9MtsPKkmp4+EVbCSbftBJPDv2A89YQEKJQ3ljya9by5/3LIS1tRwwht6RqG3NyFLXSJSmgNDkDpqMYdbX8W3lCJWG6XNAHVuWdyHSECRYGqH3oSEGjjiQF/t+wqkLvAuPVZd7BxvumW1jXURPjvKu8JnMTtznf1tzBwbuPIx0CaVetSxiE8rZ/4hvi0XE6ZaqK4VuTUKw0Z5b93oIS+b5bfwey+UvvVxaVYoNbC2yr01i10UAmmoSRv302mq7aUobxLNmSTbPIxtBQ8pInhj361lRbEuk3/3O3Qx/t9azReS+06Jc/qMgybeuVw+OEAqUcOFzYcrrnVdWBPFJHgWouul/CDYn5nDFlDRD8P7HmX3CcJur8qub6dkQSTiXWOtMrDXBq0Ba760Q7lvJXtff5FskzotX4unKm25gy/rFjP5+S5KvX/fT1Ydf7bvfzhw9owGHyqjbccdlHTTksm57uX/eipyDjZjXzDBeax6WuHA3sgowvMS6SpIVR0r4Y9Ed9KGaDdgckNdqhzFjT8NFH79AkSvxrjEILx0OP9lcw7ENaaqZJrV8FEejXOoklj7evRsbgwF6R6I0YAODPddHOOqjpJtdIGqHA/dvpttJx8GxP4s/dOyj53Nslw1s+1oJVR90T/gr3ABFzba7xh10nPKOdxfR+a9DXUn6WiBR4I2vwaEr/G+s7tEOMaPej1DanL4LyKsF472Vq7i1ZyVTu3fzrPfRELJ/QS86WPjHn5ox0cTXIGRgaxlgbJVSvyG7bdEk9ub3+O22u6RJ8Byp4ta1wc447Cc5UPPLC2lPftch9nqGjOHG1Svgzq+xrqfwS48urkxF0OrCdYSaTcuxfGZCjyeZ+gQbMT1rDL99524O/HMkJQnUfS6x1gS/Amn9yrtx9eBATsmdXomnxc0mHgTFtnEPJc4UyKQbPdNRNOBQBelVp+Lohm2N9OlWwndG7uXZwvHqR+vZ1tj+M8zFElJzTUxt2pJaJ0SiAX64fRO7ic3z2I1qfhKaAWF4bbdhFPecx6lL6lNGqXwU7sUI08gFW7dzX2V3mgMtH9tFUcPxdXUsKi1jSzBAUaSE79Zs5FjnwyQ2cRvA62Wl3F/ZnVlH2b9uY9PcI4Zgf0O3ST5TnG9fD0C3fo38qRLOerNlJl/BJoImJ7j6dREFjS1FHg741+QIAENXwOxj4MI5qd0MTZKU8GkMo5ZFufy5zPkXyYHK7mG783lduoBIyl/+m7rDY6MDrDhyAJO+/IRQbWnqTrETyF37c7hp05H0feDtdqyPYmgICUHTkpPRvd52l2wvgvIG76q10FKXBOf7T2fanJEHxqZ+3GfVldSqWZIyawzBY6OFfuEIV2/ewrjaOmaXG8T08j2vdOebbR5Otkmm20ttLkS4ynunsXOJtSb45U1U1VZx0/yb4kOuIVbjZTu9b/05y/v/IaWlwe/8etYYfvz6RO5+5+54YBH7yiTd6JmOogGHKjjuQl8A67c18qdXPwVICToeeeuLVh8nJKlDZsuKAjQ0R+nVtSXI+d6D//UcJeMluU4IkW5cXl/N8bWJibdl0szFoRd5rXkYSw9o4t0DU1swtgcjnLftWgCC4Y8Sao9s2zKSp5NGviySJQwOvUhfqhO6dWLBx5KN3eLT3ANghOY1Qb649FKi27endoF17Qvbv7I/RluCDbeEBFdjfOeDAZs0ur0Imov8K6b22ArH9Kvm76dWctHLLU3x20rhgTGuhE9jKMLmdGSTOOoOVALGUB8Qhuy9R0LLmPuvUjGGd1euhlUC9U0s6F5GT4+bXXV3eGH1WmAG/yntn7Frwn1OvrdwMfT/ejVfvNedotrE90VRBIJRw/Yy8eym8NpvANvy9MnA1ATNdN00BggURajYs56alV0wkUDCY60PQQyhLhH6D9nGX7o0wCr7Kswu78Kk3j2Jpuni8mrhiskmeIrlUsQLhKVR1gzjlncj1K/Uc93YucRu9n7DeYGUYMPdmhZeu5a11/+Cqv+9jX7/c1Pac4odszXDX31Hz6xdy6CSkqFZ7aSNNGlUFRx3oa+YxnDUM7hIrtGRjZBA99IQYacFA6BbSYhQAOqbbeJcLMh59aP1OR8jUjuI+tWX0nXTz/nRoNs4cav3sOA+2GGA7rLsblHX8tg+61b+lPrVl3oWOnvNDOP7zdeznsqUx46tb+DMReGU7o6gwY5CMk6htj/exbbJ58LyF+HIH0LIZpue8k7E9wZTUQu9wxG6RqI8Pzx9ommXJvjd+UGqfYbSVneHcbV1jN2tml9dCd+aGORbE4P84NpQQrBRZgzNIlndZLaVwYJDAna7aJQgUB0MYkR8k293D0eYXV7GmG4RhgzoySPHpyZbNoTgX8e3bD/jRFLW8Rs/VR+yk+x5MUaYP1goqvX+eA4YocQjOTRdQ1ysvDjYG96994YzJr8KsKksyNjzKnhobIDmcvtsmssjbC/z3ibbxsADzlhPxd71CVvc3aOSBif3aMpo7+vtVzgN/IORiNjXYXNFkLvHNHFe4x9Zc9HxhDPU7SuKwIXzovS99hqaihKPGzuXWN5JzaxZ/P6ebUy9PZyQVOrFryXGVFez9oaJrL3+F57dQcktfMkJq5mkGyUTRDqk8UEDDlVw/G7wyctf/Wg9gRz/zOpWErLzoDTY3/iIsUNsAZJH18aCnD6tGAXTt1sJ/7z0CNsi0zW1KwhgA/bDKjZixS1TufZ0vEbfQDaVUYFogC3vhWHuZPv/466HrrsRqvf/qCgqDXPvVxu4dOs2PtxHeHKU/4iUWM0Or8CkKQDNh9nmkXG1dbywei3vrVzF5A2b6BeOIkC/cJTJGzbR4AQKfje+GINtJfnrnyPMn7uRykiU5jTVXAFKo1GOq6tjUu+eVBWFMCIs+FriqJjYCInPDjLMLu/CmIH9eWZIkPtOTVxnzmH2RuEWDsB73zuaLRXen/Ebu8Ok3j3jN3gvxQaaJXGETqyry0+vrS1/XcdHyGTI36jcCkaELSGhJhjAYIO9YGVTSnBhsIHU1rKWyrdeQl28b8brXHlKsVFIW0tb9tWUfhAKU3yCwj+NFy64McQVPxbmDw5QVVvF9SWzuHecZDzXovXVVIwfz5ZrLkh4XV8dYgOH+3+9ifePHM7aG2+iaH11vDvr8mcNYz4MegYOaYNkr5m2Y8+llJQWqnW165i9YjY3/nIU8448mA8GHczCUcO58ZejGPLQEMZMH8PsFbMB6HvtNUhRhouYZ9qlogqOX6Ev940/1u3il1/h1V1SEgog4h1Y+NXyWL+tketOPpC7X/4kZTs/JaEA3xm5V8uCI38I8+6AsOs5hUqYYU6F5laWa4f4cODka/WaGUb3UIjLy16Jd4mAf42RZJGGIETDsOAeuHQWHHAywdev8JlXx9DjgDqgpevm8b26Mc2QMFqF/9/emYdJVd15//Orqt7opRpsWxpB0QRHQRs1xpBAlITg1hKzGOIk0TfGQRM1ovOaYZIYh5g9eR8TfTU6ZjHqRBOzGQWNGJcoZEgEJyKEJCqioo2A0At0N3RVnfnj3Kq6deveqlsN1d0Fv8/z1FNd527n3Funz7fObzk4OTtOBIxhzZERMCnOcLKlDlTb2ZZD/jCG58fU0Nre6/wCho6BBB1v/w9onw+LmwHD8s5mzlweHP2QJj0Aj+uBDavGcuTBQuexfs2wzobjE0kW7ujK+bUdRHXKZIRJet8Vx0ZZMc1+8dqcc02uMWxZ00iiL0psTJI3ZlVx80GreNupKS55kDxH1btnCwORCPecGuWCB4ONF3U+Jq5CvNnk/+u60DnebPKaAIREX4wxffkdT4AxCRjAzir5ixlDQ9sAz9/fmrkfre29LJ8mviv5Vrva2NRvB/K3JpLcfXw1gyb75aoyhomTdvFfZ4zhnCe9ETr5QnsgOcB/H1vFimm2U9/x/xLU+fiQGuDz18xk6ZReuKwKg+HChxOc8Uy2XtGevrzjahPwgcd2s+yY/P4WNoeLlwaf+xmvifPilxbxidXJzOxB/Zt9zL+vjx1nCbCJpu98jr92X41Eo6Fzp5QLFRzKqCMo0Zd7EPczu4A1kVz5vqO4a+XLeQNxIWERRFSs38htT24o6JwaFbuchNfB1Tq/NnNM//u5sOr3HEQX0tAKJy/gqOR0apx2ptO118Qi1MQi9A8Udkxw3w+/e3XUrA/B0Z+Gn87PiI6xU/rsOjEFUrdDNn07u7P/Fcd+7GM56+RYDA0T+2lsy97nWf0DvLt/gMsmH8yvhbyU7a8dDj9/3RFBdcDM7Po1CQOJqB3QOp+2sz/xyf2Q3AOPXmcFR3wi3c9u4xNPm5JTnNcmrM/HCh/B0ZxK8dQrr2U+f/7gg3K2+yWuWvA7w92njWFguud+itA2mHB8O4DJED8yASk7ol02cQIDpHwdVd0D5JL2KOc/GGzKKtT6oAyrn70/vAd02vfF3wQQfPXahPX3CaLrxfrM8Ym+GJuebubhFiHVmnvOoNwdJy833HfsIIKhOxLJiMSOXX3Q0MXSj9TzxUlvoWt3V8H2pUwqs/pubUDAimDXcFkyxQ6VM9clOf2ZcELPz+cH7No4Z4Q8h5uddTaqKP1d+eV7a4hF+nnf6vzvSG0CLlxmqE64BO0Iiw1QwaGMQtKDdaEolSCzS8rY469/5B/7pC7pGZSdRSJhUgYe+GzuCrhu59ctHM8f9hwPQFNfjFmvtbBqoxVN6UiYVqedQMEZlVaf+xF4r1yzK2lhsOOFejuL0dAA/QM5C4ylw2S95OVYiTcQP2wL8fFZD9FEtJr+ZIxG+jivp5fbjmji2bdkB+PqVIqLu/L/C2fWr3EHoCQjbFnTmJnloPtVWGxFyJY1rUNeTyUw9bd7HI5WMz6RpLMq++/Rb9CNJYQzl8MSH3e7zTlhzJIRG95tfmu6ZA8TtgX8Gi7mtNlbB7uroKXHsK1JMkLm8gcS4R0tsBEuQ7vTQTXML4skhYuXwiVLEhnRBYVzd3RHI9SmUnxj65tWaLjoiI3jhlhdUcERNlut+zvzsSdMaD8Et0+JO5OpkaE53DYMRmnqd+UCWbKbnVW7A88VFKo9kqjgUEYlQYm+0hQzuwRtb6yJlRRG21rkfN7rugmahekZSPDgc9kQurQfiVdE/OCpDRlfk8aaGBefcmRg8rPAezVlrn3/8w9g5xYapzTT+PEFmfLeJ59kx23fI9kvRGuTNidHesaiJtcLLy/HyvOPZM5LQyuxkxdw68N/5/LYfVnziisfyHk9vTnhupn2B/iWJPpKK0+Ttsn7DQxBjoU96eRq8UnQt52FO7pyTCWlhmmmw26JVOWIjfQ2t5gphF9+kBRWTPiZAMDOZtw+V9hwlKE/EqErmr1fkRLERjGfkEIkqw2plFAdsqulI6fS/g97YsVzdwxEItwwbmyO4Fja1MwNhzQHRou4KTWdPYQNI4Y9LgfXsOv0FCOSSHo+Q+O+zwhQVlRwKBVJMbNL0PaLTzkydFbSmFDwfH7XdVNKdEvaQTUtHLyzPHXVIRw+g5gyNys8PDS27abx1G7rs+EmEoGZV5R83vUrmrlpJ1wd+wXvdswrgTiCJsi3JMi5MDYmSaLP71+XIVaf4rtzqkhhfBN5BUU5jE8koaoOppwGq35EhzOY3zC2mc2xaMEwzdpUKsffozaVYuGOLqeNTqRR//bMdq+YAeuDMCaZotuzRk6Q2aU6ZbjkoVyzksHObNw+1yYrW7xtB//uMQ0FzZiUSqEZlt0xeHJqhHf8Dapc9z+seClkkvGGOHfGokyfPImP9OzkhGgDixurGRjsLnj+tvo2Ond1hhIP3pVuw/hfxCZM4I3TJ7K6dTUQXtgMDf8nkf4uFHIKNsCbcaExWSBL3D5Go1SUiiRofRX3gB20/czj/NcZyIt4cX12nw+y4bTe67opNbrFLVDS5pgtvbvzwnT3KX/+Qb7YAKhqCBQphTh/xuGsjJ4YuN0YoOEQeO811iF1d6814URyhZxEU7S252dsBWht70Wi+ftPmNHFlNu/ylPTInlrraSjSlZMy48cqE2lWLg7CvNuhOezSdDSkTJrNr7KQ7PyQ14HYvDQLFi8bTttgwnEGNoGEyzetj37q7t/O+zuhWh1znm9x3xl65ssf/U12hL5ImvFtChf/LRw1dVw+aVRNhxlOP2QLia+vYvYmARgGKxP8tR0O/Nxxf2GH900yKx1+bLaL9xUIgakuG+TIXsvf3cimXvbUwc9tdlt6w+F9/3FrhGzN7MkfvT6RGqkRPh5vJHrmusZMMHZQ2ujtXzz3d9k2bnLaKtvK5jTw2BXuf3+WblOp/e+pyrv/mXqUVPFhO98mymPPcrsRXew+IgPEk+ZUMLG7wdQmB9Fhe7t7XPzn3WagRjc+H7h0kuj3PrePdkFAsuMmID0rwcqbzvqaLP6np+OdDWUMvP9J17g4bWbM9lEq2MR+gfz/+mmw1uHgjeBWTHc1wpKNrY39fHlP2fj/69N4JInhnTKx/+2hWlPLKBVuvK2bWMsLZfcly1wnFp7O2vY8vxB7CGSiVyITx6A+ETrv+Ghe2NdTuRHa3sv8ektcNVaTvvlaYFT6m1VcU7p7eLJaJLNsSjjq5tZOOPz2eRJThSMl6X1Y3j4jWbOdUVA/PIUOP2QrjwfAl/qxsFAN5jCjnvp5FdvW28ysxrbmyB5wi5mtwX/cu98uinHGROsCLvtjAiPtOeOOm5/gu1N0D51BwAvP9dEdFcUI/5r52xtgssuKzwpPnNdks/e7+/nkCL3F67B+jP4mXmStdbHsdqlH9Kp5oMW4CtEvDrO59+Rfc5LNyzl4Vs+x4WeLLUGm1Ttx3Nzk8wBtKVg4exvUf/4aqK3/Zxx3alM/dPrpcQP77cOzt2b7Hd3zrWsufy7vivZptfpebMJ6hIRGvpKc2gfiEEi5r+oX/pZpZ91S49ddTh9PW/0zuZFL7B1c3/ZZYeaVJQDkktn29Vm08z7/8t99xtKYrE06VmPYhEukG+WCZuLZK9xZRPNKx8i7zm6lXVvXEDT+luoleyIMWCqeOOY82lx73zyAnjsazS27ab6oC5qJrvaF58Ec66F+y7N84OIT+7POpSCNYfMsetCBC1otfhdi4tnZQwQOB17BI6I8tWjHaESrWNhTz8dW0OIDcgxqRSiY1cf9S9UMXZFfcb/oaUHZHkd3W/fk9tmh+6NdXliA6zj7SefSPLocVFSHjNN2lG1OZnkqVfsOS999zi6Y1FfZ8pAc5RnPZ+PF3CqFOxA6DYNAXnXkmiKScd38z91Y4iurmFckRBXN95l5tPH7E7m9puOIzugYyX3Jn/JmcvtPU7UJ7nn1ChLpnungIS2RIJlb/8yHNlhX64F1DKsuRceuAIGXY7OD1zB4R+9iM4fPJiTVtwrnn72jfA2F4M1jQXdP4NdRmDmumTOs45IJCfjqZti+Wz2FSo4FIVwuT+GQtqh07s2zEmTx7Jq4w629u6moSaGiF3x9q6VL3P+jMPLVp88AnKEcPKCvTrttFM/zDrgkL/dxTjTxXZp5o1jzmfaqR/O3XHKXNj8HPz1/tzytIBon28/P7QoO2jXjYNpH7TmD9cvyfS+pSxolceca3MHjXRdpp9Hx/PL6Nja6Vzvc/Z63z3WV6DsDYc+XZMTOAQ+UTsutqxpJGhyvWpXhMZkim6/xf+AbpcfSXc0khmwqxP2F3jEZAe3oMG+bTBhRVgyyUE9wd4d25usSWh8IklnLOoSKrkiYfrUbuJH7GG26WfpR8bwFcePZnwiyTd3Gr7QGPUdOGeuTeasq5O7qusAN6z8Bh2/XZT5znTMuZaOphvhrOw5PjF5km/dN0ej8OuL7eyFI2y9Mxk8el3u9wZgsJ/4tltgwWfY8quVDL7+uu/97IpHGNed3ybv3dxTJdxypteslOTCRwyN/VkTVtOAjTC64v4E25wQ2tYPnMtvX/htjhAfbtSk4kFNKgcmfuaPmlgk0D+j3Nedc0wrj67fMjz18USbcPKCIflv7G0ddj95FzWtr+UJiGFnzb25g8mU0+DZu/NFyLwb7d9egVIS+emu1v+sDf9B23DMefmmouD9ITYmwYevqLFp3H1w5wv59K5D8xbCK2bGyMk3Ajz/wHgSPinZDYZDZ3RlBNNpEyf4Rup4z+fHV9sm8fNaT3uM4ebvJ30dOjOmIGN4bqNLHFbVWaHtEi+h6hWpsmIp6cpJU1VX+DvgfF+WNtSzePmXcnxNalMpvvPnfsavaMDscc0IxmxG05NesDMwVRMmcP1Jm1k+Lf/+3nxzoqAzayoWJdrQSKqri95aQGwisfQs0B2/eIn+l9SkoijDQpjcH+UgaN2YVRt3cPl73jo89SkQxTJsTJkLLbPgfbOK71tu2ufnip3vHuv7y5VHr4Or1trPS66EPQGr1vkigWImVp8isSt/gB9K1E5rey/jEzH/MFxjstE0wCd8FsJLL8O+7ugkO0VIBEXjOLQe103nM605AycYxr5lV87sjF+kjt/5/Lim81VeHn8IK+tcs30F1tVJl+cN04P9UFUPg9nnFqpeKR/H1MF+kGiwj47zfem4ai08tIgbapKZWZuFO7qYfUgf3bNqeXltM9EtXTnmoNtPh6pIFV+Z+a+8+MwN4OOfVMwxNZJIYrq6MrMfadKzQL8aql4uERUciuJQLPdHOSjkqzES9VF86N5UvDzANu5LfFJWqAAcNiNnRqX1U+fk2fyltpbWc44C87u807W299L5dDxnNVeAZmeQX7hD8gZRjOGjPb05Dq/jAswhLT2Gp155naX1YzJhwjnZPd1Nm94CH/6aXY21s5NYvaH12K48U1D6uGLnC+JlHxNRUMiq4PgzTPXxLhnsg5MugtU/AZPcu3qZZOGZDuf70rF1Ex0+jsnx1tdo/2CEdx/cSFcy9xyDqcHMkvSLn1yUWUsozVDTpYMVlQf5B4Ttc1RwKMoIMmy+GsrQCXAkJT7RvvvZ7gORrA8A5Jtv5lxLvH0+HDYjO2i3tdF61ZXE581z7Z+tT3owz4namVFFfEocuntCD6JVATMlVc7MSseuvsIDr+N3E2+fZ+sKgVE/oc5XgM0+giOdut0rK9Ir5W44ymf2IT4Rzr7evhx/nCHXS6Iw/WMZ8eJ7LYC6scFOxN2v0jV2ku8qxpt3bbZ+SL9awA1j42yORRFsWLBfgrhSiA5T1nMVHIoygoRZN0YZYYIcSdPCIWgGJA+Bkz6VNdcERDUAxOfNzw7abrzmHmdAz4vaQWDObZkInzCDqN9MicQIzIeS2zQnj4nX7yZIrO0lftlaV0yLcsX9/iNuSw/+5hq3+Av9HAMwSWsee9sn/X1+3NcKYGn9GMA/2mbDyVawdMTG0bHp1cz+i1vG5SSIa+kBMEgpGVD84qDLgCb+UpQRpFgCM2UU0D7fDqbxSVjfi0m5g2v6l6uXunG5x3zoNvtLOk1AVAOPXhe+bkHXjk+09UtnOg1zqsn9tL29O+MrEmuAtpN2ED9iT5EjsSYlPyffOdfawXZvcSVOAyseaj1LuddKFYl6f9NWoj7pL7gevc4KPwi+l0GIjyPtYL+Nnir0fenfEXjKG8Y2M/OvKS550HBwjx2g034WX+h2/Jtc9zSTRC6R5I9TI3z1Ynjpoi4OnZFNChetThZM7JaKGqrrhmeKQ2c4FGWEUV+NCsA7s+AmaAbkzG8VjrQJ4xtSjGKzLwUGNz/iUyB+7tm5v9DD/PgNGqzT7XebjfbsCp2XJMM5N8Ovs6HaeWaihgksfO0lDj/OZ5YmmuLw4wIcHFyzSr73Moj4pMLPL+j7suZekEigc+nmWJRrnkj6Ou82/PQPcBF5oeIdu/roSNXasvR9nUzOjJc7UZ5UpYgIJPdkk+xFl5WWdGyoqOBQFEXZG/wG1TBhvcV8Q/bFtUsxaaQTrRX1SfGE8hYzF3gH3zX3wm8vyw0rLVav9vnwm0/nDNQZM5FE4YPXwtoFMNluy8tC65O/JIM34ihMSHT6PpXy/NImtALZZscnkoERJ4lOT3RKwlWn/u12FshnsUDwSZQHhaNqyoQKDkVRlL2l0AxIEMVmJ/bFtcP8ak/nFEmf49cXB+/rFiWliCtvfcE/mVsh34egwdEkc8xQvoNrMdKzFX730hNFlNPeUp5fCOfihTu62N7U7Phh5BKrN1a0tM/3P1dyj72P1fW5gmndb/JnlKrqrIOr936XGRUciqIoI8FQZ0b29hpTTgvM0goUnhUpZC4otV5+5yg0uMcnBcwoFDBv5JGfaM2eo8CsUlBdS31+gXUU69/z6HV0dG/iiRlj2fO4yVlHRqIpWo/tzpp/gs7VvwMWvZRbdvb1udFQdWNt+aof279jdcDOgLrtWzTTqAfNNKooI8PuHf3UjIbEXwc6a+51Zjn8BmZPDpHhxBvVA9nZmSDzRnW9/dUfJmtsuTPbBqXB97mn3Q88wJYv/xuJnSbfLBR30q+HPFcOAffw4K9veWnrrtSRJbaoZHSGQ1EURcnSPh9eWWl/AZfiq1Fuis0o+ImRs79XmomknJRgQovPm0d89fn4ir7uTXZGZCjmuIDIqAmNcmj4hgwdFRyKoihKLmdfP3IDcyH2hXljX5iEhkKpJphCTsVDNccFmGKqIlT7btjHqEnFg5pUFGVkUJOKorgoZEIaqmAKMOtMv3Xnnmc3J8ue3lgTfymKoijKaKNYwrmh4JeIraqO13vNa3tV15CoSUVRFEVRRiP72vwTYIrZds1HS8zENjRUcCiKoijKgYKviPnosFxaTSqKoiiKopQdFRyKoiiKopQdFRyKoiiKopQdFRyKoiiKopQdFRyKoiiKopQdFRyKoiiKopQdFRyKoiiKopQdFRyKoiiKopQdFRyKoiiKopQdFRyKoiiKopQdFRyKoiiKopQdFRyKoiiKopQdFRyKoiiKopQdFRyKoiiKopQdFRyKoiiKopQdFRyKoiiKopQdFRyKoiiKopQdFRyKoiiKopQdFRyKoiiKopSd/V5wiMgZIvJ3EXlBRP59pOujKIqiKAci+7XgEJEocDNwJjAV+GcRmTqytVIURVGUA4/9WnAAJwMvGGM2GGP2AD8DzhnhOimKoijKAcf+LjgOBV51fd7klCmKoiiKMozERroCZUZ8ykzeTiIXAxc7H3fLSSetLWutRpYWYNtIV6KM7M/t25/bBtq+SkfbV7n803BcZH8XHJuASa7PE4HXvTsZY24DbgMQkVXGmJOGp3rDj7avctmf2wbavkpH21e5iMiq4bjO/m5SeRqYIiJHiEg1cB5w/wjXSVEURVEOOPbrGQ5jTEJELgceBqLAj40x60a4WoqiKIpywLFfCw4AY8yDwIMlHHJbueoyStD2VS77c9tA21fpaPsql2FpmxiT50OpKIqiKIqyT9nffTgURVEURRkFHFCCo1iac7Hc6GxfIyInhj12pAnRto87bVojIn8UkemubRtF5DkR+ctweSuXSoj2zRaRbqcNfxGRa8MeOxoI0b7Pudq2VkSSIjLO2Taqn5+I/FhEtoiIb7h5Jfc7CNW+Su97xdpX6X2vWPsque9NEpHHRWS9iKwTkYU++wxf/zPGHBAvrNPoi8CRQDXwLDDVs89ZwEPY/B0zgD+FPbYC2vYuYKzz95nptjmfNwItI92OvWzfbGDJUI4d6VepdQTmAY9V0PM7BTgRWBuwvSL7XQntq9i+F7J9Fdv3wrTPs2+l9b024ETn70bgHyM57h1IMxxh0pyfA9xpLCuBZhFpC3nsSFK0fsaYPxpjdjgfV2JzklQKe3P/R/uzg9Lr+M/APcNSs32AMeZJYHuBXSq13wHF21fhfS/M8wtiv3h+Hiqt73UaY55x/u4F1pOfbXvY+t+BJDjCpDkP2me0p0gvtX4XYRVtGgMsE5HVYrOujjbCtu+dIvKsiDwkItNKPHYkCV1HERkDnAH8ylU82p9fMSq13w2FSut7YanUvheaSu97IjIZOAH4k2fTsPW//T4s1kWYNOdB+4RKkT6ChK6fiLwH+09vlqt4pjHmdRFpBR4Rkb85qn+0EKZ9zwCHG2N2ishZwH3AlJDHjjSl1HEesMIY4/5FNtqfXzEqtd+VRIX2vTBUct8rhYrteyLSgBVKVxpjerybfQ4pS/87kGY4wqQ5D9onVIr0ESRU/USkHfghcI4x5s10uTHmded9C/Ab7FTaaKJo+4wxPcaYnc7fDwJVItIS5thRQCl1PA/PlG4FPL9iVGq/C00F972iVHjfK4WK7HsiUoUVGz81xvzaZ5fh638j5cwy3C/sbM4G4AiyDjDTPPt0kOs88+ewx1ZA2w4DXgDe5SmvBxpdf/8ROGOk2zSE9o0nm1fmZOAV5zmO6mdXyvcLiGNtzfWV9Pycuk0m2OmwIvtdCe2r2L4Xsn0V2/fCtM/ZXpF9z3kOdwLfK7DPsPW/A8akYgLSnIvIp53tt2Izkp6F/efQB1xY6NgRaIYvIdt2LXAQ8H0RAUgYuxDRIcBvnLIYcLcx5ncj0IxAQrbvXOAzIpIA+oHzjO01o/rZQej2AXwQWGaM2eU6fNQ/PxG5BxvJ0CIim4D/AKqgsvtdmhDtq9i+B6HaV7F9D0K1Dyq07wEzgfOB50TkL07ZF7AieNj7n2YaVRRFURSl7BxIPhyKoiiKoowQKjgURVEURSk7KjgURVEURSk7KjgURVEURSk7KjgURVEURSk7KjgURVEURSk7KjgUZRThLPVtnNe/BOxjRGTJcNctDCIy2anfTSNdl1IQkeNFZLGz3oSiKGVABYeijF6+LCJ1I12JA4TjsQmfJo9sNRRl/0UFh6KMTlYBE4ArR7ge+zUi0jgM1xBn8SxFOaBRwaEoo5N7gdXAIhE5qNjOjhnjJz7ln3S2zXaVLXbKporI90SkU0R2icijIvJPzj4fEpFnRKRfRDbu7dLb6fqJyDtF5A/O9baJyA/dg7GIfMvZt93nHHGnPvd5yt8nIstEpEtEBkRkTTotvGe/jSLyhIicICIPi0g3sEZEFgO3O7s97jJp/cR1bI2IfEFE1jnX6BKRB0TkBM810iaxT4rIZSLyV2AAuNrZPk1EfiEir4nIbhHZLCKPi0jHUO+tolQKB8xaKopSYRhgEfB74IvAv5bhGncAO4GvAwcD/xd4WES+BHwbuAX4MXZJ9f8Ukb8aY5bvxfWOB5ZgB/e7setXXASkgLSguQP4N+ACnEHaxXyg1tkHAEcI3QqsBL4G7ALmAreIyFuMMZ/znOMw4DHgF9gVNBuw97jNqcPXgfXOvi8616gCfge8C7gLuAm7mNcCYIWInGKMWeW5zpXY9VN+AGwGXnWE42PO9luBl4EW4CTgHcBS37umKPsLI72anb70pa/sCzsIG+Bq5/My7C/kw137GGCJ5zgD/MTnfJ90ts12lS12yh7AWU/JKb/CKe8FDnOVH+zU4Z4Q9Z/snOMmn/qlgBme8qXAINDgKnsauwx21LPvU8A2oNr53ObU626fetwAJIG3uMo2OvX4lzD3ybXtKmfb6Z7yJuzKqE/4PL/tQKtn//c72+aP9PdMX/oaiZeaVBRldLMIuzT0V8pw7huNMe7VG59y3n9rjHklXWiM2Qr8HZiyl9f7b2PMSk/ZY9iZ1smusjuwYmJuukBEjsCufHmPMWaPU3wuUAP8SERa3C+smIoAczzX207WfBKWTwB/A1Z7rlENPALM8nHuvdMYs8VT1u28nykiTSXWQVEqHhUcijKKMcb8D3AP8HE/v4a9ZIPn8w7n/SWffXdgTQT78noAbzrv7nPfg531uMBVdgEguMwpwDHO+++BrZ7XI862QzzXe9EYkyyx3scAR/tcYyvwKezS3S2eY/7hPYkx5g/AndjZlG0iskJEviwiU0usj6JUJOrDoSijn2uwv+a/BZxZ4rGF+njQwBtULiVeO+x5c85tjHlTRJYCHxCRRmNML3aWYb3J9ZVIH3MB0BlwXq/I6SuxzunrPEdhP5qtYa5jjPk/IvId4CxgFtZv5osicqUxpqJylyhKqajgUJRRjjHmJRG5BVgoIu8J2G07MM6n/Mjy1ays3AF8APiIiPwdeCvw7559nnfetxljfr+X1zMFtj2P9WN5zBiT2svrYIxZC6wFvi0izcCfgG+KyM0eE5ei7FeoSUVRKoOvAj3YWQ4//gG8U0TGpAtEZCxw4TDUrRwsxTqIXuC8UsB/efa5F9hNQII0J4y2JuT1djrvfqLtTmA8ATMcIuI12/giIuNEJOd/rjGmC2vCGoONwFGU/Rad4VCUCsAYs82Zig9yHr0JOyA/JiJ3Ac3YsM2XsYNlRWGMGRSRe4DLgbcBvzfGvObZZ5OIfAb4IbDeaffL2NmI47AzJFOx0SnFeBorar7oCLVdwEvGmD9hI17mAt8RkfdiHV17sCG2c7CRMkEzT24uAK4Skd8AL2D9VE4FTgfuNcb0hziHolQsKjgUpXK4HrgUG8GRgzHmpyIyATtAX4/1XbgOO4i+YzgruQ+5A/gsNlfGnX47GGNuF5F/YHN2XIIVWtuwUTVfwubAKIox5hUR+RQ2KugWoMq5/p8c8dOBvffnA192Dnsd+DO5jqyFeAI4ATgb+wyT2NmNq7GCUVH2a0RNhoqiKIqilBv14VAURVEUpeyo4FAURVEUpeyo4FAURVEUpeyo4FAURVEUpeyo4FAURVEUpeyo4FAURVEUpeyo4FAURVEUpeyo4FAURVEUpeyo4FAURVEUpeyo4FAURVEUpez8L7+3cF+YSFWKAAAAAElFTkSuQmCC\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(numinv_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " ax.scatter(numinv_designs[i], coilloss_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,2)\n", + "plt.ylim(0, 10000)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"Num Inverters\", fontsize = 18)\n", + "plt.ylabel('Coil Loss', fontsize = 18)\n", + "plt.suptitle(f\"Number of Inverters vs Coil Loss During Training, \\n Generated Designs\" , fontsize = 20, y=1.01)\n", + "\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle(( 0,2 ), 1, 2000, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(V_SecCore_designs):\n", + "\n", + " ax.scatter(V_SecCore_designs[i], pave_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,5000)\n", + "plt.ylim(0, 200)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"V_SecCore\", fontsize = 18)\n", + "plt.ylabel('Pave', fontsize = 18)\n", + "plt.suptitle(f\"V_SecCore vs Power Average During Training \\n Generated Designs\" , fontsize = 20, y=1.01)\n", + "\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle((0,30), 1500, 200, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "noise = generate_noise(shape=(10_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "pareto_bstray, pareto_kdiff = pareto_frontier(bstray, kdiff)\n", + "pareto_kdiff = pareto_kdiff * 100\n", + "\n", + "plt.plot(pareto_bstray.detach().cpu(), pareto_kdiff.detach().cpu())" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "noise = generate_noise(shape=(25_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "plt.scatter(bstray.detach().cpu(), kdiff.detach().cpu())\n", + "plt.title(\"Randomly generated points after GP reshaping\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Kdiff\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Noise 0\n", + "[ 0.04101419 0.34708083 -0.22188711 0.5275893 0.01459944 -0.6676489\n", + " 0.20969105 -0.9354551 -0.11613619 0.574291 ]\n", + "\n", + "Generated Parameters 0\n", + "[0.6728342 0.76642454 0.67323554 0.49700066 0.38120773 0.74987733\n", + " 0.71334994 0.51068175 0.44691524 0.3693751 ]\n", + "\n", + "Noise 1\n", + "[ 0.9502919 -0.84337425 0.8520248 0.37778485 0.7457894 -0.50416434\n", + " 0.30488837 0.677212 -0.88005257 0.4952891 ]\n", + "\n", + "Generated Parameters 1\n", + "[0.6848593 0.71806866 0.9225133 0.6557332 0.7800792 0.8705411\n", + " 0.5553367 0.26441512 0.60272783 0.46327987]\n", + "\n", + "Noise 2\n", + "[-0.66146374 0.88239443 0.8194696 -0.23779678 0.73123753 0.3056749\n", + " 0.5584065 0.11068308 0.46339512 0.3499174 ]\n", + "\n", + "Generated Parameters 2\n", + "[0.2185045 0.09542474 0.03316803 0.3031584 0.26771888 0.0468093\n", + " 0.2716334 0.7267838 0.49590957 0.6811479 ]\n", + "\n" + ] + } + ], + "source": [ + "noise = generate_noise(shape=(3, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "noise = noise.cpu().numpy()\n", + "gp = gp.cpu().detach().numpy()\n", + "\n", + "for i in range(3):\n", + " print(f\"Noise {i}\")\n", + " print(noise[i])\n", + " print()\n", + " print(f\"Generated Parameters {i}\")\n", + " print(gp[i])\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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G44Z0qXM37BURiaaRM5GAPfTux/xh9mruvTKTS07uGnScOufQkVJeXrKJKTn5rNqyhxaN0hg/tBvfGN6Dbq0bBx1PROSYNK0pkoBWb93DxX95n9H92/P3b5yiUbMvwN1ZkLeTKTmf8NqKbbg7Zw/owKTsDLJ7t9G+FZGEo2lNkQRzuKSMyc/l0rxRKr+9ZLCKhy/IzBjWszXDerZm866DPDU/n2c+2MgbK7fRt31TJmRn8NWTu9Ckgf7aE5HEppEzkYD8+fWP+Mvb63jkm6dyzqCOQcdJSoeOlPLPpVuYmpPHsk27adYwlSuyujFhRA96tGkSdDwRqec0ciaSQD7csJMH/72OS0/pqsIsjhqmhbjs1K5cekoXFm/YxZScPKbm5PHY+58wql97JmVn8KU+bUmpp/eUE5HEpJEzkVp28HApFzzwHoeOlPLqD8+gecO0oCPVK9v2HOKp+Rt4ev4GivYV06tdEyaOyODSU7vSVFOeIlKLdEGASIL41cwVTMnJ4+lrTiO7T9ug49RbxSWlzFq2hSk5+eRu3EXTBqlcdmpXJozoQa92TYOOJyL1gKY1RRJAzroipuTkha8gVGEWqAapIS45uSuXnNyVJRt3MTUnj6fm5zMlJ48zT2jHpOwMzjyhnaY8RaTWaeRMpJbsOXSE8+57jwapKfzre6frRqkJqHBvMc98sIEn5+WzfW8xGW0a880RGVye1VXTzyJS4zStKRKwHz2fy/TFBbxwQzandG8VdBypxOGSMl5dsZWpOXksyt9J4/QQl57SlYnZPejTvlnQ8UQkSWhaUyRAb6zcxguLCrhxVG8VZnVAemoKF2d25uLMzizftJspOXk8t3AjT8zL50t92jIxO4PR/dsT0pSniMSBRs5E4mzHvmLOvW8O7Zo15OUbR5KemhJ0JDkOO/YV8+yCjTw5L58tuw/RrXUjvjm8B1dmdadFY015ikj1aVpTJADuznefWsxbq7Yz8+aR9O/YPOhI8gWVlJbx+sptTMnJ44NPPqVRWohxJ3dhUnYG/TpqylNEYqdpTZEAvLxkM7OXb+XHY/qrMEsSqaEUzj+xE+ef2ImVm/cwNSeP6YsLeOaDDQzv1ZpJ2Rl8eUAHUkMaIRWR46ORM5E42bL7IOfcO4cTOjRj2ndG6PykJLZz/+HwOWlz89m06yBdWjbiG8N7MH5oN1o1SQ86nogkKE1ritQid2fCYx+wMG8ns79/Ohlt9RzH+qC0zHlz1Tam5uSR8/EOGqSmMHZIZyZmZzCoc4ug44lIgtG0pkgtenL+Bt5bW8Rd4warMKtHQinGuYM6cu6gjny0dS9T5+YxY/Empi0sYGhGKyZl9+ScQR1I05SniFQiriNnZjYGuB8IAY+6+x/KLT8LeBn4JNI03d1/HVmWB+wFSoGSiirL8jRyJokgr2g/593/HlkZrXj86mGYaTqzPtt94AjPL9rI43Pz2fDpATo2b8g3hnfnqmHdadO0QdDxRCRAtT6taWYhYA3wFaAAWABc5e4ro/qcBfzI3S+sYP08IMvdi2L9TBVnErTSMufKh+fy0ba9vP7DM+jUolHQkSRBlJY5/169nalz83hvbRHpoRQuyuzMpOwMTuyqKU+R+iiIac1hwDp3Xx8J8CwwFlhZ6Voiddj/e289C/N3cu+VmSrM5DNCKcaXB3bgywM7sG77Ph6fm8eLiwp4cXEBp3RvycTsDM4b3En3wRMR4vm3QBdgY9T7gkhbeSPMLNfMZpvZoKh2B143s0Vmdl0cc4rUiNVb93DP62sYM6gj44ZUdKiLhPVp35Rfjx3M3NvP5pcXDeTT/Yf5/rNLGHn329z35hq27z0UdEQRCVA8R84qOtGm/BzqYqCHu+8zs/OBl4C+kWUj3X2zmbUH3jCz1e4+53MfEi7crgPo3r17jYUXqY7DJWX88LlcmjdK5beXDNZ5ZhKT5g3T+NbInkwckcG7awuZmpPHfW+u5cF/r+OCEzsxMTuDk/W4L5F6J57FWQHQLep9V2BzdAd33xP1epaZ/c3M2rp7kbtvjrRvN7MZhKdJP1ecufsjwCMQPues5r+GSNUeeGstq7bs4f9NyNJJ3lJtKSnGqH7tGdWvPZ8U7efxuXm8sLCAl5ZsJrNrCyZmZ3DBSZ1okBoKOqqI1IJ4TmsuAPqaWU8zSwfGAzOjO5hZR4sMMZjZsEieHWbWxMyaRdqbAOcAy+OYVeS4fbhhJ397Zx2XndqVrwzsEHQcqeN6tm3CLy8axNzbz+bXYwexr7iEydNyGfmHt7nn9Y/YtkdTniLJLt630jgfuI/wrTQec/ffmtn1AO7+kJndBNwAlAAHgcnunmNmvYAZkc2kAk+7+2+r+jxdrSm17eDhUi544D2KS8qY/YPTad5QD8CWmuXu/GddEVNz8nhr9XZCZowZ3JFJ2Rmc2qOVptBF6jA9IUAkDn41cwVTcvJ4+prTyO7TNug4kuQ27DjA43PzeG7hRvYeKmFQ5+ZMys7goszONEzTlKdIXaPiTKSGvb+uiK8/Op9J2Rn86uJBVa8gUkMOHC5hxoebmJqTx5pt+2jdJJ3xQ7vxjeE96NxSt3ARqStUnInUoD2HjjDm3jk0TAvxr++dTqN0jVpI7XN35q7fwdScPN5YuQ0z45yBHZiUncGwnq015SmS4PRsTZEa9OtXVrJ1zyFevCFbhZkExszI7t2W7N5tKdh5gCfm5fPcgo3MXr6V/h2bMSk7g7FDuugYFaljNHImUk2vr9jKdU8s4qZRffjRuf2CjiPyGQcPlzIzdxP/eD+P1Vv30qJR2n+nPLu1bhx0PBGJomlNkRqwY18x5943h/bNGvLSjSP1qB1JWO7OB598ytS5eby2YhvuzpcHhKc8R/RuoylPkQSgaU2RL8jduWPGcvYcLOHJazJVmElCMzNO69WG03q1YfOugzw1P59nPtjI6yu3cUKHpkwYkcFXT+lC43T9MyCSaDRyJhKjGR8W8MPncvnJef25/szeQccRqbZDR0r559ItTMn5hOWb9tCsYSpXZHVjwoge9GjTJOh4IvWOpjVFvoAtuw9yzr1z6NehGc99ZwShFE0JSd3l7izesJMpOfnMXraFUndG92vPxOwMTu/bVlOeIrVE05oix8ndue2FpZSUOn+6PFOFmdR5ZsapPVpzao/WbLtgAE/N38DT8/OZ8NgH9GrXhIkjMrj01K40baB/IkSCoJEzkSo8MS+fn7+0nLvGDeabw3sEHUckLopLSpm1bAtTcvLJ3biLpg1SuezUrkzMzqBnW015isSDpjVFjkNe0X7Ou/89sjJa8fjVwzTdI/XCko27mJqTxz+XbuZIqXPmCe2YlJ3BmSe0I0UjxyI1RsWZSDWVljlXPDyXtdv28toPz6BTCz0WR+qX7XsP8cz8jTw1P5/te4vJaNOYCSMyuCyrK80bpgUdT6TOU3EmUk1/f+dj7n51NfddOYRxJ3cJOo5IYA6XlPHqiq1MzcljUf5OGqeHuPSUrkzM7kGf9s2CjidSZ+mCAJFqWL11D/e+sYbzBndk7JDOQccRCVR6agoXZ3bm4szOLN+0myk5eTy3cCNPzMvn9L5tmTgig1H92+tiGZEaopEzkXIOl5Qx9sH3Kdx7iNd+cAZtmjYIOpJIwtmxr5hnF2zkyXn5bNl9iG6tGzFheAZXZHWjRWNNeUrNc3dKypzDJWUcLinjSGkZxSVlHC4Nvz7afjjq9ZFS53BpaaT9s+t+pu9n1ikjJcV48GunxP07aeRMJEb3v7WGVVv28P8mZKkwEzmGNk0bcOOoPnznjF68vnIbU97P47ezVnHPG2sYd3IXJmVn0K+jpjzrorIy/2zBU1rGkZJwkVN8tOCpqECKKnL+216uEDpW+2eLp9L/fUZku8WRdWtyPCnFwqPC6aGUz/xOC6XQJODbyKg4E4myeMNO/v7Ox1x+ale+MrBD0HFEEl5qKIXzT+zE+Sd2YuXmPUzNyWP64gKe+WADI3q1YWJ2Bl8e0J7UkB53Vl5pmVc4enO0aCku+eyI0JHScJ8K248WMCX/Gyk6WuAUV1AgfW6UKeqzj5TW7IxaWshIiyqA0kIpNEj9XyF0tL1Revh3g9QU0kIWbo/q0yAU1T+6/ejr0DHaU6OKr6j3iTwNr2lNkYiDh0u54IH3KC4p49UfnE4zXY0mclx27j8cPidtbj6bdh2kS8tGfGN4D8YP7UarJum1muV4psI+v8w/VyBVNBVWWaH1v+2URrbvlJbV7L+/5UeB0lIt8j4UabPPFEifKVr+u87nR5LKF1HpqUZ6KBRpt88XQtHbCaXo9iuV0NWaIlX41cwVTMnJ4+lrTyO7d9ug44jUeSWlZby5ajtTc/KYu34HDVJTGDekC18e2CE8alTJVFj5ZZ8rhBJwKiy6kGlwjGWfKYQ+015upKjKAumz208Lme7DWAfpnDORSry/rogpOXlMys5QYSZSQ1JDKYwZ3JExgzvy0da9TJ2bx4zFm3hu4cYq163JqbDKiqcK28sXQnVkKkySh0bOpN7bc+gIY+6dQ8O0EP/63uk0Sg8FHUkkae0+eIRPivaTFrJIgRSKmn7736iRpsKkPtDImcgx3DlzJVv3HOLFG7JVmInEWYtGaQzp1jLoGCIJTZfPSL32+oqtvLi4gBtH9eHk7q2CjiMiIhLf4szMxpjZR2a2zsx+UsHys8xst5ktifz8ItZ1Rb6oHfuKuX3GMgZ1bs7No/sGHUdERASI47SmmYWAB4GvAAXAAjOb6e4ry3V9z90vPM51RY6Lu3PHjOXsOVjCU9cMIT1Vg8giIpIY4vkv0jBgnbuvd/fDwLPA2FpYV6RKLy3ZxKsrtjL5nBN0F3MREUko8SzOugDR10sXRNrKG2FmuWY228wGVXNdkWrbvOsgv3h5BVk9WnHt6b2CjiMiIvIZ8bxas6LroMvft2Mx0MPd95nZ+cBLQN8Y1w1/iNl1wHUA3bt3P+6wUj+4Oz9+cSmlZc6fr8jUPYtERCThxHPkrADoFvW+K7A5uoO773H3fZHXs4A0M2sby7pR23jE3bPcPatdu3Y1mV+S0JPz8nlvbRG3nz+AHm2aBB1HRETkc+JZnC0A+ppZTzNLB8YDM6M7mFlHizxvwsyGRfLsiGVdkerKK9rP72at5owT2vH10zTKKiIiiSlu05ruXmJmNwGvASHgMXdfYWbXR5Y/BFwG3GBmJcBBYLyHH1lQ4brxyirJr7TMmTxtCWkh44+XnqRn0ImISMKK6xMCIlOVs8q1PRT1+q/AX2NdV+R4PTznYxZv2MV9Vw6hY4uGQccRERE5Jt3cSZLeqi17uPeNNZx/YkfGDukcdBwREZFKqTiTpHa4pIzJ03Jp0Sid34w7UdOZIiKS8PTgc0lq97+1hlVb9vDohCxaN0kPOo6IiEiVNHImSWvxhp38/Z2PufzUrnx5YIeg44iIiMRExZkkpQOHS7hlWi6dWjTiFxcNDDqOiIhIzDStKUnp7tmr+aRoP09fexrNGqYFHUdERCRmGjmTpPP+uiKmzs3nWyMzyO7dNug4IiIi1aLiTJLKnkNHuPX5XHq1a8KPx/QPOo6IiEi1aVpTksqdM1eybW8xL96QTcO0UNBxREREqk0jZ5I0XluxlRcXF/Dds3ozpFvLoOOIiIgcFxVnkhSK9hVz+/RlDOrcnJtH9w06joiIyHHTtKbUee7OHTOWsfdQCU9fO4T0VP0/h4iI1F0x/ytmZheZ2XwzW2Jm341nKJHqmPHhJl5bsY1bzjmBfh2bBR1HRETkCzlmcWZmmeWavgkMB04BbohnKJFYbd51kF/OXMHQjFZcc3qvoOOIiIh8YZVNa37Xwk+J/oW7bwU2Ar8FyoDNtRFOpDJlZc5tLyyltMz50+WZhFL0UHMREan7jlmcuft3IqNnD5vZQuDnQDbQGLirlvKJHNOT8/P5z7oifjNuMD3aNAk6joiISI2o9Jwzd89197HAEmAm0MndZ7p7cW2EEzmWT4r287tZqzjjhHZ8/bTuQccRERGpMZWdc3a9mX1oZouBJsAYoJWZvWZmp9daQpFySsucW6YtIT2Uwh8vPYnw7LuIiEhyqGzk7LvufjLhiwBudfcSd38AGA9cUivpRCrw8JyPWbxhF3eNG0zHFg2DjiMiIlKjKrsgYJOZ3QU0AlYfbXT3ncDkeAcTqciqLXu49401nH9iRy7O7Bx0HBERkRpXWXE2FjgXOAK8UTtxRI6tuKSUHz63hBaN0vnNuBM1nSkiIkmpsqs1DwOv1GIWkUrd/+ZaVm/dy6MTsmjdJD3oOCIiInGh59xInbAofycPvfsxV2R15csDOwQdR0REJG7iWpyZ2Rgz+8jM1pnZTyrpN9TMSs3ssqi2PDNbFnlc1MJ45pTEduBwCT96PpdOLRrx8wsHBh1HREQkrqp88LmZta6gea+7H6livRDwIPAVoABYYGYz3X1lBf3uBl6rYDOj3L2oqoyS3O6evZpPivbz9LWn0axhWtBxRERE4iqWkbPFQCGwBlgbef2JmS02s1MrWW8YsM7d10fOX3uW8EUG5d0MvAhsr1ZyqRf+s7aIqXPz+dbIDLJ7tw06joiISNzFUpy9Cpzv7m3dvQ1wHjAN+C7wt0rW60L4eZxHFUTa/svMuhC+Z9pDFazvwOtmtsjMroshpySZ3QePcOsLufRq14Qfj+kfdBwREZFaEUtxluXu/51ydPfXgTPcfR7QoJL1KrrPgZd7fx/wY3cvraDvSHc/hXAxeKOZnVHhh5hdZ2YLzWxhYWFhZd9D6pg7X1nB9r3F3HPFEBqmhYKOIyIiUitiKc4+NbMfm1mPyM9twM7IuWJllaxXAHSLet8V2FyuTxbwrJnlAZcBfzOzcQDuvjnyezswg/A06ee4+yPunuXuWe3atYvh60hd8NqKrUxfvIkbz+rNkG4tg44jIiJSa2Ipzr5GuLB6CXgZ6B5pCwFXVLLeAqCvmfU0s3TCj32aGd3B3Xu6e4a7ZwAvEH5k1Etm1sTMmgGYWRPgHGB5db6Y1F1F+4q5ffoyBnVuzk2j+wYdR0REpFZVebVm5GrJm4+xeF0l65WY2U2Er8IMAY+5+wozuz6yvKLzzI7qAMyI3AE+FXja3V+tKqvUfe7OHTOWsfdQCU9fO4T0VN2KT0RE6pdYbqVxAvAjICO6v7uPrmpdd58FzCrXVmFR5u6Tol6vBzKr2r4kn+mLN/Haim389Lz+9OvYLOg4IiIita7K4gx4nvDVlI8CFZ24L1IjNu86yK9mrmBoRiuuOb1X0HFEREQCEUtxVuLuf497EqnXysqc215YSqk7f758CKEUPdRcRETqp1hO6HnFzL5rZp3MrPXRn7gnk3rlyfn5/GddEXdcMIDubRoHHUdERCQwsYycTYz8vjWqzQHNO0mN+KRoP7+btYozT2jH14Z1DzqOiIhIoGK5WrNnbQSR+qmktIzJ05aQHkrh7ktPInKFroiISL11zOLMzEa7+9tm9tWKlrv79PjFkvri4Tnr+XDDLu4fP4SOLRoGHUdERCRwlY2cnQm8DVxUwTIHVJzJF7Jy8x7ue3MNF5zYiYszOwcdR0REJCEcszhz919Gfn+r9uJIfVFcUsrkaUto0Sidu8YN1nSmiIhIRGXTmpMrW9Hd76n5OFJf3P/mWlZv3cv/TcyidZP0oOOIiIgkjMqmNXV7domLRfmf8tC7H3NFVlfOHtAh6DgiIiIJpbJpzTtrM4jUDwcOl3DLtFw6tWjEzy8cGHQcERGRhFPlTWjNrJeZvWJmhWa23cxeNjPd40yOyx9mryZvxwH+dHkmzRqmBR1HREQk4cTyhICngWlAJ6Az4WdtPhPPUJKc/rO2iMfn5nP1yJ6M6N0m6DgiIiIJKZbizNz9CXcvifw8SfhWGiIx233wCLe+kEvvdk24bUy/oOOIiIgkrFge3/RvM/sJ8CzhouxK4F9Hn6/p7p/GMZ8kiTtfWcH2vcVMvyGbhmmhoOOIiIgkrFiKsysjv79Trv1q9IxNicGry7cyffEmvje6D5ndWgYdR0REJKHp2ZoSV0X7irljxjIGd2nOTaP7Bh1HREQk4VVZnJnZhIra3f3xmo8jycTduX36MvYWl/DMFUNIT43lFEcREZH6LZZpzaFRrxsCZwOLARVnUqnpizfx+spt3H5+f07ooHsai4iIxCKWac2bo9+bWQvgibglkqSweddBfjVzBcMyWvPtL+m0RBERkVgdzzzTAUAnD8kxlZU5t76QS6k7f7o8k1CKHmouIiISq1jOOXuF/93XLAQMIHxTWpEKPTEvn/fX7eC3lwyme5vGQccRERGpU2I55+xPUa9LgHx3L4hl42Y2BrifcFH3qLv/4Rj9hgLzgCvd/YXqrCuJZX3hPn4/exVnntCOrw3rHnQcERGROqfKaU13fxdYDTQDWgGHY9mwmYWAB4HzgIHAVWb2uSddR/rdDbxW3XUlsZSUlnHL87k0SA3xx8tOwkzTmSIiItUVy4PPrwA+AC4HrgDmm9llMWx7GLDO3de7+2HCTxgYW0G/m4EXge3Hsa4kkIfnrOfDDbv49dhBdGjeMOg4IiIidVIs05p3AEPdfTuAmbUD3gReqGK9LsDGqPcFwGnRHcysC3AJMJrP3rKjynUlsazcvIf73lzDBSd24uLMzkHHERERqbNiuVoz5WhhFrEjxvUqmtMq/8D0+4Afu3vpcawb7mh2nZktNLOFhYWFMcSSmlZcUsrkaUto0Sidu8YN1nSmiIjIFxDLyNmrZvYa8Ezk/ZXA7BjWKwC6Rb3vCmwu1ycLeDbyj3lb4HwzK4lxXQDc/RHgEYCsrKwKCziJr/veXMvqrXv5v4lZtG6SHnQcERGROi2Wm9DeamZfBb5EeETrEXefEcO2FwB9zawnsAkYD3yt3Lb/+9xOM5sC/NPdXzKz1KrWlcSwKP9THn73Y67M6sbZAzoEHUdERKTOO2ZxZmZ9gA7u/r67TwemR9rPMLPe7v5xZRt29xIzu4nwVZgh4DF3X2Fm10eWP1Tddav75SS+Dhwu4ZZpuXRq0YifXTgg6DgiIiJJobKRs/uA2ytoPxBZdlFVG3f3WcCscm0VFmXuPqmqdSWx/GH2avJ2HOCZa4fTrGFa0HFERESSQmUn9me4+9Lyje6+EMiIWyKpE95bW8jjc/O5emRPRvRuE3QcERGRpFFZcVbZjaoa1XQQqTt2HzzCrc8vpXe7Jtw2pl/QcURERJJKZcXZAjO7tnyjmX0bWBS/SJLo7py5gsJ9xdx75RAapoWCjiMiIpJUKjvn7AfADDP7Ov8rxrKAdMI3jpV66NXlW5n+4Sa+d3ZfTuraMug4IiIiSeeYxZm7bwOyzWwUMDjS/C93f7tWkknCKdpXzB0zljG4S3NuHt0n6DgiIiJJKZb7nP0b+HctZJEE5u78dPoy9haX8MwVQ0gLxfKQCBEREaku/QsrMXlx8SbeWLmNH51zAid0aBZ0HBERkaSl4kyqtGnXQe6cuYJhGa359pd6BR1HREQkqak4k0qVlTm3vZBLqTt/ujyTUIoeai4iIhJPKs6kUk/My+f9dTv42QUD6d6mcdBxREREkp6KMzmm9YX7+P3sVZzVrx1XDesWdBwREZF6QcWZVKiktIzJ03JpkBri7ktPwkzTmSIiIrWhyltpSP308Jz1LNm4iweuOpkOzSt7kpeIiIjUJI2cyees3LyH+95cwwUndeLizM5BxxEREalXVJzJZxSXlDJ52hJaNk7nN2MHV72CiIiI1ChNa8pn3PfmWlZv3ctjk7Jo1SQ96DgiIiL1jkbO5L8W5X/Kw+9+zJVZ3Rjdv0PQcUREROolFWcCwIHDJUyelkvnlo342YUDgo4jIiJSb2laUwD4/azVbPj0AM9cO5xmDdOCjiMiIlJvaeRMeG9tIU/My+fqkT0Z3qtN0HFERETqNRVn9dzuA0e49fml9GnflFvP7Rd0HBERkXpPxVk996tXVlC4r5h7rsikYVoo6DgiIiL1noqzeuzV5VuY8eEmbhzVh5O6tgw6joiIiBDn4szMxpjZR2a2zsx+UsHysWa21MyWmNlCM/tS1LI8M1t2dFk8c9ZHhXuLuX3GcgZ3ac7No/sEHUdEREQi4na1ppmFgAeBrwAFwAIzm+nuK6O6vQXMdHc3s5OAaUD/qOWj3L0oXhnrK3fn9hnL2Fdcwr1XDCEtpAFUERGRRBHPf5WHAevcfb27HwaeBcZGd3D3fe7ukbdNAEfi7sXFm3hj5TZuPacffTs0CzqOiIiIRIlncdYF2Bj1viDS9hlmdomZrQb+BVwdtciB181skZldF8ec9cqmXQe5c+YKhmW05uov9Qw6joiIiJQTz+LMKmj73MiYu89w9/7AOOCuqEUj3f0U4DzgRjM7o8IPMbsucr7awsLCwhqInbzKypxbn8+l1J0/XZ5JKKWiPyIREREJUjyLswKgW9T7rsDmY3V29zlAbzNrG3m/OfJ7OzCD8DRpRes94u5Z7p7Vrl27msqelB6fm0fOxzv4+YUD6d6mcdBxREREpALxLM4WAH3NrKeZpQPjgZnRHcysj5lZ5PUpQDqww8yamFmzSHsT4BxgeRyzJr31hfv4w6urOatfO8YP7Vb1CiIiIhKIuF2t6e4lZnYT8BoQAh5z9xVmdn1k+UPApcAEMzsCHASujFy52QGYEanbUoGn3f3VeGVNdiWlZUyelkuD1BB3X3oSkf0qIiIiCSiuDz5391nArHJtD0W9vhu4u4L11gOZ8cxWnzw8Zz1LNu7igatOpkPzhkHHERERkUroBldJbsXm3dz35houOKkTF2d2DjqOiIiIVEHFWRIrLill8nO5tGyczm/GDg46joiIiMQgrtOaEqx731jLR9v28o9JQ2nVJD3oOCIiIhIDjZwlqUX5n/LInI8ZP7Qbo/q3DzqOiIiIxEjFWRLaX1zC5Gm5dG7ZiJ9dODDoOCIiIlINmtZMQr+fvYoNnx7gmWuH07SB/ohFRETqEo2cJZk5awp5ct4Grh7Zk+G92gQdR0RERKpJxVkS2X3gCLe9sJQ+7Zty67n9go4jIiIix0HFWRL51SsrKNxXzD1XZNIwLRR0HBERETkOKs6SxOxlW5jx4SZuGtWHk7q2DDqOiIiIHCcVZ0mgcG8xt89YxoldWnDT6D5BxxEREZEvQMVZHefu/HT6MvYfLuWeKzJJC+mPVEREpC7Tv+R13AuLCnhz1TZuO7cffTs0CzqOiIiIfEEqzuqwTbsO8utXVjKsZ2uuHtkz6DgiIiJSA1Sc1VFlZc6tz+dS5s6fL88kJcWCjiQiIiI1QMVZHfX43DxyPt7Bzy4cSLfWjYOOIyIiIjVExVkd9HHhPn4/ezVn9WvH+KHdgo4jIiIiNUjFWR1TUlrG5Gm5NEoP8cdLT8JM05kiIiLJRE/FrmMeevdjcjfu4i9XnUz75g2DjiMiIiI1TCNndciKzbu5/621XHhSJy7K7Bx0HBEREYkDFWd1RHFJKZOfy6Vl43TuGjs46DgiIiISJ5rWrCPueWMNH23byz8mDaVVk/Sg44iIiEicaOSsDliY9ymPzFnPVcO6Map/+6DjiIiISBzFtTgzszFm9pGZrTOzn1SwfKyZLTWzJWa20My+FOu69cX+4hJueT6Xrq0acccFA4OOIyIiInEWt+LMzELAg8B5wEDgKjMrX128BWS6+xDgauDRaqxbL/x+9io2fHqAP12WSdMGmoUWERFJdvEcORsGrHP39e5+GHgWGBvdwd33ubtH3jYBPNZ164N31xTy5LwNfHtkT07r1SboOCIiIlIL4lmcdQE2Rr0viLR9hpldYmargX8RHj2Led1ktvvAEW57IZc+7Zvyo3P7BR1HREREakk8i7OKbl3vn2twn+Hu/YFxwF3VWRfAzK6LnK+2sLCw8HizJpxfzlxO0b7D3HNFJg3TQkHHERERkVoSz+KsAIh+8GNXYPOxOrv7HKC3mbWtzrru/oi7Z7l7Vrt27b546gQwe9kWXlqymZtH9+Gkri2DjiMiIiK1KJ7F2QKgr5n1NLN0YDwwM7qDmfWxyMMhzewUIB3YEcu6yapwbzG3z1jGiV1acOOoPkHHERERkVoWt8v/3L3EzG4CXgNCwGPuvsLMro8sfwi4FJhgZkeAg8CVkQsEKlw3XlkThbvz0+nL2H+4lHuuyCQtpNvQiYiI1DdxvTeDu88CZpVreyjq9d3A3bGum+xeWFTAm6u28bMLBtC3Q7Og44iIiEgANDSTIAp2HuDOV1YyrGdrrh7ZM+g4IiIiEhAVZwmgrMy59fmluDt/vjyTlJSKLlYVERGR+kDFWQKYOjePuet38PMLB9KtdeOg44iIiEiAVJwF7OPCffxh9mpG92/PlUO7Vb2CiIiIJDUVZwEqKS1j8rRcGqWH+MNXTyRyVxERERGpx/Qk7QD9/Z2Pyd24i79cdTLtmzcMOo6IiIgkAI2cBWT5pt3c/9ZaLsrszEWZnYOOIyIiIglCxVkAiktKuWVaLq2bpHPX2EFBxxEREZEEomnNANzzxho+2raXf0waSsvG6UHHERERkQSikbNatiDvUx6Zs56rhnVjVP/2QccRERGRBKPirBbtLy7hlmm5dG3ViDsuGBh0HBEREUlAmtasRb+btYqNOw/w7LXDadpAu15EREQ+TyNnteTdNYU8NX8D13ypJ6f1ahN0HBEREUlQKs5qwe4DR7jthVz6tm/KLef0CzqOiIiIJDDNrdWCX85czo59h3l0wlAapoWCjiMiIiIJTCNncTZr2RZeWrKZm0b34cSuLYKOIyIiIglOxVkcbd97iDtmLOPELi24cVSfoOOIiIhIHaDiLE7cndunL2P/4VLuvTKTtJB2tYiIiFRNFUOcPL+ogDdXbee2c/vRp32zoOOIiIhIHaHiLA4Kdh7g16+s5LSerbl6ZM+g44iIiEgdouKshpWVObc+vxR350+XZ5KSYkFHEhERkTpExVkNm5KTx9z1O/j5hQPp1rpx0HFERESkjlFxVoPWbd/H3a+uZnT/9lw5tFvQcURERKQOimtxZmZjzOwjM1tnZj+pYPnXzWxp5CfHzDKjluWZ2TIzW2JmC+OZsyaUlJZxy/O5NEoP8YevnoiZpjNFRESk+uL2hAAzCwEPAl8BCoAFZjbT3VdGdfsEONPdd5rZecAjwGlRy0e5e1G8Mtakv7/zMbkbd/HXr51M++YNg44jIiIidVQ8R86GAevcfb27HwaeBcZGd3D3HHffGXk7D+gaxzxxs3zTbu5/ay0XZXbmwpM6Bx1HRERE6rB4FmddgI1R7wsibcfybWB21HsHXjezRWZ23bFWMrPrzGyhmS0sLCz8QoGPx6EjpUyetoTWTdK5a+ygWv98ERERSS7xfPB5RSddeYUdzUYRLs6+FNU80t03m1l74A0zW+3ucz63QfdHCE+HkpWVVeH24+neN9awZts+/jFpKC0bp9f2x4uIiEiSiefIWQEQfcliV2Bz+U5mdhLwKDDW3XccbXf3zZHf24EZhKdJE8qCvE955L31XDWsO6P6tw86joiIiCSBeBZnC4C+ZtbTzNKB8cDM6A5m1h2YDnzT3ddEtTcxs2ZHXwPnAMvjmLXa9heXcMu0XLq2asQdFwwIOo6IiIgkibhNa7p7iZndBLwGhIDH3H2FmV0fWf4Q8AugDfC3yK0nStw9C+gAzIi0pQJPu/ur8cp6PH43axUbdx7guetG0LRBPGeHRUREpD6Ja1Xh7rOAWeXaHop6fQ1wTQXrrQcyy7cninc+2s5T8zdw7ek9GdazddBxREREJInoCQHVtPvAEX784lL6tm/KLef0CzqOiIiIJBnNx1XTL2YuZ8e+wzw6YSgN00JBxxEREZEko5Gzapi1bAsvL9nMzaP7cmLXFkHHERERkSSk4ixGJaVl3P3qak7q2oLvjuoddBwRERFJUprWjFFqKIWnrx3OkZIy0kKqaUVERCQ+VJxVQ5eWjYKOICIiIklOQ0AiIiIiCUTFmYiIiEgCUXEmIiIikkBUnImIiIgkEBVnIiIiIglExZmIiIhIAlFxJiIiIpJAVJyJiIiIJBAVZyIiIiIJRMWZiIiISAIxdw86Q40xs0IgP84f0xYoivNn1CfanzVP+7TmaZ/WLO3Pmqd9WrNqa3/2cPd25RuTqjirDWa20N2zgs6RLLQ/a572ac3TPq1Z2p81T/u0ZgW9PzWtKSIiIpJAVJyJiIiIJBAVZ9X3SNABkoz2Z83TPq152qc1S/uz5mmf1qxA96fOORMRERFJIBo5ExEREUkgKs6OwczGmNlHZrbOzH5SwXIzswciy5ea2SlB5KwrYtifZ5nZbjNbEvn5RRA56woze8zMtpvZ8mMs1/FZTTHsUx2j1WBm3czs32a2ysxWmNn3K+ij4zRGMe5PHaPVYGYNzewDM8uN7NM7K+gTzDHq7vop9wOEgI+BXkA6kAsMLNfnfGA2YMBwYH7QuRP1J8b9eRbwz6Cz1pUf4AzgFGD5MZbr+Kz5fapjtHr7sxNwSuR1M2CN/h6N+/7UMVq9fWpA08jrNGA+MLxcn0COUY2cVWwYsM7d17v7YeBZYGy5PmOBxz1sHtDSzDrVdtA6Ipb9KdXg7nOATyvpouOzmmLYp1IN7r7F3RdHXu8FVgFdynXTcRqjGPenVEPkuNsXeZsW+Sl/In4gx6iKs4p1ATZGvS/g8/8RxNJHwmLdVyMiw8uzzWxQ7URLWjo+40PH6HEwswzgZMIjE9F0nB6HSvYn6BitFjMLmdkSYDvwhrsnxDGaGu8PqKOsgrby1XQsfSQsln21mPBjLPaZ2fnAS0DfeAdLYjo+a56O0eNgZk2BF4EfuPue8osrWEXHaSWq2J86RqvJ3UuBIWbWEphhZoPdPfq800COUY2cVawA6Bb1viuw+Tj6SFiV+8rd9xwdXnb3WUCambWtvYhJR8dnDdMxWn1mlka4kHjK3adX0EXHaTVUtT91jB4/d98FvAOMKbcokGNUxVnFFgB9zaynmaUD44GZ5frMBCZEruQYDux29y21HbSOqHJ/mllHM7PI62GEj80dtZ40eej4rGE6Rqsnsq/+D1jl7vcco5uO0xjFsj91jFaPmbWLjJhhZo2ALwOry3UL5BjVtGYF3L3EzG4CXiN8peFj7r7CzK6PLH8ImEX4Ko51wAHgW0HlTXQx7s/LgBvMrAQ4CIz3yKUy8nlm9gzhK7PamlkB8EvCJ7Pq+DxOMexTHaPVMxL4JrAsck4PwO1Ad9Bxehxi2Z86RqunEzDVzEKEC9lp7v7PRPi3Xk8IEBEREUkgmtYUERERSSAqzkREREQSiIozERERkQSi4kxEREQkgag4ExEREUkgKs5EpF4ws1IzWxL185Ma3HaGmS2vuqeISNV0nzMRqS8OuvuQoEOIiFRFI2ciUq+ZWZ6Z3W1mH0R++kTae5jZW2a2NPK7e6S9g5nNiDxcOtfMsiObCpnZ/zOzFWb2euSO4yIi1abiTETqi0blpjWvjFq2x92HAX8F7ou0/RV43N1PAp4CHoi0PwC86+6ZwCnAikh7X+BBdx8E7AIujeu3EZGkpScEiEi9YGb73L1pBe15wGh3Xx95sPRWd29jZkVAJ3c/Emnf4u5tzawQ6OruxVHbyADecPe+kfc/BtLc/Te18NVEJMlo5ExEBPwYr4/VpyLFUa9L0Tm9InKcVJyJiMCVUb/nRl7nAOMjr78O/Cfy+i3gBgAzC5lZ89oKKSL1g/7PTkTqi0ZmtiTq/avufvR2Gg3MbD7h/2G9KtL2PeAxM7sVKAS+FWn/PvCImX2b8AjZDcCWeIcXkfpD55yJSL0WOecsy92Lgs4iIgKa1hQRERFJKBo5ExEREUkgGjkTERERSSAqzkREREQSiIozERERkQSi4kxEREQkgag4ExEREUkgKs5EREREEsj/B99J289k0D25AAAAAElFTkSuQmCC\n", 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cEH72kYm89PoOPvVAMQ2NTUFHEhERkcOIa/EyszfM7GUzW2lmRbFpA8zsOTPbGPuzfzwz9AQzcnP43mXjeWF9GV94ZCWNTR50JBERETmERIx4nePuk909P/b+FuB5dx8NPB97L0dp5qnH8rULT+Kp1Vv5euHLuKt8iYiIdDbhALZ5GXB27PW9wGLgqwHk6HY+cdbxVNdFuP2FTaSnhvnG9JMxs6BjiYiISEy8i5cDz5qZA39w9zuAwe6+FcDdt5rZoDhn6FG+cN6JVNZFuOs/r9MnLcwXzjsx6EgiIiISE+/idbq7b4mVq+fMbH1rP2hmNwA3AIwYMSJe+bodM+MbF4+lui7C/z6/kYzUMB9//3FBxxIRERHifI2Xu2+J/VkGLAKmAtvNbChA7M+yw3z2DnfPd/f8gQMHxjNmt5OUZPyoYCIXTxjKD/66jgeXvBV0JBERESGOxcvM0s2sz/7XwIeANcATwLWxxa4FHo9Xhp4slGT88srJnDNmIP/z2Ms8vrI06EgiIiI9XjxHvAYDL5rZKmAp8Bd3/xtwK3CemW0Ezou9lzhICSfxu5mnMHXkAL74x1U8u3Zb0JFERER6NOsKtx3Iz8/3oqKioGN0WVV1Ea6Zv4R1W/Zy13VTOGN0VtCRREREui0zW97sNloH0J3re4CM1DD3Xj+FUVnpfPy+Ipa/uTPoSCIiIj2SilcP0a93CvfPm8qQvmlcd/cy1m7ZE3QkERGRHkfFqwcZ1CeNhfOm0Sc1zOwFS9lUVhV0JBERkR5FxauHye7Xiwc+fipmxsz5S3h7Z03QkURERHoMFa8eaFRWOvfPncq+hkaumb+E7Xtrg44kIiLSI6h49VAnD83knuunsKOqjpnzl7Czuj7oSCIiIt2eilcPljuiP/OvncJbO2u49q6l7K1tCDqSiIhIt6bi1cO97/hj+N3MPNZt3cvce5axr74x6EgiIiLdloqXcO5Jg7ntqsksf3MXN9xfRF1E5UtERCQeVLwEgOkTh3FrwUT+vbGCmx5aSaSxKehIIiIi3Y6Kl7zjo1OG883pY/nb2m3c/Ohqmpo6/+OkREREupJw0AGkc5lzxiiq6yL8/LkNpKeE+e5l4zCzoGOJiIh0Cype8h6fOfcEquoi/OFfm8lIC/PVC04KOpKIiEi3oOIl72Fm3HLhSVTVRfjd4tfISA3z6XNOCDqWiIhIl6fiJYdkZnzvsvFU10X46TOvkpEa5trTRgYdS0REpEtT8ZLDSkoyfvqRSVTXN/KtJ9aSnhrmw6fkBB1LRESky9K3GuWIkkNJ/OrqXM44IYub/7yKp1/eGnQkERGRLkvFS1qUlhzijtmnkDuiP597eAWLXy0LOpKIiEiXpOIlrdI7Jcxd103hxMF9+OTC5SzZvCPoSCIiIl2Oipe0Wt9eydw3ZyrZ/Xox994iVr29O+hIIiIiXYqKl7TJMRmpPDDvVPqnJ3Pt3Ut5dVtl0JFERES6DBUvabMhfdN4YO6ppISSmLlgCW9UVAcdSUREpEtQ8ZJ2GXFMbx6YN41IYxPXzF/Clt37go4kIiLS6al4SbuNHtyH++ZMY+++BmbOX0JFVV3QkURERDo1FS85KhNy+nLX9VPYsmcfsxYsZU9NQ9CRREREOi0VLzlqU0YO4I5Z+bxWVsV19yylui4SdCQREZFOScVLOsT7TxzI7VfnsrpkDx+/r4jahsagI4mIiHQ6Kl7SYS4YP4SffWQi//faDj7zYDENjU1BRxIREelUVLykQ83IzeF7l4/n7+vK+OIfV9HY5EFHEhER6TTC8d6AmYWAIqDU3aeb2STg90AG8AZwjbvvjXcOSZxZpx5LdV2EW59eT3pKiB8VTMDMgo4lIiISuESMeN0ErGv2fj5wi7tPABYBX0lABkmwT551PJ855wQeXvY23//LOtw18iUiIhLX4mVmOcDFRMvWfmOAf8VePwdcEc8MEpwvfehErjttJAtefJ3/fX5j0HFEREQCF+9TjbcBNwN9mk1bA1wKPA58BBge5wwSEDPjm9PHUlUX4ba/byQjNcy8M48LOpaIiEhg4jbiZWbTgTJ3X37QrDnAp81sOdFCVn+Yz99gZkVmVlReXh6vmBJnSUnGrQUTuGjCEL7/l3U8tPStoCOJiIgEJp4jXqcDl5rZRUAakGlmC919JvAhADM7keipyPdw9zuAOwDy8/N1gVAXFg4lcduVudTUF/H1RS+Tnhrm0knDgo4lIiKScHEb8XL3r7l7jruPBK4CXnD3mWY2CMDMkoD/R/QbjtLNpYST+P3MU5g6cgBffGQlf39le9CRREREEi6I+3hdbWYbgPXAFuDuADJIANKSQ8y/Np9xwzL51IPF/GdTRdCRREREEsq6wtf88/PzvaioKOgY0kF2Vddz1R0v8fauGu6fO41Tju0fdCQREZEOY2bL3T3/UPN053pJuP7pKdw/dyqD+qRy/d1LWbtlT9CRREREEkLFSwIxKDONhfOmkZ4aZvaCpbxWXhV0JBERkbhT8ZLA5PTvzQPzpmEGM+cv4e2dNUFHEhERiSsVLwnUcQMzuG/ONKrrIsxcsISyvbVBRxIREYkbFS8J3NhhmdwzZyrllXXMXLCEXdWHvKeuiIhIl6fiJZ1C3oj+zL82nzd21HDt3UuprG0IOpKIiEiHU/GSTuO047P43TV5vLJlL3PvKWJffWPQkURERDqUipd0Kh84eTC/vHIyy97cyScWLqcuovIlIiLdh4qXdDqXTBrGrQUT+NeGcj7/8EoijU1BRxIREekQKl7SKV05ZQTfmD6Wp9ds46uPvkxTU+d/woKIiEhLwkEHEDmcuWeMoqo2wi//voGM1BDfvnQcZhZ0LBERkXZrVfEys58Bd7v72jjnETnA5z5wAlV1Ddz579fJSAvzlfNPCjqSiIhIu7V2xGs9cIeZhYG7gYfcXQ/Yk7gzM75+0clU1UX4zT9eIz01zKfOPiHoWCIiIu3Sqmu83H2+u58OzAZGAqvN7EEzOyee4UQgWr6+f/kELps8jJ/87VXu/+8bQUcSERFpl1ZfXG9mIeCk2E8FsAr4opk9HKdsIu8IJRk/+8gkPnjyYL7x+FoeXV4SdCQREZE2a1XxMrNfAK8CFwE/dPdT3P3H7n4JkBvPgCL7JYeS+PXHcjn9hGP4yp9X8fTLW4OOJCIi0iatHfFaA0x090+4+9KD5k3t4Ewih5WWHOKOWflMHt6Pzz28gn9uKA86koiISKu19hqvu4BUM5tqZu/f/xObp4vsJaHSU8Pcff1URg/qwyfuL2Lp6zuDjiQiItIqrT3VOA/4F/AM8J3Yn9+OXyyRI+vbK5n75k5lWL9ezLlnGatLdgcdSUREpEWtPdV4EzAFeNPdzyF6XZfO8UigsjJSeWDeNPr2Subau5ayYXtl0JFERESOqLXFq9bdawHMLNXd1wNj4hdLpHWG9u3Fgx+fRnIoiZnzl/DmjuqgI4mIiBxWa4tXiZn1Ax4DnjOzx4Et8Qol0hbHHpPOwnnTaGhs4pr5S9i6Z1/QkURERA6ptRfXz3D33e7+beAbwALg8jjmEmmTEwf34b4509hd08A185dQUVUXdCQREZH3aLF4mVmSma3Z/97d/+nuT7h7fXyjibTNhJy+3HXdFLbs3sesBUvZU9MQdCQREZEDtFi83L0JWGVmIxKQR+SoTB01gD/MymdTWSXX37OU6rpI0JFERETe0dprvIYCa83seTN7Yv9PPIOJtNdZJw7kV1fnsvLt3dxwfxG1DY1BRxIREQEg3MrlvhPXFCId7ILxQ/nphyfxpT+t4jMPruB3M/NIDrX60aQiIiJx0dp/iS6KXdv1zg/R5zaKdFpXnJLDdy8bx9/XbefLf1pFY5MHHUlERHq41hav8w4x7cKODCISD7PfN5KbLxjD4yu38P8eW4O7ypeIiATniKcazexG4FPAcWa2utmsPsB/4hlMpKN86uwTqKqN8NvFr5GRGuLrF52MmQUdS0REeqCWrvF6EHga+BFwS7Pple7eqicTm1kIKAJK3X26mU0Gfg+kARHgU+6+tK3BRdriK+ePobouwp3/fp0+acl87gOjg44kIiI9UEvFq4FoYboawMzGEL22602gsJXbuAlYB2TG3v8E+I67P21mF8Xen93G3CJtYmZ865JxVNU18ovnNpCeGmbuGaOCjiUiIj1MS9d4/Q0YCWBmJwD/BY4DPm1mt7a0cjPLAS4G5jeb7LxbwvqiRw9JgiQlGT++YgIXjh/C9556hYeXvhV0JBER6WFaGvHq7+4bY6+vBR5y98+aWQqwnANPPx7KbcDNRK8J2+/zwDNm9jOixe+0toYWaa9wKInbrppMzX3L+dqil0lPDXPJpGFBxxIRkR6ipRGv5l8BOxd4DiD2uKCmI33QzKYDZe6+/KBZNwJfcPfhwBeIPvfxUJ+/wcyKzKyovLy8hZgirZcaDvH7macw5dgBfOGRlTy/bnvQkUREpIewI3293swWAtuAUqKjW6PcvcbM+gH/dPdJR/jsj4BZRC+gTyN6erEQuATo5+5u0a+W7XH3zMOtByA/P9+Liora9IuJtKSyNvpA7fXbKrnn+imcdnxW0JFERKQbMLPl7p5/qHktjXh9HKggep3Xh9y9JjZ9LPCzI33Q3b/m7jnuPhK4CnjB3WcSvabrrNhi5wIbD7MKkbjqk5bMvddPZeQxvZl3bxHFb+0KOpKIiHRzRyxe7r7P3W9195vcfRWAmWUB/3X3+9u5zY8DPzezVcAPgRvauR6Ro9Y/PYWFc6cxsE8q1921lHVb9wYdSUREurEjFi8zO9XMFptZoZnlmtkaYA2w3cwuaO1G3H2xu0+PvX7R3U9x90nuPu0Q14CJJNSgzDQWzp1GemqYWQuWsLm8KuhIIiLSTbV0qvHXREelHgJeAOa5+xDg/URvqirSLQwf0JuF86bhDjPnL6FkV03LHxIREWmjlopX2N2fdfc/Advc/SUAd18f/2giiXX8wAzunzuNqroI18xfQtne2qAjiYhIN9NS8Wp+y4h9B83T04al2xk7LJO7r59KeWUdsxYsZVd1fdCRRESkG2mpeE0ys71mVglMjL3e/35CAvKJJNwpx/bnztn5vL6jmuvuXkplbUPQkUREpJto6VuNIXfPdPc+7h6Ovd7/PjlRIUUS7fQTsvjtx/JYs2Uvc+8tYl99Y9CRRESkG2hpxEukx/rg2MH84qOTWPbGTm58YDn1kSM+rEFERKRFKl4iR3DZ5Gx+OGMCi18t5/OPrCDSqPIlIiLt19JDskV6vKunjqC6LsL3/7KO3ikv85MrJpKUZEHHEhGRLkjFS6QV5p15HFV1EW77+0YyUsN865KxRB81KiIi0noqXiKtdNMHRlNVG2H+i6+TkRrmy+ePCTqSiIh0MSpeIq1kZvzPxSdTVRfh1//YRHpqmBvPPj7oWCIi0oWoeIm0gZnxgxkTqK5v5Md/W09GaohZ7xsZdCwREekiVLxE2iiUZPzio5PYVx/hG4+vJT01TEFeTtCxRESkC9DtJETaITmUxK8/lsdpxx/DV/68mr+t2RZ0JBER6QJUvETaKS05xJ2z85mY05fPPbSCf20oDzqSiIh0cipeIkchPTXMPddN5fhBGdxwfxHL3tgZdCQREenEVLxEjlLf3sncP3cqw/r1Ys7dy3i5ZE/QkUREpJNS8RLpAFkZqSycO43MXsnMvmsJG7dXBh1JREQ6IRUvkQ4yrF8vHpg3jXAoiWvmL+GtHTVBRxIRkU5GxUukA43MSmfh3GnUNzbxsfkvsW1PbdCRRESkE1HxEulgY4b04d7rp7K7poFr5r/Ejqq6oCOJiEgnoeIlEgeThvdjwbX5lOzax+y7lrJnX0PQkUREpBNQ8RKJk2nHHcMfZp3Chu2VzLlnGTX1kaAjiYhIwFS8ROLo7DGDuP2qXFa8tYsb7ltObUNj0JFERCRAKl4icXbhhKH85MOTeHFTBZ99aAUNjU1BRxIRkYCoeIkkwIdPyeE7l47juVe28+U/raKpyYOOJCIiAQgHHUCkp7j2tJFU1UX46TOvkp4a5geXj8fMgo4lIiIJpOIlkkCfPucEquoi/G7xa2SkhvnahSepfImI9CAqXiIJdvP5Y6iqjXDHvzbTJzXMZz8wOuhIIiKSIHEvXmYWAoqAUnefbmaPAGNis/sBu919crxziHQWZsZ3Lh1HdV2Enz+3gfTUMHPOGBV0LBERSYBEjHjdBKwDMgHc/cr9M8zs58CeBGQQ6VSSkoyffHgi1fURvvvUK2SkhvnolOFBxxIRkTiL67cazSwHuBiYf4h5BnwUeCieGUQ6q3AoiduvzuXM0VncUriap1ZvCTqSiIjEWbxvJ3EbcDNwqBsXnQlsd/eNcc4g0mmlhkPcMSufU47tz+cfXsk/1pcFHUlEROIobsXLzKYDZe6+/DCLXM0RRrvM7AYzKzKzovLy8rhkFOkMeqWEWHDdFE4emsknFy7nv6/tCDqSiIjESTxHvE4HLjWzN4CHgXPNbCGAmYWBAuCRw33Y3e9w93x3zx84cGAcY4oELzMtmXvnTGXEgN7Mu3cZK97aFXQkERGJg7gVL3f/mrvnuPtI4CrgBXefGZv9QWC9u5fEa/siXc2A9BQWzpvGMRmpXHf3MtZt3Rt0JBER6WBBPTLoKnRRvch7DM5M44F50+iVHGLWgqVsLq8KOpKIiHSghBQvd1/s7tObvb/O3X+fiG2LdDXDB/Rm4bxpNLkzc/4SSnfvCzqSiIh0ED0kW6QTOmFQBvfNmUplXYRr7nyJssraoCOJiEgHUPES6aTGZ/flnuunsH1vHbMXLGV3TX3QkURE5CipeIl0YqccO4A7Z+ezubyaWQuW8uLGChqbPOhYIiLSTipeIp3cGaOz+O01ebyxo5qZC5Zw+q0vcOvT69mwvTLoaCIi0kbm3vn/7zk/P9+LioqCjiESqNqGRp5fV0ZhcQmLN5TT2OSMz86kIDeHSycPIysjNeiIIiICmNlyd88/5DwVL5Gup6KqjidWbmHRilJeLt1DKMk468SBFORl88GTB5OWHAo6oohIj6XiJdKNbdxeSeGKUh5bUcrWPbX0SQ1z8cShFOTlkH9sf5KSLOiIIiI9ioqXSA/Q2OS8tHkHhcWlPL1mKzX1jeT070VBbjYz8nIYlZUedEQRkR5BxUukh6mpj/DM2m0UFpfyn00VNDnkjuhHQV4Ol0wcSr/eKUFHFBHptlS8RHqwbXtqeXxlKYXFpby6vZLkkHHuSYMoyMvhnDGDSAnry80iIh1JxUtEcHde2bqXwuJSHl+5hYqqOvr1TuaSicMoyMtm8vB+mOl6MBGRo6XiJSIHiDQ28e9NFSwqLuWZtduoizRxXFY6M3KzuTw3m+EDegcdUUSky1LxEpHDqqxt4OmXt/FocQlLXt8JwNRRA7giL5sLJwwlMy054IQiIl2LipeItMrbO2veuR5sc0U1qeEkzhs7mCvycjhzdBbhkK4HExFpiYqXiLSJu7OqZA+FxSU8uWoLu2oayMpI5dJJ0evBxg3L1PVgIiKHoeIlIu1WH2li8atlFBaX8sL6MuobmxgzuA8z8rK5fHI2Q/qmBR1RRKRTUfESkQ6xu6aep1ZvpbC4hOK3dmMGZ5yQxYzcbM4fN4T01HDQEUVEAqfiJSId7vWKahatKGXRihLe3rmP3ikhLhg/hILcHN53/DGE9KgiEemhVLxEJG7cnaI3d1FYXMJTq7dSWRthSGYal+dmU5CXzYmD+wQdUUQkoVS8RCQhahsaeX5dGYXFJSzeUE5jkzM+O5OC3BwunTyMrIzUoCOKiMSdipeIJFxFVR1PrtpCYXEpL5fuIZRknHXiQGbkZnPe2MGkJYeCjigiEhcqXiISqI3bKylcUcpjK0rZuqeWPqlhLp44lIK8HPKP7U+SrgcTkW5ExUtEOoXGJuelzTsoLC7l6TVbqalvJKd/Lwpys5mRl8OorPSgI4qIHDUVLxHpdGrqIzy7djuPFpfwn00VNDnkjuhHQV4Ol0wcSr/eKUFHFBFpFxUvEenUtu+t5fGVpTy6vJRXt1eSHDLOPWkQBXk5nDNmEClhPapIRLoOFS8R6RLcnVe27mVRcSmPrdxCRVUd/Xonc8nE6KOKJg/vp0cViUinp+IlIl1OpLGJf2+qYFFxKc+s3UZdpInjstKZkZvN5bnZDB/QO+iIIiKHpOIlIl1aZW0DT7+8jcIVJby0eScAU0cN4Iq8bC6cMJTMtOSAE4qIvEvFS0S6jZJdNTy2opTCFaVsLq8mNZzEeWMHc0VeDmeOziIc0vVgIhIsFS8R6XbcnVUleygsLuHJVVvYVdNAVkYKl06KPqpo3LBMXQ8mIoEItHiZWQgoAkrdfXps2meBzwAR4C/ufvOR1qHiJSJHUh9pYvGrZRQWl/LC+jLqG5s4cXAGBXk5XD45myF904KOKCI9SNDF64tAPpDp7tPN7Bzgf4CL3b3OzAa5e9mR1qHiJSKttbumnqdWb6WwuITit3ZjBmeckMWM3GzOHzeE9NRw0BFFpJsLrHiZWQ5wL/AD4Iux4vVH4A53/3tr16PiJSLt8XpFNYtWlLJoRQlv79xH75QQF4wbQkFeDu87/hhCelSRiMRBkMXrz8CPgD7Al2PFayXwOHABUBubvuxI61HxEpGj4e4UvbmLwuISnlq9lcraCEMy07gsdxhX5OVw4uA+QUcUkW4kkOJlZtOBi9z9U2Z2Nu8WrzXAC8BNwBTgEeA4PyiImd0A3AAwYsSIU95888245BSRnqW2oZHn15VRWFzC4g3lNDY547MzmZGbw6WThjGwT2rQEUWkiwuqeP0ImEX0Avo0IBMoBLKAW919cWy514BT3b38cOvSiJeIxENFVR1PrtpCYXEpL5fuIZRknHXiQGbkZnPe2MGkJYeCjigiXVDgt5M4aMTrk8Awd/+mmZ0IPA+MOHjEqzkVLxGJt43bKylcUcpjK0rZuqeWPqlhLp44lBm52UwZOYAkXQ8mIq10pOIVxNd77gLuip1yrAeuPVLpEhFJhNGD+/DVC07iyx8aw5LNO3i0uJQnV23h4WVvk9O/FzNys5mRm81xAzOCjioiXZhuoCoichg19RGeXbudR4tL+M+mCpocckf0oyAvh0smDqVf75SgI4pIJxT4qcajpeIlIkHbvreWx1eW8ujyUl7dXklyyDj3pEEU5OVwzphBpIT1qCIRiVLxEhHpIO7OK1v3sqi4lMdWbqGiqo5+vZO5ZOIwZuRlkzu8nx5VJNLDqXiJiMRBpLGJFzdVUFhcyjNrt1EXaWJUVjoFudlcnpvN8AG9g44oIgFQ8RIRibPK2gaeXrONwuISXtq8E4CpowZwRV42F04YSmZacsAJRSRRVLxERBKoZFcNj60opXBFKZvLq0kNJ3He2MFckZfDmaOzCId0PZhId6biJSISAHdnVckeFhWX8MSqLeyqaSArI4VLJ2VTkJfNuGGZuh5MpBtS8RIRCVh9pInFr5axaEUpz68ro76xiRMHZ1CQl8Plk7MZ0jct6Igi0kFUvEREOpHdNfU8tXori1aUsvzNXZjB6cdnUZCXzfnjhpCeGsS9rUWko6h4iYh0Uq9XVLNoRSmLVpTw9s599E4JccG4IRTk5fC+448hpEcViXQ5Kl4iIp2cu1P05i4Ki0t4avVWKmsjDMlM47LcYVyRl8OJg/sEHVFEWknFS0SkC6ltaOT5dWUUFpfwzw3lRJqc8dmZzMjN4dJJwxjYJzXoiCJyBCpeIiJdVEVVHU+u2sKiFaWsLtlDKMl4/+gsCvJyOG/sYNKSQ0FHFJGDqHiJiHQDG7dXUriilMdWlLJ1Ty19UsNcNGEoBXnZTBk5gCRdDybSKah4iYh0I41NzpLNO3i0uJS/rdlKdX0jOf17MSM3mxm52Rw3MCPoiCI9moqXiEg3VVMf4dm123m0uIT/bKqgySF3RD8KcrOZPnEY/dNTgo4o0uOoeImI9ADb99by+MpSCotLWb+tkuSQce5Jg5iRm8M5Jw0kNazrwUQSQcVLRKSHeWXLXgqLS3hs5RYqquro1zuZSyYOY0ZeNrnD++lRRSJxpOIlItJDRRqbeHFTBYXFpTyzdht1kSZGZaVTkJvN5bnZDB/QO+iIIt2OipeIiFBZ28DTa7ZRWFzCS5t3AjB11ACuyMvmwglDyUxLDjihSPeg4iUiIgco2VXD4yu38GhxCZvLq0kNJ3He2MEU5GVz5uiBJIeSgo4o0mWpeImIyCG5O6tL9lBYXMITq7awq6aBrIwULp2UTUFeNuOGZep6MJE2UvESEZEW1UeaWPxqGYtWlPL8ujLqG5s4cXAGBXk5XD45myF904KOKNIlqHiJiEib7K6p56nVW1m0opTlb+7CDE4/PouCvGzOHzeE9NRw0BFFOi0VLxERabc3KqpZtKKUwhUlvL1zH71TQlwwbggFeTm87/hjCOlRRSIHUPESEZGj5u4UvbmLwuJSnlq9hcraCIMzU7k8N5uC3BzGDOkTdESRTkHFS0REOlRtQyPPryujsLiEf24oJ9LkjBnch8F90+iVnERacoheyaHonykh0sIheqUkvTNt//xeKfvfJ73zfv8yqeEkXdgvXdKRipdO0ouISJulJYe4eOJQLp44lIqqOp5ctYUX1pexd18DZXsbqW1oZF9DI/vqG6ltaKK+sanN2zAjVtiiZSx1fzmLFbTUd+YdVOjeKW9JBxS8wy4TDpGk06WSIBrxEhGRuGts8gPKWF2kkX31TdH3DdGiVhub9+60pgOmvbNMs0L3zvt31tv2ggeQGk5qNjK3f0Su2chdrKj1ajY6l9aszB04upd02KKn6+F6Bo14iYhIoEJJRnpqOO7fhmxqcuoiBxa6aEl7t8zta2ik9oCC13jAtHeWiX12b23DO0Wv+fLtGbdICSW9OxJ3QDlLOujUa+ig0b13y9yhCt07o3ex97oBbucV9+JlZiGgCCh19+lm9m3g40B5bJGvu/tf451DRES6v6Qki5aRlFBct+MeLXgHFLrDjsy9W9oOnLa//DVRW9/Ijup69u1qvo4mauojNLWj4IWTrFk5O8S1dfvLXspBI3atHN2LTksiJaTr8NoqESNeNwHrgMxm037p7j9LwLZFREQ6nNm7xaZfHLfj7jQ0+iEL3b6GRur2F7pDlr6m2DIHno7dva+BbXtq33Oat6Gx7Q3PjING4Q5xmvUIX6R47zJJza7dO/CLFt3lOry4Fi8zywEuBn4AfDGe2xIREeluzIyUsJESTqJvr/g+xLyh8d0RvJYKXW2z0ravvpHayIHT99U3UlUXobyy7j2neevbeR1eWrMvV7T0RYoDC927p3bTkkNMyukX6FMY4j3idRtwM3DwzV0+Y2aziZ6C/JK774pzDhERETmC5FASyaEk+qTFt+Dt/6LFgeXtwOvnDrw2r+nA8tesDO4ftdtZXX/gZ2NF71B+/bFcpk8cFtff8UjiVrzMbDpQ5u7LzezsZrN+B3wP8NifPwfmHOLzNwA3AIwYMSJeMUVERCSBEv1Fi4O/+ZrTv1dct9uSuN1Owsx+BMwCIkAa0Wu8Ct19ZrNlRgJPufv4I61Lt5MQERGRruJIt5OI2/dN3f1r7p7j7iOBq4AX3H2mmQ1tttgMYE28MoiIiIh0JkHcx+snZjaZ6KnGN4BPBJBBREREJOESUrzcfTGwOPZ6ViK2KSIiItLZ6Na2IiIiIgmi4iUiIiKSICpeIiIiIgmi4iUiIiKSICpeIiIiIgmi4iUiIiKSICpeIiIiIgkSt0cGdSQzKwfejPNmsoCKOG+jp9E+7Vjanx1P+7RjaX92PO3TjpeIfXqsuw881IwuUbwSwcyKDvdcJWkf7dOOpf3Z8bRPO5b2Z8fTPu14Qe9TnWoUERERSRAVLxEREZEEUfF61x1BB+iGtE87lvZnx9M+7Vjanx1P+7TjBbpPdY2XiIiISIJoxEtEREQkQXpc8TKzC8zsVTPbZGa3HGK+mdntsfmrzSwviJxdRSv259lmtsfMVsZ+vhlEzq7EzO4yszIzW3OY+TpG26AV+1PHaBuY2XAz+4eZrTOztWZ20yGW0THaBq3cpzpOW8nM0sxsqZmtiu3P7xximeCOUXfvMT9ACHgNOA5IAVYBYw9a5iLgacCAU4ElQefurD+t3J9nA08FnbUr/QDvB/KANYeZr2O0Y/enjtG27c+hQF7sdR9gg/47mpB9quO09fvTgIzY62RgCXDqQcsEdoz2tBGvqcAmd9/s7vXAw8BlBy1zGXCfR70E9DOzoYkO2kW0Zn9KG7n7v4CdR1hEx2gbtGJ/Shu4+1Z3L469rgTWAdkHLaZjtA1auU+llWLHXVXsbXLs5+AL2gM7Rnta8coG3m72voT3HtytWUaiWruv3hcb8n3azMYlJlq3pmO04+kYbQczGwnkEh1RaE7HaDsdYZ+CjtNWM7OQma0EyoDn3L3THKPhRGykE7FDTDu4BbdmGYlqzb4qJvrohCozuwh4DBgd72DdnI7RjqVjtB3MLAN4FPi8u+89ePYhPqJjtAUt7FMdp23g7o3AZDPrBywys/Hu3vw6z8CO0Z424lUCDG/2PgfY0o5lJKrFfeXue/cP+br7X4FkM8tKXMRuScdoB9Ix2nZmlky0IDzg7oWHWETHaBu1tE91nLaPu+8GFgMXHDQrsGO0pxWvZcBoMxtlZinAVcATBy3zBDA79o2HU4E97r410UG7iBb3p5kNMTOLvZ5K9JjbkfCk3YuO0Q6kY7RtYvtqAbDO3X9xmMV0jLZBa/apjtPWM7OBsZEuzKwX8EFg/UGLBXaM9qhTje4eMbPPAM8Q/UbeXe6+1sw+GZv/e+CvRL/tsAmoAa4PKm9n18r9+WHgRjOLAPuAqzz2lRI5NDN7iOg3mLLMrAT4FtGLQ3WMtkMr9qeO0bY5HZgFvBy7hgbg68AI0DHaTq3ZpzpOW28ocK+ZhYgW1D+6+1Od5d963bleREREJEF62qlGERERkcCoeImIiIgkiIqXiIiISIKoeImIiIgkiIqXiIiISIKoeIlIl2dmjWa2stnPLR247pFmtqblJUVEWtaj7uMlIt3WPnefHHQIEZGWaMRLRLotM3vDzH5sZktjPyfEph9rZs+b2erYnyNi0web2aLYg4hXmdlpsVWFzOxOM1trZs/G7oYtItJmKl4i0h30OuhU45XN5u1196nAr4HbYtN+Ddzn7hOBB4DbY9NvB/7p7pOAPGBtbPpo4DfuPg7YDVwR199GRLot3bleRLo8M6ty94xDTH8DONfdN8ceQrzN3Y8xswpgqLs3xKZvdfcsMysHcty9rtk6RgLPufvo2PuvAsnu/v0E/Goi0s1oxEtEujs/zOvDLXModc1eN6LrY0WknVS8RKS7u7LZn/+Nvf4/4KrY62uAF2OvnwduBDCzkJllJiqkiPQM+r82EekOepnZymbv/+bu+28pkWpmS4j+j+bVsWmfA+4ys68A5cD1sek3AXeY2VyiI1s3AlvjHV5Eeg5d4yUi3VbsGq98d68IOouICOhUo4iIiEjCaMRLREREJEE04iUiIiKSICpeIiIiIgmi4iUiIiKSICpeIiIiIgmi4iUiIiKSICpeIiIiIgny/wEIffhqlLoeBgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"KDiff Error During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Coupling %\")\n", + "\n", + "plt.plot(numpy.arange(len(kdiffs)), kdiffs)\n", + "plt.show()\n", + "\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"BStray Error During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"BStray\")\n", + "\n", + "plt.plot(numpy.arange(len(bstrays)), bstrays)\n", + "plt.show()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "optimization_date4_22_22.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 8c610914755848e436439aa80266b7b97605ea4c Mon Sep 17 00:00:00 2001 From: AndrewCurtis27 <89710614+AndrewCurtis27@users.noreply.github.com> Date: Tue, 25 Oct 2022 08:38:27 -0600 Subject: [PATCH 6/8] Add files via upload Block of graphs added, count added (old constraint boxes) --- optimization 10_25_22.ipynb | 2379 +++++++++++++++++++++++++++++++++++ 1 file changed, 2379 insertions(+) create mode 100644 optimization 10_25_22.ipynb diff --git a/optimization 10_25_22.ipynb b/optimization 10_25_22.ipynb new file mode 100644 index 0000000..62abb36 --- /dev/null +++ b/optimization 10_25_22.ipynb @@ -0,0 +1,2379 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iZN2u5WKRFqR", + "outputId": "6842718b-36d3-4c1c-b523-796de23e5470" + }, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "import os\n", + "import random\n", + "import pandas\n", + "import numpy\n", + "import math\n", + "from scipy.optimize import curve_fit\n", + "from scipy.interpolate import interp1d\n", + "from scipy.interpolate import UnivariateSpline\n", + "import imageio\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "from matplotlib import style\n", + "import matplotlib.font_manager\n", + "\n", + "# seed = 7777\n", + "# random.seed(seed) \n", + "# torch.manual_seed(seed);" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# pair_no = 1\n", + "# BASE_PATH = \"drive/MyDrive/\"\n", + "# RESULT_PATH = \"optimization_result/\"+str(pair_no)+\"_SplineFit/\"\n", + "# FULL_PATH = BASE_PATH + RESULT_PATH" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# pairs = []\n", + "# file1 = open(BASE_PATH + 'dwpt_v5/pairs.txt', 'r')\n", + "# lines = file1.readlines()\n", + "# for line in lines:\n", + "# pairs.append([int(i) for i in line.strip().split(',')])\n", + "# pair = pairs[pair_no]\n", + "# print(pair)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load Saved MP (Magnetic Parameter) Model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "B-TMPJtCKIM6" + }, + "outputs": [], + "source": [ + "# 12 input -> 42 output\n", + "class MpModel(torch.nn.Module):\n", + " def __init__(self):\n", + " super(MpModel, self).__init__()\n", + "\n", + " self.linear1 = torch.nn.Linear(12, 100, bias=True)\n", + " self.linear2 = torch.nn.Linear(100, 100, bias=True)\n", + " self.linear3 = torch.nn.Linear(100, 42, bias=True)\n", + "\n", + " def forward(self, x):\n", + " x = torch.atan(self.linear1(x))\n", + " x = torch.atan(self.linear2(x))\n", + " x = torch.atan(self.linear2(x))\n", + " x = self.linear3(x)\n", + " return x\n", + "\n", + "\n", + "mp_model = MpModel().to(\"cuda\")\n", + "mp_model.load_state_dict(torch.load(\"saved_model_state.pt\"))\n", + "\n", + "for param in mp_model.parameters():\n", + " param.requires_grad = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create GP (Geometry Paramter) Generator" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "z9cggAl1BGHo" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\lazy.py:178: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.\n", + " warnings.warn('Lazy modules are a new feature under heavy development '\n" + ] + } + ], + "source": [ + "class GpModel(torch.nn.Module):\n", + " def __init__(self, input_length: int):\n", + " super(GpModel, self).__init__()\n", + "\n", + " self.dense_layer1 = torch.nn.Linear(int(input_length), 128)\n", + " self.dense_layer2 = torch.nn.Linear(128, 256)\n", + " # self.dense_layer3 = torch.nn.Linear(256, 512)\n", + " # self.dense_layer4 = torch.nn.Linear(512, 256)\n", + " self.dense_layer5 = torch.nn.Linear(256, 128)\n", + " self.dense_layer6 = torch.nn.Linear(128, int(input_length))\n", + " \n", + " self.batch_norm1 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm2 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm3 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm4 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm5 = torch.nn.LazyBatchNorm1d()\n", + "\n", + " def forward(self, x):\n", + " x = self.batch_norm1(torch.atan(self.dense_layer1(x)))\n", + " x = self.batch_norm2(torch.atan(self.dense_layer2(x)))\n", + " # x = self.batch_norm3(torch.atan(self.dense_layer3(x)))\n", + " # x = self.batch_norm4(torch.atan(self.dense_layer4(x)))\n", + " x = self.batch_norm5(torch.atan(self.dense_layer5(x)))\n", + " x = torch.sigmoid(self.dense_layer6(x))\n", + " return x\n", + "\n", + "\n", + "gp_model = GpModel(10).to(\"cuda\")\n", + "optimizer = torch.optim.Adam(gp_model.parameters(), lr=3e-4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# System Constraints" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "Bse1wsKN1NxY" + }, + "outputs": [], + "source": [ + "# x-direction of the primary winding (mm)\n", + "lpx_min, lpx_max = (50.0, 650.0)\n", + "\n", + "# y-direction of the primary winding (mm)\n", + "lpy_min, lpy_max = (50.0, 2050.0)\n", + "\n", + "# width of the primary winding (mm)\n", + "wp_min, wp_max = (25.0, 325.0)\n", + "\n", + "# length of the edge of the primary core (mm)\n", + "a_min, a_max = (0.0, 200.0)\n", + "\n", + "# pitch of the adjacent cores (mm)\n", + "p_min, p_max = (0.0, 200.0)\n", + "\n", + "# length of the secondary winding (mm)\n", + "ls_min, ls_max = (50.0, 450.0)\n", + "\n", + "# width of the secondary winding (mm)\n", + "ws_min, ws_max = (25.0, 225.0)\n", + "\n", + "# input RMS current (A)\n", + "ip_min, ip_max = (50.0, 200.0)\n", + "\n", + "# turn number of the primary side\n", + "np_min, np_max = (4.0, 10.0)\n", + "\n", + "# turn number of the secondary side\n", + "ns_min, ns_max = (4.0, 10.0)\n", + "\n", + "# switching frequency (kHz)\n", + "f = 85 * (10 ** 3)\n", + "\n", + "# output power at the center (kW)\n", + "p_out = 50 * (10 ** 3)\n", + "\n", + "# input DC voltage (V)\n", + "v_dc = 400\n", + "\n", + "# output DC voltage (V)\n", + "v_bat = 400\n", + "\n", + "# length of the edge of the secondary core\n", + "b = 50\n", + "\n", + "# ???\n", + "w = 2 * numpy.pi * f # [rad/s]\n", + "\n", + "# ???\n", + "q_coil = 400" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "brlwMfzj1Nxh" + }, + "outputs": [], + "source": [ + "# Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive.\n", + "# Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch.\n", + "\n", + "@torch.jit.script\n", + "def linspace(start: torch.Tensor, stop: torch.Tensor, num: int):\n", + " # create a tensor of 'num' steps from 0 to 1\n", + " steps = torch.arange(num, dtype=torch.float32, device=start.device) / (num - 1)\n", + "\n", + " # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings\n", + " # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript\n", + " # \"cannot statically infer the expected size of a list in this contex\", hence the code below\n", + " for i in range(start.ndim):\n", + " steps = steps.unsqueeze(-1)\n", + "\n", + " # the output starts at 'start' and increments until 'stop' in each dimension\n", + " out = start[None] + steps * (stop - start)[None]\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "dMoXnTFV8U1P" + }, + "outputs": [], + "source": [ + "# Generate a pytorch tensor full of random floats in the range [-1, +1] with the given shape\n", + "\n", + "def generate_noise(shape):\n", + " return torch.distributions.uniform.Uniform(-1, +1).sample(shape).cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "xloGE25R8Jtm" + }, + "outputs": [], + "source": [ + "def stack_parameters(*params):\n", + " return torch.stack(params).transpose(0, 1)\n", + "\n", + "\n", + "# Scale an array to be between our specified [min, max] contraints\n", + "def scale(arr):\n", + " scaler_min = torch.tensor(\n", + " [a_min, lpx_min, lpy_min, ls_min, p_min, wp_min, ws_min, ip_min, np_min, ns_min],\n", + " device=\"cuda\",\n", + " )\n", + " scaler_max = torch.tensor(\n", + " [a_max, lpx_max, lpy_max, ls_max, p_max, wp_max, ws_max, ip_max, np_max, ns_max],\n", + " device=\"cuda\",\n", + " )\n", + "\n", + " return arr * (scaler_max - scaler_min) + scaler_min\n", + "\n", + "\n", + "def extract(arr):\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns = [\n", + " numpy.squeeze(x) for x in numpy.hsplit(arr, 10)\n", + " ]\n", + "\n", + " ys_min = (\n", + " 5 * wp +\n", + " 4 * a +\n", + " 3 * lpy +\n", + " 2 * p\n", + " )\n", + "\n", + " ys_max = (lpy + wp)\n", + " ys_max = ys_min + (ys_min - ys_max) / 2\n", + "\n", + " ys_min /= 2\n", + " ys_max /= 2\n", + "\n", + " ys = linspace(ys_min, ys_max, num=5)\n", + "\n", + " np = torch.round(np)\n", + " ns = torch.round(ns)\n", + "\n", + " # take all 15 tensors of shape (X) and convert to (15, X)\n", + " return a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys\n", + "\n", + "\n", + "def scale_and_extract(arr):\n", + " scaled_array = scale(arr)\n", + " return extract(scaled_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "44TlTSnclCKs", + "outputId": "c2d4a61b-35c8-42ad-99f9-98c99022a0b2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " GP Parameters Shape: torch.Size([256, 12])\n", + " Extra Parameters Shape: torch.Size([256, 3])\n", + " MP Model Output Shape: torch.Size([256, 42])\n", + " \n" + ] + } + ], + "source": [ + "# Enclose in a function so we don't leak variables\n", + "def test_models():\n", + " noise = generate_noise(shape=(256, 10))\n", + " gp = gp_model(noise)\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "\n", + " gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + " extra_parameters = stack_parameters(ip, np, ns)\n", + "\n", + " mp = mp_model(gp_parameters)\n", + "\n", + " print(f\"\"\"\n", + " GP Parameters Shape: {gp_parameters.shape}\n", + " Extra Parameters Shape: {extra_parameters.shape}\n", + " MP Model Output Shape: {mp.shape}\n", + " \"\"\")\n", + "test_models()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "eEb7J5bOFEEr" + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "Method to take two equally-sized lists and return just the elements which lie \n", + "on the Pareto frontier, sorted into order.\n", + "Default behaviour is to find the maximum for both X and Y, but the option is\n", + "available to specify maxX = False or maxY = False to find the minimum for either\n", + "or both of the parameters.\n", + "\"\"\"\n", + "\n", + "\n", + "def pareto_frontier(x, y, max_x=False):\n", + " if max_x:\n", + " return _pareto_frontier(y, x)\n", + " else:\n", + " return _pareto_frontier(x, y)\n", + "\n", + "\n", + "def _pareto_frontier(x, y):\n", + " # Combine inputs and sort them with smallest X values coming first\n", + " sorted_xy = sorted(zip(x, y))\n", + " p_front_x = torch.zeros(len(x), device=\"cuda\")\n", + " p_front_y = torch.zeros(len(y), device=\"cuda\")\n", + " \n", + " # Loop through the sorted list\n", + " # Look for lower values of Y…\n", + " # and add them to the Pareto frontier\n", + " min_y = sorted_xy[0][1]\n", + " for index in range(len(sorted_xy)):\n", + " x, y = sorted_xy[index]\n", + " if y < min_y:\n", + " min_y = y\n", + " p_front_x[index] = x\n", + " p_front_y[index] = y\n", + " \n", + " p_front_x = p_front_x[p_front_x.nonzero()]\n", + " p_front_y = p_front_y[p_front_y.nonzero()]\n", + "\n", + " return p_front_x, p_front_y" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "#testing for creating total pareto f\n", + "\n", + "def oldgraph_pareto_frontier(Xs, Ys, maxX = False, maxY = False):\n", + "# Sort the list in either ascending or descending order of X\n", + " myList = sorted([[Xs[i], Ys[i]] for i in range(len(Xs))], reverse=maxX)\n", + "# Start the Pareto frontier with the first value in the sorted list\n", + " p_front = [myList[0]] \n", + "# Loop through the sorted list\n", + " for pair in myList[1:]:\n", + " if pair[1] <= p_front[-1][1]: # Look for lower values of Y…\n", + " p_front.append(pair) # … and add them to the Pareto frontier\n", + "# Turn resulting pairs back into a list of Xs and Ys\n", + " p_frontX = [pair[0] for pair in p_front]\n", + " p_frontY = [pair[1] for pair in p_front]\n", + " return p_frontX, p_frontY" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def findSpline(x, y, smoothing_factor = 2):\n", + " spl = UnivariateSpline(x, y)\n", + " spl.set_smoothing_factor(smoothing_factor)\n", + " return spl" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def pair_loss_calc(x, y, x_numpy, y_numpy, pareto_x_numpy, pareto_y_numpy, pareto_x_forx_numpy, pareto_y_forx_numpy):\n", + " if len(pareto_y_numpy) > 3:\n", + " # f = interp1d(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0])\n", + " # f2 = interp1d(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0], kind = 'cubic')\n", + " # plt.plot(pareto_bstray_numpy[:,0], f2(pareto_bstray_numpy[:,0]), '--', pareto_bstray_numpy, pareto_kdiff_numpy, 'o')\n", + "\n", + "\n", + " spl_y = findSpline(pareto_x_numpy[:,0], pareto_y_numpy[:,0])\n", + " y_calc = torch.as_tensor(spl_y(x_numpy)).cuda()\n", + " # plt.plot(pareto_bstray_numpy[:,0], spl_y(pareto_bstray_numpy[:,0]), '--', pareto_bstray_fory_numpy, pareto_kdiff_fory_numpy, 'o')\n", + " # plt.show()\n", + " y_loss = y - y_calc\n", + " y_loss_relu = torch.nn.functional.relu(y_loss)\n", + "\n", + "\n", + " spl_x = findSpline(pareto_y_forx_numpy[:,0], pareto_x_forx_numpy[:,0])\n", + " # plt.plot(spl_x(pareto_kdiff_fory_numpy[:,0]), pareto_kdiff_fory_numpy[:,0], '--', pareto_bstray_fory_numpy, pareto_kdiff_fory_numpy, 'o')\n", + " # plt.show()\n", + "\n", + "\n", + " x_calc = torch.as_tensor(spl_x(y_numpy)).cuda()\n", + " x_loss = x - x_calc\n", + " x_loss_relu = torch.nn.functional.relu(x_loss)\n", + " \n", + "\n", + " if len(pareto_x_numpy) > 3 and len(pareto_y_numpy) > 3:\n", + " combined_loss = torch.sum(y_loss_relu * x_loss_relu)\n", + " else: \n", + " combined_loss = torch.zeros(1, requires_grad=True, device = \"cuda\")\n", + " # print('No new pareto points found, used zero grad')\n", + " \n", + " \n", + " return combined_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_pairloss_and_pareto(x, y):\n", + " \n", + " pareto_x, pareto_y = pareto_frontier(x, y)\n", + " pareto_y_forx, pareto_x_forx = pareto_frontier(y, x)\n", + " \n", + " pareto_x_numpy = pareto_x.cpu().data.numpy()\n", + " pareto_y_numpy = pareto_y.cpu().data.numpy()\n", + " \n", + " pareto_x_forx_numpy = pareto_x_forx.cpu().data.numpy()\n", + " pareto_y_forx_numpy = pareto_y_forx.cpu().data.numpy()\n", + " \n", + " x_numpy = x.cpu().data.numpy()\n", + " y_numpy = y.cpu().data.numpy()\n", + " \n", + " x_y_loss = pair_loss_calc(x, y, x_numpy, y_numpy, pareto_x_numpy, pareto_y_numpy, pareto_x_forx_numpy, pareto_y_forx_numpy)\n", + " \n", + " return x_y_loss, pareto_x, pareto_y, x_numpy, y_numpy, pareto_x_numpy, pareto_y_numpy, pareto_x_forx_numpy, pareto_y_forx_numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def pave_tomax(pavemin, pave_max_value=10000):\n", + " pave_max = torch.sub(pave_max_value, pavemin)\n", + " return pave_max" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load DWPT data" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "jkTrlAJMH5UO" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1010, 12)\n", + "(1010, 42)\n" + ] + } + ], + "source": [ + "def load_dwpt():\n", + " folder_name = \"./dwpt_v5\"\n", + " file_names = [\n", + " \"DWPT_v5_N10\",\n", + " \"DWPT_v5_N100\",\n", + " \"DWPT_v5_N200\",\n", + " \"DWPT_v5_N300\",\n", + " \"DWPT_v5_N400\",\n", + " ]\n", + "\n", + " df = pandas.DataFrame()\n", + "\n", + " for file_name in file_names:\n", + " new_df = pandas.read_csv(f\"{folder_name}/{file_name}_after.csv\", index_col=0)\n", + " df = pandas.concat([df, new_df])\n", + "\n", + " return df\n", + "\n", + "df = load_dwpt()\n", + "df_gp = df.loc[:, :\"ys4[mm]\"]\n", + "df_mp = df.loc[:, \"k0mm_ys0\":]\n", + "\n", + "print(df_gp.shape)\n", + "print(df_mp.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define Loss Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "f = 85*10**3 #[Hz]\n", + "w =2*math.pi*f #[rad/s]\n", + "Pout= 50000 #W\n", + "Vdc = 400 #800 # V\n", + "Vbat = 400 #V\n", + "QCoil = 400\n", + "b = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def kdiff_loss(k_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + "\n", + " kdiff = abs(k_0_0 - k_1_0)\n", + " kdiff = kdiff / torch.max(abs(k_0_0), abs(k_1_0))\n", + "\n", + " return kdiff\n", + "\n", + "def calculate_Is(l_parameters, k_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + "\n", + " a6 = lp_0_0 * np ** 2\n", + " a11 = ls_0_0 * ns ** 2\n", + " # Paper formula\n", + " n1 = (torch.pi * w * a6 * ip) / (2*numpy.sqrt(2) * v_dc)\n", + " t1 = (torch.pi ** 2 * w * p_out * torch.sqrt(a6 * a11))\n", + " t2 = 8 * k_0_0 * n1 * v_dc * v_bat\n", + " n2 = t1 / t2\n", + " Is = (4 * v_bat * n2) / (torch.pi * w * a11)\n", + "\n", + " return Is\n", + " \n", + "def bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters):\n", + " # Unpack parameters\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " Is = calculate_Is(l_parameters,k_parameters)\n", + "\n", + " bx_100 = (\n", + " (bx_p_1_00 * ip * np + bx_s_1_00 * Is * ns) ** 2 +\n", + " (bx_p_1_90 * ip * np + bx_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " by_100 = (\n", + " (by_p_1_00 * ip * np + by_s_1_00 * Is * ns) ** 2 +\n", + " (by_p_1_90 * ip * np + by_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " bz_100 = (\n", + " (bz_p_1_00 * ip * np + bz_s_1_00 * Is * ns) ** 2 +\n", + " (bz_p_1_90 * ip * np + bz_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " b_100 = (bx_100**2 + by_100**2 + bz_100**2) ** 0.5\n", + "\n", + " bstray = b_100\n", + "\n", + " return bstray" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# import seaborn as sns\n", + "# plt.style.use(['science','no-latex'])\n", + "\n", + "# fig_width = 8 #cm # Setting for Conference paper \n", + "# fig_height = 3 #cm\n", + "# font_size = 7 # pt\n", + "# fig_update = True\n", + "# marker_size = 5\n", + "# x_tick_pad = 2\n", + "# y_tick_pad = 2\n", + "# x_label_pad = 0.5\n", + "# y_label_pad = 1\n", + "\n", + "# def plot_(**kwargs):\n", + "# import numpy as np\n", + "# plt.close('all')\n", + "# kdiff = kwargs[\"kdiff\"] * 100\n", + "# pave = kwargs[\"pave\"] \n", + "# coilloss = kwargs[\"coilloss\"]\n", + "# bstray = kwargs[\"bstray\"]\n", + "# vpricore = kwargs[\"vpricore\"]\n", + "# vsecwind = kwargs[\"vsecwind\"]\n", + "# n_inv = kwargs[\"n_inv\"]\n", + "# vseccore = kwargs[\"vseccore\"]\n", + "# epoch = kwargs[\"epoch\"]\n", + "\n", + "# marker_size = 60\n", + "\n", + "\n", + "# font = {'family' : 'normal',\n", + "# 'weight' : 'bold',\n", + "# 'size' : 12}\n", + "\n", + "# plt.rc('font', **font)\n", + "\n", + "# #plt.style.use(['science','no-latex'])\n", + "\n", + "# fig, (ax1, ax2, ax3,ax4) = plt.subplots(1, 4)\n", + "\n", + "# #fig.subplots_adjust(hspace=10)\n", + "\n", + "# fig.set_size_inches(16,4)\n", + "# #ax1.set_aspect('equal')\n", + "# #fig.tight_layout(pad=6)\n", + "# fig.tight_layout(pad = 2)\n", + "\n", + "# xlim = 1 #Number of Inverter [1/m]\n", + "# ylim = 2000 #Coil loss [W] \n", + "# ax1.set_xlabel(r\"$Number\\ of\\ inverter\\ [1/m]$\", labelpad = x_label_pad) # not shown\n", + "# ax1.set_ylabel(r'$Coil loss [W]$', labelpad = y_label_pad) # not shown\n", + "# x1 = np.arange(0, xlim + 0.1,0.1)\n", + "# y1 = 0\n", + "# y2 = ylim\n", + "# plt_ax1 = ax1.scatter(n_inv, coilloss, rasterized=True)\n", + "# #fig.colorbar(mappable = plt_ax1 , ax = ax1, label=r\"$B_{\\rm stray}~[\\rm \\mu T]$\")\n", + "# ax1.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + "# ax1.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + "# ax1.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + "# ax1.axis([0,2, 0,5000])\n", + "# ax1.tick_params(axis='x', pad=15)\n", + "# # ax1.set_aspect('equal')\n", + "\n", + "\n", + "\n", + "# xlim = 40\n", + "# ylim = 15\n", + "# ax2.set_xlabel(r\"$B_{\\rm stray}~[\\rm \\mu T]$\", labelpad = x_label_pad) # not shown\n", + "# ax2.set_ylabel(r'$Coupling [\\%]$', labelpad = y_label_pad) # not shown\n", + "\n", + "# x1 = np.arange(0, xlim + 0.1, 0.1)\n", + "# y1 = 0\n", + "# y2 = ylim\n", + "\n", + "# plt_ax2 = ax2.scatter(bstray, kdiff,rasterized=True)\n", + "# #fig.colorbar(mappable = plt_ax2 , ax = ax2, label=r\"$V_{PriCore}$\")\n", + "# ax2.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + "# ax2.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + "# ax2.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + "# ax2.axis([0, 100, 0, 50])\n", + "# ax2.tick_params(axis='x', pad=15)\n", + "\n", + "# # ax2.set_aspect('equal')\n", + "\n", + "# xlim = 3500\n", + "# ylim = 30\n", + "\n", + "# ax3.set_xlabel(r\"$V_{SecCore}$\", labelpad = x_label_pad) # not shown\n", + "# ax3.set_ylabel(r'$P_{ave} [KW/M]$', labelpad = y_label_pad) # not shown\n", + "\n", + "# x1 = np.arange(0, xlim + 0.1, 0.1)\n", + "# y1 = ylim\n", + "# y2 = ylim*10\n", + "\n", + "# plt_ax3 = ax3.scatter(vseccore, pave, rasterized=True)\n", + "# #fig.colorbar(mappable = plt_ax3 , ax = ax3, label=r\"$V_{SecWind}$\")\n", + "# ax3.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + "# ax3.plot([xlim, xlim], [ylim, ylim*10], 'r--', lw=0.5) \n", + "# ax3.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + "# ax3.axis([0, 5000, 0, 200])\n", + "# ax3.tick_params(axis='x', pad=15)\n", + "# # ax3.set_aspect('equal')\n", + "\n", + "\n", + "# xlim = 3500\n", + "# ylim = 6000\n", + "\n", + "# ax4.set_xlabel(r\"$V_{SecCore}$\", labelpad = x_label_pad) # not shown\n", + "# ax4.set_ylabel(r'$V_{PriCore} $', labelpad = y_label_pad) # not shown\n", + "\n", + "# x1 = np.arange(0, xlim + 0.1, 0.1)\n", + "# y1 = 0\n", + "# y2 = ylim\n", + "\n", + "# ax4.scatter(vseccore, vpricore, s=marker_size, cmap ='viridis',rasterized=True)\n", + "# #fig.colorbar(mappable = plt_ax3 , ax = ax3, label=r\"$V_{SecWind}$\")\n", + "# ax4.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + "# ax4.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + "# ax4.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + "# ax4.axis([0, 5000, 0, 7000])\n", + "# ax4.tick_params(axis='x', pad=15)\n", + "\n", + "# plt.suptitle(\"Epoch \"+str(epoch), y = 1.05, c='r')\n", + "# # plt.savefig(\"images/\"+ str(epoch) +\".png\")\n", + "# plt.show()\n", + "# plt.clf()\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "import seaborn as sns\n", + "\n", + "fig_width = 8 #cm # Setting for Conference paper \n", + "fig_height = 3 #cm\n", + "font_size = 7 # pt\n", + "fig_update = True\n", + "marker_size = 5\n", + "x_tick_pad = 2\n", + "y_tick_pad = 2\n", + "x_label_pad = 0.5\n", + "y_label_pad = 1\n", + "\n", + "plt.style.use(['science','no-latex'])\n", + "def plot_(**kwargs):\n", + " import numpy as np\n", + " plt.close('all')\n", + " kdiff = kwargs[\"kdiff\"] * 100\n", + " pave = kwargs[\"pave\"] \n", + " coilloss = kwargs[\"coilloss\"]\n", + " bstray = kwargs[\"bstray\"]\n", + " vpricore = kwargs[\"vpricore\"]\n", + " vsecwind = kwargs[\"vsecwind\"]\n", + " n_inv = kwargs[\"n_inv\"]\n", + " vseccore = kwargs[\"vseccore\"]\n", + " epoch = kwargs[\"epoch\"]\n", + "\n", + " marker_size = 1\n", + "\n", + "\n", + " font = {'family' : 'normal',\n", + " 'weight' : 'bold',\n", + " 'size' : 12}\n", + "\n", + " matplotlib.rc('font', **font)\n", + "\n", + " #plt.style.use(['science','no-latex'])\n", + "\n", + " fig, (ax1, ax2, ax3,ax4) = plt.subplots(1, 4)\n", + "\n", + " #fig.subplots_adjust(hspace=10)\n", + "\n", + " fig.set_size_inches(16,4)\n", + " #ax1.set_aspect('equal')\n", + " #fig.tight_layout(pad=6)\n", + " fig.tight_layout(pad = 2)\n", + "\n", + " xlim = 1 #Number of Inverter [1/m]\n", + " ylim = 2000 #Coil loss [W] \n", + " ax1.set_xlabel(r\"$Number\\ of\\ inverter\\ [1/m]$\", labelpad = x_label_pad) # not shown\n", + " ax1.set_ylabel(r'$Coil loss [W]$', labelpad = y_label_pad) # not shown\n", + " x1 = np.arange(0, xlim + 0.1,0.1)\n", + " y1 = 0\n", + " y2 = ylim\n", + " plt_ax1 = ax1.scatter(n_inv, coilloss, s=marker_size, rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax1 , ax = ax1, label=r\"$B_{\\rm stray}~[\\rm \\mu T]$\")\n", + " ax1.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax1.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + " ax1.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax1.axis([0,2, 0,5000])\n", + " ax1.tick_params(axis='x', pad=15)\n", + " # ax1.set_aspect('equal')\n", + "\n", + "\n", + "\n", + " xlim = 45\n", + " ylim = 15\n", + " ax2.set_xlabel(r\"$B_{\\rm stray}~[\\rm \\mu T]$\", labelpad = x_label_pad) # not shown\n", + " ax2.set_ylabel(r'$Coupling [\\%]$', labelpad = y_label_pad) # not shown\n", + "\n", + " x1 = np.arange(0, xlim + 0.1, 0.1)\n", + " y1 = 0\n", + " y2 = ylim\n", + "\n", + " plt_ax2 = ax2.scatter(bstray, kdiff,s=marker_size,rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax2 , ax = ax2, label=r\"$V_{PriCore}$\")\n", + " ax2.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax2.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + " ax2.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax2.axis([0, 100, 0, 40])\n", + " ax2.tick_params(axis='x', pad=15)\n", + "\n", + " # ax2.set_aspect('equal')\n", + "\n", + " xlim = 3500\n", + " ylim = 30\n", + "\n", + " ax3.set_xlabel(r\"$V_{SecCore}$\", labelpad = x_label_pad) # not shown\n", + " ax3.set_ylabel(r'$P_{ave} [KW/M]$', labelpad = y_label_pad) # not shown\n", + "\n", + " x1 = np.arange(0, xlim + 0.1, 0.1)\n", + " y1 = ylim\n", + " y2 = ylim*10\n", + "\n", + " ax3.scatter(vseccore, pave,s=marker_size, rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax3 , ax = ax3, label=r\"$V_{SecWind}$\")\n", + " ax3.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax3.plot([xlim, xlim], [ylim, ylim*10], 'r--', lw=0.5) \n", + " ax3.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax3.axis([0, 5000, 0, 80])\n", + " ax3.tick_params(axis='x', pad=15)\n", + " # ax3.set_aspect('equal')\n", + "\n", + "\n", + " xlim = 750\n", + " ylim = 6000\n", + "\n", + " ax4.set_xlabel(r\"$V_{SecWind}$\", labelpad = x_label_pad) # not shown\n", + " ax4.set_ylabel(r'$V_{PriCore} $', labelpad = y_label_pad) # not shown\n", + "\n", + " x1 = np.arange(0, xlim + 0.1, 0.1)\n", + " y1 = 0\n", + " y2 = ylim\n", + "\n", + " ax4.scatter(vsecwind, vpricore, s=marker_size, rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax3 , ax = ax3, label=r\"$V_{SecWind}$\")\n", + " ax4.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax4.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + " ax4.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax4.axis([0, 2000, 0, 8000])\n", + " ax4.tick_params(axis='x', pad=15)\n", + "\n", + " plt.suptitle(\"Epoch \"+str(epoch).zfill(5) + \"\\nFound \"+ str(accepted_solution_so_far)+ \" solutions out of 1000 input \\nSpline Fit Loss with V_PriCore and V_SecWind\", y = 1.05, c='r')\n", + " plt.savefig(\"SplineFitLoss_V_PriCore_V_SecWind/\" + str(epoch).zfill(5)+\".png\")\n", + " plt.show()\n", + " plt.clf()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def accepted_solution(**kwargs):\n", + "\n", + " kdiff = kwargs[\"kdiff\"] * 100\n", + " pave = kwargs[\"pave\"]\n", + " coilloss = kwargs[\"coilloss\"]\n", + " bstray = kwargs[\"bstray\"]\n", + " vpricore = kwargs[\"vpricore\"]\n", + " vsecwind = kwargs[\"vsecwind\"]\n", + " n_inv = kwargs[\"n_inv\"]\n", + " vseccore = kwargs[\"vseccore\"]\n", + "\n", + " count = 0\n", + " for i in range(len(kdiff)):\n", + " if n_inv[i] < 1 and coilloss[i] < 2000 and bstray[i] < 45 and \\\n", + " kdiff[i] < 15 and vseccore[i] < 1500 and pave[i] > 30:\n", + " count += 1\n", + "\n", + " return count" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def number_of_inverters(gp_parameters):\n", + " (\n", + " a, lpx, lpy, ls, p, wp, ws, *ys\n", + " ) = gp_parameters.clone().transpose(0,1)\n", + " \n", + " number_of_inverters = 1/(lpy+2*wp+2*a+p)*10**3\n", + "\n", + " return number_of_inverters" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def core_losses(extra_parameters, gp_parameters):\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " (\n", + " a, lpx, lpy, ls, p, wp, ws, *ys\n", + " ) = gp_parameters.transpose(0,1)\n", + " \n", + " V_PriCore = ((lpy +2*wp+2*a)*(lpx+2*wp+2*a)*5)/(10**3) # cm3\n", + " V_SecCore = (((ls+2*ws+2*b)**2)*5)/(10**3)\n", + " V_PriWind = (2*(lpx+wp)+2*(lpy+wp))*6.6*6.6/(10**3)*np\n", + " V_SecWind = 4*(ls+ws)*6.6*6.6/(10**3)*ns\n", + " \n", + " V_PriCore_ave = V_PriCore/(lpy+2*wp+2*a+p)*10**3\n", + " # V_PriWind_ave = V_PriWind/(lpy+2*wp+2*a+p)*10**3\n", + "\n", + " return V_PriCore, V_SecCore, V_PriWind, V_SecWind, V_PriCore_ave" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_coilloss_pave(k_parameters, l_parameters, b_parameters, extra_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + "\n", + " a6 = lp_0_0 * np**2 # LP0MM_YSO\n", + " a7 = lp_0_1 * np**2 # LP0MM_YS1\n", + " a8 = lp_0_2 * np**2 # LP0MM_YS2\n", + " a9 = lp_0_3 * np**2 # LP0MM_YS3\n", + " a10 = lp_0_4 * np**2 # LP0MM_YS4\n", + "\n", + " a11 = ls_0_0 * ns**2 # LS0MM_YSO\n", + " a12 = ls_0_1 * ns**2 # LS0MM_YS1\n", + " a13 = ls_0_2 * ns**2 # LS0MM_YS2\n", + " a14 = ls_0_3 * ns**2 # LS0MM_YS3\n", + " a15 = ls_0_4 * ns**2 # LS0MM_YS4\n", + "\n", + " Is = calculate_Is(l_parameters,k_parameters)\n", + " \n", + " loss = (w*a6*10**(-9)*ip**2/QCoil + w*a11*10**(-9)*Is**2/QCoil) \n", + "\n", + " p0 = w*k_0_0*(a6 * a11)**0.5*ip*Is\n", + " p1 = w*k_0_1*(a7 * a12)**0.5*ip*Is\n", + " p2 = w*k_0_2*(a8 * a13)**0.5*ip*Is\n", + " p3 = w*k_0_3*(a9 * a14)**0.5*ip*Is\n", + " p4 = 2*w*k_0_4*(a10 * a15)**0.5*ip*Is\n", + " pave = (p0+(2*p1)+(2*p2)+(2*p3)+ p4)/8\n", + " pave_kW = pave / 1000\n", + "\n", + " return loss,pave_kW" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Network Training Loop" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "OXzpQf5O1Nxk", + "outputId": "1bc1a1b1-95ac-43d8-f1d8-5dd49e4bbb03" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accepted Soluntions : 12\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0\n", + " Kdiff : 21.6251%\n", + " Bstray : 50.4493\n", + " Pareto Kdiff : 6.1025%\n", + " Pareto Bstray : 31.0903\n", + " Coilloss : 2413.7827\n", + " num inv : 0.6053\n", + " Pareto Coilloss : 625.1817\n", + " Pareto num inv : 0.4442\n", + " V_SecCore : 1838.4954\n", + " pave : 46.9718\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\scipy\\interpolate\\fitpack2.py:280: UserWarning: \n", + "The maximal number of iterations maxit (set to 20 by the program)\n", + "allowed for finding a smoothing spline with fp=s has been reached: s\n", + "too small.\n", + "There is an approximation returned but the corresponding weighted sum\n", + "of squared residuals does not satisfy the condition abs(fp-s)/s < tol.\n", + " warnings.warn(message)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accepted Soluntions : 1\n", + "Accepted Soluntions : 5\n", + "Accepted Soluntions : 2\n", + "Accepted Soluntions : 3\n", + "Accepted Soluntions : 1\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 50\n", + " Kdiff : 30.4537%\n", + " Bstray : 47.3994\n", + " Pareto Kdiff : 6.1079%\n", + " Pareto Bstray : 34.7388\n", + " Coilloss : 2008.6498\n", + " num inv : 0.6147\n", + " Pareto Coilloss : 946.7493\n", + " Pareto num inv : 0.4035\n", + " V_SecCore : 1792.9204\n", + " pave : 51.3174\n", + "Accepted Soluntions : 1\n", + "Accepted Soluntions : 12\n", + "Accepted Soluntions : 6\n", + "Accepted Soluntions : 8\n", + "Accepted Soluntions : 5\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 100\n", + " Kdiff : 26.6247%\n", + " Bstray : 48.3968\n", + " Pareto Kdiff : 5.8612%\n", + " Pareto Bstray : 35.5512\n", + " Coilloss : 2115.3540\n", + " num inv : 0.6116\n", + " Pareto Coilloss : 1025.4609\n", + " Pareto num inv : 0.4245\n", + " V_SecCore : 1785.5590\n", + " pave : 49.3768\n", + "Accepted Soluntions : 7\n", + "Accepted Soluntions : 1\n", + "Accepted Soluntions : 5\n", + "Accepted Soluntions : 4\n", + "Accepted Soluntions : 6\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 150\n", + " Kdiff : 27.7711%\n", + " Bstray : 48.4618\n", + " Pareto Kdiff : 5.8411%\n", + " Pareto Bstray : 32.9400\n", + " Coilloss : 2136.6038\n", + " num inv : 0.6105\n", + " Pareto Coilloss : 848.7180\n", + " Pareto num inv : 0.4139\n", + " V_SecCore : 1874.3749\n", + " pave : 49.7967\n", + "Accepted Soluntions : 3\n", + "Accepted Soluntions : 2\n", + "Accepted Soluntions : 4\n", + "Accepted Soluntions : 7\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pareto_bstray_points = []\n", + "pareto_kdiff_points = []\n", + "pareto_numinv_points = []\n", + "pareto_coilloss_points = []\n", + "pareto_V_SecCore_points = []\n", + "pareto_pave_post_points = []\n", + "\n", + "accepted_solutions = []\n", + "max_solution = -float('inf')\n", + "\n", + "kdiffs = []\n", + "bstrays = []\n", + "numinvs = []\n", + "coillosses = []\n", + "combined_losses = []\n", + "\n", + "#testing for graphs\n", + "kdiff_designs = []\n", + "bstray_designs = []\n", + "numinv_designs = []\n", + "coilloss_designs = []\n", + "V_SecCore_designs = []\n", + "pave_designs = []\n", + "\n", + "pareto_bstray_points2 = []\n", + "pareto_kdiff_points2 = []\n", + "\n", + "x_calc_numpy =[]\n", + "y_calc_numpy=[]\n", + "\n", + "#training parameters\n", + "n_epochs = 200\n", + "n_noise = 1000\n", + "\n", + "for epoch in range(n_epochs):\n", + " \n", + " # Clear old gradients in the GP model\n", + " for param in gp_model.parameters():\n", + " param.grad = None\n", + "\n", + " # Create GP parameters from noise\n", + " noise = generate_noise(shape=(n_noise, 10))\n", + " gp = gp_model(noise)\n", + "\n", + " # Unpack and filter GP parameters\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + " gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + " extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + " V_PriCore, V_SecCore, V_PriWind,V_SecWind, V_PriCore_ave = core_losses(extra_parameters,gp_parameters)\n", + " numinv = number_of_inverters(gp_parameters)\n", + " \n", + " # Scale GP parameters - why?\n", + " min_x = torch.min(gp_parameters)\n", + " max_x = torch.max(gp_parameters)\n", + " gp_parameters = (gp_parameters - min_x) / (max_x - min_x)\n", + " \n", + " # Push GP parameters through MP model\n", + " mp_parameters = mp_model(gp_parameters)\n", + "\n", + " # Scale MP parameters\n", + " y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + " min_y = torch.min(y, 1).values\n", + " max_y = torch.max(y, 1).values\n", + " mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + " k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + " l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + " b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + " # Calculate losses\n", + " kdiff = kdiff_loss(k_parameters)\n", + " bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " # coilloss,pave = coil_loss_and_power_average(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " # print(pave)\n", + " coilloss,pave = calculate_coilloss_pave(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " \n", + " #pmax - pave_kW, to get tensor to minimize, pavemin is tensor to minimize\n", + " pavemin = torch.sub(10000, pave)\n", + "\n", + " # Call pairwise pareto and loss\n", + " bstray_kdiff_loss, pareto_bstray, pareto_kdiff, bstray_numpy, kdiff_numpy, pareto_bstray_numpy, pareto_kdiff_numpy, pareto_bstray_forx_numpy, pareto_kdiff_forx_numpy = compute_pairloss_and_pareto(bstray, kdiff)\n", + " numinv_coilloss_loss, pareto_numinv, pareto_coilloss, numinv_numpy, coilloss_numpy, pareto_numinv_numpy, pareto_coilloss_numpy, pareto_numinv_forx_numpy, pareto_coilloss_forx_numpy = compute_pairloss_and_pareto(numinv, coilloss)\n", + " V_SecCore_pavemin_loss, pareto_V_SecCore, pareto_pavemin, V_SecCore_numpy, pavemin_numpy, pareto_V_SecCore_numpy, pareto_pavemin_numpy, pareto_V_SecCore_forx_numpy, pareto_pavemin_forx_numpy = compute_pairloss_and_pareto(V_SecCore, pavemin)\n", + " V_PriCore_V_SecWind_loss, pareto_V_PriCore, pareto_V_SecWind, V_PriCore_numpy, V_SecWind_numpy, pareto_V_PriCore_numpy, pareto_V_SecWind_numpy, pareto_V_PriCore_forx_numpy, pareto_V_SecWind_forx_numpy = compute_pairloss_and_pareto(V_PriCore, V_SecWind)\n", + " #get pave points for plotting\n", + " pareto_pave_post = pave_tomax(pareto_pavemin)\n", + " \n", + " if epoch%10 == 0:\n", + " kwargs = {}\n", + "\n", + " kwargs[\"kdiff\"] = kdiff.clone().detach().cpu().numpy()\n", + " kwargs[\"pave\"] = pave.clone().detach().cpu().numpy()\n", + " kwargs[\"coilloss\"] = coilloss.clone().detach().cpu().numpy()\n", + " kwargs[\"bstray\"] = bstray.clone().detach().cpu().numpy()\n", + " kwargs[\"vpricore\"] = V_PriCore_ave.clone().detach().cpu().numpy()\n", + " kwargs[\"vsecwind\"] = V_SecWind.clone().detach().cpu().numpy()\n", + " kwargs[\"n_inv\"] = numinv.clone().detach().cpu().numpy()\n", + " kwargs[\"vseccore\"] = V_SecCore.clone().detach().cpu().numpy()\n", + " kwargs[\"epoch\"] = epoch\n", + "\n", + " accepted_solution_so_far = accepted_solution(**kwargs)\n", + "\n", + " \n", + " accepted_solutions.append(accepted_solution_so_far)\n", + " print(\"Accepted Soluntions : \", accepted_solution_so_far)\n", + " if max_solution < accepted_solution_so_far:\n", + " torch.save(gp_model.state_dict(), f\"./models/gp_model_{epoch:06}.pt\")\n", + " max_solution = accepted_solution_so_far\n", + " if epoch%50 == 0:\n", + " plot_(**kwargs)\n", + " \n", + " #combine pairwise losses\n", + " # combined_loss = (bstray_kdiff_loss + numinv_coilloss_loss + V_SecCore_pavemin_loss)\n", + " combined_loss = (bstray_kdiff_loss + numinv_coilloss_loss + V_SecCore_pavemin_loss + V_PriCore_V_SecWind_loss)\n", + " # combined_loss = (torch.mean(pareto_bstray) + torch.mean(pareto_kdiff) + torch.mean(pareto_numinv) + torch.mean(pareto_coilloss) + torch.mean(pareto_V_SecCore) + torch.mean(pareto_pavemin))\n", + " # combined_loss = (torch.median(pareto_bstray) + torch.median(pareto_kdiff) + torch.median(pareto_numinv) + torch.median(pareto_coilloss) + torch.median(pareto_V_SecCore) + torch.median(pareto_pavemin))\n", + " # combined_loss = (torch.mean(bstray) * torch.mean(kdiff) * torch.mean(numinv) * torch.mean(coilloss) * torch.mean(V_SecCore) * torch.mean(pavemin))\n", + "\n", + " # Push loss through the optimizer and update the GP model\n", + " try:\n", + " combined_loss.backward(retain_graph=True)\n", + " optimizer.step()\n", + " except Exception as e:\n", + " print(f\"Error on epoch {epoch} regarding\", e)\n", + " break\n", + "\n", + " \n", + " if epoch % 50 == 0:\n", + " \n", + "# # plt.plot(pareto_bstray_numpy[:,0], spl_y(pareto_bstray_numpy[:,0]), '--', bstray_numpy, kdiff_numpy, 'o')\n", + "# plt.plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# plt.plot(bstray_numpy, kdiff_numpy, 'o')\n", + "# # plt.plot(x_calc.cpu().data.numpy(), kdiff_numpy, 'o')\n", + "# plt.xlim([0,100])\n", + "# plt.ylim([0,0.5])\n", + "# plt.show()\n", + " \n", + "# plt.plot(pareto_numinv_numpy, pareto_coilloss_numpy)\n", + "# plt.plot(numinv_numpy, coilloss_numpy, 'o')\n", + "# plt.show()\n", + " \n", + "# plt.plot(pareto_V_SecCore_numpy, pareto_pavemin_numpy)\n", + "# plt.plot(V_SecCore_numpy, pavemin_numpy, 'o')\n", + "# plt.show()\n", + " \n", + "# plt.plot(pareto_V_PriCore_numpy, pareto_V_SecWind_numpy)\n", + "# plt.plot(V_PriCore_numpy, V_SecWind_numpy, 'o')\n", + "# plt.show()\n", + "\n", + " #paretopoints graphing test\n", + " pareto_bstray_test, pareto_kdiff_test = oldgraph_pareto_frontier(bstray.cpu().detach().numpy(), kdiff.cpu().detach().numpy())\n", + " pareto_bstray_points2.append(pareto_bstray_test)\n", + " pareto_kdiff_points2.append(pareto_kdiff_test)\n", + "\n", + " # Store metrics for plotting or further logging\n", + " combined_losses.append(combined_loss.cpu().data.numpy())\n", + " kdiffs.append(torch.mean(kdiff).cpu().data.numpy())\n", + " bstrays.append(torch.mean(bstray).cpu().data.numpy())\n", + " numinvs.append(torch.mean(numinv).cpu().data.numpy())\n", + " coillosses.append(torch.mean(coilloss).cpu().data.numpy())\n", + " \n", + " kdiff_designs.append(kdiff.cpu().data.numpy())\n", + " bstray_designs.append(bstray.cpu().data.numpy())\n", + " numinv_designs.append(numinv.cpu().data.numpy())\n", + " coilloss_designs.append(coilloss.cpu().data.numpy())\n", + " V_SecCore_designs.append(V_SecCore_numpy)\n", + " pave_designs.append(pave.cpu().data.numpy())\n", + " \n", + " \n", + " pareto_bstray_points.append(pareto_bstray[:].cpu().detach().numpy())\n", + " pareto_kdiff_points.append(pareto_kdiff[:].cpu().detach().numpy())\n", + " pareto_numinv_points.append(pareto_numinv[:].cpu().detach().numpy())\n", + " pareto_coilloss_points.append(pareto_coilloss[:].cpu().detach().numpy())\n", + " pareto_V_SecCore_points.append(pareto_V_SecCore[:].cpu().detach().numpy())\n", + " pareto_pave_post_points.append(pareto_pave_post[:].cpu().detach().numpy())\n", + "\n", + "\n", + " print(f\"Epoch: {epoch:4d}\")\n", + " # print(f\" Combined Loss: {combined_losses[-1]:.10f}\")\n", + " print(f\" Kdiff : {100 * torch.mean(kdiff):.4f}%\")\n", + " print(f\" Bstray : {torch.mean(bstray):.4f}\")\n", + " print(f\" Pareto Kdiff : {100 * torch.min(pareto_kdiff):.4f}%\")\n", + " print(f\" Pareto Bstray : {torch.min(pareto_bstray):.4f}\")\n", + " print(f\" Coilloss : {torch.mean(coilloss):.4f}\")\n", + " print(f\" num inv : {torch.mean(numinv):.4f}\")\n", + " print(f\" Pareto Coilloss : {torch.min(pareto_coilloss):.4f}\")\n", + " print(f\" Pareto num inv : {torch.min(pareto_numinv):.4f}\") \n", + " print(f\" V_SecCore : {torch.mean(V_SecCore).cpu().data.numpy():.4f}\")\n", + " print(f\" pave : {torch.mean(pave).cpu().data.numpy():.4f}\")\n", + " \n", + "\n", + " # Save model for further training or testing\n", + " # torch.save(gp_model, f\"./models/gp_model_{epoch:06}.pt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "#use inside every so many epoch printing \n", + "\n", + "\n", + "# figure, axis = plt.subplots(2, 1, figsize=(10,10))\n", + "# plt.figure(figsize=(10, 5))\n", + " \n", + "# if normalize == True:\n", + "# axis[0].plot(normalized_pareto_bstray_numpy, normalized_pareto_kdiff_numpy)\n", + "# axis[0].scatter(x_calc.cpu().data.numpy(), normalized_kdiff_numpy)\n", + "# axis[0].scatter(normalized_bstray_numpy, normalized_kdiff_numpy)\n", + "# axis[0].set_xlim([0,1])\n", + "# axis[0].set_ylim([0,1])\n", + "# axis[0].set_xlabel(\"Bstray\")\n", + "# axis[0].set_ylabel(\"Kdiff %\")\n", + "# axis[0].set_title(f\"x_calc vs. designs, epoch: {epoch}\")\n", + " \n", + "# axis[1].plot(normalized_pareto_bstray_numpy, normalized_pareto_kdiff_numpy)\n", + "# axis[1].scatter(normalized_bstray_numpy, y_calc.cpu().data.numpy())\n", + "# axis[1].scatter(normalized_bstray_numpy, normalized_kdiff_numpy)\n", + "# axis[1].set_xlim([0,1])\n", + "# axis[1].set_ylim([0,1])\n", + "# axis[1].set_xlabel(\"Bstray\")\n", + "# axis[1].set_ylabel(\"Kdiff %\")\n", + "# axis[1].set_title(f\"y_calc vs. designs, epoch: {epoch}\")\n", + "# else:\n", + "# axis[0].plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# axis[0].scatter(x_calc.cpu().data.numpy(), kdiff_numpy)\n", + "# axis[0].scatter(bstray_numpy, kdiff_numpy)\n", + "# axis[0].set_xlim([0,100])\n", + "# axis[0].set_ylim([0,1])\n", + "# axis[0].set_xlabel(\"Bstray\")\n", + "# axis[0].set_ylabel(\"Kdiff %\")\n", + "# axis[0].set_title(f\"x_calc vs. designs, epoch: {epoch}\")\n", + " \n", + "# axis[1].plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# axis[1].scatter(bstray_numpy, y_calc.cpu().data.numpy())\n", + "# axis[1].scatter(bstray_numpy, kdiff_numpy)\n", + "# axis[1].set_xlim([0,100])\n", + "# axis[1].set_ylim([0,1])\n", + "# axis[1].set_xlabel(\"Bstray\")\n", + "# axis[1].set_ylabel(\"Kdiff %\")\n", + "# axis[1].set_title(f\"y_calc vs. designs, epoch: {epoch}\")\n", + " \n", + " \n", + " # plt.xlim([0,100])\n", + " # plt.ylim([0,0.5])\n", + " # plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "4\n", + "7 7\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#scatter plot of final pareto points\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff Combined Final Frontier\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.5])\n", + "\n", + "print(len(pareto_bstray_points2[:]))\n", + "print(len(pareto_kdiff_points2[:]))\n", + "# print(pareto_bstray_points2[:])\n", + "final_x = []\n", + "for list in pareto_bstray_points2:\n", + " for item in list:\n", + " final_x.append(item)\n", + "\n", + "\n", + "final_y = []\n", + "for list in pareto_kdiff_points2:\n", + " for item in list:\n", + " final_y.append(item)\n", + "\n", + "final_x, final_y = oldgraph_pareto_frontier(final_x, final_y)\n", + "print(len(final_x),len(final_y))\n", + "\n", + "plt.scatter(final_x, final_y)\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n", + "4\n", + "7 7\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#line plot of final pareto points\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff Combined Final Frontier\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points2[:]))\n", + "print(len(pareto_kdiff_points2[:]))\n", + "# print(pareto_bstray_points2[:])\n", + "final_x = []\n", + "for list in pareto_bstray_points2:\n", + " for item in list:\n", + " final_x.append(item)\n", + "\n", + "\n", + "final_y = []\n", + "for list in pareto_kdiff_points2:\n", + " for item in list:\n", + " final_y.append(item)\n", + "\n", + "final_x, final_y = oldgraph_pareto_frontier(final_x, final_y)\n", + "print(len(final_x),len(final_y))\n", + "\n", + "plt.plot(final_x, final_y)\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points), len(pareto_kdiff_points))\n", + "\n", + "while i < len(pareto_bstray_points):\n", + " plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Number of Inverters vs Coilloss During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Num Inverters\")\n", + "plt.ylabel(\"Coil loss\")\n", + "plt.xlim([0,2])\n", + "plt.ylim([0, 5000])\n", + "\n", + "print(len(pareto_numinv_points), len(pareto_coilloss_points))\n", + "\n", + "while i < len(pareto_numinv_points):\n", + " plt.plot(pareto_numinv_points[i], pareto_coilloss_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "# plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto V_SecCore vs Power Avg During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"V_SecCore\")\n", + "plt.ylabel(\"Pave\")\n", + "plt.xlim([0,3000])\n", + "plt.ylim([0, 200])\n", + "\n", + "while i < len(pareto_V_SecCore_points):\n", + " plt.plot(pareto_V_SecCore_points[i], pareto_pave_post_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "# plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4 4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points), len(pareto_kdiff_points))\n", + "\n", + "while i < len(pareto_bstray_points):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "print(len(bstray_designs))\n", + "#colors = numpy.arange(len(bstray_designs))\n", + "colors = numpy.arange(1000)\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Bstray vs Kdiff During Training, Generated Designs\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "while i < len(bstray_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " fig = plt.scatter(bstray_designs[i], kdiff_designs[i], label = f\"epoch: {i*100}\")\n", + " i+=1\n", + " \n", + "# plt.colorbar()\n", + "plt.legend()\n", + "# from matplotlib.patches import Rectangle\n", + "# someX, someY = 45, 0.5\n", + "# fig,ax = plt.subplots()\n", + "# currentAxis = plt.gca()\n", + "# currentAxis.add_patch(Rectangle((someX -0.1, someY-0.1), 0.2, 0.2, alpha=0.5, facecolor='red'))\n", + "#plt.savefig('Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(bstray_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " ax.scatter(bstray_designs[i], kdiff_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,200)\n", + "plt.ylim(0, 0.2)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"Bstray\", fontsize = 18)\n", + "plt.ylabel('Coupling %', fontsize = 18)\n", + "plt.suptitle(f\"Bstray vs Kdiff During Training, \\n Generated Designs\" , fontsize = 20, y=1.01)\n", + "# plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle(( 0,0 ), 45, 0.15, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(numinv_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " ax.scatter(numinv_designs[i], coilloss_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,2)\n", + "plt.ylim(0, 10000)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"Num Inverters\", fontsize = 18)\n", + "plt.ylabel('Coil Loss', fontsize = 18)\n", + "plt.suptitle(f\"Number of Inverters vs Coil Loss During Training, \\n Generated Designs\" , fontsize = 20, y=1.01)\n", + "\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle(( 0,2 ), 1, 2000, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(V_SecCore_designs):\n", + "\n", + " ax.scatter(V_SecCore_designs[i], pave_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,5000)\n", + "plt.ylim(0, 200)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"V_SecCore\", fontsize = 18)\n", + "plt.ylabel('Pave', fontsize = 18)\n", + "plt.suptitle(f\"V_SecCore vs Power Average During Training \\n Generated Designs\" , fontsize = 20, y=1.01)\n", + "\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle((0,30), 1500, 200, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "noise = generate_noise(shape=(10_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "pareto_bstray, pareto_kdiff = pareto_frontier(bstray, kdiff)\n", + "pareto_kdiff = pareto_kdiff * 100\n", + "\n", + "plt.plot(pareto_bstray.detach().cpu(), pareto_kdiff.detach().cpu())" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "noise = generate_noise(shape=(25_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "plt.scatter(bstray.detach().cpu(), kdiff.detach().cpu())\n", + "plt.title(\"Randomly generated points after GP reshaping\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Kdiff\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Noise 0\n", + "[-0.25599623 0.23926842 -0.22959292 -0.19015706 0.43794477 -0.03576255\n", + " -0.9996141 0.33944595 -0.38004458 -0.8081919 ]\n", + "\n", + "Generated Parameters 0\n", + "[0.65768933 0.6605672 0.70209956 0.41472813 0.66993153 0.6989279\n", + " 0.6150084 0.6554726 0.2424616 0.4109589 ]\n", + "\n", + "Noise 1\n", + "[-0.8974911 -0.78110754 0.64632857 0.7011901 0.3838761 0.16286695\n", + " 0.91939116 -0.04209542 0.84262836 -0.6387352 ]\n", + "\n", + "Generated Parameters 1\n", + "[0.5736714 0.18341808 0.38992837 0.8087469 0.3973963 0.42718866\n", + " 0.18307063 0.45503306 0.4986779 0.5950704 ]\n", + "\n", + "Noise 2\n", + "[-0.05708325 0.9941913 -0.10390449 0.2827102 0.8356006 0.33764756\n", + " 0.93553007 -0.53755236 -0.72090733 0.9775162 ]\n", + "\n", + "Generated Parameters 2\n", + "[0.26180783 0.64100635 0.3723605 0.21850154 0.47172338 0.3371846\n", + " 0.724087 0.36743498 0.72835726 0.48854136]\n", + "\n" + ] + } + ], + "source": [ + "noise = generate_noise(shape=(3, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "noise = noise.cpu().numpy()\n", + "gp = gp.cpu().detach().numpy()\n", + "\n", + "for i in range(3):\n", + " print(f\"Noise {i}\")\n", + " print(noise[i])\n", + " print()\n", + " print(f\"Generated Parameters {i}\")\n", + " print(gp[i])\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"KDiff Error During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Coupling %\")\n", + "\n", + "plt.plot(numpy.arange(len(kdiffs)), kdiffs)\n", + "plt.show()\n", + "\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"BStray Error During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"BStray\")\n", + "\n", + "plt.plot(numpy.arange(len(bstrays)), bstrays)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "accepted_solutions[0] = 0\n", + "max_value = max(accepted_solutions)\n", + "max_index = accepted_solutions.index(max_value)\n", + "\n", + "plt.plot([ i*10 for i in range(len(accepted_solutions))], accepted_solutions)\n", + "# plt.plot(epoch, accepted_solutions)\n", + "\n", + "# # plt.scatter(mark, marked_points, marker='o',color='y')\n", + "# plt.scatter([max_index], [max_value], marker = 'o', color = 'r')\n", + "\n", + "# plt.annotate('Max number of solutions ('+ str(max_value)+') at epoch '+str(max_index), xy=(max_index,max_value), xycoords='data',\n", + "# xytext=(-90,60), textcoords='offset points',\n", + "# arrowprops=dict(arrowstyle='fancy',fc='0.2',\n", + "# connectionstyle=\"angle3,angleA=45,angleB=90\"))\n", + "plt.xlabel(\"Number of epochs\")\n", + "plt.ylabel(\"Accepted solutions out of 1000\")\n", + "plt.title(\"Spline Fit Loss with V_PriCore and V_SecWind\")\n", + "plt.savefig(\"SplineFitLoss_V_PriCore_V_SecWind/\" + \"soultions_per_epoch.png\")\n", + "plt.show()\n", + "plt.clf()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "optimization_date4_22_22.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From dfd6c0814539c54ae8a8266d39e452d0feedec6f Mon Sep 17 00:00:00 2001 From: AndrewCurtis27 <89710614+AndrewCurtis27@users.noreply.github.com> Date: Thu, 27 Oct 2022 08:50:34 -0600 Subject: [PATCH 7/8] Add files via upload --- optimization_10_26_22.ipynb | 2404 +++++++++++++++++++++++++++++++++++ 1 file changed, 2404 insertions(+) create mode 100644 optimization_10_26_22.ipynb diff --git a/optimization_10_26_22.ipynb b/optimization_10_26_22.ipynb new file mode 100644 index 0000000..fcf3f39 --- /dev/null +++ b/optimization_10_26_22.ipynb @@ -0,0 +1,2404 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "0m5JnUV30Vjt" + }, + "source": [ + "# Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "iZN2u5WKRFqR" + }, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "import os\n", + "import random\n", + "import pandas\n", + "import numpy\n", + "import math\n", + "from scipy.optimize import curve_fit\n", + "from scipy.interpolate import interp1d\n", + "from scipy.interpolate import UnivariateSpline\n", + "import imageio\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "from matplotlib import style\n", + "import matplotlib.font_manager\n", + "\n", + "# seed = 7777\n", + "# random.seed(seed) \n", + "# torch.manual_seed(seed);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Kc0NV7P40Vjy" + }, + "outputs": [], + "source": [ + "# pair_no = 1\n", + "# BASE_PATH = \"drive/MyDrive/\"\n", + "# RESULT_PATH = \"optimization_result/\"+str(pair_no)+\"_SplineFit/\"\n", + "# FULL_PATH = BASE_PATH + RESULT_PATH" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oS2YO-1H0Vj0" + }, + "outputs": [], + "source": [ + "# pairs = []\n", + "# file1 = open(BASE_PATH + 'dwpt_v5/pairs.txt', 'r')\n", + "# lines = file1.readlines()\n", + "# for line in lines:\n", + "# pairs.append([int(i) for i in line.strip().split(',')])\n", + "# pair = pairs[pair_no]\n", + "# print(pair)" + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install SciencePlots" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "SAiQqoxi1kAo", + "outputId": "c648d6e0-56f8-4fc8-e1e6-f72ffe4605f3" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", + "Requirement already satisfied: SciencePlots in /usr/local/lib/python3.7/dist-packages (1.0.9)\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from SciencePlots) (3.2.2)\n", + "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->SciencePlots) (0.11.0)\n", + "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->SciencePlots) (3.0.9)\n", + "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from matplotlib->SciencePlots) (1.21.6)\n", + "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->SciencePlots) (2.8.2)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->SciencePlots) (1.4.4)\n", + "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->SciencePlots) (4.1.1)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->SciencePlots) (1.15.0)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B-Kd3dIE0Vj1" + }, + "source": [ + "# Load Saved MP (Magnetic Parameter) Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "B-TMPJtCKIM6" + }, + "outputs": [], + "source": [ + "# 12 input -> 42 output\n", + "class MpModel(torch.nn.Module):\n", + " def __init__(self):\n", + " super(MpModel, self).__init__()\n", + "\n", + " self.linear1 = torch.nn.Linear(12, 100, bias=True)\n", + " self.linear2 = torch.nn.Linear(100, 100, bias=True)\n", + " self.linear3 = torch.nn.Linear(100, 42, bias=True)\n", + "\n", + " def forward(self, x):\n", + " x = torch.atan(self.linear1(x))\n", + " x = torch.atan(self.linear2(x))\n", + " x = torch.atan(self.linear2(x))\n", + " x = self.linear3(x)\n", + " return x\n", + "\n", + "\n", + "mp_model = MpModel().to(\"cuda\")\n", + "mp_model.load_state_dict(torch.load(\"saved_model_state.pt\"))\n", + "\n", + "for param in mp_model.parameters():\n", + " param.requires_grad = False" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7q4wHhFd0Vj3" + }, + "source": [ + "# Create GP (Geometry Paramter) Generator" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "z9cggAl1BGHo", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8b9bbb55-3b81-4b15-cabc-0f70a6d58fec" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/torch/nn/modules/lazy.py:178: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.\n", + " warnings.warn('Lazy modules are a new feature under heavy development '\n" + ] + } + ], + "source": [ + "class GpModel(torch.nn.Module):\n", + " def __init__(self, input_length: int):\n", + " super(GpModel, self).__init__()\n", + "\n", + " self.dense_layer1 = torch.nn.Linear(int(input_length), 128)\n", + " self.dense_layer2 = torch.nn.Linear(128, 256)\n", + " # self.dense_layer3 = torch.nn.Linear(256, 512)\n", + " # self.dense_layer4 = torch.nn.Linear(512, 256)\n", + " self.dense_layer5 = torch.nn.Linear(256, 128)\n", + " self.dense_layer6 = torch.nn.Linear(128, int(input_length))\n", + " \n", + " self.batch_norm1 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm2 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm3 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm4 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm5 = torch.nn.LazyBatchNorm1d()\n", + "\n", + " def forward(self, x):\n", + " x = self.batch_norm1(torch.atan(self.dense_layer1(x)))\n", + " x = self.batch_norm2(torch.atan(self.dense_layer2(x)))\n", + " # x = self.batch_norm3(torch.atan(self.dense_layer3(x)))\n", + " # x = self.batch_norm4(torch.atan(self.dense_layer4(x)))\n", + " x = self.batch_norm5(torch.atan(self.dense_layer5(x)))\n", + " x = torch.sigmoid(self.dense_layer6(x))\n", + " return x\n", + "\n", + "\n", + "gp_model = GpModel(10).to(\"cuda\")\n", + "optimizer = torch.optim.Adam(gp_model.parameters(), lr=3e-4)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hyDsTMP10Vj6" + }, + "source": [ + "# System Constraints" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Bse1wsKN1NxY" + }, + "outputs": [], + "source": [ + "# x-direction of the primary winding (mm)\n", + "lpx_min, lpx_max = (50.0, 650.0)\n", + "\n", + "# y-direction of the primary winding (mm)\n", + "lpy_min, lpy_max = (50.0, 2050.0)\n", + "\n", + "# width of the primary winding (mm)\n", + "wp_min, wp_max = (25.0, 325.0)\n", + "\n", + "# length of the edge of the primary core (mm)\n", + "a_min, a_max = (0.0, 200.0)\n", + "\n", + "# pitch of the adjacent cores (mm)\n", + "p_min, p_max = (0.0, 200.0)\n", + "\n", + "# length of the secondary winding (mm)\n", + "ls_min, ls_max = (50.0, 450.0)\n", + "\n", + "# width of the secondary winding (mm)\n", + "ws_min, ws_max = (25.0, 225.0)\n", + "\n", + "# input RMS current (A)\n", + "ip_min, ip_max = (50.0, 200.0)\n", + "\n", + "# turn number of the primary side\n", + "np_min, np_max = (4.0, 10.0)\n", + "\n", + "# turn number of the secondary side\n", + "ns_min, ns_max = (4.0, 10.0)\n", + "\n", + "# switching frequency (kHz)\n", + "f = 85 * (10 ** 3)\n", + "\n", + "# output power at the center (kW)\n", + "p_out = 50 * (10 ** 3)\n", + "\n", + "# input DC voltage (V)\n", + "v_dc = 400\n", + "\n", + "# output DC voltage (V)\n", + "v_bat = 400\n", + "\n", + "# length of the edge of the secondary core\n", + "b = 50\n", + "\n", + "# ???\n", + "w = 2 * numpy.pi * f # [rad/s]\n", + "\n", + "# ???\n", + "q_coil = 400" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Mhkb7n2t0Vj9" + }, + "source": [ + "# Helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "brlwMfzj1Nxh" + }, + "outputs": [], + "source": [ + "# Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive.\n", + "# Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch.\n", + "\n", + "@torch.jit.script\n", + "def linspace(start: torch.Tensor, stop: torch.Tensor, num: int):\n", + " # create a tensor of 'num' steps from 0 to 1\n", + " steps = torch.arange(num, dtype=torch.float32, device=start.device) / (num - 1)\n", + "\n", + " # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings\n", + " # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript\n", + " # \"cannot statically infer the expected size of a list in this contex\", hence the code below\n", + " for i in range(start.ndim):\n", + " steps = steps.unsqueeze(-1)\n", + "\n", + " # the output starts at 'start' and increments until 'stop' in each dimension\n", + " out = start[None] + steps * (stop - start)[None]\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dMoXnTFV8U1P" + }, + "outputs": [], + "source": [ + "# Generate a pytorch tensor full of random floats in the range [-1, +1] with the given shape\n", + "\n", + "def generate_noise(shape):\n", + " return torch.distributions.uniform.Uniform(-1, +1).sample(shape).cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xloGE25R8Jtm" + }, + "outputs": [], + "source": [ + "def stack_parameters(*params):\n", + " return torch.stack(params).transpose(0, 1)\n", + "\n", + "\n", + "# Scale an array to be between our specified [min, max] contraints\n", + "def scale(arr):\n", + " scaler_min = torch.tensor(\n", + " [a_min, lpx_min, lpy_min, ls_min, p_min, wp_min, ws_min, ip_min, np_min, ns_min],\n", + " device=\"cuda\",\n", + " )\n", + " scaler_max = torch.tensor(\n", + " [a_max, lpx_max, lpy_max, ls_max, p_max, wp_max, ws_max, ip_max, np_max, ns_max],\n", + " device=\"cuda\",\n", + " )\n", + "\n", + " return arr * (scaler_max - scaler_min) + scaler_min\n", + "\n", + "\n", + "def extract(arr):\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns = [\n", + " numpy.squeeze(x) for x in numpy.hsplit(arr, 10)\n", + " ]\n", + "\n", + " ys_min = (\n", + " 5 * wp +\n", + " 4 * a +\n", + " 3 * lpy +\n", + " 2 * p\n", + " )\n", + "\n", + " ys_max = (lpy + wp)\n", + " ys_max = ys_min + (ys_min - ys_max) / 2\n", + "\n", + " ys_min /= 2\n", + " ys_max /= 2\n", + "\n", + " ys = linspace(ys_min, ys_max, num=5)\n", + "\n", + " np = torch.round(np)\n", + " ns = torch.round(ns)\n", + "\n", + " # take all 15 tensors of shape (X) and convert to (15, X)\n", + " return a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys\n", + "\n", + "\n", + "def scale_and_extract(arr):\n", + " scaled_array = scale(arr)\n", + " return extract(scaled_array)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "44TlTSnclCKs", + "outputId": "a553023a-072d-45da-f38c-0f11d1a04cea" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + " GP Parameters Shape: torch.Size([256, 12])\n", + " Extra Parameters Shape: torch.Size([256, 3])\n", + " MP Model Output Shape: torch.Size([256, 42])\n", + " \n" + ] + } + ], + "source": [ + "# Enclose in a function so we don't leak variables\n", + "def test_models():\n", + " noise = generate_noise(shape=(256, 10))\n", + " gp = gp_model(noise)\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "\n", + " gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + " extra_parameters = stack_parameters(ip, np, ns)\n", + "\n", + " mp = mp_model(gp_parameters)\n", + "\n", + " print(f\"\"\"\n", + " GP Parameters Shape: {gp_parameters.shape}\n", + " Extra Parameters Shape: {extra_parameters.shape}\n", + " MP Model Output Shape: {mp.shape}\n", + " \"\"\")\n", + "test_models()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eEb7J5bOFEEr" + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "Method to take two equally-sized lists and return just the elements which lie \n", + "on the Pareto frontier, sorted into order.\n", + "Default behaviour is to find the maximum for both X and Y, but the option is\n", + "available to specify maxX = False or maxY = False to find the minimum for either\n", + "or both of the parameters.\n", + "\"\"\"\n", + "\n", + "\n", + "def pareto_frontier(x, y, max_x=False):\n", + " if max_x:\n", + " return _pareto_frontier(y, x)\n", + " else:\n", + " return _pareto_frontier(x, y)\n", + "\n", + "\n", + "def _pareto_frontier(x, y):\n", + " # Combine inputs and sort them with smallest X values coming first\n", + " sorted_xy = sorted(zip(x, y))\n", + " p_front_x = torch.zeros(len(x), device=\"cuda\")\n", + " p_front_y = torch.zeros(len(y), device=\"cuda\")\n", + " \n", + " # Loop through the sorted list\n", + " # Look for lower values of Y…\n", + " # and add them to the Pareto frontier\n", + " min_y = sorted_xy[0][1]\n", + " for index in range(len(sorted_xy)):\n", + " x, y = sorted_xy[index]\n", + " if y < min_y:\n", + " min_y = y\n", + " p_front_x[index] = x\n", + " p_front_y[index] = y\n", + " \n", + " p_front_x = p_front_x[p_front_x.nonzero()]\n", + " p_front_y = p_front_y[p_front_y.nonzero()]\n", + "\n", + " return p_front_x, p_front_y" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TUybqb910VkC" + }, + "outputs": [], + "source": [ + "#testing for creating total pareto f\n", + "\n", + "def oldgraph_pareto_frontier(Xs, Ys, maxX = False, maxY = False):\n", + "# Sort the list in either ascending or descending order of X\n", + " myList = sorted([[Xs[i], Ys[i]] for i in range(len(Xs))], reverse=maxX)\n", + "# Start the Pareto frontier with the first value in the sorted list\n", + " p_front = [myList[0]] \n", + "# Loop through the sorted list\n", + " for pair in myList[1:]:\n", + " if pair[1] <= p_front[-1][1]: # Look for lower values of Y…\n", + " p_front.append(pair) # … and add them to the Pareto frontier\n", + "# Turn resulting pairs back into a list of Xs and Ys\n", + " p_frontX = [pair[0] for pair in p_front]\n", + " p_frontY = [pair[1] for pair in p_front]\n", + " return p_frontX, p_frontY" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "aNSYKyZn0VkD" + }, + "outputs": [], + "source": [ + "def findSpline(x, y, smoothing_factor = 2):\n", + " spl = UnivariateSpline(x, y)\n", + " spl.set_smoothing_factor(smoothing_factor)\n", + " return spl" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7kYGHbkc0VkD" + }, + "outputs": [], + "source": [ + "def pair_loss_calc(x, y, x_numpy, y_numpy, pareto_x_numpy, pareto_y_numpy, pareto_x_forx_numpy, pareto_y_forx_numpy):\n", + " if len(pareto_y_numpy) > 3:\n", + " # f = interp1d(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0])\n", + " # f2 = interp1d(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0], kind = 'cubic')\n", + " # plt.plot(pareto_bstray_numpy[:,0], f2(pareto_bstray_numpy[:,0]), '--', pareto_bstray_numpy, pareto_kdiff_numpy, 'o')\n", + "\n", + "\n", + " spl_y = findSpline(pareto_x_numpy[:,0], pareto_y_numpy[:,0])\n", + " y_calc = torch.as_tensor(spl_y(x_numpy)).cuda()\n", + " # plt.plot(pareto_bstray_numpy[:,0], spl_y(pareto_bstray_numpy[:,0]), '--', pareto_bstray_fory_numpy, pareto_kdiff_fory_numpy, 'o')\n", + " # plt.show()\n", + " y_loss = y - y_calc\n", + " y_loss_relu = torch.nn.functional.relu(y_loss)\n", + "\n", + "\n", + " spl_x = findSpline(pareto_y_forx_numpy[:,0], pareto_x_forx_numpy[:,0])\n", + " # plt.plot(spl_x(pareto_kdiff_fory_numpy[:,0]), pareto_kdiff_fory_numpy[:,0], '--', pareto_bstray_fory_numpy, pareto_kdiff_fory_numpy, 'o')\n", + " # plt.show()\n", + "\n", + "\n", + " x_calc = torch.as_tensor(spl_x(y_numpy)).cuda()\n", + " x_loss = x - x_calc\n", + " x_loss_relu = torch.nn.functional.relu(x_loss)\n", + " \n", + "\n", + " if len(pareto_x_numpy) > 3 and len(pareto_y_numpy) > 3:\n", + " combined_loss = torch.sum(y_loss_relu * x_loss_relu)\n", + " else: \n", + " combined_loss = torch.zeros(1, requires_grad=True, device = \"cuda\")\n", + " # print('No new pareto points found, used zero grad')\n", + " \n", + " \n", + " return combined_loss" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "aRaOChQS0VkE" + }, + "outputs": [], + "source": [ + "def compute_pairloss_and_pareto(x, y):\n", + " \n", + " pareto_x, pareto_y = pareto_frontier(x, y)\n", + " pareto_y_forx, pareto_x_forx = pareto_frontier(y, x)\n", + " \n", + " pareto_x_numpy = pareto_x.cpu().data.numpy()\n", + " pareto_y_numpy = pareto_y.cpu().data.numpy()\n", + " \n", + " pareto_x_forx_numpy = pareto_x_forx.cpu().data.numpy()\n", + " pareto_y_forx_numpy = pareto_y_forx.cpu().data.numpy()\n", + " \n", + " x_numpy = x.cpu().data.numpy()\n", + " y_numpy = y.cpu().data.numpy()\n", + " \n", + " x_y_loss = pair_loss_calc(x, y, x_numpy, y_numpy, pareto_x_numpy, pareto_y_numpy, pareto_x_forx_numpy, pareto_y_forx_numpy)\n", + " \n", + " return x_y_loss, pareto_x, pareto_y, x_numpy, y_numpy, pareto_x_numpy, pareto_y_numpy, pareto_x_forx_numpy, pareto_y_forx_numpy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "fMOOibBt0VkE" + }, + "outputs": [], + "source": [ + "def pave_tomax(pavemin, pave_max_value=10000):\n", + " pave_max = torch.sub(pave_max_value, pavemin)\n", + " return pave_max" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4xTV--hM0VkE" + }, + "source": [ + "# Load DWPT data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jkTrlAJMH5UO", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c852e6d6-2080-4755-fecb-72b06660c854" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(1010, 12)\n", + "(1010, 42)\n" + ] + } + ], + "source": [ + "def load_dwpt():\n", + " folder_name = \"./dwpt_v5\"\n", + " file_names = [\n", + " \"DWPT_v5_N10\",\n", + " \"DWPT_v5_N100\",\n", + " \"DWPT_v5_N200\",\n", + " \"DWPT_v5_N300\",\n", + " \"DWPT_v5_N400\",\n", + " ]\n", + "\n", + " df = pandas.DataFrame()\n", + "\n", + " for file_name in file_names:\n", + " new_df = pandas.read_csv(f\"{folder_name}/{file_name}_after.csv\", index_col=0)\n", + " df = pandas.concat([df, new_df])\n", + "\n", + " return df\n", + "\n", + "df = load_dwpt()\n", + "df_gp = df.loc[:, :\"ys4[mm]\"]\n", + "df_mp = df.loc[:, \"k0mm_ys0\":]\n", + "\n", + "print(df_gp.shape)\n", + "print(df_mp.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8GULefwA0VkF" + }, + "source": [ + "# Define Loss Functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "JxyuSPg-0VkG" + }, + "outputs": [], + "source": [ + "import math\n", + "f = 85*10**3 #[Hz]\n", + "w =2*math.pi*f #[rad/s]\n", + "Pout= 50000 #W\n", + "Vdc = 400 #800 # V\n", + "Vbat = 400 #V\n", + "QCoil = 400\n", + "b = 50" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OtVLwoEF0VkH" + }, + "outputs": [], + "source": [ + "def kdiff_loss(k_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + "\n", + " kdiff = abs(k_0_0 - k_1_0)\n", + " kdiff = kdiff / torch.max(abs(k_0_0), abs(k_1_0))\n", + "\n", + " return kdiff\n", + "\n", + "def calculate_Is(l_parameters, k_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + "\n", + " a6 = lp_0_0 * np ** 2\n", + " a11 = ls_0_0 * ns ** 2\n", + " # Paper formula\n", + " n1 = (torch.pi * w * a6 * ip) / (2*numpy.sqrt(2) * v_dc)\n", + " t1 = (torch.pi ** 2 * w * p_out * torch.sqrt(a6 * a11))\n", + " t2 = 8 * k_0_0 * n1 * v_dc * v_bat\n", + " n2 = t1 / t2\n", + " Is = (4 * v_bat * n2) / (torch.pi * w * a11)\n", + "\n", + " return Is\n", + " \n", + "def bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters):\n", + " # Unpack parameters\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " Is = calculate_Is(l_parameters,k_parameters)\n", + "\n", + " bx_100 = (\n", + " (bx_p_1_00 * ip * np + bx_s_1_00 * Is * ns) ** 2 +\n", + " (bx_p_1_90 * ip * np + bx_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " by_100 = (\n", + " (by_p_1_00 * ip * np + by_s_1_00 * Is * ns) ** 2 +\n", + " (by_p_1_90 * ip * np + by_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " bz_100 = (\n", + " (bz_p_1_00 * ip * np + bz_s_1_00 * Is * ns) ** 2 +\n", + " (bz_p_1_90 * ip * np + bz_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " b_100 = (bx_100**2 + by_100**2 + bz_100**2) ** 0.5\n", + "\n", + " bstray = b_100\n", + "\n", + " return bstray" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "t_zwe2h20VkI" + }, + "outputs": [], + "source": [ + "# import seaborn as sns\n", + "# plt.style.use(['science','no-latex'])\n", + "\n", + "# fig_width = 8 #cm # Setting for Conference paper \n", + "# fig_height = 3 #cm\n", + "# font_size = 7 # pt\n", + "# fig_update = True\n", + "# marker_size = 5\n", + "# x_tick_pad = 2\n", + "# y_tick_pad = 2\n", + "# x_label_pad = 0.5\n", + "# y_label_pad = 1\n", + "\n", + "# def plot_(**kwargs):\n", + "# import numpy as np\n", + "# plt.close('all')\n", + "# kdiff = kwargs[\"kdiff\"] * 100\n", + "# pave = kwargs[\"pave\"] \n", + "# coilloss = kwargs[\"coilloss\"]\n", + "# bstray = kwargs[\"bstray\"]\n", + "# vpricore = kwargs[\"vpricore\"]\n", + "# vsecwind = kwargs[\"vsecwind\"]\n", + "# n_inv = kwargs[\"n_inv\"]\n", + "# vseccore = kwargs[\"vseccore\"]\n", + "# epoch = kwargs[\"epoch\"]\n", + "\n", + "# marker_size = 60\n", + "\n", + "\n", + "# font = {'family' : 'normal',\n", + "# 'weight' : 'bold',\n", + "# 'size' : 12}\n", + "\n", + "# plt.rc('font', **font)\n", + "\n", + "# #plt.style.use(['science','no-latex'])\n", + "\n", + "# fig, (ax1, ax2, ax3,ax4) = plt.subplots(1, 4)\n", + "\n", + "# #fig.subplots_adjust(hspace=10)\n", + "\n", + "# fig.set_size_inches(16,4)\n", + "# #ax1.set_aspect('equal')\n", + "# #fig.tight_layout(pad=6)\n", + "# fig.tight_layout(pad = 2)\n", + "\n", + "# xlim = 1 #Number of Inverter [1/m]\n", + "# ylim = 2000 #Coil loss [W] \n", + "# ax1.set_xlabel(r\"$Number\\ of\\ inverter\\ [1/m]$\", labelpad = x_label_pad) # not shown\n", + "# ax1.set_ylabel(r'$Coil loss [W]$', labelpad = y_label_pad) # not shown\n", + "# x1 = np.arange(0, xlim + 0.1,0.1)\n", + "# y1 = 0\n", + "# y2 = ylim\n", + "# plt_ax1 = ax1.scatter(n_inv, coilloss, rasterized=True)\n", + "# #fig.colorbar(mappable = plt_ax1 , ax = ax1, label=r\"$B_{\\rm stray}~[\\rm \\mu T]$\")\n", + "# ax1.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + "# ax1.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + "# ax1.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + "# ax1.axis([0,2, 0,5000])\n", + "# ax1.tick_params(axis='x', pad=15)\n", + "# # ax1.set_aspect('equal')\n", + "\n", + "\n", + "\n", + "# xlim = 40\n", + "# ylim = 15\n", + "# ax2.set_xlabel(r\"$B_{\\rm stray}~[\\rm \\mu T]$\", labelpad = x_label_pad) # not shown\n", + "# ax2.set_ylabel(r'$Coupling [\\%]$', labelpad = y_label_pad) # not shown\n", + "\n", + "# x1 = np.arange(0, xlim + 0.1, 0.1)\n", + "# y1 = 0\n", + "# y2 = ylim\n", + "\n", + "# plt_ax2 = ax2.scatter(bstray, kdiff,rasterized=True)\n", + "# #fig.colorbar(mappable = plt_ax2 , ax = ax2, label=r\"$V_{PriCore}$\")\n", + "# ax2.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + "# ax2.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + "# ax2.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + "# ax2.axis([0, 100, 0, 50])\n", + "# ax2.tick_params(axis='x', pad=15)\n", + "\n", + "# # ax2.set_aspect('equal')\n", + "\n", + "# xlim = 3500\n", + "# ylim = 30\n", + "\n", + "# ax3.set_xlabel(r\"$V_{SecCore}$\", labelpad = x_label_pad) # not shown\n", + "# ax3.set_ylabel(r'$P_{ave} [KW/M]$', labelpad = y_label_pad) # not shown\n", + "\n", + "# x1 = np.arange(0, xlim + 0.1, 0.1)\n", + "# y1 = ylim\n", + "# y2 = ylim*10\n", + "\n", + "# plt_ax3 = ax3.scatter(vseccore, pave, rasterized=True)\n", + "# #fig.colorbar(mappable = plt_ax3 , ax = ax3, label=r\"$V_{SecWind}$\")\n", + "# ax3.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + "# ax3.plot([xlim, xlim], [ylim, ylim*10], 'r--', lw=0.5) \n", + "# ax3.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + "# ax3.axis([0, 5000, 0, 200])\n", + "# ax3.tick_params(axis='x', pad=15)\n", + "# # ax3.set_aspect('equal')\n", + "\n", + "\n", + "# xlim = 3500\n", + "# ylim = 6000\n", + "\n", + "# ax4.set_xlabel(r\"$V_{SecCore}$\", labelpad = x_label_pad) # not shown\n", + "# ax4.set_ylabel(r'$V_{PriCore} $', labelpad = y_label_pad) # not shown\n", + "\n", + "# x1 = np.arange(0, xlim + 0.1, 0.1)\n", + "# y1 = 0\n", + "# y2 = ylim\n", + "\n", + "# ax4.scatter(vseccore, vpricore, s=marker_size, cmap ='viridis',rasterized=True)\n", + "# #fig.colorbar(mappable = plt_ax3 , ax = ax3, label=r\"$V_{SecWind}$\")\n", + "# ax4.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + "# ax4.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + "# ax4.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + "# ax4.axis([0, 5000, 0, 7000])\n", + "# ax4.tick_params(axis='x', pad=15)\n", + "\n", + "# plt.suptitle(\"Epoch \"+str(epoch), y = 1.05, c='r')\n", + "# # plt.savefig(\"images/\"+ str(epoch) +\".png\")\n", + "# plt.show()\n", + "# plt.clf()\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YfEV3Wwr0VkI" + }, + "outputs": [], + "source": [ + "import matplotlib\n", + "import seaborn as sns\n", + "\n", + "fig_width = 8 #cm # Setting for Conference paper \n", + "fig_height = 3 #cm\n", + "font_size = 7 # pt\n", + "fig_update = True\n", + "marker_size = 5\n", + "x_tick_pad = 2\n", + "y_tick_pad = 2\n", + "x_label_pad = 0.5\n", + "y_label_pad = 1\n", + "\n", + "plt.style.use(['science','no-latex'])\n", + "def plot_(**kwargs):\n", + " import numpy as np\n", + " plt.close('all')\n", + " kdiff = kwargs[\"kdiff\"] * 100\n", + " pave = kwargs[\"pave\"] \n", + " coilloss = kwargs[\"coilloss\"]\n", + " bstray = kwargs[\"bstray\"]\n", + " vpricore = kwargs[\"vpricore\"]\n", + " vsecwind = kwargs[\"vsecwind\"]\n", + " n_inv = kwargs[\"n_inv\"]\n", + " vseccore = kwargs[\"vseccore\"]\n", + " epoch = kwargs[\"epoch\"]\n", + "\n", + " marker_size = 1\n", + "\n", + "\n", + " font = {'family' : 'normal',\n", + " 'weight' : 'bold',\n", + " 'size' : 12}\n", + "\n", + " matplotlib.rc('font', **font)\n", + "\n", + " #plt.style.use(['science','no-latex'])\n", + "\n", + " fig, (ax1, ax2, ax3,ax4) = plt.subplots(1, 4)\n", + "\n", + " #fig.subplots_adjust(hspace=10)\n", + "\n", + " fig.set_size_inches(16,4)\n", + " #ax1.set_aspect('equal')\n", + " #fig.tight_layout(pad=6)\n", + " fig.tight_layout(pad = 2)\n", + "\n", + " xlim = 1 #Number of Inverter [1/m]\n", + " ylim = 2000 #Coil loss [W] \n", + " ax1.set_xlabel(r\"$Number\\ of\\ inverter\\ [1/m]$\", labelpad = x_label_pad) # not shown\n", + " ax1.set_ylabel(r'$Coil loss [W]$', labelpad = y_label_pad) # not shown\n", + " x1 = np.arange(0, xlim + 0.1,0.1)\n", + " y1 = 0\n", + " y2 = ylim\n", + " plt_ax1 = ax1.scatter(n_inv, coilloss, s=marker_size, rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax1 , ax = ax1, label=r\"$B_{\\rm stray}~[\\rm \\mu T]$\")\n", + " ax1.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax1.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + " ax1.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax1.axis([0,2, 0,5000])\n", + " ax1.tick_params(axis='x', pad=15)\n", + " # ax1.set_aspect('equal')\n", + "\n", + "\n", + "\n", + " xlim = 45\n", + " ylim = 15\n", + " ax2.set_xlabel(r\"$B_{\\rm stray}~[\\rm \\mu T]$\", labelpad = x_label_pad) # not shown\n", + " ax2.set_ylabel(r'$Coupling [\\%]$', labelpad = y_label_pad) # not shown\n", + "\n", + " x1 = np.arange(0, xlim + 0.1, 0.1)\n", + " y1 = 0\n", + " y2 = ylim\n", + "\n", + " plt_ax2 = ax2.scatter(bstray, kdiff,s=marker_size,rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax2 , ax = ax2, label=r\"$V_{PriCore}$\")\n", + " ax2.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax2.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + " ax2.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax2.axis([0, 100, 0, 40])\n", + " ax2.tick_params(axis='x', pad=15)\n", + "\n", + " # ax2.set_aspect('equal')\n", + "\n", + " xlim = 3500\n", + " ylim = 30\n", + "\n", + " ax3.set_xlabel(r\"$V_{SecCore}$\", labelpad = x_label_pad) # not shown\n", + " ax3.set_ylabel(r'$P_{ave} [KW/M]$', labelpad = y_label_pad) # not shown\n", + "\n", + " x1 = np.arange(0, xlim + 0.1, 0.1)\n", + " y1 = ylim\n", + " y2 = ylim*10\n", + "\n", + " ax3.scatter(vseccore, pave,s=marker_size, rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax3 , ax = ax3, label=r\"$V_{SecWind}$\")\n", + " ax3.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax3.plot([xlim, xlim], [ylim, ylim*10], 'r--', lw=0.5) \n", + " ax3.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax3.axis([0, 5000, 0, 80])\n", + " ax3.tick_params(axis='x', pad=15)\n", + " # ax3.set_aspect('equal')\n", + "\n", + "\n", + " xlim = 750\n", + " ylim = 6000\n", + "\n", + " ax4.set_xlabel(r\"$V_{SecWind}$\", labelpad = x_label_pad) # not shown\n", + " ax4.set_ylabel(r'$V_{PriCore} $', labelpad = y_label_pad) # not shown\n", + "\n", + " x1 = np.arange(0, xlim + 0.1, 0.1)\n", + " y1 = 0\n", + " y2 = ylim\n", + "\n", + " ax4.scatter(vsecwind, vpricore, s=marker_size, rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax3 , ax = ax3, label=r\"$V_{SecWind}$\")\n", + " ax4.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax4.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + " ax4.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax4.axis([0, 2000, 0, 8000])\n", + " ax4.tick_params(axis='x', pad=15)\n", + "\n", + " plt.suptitle(\"Epoch \"+str(epoch).zfill(5) + \"\\nFound \"+ str(accepted_solution_so_far)+ \" solutions out of 1000 input \\nMean Loss\", y = 1.15, c='r')\n", + " plt.savefig(\"MeanLoss/\" + str(epoch).zfill(5)+\".png\")\n", + " plt.show()\n", + " plt.clf()" + ] + }, + { + "cell_type": "code", + "source": [ + "def generate_solution_plot(**kwargs):\n", + " kdiff = kwargs[\"kdiff\"] * 100\n", + " pave = kwargs[\"pave\"] /1000\n", + " coilloss = kwargs[\"coilloss\"]\n", + " bstray = kwargs[\"bstray\"]\n", + " vpricore = kwargs[\"vpricore\"]\n", + " vsecwind = kwargs[\"vsecwind\"]\n", + " n_inv = kwargs[\"n_inv\"]\n", + " vseccore = kwargs[\"vseccore\"]\n", + "\n", + " accepted_n_inv_ = [] \n", + " accepted_coilloss = [] \n", + " accepted_bstray = []\n", + " accepted_kdiff = []\n", + " accepted_v_sec_core = []\n", + " accpeted_pave = []\n", + " accepted_v_sec_wind = []\n", + " accpeted_v_pri_core = []\n", + "\n", + " for i in range(len(kdiff)):\n", + " if n_inv[i] < 1 and coilloss[i] < 2000 and bstray[i] < 50 and \\\n", + " kdiff[i] < 40 and vseccore[i] < 1500 and pave[i] > 30 and vsecwind[i] < 750 and vpricore[i] < 6000:\n", + " accepted_n_inv_.append(n_inv[i])\n", + " accepted_coilloss.append(coilloss[i])\n", + " accepted_bstray.append(bstray[i])\n", + " accepted_kdiff.append(kdiff[i])\n", + " accepted_v_sec_core.append(vseccore[i])\n", + " accpeted_pave.append(pave[i])\n", + " accepted_v_sec_wind.append(vsecwind[i])\n", + " accpeted_v_pri_core.append(vpricore[i])\n", + "\n", + "\n", + " import matplotlib\n", + " import seaborn as sns\n", + " import numpy as np\n", + "\n", + " plt.subplots_adjust(left=.1,\n", + " bottom=0.1,\n", + " right=0.9,\n", + " top=2,\n", + " wspace=2,\n", + " hspace=0.4)\n", + "\n", + " font = {'family' : 'normal',\n", + " 'weight' : 'bold',\n", + " 'size' : 15}\n", + "\n", + " matplotlib.rc('font', **font)\n", + " color_list = sns.color_palette(\"Paired\", n_colors=len(accpeted_v_pri_core))\n", + "\n", + " plt.style.use(['science','no-latex'])\n", + " #matplotlib.rcParams.update({'font.size': font_size, 'font.family': 'STIXGeneral', 'mathtext.fontset': 'stix'})\n", + "\n", + "\n", + " #fig1=plt.figure(figsize=(cm2inch(fig_width/2),cm2inch(fig_height)/1.2), dpi=400)\n", + "\n", + " marker_size = 50\n", + "\n", + " #plt.style.use(['science','no-latex'])\n", + "\n", + " fig, (ax1, ax2, ax3,ax4) = plt.subplots(1, 4)\n", + "\n", + " #fig.subplots_adjust(hspace=10)\n", + "\n", + " fig.set_size_inches(16,4)\n", + " #ax1.set_aspect('equal')\n", + " #fig.tight_layout(pad=6)\n", + " fig.tight_layout(pad = 2)\n", + "\n", + " xlim = 1 #Number of Inverter [1/m]\n", + " ylim = 2000 #Coil loss [W] \n", + " ax1.set_xlabel(r\"$Number\\ of\\ inverter\\ [1/m]$\", labelpad = x_label_pad) # not shown\n", + " ax1.set_ylabel(r'$Coil loss [W]$', labelpad = y_label_pad) # not shown\n", + " x1 = np.arange(0, xlim + 0.1,0.1)\n", + " y1 = 0\n", + " y2 = ylim\n", + " plt_ax1 = ax1.scatter(accepted_n_inv_, accepted_coilloss, s=marker_size, c= color_list, rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax1 , ax = ax1, label=r\"$B_{\\rm stray}~[\\rm \\mu T]$\")\n", + " ax1.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax1.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + " ax1.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax1.axis([0,2, 0,5000])\n", + " ax1.tick_params(axis='x', pad=15)\n", + " # ax1.set_aspect('equal')\n", + "\n", + "\n", + "\n", + " xlim = 100\n", + " ylim = 40\n", + " ax2.set_xlabel(r\"$B_{\\rm stray}~[\\rm \\mu T]$\", labelpad = x_label_pad) # not shown\n", + " ax2.set_ylabel(r'$Coupling [\\%]$', labelpad = y_label_pad) # not shown\n", + "\n", + " x1 = np.arange(0, xlim + 0.1, 0.1)\n", + " y1 = 0\n", + " y2 = ylim\n", + "\n", + " plt_ax2 = ax2.scatter(accepted_bstray, accepted_kdiff,s=marker_size, c= color_list, rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax2 , ax = ax2, label=r\"$V_{PriCore}$\")\n", + " ax2.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax2.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + " ax2.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax2.axis([0, 200, 0, 60])\n", + " ax2.tick_params(axis='x', pad=15)\n", + "\n", + " # ax2.set_aspect('equal')\n", + "\n", + " xlim = 3500\n", + " ylim = 30\n", + "\n", + " ax3.set_xlabel(r\"$V_{SecCore}$\", labelpad = x_label_pad) # not shown\n", + " ax3.set_ylabel(r'$P_{ave} [KW/M]$', labelpad = y_label_pad) # not shown\n", + "\n", + " x1 = np.arange(0, xlim + 0.1, 0.1)\n", + " y1 = ylim\n", + " y2 = ylim*10\n", + "\n", + " ax3.scatter(accepted_v_sec_core, accpeted_pave,s=marker_size, c= color_list, rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax3 , ax = ax3, label=r\"$V_{SecWind}$\")\n", + " ax3.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax3.plot([xlim, xlim], [ylim, ylim*10], 'r--', lw=0.5) \n", + " ax3.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax3.axis([0, 5000, 0, 60])\n", + " ax3.tick_params(axis='x', pad=15)\n", + " # ax3.set_aspect('equal')\n", + "\n", + "\n", + " xlim = 750\n", + " ylim = 6000\n", + "\n", + " ax4.set_xlabel(r\"$V_{SecWind}$\", labelpad = x_label_pad) # not shown\n", + " ax4.set_ylabel(r'$V_{PriCore} $', labelpad = y_label_pad) # not shown\n", + "\n", + " x1 = np.arange(0, xlim + 0.1, 0.1)\n", + " y1 = 0\n", + " y2 = ylim\n", + "\n", + " ax4.scatter(accepted_v_sec_wind, accpeted_v_pri_core, s=marker_size, c= color_list, rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax3 , ax = ax3, label=r\"$V_{SecWind}$\")\n", + " ax4.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax4.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + " ax4.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax4.axis([0, 2000, 0, 8000])\n", + " ax4.tick_params(axis='x', pad=15)\n", + "\n", + " plt.suptitle(\"Epoch \" +str(epoch).zfill(5) + \"\\nFound \"+ str(len(accepted_bstray))+ \" solutions out of 1000 input\", y = 1.10)\n", + " plt.savefig(FULL_PATH + \"Solutions/\"+str(epoch).zfill(5) + \".png\")\n", + " # \"Epoch \"+str(epoch).zfill(5) + \"\\nFound \"+ str(len(accepted_bstray))+ \" solutions out of 1000 input\"\n", + " plt.clf()" + ], + "metadata": { + "id": "T-rEIHhDVcCQ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "YJTlhVSi0VkJ" + }, + "outputs": [], + "source": [ + "def accepted_solution(**kwargs):\n", + "\n", + " kdiff = kwargs[\"kdiff\"] * 100\n", + " pave = kwargs[\"pave\"]\n", + " coilloss = kwargs[\"coilloss\"]\n", + " bstray = kwargs[\"bstray\"]\n", + " vpricore = kwargs[\"vpricore\"]\n", + " vsecwind = kwargs[\"vsecwind\"]\n", + " n_inv = kwargs[\"n_inv\"]\n", + " vseccore = kwargs[\"vseccore\"]\n", + "\n", + " count = 0\n", + " for i in range(len(kdiff)):\n", + " if n_inv[i] < 1 and coilloss[i] < 2000 and bstray[i] < 45 and \\\n", + " kdiff[i] < 15 and vseccore[i] < 1500 and pave[i] > 30:\n", + " count += 1\n", + "\n", + " return count" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "URm5-U_-0VkK" + }, + "outputs": [], + "source": [ + "def number_of_inverters(gp_parameters):\n", + " (\n", + " a, lpx, lpy, ls, p, wp, ws, *ys\n", + " ) = gp_parameters.clone().transpose(0,1)\n", + " \n", + " number_of_inverters = 1/(lpy+2*wp+2*a+p)*10**3\n", + "\n", + " return number_of_inverters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "n1ztkTub0VkK" + }, + "outputs": [], + "source": [ + "def core_losses(extra_parameters, gp_parameters):\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " (\n", + " a, lpx, lpy, ls, p, wp, ws, *ys\n", + " ) = gp_parameters.transpose(0,1)\n", + " \n", + " V_PriCore = ((lpy +2*wp+2*a)*(lpx+2*wp+2*a)*5)/(10**3) # cm3\n", + " V_SecCore = (((ls+2*ws+2*b)**2)*5)/(10**3)\n", + " V_PriWind = (2*(lpx+wp)+2*(lpy+wp))*6.6*6.6/(10**3)*np\n", + " V_SecWind = 4*(ls+ws)*6.6*6.6/(10**3)*ns\n", + " \n", + " V_PriCore_ave = V_PriCore/(lpy+2*wp+2*a+p)*10**3\n", + " # V_PriWind_ave = V_PriWind/(lpy+2*wp+2*a+p)*10**3\n", + "\n", + " return V_PriCore, V_SecCore, V_PriWind, V_SecWind, V_PriCore_ave" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rekYEBqX0VkL" + }, + "outputs": [], + "source": [ + "def calculate_coilloss_pave(k_parameters, l_parameters, b_parameters, extra_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + "\n", + " a6 = lp_0_0 * np**2 # LP0MM_YSO\n", + " a7 = lp_0_1 * np**2 # LP0MM_YS1\n", + " a8 = lp_0_2 * np**2 # LP0MM_YS2\n", + " a9 = lp_0_3 * np**2 # LP0MM_YS3\n", + " a10 = lp_0_4 * np**2 # LP0MM_YS4\n", + "\n", + " a11 = ls_0_0 * ns**2 # LS0MM_YSO\n", + " a12 = ls_0_1 * ns**2 # LS0MM_YS1\n", + " a13 = ls_0_2 * ns**2 # LS0MM_YS2\n", + " a14 = ls_0_3 * ns**2 # LS0MM_YS3\n", + " a15 = ls_0_4 * ns**2 # LS0MM_YS4\n", + "\n", + " Is = calculate_Is(l_parameters,k_parameters)\n", + " \n", + " loss = (w*a6*10**(-9)*ip**2/QCoil + w*a11*10**(-9)*Is**2/QCoil) \n", + "\n", + " p0 = w*k_0_0*(a6 * a11)**0.5*ip*Is\n", + " p1 = w*k_0_1*(a7 * a12)**0.5*ip*Is\n", + " p2 = w*k_0_2*(a8 * a13)**0.5*ip*Is\n", + " p3 = w*k_0_3*(a9 * a14)**0.5*ip*Is\n", + " p4 = 2*w*k_0_4*(a10 * a15)**0.5*ip*Is\n", + " pave = (p0+(2*p1)+(2*p2)+(2*p3)+ p4)/8\n", + " pave_kW = pave / 1000\n", + "\n", + " return loss,pave_kW" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YJffUeld0VkM" + }, + "source": [ + "# Neural Network Training Loop" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "OXzpQf5O1Nxk", + "outputId": "d033b3de-0d57-4298-9123-00a57785694a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:matplotlib.font_manager:findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Accepted Soluntions : 9\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:matplotlib.font_manager:findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", + "WARNING:matplotlib.font_manager:findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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W91oN/BVvJa3lwJl+XwLfAn4DTMH73R3u92ld6FjTgUvwCmcvAi7DK579VB79zsbDeAG2Z/H+bj2O9/sN/Ba40/95CS9L6Y7IMX6F9zv+B97fvfOL1FcREZE2Y83/DxcRERERKXNms4E3cO6SUndFRESk3CijR0RERERERESkSijQIyIiIiIiIiJSJTR1S0RERERERESkSiijR0RERERERESkSijQIyIiIiIiIiJSJRToERERkdIzuxczh9mjMfvO9Pc1laBn8cxWYfa9UndDREREJEqBHhERESkXq4HTMTs4sv0y4M0S9EdERESk4ijQIyIiIuXidWAecHFqi1k/YBzwmxatzY7F7C+YbcdsE2aPYtY/tH+gv20tZjsxW4TZ5yPHmI3ZFMyuwWw9Zlswuw+zLq26ErMJmL2MWSNmGzH7OWb7h/YfhdlMzN7FbAdmrzbrm9kl/rYGv09zMTu0VX0SERGRmqBAj4iIiJSTe4BLMDP/9SXAX4lm9JgdCcwB/g84Dvg4sAd4GrOOfqsuwN+ATwHD/GP/BrOxkXOeDfQAxgDnAacD/533FZgdDfwJmAscA3zBP+ZdoVa/B94GPur37RvAO/77j/Xb/gA4AhgN3Jd3f0RERKSmaHl1ERERKT2ze4FD8QIia/CCL3PxAjxfB7oBU3CuXah9R5w7L3SMDnjBkkk4Nz3hPI8BG3Huy/7r2cABOHdMqM0vgOE4d2Ka/q7y+/P9mH2/A47AuY+Etp0J/BEYiHNvYrYV+A+cuzfm/Z8B7gU+iHP1iX0QERERiaGMHhERESkfzjUAvwO+DEwE2gF/jml5PPAZf9qW9+NlyHQEDgfArDNmN2O22J/+tB2YAPSPHGth5PVaIFonKBdH4QWpwuYABhzpv/4xMMWfOjYZs5Ghtk8DK4CVmD2I2aWY9WpFf0RERKSGKNAjIiIi5eYe4LPAt4Df4Nx7MW32wQsIDY/8DAam+G1+BFwIXAeM9fc/CbSPHGt35LWj2J+RnLsBr68PAUOBeZh939+3HW862meAZcDlwBv+lC4RERGRtBToERERkfLi3BLgReBj7A3aRL0EHA0sx7k3Ij/v+G1OAe7HuYdwbiFelszgYncfWOyfO2w0XgBpcWqLcytw7uc4dzbwP8BXQvv24NxcnPsf4FhgHTCpyP0WERGRKtCu1B0QERERiXEaXg2eLQn7bwJeAKZidjuwCRgAnAXcjnMrgNeAMzF7BNiOV/C4D7ChQH3sjdnwyLbNeJlE8zG7Fbjb79f/4gWdVvsret0CPAKsBD4AjAeWAEE9n0F407824QV6PpjaLyIiIpKGMnpERESk/Di3M02QB5x7FW/Fqi7ATLwgyC+BTsC7fqv/xCvmPAtv5a41wMMF7OUVwILIz/dw7p/AGXhZPQvxppg9gTcFC6AJOAD4FfCq3/8N7M3YeQf4NDADb+rWD4Hv49yvCth3ERERqVJadUtEREREREREpEooo0dEREREREREpEoo0CMiIiIiIiIiUiUU6BERERERERERqRIK9IiIiIiIiIiIVAkFekREREREREREqoQCPSIiIiIiIiIiVUKBHhERERERERGRKqFAj4iIiIiIiIhIlVCgR0RERERERESkSijQIyIiIiIiIiJSJRToERERERERERGpEgr0iIiIiIiIiIhUCQV6RERERERERESqhAI9IiIiIiIiIiJVQoEeEREREREREZEqoUCPiIiIiIiIiEiVUKBHRERERERERKRKKNAjIiIiIiIiIlIlFOgREREREREREakSCvSIiIiIiIiIiFQJBXpERERERERERKqEAj0iIiIiIiIiIlVCgR4RERERERERkSqhQI+IiIiIiIiISJUoWqDHzGabmYv81IX2X2Fmy82s0cxeM7MvRN5/jJnNMrNdZva2mf3SzLqG9nc0s/81s41+m+fM7IRiXY+IVDczOz80Vt0W2p52rBIRycTMhpvZTP/zzE4zW2Jm/x7a/zkzW+yPM6vM7Nul7K+ItK3WjhFmNsDMHjOz7Wa21cweMrPeof37mNlkM3vLP8YrZjahLa9RRNqWOeeKc2Cz2cBo4PbQ5nXOuVvM7Dzg98Am4EngDOAAYLxzbqYf0FkOHAg8AgwERgIPOufO949/F3AZUOf/nAtsBwY55zYX5aJEpCqZ2aHAIqAL0A643Tl3ZaaxqlT9FZHKYmargP7Ai8BrwAWAAR8HGoDngB3Ao8CpQF/gcufc3aXor4i0rdaMEWa2D95nmCOBvwAd8O7B5jnnTvSPfxXwA2AVMBfvvqkdcIxzbnGbXKSItKmiT91yzl0Z+rnF33yV//gV59zFwLf811f7j1/CC/I87pw7G2+wagDOMbNBZnYQ8EXgfeBUP/hzP9AV+Gqxr0lEqoeZGfBbYC1eYDks01glIpKWme0HfNB/+UXn3OeB+f7rAcB/493QTXbOfQEIsgY1zojUgAKMEWfgBXkWOedOwwsEvQmMMrMxZtYO+C+/7dn+MX4E7MvezzUiUmXaFfsEZvYO3uD0Mt5N0wJgqL/7pcjjcP9xRHi7c267mS319x8NbAX2A1Y55zaG2l4YOkbKqFGjXMeOHQEYMGAAAwYMSOzvqlWr0u5vTftCtJ23bBN/W7yejx/Vm1GDDyxJH9qybbn0o5rbFvrYq1atYtWqVQDMmTMn9W1SGbsSOAk4wX8OgP/BKNNYlVJN40w5tN3Z2MQ/33yHo/sfQOcO7WLbxrUJLH19Oe+6brH7itXntmgbXPMHrJ4hhx9Wkj7k07bQx66kccY5956Z3Q78J/BrM3sNL0t5IfBHYLLfNDrO9DezDzjn3g2ONWrYMNfRz8QecPDBDDjooBbnW7VxY+z2WmzT2LSHlRu3M/CgLnRot2/e5ynUcSquzX77waBBzdtk8W8z07/zSmnTVuNMa8cI9t43vewfb4+ZLcDLEBqOF/TpifcF+fzIMfR5poralks/qrltoY9d1HHGOVeUH+DP/s9deAOVA7YAvf3nDujpt/1QaFtHYIb//Juh4z3rb7scOM9/vii0/xJ/27xoX0aPHu2yde2112bdNtf2+bbdVL/L3fr4YrepfpfbVL/Lff+Rhe77jyx0m+p3ZTxu+L1t1d9Cti2XflRz22IeG5jtijTGFOIHL5DTAPy3//pefxy5LZuxKnysSh9nitE2Ov5Ebarf5U47/98T92fqQ7rjF/t3nOna8j1uJrc+vth1+fz97rTz/72gxy1222Ieu9zHGa+LnAKsDI0fu/Fu3vb1xyAHHOu3bRdqNyR8nNEjRzr3pz+l/bn2vPPUxv+Zfs3P3OfGfNPd9G83uq3THs77PMFxpl/zs5JfU5u2ueaanP9tZvNvtxLbFHucac0Y4d9rOeB/Q8eb6m+7GRjlP98W2v8Jf9v6aF/0eaZy25ZLP6q5bTGPXehxppgZPWf4HcbM2gPL8CLL44A9/sDVBXjbfwTY6pxrMLMN/usuoeMFz9fjZfSk25+3MWPGFK19vm2nzl3BNdNeAeDKiUeyf4d2XDPtFfbv0I4rJx6Z9jjR97ZFfwvZNlfl0OdiXV8x+1AO11ci/w9oD4w2s5OBY/ztZwC7yDBW5XvSSvzzyea4m7c1MHXuCkZ85KNAy/EnaurcFczZfABT565otj84zoWnDKJX146J50s6/uZtDWzuMIDN2xrSvj/X6wu3zXRtSdJdWzZ9uPAU79v1gdYrp/4WQyX+PS4FM+sJPAV0Bk4GFgMzgWuBjcAGoB97x5fwZ5ucP9OMGTasrNpko1j9GTu0N0ve2sqClVuYVbc+7/6OHdq72WPN/I4/85mWbTL82yzUv91sjtOWbYqpAGNEpvumYH9nM9vHOfc+VXzfVEjl0F/dN+VH901FKsZsZp2BDzjn1vqvw4Ge84Dv4E3BOsc59wcz+zJwDzDXOTfazP4T+CnwpHNuol+ceRPeDdnheEWX/4V3A9bHObfBzO4HJgHXO+euDffn4osvdvfee2/Br7OtRG8MNm9r4O6nlwFw2bjB1L08L/EvUbY3TOVs9uzZJf9PuFiq+doAzOy3zqttU5bMbDLeB6k4c/AKLyeOVeHGlTjOFPrv321PLOGaaa9ww7nDuXLikRnHn6T90eMk9TdubJw6dwU7Gpu4eXpdi/cXUlLfo9ujfQ5f24WnDCq78bkSx6QKGGeOwyuw+h7Q1TnXaGa/BS4CfoZ3A3cG8G3n3I/MbBxeQdXVzrn+4WNd/OlPu3svvbRtL6CVZi9aVLBgRZL6XbuZVbeesUN7061T+8R9QGK7tuxvoRWtz8uXw5VXZm6XI40zLY7dqjHCzD6DV6R5MTAMrwbrSry6Px8H/o4X7OkBfMQ596KZ3Yh3P3af82r2pFTi55lcVOLfv1xU8/VV87VB4ceZYmX0HAS8ZmZ/w5sXeiJekGcD8Fe8AM39wJ1mNhE403/fzf7jFOC7wAQzexgYhFdB/iHn3HIAM7sX+DLwV3/Z9nPwAkA/i3Ymlzl35ahX144tbgiaZ/WMiX1fNQR5oPTftBRTNV+bb1WpO5COc24ye+e+B+PKF9i76tYk0o9VKZU4zhTq718w1kwY2Rfwsk6yGX96de3YIhCzeVsDOxqbuOqsoanslaT+Rt8fZNlcddbQVCClWOL6Hu4DxGdRBn0KxvR8soKylc//ARU6Jq0qdQcyeBVv6noPvM8sy4Hz/X3PAm8BnwauNbOheFMqIG6c6dOn+L0tsLYImsyqW8/9f18JwJnH92u2r1un9qltj724OrFdINzfdAGkclK03/GWLUU5rMaZFlo7RjwGLAWOwssE6oAX5HnBOTcLwMx+AtwI/MHM5uLdN+3BK8rcTCV+nslFhf79y1o1X181X5tvVSEPVqxAz9vAfXhR5DF4U62mA99z3tLnD5hZL7zCp5PwLuqbzrmnAJxz2/xo9a3ARLwpFL/GK1IW+A+8yPc5eHUz5vnH2FSkayqp6A3BhJF9eXbpxtSNVTbvEZHcOOfSjlXiiRtrgsyV8LZsjxVk4+QaoA4HUXJ9b6EC4+E+FLJtPvR/QHlwzu0wswnA9/EKrI4E3gDuds5NAzCz8/GyC8/Hm0pxNV7dDclCdGpVa9sF0gWQasIll5S6BzWhtWOEc+59//13AGPxau88Anw9dJpbgE54qxafhxdc+q5zrq7oFygiJVGUQI9zbhtetk26NnfgDUhJ+xfgBYmS9u8CrvB/ql70huDJ+WuYuXAtIwb2YP8O7WJvTop9EyFSbfx0yYsj29KOVYFVq1YxefJkxowZUwvfODQTN9ZMGNmXv9WtY1N9Q061clozbiVl2WSjUEGRTH2InqeYAZha+D9g9uzZ4C0/XNacc8/j1ShM2j8NmJbpOKvWrmXyAw8wZtiwipteVEzhrJ1CtAvkGhiqOlOmwPXXl7oXJdcW40xrxwjn3Eq8rJ+k/XuAa/yftGr584xIqRRjnCn68urSenHfNAcf3Hc0NiXenGS64aiWqV0i5WDAgAFMnjy51N0oibix5sn5a5i1eAOzFm/gwG5792cad6LHKtY4FT1uWwVF2jL40prAV6Xwb0JWlbYXbWdAnz5MPv30UnejZuQaGKo6Q4eWugdloebGmRr+PCNSKsUYZxToqQBx3zQHH+A3b2tIZfQU4rgiIoVw4SmD2NHYlHoeyDTuRAMwrR2nkgJF0eO2VVCkFoIvIlIlunTJ3EZERMpSTQR6Kj0FMekb4GxXfUnaXgtp/VI6lTKlQvKXLtumV9eOfPezR7d4T6ZxJ596ZOn6lBQoynb8U+ajiNSsefNgwoRS90JERPJQE4GeSk9BzHZ1l1y365tlKaZaS3WuRflk22Qad5LqkZ005CAGT+we+55wMCbap6SATrbjXyVmPio4JSIFce65pe6BiIjkqSYCPdUg+sE93RLESTc2yuARKZ5KzxzMRzCWTBjZl9ueWMKEkX15cv6aVgUYogGY6DniMhUvv2ceMxeubdY+vMT7hJF98w58tNW4WcjgTCUGp/JVa5mDtViMuVKWOM9FxVzTk0/CUUeVuhclV3PjTA1+nhEpNRVjrmHBB/dnl27krktHpZYgPu2YPi3aJn1TrQwekeKp9MzBfARjSrCU+u/mrmDZunp2NDYlrgaYJCnQET0HtFKbTsAAACAASURBVMxUnLlwLacd0yf13qB+WRAAenbpxlQgKNcxsK3GzUIGZ2opqF9rmYO1WIy5Gpc4r5hramwsdQ/KQs2NMzX4eUak1FSMuYZdeMog/la3jpkL13L308u4bNzg1M3L1LkrCnYjopR/EQnLZky48JRBqfEoCD7nGrTIFOjIJlMxWmw56M9Nk0Zw0pCDyjrwUcjgjIL6Uk3SLXFeMZkxERWzbPt555W6ByIikicFeipEr64dOeHwA5m1eAM7G5uYOndF2puXfAM2tZTyLyKZZTMm9OraMZVpGIxHua4GmCnQkWumYjQAlFTfp1hyHYMVnBGJ161Te8YO7R0b0KmYzJiIilm2/b774PrrS90LERHJgwI9FeSycYPZv0M7djQ2Zbzxit6cZXvTUUsp/yKSLFzfBjKPCdFARbZBi2XrtvKdBxZw06QRBQl0hMe6UgZOFDQXSZZrJk5SQCcpM6ZSM33KzsiRpe6BiIjkSYGeChKuPRH+tjwuiBMN2GR706FvlUUE2i5Q8Z0HFqTq5zz8zTGtPl4pAyzhsVhBc5FkuWbiRAM64UBO3PuD4y95aytXjD9CwR4REak5+5S6A5LZ5m0N3PbEEjZva4jdH9zYTJ27IrUtCNiEAz83nDtcNx0iRRKsUuFXza94bTVm3DRpRKqODmQe7+KE31OIfufTB2g+FkfHYCmOmlsNx191a/aiRaXuSquMHdqbC04emHWNmmCqUxCwCQI5s+rWJx5/xMAeLFi5JbFNJvW7dvPYi6up37U7r/dXhfnzS92DslBz40yVfZ4RqQRadStPlb5MYPQb6uB1sLJNNlMrlKkjba3WPhhV2yoV6caMXOvPpGs/+JDuzTJ58snIib6ntWNdvllBuWbxqPh969XcajgVvupWpkycbGUqZtytU3uuGH9E6lz5qNT6PwV10UWl7kFZqLlxpso+z4hUAq26ladKH7CiNw/BY1CrJwj45Eo3GVJMtfbBqJbkGgiJa580/sQFSzKNVUkBlnzHuHynXeUaUM/l9yJSDQoVPEkqZhytzVPMYFJNePBB+N73St0LERHJQ00Eeipd+OYhXCD1D//3JledNRTwljLeVN/Aa2vruWnSCAYfknmFGRULFZF85BoIiWufNP7EBUvCbS88ZVCLQEi4ftltTyxJ7cu3KH1rMiBzCdTk8nsRqQbFCJ6EgzvhQFLSSl3ZqpiVsYqpQ4dS90BERPKkQE+FCW4Cnl26kZkL16ZqUezfoR1/q1vHrMUbAK/uRbCSTVLQJ5ebtejNi751FqldmQIh0fEhrn0u40+4bbpAyK2PL+GOp5ayqb6BG88fmXdR+lxErzWXcyQF8cN9Fqkm2QZPMq2alS64Ezxq6lUBTJhQ6h6IiEieFOipMMGH/wkj+zJiYA92NDYB3g3FhJF9U8GduJVssrn5SpJUJyh4LVLrKr0WWCFlGh9yDRSHx6p0AaJFq99p9hgd44qxElb0WvM9h8bU/NRaLbCgGPOYYcMYM2xYqbtTNJmCNEnBnXAgqVDZQzW9VPu0aXD99aXuRcnV3DijzzMibU7FmGvcsnVbm2Xp7N+hHddMe4X9O7TjyolHpoqabt7WwBF9urG7aU9qJRuAu59exs3T69jR2MR3P3t0TudOqhOkb51FPJVeC6yQMo0PrQlq9OraMXb61uZtDRzd/wB2N73P0f0PYPO2htjsw0IHUS48ZRA7GpvY0diUOmc+wS2NqfmptVpglV6MOVvHHdaTJW9t5bjDesbuzyaIk032UDZBnLigU80Ef0aNKnUPykLNjTP6PCPS5ooxzmh59QoSZOn8130vcdsTSzjxiAM57Zg+nHjEgc2WAp46dwV3PLWU9u32pUeXvfOrd/rZP8FjLqJLBWvpYKkWZvZbM1tjZo1mttnMZpjZCH/fZDNzMT+9St3vchYdHzZva+DGR//JjY/+syBLoIeXMQ9vu/3JpbRvtw+3P7mUu59elti2EIJ6QAD7d2jHzdPr0p4jUz80plYvMxuTMI44M7vYb/M5M1vsj0OrzOzbJe52Sb20/G0WrNzCS8vfjt0fXm4901Lr6WTz3ril4FtzzoqyfXupe1ATCjFGmNkAM3vMzLab2VYze8jMeof27+N/pnnLP8YrZqa5eSJVTBk9ZS78LXCQnXNEn25cM+0VTjumT2p6VvAYTB0IavhMnbsi9e1yZ39lrs55rNAlUsX6A3OArcDHgdOAD/vbA48Ab4Ve72qz3lWBqXNXcPP0OoBUBmJrMmvisl+C55vqG1K1ypLa5jJ1LKlttEB09BzR9xYrY0f10irCW8DtodddgC/5z98wsxOBacAO4EHgVOAWM9vqnLu7TXtaJnKZdpXUNpusm3wzg2pmRa66OjjnnFL3oha0aowws32AJ4Ajgb8AHYDPAR8ETvSP823gWryMgQeBc4E/mdkxzrnFRbw2ESkR3fGXuegUh2Bq1oHdOjJhZF9OGnJQ6jG4gejVtSN3XToq9eE/cNm4wezfoZ2mBoiEOOfGBM/NbCTwMnCome0XavYz59zsNu5a1Zgwsi9/q1vHsH4H5B1wCYubHhVeeevAbh2bjYfRqV7BuLqjsSk1JiadP2kJ9B2NTVx11tC0U8LCxfPvunRUUWrvqLZP+XPOvQFcGbw2s6/5T+c75541s+mAAZOdcz8xs1OBZ4CrgZoM9OSy4lVS22yKMee7slbNrMh1ySWl7kFNKMAYcQZekGeRc+40M9sXWA6MMrMxwLPAf/nHPNs597KZrQa+B3wLuLjY1ygiba8mAj2VXFQs7lvg8E3F4Indmz3GtUm3TaRYKql4oZl9Fe9D0qn+pp84594zs6DJdDNrD7wO3OKce6AE3SwruQRpnpy/hlmLN/DxoYckZsW0dmwK9ydpefYg4BKMpzsamzKeP2kJ9Jun13HDucPTXntSdmUhqbZPZTFvUPm6//I2/zEopvdS5LG/mX3AOfduW/WvXBSiBk4uWTc1U3MnV1OmqBhzG8tnjAjtfxnAObfHzBbgZSYPB94EegLvA/Mjxxhe6GsQkfJQE4GeSi4qlq6wZ1CD4rJxg7P+NryQaf6aMiDpVFjxwrOB0f7zt4Dn/OdNwFxgKV7Q6pPA/Wb2tnNuZvgAQUAZqMigcq5yCdIkBSMKGaRI159wwOXye+alsms2b2vImOXYmqXhg+zKu59e1qxYcyHVagB/9uzZQTAZKiSg7Dsd+BCwDm8qBsDB/mNQEGVHqH1vIBXoCVbdAspy5a1clkVPF1AJsnGWvLWVK8YfkVfwJZesGy3FnqBHj1L3oKRKNM7kM0ZE94fbhPfvdM65mP3N1NrnGZFSKuY4UxOBnmoUV/Mi2/fl8w16XFBHUwakWjjnxphZR7z6PI8CD5vZh4AbnXPfD9qZ2e+B84DPAs0CPZUcUM5HLkGa8LSq255YkhpHChmkSNefIOBy+T3zmmXX5Hv+dO8LxsoJI/vy5Pw1XHjKoNQKieCN1+F9CpLnJ3zzcd11160qaWdyE0zP+IVzbrf/fAPQD68uB6FHgGbVfst91a1clkVPF1AZO7Q3S97ayoKVW5hVt77owZeaqbmTq9GjM7epYiUaZ/IZIzbEbO8Ss7+zme3jnHs/sr+ZWvs8I1JKxRxnFOipUMGSvsHzsHSZNvl+gx4X1NGUAal0ZtYJ2O2c2+OcazCzGXjfiHUDBgH7AW/EvPX9NuxmWcom2JGugHE2AZZcsgazWdY8rnZZa6QLgAcZRME4fdVZQwGa7QMFyWuJmQ3DK/jeANwV2vUK3k3cR/AKwx/vb19dadO2MgVMsg2odOvUnivGH5HK/im21tTcqeppX3/8I4wYkbmdFES+Y4SZveK/Pt6f+rUPMNLfthD4F7AF6AEcC7wYOsbC4lyNiJSaAj0VLKmIaLqbqXy/wc5UK0ikQp0APGBmc4F3gJPxgjyb8Oaxv2Jm64FFeB+yTsML8jxYmu5WhqQxKNfgcPQ4+UwXjR6jkGNWugB4UCR/R2NTqp5PkN0TLaAvNSP4pv5+59ym0PYfAp8GrjWzocAn/O03t2XnCiFTwCTXIstjh/bOO4iSFIApdGAmLkupaoI/NZ7RUwL5jhGP4U0xPwov27gD3opbLzjnZgGY2U+AG4E/+J95zgH2AD8q3uWISCkp0FMBcp02lc1Sv7lOF1BQR6rUWmAZMA7oihfg+QNwvXNuq5n9Em8J0vPxPhD9A7jJOTenRP2tCEljUK7jSPQ4+UwXDQdewtPGoqLjYzbjZTbF8sN1gOIK6UttMLNewCT/5W3hfc6558zsfLylj8/Hm0pxNc2/0a859bt2c+eM11iwcguQe+2cpGli6aaPBQGa4w7ryUvL384qUBOXpVQ1NX/Wri11D2pGa8YI59z7ZjYBuAMYCzjgEfYWdQa4BegEfBFvCvqrwHedc3XFuiYRKS0Fesrc5m0NqboS4aWAg+WKN9U3NCvwme7mJHyTFF1uWIWVpRY555YBY9Lsvwm4KZtjVfLqfhA/duQ6LqRb+Srbc4ZFA0PBlNUdjU0sW7c1qzo3wTFue2JJ2iBRNIiUTVApm8BVtM2ydVv5zgMLuGnSCAYfomBPa1XK6n7Ouc14N1lJ+6ext/BqoqAYczkWYi60WXXrWbByCyMG9shr+lbSNLF008fCRaAXrNxC43t76LDfvmkDPnGZR1VT8+f110vdg7LQFuNMa8cI59xKvKyfpP17gGv8n7Qq/fOMSCUqxjijQE+Zmzp3BTMXruW0Y/qw058CEAR8Zi3ewKzFGziw294bibufXpZq893PHt3sWOFvn/O5qRGRZJVevDBuDMh16lSmcST6/lzHneCcN0+v4/nXNzFr8Yas35tp2lh0fzioVMjVsr7zwIJUfZ6HvzmmIMesZRW2ul+rlXsx5kIKB0vyXXUrbupXuuljY4f2pvG9PTQ2vc9hB3u1auMyc6JTs6IZPK2p+VNWLrmk1D0oCzU3zlT45xmRSlSMcaYmAj2VHJkO33QEy6n//dUNHDuoJ/8xYQid2qdfGjh6Y5VUSFmFlaXQKuWbdvHEjQG5Tp0Kt89UqPiuS0elHXcyBZWO7n8AJxx+YMZATHgVrCSbtzWkxtdAr64dU6tl5bKyYSY3TRrR7FGk1iXVs4kLluRadycIwGSTmROcs8N++/LwvNVccPJAxg7tnXpfWDSwE+w/7rCePPbi6sqvzROYMgWuv77UvRARkTzURKCnkiPT4eDMZeMGs2DlFmYuXMtzr23ihnOHN7v52LytAfBWd7ls3GCg5Y1VcDMUnU6gGjxSaLX2DVilixsD4qZOhR/THSNuqtSFpwxKrTgVLHEeBLF3NjbRuUO71NgVTFkNvx+8cTCYwhqMb+kCMdFVsKLHC9rcPN0rUxA+VqbAVT4GH9JdmTxSs+Jq4ORSzyabujvhLJ4gANP43p6szxHNJIprH52aFbR77MXV1VGbJ9C7wqeeiYjUsJoI9FSLXl07ctelo1LfPEdvtoKblRvOHZ66EYm7sRIRyUcuAeGkQsXRJc7DQRbwAi1AaspqtIhyUmZiUiAmugpWXJAqmKYV1990gauwYtQ5U+00qTbRGjiQ/ZLr6dqGt0eDQWce34/6XbtjM3PiRIM7cdlC2QaAKt6xx5a6ByIikicFeipMr64dW9TeCWR7YyUiUijpVquKC4gkFVgOMnqiU8eCjJxwMfq4zMRMgZgeXTq0KEIf7lPSuBruS/gxqhh1zlQ7TapB/a7dzFiwBoCPDTkI8KY4HXlo97RZM9EAS7oly8P1eI47rCfQPNiSa82c8LlyyTiqmto8gSeegBNOKHUvREQkDwr0VJG4b9vDK7zoG2GR4qnkWmCt0drC7klBlmjWzo7GpqzrAyX1D8iqb7lk0kRrABUioF6MY1arWqsFVomrbs2qW8/D81YD0GG/fVOBkL499s/4vnCAJVPAJdP+dIGiqBkL1vDwvNU0vreH8SO8f4dVk6WTi098otQ9KAs1N87U6OcZkVKqyFW3zOx84AH/5e3OuSv97VcA3wAOxavjcZNz7reh9x0D3AaMAnYCjwLfcM5t8/d3BH4EnAt0Beb7+58v9jW1tXxuOoK20RVeNBVApDgquRZYaxSrsHs0M2jztoZURk+ccKA7XFz5cyf2b9GfTH2LyyJKCmApk6e0aq0WWCWuuhWsZBU8D0sXfIlOgzrusJ4seWtrKmMn+v5M06ZyLcwcqLosnVy8/jqcckqpe1FyNTfO1OjnGZFSqrhVt8zsUODnQFP4XGZ2HvAzYBPwe+AM4F4zW++cm2lmXYGngQOBR4CBwCVAF+B8/zC3AZcBdcBf8QI+T5vZIOfc5mJeV1uL+9AfztQZfEj3Fm2D4svRFV50AyEihVSMwu6btzW0KMacy3GTiisHx8okLosoKYCVbWArHHy6bNzgtIH2tlwFUcF/KbZundpzzkcHxu6Ly8IJB2/CAZbnlm5kwcotHHZwl9Tx4urxJAkCQPU730tl6yT1a/yIvrE1fcLT0MaP6FsdK2ul8+abpe6BiIjkqWiBHjMz4LfAWmARXiAmcJX/+BXn3CNm9iVgCnA1MBP4El6Q53Hn3Nlm1gUvKHSOmX0X2A58EXgfONU5t9HMmoALga8Ck4t1XaUQ96E/KVNnwsi+LYovh1d40TLqIlIohQ4SBMfb0diUKsacz1iVVFw5lz4E7wvXBcqm5lCSdMGnfI9ZCAr+SynFZenkknkT9/6wcGDm8EO68de6dezfPvNH36QsnqRpaMWSy3SzorjkkrY/p4iIFEQxM3quBE4CTvCfA2Bm7YCh/suXIo/D/ccR4e3Oue1mttTffzSwFdgPWOWc2xhqe2HoGFUhetNx2xNLmDCyL0P6dmN3057YTJ10xZeTbiD0ra6I5CpdoeTWHO+qs4Zyw7nDm417uR473/5EsyILVcunNcGnYlLwX0rppeVvs2DlFo48tHuqZk/SkuhxWTZx74e9AZLG9/akAjNdOrVj+y7v3+CIgT1StXfC7wkXjQ6Wfw8HWNJNQ4sqRJAml0LQRTFlClx/fdufV0REWq0ogR4zGwr8APgf59wrXnJPSi9gX//5dv9xh//Y3a+9c3Bkf7hNb6Bjhv3NBEXFgIorLBZXSDTI2Lnh3OGpaVvhD+vRYE42NyP6VlcKZfbs2UFBMVDxwqq1eVsDOxqbuOosL25fiPEjOo5B5tW0wv0Jxrm7n17GzdPr2NHYxGXjBrcY/9KNiRNG9uV3c1c0y4pMWllsR2NTKksn03Vns7JXscVdd1tmDxVDzRVJrcBizOnEZeQE2TTRJdHDWTZBECVuhS3YGyA5e1Q/hvX7AItWv8vxh/Xk1TVbObp/Dz41vE+LIEw4W2f5hu2p5d+DvgTtk6Z7RRUiSFPy5dr79y/NectMzY0zNfZ5RqQcVFIx5v8HtAdGm9nJwDH+9jOAXcAevGBPF+Bt/xFgq3Ouwcw2+K+77D1k6vl6vIyedPubqeSiYnHftk4Y2ZeThhyU9Tew2dT40be6UijhDwbXXXfdqpJ2pg1V8jiTj2AqUpB5k65Qcrbigg5BJsyOxiY2b2vIKlgd2NnY1KLWT1z9n3AA5Mn5a1i2rr7ZtLGklcWimUflKHxt1RjQr7kiqRVYjDmdpIycTJKCKOEAUJB588WPf6hFds5jL65uMT1s7NDe1O96j1Ubt/PZE/qlln9Pd750ChGkKXkh6MMPL925y0jNjTM19nlGpBxUUjFm838+Fdk+EDgRWIw3BesjwJvA8f7+hf7jK8BF/n784sxDAIdX72c78B7Qz8wOds5tiDlGVYje+ATPB0/s3qxd+Bvs6LfG2dT4qfRvdUWkbaXLImytcM2xJ+d7Uylunl6XtrZNdJzbv0O72Fo/U/1snbhATjC16uufGkLnDu0Sjx2XeZTuOko5JTYc3FFAX8pNNBgSzpxJV6g5UyYPeDV07v/7SpZv2M4V449oMQUL9k4PCwI+Hdrtw6LV73J0/63NAiz5BG1KHqQphGee0apbIiIVqiiBHufcZEIFkc3sXuAL+Murm9kk4H7gTjObCJzpN73Zf5wCfBeYYGYPA4OADsBDzrnloWN+GfirmdUB5+AFgH5WjGtqa9HaPK25WYi7CYuuxtWa/qmmj0jtaU1wJ9P4Ea6TM3Ph2qwyZ6LLq8PepdXD5w2mm4VXvgqvsnXz9DpOO6YPMxeuTQWW8l1ZrBwyaIoZkBNprWgwJByoSVeoGeIza6IBmSVvbWXByi3MqlvfrH14ehjAa2vrWbT6XT593KGMGNiDIX2789iLq1NZQFURtMnHxIml7oGIiOSpqMurJ3HOPWBmvfCKNE/CS1P6pnPuKX//NjMbB9wKTMSb7vVr4D9Dh/kPvKyec4APAfP8Y2xqq+soprjaPNDyZiG4YQrf0KSb3hAYfEj3ZqtxtaZ/unEQkVzEjR/h4E8QnAhPU00a0+KCRtHxM7zaVTDdLHy8IACyeVsD+3dolzrvhJF9sy4EHdePcsigUXBHyk04Kyc6pSocqJlVtz6xUHNcBlBcQOaK8Uek9ie177Dfvixa/S7D+n2AVRu3s2j1uwDNavTUrJdfhhNOKHUvREQkD20S6HHOXQxcHNl2B3BHmvcsAMak2b8LuML/qTpxNwhxNwvhG5r9O7TjmmmvZFy6t1j9E5HqlZSFk092X9z4EQ3+BDVlMh03+r5w1k5QmDndecPCQZHBE7u3KASd7lrjgleZgizKjJRaFGTlBNk2sDeYEg7UxE2XSpcBFBeQibafsWAND89bzT/ffCdVuyfIGApW6BoxsAcXjR7EkYd257jDejbL7MlF3KpbJV8uPVfrW5S9FBGRClGSjB7JLKk2T1RQqHTztgacI3Vz09b9E6l11b5KRVIWXz7ZfUlFl8OP2S7dHve+cNbOZeMGN3t/LuNW9Ni51kLLRJmRrVdzq+FU2KpbcYGNIHBz3GE9mxU8jspmutTYob2p3/ke/3zzHY47rCd9e+yfVTBl0ep3+dGflrB2y06A2FW+AJ5bujG1Ele6vsSdMwhChQs+l3y59Fxdckmpe1AWam6cqfLPMyLlqBjjzD6FPJgU3uZtDdz2xJJUzYnovqlzVwBw+5NLueOppQD6ZlgkB2b2WzNbY2aNZrbZzGaY2YjQ/ivMbLm//zUz+0LccYJVKqr1Q9GFpwyKrZOTtD2QbgwL69W1YyqLZ9m6rS2Wbg/Gurj3BXV04voT3Z+LXN4b7n/StS5bt5WzfzKbZeu2xvYVsv99iaeSVsMxs8+Y2YtmtsvMtprZs2Z2gL/vc2a22B9nVpnZt+OOMaBPHyZPmlQRQR7YG+yYVbc3MyQI4PTtsT9nHt+v1ZktL6/cwqLV73LfnBXNzvnYC//iB39cxIKVb/ODPy5izZYdjB/RN7Xk+totOxkxsIe/2tbe6WSz6tYzY8GaVEDmgpMHNgv+1O/azWMvrk7V90m6zrFDe3PByd5S7MG+YFvJlkvP1ZQppe5BWWircaY1Y4SZDTCzx8xsu//eh8ysd2j/PmY22cze8o/xiplNiOtHtX+eESlHlbTqlhRIdMWUcJp/eInfsUcdzKzFG9i1uynrmhJJNm9rSE13CBcsFalS/YE5wFbg48BpwIeB/mZ2Hl6B903A74EzgHvNbL1zbmaJ+lsSSdkwmbJkMtXjCY8vQdvfzV3BsnX1zYIgccurxx2nmNmG4eygbK81LJvVDssly0fTygrLzM4HHgAagel4i0d8BOhsZkOAacAO4EHgVOAWM9vqnLu7RF0uiEIsMZ5kzZYdqaycPj06c9HoQc3O9c8332HR6ndZvmEb9TvfA+DqzwzjnI8ObJGBEyy3HkwnO3tUv1RAJhqIisvUCV9n+NjRTKGKK+qs5dXbTGvGCDPbB3gCOBL4C94CNp8DPoi32jHAt4Fr8W4kHwTOBf5kZsc45xa3yUWKSJuqiUBPJacghqcDxNWwCPZdNm4wdz+9jOdf38SsxRtSbfIRTH0A2qTej1SnSkl1ds6NCZ6b2UjgZeBQM9sPuMrf9RXn3CNm9iW8VQGvBmoq0JNOuqBANvV4wsupByttBcuf9+raMbb+2OZtDVx+z7xU4CSpUH0hAxWZgkjppm9t3tbAEX26sbtpT9rVDsul/lm5BJyqgZkZcIv/crxzbnZk/52AAZOdcz8xs1OBZ/DGmYoO9MQFNtJNrVqzZQf3zVnBRaMH0bXTfs0KKUffc9+cFakgz/XnHpPaHpzzuMN6ct+cFYwf3ocZr6zlsyf0a1ZvJ2759PB0sqRMo+jS7OBNwwqmZgW1foLtFRfcCevTp9Q9qAkFGCPOwAvyLHLOnWZm+wLLgVFmNgZ4Fvgv/3BnO+deNrPVwPeAbxGpoyoi1aEmAj1BCmIlCk8HmDCyLxB/AxDcyMxavIGxRx3cqpuEoO5P0rlEslFhUyq+ivch6VR/008ABwz1X78UeRzedr0rf+mCArnU4wFSQZBLxx3OF3/+HMP6HcAXxhzWrH3wnnBAKCqpns6ydVv5zgMLuGnSCAYf0r1V1x2VLhA0de4K7nhqKTecOzzteYuRkVSogtmSt8PxvlnfBXzbzB4H1gO3OufuBILIX3Sc6W9mH3DOvdumvS2ydHVq7puzIlWg+chDu6faAS3eE2TwXDR6UGxQpm+P/bn6M94UtxEDe6ayduLOGw7GBCt8BZJW9goydYKCzUGAJ5wNlHSMdMcvK3PmwKmnZm4nrdWqMSK0/2UA59weM1uAl7E8HHgT6Am8D8yPHEOfZ0SqVE0Eeipd0g1L9OZq124vOHN0/wPo1bVj3t9o9+rasUWhUZEqdzYw2n/+FvAc0AvY19+23X/c4T92N7OOzrlUIZUgcxCoyOzBfEVXucpGNJgRzVycuXAtL694m83bGpm1eAMHdmvefvO2Bla/vYPDDu7Ct848KnY6104/WB0VnT5VSLlmNrXVuQtVMLuczJ49O8gahPLPHOzlP3YCpk3QdgAAIABJREFUBgEPAecDPzOzNcDB/v7oOAPQG0gFeoJizEDFFGSOSjedKxy86dppvxbtws/DgZxsHHdYT5a8tTW1ylY26nft5s4Zr8UutR4EfIIAUjTAEw7eJAW36nft5vYnXmXR6ndpfG8P53x0YNZ9axOf+Uype1BSbTjOtHaMiO4Ptwnv3+mcczH7m1n/l78w+5FHWPLBDzJu3305vGtXGD8eZsyAAQOgY0dYuhRGj4YXXoCmJu/5M8/Ahz7kHeSNN+ATn/CChe3awUc+4j0fMgQaGmDVqr3H7NYNhg2D557zHrdsgTVr9u7v0cObRvj88zBypLdvw4a9+w8+GPr2hfnz4YQT4PXXvWME+/v29Y6xaBF87GPeY31921zT/Pnw+c9DjXwmlNwVc5xRoKeCRW8cOrVv1+wx+HD/7NKN3HXpqNSHf9VeEGnOOTfGzDri1ed5FHgY+BCwBy/Y0wV4238E2BoO8kBlZw62RnSVq3TCU7SenL8mNQaFgwoXnjIoNX3rsIO7MHHkoS2CI1PnruCXz7wOwI8eW9wsYBOuXRau8ROc+9Jxh/PG+nr69ercouZPLuLG0aQpaW2RFZMumFON2TnhYOp11123qqSdyWxT6PnnnXMvmtku4N/xplxsAPqxd3zpEmrfbH3rAX36MPn004vZ16JLN5UpGrwJt8tl+lNclsxLy99mwcotHHlo9xZZO0lm1a1nwcotqaLNcYIA0seGHJQ6bjR7KCm4NatuPYtWl3HC1pw5MCJ5qmm1a8NxprVjxIaY7V1i9nc2s32cc+9H9jfTe9gwxpx+OmOiO84/f+/z4O/FWWfF7z/+eO/x7LPj9594Ystt/WL+jYf3D/L/DzviiPj9wfZBg+L3Dx3a8jzFvqbhw+HppxXokUTFHGcU6ClT4RuEaAHQ8L7wB/rPndif51/fxK7dXtHS8A3T5ffMSwV7VHtBxGNmnYDdzrk9zrkGM5uB941YN7xv1RYDR+MVRHwT8P+XZ2Ep+luOcgkihIPPSbV1enXtyF2XjmoWRAlWogpeX3jKIDZva+Cfb77DTZNGNBsTg34EwaRAkBk59qiDWb5hO8s3vEG/Xl3yGgPD9YHCgfR0U9KArMbdbAPx0Xbp/hzKPTunBrwJ1OONK1HbgVfwbuI+glcYPhhnVlfbtK2oYk1bCjJolry1lSvGH0G3Tu3zKgwdfk/Qv2if4wJIQfBnSN/uqbpAcYGqsUN70/jeHgA+NuSgZjWEysKWLaXuQa1o1RhhZsF/NMf79X72AUb62xYC/wK2AD2AY4EX0eeZttMz+yxCkUJSoKcMxRUZzVSAdPO2Br7zwAJmLd7ArMUbWLqmnrsuHcVdl45KtZ86d0WLIs4iNe4E4AEzmwu8A5yM90FrE9489luA+4E7zWwicKb/vptL0NeKFp7i9bkT+3PSkIMSx6BoYCIanO7VtSPfP29kav9tTyxpUag+Ok4GU7l2Njbx9U8NoXOa1bMyCaaXDT6kW7OxNd2UtOi2TCuPBf1O14fo70TBnPLknNttZrcB/wPcZ2b/hzctYw/e+NIO+DRwrZkNBT7hv7Xqx5l09XqShJdCf2n527GBkbFDe6dW0LpzxmvNgj3ZBpaSglDhPgeBmrNH9WsWQAqCP0DqMe7c3Tq1T03XSldDqGQuuaTUPagJBRgjHgOWAkfhLRTRAa/mzwvOuVkAZvYT4EbgD/5nnnP84/+o+FdY45TNIyWiQE8ZSldkNGlfsH3sUd403PDNR/jbcdC3uyIha4FlwDigK16A5w/A9c65rXhBoF7AlcAkvOLS33TOPVWa7radbDNLcglMBFO8Bh/SncETsy+EHA6YxBVTjsukiY6TnTt4/909/8bbnH7sB1uVVRPNGso2YBUXvNrR2JTK2MyUmZP0O5GKcAPQHm91m3OBOuBa59zzkFpa+Vq8m7v1eKvp3FWSnrahIX2706dHZ4b0bT4exAVZgm1B0eNg+fS42jbdOrXnivFHpOrrzKpbz5nH94tdGj2pOHJwnnBWEDTP8plVt56H561mxMAezY4Rt4pXpqBWthlHbVq8ecoUuP764p5DAnmPEc65981sAnAHMBZvMYlHgK+Hjn8LXg2gLwLnAa8C33XO1RX7wmreI4949XpE2pgCPWUo/AE+XUHP8L5gWeKbJo2gR5cOWQd2VK9Haplzbhm0nIYeaXMH3oentIJizNVSiDnbAE6xAxPRMSqcqRPU5kmXSRNM/QLSZvKEz5Pp2sPnyyVgFRb0YUdjU16ZOdm2q/Yx3i9gOKC0vcjMOdeEd2N2dcL+acC0TMcJijFXaiHmqEefX83aLTt59PnVzWrzxAVkZixYw8PzVvPp4w7lgpMHUr/zPRatfpdnX9vYrD5OIAj2hJdoj1saPZppE5z77FH9GDGwR7NAUXDc4Hk4cyipTdCvpEBOOHCTLpMnGoCCNsj8CWqa1Li2GGdaO0Y451biZf0k7d8DXOP/pFVt40zJVcFnQim+YowzCvSUoXQf4MP7wh/gn5y/hpkL13LSkIOaTfWKin7oV70ekcKotmLM2QZmCh2YiIqOUcHy68Fj0rmCgE3wGGQThbcnTZnKJyiVb0Dlcyf2b1aDrdCqfYz3g6qrStuLtlMNxZhhb9Disyc0Xyo9MHZob+p3vcezr21k/TsNNL63h9fW1gPQod0+nHl8P9Zs2cGcVzew/p0Gfv23Nzi6/wEtslyihZ/DS6MD1O98L7XqVZC5Ew3IhANFUeFgUrDMelKmTdLUsWynr4UDUNHl24umS5fMbWqAxpny06aZba21dm2peyAVoBjjTE0Eeqrtm/ZA+AN8kNEzYWTfrN+TS72eav9WWAqvUr5pl3jFnuKZ7ZgSHaMGH9I9q2XRkwI3SYGPaBZQrtced9xCL3mezzisKV5SjoKgxQUnD4xdIr1bp/a89fZO1r/TQJ8enQFYtPpdRgzswfgR3uecl5a/Tf3O9+jTozMDDuySU32bbp3a02G/fVOZMX16dE5l5YQDKOlWCIu2yabGTlxQJ9spW3GFoYtu3jyYMKFtziWSg3zqe5XMsmWl7oHUqJoI9FTTN+1xq8sENy9BRs/gid0TlzHO90N/tX8rLIVXa9+ASW7SLUUeDmIkZTEGbZLelxS4CW+Pvrc1Y1vc2FroJc/zGYeTrkvBeymlaHAj7tv5i0YPomnP+ww4sAvHDOjB8g3bU5k/j724mn699qdPj8585ZODOeSATnTYbx8a39uTytYJF2yOK9wcFFJubHofHHx0cK/YWjrpMgfC+8LXlPSebK47STZBp4I799y2PZ9Igui/lXxW0SuZSy8tdQ+kRtVEoKfS5PItcNzNS7hddBnjTKvZJNG3wiJSSNGAS9JqgpmmmiYVNY5O34oLZgTv/d3cFfz+ypNTxZ3zERdQKfSS54UchxW8l1KKBi3ivp3v22N/jujTjYfnrWbVpu0sWv0uRx7anfpd7/Hnl97iwO4d2LS1MVXfp8N++3L/31fSYb99AVLLqy9YuYX/z965x8dV3df+uyWNRpKlkSVZsl7IkkUk2ZYxwnZscMAxKWDMdSDUMUmgpCUpSUoTcpubliTNbUMaQsrtLTSlCVxCE5KQhlCCwyPmEQwBg41thC35JSxLlvW03qPnaCTt+8eZvX3maGY0eowe1lmfjz8j65zZZ5/Bszm/tX9rrT1HW2js6PcbXyVeqU6cW68sCFhAhiJ+rMfU74L56FjvW/kOHTnTyd03rJh7EpQXX4RVq2Z7FjZsjPmuzQrxOVk8+ihcIA0HNuYXbKJnDiJQ4aJ+v+2yHPo8w/R5hmnrGdTFSzAz0m2X5YSMMZ5uHw4bNmwsLEy2M8S8pjz4wrGgaYLjSU2DmRoHI4+skq6f//E0VU1uvvlkuZaETVe3y1xeN23y3sZ0YjJ+Ge6BIXaXNwCwqSQDGNsNo5Cfnsgly1JYV5jGD3YZIUEpCbHkpi7SXT6BdvjXFaYBRsR5WUFqwN3/dYVpHDnTiXvAi3tgaEwBqTt/fN1C1nh18+tEfHTcA0Pad6iirsvPzHnOwOOZ7RnYsIF7YAiPd4QdG/MCfqfmvF9PVtZsz8DGAoVN9MxBBCpcAP3zImcM3/71+yxyxoRl2qykXA++cCykLCIU7DZ/GzZC40L1AhsPj7xSxf3PVtLnGeZbN18C+K8X6hwwjIfNUlKFYGmCVjIi0HqlftfWMziGGH/pcCNFWS4/7zLrtX711St1XHuoewoHkVwngxFXk8VcJqEmgoXmBTZX03Am45ehoskVVES62etma1mOXwz6rgN12rfnS9cV60QrVeiV5CTz8O6T3LwhD493hL0nznH75uUULg1sKuweGOKJN05TUddFRV0XrnjHmPkrP59fvlnjJx9Tc1Lnm4vRrWU54xaceyqbqajrYnXeYoqzXdMiQZn2gvdTn5r6GBcA7HVmdqHWih0b8/z+fYeTRDcnSKB162bnujbmFezUrQWCYIULBN7JDgfj+WGMV6DYbf42bITGheQFNlWY1wuA+581duDLazoCEhWhfHgm60Vz21XLtXT1xfcadAx6R69HG9cvSYoL29wZoKqpW5NCgWRekVwnFXFl7Xpa6FhoXmBzNQ1nMn4ZW0ozOXKmk4q6rqBjBequMR9TXUEnG91U1HWRnZpAY0c/Ld2DWqpV3dJL4dJEnt5Xh9MRPUYyVl7Tweq8xeRnJI7x+DH7gRw500l5TQeFSxM1KWWGKkZvvfI8YRXK10d1G01nATrtBrVPPAH33jv1ceY57HVm9qAI1O1rc/X3HIx/3+F00M0J0+bnnoO1a2fn2jbmDezUrQUGa+ES7GczghUi1lQua0ESzOdCwW7zt2HDRiB84ZqioIT0tsty+M07Z/jK9SUkOGP45OXLQkpJITRZMhEz5iVJcfz4zo1+nUUA33yyXJNNwQieQPcUznsjuU5aO5HsLksbcwmT8ctwxcdy9w0rQiZdWWVcgaLJ1U5+Zkocn928nN3vN3LzhjwO13ZwstGtyRlVCJqJFtWBs6kkg8dfO+VHOlm9c4qzXVTUdeHxjmoSxzwnMwkVqriMZOE57Qa1l102PePYsDFJqO94WUGqTt6z/jtfV5jG3hPn2F3eMKabbl1hGsfquzWxOiu49trZu7aNBQ2b6LnAYC5EzEXOi+81+KVyWQsSRQT1e4b17nuwHXcbNmzYUAglp3rwhWPc/2wl163J5sd3bjQ6aG4IbXgciiwJZcZs/l2ouSmJlnoNRJgEW++s77UiklLYyRrpzxXYxJSNyUCRIh7vCNUtvZTXdHCsvpvbNy/XaVqqK6i5c5C6tj6+8YnVuAeGONEQzc4r8omJrmNTSYaWeSlpmDJqLitIBRjTWaR+t+vAWVzxDjaVZOB0ROPxjmiiBvAjbcKJTI9kWtC8Mqi1YWMcmOWQm0oyxkgw1b/3XQfqNOFb3dLLXVuLNdlzsLqd8poOVuYm6zVgxlFVBVdcMTvXtrGgsSCIngvZO8P68GwuRKymo+ZXszzswReO0ecZ5qXDjZQVpPLdWy4NWGQFi2y3YSMQIqlpF0JcNYHTB6WU70ZiHjbOI1Ahb5ZP/eKPp6dMSIQyY1av4xEKVqmWOaFQkVHB7u/F9xpCnhMupoOkmeh9zzbmGzFlIzKw+mWYiRyzF486VxV5YEg/s1MTKK/p0H8HuPuGFewub6Bn0MuRM506Sv2Xb9awOm8xFXVd5KYm4Epw6M4bw1x5lOGRUd3xs31tLrWtvWwqySAp3qFlIvs/aOVctwePd4StZTnsLm/QprA9A8Y13f3njZxhYoRLOB4i4Ua8T7sHyXvvwU03Te+YNmyECXM3j/LrUsl6W0oz2XXgLLXnetl5RT7b1+VyoLqN8poOHt59UpM9gYhVsxF8OH5aU0ZtbWTHt2EjCBYE0XMhe2dYyRxzIZJ6lVP/PthOs3r/PTeVcs9NpX7nBzvXGtluw0YgRFjT/jogwzy3FiiM0DzOX+QCJpTDQaBCPpB8arJ+YKEkWlPpdAlFRpmvGWhcq+l0uERLuBKvUJ9VqPseL1Z+NhApWZttkjp/4B4Y4uHdJzVBo+LIAb8uGUWQmD1vtpRm4nREaxJnXWEaK3OTNbmx84oCvv/bCirqunjgd8f4+sdX+q7ppaKuS8ezq/Gdjmht7nrJshQtt6qo6+KJN05z19Zi7r5hhd98rXNSRJUycnY6ogKSVUomdrC6PaCsK1R8u1miNhtSMG6/fXrHm6ew15mZh3tgCHe/l8yUOMprOviX544yMiopykrCPeBld3kDzx2sB6C15wTpSXE0dw7q8//+v8r5uxtLyUldFPA7ozqArL5dEcGdd0Z2fBsXBGwzZhtjYH54thYiS5Ligj7wt/UM8q/PH+PQ6Xbu3lbCJy9fpmVfwdK8wo1st2FjBuABmsM8dySSE1G4kAnlcGBei0IZKo9HxATrVOnzyUqtnTfWa9121XL6PMP0eYZp6xkcl+gI5uVjnWsgosJqOh0uwRSuxGsipFWo/xdMBtPdIRQp+W8kCeW52Dk4l0xSJwplfJydmjDGL0PJosy77sFMmZX8Iine4UeI3L55uTZh3nviHE5HNFeXZuKMicLjHaU428W6wjR2HajT11cEjLqOMlze9e5ZAAa9w1y7JgtXvIM1+ak89XYt29fmsq4wjafersE9OMyKXBcXZ7oAApJVZpmY9b4CvQYibmZLCsZ//Rf8/d9P/7jzDLYZ88xjT2Uzzx0yiBxXgoPj9W59rKqph+1rc1mZ66Ktx0Nz5yDNnYOUFaSSm5rAc4fqae4c1KStlThVks/8jMTIfG+sePRRWMDPhzbCw4yYMc/FBxsbgWGWUqlXCF6IWIutf/v9CQAS4xzawydUoos1st2GjVnEPinllnBOFELsifRkbPivDw++cCwo0RCss8NMLAQihu65qZTr1mSP6bwxG8mbcf+zlZTXdIQltQpGQpjJbbXGqqj4L1xT5EcqffLyZQHvayqYSBeM+R6mo3tmqmTRXJeShYnXmWOdg3MdoWRE5pSt1yqacSU4cA94ee5gPR7vyJgkq/HkT2YS5a6tRtT6vbes0XHLiixR3Tu3XlmgJV1qPg+9cJyKui7c/V5cCQ6yU+OpqOviVItbF5bd/cPce8safe7w6Cj1Hf1+nT6rchcDsGNjniaTzAbNqgNpXWHamM9HyUsCmTqbPwvzOebPNqK+PE5nZMa1YWMcbCnNxN3v5cDpNpo7B0lPdtLa7SE/PYHB4VF6PV6O1bvZvi5Xk7kAnpFRirKSQEC6y6m/t3CeOD1Y3U5FXReXLEuZmcj1/PzIX8OGjQAI1NHzOvaDzZxCMG+cR16p4v5nK3mtsok9R1sA/Hx3brtqedAH/tuuWk6re5CKuk6+fuMqXj3SxD03lfKFa4rm80O5jYWDTwU7IIRYArRLKeV459qIDEIRDcGi1IMRC9axrJ036uc+k5F8MFJoojCbSltlq6rzcZEzRs97kTP8JtlwiJDJdsFM5H3B5jFVsugC8eSZc52Dcx2hZETm5ColpVqdZxAkR+u7+Pkb1TgdUWwty9FjmWPUzX9v6OjjyJlOVuS6KK/pYE9lMzeuz9Okh3tgSHcI9Qx4depOUrzD588zwq4DZ3UBqIidFblGZ87FmS4uXuriwOk2Gjv62VPZTH56oj7fHMmORPv57NiYxxNvnPaTp5k/hwd+d0zHvltj3gOZOpvve1Ziordtm5nr2LDBWKLYleCguXOQ7NQEvnRtEScaunmvpp3aVjfDw75HPAk7ryjQJusK2akJvHy4CcAvqQvOd/S4B/y9tSKGoqLIjm/DRhAEeiq1H2zmCKqauvlfTxzEOzLKWyda+fkfT1PVZOwwmR+cL1mWwtWlWfqBXBFAfZ5hvnXzJQHlW0uS4vjep43YTJWM891bLrVJHhvzAlLKFuvvhBCXAc8COUCPEOIvpJS/DXSujcjCavYeyMC9rWeQLz66TxMnwYgFKzEU6Fq3XbWcR16p4u5tJcTHxvCFa4yHKkWQf++ZIwCTJrLNnT0qocc63z7P8ISIjblChASbRyiyKBySKpJR8zMIu3NwghhPRqQMVUtykomJrmPrpdm09w5xvP58B43TEQ0Q1MdmXWEa//DUYdz9XlbnLdYePhDYZFVJxgBu3pDH21UGeZOZYvzbXZ23mPz0RI7Xu8lbsog4RwxXl2Ya3h4fvsgv3t2V4PDz23HFx/LU2zV+pFUgeZp7YEiTPNmpCWM+H+vnpgpej3dEe4lEVKIVDL/+Ndx778xdz8aChpXMNHcBHq7tYOcVBbj7vRyvd9M/ZHTxOh1R2rh9S+lSKs92kbIolqrGHlbnLaY428XWshx6Brw8vPukTuxT3lqueEfkidOXX7ZTt2zMCgIRPZVSyvXhvNl+sIksvvlkue7UKcpyUdXkpijLpSVaqpjp90kHgmGinhjh4AJpy7dxYeGbwG4Mojod+D7w21md0QWAqXzXQxm4/+KPp/3kouF0oYSSoiqy2vx7c8Q7ENR/bLz7NM/tWzdfMuaYIpruuak07HV0rhAhk5lHOCRVpDx5Zhi7J3Dugu0ctO7ChyqazHHIinxRpMsleSm44h3kLVnEY699QFFWkiZLzCTHw7tP4u73khgfQ1ZKvDZmVYlYZpNVJf9QBq3Kw8fpiNKeHndtLaZnwEt9Rz+x0dE6hUu933w/W0oz2V3egGd4VF9TobVnkJ35+ToG/mB1u/YT2lPZrEmer398pZ9Zc6D0H1Xwrsh1sTpvMesK00J+thFL3tq4cfrGsmFjHFjJTFd8rO6k21PZzNH6Lj7zkeUcqunQ36etZTnaXDk7NYHWbg9dfR6WL00kPyORTSUZ7Kls1oQRwO2blxsePekz5NGzfXvkr2HDRgAEInoWCSFypJQNYbx/wT7YzATuvOZDHDjVxg1rc/nqDSu0WfKL7zVoj5zymg5dQIFRhHzhmiIWOWO0KWqfZ3jcAqTPM8wjr1T57XiHKnzmym60jYUJIcSvgHuklGcsh/4TaAIygE0zOacLNXUr3O96sHh1CGzgbiYYrEbx5nHMfw9mtByIrGjrGeSRV6ro9wzzletLSPCtiVO9z2DvnWhX5HQTIZMl5CYzj7lCUkHE03A+I4R4CTgupfSEOnGmOgfnQhqOFZORFJkjzge9wxyvd/ORYgc7ryjgqz89QGu3h9ZuD3tPnGPnFQV+JMfNG/Jo6R6k9KJkLc2A851AACtyXXi8I0Yyj8/QtawglatWZPDjV6rweEdZnbeY3NQETbQogmfHxjwtxbLGvpvTetQ1N5Vk8PKRJpo7B3lmf52f+atCSU6ylp8o8kd9doHSf7aUZvoZOJtJo+n6bxAWenunb6x5DDt1a2YQiMx0OqIAaO8dor13iB+9XMWXri3imf113L55Oa74WNYVpvFeTTtD3lEcMeAdhtMtvZxu6aW+3fDTuu7SLNp7h7h5Q57u6CnOdkWGILXi4EFYuzZy49u4IDBTqVsCuF8IEQX8UEq5L9ibbUlEZPHoKx/Q0TfEue5BirKSxyTDqB3xwqWJVLec/5+x+cH9e88c4f5nK7nnptIxD//WNBvw3/EOVfiE86Bvd/3YiCCeBp73FWH/JKXsAt4E9nLeY+ynMzmhCzV1K9zvulmGZY5XD2bgHoxgMJsrL3LG+K1PZk8c81oVaCxFvgBjOn0CYdtlObx14pzumJwI5gLxMZPk+1zq1olwGs5q4BAwKoSoAY76/lT6Xk8Az0gpZ2y7di6k4VhxnrQZCcvvQnWfADx3qF5LqBS+dG0RP9hVSe9A4E7lEw3dNHb0k5YYy/a1uSDAGROliRWnI1pLnnZszGPHRqNw3FqWw8O7T+LxjpKdmkBxtkuTLNeuyeLWKwtYV5jGE2+cpqKuS8s0rVISj3cEz/Aozpgo1hWm8fhrp3TXkCo8rcXqM/vraOzoH0MEqfHU56jgio/lrq3FmoQar+sgYrKuykrYuXN6x5yHsFO3Zg9by3JwD3g5cKqNXs8wjR39HK41SNm9J86xqSSDJ944raWfKYkOHNFRrLpoMWmJTjaVZLAyNxmPd0S/F9Drwoz4XjU1jX+OjQWPGUndAu6QUr4jhLgI+LIQ4h8xdsl/I6Ucnc6LzxTm6077fZ8pY2h4hJIcF1VN3WM8Lsy75erYRMgVVRhsWbVUe1sE23G3YioyCxsLA5HcAZNS/rcQYhfwJWC/EOIR4IcYxVcZxkL53+ONI4R4DKPz5yIMf7L9wN9KKSt9x/8cY/2zYr2U8uDU72TuI9zvupJhbbssR5vBT8UPR3ne3HNTKd+95dIx65G1eydQN5GStIZDwKjkwY+UZEw4VTDYZxRqPZ5uInwukE0XKAQQDVzs+/Nx07ERIKznIiHE68Bmy6+PSilLfcfvAv4GyMVYv+6TUv5sKhOfKbjiY3E6ovnlmzV+XSnB5ESq+2THxjzKClIpr+mgrCBVy6CKs5N58M/Xs6eyWceYe7yj2qh5XWEaf6hs0jvya/JT+eHvj3PkTCd/ttnIB1mTn0p1Sy+bSjL8OmG2XppNdUsPn928nIzkOHYfbqR3YJimzgE+/7EiLSlTki4wiCM1D/CXWCl/HoCPFGcE7bq5ffNyhkdGdQfR0/vqdLeQeTzr52pNIpssJi3t+vznp+X6NsbHVNcIIcQa4EFgI9APPAP8jZSyx3c8DngAuAVIAt7zHd8fubuaOlzxsaS74ujs8wKQGB/D0fouTey8fqyFNreHmCgYHoXOXi87Nub5deIBPP7aKbavywXQKXxbSjP1eRHFnXdGdnwbNoJgDNEjpXzH93oW+FshxCLgL4BXhRC/B/6fb/d83mC+7rQXZSVzdWkW3/71+5xocAfcLbcaLasOntcqm3j8rzb5ybisuO2q5do74+rSrDGFymSKF+v45lcbCwuR3gGTUg4DPxRC/BT4BlAO3CulfGACw3wO2Ae8BfwJcD1wiRBKBdhEAAAgAElEQVTiYiml2fn3FeCY6e92N6MJ5u96MII33HXDbOSs1i4l4Qpmqmy+pnlNtPrphJqDtaNnvPmGcz+hyO5wiPCJkEHW9druqJwWfAL4NnAYo7NnBbDK9ycd4xkq3JRShYdMPzcBCCE+Bfw70Ar8CoNM+qkQollK+dJUbmCmEKijJJicyHquWeakYsnNXj5WqRRAc+f55fnhl05wrtvDuW4PD/zuKO5+ryaQwJB6PfV2LVmpCRw504G738vzh+qJiY6id2CYzJQ48tMTcQ8Msa4wjWP13bozxz0wBMDeE+cCSqwUVuct9nUe+BMqZg8e1UG0Y2Met15Z4BcBP9VugvGkW5OWdj32mG3GPPOY8BohhEjCeE5Jx9jkKgA+DyQCn/aN9SDwBYyOxD9gED6vCCGWSynbIn5XYSCYZ5WSZrX1GJLO4/VuHNHgHYE2t6GqHfZR7kVZSYDRqfOHyibWLEvh8JlOmjsHiYmO4q6txX4kkPn70NDRx+OvnSI/PZEbP3zR9Mm5Hn0U5mEdamP+YwzRI4TYAGQH+JMJ3A/8byHEE1LKu2ZyogsVale63zNMcbaLs+29fPwHf+D/3L6OoqzkMcVCv28He8/RljExxNYH/yVJcX5ysFDeGMGKqlA7/XOpvd/GhQUhRAnwz8By4AjwdeBHwD8JIf4n8HUp5VthDLVOSnnIN2Y+UIOR2rUSY7dL4Ukp5U+na/4XGszf9WAE70Q7/Kzrh1mKVV7TwY/v3BjQo2ey5Iq1o8eaXmhFqONq7VSkUTCiPdgxNUYgOZz1GsGIHLujcuqQUu4SQrwAfBGD9PmBlPLLAEKIJUAp8PAEx/xqgF/f43v9kq9b8XPAYxgE9rwgegLJlYLJiaznqp/N8cgqIt3jHTF24aXh1aHGUnKnrWU59Ax6ae5sIs4RjbvfS3qyk5s3GGOaDZhV5012agJZKfG8fLhJp/I8va8OV4JDv2dlbjI5qYs0QbI6b7Geh7vfy1Nv17CpJAOA7WtzcTqi6BnwjolWN3vwKILHTAKpbiEzwRUKwTpzxpNuTVralZo6sfNtTBmTXCM+h0HyPC+l3CGESMQghXYKIb4F9AJ3YHQgfkxKeU4IMQzcBvw18I+RvKdwEcyz6mB1O8fr3WQkO0lJdNDZ68Vryn12RENOagK1rf1ERws2lWTodL3mzibfeFGsL0zjoReOk5+eGPD6SrZZUddFfUc/d20tnh6yx45XtzFLCCTdegf/HSphOb4I46HHJnpmAEuS4ljkjOH+Zyu5bk22fuj/5pPlPP21j44pFhKcxn/SwqWJbLssx+9hHxjz4G8uph584ZhOyFEEUKBCwe7UsTEH8HOMtagN+DDwoJTyk8BnhRBlwD8LIfqklDeFGkSRPD6o/5uP4NtFM+EhIcSPgDPAj6SUD1mOa4koMO9kotOJYASvdd0Il1g2v7/PM8z+D1p56XAjX3x0nyZ7wiGaILQPj9nouaqpm/0ftIa8zwFftKt6NWM8kkXd6+XF6Xzx0X3c95kyirKSx4xhTiWzfj6RSFOcT3j99deVPBQiaJLq6xz8dyHEz4FvCSH+GviWlPIo8LoQ4t8nMp4QohPjueoQRvFWjkEYARy0vF5qfb8ySQXmlCFzIIyXwGVFoE4fJbGwjmOWNH3y8nyWJMXxXo1RDLZ2e3j3gzYKlyZSuDSRD2W5+M/XT7EiJ5lEp0HmnGoxZB/5GUbBt2NjHusK03itolmnXKm5KFPkS5alAOc9PVS6VnZqAo0d/frvq/MW4/GO0NDRp4kqZ0zUGImWuWtJjbmlNFNL1swR7grBOnPCTTubMDZblUQLCzO1zpgxyTWizPx7KWWvEOKE7/glQDfgAGqllOdM597GHFpngnlWbSnNZM/RFho7+v3Oj4uNZnBoBO8IxPvqn+P1bu77bQWlFy0m3hFFR98Qg0MjDAyN8Mu3TtM3OEJFXRdOR9QYeZeSWA6PjlJe08Geyubp8e7Jzp76GDYuWERynQlE9Pwe2AoMARVAI0bR02j52cYMwezFU5ztoqKuk/s+Y6zp1qLqC9cU6SQu5dtjHsP6s/U6Ssr1yCtVAAHTuuxOHRtzADHAKimlFELEA3vUASllOXCNEGJruIP5dr+UF8//lVIqomcUOIAh3UjDaJd+UAgxIKV81DzGfJWIzhSsUlMrURGIuLCSG1+45vyu2EuHG/nFH0+HJTk1G88H8+FRpPq3f/2+8YB3tIXr1mT7XdOM+NgYv1czxiNZ1L0WZbmoajIKzqe/9tGgYyxJitNEvPp8FDGlJG1WOduFvk6bydTvfOc7tZG+npSyG0POfgPwByHE88D/llL+KMwheoDngQbgcuBqjJ34lRgeQGDsugP0+V6ThRBxZhnpXDJJnQhUJ4oiMKxEhvm42agYCNrxYu5uuXG9QdT8YFclzZ2DHDjdRnPnILdeWUBdWx/NnYN8rDQLOE/UZKcmgETLqczdOOa0r5s35NHU1U9rj4frL83Whajq6FFkz80b8rTp69P76nRHQSCiyjx3M8Gl/HtUFPSx+m6/roKImS4Hw29/C2Vl4593gWKG15lJrxHAUstx8zmZQNw4x/0wm+uM6nKzdq6VXpRMc2c/o6ZWhLy0BDr7h2jt9nBxpouYqCgq6rpo7fawp7uFxPgYP1P32Ogo+hhhicuJxzvK0/vqtFRz7wmD/7r7hhW6O08RvlPG66/DAt38szE+IrnOBPLouUEIUQx8FYNV+qmU8tmJDiyE+BmG58USjMXrIPANXxG2YE3FJgNzgfTZjxby4nuGfjWQ4alVjmX2uxjPr2FJUpwmkAaGhnnoxRNctyY0C237QNiYJXQCXUKILgwC5jfWE6SUu8MZSAiRDrwIrAP+H/B3psM/l1I+YTr3+xg7bH8K+BE9CwWT/c5bpUjBzJXN5EggMkilCF5dmhV2t4qSWX3l+hI/Y2crAsXBB7vHUP5n45Es6j2XF6fzwK6jet0NNkZbzyB9nmE/4t3c7Qn+iYlThb2uGxBCpAIbMJ5DNgLrgWSM3fa/wHg2+UqYw31cSil948YCVcAy4BqMLsJoDD+Ndt8rQLfFK2xewj0wxMO7T2oCtbGjn+cO1ePu92pDYkWOKILD3e/FleDQHS6Bulisv89JXcQ/fapMX6usIHVMV0DPgJcjZzoZHh3leL2b5IQYowNn2NjBz0yJo7lzkJONbp0g9sz+Opo7B2nubMQVF+PXBXDX1mJ9vRMN3Vpyprp7ygpSAxo5q7krIsdKBOWnJxITHTWmq2DSnTmTxQLv6JlhTHqNEEIo30CzJkn93IzR0RPq+JyA+XthlUC+fNi/0doRI6hq6mH72ly9VgD8865Kqhp7SHBGa5InPz2BeGeMNnBWvj7Ky2t4ZFRLO5UPWHmNkexl7fqZFP70Tyf/Xhs2poBAHT1IKU8CX/I95HxRCPE1jDjjn0gpewO9JwCWAW9gLC5XA9dhGBkuWyimYtMJVfC8dLiBt0606tfXKptYnZdCgjNmzI5uoPdDaL8G5VNRVpCqpWKBds0nOq4NG9OMW4FvYawNlcB9kxlECLEMeBkoAr4vpfym5ZRC4FSAt87LBMLpgDX+PFwywCpFspIhgcgRK/mjpFefvHzZGKlTOEjwzTcYiWGeQ+pVzoDnmUmQya555utYO3kCQRFc373lUr+5TDRZLFzY67pGG+el7GYZexNGQl9Y/jlCiARgMYG7oYcw0gIvwZChnsEglMDoJJz32FPZ7CdxciU4NJHT1DXA3hOtbCpJ9xEuRrdMbWuvLryUnKPVPch3nz7MzivyOdHQTUlOMqvzFtPa4+Gpt2t0apeSbJmlUooc2VPZTEVdFzs25hHniNHFpMc7wuq8xey8Ip9n9tf5ESxKzqFkXlbSSV3P3HlkjlAP5DuyrjCNPUdbKK/p4OHdJ3XXztayHL8UILNR9ayg0W7gnwlMwxrxPnC77zi+OqoEY/2qwOjk8QJ5QoilUsqWAGPMOsxdfCtzk9lSmqm9uvLTDQ8ehfSkWBo7PRyr72Jl7mJ2vXsWpyOK4qxkqhp76PeMsCgumr7BEWJjoomJijLel+yktdsgetR31+M1iJ7VeYv9vm/TZpj++uswh2W2Ni5cBDJj/omU8nOm4y9ipM38DfAdIcRPgB9KKWtDDSyl/KhpzMswtKa5QggHC8RUbDqhHuJ3v29080iJJmL2HD0fALTIGUOfZ1ibhKpCzOw/0dYzGLQwsxZWVkPn8c63YWMm4JNW/fU0DPU2htl8HZAghHjQ9/snpZTvAo/5CO8DQArno5V/OQ3Xnpewxp8rT6/xyB4zKREK1m4SM9Hwm3fOaCI6kEFyMKjum22X5eiuolBElbn7qK1nkBMNbu2jE4gEqWrq5ptPlo/x2pmuzphg6+ySpLgJJYtN9XoLFAMYHcn7gXeB/VLK+gmOkQGcFEK8hlGkXY6xGdaCsVEVjbGmPOyTht3oe9/9U5/+7EMlWd28IU8TND96uYrGjn4On+kE4PCZTr37npkSR1ZqAsXZLl10nWx0a+Knvdd47+q8xdo4Fc7vxCspVjDTYo93hJ5BL4PeYYqykqhq6qGqqcd3Ri13XH2xX5F5sLqdu29Y4ZfC5e738vM3qjUhdeuVBRysbueXb9boLiUlP1lXmIa730tta6+Wguw9cY7Gjn4yU+L8SKVgRtWzhg8+mN3rLxxMdY14DGPza5sQ4mmMoAon8JSUshrAl1D6lxjS00pgJ0Y9NSGfsUjCFR+rJYwKSs6YGBftd25Xv7FeVLf0Ut1yvgchJTGWa9dk4Yp34B7w8vLhJqKjBRV1XWSnJvCla4s4XNuh15RbrywwSGFfV5BZJrm7vIEdG/OmTra2t0/t/TZsTBKBOnpuEUJch7HoRFuOCeB/Al/mvHFpUPhMC1cCH/P96l8w2OUZNRW7EKAKnm2X5eiCIjXRyR3/sZc9R1soXJpIv4/g2bJqKV+5voT9H7RqEui2q5Zr7x7V4h+oILAWVuPt5l7oPhA25iaEEE9KKT8zDecqbWIecLfp9+9jFHW/AO7EkGpFYyR8PSSl/MWkJn4BwCwHVWtKqK4/8/vMHjiByKHxkqZCGSCHM+cHXzimu4pgrDm9grn76MiZTr2OBjLAB8McX83Z3KEzXZ0xE1lng0XNT4T0sdd1jTaMjr5koBgYBpxCiKPACSmlRwjxb1LK8eRb7cATGN3NH8V4hnkW+HtfB/KTvhSvrwKfwZCzf01K+fvpv6WZx8Hqdj8ZRFZKPPfesoY9lc3kLVnEz944zWc3L+eDJjfH6rs4Vu+mudMgdOF8Fw4YJNBnNy/n+UP1mjhOTYxl6eI48pYs4vlD9UY6FowhXfaeOId7wMuRuk4dz74i16XlWgAVdV3aO2hPZTPuAS/PHazH4x3Rnj1OR7Rf5Lu1C8Dd7+XpfXW8cqSRc90ePN4RXAkOKuq6eOKN09y1tRiPLwv6kmWpfKzU6f/+IMlas4LPf352r79wMKU1QkrZI4S4BvhX4AYMgvpxjJpN4W6Mrp6dwMXAPt8YoZMHZgjq372ScSqcbDTkVusvXsIfj7cw4kvb6veMkJkShys+lqpGN44YgXdY0tk7xJG6Tj5SnMH1ZTmku+L8yOUTDd04HdFU1HVpaeXu8gY8w6O6K2hrWY7uxFPrkHmOE/5u3nnntHxGNmxMFIGIngTfHysG8DdlDgc7ACXwrQf2Ynj2zKip2IWUhlOUlexXSDz+V5t0YZTgjNFdPrEx0ew52sKWVUvp8wzzyCtVfrIJmHoRYvs42AiEGUqp2CKEeDzMc0uDHZBSWlMFrccfw9gpGxdqnZnva0y4sPqBhQOz4bsih8zriFXepaDOkT4RTSAD5PFg9rlRBsvBPHbMZE5Hr0eT6+q+reulOmb22gnkqzMTCDdq/kKBb63Jj9DwP5FSfgNACJEHrMJYT64FSnzPK3mM49Pj8xb8y3HO+Tfg38abkErDmeuJW2YoEsM9YBAgijRRZMrXP76Sg9XtWnp1rN6tO11UAbZ8aSI9g17uuq6Ew7UdmvgB6OgdoqN3iM6+UzR3DtLeO8TXP278e1e79sr7RyEj2cnS5Hjy0xN57lA9mSlxXJKXgive3xdoRa5Lz13JslRX0NH6LiPlq2eQngEvOamLuHF9nvbjOeeTh6jPQPmOPLz7JLmpxmO2Ky5mTNdOME+iUIgYOfTYY3DvvdM33jxFhNeZaVkjfB6oHw1xfAAjMXnc1OTZWGfUv/sdG/PYsfH8v3slqUpLdPK3Hy/lsdc+ICnOwbL0RbS5Pey8Ip/DtR00dQ1QXtPBiByluXNQG6J/dvNyHn7pBM2dg2SmxOmuuiNnOkl3OXn8tVN+awOg5ZPqO6s67oJ9N8f9/j36KNhhHTbGQSTWmUBPy2eBH3Ge0GkEGqWUXQHODQkp5Ud9D0LXYZgpP43BIs+oqdiFlobT1jOoU7G+cE3RmGLrF388zbbLcigrSOWtEy3c/2wld287b0KqSJlQUcPBrmu+TqiddxsLFzOUUrEU+Cz+vhnBEKk5+OFCW2fCwUQ7PwKRQ9YOFDDWJrWOvfheg5aj3nNTqZ+Zcrhks7lTyOxzE2zu5vtakhQ3ro+OlYBX9xXIVyfSCDdqfqqYK0S/b62pjcTYiuTx/VyHIfHUXTZCiCgML8IZw3xM3VJyJEWAKFgTpgDtUbOuMI3fv9/I7vcb6R0838H3zP46ctMMkmRJkpO2Hg8ZyU7OdXu4JC+FKNFNY0c/B6vb9a59RrLTL5Y5PdnJpfmpuOIdbCrJoL6jn/KaDtKTBnDFO+gZMEyid2zMw+M1TJubOge0GeyN6/PYWpaDxzvK2fZ+mjsH+YenDvOdnWvISV3E1rIcTTCtzlusvYLMxs2D3mFW5y3WyV1mhErWClZQToYcCguZs+gPNIcQyXVmLmI21hkl8dxUkkFSvIM9lc0sSXLiSnCQnODg6X11bF+XS27qIt93aJTGjn5auo8TFSV0V55CdDQ0dvTzL88dY8jXQdfcOcjB6nY83hE/2WdsjGBoWLLE5STd5aQkJ5k9lc3cvnm5lnGCv4eQOQlw3O/fPCHlbcwuIrHOBCJ6bpVSvjWVQX1xx0NSyhEfcbMbo/vGhaEbXRCmYpGCKiDgfNJKICnWImcMb50wOjKl9C9q2noGtdwgUNRwsOuqggwIuPNuw8YM4TsTOHfCJLWN6UE48lBrjLiSWCn/n5cON/oRPGZSwdqxEox8CNYpFGqe4RAZoc4J1xctkoikBGshdAsJIa4C9kopRwIdl1KOCiG+OsPTmrewGg17vEbxlZ0azyXLUnTRpAqlyrouegeHSYyP4YqidJo6B/xikC8vSseV4KAkJ5kn3zpNdXMP8Y4oVuS6WFeYRlK8wy+9B4z0naT4WE3aeIZHuWtrMQ+9cFwXfoqk2bExjxs/fBFORxSe4VGKs106QcvsGeSINuRaj75axarcxQDccfXFep5gEDS7yxvITU3wS/g5WN1OTuoiv88pVLKWuaBUnUfWiPZpxdq10zveAoMQ4gFgG4anToqU8sAsT2nOQkk8AXJTE3juUD2JcTH0Dg7zfq3h5XWguo3mzkHKClK5eUOelmMBJMbHsNQVR3VLLwnOaPo9xrKtSJ4lLieXF6X7eQA5YsA7DEPDEleCgza3hza3RxuyAzpJTxE7N67PY9eBOj9iZ9zvX2Ji4N/bsBFhBIpXnxLJ48MGDD3pHzFikK/EIHlaMWLQf8ACMBWbTpjNPoMlrVjTcLZdlsNrlU3sOdpCgtP/P/V4hU8gBNodnu3dXBsLE1LKiRA9NmYAgUiPcBK6QqVtjRdzHsg83ko+WCVbgcYJNM9wSKRQZIfZk2g6o8/nChaIYfNrQK/PIHU3sNvX2aMhpTw0KzObp/B4R9hd3uAjfYwUnKQ4R0Bi40vXFvGjl6v40rVFFGcbm1HKDHnHxjwt9Xp490kdm6zwxBunuX3zcgqXJlLb2ktnr/G+1h4Pta39usA71exmT2Uz+emJVNR1sSLXhcc74pvrqJ8nj/LqUP4hifEx9A4Mk7zISZvbwzm3h+OmdC2nI5pfvlnjZxKt5l6cbUjCrN48qgA1J4aZYS4orV0EETFufuEF2LBh+sddOFgNvIORGDyKEexgIwDMSXTDIwY5s/7iNE429vDJjXn8+p1amjsHKcpOYnhklMS4GO69ZQ273j3LgdMGAXRRmrGmjEr/saOjjUh1Z0wUeyqb2VSSwdtVbTR29JOR7CQpzkHXgJdNJem4+73cvCGPlbnJmthVMk2VFqjkX+r7GIqcBeCdd+C666b9M7NhYzwESt2aDpPTRqAKuAZIwiB4fgPcK6XsZgGYik03VPfN0PAIV5dmBSxYlBRLmTKD4eETyEPDuoseDiZq1GzDxkzAlwS4EaPb74jvtWK8ZEAb04tApIc1oct8LBTMa02wbsNAxEsg8iEcCVWgeYZDIlnPsc5pql091vHmilwKFoxhczqG9Pxa4B+AHwkhTmKQPr8H/iil9IR4vw0TzDHjnuFRas/1ct2lWYBBcpiJDffAECcaurn3ljVjJErmVC2Pd4Tymg5txAqQkuigvKaDPo+XqsYeMpKdACyKi+bDFy9hT2UL3mFDwuXxjvLLN2soykrSUq3nDhmharWtvbgHhthSmsmRM52U13SQm5rA9rW51Lb28j/W5rL7/Ua2XprNz944rTsLrObM5zuYDAIpGIlj/nwAdl5RMOYcc0EZsS4eM/7kTyI39sJAE/B9jHrIbusIgYPV7TR29FNWkMrtm5drU3QwyFwlzXIPeGnu7OHRV6uIc8Sw9dJsTrW4WRQbQ6vbWI4Hh843YUYJGBmBoqwk3jjeQmu3B3e/l7UFqSTGxRAdBe09HtrdHiqGu3D3eynONrp5rP49p1rcHK93c+RMp07iCwuf/vR0fEQ2bEwYgaRbUzY5lVJWEcIQzHfOjJmKXQi47zNlDA2PMDQ8GnSH/MX3GnT0sFnmEM7D+EQLiLlUcNhY8LgBIyVwBfBJ9UshRC/wKvCAlHJfpCex0MyYrQhEspgTuoCwCI9w15ZAxEuo7qBQnSfmeZrX1WASM+v7rHN6rbKJDR9K17Hu43X1hJKcme9xIcilJooIm6ReLqX8L+C/AIQQazBIn63AXwEjQog3gN9LKX8YoTn4YT6aMSsoE2M4b5Lc3jtEY0c/Tke03464tVtFedOU5CRTVpCKxzuqCZ+yglTKazq0ofLek4Zkqqqxh9TEWM51e/xilVfmujhW7yY6SnDaF8tc1dRDcXYyB063AQYJVFHXxe7yBnZeUUBxtouKui6cjijt/VOc7WJlbjIfNLlp7OhnRa6LmKgodl6RryVV6p7cA0N+sjWzv4c67u73kpoYS0fvEO4B77if57hdBNOBDz6Aq66K7DXmAaawzrRjbHz3YESl/59pm1QEMRvrjFofPN5R9p44x6aSDD8yd3XeYoqzXazJT+Wpt2tp6R7gXLeR1KdkoGBIKX3LDEtcRrcdQGNnP72DxoH9p1o51+3RXXlFWUlERQkKlybh7vf6Eb6r8xaTnRpPY8cAWakJHK831i5l0BwK7oEhfv92Df/D+yaLvnlPBD41GxcSZsqMec6ZnC50tPUM8uJ7DWz4UDr3P1sZNBo4UJdOMO8Jq5FyMMlDsILLLjhszCG0YqxZKzBScdZjeIAlAZ8AtgohNkkp3w8+xNSxEM2YzQhFKk9ExhTu2hKudGg8stu6zoVj0BxqTspXaM/RFvo9wyQ4Y8ZN3gomcbN2BC0QudSEEGGT1P8jhDgspWwAkFIexvABfEAIsQgjCnkr8GVgRoie+WjGbDYQVl0qJxu7ae+t4rObl1PX1kdJTjLf/20Ft29eTlK8Q5shK9NTFXO+Om8xFXVdFC5N5NYrjeSungEvg15Dzu6MiaZv8PxufkfvENmpCTR29GvfjR0b83A6Yiiv6SAxLprewRHSk53UtvbS3DlIdmoCawtSdaEHsKkkg+qWXm0UC0aHzi/frGH7ulxW5Lpo6/HQ2u0BanXSl9r1V8SVxztCdUuvn/8HGMSW+XpNnQMR/W8SNs6cme0ZzAlMYZ35MPBtYB1hJnjOBczGOmOWSQK6+2b72lxuvbKAkpxkntlfx6aSDE28OmIEHu+oll9Vt/TiHTlP8CTHG747ZsInzeXUiXi9A8O4EhxcX5bDb/bVsdfna1qc7dLJXypq/aX3m/x+H04n3Z7KZh579xy5S7vZNO2fmI0LDTNlxmybnM4xqCJAGZJeXpzO9/77CHdvKwm6u6yKlz6TjEv5TvR5hnnpcKOOXlcFRKt7kNcqm7i8OJ13Trb6vXcyu+Q2bMwQBoB3pZQvqV8IIS7DkIXuB/4auAf41OxMzwaML3Oa6HkTlQ5ZUwNVqpeSxcLESOtg45UVpNI76GXvyVYOnW5n78lW7rmpdAxZbk5P/OTly4Dz0jEz4WMlyCLRoWkjKARwvy9d64fmzkApZR/wnO+PjRAIlEhzosFIx6pr6+PG9Xl89+nDVNR1MTximB4/va+OzJQ4vbO+Os8wOc5PTyQ/PZGTjW7uuPpiega8PPC7Y1o2FRMVpTt8YmOi2FSSztWlWTyzv077bqgCTXUIPbO/jts3G9/jx187RX5GIleXZoIwOo8aOvq0Uaz5/Sox62SjW3sELXE5yUpN0MbOatffLN8qr+mgrCB1jLzL4x3BPeClqXOAO66+eFKf9bTHrH/+81MfY2HjD1LK+2Z7EvMFSiZZUdflI00N3Lg+j+//toLymg4aOvpQFjxKrgnwsdVZtLhrWF+YRnPXAG1uD01dAxRlJdHSfT6RqyTbhXuxl6yUeM6293G83s1PXvuA3sER0lxOshfHs6kkw88k3SyTnMj3aktpJoNJyay4fv34J9uwEQEEMmO2TU7nGKyFz3X/9CpVTW5iY6L9unTMkeuKHLp7WwnXrcnm8uJ03cWjCCNF5F1PpdIAACAASURBVKgC4mSjmz1HW2jo2E9Vk3tMlLEZC8Sfwcb8wB7gHSHEPwGv+FL4yoEfSCn/lxDid8DeWZ3hAoSVbAgmcwJ/gsVKWFu7DwONHQ6sqYFKYrXnaAtbVi2dMGmtxnvrxDnKClK5/9lKv5SwrZfm0OoeZO/JwLZxgdITlXQslFfQRO/VXqenhDuklO8IIS4CviyE+EfgP4HfSClHQ7/VhkIgLxnz79wDQwyPGh/n8OiolmE0dw5S29qrd/Njouu4enUmT7xxmoq6Lr796/dJiI3mXLcRsZ6W5CQ/PZGrV2eyMjeZkpxknnq7liffOq2JmNs3Lx8jq/rGJ85LU/LTE3nuYD29g17ePdVG3+AIj75axZ1/UsSx+m7WFabpWPg/VDaxfvkSo7PAJxeRgCvOeLROT3bi7vfS0NGnE7g2lWRoCZe5YHTFxwb05FEIl8BRpNqx+m7u2lo8dbLnscfg3nunNsbCxnYhRAKwC3hPSjk43hsWEqz/rl3xsdx9wwp2lzdoA2Rl2n7zhjyqW3p0N06ay8mAZ5h+zwjnuj389I1qhryjHKhu58tbS6huOUq/Z4Sqph6/ax6v76ajd4hLlqXgjInmeL1bS7rSEmOpqOti74lzft9HJZM0p2+F891yxcdyyxWZ8PSvoPQfp+lTs2EjfATq6NEQQlwPOIAXpJQjQoh4nzfOvMJ89s6wFjQPvnCMqiY3RVku7vtMmT7PWjSooqDVPchLhxv1DvOWVUu1kbPZjwLQ4339xlW8c7KVbZfl8Jt3zvDIK1VB02ps2AiGCHtnmPE94E+BnwEIITow0i2iAKSUHiFE3wzMw4YJwcgGtaZtu8xIzAlFYDzySpXuPrztquUhOxXHI30CESat7kH2HG3xOy9cA2SzTEv5ollTwtp6Bkl3xY25R5UEdve2EuJjz6/B4XgFhZprqHudKhZil5CU8h3f61ngb31yrb8AXhVC/B74f1JKu7PZgkDFm9XLwhUfq5Oj3P1ejte7yUyJ43i9m1W5i7l2TRZH6jrZeUU+xdnJPPV2DeU1HRQuTeT2zcupbunB3e/V0osvX7+CEw3d/PLNGlwJDt0BoIxUF8VF6zSfirou3ANeas/1kp+RyI3rL9JFW22r4dmz92QrQz7Cqa3Hw94T53RHj4IionZszONgdRu1rf2U+NK0VuS6OF7v5rlD9dS29up5WL2IQn1uZgTqigqELaWZOlI+HA+RcbFs2dTeb6MAo6v468AwMA1tVhcOFGnq8Y6wtSzHL3VOSaaUfNPjHcHd79VeWMOjoxyvN0ifKAGx0YIhryHH2v1+I0NeGfCaEkl2agJ5SxbxvE8umZHs5NL8VGrPGd9/s+ePGYGI1LBI2LVrp/Ix2bAxaYQkeoDvAn8AXhNCPAN8TAjxOvBpKeW5SE9uujCfvTOsxZI5dvjF9xpIvcrp5+UA+BUH33vmiN94Gz6Urh/SrQVEUVYyT3/to8Z5F6fz4AvHxuw427ARLiLsnaEhpXQLIT4K/ArYBKT5Dv27ECIR2AFEMJbEhoKZDAhGNjzyShX3P1tJn2eYb918yZj3BSIR1Lr14AvH/GSsgWLQzeOpdTIQYaIIlZONbl463Mgv/ng6oF9ZqPHv+0zZmPh3c0pYMJLGnAQW6Hi4HZOBPsuJvF8hHBJnIXYJCSE2ANkB/mQC9wP/WwjxhJRyQYRChAtz8RaqS0UVTStyDXLEFedg/bolxs/xDpo7B3nq7VqyUuJ5z+dpc7S+i61lOXxn5xoef+2Ur9hzc7i2A493lNV5iynJSWbXAUOq1dTVT3PnoPbtWZLkJDs1gZMN3VQ19VBR14Ur/ny8+x1XX6ylYAlOIxZdSUi2r8vlwKk2ELCldCltbg93XH0xe0+co7bVkI519nrYe6JVS80AslLiyU83Yt5VLHOozwPGkjkTSdgqXJpI4dLE6Unj+tCHpj7GwsbHMVKBL8VIB7URBObUOUWI3rg+j10H6gwvrLW5rM5brLv2fl/eQHVzD0PDklEJvYMjpCbGsqk4g16Pl4xkJ05HNGfb+nElOBCAd2QUV7yDM639/PiVKjp7h0jxGba/X9uhu4Wcjig/A3gl7zRHwO8ub8DpiMbjHdHzjrg5ug0bE8R4RE+XlPLvhBBXAypjcQvwL8CfRXRmNgACxvMqAsf8gL8kKc7vQV9Bpb6YC57xYC6SzOTRRLEQd4BtzA6klPXAlUKI9RimzOeAlzGKsjXMgAnifO4cnC5YyYBAhEC/b01Rr+b3Wc2I1fplJY3Uemb+nTVSXcmqzLKvQHKyH9+50c9rZ7zXQPc5UUy24yZSa6r58/rxnRsDjj1Xfdki3Dn4DmDeFraGVCwCvsgMpn/O59QtKxQR4R4wOnqqmnp0otWOjXnaeNkcb3y83q0LLEWyrMpd7JeS095bRWNHP7deWcA/faqMXe+e5VSLm4uXuqht7aWxo5+VuS5W5LoYGTU6+556u4ZNJRkcrG7n6x9fyRNvnNaGyWUFqWwty+GhF45rGUjv4Aj33rIGMDx6FAqXuigrSGNdYRqPvlplSE9ionElOKio6+Jgdbv2/nAPDOkOhk0lGdqAOhBBE27CliqWb72yYHo8el591U7dYkrrzCGMuumQlPLQNE4popipdWZrWY5fGp1K5TN/B9YVpnHkTCcnm7qpajTI2fqOfv39dMQI7dUjMUia5w4ZnbrxPrLW3X8+xS7BGeN3rb5B45gieVbmuvB4R3noheNU1HVpM/eW7kGuKFqiI+ABfvlmDTs25mlj+KA4dAi2b5/8B2VjQWCmUrfMyPC93ux7/RHwOMbOuY0ZwETSaqywFgXmneZQMBcxgcijcBGseLNhI1KQUh4ADph+VQ/8z5m49nzuHJwuhEMGCOH/aj7f7E3z1RtWjulMUX9XnT3B1hYzIVRWkKqJ8mCR7Fb5VyjPoHBlZ8EwFbLGOn8rETbZuWy7LEeTYqqzyYq56ssW4c7B32Okag0BFUAj0OR7Nf88Y5gPqVvW4s0Mq8xhS2kmu8sbKMpOoqqxh8auAbavzWVTSYYmUIqykkAIPN5h+gZHOHKmk6qmHm3aqlJwABY5o2ns6Gd13mJdyLkSHByvd3NZQRo7r8invbeKG9fnsfv9RsprOqjyXUdFvnu8I9y1tdhPRuKKjyU7NZ6Kui4SfNfYU9kMQEVdl45+VmRRUryDVbmLOV7vxjMyypEznWxfl+v3mZg7GFQSVyCCZiIGyxPp/AkLN9wwPePMc0xhnfk1sB3oE0Jsk1K+OY3Tihhmap2xEpiBOgD3njjnR/YWZScx6B3m2jVZNHUO+DyyBN4RScqiWI7Wnz93wGOsAQKDBHJEC25an0eco5GrVmTwn69X4+73kpkSxyV5hmdPbWuvJo3LClK5eUMeP3rZII9PNrrZvjYXpyNKm7GDQUaF/I7++Z9P7YOysSAwU6lbZriFEEeAD2F8Rx6RUh4RQszog81CR6Diydyp8+ALxwJGqodKzZro9RQmUqQEK95s2JhOCCFuBL4K9APHgSPAUeDbUsqbZnNuCw3hkAHxsTF+r9b0qnCIi3DXltREpx9RHmxtCyXPMq914XbyWM3xzWvlVAhwM4Gl1n3rPCayRpvvx9rZZAOklDcIIYox1pd84KdSymdnd1ZzH6G6T6zyJEV2KPlWu9vDqRbDgFURKHffsMLvfW09HoqykmjsMiwjj5zpJDraYI77PCOUFaRSuDSRp/fVUd3Sy80b8liR6+LAqTbeq2mnsaNfkzxKCuJ0ROHxjvqInlH2VDZrgkchKc6IVV+WvohVuYvHmEu74mO1zESRTDs25mkCKSY6ys/TY11hmj5vU0mGTvQyHz9Y3Y6738tzh+r94tpnDIcOwYYNM3e9Cw/1wDbgKuBbGMSxjXFgJjfNfjlqnThe7yYmKoq7b1jBw7tPUl7TQXZqAhdnuXj5/fMlqtMhiImO4uYNefz23bP0Dgzz/KF6Kuq6WJmbzP/97Dr9/o8UG/HsFXVdZCQ7WZLk5PbNy8lJXcS9t6zR57X3DtHY0Y/TYXQLPb2vTn/HIYh868kn4ZvfjOAnZsNGYIxH9Pwt8CLgBA76SJ6LgaURn5kNYPxo4UA729Y49mAP7tYCy1yYBCtiJiJZCGQsasNGBPAokO77+Xr8pRY2ZgATIRe+cE3RmPeFknsFIk3GW1vMY5rJnWBEVLjyrHDlS4EStdS99HmGueemUvp9RLzVXycUrOu+eW6h5h0M4Xw2Cx1SypPAl4QQqcAXhRBfA54GfiKl7J3d2c0vuAeGxsiT1Gure1AnYwXCltJM3P1e9p1qpbXbg3vQS7vbkFooOZXqqtlaZnTcnWx0awNm89hqlx7QhVxDRx+Pv3aK6y7N0ubJe4628PWPr9RSqzX5qbx8pEl3BynCxVzYmSUoSkJ1x9UX88Qbp3WEuyKtzOO7B4b0GGbDV0VGAX5x7cEQrmlz2GhunvoYCxvxwPtSyt1CiBnpLr4QYP53bBbMrspdTHuvh+P1bpa4nMB5T6pNJRk8+mqV3zger8TjHeH9mk62rMzkuUP1eIaNNUh14Wy9NJuW7kHaezyU13SQmRJHc+cg57o9Oi2vZ9DLoHeY5UsTOd3Sy4pcl+5GBCMtMJjsEgCnc3o/IBs2wsR4RE8yhvb8LHBQCLEC+AXGrrmNGYD1od1aUFl3tn/+x9P8x19u4J6bSgECFl5qjLaeQR568QRtPYOcaHBrL4tQErHJ+DTYBYSNCKMOuBPDhHklsAooBXJmc1ILCRMlgMPpsjGPHcwUPtDaYiZTwiUwAp0TaF7hrmVWc3zrvXz3lkvHHWO88a1jj3csEBlnr82hIYT4iZTyc76/xmBsfB0D/gb4jhDiJ8APpZS1szTFeQWzfwygY4q3lGby0AvGY2VmShx3/kkRSfEOLWdS5MafbS7kxg9fxO7yBtwDXlIX9eHxjlLd0kt6spOdV+RzoqEbMLqKirNdWoq1JMlJW4+H9GRjl/5gdbtO8XI6orUMrLqlh37PiJZnPfC7Y9x7yxpc8bE8s78Od7+X7NQEk7+Qf/eNim13Dwz5xaibI9y3lGay52gLjR39PPHGab7xidV+ha3RxTCCxzuqC1hVcFqva5WKTLt06/Ofn55xFi6agAYhxFmgA/jXWZ7PnEQgSScYkqjHXzsFnO/mafZ18jV3DWgfHSP5rn0MWeyIBu+IQbyeajGOVTX2sL5wCQer2/nlmzUkxkXTOzjC0LBP6hnvwBXnAIGOeFdIjDfK5pgooztva1mOXqfiHCFK6jkut7Vx4SLc1K1dwG8xDJnfwDAftDEDsD60BzLNvO2q5TzyShWFSxOpanLzwK6jfKQkQxdeVnmAGmPLKqMx6/lD9VS39LJl1VI2fCg9JIlj3km3SsZs2Jgl/BZ4WUo5YP6lEMI1k5NYyGbM5nUqnO4es8k8hCaHApEmoa6hyJR7biodc85EOo+mQoKYzfHNa6V1PZ9sp2OouYVK+roQJbQRNmO+RQhxHYZfYbTlmMDw//oyMxiZPJ/NmM0khCI2PN4RLZcoK0jVkcWA9snxeEdo6OjTRIrTEc3L++q0LAqMZKxn9tdRXtOBxzuC0xHNppIMXYQBpCc7afXt0m8ty8HjPe/3s2XVUr2TDzAiR4l1RBkyL5/5s7kLSM3R2n1zrL5bE0nqfhWhpc7fUpqpDZ9Vl4/5s3HFx+J0RGtSLCd10RjvkmCdO+GaNoeNxx6De++dvvHmKSazzgghbgJeANqAjwD/Pt3zihRmcp1xDwxpWRScXx/UqzJEvmiJIccsyk7SkkvlpaPkXdvX5XKq2c3xerc2aV7icuquvxW5Li277BnwasIVDH+voQQHVY09fvNbnbeYJS4nlWe7KMpy0dHrIT89EffAEK74WO7aWqznH7Tj7le/ggXu4WhjfMyGGbM5desa3+8+yjxL3ZrPBZj1of22q5aPMc1Uhc1Xri/hZKOb+z5TRmqi0SYYyMPC7PPwzSfLeelwI9etyQ6athIIF2rRYGP6EOECzIy/Au4QQvwaI+HiGPCBlDK4DiACWMhmzOZ1KpCsKFDaVbgm84ESBUOtP6H8e6Zr3bLKXs33Zr3XUNK0cEjzYOTURE2dFWHW6h7ke88cGeMdNJXPYDbJ/gibMSf4/lgxgL8p84xhPpgxB4OZhDBLnMprOigrSOX2zct1gQdos2PltWMuBD3eEe2LkRgfwxVF6VxflsPK3GQ83hFNgty1tVjv+i9JcuqodEWmqAKwpXuAjxRn8PS+OqKiwDMkAUlmSpy+zq1XFnDX1mL2VDaTFO8Y03kA6DmqV0DPRcm5VOy8ucvHStCM15kz7Z07wWDHqwOTXmd2At+TUv6rEKIDaJnmaUUMM7nO7Kls1muAlQQGdPpeWqJBrlY19pCdmsDOK5bgdEQBBtGjvL4uSltEZ9+QJm3NWv5+nzlzU+eANlhemesiOiqKkdFRzrT2k+CM1ueZPcL2VLbQ2t3K6rzFPHeontrWXu2ZpdaFdYVpmtj189K6/PIIf4o2LgTMhhnzBZG6NZ8LsEAF0n2fKQMYk/xifeDedlkOX3n8XT58cRqt7kGqmrp1xLoqNswGnBMpIsKRcM2VQsDG7CDCBZgZ2b7Xb3D+/+leIUSVlDKk+YkQ4jFgE3AR4AH2A38rpaw0nfNJ4B+BizGKu/+QUv7ztN7BBYTp9rsJ9xoKofx7VLqUWjsnC/P9AH73Zr3XcKRpocinYMcnSlopci2YDA4mb+R8AZP9ZzGeexrNf6SUXSHfFQJCiE8DT/r++pCU8qu+39+FIQnLxVg375NS/mzyU5/bUMSGWeLk58uBf3TxusI0bVasSJqKui5cCQ7c/V5a3R5yUhdpvxtz6ldxtssvEctMkrxXY8g9Cpcax9+uatM7/JkpcVyyLJWX329kdd5i7clhJmvM92H2ClFzVTD7eUzk85ns8WlDdvb459gIhv1SyqO+n3+F8RwxriPvZNcIIcQa4EFgI0Y4xTPA30gpe3zH44AHgFuAJOA93/H9U7rLKcLazabliT4T8u1rc7lkWQp5SxbR2jOI1ztKY0c/T71dq0kY96DR9Xu83q2lVhnJThYvcjLkHabdd60zrX2cae1j9+FGegeGiXVE8emPLCcrJZ5d754lOioKj3eEqqYeFsVFk5+RCJyPeM9Kiedsex/g75ml5m3uTPL7fvbadm42Zgd26tYcR6CH6Rffa+Clw418pCTDLzK9o9fjt8v86QffpKrJWPDePdXO7vcb9d/N0cKTafMPR9awQAoBG7OPI8BDGL486k8WhlfPePgcsA94C0Oaej1wiRDiYinloBDicox41D7gv4CPAT8QQnRLKR+Z9juZhwhERk+n300gTNZ3x7x2pl7lDJvUsN6jmTBS3ZPWe1Sv4811PCIo2PHJEGXBvIMUJmvkfAGjSkp5f6gThBAxUsrhcAYTQuQC/wEMY3r+EkJ8CkPW0YpREH4c+KkQollK+dJkJz8foAoktRsORlG198Q5dmzM80u+Sop3aLJkU0kG7gEvR8924XQYqTpmvxyAngEvj792Snt4mIkgtev+te2r/GQiKpo9KyWeps4BkMbeQXG2K2Taldl/SF1HQRV8SkpmjmWe83jjDfjYx2Z7FvMVmiWTUg4JIc6N94bJrhFCiCTgFYxgiv8GCoDPA4nAp33DPAh8AajEsOW4BXhFCLFcStk2xXudMMy+PIHkh0+97W/G/Pyhepo7B0n1dfacbe/j+89UUN3Sy7J0o/GycGkimSnxHKhu41y3h3O+Dj6ABGc0aYmxtPcO0TtgLNlD3lGe2V/HytxknjtUr0nlB353jMaOfp47WE99ez+FSxN1ct7xejcrcl3EREWxrjBN34e5O9GvswfoP1jOo3Er7I1vGzOO8Yiev+N86tYBO3Vr5hHoYTqUb89Lhxvp8wxTXtNBVZOb1MRYOnqHWJa+iKomN1tWLR3XpNNqZjqdc7dhIwL4iDX9RgiRgkH4jId1UspDvvfkAzUYJs4rMXa7/g7jMeMfpZT/IoT4GPAqRveQTfQQHjkw3aa/43WeBDtulq1+8dF92oB+oqS1mTCyyrEmeq/m8ydimDyZzzSQDM6MiazZC8TIOUcIES2lHAlxzh+BK8YbSAghgJ9hdAVVYBRZCvf4Xr8kpfxvIcTngMcw1pkLmuiBsX4zuw7UadLETK4oMgXA6Yim9lwvp1uMpf9EQzeHazt4el+dNlY2+2+YI5p3vXuW5w7Vc6C6jeKsZJyOKHoGvDoRbGtZDg/vPqnjkm+9skB3GWwty/HrFlIIR0qlDKBX5ib7EUFzGp/4xGzPYD4jTwhxH0YyaDNQFurkKa4Rn8MgeZ6XUu4QQiRikEI7hRDfAnqBO4BR4GNSynNCiGHgNuCvMbqNZhTW773VkFl915QUc/u6XPj/7J15eF1Vuf8/K9NJ0jRpkw5pEtIhJR1Ih5QWSguUgkAtl0GoVAbRB72gl6vyu06gokUcUK+KXrkCeh2QomBBKoItKKWMhbakJekUSRNCkiZtM52MJ9P6/bHO3tln54zJOScnyfo8T56Ts4d11j6UN3t99/t+X6DGnVHT0tFLS0cvAKfcHfg6evr41wknPb2SVEc8eVmpnGztpqWjl8T4OD5o9LByZHqGg7zMVPKnTSInM5WFuRlMTklkTeE0XL0DVJ1SJaN5malmtz6jPHTbnmr2VahcIWv2oT078ercbLYu+pB+8K0ZFfwKPVLK14UQOcDZwH4hxGLgD+iuW1HD1yLA7tsDavFy/sIZdLj6TN+d791YzPPv1HLK2c3P/36Uc8+c7tW81F56YHSGCVTO5a31sbe5azThRAhxCGVwuB74byFEGeop1SEp5SkpZbMQIuBjU0PkcWOsKPpRJVoweGO2z/Y6WwgxZSQlHOOFSAu63oToQCKNN9N66ziPvXLcjJHBzNtXts5IrtlbPLW3hR9p6Wuo5bM6Zg8hByXoDmk9JIQoRC3gzg1yrDtRhqznun83xklgUJS2x5mRtWcbI9hFEm+iidGa/cqVeTgS1JP0vRUqCWHaZAcLczN48o0qAObMSCMhPs6jVXLVqXbTPLXqlBKHyuvaTONVwwPIEJcMk2Sj/bqBtWTK2dVjZhhtKM4NWEoVNV+dcLJ7NxT71Sc0vvku8DrqgRHAXwMcP5IY4XGvIqVsF0Icde9fCrQCiUCVlPKk5dibGaU4Y///wS78+CrvNATYxHhBb7/KuMtKc5CeOkB9czepDuWbn5IY72Gs3NqpRKG4OMjPmkSKI57sjBSe3V/DnvdOcarVxf/8/QhxcYL65m5uumAu16+Zw6m2oxw70Up5XRt9/QMsyEln2ZxMKhraWVmQxeSURNNPyF5+ZrzedOQfdGy+UT/41kSdQBk9AFei6j3XAU9LKc+O7JQ0vvDXat24OS+8IsPDm2La5GRz2/T0wXbs1nODXcQ8/GI59z9TRoerz3wq7K/1sUYTQTpQT9Gno1qrm357QojTwCFgESqFOSDup1+/db/9iZTSEHqM7EUjY6jDclo2YAo9huk7MCaN34dLpMUBb0K0VaTxJmb4Mq2/54kDZunSXdcUBW1IbL/GcFyzrw6KxvzDUfrqa4xo+adF8nNefvllw/AdImf6/gVgrhDibinl9wGEEIkon427UNnO0s/5uM8pAr4PfFNKeUA9uDeZxmBHL3ucyRBCJEspu42DjW44wJjsvOUNu9+MtZzLWDhZS6OMsg5jEXe6TXXcMjp3Xb3qDACzjOvR3cc9OuLcevF8fvPSe8zKTMURH4cjMY61C2dQMDMNV28/zq4ecjMneZgle8OeYRRI6Imar044aWoKfMw4ZiRxRkpZJoRYjerMFw/4bF820hjB0HsV6zHZQHKA/R5EI84Ea0BuxIMdJbU0trtIccTR5RrgnDOncbCqifbufj5o7GTT6nzTY8uRGEdje485RmKCYPpkB86uXtq7+6k6pS69sU1lAk1NTSLeLfAAJCUKTrW5ePeNKuqbu6lv7iY9NZHS6hZKq1tMYXhxXgZXr8rHkRjP1lcrzThgv7bUnJm6Y7HGJ5G8n/Er9AghvooKPMaNzBYhxM9R5l0Bb240I8cuyBgtiY3t3m7ivS1C7Nvsos1wFzGBPB80mgixHsiRUjYIIT5AeewUAQtQ4s9FBLEAAxBCTEeVqK4EfsXg0zdQXTLyUXXuWF5BpWKbjGXT91jGLjwb/jiGKb237J5pk5M9jOat53e4+ny2Xw8nwRja28Uoa+y1z9tfpy9f+BLto+WfFsnPsYqp9957b1VYB3djGJ0KIf4ghNgEnAQeQsUZgcr8+0IQQ12HyhhcJ4S4AFjm3n4VqoNXP2ohlwY0MhhnWq0iD4ztrluhYO+84+odYNPq/CGLQKNdstUA2Sj3MhZaRkcc49zczEncs2mZOYZRMgKqw1cwog0Mdv8yfh+XfHpIMtuEYrhxRgjxJKqs6irgz8BeYDbgy5F7RDFCCGF09LLeoxi/16Myevzt92A04ow/IdQwQbdSUtlkdsdSIu0AS/IzGJAqs2dGhoMpqUmcdKrSrbrmbi5bNot3q5tNQadwVjpxcW2m6XJ98wni46CnV/LCgTqzXKxvYIAjNU7V1n1GGkj1mf6yED1w/xvS3qUab0TyfiZQRs+twF+AXqAQWAJ8HlW69Ug4J6Lxjj0oWFsSR9sD5/ZLC4d0sQnk+aDRRAIpZQfwL/fbW6SUuwGEEPGoWGWYMvtFCDEbeMF9zvellPaOGAdQQs85wG5glXt7tS7b8o2/kk5vx/oTLezCs+GPUzw3k5LKJp8lWL6ycIyMR2v79XCUSdkJxtD+odtW8/CL5XS4+jy6InoztbaK88bfAV9j+/oODKL1t2Os+7QJIX4vpfwEyv9iByqzWeBuTgF8VUrpDGYo98+HbdvnAuehMhCXouLM+wzGmYMjvYaxirX9urHAs3r2GMbGP9l4SgAAIABJREFU1tIqX743gTJpDFHJ6rERDOkpSVy/Zm7Q1zQm+fWv4ds+E1E0vqkBMhl8WC5RYkuWj+NHGiMOALe49+M2Z17o/txSVCZPL8o3aKaUssHLGDGD3a/HzqTkeDq6B63TOnr6eHZ/DaB8dzLTkkwz5sSEweyoI7Wt1Dd3MzUtkeb2XhrbXe6MnRMsyZ8CQP8ApCUnsGbhDK5edQbpKUke8zHihTUeWcvMvLZXf+opWLJkzP9N1Iw9Agk9p6SU1xlv3OUNn0KVSmihJwr4K6syUv0fe+U4G1fkeiwSAmEXbcpPtPK1x0v43o3FFM7K8HqO9m/QxApCiAXAv6SUA4bIA+A2TT3i/vlzEEO9gfLhqAZShRAPuLc/LqV8G/ghqnz1W+7U6g+59/vtxDPRCaWkM9QnXNbMHEPkMUqfgsFb+/VIPGULdENnCFwA9z9TZopWwcxhrNwsjoO/GZcJIb6DWmR9DyUINwG3SynfBBBCrJJS7vU3iJRyCxazUyHE74BP4G6dLIS4EdgKPCiEuAK42n3ohI0z1oWTgVWAsRobT05J9LsoDIS9vbPGQlEwPQ00dqSU/wUghFgPOFG+Oyv9HL+FkcWIXwNfBzYKIbYB81ClpU9KKSssY/478E+3r+H1KAHoFyO+4DBjiCmHa1q5Y8MCNhTnAiqzz5EYx7I5mTz+2nH6+yXx8cLMtjnV1m1m6wDEx0Fv32Byd32zMmdv7+plSf4UUpJURdzs6anMmZ7GnBlpVJ1sp7S6hemTHUOEHPDtIWZ03jKEaUMUWnfBJKa4szXGwd9EzRgjkNCTIoT4HPBbKWW7u7PNz4QQH43C3MKG4Z0xFn0zvJkxb1yR62Eqau24BcE9nbYHmy89uo9dhxro6evnN/+xNuSn29HyfNCMHdz1pnMiNPwRoFsIcQT1tMr8Mfx1hBDXSSkDefQY7U/z8SzBOAC87TakvwH4FqpFaT0qHfuhsF3JOCSUks5QRQtvQo3dbDlU3x1fcxhJXAt0Q2fE7ruuKeK+zctNM31f34NVnB/pzaJOHw+amaj/3630AF9xL5QOA18GVozkQ6SUjwshpqEMWG8EqoAvSin/PpJxxwO+smasiy27ietwPmPMeedEi7S0wMdo/PFt4GNSyhHdMwSKEVLKNiHEpcBPgStQ5V6/Af6fZZgvoLJ6rgfmA3vcY5waydwiwfqibN59v5mSyiZ2lNSaQo8jMY4zZ6XzyxfK+exlhSzIyTBFlpUFWfy9pJbGtnp6+yRCqOycFEc8Xe4Sr+npyZxo6aa3H0qrW0h2xAFQ19zF+6dquPLsPPoGBliUl87KAs/kK7v5ujXTxxB4vHXeaklOY0ZzA6vODv6BlEYTLgIJPf+HUnp/LIQ4DBxD1ZBOi/TEwsl48c7wJeoAHosEu8mnca69W5Z129LZU9l1qIGls6cOaxFgNTq1LkY0Exe3qFoVoeElylyw2P1jNWNuRok+CwlgxiylFP72u495Angi0HFjWVAON6GUdA5XtLCf583c2B/2GOive1cocS1YccguhhnG+cFe73DmYH1YYHzucMeKFSIsKNcD7wJnAbnubXPcP1cNd1Ap5SeBT9q2/Rz4eaBzDZPU8WLEPFwCPWHXhIk9e2DjxtGexagzgjjzkqW5Q9AMJ0ZIKUtQ/oS+9ncBd7h//DLacSY9JYk509MorW7B1TvgYXyenpqIs7OXX75Qzrc3Lxtiug6Q6ojHkRhHc3svkxwJdLn6yUp3UNfsYXmGIz6ebgbo7ZMsykun6lQ7R2pUNe6+ikaPbEFv5uveyj6NLCAjHrUAJS+8xbGLjusHKxq/ROJ+JpDQ8xCwGPgPVG2ocef+X+GchCY4vIk61pt/Y5FgN/k0zEft3bKsYo5hBGq98Q/l6ba1nEI/KdZEgf9FPSlbivIOW+L+fTGqLn4dQZoxh4vxIiiPBYLttOWPYARtI66dcnZ7xFCrYGIvmQ1WKJ82OdnDc83eSXEkooqvOYxExA/lnGgSYUH5QSnldwGEEOkowafI/XoWKu5Mj9Bne2WimDGHgs7IiSCbN4/2DGKCEcSZK4UQqajW6vvt5uqxSizEGUdinPlqNT4/c1Y6v999nM9eVmiaNP+z7AT1zd2kpSTQ3tVHp6ufTlc/OZmpfPayQl472sBLZfWWsQWuXsnUSYlm23VgsCPf1BRcvf1s3/sBz+6rwdXbz4biXFy9/bh6B8zufCsLsjhc08rahTOGeIQZcakleRJ/zvscH4nxUmvN6BOJ+xm/Qo+UcgD4TyHEL1D1oDOA16WUT4dzEprg8Cbq+DrO6DizcUUuX3p0HwCdrj4eeO4wG1fk0uHq4wsbF5odvOxPi/093fa231s5hUYTKaSUn3P/+k9UvXkmyrSwAUhlUPzRjBHsIoe/995iUVO7i56+fj7/4YVBxZ9gSsaMuPbdp9/12G58/h9eOU75CafHPEIpRbMfa72ukRhE++rYFUomj6+xJhKGyOP+3Qm86f4xEULsiva8NJqo8fzzcNZZoz2Lscw8YBGqxLMPVRWhseHNfHlDcS6OxHhz24biXHaV1VOQPdnM5HH1DgCYXbbOnJVO+QknRWdMISvNwbI5mTz5RhXHT7aZXj2THPFkT0mhoqGdpMR4MtOSaGrvMceaMyONvRWnqW/uZt5MVbr4TmUTG4pzuX7NXLbvrTZLRY/VOSmtbqFgZprXElNnVw87j7ZyU9NRUm+5KNJfo0YzhCFCj9Xk1NgmpTwKHI3mxDS+CcU4+YHnDrPrUAOXL8sh1f302FicXL4sh50H6zzMUoPJ2vF2w289Lxaf+mrGL0KIj6Paoie6N5UCX3ObG2rGCHbxxt97aywyYs9LZSfYdaiBpARlrvjAc4dD8inzh9283po9ZO34Ze02Fgz2OVjHH4no46usDULPytHmkQH5zGhPQKOJGC7XaM9gzOLu6nk1qunDcmD16M4odvHms2XP1LMeA7D11UquXJlH9tRk6pu73V22lN3Q6QwXWWkOnnyjitJq1SB1UnI8MzNSON7QTv60STi7e2lq76GpXRm+H29o50d/PUxdkzJsTktOoL6ly9z34I5j3LFhgUdHQGNsA7tgtausnl+/fZJp8xO4JOzfmkYTGG8ZPeEyOdWEEesT2RseeNV8irztixd5Pc5aSnXXNUXcfmkhTe0unn6r2hR5vndjsYf55+m2bj7zyB6fnV/83fAHWkjEus+DZkxzH55PyZYCzwohrtdxauzgr8Og8XrK2c1LZSfYuCLXjDMPPHeYe544wOc/vJCkhHi+d2PxkHg00vjjrVW7kTVpL9syuo2VVDZ59QryNxdfBtH+4msw1zaRs3JGghDicSnljf6OkVIeC/ZYjWbM8bGPjfYMxjLbUZnFH5ZSvgDsH+X5xCzB+GwZZVILczM4WNXEptVKBKpv7mZRXjoAmWkODr7fTGl1C6XVLVy+fBaVJ9to7+4nNzOV+Dhly3i6zcWpViViGmbNqY546po6KZiZRkNrN+3dykNvUnI8KUkJlFQ2mWKPvSOgYRZtF6zWF2XTPTmDFefqv72a0cGb0BMWk1NNeLEajZafcDJtsoMvX63Sab2VNLx29CTFczO5/5ky7tu8HICvPV5iijzGAqTwigxOt3XzwHOHPdoVe3tCffulhX4XEh2uPo9SMG/zh9j0edCMaU4Da4EOVJr0OcDHUa1KdZyKICMVUPwZInt7f6zOya5DDdzwwKvs/MaHPHzFrHPIvNBhboPQTZWDuS5vwrcRB9/61ymfXkGhePgEUw4WzHg6K2fYrBdC/CbIY3Ufas3449FH4dvfHu1ZjFUagXuBjwEvjPJcYhp/PltGloyzs5eSyib6+gcorW4xzY8difFm16vpGQ7au/pITYpnyqRE/vHuCfrd9SnldW3qs1ITubgom/rWLk61ukhJVEJPp7szV31LFx2ufhblpXNW3hRcvQM8u7+GGRkOswuYUaZlLS2DoYJVekoSm9dkw4svwIVr9ENvTdTxJvTEnMmpZnAB0dXTR09fP7sONfCj7Yf43o3FfO3xEo8uXEZJQU9fP3ddU2QKQIaIY3/KbG3ze9c1g/eq9gwfa4mXHW/Govb5W181mjCyE+iVUrYA7wDvCCEeBg5GcxITsevWSAXcUM//3o3FVJ5sp/yEk4dfLDdFm0DZh8GaxRs3YYaBvXFcsDdnRrcxe2alleHEQn9CTSCRfbiMlRvSCHfdmgl8AgjYnY/IGUJ7fojuuqWJJitWjPYMYoJhxpm9wHeAuwGEEFlSysawTixCxFKcMbJkluRPAZSHztLZU02BZX1RNjtKatm0Op9332/mVKuLzp5+Onv6h4yV7IjD2dnLH1+v5FSryyz7AiicNZm6li7au1QmzxnT0thQnMvPnjsCwJRJDk62unB297F9b7UpLh2uaeWODQtIT0nyLVhddhmgH3pr/BOtrlu/ABqllP9EGZ0CIIQQwJko0Uc/uYoyhpBy/zNl3HVNEUkJ8aYAs/NgHYWz0tm4ItcsKTAEmouLZvl86m1gbfMLcP8zZUxyJJhjrz9rJueeOX2Isad9LH8LGP1EWRNBvgJ8TgixE3VjVQWkoOrio8ZE7Lo1XAHXbg68cUXuEE8db3GmcFYGO7/xIVOMCbacKVizeKvofd/m5V5NkoOJY9Z452suoXxX/rIqA4nswxVsvGVBGdtjSfyJcNete0M4tiXwISMnFrrhaDQTjWHGma8AtwM1QogBVMOI28I6sQgRS3HGyI5ZWZDFvopGjwwawGx7vml1PgtyMkiIF8zMSOGN8pO4eiVJCdDjXuKkJibQ7eohJSmeRXnpNLQokWd6hoM5MydTfqKN+DjoH4APTrez/e0PKK1uYVFeOvNnplNe5+REUycvHKhj0+p8iudmUlLZxK6yelPg8WYsTXk5rFmjH3pr/BKtrluHAZcPj55yoFwIMaYyesbSk3Z/bXu9dWgxFko7D9bx/Du1FF6RYYo9D79Y7vGU15ehp/H7/c+U8YWNC7lv83I2rsjlz2++b/r7eMsAAvyWWoR6fZrxRYSftFuJB9KA64BrLds7hRDFwCGgTEr5kyjMZUIxXAHXHkMMnx3jvbdj7J/pT7QZbozyJYgHa0RvPSdQdlCwMdDq++Mrq9LX/AL5rvnDWxYUMKGeRkopQxF6NJrxxzvvwDXXjPYsYhYhRLGUssTH7idRpeUXoDJ7YIwIPbGENUvG3sIc8DBHfnZ/DTmZqeRlxeHqlaQlJ9De3ceivHQS4uKYlu5gV1kDkxwJHK5xmmOcanXxwel2ALPUC+BYXav5+9XnnIEjMQ5nl2rHvnbhDLMTmDEHZ1cPD+44RkllkzrHyO6pqgL0Q29N9PHVXn1cefSMpSftVo8d+825rxbo37uxmJ6+fk63dVN+otVcPADmAuHr1y4N6ql0SlKCufAy/H1C6b41kuvTjC8i/KTdyqsoQ+azbD/pwCr3jwS00BMjBDJe9rXNSqByJn/n+sLXmN6224Wc146e9CiLNWLdFzYu5PJlOaYoHygG2rsqWjMuvQk5/rodWkt2h/tdeBPU9NNIjWaCcMstoz2DWOdxIcR6KWW9l323SCl7AIQQUwktQ1Djxp4hY39vCEHOrh5eO3aSuqZOWjuV0bI6JtEss8qemszly2eRFB/PyVYXp9tcZKYlkZuZyrR0B0dqnBTmTMaREG9m8gDMz04nPSUJR2I8L+ypBmBfRSNXr8r3yOQxRJ7iuZmextK3aX1PMzpoj54Yw7iB3rgi16MjljesC41dhxrYdaiBo7VOc/Hga2xvJVje2gdbX62MRJEO5fo0mmCQUq5z//oP63YhRB6ewo8mwoTiZWPPMAyly18gfJ073DImb+dZy7wuX5YzxHzZmhFjxOSHblsdMAZaPde2ffEi0/fHPp+HXyznrX+dYtehBsBTLLKXxhnzHs71+3rAoAEhxI+AjagWylOllHtHeUoaTXj505/gG98Y7VnEMvHAb4QQpcD9UspmY4ch8rh/bxZCfHM0JjjWsXey8taK3WDqpCTqm7vp6O6neG4mV686g/SUJGqbOnjh3RPUN3cTJ1qpa+rkyrPzqDrVTt+AMnc2PIAcCfFcv2YOCfHVXHtuPgerVHaOs6uH9UXZuHqV94+9Q9iusnpT5DE8e0weeQTGSMKBZnwxROiRUn7O/asvjx5D/NFEAOtNdeEVGeb2QAuNu64potPVR3NHD7OnT6L6dDufuWwBkxwJHv4XxhNao8vW/c+Uee1EE6n0Ql/Xp9GEGyllDVCDMmuOOGOpRDRShOJlMxqmhMP9TG/nWcXwpnb19NAQVsAzI6aksslDCPIXA793Y7HHKwyN/w+/WG5ma64/a+YQscg6X6ugZpzX4eobIh6NVaJYIuqNJcCbwA3AAMojLKLEkkmqZgLgcIz2DGICP3Hm36WUu4UQlwNPCSH+CfxUStlpP9DdMGJMEEtxxt7JynhdmJvB9/9Syi3r5pGbOYldZfUcqXGSlpLAqnlZZE0e/Le7r6IRZ2cvOZmpfGLdPHYcqOPiJdm8fvQk2/ZUsyR/CrdePJ/fvPQepdUtQBWl1S0UzEyjoqGdksomHInxXL0q3+y45W+eHiIPwJw54f1SNOOSaJkxe0VKKYFy98+YKdsaLwRaaEybnMwDzx3m538/CsCv/vke+dPSPPwvjNICu+FoKKamGk2sIYRwonx4DgFlltfvSCk/Fa15jKUS0XBhjw+hlEyFcqz9c4Ybl4Zb0hXovOffqWXnwTrOXzjDq3izICednr5+DyHIF4WzMtj2xYs8tvkTqM49c7rf8lrrueORKJaIeuME8H3gUpRPWMSJJZNUzQRg48bRnkFM4CvOSCl3u193AjuFEB8Fdggh/gw8JKXsjeI0w0YsxRl7Jyvj/ff/Ump64dz9kSWsL8pm16EG6po6OXaijbpDDaY4YxVhjMybxXkZuPqUIc+cGWnkZk5izvQ0SqtbmDU1hQU56RyuaeFwjZPsqcmsLMjyOUe/5WUAhYUR+34044eomDELIQ6hzMPWo8q4ytw/h6SUp9zHXCel9Cn2CCF+DawFzgBcwFvAV6SUZZZjPgpsAeajbpb+V0r5Q8v+OcDPgEuAftRT+c8bdbBCiDjgm8CngenAEeBrUsrnQ/8aYhdvKfgG9qybjSty2XGglt7+AVafOd3M5DlvwXQKZqax82AdD79Yzu2XFprHP/9OLR89b/aQzB971hD4fgKuxSDNKJMGnOv+sZeVRk3omYjY44O/TMCRdJ+yf479fSglY9asxmBjlre52rNmrK/24wwB3jDMDxX7+EYMt//uzbPH2n7diPWBOij6I1yi2zihCfXwqw1oAP57dKej0YSZJ56Ab397tGcxZpBS/lkI8RTq3uNtIcRPgT+4H5Zrwsgt6+Z5vKanJPHlqxbzyD/K6R+QXHl2ninwWMUiaxev37z0HgBVJ9upbeqg6lS7+/hEHInxpmFzfXM3+yoaPcygrWKOtZxsfVG2hyHzuqlTOfrbp5i/ZMVE/BupGWW8ZfR0AGtQ4slteJoxn0Y9LV+E/6yeTwF7gNeADwEfBpYKIeZLKbuFEOcBT7g/608oMecHQohWKeXDbhHnOZQv0AuAA/goSjg6z/0ZXwG+hVK+/gRsBv4qhFgmpTwU4vcQs4QitHS4+nj92Cnu27zcI5OncFY6FQ0qeL31r1PcfmkhN184b0g3FnvmT7BP6EejBEOjsZCPpxfPKqAI7SUWcULJkBlJnAhk3hzq2IGO9yZe+MteCmQM7ctMOdDn+jJb9ubb4+u6/LVf9/c9+BJwAoluE4wvA7XA+cCvR3kuGk34Wb16tGcQ0wghHgE+hyrjXAYsd78uBSYDv0WtV4pGa47jldzMSdz9kSVDtiUnJlBS2UR7dz9Xn3OGhyADsKOkFlffgFmmlZOZSml1C4/uPu7h1bOyIAtXbz+u3gEciXFePXms4o7xavXqWV+Uzd8P1PGL1hyus3j4aTTRwpvQsx7IkVI2CCE+QIk1S4BClPhzEYEXUCullPvBzMypBHJRws07wFcBAWyRUv5YCHEJykj1buBh4Cr3saVSysuFEPFABbBaCHGRe05fcn/WJinlfiFENfAN1I3XJ0P6FmKYUIQWw6vHaKl+84XzzM4u68+aSUe3Mm3+6d8Ok+pIYOfBOlKS4snLSuWB5w6zcUWuebzhJRHME3B/Js/G/Cbo015NFPDmxeMWkz80apOaIISSlTOSbn32z7G/t44dTIaJ1RDZW1zzJl748r0JFNd8iTLesHvohJpR6es79rXdiPneSsp8fXYwHdMmEE8Du4EbpJSu0Z6MRhN22ttHewaxzidQa454yzZhO0YbUkaRW9bNo6G1m7qmTnaVqWZohiADsM3dNQtgSf4U5sxIY03hNJbNyQQgLzOVbXuqcSTG+/TjgaGePPaMIWP75cunMu3991g2Mf9GakYZb2bMHcC/3G9vMepP3WJLIUr08dvBxhB53BiOVP2oEi1QbdsB9tleZwshplj273eP1y+EKAFmo9Ty94EslPnhO7YxltvnU//Xv/Lytm0cPuMMLk1I4My0NLj8cti5UxlkJSfD0aNw4YWwdy/09sK6dfDPf8L8+WqQ996DSy6B3bshMRFWrYJXXoGFC6G7G6qqBsdMT4eiInjjDViyBJqaoLZ2cH9mphr37behuBjq6qChYXD/zJmQkwMlJUw75xzufO89eKxpcH9urhqjtBTWrOH2dw6w4f0T5J78CLV//AvPNSZQ/tQZrBlo4sFlqyg7+g9Wdqbze5nLZ/a/SX/NPHIzUyncV8bL2Wcx8PEHyZRxfHDZhWw9dZT9SdNZ/lIJPFZjfmZdcx8fNKZyOvUU0y4/3+Oapu3cyZ2ZmVCurulgQjaJe4/QME1Qs+p8cv/yHNXLCpi2dgmUlMA556jvs8n3NVFWBk6n2t/SAnfe6e+fnCaGePnllw1DMYiCSaoQ4jWgFDiMyjg8AhwAvhbpz7aizZj9EwmDd28ZL94yE33x5zffN02N/YkZ4CmKRCuLxZeIbpTd2j14rEbPVnx99/68hXwJOIFEt2gzymbMNwCzgP8VQvxISnk00h8YSyapmglAWRlcf/1oz2LU8RNnEi2/9wHHUPcf5o+UsjHC0ws7YznO5GZO4tubl3lk8ajMnH7WLpzBu+83U1rdQmHOZE61dVNa3cJNF8zlYFUTJZVNdPf2ceXZebh6+3F29Qw1VnZj9w7ytX1KqoNLpgH6YbcmAFE3Y3Y7yX8MWI3y7fmLlPLJYAcXQqSh0hYBfiKlNISeme5X41FBh+W0bC/7rcdY93da6l6t+z3IXrCAi268kYvsO268cfD3FSvUa16e9/3nnKNerX/wrPvXrBm6zZvLunW/ISItWuR9v7HdOM6930xD/Mgm0lOS6J2Zw7GyenIyM5l86y10vvQeFdPTKDrnDHaX1LJt0mLemDSF69fM4emMLLOedWdSLh1NnWxLOYfsqclccu5iHJmrWIOqPd1eVs/61Mmk33gjc7p6uLCsnpyibLAHPds1Nb5Rye6aeKavzmdDcS67Jk9hvnGej2syMf6gWL+7yy4b+j1qYhar0HHvvfdWReEj1zBY0mmlLQqfbTIRzZijga8MndNt3UPKTwGPTMbHfKRK2w3pA4kZ4CmKRCqL5fZLCz08dOzzMOZtXJ91DsOZi79zR1vACZZRNmM+A3V/lAi8AsyI9AfGkkmqZgLw6U+P9gxiAj9x5n3gOyhRp2y8ZPaN9TjT1tXL4ZpWVhZkkZs5CUdiPFtfrcSRGM+tF8/n0d3H6ejupb65m+ypyawvymZHSS0AR2qcJMTFubtv4TerJ2huu23kY2jGPZG4n4nzt1MI8VXgcVT96b3Au0KIn7pbrftFCDEd2IVahP0KVa5l0OB+TbO9AtR72W/93bo/1e3nY98/7nB29bB9b7Up8mx9tdJMSdxRUsvWVyvZUVLLvopGSqtbeHZ/jbkfoLS6haffquaODQvIzZzEvopGyk+0UTBzMqCMxl4/etLnZxgKtV3Zts7LeA+wyS3y+DovJB55ZPjnaiYCR4F7gG2op2kD7u1B/cMRQtwphHhXCNEvhJBCiC2WfZ90b7P/rAz3RWi8Y4gbj71yfMj2nQfruHxZzhCT+oduW+1VwDG4+cJ53Ld5ObdfWmiWqAbCOCdUQcUoez3d1h3wWENcsfoCWc815vC9G4vNMl0g6GsI9HmakDkK/A5YB7zk70AhxO+FELVCCJcQ4rQQYocQotiy/w4hRIV7/zEhxCciO3WNJgh+ra2nArBZSvl/Usr9IxV5RhojhBDLhBC7hBBdQohGIcSvhBCTLfuThRD/I4Q46T7mdSHEuSOZc6zy6O7jlFQ28ehudd+wviibmy6Yy8qCLHNffLxayq6aN430lCQ2FOeyaXU+V56dR9+Auo109Q6Yaxxv6x3re7/odYxmlAjUXv1W4C9AL4NlW59HlUb4/FcrhJiNMlEuBL4vpbSXUBxAGaieg6pvX+XeXi2lbBFCGL1gV7lFpTjAnXLDQeADVLeLTOBsYK9ljIMBrmlM4s30a2VBFk++UcmhmhbzuPVF2bh6+z2Oc/UOsPf4aUoqm/jmEwf58lWLPcZ4/ehJ8/ytr1aa5weTumid19Wr8tlVVs+2PdXcdIFSwLfvrTbrVIeNbkuo8c9GKWWV8cYt/iZIKYP46wuoGNKEiiuzfRzzIqo0zKDBx3GaMBOM74xdqAiUjRJov7csIqtfmbdMIl+MpHOhv65mvgyWQ2WCd80aKfcCv5JS1gVx7GzU/U4rcDFwOaqxxWx35vQvgFPAH1E+hb8TQtS72zZrNKNDZuZozyCmkVK+Hcbhhh0j3ILOiygv1aeAuaiuxGmoElOAB4DbUZ2U/4lqYvOiEGKelPJ0GK9j1Ln23HwaWrvZsDzHXIdcvSqf7XurKalsIiczlRvPn8dCZPE7AAAgAElEQVTRWpX1Yxxz/Zq5bN9bzZEaJ8VzM3EkxpnrooqGdrOTlrHesa5//KLXMZpRIpDQc0pKeZ3xxl2K9SlUNy5/8uQbQA5Qjcq6ecC9/XF3UPwhcCXwLSFEEYOmqfe7X7ejnpSdhTJYdaBSpN+WUu5yz+XHwHeBPwshXgGuR/kA/SjQRY9FvJl+bd9bbZqKFc/NNDNorGmGzq4eapo6qW/uJtURT11TJ7984Rjf+dgKMzAZx9c2dVDR0I6rd4Bn99dQPDeTksomHInxQ4KYkfWzsiBryPyMVyMIunr7cSTGD1/wyckJ/RzNhEFKWWUrMX1aSnk4wGnW8z8OIIR4Bt9Cz+NSyt+NdK6a0PElyoSztMgudthNka0YmUTrz5ppGt/7E0hC7VxoNXr2d26oJVvBikmakCgG7hRCvAWkSin/4OtAKeVFxu9CiBUoD8I8IUQicJd712ellE8JIT6F6uJ1NxaTeY0m6qxbN9ozmDCMMEZ8CiXy/E1Kucm9XjsFXC+E+DrKCuNWVMbzJVLKk0KIPuBm4D+BLVG4xKhxtLaVuqZO/ra/htLqFg9/nkV56RypcXK0ttVcS1kFG/t6xpEYj6u336OTlnW/vRuXV/Q6RjNKBBJ6UoQQnwN+K6Vsl1K2Az8TQnw0wHnGv+h84AuW7QdQYs3rQogbUO3Rb0CVW90NPAQgpRwQQmwEfo7qAiZRCvXnLWP9AEhBBa6PobKMvi6lLAswtzFJekqSKZ6sLMhiX0Wj2foPYEOx6pqyfW+1x34jRbF4bibVjR10uvpp6ejxEGr2VTSyMDeDX75QTl1TJ339A2xanc/ahTNYnJfhNYj5UrK9Oc+7evvZ+molh2tauWPDAtJTknB29Zj1sIZA5ZOXXwZtbqvxgbvE9PsMdgO8Vwjxc+C/LB5eI+VnQohfourxfyml/FmYxtXEAKG0WzeElQ5XH/c/UxYwoyYYQcoq2thFJl/n2scNlJljv0arsbN1DpqQKEA1nPg0qrOOT6EHQAjxn6iOope4N/0YFbeM1sv2BhVDmksgBMTHD9ms0USEv/xFNQ3RRIURxAiPJjdSynYhxFH3/qWoLKFEoEpKedJy7M14izNjHGP94ezqNb12jPbp2VOT2bR6qKBjvNrNlK9elY+zq2fIA2tfZsxe0esYzSgRSOj5P1Sq4I+FEIdR/hdJwDR/J0kpA3r4SCmfAJ7ws78SlfXja38/ypfjnkCfNV4wxJXDNa1m+uDahTN4dPdx2rp62VfRyNZXK9m+9wPau/tMZ/niuZncsWEBJ5q7+OUL5Xz2ssIhY2VPTaa+uZu0lARKq1tYOnsquZmTyM2cZNahWgNcMEq2EQSdXT1myuODO45xx4YFZokX4DVjyIPrrvO9T6MZZolpkAygSkMPojr9XQU8IIToklJ6jD2Wu/uF3AkvQtfUlZzKm0kzWdv0Ho7i5SO7pi99Cc4+O6j/yPbsGLspsj3jpsPVR1dPH3ddUxRQIAmmNMqXGBRKWVUgscp+jWM9kyfa3f18cAjVWrmXwSfu/tiE8vMBqAFeR91PGcqNvUFFhhAiWUppGjzVV1Xx8g9/yOFFi7g0MZEzMzOV0ecjj6g4kJYGb74JN9wAf/sbuFyq4cHvfjf4/8P+/fDJT8Ljj4PDAf/2b/DHP8J556l22qWlg2NmZakFylNPqde6OigvH9w/axasXAnPPqsaJ5SXq/+njf1z5qiyhRdegCuvhH374MSJwf2Fher/2ZdfVn/rX34ZGhv1NcXKNZ1/fhD/rMcvoxBnhhUjCNzEJjnAfg+MrlvAmOy8ZV1/pKcksr4om7auXk60qAoH45hQxxs2eh2j8UMk44zw98Db7XXxc+A/bLv+S0r5gJdTYpItN9wgt1g7O40BzM5aFnHFnoWzviibB3ccMzN27tiwgG8+cZC6pk4ArlyZZwY46xg7Smpx9Q3gSIhj7cIZ7Kto5JSzmxcOnuCyZbOYnp7scY6R1njTBXO9BjpzzN4BHImDY9o/15jrTRfM9XC4D5jRU1UFn/tcuL5aTRQRQtwrpdwS4c94TUp5vuW9UWL6cSll0KbJ7tKtqwFzzkIIYc0KEkJ8H7Wge0FKebn1/LEYZ2INe6zxFgeDJsi4EYyYYj3GKpDct3l5QJHEaPcezLG+Pst6rr8uZKF47QzHmydW/XyiEWd8fO61wP+gGk/Ml1KuDuKcZJT3xtMoIXk+UIFayM2RUr4vhFgOlACtUsop1vO3bNkidXc/jSb6RCvODCdGCCF+D9wCbJFS3use5wCwDPgIKqPnJVRGz1z3/juBnwLbpZTXWOew5fbb5VjuuuWLJ9+oZNuearNhzK6yely9/WzbU22uo0bkKeqNzEx45x29jtEERbjjzJCMHiFEHioFsNRtcPqfQohfoBZAM4DXpZRPh2sCGu94K42yKsq5mZMAuGXdPPr6B8jLTAXgy1ct5jcvvcecGWmcM38aT79VbfroWAMaqM5YhiDT1tXLKaeLDxfnkps5idqmDn723BFyMlNIio/3a8xszc4BhhiWGXM3MnmMRVvQLQsbG0P+/jQTiuGWmAZDAfCel+0DXrZpRog9UzAks0M7QcaNYDJbrBk3RkaP8TsoAeThF8sBlQ1kFUFC9dKxf5b93OFk4vgzlw6FsZ4FNByEEAlAmpSyxb5PSvm02+fi31Hl5b7GSAF6pJT9UspuIcQO1JP1dGAeKjNoKapBxfuM8+YSGo3GkzDEiAMooecc93iTgYWosq9S91i9QL4QYqaUssHLGOOeDcW5ZgmWcX+xaXW+6Um6q6x+ZJk7vtDrGM0o4a106w7gK8ABIcQmKWWllPIocFQIsVFK+Xx0pzhyqk6eZMvjj4+p9EP7gsfXk+3czEksnT2Vra9Wkp6ayNWr8rln0zIAvv+XUkoqm2ho7WZN4TRTxd60ejCIGYsowBRn7tiwwKxlNWpb/RkzG52+rBk93rx9hp36eNttXjfH6tNljcKdhjgnCh81rBJTAyHEp4HzGezsd40QYg7wDMpoNRNVvjUVVboFsDVck9d4xjdrjAjJ7NCOj7hh5XRbNx0uzxKsQHFl2uTkIQbNhq8OMMSzZySm0d7O9SUc+RNhwiXQhCpajXWEEOejSsyzhRBVwN1Syietx0gp/wr8NcBQ5wKPuxtHNAMXoBZwp4B3UJ6DW4EHhRBXoB6swWCDCo1GM74ZaYz4NfB1YKMQYhtKHHIAT0opKwCEEL9DidL/FEKUoZrYtKPunyYE3nxErQ+WhnWvEQxB3I9oNJHAm9DzIeB8KeWbXvZ9VQhxG7BJStkX2amFjzkzZjDWSirsooi3J9vG4mjaZAfpqYnkT5vkMca15+ZT0dBmlnIZJVOGUHSsrpU3yk+zMDeDWVNTTL+enz13hGnpDqZnOCg6YwpZaQ5TvLG2IbQaktmzc4yMo7DwyCPgJVV9Ij5dHktcpIznqqLwUQ+hzAv/A/XEy1iF/1eQ558PfMLyfpn7pwp4DNVl8DpU2vS7wM+klI+NeNYak2DM3X3hs7zLS9ywiziPvXKc+58p477Ny01Rx1tc8Sf+nG7r5q1/nQJg/VkzIy6C+BKOwtmhK9TPHsc8AMxy/z4X+KMQ4pTR/TME6oBy4FJgMmrx9mfg21LKVtQCbxpwJ3AjKvZ8UUr595FfgkajGQOMKEZIKduEEJeiSrGuALqA3wD/z/IZX0Bl9VyPKgfb4x7jVMSvbhTxdY/gzXQ5YvhYx2g0kcab0NPuQ+RBSrlOCPEdVMbP9yI6M40H3p5sG4uj9NREnJ29PLjzGD/5xEozkB2tbcXZ2evRet3K029Vu9utl/Ptzcu4Y8MC00fHIGdpqhn8JqckeuyPaFC04iMLa6I9XdYMEs4SUynlJ1GGqr74dTDjjMXMwVghmMwdXzdrPsu7vPw3sIs43mJIqOVSj71ynF2HGrh8WQ4P3bZ6xNmFw81U9CfCeNs3XjIiI5w5eBbwM1R3mmKU6HsnypMnaKSU5cBFAY75OcoT0S9VVVVs2bKFiy66yBDTNRpNhIl0hnI4YoSUssTfGFLKLlTVxh2B5mOYMY+H+5kRlYCHizH+HWqiQyTijDehJyvAOfcAO9FCT1SxOshv31vNwtwM3n2/mSvPzqMofwoP7jyGs7PXo77Uunjy1tL8lnXzaGjtpq6pk+1vf0BNUyfXnptPwcw0swzLLiwZxs8RS2/0RloaMHRhMgGfLmsGibkS07GYORgreMvcsQs7vm7WfIpE7rhhZeOKXF47epKNK3J9Ch2ByqXs51n3BRJMghFXfIlK4RZmxktGZIQzBw9JKY0n4luFEH8kQAv1SDNnzhy0GbNGE12imKEcE8zJyWG8mDGPqAQ8XHi5H9Fo7EQizngTepqEEPdIKe/zdoKUUgohHOGchCZ47Fk8CfFxXH3OGaxbNJOqU+2sLMjyWCAZhmMrC7J4dPdxMxvH8Nr59uZl7CqrN1uxA9z9Ee/Ks104MvDXIWxY3XLsvPkmXH6534XJeHk6rQmacVdiqvHELuz4ulnzWd7ljhtWnn+nlp0H6zh/4QyAoIUOq/hjdNEyzgtFcA5GXPGWUXS6rZvPPLKHnQfrgpqvL6xxUmdEBkWuEOK7wBGUGeph4LT1ACHEHVLKB0djchqNRqPxz4hbo4cDL/cjGk008Cb0/Ar4g7vW81fAP6SUJ4yd7u0FUZrfhMabWLK+KNsUZWZkOMjLTGVHSS3P7q8BYF+Fcnbf+molh2taKZiZxrY91ab/zpL8KSzISTcXS0YANISgW9Z5v+n3J9x4e9Ie1lTJG24A/JdqjZen05qg0SWmMUw4hF67sBPyzZo7bljxV6oVLKEKJKGKK96Eo8deOc7Og3VcviyHjStyeeC5w8MStY042eHqY5IjQQvjgZkJ3GV5LwGXEOIvKOHnEPApQAs9Go1Go/GOl/sRjSYaDBF6pJRbhRDXoMxH1wIIIZqBkyiDsByUQakmwvhqsb4gJ53S6hZmZqTw7P4arjw7jyX5U5gzI431RdmcaO4iPTWRksomCmamcdMFc1lZkGV2wrIvvJxdPeyraOSWdfPYV9HI5JTEIaVert4Bnt1fg6u3f4jx8mDXrcH262FNlfzb32DBAr9PzvXT6QmHLjGNYcIh9IYi7HgVltxxw4o9hgxHFA61ZNQuQvs6119WojW+jUTUNsY55ezm/mfKeKnsBL/5j7U+xR5jThtX5PL8O7UTURiqRxmwnwXkAgJIQXmBXeXnPI1Go9FoFF7uRzSaaOAtowfgY8B3UKaDDiDT/QPwPvC1yE9N40ss2VCciyMxnpUFWeyraMTV209pdQtLZ08lPSWJB3cov56czFQPE2ZfnbB2lNSybU+1R/nW1avy2VVWz7Y91QAsyZ/ic57pKUk4EuPZ+mqlWRIWrlRJZ1cPpe9+wIK2br8LDO3XM+HQJaYxjDfxN5J4FZZcrrB+RiDRw5dQE0iENs7rcPWZLdrvvGLxkPGM+GaMM5zMHmOc7z79LgC7DjXw2CvHfcZOQ1R67ehJdh6sm4iZQA9KKb8LIIRIRwk+Re7Xs4AlwPTRm55Go9FoYp4w349oNMHiVeiRUvYDdwsh/hv4N9TNTDIqTfkPUsr26E1x4uJLLLFun5ySyI6SWjatzjdbn197bj59/QPMmeHd/Mt4+m0KRX0DAMyZnsbS2VNNYWllQRbvvt/MrMxUHPFxLMhJZ0NxrtcxI2V2tqusnq+1zeXf/SxGNBOSmCsx1V23BvEm/kYSr/EnzMbYhujxh1eOU37CCXhm1PjKtAkkQhvn3XVNEfdtXm4KOf7Gu/nCeSPy7Ln90kLzd39ZkFZR6fyFM+hw9XnMKRa80SLZDccQedy/O4E33T8mQohQW62PCN11S6OJPpHuuhVrjKeuWzGBbtShCYJodd0ykVI2Ar8P5weOBuN5AWZk4xgZN9v2VHPTBXNZOnuqMm1OSTQXWYbA4+rt9/Dt2bQ6n5sumOtR9lDb1MEPtpdR39xNQ2sXJ1tdXLkyz6fvRqgZPMF6eKwvyub/3nuJ+RfeHupXoxllIrwAi7kSU911y5NodrrwGn9+9zsIY3eimy+cZ2a2XL4sxxRBTrd18/CL5XS6+rjrmqIRef4Emwlk9ewZTrnqtMnJfP3apR7bvIk2VpGq8IoMTrd1mxk9xjxG2xstBrrhfCaaH6a7bmk00ScG4kxUGU9dt2KCMN+PaMYn0eq6Ne4YLwswf+KIUXJ12bJZvFPZSPaUFJbkT2FlwaCViVHeYAg7hm/PwtwMnn6rmpUFWea4j+4+Tn1zNwAnW1XKYdXJdkqrW3D19uNIjDcXcMMxXQ3WwyM9JYnVN2yAiVEmMK6Iwo2RLjGNYUa908XZZ4d1uGmTk3nottVDxJDHXjlullzdt3l5yJktVjHFV7mWnZsvnEeHK3BDuVAybuyiTSDhx5iH9XUiIqU8Ntpz0Gg0Gk0ME+b7EY0mWCaE0DNesIsjtU0dHKtzctmyWZxo7qK0uoXG9h7qmjo5UqNKC/ZVNJrePPb26M6uHgAef+04R2qc9PUPcM+mZQBce24+J1o6WZSbQZojEUdiHGsXzjA9gYx5wGCHrzs2LAha7Inm037N+ESXmGp8YYjiay8a6u1lFzBCEUO8iS83XziPU85uSqub2bjCe2lrsASbITNtcjKTHAnc88QBJjkSPI61Xk8oGTd20SaYc7U3mkaj0Wg0Gk1sooWeMYRdHHl093FKq1tIiI/jC1csYldZPQtzM3hw51Hqm7tZkj/Fr5BiCEczMpRn7Zzpg54+R2tbqW/u5pKiWawvymZXWT2T3WVgzq4e0wz69aMnWZI/hZLKJnaV1Qd8gm/NSgr6af/+/XDllcEdq5lwjJcSU0342FVWz6HtL1F57nqvrcqtAoYvQSNYAWja5GSmpyez61ADz79TS+EVGcOedzAZMlZTaG/HWq8nlPFuvnCeztbRaDQajSbc6HWMZpTQQk+MYy/Xsoojt6xTN+DXnpvvccx3PlZsvgfYvrfaFGuM7Jtb1s3D1dvPkvwplFa3kD01GQRmlo+rt59Nq/NZX5Rt+gDtrWjEkRDHrRfPZ31RNg/uOGZ6/FhNnP0xrLbLn/xkCN+YRjM6jGcvsLHGyoIsdvzbR/l/XjJs7AKGL0FjJNkw3ghGOAomQybQvOyeP8Mdb6xk60w4k1RtxqzRRJ0JF2e0GXN40esYTRBE3YxZM/r4EkacXT3sq2jklnXzeHT3cUoqm4Yc09bV67FvfVG2acAMUFLZxJL8KSzKS+dIjZNn99VQ09hJwcw009TZWopVXqfKwR7dfZyCmWnm+UZJVzAMq2Tr8cfha9puRRPbjBcvsPHAvopGsp9/hefXLB2SYWMXMHwJGsPJhjnd1u2z5bm1VflDt60edpeqQPMKVaAZ65k7E84kVZsxazRRZ8LFGW3GHF70OkYTBNqMeQJiF0Z8dc5akj8FV2+/ud/I3CmpbKJ4bqZ5fl5mKn39A2xYnkNDazel1S0syksHYHqGg5LKJvIyUymem2kaORst1Z3dfZxo6uSWdfN4/ehJABbkpLOvojHoLJ1hGbQ6HKEdr9FoJjTri7KZWjubxSMQL0LNhgnU8tzateuxV46H1J48WJPm4TBWMnc0Go1GoxmT6HWMZpTQQk+MYxdGDBHnsmWzyMlMZcPyHBbnZZjCz6Ea1X3ryrPzuHhJNovzMsySru17q3l2fw0ACfFx1DV1Ujw3k7zMVI7UOJk22cG6RTMBle2zOC+D3MxJpKckcf2auR7zMsQfwBSE7Fk6wbZQ94bHufqpgkYzYRlOHElPSeLCr3yK0+AzwyYcWLNhArU8t3ftguDLw/wdF4qZtJ2RnKvRaDQajSYI9DpGM0pooWeMYYgp777fTF1TJzsO1HH3R5bg7OqhoqHdLMtq7ugBAUjYUVLL2oUzcHb2sigvnfkz0z1EoLauXvYeP82RGidn5U1hQ3GuR/t0K9ZFlyMxnq2vVlLR0M4dGxYAg35A6SlJHplFoXTkAlvJ2uFS0KnqGs2EZFi+XgB//COPrbo+aJ+d4WDNhrF74wQ63n6ONwIZL0NoXkLBnqsFII1Go9FowsQf/6jXMZpRYUIIPePFJNUqsqwsyOLR3cdNQ+b0lCTu2LCA7Xs/YG/Faeqbu3l2X415rlUESoiL4+8ltZxo7mJlQRb7Khqpb+42j/VXXmVddFk9f3aV1QN4LMjs+0NZpHmUrGXMD/o8Teww0cwLNZFhWL5eAOedx81rQvefCSRy+No/nBKoQOcEI+KMxGMnHEbUGo1Go9Fo/HDeeaM9A80EZUIIPePFJNX+ZPvujyjRyioAffzCAi4uyuaRf5TT3y+ZMyON9JRE1i6cQcHMNI7VOSmtbqG0WpV4Pbr7OHdsWICrtx/wLMkyxt5RUmvusy66DHHJ2uHL2AcM2R9KCUZ6SpLZKWx9YSbpI/3yNFFnopkXjhdBOdYYlq8XQHv7sMSXQCJHsCJIOEqq/GXyGIzEY2ckRtSxwkQTlHXXLY0m+ky4OKO7boWX9vbRnoFmDKC7bk1wVhZkcbim1fTEMbALQPsqGjlSozpkLZ09lQ3Fuewqq2dDcS5rF87gB9vLqG/uZnqGg7zMVIAhHjzWsbftqQbAkRjP1avyPRZd9kWYfUFm3b99b3VIJRjGdc187zCrb74h4PEazWgyXgTlcUNpKVx3Xcin+RI5QhFfYGRZMQ+/WM79z5TR4erj69cuDenccBCqeDSapV4TTVDWXbc0mugz4eKM7roVXoZ5P6KZWEQizsSFczBNZNlX0UhJZdOQVubri7K56YK5Htk2S/KnAODs6uWbTxxk66uV7CqrN8u0iudmsm7RTJ7dX8ODO47h7Ooxx3N29bB9bzXOrh7WF2WzaXU+m1bns7Igi+17q6lt6jD324+3Yt3u7OrB1dvvMY63z7SObVxX4ZYvRuT71GiEEHcKId4VQvQLIaQQYott/0eFEIeEEC4hRJUQ4iujNFVNqNx227BOM0QOX+3Rn3+n1ut+OzdfOI/7Ni/367/zwHOHOd3W7XX/WML4bh575fhoTyUmEUL8WghxRAjRLoRoFEI8L4Qosh2jY41GM0EJR4wQQswRQmx3j9EqhHhSCJFt2R8nhNgihKhxj3FACLExWtc4oRnm/YhGM1J0Rs8YwpdXhTVrxiiPuvXi+eyraDRNm3MyU83zjDKttQtnmN49O0pqTQNme4aQke1jZOQYvjvGfl9mqdbtRlewTavzvbZjt7eEN/atL8rmwD0/4tXNn+X2Swu1Magm3JwNNAEfALOtO4QQ5wFPAB3An4BLgB8IIVqllA9He6KaEHnkkbCaH4ZazhSs/06Hq49JjgSPbJjbLy00t40FxlKp1yjxKWAP8BrwIeDDwFIhxHwpZbeONRrNhGdEMUIIEQc8BywGXgAcwEeBMwDDIOYrwLdQGQN/AjYDfxVCLJNSHorOZU5Qwnw/otEEixZ6xhDBeFXYzZKdnb0A3HrxfNJTknB29Zg+PYBp5uzqHWDbnmpcvf24+gZYkj9lSImYIRStLMgyO3ZZt9sFKOt2w+fH1/G+xt5VVs+rlZ08/EwZkxwJ2hhUE1aklB8HEEI8g03oAb6K6l23RUr5YyHEJcA/gLsBvfiKdbKyAh8TAv6Em+GULhmiSIerb0iJ10h8d3wRyfKqSMx3nLFSSrkf1FN3oBLIRS3K3kHHGo1mojPSGHGV+9hSKeXlQoh4oAJYLYS4CCUgfcn9WZuklPuFENXAN4AvA5+MwjVOXMJ8P6LRBIsWesYZdnHl2f01bFqdT27mJEAJJ4bIA/D60ZOUVDZR09TB+qKZvHbspNmBa19Fo3lebVOH2eUrN3OSuR1sxskWo2WrMGVt2e5NsLJus469viibhqlXcdfsM/XTYk20KXa/7rO9zhZCTJFStlgPNsyYAW1gGAuM0Kg2FGFkOH48hjhyuq07Ktk7462T1ssvv2wYF0KMm6QaCzg3RieCfuCE+/egY41hxgxoQ2aNJsJEK86MNEZY9u93j9cvhChBPcBaDrwPZAEDKOHIOsZy+3wMM2bQ9zNhQcdpjR8iGWe00DPOsAomrr4BAJzdfTz5hsryWbtwhsra6VX7jNdTrS729zbh7Owle2oy5y+Y4ZFx8+ju45RUNtHXP8DS2VOHdM7aUVJrZgR5M3Yebuec9JQkbmvdD9dqEzNN1JnpfjXaJXRY9mUDHkKPNmOOMZ56CkZwcxqKMDKS0qVoZcNsXJHLa0dPmmbSYx2ryHHvvfdWjepkgkQIkQb81v32J1JKYxEXdKzRZswaTfSIdpwZQYyw77ceY93fKaWUXvZ7oM2Yw8wI70c045tIxpkJIfSMt7bH1jblgEf7c6v44khQXttVDW2Un2hT2xLjuX7NXJ58o5Jte6pZnJfOZctmcaK5i387O48dB+rMrB0rRolXXlYqW1+txNXb75GhYxeO7PMM1E7d3sbd43ithI9JxkE70gYgH0hzv0+z7KuP/nQ0ITHCuBGKeDNtcjI3Xzhv1DpPBcPz79Sy82Ad5y+cQeEVGaM9nQmHEGI68DywEvgVqhTDQMcajWaCM8IY0eBle5qX/alCiDgp5YBtvyaS6HWMZpSYEELPWHzS7k8ksfrwAGb7c1AiiSGYrF04A0diPCWVqktX9tRk5dvj9ukBOFzjZHHeFD59SSEAxXOH1pE6u3rYV9HIHRsWAJCekoirt9/DUNmRqEQlR2KcOXfDgPlwTSt3bFjgV+zx1sbdpK4uiG9ME2uMg3akB1A3VucAu4FV7u3V9rItTfQJKCSPMG6EmmljNVc2iCUD+ZsvnEeHq48OVx+n27pjZl4TASHEbJRBaiHwfSnl12yH6Fij0UxgRhojhBAH3O9XCSEEqqvyCve2g6iGE01AJqoJxV7LGAfDf0UaD/Q6RjNKTAihZyziq5MVeBoXv370JIvy0jlS48TVO+4w4/YAACAASURBVMCDO46ZXasArl8zl5UFWaa/TnpKEtv3VlNa3UL21GTTjwd8L5yMsixnVy/pKYnm5xsZPeDpwWPMfdPqfJbkTzG7etlLuqyft74o2+wGZjd17iw9zCPPHY7ZJ+WasYsQ4tPA+QzeEF3jNkJ8BvghcCXwLXeb0w+5j7k/ytPUeMFfjASgvDyq87GaK9//TBlATBnIT5uczCRHAvc8cSCm5jVBeAPIAapRT9QfcG9/XEr5NjrWaDQTnZHGiO3AUeAsYCeq69YZwNtSyl0AQogfA98F/iyEeAW4HuUD9KMIX5smyvcjGo2BFnpiFF+drGDQ72b73mq27anmsuU5NHf0cKyulfITbUMEnNzMSdz9kSUeY7t6+3F295Ge3MbhmhZqmzqGtD03hBhnl+rc9V69kyM1TnO/vaW7tX37ptX5rF04w8wc8oavNu52ti760LgyEdXEFOcDn7C8X+b+qZJSbhFC3IBqR3oDKr35buChqM9ynBJKeacdfzESgNtuG+n0QsJqrmzgr+wrkl2wfKHboI8aOe7XfOALlu0HUAux13Ws0WgmNCOKEVLKASHERuDnwHpAAk8Bn7eM9QMgBbgV+BhwBPi6lLIsUhelcRPl+xGNxkALPTFKMObFxgLn3febqW/upp5uiudmcsu6eeyraDT32xdT6SlJOBLjecFS8vWrf5Tz7x8q5HBNq9lW3RBiFuelA3DGtDRWzM0yy7+sLdOt5WPb9lRz0wVz2VfRSGl1C8VzM9lQPNQANOBCzc1NR/5Bx+Yb9eJEE3aklJ/ET1tRKeUTwBPBjDXevMCiQcCsHD/YY+QQ0eiRR8CHaW2kW41//dqlAY8bjS5Y47EN+ljwApNSiiCOCSrWGF23dMctjSZ6RDrOhCNGSCkrUVk/vvb3A/e4f/xidN3S9zNhws/9iEZjEIk4o4WeccD1a+bQP3Ccvn7J9IxkXiqtNz1zYLD0qqSykcV5U9hQnGtm9bx8uIHTThcS1U69pLKJgplpOBLjTcHH2dnL4Ron6ckJ5sLKyCYCuHJlHsVzM1mYm8HBqiY2rc73EG98Pa0PthNX6pwzxt3iRDP+GIteYKNNsGJvMAwRjWbN8nlsLLQa19k14WEceIGFhO66pdFEnwkXZ3TXrfDi535EozGIRJyJmNAjhLgTlR54FsoU7F4p5RbL/o8CW4D5wAngf6WUP7TsnwP8DLgEVUO6E/i8lLLevT8O+CbwaWA6KgXxa1LK5yN1TbGGsbC56YK5FM/NYuurlWZ3LRhqany4xsnhGqe5/fo1c1m7cIbp3zM5JRFQwo61Vbqzq4f01EQzk2dXWT0rC7JMTx3A9AUqqWzipgvmkp6ShLOrx+u8A5VrDNm/cmVYvi+NRhNbBCv2BsMQ0chP3IgFkWU8ZtdoNBqNRqOxodcxmlEikhk9Z6Mc3j8AZlt3CCHOQ6UfdgB/Qok5PxBCtEopH3aLOM8Bi1Eu9A7goyhjsfPcw3wFVata5R5jM/BXIcQyKeWhCF5XzGBf2Lz7fjOl1apBh9FhC5RR8rE6p2nAbGTqAExOSWRxXgaTUxLNRdcfXqkAwNU3MER02b63ekh7dcDMACqYmab8f9zneSvLMLbbW7Tb95vnPfssnH12RL5DjUYzPhgiGvmJG1pkCZ3R8BTSaDQajWbMo9cxmlEiYkKPlPLjAEKIZ7AJPcBXAQFskVL+WAhxCfAPlLHYw8BVKJGnVEp5uRAiHqgAVgshLgJeA77kHmuTlHK/EKIa+AbwZfx4boxX0lOS+MIVi9i+9wOqTrZz68XzAXjyDSWY3HrxfB7dfZySyib2VTSSmzkJGCqqOLt6qDrZDoAjIc7c7+zspaapk2vPzWfT6nxTODLOMxZYjsR4tr5a6SEC2csyrKbN3oSgIedddlkYvymNRjMhiNG4MVYFk1god9NoNBqNZswRo/cj/7+9e4+ToyrzP/55AiEkhCQmQgIJISGsgZAIMQFBQBK8gBdQVlRYvGTV5fdT3JX9eWW9BS/r7YcLq6wuui6yqyCLrizqBlwlXlCExAAJhKCYMCQh4TLkRkIyTJ7945zqqe70THfPdHdVd3/fr1e/arqquuqpru4zVafPeY60v6xy9MyN02Ul0yPNbFxq+XIICcTMbAWhwugE4BFgArAX+H3JNk4o3VmSJBVoq8RipZU0Y0YewFtfOqOwPJ1H5+HNO3jbGUcxa8rYooqX+TMm8MD6rRwzeSw33x26a5VLoJxuLTRrylhWdm1hztRxhdY7SYuc9Pb6656V/PK+bdeeogqh0uUFDz0EL3lJv+9Dq944taulS5cmCcUg50lS60nJmHOmQrmRlVatMMlDd7dyWiEZcz0pGbNI83VcOaNkzPWV0+sRyZd2SsY8MU53xOkzqWWTyixPr5NevtPdvczyIu2QJLVcXpvSli+lw5xv29XDsXHErBVru5kyfhTru3cWVcIkCZg3b32Wjd07mTN1HOefPLVQyZPk49m2Mwyxns7ls7unl5vu7CpU1ty+ahO7e3pZsbab53r3srJrS1EC6P4qfCpat27Axa1649Su0jcgl19++bpMg2midihn2kqFciMrea0wqSSv3d06LkmqkjGLNF3HlTNKxlxfOb0ekXxpqWTMFWwGpgKj4/PRqWWb4vLS+aPLLB9lZsPcfW/J8rZTLt9NaUVJeh2AW5atByhU3iTdrR7bspNNTz/L7p5ejp82njGjhrOxeyeHjx/Fyq4tvPDI5xXl43lg/VZWrO1m7vTxRbl80i1ykn0n+9rdEyp6ShNA1yKpuDr9LW9j/ADrteqNk4g00MUXZx1BWXmtMKmGWk+KiIjUKKfXI9L+hlVepSHuidOT4vTEOO1y9y2p5SdasB/wojjvXkKC525C/PNKtnFvw6LO0MLZk7jo9On7dHPatmsPN9/dxYbuZ9jd01sY2nz+jAnMmTqOY6eMYWXXFtZs3MZhzxvJnKnjeOGRfdUmP/hdF9t29nD4+FF88NxZRfuYP2MCc6eP589fPJW508ezYm03t6/aVNhvuoXRwtmTmDt9fKF714jhwzhn3hTOOuEw5kwdV5QAulpJ5dFDi68YcL3kxkk3HiJScM01me7+ye3PcuWPH+DJ7c9mGkc9Ja0n//2Xf8o6FBERkdaQ8fWIdK5GDq/+LuA0+ipoXh+HTP8h8EXgHOCTZjYbeHlc5/NxejPwIGFo9lsJo24dAdzl7rfH7V8BfBb4DzP7JfAmwjDsX2rUMeVRUhly+/2b2di9szC0+e2rNrGyawvnzJvC1p3PsbJrCyu7thQqcg45eAQLZ09i+66+LlmTxx9USNIMFLp1zZoylkvOnlnULaxcC6MZE0czZcKoQsuhOVPHAbCya0tRAuhqJfs67vB9h2AXERnQtGmZ7r6/LqWt3CpGrSdFRERqlPH1iHSuRnbdOg14e+r58fGxzt0Xm9mFhOHRLyR0t7oM+DqAu+81s1cD/wgsBBz4PvA3qe19ARgJvAO4AFgNfNTdVzXwmDLT31DlC2dPKupatXD2JLbt2lNo3QMUcu9MOyQMfZ7expiRB3DZeeUTraVzAJV2EyvND3T7qk3cdGdXoVVP0g0MKJu0uRqFfU6ovTUQtPYNlYgM0QtekOnu+6sUaeWcYq3c7UxERCQTGV+PSOdq5PDqixhgmHN3/x7wvQGWryW0+ulveS/w8fhoewMlXr7k7JksWbGhsO6SFRu46c6uoqTKiSR5crl8OaXdscaMPID5MyZw1Y9XM+2Q0bzupCMKFTXlKn529/Syu2cvU8aPAmDe9PGMGD6s4n4ruu22QWWrb+UbKmk9GnUrZwZZbtRLf5UiahVTXx03Go5G3RJpuo4rZzTqVn1lfD0iraGdRt2SGg2UePl1J05lxPD9Cs/XbNxW9Lpk2fknT42Jkotb1yQVPMkoWsk2t+3aw5f+6wE2du9kZdcWxowavk9FTbpyaMTw/Qqtelas7eai06cPOIx61c7pt75vQLqhkmbSqFs5M8hyo9HUKqa+Om40HI26JdJ0HVfOaNSt+srp9YjkSzuNuiVDVNrCp9DSZ2dPIT9O0ponvU5SQZS0rtm2aw9XL1nDirXdnDN/CnOnj+eYyWO5+e6QpHlj904OHTuCFx99SNmKmmR7tyxfzyVnzQwVSc/tZcbE0YX106N03Xx3V9EQ8VVZtgzmzau8XgndUIl0sH7KDXXpFBERkaYZ5H2MyFBlNepWUyVdKpauXJl1KHWTVJ6ku1ItnD2JdU/sAGDm4WP26WaVrJMeWev2VZsK+X1G7D+MFWu7+cHvwrDqybZeeuxE3nrGjLKVMwtnT2LMqOFs29nDt38RRmK5Zdl6dvfs5fZVm9jQ/UyoNIotf77zq7WFkbuq9thjg3qPJFud1tRZcqafckMjR4mIiEjT6D5GMtIRLXrarUtFUmkyf8YElj38VKHS5uola1jZtYW508dz9tzJbNu1p5C75+y5kwt5d5KWPQtnTyoaSv2OBx/nlccfxqNPPcM586dw5uxJRdsv3X/SMueD5x7H1257iHe/8gXc9ccnAbjzj0/wxNbdhUTRsG/ro6pdfPGg3yvJTqc1dZac6afcUJdOERERaRrdx0hGOqJFT7tJWsZc94s/FVrIpFvmXHL2zMIQ6zfd2cVNd3axZMWGfVrWXL1kDRDy8Sx7+CluurOLVY9uZfX6bSz/UzcHjxxeaAm0bdcebvzNWm78zVqWrNhQ2O+2XXt4cMNWPvXm45l5+FhG7B8+Uk9s3c3c6eN52xlHFbUgKifp0rVt157yK1xzTd3fQ5F6a8eWgy2tn3Ij6dKpblvtodNaDibJmONxi0gTdFw5E5Mx63qmTnQfI1VQMmYBKGqFM2vK2KJKlHT+m2QkrMR3frWW+x55mmmHjGbO1HGsWNvN1UvWcMnZMwvbOGbyWK6+9UE2du9kyYoNnD138j6Jms8/eWqh8iadFDrZxlknHMbG7l287YyjmDz+ICaPPwiAm+8OXcJ29/QWkjMnFVLlho4v0LCE0gLareVgy1O50RE6reWgkjGLNF/HlTNKxlxfuh6RKjSinFGLnhaU5NyZPP6gQoub0pw9yXpJQubdPXuZM3UcK7u2cMvy9Rw+fmRhdKzbV20qvP6w543kkNQv3UklzJqN23jl8YcxZ+o4Tj3m0EKFzO6eXs4/eWqh0uemO7t4fOtuVnZtYdnDTxXFneQHAopy9ZTmDYKSVj6HH96YN1KkCma21My85LEq67ikApUbkhNmdqmZ3WdmvbH8WFyy/I1mdr+Z7TazdWb2oYxCFZGMDLWcMLNpZnazme0ws61mdqOZTUotH2Zmi81sfdzGPWb26iYdXmfT9YhkRC162lh6RC0ILXGe2P4sm55+lo3du3jHmUfzXO9etu3qKQy3fvuqTUV5foBCnp399xtWqMCZPP6gQsXORadPL+T+gdDSaMbE0Wzb1cONv1lblB8oPdz6/BkTCqNwlbbkKWrlM2wlhFpOkSxdlfpbmfXybulSlRuSF/OAbuBR4Mj0AjM7Bfge8AxwA/Ay4AtmttXd/7nZgYpIZgZdTpjZMODHwCzgNmAE8EbgCOCUuJkPAZ8ktBi4AXgz8F9mdry739/YQ+twuh6RjKiip40leXvmTB3HzMPHcPbcyZx6zKFc94s/8bYzjmLZw0+xsmsLK7u2MCbm40knTE5y88yYOJoZE0dz6jGHFnUVK50mFTkAI4bvV+jqlQzlnkjWS7pyJdtIJ44u2vaxL230WyVSkbtfmnUMUoM3vCHrCEQAcPe3ApjZDym5gQM+DBiw2N2vMLOXAf8DXAaookekQwyxnDiXUMmz0t3PMrP9gIeBk81sAfBr4ANxW+e7+3Iz6wI+BnwQWNTIY+t4uh6RjKiip42VVtpAqGS57Lw5bNu1h909vZwzbwojhg/rN1lyutVOOt9Osq2yOXUIrXqSfEALZ08qOwJYOr5kP9BXMVTY9tKlMGfOkN8PkaEws6cJF1rLgY+4+90ZhyQDUbkhrWFunC4rmR5pZuPcfUsGMYlIvgxYTqSWLwdw914zW0GoMDoBeASYAOwFfl+yjRMaGLeArkckM6roaWMDVcSkK3DS65QmRp4/YwIPrN/K/BkT+t1P6XDrQKG10AuPfB5jRh7AzXd37VORk44vnTh6n0qnp4pz/Yg02XbgR8AGQhPoM4FbzWyWu29KVkpG3QJYMGcOC/RPPVsqN9ra0qVL0yNPTcsukiGbGKc74vSZ1LJJQFFFTzLqFoTEjQvUHUCkYXJUzlQqJ0qXp9dJL9/p7l5meZFk1C3Q9Uxd6HpEBtDIcqYjKnqSG7BOKqzKVb6klXa76m/+HQ8+zoq13cyYOLowAleyLPm73KhZpRVEA1bkECql3vSS6eUP5uKLazp2yYc2Go703OTCyMwOAB4i/Eq2ELg+WUmjbuWMyo22lq7kuPzyy9dlGszQbAamAqPj89GpZZtKV9aoWyLNk6NyplI5sbnM/NFllo8ys2HuvrdkeRGNulVnuh6RATSynOmIUbeSG7BOqeSBvpY5ychWpdKtaQqjW6Xml6scWrJiA9/51VqWrNhQtP35MyYwd/r4olY/yx5+ihVruwsjbyUVOW96yfSibReNrtWfa66p+fgle+0wHKmZjQIO62fx3mbGIjVSuSGt4Z44PSlOT4zTLnXbEpGoUjmRLD/Rgv2AF8V59xISPHcT7vvmlWzj3oZFLYGuRyQjHdGipxP112InkbT42d3Ty013drG7p5cRw/fbpwXQ2XMnF+YnOXZKt58kfZ4xcXRh3Ur7T5RrDbSPDqqgk9w5FFhjZj8n9HE/hdCaZzPwsywDkwpUbkhOmNm7gNPou/F6vZlNA34IfBE4B/ikmc0GXh7X+XyTwxSRDA2xnLgZeBA4DriVMOrWEcBd7n573P4VwGeB/zCzXwJvAnqBLzX0wETXI5IZVfS0qYHy86SHXT//5KlcdPp0dvf0lq1wSW8nXelTml8H2Gcb/VbcpFRVITR6dP/LRBrrKeA6Ql6eBcBWwkXXx9z9yQzjkkpUbkh+nAa8PfX8+PhY5+6LzexCwrDHFxK6UVwGfL3pUYpIlgZdTrj7XjN7NfCPhG7lDnwf+JvU9r4AjATeAVwArAY+6u6rGnlQgq5HJDMd0XVLiiUtcOZOH180lPn5J08dsMKlv25dyfxTjzl0ny5clQzUVazgt7+tensi9eTu2939r9x9hruPdPdJ7n6eu99fum6SC2zpypVZhCqlVG50hFbIBebui9zdyjwWx+Xfc/dZ7n6Au09198+nEqYWSZIxpxI3ikiDNaOcGWo54e5r3f0cdx/t7ge7+/nuvjG1vNfdP+7uk+M2jnf3H5WLJUnGrOuZOtH1iFShEeWMWvR0oNJh15MRsS46ffrAFS4VJHl5Zk0ZWzQM+5BdeGH9tiXSIErGnDMqNzpCO+QCq4WSMYs0X8eVM0rGXF+6HpEqNKKcUUVPByrt1lVtPp1K6rWdffzoRzBzZn23KSLtTeWGiIiIZE3XI5IRdd0Sxow8oJBUecDRr6rYTsVuWIOxe3d9tyci7U/lhoiIiGRN1yOSkY6o6FHujMoqDcdeL1UNp15K3WFaUivkzpA2pnJDREREsqbrEclIR1T0JLkzFmh4u34tnD2Ji06fXv9uVyUGVaF07bUNi0cap9P6tEvOqNwQERGRrOl6RDLSERU9UlnDul2VGFSF0rx5jQtIpE7UcjBnVG50hE5rOahRt0Sar+PKGY26VV+6HpEqaNQtydy2XXu4fdWmwohdtSpNBC3SLjTqlkjzdVrLQY26JdJ8HVfOaNQtkaZrRDmjFj1Sk2bl8imyfHnz9iUi7UHlhoiIiGRN1yOSEbXokZo0bAj1gSxa1Lx9iUh7ULkhIiIiWdP1iGRELXqkJs3K5VPku99t3r5EpD2o3BAREZGs6XpEMqKKHsm/ESOyjkCkIiVjzhmVGx2h45KkKhmzSNN1XDmjZMz1pesRqYKSMUtnUkI4aQFKxpwzKjc6QsclSVUyZpGm67hyRsmY60vvpVRByZgHSb+0t7jrr886AhmETvsFTHJG5YaIiIhkTdcjkpGOaNGjX9pb3CmnZB2BDEKn/QImOaNyQ0RERLKm6xHJSEe06JEWt2NH1hGISKtRuSEiIiJZ0/WIZEQVPZJ/6nInLUBdRHNG56EjdFoXUSVjFmm+jitnlIy5vvQ+ShWUjFk608UXZx2BSEXqIpozKjc6Qqd1EVUyZpHm67hyRsmY60vXI1IFJWMuYWYHmtlXzOxxM9tlZneY2Yuzjkvq7Jprso5AOpjKmRalckNaiMoZEWk0lTMZ0fWIZKSlK3qAK4H3ApuBHwKnAD81s+enV1r3+OMZhNY87d608g/d3VmH0DAd0Px+WtYB1EHbljOtVnbUFO+ECY0LpAat9h1vtXijaVkHUAfVlTPr1jU/siFqtc9Uq8ULrRdzq8UbTcs6gDqorpzZuDGD0Jqn6dc+Tb4eadHvV1Xa+diiafXcWMtW9JjZocA7gL3Ay9z9QuA7wMGEQqxg3ebNzQ+wiVrtZq1WP+3pyTqEhlGBlW/tXs60WtlRU7yhCWzmWu073mrxRtOyDmAoaipnVNHTcK0WL7RezK0WbzQt6wCGoqZyRhU99dXk65EW/X5VpZ2PLZpWz421bEUPcBwwHOhy9+Sn9GVxesJgN1rrl7+W9Ru1bi3yEG+txzZr9erqt11DAZCHdWvRyBjycHw51dblTB5iaFS86664oqb18/AdaOcyqdb1Vc4Mvpyp5r1r5jrVaFY8rRZvtetUQ+/x0NZpA9WXM2Zw4IFVPZauWVP1urWu36h12X//5sVwwAHw/e/vczLy8v8wD9cdebhGadfrGXP3zHY+FGZ2AXA9sMrd58R57wK+AfzO3U9OrftbYHd8uo6BEx1Nq7B8KOtr3drXzUsc7bxuvbc9jb4a6RHufkoNceSKypmOWTcvcbTzuvXe9jRUzqyj/Hs0rZ/5WidfsbTrOnmKZajrTEPlzDp0PdNK6+YljnZet97bnkaDyplWHnUr6ScxOjUv+XtTesVWLphFJFMqZ0Sk0VTOiEijqZwR6TCt3HXrAaAHmGpmE+O8E+P03mxCEpE2o3JGRBpN5YyINJrKGZEO07JdtwDM7Brgr4D7gVXAm4BngKPc/YksYxOR9qByRkQaTeWMiDSayhmRztLKXbcA3keonX4TcDRwJ/B+FVYiUkcqZ0Sk0VTOiEijqZwR6SAt3aJHRERERERERET6tHKOHhERERERERERSVFFj4iIiIiIiIhIm1BFj4iIiIiIiIhIm1BFj4iIiIiIiIhIm1BFj4iIiIiIiIhIm1BFj4iIiIiIiIhIm1BFj4iIiIiIiIhIm1BFj4iIiIiIiIhIm1BFj4iIiIiIiIhIm1BFj4iIiIiIiIhIm2j5ih4zO9DMvmJmj5vZLjO7w8xeXK/1szaI41tqZl7yWNXMmKtlZpea2X1m1hvjXFxh/VY7d7UeXyudu2+a2Woz22FmT5nZT8xsdoXXtNT5q1aej6ua82RmbzSz+81st5mtM7MPZRVvKTO7MPVduDI1/xIzezjGvMbM3p5lnDGm88zs7vgZ2Gpmvzaz58VluXuPzewEM7s1fi52mtkDZvae1PJMY65UflaKz8ymmdnN8bO/1cxuNLNJzTyGesqynGn0uTCzYWa22MzWx23cY2avHmLMQy77mh23mX3bzDbEbT1pZkvMbG5q+YDlnpkdb2a3x8/HU2b2DTM7OLW8IZ+hwZbTWcRrFa6z8hhzM7Vy/I0+t81kLVjm1mKg4zOzRWXOo5vZ/NQ6uf0eWt7+97h7Sz+ArwMOrASuB/YC24Dn12P9rB+DOL6lcf0rU48PZ30c/cT6bzHedTHmxfV8L7J+DOL4WuncOfBb4BvA2vh8PXBgu5y/Gt6L3B5XpfMEnBLj3Q58Oy5z4P/kIPYpwNNAT/K9iPMviM8fB64FuuPzszKM9cIYw7PADcA3gfuAyXl9j1Pl0l2xrNobny/MQ8wDlZ+V4iP8iHV/nHdrqmz9bdaf6yG8H5mVM40+F8BH4ry1cRvPAs8Bxw0h5iGVfVnEHffxXeBrwJq47UfisgHLPeDguMyBm4Dl8e/rG/kZYpDldIbxJudxn+usvMbczEcrx9/oc9vkY2m5MreOx7cozrut5FweUe25yvJznLzX5OR/T+ZfzCG+mYcCe4Be4NDUh6foQzPY9bN+DCbe5AORdew1HucPK52DVjt3tR5fq507YF7q72nx+Bx4UbudvwrvQ66Pq9J5Sn023x+fvyw+X5dx3Ab8LP6zu4HiG4h74vM3xOfvjM+XZhhrV4xhQZnluXuPgeHxM+vA7DhvWXz+l3mKuVz5WSk+4PXx+X3x+X70XVDuc47y/shLOdOIcwHsDzwZn8+L63w6Pr92CLEOqezLKu5UzC+K2+qN39cByz3g0vj8lvh8NLArvv6oRnyGGEI5nUW8cRtL6ec6K68xN+vRBvE37NxmeEwtU+bW8fgWxXmL+nlNrr+H5Ox/T6t33TqO8A+wy90fj/OWxekJdVg/a4OO18yeNrMtZvYzMzuxkUE2Saudu0FrhXPn7stTTw+I017gsX5e0q7nL9fHVcV5SroFLCuZHmlm4xoc3kAuBU4DLiL8UgGAme0PJE1gS2PO6v3+M+AIwoXGh2JT2z+a2SVxee7eY3fvAa6KT79lZv9GuLG8F/hPchhziUrxJcuXA7h7L7Aizsv8ezkIeS5nhnoujgAmEH7h/H3JNgZ9bHUo+zKJ28zea2b/RPgVGuAKYoVsP/Em+yo6HnffATxI+HX4hTTmMzSUcjqLeAtKr7NaIeYmaPX4gYad2zzJZdnVAFfFblcPmtn7UvNz/T3M2/+e/Qd1FPkxMU53pOY9E6fl+uLXun7WBhPvduBHwAZC87AzgVvNbJa7b2pIlM3RauduMFru3JnZaOBfftenVwAAEjJJREFU49Mvu3t/FT3tev5a4rgGOE+l8T+TetkkYEsTwisS+zJ/DviEu99jZunFzyf8ugH7xjzWzA5092dprufH6UjCr0k3ErpyfdXMNpDD9zj6IXAecGJ89MR528lvzIlK8bXE97IGeT6eoZ6LZPlOjz9NUsdjG0LZl1Xc5wNnxL/XA3dQRblXRbwHVlhek6GW082ON6XsdRYwK8cxN0uey5lqNPLc5kley6562QvcTfjhaQJwLnClme1y92toke9hXv73tHpFz+Y4HZ2al/xd7sa41vWzNph4z01OvJkdADwEHEnIu3B9P69pBa127gajpc6dmR0C/ASYT+iL+uEBVm/X85f746pwnjYDU+mLOX0cWcX/BsKvIGeY2enA8XH+ufQ1z92PEOtT9MW8NYNKHoAnUn+/1d3vNrNdwHsIMefuPTazCcB/A6OA0wldL24FPkno+567mEtUii/338sa5fl4hnoukuWjzGyYu++lTsc2xLIvk7jdfUG88TwL+AEhB8XRVCj3zKxSvFsrLK/VkMrpDOJN9Hed9Yocx9wseS5nqtHIc5snuSy76ujf3P265ImZfY6Qk+YNwDVUPr7Mv4d5+t/T6l23HiD8CjnVzJIarqSry71mNtbMjjGzadWs34R4a1XT8ZnZKOCwfra1t5GB1lsbnLsBtfq5M7MjgV8TCrHPufvFqZrltj9/Kbk+rkrnidBvHeCkOE1i73L3rFptWHy8CngNIdknwHTCr3T3x+elMWf1fj9CSPJXzg7y+R5PJ1Ty9AB3u/vTwOq47FjyGXNapfiS5SdasB+haxrk4Hs5CHkuZ4Z6Lh4lJEYdBswr2cagj60OZV9T4zazkXEfxArrJYTyY39CS8FK5V7R8VgYgeYY+hKS1vszNNRyutnxVrrO2pPHmJusZeNvwrnNk1yVXQ0wo5/5yb1Qrr+Hufvf0+ikRI1+EGr3HFhFSAaXZLI+hL6ETvdUs37WxzLU4yMkfdpN+KX26/GEO6GGL3cZ84F3EbLfJ4lM74nPX98m567q42vBc7chxvcIxVnxT4rLW/781fBe5Pa4qjhPp8Z4dxAy9yfrvzvr2FPHcC3FST7/Ij5PRs94Oj5/VYYxXh5jWA18i/CL9nPAi/P4HgMHEX7VdMIFybcJF8MOvDkPMVcoPweMj3ABtDrOuw34Rfz7d1l/nofwfmRWzjT6XAB/F+etA66jbwSR2UOIeUhlX7PjJiTZ3BjP7dfieU7KubFUKPcII9E8GefdRMjd4MD3mvEZosZyOot4qXCdlceYm/1o1fibcW6bfDwtV+bW8fiWEkYt/RdCq8bn4jpvqfZcZfk5Jmf/ezL/ctbhDR0JXE1oPv8s8BvglLhsUfIBqmb9PD5qOb744f8G8DDhRmMTIbFmU4bLG8SxXUtfNvL0Y3GbnLuqj68Fz12543Jilvx2OH81vBe5Pa5K5ymu82bCLyB7CP90PwJY1rGn4ku+R1em5v0N8KcY80PAOzKOcX9CvorHCH2l7wZenef3mFAJ9VNChc/OGN/78hLzQOVnNfERWhbcQriY2k64IDw8y/d8iO9HZuVMo88FoUvFpwkXvHsIN2mvHWLMQy77mhk38ALCDc5TcVsbCPm+ZqfWGbDcIyTxXEq4hugm3CiNacZniEGU082Olyqus/IWc7MfrRp/M85tk48n+T61TJlbr+MjVALdRcgFuJ1QkfP2Ws5Vlp/jfo4rs/89Fl8gIiIiIiIiIiItrtVz9IiIiIiIiIiISKSKHhERERERERGRNqGKHhERERERERGRNqGKHhERERERERGRNqGKHhERyZyZfczMHjMzN7NNZvZrM3vczDaa2VfNbL+M4lqciumGKl/zoJktjY8nU69fmnpMitNn4/IFDT4UEanAzF5sZuvid3KPmX0ztWyBmf3BzNaY2bws4xSR1qPyRZpNFT0NYGafMrPN8Yv87pJl/2RmW+JNzPwG7f+Hcd97zezgRuyjxngON7MfmNmjZrbKzH5Qsny0md1lZg+YmWUVZ6MNdMNoZqeb2cNx+eIyrx1vZr2DuRnUDaW0Anf/DHBffPqv7n4acDQwCrgEeH1WsUVL3P2CKtfd5O4L3H0B8OvU65N5uPum+PemukcqIoPi7r8DPhOfDgP+OrVsKWH45jPdfXnzoxORVqbyRZpNFT0N4O6fAB6MTz9lZuNSy94DPAKc7u7LGhTCp+L0YXff3qB91OLbwHmEm7VTgF+WLD8cmAd0u7s3ObaCWCHiZrbLzPZv4K6KbhjN7J3Ap4HdA7zmlcAzwB217kw3lNJCjovTVXG6A+iNf7dSJfDnh7hcRLJzT5zuB8xJZprZ2cBKd9+QSVQi0g5UvkjTqKKncY6O0+cDn0hmmtlI4NkGV2i8ME5XNHAfVTGzscDLgOeA29x9u7tfmV7H3R8CJgMLmh9hkRPi9H53f66J+/0tcCbw+ADrnAX83N17mhOSSHPFsmJyfLrKzIYBHwLGE8qyn2QVWykz+2hsmelmtsjMzowt9dzMFrv7koFeX2m5iGRqFeGaBeB4gNja+MOoklZEhkblizSNKnoawMwmAvfTd2PyXjN7Qfz7WGB1XG+6ma2ONwe/ifOOiM93WHCwmd0b5z1mZp+IXZw2mdnLzOztZnZP7Cr2uriPpIZ4tJn9u5l1m9lPzGxCKsbhZvZBM/t9fP0aM3uXmR2W2t9aM/uMmd0Rt7/PL+pmtp+ZvSdu5w4ze8jMPhJjn0LotmCE1ihLUjEmr19gZl3AY8A/xXnpY95gIXfHMgv5Os4zs8/GZT1mNiK+5rVmtt3MDujv2OJ6/R3fE8A3YliHxq5OrxjovaqwvbLvVznu/oC7762w2lnAkjLvT7WfCZG8S1rzOHA1oeLz74H/BM5w951ZBVbK3T9L369yuPvPid9PEWlt7v4ssCY+TX4AeiPhx5ansolKRNqByhdpJlX0NMZxhIqe9xNqbYcDXy5ZhruvBf49zl+VWg7wgAfbgavivB3Al4CvABOBrwP/BXwWOJTQNQr6Knr+4O5vAW4EXkWqZRGhUuWLwOXASYQWSNcAO1P7ez5wLXAGcEs/rZCuItyUXefup8bj+Bxwobuvj8ugLz/FzekXxz6pSQ6LVXFe+pj3AP9A6NN6CHCpu38U6Ab2B2bF9ZbG493T37GZ2Vh3f6yf47sZ+FWc/6UY608Heq8qbK+/96tmZnY8cBjxRnKQnwmRvJsdp3+I+XkmEz7L5wG3VVtxKiJSB0lF7vEWEsH/NeFaRERkqFS+SFOooqcxZhO6/zwIfC3Oe42ZnUWomLg/tW7SzSqZV5qjItkewI3uvotQoQDwS3d/OvX8mZJtfitOk1/CZwKY2XHAu4CtwI+BRYTPwu+Bban9Xefuf3T359z9XaUHaWbHEioSelP7SroWnRynSUXMA6WvL3N85Y75e+7+DJC0RkqO5d44PT5O/xy4pYpjS2+79PiSCrIkIWw179VA26uXs4A17r4uNa/Wz4RI3hUquQHcfTeQJG4/GTgii6BEpCMlN2IvBN4J3OTuOzKMR0TaRy7KFzP7BzO7qcp155rZM2Z2VeW1JS8amXC2kx1HaNkBsBh4C/A8QqueR+ir/IHaKnqSguHYfp6vNrNDCC07ngVWxvlJvqAkwdc5cdoL/Cyuexlwtbu7mSX7+8UAxwiQdAt6wN2TSo+ki1rS/LDo5q2UhaTHM+PTdAVYEkOSsDqpMFodp/cBCwm14cOBDwCvAP5yoGMr2Xbh+OI2kjgKFT1UeK/6216dnc2+3UKq/kw0KCaReitXVsyI063A5nIvMrNpwFrgL9392gbFNpDkB5ORGexbRBoj+V96MOEHrZPSC83svcCfEcqe2YTBLz5X7cbjddbHgEcJ12YvBv7D3X8w4AtFpB00rHwxs1MJ9yt/As4n/Ci9Gvicu/+9mX0Z2Eu4l7keeLrKmLsIo6DeXeX6kgOq6GmMWfT9Kt1tZp8iNMmbBUxx9y4I+W0IX2ToG6XrxDgtV+nRX2VQ8nwlfRVHj6byviQFSJIzaFKc/tjd3xZjmUP41fynlG9hU86hcbombuNg+lrFJBcrlVr0vAA4AHjS3dM3ckkMyevSxwjFLXoWA993981mVunY0ttOH99MQhe79bFFTGKw26sLMxsNnEroOpZWy2dCpBUUfect5DX7v3HeP7j7bjN7DfBR4HuE7+BHCCPSAbzSzNbF55cB3yVU0I4jDFm6EXgr8F7CDdZCYD4wldBS7y9iK6JaTYpl+QkV1xSRVnFP6u8r02WDmc0kdJd+D+FGqQeYEcuBLxJaIB9G+P/7YUJ39j8jdA9/CaEb/W+Ar7n7h+M25wBjLCSh/0x8/S5gCnAB8CJCV9Z1cfoy4K70vtz9A/V+E0SkIRpWvrj7+Wb2MPCcuz9gZv+fUEFzdNzGLnf/qJktAG4H/s7M/hv4H0LF0vcJ11CvAv4IXEf4kTu5N2rUiNHSAOq61Rjj3H1r6vnVwEPx73QLi/3pOwdPm9kplFQamNl4whe6B/hD/JLOTK9D8U1+UtFyoJkNszBc30Tgd4Q8NNBXSXJMTJp8IKEiqrt0fxWOMxnq+4A4fQdhuMCvuPvKuK1JFbZVWmGxzzGXOcb0MZxKqLH+QqVjG2DbANPi9I9mNtrMbh3i9uplIaHmPd36qNbPhEhumdkxZraUUE4BfNDMlgE/J3yn3unul8dlpxM+20cTbnY2A7fFZbfFnF/XxOf3xsrZdxPKqJH03TR9gFD+HkPIBfS3g6zkgTAy2BJCviyARbFCCjO7ATgtzj/bzL4yyH2ISBO5+5OEljYPEm500jYBPyJcCzxN+P+8BbgU+H+EHHnrgdcA7wM+SMgv+GVCrr/3En7JT8oq3H2lu98R17+MUBl9C6FV8WnAncCBhIStiwllTum+RKQFNLh8gXDtdJSZvZZwjfQo4brpr4B/ieskPTHuItyDHUS4T/wXYExc/8uESubz4z630ZdIWlqAKnrqyMxOMLNfAkea2TeT+XFI7OSXlvtT83cDVwDbCQmTX5Ha3GfiNJ2gtIfwxRsBPO7uT8Sb/uQG6eP0VfQsIzTd+yahRvic1JDh1xFqgCcRmuDdAHzB3ZeX2V+/3P37hITTx1oYNezthBrov42rJK15/jjAtpL9HWtmbyx3zBaGXZ6SOkYI7+NzhMqO82MW+0rHts+2U3HcR6gUOYHQGilJnj3Y7VXNzM6ON7pJi4BFcdSvyYRuW7+IeXgStX4mRHLL3R+Myc8tPk5w9/nuPsXdT3P3b6VWv45QzowitNg5kzBKFwBmlu4+9Xicfgi4CPg0IWfVKHe/j/BL1qWkWlkO0qXu/gp3nxfjn+buP47HdoG7Pz/On+Tufz2E/YhIE8Uy6Fh37y1ZtMDdzyHcGJ0HzAOOIlTIbAdeTujm8Lo47zF3f8TMxhHyDCat6R8DMLMRZrYozjud0CJ7Q/x7J7Cc0Hp5JH0/2M0vsy8RaRENLF8gVPSMBl7u7rfF53OA/d39T3GdkwjXT3cT7j8OJAxsk/QsWRa3vzzeP74k/l2XgWakOdR1q47c/R7gpf0su4UwzHjp/A8SamMTnypZ/sv069x9Tcnz7jLbfWeFOHuBv4uP0mVF+6sk1iB/uZ/FFVuVuPsnKB4NrNwxby2NKVaSDS+zvX6Prdy2U/O76KskG/L2auHuS+hnaOaYwPurA+2zys+ESDs4n/BL+GZgBaHp8uOEit/XErquJt09X2lmyWh6iwjl4sHALDObAlxJuGm6nIFtIeRWm29mN7j7BfU4kNjN9AZChfUjhKbRIpJ/55nZKwndHI4BFrv7UjM7iPBDzMGElrjPI4zaOcvMPhLnf4VwnTcR+GczW0HoBp+MwPpV4Mtm9n5CZc5r3P0pM7uQ0BU1+ZHpm2X2JSKtb6jlC4SKnS2EH6qT54cD/5zaz4mE/IdHEnKE9RIGmvlwavvXAm8zsw8QKp6vbMDxSgOZKuakUczsP4HXA+9w93/NOp6smdmlhBYEO4BV9bphrGK/yQ3lFELl7gXufmcz9i2SR3GodiO0ejxzML9QxVZ4Z5BdEmgRERERkbJU0SMNYWYfB95IqA2+MpUYWkQkU2b2GcIvWY+6+w1ZxyMiIiIiUk+q6BERERERERERaRNKxiwiIiIiIiIi0iZU0SMiIiIiIiIi0iZU0SMiIiIiIiIi0iZU0SMiIiIiIiIi0iZU0SMiIiIiIiIi0ib+F5dAWp1VL+iZAAAAAElFTkSuQmCC\n" 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NsArwu3bF3XaHIX0z+259i7Q63WVF+x8Avp595gOAEaTPodgUYK/sO79uSYeXmZlZrvyfkpmZ1aeIBcCBpNEED5MuPN8DDl7WARAxm7RU+e6kXDTnAmd24L3mA4cA65A6Ma4ldarMbMOr7yaNkPgaaSTEuOxWPBLki8CDwK2klZR6AAdmx1iI4XLgfNLKYONJHRVHZHlcCmWeAI4irQ41HrgAOIeIllaKmkNaaet24L+ki/RrSRf9hXxFBwIbkaZ23UkaYXJMszW25XNP9Z5NSrb8Bs0l0o24G/gKadWriaTP/arss2irD4C1gD+QEk3fC8wgJfRu7hj+AxxBGtUzntTpchfFS4ZHPAD8EPhBVmY/0mde7DbSqmZ3kTomm/7+deRzbt7OLP+ebUhK9DwOGF70fq9m7/dJ0qicq7PbOUVllpK+S1NJq8LdTxpFdWTW+VjwPeCPWf1PAVsCBxDRkalbbXEBacTQeNJnfyYRtxbt/y7ps7sXuIfUPjxRUsd5wJqknF6zgE3KFKuZmVm7acX/Z83MzMzMapQUwBeJ+EveoZiZmZWLR/SYmZmZmZmZmdUId/SYmZmZmZmZmdUIT90yMzMzMzMzM6sRHtFjZmZmZmZmZlYj3NFjZmZmZmZmZlYj3NFjZmZm+ZNGIAXS35rYd2S274McImua1Ij0w7zDMDMzMyvljh4zMzPrLqYChyFtULL9a8ArOcRjZmZmVnXc0WNmZmbdxX+BscDJy7ZImwAHAH/8UGlpJ6T7kOYhzUL6G9KmRfs3y7a9jjQfaQLSF0vqGI00HOlcpOlIs5FGIvXt1JFIhyA9hbQQaSbSVUirFe3fBulepHeQ3kN6boXYpKHZtvezmB5G2qhTMZmZmVldcEePmZmZdSdXA0ORlD0fCvyD0hE90tbAQ8CjwM7AfsAS4H6kPlmpvsA/gc8AA7O6/4i0b8l7HgOsDQwGhgCHAd/v8BFI2wJ/Bx4GtgNOyur8bVGpvwJvAXtmsX0HeDt7/U5Z2Z8CHwcGASM7HI+ZmZnVFS+vbmZmZvmTRgAbkTpEXiN1vjxM6uD5X2ANYDgRKxWV70PEkKI6epM6S04g4rZm3ud2YCYRX82ejwbWImK7ojK/AbYnYo8W4m3M4rmwiX1/Bj5OxK5F244EbgU2I+IVpDnA6USMaOL1RwMjgI2JmNtsDGZmZmZN8IgeMzMz6z4i3gf+DHwVOBRYCbijiZK7AEdn07bSLY2Q6QNsCYC0KtLFSJOy6U/zgEOATUvqGl/y/HWgNE9Qe2xD6qQq9hAgYOvs+c+B4dnUsWFIOxaVvR94GZiCdD3SqUjrdiIeMzMzqyPu6DEzM7Pu5mrgs8D3gD8SsbiJMj1IHULbl9y2AoZnZS4FvgCcD+yb7b8bWLmkrkUlz4NynyNF/JgU643AAGAs0oXZvnmk6WhHAy8CXwdeyqZ0mZmZmbXIHT1mZmbWvUQ8CzwB7MXyTptSTwLbApOJeKnk9nZWZh/gWiJuJGI8aZTMVuUOH5iUvXexQaQOpEnLtkS8TMRVRBwD/Aj4RtG+JUQ8TMSPgJ2AN4ATyhy3mZmZ1YCV8g7AzMzMrAkHkXLwzG5m/0XA48BfkH4JzAIagKOAXxLxMvACcCTSLcA8UsLj/sCMLoqxH9L2JdveJI0kehrpF8DvsriuIHU6Tc1W9LoEuAWYAqwJHAw8CxTy+WxOmv41i9TRs/Gy/WZmZmYt8IgeMzMz634i5rfQyQMRz5FWrOoL3EvqBPk9sArwTlbq26Rkzg+SVu56Dbi5C6M8DRhXcvshEf8BjiCN6hlPmmJ2F2kKFsAHwFrAH4DnsvhnsHzEztvA4cAo0tStnwEXEvGHLozdzMzMapRX3TIzMzMzMzMzqxEe0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPc0WNmZmZmZmZmViPK1tEjabSkKLlNLNp/mqTJkhZKekHSSSWv307Sg5IWSHpL0u8lrV60v4+kKyTNzMqMkbRbuY7HzGqbpOOL2qrLi7a32FaZmbVG0vaS7s3OZ+ZLelbS/xTt/7ykSVk70yjpzDzjNbPK6mwbIalB0u2S5kmaI+lGSf2K9veQNEzStKyOZyQdUsljNLPKUkSUp2JpNDAI+GXR5jci4hJJQ4C/ArOAu4EjgLWAgyPi3qxDZzKwHnALsBmwI3B9RByf1f9b4GvAxOx2HDAP2Dwi3izLQZlZTZK0ETAB6AusBPwyIs5ora3KK14zqy6SGoFNgSeAF4ATAQH7Ae8DY4D3gL8BnwY+Cnw9In6XR7xmVlmdaSMk9SCdw2wN3Af0Jl2DjY2IPbL6zwJ+CjQCD5Oum1YCtouISRU5SDOrqLJP3YqIM4pul2Sbz8ruvxERJwPfy56fnd2fQurkuTMijiE1Vu8Dx0raXNL6wFeApcCns86fa4HVgW+W+5jMrHZIEvAn4HVSx3Kx1toqM7MWSeoFbJw9/UpEfBF4OnveAHyfdEE3LCJOAgqjBt3OmNWBLmgjjiB18kyIiINIHUGvALtLGixpJeC7WdljsjouBXqy/LzGzGrMSuV+A0lvkxqnp0gXTeOAAdnuJ0vut8/udyjeHhHzJD2f7d8WmAP0AhojYmZR2S8U1bHM7rvvHn369AGgoaGBhoaGZuNtbGxscX9nyrts+8t2lzhquWxX193Y2EhjYyMADz300LJfk7qxM4BPAbtljwHIToxaa6uWcTtTvWW7Sxy1XLar666mdiYiFkv6JfBt4BpJL5BGKY8HbgWGZUVL25lNJa0ZEe8U6tp94MDok43EbthgAxrWX/9D79c4c2aT212me8VSFWV69YLNN1+xTBv+bbb277xaylSqnelsG8Hy66ansvqWSBpHGiG0PanTZx3SD+RPl9Th85kaKttd4qjlsl1dd1nbmYgoyw24I7v9ltRQBTAb6Jc9DmCdrOwWRdv6AKOyx/+vqL5Hsm1fB4ZkjycU7R+abRtbGsugQYOirc4777w2l21veZdtf9nuEkctly1n3cDoKFMb0xU3UkfO+8D3s+cjsnbk8ra0VcV1uZ2p3rLdJY5aLlvOurt7O5NCZB9gSlH7sYh08dYza4MC2Ckru1JRuU8U1zNoxx0j/v73Fm/nDRniMlUQS1WUOffcdv/bbMu/3WosU+52pjNtRHatFcAVRfX9Jdt2MbB79vjdov37Z9uml8bi85nqLdtd4qjlsuWsu6vbmXKO6DkiCxhJKwMvknqWDwCWZA1XX+Ct7B5gTkS8L2lG9rxvUX2Fx9NJI3pa2t9hgwcPLlv5cpVtj+4Qb7mOrZxxdIfjK2cM3eH4cvI5YGVgkKS9ge2y7UcAC2ilrerom1bj36eW/720t3x3iLna4m1v+VpqZyStA9wDrArsDUwC7gXOA2YCM4BNWN6+FJ/btPucZvDAgd2qTFtUKp5qi7etZdqiQ+919NEfLtPKv82u+rfblnoqWaacuqCNaO26qbB/VUk9ImIpvm7KNYbucGzljKM7HJ+vm8qUjFnSqsCaEfF69ry4o2cI8APSFKxjI+ImSV8FrgYejohBkr4N/B9wd0QcmiVnnkW6INuSlHT5VdIFWP+ImCHpWuAE4IKIOK84npNPPjlGjBjR5cfZXYwePTr3/6TKqZaPr5aPDUDSnyLltumWJA0jnUg15SFS4uVm26riwtXYzlTb96/a4oXqi7na4oWqaGd2JiVYXQysHhELJf0J+BJwJekC7gjgzIi4VNIBpISqUyNi0+K6Tj788Bhx6qmVPYBOGj1hQpd1VlRCtcULZYx58mQ444zWy7WT25kP1d2pNkLS0aQkzZOAgaQcrFNIeX/2A/5F6uxZG9g1Ip6Q9BPS9djISDl7lqnG85n2qMbvX3vU8vHV8rFB17cz5RrRsz7wgqR/kuaF7kHq5JkB/IPUQXMt8GtJhwJHZq+7OLsfDpwDHCLpZmBzUgb5GyNiMoCkEcBXgX9ky7YfS+oAurI0mPbMuatGtfyFh9o+vlo+tkxj3gG0JCKGsXzue6FdOYnlq26dQMtt1TLV2M5U2/ev2uKF6ou52uLNNOYdQCueI01dX5t0zjIZOD7b9wgwDTgcOE/SANKUCmiqnenfv/zRdrFq6zSptnihjDHPnl2Wat3OfEhn24jbgeeBbUgjgXqTOnkej4gHASRdBvwEuEnSw6TrpiWkpMwrqMbzmfao0u9fm9Xy8dXysWUau7Kycq269RYwEtiKdNG0AXAbaYWsNyPiOuB0UsfMCaTROqdExD0AEfEuaYrXQ8ChpIzz15A6dgpOB67K6j4KGAscGBGzynRMZlZnWmurzMxaExHvAYcADwCfBD4PvAScERE3RMQY0kXd1Ox+CWk1nd/mE7FZZujQvCOoC51tI7KpWIcAdwJ7khI53wIUz727BLiQtJjNENIS7kdFxMRyH5+Z5aMsU7e6m2HDhsWwYcPyDsOs7kg6Pxs1U/NOPvnkaGhoYPDgwfXwi4NZtzB69Gj23Xffbj11qyudfPjh0dC3L4MHDqzKkSdWZZ54Ai64IO8ocld37YzPZ8wqrhztTNmXVzczqwcNDQ24Q9mssrKLkMZ8o6ichv79GXbYYXmHYfViwIC8I+gW6q6d8fmMWcWVo50p19QtMzMzMzOrVn37tl7GzMy6pbro6GlsbGTYsGGMHj0671DM6kb2760h3yjMzMysQ8aOzTsCMzProLqYuuUhiGaVV29Dnc3MzGrKccflHYGZmXVQXYzoMTMrN48cNKu8ehs52Pj66wy77jpGT5iQdyhWD+6+O+8IuoW6a2d8PmNWceVoZ+piRI+ZWbl55KBZ5dXbyEEnY7aKWrgw7wi6hbprZ3w+Y1ZxTsZsZmZmZmblN2RI3hGYmVkHuaPHzMzMzMxWNHJk3hGYmVkHuaPHzMzMzMxWtOOOeUdgZmYd5I4eMzMzMzMzM7Ma4Y4eM7Mu4FUqzCqv7lbD8apbVklPP513BN1C3bUzPp8xqzivutVBhQZr8ODBhYzWZlZm9XZi5FUqzCqv7lbD8apbVklf+lLeEXQLddfO+HzGrOK86lYHFRosd/KYVU69nRiZmZnVlOuvzzsCMzProLro6DEzMzMzs3bo3TvvCMzMrIPc0WNmZmZmZis65JC8IzAzsw5yR4+ZWRdw8kKzyqu3XGBOxmwVdcMNeUfQLdRdO+PzGbOKczJmM7NuyskLzSqv3nKBORmzVdTuu+cdQbdQd+2Mz2fMKs7JmM3MupikP0l6TdJCSW9KGiVph2zfMEnRxG3dvOM2s+ogaXAz7UhIOjkr83lJk7J2qFHSmTmHbQbz5uUdQV3oijZCUoOk2yXNkzRH0o2S+hXt75Gd00zL6nhGkufmmdUwj+gxs3q3KfAQMAfYDzgI+GS2veAWYFrR8wUVi87Mqt004JdFz/sCp2SPX5K0B3AD8B5wPfBp4BJJcyLidxWN1KzYxIlw7LF5R1EPOtVGSOoB3AVsDdwH9AY+D2wM7JHVcyZwHmnEwPXAccDfJW0XEZPKeGxmlhN39JhZXYuIwYXHknYEngI2ktSrqNiVETG6wqGZWQ2IiJeAMwrPJX0re/h0RDwi6TZAwLCIuEzSp4EHgLMBd/RYfoYOzTuCutAFbcQRpE6eCRFxkKSewGRgd0mDgUeA72Z1HhMRT0maCvwQ+B5wcrmP0cwqry6mbjmpmFnlVVPyQknflHQV8Nds02URsbioyG2S5ksaL+mEHEI0sxogScD/Zk8vz+53yO6fLLnfVNKalYrN7EOGD887grrTwTaisP8pgIhYAozLtm1PGtmzDrAUeLqkju27MrPTZU0AACAASURBVH4z6z7qYkSPk4qZVV6VJS88BhiUPZ4GjMkefwA8DDxP6rQ6ELhW0lsRcW9xBYUOZUjHnh2/mZXB6NGji3+8acgvknY7DNgCeIM0FQNgg+y+kBDlvaLy/YB3Ck8Kq24BDB44kMEDB5Y1WKtza6+ddwS5yqmd6UgbUbq/uEzx/vkREU3sX4HPZ8wqp5ztTF109JiZtSQiBkvqQ8rP8zfgZklbAD+JiAsL5ST9FRgCfBZYoaPHHcpmlVN88XH++ec35hpM+xSmZ/wmIhZlj2cAm5DyclB0DzC9+MVedcsqatCg1svUsJzamY60ETOa2N63if2rSuoREUtL9q/A5zNmlVPOdqYupm6ZmTVF0irZXHYi4n1gFOkXsZWAzYGPNfPSpZWJ0MxqhaSBpITv7wO/Ldr1THa/a3a/S3Y/NSLewSwvt96adwR1pRNtRGH/Lkp6Ajtm28YDrwKzSdd9O5XUMb5LD8LMug2P6DGzerYbcJ2kh4G3gb2BNYBZpHnsz0iaDkwg/Zp2EKmT5/p8wjWzKlb4pf7aiJhVtP1nwOHAeZIGAPtn2y+uZHBmH1LnI3py0NE24nbSFPNtSKONe5Py8jweEQ8CSLoM+AlwU3bOcyywBLi0fIdjZnnyiB4zq2evAy8CB5CWMl0LuAnYLyLmAL8HVgWOB3YH/g0cEREP5ROumVUjSesChUTulxfvi4gxpDZmana/hLSaTvEv+maV9/rreUdQNzrTRmRTsQ4B7gT2JI3muQU4uqiaS4ALgV6kKegvAEdFxMTyHJGZ5c0jesysbkXEi8DgFvZfBFzUlroKyQuduNCscqpldb+IeBNYpYX9N7A88WqzCsmYnYjZKuK//807gm6hEu1MZ9uIiJhCGvXT3P4lwLnZrUU+nzGrvHK0M+7oMTPrAk5eaFZ5Vba6X6c5GbNV1NCheUfQLdRdO+PzGbOKK0c7UxdTtwo900VLl5lZmVXLL+1mZmbWhOHD847AzMw6qC5G9Lhn2qzy6u0XMDMzs5rSr1/eEZiZWQfVxYgeMzMzMzNrh512ar2MmZl1S+7oMTMzMzOzFd11V94RmJlZB7mjx8ysCzgXmFnl1VsusMKqW6MnTMg7FKsH+++fdwTdQt21Mz6fMau4crQzZe/okXS8pMhulxdtP03SZEkLJb0g6aSS120n6UFJCyS9Jen3klYv2t9H0hWSZmZlxkjardzHY2bWlEIuMC9FalY59ZYLrKF/f4adcIKXVrfK8PLqQB22Mz6fMau4qlt1S9JGwFXAByXbhwBXAqsDfwXWA0ZIOijbvzpwPzAYuIt00EOBq4uquRz4JjADuA3YA7hf0rplOyAzMzMzs3rwyit5R2BmZh1Uto4eSQL+BLwO3FKy+6zs/hsRcTLwvez52dn9KaTOnzsj4hhgEPA+cKykzSWtD3wFWAp8OiKOB64ldRx9szxHZGZmZmZWJ4YOzTsCMzProHKO6DkD+BRwIqmTBgBJKwEDsqdPltxvn93vULw9IuYBz2fxbgtsA/QCpkbEzGbqMDMzMzOzjhg+PO8IzMysg1YqR6WSBgA/BX4UEc+kwT3LrAv0zB7Py+7fy+4/IqkPsEHJ/uIy/YA+rexfQSGpGKT5b55zalY+o0ePLk7g15BfJJVVaGfcxphVTt0lSc2SMQ8eONB5eqz8Nt007wi6hbprZ3w+Y1Zx5WhnytLRA3wOWBkYJGlvYLts+xHAAmAJqbOnL/BWdg8wJyLelzQje953eZXLHk8H5rSyfwWFpGJmVn7FJwbnn39+Y67BVJDbGbPKq7skqf37M+yww/IOw+rFllvmHUG3UHftjM9nzCqumpIxK7t9BjgU2CjbvhkpafKk7Pmu2f0u2f347P6Z4v1ZcuZPAAFMAJ4FFgObSNqgmTrMzMzMzKwjHngg7wjMzKyDytLRExHDIkKFGykpM8AvI2IwcEn2/NeSRgA/y55fnN0PJ430OUTSzcBDQG/gpoiYHBEzgBFZ/P+QdD1wPGkq15XlOCYzMzMzs7px6KF5R2BmZh1U1uXVmxMR1wGnkzpmTgBmAadExD3Z/neBA0gdPIeS5qtdA3y1qJrTSUu3bwAcBYwFDoyIWZU5CjMzMzOzGvXUU3lHYGZmHVSuHD0ryJZQP7lk26+AX7XwmnHA4Bb2LwBOy25mZmZmZtZVpn8o7aWZmVWJXEb0mJnVmsIqFUUrjplZmdXdajjZqlujJ0zIOxSrB0OH5h1Bt1B37YzPZ8wqrhztjDt6zKyuSfqTpNckLZT0pqRRknYo2n+apMnZ/hckndRUPYVVKrwUqVnlVNNqOJKOlvSEpAWS5kh6RNJa2b7PS5qUtTONks5sqo6G/v0ZdsIJXlrdKmP48Lwj6BYq1c50po2Q1CDpdknzstfeKKlf0f4ekoZJmpbV8YykQ5qKw+czZpVXTatumZlVi01J+cCuISWBPwi4DUDSEFKC99WBvwLrASMkHZRPqGZWjSQdD/wNGAjcDtwErAGsKmkP4AZgE+B60rT6SyR9LadwzRIvr14xnWkjJPUA7gKOAMYA44DPA7cWvcWZwHmkVYuvJ61m/HdJ25T94MwsF3XR0eMhiGaVVy1DnSNicEScEBHfIK3eB7CRpF7AWdnzb2S5xr6XPT+7wmGaWZWSJJavNnpwRAyJiKERsW1EvAZ8HxAwLCJOAgqjBt3OWL769887grrQBW3EEcDWwISIOAj4NPAKsLukwZJWAr6blT0mq+NSoCfLz2vMrMbURUePhyCaVV6VTan4pqSrSKN2AC4DAhiQPX+y5H77CoZnZtVtS2BjYAFwZja14iVJhcUkClNFS9uZTSWtWcE4zVb00EN5R1AvOttGFPY/BRARS0ijeiCdr2wMrAMsBZ4uqcPnM2Y1qiKrbpmZdXPHAIOyx9NIQ5/XJf3aBTAvu38vu/+IpD4R8X6hgsLIQUidXO5YNiuf0aNHF4/SbcgvkjZZN7tfBdgcuJE0evBKSa8BG2T7S9sZgH7AO4UnhWTMAIMHDnSuHiuvo4/OO4JcVbCd6WwbUbq/uEzx/vkREU3sX8H0++5j9C238OzGG3NAz55sufrqcPDBMGoUNDRAnz7w/PMwaBA8/jh88EF6/MADsMUWqZKXXoL990+dhSutBLvumh5/4hPw/vvQ2Li8zjXWgIEDYcyYdD97Nrz22vL9a6+dphE+9hjsuGPaN2PG8v0bbAAf/Sg8/TTsthv897+pjsL+j3401TFhAuy1V7qfO7cyx/T00/DFL4LPCa0Z5WxntPzfe+0aNmxYFC7AzKxyJJ0fEcPyjqMtJPUh5ef5G+lXry2AyaTOnoaIeEXS9qRfyeZExAq/tLudMctHd29nJG0JvJg93TUinpD0a+B/gD+SpllsAgyOiIeyX+jfzsqvFRHLOnqGfe1rMeywwyoYvdW1yZPhjDPyjqJbKGc709k2Avg28CNgRER8OavzNuDIbN/twMukc5teEbFU0lGkHD7jI2KFUT1uZ7rQ2mvD/feDzw+tDbq6namLqVtmZk2RtIqkngDZ6JxRpF/EViL9qjYpK7prdr9Ldj++knGaWVV7BZjbzL55wDPZ49J2ZmpxJ49Zxc2enXcE9aKzbURh/y5KegI7ZtvGA68Cs0nXfTuV1OHzmXJbZ528I7A65albZlbPdgOuk/Qw6dexvUmrXMwizWO/BLgW+LWkQ0m/jgFcnEOsZlaFImKRpMtJv7iPlPQoaVrGElL7shJwOHCepAHA/tlL3c5YvoYOzTuCutAFbcTtwPPANsC9QG9SXp7HI+JBAEmXAT8BbsrOeY7N6r+0/EdY5zxty3LiET1mVs9eJw2XPgA4hTQE+iZgv4iYExHXAaeTflE7gdQBdEpE3JNTvGZWnX5MuihbEzgOmAgcERGPRcQY0kXdVJZf3J0N/DanWM2S4cPzjqCedLiNiIilwCHAncCepNE8twDFSZYuAS4EegFDgBeAoyJiYtmPrN7dckveEVid8ogeM6tbEfEiMLiVMr8CftVaXYVkzE7EbFY5WQLDhnyjaF1EfEC6MGtyyfSIuAG4obV6CsmYnYjZKmLAgNbL1IFKtDOdbSMiYgpp1E9z+5cA52a3Frmd6WI+J7Q2KEc7444eM7Mu0NDQgJMxm1VW1qnamG8UldPQvz9OkmoV07dv3hF0C25nrFNefz3vCKwKlKOdqYupW4Vf2ouWLjOzMquWX9rNzMysCWPH5h2BVbG5CxZx+xNTmbtgUd6h5OvFF1svY1YGdTGix7+0m1Vevf0CZmZmVlOOOy7vCKyKPThxOtf+awoAR+6ySc7R5OjUU/OOwOpUXYzoMTMzMzOzdrj77rwjsCq274B+nLj3Zuz8sXXqe2TP1VfnHYHVqboY0WNmZmZmZu2wcGHeEViVmrtgEQ9OnM7OH1uHkQ+9zLgps4E6Hdmz4YZ5R2B1yiN6zMy6gHOBmVVeveUCK6yGM3rChLxDsXowZEjeEXQLbmfaZ+6CRVx2xySu/dcUfnPfC4ybMpuBm6zJvgP6dXGkVWLnnfOOwKqAV90yM+umnAvMrPLqLReYV8Oxiho5Ei64IO8ocud2pu3mLljEr0e9wHPT5gLw6pvzAdhwrVUAuP2Jqew7oB9rrLJy1wRbDe64A3baKe8orJsrRzvjjh4zMzMzM1vRjjvmHYFVkUInz7gps/nkRmswc+5C3pqbpv890zibaW+9x7PT5rJw8RKO3XOznKOtoAMPzDsCq1OeumVmZmZmZmYd9uDE6YybMpsdNlubo3bZhHfnp+TLPXrAzDkLeTYb5VN3vLy65aQuRvQUcmcMHjy4MCzKzMqsnHPaJe3TjuLvR8Tj5YjDzMysZj39NBx1VN5RWJXYd0A/Fi5ewlvvLuRnf5/IkiVp+9Kly8t8cqM1WPjBUv780GR69+rBwTt8tPancTU25h2B1am66Ohx7gyzyivznPbRQLSxbCPwsTLFsfxN3KFsVnH1miR18MCBDB44MO9wrNZ96Ut5R9AtuJ1pm0KHzYOTZjS5v/fKPXjz3YU8N23a8m29etb+Slynnpp3BFYFnIzZzCxZCExvY9kl5QykwB3KZpVXzg7l7jhy0MmYraKuvx5++MO8o8idkzG3zWuz3+ORF2Y2u3/hoqXMWrSQ1fr05FOfWJ/V+/Sqj5W4rr4afH5orahIMubueGJjZlZibETs25aCkh4sdzBmVpNG081GDppVVO/eeUdgVWTkQy8z/e33kSBaaDnfe38JM+cs5PN7NNT+tC2Ahoa8I7A61dSIntH4xMbMurchze2QtC7wVsSy04xmy5qZtaDbjRw0q6hDDsk7Aqsin91tEybPeJe58xe3WK5XTzFuymwenDi99qdtAWy1Vd4RWJ1qqqPHJzZm1q1FxIcmgEvaEbgN+CjwrqQvR8StTZU1M2sDjxy0+nbDDXDBBXlHYVVg7oJF3PjvxlY7eQAWLwl22Gzt+pi2BXDffbDnnnlHYXWoqY6eiRGxS1te7BMbM+tGfgCMInVUrwf8FLg114jMrJqNakdZjxy02rP77nlHYFXiwYnTmTD1nTaVXaknrLlaL25//NX6WHnr8MPzjsDqVI8mtq0m6aNtfL1PbMys4iT9VdKmTez6I3BNdr+okjEVVt3KsuabWQWUeTWcEyRtL6nVRCWVGjlYWA1n9IQJlXg7q3fz5uUdQbdQr6tutaed2XdAP1bp3dRl5Yd9sAQenDiDO56axs1jp/LgxLZOJKlSTz6ZdwRWBSq16paAiyX1AK6IiLHNvdhTIswsJzcDd0q6F7gwIt4B/gWMYXmOsRGVDMirbplVXplXwxkIPAUslTQFmJTdJmb3zwN/i4iK/VzrVbesoiZOhGOPzTuK3HnVrTYKtav4+h/pzW5brlf7U7jeeCPvCKwKlKOdaarr9SsR8UXgLOCzkkZJOi7r+KlK/qXdrPLK+QtYRNwC7AC8Ajwm6TvAVcCBpLZrCPDV1uqRNFzSc5LmSXpL0t2SBhTtP1lSNHHbuRzHZWbdjoCewBbAkcDZwLXAM8B7wAFtqkQa3UQ7MrFo/2mSJktaKOkFSSd1/aGYtdPQoXlHUDc620ZI2k7Sg5IWZOczv5e0etH+PpKukDQzKzNG0m5dFf/tj7/KgkVtS90qwWp9ejJzzkLWWKVXbU/bAjj11LwjsDr1oRE9EfFodv8qcKak1YAvAw9Iugf4ffbredXwL+1mlVfuX8Ai4gPgCkkjSBdf44ALIuLSdlRzCjAWeATYH/gMsK2kLSLi/aJy9wPPFj33aEaz2nc0cC4wnjSy55PANtltPdI5VFtXKS34ZdHjNwAkDQGuBGYBfwWOAEZImh4R93bmAMw6ZfhwJ2OuvHa3EVmHzv2kdukWYDNgKNAXOD6r63Lga6QRif8AjgPul7R5RLzZ2aAbZ7V9ml9EWmK931p9WLh4CXMXLKrtzp6rrwZfh1oOPtTRk/Xu9m/i1g+4GPiRpJERcVolAzUzK5D0CeBnwObAf4DvAb8BLpT0beB7EfFIG6raOSKeyupsAKaQVu3aGni6qNx1ETGiq+I3s+4vIm6XdBfwdVKnzyUR8S0ASesCA4Bft7POM5rYfFZ2/42IuEXSKcBwUge2O3osP2uvnXcEdaeDbcQppE6eOyPiGEl9SZ1Cx0o6B5gHfAVYCnw6ImZK+gD4AvBNYFhH431t9nuMfOhlDttpI6bNfo+357W+6hZAr5XEtpusxc1jp9K7V8/aXmbdy6tbTprK0fMoK/5CVTrhcjXSSY87eswsL38mtUVvArsCl0fE54GTJO0A/EzSexFxVEuVFDp5MoWfk5aQ/YpW5JeSfkOaKvabiPhlyf5lU0QhjWbKRjSZWRmMHj26eDp2Q7neJxs5eKWkPwPnSPomcE5ETAJGS7qyPfVJept0XvUU6eJtHKnDCODJkvvtS19fSJIKMHjgQAYPHNjOIzJrh0GD8o4gV5VqZ4p1sI3YoXh7RMyT9Hy2f1tgDtALaIyImUVlv0An25mRD73MuCmzmTHnfZZ8sLTNx7n4g2CNVXpx4t6b1X6Onv79847AurFytjNNdfTcAxxMWrFmAvA66aLn9ZLHZmZ5WQnYJiJC0irAg4UdETEOOEDSwW2tLPv164/Z0/+LiEJHz1LgCdLUjXVIw6Uvl7QgIq4ursNTRM0qp7gz9fzzz28s9/tFxBzSdPZDgX9IuhP4UUT8po1VvAvcCbwG7AHsR/olfmtSDiBIv7pDyv0D8BFJfYqnkToZs1XUrbfCDju0Xq5GVbid6XAbAWxQsr+4TD+gTyv7V9Ceduazu23C5Bnv8vrs+W0qD9Cw3qqsvsrK7PWJ9fno2qu1+XVVa/Ro8I9/1oxytjNN5eg5VNLHgTNIvUojIuK29lYs6U+knBfrkhqvJ4Gzs4swJJ0GfAfYiJTH46KI+FPR67cjzSfdHZgP/A34TkS8m+3vA1xKmmO6OmmaxXci4rH2xmpmVedt4B1J75A6YG4qLRARo9pSkaT1gLuBnYHfA98v2v3niBhZVPanpF/YPges0NFjZrVF0trAbqTzkN2BXYCPkH5t/zLp3OR/21jdERERWb0rAy8Cm5KSOS8hXcj1Bd7K7gHmlOQKM6usOh/RU2EdbiMkFfIG9i2qr/B4OmlET0v7O+z51+Ywd/5ievfqwcLFbRvRM3PuQhpnzefJyW/VR0fP5z6XdwRWp5pcSSsiXoiIbwAnAltL+pek07NfvdtqU+Ah4BpSo3QQcBuskFRsdVJSsfVIScUOyvYXkooNBu4idQQNZcULq8tJ80pnZPXuQUoqtm47YjSz6nQiafrWRFKOjKbmtLdK0qakRMw7Az+NiFMLJ1qZjzXz0raPTzazavUm6Rf2H5IuttYkdfK8AdxOG/PnSFoV2LCZ3YtIS7VDmoYKqUMJ0khCs/y87gH8ldAFbcQzxfuz66hPkFJxTCAtJrEY2ETSBs3U0SH7DujHiXtvxkmDmjtd+rD5C1Mi5pqfslXgVZ8tJ00lY/5DRJxStP9uUgPxHeB8SX8AroiIxpYqjojBRXXuSJprupGkXnTjpGJm1v1lU6u+2QVV/ZuUbH4qsKqky7Pt10XE48Dw7Ff9J4C1SFO3IC2vbGa1bwFpRPJjwOPAYxExrZ11rA+8IOmfpDxfe5B+DJtBWv2mJ6lN+XU2NezI7HUXdz58s07473/zjqBedLaNGA6cAxwi6WbSQhW9gRsjYjJAtkLpV0lTTycCx5Kup9qVZ6zUuwsWM27KW0ye8W67XrfL5uvW9kpbxd56K+8IrE41NaLnOEnTJC0i/Wr1FGmpvr2ANYBvk4YTtkrSNyVdRRq1A3AZqXe53UnFgOezeLclLW3aC5haklSsuA4zq1GSruuisoUMeZsApxfdts62/wV4nzRV6wDSCl8nR8Rf2huzmVWdN0n/5j8CfBzYEdhH0naSegNI+lUb6nkLGAlsBZxEyqdxG+mHqjcj4jpSuzMPOIH0w9YpEXFPFx+PWfsMHZp3BPWiU21EltbiANJMikNJqTeuIXXsFJwOXJXVfRQwFjgwImZ1JvBr/vkSz06by8LF0Xph4COr9mKrDVdnv4F1MpoH4NRT847A6lRTyZhXzW6lFrBiUua2OAYoTPCdBowh5eypaFIxr4ZjVjkVWqViX0nXtLHsgOZ2RETpqoKl+4eTfilrVaGdcRtjVjlZW9NQpur/EBFnA0jahPQj0wDgQOAT2fnKJrSSpye7CPtqK2V+BbTaaVRYDccrbllFDB8OF1yQdxS5K3M70yVtRJYDdXAL+xeQVkxuddXk9rQzDev1ZcLUd1qrcpk58xczZ/7i+snPA3D11eDFOqwV5WhnmuroeRX4Dcs7dF4HXo+Itv8rzkTE4OxE6CBSMuWbgS2ocFIxr4ZjVjkVWqViA9KvXi121GTKFcMK3M6YVV7W1jSWo+5CJ0/2eCppiueyUTaSepB+Qa8Yr7plFdWvjkZdtKCc7Ux31J52Zr+B/XjspVnMnLOw1bI9e8CSpbDOGr3Z+WPrdDbM6uFOeWuDcrQzTXX0nBgRj3Sm0my540URsSTruBlFGn2zBmne6CTSFKxdSXNRm0oq9iWaTyo2j6KkYhExo4k6zKx2nd+Osu3upDYzk7QPMCYiljS1PyKWSupQInizqrDTTnlHUNUkXQocQsqps1ZEPJFzSF1uzPMz29TJA6mTB+CtuQvra0RP3/asZWTWdZpaXr1TnTyZ3YDrJD1MWgZ5b1InzyzSMuiX0E2TiplZ9xcR7enoMTPriH8C87IEqaOAUdnInmUi4qlcIjOrhLvugt12yzuKajYQeBQ4nrSATM119Lz7/mIAJIhW0vSs1bcXn/r4BvTu1aN+VtwCePRROOigvKOwOvShZMxdlOT0dVLC5gNIK2itBdwE7BcRc7pzUjEzqy6S/iBpkqTrJf1A0uGSGvKOy8yq3nrA10mjAs8Dpkh6VtL/STqgkJDZrGbtv3/eEVS7N4Cfkq5z2jbspcpMfTOlSG2tkwdgrdV688VBH+PYPTernxW3AI4/Pu8IrE41NXWr00lOI+JFWkgIlpWpWFIxM6tph5KWJv0k8PnCRknzgAeASyNibLmDcDJms8orc5LUPSLieuB6AEnbkRIxHwz8D7BE0kPAPRFxRZliWIGTMVtF/fe/sM8+eUeRu060M2+Rfvh+l7RU+s+7LKgyak87s0W/NXhu2twWy/TqKRYvCXr3amqx5zpw553w8Y/nHYV1c5VKxtztkpyambVgFqnN+iRpVZxdSDnAVgeOBg6WtFdEPFPOIJyM2azyypwk9eeSxkfEawARMZ6UB/BSSasB+5E6fb4FVKSjx8mYraJeeSXvCLqFTrQzuwLnAjvTxhU8u4N2JWMe0I8XXpvDlJnvsrjJbGbQf+1VWGOVlfnKflt0YZRVZGFNDuayLlapZMxOcmpm1WQB8HhE3FvYIGlH0rTQx4BvAmcBQ/IJz8yqlICLs9W1rigeGRgR7wF3ZDez2jR0aN4RVLt/RMRFeQdRTqOeeY0X33i3xTKvzJrPiXtvUD/Jl0udcELeEVidaioZs5Ocmlk1eRB4VNKFwP3ZKnzjgEsi4ruS/g6MyTVCM6tGX4mIRyVtDHxL0jDgj8BNEbE039DMKmD4cLjggryjqGaHS1oVuB14OiLezzugrjb+lbdbLbPOGr2Zu2Axcxcsqq/cPAUjRoBHfFsOWpwsKekzko74/+zdfZzUdb3//8eLqwVcUMELBMVFzUsQETAwPUCWmh2vjmhSJ+WbRqdTnTxdaHZ+HfFUx6xjaeXJg1ZYR0zzMq2DloGZaQqCgiKUsa6AoHK1crXA7uv3x/szMCy7O7M7M5/PZ2ae99ttbrMzn8985vXZmX3te97zfr/eZtY9ut0nnrCKK1M7I5r7JiIxKHHtjGzfJHRa3wmsMrO3gdXASQDu3gRsjiEOEakg7v5MdP2Gu18NXEQo0Pw7M/uyme2XaIAipXb44UlHUO6GEUYV/xHouJBNmfrMWcdS07Pjah9rG5t4ZN4KZi9YGVNUKTN6dNIRSJVqa+pWtq8DTwC/N7MHgDPMbC4wxd3fKnVwxaLaGSLxK3HtjF3cvdHMJgJ3A+8DBkabfmhmtcBkoIrW8RSRYjCz9wKD27gMAr4F/LuZ/czdtSiEVKb3vCfpCMrdeYRVgU8CxiUcS0kcM3hfWlryWHJLRGKXq/z5Bne/hlBM7AOE+eqTgJtKHZiISL7cfYW7nw68F5gKnANcBewHjCSGIogaOSgSvxKPHHwGuI+wQuhXCEXfPwgcS2gP7UNYfj02mdVw5i5aFOfTSrX63e+SjiAVCsgz8wmfm/7m7rcWMaSS6kyeady6Haz9ET01vYwzTxrM5HFDOXvUkGKGWT7mz086AikDca26le2g6PofousfAT8hfHMuIpIq7v488HzWXSuAf43juTVyUCR+JR45+H+EVbW2A4uAVcCb0XX2z7HRTnvSBQAAIABJREFUqlsSqw9/OOkIUqGAPHMPcC6w2czOcfenihhWyXQmz8xZvJodO9sf0dO03TmwXw3njx1arPDKz9SpSUcgZaAU7ZlcI3oazewl4ArAgf9x9/nE3LAREWmPmZ1vZnPM7Ndm9l9mdpmZjTazh5KOTUTKl7t/GDgemAmsBWa6+6fd/evu/mN3/427L0w0SJFS0kiEQq0gjDD+AfBvCcdSEmOOHEhNj/Y/Ttb07Mak4VU+e37WrKQjkCqVa0TP1cBvgBpgnru/ZGZHAQeXPDIRkfzMIBRIBfgQoVNaRKRg7r4U+LSZDQD+ycy+SJjO9WN335RsdCIltnp10hGUuz7AQnefbWaxjC6O27zX1tK0s/1FCLt1M+YsXs2k4YOqc8UtgJqapCOQKpVrRM++wGeAicBEMzuOMAxxSYnjEhHJVwNwIXAl8F3gccKow46XgRAR6YCZ/TjrZg/CF183EVbfWmFmN5lZXQKhicTjyiuTjqDcvQmsNLN64OMJx1ISk4YP4siDawHoFrW6enaHwfv3prZ3d8YddQB3PbWcOYuruNNQ020lIbk6er4OnAi8ADwILAY2EnPxQRGRDjwIPO7uP3H3L7n7h9z9MEIh5tioGLNI/EpcjPkjZrbCzLYTPrDNB+4nrO7Xn1D/a1mJnrtNKsYssbqj5OsYlIWu5BkzuwD4NfAlQu74YrHjKpXO5Jn+fXoxatgAADKLb+1ohlXrtzHsoH6cN/YwPnb6sOqevnW3SttKbkkUY97g7teY2fsJK01AGN1zE2XUM535ADZx4sRMoSMRKbESfwDL9s/AJ8zsHkJj6hXgL+7eGMNz76JizCLxK3Ex5r7RpbWt7FmUOTYqxiyx0vLqQJfzzCXAN939e2a2DlhT5LBKprN55uxRQ3h5xQaWrNiz2bWoYQPzXltb3YWYAcaPTzoCKQNJFGNua9WtsYQljMtG5gOYOnlE4lPiD2DZBgNHANcCvwReJqxw8VKuB5rZHWa2xMw2mdlaM/uNmQ1vtc/FZvaymTWZWb2ZXV2SsxCRtHkD+CowFTgTGA4McPd93P0od/87d7+0Mwc0sylm5tHl5qz7P2Nmr0V5ZqmZXV7MExHpksGDk46gnP3Z3V+Ofr4b+Md8HtTVHGFmI6OFKbZG7Znbzaxf1vbeZvYDM3sr2udpMyvK57l3t+5g/ebtAPSt6c4RB9fuWlK9qkfyZGxSOTdJhlbdEpFyl8lR3wN+B6wGegEn5PHYK4ANhEZYI6GY82wz6w1gZuMJdcmGAr8gjIK80cw+VeRzEJH0Webu33L3n7n779z9FXffkL2DmeUaGZ2976HAfwM7W91/KfBDoB8hFx0IzDSzswo+A5FCPPlk0hGUs129ZO6+HXgr1wO6miOiDp3fEmZd/JrwJduVhMUqMm4GPksYWfQQMB74rZkd0JWTy/aT3/+V1eu30b0bbGlqZs3GrXzopMFccuqw6i3AnE1TbSUhuTp6rgEOR6tuiUh6nebuP3X3L7r7We4+BBhIaPDkMsbdx7v7J4FJ0X1DCEsqQ8iBBkx398uBzDdo1xYvfBFJqSFm1j3HPn/I50BmZsCdhC/K7m+1+SvR9afdfSrw5ei28owk68ILk46gnA01s/80s7roy6NRHe1cYI64gtD586i7TwYmANuAS8zsCDM7CPgE0AKc4e5TgLsIHUefLeAcAejftycAzdHiW5u3NfOzJ/9W6GErx7RpSUcgVarDjh53f5rQIz0ReL+ZHY9W3RKRhEVTqZ40s27Af5nZZ81sopkdCODu69k99bRd0QjFjMzXTs2E+huwu2E2r9X14WYWa7FnEYndYOB/2tpgZkeb2Vzyn8p+FXAa8DHCB7DMcXoQpoTB3nnmpE7GK1JcGtFTiG8SVi5+DdhMKODekUJyxB5tFXffBLxK+Jx3ImGEc0+gwd3faucYXfbamneBMG3r6EP6cfyh/blswhGFHrZyzJiRex+REshnyPG5wDhC7/AD7j66tCGJiOS0GTiV8A3WNMLUUgDM7B1CnZ7j2PtbsTaZWS3w0+jmd90909GTGb2YmWC9OethgwjTvoDdRd8BFX4XKbG5c+dmr3BXV6Kn+TwwzMyudfcbAMysJ6Fuz1cIo529g8cTPWY4cAPw7+6+MHxxv8sBQGbUUOs8s6+Z9Xb3XR/6MqvhAEwcMYKJI0bkfTIr123m5l8voeHtzbuC7t4dRh8xkKED9+HsUUM0zUL2tG5d0hEkqpA84+6LzWwc8DnC3/h/tLdvoTmCvdsq2fsMAnrn2L6HzuaZz5x1LD96fBmfPvNojhm8b4f7VqWBA5OOQFKslO2ZDjt6zOwaQuLJtAmmm9n3gS+4e87GjYhIiUwCBrv7GjN7A/gj4RuvYwidPxPJ4wMYQDQK6DfAGOB2wnStjDWE+jy10e3arG2rs4+jVbdE4pPdmXr99dfXl+I53P1OADP7uZlNJtTYuI2QZ4ww8u/zeRzqIsKIwQlmdjowMrr/PMIKXs2ED3K1wFp255mN2Z080PVVtxq3buerd7/A1qaWPe5vbobn/rKW5/6ylpqe3bU6juzpyiuTjiBRXc0zZnYvYVrVeYRFIp4nlMJY2c5DCsoRZpZZ0Su7jZL5eTWwMcf2PXQ2zxwzeF9unjqWxq3befj5Bo4dsi8P/LmByyYcwZAB++R9nIqlL/6kA6Vsz+Qa0fMJ4EFgB3A0MAL4F8LULY1DE5FEuPtm4C/Rzcvc/UmAqJ7G0YROn+HtPHwXMzsceDx6zA3u/tVWuywkdPScAjxJWHUQwvDnDYhIxTKzO6PaXFcAswkjm41ocQrgGndv7OAQuw4VXT7U6v5hhIKoLxOmV5wCvM7uPPNioeeQ8dM5f92rkydbrx7GsUP0Tby0cscd8B/tDkSR9q0ABrD7y3IndLa0N7Sj0ByxELgs2p4pznxs9LyLCCN5dhDqBh3s7mvaOEZBGrdu59bZS1mwfB2DB/Rl1bot7Gxu4cTD92fS8EHVPVrw/vuhE6MvRYolVzHmt939Ine/1N1PBvYHvkCYKiEikggzOyaqz0Omkyf6udndl7j7L939ujwO9SdCJ08D0NfMbo4up0Tbv01oKF1nZncCM6P7v1WscxGR1DrTzL5B+Lb9PwkjeBYTCsB/2t0bzWxsh0cA3H26u1vmQii4CnCLu08Eboxu32pmMwl5B4qUZ1au28zTr77d4T7bdzr3/qm+GE8nlWR4zu9LpA3u/gV3f54w+ngMoeDxgx3sX2iOuIMw0uccM7uP8MVUDfBLd38t6tiZSfjc94SZ/QKYQugA+mERTpk5i1ezYPk6Rg0bwKfPPJpRwwZQd1Atdz21nDmL9xo0VF00okcSkmtETx8z+xzwU3ffFBX3usXMLo4htqLJ1M5Q3QyR+ETzTetKdPglwDYzW0L4tmrXJVNfx8wucvdcNXoyy58OZc8pGAuB59z9aTObAlxHaBStJgzHvq1oZyIiaXUwe698tR242swWA68QVr85uZAncfdZ0RLHVwEfJSyN/EV3/79CjpuR7+o3dQfW5t5Jqkut3hMF+g/gUncvqM2QK0e4+7tm9kHge8CHCdO9fgL8a9ZhPk8Y1XMJcBTwbHSMjnuB8zRp+KA9ro8/dF/GHDmQ/n167rqvaq1alXQEUqVydfT8mNDTe5OZvQIsJcwhPaDUgRWTameIxC/qVK0v0eGdUFxwVHTJLsa8ntDpcyw5ijFH35x1/ETu9xBWG+yQOpRF4lfiDuXVwEuEFWuGRPfVRZfzunrQaHnkqa3u+z7w/VyPzRRJ7Uwh5ssmHMGC5R0X1R01bADnn3JYXseTKvLss3DOOUlHkbgC8szvsxZ3yFtXcoS7LyDUJ2xv+1bCKmCfyfX8Xckz/fv02lXj6+HnG7jrqeUAqvsFsGxZ0hFIGShFeyZXR89twPHAPxPmhp4Y3f+FYgYhItJJ/034puxEQu2wEdHPxxPmxU8gz2LMxaIOZZH4lbhD+VZ3/yaAmfUndPgMj65PIOSdA0v03G3qSjHmIQP2oZtBSzsZsaancdmEI6q7hoa07SMfSTqCVCggz5xrZn2BXwHzWxdXT6uuFn3PaD26p+pNU8UTya0U7ZkOO3rcvQX4rJn9EDgfOAh42t0fKGYQIiKd4e6fi358gjDffAChaOEaoC+7O39ERLok08kT/dwIPBNddjGzOXHH1RUdDV1s2uE8/epbXHLqsNjikTLxm9/ACSckHUU5OwI4jjDFcydhVkTFyx7dI8CMGaAvAiUBe3X0mNkxwF+iTh4A3P1V4NU4AxMRyYeZfZywLHrP6K5FwFfdfXpiQYlItfinpAPIR/99erJ+046kw5By09SUdARlK1rV83zCog8nAeOSjUgSc8ghSUcgVaqtVbeWAJvMbL6ZzTSzL5rZmWa2611qZhfFF6KISIe+TviWLLM86YnAI8pTIlIIM5uVax93X5rvvkn6wt+3Pyqjb0133t22g8at22OMSMrCpZcmHUE5exiYC5zh7vPd/daE45GkjBmTdARSpdqaulWUIqciIjF5B3gfsJkwTPoU4OPAdJSnRKTrJpnZT/LcN9XrUB+yf592t21pauaxhW9yQL/emm4he/rZz+A//iPpKMrVWuB64FLg8YRjkSQ98giMHp10FFKF2uroSV2RUxGRDjwG7HD3DcALwAtm9j/Ai3EGoVW3ROJX4lW3DgYup+MSNxn1JYphzyfpwmo4AHMWr+5w+0H71qhwquzt5JOTjiAVuphnnge+AVwLYGYD3X1tUQMrka7mGWnHmWcmHYGUgbhW3fohsNbdnyAUOgXAzAx4D6HTJ9XfXIlIVbka+JyZPUZoWNUDfQjz4mOjVbdE4lfiVbeu78S+G0oUwx66uhrOpOGDdi133FrP7nDthSO06pZIO7qYZ64GPgWsMLMWwoIRZbH8UqGrbkkry5bBqacmHYWkXFyrbr0CNJnZEsI0rV0Xd18GLDOzshrRo2/aReJX4m/as3UHaoGLgH/Iun+LmY0CXgYWu/t3Y4hFRCqEu3emoyfV+vfpxSWnDuXePzUA0L1bmM61fWcLn/vQcQwZsE/CEUoqvfACXHBB0lGklpmNcvcF7Wy+lzC1/HTCyB4ok44eKbL6+qQjkCrV3vLqFVWjR9+0i8SvxN+0Z3uKUJD5hFaX/sDY6OKAOnpEpGpNHjeM8UcfxM+e/BuXTThCnTuS22WXJR1B2s0ys0nu3tbcyMvcfTuAme1P50YISiWZpv49SUZbq279N2Fe+geBLwAzCXUvtrG7Rs9BMcUnItIhd5/g7r9z91vcfZq7v8/d9wOGAh8CvkTIYyIiRWNm3zGzl83sKDMbm3Q8+RgyYB+uvXCEOnkkP7/4RdIRpF134CdmdmPUmbNLppMn+nk98O9xBycpMWNG0hFIldprRI+7fy76sb0aPZkCzSIiqeXuK4AVhGLNJacpoiLxi3GKaFtGAM8AU4AWQo2wklKRVIlVTU3SEaRCB3nmk+7+pJmdBdxvZk8A33P3La13jBaMKAvKM0VWV5d0BFIG4irG3CZ3d2BZdCmbaVsiUtnMrJFQh+dlYHHW9Tfc/Yq44tAUUZH4xThFtC1vAjcQRkDXxvGEKpIqsTrnnKQjSIX28oy7PxldPwY8ZmYXA7PN7JfAbe6+I8Ywi0Z5psiOPjrpCKQMlKI9s9fUrWgY8pNm1s3MbjOzz5rZRDM7MGufizo6qJndYWZLzGyTma01s9+Y2fBW+1wcPVeTmdWb2dWttteZ2cPRMTaa2b1mNihrezczm25mK6JjLDQz/UcSqT61wHuBTwA3AbMJI3mmJhiTiFS+dYQvv74FfDLhWESK7557ko6grLj7L4GJhHIXz5nZZdGMCKlmjz+edARSpdoa0bMZOBU4kFAdPrsY8zuEb8uPo+NRPVcAzwJ/BD5AqJNxopkd5e7bzGw8cE/0XL8AzgBuNLON7v4/ZtYN+DVwPPA4UANcDBwGjI+e42rgOkLP1y+AjwC/MrOR7v5yJ38PIlK+hrJnEeaxwHCycpeISAl8GVgJnAbckXAsIsU3blzSEaSamc0APkeYxjkSOCm6PhHoB/yU8HlleHvHkCpw7rlJRyBVqq2OnknAYHdfY2ZvEDprRgBHEzp/JpL7A9QYd58PYWQOsBwYQui4eQG4BjBgurvfZGZnAL8DrgX+Bzgv2neRu59lZt2B14BxZjYxiulL0XNNdvf5ZtYA/H+EhtfUTv0WRKRstVWLJ+pM/kBiQYlINXgAeBKY4u5NSQcjUnSbNiUdQdpdTvjM0T3rvtYjePaNLRpJp3nzYPTopKOQKtRWMebNwF+im5dl5p9GnS1HEzp9TujooJlOnkiv6LqZMJ8dwrLtAPNaXR9uZvtlbZ8fHa/ZzBYAhxN6y18HBhKKH77Q6hgntY5n9a9+xdz77uOVww7jgz168J7aWjjrLHjssVAgq3dvePVV+Lu/g+efhx07YMIEeOIJOOqocJC//hXOOAOefBJ69oSxY+EPf4Bjj4Vt26C+fvcx+/eH4cPhT3+CESNg3TpYuXL39gEDwnGfew5GjYJVq2DNmt3bDz4YBg+GBQvglFPCc69bt3v7kCHhGIsWwamnwuLF0NhYuee0YQNcdVVHbzlJkblz52YKikEMRVLN7I/AIuAVwojDJcBC4Kulfu5sKsYsEr+EizFPAQ4B/tvMvuPur5b6CVUkVWK1eDFccknSUSSugzzTM+vnncBSQvtj18Xd15Y4vKJTnimyN9/MvY9UvVK0ZyzUWO5gB7NLgXHAO8CDnZkWZWa1hG/ZTwW+4+5XR/dvI0zHGhONxukBZAqWHQdcBXwK+GFmFTAz+1/gY8CNwEOElS42uXu/aPsHgN8Ca9x9Vy0fgOlTpvj0j34037AlbUaPDp1EUnbM7Hp3n17i52ih7VGG70bLrMdi+vTprmLMIvGLI8+087xHE9pHHwDOdveDSv2c0z/1KVeRVInNyJEwdGjSUaRCW3nGzJYD3yB06iyulJF9yjNFNGAADBumzzGSl2K3Z/Yqxtzqya4BZhHmn14PvGRm38unsFhUvHkOoZPndsJ0rYw10XVtq2uA1W1sz/45e3vfqJ5P6+1SSWbMSDoCSbdXga8B9xG+TWuJ7s/rjWNmV5nZS2bWbGZuZtOztk2N7mt9GVPskxCRsvMqMBOYAPy+ox3N7E4zWxktHvGOmc02s1FZ2z9jZq9F25ea2eWlDV0kD3eo9FQOH3H3H7v7/EI7eQrNEWY20szmmNnWaCGc282sX9b23mb2AzN7K9rnaTN7byExS570OUYSkmt59U8ADxJG22Smbf0LYWpEu+9aMzucUET5aOAGd289hWIhoYDqKYT57WOj+xvcfYOZLYxuj406lboBJ0f3vQi8QVjtYgAwGng+6xgv5jgnKTdallA6do6712duRJ2/Pdx9e56PH03IJ28Qpoe25beEqWEZa9rZT0Sqx/XA7e6+Ko99Dye0dzYC7wfOIoxgPjwaOf1D4G3gbkKdwplmtjpatlkkGQMGJB1Bqrn7c0U8XJdzRNSh81tCLdX7gWHAlYQvwadEx7+ZMFtiMfAEYRGb35rZEe7+ThHPQ1rT5xhJSK6OnrfdfddS6tFUrCsIq3F11D35J2Aw0EAYdXNzdP+sKCl+GzgXuC5adj1TNPVb0fXDhG/KTiBM/aohrLj1nLvPiWK5Cfgm8Esz+wNwCaEO0HdynbSUGQ13lA64e32rKaYPuPsrOR6W/fiPA5jZQ7Tf0TPL3WcWGquIVJRRwFVm9megr7v/vL0d3X1i5mczO5lQg/BQM+sJfCXa9Gl3v9/MriCs4nUtWUXmRWI3YULSEVSNAnPEFYROnkfdfXL0ee1t4BIz+zdgE+HL+xbgDHd/y8x2Av8IfBaYHsMpVi99jpGE5Oro6WNmnwN+6u6b3H0TcIuZXZzjcZl39FDg81n3LyR01jxtZlMIy6NPIUy3uha4DcDdW8zsHOD7hFXAnNBD/S9Zx7oR6ENIXJcSRhn9m7svzhGblJu5c0HFbaUd0RTTG9hdp+d6M/s+8AXPVYQsf7eY2Y8IheB/5O63FOm4IlK+jiQsOHElYWWddjt6AMzss4QVRc+I7rqJkLcySy+3XqBir8UlMIPu3fe6W6QkHnwwLLAhsSggR+yxyI27bzKzV6PtJxJGCfUE6t39rax9/5G28owUlz7HSEJydfT8mDBU8CYze4VQ/6IXcEBHD3L3nDV83P0e4J4Oti8njPppb3szoS7H13I9l5S5iy7KvY9Usy5NMc1TC2Fq6IuElf7OA242s63uvsextbpfys7pS1/ScqYVLO7V/drxMmFp5R3s/sa9I5MJ9XwAVgBPE9pTmZ6bzFrWm6Prfc2st7tvyxxgdX09c7/9bV457jg+2LMn7xkwAKZNCzUgRoyA2lp45hmYMgUefRSamuCjH4WZM3f/PcyfD1OnwqxZUFMDf//3cPfdMH58WE570aLdxxw4MHxAuf/+cL1qFSxbtnv7IYfAmDHwyCNw5plhW3397u11dWHawuOPw7nnhmWG33xz9/ajjw5/s3Pnhv/1c+fC2rU6p7Sc02mn5fG2rlwJ5Jku5Qjg4Fbbs/cZBPTOsX0PmVW3AK28VQz6HCMdKGWe6XDVrajWxfeBf2616QvufnMbD0klrbpV5urr4XOfSzoK6YKYVt36o7uflnU7M8X04+6ed9HkaOrW+cCumM3MskcFmdkNhA90j7v7WdmPV55JGeWNqpHgqlv/APyAsPDEUe4+Lo/H9CbU3niA0JF8FPAa4YNcnbu/bmYnAQuAja1XDtTqfiLJiCvPdCVHmNmdwGXAdHe/PjrOQmAkcCFhRM/vCSN6hkXbrwK+Bzzs7hdkx6BVt4powAB44QW1RyQvJV91y8wONbNzzazO3VvcPTOM8FpCQphcTp08UgHWrk06Akm3Pmb2uaiDh2ia6S3AthyPy8eR7dzf0s79khbKG1IEZtbDzPZra5u7PwB8mjBt6/4OjtHHzLpHj9kGzCZ8s94DOIIwMgjCAhWgxSVEqkoRcsTC7O1RceZjCdO+FhEWk9gBDDWzg9s5hpSK2iOSkLambn0GuBpYaGaT3X25u78KvGpm57j7b+INsXD1b73F9FmzNPywXE2blnQE0gXRMMS6GJ6qS1NMM8zsSuA0dq/sd4GZ1QEPEQqtDiBM39qfMHUL4K5iBS8lorwhBTKz0whTzAeZWT1wrbvfm72Pu/8K+FWOQ70XmBUtHLEeOB3oTyiW+gKh5uBdwK1m9mHCyELYvUCFiFS2QnPEHcC/AeeY2X2EzqEa4F53fw3AzGYCnwSeMLPFhEVsNhHaT1JKao9IQvYa0UNYAes0dx8d1cnJdo2ZPWRmuWr7pErdQQcx/aMfVSdPuZpRaJkVScLEUHiuPoanug34b0LH9YnAxYRG0G15Pv404HLCyn4QhjpfTihQ+L+EkUEXAR8EXgKmuvv/Fit4KRHlDSnczcAhgBGWK77bzCZ14TirgGWEHHIFodP4l8D73X2ju88iLFyxCfgo4cPdFe7+f4WfgoiUgYJyhLu/Gz32SeDDhC/ZfkLo2Mn4PKGtdDBwAfAscKa7v13qk6t6ao9IQtrqsNnk7s+0tbO7TzCzbxBG/PxnSSMTyVAHnbRiZocSVplY5O71wGfN7IeEDp6DgKejaRU5uftUQkHV9tyRz3E0cjBl9BpUhRKPHDwBuIWwOs0oYBpwFaEmT97cfRkwMcc+3yfUROxQfX0906dPZ+LEiZnOdBEpsVKPUC5GjnD3BR0dw923EmZtfCZXPJlizGrPFIl+h5KHUuSZtjp6BuZ4zNeAx1BHj8SltjbpCCR9UjfFNDNyUFJCeaMqlHjk4Mvu/q/Rz3eZ2d3kWEK91Orq6lAxZpF4xThCORXqBg9GxZiLSO0RyUMp8kxbU7fWmVm7S5ZHK9DUFDMIkQ490+YAM6luFTfFVIpMeUMKN8TMvmlm/2hmowgFTd/J3sHMcn47LiIiVUztEUlIWx09twPXm9kfzOzjZnZI9kYz+yDtr0QjUnxTpiQdgaRPh1NMgcWEET9SrZQ3pHAHA18B7iRM33oXGG1mD5rZN8xsCqGehoiISNvUHpGE7NXR4+53EZYJPQ2YCawws3fM7BUze4Ow5N8TsUYp1e3RR5OOQNInnymmE2OIQ9JKeUMKtxr4LaFQqhHaTH0ItcCuJRRrH5lYdCIikn5qj0hC2hrRA3ApYSm/7YTGzQDgWGAI0AB8NZboRACampKOQNJHU0ylY8obUrhb3f1sdz8M2A94H/ApQkHU3xNWvhEREWmf2iOSkDY7ety92d2vBQ4F/h/wXcKSfJ8BRrj7yvhClKqnAreyt9RNMc2sujV30aI4n1bao7xRFUq5Go67fzPr50Z3f8bdb3f3q9z9g+4+CPhDKZ67PZlVt6LzFpEYlHrVrbTJrLql9kyRqD0ieYhr1a1d3H0tYW56WdOyx2Vu5kzQKiNlp8QfwO4yswuAiwjfsmNm64G3gH7AYMK0itho1a2UUd6oCilYDeef4nwyrbolEr8U5JlYadWtIlN7RPJQijxTFavS6ANYmRs9OukIpAtiaBhdCnwDuIowTWtAdAF4HU0xrW7KGxIDd1+adAwiIpJiao9IQqqio0dEKo+7NwPXmtl/AX8PjAB6Ay8DP3f3TUnGJyIiIiIikoT2ijGLpMf8+UlHICnm7mvd/U53/5K7f9bdf6ROHlHeEBERkcSpPSIJUUePpN/UqUlHIJKTijGnjPJGVai6IqkqxiwSu6rLMyrGXFxqj0geSpFn1NEj6TdrVtIRiOSUqQWmgu8pobxRFaquSGpUjDk6bxGJQdXlmcGD1Z4pJrVHJA+lyDPq6JH0q6lJOgIRKTfKGyIiIpI0tUckIerokfTTEo8i0lnKGyIiIpI0tUckIerokfS7++6kIxCRcqO8ISIiIklTe0QSUhUdPSqSWua8GAEJAAAgAElEQVTGj086AumCaiteKCmjvCEiIiJJU3tEElIVHT0qklrmNmml7HJUbcUL1aGcMsobVaHaOpS16pZI/Kouz2jVreJSe0TyUIo806OYBxMpiUWL4KKLko5CpEOZDmVJCeWNqlBtHcqZVbdEJD5Vl2cGD2a66soUj9ojkgetuiXVadq0pCOQCmVmV5nZS2bWbGZuZtNbbb/YzF42syYzqzezqxMKVTpLeUNSwszuMLMlZrbJzNaa2W/MbHirfZRrRKpUMXKEmdWZ2cPRMTaa2b1mNihrezczm25mK6JjLDSzc+I6x6qm9ogkRB09FaZx63Yefr6Bxq3bkw6leGbMSDoCqVyjgXXAG603mNl44B5gKPALwgjIG83sU7FGKF2jvCHpcQWwAbgbaAQ+BMw2s96gXCMiheUIM+sG/Bo4D3gaWABcDDyY9RxXA9cBO6JjHAv8ysxOKPXJVT21RyQh6uipMHMWr+aup5Zz6+ylldPZM3Bg0hFIhXL3j7v7RGBhG5uvAQyY7u6XA5dH918bU3hSCOUNSY8x7j7e3T8JTIruGwIcH/2sXCNS3QrNEedF+y5y97OAM4DXgXFmNtHMegBfivadHB3jO0B34MslPC8BtUckMarRU2EmDR/EKys2smD5OuYsXs35Y4cmHVLhwpxFkbiNiq7ntbo+3Mz2c/cN2TtnijEDTBwxQsXfk6a8UdHmzp2bXZC4LrlIcnP3+Vk3e0XXzcCb0c9555pMMWYI8/kn6n0uUjJx5ZlCc0TW9vnR8ZrNbAFwOHASodNnINACvNDqGCe1jidTjBnUnikK5WnpQCnzjDp6Ksy7W3ews7mFc0cfyqThg3I/oBzcfz/on4zE7+DoOrNcwuasbYMIw6x3UTHmlFHeqGjZnRzXX399faLB5MnMaoGfRje/6+6ZD3F55xoVYxaJT9x5poAc0Xp79j7Z27e4u7exfQ8qxlxkao9IB0qZZ6pi6lY1LXv8syf/xqKGDTz+0ireXL816XCKQz3hZakCliNdE13XtroGWB1zLNJZyhuSImZ2IDAHOBW4nTAVI0O5RqTKFZgjWm/P/jl7e9+onk/r7VJKao9IQqqioyfzTXs1DD28bMIR1PTsRtOOFn70+LKkwymOVauSjkC6oAKWI83U7Tkluh4bXTe0nrYlKaS8ISlhZocDfwTGADe4+7Ssb9VBuUakqhUhR2S2j7WgO3BydN+LhAUn1hE+941udYwXi3oysje1RyQhmrpVQRq3buf3i1dzYP8a1mzcxsXjKqA+D8CyCumwktQxsyuB09jdILrAzOqAh4BvA+cC10XLnH4g2udbMYcZq8at25mzeDWThg+if59euR+QVsobkh5/AgYDDYRv1G+O7p/l7s9RpblGRHYpNEc8DLwKnAA8BtQAhwHPufscADO7Cfgm8Esz+wNwCaEO0HdKfG6i9ogkpCpG9FSLOYtX88i8FaxYu5UdO50/LHkr6ZCKY9q0pCOQynUaYfWKw6LbI6PbJ7n708AUQsNrCqFBdC1wWwJxxiazct+cxXuP5m7cup2Hn28ojxX9lDckPQZH10OBz2ddjgeo1lwjIrsUlCPcvQU4B3iUMPXrZOB+4MKs57gR+AbQE7gUWApc4O6LS3heAmqPSGLU0VNBJg0fxAH9awDoW9OdyyYckXBERTJjRtIRSIVy96nubm1cpkfb73H34929l7sPdfdvtRpOvUul1AKbNHwQHzt9GGOOHLhXp05HnUCpo7xRFcqhFlg7OcbcfWbWPnnlmsyqW1krdIhIiZU6zxQjR7j7cnc/191r3b2fu09291VZ25vd/WvuPiQ6xkh3f7SteDKrbpV7eyY11B6RPJQiz2jqVoXp1T1c9zBLNpBiOuSQpCMQyancVt3KnqIF7DFd6/yxQ3n4+Qbuemo5TTuaqenZnUnDB+3atyxW9FPeqAoVUAusU7Tqlkj8qi7PaNWt4lJ7RPJQijxTshE9ZnaVmb1kZs1m5mY2vdX2i83sZTNrMrN6M7u61fY6M3vYzDaZ2UYzu9fMBmVt72Zm081sRXSMhWZ2TqnOpxzMWbyaVeubAGjctpP/fHBReUyxyGXMmKQjEKk42aNz2hqpkxnZA+zalukEKovaPcobIiIikjS1RyQhpZy6NZpQ4f2N1hvMbDxwD2Eu6i8II4tuNLNPRdu7Ab8GzgOeBhYAFwMPZh3mauA6YEd0jGOBX5nZCSU6n9Qbc+TAPW6/vbGJ2QtWJhRNET3ySNIRiFScTEdOZqRO5ueMTKfO2aOG7LWtLChviIiISNLUHpGElKyjx90/7u4T2b3kX7ZrAAOmu/vlhOKnEAqLQejgOR5Y5O5nAWcArwPjzGyimfUAvhTtOzk6xneA7sCXS3E+5WDea2v3uq9pZ0sCkRTZmWcmHYFIxckendO/Ty/GHDmQW2cvZeW6zbv2ady6ndkLVtK0oznBSLtIeUNERESSpvaIJCSpYsyjout5ra4PN7P9srbPh1BAjDCqB+Akwgo5A4EW4IVWxzip9ZNliqRWemGxoQfss/edbZaNLTNalrCszJ07l+nTp2fqSNQlG018yr0Y88+e/BsLlq/jO796ZdeUzzmLV3Pfsw3c92wD1856gZ8/+dqubalfgUt5oyqUQzHmYlIxZpH4VV2eUTHm4lJ7RPJQScWYD46uN0XXm7O2DWpje/Y+2du3ZFWcz96+h3IrktpVP537173ua9pZht/Et1Zfn3QE0gkTJ07MFBTj+uuvr080mBiVe565bMIRrNm4jVXrtjBn8WrOHzuUScMH8cLytSxZ0cjbG5t4ZP4K5ryymmvOH86rKzdy11PLgTANLLuYcyoob1SFqiuSqmLMIrGrujyjYszFpfaI5KEUeSapjp41hPo8tdHt2qxtq6Ptre+vbWN7XzPr5u4trbZXpaED92H1+m173Ff/9qZ29i4j06YlHYFIRWvcup15r63ly+cdz7zX1nLskH257t4FNDc7W7eHzuKe3Y0dzc6mrTv5/v8tYdxRBzJi6H4MPWAf/v2eF1m1bgsA548dmuSp7Ka8ISIiIklTe0QSktTUrUzdnlOi67HRdYO7b8jaPtaC7sDJ0X0vEgo8ryPEP7rVMV4sWdQpt7yNTp3u3SpgmfUZM5KOQKRiLV21kS/cOY+7nlrOvNfWcv7Yocz6499YsqKRZW++yxtrQwfOjmYPldWA/fv24pH5K1jUsIEfzH6VVeu2MGj/3ukq2Ky8ISIiIklTe0QSUrIRPWZ2JXAauztoLjCzOuAh4NvAucB1ZjYc+EC0z7ei64eBV4ETgMeAGkJdnufcfU50/JuAbwK/NLM/AJcAzYSizFXpiINqeXtj0x73ffS0IxKKpojq6pKOQKQiNW7dzg0PLmJLUzM9uxuNW3awct1m3mpsavsBDt27Q91BtfTobjTtaOG1NaGD+cTDB+yattW4dXvyU7mUN0RERCRpao9IQko5des0dq+mBTAyutS7+3Qzm0JYHn0KYbrVtcBtAO7eYmbnAN8HJhFKCt8P/EvW8W4E+gCfAC4FlgD/5u6LS3hOqfbXNXuP6Hl15UaOGbxvAtEU0dFHJx2BSEWavWAlW5rC1Kwdzc4j81cwf/k61rbX0QM0N8PjL74JQJ+a3YNC33hnEz//w2vU9OhG044WHpm/gpdeX8/nP3xcMp09yhsiIiKSNLVHJCGlXF59qrtbG5fp0fZ73P14d+/l7kPd/VtZhZVx9+Xufq6717p7P3ef7O6rsrY3u/vX3H1IdIyR7v5oqc6nHPSr6b7XfW2uxFVuHn886QhEciq3Vbcat25n6arGXbf79OrO0Yf0Y9W6LfTskd+Uz61NLQCYwZIVjTwybwX3PdvAM395G4BFDRuYvWBl8YPPh/JGVai61XC06pZI7Kouz2jVreJSe0TyUEmrbkkJNLfsvZb6nU/+jVHDBiYQTRGde27SEYjkVG6rbs1ZvJpFDRsYtH9vVq/fxtbtzTRu28GIofuxqGFDp47lrVLPO41N1PbuzqZtYbRQ49btzF6wkqYdLdT07MbZo4aUfpSP8kZVqLrVcLTqlkjsqi7PaNWt4lJ7RPJQSatuSQm0VVfj8gkVUKNn3jwYPTr3fiKSt0zh5DFHDuT3i1bz/N/eYfX6bZx2zEHsbGlhyYrGHEfYW8/u0NwCLQ6btjUzYuh+nD1qCLMXrOS+Zxv22PeSU4cV5TzapbwhIiIiSVN7RBKS1KpbsSq3KRVd1dLSstd9De9sTiCSInvzzaQjkC6otqHO5aZ/n16cP3Yo/fr0pH/fnlxz/nA+dvow3nfsQfTo1rV/DTuaQydPRt2BtfTv04t3t+0AYP/aMIpn6apGGrduL/gcOqS8ISIiIklTe0QSUhUdPZkpFRNHjEg6lJLa0bz3fWOOLPNpWwDTpiUdgXRBtQ11LldzFq/eY2n1ea+tZVHDBmp7713zq9MsTNt6Yfk6AA7sV8OoYQNY1LCBW2cvLW1nj/KGiIiIJE3tEUlIVXT0VLN5r61NOoTCzZiRdAQiOZXryMFJwwfxsdOH7ZrKlbl9zQUjOGjfmoKOvW5TE9fOeoG3N4Zppes2b2fskQOp6dmNBcvXMWfx6oLjb5fyRlWotpGDKsYsEr+qyzMqxlxcao9IHlSMWTqtIkb0aFlCKQPlVoy5PZkpXQD/+dGT+fTtz7Jj596F3sO+PWjcurPdYz396tt73H6nsYmfzPkLO5uhZw9jzJEDady6nTmLVzNp+KDiFmhW3qgK1TZyUMWYReJXdXlGxZiLS+0RyUMp8oxG9FSI9qZAVMSInsGDk45AqpiZzTUzb3VZnHRcxZKZutXe6Jr3HnVAu4/tqJOnPTujKaY7djo/emwpX531Anc9tZxbfr2kuFO5lDckJczsKjN7ycyao/wxvdX2i83sZTNrMrN6M7s6oVBFJCGF5gkzqzOzh81sk5ltNLN7zWxQ1vZuZjbdzFZEx1hoZufEdHrVTe0RSYg6eipEWx/SzHavrFPWNERd0uGWrMvPE46laFpP3co2Z/Fq/thqVE4xLXvzXd6KpnUtatjA7AUri3dw5Q1Jj9HAOuCN1hvMbDxwDzAU+AVhpPWNZvapWCMUkaR1OU+YWTfg18B5wNPAAuBi4MGsw1wNXAfsiI5xLPArMzuhROcjGWqPSELU0VMh2vqQduoxBxZ3KkRSLroo6QhEcPersi43Jh1PsWSmarWVKyYNH8SIofsB0Ldmd3Hm7t1CRzJANytCDH3DLOLM6lxFobwhKeHuH3f3icDCNjZfAxgw3d0vBy6P7r82pvBEJAUKzBPnAccDi9z9LOAM4HVgnJlNNLMewJeifSdHx/gO0B34cinOR7KoPSIJUUdPhWjrQ9qAfQorpJoa6gmXFDCz9Wa2wcyeMLOxSccTh/59evH5Dx/Hx04fxhnDD9l1f3MLeFS2Z9++Pendq7B/JY1bwhSwhnc273n/1u08/HxD16Z0KW9IeRgVXc9rdX24me2XQDwikj658kRm+3wAd28mjOoBOAk4DBgItAAvtDrGSSWKWTLUHpGEqBhzBVu3uSnpEIpjbQXUGZJy9i7wKLASGA+8H3jMzI53911zJjOrbgFMHDGCiSNGJBFr0WVG/CxdtZE//eVtWppbGFBbw9AD92HJyo2sXr8NCCN+tjQ1F/Rcg/bts0dx5kz9IGBXgei8KW9UtLlz52avPFWXXCQFOzi63hRdZ/d2DgI2ZO+cWXULQuHGqHijiJRAivJMrjzRenv2Ptnbt7hnvqbZY/seMqtuQWW1ZxKj9oh0oJR5pio6ejIfwCo5WbX1jffzf30ngUhKYNq0pCOQLqig5UjPyzSMzKwXsAw4HJgE3J3ZqVJW3WrPA39uYG1j6Dxev3kH444+kI+dfgQPP/cG9W9v4oD+NcxZvKbDY5jtHgnUltUbt3Lr7KUsWL6Od97dxouvr+fMkYcwafigzq/OpbxR0bI7Oa6//vr6RIMpzBpC3Y3a6HZt1ra9iu9p1S2R+KQoz+TKE2vauL+2je19zaybu7e02r4HrbpVZGqPSAdKmWeqYupW5gNYpXbyQNvFmLe3syRy2ZkxI+kIpAsqYTlSM+sLHNLO5pY4Y0naZROOYMTQ/Thz5CFMHjd0V4fLxyccydcmj2Rgbe6poh118gC8/vZmFixfx+ABfXlh+TpWr9/GSw3rmbN4NbMXrOxwdbC9KG9IecjU4zglus5MC21w9w1t7C8i1SdXnshsH2tBd+Dk6L4XCQWe1xE+941udYwXSxa1BGqPSEKqYkRPNZg0fNCuKQ4ZB/SrkBo9FdxBJ6l3ELDUzH5PKGw4njCaZw3wRJKBxW3IgH342uSR7W4/e9QQXnp9PcvefLfN7ccd2p/tO1p4bc2mNrcDbGlqpmcPY9W6LdQd2JeNPbtx5MH9uOup5UweN7Td1cHapLwhKWFmVwKnsfuD1wVmVgc8BHwbOBe4zsyGAx+I9vlWzGGKSIIKzBMPA68CJwCPATWEujzPufuc6Pg3Ad8EfmlmfwAuAZoJRZmllNQekYRUxYieatDWVIZulfLq1tbm3kekNNYCPwOOJqxycTCh0XWGu1fI3Mji6N+nFycevn+b2447tD8nHLofxx+au7Zs927GiKH7Uf/2FrbvaOHtjdsYPKAvI+sG7Krbk1dxZuUNSY/TCPnjsOj2yOj2Se7+NDAFaIiumwkr6dyWQJwikpwu54loKtY5hHqCpxI6i+4HLsw6/o3AN4CewKXAUuACd19c0rMStUckMZXSFSCEdRezXfH+9yQSR9E980zSEUiVcvd33f2T7n6ku/dx90HufqG7v9x630wtsLmLFiURaiqcPWoI544+lAG1oeP5yINrmTxuKCccuh/3PdtATc9uDNq/d5uPzXRMb9vewjGD+3Pu6EMZMXQ/enQPI3we+HPDruLMeU3fUt6oCuVQC8zdp7q7tXGZHm2/x92Pd/de7j7U3b+VVTB1D5lizHO1iotIbOLIM4XmCXdf7u7nunutu/dz98nuvipre7O7f83dh0THGOnuj7YVS6YYczW3Z4pK7RHJQynyjKZuVZBJww/m91nFUBve2cyoYQMTjKhIpkxJOgKRnCq9GHM+MjV7anp2475nGxg1bACXnDqMxq3bqenZnUnDB/G+Yw9ixu+WsWnrTtZuauLA/jXsU9ODV1Y0MmLofhwzuD9njxoChNpjxw7Zlwf+3MBlE46gX5+eAPlN31LeqAqVUAusM1SMWSR+VZdnVIy5uNQekTyUIs+oo6eCZK94c+C+NfnXski7Rx+FY45JOgoRydPZo4bs6tiB3Uu0Z34+edhA7npqOR87fRjnjx3a5opaDz/fsGufay/cPb8972XWlTdEREQkaWqPSELU0VMhGrduJ3uc95Xvf09+SxCXg6ampCMQkU7I7thpS6YDqK2OoPb26TTlDREREUma2iOSkKqo0VMNtTNa16x4dP6KhCIpgSqfDlOuyqF2hiQj07HTUWd0Pvt0SHlDREREkqb2iCSkKjp6MrUzJlbw8naThg/iuEP777pdd2AFVXifOTPpCKQLqm1Ou6SM8oaIiIgkTe0RSUhVdPRUg/59evHFc09g8rihTB43lPNPOSz3g8rF6NFJRyCSUzWMHCwryhtVodpGDmrVLZH4VV2e0apbxaX2iORBq25Jh/r36cUlpw5LOgyRqqRVt0TiV20jB7Xqlkj8qi7PaNUtkdiVIs9oRI+k3/z5SUcgIuVGeUNERESSpvaIJEQdPZJ+U6cmHYGIlBvlDREREUma2iOSEHX0SPrNmpV0BCJSbpQ3REREJGlqj0hC1NEj6VdTk3QEIjmpGHPKKG9UhaorkqpizCKxq7o8o2LMxaX2iORBxZilOqkgnJQBFWNOGeWNqlB1RVJVjFkkdlWXZ1SMubj0u5Q8qBhzF+mb9jJ3991JRyBdUG3fgEnKKG+IiIhI0tQekYRUxYgefdNe5saPTzoC6YJq+wZMUkZ5Q0RERJKm9ogkpCpG9EiZ27Qp6QhEpNwob4iIiEjS1B6RhKijR9JPU+6kDGiKaMrodagK1TZFVMWYReJXdXlGxZiLS79HyYOKMUt1mjYt6QhEctIU0ZRR3qgK1TZFVMWYReJXdXlGxZiLS+0RyYOKMbdiZr3N7Adm9paZbTWzp83svUnHJUU2Y0bSEUgVU54pU8obUkaUZ0Sk1JRnEqL2iCSkrDt6gJuBzwJrgIeA8cBvzeyA7J3q33orgdDiU+lDK/+ybl3SIZRMFQy/r0s6gCKo2DxTbrmjU/EOHFi6QDqh3P7Gyy3eSF3SARRBfnmmvj7+yApUbu+pcosXyi/mcos3Upd0AEWQX55ZtSqB0OITe9sn5vZImf595aWSzy1SV8yDlW1Hj5kdBHwCaAHOcPcpwF1AP0IS26V+zZr4A4xRuX1Y66zf7tiRdAglo4SVbpWeZ8otd3Qq3jAENnHl9jdebvFG6pIOoBCdyjPq6Cm5cosXyi/mcos3Upd0AIXoVJ5RR09xxdweKdO/r7xU8rlF6op5sLLt6AFOAHoCDe6e+Sp9XnR9UlcP2tk//s7sX6p9OyMN8Xb23I5fsiT/Y3ciAaRh384oZQxpOL+Uqug8k4YYShVv/U03dWr/NPwNVHJO6uz+yjNdzzP5/O7i3CcfccVTbvHmu08+9DsubJ8KkH+eMYPevfO6zF26NO99O7t/qfalR4/4YujVC+6/f68XIy3/D9PQ7khDG6VS2zPm7ok9eSHM7FLgbmCxu4+I7rsSuB34s7uPy9r3GaApullPx4WO6nJsL2R/7dv5fdMSRyXvW+xj17G7R7rG3cd3Io5UUZ6pmn3TEkcl71vsY9ehPFNP27+junbu1z7piqVS90lTLIXuU4fyTD1qz5TTvmmJo5L3Lfax6yhRninnVbcy8yRqs+7L/Lw6e8dyTswikijlGREpNeUZESk15RmRKlPOU7deAXYAQ83s4Oi+sdH1i8mEJCIVRnlGREpNeUZESk15RqTKlO3ULQAzmwF8EngZWAxcAmwGjnD3t5OMTUQqg/KMiJSa8oyIlJryjEh1KeepWwCfJ/ROXwIcBTwLfFHJSkSKSHlGREpNeUZESk15RqSKlPWIHhERERERERER2a2ca/SIiIiIiIiIiEgWdfSIiIiIiIiIiFQIdfSIiIiIiIiIiFQIdfSIiIiIiIiIiFQIdfSIiIiIiIiIiFQIdfSIiIiIiIiIiFQIdfSIiIiIiIiIiFQIdfSIiIiIiIiIiFQIdfSIiIiIiIiIiFQIdfSIiIiIiIiIiFSIsu/oMbPeZvYDM3vLzLaa2dNm9t5i7Z+0LpzfXDPzVpfFccacLzO7ysxeMrPmKM7pOfYvt9eus+dXTq/dHWa2xMw2mdlaM/uNmQ3P8Ziyev3ylebzyud1MrOLzexlM2sys3ozuzqpeFszsylZfws3Z93/GTN7LYp5qZldnmScUUwXmtnz0Xtgo5n90cz2j7al7ndsZieZ2WPR+2KLmb1iZv+ctT3RmHPlz1zxmVmdmT0cvfc3mtm9ZjYoznMopiTzTKlfCzPrZmbTzWxFdIyFZnZOgTEXnPvijtvM7jSzldGx3jGz2WY2Kmt7h3nPzEaa2Zzo/bHWzG43s35Z20vyHupqnk4iXsvRzkpjzHEq5/hL/drGycow53ZGR+dnZlPbeB3dzMZk7ZPav0NL2/8edy/rC3Ab4MAi4G6gBWgEDijG/klfunB+c6P9b866XJP0ebQT68+jeOujmKcX83eR9KUL51dOr50DzwC3A8uj2yuA3pXy+nXid5Ha88r1OgHjo3jfBe6MtjnwqRTEfiiwHtiR+buI7r80uv0WMBNYF90+K8FYp0QxbAN+AdwBvAQMSevvOCsvPRflqpbo9qQ0xNxR/swVH+FLrJej+x7Lyq3PJP2+LuD3kVieKfVrAXwlum95dIxtwE7ghAJiLij3JRF39ByzgB8BS6Njvx5t6zDvAf2ibQ7cB8yPfr67lO8hupinE4w38zru1c5Ka8xxXso5/lK/tjGfS9nl3CKe39TovsdbvZaH5ftaJfk+zvyuScn/nsT/MAv8ZR4EbAeagYOy3jx7vGm6un/Sl67Em3lDJB17J8/zoVyvQbm9dp09v3J77YDRWT/XRefnwMmV9vrl+D2k+rxyvU5Z780vRrfPiG7XJxy3AU9E/+x+wZ4fIBZGty+Kbl8R3Z6bYKwNUQwT29ieut8x0DN6zzowPLpvXnT7/6Up5rbyZ674gAui2y9Ft7uzu0G512uU9kta8kwpXgugB/BOdHt0tM/Xo9szC4i1oNyXVNxZMZ8cHas5+nvtMO8BV0W3H4lu1wJbo8cfUYr3EAXk6STijY4xl3baWWmNOa5LBcRfstc2wXMqm5xbxPObGt03tZ3HpPrvkJT97yn3qVsnEP4BNrj7W9F986Lrk4qwf9K6HK+ZrTezDWb2hJmNLWWQMSm3167LyuG1c/f5WTd7RdfNwJvtPKRSX79Un1cer1NmWsC8VteHm9l+JQ6vI1cBpwEfI3xTAYCZ9QAyQ2Bbx5zU7/s9wGGEhsbV0VDbv5rZZ6Ltqfsdu/sO4Jbo5k/M7OeED5YvAg+SwphbyRVfZvt8AHdvBhZE9yX+d9kFac4zhb4WhwEDCd9wvtDqGF0+tyLkvkTiNrPPmtl/E76FBriJqEO2nXgzz7XH+bj7JuBVwrfDJ1Ka91AheTqJeHdp3c4qh5hjUO7xAyV7bdMklbmrBG6Jpl29amafz7o/1X+Hafvf06NLZ5EeB0fXm7Lu2xxdtzUXv7P7J60r8b4LPAqsJAwPez/wmJkd7+6rSxJlPMrtteuKsnvtzKwW+Gl087vu3l5HT6W+fmVxXh28Tq3j35z1sEHAhhjC20M0l/kG4N/dfaGZZW8+gPDtBuwd875m1tvdtxGvA6LrPoRvk+4lTOX6oZmtJIW/48hDwIXA2OiyI7rvXdIbc0au+DX1QXEAABIKSURBVMri77IT0nw+hb4Wme1bPPpqkiKeWwG5L6m4JwMTop9XAE+TR97LI97eObZ3SqF5Ou54s7TZzgKOT3HMcUlznslHKV/bNElr7iqWFuB5whdPA4HzgJvNbKu7z6BM/g7T8r+n3Dt61kTXtVn3ZX5u64NxZ/dPWlfiPS/zwptZL2AZcDih7sLd7TymHJTba9cVZfXamdmBwG+AMYS5qNd0sHulvn6pP68cr9MaYCi7Y84+j6Tiv4jwLcgEMzsdGBndfx67h+d2J8S6lt0xb0ygkwfg7ayfP+7uz5vZVuCfCTGn7ndsZgOB/wP6AqcTpl48BlxHmPueuphbyRVf6v8uOynN51Poa5HZ3tfMurl7C0U6twJzXyJxu/vE6IPnWcADhBoUR5Ej75lZrng35tjeWQXl6QTizWivnfXBFMcclzTnmXyU8rVNk1TmriL6ubv/LHPDzG4g1KS5CJhB7vNL/O8wTf97yn3q1iuEbyGHmlmmhysz1eVFM9vXzI41s7p89o8h3s7q1PmZWV/gkHaO1VLKQIutAl67DpX7a2dmhwN/JCSxG9x9WlbPcsW/fllSfV65XifCvHWAU6LrTOwN7p7UqA2LLh8CPkwo9gkwjPAt3cvR7dYxJ/X7fp1Q5K8tm0jn73gYoZNnB/C8u68HlkTbjiOdMWfLFV9m+1gLuhOmpkEK/i67IM15ptDX4g1CYdRuwOhWx+jyuRUh98Uat5n1iZ6DqMN6NiF/9CCMFMyV9/Y4Hwsr0BzL7oKkxX4PFZqn4443VztrexpjjlnZxh/Da5smqcpdJXBkO/dnPgul+u8wdf97Sl2UqNQXQu+eA4sJxeAylawPZHdBp4X57J/0uRR6foSiT02Eb2pvi15wJ/Twpa5iPnAlofp9ppDpwuj2BRXy2uV9fmX42q2M4nudPavinxJtL/vXrxO/i9SeVx6v0/uieDcRKvdn9v900rFnncNM9izy+dHodmb1jPXR7Q8lGOP1UQxLgJ8QvtHeCbw3jb9jYB/Ct5pOaJDcSWgMO/CRNMScI392GB+hAbQkuu9x4Mno5z8n/X4u4PeRWJ4p9WsBfDW6rx74GbtXEBleQMwF5b644yYU2VwVvbY/il7nTJ7blxx5j7ASzTvRffcRajc4cE8c7yE6maeTiJcc7aw0xhz3pVzjj+O1jfl8yi7nFvH85hJWLf0xYVTjzmiff8z3tUryfUzK/vck/sdZhF9oH+BWwvD5bcCfgPHRtqmZN1A++6fx0pnzi978twOvET5orCYU1oxlubwunNtMdlcjz75Mr5DXLu/zK8PXrq3zcqIq+ZXw+nXid5Ha88r1OkX7fITwDch2wj/drwCWdOxZ8WX+jm7Ouu9fgL9FMS8DPpFwjD0I9SreJMyVfh44J82/Y0In1G8JHT5bovg+n5aYO8qf+cRHGFnwCKEx9S6hQTg4yd95gb+PxPJMqV8LwpSKrxMavNsJH9L+vsCYC859ccYNHE34gLM2OtZKQr2v4Vn7dJj3CEU85xLaEOsIH5T6x/Eeogt5Ou54yaOdlbaY476Ua/xxvLYxn0/m76lscm6xzo/QCfQcoRbgu4SOnMs781ol+T5u57wS+99j0QNERERERERERKTMlXuNHhGR/7+9Ow+yqyzzOP79sYOJIoMQII6oaEgAyQwRF2AIQRYLKM1UqIozjkbBKmWgJo4gIEqFGMRlJoQCXFExZWl0BMdhcDJxhokoLhhkScjCIkvALGIAE9YkPPPH8x765HK7b3enb9/u9vep6jr3bO9yc+/JfZ/zvu8xMzMzMzOzwoEeMzMzMzMzM7MRwoEeMzMzMzMzM7MRwoEeMzMzMzMzM7MRwoEeMzPrOEmflLRGUkhaK+nnktZL+r2kqyTt2KFyzaqVaUEvz1kpaXH5e6x2/uLa35iyfLbsn9zmqphZC5LeIunB8p18XtI1tX2TJd0raZWkIzpZTjMbfnx9scHmQE8bSJotaV35In+kYd8XJT1RGjGT2pT/v5e8X5A0uh159LE8+0u6XtJqScskXd+wf5SkWyUtl6ROlbPdemowSjpG0v1l/6wm5+4laWt/GoNuUNpwEBFzgLvK6jcj4mjgIGAP4B+Bd3eqbMXCiJjey2PXRsTkiJgM/Lx2frWNiFhbXq8d8JKaWb9ExK+BOWV1B+Cc2r7F5OObp0TEbYNfOjMbznx9scHmQE8bRMTFwMqyOlvSnrV9ZwEPAcdExJI2FWF2Wd4fERvblEdffAuYSjbW3gbc3LB/f+AIYENExCCX7UUlIBKSnpG0Uxuz2qbBKOkM4NPAcz2ccyLwFHBLXzNzg9KGkUPKcllZbgK2ltfDKQj82e3cb2adc0dZ7ggcVm2UdDKwNCIe7UipzGwk8PXFBo0DPe1zUFnuDVxcbZS0O/BsmwMabyrL29uYR69IegVwPLAFWBQRGyNiXv2YiLgHOACYPPgl3MbEsrw7IrYMYr6/BKYA63s45iTgpojYPDhFMhtc5VpxQFldJmkH4OPAXuS17MedKlsjSReVnpkhaYakKaWnXkiaFRELezq/1X4z66hl5G8WgMMBSm/j83GQ1sy2j68vNmgc6GkDSfsCd9PVMDlb0hvL6/HAinLcayWtKI2DX5Rtry7rm5RGS7qzbFsj6eIyxGmtpOMlvV/SHWWo2LtKHlWEeJSkb0vaIOnHkv6iVsadJZ0n6bfl/FWSzpS0Xy2/ByTNkXRLSf8ld9Ql7SjprJLOLZLukXRBKftYctiCyN4oC2tlrM6fLOlhYA3wxbKtXudHlXN3LFHO1zFV0qVl32ZJu5ZzTpW0UdIu3dWtHNdd/f4AfK0Ua58y1OmEnt6rFuk1fb+aiYjlEfFCi8NOAhY2eX96+5kwG+qq3jwBXE0GPj8D/BA4NiKe7lTBGkXEpXTdlSMibqJ8P81seIuIZ4FVZbW6AXQ6ebPlj50plZmNBL6+2GByoKc9DiEDPR8jo7Y7A3Mb9hERDwDfLtuX1fYDLI+0EbiibNsEfAG4EtgX+DLwH8ClwD7k0CjoCvTcGxHvBb4PvJNazyIyqPJ54BLgSLIH0leBp2v57Q1cCxwL3NBNL6QryEbZ/Ig4qtTjMuA9EfFI2Qdd81P8qH5yGZNazWGxrGyr1/l54HJyTOurgJkRcRGwAdgJmFCOW1zq+3x3dZP0iohY0039fgT8rGz/QinrT3p6r1qk19371WeSDgf2ozQk+/mZMBvqDi3Le8v8PAeQn+WpwKLeBk7NzAZAFcg9XDkR/DnkbxEzs+3l64sNCgd62uNQcvjPSuBLZdspkk4iAxN3146thllV2xrnqKjSA/h+RDxDBhQAbo6Ix2vrTzWk+Y2yrO6EjwOQdAhwJvAkcCMwg/ws/Bb4Uy2/+RFxX0RsiYgzGyspaTwZSNhay6saWvTWsqwCMcsbz29Sv2Z1/l5EPAVUvZGqutxZloeX5d8CN/SibvW0G+tXBciqCWF78171lN5AOQlYFREP1rb19TNhNtS9GOQGiIjngGri9rcCr+5Eoczsz1LVEHsTcAbwg4jY1MHymNnIMSSuL5Iul/SDXh77V5KeknRF66NtqGjnhLN/zg4he3YAzALeC7yS7NXzEF3BH+hboKe6MIzvZn2FpFeRPTueBZaW7dV8QdUEX6eV5Vbgf8uxFwJXR0RIqvL7aQ91BKiGBS2PiCroUQ1Rq7ofbtN4a6Sc9HhcWa0HwKoyVBNWVwGjFWV5F3AcGQ3fGTgXOAH4QE91a0j7xfqVNKpyvBjoocV71V16A+xkXjospNefiTaVyWygNbtWvL4snwTWNTtJ0oHAA8AHIuLaNpWtJ9UNk907kLeZtUf1f+lo8obWkfWdks4G3kBeew4lH35xWW8TL7+zPgmsJn+bvQX4t4i4vscTzWwkaNv1RdJRZHvld8A08qb0CuCyiPiMpLnAC2Rb5rvA470s88PkU1B/08vjbQhwoKc9JtB1V3qDpNlkl7wJwNiIeBhyfhvyiwxdT+l6c1k2C3p0Fwyq1pfSFThaXZv3pbqAVHMGjSnLGyPifaUsh5F3zX9C8x42zexTlqtKGqPp6hVT/Vhp1aPnjcAuwGMRUW/IVWWozqvXEbbt0TMLuC4i1klqVbd62vX6jSOH2D1SesRU+pvegJA0CjiKHDpW15fPhNlwsM13Xjmv2YfLtssj4jlJpwAXAd8jv4MXkE+kAzhR0oNl/ULgO2SAdk/ykaW/B/4BOJtsYB0HTAL+kuyp93elF1FfjSnX8oktjzSz4eKO2ut59WuDpHHkcOmzyIbSZuD15TrwebIH8n7k/7/nk8PZ30AOD387OYz+F8CXIuL8kuZhwMuVk9DPKec/A4wFpgN/TQ5lfbAsjwdurecVEecO9JtgZm3RtutLREyTdD+wJSKWS/oXMkBzUEnjmYi4SNJk4P+AT0j6L+B/yMDSdeRvqHcC9wHzyZvcVduoXU+Mtjbw0K322DMinqytXw3cU17Xe1jsRNe/weOS3kZD0EDSXuQXejNwb/mSjqsfw7aN/CrQspukHZSP69sX+DU5Dw10BUkOLpMm70YGojY05teintWjvncpyw+Sjwu8MiKWlrTGtEirMWDxkjo3qWO9DkeREevPtapbD2kDHFiW90kaJem/tzO9gXIcGXmv9z7q62fCbMiSdLCkxeR1CuA8SUuAm8jv1BkRcUnZdwz52T6IbOysAxaVfYvKnF9fLet3luDsR8hr1O50NZrOJa+/B5NzAX20n0EeyCeDLSTnywKYUQJSSFoAHF22nyzpyn7mYWaDKCIeI3varCQbOnVrgf8kfws8Tv7//AQwE/hnco68R4BTgH8CziPnF5xLzvV3Nnknv7pWERFLI+KWcvyFZDD6BrJX8dHAr4DdyAlbZ5HXnMa8zGwYaPP1BfK30+sknUr+RlpN/m76EPD1ckw1EuNWsg32MrKd+HXg5eX4uWSQeVrJ8090TSRtw4ADPQNI0kRJNwOvkXRNtb08Eru603J3bftzwL8CG8kJk0+oJTenLOsTlG4mv3i7Ausj4g+l0V81kD5FV6BnCdl17xoyInxa7ZHh88kI8BiyC94C4HMRcVuT/LoVEdeRE06PVz417P1kBPqj5ZCqN899PaRV5Tde0unN6qx87PLYWh0h38ctZLBjWpnFvlXdXpJ2rRx3kUGRiWRvpGry7P6m12uSTi4N3apHwIzy1K8DyGFbPy3z8FT6+pkwG7IiYmWZ/Fzlb2JETIqIsRFxdER8o3b4fPI6swfZY2cK+ZQuACTVh0+tL8uPA38PfJqcs2qPiLiLvJM1k1ovy36aGREnRMQRpfwHRsSNpW7TI2Lvsn1MRJyzHfmY2SAq16DxEbG1YdfkiDiNbBhNBY4AXkcGZDYC7yCHObyrbFsTEQ9J2pOcZ7DqTb8GQNKukmaUbceQPbIfLa+fBm4jey/vTtcNu0lN8jKzYaKN1xfIQM8o4B0RsaisHwbsFBG/K8ccSf5++g3Z/tiNfLBNNbJkSUn/ttJ+fHt5PSAPmrHB4aFbAygi7gD+ppt9N5CPGW/cfh4Zja3Mbth/c/28iFjVsL6hSbpntCjnVuAT5a9x3zb5tVIiyHO72d2yV0lEXMy2TwNrVucnG8tUgmQ7N0mv27o1S7u2/WG6gmTbnV5fRMRCunk0c5nA+6qe8uzlZ8JsJJhG3glfB9xOdl1eTwZ+TyWHrlbDPU+UVD1NbwZ5XRwNTJA0FphHNpouoWdPkHOrTZK0ICKmD0RFyjDTBWTA+iGya7SZDX1TJZ1IDnM4GJgVEYslvYy8ETOa7In7SvKpnRMkXVC2X0n+ztsX+Iqk28lh8NUTWK8C5kr6GBnMOSUi/ijpPeRQ1Oom0zVN8jKz4W97ry+QgZ0nyBvV1fr+wFdq+byZnP/wNeQcYVvJB82cX0v/WuB9ks4lA8/z2lBfayM5MGftIumHwLuBD0bENztdnk6TNJPsQbAJWDZQDcZe5Fs1KMeSwd3pEfGrwcjbbCgqj2oX2etxSn/uUJVeeMfSuUmgzczMzMyacqDH2kLSp4DTyWjwvNrE0GZmHSVpDnkna3VELOh0eczMzMzMBpIDPWZmZmZmZmZmI4QnYzYzMzMzMzMzGyEc6DEzMzMzMzMzGyEc6DEzMzMzMzMzGyEc6DEzMzMzMzMzGyEc6DEzMzMzMzMzGyH+HzCbrvhYz0XnAAAAAElFTkSuQmCC\n" 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\n" 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\n" 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\n" 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\n" 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" + ] + }, + "metadata": {} + } + ], + "source": [ + "pareto_bstray_points = []\n", + "pareto_kdiff_points = []\n", + "pareto_numinv_points = []\n", + "pareto_coilloss_points = []\n", + "pareto_V_SecCore_points = []\n", + "pareto_pave_post_points = []\n", + "\n", + "accepted_solutions = []\n", + "max_solution = -float('inf')\n", + "\n", + "kdiffs = []\n", + "bstrays = []\n", + "numinvs = []\n", + "coillosses = []\n", + "combined_losses = []\n", + "\n", + "#testing for graphs\n", + "kdiff_designs = []\n", + "bstray_designs = []\n", + "numinv_designs = []\n", + "coilloss_designs = []\n", + "V_SecCore_designs = []\n", + "pave_designs = []\n", + "\n", + "pareto_bstray_points2 = []\n", + "pareto_kdiff_points2 = []\n", + "\n", + "x_calc_numpy =[]\n", + "y_calc_numpy=[]\n", + "cumulative_solutions = [0]\n", + "i = 1\n", + "#training parameters\n", + "n_epochs = 5001\n", + "n_noise = 1000\n", + "\n", + "for epoch in range(n_epochs):\n", + " \n", + " # Clear old gradients in the GP model\n", + " for param in gp_model.parameters():\n", + " param.grad = None\n", + "\n", + " # Create GP parameters from noise\n", + " noise = generate_noise(shape=(n_noise, 10))\n", + " gp = gp_model(noise)\n", + "\n", + " # Unpack and filter GP parameters\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + " gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + " extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + " V_PriCore, V_SecCore, V_PriWind,V_SecWind, V_PriCore_ave = core_losses(extra_parameters,gp_parameters)\n", + " numinv = number_of_inverters(gp_parameters)\n", + " \n", + " # Scale GP parameters - why?\n", + " min_x = torch.min(gp_parameters)\n", + " max_x = torch.max(gp_parameters)\n", + " gp_parameters = (gp_parameters - min_x) / (max_x - min_x)\n", + " \n", + " # Push GP parameters through MP model\n", + " mp_parameters = mp_model(gp_parameters)\n", + "\n", + " # Scale MP parameters\n", + " y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + " min_y = torch.min(y, 1).values\n", + " max_y = torch.max(y, 1).values\n", + " mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + " k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + " l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + " b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + " # Calculate losses\n", + " kdiff = kdiff_loss(k_parameters)\n", + " bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " # coilloss,pave = coil_loss_and_power_average(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " # print(pave)\n", + " coilloss,pave = calculate_coilloss_pave(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " \n", + " #pmax - pave_kW, to get tensor to minimize, pavemin is tensor to minimize\n", + " pavemin = torch.sub(10000, pave)\n", + "\n", + " # # Call pairwise pareto and loss\n", + " # bstray_kdiff_loss, pareto_bstray, pareto_kdiff, bstray_numpy, kdiff_numpy, pareto_bstray_numpy, pareto_kdiff_numpy, pareto_bstray_forx_numpy, pareto_kdiff_forx_numpy = compute_pairloss_and_pareto(bstray, kdiff)\n", + " # numinv_coilloss_loss, pareto_numinv, pareto_coilloss, numinv_numpy, coilloss_numpy, pareto_numinv_numpy, pareto_coilloss_numpy, pareto_numinv_forx_numpy, pareto_coilloss_forx_numpy = compute_pairloss_and_pareto(numinv, coilloss)\n", + " # V_SecCore_pavemin_loss, pareto_V_SecCore, pareto_pavemin, V_SecCore_numpy, pavemin_numpy, pareto_V_SecCore_numpy, pareto_pavemin_numpy, pareto_V_SecCore_forx_numpy, pareto_pavemin_forx_numpy = compute_pairloss_and_pareto(V_SecCore, pavemin)\n", + " # V_PriCore_V_SecWind_loss, pareto_V_PriCore, pareto_V_SecWind, V_PriCore_numpy, V_SecWind_numpy, pareto_V_PriCore_numpy, pareto_V_SecWind_numpy, pareto_V_PriCore_forx_numpy, pareto_V_SecWind_forx_numpy = compute_pairloss_and_pareto(V_PriCore, V_SecWind)\n", + " # #get pave points for plotting\n", + " # pareto_pave_post = pave_tomax(pareto_pavemin)\n", + " \n", + " if epoch%100 == 0:\n", + " kwargs = {}\n", + "\n", + " kwargs[\"kdiff\"] = kdiff.clone().detach().cpu().numpy()\n", + " kwargs[\"pave\"] = pave.clone().detach().cpu().numpy()\n", + " kwargs[\"coilloss\"] = coilloss.clone().detach().cpu().numpy()\n", + " kwargs[\"bstray\"] = bstray.clone().detach().cpu().numpy()\n", + " kwargs[\"vpricore\"] = V_PriCore_ave.clone().detach().cpu().numpy()\n", + " kwargs[\"vsecwind\"] = V_SecWind.clone().detach().cpu().numpy()\n", + " kwargs[\"n_inv\"] = numinv.clone().detach().cpu().numpy()\n", + " kwargs[\"vseccore\"] = V_SecCore.clone().detach().cpu().numpy()\n", + " kwargs[\"epoch\"] = epoch\n", + "\n", + " accepted_solution_so_far = accepted_solution(**kwargs)\n", + "\n", + " \n", + " accepted_solutions.append(accepted_solution_so_far)\n", + " print(\"Accepted Soluntions : \", accepted_solution_so_far)\n", + " cumulative_solutions.append(cumulative_solutions[i - 1] + accepted_solution_so_far)\n", + " i+=1\n", + " generate_solution_plot(**kwargs)\n", + " \n", + " if max_solution < accepted_solution_so_far:\n", + " torch.save(gp_model.state_dict(), f\"./models/gp_model_{epoch:06}.pt\")\n", + " max_solution = accepted_solution_so_far\n", + " \n", + " if epoch%1000 == 0:\n", + " plot_(**kwargs)\n", + " \n", + " #Spline Fit Loss\n", + " # combined_loss = (bstray_kdiff_loss + numinv_coilloss_loss + V_SecCore_pavemin_loss)\n", + " \n", + " #Spline Fit Loss with V_PriCore and V_SecWind\n", + " # combined_loss = (bstray_kdiff_loss + numinv_coilloss_loss + V_SecCore_pavemin_loss + V_PriCore_V_SecWind_loss)\n", + " \n", + " # ParetoPointMeanLoss\n", + " # combined_loss = (torch.mean(pareto_bstray) + torch.mean(pareto_kdiff) + torch.mean(pareto_numinv) + torch.mean(pareto_coilloss) + torch.mean(pareto_V_SecCore) + torch.mean(pareto_pavemin))\n", + " \n", + " # ParetoPointMedianLoss\n", + " # combined_loss = (torch.median(pareto_bstray) + torch.median(pareto_kdiff) + torch.median(pareto_numinv) + torch.median(pareto_coilloss) + torch.median(pareto_V_SecCore) + torch.median(pareto_pavemin))\n", + " \n", + " #SimpleMeanLoss\n", + " combined_loss = (torch.mean(bstray) * torch.mean(kdiff) * torch.mean(numinv) * torch.mean(coilloss) * torch.mean(V_SecCore) * torch.mean(pavemin))\n", + "\n", + " # Push loss through the optimizer and update the GP model\n", + " try:\n", + " combined_loss.backward(retain_graph=True)\n", + " optimizer.step()\n", + " except Exception as e:\n", + " print(f\"Error on epoch {epoch} regarding\", e)\n", + " break\n", + "\n", + " \n", + " #if epoch % 50 == 0:\n", + " \n", + "# # plt.plot(pareto_bstray_numpy[:,0], spl_y(pareto_bstray_numpy[:,0]), '--', bstray_numpy, kdiff_numpy, 'o')\n", + "# plt.plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# plt.plot(bstray_numpy, kdiff_numpy, 'o')\n", + "# # plt.plot(x_calc.cpu().data.numpy(), kdiff_numpy, 'o')\n", + "# plt.xlim([0,100])\n", + "# plt.ylim([0,0.5])\n", + "# plt.show()\n", + " \n", + "# plt.plot(pareto_numinv_numpy, pareto_coilloss_numpy)\n", + "# plt.plot(numinv_numpy, coilloss_numpy, 'o')\n", + "# plt.show()\n", + " \n", + "# plt.plot(pareto_V_SecCore_numpy, pareto_pavemin_numpy)\n", + "# plt.plot(V_SecCore_numpy, pavemin_numpy, 'o')\n", + "# plt.show()\n", + " \n", + "# plt.plot(pareto_V_PriCore_numpy, pareto_V_SecWind_numpy)\n", + "# plt.plot(V_PriCore_numpy, V_SecWind_numpy, 'o')\n", + "# plt.show()\n", + "\n", + " #paretopoints graphing test\n", + " # pareto_bstray_test, pareto_kdiff_test = oldgraph_pareto_frontier(bstray.cpu().detach().numpy(), kdiff.cpu().detach().numpy())\n", + " # pareto_bstray_points2.append(pareto_bstray_test)\n", + " # pareto_kdiff_points2.append(pareto_kdiff_test)\n", + "\n", + " # # Store metrics for plotting or further logging\n", + " # combined_losses.append(combined_loss.cpu().data.numpy())\n", + " # kdiffs.append(torch.mean(kdiff).cpu().data.numpy())\n", + " # bstrays.append(torch.mean(bstray).cpu().data.numpy())\n", + " # numinvs.append(torch.mean(numinv).cpu().data.numpy())\n", + " # coillosses.append(torch.mean(coilloss).cpu().data.numpy())\n", + " \n", + " # kdiff_designs.append(kdiff.cpu().data.numpy())\n", + " # bstray_designs.append(bstray.cpu().data.numpy())\n", + " # numinv_designs.append(numinv.cpu().data.numpy())\n", + " # coilloss_designs.append(coilloss.cpu().data.numpy())\n", + " # V_SecCore_designs.append(V_SecCore_numpy)\n", + " # pave_designs.append(pave.cpu().data.numpy())\n", + " \n", + " \n", + " # pareto_bstray_points.append(pareto_bstray[:].cpu().detach().numpy())\n", + " # pareto_kdiff_points.append(pareto_kdiff[:].cpu().detach().numpy())\n", + " # pareto_numinv_points.append(pareto_numinv[:].cpu().detach().numpy())\n", + " # pareto_coilloss_points.append(pareto_coilloss[:].cpu().detach().numpy())\n", + " # pareto_V_SecCore_points.append(pareto_V_SecCore[:].cpu().detach().numpy())\n", + " # pareto_pave_post_points.append(pareto_pave_post[:].cpu().detach().numpy())\n", + "\n", + "\n", + " # print(f\"Epoch: {epoch:4d}\")\n", + " # # print(f\" Combined Loss: {combined_losses[-1]:.10f}\")\n", + " # print(f\" Kdiff : {100 * torch.mean(kdiff):.4f}%\")\n", + " # print(f\" Bstray : {torch.mean(bstray):.4f}\")\n", + " # print(f\" Pareto Kdiff : {100 * torch.min(pareto_kdiff):.4f}%\")\n", + " # print(f\" Pareto Bstray : {torch.min(pareto_bstray):.4f}\")\n", + " # print(f\" Coilloss : {torch.mean(coilloss):.4f}\")\n", + " # print(f\" num inv : {torch.mean(numinv):.4f}\")\n", + " # print(f\" Pareto Coilloss : {torch.min(pareto_coilloss):.4f}\")\n", + " # print(f\" Pareto num inv : {torch.min(pareto_numinv):.4f}\") \n", + " # print(f\" V_SecCore : {torch.mean(V_SecCore).cpu().data.numpy():.4f}\")\n", + " # print(f\" pave : {torch.mean(pave).cpu().data.numpy():.4f}\")\n", + " \n", + "\n", + " # Save model for further training or testing\n", + " # torch.save(gp_model, f\"./models/gp_model_{epoch:06}.pt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "id": "VOD_SGAl0VkO" + }, + "source": [ + "# Plotting" + ] + }, + { + "cell_type": "code", + "source": [ + "plt.plot([i*100 for i in range(len(cumulative_solutions))], cumulative_solutions)\n", + "plt.ylim(0,1000)\n", + "plt.xlim(0,5000)\n", + "plt.title(\"Cumulative Solution Plot \\nMean Loss\", y = 1.10)\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Total Solutions Found\")\n", + "plt.savefig(\"MeanLoss/\" + \"CumulativeSolution_MeanLoss\"+\".png\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 247 + }, + "id": "QE4TK4XtFCAZ", + "outputId": "56ce0d08-88ab-4808-fae1-fc196284cc58" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "An2Zfd5R0VkO" + }, + "outputs": [], + "source": [ + "#use inside every so many epoch printing \n", + "\n", + "\n", + "# figure, axis = plt.subplots(2, 1, figsize=(10,10))\n", + "# plt.figure(figsize=(10, 5))\n", + " \n", + "# if normalize == True:\n", + "# axis[0].plot(normalized_pareto_bstray_numpy, normalized_pareto_kdiff_numpy)\n", + "# axis[0].scatter(x_calc.cpu().data.numpy(), normalized_kdiff_numpy)\n", + "# axis[0].scatter(normalized_bstray_numpy, normalized_kdiff_numpy)\n", + "# axis[0].set_xlim([0,1])\n", + "# axis[0].set_ylim([0,1])\n", + "# axis[0].set_xlabel(\"Bstray\")\n", + "# axis[0].set_ylabel(\"Kdiff %\")\n", + "# axis[0].set_title(f\"x_calc vs. designs, epoch: {epoch}\")\n", + " \n", + "# axis[1].plot(normalized_pareto_bstray_numpy, normalized_pareto_kdiff_numpy)\n", + "# axis[1].scatter(normalized_bstray_numpy, y_calc.cpu().data.numpy())\n", + "# axis[1].scatter(normalized_bstray_numpy, normalized_kdiff_numpy)\n", + "# axis[1].set_xlim([0,1])\n", + "# axis[1].set_ylim([0,1])\n", + "# axis[1].set_xlabel(\"Bstray\")\n", + "# axis[1].set_ylabel(\"Kdiff %\")\n", + "# axis[1].set_title(f\"y_calc vs. designs, epoch: {epoch}\")\n", + "# else:\n", + "# axis[0].plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# axis[0].scatter(x_calc.cpu().data.numpy(), kdiff_numpy)\n", + "# axis[0].scatter(bstray_numpy, kdiff_numpy)\n", + "# axis[0].set_xlim([0,100])\n", + "# axis[0].set_ylim([0,1])\n", + "# axis[0].set_xlabel(\"Bstray\")\n", + "# axis[0].set_ylabel(\"Kdiff %\")\n", + "# axis[0].set_title(f\"x_calc vs. designs, epoch: {epoch}\")\n", + " \n", + "# axis[1].plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# axis[1].scatter(bstray_numpy, y_calc.cpu().data.numpy())\n", + "# axis[1].scatter(bstray_numpy, kdiff_numpy)\n", + "# axis[1].set_xlim([0,100])\n", + "# axis[1].set_ylim([0,1])\n", + "# axis[1].set_xlabel(\"Bstray\")\n", + "# axis[1].set_ylabel(\"Kdiff %\")\n", + "# axis[1].set_title(f\"y_calc vs. designs, epoch: {epoch}\")\n", + " \n", + " \n", + " # plt.xlim([0,100])\n", + " # plt.ylim([0,0.5])\n", + " # plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [], + "colab": { + "base_uri": "https://localhost:8080/", + "height": 741 + }, + "id": "R9cL2-V80VkP", + "outputId": "5696eead-bca5-4d97-9963-f534f63db744" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0\n", + "0\n" + ] + }, + { + "output_type": "error", + "ename": "IndexError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0mfinal_y\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mfinal_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moldgraph_pareto_frontier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 25\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfinal_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36moldgraph_pareto_frontier\u001b[0;34m(Xs, Ys, maxX, maxY)\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mmyList\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msorted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mXs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mYs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreverse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmaxX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m# Start the Pareto frontier with the first value in the sorted list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mp_front\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmyList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;31m# Loop through the sorted list\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpair\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmyList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mIndexError\u001b[0m: list index out of range" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ], + "source": [ + "#scatter plot of final pareto points\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff Combined Final Frontier\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.5])\n", + "\n", + "print(len(pareto_bstray_points2[:]))\n", + "print(len(pareto_kdiff_points2[:]))\n", + "# print(pareto_bstray_points2[:])\n", + "final_x = []\n", + "for list in pareto_bstray_points2:\n", + " for item in list:\n", + " final_x.append(item)\n", + "\n", + "\n", + "final_y = []\n", + "for list in pareto_kdiff_points2:\n", + " for item in list:\n", + " final_y.append(item)\n", + "\n", + "final_x, final_y = oldgraph_pareto_frontier(final_x, final_y)\n", + "print(len(final_x),len(final_y))\n", + "\n", + "plt.scatter(final_x, final_y)\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GBOUVDDg0VkQ" + }, + "outputs": [], + "source": [ + "#line plot of final pareto points\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff Combined Final Frontier\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points2[:]))\n", + "print(len(pareto_kdiff_points2[:]))\n", + "# print(pareto_bstray_points2[:])\n", + "final_x = []\n", + "for list in pareto_bstray_points2:\n", + " for item in list:\n", + " final_x.append(item)\n", + "\n", + "\n", + "final_y = []\n", + "for list in pareto_kdiff_points2:\n", + " for item in list:\n", + " final_y.append(item)\n", + "\n", + "final_x, final_y = oldgraph_pareto_frontier(final_x, final_y)\n", + "print(len(final_x),len(final_y))\n", + "\n", + "plt.plot(final_x, final_y)\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [], + "id": "JEl-nlGO0VkQ" + }, + "outputs": [], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points), len(pareto_kdiff_points))\n", + "\n", + "while i < len(pareto_bstray_points):\n", + " plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dk7Zapn60VkR" + }, + "outputs": [], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Number of Inverters vs Coilloss During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Num Inverters\")\n", + "plt.ylabel(\"Coil loss\")\n", + "plt.xlim([0,2])\n", + "plt.ylim([0, 5000])\n", + "\n", + "print(len(pareto_numinv_points), len(pareto_coilloss_points))\n", + "\n", + "while i < len(pareto_numinv_points):\n", + " plt.plot(pareto_numinv_points[i], pareto_coilloss_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "# plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xGvOxnZs0VkR" + }, + "outputs": [], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto V_SecCore vs Power Avg During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"V_SecCore\")\n", + "plt.ylabel(\"Pave\")\n", + "plt.xlim([0,3000])\n", + "plt.ylim([0, 200])\n", + "\n", + "while i < len(pareto_V_SecCore_points):\n", + " plt.plot(pareto_V_SecCore_points[i], pareto_pave_post_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "# plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "U5hmkZRF0VkS" + }, + "outputs": [], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points), len(pareto_kdiff_points))\n", + "\n", + "while i < len(pareto_bstray_points):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "U4ZPapw10VkS" + }, + "outputs": [], + "source": [ + "i = 0\n", + "print(len(bstray_designs))\n", + "#colors = numpy.arange(len(bstray_designs))\n", + "colors = numpy.arange(1000)\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Bstray vs Kdiff During Training, Generated Designs\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "while i < len(bstray_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " fig = plt.scatter(bstray_designs[i], kdiff_designs[i], label = f\"epoch: {i*100}\")\n", + " i+=1\n", + " \n", + "# plt.colorbar()\n", + "plt.legend()\n", + "# from matplotlib.patches import Rectangle\n", + "# someX, someY = 45, 0.5\n", + "# fig,ax = plt.subplots()\n", + "# currentAxis = plt.gca()\n", + "# currentAxis.add_patch(Rectangle((someX -0.1, someY-0.1), 0.2, 0.2, alpha=0.5, facecolor='red'))\n", + "#plt.savefig('Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jFHxYg9o0VkT" + }, + "outputs": [], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(bstray_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " ax.scatter(bstray_designs[i], kdiff_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,200)\n", + "plt.ylim(0, 0.2)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"Bstray\", fontsize = 18)\n", + "plt.ylabel('Coupling %', fontsize = 18)\n", + "plt.suptitle(f\"Bstray vs Kdiff During Training, \\n Generated Designs\" , fontsize = 20, y=1.01)\n", + "# plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle(( 0,0 ), 45, 0.15, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rwKnPlko0VkU" + }, + "outputs": [], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(numinv_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " ax.scatter(numinv_designs[i], coilloss_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,2)\n", + "plt.ylim(0, 10000)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"Num Inverters\", fontsize = 18)\n", + "plt.ylabel('Coil Loss', fontsize = 18)\n", + "plt.suptitle(f\"Number of Inverters vs Coil Loss During Training, \\n Generated Designs\" , fontsize = 20, y=1.01)\n", + "\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle(( 0,2 ), 1, 2000, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XRime0SS0VkV" + }, + "outputs": [], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(V_SecCore_designs):\n", + "\n", + " ax.scatter(V_SecCore_designs[i], pave_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,5000)\n", + "plt.ylim(0, 200)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"V_SecCore\", fontsize = 18)\n", + "plt.ylabel('Pave', fontsize = 18)\n", + "plt.suptitle(f\"V_SecCore vs Power Average During Training \\n Generated Designs\" , fontsize = 20, y=1.01)\n", + "\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle((0,30), 1500, 200, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yhbN9uS50VkV" + }, + "outputs": [], + "source": [ + "noise = generate_noise(shape=(10_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "pareto_bstray, pareto_kdiff = pareto_frontier(bstray, kdiff)\n", + "pareto_kdiff = pareto_kdiff * 100\n", + "\n", + "plt.plot(pareto_bstray.detach().cpu(), pareto_kdiff.detach().cpu())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "VRQU48QF0VkW" + }, + "outputs": [], + "source": [ + "noise = generate_noise(shape=(25_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "plt.scatter(bstray.detach().cpu(), kdiff.detach().cpu())\n", + "plt.title(\"Randomly generated points after GP reshaping\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Kdiff\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TytB-H_q0VkX" + }, + "outputs": [], + "source": [ + "noise = generate_noise(shape=(3, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "noise = noise.cpu().numpy()\n", + "gp = gp.cpu().detach().numpy()\n", + "\n", + "for i in range(3):\n", + " print(f\"Noise {i}\")\n", + " print(noise[i])\n", + " print()\n", + " print(f\"Generated Parameters {i}\")\n", + " print(gp[i])\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Sm14O1220VkZ" + }, + "outputs": [], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"KDiff Error During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Coupling %\")\n", + "\n", + "plt.plot(numpy.arange(len(kdiffs)), kdiffs)\n", + "plt.show()\n", + "\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"BStray Error During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"BStray\")\n", + "\n", + "plt.plot(numpy.arange(len(bstrays)), bstrays)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "i-L-Q7cm0Vkb" + }, + "outputs": [], + "source": [ + "accepted_solutions[0] = 0\n", + "max_value = max(accepted_solutions)\n", + "max_index = accepted_solutions.index(max_value)\n", + "\n", + "plt.plot([ i*10 for i in range(len(accepted_solutions))], accepted_solutions)\n", + "# plt.plot(epoch, accepted_solutions)\n", + "\n", + "# # plt.scatter(mark, marked_points, marker='o',color='y')\n", + "# plt.scatter([max_index], [max_value], marker = 'o', color = 'r')\n", + "\n", + "# plt.annotate('Max number of solutions ('+ str(max_value)+') at epoch '+str(max_index), xy=(max_index,max_value), xycoords='data',\n", + "# xytext=(-90,60), textcoords='offset points',\n", + "# arrowprops=dict(arrowstyle='fancy',fc='0.2',\n", + "# connectionstyle=\"angle3,angleA=45,angleB=90\"))\n", + "plt.xlabel(\"Number of epochs\")\n", + "plt.ylabel(\"Accepted solutions out of 1000\")\n", + "plt.title(\"Random Generation\")\n", + "plt.savefig(\"RandomGeneration/\" + \"soultions_per_epoch.png\")\n", + "plt.show()\n", + "plt.clf()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "V4utlWvi0Vkb" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From fb179326f5ef53f3562f59eb03975cd705827f90 Mon Sep 17 00:00:00 2001 From: AndrewCurtis27 <89710614+AndrewCurtis27@users.noreply.github.com> Date: Thu, 27 Oct 2022 08:52:54 -0600 Subject: [PATCH 8/8] Add files via upload Graphics: 4 plot block with all designs, 4 plot with passing colored, cumulative solutions, solutions per epoch, also can turn on various spline lines for plots --- optimization 10_27_22.ipynb | 2673 +++++++++++++++++++++++++++++++++++ 1 file changed, 2673 insertions(+) create mode 100644 optimization 10_27_22.ipynb diff --git a/optimization 10_27_22.ipynb b/optimization 10_27_22.ipynb new file mode 100644 index 0000000..e788094 --- /dev/null +++ b/optimization 10_27_22.ipynb @@ -0,0 +1,2673 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iZN2u5WKRFqR", + "outputId": "6842718b-36d3-4c1c-b523-796de23e5470" + }, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "import os\n", + "import random\n", + "import pandas\n", + "import numpy\n", + "import math\n", + "from scipy.optimize import curve_fit\n", + "from scipy.interpolate import interp1d\n", + "from scipy.interpolate import UnivariateSpline\n", + "import imageio\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "from matplotlib import style\n", + "import matplotlib.font_manager\n", + "\n", + "# seed = 7777\n", + "# random.seed(seed) \n", + "# torch.manual_seed(seed);" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# pair_no = 1\n", + "# BASE_PATH = \"drive/MyDrive/\"\n", + "# RESULT_PATH = \"optimization_result/\"+str(pair_no)+\"_SplineFit/\"\n", + "# FULL_PATH = BASE_PATH + RESULT_PATH" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# pairs = []\n", + "# file1 = open(BASE_PATH + 'dwpt_v5/pairs.txt', 'r')\n", + "# lines = file1.readlines()\n", + "# for line in lines:\n", + "# pairs.append([int(i) for i in line.strip().split(',')])\n", + "# pair = pairs[pair_no]\n", + "# print(pair)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load Saved MP (Magnetic Parameter) Model" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "B-TMPJtCKIM6" + }, + "outputs": [], + "source": [ + "# 12 input -> 42 output\n", + "class MpModel(torch.nn.Module):\n", + " def __init__(self):\n", + " super(MpModel, self).__init__()\n", + "\n", + " self.linear1 = torch.nn.Linear(12, 100, bias=True)\n", + " self.linear2 = torch.nn.Linear(100, 100, bias=True)\n", + " self.linear3 = torch.nn.Linear(100, 42, bias=True)\n", + "\n", + " def forward(self, x):\n", + " x = torch.atan(self.linear1(x))\n", + " x = torch.atan(self.linear2(x))\n", + " x = torch.atan(self.linear2(x))\n", + " x = self.linear3(x)\n", + " return x\n", + "\n", + "\n", + "mp_model = MpModel().to(\"cuda\")\n", + "mp_model.load_state_dict(torch.load(\"saved_model_state.pt\"))\n", + "\n", + "for param in mp_model.parameters():\n", + " param.requires_grad = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Create GP (Geometry Paramter) Generator" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "z9cggAl1BGHo" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\arist\\anaconda3\\lib\\site-packages\\torch\\nn\\modules\\lazy.py:178: UserWarning: Lazy modules are a new feature under heavy development so changes to the API or functionality can happen at any moment.\n", + " warnings.warn('Lazy modules are a new feature under heavy development '\n" + ] + } + ], + "source": [ + "class GpModel(torch.nn.Module):\n", + " def __init__(self, input_length: int):\n", + " super(GpModel, self).__init__()\n", + "\n", + " self.dense_layer1 = torch.nn.Linear(int(input_length), 128)\n", + " self.dense_layer2 = torch.nn.Linear(128, 256)\n", + " # self.dense_layer3 = torch.nn.Linear(256, 512)\n", + " # self.dense_layer4 = torch.nn.Linear(512, 256)\n", + " self.dense_layer5 = torch.nn.Linear(256, 128)\n", + " self.dense_layer6 = torch.nn.Linear(128, int(input_length))\n", + " \n", + " self.batch_norm1 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm2 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm3 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm4 = torch.nn.LazyBatchNorm1d()\n", + " self.batch_norm5 = torch.nn.LazyBatchNorm1d()\n", + "\n", + " def forward(self, x):\n", + " x = self.batch_norm1(torch.atan(self.dense_layer1(x)))\n", + " x = self.batch_norm2(torch.atan(self.dense_layer2(x)))\n", + " # x = self.batch_norm3(torch.atan(self.dense_layer3(x)))\n", + " # x = self.batch_norm4(torch.atan(self.dense_layer4(x)))\n", + " x = self.batch_norm5(torch.atan(self.dense_layer5(x)))\n", + " x = torch.sigmoid(self.dense_layer6(x))\n", + " return x\n", + "\n", + "\n", + "gp_model = GpModel(10).to(\"cuda\")\n", + "optimizer = torch.optim.Adam(gp_model.parameters(), lr=3e-4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# System Constraints" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "Bse1wsKN1NxY" + }, + "outputs": [], + "source": [ + "# x-direction of the primary winding (mm)\n", + "lpx_min, lpx_max = (50.0, 650.0)\n", + "\n", + "# y-direction of the primary winding (mm)\n", + "lpy_min, lpy_max = (50.0, 2050.0)\n", + "\n", + "# width of the primary winding (mm)\n", + "wp_min, wp_max = (25.0, 325.0)\n", + "\n", + "# length of the edge of the primary core (mm)\n", + "a_min, a_max = (0.0, 200.0)\n", + "\n", + "# pitch of the adjacent cores (mm)\n", + "p_min, p_max = (0.0, 200.0)\n", + "\n", + "# length of the secondary winding (mm)\n", + "ls_min, ls_max = (50.0, 450.0)\n", + "\n", + "# width of the secondary winding (mm)\n", + "ws_min, ws_max = (25.0, 225.0)\n", + "\n", + "# input RMS current (A)\n", + "ip_min, ip_max = (50.0, 200.0)\n", + "\n", + "# turn number of the primary side\n", + "np_min, np_max = (4.0, 10.0)\n", + "\n", + "# turn number of the secondary side\n", + "ns_min, ns_max = (4.0, 10.0)\n", + "\n", + "# switching frequency (kHz)\n", + "f = 85 * (10 ** 3)\n", + "\n", + "# output power at the center (kW)\n", + "p_out = 50 * (10 ** 3)\n", + "\n", + "# input DC voltage (V)\n", + "v_dc = 400\n", + "\n", + "# output DC voltage (V)\n", + "v_bat = 400\n", + "\n", + "# length of the edge of the secondary core\n", + "b = 50\n", + "\n", + "# ???\n", + "w = 2 * numpy.pi * f # [rad/s]\n", + "\n", + "# ???\n", + "q_coil = 400" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "brlwMfzj1Nxh" + }, + "outputs": [], + "source": [ + "# Creates a tensor of shape [num, *start.shape] whose values are evenly spaced from start to end, inclusive.\n", + "# Replicates but the multi-dimensional bahaviour of numpy.linspace in PyTorch.\n", + "\n", + "@torch.jit.script\n", + "def linspace(start: torch.Tensor, stop: torch.Tensor, num: int):\n", + " # create a tensor of 'num' steps from 0 to 1\n", + " steps = torch.arange(num, dtype=torch.float32, device=start.device) / (num - 1)\n", + "\n", + " # reshape the 'steps' tensor to [-1, *([1]*start.ndim)] to allow for broadcastings\n", + " # - using 'steps.reshape([-1, *([1]*start.ndim)])' would be nice here but torchscript\n", + " # \"cannot statically infer the expected size of a list in this contex\", hence the code below\n", + " for i in range(start.ndim):\n", + " steps = steps.unsqueeze(-1)\n", + "\n", + " # the output starts at 'start' and increments until 'stop' in each dimension\n", + " out = start[None] + steps * (stop - start)[None]\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "dMoXnTFV8U1P" + }, + "outputs": [], + "source": [ + "# Generate a pytorch tensor full of random floats in the range [-1, +1] with the given shape\n", + "\n", + "def generate_noise(shape):\n", + " return torch.distributions.uniform.Uniform(-1, +1).sample(shape).cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "xloGE25R8Jtm" + }, + "outputs": [], + "source": [ + "def stack_parameters(*params):\n", + " return torch.stack(params).transpose(0, 1)\n", + "\n", + "\n", + "# Scale an array to be between our specified [min, max] contraints\n", + "def scale(arr):\n", + " scaler_min = torch.tensor(\n", + " [a_min, lpx_min, lpy_min, ls_min, p_min, wp_min, ws_min, ip_min, np_min, ns_min],\n", + " device=\"cuda\",\n", + " )\n", + " scaler_max = torch.tensor(\n", + " [a_max, lpx_max, lpy_max, ls_max, p_max, wp_max, ws_max, ip_max, np_max, ns_max],\n", + " device=\"cuda\",\n", + " )\n", + "\n", + " return arr * (scaler_max - scaler_min) + scaler_min\n", + "\n", + "\n", + "def extract(arr):\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns = [\n", + " numpy.squeeze(x) for x in numpy.hsplit(arr, 10)\n", + " ]\n", + "\n", + " ys_min = (\n", + " 5 * wp +\n", + " 4 * a +\n", + " 3 * lpy +\n", + " 2 * p\n", + " )\n", + "\n", + " ys_max = (lpy + wp)\n", + " ys_max = ys_min + (ys_min - ys_max) / 2\n", + "\n", + " ys_min /= 2\n", + " ys_max /= 2\n", + "\n", + " ys = linspace(ys_min, ys_max, num=5)\n", + "\n", + " np = torch.round(np)\n", + " ns = torch.round(ns)\n", + "\n", + " # take all 15 tensors of shape (X) and convert to (15, X)\n", + " return a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys\n", + "\n", + "\n", + "def scale_and_extract(arr):\n", + " scaled_array = scale(arr)\n", + " return extract(scaled_array)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "44TlTSnclCKs", + "outputId": "c2d4a61b-35c8-42ad-99f9-98c99022a0b2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " GP Parameters Shape: torch.Size([256, 12])\n", + " Extra Parameters Shape: torch.Size([256, 3])\n", + " MP Model Output Shape: torch.Size([256, 42])\n", + " \n" + ] + } + ], + "source": [ + "# Enclose in a function so we don't leak variables\n", + "def test_models():\n", + " noise = generate_noise(shape=(256, 10))\n", + " gp = gp_model(noise)\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "\n", + " gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + " extra_parameters = stack_parameters(ip, np, ns)\n", + "\n", + " mp = mp_model(gp_parameters)\n", + "\n", + " print(f\"\"\"\n", + " GP Parameters Shape: {gp_parameters.shape}\n", + " Extra Parameters Shape: {extra_parameters.shape}\n", + " MP Model Output Shape: {mp.shape}\n", + " \"\"\")\n", + "test_models()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "eEb7J5bOFEEr" + }, + "outputs": [], + "source": [ + "\"\"\"\n", + "Method to take two equally-sized lists and return just the elements which lie \n", + "on the Pareto frontier, sorted into order.\n", + "Default behaviour is to find the maximum for both X and Y, but the option is\n", + "available to specify maxX = False or maxY = False to find the minimum for either\n", + "or both of the parameters.\n", + "\"\"\"\n", + "\n", + "\n", + "def pareto_frontier(x, y, max_x=False):\n", + " if max_x:\n", + " return _pareto_frontier(y, x)\n", + " else:\n", + " return _pareto_frontier(x, y)\n", + "\n", + "\n", + "def _pareto_frontier(x, y):\n", + " # Combine inputs and sort them with smallest X values coming first\n", + " sorted_xy = sorted(zip(x, y))\n", + " p_front_x = torch.zeros(len(x), device=\"cuda\")\n", + " p_front_y = torch.zeros(len(y), device=\"cuda\")\n", + " \n", + " # Loop through the sorted list\n", + " # Look for lower values of Y…\n", + " # and add them to the Pareto frontier\n", + " min_y = sorted_xy[0][1]\n", + " for index in range(len(sorted_xy)):\n", + " x, y = sorted_xy[index]\n", + " if y < min_y:\n", + " min_y = y\n", + " p_front_x[index] = x\n", + " p_front_y[index] = y\n", + " \n", + " p_front_x = p_front_x[p_front_x.nonzero()]\n", + " p_front_y = p_front_y[p_front_y.nonzero()]\n", + "\n", + " return p_front_x, p_front_y" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "#testing for creating total pareto f\n", + "\n", + "def oldgraph_pareto_frontier(Xs, Ys, maxX = False, maxY = False):\n", + "# Sort the list in either ascending or descending order of X\n", + " myList = sorted([[Xs[i], Ys[i]] for i in range(len(Xs))], reverse=maxX)\n", + "# Start the Pareto frontier with the first value in the sorted list\n", + " p_front = [myList[0]] \n", + "# Loop through the sorted list\n", + " for pair in myList[1:]:\n", + " if pair[1] <= p_front[-1][1]: # Look for lower values of Y…\n", + " p_front.append(pair) # … and add them to the Pareto frontier\n", + "# Turn resulting pairs back into a list of Xs and Ys\n", + " p_frontX = [pair[0] for pair in p_front]\n", + " p_frontY = [pair[1] for pair in p_front]\n", + " return p_frontX, p_frontY" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "def findSpline(x, y, smoothing_factor = 2):\n", + " spl = UnivariateSpline(x, y)\n", + " spl.set_smoothing_factor(smoothing_factor)\n", + " return spl" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def pair_loss_calc(x, y, x_numpy, y_numpy, pareto_x_numpy, pareto_y_numpy, pareto_x_forx_numpy, pareto_y_forx_numpy):\n", + " if len(pareto_y_numpy) > 3:\n", + " # f = interp1d(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0])\n", + " # f2 = interp1d(pareto_bstray_numpy[:,0], pareto_kdiff_numpy[:,0], kind = 'cubic')\n", + " # plt.plot(pareto_bstray_numpy[:,0], f2(pareto_bstray_numpy[:,0]), '--', pareto_bstray_numpy, pareto_kdiff_numpy, 'o')\n", + " \n", + " # f2_y = interp1d(pareto_x_numpy[:,0], pareto_y_numpy[:,0], fill_value=\"extrapolate\")\n", + " # y_calc = torch.as_tensor(f2_y(x_numpy)).cuda()\n", + "\n", + " spl_y = findSpline(pareto_x_numpy[:,0], pareto_y_numpy[:,0])\n", + " y_calc = torch.as_tensor(spl_y(x_numpy)).cuda()\n", + " # plt.plot(pareto_bstray_numpy[:,0], spl_y(pareto_bstray_numpy[:,0]), '--', pareto_bstray_fory_numpy, pareto_kdiff_fory_numpy, 'o')\n", + " # plt.show()\n", + " y_loss = y - y_calc\n", + " y_loss_relu = torch.nn.functional.relu(y_loss)\n", + "\n", + " # f2_x = interp1d(pareto_y_forx_numpy[:,0], pareto_x_forx_numpy[:,0], fill_value=\"extrapolate\")\n", + " # x_calc = torch.as_tensor(f2_x(y_numpy)).cuda()\n", + " \n", + " spl_x = findSpline(pareto_y_forx_numpy[:,0], pareto_x_forx_numpy[:,0])\n", + " # plt.plot(spl_x(pareto_kdiff_fory_numpy[:,0]), pareto_kdiff_fory_numpy[:,0], '--', pareto_bstray_fory_numpy, pareto_kdiff_fory_numpy, 'o')\n", + " # plt.show()\n", + " x_calc = torch.as_tensor(spl_x(y_numpy)).cuda()\n", + " x_loss = x - x_calc\n", + " x_loss_relu = torch.nn.functional.relu(x_loss)\n", + " \n", + " \n", + "\n", + " if len(pareto_x_numpy) > 3 and len(pareto_y_numpy) > 3:\n", + " combined_loss = torch.sum(y_loss_relu * x_loss_relu)\n", + " if epoch % 10 == 0: \n", + " plt.style.use(['science','no-latex'])\n", + " font = {'family' : 'normal',\n", + " 'weight' : 'bold',\n", + " 'size' : 15}\n", + "\n", + " matplotlib.rc('font', **font)\n", + " \n", + " plt.figure(figsize=(6, 6))\n", + " plt.xlabel(\"Bstray\", size = 'medium')\n", + " plt.ylabel(\"Coupling %\", size = 'medium')\n", + " plt.title(\"Epoch \"+str(epoch).zfill(3) + \"\\nCalculated and Actual points \\nFrom Spline Fit\", y = 1.0, size = 'large')\n", + " plt.xlim([0,100])\n", + " plt.ylim([0,40])\n", + " \n", + " \n", + " \n", + " # plt.plot(pareto_x_numpy[:,0], 100 * spl_y(pareto_x_numpy[:,0]), '-', c='r',lw=3)\n", + " # plt.plot(pareto_x_numpy[:,0], 100 * f2_y(pareto_x_numpy[:,0]), '-', c='r',lw=3)\n", + " # i*100 for i in\n", + " # plt.plot(pareto_x_numpy,100 * pareto_y_numpy, '--', c='m', lw=3)\n", + " # plt.plot(x_numpy, 100* y_numpy, 'o', c='b', ms=2)\n", + " #plt.plot(100*y_calc.cpu().data.numpy(), x_numpy, '-', lw=3, c='c')\n", + " # plt.plot(x_numpy, 100*y_calc.cpu().data.numpy(), 'o', lw=3, c='c')\n", + " # plt.plot(x_calc.cpu().data.numpy(), 100*y_numpy, 'o', lw=3, c='r')\n", + " # plt.plot(x_calc.cpu().data.numpy(), 100*y_calc.cpu().data.numpy(), 'o', ms=2, c='c')\n", + " \n", + " # plt.savefig(\"SplineVisuals/Tests/\" + str(epoch).zfill(3)+\".png\")\n", + " # plt.show()\n", + " # plt.clf()\n", + " else: \n", + " combined_loss = torch.zeros(1, requires_grad=True, device = \"cuda\")\n", + " # print('No new pareto points found, used zero grad')\n", + "\n", + " \n", + " \n", + " return combined_loss" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def compute_pairloss_and_pareto(x, y):\n", + " \n", + " pareto_x, pareto_y = pareto_frontier(x, y)\n", + " pareto_y_forx, pareto_x_forx = pareto_frontier(y, x)\n", + " \n", + " pareto_x_numpy = pareto_x.cpu().data.numpy()\n", + " pareto_y_numpy = pareto_y.cpu().data.numpy()\n", + " \n", + " pareto_x_forx_numpy = pareto_x_forx.cpu().data.numpy()\n", + " pareto_y_forx_numpy = pareto_y_forx.cpu().data.numpy()\n", + " \n", + " x_numpy = x.cpu().data.numpy()\n", + " y_numpy = y.cpu().data.numpy()\n", + " \n", + " x_y_loss = pair_loss_calc(x, y, x_numpy, y_numpy, pareto_x_numpy, pareto_y_numpy, pareto_x_forx_numpy, pareto_y_forx_numpy)\n", + " \n", + " return x_y_loss, pareto_x, pareto_y, x_numpy, y_numpy, pareto_x_numpy, pareto_y_numpy, pareto_x_forx_numpy, pareto_y_forx_numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "def pave_tomax(pavemin, pave_max_value=10000):\n", + " pave_max = torch.sub(pave_max_value, pavemin)\n", + " return pave_max" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load DWPT data" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "jkTrlAJMH5UO" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1010, 12)\n", + "(1010, 42)\n" + ] + } + ], + "source": [ + "def load_dwpt():\n", + " folder_name = \"./dwpt_v5\"\n", + " file_names = [\n", + " \"DWPT_v5_N10\",\n", + " \"DWPT_v5_N100\",\n", + " \"DWPT_v5_N200\",\n", + " \"DWPT_v5_N300\",\n", + " \"DWPT_v5_N400\",\n", + " ]\n", + "\n", + " df = pandas.DataFrame()\n", + "\n", + " for file_name in file_names:\n", + " new_df = pandas.read_csv(f\"{folder_name}/{file_name}_after.csv\", index_col=0)\n", + " df = pandas.concat([df, new_df])\n", + "\n", + " return df\n", + "\n", + "df = load_dwpt()\n", + "df_gp = df.loc[:, :\"ys4[mm]\"]\n", + "df_mp = df.loc[:, \"k0mm_ys0\":]\n", + "\n", + "print(df_gp.shape)\n", + "print(df_mp.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define Loss Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "f = 85*10**3 #[Hz]\n", + "w =2*math.pi*f #[rad/s]\n", + "Pout= 50000 #W\n", + "Vdc = 400 #800 # V\n", + "Vbat = 400 #V\n", + "QCoil = 400\n", + "b = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "def kdiff_loss(k_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + "\n", + " kdiff = abs(k_0_0 - k_1_0)\n", + " kdiff = kdiff / torch.max(abs(k_0_0), abs(k_1_0))\n", + "\n", + " return kdiff\n", + "\n", + "def calculate_Is(l_parameters, k_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + "\n", + " a6 = lp_0_0 * np ** 2\n", + " a11 = ls_0_0 * ns ** 2\n", + " # Paper formula\n", + " n1 = (torch.pi * w * a6 * ip) / (2*numpy.sqrt(2) * v_dc)\n", + " t1 = (torch.pi ** 2 * w * p_out * torch.sqrt(a6 * a11))\n", + " t2 = 8 * k_0_0 * n1 * v_dc * v_bat\n", + " n2 = t1 / t2\n", + " Is = (4 * v_bat * n2) / (torch.pi * w * a11)\n", + "\n", + " return Is\n", + " \n", + "def bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters):\n", + " # Unpack parameters\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " Is = calculate_Is(l_parameters,k_parameters)\n", + "\n", + " bx_100 = (\n", + " (bx_p_1_00 * ip * np + bx_s_1_00 * Is * ns) ** 2 +\n", + " (bx_p_1_90 * ip * np + bx_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " by_100 = (\n", + " (by_p_1_00 * ip * np + by_s_1_00 * Is * ns) ** 2 +\n", + " (by_p_1_90 * ip * np + by_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " bz_100 = (\n", + " (bz_p_1_00 * ip * np + bz_s_1_00 * Is * ns) ** 2 +\n", + " (bz_p_1_90 * ip * np + bz_s_1_90 * Is * ns) ** 2\n", + " ) ** 0.5\n", + " b_100 = (bx_100**2 + by_100**2 + bz_100**2) ** 0.5\n", + "\n", + " bstray = b_100\n", + "\n", + " return bstray" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# import seaborn as sns\n", + "# plt.style.use(['science','no-latex'])\n", + "\n", + "# fig_width = 8 #cm # Setting for Conference paper \n", + "# fig_height = 3 #cm\n", + "# font_size = 7 # pt\n", + "# fig_update = True\n", + "# marker_size = 5\n", + "# x_tick_pad = 2\n", + "# y_tick_pad = 2\n", + "# x_label_pad = 0.5\n", + "# y_label_pad = 1\n", + "\n", + "# def plot_(**kwargs):\n", + "# import numpy as np\n", + "# plt.close('all')\n", + "# kdiff = kwargs[\"kdiff\"] * 100\n", + "# pave = kwargs[\"pave\"] \n", + "# coilloss = kwargs[\"coilloss\"]\n", + "# bstray = kwargs[\"bstray\"]\n", + "# vpricore = kwargs[\"vpricore\"]\n", + "# vsecwind = kwargs[\"vsecwind\"]\n", + "# n_inv = kwargs[\"n_inv\"]\n", + "# vseccore = kwargs[\"vseccore\"]\n", + "# epoch = kwargs[\"epoch\"]\n", + "\n", + "# marker_size = 60\n", + "\n", + "\n", + "# font = {'family' : 'normal',\n", + "# 'weight' : 'bold',\n", + "# 'size' : 12}\n", + "\n", + "# plt.rc('font', **font)\n", + "\n", + "# #plt.style.use(['science','no-latex'])\n", + "\n", + "# fig, (ax1, ax2, ax3,ax4) = plt.subplots(1, 4)\n", + "\n", + "# #fig.subplots_adjust(hspace=10)\n", + "\n", + "# fig.set_size_inches(16,4)\n", + "# #ax1.set_aspect('equal')\n", + "# #fig.tight_layout(pad=6)\n", + "# fig.tight_layout(pad = 2)\n", + "\n", + "# xlim = 1 #Number of Inverter [1/m]\n", + "# ylim = 2000 #Coil loss [W] \n", + "# ax1.set_xlabel(r\"$Number\\ of\\ inverter\\ [1/m]$\", labelpad = x_label_pad) # not shown\n", + "# ax1.set_ylabel(r'$Coil loss [W]$', labelpad = y_label_pad) # not shown\n", + "# x1 = np.arange(0, xlim + 0.1,0.1)\n", + "# y1 = 0\n", + "# y2 = ylim\n", + "# plt_ax1 = ax1.scatter(n_inv, coilloss, rasterized=True)\n", + "# #fig.colorbar(mappable = plt_ax1 , ax = ax1, label=r\"$B_{\\rm stray}~[\\rm \\mu T]$\")\n", + "# ax1.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + "# ax1.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + "# ax1.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + "# ax1.axis([0,2, 0,5000])\n", + "# ax1.tick_params(axis='x', pad=15)\n", + "# # ax1.set_aspect('equal')\n", + "\n", + "\n", + "\n", + "# xlim = 40\n", + "# ylim = 15\n", + "# ax2.set_xlabel(r\"$B_{\\rm stray}~[\\rm \\mu T]$\", labelpad = x_label_pad) # not shown\n", + "# ax2.set_ylabel(r'$Coupling [\\%]$', labelpad = y_label_pad) # not shown\n", + "\n", + "# x1 = np.arange(0, xlim + 0.1, 0.1)\n", + "# y1 = 0\n", + "# y2 = ylim\n", + "\n", + "# plt_ax2 = ax2.scatter(bstray, kdiff,rasterized=True)\n", + "# #fig.colorbar(mappable = plt_ax2 , ax = ax2, label=r\"$V_{PriCore}$\")\n", + "# ax2.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + "# ax2.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + "# ax2.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + "# ax2.axis([0, 100, 0, 50])\n", + "# ax2.tick_params(axis='x', pad=15)\n", + "\n", + "# # ax2.set_aspect('equal')\n", + "\n", + "# xlim = 3500\n", + "# ylim = 30\n", + "\n", + "# ax3.set_xlabel(r\"$V_{SecCore}$\", labelpad = x_label_pad) # not shown\n", + "# ax3.set_ylabel(r'$P_{ave} [KW/M]$', labelpad = y_label_pad) # not shown\n", + "\n", + "# x1 = np.arange(0, xlim + 0.1, 0.1)\n", + "# y1 = ylim\n", + "# y2 = ylim*10\n", + "\n", + "# plt_ax3 = ax3.scatter(vseccore, pave, rasterized=True)\n", + "# #fig.colorbar(mappable = plt_ax3 , ax = ax3, label=r\"$V_{SecWind}$\")\n", + "# ax3.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + "# ax3.plot([xlim, xlim], [ylim, ylim*10], 'r--', lw=0.5) \n", + "# ax3.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + "# ax3.axis([0, 5000, 0, 200])\n", + "# ax3.tick_params(axis='x', pad=15)\n", + "# # ax3.set_aspect('equal')\n", + "\n", + "\n", + "# xlim = 3500\n", + "# ylim = 6000\n", + "\n", + "# ax4.set_xlabel(r\"$V_{SecCore}$\", labelpad = x_label_pad) # not shown\n", + "# ax4.set_ylabel(r'$V_{PriCore} $', labelpad = y_label_pad) # not shown\n", + "\n", + "# x1 = np.arange(0, xlim + 0.1, 0.1)\n", + "# y1 = 0\n", + "# y2 = ylim\n", + "\n", + "# ax4.scatter(vseccore, vpricore, s=marker_size, cmap ='viridis',rasterized=True)\n", + "# #fig.colorbar(mappable = plt_ax3 , ax = ax3, label=r\"$V_{SecWind}$\")\n", + "# ax4.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + "# ax4.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + "# ax4.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + "# ax4.axis([0, 5000, 0, 7000])\n", + "# ax4.tick_params(axis='x', pad=15)\n", + "\n", + "# plt.suptitle(\"Epoch \"+str(epoch), y = 1.05, c='r')\n", + "# # plt.savefig(\"images/\"+ str(epoch) +\".png\")\n", + "# plt.show()\n", + "# plt.clf()\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib\n", + "import seaborn as sns\n", + "\n", + "fig_width = 8 #cm # Setting for Conference paper \n", + "fig_height = 3 #cm\n", + "# font_size = 7 # pt\n", + "fig_update = True\n", + "marker_size = 5\n", + "x_tick_pad = 2\n", + "y_tick_pad = 2\n", + "x_label_pad = 0.5\n", + "y_label_pad = 1\n", + "\n", + "plt.style.use(['science','no-latex'])\n", + "def plot_(**kwargs):\n", + " import numpy as np\n", + " plt.close('all')\n", + " kdiff = kwargs[\"kdiff\"] * 100\n", + " pave = kwargs[\"pave\"] \n", + " coilloss = kwargs[\"coilloss\"]\n", + " bstray = kwargs[\"bstray\"]\n", + " vpricore = kwargs[\"vpricore\"]\n", + " vsecwind = kwargs[\"vsecwind\"]\n", + " n_inv = kwargs[\"n_inv\"]\n", + " vseccore = kwargs[\"vseccore\"]\n", + " epoch = kwargs[\"epoch\"]\n", + "\n", + " marker_size = 1\n", + "\n", + " plt.style.use(['science','no-latex'])\n", + " font = {'family' : 'normal',\n", + " 'weight' : 'bold',\n", + " 'size' : 15}\n", + "\n", + " matplotlib.rc('font', **font)\n", + "\n", + " #plt.style.use(['science','no-latex'])\n", + "\n", + " fig, (ax1, ax2, ax3,ax4) = plt.subplots(1, 4)\n", + "\n", + " #fig.subplots_adjust(hspace=10)\n", + "\n", + " fig.set_size_inches(16,4)\n", + " #ax1.set_aspect('equal')\n", + " #fig.tight_layout(pad=6)\n", + " fig.tight_layout(pad = 2)\n", + "\n", + " xlim = 1 #Number of Inverter [1/m]\n", + " ylim = 2000 #Coil loss [W] \n", + " ax1.set_xlabel(r\"$Number\\ of\\ inverter\\ [1/m]$\", labelpad = x_label_pad) # not shown\n", + " ax1.set_ylabel(r'$Coil loss [W]$', labelpad = y_label_pad) # not shown\n", + " x1 = np.arange(0, xlim + 0.1,0.1)\n", + " y1 = 0\n", + " y2 = ylim\n", + " plt_ax1 = ax1.scatter(n_inv, coilloss, s=marker_size, rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax1 , ax = ax1, label=r\"$B_{\\rm stray}~[\\rm \\mu T]$\")\n", + " ax1.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax1.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + " ax1.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax1.axis([0,2, 0,5000])\n", + " ax1.tick_params(axis='x', pad=15)\n", + " # ax1.set_aspect('equal')\n", + "\n", + "\n", + "\n", + " xlim = 45\n", + " ylim = 15\n", + " ax2.set_xlabel(r\"$B_{\\rm stray}~[\\rm \\mu T]$\", labelpad = x_label_pad) # not shown\n", + " ax2.set_ylabel(r'$Coupling [\\%]$', labelpad = y_label_pad) # not shown\n", + "\n", + " x1 = np.arange(0, xlim + 0.1, 0.1)\n", + " y1 = 0\n", + " y2 = ylim\n", + "\n", + " plt_ax2 = ax2.scatter(bstray, kdiff,s=marker_size,rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax2 , ax = ax2, label=r\"$V_{PriCore}$\")\n", + " ax2.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax2.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + " ax2.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax2.axis([0, 100, 0, 40])\n", + " ax2.tick_params(axis='x', pad=15)\n", + "\n", + " # ax2.set_aspect('equal')\n", + "\n", + " xlim = 3500\n", + " ylim = 30\n", + "\n", + " ax3.set_xlabel(r\"$V_{SecCore}$\", labelpad = x_label_pad) # not shown\n", + " ax3.set_ylabel(r'$P_{ave} [KW/M]$', labelpad = y_label_pad) # not shown\n", + "\n", + " x1 = np.arange(0, xlim + 0.1, 0.1)\n", + " y1 = ylim\n", + " y2 = ylim*10\n", + "\n", + " ax3.scatter(vseccore, pave,s=marker_size, rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax3 , ax = ax3, label=r\"$V_{SecWind}$\")\n", + " ax3.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax3.plot([xlim, xlim], [ylim, ylim*10], 'r--', lw=0.5) \n", + " ax3.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax3.axis([0, 5000, 0, 80])\n", + " ax3.tick_params(axis='x', pad=15)\n", + " # ax3.set_aspect('equal')\n", + "\n", + "\n", + " xlim = 750\n", + " ylim = 6000\n", + "\n", + " ax4.set_xlabel(r\"$V_{SecWind}$\", labelpad = x_label_pad) # not shown\n", + " ax4.set_ylabel(r'$V_{PriCore} $', labelpad = y_label_pad) # not shown\n", + "\n", + " x1 = np.arange(0, xlim + 0.1, 0.1)\n", + " y1 = 0\n", + " y2 = ylim\n", + "\n", + " ax4.scatter(vsecwind, vpricore, s=marker_size, rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax3 , ax = ax3, label=r\"$V_{SecWind}$\")\n", + " ax4.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax4.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + " ax4.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax4.axis([0, 2000, 0, 8000])\n", + " ax4.tick_params(axis='x', pad=15)\n", + "\n", + " plt.suptitle(\"Epoch \"+str(epoch).zfill(5) + \"\\nFound \"+ str(accepted_solution_so_far)+ \" solutions out of 1000 input \\nRandom Generation\", y = 1.12)\n", + " plt.savefig(\"RandomGen_400ep/all/\" + str(epoch).zfill(5)+\".png\")\n", + " plt.show()\n", + " plt.clf()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def generate_solution_plot(**kwargs):\n", + " kdiff = kwargs[\"kdiff\"] * 100\n", + " pave = kwargs[\"pave\"]\n", + " coilloss = kwargs[\"coilloss\"]\n", + " bstray = kwargs[\"bstray\"]\n", + " vpricore = kwargs[\"vpricore\"]\n", + " vsecwind = kwargs[\"vsecwind\"]\n", + " n_inv = kwargs[\"n_inv\"]\n", + " vseccore = kwargs[\"vseccore\"]\n", + "\n", + " accepted_n_inv_ = [] \n", + " accepted_coilloss = [] \n", + " accepted_bstray = []\n", + " accepted_kdiff = []\n", + " accepted_v_sec_core = []\n", + " accpeted_pave = []\n", + " accepted_v_sec_wind = []\n", + " accpeted_v_pri_core = []\n", + "\n", + " for i in range(len(kdiff)):\n", + " if n_inv[i] < 1 and coilloss[i] < 2000 and bstray[i] < 45 and \\\n", + " kdiff[i] < 15 and vseccore[i] < 1500 and pave[i] > 30 and vsecwind[i] < 750 and vpricore[i] < 6000:\n", + " accepted_n_inv_.append(n_inv[i])\n", + " accepted_coilloss.append(coilloss[i])\n", + " accepted_bstray.append(bstray[i])\n", + " accepted_kdiff.append(kdiff[i])\n", + " accepted_v_sec_core.append(vseccore[i])\n", + " accpeted_pave.append(pave[i])\n", + " accepted_v_sec_wind.append(vsecwind[i])\n", + " accpeted_v_pri_core.append(vpricore[i])\n", + "\n", + "\n", + " import matplotlib\n", + " import seaborn as sns\n", + " import numpy as np\n", + "\n", + " plt.subplots_adjust(left=.1,\n", + " bottom=0.1,\n", + " right=0.9,\n", + " top=2,\n", + " wspace=2,\n", + " hspace=0.4)\n", + "\n", + " font = {'family' : 'normal',\n", + " 'weight' : 'bold',\n", + " 'size' : 15}\n", + "\n", + " matplotlib.rc('font', **font)\n", + " color_list = sns.color_palette(\"Paired\", n_colors=len(accpeted_v_pri_core))\n", + "\n", + " plt.style.use(['science','no-latex'])\n", + " #matplotlib.rcParams.update({'font.size': font_size, 'font.family': 'STIXGeneral', 'mathtext.fontset': 'stix'})\n", + "\n", + "\n", + " #fig1=plt.figure(figsize=(cm2inch(fig_width/2),cm2inch(fig_height)/1.2), dpi=400)\n", + "\n", + " marker_size = 50\n", + "\n", + " #plt.style.use(['science','no-latex'])\n", + "\n", + " fig, (ax1, ax2, ax3,ax4) = plt.subplots(1, 4)\n", + "\n", + " #fig.subplots_adjust(hspace=10)\n", + "\n", + " fig.set_size_inches(16,4)\n", + " #ax1.set_aspect('equal')\n", + " #fig.tight_layout(pad=6)\n", + " fig.tight_layout(pad = 2)\n", + "\n", + " xlim = 1 #Number of Inverter [1/m]\n", + " ylim = 2000 #Coil loss [W] \n", + " ax1.set_xlabel(r\"$Number\\ of\\ inverter\\ [1/m]$\", labelpad = x_label_pad) # not shown\n", + " ax1.set_ylabel(r'$Coil loss [W]$', labelpad = y_label_pad) # not shown\n", + " x1 = np.arange(0, xlim + 0.1,0.1)\n", + " y1 = 0\n", + " y2 = ylim\n", + " plt_ax1 = ax1.scatter(accepted_n_inv_, accepted_coilloss, s=marker_size, c= color_list, rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax1 , ax = ax1, label=r\"$B_{\\rm stray}~[\\rm \\mu T]$\")\n", + " ax1.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax1.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + " ax1.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax1.axis([0,2, 0,5000])\n", + " ax1.tick_params(axis='x', pad=15)\n", + " # ax1.set_aspect('equal')\n", + "\n", + "\n", + "\n", + " xlim = 45\n", + " ylim = 15\n", + " ax2.set_xlabel(r\"$B_{\\rm stray}~[\\rm \\mu T]$\", labelpad = x_label_pad) # not shown\n", + " ax2.set_ylabel(r'$Coupling [\\%]$', labelpad = y_label_pad) # not shown\n", + "\n", + " x1 = np.arange(0, xlim + 0.1, 0.1)\n", + " y1 = 0\n", + " y2 = ylim\n", + "\n", + " plt_ax2 = ax2.scatter(accepted_bstray, accepted_kdiff,s=marker_size, c= color_list, rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax2 , ax = ax2, label=r\"$V_{PriCore}$\")\n", + " ax2.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax2.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + " ax2.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax2.axis([0, 100, 0, 40])\n", + " ax2.tick_params(axis='x', pad=15)\n", + "\n", + " # ax2.set_aspect('equal')\n", + "\n", + " xlim = 3500\n", + " ylim = 30\n", + "\n", + " ax3.set_xlabel(r\"$V_{SecCore}$\", labelpad = x_label_pad) # not shown\n", + " ax3.set_ylabel(r'$P_{ave} [KW/M]$', labelpad = y_label_pad) # not shown\n", + "\n", + " x1 = np.arange(0, xlim + 0.1, 0.1)\n", + " y1 = ylim\n", + " y2 = ylim*10\n", + "\n", + " ax3.scatter(accepted_v_sec_core, accpeted_pave,s=marker_size, c= color_list, rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax3 , ax = ax3, label=r\"$V_{SecWind}$\")\n", + " ax3.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax3.plot([xlim, xlim], [ylim, ylim*10], 'r--', lw=0.5) \n", + " ax3.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax3.axis([0, 5000, 0, 80])\n", + " ax3.tick_params(axis='x', pad=15)\n", + " # ax3.set_aspect('equal')\n", + "\n", + "\n", + " xlim = 750\n", + " ylim = 6000\n", + "\n", + " ax4.set_xlabel(r\"$V_{SecWind}$\", labelpad = x_label_pad) # not shown\n", + " ax4.set_ylabel(r'$V_{PriCore} $', labelpad = y_label_pad) # not shown\n", + "\n", + " x1 = np.arange(0, xlim + 0.1, 0.1)\n", + " y1 = 0\n", + " y2 = ylim\n", + "\n", + " ax4.scatter(accepted_v_sec_wind, accpeted_v_pri_core, s=marker_size, c= color_list, rasterized=True)\n", + " #fig.colorbar(mappable = plt_ax3 , ax = ax3, label=r\"$V_{SecWind}$\")\n", + " ax4.fill_between(x1, y1, y2 ,facecolor='r',alpha=0.3)\n", + " ax4.plot([xlim, xlim], [0, ylim], 'r--', lw=0.5) \n", + " ax4.plot([0,xlim], [ylim, ylim], 'r--', lw=0.5) \n", + " ax4.axis([0, 2000, 0, 8000])\n", + " ax4.tick_params(axis='x', pad=15)\n", + "\n", + " plt.suptitle(\"Epoch \" +str(epoch).zfill(5) + \"\\nFound \"+ str(len(accepted_bstray))+ \" solutions out of 1000 input \\nRandom Generation\", y = 1.12)\n", + " plt.savefig(\"RandomGen_400ep/accepted/\"+str(epoch).zfill(5) + \".png\")\n", + " plt.show()\n", + " plt.clf()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "def accepted_solution(**kwargs):\n", + "\n", + " kdiff = kwargs[\"kdiff\"] * 100\n", + " pave = kwargs[\"pave\"]\n", + " coilloss = kwargs[\"coilloss\"]\n", + " bstray = kwargs[\"bstray\"]\n", + " vpricore = kwargs[\"vpricore\"]\n", + " vsecwind = kwargs[\"vsecwind\"]\n", + " n_inv = kwargs[\"n_inv\"]\n", + " vseccore = kwargs[\"vseccore\"]\n", + "\n", + " count = 0\n", + " for i in range(len(kdiff)):\n", + " if n_inv[i] < 1 and coilloss[i] < 2000 and bstray[i] < 45 and \\\n", + " kdiff[i] < 15 and vseccore[i] < 1500 and pave[i] > 30 and vpricore[i] < 6000 and vsecwind[i] < 750:\n", + " \n", + " count += 1\n", + "\n", + " return count" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def number_of_inverters(gp_parameters):\n", + " (\n", + " a, lpx, lpy, ls, p, wp, ws, *ys\n", + " ) = gp_parameters.clone().transpose(0,1)\n", + " \n", + " number_of_inverters = 1/(lpy+2*wp+2*a+p)*10**3\n", + "\n", + " return number_of_inverters" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def core_losses(extra_parameters, gp_parameters):\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + " (\n", + " a, lpx, lpy, ls, p, wp, ws, *ys\n", + " ) = gp_parameters.transpose(0,1)\n", + " \n", + " V_PriCore = ((lpy +2*wp+2*a)*(lpx+2*wp+2*a)*5)/(10**3) # cm3\n", + " V_SecCore = (((ls+2*ws+2*b)**2)*5)/(10**3)\n", + " V_PriWind = (2*(lpx+wp)+2*(lpy+wp))*6.6*6.6/(10**3)*np\n", + " V_SecWind = 4*(ls+ws)*6.6*6.6/(10**3)*ns\n", + " \n", + " V_PriCore_ave = V_PriCore/(lpy+2*wp+2*a+p)*10**3\n", + " # V_PriWind_ave = V_PriWind/(lpy+2*wp+2*a+p)*10**3\n", + "\n", + " return V_PriCore, V_SecCore, V_PriWind, V_SecWind, V_PriCore_ave" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_coilloss_pave(k_parameters, l_parameters, b_parameters, extra_parameters):\n", + " (\n", + " k_0_0, k_0_1, k_0_2, k_0_3, k_0_4, k_1_0,\n", + " ) = k_parameters\n", + " (\n", + " lp_0_0, lp_0_1, lp_0_2, lp_0_3, lp_0_4,\n", + " ls_0_0, ls_0_1, ls_0_2, ls_0_3, ls_0_4,\n", + " lp_1_0, ls_1_0,\n", + " ) = l_parameters\n", + " (\n", + " bx_p_0_00, by_p_0_00, bz_p_0_00,\n", + " bx_p_0_90, by_p_0_90, bz_p_0_90,\n", + " bx_s_0_00, by_s_0_00, bz_s_0_00,\n", + " bx_s_0_90, by_s_0_90, bz_s_0_90,\n", + "\n", + " bx_p_1_00, by_p_1_00, bz_p_1_00,\n", + " bx_p_1_90, by_p_1_90, bz_p_1_90,\n", + " bx_s_1_00, by_s_1_00, bz_s_1_00,\n", + " bx_s_1_90, by_s_1_90, bz_s_1_90,\n", + " ) = b_parameters\n", + " (\n", + " ip, np, ns\n", + " ) = extra_parameters\n", + "\n", + "\n", + " a6 = lp_0_0 * np**2 # LP0MM_YSO\n", + " a7 = lp_0_1 * np**2 # LP0MM_YS1\n", + " a8 = lp_0_2 * np**2 # LP0MM_YS2\n", + " a9 = lp_0_3 * np**2 # LP0MM_YS3\n", + " a10 = lp_0_4 * np**2 # LP0MM_YS4\n", + "\n", + " a11 = ls_0_0 * ns**2 # LS0MM_YSO\n", + " a12 = ls_0_1 * ns**2 # LS0MM_YS1\n", + " a13 = ls_0_2 * ns**2 # LS0MM_YS2\n", + " a14 = ls_0_3 * ns**2 # LS0MM_YS3\n", + " a15 = ls_0_4 * ns**2 # LS0MM_YS4\n", + "\n", + " Is = calculate_Is(l_parameters,k_parameters)\n", + " \n", + " loss = (w*a6*10**(-9)*ip**2/QCoil + w*a11*10**(-9)*Is**2/QCoil) \n", + "\n", + " p0 = w*k_0_0*(a6 * a11)**0.5*ip*Is\n", + " p1 = w*k_0_1*(a7 * a12)**0.5*ip*Is\n", + " p2 = w*k_0_2*(a8 * a13)**0.5*ip*Is\n", + " p3 = w*k_0_3*(a9 * a14)**0.5*ip*Is\n", + " p4 = 2*w*k_0_4*(a10 * a15)**0.5*ip*Is\n", + " pave = (p0+(2*p1)+(2*p2)+(2*p3)+ p4)/8\n", + " pave_kW = pave / 1000\n", + "\n", + " return loss,pave_kW" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Neural Network Training Loop" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "OXzpQf5O1Nxk", + "outputId": "1bc1a1b1-95ac-43d8-f1d8-5dd49e4bbb03" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Accepted Soluntions : 1\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n", + "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0\n", + " Kdiff : 24.1659%\n", + " Bstray : 51.6425\n", + "Accepted Soluntions : 4\n", + "Epoch: 10\n", + " Kdiff : 22.9311%\n", + " Bstray : 52.1386\n", + "Accepted Soluntions : 3\n", + "Epoch: 20\n", + " Kdiff : 25.0798%\n", + " Bstray : 51.4415\n", + "Accepted Soluntions : 6\n", + "Epoch: 30\n", + " Kdiff : 23.7308%\n", + " Bstray : 51.7838\n", + "Accepted Soluntions : 2\n", + "Epoch: 40\n", + " Kdiff : 24.8478%\n", + " Bstray : 51.6758\n", + "Accepted Soluntions : 7\n" + ] + }, + { + "data": { + "image/png": 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iktTBsSiKoihKRKhwoyiKovQ2pI3tw1XVaSlduCHMdgnxOlT/bR2jw/VYi9ZIo22sARjzA6wok9uxvEy+Arxne6J0dEzuaVrY19HU63aNzRvpFK4/Y8w7WGXVr8byRZqB5VuzRUTSOnFcXfn+QMd/f51JIcHpTV4yCU6LKvRsD9WWEO0j7bs9hPubgebvcbjfaaTtQG0JFEVRlE5AhRtFURSlt+GYrJ4ZYt8Z9rP3rnopEGqB3tHoBGcck0PsC7UtEm7CMst9qq0HGmP2G2N+Y4z5HJZPyFrgu570qX1AhogMCnH4GUAZlmFqOErt50jfy0jLUEPT7yvU73QS1veVdhs+G2MqjDF/N8b8lzHmTCxz4clY73dL7LPPHer36fytHbCfu/L9ccYCkf/ddyXvY6WfnebdaP+cQbDH0/v289wQ/czBeh8+aGffXc1gX8Urh0nAEWNMpf1zS7/7UBFZbf3dK4qiKKc4KtwoiqIovY1NQD7wJe+iSqzSzndgLYpe9LTPBSaJSKanbTzW4r0j/NM+17fEU65aRGYCF7W1M7uq0nTgJWPMkTYclyq+stbGmBqa0nsG288rsP7vB5nyisinsLxR/mmMaWzhVAeAenzXJiLnYC3A/VRgLXxbjRSxr/dt4HIRmeLpW7Cq+YAVLdNmQlQnA+tvCEIvtL2ssJ+/770Oe4yfAdYZY5xqQF32/tg4f293eH/fIjISy3j6INCeClftwal2drtvu/PzX5wNxpi9WGLLNSLiGBVjv74GeMOXjhRx392E//PyWSwz8BWezbn2s/93/wWazJm9VND6356iKIqiuGj4pqIoitKrMMY0iMh/YS3k3xeR32GVA78Wa4H8gK+i1P9ilQp/3a4g0x+relKHvDKMMbvtSlb/BbwhIn/HKgf+X8BW2m4U60R//KGNxy0Efmeffw/WovAsrHSpd40xe+x2TwJfBL4nItlYETnjscobF2N5wYTFGFMhIk8CXxGRvwJrsNKzvgR8iCU6edkAfBr4XxF5GytF5Y0WRKnbsMqBv2W/r4ft4y8BloeoKBUpu0RkA/AuVhrOSKwS2rXA31o60Bjzmog8i/X3M1hEXqapHHgNVlUxp22Xvj/GmD0i8lOscuBr7cpWTjnwZOC6FlLHWkVEpmGJUWBVHwO4wWPq/BtjzEl7LK/Y78W3RCQVq9T3XKy/4WUh/JluwzIAfktEfmNv+39YQuK3fdfZ1r67kqPAVbbItIamcuDFWFWlnDHvsauz3WILcVuwUvI+C+wFgoRVrN/9TSJyL5bA2ogl2FaiKIqiKKEwxuhDH/rQhz70ERUPYAFWVMF3Img7H6t6UxnWInoz8JUwbb+IJWrUYkVGfBer7LYBlnjaLbG3LQjRxxogz7etH3A3VrRDAMsk9jqsRZ0BsiO87kTgBPAx0K+N79kY4DGsBWAZUGm//gmQ6mubhFVVar/9XhwBngZG+9pl2+Nf6tueDPwey8y1CliHVQ3qSewCPb5zPYG1yG3wvq+h2tvbp2NFMpTa7+cu+3cV42sX8nh7n8Eqpe78fCeWSHXE7vNj4DlgZoTvbyzwPXssAXtsK4CpIdp2yvvTynj+E+tvvcb+fb8GnB/J32sr/S6xxxDuke1rn4BV6S3Pfl/2Az8E4sL0Pxf4N5awWA6sCvc7aGvfIY5v9vcb7m/a3rfUf43O+4eV5vai/V6X26/Hh+hjhP13VWZf4/9hpdg1+z1gCbx/t/+WGkO9v/rQhz70oQ99eB9ijKbZKoqiKIqiKIqDiKzBElOye3goiqIoiqIeN4qiKIqiKIqiKIqiKNGKCjeKoiiKoiiKoiiKoihRigo3iqIoiqIoiqIoiqIoUYp63CiKoiiKoiiKoiiKokQpGnGjKIqiKIqiKIqiKIoSpahwoyiKoiiKoiiKoiiKEqWocKMoiqIoSqchIgtExIjIkp4ei9I+RCTPLoetKIqiKEoUoMKNoiiKovQSPKKI91EhIptE5JsiEtvTY+wtiMjlIvKCiBwSkYD9Pu4SkSdE5MKeHl9XIyJLReTKnh6HoiiKoiito1/wFEVRFKX38VfgVUCAEcCNwC+AycDNPTiuqEdEEoHlwJXAHuApYD8QA0wAPg18WUQWG2P+2lPj7AZ+DPwZWBFi30RAq1coiqIoSpSgwo2iKIqi9D42GWOWOT+IyKPAbuArInK3Maak54YW9fwWS7T5KXCnMabRu1NEvgN8Fqjq/qG1HVuIqjPG1HdWn8aYQGf1pSiKoihKx9FUKUVRFEXp5RhjKoENWBE445ztItJPRO4WkbUiclhEakUkX0R+KyJDvH2ISLaderVURD4tIu+LSI2IFInIT0OlYYnIFSKy2W73sYj8BIgLNUYRGSoij9jtau3nR0KMY4k9jgtF5EciclBEqkXkXRGZY7eZLyLrRKTSHt8PI3mfRGQa8EVgPfA9v2hjv5fGGPOCMWal71gRka+JyAciUiUi5SKyWkQWdsL7eLqIPG23qbU9Zn4qIkm+dk/afaeLyB9FpBioBEbZ+78uIv8SkQK7nyIRWSYi2f7x2T9+0Zt252kT0uNGRK4UkfV2WlmF/fqKEO3yRGSNiEwSkVfs9+qkiDwvIiOa/2YURVEURWkJjbhRFEVRlL6BI9iUerb1B+4A/g68iLXIPxu4CThPRM4yxtT6+lkEfB14DPgjcAXwHeA48IDTSEQ+a/ebB/wEqAe+hJVqFISIpAJvA+PtPjcBOcDXgAtE5BPGmHLfYQ9hpS/9yr6ObwOrROSLwBPA74C/AJ8DfiIiB7xRSGH4D/v5CWNMW1OBnga+ADwP/AmIB64DXhORq4wx//S1j/R9PAt4AzgBPA4UANOBW4FzRWS+MabO1/drwGHgXiAJqLC3fwdLwPs11t/BFOArWO/xVGPMMaAEuMG+nrew3sdWEZGvA49gRXbdh5VKtQRYISK3GGP8/WQCa4B/YP0NTgduAVKAT0ZyTkVRFEVRbIwx+tCHPvShD33ooxc8gAVYC+YfAUOBdGAq1oLaAO/52guQGKKfm+z2n/Nsy7a3VQLZvj62A0WebTFAPnAUGOrZngoctPtZ4tl+v73t675xfMPefq9n2xJ72yagv2f7Z+zt9cDZnu39gSLgnQjev7/bfeSE2Jdmv6fOI8Wz77P2cTf7jokFNgIHAGnr+2hv34olhgz0bXfO6X0fn7S3LQtzfUkhtl1oH/Nd33YDPBmmnzxgjefnwVji0F7f+5IC7APKgUG+44P+vuztzt/ppJ7+LOlDH/rQhz700ZsemiqlKIqiKL2Pe7AiJ44AH2JFdryAJW64GItqABGJEZFBIjIUK8IDYHaIvlcYY/K8fQCrgREikmxvPgs4DfiTMeaop+1JrAgTP5+1x+uPyngcS/z5bIhjfmuCo4Hesp83GGPe95yzFngPOD1EH35S7OeyEPty7TE6j+WefddjiRMr7JSvofb7OAh4CUus8Z+/1fdRRKYC0+xzxfv6Xocl/oSKTvlZqIszVsqckyKXavezFThJ6N91pFyMFdnza2OM+97Zr38DJAMX+Y4pNMY869vm/N2N78BYFEVRFOWUQ1OlFEVRFKX38TvgOSw/manA97B8Tmr8DUXkc1hpRjk0958ZHKLv/SG2HbOfh2BFXoy1f94dou3OENvGABuNz0DXGFMvInuAma2NwxhzXETAim7xc9weW2s4okNKiH1XYUXvgJWK5GUyMBAobqHv4Vjij0Mk7+Nk++d77Ee4fv3khtiGiFyAFY01G0jw7Q71u46UMfbzjhD7ttvPY33bW7t+RVEURVEiRIUbRVEURel9fGSMed1+/X8isg4rQuMx4PNOIxG5CngGKyLlNuBjLHEnBlhJ6CIFDS2cV3zPoXxiJMS29hBuHC2NrzW2Ywk0M4DN3h3GmLXOa1sg8iJYUTiLW+nbS1vex59j/T5Ccdy/wRjTrOKViJwN/AsrnelOLIGrGut39Dc6VpCiPb/TSK5fURRFUZQIUOFGURRFUXo5xpi3ReRp4EYR+bUx5m171w1YQs1C72JfRCZ18JT77OfJIfaF2rYfmCgisd6oG7vC0gRCR2d0BX/Hiki5SUSetNOXIuEjrHFuMMZUtNa4DXxkPzd4hLj2shhLkPuUMcaNSrIrU3Uk2gaaft9nAv/27TvDfu6u36GiKIqinHKox42iKIqi9A3uxYpy+IlnWwNWxIX7/16scJIfdPBcHwCHgC/ZPipO3ynAV0O0X4FlpPwV3/b/tLf/o4PjiQhjzIfAU8C5wEMi0ux7kIQIt7GP6Qc8GKpfEQmVzhQJm7Eidb4qIv5UI0QkVkTSIuzLiXDxj/8uQn/fq8AyZI6E17D8dv6fiAz0jG8g8P/svvzpZYqiKIqidBIacaMoiqIofQBjzF4R+RtwnYicb4x5C6t09X8Ab4jIU1geN1cCAzp4rgYR+SbwLPCeiPweq9rTl7F8TLJ8h/wPcA3wiIjMxBIscrCqW+2x93cXX8WqfvVd4AoReQErWiQOa9xX2+3cqBVjzPMi8ifgv+zxv4xlqjwKmItltttMeGkNY4wRkRuwTHs/FJE/YvnIDLD7vAr4PlY1qdb4B/BN4FUR+R1Qi2UqPM0eq58NwEUi8j2sCmHGGPO3MOM8ISLfxaoK9a6IOONZYo/zFtuYWlEURVGULkCFG0VRFEXpO9wPfAEr6mahMeZvdlTEN7EqER3HqoJ0J01Gse3CFjOuxko9WopV4epJYC2W14q37UkRORfLgPczwJewjH4fA35sjCnvyFjaOO5qEfmsPY4lwBexon7qsDyA3sIq+73ad9yXRWQ1cDOWmNIfOIxVtvz7HRjPFhHJsfv4DJawVI5VUvtJmqcmhetnvYj8B/BDrOirauB1YD7W78TP17GEmLuxjJfB8sIJ1/+jIlIE3AH82N68FfisMWZFJGNUFEVRFKV9SOTp3YqiKIqiKIqiKIqiKEp3oh43iqIoiqIoiqIoiqIoUYoKN4qiKIqiKIqiKIqiKFGKCjeKoiiKoiiKoiiKoihRigo3iqIoiqIoiqIoiqIoUYoKN4qiKIqiKIqiKIqiKFGKCjeKoiiKoiiKoiiKoihRigo3iqIoiqIoiqIoiqIoUYoKN4qiKIqiKIqiKIqiKFFKlwo3IrJEREyYxwpf25tEZKuI1IhIiYgsE5HTQvSZZe8rEZFq+5ibwpw/oj4VRVFCISIX+eat83z7dY5RFKVNiMiNIvKOPWdUisgeEXlQRAb72un8oih9nM6eD3SdpCh9FzHGdF3nIkuAP4XZ/aIx5kq73Q+Ae0O0OQScbYw5bLcbAWwEMkO0/YEx5n7PuSPqU1EUJRQiEgd8CEzybD7fGLPO3q9zjKIobUJE7gD+J8zud40xc+x2Or8oSh+ns+cDXScpSt+mu1KlDhpjxPe4EkBERgM/stu9C4wEbrB/HgUs9fRzD02T0Q1223ftn38sIlnt6FNRFCUU38QSbar8O3SOURSlnVxvPzcA84GhwHv2ttkicobOL4pyytDZ84GukxSlDxMNHjdXA3H2618YYw4bY5YBu+xtnxeRfiLSD7jW3rbLGLPMVoN/YW+Ls/uKuM+uuiBFUXo3IpIB/BA4Avw+RBOdYxRFaQ8N9vNhY8xaY8wx4DXP/kR0flGUU4VOmw90naQofZ/u+lBmiMgxEakVkVwR+YmIxNv7Znra5YZ4nQqMAcbZr8O1A8hpY5+Koiih+DmQDHwPOBFiv84xiqK0h9/ZzyNFZJ6IDAEutrcVAtvR+UVRThU6cz7QdZKi9HG6S7iJA9Ls59Ox7mS/aO8b6mlXFub1sDa0a0ufiqIoQYjIfODzwNvAn8M00zlGUZQ2Y4x5DLgdEOBN4CjwCWAzcJkxJoDOL4pyStDJ84GukxSljxPbxf1/BNwE/Bsr5WA28DdgOHCJiCzAmqxC4d1uCC8y+dv5t7XW1mXOnDkmISEBgOzsbLKzs0N2kpeXF3ZfT7Y7Vl7Dv7cdZkxyDQ0JQ5g2ejAD4oN/xVWBej48eJxpowdzpOhQROftzDFqu+5t11l95uXlkZeXB8Cbb765wRgzN6KT9zJEJBb4X6zw5W8YY4xIyOmkzXNMpPMLRP/fldPOO5/455q29Lf7o32cMCkh+9mQW8IbOw5zwZkjGNG/skPX4e1rzoT0qP28nWrtTpX5BUBEFgM/o/kcMgKYDmwJsc893PO6+XeYiRNNQpyV/ZA9fDjZwzq27so7cqTDffSm/rqiz2jv71B5OaNmzeq0/toyV3Znn9E6x3TyfNCl66S++B1G27WvXU+eOxrbdef80qXCjTFmPbDes2mNiPwKeMD++WygxLM/xfN6oOd1CcETUkvtvM+RtHVJSEhgzZo1/s3NWLp0KUuXLo26dlf/fA0flxRyfN/LVIw7l+ShGTx28xyGDkzgaHkNy9buJy5Qz/qC7Zx/9hQ+fP5xfvbtOxk6MCHqrkXbdU67ruhTRAIRnbh3ciUwBXgZQERmYH2BchgvIodpxxwT6fwC0f931ZXtjpbX8PhrVrT2LRdP4L+AZWv3s2hmJrd++66gOcuZ166fNzZoHgt3Xn/73vB5O9Xa9eX5xfaM+A3Wd68CrJSIQ/a2LwJ/EpEdtPc7TFwcax58sNPGu3T5cpYuXnzK9NcVfUZ7f6t++UsuiXBui4S2zJU91We0zDFdMB906TpJv8Nou2g4d7S36+r5JZw62ymEMbbyKriNwCbPzxNCvD4JHAD22a/DtQMrtJA29NkuFixYEJXtHlicwyXTM7jsrEwumZ7Bqq2FLFu7H7AWPj98ZgsA9147A4A3jw5293fXGNvaLlKi9XfSVrrivD11Lb2UZPv501jzyWbgFs/+PwF/4BSdY9pKe867bO1+HlqxnYdWbGfZ2v0MHZjA7ZedwaubCoLmrKPlNXz1dxv44TNbms1j4c7r9BWJWO3v72h5DQ+/spOj5TURX0t3touUvvK31UsZhpU2DrDWGLPLGFMOLLe3CbCQLp5fImXB1KmnVH9dQbRf8+mnndap/XUFXTB35HV2h+2ks+cDXSfp/8tuO29fuZbe9h1GjGkWbdt5nYu8DPwLWIGVKjWHplQpgHOwVOa9WP43G4DPAhcCy+w2jxtjvmr39zhws739euB1LK+c2UAdMM4Y87Fd7i6iPr0sWbLEPPnkk51w5d1HqDvOa9asYcpZc4K2+9uFu1MdbaxZsyZqPiwdoa9cB4CI/NkYs6Snx9EViMgSLHGmJd4EbqSNc0xvnF/C0ZV/z/6IG2d+yi06yV3LN/PA4hwmjEzl4Vd28sNntnDJ9KbIwrYS6XU4ItGqrYXce+0Mbr/sjLDtempe7StzTB+fX+KxzM4TsL77XGQ/O3fYwfqOs4r2fIe58ELz5O23d90FnAKs2batVwhCnUZ2NnTi9faGeUhE7jHGLI2CcXT6fNCV6yT9DhN99JXrgL5zLV39HaZLI26AUcCvgINANbCaJtFmuTHmHWNMPvATe9scoIimiaMAWOrp78f2Nuw2h7EmI4B7jDEfA7SxT5fOzsvtasLdcV6wYAGlFQHW7T5CaYUVsTV0YALXzxvLsrX7OVpe0+47z91NX/gQQ9+5Dpu8nh5AV2GMedIYI94HcI+nyfnGmAXtmWN62/zSEl3995wUHxsk2gC8uqmAVVsLeXWT9S/g+nljuffaGe0WbSDy61i2dj+rthZyyfQMrp83tsV2oSKAuoM+NMfk9fQAugrbaPS39o+ZWKV3y2hapBUDL7T7O0wn+72cipxSog3A8uWtt2kDfWge6nK6aD7osnWSfoeJPvrKdUCfupa8ruy8q82JfwQsBmYBGVhpUruw7mg7kxXGmPtEpAi4FZgIVGApzN83xhz2tDssIucADwKXYKU17AF+bYx5wnviSPvszYRbTBwtr+Fzv3iTfcUV1NY38M/vXei2d9Klwt0xVhQlMk6FOaYnCDdPOXOc8+yIz51NqKgZ77lbEon8Y1SUENyBdTPri1jzRn+sBdobwFJjzDHQ+UXpJmbM6OkRnOp06nyg6yRF6dt0tTnxP4F/Rtj2CeCJCNrlA9d1Zp+9Ff9iwllwVAbq2VdcAcC00YNDtu8tqVKK0tPYIdVLw+zr03NMR2nLPOO0XTQzE2gufrRXqPH2++qmglbHEko4ivTcXSUmKX0HY0wDViTyryJoq/OLovRhumI+0HWSovRdujpVSulC/OlOzoKjKlDPwjOHc9uiSUGLCG/7ngzpVxTl1KAt84zT9tVNBe1O4wxlIOz0e9fyzRGNxUnB8gtH4cyJIzUtVhRFiTq2bOnpESiKoigR0tWpUr2KvLw8li5dyoIFC3plrp2z0KgM1LN6RzEXTBnZbPET7q62RuAokWCXgszu2VEovYW2pA51RppRqGgZp79FMzM5b9KwVvsPFzUTLoXL2V4ZqCcpPlbnUEVReg+dXE5dURRF6TpUuPGQnZ0dcf36aMRZcBwtr3EXEH5aW3z4tyuKF1vQzOvZUSi9hbakDnVGmlEo8cfb74TLUju1b+/PlYF6nUMVReldPPdcp1aVUhRFUboOFW76MKUVgRZNNr2oqaaidC29PaKvNxBO/GlPRKH/mHB9RyKYK5GhEX2K0s3Ex/f0CBRFUZQIUeGmD+JEz7yxvYjVO4qpDNRz91XTgPALGzXVVJSupbdH9HUGPZWS2Z6IwrYeo3Nox9GIPkXpZi69tKdHoCiKokSICjd9EOeOb0lZDat3FPfwaBRFUSx6KiWzLRGFrVW3UhSld1FWXcvq7YdZOGUEKYn9e3o40cXzz8NZZ/X0KBRFUZQIUOGmD+IN3U9PSdDy34qiRAXhBJRw81NnzVttiYZRvy+lt5J35AhLly9nwdSpLOjDviVtFWJWbz/MX946AMAVZ2d19fB6F2ef3dMj6FY0HbP9aLq3orRMd8wvKtz0clpa2HgXKw+/srPZYkTFHEVRuouW5pvuMk2PZM5Tvy+lt5I9bBhLT4EqQW0VYhZOGRH0rHiorOzpEXQrmo7ZfjTdW1FapjvmFxVuejn+hU24hUmoxYjeWVYUpbtoab7pLtP0cGPwz5sqbitK9NJWISYlsb9G2oRj586eHoGiKIoSISrceOhtYYBHy2uoDNRz55VT3IVNuIVJqFQB76JIFyhKJGiYsdJeWhJhOts0vS0CNsDjr+Xy0IrtQUbuoOK2okQjKsR0IkuW9PQIFEVRlAjp19MDiCacMMDeINqAtah4aMV2kuJjg8p933vtDBbNzOThV3aSW3SSh1/ZydHyGo6W17ivoWlRNHRggrtAWbZ2f09ekhLlaJix0l688004/HNUewk3n0UyBu9Y/MK4oihKn+LJJ3t6BIqiKEqEaMRNLybU3WNnYeJ42qzbfYRVWwtZt/sIOWPSeGjFdiDyVAVFUZSuxBsd01kRLm2dz265eAJJ8bHNUkkfWrGde6+doVGIiqL0TdLSenoEiqIoSoSocNML8S50wnkxOAsQp6Ttqq2F5IxJC4rG8aYRtDclQVEUpa2EE2s6S0Bu63zWWippZ6DpqIqiRB3nndfTI1AURVEiRIWbXoh/obNs7X4qA/VB0TTekuATM1KorW/gmrmjmTAyNWSFKUVRupbe5qHVlYQTazpTQO5oifHOFrPVL6d11ENLUbqZf/4T5s7t6VEoiqIoEaDCTS/Eu9BxFgN3XjmFe6+d0ezu8LK1+/n1/+0G4Ll3DnL3VdM0LUpReoC+XkqzLRElrYk1nRGdEmmJ8e6KhNF5t3XUQ0tRuhmNuFEURek1qHDTC/EudPwLID/XzxvLG9uLWL2juNnxjhGotyKVhvEritIe2hJR0lo0i9NXZaDe9Z5p67wUaYnxSISczhB3NB1VUZSo4/Dhnh6BoiiKEiEq3PRy/IsB/wJj6MAE/vj1c91t4bwlAA3jVxSl3YQTStojelw/byyVgXre/ajEFZ3bOi9FWmK8NSHHv82ZO1XkVhSl17N3b0+PQFEURYkQFW76GKEWHd6Firfa1AOLc4DghZaG8SuK0h7CCSXt8XYZOjCBpPhYVu8o5pLpGSHFoMdfywWsilDOeSIRU0KJ2y0JOf5t6lWjKEqfYcmSnh6BoiiKEiEq3HjoC+ahzp3qykA9R8trmi1irp831i0Rft6kYUELD12EKK2h5qFKW2mvt0tLaaBOqW6ApHjr31ikYkpraVihBKhw6amKoii9miefhAcf7OlRKIqiKBGgwo2HvmIe+tauYtbvKaEqUM/9X5jpbnfuND+wOIfzJg3ThYfSZtQ8VIkEf1RLe0Rh73H+/hyBGtoeMei0qQzUtyj2hEvxUq8aJVrJO3KEpcuXs2DqVBZMndrTw1F6A8OH9/QIuhW9+dR++sLNbUXpSrpjflHhpo+xbO1+1u8pAWBb/vGwnjbq06AoSlcRaTpRpP43/v6GDkzg7qumBbUJJ754U6q8qVFHy2vciJtQY6oM1LtRPTpfKr2B7GHDWLp4cU8PQ+kEyqprWb39MAunjCAlsX/XnSgnp+v6jkL05lP76Ss3txWlq+iO+UWFmz7G9fPGUlJWw7b84/zsxlnNxBrnuadK4iqK0ncIN28smpnJut1HWDQzs8Vjv/q7DazaWgi0LPC0Nz3Jn1LlPUdrnjx3XjmFe6+dob42iqJ0O6u3H+Yvbx0A4Iqzs7ruRCtXwvz5Xde/oiiK0mmocNPHGDowISg9Km1ePECzlAW/F44uTBRFaSvh5o1XNxW4PloTLksNe+yqrYUhzYf9hBNZWhOcF83M5I3tRUzNGhyx6BPKWycSIUpRlN5Bt0WzdICFU0YEPXcZF1zQtf0riqIonYYKN30cxw9i2dr9LJqZyaubCtwFSVJ8LD98ZgubD5SGrDClKIrSEuEiYSKJkGnJfDhSWhOcX91UwOodxVwwZWTE5wglEkUiRCmKEh20Jsx0WzRLB0hJ7N89Y9u7Fy6+uOvPoyiKonQYFW76IP670M7i5sk1e9lXXEFloJ67r5rWYoUpRVE6l75o7BcuEiYSA9+2mPyGi6wJJxA57Z0ImevnjSW36CR3Ld/MA4tzmDAyvPgS6lxzJ6YzYWQKcyemtzgepWOocajSGYQTZhxBZ9a4IUDr0Sy9ITKnw+Tn9/QIFEVRlAhR4aYP4gg1b2wvYvbp6Vwzd7Qr0AA8904e18wdzYSRqTx28xx3AaIoStfRl439IhEyOiJ2hIusac2nxtve66fz/LcXtOlcP31xB7lFZfz0xR08/+0FYcejgk7HUONQpTMIl2bU1kib3hCZ02GWLOnpESiKoigRosKNh75yR9wbSbN6RzFJ8bE8dvMcfvnyTv78phV1c+sf32Pl3RdraVulTegdcSUUoYSMcJF/LbUJhyMsL5qZycOv7HR/DndsqEgcJx3UeQ5HKD8b/7HhIn3UK0xRep5waUZt9Y3pNp+ZnuTJJ+HBB3t6FIqiKEoEqHDjoTfeEQ9X7vaBxTnU1je4ppxDByaQnpLAyap6AOoaGnty2EovRe+IK6EIJWT4RYxI2oTDEZgffmUnP3xmC+t2HyFnTBoPrdjuRhbecvEEt8/r541t1t+EkaktRto4+P1sjpbX8OqmAh67eY4rEIUTvNXEWFGil7b6xnSbz0xPktXHr09RFKUP0W3CjYhcBLzm2XS+MWadZ/9NwK3ARKAcWAV83xjzsa+fLOAB4BIgGcgFfm2MeSLEOSPqszcTrtytY8o5+/R0dyFz/byxvLTxY97bd4wZ2YN7ctiKovQhQgkZfqGmtTaRRN94owlr6xtYeOZwVu8odiMLgQ5HvPjH7YhLlYF6kuJjWxxfe02MNcVKUZQeYfz4nh6BoiiKEiHdItyISBzwmxb2/wC417MpHrgOmC8iZxtjDtvtRgBvA97bmdOAP4jICGPM/W3ts7fjlPV2Xnu3A1QG6oMWMhdMHcl7+46RlmwtDnTBoChKV9BWg2InmgasuSrU3DR0YAKP3TyHLz+6ntU7irn1U5OYfbplGBxq/uuMcYebS0MRSTWtUGiKlaKcGkSd4fEbb2hVKUVRlF5Cd0XcfBOYBFQBA7w7RGQ08CP7x3eBK4GLgKeBUcBS4Kv2/ntoEm1uAF4HVgCzgR+LyNPGmPw29tmrGTowgbuvmhZyu7P4ce4SHy2vAeDOK6cEpRXogkFRlJ4mXKQLNDcknn16Oqt3FDMgPrbZ/NfWeSxS8fqauaPduTQc7fUMa6/goyinGu0RPtp6TFl1LSs3FwBwaU5mpwosUWd4fOmlPT0CRVEUJUL6dfUJRCQD+CFwBPh9iCZXA3H2618YYw4bY5YBu+xtnxeRfiLSD7jW3rbLGLPMjpr5hb0tzu4r4j474/qilaPlNTz8yk7AWsg45qAPrdhOUnysu0BZNDOTS6ZnsGhmpnuMI/AoiqKEoivmCkf0cOam6+eN5d5rZ4QUM265eAL3XjvDFaDbgn/sjkC0bO3+kO2d/a9uKggaX6i+2jsO/7UrSnvIO3KEpcuXs2bbtp4eSpfhCB+rt0ceNN3WY1ZvP8zzG/J5fkN+m84TCQunjOC688dEj+Hx5s09PYJuRQsstB+ngIv9HiqK4qM75pfuiLj5OZYXzf8j9MXM9LzO9b2eDKQCY7BEptQw7RycciGR9rkvkgvobRwtrwkqfevcAfZWZrn/hQ/d9o4nA3TcH0JRlL5PZ0bqhYt4aSl6pSPV8CIxTfbS0v6OvA8a7ah0NtnDhrF08eKeHkaXsnDKCAJ1DQTqGiirro0oGqY91aQCdQ0tHtPelKeoMzwuLu7pEXQrWmCh/fTGAi6K0p10x/zSpcKNiMwHPo/lS/Nn4Mchmg31vC4L83qY75jW2kXaZ68VbloK71+2dj+rthYyYWRKUHUTb2UWx9D4tkWTmt3V1nB9RVFaojNTex5/LZeHVmynMlAfMu2zM/DOl5GYJntpaX+49yFSo+VQxyqKEp6UxP7Ex8Xwl7cOEB8XE5EI0p5qUp87Z0yLbaIu5am9LFnS0yNQFEVRIqTL0oVEJBb4X6AB+IYxxoRrGsF204Z2bekzCCcMsDeEArYU3n/9vLFcMj2D3KIyXt1UEHL/wjOHA5DYP9YN0ddwfSUca9ascT8baJhxu+hLYcaRzBWdlUbUGX1658vOnOfC9dVa+lVLx57KaBqDEgmdmW5UVl3Li+/nU1ZdG9H2rhhDj/Lkkz09AkVRFCVCujLi5kpgCvAygIjMALz/4caLyGGgxLMtxfN6oOd1CcEiU0vtvM+RtHXpTWGA3rSnh1/ZGWToef28sTx28xyWrd0fdv8fv36u+1pRWmPBggVOCCD33HNPXo8OppfSm+aXziDSVKBbLp4QZPobLtUz0j6Pltfw+Gu5bt9DBya0ObrFiZhZNDOT5945GNSXRtN0HZrGoERCZ6YbhYucaS2iJupSntqLlgNXFEXpNXSlcJNsP3/afvj5E/Am8BLgJGVPALZ4XgOcBA54Xqd69uF77bisbWpDn70Sb9qTs5CBYI+acPvf2F7E1KzBDIjvrqJiiqKcarQkXvjFD68I8/hruazaWsjCM4c3O3bRzEze2F7E0fIa19TXj5N6BZAU3xRR2JJ45B+PIxCt233EFZCcvsKldnnFnlc3FbRapUpRlJ4nnP9NuO0dLecddeXAR/TyiCFFUZRTiGhYuT8HPIhVBeqbIrIWuBDLRBjgb8aYRgAReQa4GZgsItdhlQP/lt2uzu6rTX32dryLo9KKAOt2HwnytfHvf3rtflbvKGb1DsuQzlmMRFoSV1EUJRJaEkv8kTPeKJnq2noAZp+e3mwuenVTgTt/tSbGjBuezNyJ6W7EYah5zZn3KgP1rthz+2VnsGhmJut2H+GOK84kZ0wa0Hr0TCixR02HFSW6CRc5E257R71tnOMDdQ3Ex8X0vICzbh1cdlnPnV9RFEWJmC4TbowxTwJPereJyFKaDIrPN8ass7f/BLgXmAMUeQ4pAJZ6fv4xcBmQCSzznfIeY8zH9rnz29Bnr8a7eHFMic+bNIwJl6W6+xfNzOSrv9tA1tAB5BaVMTo9iYMllSw8c7ibSuVduFw/b6yKOMopiYhcCNyBleY5FMsL6wCwAnjAGFPhaXsTcCswESgHVgHfd+YhJTz+aJxla/e788+dV04JWwb8+nljqQzUBx3r55aLJ/DuRyWs3lHM/X//kNU7iqkM1LvpWN45zRFb/Od8dVMBq7YWkjMmrdlx/tQu/zUtmpnJeZOGaZqU0iIicgPwNWAqVir4x8A/jTHf9bTROaadhIps6Yxol7ZWqAp3fKCuITrMjT/zmZ47t+LSmfOBiGQBDwCXYGU/5AK/NsY8EeK8OscoSi8iGiJuMMbcJyJFNE0eFTRNHoc97Q6LyDlY0TTOhLSHEBNSpH32JZxFTWWgntyik7y6qYC5E9P5/C/XcrQ8wOCkOAAumT6S04Ykc/28sW7Y/7kT07nzyimuaOO/G65CjnKKcDbW3OJlsv3IAT4FICI/wBKGHeKB64D5InJ2X51jOgvHd8Zb6amkrIYP9h+jurbe9ZMJdVxrlaeGDkxg9unprN5RzNSswVwwZSRHy2tCpjd5BSTv+ZztlYH6Zp46rUX6pCXHa6SN0iIi8r/AN3ybJ2KleH/XbqNzTAcIFRnTGZWgOupt4xxfVl3rRtz0KOvWwdy5PTuGU5zOnA9EZARWJd9MT9tpwB9EZIQx5n7PeXv1HKNrE+VUpFuFG2PMUsJEu9jCSzM1OES7fKyJJZLzRdRnX2HowASS4mP54TNb3DvO44Ync7Q8QGL/GI5X1gGQlty08Kiy716v31PCpTMyQxp5Rmoyqih9gC3ANVhffI4DFwPPYn2huVRE0rBMzn9kt38Xy4j9IuBpYBTWHPfVbhxz1BHJFyr/vDIgPpb1e0pYv6eExP6xISNkIuWWi5uszxbNzOQ7T20M2S6cCOPMg4+/lusK2t5r85sfh7oeRQmFiHyapkXac8DdwCFgLDDfbjManWM6RKjImI5Gy7RGWyJ6osbcuLS0p0dwStMF88E9NIk2N2BZSqwAZgM/FpGn7ayEXj/H6P9c5VQkKiJulM7DWWCUlNWwekcxF04dwfgRVWQNHcDv/72XhWcOD1rUOJw7Md091r+Y0QopyqmCMWalb9M/RWQHMNP+uQ64Gss/C+AX9l2pZSJyF1ZkzudF5Ot9xUerPThfqPwpSl5BJ9y8svDM4VTZqZuOAXFbBRyviL35QCmrdxRzyfSMkHNfS9fw0Irt3HvtjGbpVX7zY+916DyptMKt9nMecL0xxqk3vcN+gM4xHSaUMNLVYkm4iJ6oMyT2smRJT4/gVKfT5gN7/7X28y5jzDIAEfkF8Izdx9XALyLts6fnmJZuAun/XOVURIUbD3l5eSxdujSo9HG0Em4yc0SX3KKT7Cks45aLJzJhZCq5RSfJP1rFA4tzgto7laX6x/Zrdg5/n4qyZs0agOyeHUX3ICKJwCeBM+1NfzHGlIvITE+zXN/ryViV78YA+7ploFGIk7bpRP4BISsyeecVr3eME9Hy4cHjQR413opNQLM50JkX505M543tRdy2aBI3zh/nes6EE39Czadez5r7X/jQHWM4nx1/+peGbit+RCQGONf+8RDwDxE5F2gE/g/4jjGmiCahGHSO6TXMGjeEnYdOMmvckKDtnZGi1WU8+SQ8+GBPj+KUpAvmg37261DtHHLs514xx7QUVaNrE+VURIUbD9nZ2SxdurSnhxERoSYz7+LDMdd0jIr9PzvtwbrDvXpHMcvW7g+aBDV/VPFjC5p5PTuKrkVEkrFM+ry8AHzZfj3Us70szOthnMKLKifixYl0CXdHLFxZcEfEcYQax2vGW7HJMVR3RKCj5TV89XcbWLW1kAkjU8gtKqN/bAxpyfGtjjfUfOqM5+FXdjaLsAnnsxOuVLii2AwBBtivz/PtWwzMEpEcTqE5prVolKiOVvGxcd8xNh8o5YxRqWSmJbnbuzpFq0OcoQvfHqSz5wPa0K7tc4wx0Ni9ATjXn5cNjY3uc6+iX/gb4orSXlS46aWEChH0Lj68RsVHy2uC2jseDc7d8DuvnMIFU0Y2W1xp/qjSGxCReW08pNYYs6GNx1wF/AnLX0vCDcXz2vh3OhF9QK+I6uso/jnq4Vd2cs3c0UEVmcLNMf7oFcAVcs6bNCykb41TVe+S6RncccWZ3P/3D5mUmeKKKf5ztDRWh3f3lvDkmr3ccP4YMockuVX4VMzuXNasWeNE80HfjuiL8/38ReAfwE+BW4AJdGSOOXKEpcuXA7Bg6lQWTJ3a0fF2OaGiUbxijX9/pNWiekIQCifQRI2fTSiSklpv0weI0jmms+eDcEpBqHmjzXPM4ddfZ83f/87O007j4thYTk9OhksugVWrIDsbEhJg926YNw/efx/q6mD+fPj3v2H8eKuTvXvhwgvhzTchLg7OPhvWroVJk6CmBvLymvpMSWHolCnc/vbbsG2q5cdUUNC0Py3N6ve99yAnBwoLobi4af/w4ZCRAZs3wyc+YZ27tLRpf2am1ce2bXDOObB9O5SVdd41nXcefMPvOa30RbpzfhFjmv3vP2VZunSp6S0RN6Hw371++JWd/PCZLdx77Qy3OtTjr+Xy1q5i1u8pAeCS6Rk8dvOckIsQjbhRQiEi99hG41GBiDQSYhHTAoeNMZmtNRKRAVhVpp4GTrM3zwK+hXU3DCDHGLPFbr8CuMLePt4YE3Q3vLfPLx3BPxc5OHNSVaCeAfGxQWa/4Y7x7vPOX+HmvzuvnBLS6DiS+e2s771MblEZE0am8MF/f7rFMXn79KZ06dzZNqJtfulM7DmlAmtxdNwYk2ZvnwZstZs9AgymPXPMF75gli5eTG8ilIDy4vv5/OWtA1x3/hhXvHH2h9oXqGvg+Q35XHf+GFcgcdrljEnjG5dObCboOMf494cbU5/l7bdPuVSpaJljOns+wBJunLSnF40xV9rtZgCb7e3LjDE3iMhfIunTO8cs/drXzNJFizp41V1LVH12330X7ruvZ8egdDtdPb9oxE0fIpypsHOX2EktcMhOT2rmedNSf4oSxYS7e9TutsaYKuBNEfk7cLu9+XRgE01feCZgVaJyXgOcBA60YTx9mqPlNVQG6ptVZ4KmlCp/KlKoY8IZGzvzV0um6n4PHCeap7WIwkf/czZf//27PPqfs5v1GQonUshJ2Sopq2FPYRkPLM5hwsjUkMcopw7GmCoR2QNMIrzYXI01f/TpOca7wPJHo3gjV/zRKt59TjTO1XOyXCHH2+7Dg8fZfKCUlZsL+Nw5YwCCjskZk2aZl28/HHSOSD1p/IvEqFo0RsrVV/f0CE5Zumg+OInlT+N14ve+dgScXvs9pqXPWVT5SV10Uc+eX+mTqHDTh3EWMve/8CEPrdjO6PQkPjFuCO/tOwZAXkkldy3fHDbiRlF6CQXADyJsK8D3wu4U+V/gRay7XWXAWcB/eJrsxyoV/iBWmPM3RWQtcCGWoR/A33q6EkM0Ea46k8P188ZSUlbDtvzjLJqZGXTMJdMzgvrxCi2hvL1CGbX7x+J45dxxxZlcMj3DPacXb58f/Peng/oMZ0DsHFMZqHdTtrblH3fNmZ//9oI2vnNKH+VvWKV200TkRiz/rK979r8JfEgfn2NaWmCFSi0KJfT4BR5/u4kZKWzLPxHUjz+dyWnbUptIr6G9gk+PsnIlnHVWz47h1KZT5wMReQa4GZgsItdhlQP/lt2uDqvkOPZzr5xjWvqcRZWf1EcfQR9PiVe6n2bCTTf5RSjdSJVdAeVgSSVjhyVz55VTqArUsy3/OKu2FvLlR9cz+/T0oDQFRelF5Btj/hxpYxH5cgu7rwfCJSX/0xjznt3HT4B7gTlAkadNAdaXMMUmkiiV9JQEVu8o5tVNBUy4LJXr5411jYgd0/RQ/XgNiaF1Ly5vv0Azw3aHlqJxwhkQO8fcfNF4xg1PJmvoAK49dwz9Y3fwwOKcoD40DbX30AXfiX6OVbJ3MvBn++HwGvCKMcb09Tkm3AKrrLqWlZsLALg0J9MVNkIt1kIJPN52504axr7iCs6d1OTb6j8mlMASqSeN/xoiWTSWVdfyyMo9bD5QGvL83S7qBAJdfw6lJTp7PvgxcBmQCSzzneseY8zHAMaY/N46x7T0OYsqP6mDB3t6BEofJFTEzRra6BeBNUEo3UykX/6dkt/jhifzsxtnuSH73kXP6h3Frg+ELiiU3oQx5tzWWwW1b2kh9ghWCfAxwCCs/POdWHfFfuvp4z4RKQJuBSba7VYB3zfGHG7LeHo7rc1D3siXcG39oszQgQk8dvOcIHPicBE0TnRLOGHIPxanX8foONRxrYlNoXDavrG9iH3FFewr3kvW0OSQkTaOyOOUOdf5NqpZQyd+JzLGVIjIfOA+LC+JNOBjYDlwn7GNB/v6HBNugbV6+2Ge35APwL7iCtd/xlmkzRo3hBffzw+bnuRPpXIEEr+PTVdcQySLRmdMOWPSQi48uz3V45pruv4cSlg6ez4wxhwWkXOwomkuAZKBPcCvjTFP+M7da+aYsupaXnz/Y/KOVPDlC8a7RuXPvm19VhyRN6qi2W66qWfPr/RJwqVKdbpfRG/AqfrSWyq++O8Kh1oU5Rad5K1dxZw7MZ1ff/kTTBiZGtTusZvn8MuXd7ppCuHuNOsdYgVwXNOze3YUrSMiccCdWHee4oDVWHeb/GW+gzDG3A3cHck57C9BT7TasI/TljmjpSpSflEmEo+tUD42XkKNwduvP9Im3Lm9/dxy8YSg7f5+505MJ//oO5w7MZ3KQD25RSebGRU743bKnPvfDyXq6NTvRMaYEqyqMbe00q5XzzHhomdaYuGUEQTqGthTWOb608THxbgpUo7pcKCugfi4GNdkGCyhwyueLJwygp2HTjbrpycXdKHSu8Lt7xaWLz/lzImjjc6eD4wx+VjVqCI5d1TPMV4z8Zc2HgLgqTf3841LJwZFrsXHxbQpXbFbeOIJNSdWOp1Qws1h4PsRHt+iX0RvIzs7m95U9cV/VzjUoujWP77nVpB67p2DJMXHuibF63Yf4YHFOewpLHPTFMLdadbS4ArgCJp5PTuKiLgTuMfzcw4wFFjSI6Ppw7RlzmgtkqWtAnFr4k5b5y3/+b2+Nd6S4knxsfzwmS2umbKXd/aUsK+4gvEjUnhq7QE2HyhtlsrljPtoeU1QeXQlKuk0D61TDW/0jLOwao2UxP587pwxQQs270LMETSc7aGMib19fePSiUH97Dx0stOib9pzd7+1qJxuT/WYMaP7zqUobcRrJv7JGRl8eLCUq2ZnuZFrk0elENuvH7PGDQGsiLydh066P/coo0f39AiUPkgo4aaoE/0ilC6kpSoqzoKjrt7yFstOT6K61lp8nDsxnXMnprNqayG19Q2s3lHsphqEWwi1J3VAUboDEYkxxjT4Nl8AXA1sx0pzOAN4uJuHdkrQ2pzhVLVraX5x6AyB2CkxDnDN3NFBY2kN//mdn++8cgp3XjnFjaAJVyXLey4nFaullKyWzI6VqKEzPbROKZzoGed1W4QOR8Aoq651I2Wc7QunjODF9z5matYgzp00jIGJcWH79fazr7giZBWp9hJVd/fbQNDvoacHoygtMGvcED48eJxAXSPxMf04fLyG5ev2M354ClfPySJQ38hLGw+xfvcRPnfOGDbuO8bmA6WcMSqVzLQkoAfNwE8/vfvOpZwyhBJuJorIt4FHjTHVrXXQil+E0o14F0UPv7KTHz6zhVs/NYnByfE8sDiH596xjLLW7ylh4ZnDAZiaNZgLpoxk0cxMd7HjNynWNCklytkqIrcYY9Z7thmg0X4Yz0PpJpz5yJmLgIi8bioD9WFTjCLBqUgFweXFveJROBbNzGTd7iNupSmvYP3wKzv51au7eWtXMev3lIStkhUqFStcSpYzXo1mjGpaTK/0o9+JmnCiZxz8aU7+yk6RLKr85r7rdx9xBZmdh05y4/yxbNx3jFnjhrBx37GgfscNT2bc8GT3vB1d0EXV3f02ECQ41R2Ca6/t4REpSmg27jvGtvwTbMs/wYjB1v/bXYfK2HWojKvnZBEf2w+AlVsLSUuO59/bi7hkxsggodifTtltvP66VpVSOp2QETdAErBZRP4APBKJgKNEF84CCHBFm+raem791CQGxMdyzdzRQalR3sos/vB/XVgoUc7DwAoRWQHcYYw5geVp8wLBYs3T3T4yD73NQ6uzCBWt5y3L/djNc4J8YgAeWrGddz8qcUtpt2Xe8c59LaWRhuLVTQVBlaa8IsyHB4+77e69dobbd0eFbY1mbCJKPbSmiMiFwBZjzLGeHkxvxp/m5NBS1EqoktubD5QyNWsQEzOseJHNB0rJSBvgijmOiOM8O+lSz2/I57rzxzSrVNXe9Kk37LGMGjKAG+aNa+O70Tm0R3wK8tGZrDqjEr14Pa8c8ebw8RrAEmv+36WTSEqIoaK6nife+IiGRugnJ7npgv6uUNxSOmWXctll3Xs+5ZQglHBzhzFmhYg8DNyOCji9knALoHuvnRF0N9hbWWrc8GQumzmq2QJCFxZKNGOM+YMt2vwc2CMi3wEeAvphmRPHYlWGWdpDQwR6n4dWZxEqPSpUuW8/tfWN3HnllGapVpFUsfKW6XbO530Ox6KZmbyxvYiSspog42GAn904i7uWb+aBxTluZT5oWRSKRNSJxIT5VCFKPbQygH8BiEgBsAnY7Dyc8roi8pYx5vweG2UvIFz6E4Q3422p5LZTRSY+LsaNsJk1bghnjEp1o2Cc1KhQpr9e82J/+lRrgkhZdS3vfmT5B+49XNbidXdlqkZ70rWCfHSeew6mTu3UMSlKZ5GS2J9Lc6wI2IkZKUzPTuPZt/M4cKSciup6/vzmfjIHDyC3qJzYmH4MGRjH1z5pFRBwIuLOnTTMTZvqVj74AObO7f7zKn2aZsKNMWaF/VwG/EREfoUl4GwRkd+jAk6vY2rWYGafng4EL1y8os2EkSnkFpWRnpLgLjC8iw5dWCjRjDHmKPBFEVmAVbb7y8BXjTH3tHSc0vV45xEgqKKdd7vDLRdPcMXm/nYYtNcYuD2V7yIVR17dVMDqHcWs3lHMgPjYIAFowsjUkKW9veldfrFHoxX7FAKMwir1fbm7UaQUS8TRX3CEeIWDsurasO0cwWPWuCFBwkeoEtxOPwMT49xS4FfNttrNGjfE9cbxV7lyonGcMuOOAORPr/CLL6u3H+bIyQAA40e07BTTlV44Ha5CFR/fiaNRlM7HGy23Na+UbfknWHjmcHYVnmTKaamcNXYIv3h5J4G6RmJjYhg5OBEgpN9Nt3I4qiqqK32EcOXAXYwxJ23B5l3gf4A7ROSnWB44VV09QKXtOAuYa+aOdiuWhLrb+/hruazaWsjCM4fzsxtn8ec1+3hjexGLZmYyYWSqLjqUXoWIDMSap6ZhVcbbYAvPDxhjwq8OlC4jt+gkX3j4LXKLyqgM1DersBRqXhk6MIHZp6e7AgoQZATcWZXvQlWQqgzUc+7EdLcSXyTHAu51+dNMw41VfcN6FTuB07EibSYBfsOiIcBFRIGHVt6RIyxdvpwFU6eyoJdEUazcXMDzG/IJ1DUE+eGUVdfyq1d2sS3/BB8ePM62/BMArijjj17xiiNgpV/ljEkLWrh5q1ztK65w06O8ZcadCBx/eoVffHFMU7PTk7lgyghefD+/R0p8d7gK1aWXdt5gegFRmo7ZK8grLOzS+SVcZJrzuZk1bgh/fGMvAEMGxnPhlJH85a0DFB2vJlDXSP+4fhSWVrFyc4Ebhec9vtu56aaeOa/SY3TH/NJMuBGR+4AszyPT106A/wa+A/TQp6Fr6M0eFLlFJ90w/lc3FbgLGH/FEv+CA2D26elMGJnqlgW/a/lmnv/2gmZGnYoSjV96RGQi8Beskt8G+DdwHfBX4BFgu4h8zRjz754b5anJd57aSG5RGeOGJwOwamuhW8GuJW65eALVtfW8/MEhVu8o5oIpI1tNk4okHcp7fKgKUg+t2M6dV07h0hmZLfbjPdZ7Xf60rnCRPm0RmcKVKFfRp9uYAdwJXANchZXKleN7jOyhsQWRPWwYSxcv7ulhtEpZdS0rNxcQqGtkX3HoNKPV2w+7Yk12ejLTRg92RZtQ0SuhxJFJmZbG5i0V/OHB4zQ0NjZLj3LSKq6ancUZo1LDLh4d09On3tzPtvwTTBs9mI37jrXoleNE+/gXpT1W7cbL88/DWWf1zLl7gChNx+wVZGdksHTRoi7rv6XItEBdA398Yy/b8k+QMyaNS3MyKa+usytOWZXrausayRmTBrTsm9VtPPEE3Hdfz51f6Xa6Y34JFXFzF013jiTMcQKkd8mIepDe7EFx1/LN7p3sBxbnuILL46/l8tCK7W4If2Wg3k07uOXiCW5EjnOc99lv1KkoUfql51FgOnAYa266ELjXGPM14JMich2wTET+bYy5vgfHecoxbfRgVu8o5tNnjeKauaPZfKCUBxbntCo4OKLHvuKKIKHHK3hcP29sUNlvp2peSzjzYWWgnlsutvLgHbFl0czMIFPjlsYYSiQKJQa1dHy49Co/4UqUt3YOpXMwxtQD94nIs8BjwEHg28aYF5w2IjIcWNdDQ4xK/KKE92dv5AvA1KxBro+Fw8IpIyirqiOvpIILpo5wUx3CRa/4I0+uODuLZ98+YJkHpw0gZUAcgboGtuWfYGrWIK6ekxXUhzetYta4ITyycg83zh/rntcrvgTqGlxD5FnjhjAwMS6sV45DqMiiSFKoulzcOfvszu9TUdpBqM+2v4pcRtoAbpxv/d91xFPANSt35hEn4sYfCdetYqmWA1e6gFDCzUdYYcGOeFMCfOx55HteK1GCV3RxBJecMWmued6HB4+zekcxty2a5N4ZHjowISgiZ8LIVNd3Im1evJoSK72FbCDdriaFiIzAqigFgDHmLyLyCpZhsdKN3H7ZGUHzTGtCsDeaxD//OOKKs81b9tubguVPV/JSZQszVYF6Nxrm/hc+dMWcpPhYfvjMlqAy4qGiW/yRNE7bykB9UFpXqOsCS4wBy7unpfF6r7+1VDGlazHG5AIXiMh/Au+IyL3GmGX2vmIR+b+eHWF04YgSTulvr2eMUylmx6ET7DpUxsSMlJBRKikD4tiWf4KN+44xMDHOXXA5njMtpSd5ySupYFv+Ca6ek+WmT00bPbhZRE2griHozj7geuB4o30unzWKEYMTKCytYv3uI1yak9ms1LhDU0nixmbjiiSFyi/udPrCs7Ky430oSicQKu3PqSI3eVQKxytrKSytcj2onO1njhrkelY5LJwyIkjwcfrtSr+pZoyMikBMpY8RSriZDFwP3A1sBH5gjDkQop3Sw3gXA45xpnfxALB6RzGXTM/gjivOBD5k475jrN9T4i6eWrubq3d0lV6AAD8QkV326xwgyH/LFnW+2v1DO7XxChytCQ5es3QInn8efmVn0LzkzHO3LZpEYv9Yrpk72g2R9qcrefv/YL9VzVnCxJL6x+iN0PFXqvLjCEn3XjujWQSNP7Xqh89s4c4rpwSVFQ+HXyTSKlQ9izHm9yLyT+DXIrIEuMUYs88Yc2sPDy2q8Jf+djxjZo0b4hoELz5vLC+8m8+5k4a12IdXNNl56CQ3zh/LU2/ub7Yo84sal+ZkEh8Xw6TMVGJjrPNcmpPptvG3j4+LsYSZs0YRG9OPG+ePDVroea/JKUkMweapfjHFFXvOGkXOmDT3WiMVYPzijn88HRZxdu5s33GK0g14P3PPb8h3o9ze2N5k/OuINt7P1MrNBa6wE6hroKy61o2a8/bbpaxdC5/8ZNefRzmlCFVVqhF4SkSWATcAr4rI21ipB3ndPD6lBZzFgHOn2HsX2lkQeLc7Rp8Lzxze6l3ccIsfRYlCngDuxYoSFPv5Cz06IqVZtEprgoMTkRPKA8dfpts7zzl9OsKKX+Tx9u+YDif2b/rX500Z9Y7xaHkNb+2y5kwnUqel62tJmAqXWhXp3Kq+NtGBiAwF5gHzsW5yTQG2ichVxpiVPTq4KMAvRvhLf6ck9ufF9/PdyJs9hWVsyz/BuOHJfO6cMa73DeB6WOw8dJJZ44YEle4GK8ouZ0yauwDzplR4vWYc42EnDeqKs7NcoccxJAZL/PGaoK7ffYT1u4+4Qov/mhy8KV6hUjO8C0+/UXJLd/6972U4L59OiR5YsqR9xylKN+CILS++93FQlNvew5Y31q5DZe7nxDEzdzxvAGL79eP5DfnEx8VwxdlZHTfzbguf/Wz3nEc5pQhlThxnjKmzBZw/2wLOjcAqEXkLuF8jcKIDZxFQGagP8n1wnp3FxOOv5XKsIkB2ehJ5JcFhsc5C5d29JVxy3+s8+p+zuf2yM8IufhQl2jDG3C8iO7AWU3XAy8aYtT08rFMevx9NS8JDbtFJ3thexK2fmsQ3P32G28YRLCoD9UFluqGp0lQkAooToXPrpyYxID7W9bcBgubJqkC9u98r9NTU1XP1z9fwwOIcJoxMDbo+r3Aebq4MlVrV3vdS5+PuRUSuwZpb5hO65HcCMAE45YWbcNEg3oiYsqo6Jo9KYfzwFAL1DfZCq9E93ut983buUQpLreDJ7392alDp7nHDkwnUNbJyc4EbReP4zoQzHnZMih38251F3bNvH3DH4Sz4nPE71+StgAUEHef1sQklYDnvj/c53HvpNzz2eu3MGjfETe9yIgrazJNPwoMPtv04RekmVm8/zEsfHAJgQsZA1u4q5sjJAACTR6W4ETZOeuO6PUe4dm42GWkDuMARdqvq3M+IXyDuMq+btWtPKeNvpXsIlSr1PlYVBUQkDTgNy+fmEeBbwA0i8jQq4PQ4zmLgaLkVsusYa/rvMjs+EAATRqawekcxy9buD7qz/PlfruVoeYDP/3Itf/vmPN7YXsRtiyapj4IS9YhIkjFmBbCiDe27PbG/N1etaw9eAaU14eGu5ZtZvaOY2vpGvvq7Da5A4hy38MzhbloUEJSS5BeZ/d5dQwcmuClPd145JWTKk3+edHCEnnc/KnEjFp//9oKg6/MK595r68womVPF1yYaq9YBz9AUzeelAdgKrAX+1d2DijbKqmsJ1DW4pr+hhIeVmwvcBdiZowaRV1IBQHxcP8DrM9PInsIyCkuryEgbwFWzs9xIliaD4Ea3L68gMmvcEDbuOxaUCuWPdnFYv/uIa14cH9ePQH0jGNxKVyMGJzApM5UX3893SxE75clvu2xyiws+f/SR/05/a3f+vRFGTnljpy+vQOakd3kFpjaRltb2YxSlC/F/dhZOGcGHB4+zLf8EZdV1rmgDVkSNn8PHa/jta3uorTM8tyGfwtIqtuWfIGVAXDPvm3Z/biLh2LGu6Vc5pQkl3JwuIruxBJtQ3zYF+BJWGlV8F45NiZChAxOamWo6OCkG40emMCQ53q2+4lQzAfjq7zZwtDzAgPgYjpYH+Prv3yW3qIyC0mpunD9OQ/SVaOcdoGUDkmDeAmZ20VjC0pur1rWHtvjb3HHFmRw4UkFVoJ71e0rYe7iM13/0Sa6fN5Z1u4+wamshF0wZ6QrV3mp4ofp2BJ91u4/w2M1zwo7REVecilJVgXqqa+t57p089hVXuKlYuUUnuWv5ZtcE3nt9/vH4xwAdj5I5VXxtorRqnUMdlu/fWvuxzhhT3rNDih78Pi+zxg1h9Y7ikJWWpmYNAmBb/gky0ga46Ugpif353DljePH9fLfsrxNl4wgVYJX6dfpwxBXvQs8xMnZ8MRx/GSeypqy6lhff+5h391oRdY55sZfkxFgOH6/hhXetNKvVO4rd6J9t+SdYubnATe9yzu146kSaxtSSz01KYn++celEVm4ucFPKoEnc8lfFardnx3nnte84Reki/MbmC6eM4LbLJrN6+2EmZabyyKrdHD5eQ3JCDNvyT/Dzl3Zw2pAkYmKgwc6Sqq0zZKQN4GufnMDWPEukcT6Xmw+UulWoutTr5qabuq5v5ZQllHCTiFVVKlwpcOx9cV0yoh6kN98RXzQz0y0B7nC0vMa9k+0segBX5IGmaiyXTM/ggcU5PPfOQY5VBKitbyC3qIzvPLXRvdN8KiwclJaJ0jviw0XkR21on9xlI1FC0prw8M6eEnKLyjh3YjoA+4orePy1XJLiY3lgcQ7nTRrmCiORmPV6BZ8vP7qeqVmDufPKKUEpUhAsrjiROFf/fA37iisYNzzZFbj9FfecY72+OEfLa4J8wbyC0tHyGrd0+S0XT2gmgrcWnaMeNz1OHValureBzcBWY0yFt4GIfMkY86eeGFw04E/92bjvGIWlVa5g8uzbByirttKkstOTOXfSMPYVV7D5QKlbNcpJX5ienUbOmDSump3lpgR5+wYrssYxKP7tv3IpLK3i39uLOHvcUOJjLV8LxxA5lL+ME60zYnACIwcnUt/YSNbQJD4qKmd/cQUV1fXkjElzSw87i736xkZ2HSpzx+EXaJy0qFDiip/WxB3HMNkRsbyl1L1GyC1FDLRqgvzPf8LcuWGPV5Tuxm9sDgR5U33jkkn894vbqai2sgx2HSoL+kwCDE2JZ0hyf5ITYoPSGr3zVJeXA3/iCbjvvq49h3LKEUq4AUuYqQUKCC4F7n/0KXrzHXGnBDjAYzfPYejAhLBmn94Qf69o8+om60vT71//iHMnppNXUkllTejytsqpSZTeEU8HftyG9sVdNRClfVw/byyVgXqOVVgh0M5CzR+x4o2QeXVTgfvsFTQckWRSZgq19Q2uN06oak+honWcqJqJGSk8tGI7mw+UuqKNE8WTMybNTa1yxuaPsPEKSk7JcQd/ulZr0TnqcdOj7MGKMM6xH4uBKSJShCXiOI87gVNWuPGn/vhNdL3eNbsOlXGotIob54/ljFGpzBo3JCh9wRth4q8c5e3zqtlZFJ+sobC0ivi4fhw+XsNLGw9x+aymCk6ZaUkh/WXKqurIK6kgOz3ZFXEaGg1HTlYDVsTNVbOz2LjvGDfOH8uotAHklVSw+LyxbM0rJVDXyNNr94GhmUATrsqUV0QBwoo73nb+haY3JSyScuitRv5oxI0SZThzSUFpJfuKK5p5Uy1ft98VbYalxgelToH12U1JjGNb/gn++8XtfOOSSTz7dh4ZaYn0j4lxUzO7nClTuuc8yilFKOFmLzDPGHM4xD4lSrl+3lje2F7Eqq2FPP5aLndfNS2oQpR3seOYaHpD/J2FgVOitqSshvV7Snhv3zEun3Wa3uVVop2WIgSVKMdJ93TEjUtnZAZVxQPLwPgLD79FblGZG03jPENTmXBvSfE7r5zC1KzBbMs/HhSN6D1vKFHovEnDWDQzk235x1m1tZCHX9lJYv9YFp45nFVbC8kZk8a9184Iqr7niE9OlE5b5szWUslOFY+bKOX7xpiNWGlSAIiIABNpEnO+A3RTqZLegVfImTVuCB8ePO4unPJKKlxvmbySCjYdOMauQ2UkJ8ZSUV1Pdnoy00YPZta4Ia6w8+zblvhwaU4mKzcX8PyGfKZmDaKwtIoRgxM4fLyGhPh+JMfHUVFdx+YDpdQ3NLpeNH5/mRvmjwMskcRJlcottDLfYmKgorqeZ9/Ocz1tmiJt8pg2enCQEHX1nKwg8SScGbJXRHHSuKZmDQqqTOVv54008L6v/opY4Wi1/PFh/arfmYjIGcABY0x1T4+lt7Nx3zE2Hyhl3PCmIGnvZ2VISjyzx6eDwIaPSiixBZyK6nriYqzXh4/XuKlV3nTILvW2cUjW4G6l8wkl3FzUGaKNiFwI3IFVLnMolrnfASwD0Qe8YcYichNwK9YXoXJgFdaXpY99fWYBDwCXYKU75AK/NsY8EeL8EfXZF3DuMNfWNzbbVhWod9Od/Isc76LFSbW6Zu5oJoxMdf1vwi14FCWKGNN6kyAaWm+idJRI0n+86UOO8AEEpSA53LV8M7lFZYxOT6Kipo5bPzWJLy4YF5RG9fhruazaWsi5E9M5f/JwtzrU6h3FvLqpgAmXpYYdmzeixpknZ5+ezuodxXx48DirdxRz55VTuGDKSPc4rzHy9fPGuqmnfq8xb4qWP10LWk8lO1U8bqIR2/jcv80Au+3HXwFEZFv3jqw5eUeOsHT5chZMncqCqVO79dwFpZU89eZ+bpw/NsgAGKwF2Lb8E0zMSCE+LoYvXzCejfuOuYIN4IovGWkD+MTpQ9maV+qW43ZMgcFacJXVWPNE6oA4pmYNcueNmkAjNYEA7+07ClheNI+s3BNUlckhuEJUNiXlu8kaksTOgpNUVNeTnBBLeXWd28+A+BgAstOTXZ+ZnYdOsNOXolFWXeumcNU3NDIxI8WtXDMpM5WMtAFkDU3iZTvKx/HLcbg0JzNkVA3QzLAVWve2abX88d69LR7f1+jKdG8RuR8r8u6IiMwFrgaeMMYc74rzdTd5hYXdOr84nzNvBF7AXudMyBhITD/hpQ8OMXlUCoMH9Cepfywfl1bS0ADHK6zPbnJiLMMGOnNLIsnxscTGSDNRtUvYsAE+/emuP48SNXSHnUQz4cYYkx+qYTs4G0tg8TLZfuQAnwIQkR8A93raxAPXAfNF5GxHRBKREVj55V4VYRrwBxEZYYy539kYaZ99BadiCsAl0zPcxYo3NN9Jh3IWOd6FC1iLIn+qVXpKQrMFj6JEG8aYgz09BqU5rZUD985RjtARquKTwwOLc6itb2D/kQrW7ykhOSGOCSNTQ85NjmjjiNf+dM9QqUfeCEWvGJQUHxsyJctp63iLeVNTvZE4QwcmMHRgQtC1hRKO/HNyS/vDRfOoF07nIyKfBVYZY6paafrt7hhPS2QPG8bSxYt75NyOuFLf0MgPr57ubi+rrqWsuo6pWYMI1De65bLj42I4bUiSK9ycPW4oh45VBXnWQHDa1NSsQcwaN4QH/2FpZDsPnaS0otY914D4GKoCDVTWNDBicAKDk/qz+UBpM/GmrLqWX72yy6pSU1XH+/uPcvi4dbOqorrejfypsAUigKpAA8NS44mP60d5dZ17x/5E1X6mZzdVZnLMT1MGWKka2/JPuG1feNeqcPPnN/dTWFrlGqQCzcqP+6NqgCDD1kmZqew8dLKZMXObWbKk7cf0Yro43ftGYDlwHlYV3r8CPwe+3EXn61ayMzJYumhRl/XvTyX0GnOPGJzA2WOHknekgl2HykgZEEdZVR0jBie4c8iA+BjXnHhAfAxJCbGUnAywu9DaX3yimkL7/vYf39jbamW4DnPttV3XtxKVdIedRDPhpq2lcltovwW4BktsOQ5cDDyLJaJcapcaHwg4pqLvAlcCFwFPA6OApcBX7f330CTa3AC8jhW9Mxv4sYg8bYzJF5HRbeizTzFueDIPLM5xjTErA/WUVtTwUVE5DyzOIS3ZMtUsrQi4Qs263UeYmJHCqq2FjE5PapZqFSr0XxcHiqK0RmvlwP0RNg7h5pcJI1PdCBhnrvNzy8UTgtI/HWHo1k9NCuorVOqR12S4MlDvpkddM3e066fjjRAC3GjGnDFp7rZIyp+H2u/dBs39fSLxuVEvnC7h70CNiPwb6zvHy8aYZj5ZxphTuiR4dnoy2/JPkJ1upQd4S3G/tNGKLnE8XcASIa6ek8XVc7II1DcSqGugsqaOoQPjKSytYliqnQaBFZUyNWsQt102mRff/9j1tCivbhJtHLHFEWx2HSrjvInDSIiLDSqpPWvcEH73eq672NtbXOaKNpMzU7lwykgKS6tYvaOYtGSrOlZuYRl5JZUMTIjj+Q35rqmyEyX07Nt5rljlLePtr1zjGB1fNTuL3QUnXbGlzHMdoSpFebc5hq0ZaQMoLK1yPX4A1xjZK+S0ak785JPw4INt/n0rISkClmBlGTxnjLlVRO7p2SH1HkKlEkJTNF58XD++fMF4fvrPnRSWVpGRNoCpWakcPl4EQEKcJdzGxQpVgQZGpycxIjWRC6aM4NF/7aGu3rji7rb8E82q3XU6r74K3Rz5qPR9QqVKdUppXWPMSt+mf4rIDk/bOqwwQqc61S/sSJhlInIXVmTO50Xk6/Z+R7rcZYxZBiAivwCesfu4GvhFpH0aY5ryino5t1w8wQ3Rf+6dg+7C5e6rpnH/Cx+yesdevvPURqaNHsyvXt3Nyi0FrN9TQmL/GFZtLaS23pKoR6UN4GBJJe9+VOIKNaHKjOviQIlWROSXWELtB1ieFBuBLcaYmh4d2ClIa+XA/VEoDpHML9fMzSYtOZ77X/gQaKrU5IjWjlfNk2v2sq+4gm35wZHq/nbeaBp/tKIzt3pTqJztTsW9cNfrj7xxCPV+tLTNvz+cuKVeOF3CZ4ArgE8DlwGNIvIelojzT2PM7h4cW9RwxSdOI2VAXFBajyPODE2J52hZgNyicqaNHgxYvjBOCtGL7+fzF1vccThyMkB8XD/OnTTMNREur67j3Y9K3DZ1DRDTD1IGxHG8oo701HgOH69hWtZgEuJiOXfSMNcTx7lz/+/tRa5QMyw1noZG4/ZXcKyK5Pg4DtsGxaUVtaQkxjEw0fpK2T+uH9edP8b13ikpD3D4eCENjY2UVdeSktiflMT+3Dh/LPUNjWQPS3avESAzLYkb5491U8qc7U4ZdD9OSpQjvDjCjBNx8+zbeYwcnMg5E4Y2e9/BEnJaNScePrwtv2alZQqxfEJfBYaIyBew7BqUCPAKlU764NSsQYwcnMjh40UE6hrJTEviJ9dOd83Mz5kw1BWDTx+Zwp/W7CVraBI7D51k16Eyrjt/DFNHD2ZQUn9KTgYYlTaAiRmp5JVYpsetCpsdIRBovY2itJFQwk2nl9YVkUTgk8CZ9qa/GGPKRcQr+OT6Xk8GUrH8K/rZr0O1c3Buv0ba577Wxt1bGDowwa16UhmoD7noWb2j2PXAqbOfq2sbmDAyhZ/dOIvn3jlIdW09/WP7sXpHMcvW7uf2y86IeIGhKFHCVViRdWdgReYBNIjILuB9rLvlK3pobKcs4TxacotOctfyzTywOIcJI60pvqX5JVxETThh+dlvzXf79+I1MfZ7fzmRQNW19W7EzXmThjF3YjoVNVbefJVdkW/hmcOZfXp6M98a53r9Hjj+EuItvUct7ff221ppdKVjGGNeBl4GsH0rrsAScx4CHhSRvdgiDvC27X9zyhGuqtSkzFRWbrEWYemp8QTqGnnpg0PuYuvF9/OZNW4IG3JL2FdcweDkONJTElyjYMcfZ1v+CdbvORKUGgXQ0NjkZ5E+MJ6SkwE+PlbJrkNl7Dh0nG9/2vrauS3/BMkJsa5oMyQlnkFJ/d3zAOQWlZNbVO6OteRkgLLqOrKHJbvlwgN1DQxMjHMXl1OzBrleOjfOH8v63UdckchJ8bph3jj3HF7/m2mjBwctGEMtIh0j5kBdA587Z0yQmDMxIyWoelWoMuSteuHkNI9cVNrNT4Dnga/ZPy/DmheUCPDOIZfmZLqV4F5837YmlaZ237h0Iqu3H2bWuCFs3HeMWeOG8NtVezh8vMb9jKcMiGPWuCGs3FxAyckAw1LjmZhhfc/Yln+C9buPEB8Xw1/eOsDOQydDemF1CE2VUrqAUMJNp5XWFZFkLGNgLy/QlO851LO9LMzrYb7jW2sXaZ99RriB4DB/wE1vchYUVYF6907VjDGDmTMhnW35x/nZjbNIS47n3Y9KWL2jmFs/NYnZp6cHpUe1tsBQlChiOLATGAc4oQixWCbpU4Avicg/jDFX99D4TllCRYnc+sf3WL+nhOMVAS6fdVpYUcPBiZRxvGtuWzSJxP6xQVEojpm609fz317QrB+vH43X+8s5hz8SaMJlqTz8yk7W77Hm0PMnD+fea2e0mi7aWrpYe1HxvPsQEXHEGGPMO1hRyXeKyOlYqdifwfK3+Q5wVERuNcY801Pj7W7C3bF2BIYfPbOVipoGUgbEcddnp/LGdstiMFDfGBQNEtPPWpUdr6gjLSmey88aBVjCj1Py1yva9BNwgmWGpsRz1tg0+sfEMG54CnuKrK97tXWGR1btYc4EK+WqoqbeFWQEXNFm0IA4TlTVkRgfQ3WggaEp8TjyW9HxarblnyBnTBqrtlgpGXsKy1zR5Oo5WcTG9GPzgdKgtCUndSvvSAWbDxzjNyt3c/a4IVw121qYDkuNdz1rnGibUNExjhlreU2da1TsjWa6ek4WgboG9/fgjMn7O2kxHWTlSpg/P+Lft9IibwI/xYq8mQMcwfK4UdpAsznFkcJ9knigrsFNe9x04JgruoLlcVNWVcf63UfcbYOS+vPSB4eYYPtKQXBqo3POTovAefppuO++jvWhKD5CCTfQtaV1rwL+hGUWHO483u0GK+Imknb+ba21DSIvL4+lS5cClsGQbTLUqwiV3nT3VdP4wd828e7eYwCkJSe4BsZpyfFu1RWHcJVR/KjXzanDmjVrHLd06GLH9A6Sa4yZJiL9aCrXuxD4AlAPpACfFZFP23fSuw1nfumtc0tH8QsXR8trOGQvcorLaiIWNbyRNvdeO6NZFEploJ6k+HD/2iy8wsfQgQnNDI5DzW1zJ6YzbngyF04d4QrirRkIt5Yu1l76qnjeHRUZ2sFG4Cz/RmPMR1iLtJ+KyFAsAeczwGndO7yeJVwqTll1LY+s3OOW6z573FAGJsYRH2t9nYuP7edWjQnUNVDvSVnaV1zBgPhYtuWfYE9hmetpA9A/VqitN65oExcrHC0LUFhqCSyOyAPQP074xiUT3QpOjsHpSx8c4mhZU591jXYUcqDB9dMA6479587JZmJGKYH6Rk5W1bK/2Codnj0smavnZFFWU09NXT2TR6Ww61AZw1LjGTownivOzmLllkJunD+WH/xtM5U1DazeXsyJyjqump3FI6uaMuychapT6caJjimrrmXvYUuEyj9ayaotRdb7Vd/I1KxBnDtpGBv3HeMvbx1woxMAyqrqgqJ0WuSCC1r9HSsR85Ix5if26z/06Eh6Mf45pSJgRdQ5z9AUieYQqAt2vxgxKJH9xRWUVdcRHxvD5FEplNif+fqGRutzfqKan7+0g6yhSW6UmnPuTonAmdnMRURROkyob7edVlrXLvktIjIAq8rU01hfahbb/jQlnuYpntcDPa9LCBZuWmrnfY6kbRDZ2dmucBPttCSahFogfHjQ8ngYnBRHaUUNX350vSvWOAuSC6aMYEB8rHsn2r/A8J9TvW5OHbxiwz333JPXo4NpmXoRmW6M2Qrssh/LReTnwOexUh6cKg/dKtz0pvmlK/DPS8vW7udgSSUTRqbw6H/O5p09JSFFDf+84zc1dsqKV9da1aMAt6z3A4tzQlaDCoX3PKHmtp++uIN9xRWMH1HVrBR4JP5fQwcmsGhmJl/93Yag1DClie6oyNAOJorIt4FHjTHVoRoYY44Cf7QfpxRe8aWgtJKN+465C6DNB0rJGZPGuOHJPL8hn7wjFa7gcWlOJuXVdazbc4TDx2uYMNL6ihbTz0p/yk5PZtrowRwtr3FTjiaPSmH88BQQqK1vYOvB467Ikp1upTN5K8ycN2kYHxwopTJQz9CUeCbbaRKXnzWKQEMjHx+toKHBkFtUzpDk/sTECOOHD+Tw8RpELAFk3e5iVxRKS25axOUdsXx3/rXFSrOckJHiGgYfORngjFFlfP+zUy1PmtgYKmkgIc6KzCk6UcXh4zWMGGzNSd5FqFf8Wrm5wL2e8cNTmDlmSJDZs/NeO78HJ7rm2bebKlG1yt69cPHFkbdXWqLGrmr7G2PMyZ4eTG/Fn963q+Bk0DM0CTWpA+I4WVXHsXJLlBmUFMeJyiaB58P8pjkCLPG2vKaOkpMBd7vjhZOSaJmRr95R7EbgdKl5saK0g1DlwN3SuiIyIIISmK1i9/GmiPwduN3efDqwCXBqV07AqkTlvAY4CRzwvE717MP3erP93JY+ey2tLRCcbc5i5O7/mEZB6bvkFpXxu9f3ApCdnsSimZnc9qf32FdcwZCBJ7hgygjuvHKKa/bp7aMyUO/e6Q7nf6MoPcyrwCp7ofWsMaYOwBizW0QWGGOW2uV91/foKE9B/FEi/qgXp4KMn8dfy+WhFdspKashPSXBNV6HYK8awE19cqIGa+sbWL2jmMpAPXdfNS3sXAbNy5dDsLmw45PjPC+amckb24soKatx00pDzYleQcip5ge4vmQasRj1FAFJwGYR+QPwSDgB51QkJbG/6xPxdu5RCkurCNRZ9/O8As3buUdd3xcnlWfzgWPu4mliZirxcTFsyz/B5FEpxMf1a2ZS6qQn5YxJ4xuXTuSaudnu/ktzMvn5SzvccVUFGvjX1qKgsTo3q66ek0XKgDj+taWQISlWtc2T1bXUN0CjsYQSJ1Xqg/2lbnSOExOUlGCNs6S8aUF4ojLAkZMB4mKFunpDoK7RLTteWlHLiMEJTBudxr+2FDJtdBojB9UwKm0Az2/I5/KzRpEzJs2NuHFwFqeTR6VY5s8hKlCFSoXy+oO0Sn5+622USFmC9WfyYxHZA2wHthlj7u/RUfUyvD5Os8YNYfrowcBxvrRgPM++fYBAXWNTOmSD9Rk5WWWJNbEx/exncf2nxg5Ppqy6jpTEOGJjhNzCcuJiheGpCRw6Vk16ajxZQ5N48B/bSE+xqtpNzRoU2eenJTZtgquu6lgfiuKj5Xhy+KOIXAh8Cav89jeBGODPxpiPWzpQRP4XeBHYiuUvcxbwH54m+7FKhT+IVQXqmyKyFrgQy0QY4G9O9ScReQa4GZgsItfZ4/mW3a4OeM5+/VykffZmIhFNjpY3RdbceeUUVv3gIh5/LZe/rj/AwZJK8koq+c5TG/nIzgs9WFLBQyu2s/DM4UFmm86C5s4rp7i+DtB3w/WVXs2vgK8DTwGPi8j7WFUeMrDTHYwxpSISNlJQ6R7880e4KMIqO7rmxY0fc7CkEggWW1ZtLeTciekcPlHtijVTswaTMyaNqkA9q3cUu5XynLns1k9N4pLpGa4fDjQXkvzmwrdfdkaQX86rmwpYvaOY1TuKSU+x2nu9xhzBxysIecWflsR3TUONKu4wxqwQkYexbjypgOPD6xORMyaNQH0jL220DIjLq+vc8r1TswaRnZ7smvcOtUWTCSMHcsGUEfzxjb1un89vyGfHoROMH5Fi+d0IHKsIkJQQw+YDpbz43sfEx1mLtEtzrM+xkw8/JCWeY7bYctqQRI5X1jJh5EB2F5ZTFWhgx6ET3HzRBD48eNyN5rGLa2IaDZ+cPpJ1u49QFWigsdG46VfpKfGMShvA587J5oV3892S39BkflxRY81XiBUx4/Q/LWswRaVVXD5rFFec3STCpAyII1DXwOYDpZwxKpXMtCT3PXCu78xRg1qtQOWlVV8bL0uWRNZOiYQ8rLXJNKxiLGcA1wAq3LQRb8qS8zl7+YND7ufJIWNQIvuKKwArmmZyZiqrtxeTmTaA4hPW9Hz4RDVVgYbg9Mh6w+CkeNJTEtl8oJTf/N9uKmrq3ai6jLTEjnvc3HBD620UpY20JtxMB64HVmNVULjN3v4tETnfGLMj7JHWcd8Is++fxpj3AETkJ8C9WEZe3tsjBcBSz88/xirFmYnl1O7lHkdIMsbkt6HPXkskoonfu8Yx3bxm7mi+89RGKgMNQfv727nn3qpSEHz3+dVNBZ19KYrSaRhjSkTk81iVHAYA8+wH2FF5IhJLk3GxEiWE8sBZtna/u99JqwoltjgRNBNGprhiyr3XzuCWiyewp7CMVVsLXSGk0jZqX72jmJwxaW6VKieV6cuPrmdq1mC++emWowr9KVvhrsVpV1JWw3PvHOSxm+cwdGACafOsRWuokuH+41sTcVTo6TqcKnTGmDLgJyLyKywBZ4uI/B4VcIKqvHijZAB+93ouhaVVxPSDT581ivyjlWzLP0HKgDh3IdXQaPjvF7dz+HgNOWPSGJQUx65DZe7DMRT2sre4zE0j2ldcwbjhyew8VEb/uH6MG55MP4GSkwHqGg0VNQ3sLa6kKmCpM7sOlbF+9xEy0hLZln8CkaYIm2N2CfALp47k/X1H3YigEYOtSlfXnT+GiRmpfOPSiazcXGBFxQhMzEihrKbeTZ3ae7iMhgar02Gp8Rw6VsnOQ2XExvQLEmGc9+vys0Y1SzdzBCnAjbSJ1Dg14jLHTz4JDz7YYl9KxFxhjNnm/CAi44CpPTieXosT7eJEoW0+UAo0mX4DjBqSSNGJakYNSWRgYhw3XzSBN7ZZ5ufv7z1KRY31eXc+917iYsX2gMqmvqHRFYT6iSX/FpZ2wpT+zDPwo7YUaVaU1mlNuKk0xqwSEcESYsC6qTEI+B5wYwvHPoJVAnyM3b4Cq9rL34DfOo2MMfeJSBFwK5aZaAWwCvi+Meawp91hETkHK5rmEqwy5HuAXxtjnvCeONI++yre6iolZTVsyz/O2eOH8Jn//jfjR6aQGBfD7NPTKThWyXt7jwIwbngyP/iPadz39w+5cOqIoIVIuLvP3nPpgkGJFowx/xKRhcCjWOIzQC3wffuL1OPAsZ4an9Kco+VWutHCM4e7wow30u/OK6fw1q5i1u8p4bl3DrqpUt4Il6T4WBbNzOSpN/fx4cHjLJqZSWlFgNr6Bm791CR3jkqKj2X1jmIumZ4BNPnhPHbzHO5avrlZFE1LFa781acc/NE7SfGxbmoW4IpF3nnVMVW+ft7YNlejUr+x7sMYc9IWbN4F/ge4Q0R+iuWB0+HU8t6KN8rj3EnD2FNYRqCuETuTgYZG+POb+/nJtdPdSJehA+M5Wh4IumM+bngyOw6dCOrbKeV75GSAxP4xJMXHMmJQoivqbD5Qyqi0AaQMiKOsqo73PrKm94y0AXxx/lhWvJ9PdaCeypo6dzzlNXVulThHtBmaEk96SrwbMTQ1axApCXHkFpVTW9fIhIyBlFXXuSLKvuIKd0HpRBNdPmsUeUcqgiIDjpwMMHt8OvFxsVw1u8mD5txJw9zy4M7YnXQzsBavzjni42II1DW4psPOsTfOHxsUpePgLyPuJUjUyVIPj07kgIj8CBiNVX3uGUf4VSLHLzo6IqkTqedUfys6UU1DA1QFqpmaFW+Zn9tRahU1ltF4RXWdK+CkJfcnJTGOo+U1VNQ0kFtUziOrdnP22KFsyz9Bemo8U04bxOET1WQPS7Yi4uzIuHZVmoqP74q3RznFaU24cUwHZmCV2TY0CTgPtHSgMeZu4O5IBmELL09E0C4fqxpVp/XZ2wklnDieEG9sL2L26ems3lFMQWk1uUVlQRE244Ynu8/Pfms+X3j4LfYVVzBy8MmQBpqh7j7rgkGJRuyyvTkiMh7rS9QOW/xNB/4PKG6xA6VLCCf0Llu7n1+9alVZeXVTAWnz4qkM1HPrpyZxrLyGvYfLOWNUqrvQ8uONQBw6MIHVO4p5dVMB63YfYfWOYvrHxrjn889jjh/OsrX7eWBxjptqFSoSJtJrCuXn40TnAEFzpjdqyLu9LdWo1G+s6xCR+4AszyOT4O9OAvw3VjnwDpoi9A3W7z7i+tksPHM4h45VEh/XjymnWd8nstOT2ZZ/AmPnNo0dnkx5TR1J/WN5fkM+2ekDiIsR6hqaKk3NyE5zS3NX1za4XjmBukZe+uAQG/aWMDkzhU0HSqmrNyQnxlJYWsXKLYXE9utHXoklhvSPFU4bkkT+0Uoqquvp1w8aG61F3dGyAEfLApw5ahBnjEpxxSWA0opaSitqyS0sJz62H/FxMW4KR0Njo3u9150/htsum8zKzQUcqwiw42OrlLjjUfPs2wdcI2JHpHFEm/i4fhSWVpEzJi3I4DkjbQCzxg1xSxsH6hrdCKX6hkZ+ePV02kJQ1Z7zJ7TSWmkDv6VpjbIE+K6IXGSvXZQIcf4+A3UNrlfT584ZQ0Fppft3D9DgCaTZln/CjfS7ZMZICkur+dw52fYNnyNU1jRQEaintMISXQfEx1AVaODw8RrySipcP5zVJ61I3Jc2HiIlMY4rzs4KWzmvVRYt6pw3RFE8tCbcVInIEzSV5ywxxiwHsFVlpYdpSThZvaOY2aenc++1M5g7MZ37//4h6SkJvLf3KBdPz+Dac7L56Ys73MoruUVlTBiZQm1dI+t2W14R//zehW5/ofwbdMGgRDPGmL1YHjfOzyVYVaWUHiDcfOVPO3JKfl8yPcM18wVcjy2/WOKPMnQid5zoHcdbBpqLKl6TYIALpoxsMdKlvdX1nGga72tvX/424cYbCvUb61LuosmXVsK0EZpudPUYeUeOsHT5chZMncqCqd2foeHcmXZMdadmDeJoeYCqQINlBry1iPjYGPYWWylOx8oC5IxJY9SQAby08ZCbDuWILF7iY2OCfj5aFqC8po7+MTEkJ8ZScjLgHu+U9B4xOIHNB0r55IwMik5UUxWopyrQwL7iCi6fNYqTVfVudIv0s361k0dZxUjrbdHoaHmAyaNSOFYecMuMH6sIkBwfx4SRA6lvbOS0IUnsPFRGcmIskzJTXR+ae5/fypGTVqly/536/rZIMzVrEBlpiWw6UErJyQBTswa5ZYi93kEb9x1zTYcDdQ1B1bRC0ZJBcVDVnjfeOKWqSq1Zswaa1jSdzSzgaiwz89nAZ7BE3S900fm6lbzCwm6ZX5y/z0BdA3956wD/3l7E2WOHEh/XL6hClENcrLDwTOuY5zfkc/WcLKaNTuC9j47yr61FnDEqhfyjVVTU1NNPoNFY6VMjBicwOKk/2/JPWD5aQPawZD4xfijQlKblr3IVMc88Az0wDys9RxfPL0Drws3fsLxljP1Y6dlnQh7Ri8nLy2Pp0qVBpY+jHa9w4iwCrpk7mqpAPR/sP0ZVoJ5r5o7muXcOMvv0dN79qIS8kkr2FpXx+odFTMpM4bl3DnLN3NHuwqmgtIr39h1j1JCkkHec/QsVXTCcOnTHpNQRRGQS8CMsf5vdWFXlNgO5wMvGmMt6bnRKOKHXn3bk9dWamJHCtvzj/OzGWW4EoD9t05mT1u0+4go9r24qaGYoHAqnGpS32pQzF9555ZRmFaKcKlZOapMjDrUkXoebM/3XoXNpVPIRVhVM5ztPCfCx55Hved2jZA8bxtLFi1tv2EU4d6avnpPFdeePYeGUEZRX1/HUm/sZlhrP4eNFQd40Y4cnk56awLsfWZF0k0elcLTcEmCcSJihKfFMzEhh9Y4iKmoamJAxkILSKiprgsuBJ/aPobq2gWGp8UwfPZjDx4uYPnowIwcFwJggY9Kxw5OZctog3t93lHHDk9lXXMGxsgATRg7k42NV7DpURlKCJRQlJcTwyWkZvLH9MI2NlpDz/r5jrs+Gg+O9sXzdfmaOGcLCKSPcyKLsdCvtwvHEcYSl5MRY6hsbWbWliKlZgyg5GWBiRkqQB46TJlJWVcfKzQVBvjdAs58dWjIoDtp36aVt+RX3euzv9nld1H051veMWuBpEfkO8GYXnavbyc7IYGk3RJE4f59l1bVuVNpLH1hm55NHpbDrUBlxMTB1dBrb8o9TV2/YnFdKTnYaU7MGWV5TG/LdaLmdh8pIS+7vRtmAFXFz+HgN500cxswxQwjUWVF800YPZnfBSTcF0hFR21UWfM6cTntPlN5BF88vQOvCzf1Yi7TrsCakn9keETcR/s5TryU7O5ulS5f29DDaRWlFwC01u273EXLGpLF+Twnr95SwckshuXbpvGlZgxg3PJnxIwY281wAeGjFds6daN04PHSskqftxYzXwFOjbE5dumNS6iB/wUrtBLjcs72K1uc7pYvxiiReQdgfxeJt981PN1WQSpsXH3IOciJ2qmvrmZiRwoAQkStewkXNLDxzOHdeOQXAjfjx4lSx8vrjQPhIm6PlNfzy5Z18sP+Y67PjPbfOpb2CyVgp4ncDG4EfGGMO9OyQohPvnWmv+PD9z06lrLrWjY5zhJvik9Xst/1tMtIG8O3LzwyqQgV2ZE31MTeKZ1rWYG6YN47f/iuXL84f61aDsQNm3AiXq+dYCy1HFAFcMejIyWqeeOMjjtjeOU7K1Rs7iqisaSCmH1TWNNA/rh+VNQ3876rdNDRY1aoco+Rxw5M5UV3HoMQ4AFfIaWgwbpoHYkUdXTDVSntyUqQunzWKD/aXUlhqiUQ5Y9K4anYWsTH5nDtpWNB76pRbd44F+Nw5Y7g0J5PV2zvBsnHzZpg/v+P9KGBF9+4VkReATUA1kNKzQ+rdnDUmjUEDYhk3PMX1zgKoa4DdBSepq7f09JKTAf611apF49x4OVreJNaWVtQyJCW+SbjpH8uinEzOnTSMjfuOMT07jT2FZZRV13GBJ9LtkZV7XPGmzVRUdOTSFSUkrS1k+htjviQi3wBqjTH1InI9ljnwe10/PKU1/HeaJ4xMYdXWQnLGpHHrpybxyqZD5BaVuXejPrQN87KGlpOdnkReSSXjhidz/byxPP5aLgBnjR3CpTMy3SpSfu8FDctXopgzsPxrErBM0R2S6OEowd4Y0dcVhEotamlbZaDe9aFx9vvnIK8B8L3Xzoio4p7fZ8aZQ51UKeecj7+W28w0eNHMTJ5752CziJxQ5/n1/+0O+vloeQ2/enU3lYF67r5qWpvm0r5uBh+NEX3GmEbgKRFZBtwAvCoibwP3GmPyenRwUUaoO9NeY88rzm4y5gVLHJk8KoXYfv343DnZrN5+mFnjhriLtVFDklxfm+TEWM6dmO4KFoWlVbz8wSHOHjeEXYUn+dKC8XxUVOYamE7MsNbLl581ilrbDCN1QBzrdpdQUdPA8NREjpwMMGFkCvFxMRwrD1Bpm5ieNmQAeSVVnD1uCBv3NYlGx8oCDLFLmFfW1nOsLEB8bAz7iivcKJqYGOGTMzJYu6vYTa1yqkQF6qz+z500jPjYfgTqG4mP7edeU6iS4GAJYd7S5WXVtTyyco8bFdCuaACHYrV760S+B7yBVRjF+b7xdM8Np3ezevthXvrgENedP8adO7ym395qUf1jhYEJcRyrqOXwidAVoU5UBvjE+CHsLDjpijqOOXjOmDTXpyo+1qpMV9/QyOYDpe781WaD4u3bW2+jKG2kNeHmjyJyIfAl4HURuQOIAa50ym8rPYt3IXHepGGu2OLcrd5XXEFacn9KK2oZnBTH5WeN4uNjVazeUcxtiyaxu6DM9X+oCtSz8MzhfHHBODclYcJlqW7FFr0rrPQCNgLzjTGNIpIBTMOqLOU89xi9OaKvMwkVZdLStspAvRvl0lkGvU6EztHyGu5/4UNuuXhCkNfN0IEJ7s9e4drp+7l3DroiUUsCyvXzxrJyS4FrqvzDZ7a4pvDhaEmc8Qr1TlnxvkQ0RvSJSJwxps4WcP5sCzg3AqtE5C3gfo3AaU6T341VCWnnoZN849KJnDtpGOv2HHE9aG6+aAKZaUm8+H4+f3nrgHunG6C0spYvLRgPWF4uV3ziNLvvOkYMTnAXcTlj0hg3YiA5Y4a4KUkf5h8nt7CcCRkDyS0sB+CT00e6kTiBukb2FVdwsqqOv7x1gORE6+twcmIst112But3H3ErYyUnxhLbTzhRWcexMsvzZkRqIg2NhrHDkhiS3J/PnZPNC+/ms/lAKSer6l3RZmrWIHex51R3evH9fJ7fkO8uSKFlH42UxP7cdtnkoAWks9h02re78s2SJZG3VUIiIgOBZGPMxyIyE7gW+ARwEPhVjw6uFzNr3BA+PHicsiqrkpsjoDoMiI8hZ0wa7+09Sm29IT5OEGkSdGJigg2MGxpgy8Hj1NY1WilV1XVsPlDK5FEp3Dh/LKPSBpBXUuEano8YnMDlZ41yP3NtNii+6aZOeR8UxUtrws10rBDh1cBDwG329m+JyHnGmJ1dOTildbx3ntPmxQcZXVbaQoxTSep4ZR2nj0zlnmtzWLZ2P3MnprO7YAdgVaJy7gw75XaPltfw+Gu5VAXqEbHaXDRtpGto7K04pShRwgqsyi6FxphCoJBgby6lhwkVseffFs60tyWhorVIQL8g4i3RnRQf63rMOGIOwC0XTwg6v7dEuWOU3NI5AM6fPJzzJw/nmrmj3XTWS6ZncMvFE8gtOsldyzdzxxVn8s6eEhbNzHTbQPMULG9k0LK1+zXysXt4Hzv9UkTSgNOwfG4eAb4F3CAiT6MCThBev5ucMWnunWuAw8drSBkQx+HjNazffYT4uBjXCHRSZipFJ6o4fLyGw8dr+NOavRw+XmPdCY/rF1SCOz01nkZjVYZ78b2PSRkQx8IpI4iPi3HFmhOVte6Yio5X85ULrc90WXUtKQPimGVH1QwdGM+f1uzjG5dMJDMtifi4GLblnyAjbQCFpVVMyBjIico6AGL79XO/V5WctETZaaMtYWr19sNMykzl2bfzyB6WzBVnn9ZMSAkl0rTmo+HdHyolrd2Vb558Eh58MPL2Sii+g7UuuscY8zPgD7aoe8gYU9nDY+u1bNx3zI2CSRnQVOo7Lsb6PByrqGVfcXlTulR5LcZYxt+DBsS54qmDALW2+FPf2EheiZXKdLQ84JYS35Z/guxhye7n/lBplWsUDm00KH7iCbjvvg6+C4oSTGvCTaUxZpWICE1lwAUrBeFOrLtOSpTgDf8Hy6Nh4ZnDuW3RJIzB9X1wPCIuue911/smZ0xaUF+5RSf5wsNvufsdXng3n9yiMg4cqWDVDy7qc3d8lV7PzcAdIvI7LMF5izHmeA+PSYkQR/hwTIKhKZWppRShlqpMeSMQ/elRlYF6qgL1bvSN43cTTtCpDNRz55VTuOXiCSHH4T/H46/l8tCK7dx55RQmjEzlsZvnuCmpmw4c44bfrKMq0MCBIxXkFpW5oky46CJvJJBGQHYbp4vIbizBJtQ/PMGKSr4BiO/OgUUz/oWOEw1SXl3H6h3FbtlroMkTBnj27TwOH69xU6hGDk7k8PEiRgxOIFDf6N4hP15ZG1RhJq+kwo3AWThlBAdLKnhv31GuPDuLouPV5JVU8OULrOgdf3SKE/FTVlXHyi2FjBsx0B23I+yUVdWRW1jO1KxBfPmC8azffYRAfSMYiI/r5/bl3J2/7bLJISNf2h0Z4yGUyNPuyjfjx7drDEoQFwNn+DIRTgOeE5FPGWMKemhcvRon4iY7Pdn9u3bMikWE5IQYdw6YmjWIC6aM4A9vfMSI1ERGDE7kyMkS1yYCgnPldx0qY3Cy9fkrORngf17czkTnZrSBKael0mgauWp2B9IQhwxp/7GKEobWhBunvOUMYCjW370j4DzQRWNS2oF3UeF8oXcWAbNPT292x3rZ2v3kFllO68NS4jlWEeAT44YQF9uPs8cP4eJ7X6O0opbThgwga2gSZ5w2iCHJ8Vw0bSRf//275BaV6R1fJRpxqr/cbT8QkQJgK7DVGPODlg4WkRuBz2LNecOxTNm3A/cZY1b72t6Elcs+0W63Cvi+ppG2H79J8KKZmTz8ys5mQk6448CKSnEqPzlzYElZDdvyj7sGwWCJILdcPIEvP7qe1TuKqQrUk56SwNyJ6Sw8czjjRwykMlBPbtFJ1+vLEWG8IlFu0Um+89RGRg1JYn9xedA5/DiRPj98ZgtDB1pGiQPiY3j0P2e7ETfnTRrWYnSReox1O4lY80pLBRkEiGtrxyJyEfCaZ9P5xph1nv29do7xigtl1U1RLxv3HXNFm29cOtHd7njTgHUj6cb5Y9m47xizxg2hpCzA5gOl5B2pcFOdnt+Qz9SsQWQPSyZQ10DekQqy0wewaov1ed9bXE5tneHlTQU8vORsdxzPvn3APZeTvuUvvb16+2GuODvLHf/AxDhWbi7g8rNGER/Xj4GJcW7akx9/5Isj1Mxy/XIaXKNh7/vTFWJORIxoo9CjhKLW/5k0xrwuIjdjZSvcEEknnTUfiEgW1hrtEiAZq6rmr40xT4Q4Z9TOMU7EzbTRgwFYubmAKVmDaDSNQaLt4OT+ZKQlsr+4goGJcewrrqC4zNpfXdtASmIsZbZ5+DA7Su9oWYDjFU3zUm5hOfGxMe788tIHhwDLAHliRmr7ItrmzevYG6AoIWhNuKkSkSdoMgssMcYsBxCRH3XlwJS24dwl9i4qQnk03H7ZGby7t4Q/vPERp6UN4OPSKp5+Kzi6+/CJDyi1J7SsoUms31PCpTMy3bvOjgKtd3yVKCSAVclhkGfbKPuxCGhRuAHuwvoC45AIXABcICJfMMb8DUBEfgDc62kXj1V9b76InG2M6YRyH6cefpNgx0vm1k9NCkpNciJq5k5M56cv7uCOK87kziunUBmo5/HXcl2j9psvPh2AD/Yfc31m/NWknJSHbfnHWb2jmEumZ7B6RzH9Y2P4/b/38tauYtbvKeET44dw55VTqK5tKhl+91XTuGv5ZrcPgP6x/dxz3HLxBLdk+MOv7AwyOD7jtFS+/5fNPPqfs5k9Pp1xwwe2ajzcHnPivm5o3E0IUAsUEFwK3P+IvEOROOA3LezvM3OMd9ETKs3HSUuamjWIiRkprlmvc8w3Lp0YZMY7cnCiG/mSmZbEvc9vJbeo3D3fv7YWuRWgvvbJCUHjcESTjLQBzarGOKlO/qiVlZsLXKHIStmKCVq8eYWXUJFGXu8eb6n0UO9Ph4yG28O6dXDZZd17zr5HhogkG2OCyggZYzaIyI8j6aCz5gMRGQG8DXjrxE/DSt8aYYy5v6199hTez5LzGQT45IwMJmc0sHZXMQ2NcLyillVbitzj4uP6UVFdz+Dk/lTU1LmiTcqAOGaPT+elDw5Zom96MqWVAbbklTJ0YLxran5pTiaBOiuValJmKi++n++mcrYpou0f/4Czzuqkd0NRLFoTbv4G/BjrDrYh2CuiRyu0dAW9ueqL19fGqcTiNRSurq3nje1FzJ2Yzud/udZ1VHdKf5dX1/Fh/gk+MX4o00cPYl/xXrLTk7jn2hnunWDvnW//XWfl1CAaq774WGWMuVJEsrGiZryPSL4Rn8ASd5YBpVgpoXfZ+34I/E1ERgOOcP0ucCVwEVb1iFHAUuCrHbqKUxRvKtCimZl856mNANTU1bNu9xEWzcwMKt89YWSKm8553qRhrv/MJdMzWLW1kN+99hGrthZy66cmkZwQx8SMlJDpUgDXzB3Nq5sKgozez5s0jJVbrCj39/YeY87p6QyID/63+cDiHGrrG9yIm9r6xqBz3H7ZGTz8ys5m2wA+Oa3pu3UkxsOhqm+1RnuOUYLYC8zrgkXMN4FJQBUwwLujr80xfrGmpTQfR8zxb/OKN05kzsZ9xxiYGGelLAEZgxM4WV1HfGwMx8oC5IxJY2JGatB5nJSs6dlpPLJqN5sPlLJycwGfO2dMq1Er9Y2NXD0nq9nizS+8ePvwplydMSo1ZFRNu9OcOoPPfKb7z9n32ASsEJEvG2Pcuu0iEkvwjaCW6Kz54B6aRJsbgNexvP9mAz8WkaeNMfm9YY4J93n8YL8VudbQCDH9oMHjWRwXYxmPx/QjKKImph+UVdWx49AJhqbEE6hrYErWIP60Zi+VNQ1kDW36v56S2J+UAXH2PJPHtvwTBOoawkbZhUUjbpQuoDXh5n6sRdp1WCF0PxORscBNtBw23CvpzVVfnPB5pwKUP8pmd0EZq3cUs/Xgm5RW1JI6II6xw5Kpa2jkvb3HyE63yk8eK6/hq5+cSP7RKlZtLeSdPSVBC4/bFk3ikukZVLWSuqD0TaKx6guAiIixuBLALtObh/WFxWkTiZv2Rd67ZvYdqf8CUgDHDOBqmtIifmEv6JaJyF3AZODzIvJ1uwqN0kacuezhV3a6ETAfFZW7US3Pf3uBK0g7ETcPLM4hLTneFWHuuOJM99mbenS0vIb0FMvjyzFfB5o8a2bCd57ayNSswaQlx3P7ZWewaGYmF/7kX5yorOOptft4/tsLgkTxCSNT+ef3LgRw50m/R00kFa8Wzczk6bX7WzQebkvlrI4cowRxX2eLNnbFux8CR4C/0lT4waFPzTFtMd6F0KlDjnizcnMBgbpG11dm9fbD5BZa4m1qUn8Kj9dw7vRhlJQFuHH+2Gbn8VZ2ctItAnWNvPh+fthUpUtzMl1T5JljhrQqvPjH71ybv8x3pO9Pl7JuHcyd2zPn7js8ALwH7BORd7GqW5ZhCSGteux11nxg77/Wft5ljFlm9/8L4Bm7j6uBX0TaZ7TMMdOz03hzVzGNBo6VWTeevVWjnNd1DVbUa21907DjYoS6BivWYH+xbUhcFuDnL+90zYrHD0/hzFGDAOvz2/RZrmNb/gn2FJZZhuZtSWUsKmq9jaK0kf/f3pnHR1Wei//7EEJYA4Q9wZCAsihbABVc0bpQrHUp1Spqbe21i/f2171Vu2DV2tvb9ra93bTV2rrV1lqtS90KiBsUJCxhVUgMEMKSAEkImUyS9/fHWXJmMpOZSWYyS57v5zOfOXPOe97zvOfMPHPe5zxLp4YbY0wL8CkRuQ0rhrNFRG4AFmEpKSUFCHaFdww4AIfqrAopX7/iNN6vrmOXrbRGDsmhtKL9/+SSWeNYueUgO/fX8eL6fW6J8MVzLMN9cGne5pZWN5+OuuIryUREXgPmisjNwBza89nsCmo6C1jVWV/Brs5APyDLXnYSDM7xbN8ZtDwNGAoUA8HH79XEqie8BofaBh93PF7q6iUnwfqjq3YHeKc4+WNKy2t5eWMV50wdHWAA8eaH+dkLWzskIf7an9axYssBVmw5wKhcq+3kcUO56bxJ/OKf2zl63M//PLuFp766MKLM3jF65Q01/sP1TdzxeCk799dxwWljApIlh9Lv0ZxT7zY1rneLrwF/itRIRP7TGPPLKPv8CVbuif8itAdjr9Yx4UKHvIYXB68Xjc/fxra9deQOyHarR4XDux/Q4XjBxpdwYVSOXF45g0PDupu/JqHU1iZbgrTHGLNJRD4M/A04C1hA+8Ptj0fRRbz0QR97OVQ7h5IY+0yqjnF+h5s+OMKhYz5mFA7jrFNGsXb3YdfwOnhAXxrscCiAs6eOomzPUeoam/H5DSOG9KOmwUefPln4mlvpmyW0tBqa/W1k9xXOmTKa6YXD+NXLO6hrtCrH5WRnub/1vTWNrmce4BqOF5UUdP6bfu+9BJ0VpTcTyePGSXJ1BdBPRN61LbiPJlwyJWqCE3M6YQbO5AWsicllc8bzi39up2jUIHYdaHCVV97gftx+1Uxuv6o9/8Oj9pNfb7Wpz148mdoGH0+vqWTFlgNcOH0cI4f07xAGoCg9zKlYHjG3A6c7K0WkAdgMbMAy5tyBdSMSC18DnMekTmK/kZ7tdWGWR5Ohk6qucLi+yU0YDNHpCa+RwhtClXdeTkC4lLc/rycOtBueQ+ENk3IM0M5TurOnjArwUPnyR6z+N1cecY1HzriiNUZ1FrLk6NtLZ+VTUpzHD58pc41Jzn7B4a+hqm9FezwlJiaJyDRjzLZwDURkPJZ+iWi4EZHzgU9g5aH4I1Y4ejC9VsfUnWjG528NGZIUCq8xxynx3ZX9vBM16Gg8isUrxuuBk9T8NdFw883JliAjMMassOdLHwfOxsqJ9RdjzMrO9ouzPiCGdmmhY5zfz+VzxwNQNHowF04fi6+lFX9rLWKsct59+kBbGwzol8XMwuG8u7uWKeOGsqnyKEeO+/G3ALSS3RcG9uvLMdtA428xbNpzlG1Vx6hr9JM7MJu6E35eWW1VmbvijJOYNGYwk8YMxudvc5MWAx1yXXXgllsSeGaU3kqnhhsROQsrw3l/z7oy4BOd3cQoPYv3Ka9zs768bD8rthzgjJNHMCinLwumjOLev20CYPyIgYzO7c+/d9UwbFA2f/nK+e6kI3gC5J0YOCFYzlPhYBd8dcVXksQNWHlpHgPO8KwfgvXkq0t+4HaFqe/bH1cAP3I2hdvFs9whB5iTQwtIyzxaXcVrtAlX5jqaPpzqT8vL9vPQF84OqXcczxanbXNLKw994eyQRpWRQ/pz59Uz3c8/e2Erb+04xORxufzi02e46xyjzL3XzQnYf+f+Y1z3szfcHDteI4vz2UtnejLYu6i0vDakt6PTN+Dm8/EmbY72eKnGypUrnfxZkHo5tPzAEyKywBhzInijiCwB7icwIXpI7JwXvwRagduMMUYkpDrpmo45eJBljz8OwMIZM1g4Y0YkkVIOJ4Hw0nOLY/ZQiWRcCVe9Ka7ltYP6S2r+mmh4+GG4775kS5FwEqVjRORG4LAx5p/AaGPMH4A/eLb3CRdulAB90CfKdrH06VJRVdXj+mXepBFs3XuMC2eMZd2uGh57o9z1gHEYmJNFo8/ynps/eSR/WLnLymez7ygAg3P64vNb+W78LXCsxTLaOMYeKx/WMBqaWpg7MY89NccBWP3+ISoONbC58iglxXmMz7NSD00bb4VVRfxNP/gg3HNPPE+HkqL05D1MJI+b72NVVfEyA1guIjONMYcSI5YSC94n085T5De2WfkgBuX0tSukbGHFlgPkDe7Hm9sPMSbXmsgUjhjIa5v284+1exiY09fN9eD0uXP/Mda8d4iZE4YHJAs985RR7mRIy9MqycQYsxxYDiAi3wBuIzAp8RSscKeoE6qLyCeBh7BuhN4ErjDG+O3NXr2X61ke4lnuoBvTOYdWd/B6k4RLuhsKrzeLt/rTii0H3BwwwXrHMRKFahsJb0WrF9dbLtFeI8zh+ib+9/mtbK48wo9vmueGNk0elxuVEdurJ4M9dbzbnKpYJcV53Hn1zA75y4Jz50QqG364vinAAJWKeA2Zd911V0VShenIr4Aa4DfAzc5KERmENem6CWuyE41+uRKYDjxv9zEb8N79nywi1XRVx4wezbLrr49CjNQlkYaOWLxf4pV3Jqn5a6Lh1N5x75ZAHfNjYLiIjADeF5HjWJ6+m4Ay4DNY9yGhuJL46oM+UbbzvkfTFoCi/HyWLV4cPIa44zWwrttVQ2l5rZvYGyxjTpO/hW17rYcmjtEGoGzPUSaNGcSWvXVce9YE1u2q4YSvhWMnmmlpbxZg7AHYsreOZn8bK8oOcPm88RxrbKGqtpFDx3xuBbpJYwa7FeGiMiqPTVFjrRJ3evIeJpLhZg7weawyl0XAXOASrIzltwNfSaRwStdY894h3tpxiAtOG8OdH5tJv75b3GSdTqjCsUbL+rynptH1qAFo9LW4yTtHDunPi+v3uWFRzvKls/L5+IIJKT8ZUHolHzbG7MaqpACAiORgGZxnRdOBnSvnQayboOXAR40xxz1N1gPO7GgyViiWswxwDCjvmviZR7i8L8EEGzOCQ0AdL8Kzp4xyc8B4c99MHjfUNRJdcNoYZhQOZ6Bt6AgV0hRq3ZT8XJpbWlk8p8BNduwc69FVu/nFP7cDuMdsaLJsebUNvg4GGIed+48FyAiWceaHz5SxvGw/P75pHi+u3+fK0WiHbznvDsF9d2aMCjZ6achU1zHG3AkgIs+IyM3GmIdF5AwsD7+JtD+h/mMU3Q223z9iv4L5A/A68By9VMck0tCR8t4vyWBQ6ITJStRcAkw3xtSLyFGgGitke769vTODbiL0wTGs/DTeJE/e5VL7PWXvY4JzRDnvXt1w60WT+e9ny9w8N6OG5uBvaePQMR+HjlnJi9fsPEzVkUYamtoNNE4VKq/RRgSa/W2MHd6f0yeN5MLpY8HAsIF9mTQm1/X2iTlPlZYCVxJAJMPNEWPM/d4VYvnxfQ6rdJ0ablIM75NpgNc27Xef3lqlMXN5d3cNb+04RN6gfvzu8wv45T+3u/s8u24PHxw6znFfC3dePTPkE2SdDCiphIjMBTYZY/y20SYAY4wPq8rDuij6+hTweyyjzUvAVcaYpqBmfwXuw6rI8GURWQV8CCuhH8CfU6USQyrgNTiEMmI4BOuUYIPPQ184OyCvy6Ccvq6HDARWmwo2EoXKwxV8vPtf3ekaZhxDipMnzPF0OVTX5Oa5mTxuKIP7Z/PyxirueLw0bMLiOx4vDZDRy4otB9ztThlwp9x4cNnxWAg2ennfldgQkQuMMSuATwFvishMLK++vlhGm6PA54wxf4njYVXHJICU935JBmvXwtVXJ1uKtMUYsxErhx7A/zPGPCIi/YDTsB4WTY/DYaLWByLyJHArME1ElmI9xHLman67r5j67GmCjTVOrqh5k0a4BpR1u2qoPtLEoP5ZFOQN5GNnTuB//lEW0M/O/fUd+p40dgg7q6z1/XP60ORrwxjI7itUH2kid0A263bVuLlsSopHMGRAtrt/uHDLkLzwglZsU+JOpDvDVhH5vDHmN84KY4wBfiMiX02saD2Pk4MinfNPOJOLZ9ftYcWWA4zPG8jkcbnU1Dfxu3+9z6Wz8pk3aQRv7TjErRdP5pKZBRSNGszX/rSO5pY23toR6H0d7imvTgZ6J3YMZ1FypejAWsBv59961/PaZIxpdhqJyHnGmE6rSmElBnTcjRcBJ4JizouNMRUi8n3gbqynat6aj/uAZd0YS0bTmREjWKeEC8H8+IIJriFlwZRRlB9scD0Kg8ODFs8p4MX1+1gwZRSXzspn8ZwC1xvFu86L49HjhCw5bR5dtZsvf+TUAIOQk6jYm7A4mFBtPntx+wPQjy+YAOCWAf/4ggmUlte66yPhlDU/0dzCgH5WuGuwAUuN693i58BMY8wR27D7Bu1ldN8EbjDGVEbTkTHmYeBh7zoRWUZ7QtJzjTFv2utVxyiJZ8mSZEuQSfxGREYaY/4Xy7OltLPGCdIH3wMuw4qMCC4kc5cxZo997MpU1THhqrSt2HKAqtpGAKYWDCUnuw/Hm1rZWVXPH1/f7YZCZWVBdp8+NPnbXA8bsLxyRuX2Z8/hRk40t+L3lAz3txhmFA6zqkoJXD53PDnZfZg3aQS/emlHQE6dx94ox+dvdROahzXgXHRRfE+MohDZcPMi8EsRuQMrSfG7wAdAITA8wbL1OJmQg2LkkP6Myu3PB4esyI6VWw+wp6aRQTlZjBySE1DK25k8OCFQ37pyOqeOH8rysmpOP3mEGwoFBIQUaPnv3ott0KxIrhQhycYqczkbcFL5t4jIFiy9tQ74FrFXlQqJMeYeEdkPfBErh04D8DJwuzGmOh7HyEQ6M3R0lgMG2r1IDtU1saOqjsVzCnhnxyF27q/jC79bwxNfOpe8wTk8umo3h+qa+MU/t7vhVZfOynfLg4OVu8a7bvJlQ/nsxZPdBOw/fKaML354KpfOyufrV5wWYHDyGkEmjxvaodpVMHmDcygpzuOv73wQkEPMmxjZ6cNb0e+cqaPJOy8noq59dNXugHBXpxqVGmvixski8gztE7FfYpXtvQv4gf0wCxFZbIx5MV4HVR2j9AgvvaQhHfHjH7bRJu5Eqw+MMdV2YZn7gEuxwrF2AL8wxjzYlT6TzQXTx7J17zFKy2spKc7jgulj+dVLO/D52xg8oC8XnDqWM04ZySOrdlFxsIHmFkOrba1pbYORuTnkDe7Hzqp6Dh1rfzjdaht6Rg7JYe6kPKpqT7ieNkvmF5KTncVb2w9SWl7LyNwcXtlUxWcuPIWl5xbj87dGzpf13nuQpk4ASuoSyXDzA+AaLMvtJ+2Xw6uJEkrpHt58EKNy+7OnppHD9T4O1/sYmJNlJyvOCmgPVgnda376OrUNzXzjkXfZdaCBN7cfdMvTQuTKKYqSBA5guf3OxTLcOAnVs+3Ps4BPR9ORMaYo2oPaN0EPRmyouEweNzRsSJGXzkp9O7oNLAPQz1/cxs79ddzxeCnnTB3Nd57cwAWnjQFgRuFwLpw+jsVzCjhn6ugAD8HgdcFJgA/XN7mGbsf4s3hOQYfcXp3pw+Ay6KXltSETNHuPfdzXwreunB42JNXxsAFc75rjvhbX40a9IONOf+By++VwAlgIDBWR9VgGnR9hPeyKCWPMMsI83VYdoyQcny/ZEmQSTSLybeD/jDHHutJBPPSB7QG4NMrjpbyOyR3Qj9sWTQkIUbrpfOt/7uozC9m+7xiD+/clp28WzS2Gkbk55A7IpupoI00+y4Bz5LjlfD0yNweAw3U+srPA3wpGoPLwcbbtrbPy3Ey0KqU7Zcjz8wa6nj5/fH03P7v5dOpONLseN2H54INEnRKlF9Op4cYYc9C23D5NYFb0Bqzyu0oK4s0HcaiuifXltSyanc/rWw+yc3+d63nj3PyDNQH43AOrqW2wlNuF08dy8thGNz+Ot+RsZ2FS6o2jJIE77BKciEgf4FRgHpYhZx6W4aY/MVSVUnqWYL0RrGO82xfPKXDz5Ly4fh+H633kDe7H6KH9OVTXxLeunM7HF0wISPgLBHivOEaQyZcN7SCLY0S59+lNQLvxJ5whJZQ+dOQ97mtxkyVDezhUOIO34z1z97WzQ54HbxuHO6+eGeC9oySE4NK5A4AL7ZeipC8f/3iyJcgkbsa6z/ieiOzAqiq12Rhzb1KlygCCw6cK8gZx+1UzeHZtJY+9Uc7WvcfYXHkUgLkT8yg/2ECTr40+YhlpANcoc8YpI3l6TSWjhvbnlQ1V1NT5qKnz0S+7j5XnZmA2F0wfS052Fj5/K1W1jUweN4S6Jj+fv2RySHlCcsstnW9XlC4QMfuhMaYCmGMnAD0H60nTP1LJjU4JzycXTnKrRN1xNe7T38njcqlp8PG7194DrEpUd35sJs0trcwoHM6XP9KewDPYCNNZzoRIT5/VqKPEG8doYy+3Yd0slWHHjotIFlaiQPUHT1EiefEFJ9s9Z+po8gbnsHhOAY+s2s3O/XU8ssrKS333tbPJG5wTsL/X82V52X7OPGWUGyoaSietef8Qf32nglsvOjkgr01nBiXv/k7VqC9+eGqA0dtpG45o8vx4PSq7i+rkqNiC5W1TQns4ZgmWJ7IXNQwr6cfjj8N99yVbikyhAisZ8Eyse45TgY8DariJE8HJgedNGsHWvcc4fdIIyvYcwd9iWPd+DTX2Q+g2A6OH5nDetDH4/G089+5eXt5URbO/jWnjc5k0ZjD7bM+cZn8b+XkDmTdphHsML2dPHc26XTWMGz4guupSDz4I99yTiNOg9GI6GG68FVq8640xTsJPJQ0INxH67a3z3QnMqNz2yY1V8vsQ//jmhwL6iTUUqjNvHA2xUnoCERmMNbFqBjbaVaE22S8lBQnWG51VmPJuA9i5v44LThvDKeOG8N7+ehbPKeB/n9/KL/65nUN1Tdx73Rw3b8zkcbms2HKAFVsOMMiu2hRKJ33hd2vYdaCBY417uP2qdm8WxwsmVKlt7/pQBO8bylASTSJhr0dld70eVSdHxX/ZD7AqgL87K0VkJO3GnBJCl/NVlNRm9uxkS5BJfNQY47pDisgkYEYS5ck4vKXCrzi9kHW7aigtr2XXgXr8LZbt3DHaOBSPHsyOqjrqT1jT2mZ/GyKwbW9dQLvBA/ry+Usm89b2gzy1upK6Rj+5A6089E+trmTXgQY3SXFU1ekmRFdgQFFiIZTHTTwrtCg9jJP/oNGTJ8Epwfv1K07jtU37mVqQS0lxHieaW3hrxyHOnjKKc6eN4YbzJro3/YvnFPDXd6z4zM6eTAfT2cRDK1EpiUZELgSeApwYmOMi8hTwVWPMkeRJlhlV6+JBKMNCcGJib54XZ7tj9HCqQHn1iLPtgdfe58X1+9hcaV1q591p6+i1Rl8Lx30tbuWmYJ1039ISbvy/Nzlc73NDm7zhT06oUiiD0iOrdvPr/ziTQTl9OVTXxHee3MBxXwt3Xj3TbXPc1+JWxvLq02iNLp0lcg42xnTWZ6rp5FSsWmeMWRlm/WGsXH+vAojI5h4US1GUFEFELgXutxbll8aY/wEwxuwCdiVVuAzDWyrc+z4opy8Pv76L0bk57K05QXaW4G+1DDmbK4/S6GsN6McpAe4Ye7KyoOFEC4+/uZvD9VZoVcWhBjZXHmVG4TCWzC9kVlEeAPMmjYhO2FNO6d5gFSUE4UKlerRCixI/vPkPnDwJjofNu7trXIXkVEvZ9MERZhQOd40zTts3tx90E2p6n0y/uf2gm1wzVjd7LUur9AA/A4Z5Pg/GSqq+UETOMcZUJUMoyIyqdfEgmrAob56XSPt5c80ctw0yd35sJv36bnGrV3l1z51Xz+RnL2zlO09ucCswBbN1zzEafa1cOivfNWg7uvFbV053w5+8/d5w3kQ3bOvev23iwunj8FaS9xqkoKM+jebcBBOc/DjYOylSn6mmk1Oxap2IDDLGHI+i6X/E2F5Rks+GDXDttcmWIt35GVa1XYAfisgRY8zvkyhPxhKcW8b5fPdTG2n2tzFkQDYzCnPcfDcAOdlZDO7fl4PHfPY+2dSd8OO3Exk3NbfQ0NRK7sBs1wtncP8s8vOsOhebK48yc8Jwtu+zKludOn4oBXmDIgv72mtaVUqJO6EMN3Gr0JJuZMITcWfyUtPgY3nZfhZMGcXUglzer65j14EGLjhtDM0tbQHVUpzqU2Alz7x0Vj4/uL6EkuI8t0/ANeY4T6DVzb73kYpPxIM4BfgH1uRvHDAdmIol830EVsZTkkAkL49w28Ot9xqQB+X0dQ0ynVWvWjyngDe3H3S9dzqTYeSQ/tz79CY3ybCT+DjU8Z/40rl87U/raG5p4ztPbnBLil80c5xrYLn72tksnlPA02sqOyQrDpdDZ/Gcgg7JlgE3BMwxMEFHY0yqedWkIe9g5azoFGPManvxDWBOQiVSlHhx/fXJliATGIsVKtkIfAO4CVDDTRzx5rYBeKnU+g9eVFJA7oB+jBs+gM2VRzlpxCByB2S7hpuBOVkcaWjmklnjKK2o5dAxKwkxJ6x+D9f5uGTWOMr2HOOT50/kmbWVbNtbR0NTKy9v2M+S+YXMnDA8IN9Np5WkvFx2WdzGrygOoQw3vbZCSyY8ER85pD93Xj2TJT9ZyYotB9hXu8bNAfHxBUV89uLJ3P/qTt7acYiZE4Zz5imjAKsU+L1/28QXPzzVTcYZXKnkt7fOD8iroBOC3kcqPhEP4t/GmCu9K0RkHPBdYElSJFICiOTlEa0XSHDo0nFfC0BAiFU4Xly/j5c3VnHO1NEhK0uFY0bhcO54vJSXN1a54U7e0KkvXXYqF04fx3ee3MCls/IZmNPX9YZxDCxOVSxHLx/3tbBz/zHXMOMdu2Mc93pAhjPKhPN6TDWvmjRkjIh8N4b2gxMmSQQqDh5k2eOPs3DGDBbO0NQaShT89a/Qi74rCXr4tMUY8yKAiKwG1jgbRESMMRkxX6qoqkqafvHmtgEr54xDTnYWOX2zANhUeYRrFxQxuH9fGppamDBqENv21rGp8giHjvnIHZjN4TofY4f3p/pIE4MHWNPgqtpG1pfXcOR4MxdMH8PgnGyaW1vZsvcoJ4/J7ZrQ774LCxZ0fdBK2tETD7c7GG7iWaFFRG4CrsLy1BkD1Nt93WOMWRHU9hbgi8AUu93LwO3GmD1B7QqBHwCXYt0g7QR+YYx5MMTxo+ozE/A+9QWYkp9LQ5OfU08axqjcHLcCiZPT4VtXTuezF092b/bbDT0n3IpSwQRPAJzPh+ub+NkLW7UyiZIKvCoi84wx65wVxpj9wOdF5MNJlEvpJk6lpuB8MU7okmNACQ6x8uL1YIHQJbwdw4rXUPLZiye7RhrH82bNe4dYseVAQOiUNxzKCT8dlNOXxXMKXA/Gv77zgZsoeeaE4fzwmTJKy2s7NcwsnlPAOVNHdzBIBevk7laJ0ipTIRkFfC+G9t0v99VFikaPZpl6UCixkJMTuU0GkaCHT2eJyGFgI1YRhGwRmYU133kJuDjOx0sKRfn5LFu8OCnHDs5p4/O34vO3saOqjs2VR1kyv5D8vIFU1Tbym1d20tzSxuihOYwdOoA9NY1UH2lyt08bn0uzv43svkLDiRb2HznB0nOL+VfZfqqPNNHoa+Wua2bx38+WUX2kiW1769wkxd7EyBGp1uLLvY2eeLgddVUpB2NMK9FXaLkDy2jiMAC4ELhQRK4zxvzZPua3gbs97XKApcD5InK6U3pcRMYCbxNYhnMm8HsRGWuMcUvuRdtnpuB9MltSnMcv/rmdS2fl87vX3uOC08YAVuUoZ0LyrSunB9yg/+D6Et6vrmPn/jruf3Unn714slYmUdKR7wLfFZEXgOXABqAaOBmrrK+SIQR7mxyub3IT/oYjXBUob1Lfn7+4jcP1vpDhR84xHCPRpbPy+fiCCfz1nQ+4/9WdAK7xyDme078TxvWtK6dz6ax8Xt5YxdVnFrqhU5EMM9F4Bnn/B7y5c6JFdXlYJHITRUlDFi1KtgSZgB8rt94F9gtgPdBEBkYnJIPg3DbXnFXMs2sr2Vx5lJLiPBaVFDCrKI//fraMhhOW921rq3EfWo8d3p/pJw1lxOB+tLS1setAA2AlKK46eoKPzB3PtPyh1NT7qGv0c+cTpW5C42njc7sWKnXLLZHbKEqMJLqq1FHg28CjQC1WQuM77G3fAf4sIhOwJltguRdeCVwEPAKMB5YBn7O330W70eZG4DXgGeBM4Hsi8ogxpjLGPjOCG86b6LrTlxTnBUwGnEoqJ5pbMAZKivNo9IQX3Hn1TCaPG8rHFxTxw2fKWPPeIYCAyinOpMTrpeM9tpMU9HB9kz6pVZKJo9M+ar+8rBSRb2ElV19njDnak4IpnRMpn4vj9RIul0s0IUHhyoo7SX2d5MKTx+WGNHwEG3Ccfhxd6fW+CTaihEoa7DU6xQPv/4A3d04s+3vfFSD2IgytkZsoSorw1FMwN6IDvWIjIn8zxnwsaPUjWN79s7DyW5XYr9Ow8oMqCcDrhZM7oB8ryqpdow20lwUfO7w/p08cyXPv7gVg4pjBTBozmP1HT9Doa6WmzsePn9viVpgCXKPN5HFDOG38MKCj8SgiDz4I99zTnSEqSgcSXVXqImNMg/PB9oL5TyAX6wk4WHknHMX2U9sT5lERuQOYBnxCRL5gb3dS328zxjxq9/lT4Em7jyXAT6Pt0w4FywhGDukfkIPGmXBMvmyoO8k40dzCL/65nS9+eKpbJhfaJ0wXzRzH02sqXQu1kyvCOzEpLa/tMKHxPk0OV6VFUXqIeuA9rBumYB/whfYLrKdg4fSfkgQi5XOJZJiJNcwn2Egxckh/nvjSudzxeCk/uL6kQx+H65sCDNjB1ayc9c5+oYwooapheccevD7UuDobp/d/YPGcgphDWDUfTkeMMR8kWwZFSRinn55sCdKNRSLyeeC3Tu4aY8xn7G3v2C8ARCQbeL3nRcx86k40uwmKHS6YPhaf38pL41SHGju8P9+8wqrkuGJLNQ1NLew+0EDuwGzXONOvr9BsG21GDO7HiNwcauubOVzvo2+W8NTqSnwtbeT07QO0J0SOiJYDVxJAQqtKeY02Nv2ALHvZ+cV5qy/sDFqeBgzFMhD1sZdDtXMoibHPXREHkUaEuuk+XN/EDb94g7d2HOKkvIEAvLB+L7sONHDprHw3JOo7T25g0pjB7DrQQN8sYcWWA1w4fRwjh/R3JyZr3jsU9kmuPqlVUoRHjDH/KSJ9sRKre59+zQKGJFM4JTyR8rlEIpown+A2TjuvMSRcNSqvAdtroA6VyN1ZH5zQPRyd6U9H5nDJkEMd90uXneqWPA/XTokPInIqUG6MOZFsWRQlZo5r5foYOYB1P7FWRP7LGPNOuIbGGL+I3NhzovUeVpRVuwmKc7KzuOL0QnIH9OOas4pdo46T/2bdrhp8/lYamloYmGNNQesa/WRlQWsrLDxtLBsqajl4zEdWlvD5S6wMHw8tf999KPN+dZ1rDNp1oIHbFk2JbLwZNy5Bo1d6Mz1dVeprwCB72UkmPNKzvS7M8uigfiK1i7bPjDLcBHO4vonPPbCat3ZYoU97ahtd48zkcbnuU+UFU0YxckiOG/PZ0mrIzhIWTLEqTjkTk+AEyF70Sa2SChhj/tN+b6E9F9fDznYROZl2Q46SQsSazyUYr+EnnKdJuHLbXmOIN/cNtIc0eT1rnETEkTx8otWLnbVz5DjuawlIxhzJoyaa8uka1to9ROReLO/jgyKyAMvb90FjzJHO91SUFGHr1mRLkG7cYYz5s4icAfyfiGwFvmGMORSqsTEmo+cZycLxrnGWIdALZ1FJAWdPHc1Dy9/nUF0Te2osA6XjZZOT3Qefv42xw/uzp+Y4B4/5GJSTxcFjPh5a/j4zJwx3S4oDnDw2l9PGD2NHVR2l5bVuaXLnPaQRZ9UquOSSBJ4FpTeS0KpSXuwKU9+3P64AfuRsCreLVywsj5to2sXSZwDV//gHK596iq0nncTFfftyyuDBcOml8PLLUFQE/fvD9u1w3nmwdi34/XD++fCvf8HJduTX++/Dhz4Er78O2dmWG+qqVTB1KjQ1QUVFe5+5uTB9Orz9tlWOsbYW9u1r356XZ/X7739DSQlUVcGBA+3bx4yB/HwoLYUzzrCOXVvrbq9oymF6+QlubNjLquEnc9rRPcwd1Y9VhbMZ++QqDu2cyuGBA/Bt3spHBxRzXkMlg7MML+ZO4py9ZVTueZEZc8dTuXojwy7/MLUvvsrnCobTv3J+1GNqrD5I5cadFFx3FUNeXx77mBYvhilTgi+V0sOsXLnSKXMHCS51l0iMMe8D72N5FfYoFRUVLFu2jIULFzqZ55VuEmyE8HqaOLllgIAQJ8cw460e5c1N41SvWl62nzNPGRXg3eL1rAl1nEgGkWDjdzQGFGdcO/cfo7S8lo8vmMDkcUMjetSE88D83AOrQ4aipQI9UUozztwEPA6cA3wFeAL4CVF6JStK0rn55mRLkFY4RVWMMf8WkfnArcBqEflf4NeZlIIhlXG8a7wEe+EAbK486hpgRg/N4eAxHzMKh3HNWUX85e0KWtraXE8aZ1I4Lm+gaxjy+dvIye7jhkfVnWgOMNp0WmXqqqviP3Cl1xMyx4OIiBO7GUyMVaWc/j4JPIRlfHkTuMJTtcprpc71LHtDGg4RaLjprF0sfQYwdsoUFl5/vZsEw8VbXnOOHYU1fnzo7WecYb1fc03o7Wed1XFdUVHwEQO3O0ahadNCb3fWO+2AuquWsLt0H6dOhalFeWx6u4LsUSUUn3ESxcBLxfkst90Ip826gP176yidNIGd++u5ZNY43p9cxE3nT+TVXTU81sdPydZ6Ss1klhYXc8X48VGP6dWmSh7r05elVc1c0ZUxvfKKGm5SAK+x4a677qpIqjCdICJ1wGagFKui1AZgszHGJyJPGGOuS5ZsRUVFLFu2LFmHz0hChUfdcN5Elpft5+WNVdz/6k4G5fQNCHEC+M6TG9xExE7IqJM/xknOvmLLAc48ZZRr0HHwJlH25rA5HpTwPZK8jhxe2UPh9Qp6eWMV50wdzeTLhnYpPPXRVbt5eWNVQMWsVKInSmnGmf3AzVhevn81xnxRRO5KrkiKEgMPPwz33ZdsKdISe550v4g8BfwAWGeHT72VZNF6JaG8cLw5b0YMyeHMk0eBwMaKWqbk5/LU6kpmFA7jUH0T1UesIgE5WX1YUVYdkMtmX+1xfvXSDm46f6JrpHGOMW/SCJ5dW9nR82bVKk38rcSdUOXAXwPmisjNWPkhNgIbg939oqwqhd3Pg1iGl+XAR40x3qDa9YAzY5+MNdFylgGOAeWe5aGebQQtl3ahz4zEsTwvPbeYccMHMHPCcC6YPpb6E37+9PpuxucNtIw243MR4PK540Fg5/56cgdkc81ZRawoq2bepBGApZhOHT80bBk8rxXaq7i8Wd+7RE1N1/ZTeiuDgfn2y6FVRHYAk5IjkpIoQlW0GzmkPzMKh7NiywEafS18fMEElpftZ0bhcNdY4RhcLp2VH5Bs/dFVu1mx5QCTxgzmsjnjQ1bR8+acKSnOo6Q4L6DyXiR5ve/By6Fwjuf1CoKuhacGHz/W5MVKB/ZhefC9CIwQkesAfdKgpA95ecmWIK0QkWzPg2cAjDE1IvJ14DZghYg8gRU+dSApQvZSQnnhODlvfv7CNjZXHqW1zbCzqh6AJfMLWXpusTs3emj5+xSNGkxOdp8OnjR/en03peW17DpQz13XzKIgb5BbZerZtZU89kY5W/ceC8x9o/MXJQGE8rg5FctL5XbATTcvIg1YT7I3YBlz7iBCVSkR+RTweyyjzUvAVcaY4LqnfwXuw0p+/GURWQV8CCuJMMCfHddDEXkSyy1xmogsxSoH/hW7nZ/28Ieo+8xUvAYTx53P52/lzR0HqT7SxHGfnxmFw6iqbXRL5n3l8tPcrOkvle5zXQ4dxVWQNyjA6lyQZ6UrqjvRzK9e2kFpeW1Ae+hC+bxgbr216/sqvZFHsPJwTaO9slxfrPDOruTlUlIYx+DgeNQ4ni4Dbc+agTl9eXH9PlZsOUC/vlnuPqEq8EG7t86KLQcQCR3K5M0588Nnyrj72tmMHNK/Q7nycPIGV5Y6XN8Uda4aZ3uoSlPecLBocu5o8uK48FXgX8Dn7c+PAv9InjiKEiPnnJNsCdKNf9v5beZgzZHOsN9PwUrHIMANwEeB4ckSsrcS7iGyQ6093xk+uB87qur49IUnkzugH7kD+vGdJbOoO9HMX96uYOzw/kwtaM+1d9P5E9l1oJ66Ruvh9+1XzXC3XTB9LFv3HnNz37hznltuQVHiTSjDzQ3At4HHsBSSwxBggf2Klu/RHuK0CDghEpB+ptgYUyEi3wfuxnpKvt+zfR+wLKi/y4ACrBskL3cZY/YAGGMqY+gzI/EaTOZNGsHWvcfw+dtcV8CahmZq6nxue2dG62Rhv3zeeJbML8Tnb6XuRLOrAB2rM+AqrhVl1ZSW11JSnNd1z5pwPPAAaHiJEiXGmE+CW4bzNKwkxBdiJQ0NNhorGcSa9w65XjfBRhTHw8YJnVo8pyBkHyOH9OfMU0axYssBNn1whBVbrAemocqSH65vCjhGVxO0h6uG5TXOBPcbvE+4iledoZUA44Ify1B8Dda9xkGsHDeKkh784x+wIJbb+l7PaUADHedPErQce4Z9pduEyjuzoqyazZVHKSnOY9igbFaUHaChyc/myqM8tPx9puRbGTUWlRTwUuk+XtloTRmfeHM3y64poe5EM+t21XDbpVP44+u7ufrMwIfRuQP6cduiKa7ByOXBB+Gee3pg1EpvIlRy4uVYIU2IyDewXP9me15TsEp6x+3ptTHmHhHZD3zR7r8BeBm43RhT7WlXLSJnYXnTXIoVFrED+IUx5sGu9JmpOFbneZNG8NDy99lceZQmfwuTxw1h5/56aup8TM63Uv5k9RFuvWiyq9wAcvr2ISc7i8feKGdHVR1T8nNZVFLA1WcWcuBYU4Di8nr3RCyPFyszZkRuoyhB2K7MG+zXH0Tkl8D0ZMrEG2/AdddZOaHKyqCuLq2Tn1NQYPWxeXNSxnR88lS2bSrnv0wDuf1PZuxvVrF31QRGXnQGIydO5EtXXOGeesfDxqnM5BhylpftZ+aE4Qzo19f1VHGMPgumjKJf3y2dGnm666lyuL6J474WvnXl9A4GlM4MOs4+C6aMYslPVvL1K07jW1dOpzEobCzU8YKTOSvdYjXtXsjfNMYk1Te+urqalffdx6BrruH0xkY4cgRuvBEeecTKGzdoEKxbZyXNfPVV8Png6qvhySdhpp2XadMmuPZaePppyMmBiy+Gv/8d5s2zSkdv29be5/Dh1m//hRes9+pq2L27ffuYMTBrlpWrbuFC2LUL9uxp337SSTBpEqxcaVVf2bjR0kHO9okTYexYS4dddpn1rmOK75h2Rg7xzCTikAC9L9b8J7gISj2wDvi3/VrTjWOkJBVVVSx7/HEWzpjBwhS9Nw+VnsGb+8bnt4It/C2G3IHZFI0a3CGhsUNLqzXNdYxBJcV5VNU2sn3fMabkB9rlQkYXTE/uLafS8/REgQUJk4PY2igy0RizO2hdDjADmBVsLEl3ll13nVnmTZCbxjgxlyXFea6HDMC08bluBnUnw3pJcR63LZpC/Qk/D7xm/YnfetFkhgzIDgiBWnpuMT5/K0+trmTJ/ELXOg2EzLgeFyNOv37WpE1JGUTkLmPMsmTLEQoReRlrErXBfm33hFq+Y4xJ2qPFTNIviSBW3eHoOCdGPWDfIL3hTSj84vp9ARWlHO6+drZryPBWX/rWldNdz5pYcsGEK7vtXe8YZ7zHjrS/E+J097WzO+TqcWQO1Z93366OqSdIZf0SjIgMN8YcEZHTge8C/zDG/C5Z8ixbtsxoAnRF6Zzu6BgRaQNasFJHrKHdULMtXFGXTGHZ5z9vli1enGwxQhLp/sG5X5hROIyRuTls23eM2y6dyrjhAwLmMQA/eW4L2/bWcc7UUew+eJxPnj+RysPHmTdpBOt21bhGoYj3KyLwkY8kZsBKypLoe5iQVaUcjDG7RaQQuALoB6wzxryOZVVelyihlO7jKJapBUNpaW1jXN5AGpv8rHn/sNvm4DEfY4f3p7S8lmfX7qHiYINr1PnT67u5bdEUbls0hZdK9+Hzt7ml8Ry8pfd2HWjgpvMnut49Pn9rhyRhXeKdd9Rwo8TCxcBFns8+EXkf6+nYyaF3UVKBiKU1gwj29AvYJ0hvhPJe+e2t87n/1Z2caG5hQL/A3DTe6ksQXfWnYMJ5zHiTGwN88cNTQ3rJhPOIceRcPKeAQ3VWvrIp+bnc/+pOV+bFcwpC5s3x5ufR/DbdxxhzxF7chBWa/UcR+ZAx5hNJFEtRlMTxHjDDGNOcbEGUdiLdP3jz0JQU51F9pImn11Ry0/kTycnOCjDAfPXy01hRVs2/yvZTfaSJX728g69/9DT+9Ppubjp/IrkD+rmGoHDHA2D1ajXcKHGnU8ONHZb0KtDfs64M+IQxZluCZetxKg4eTHk3wGjwWp5fKt3H5sqjtLS1sau6AX+LQQQWnjaGEYNz8PnbeO7dvVQcbHDDpBxjjpOEGKDikLU9OAv7pg+O0NLW5nrlOH3EImOnT9evS1r1ZiUEPeEG2E2CXZj70x4ilXE6K5OIpgKdV290SpDeWDyngOVl+zlU1xRQfSpc2e7g/C/eXDbhPGEi9RG83kluPHlcLjv31wXkpvEeAwKTJI8c0p8bzpvoetdcOiufX/xze0DVqWCjUXC+nOD8PErXEJHHsHLcTMYKIRd7WQ03ipKZnKtGm9Qj+P4h1Bxj0pjBTBozmFlFeew/2khpeS0trW3uvMUxwDgPgqYWDOV//rGFukY/v3llJ1W1jYCV3zOqirnXXpuIoSq9nE4NN8D3gQFB62YAy0VkpjHmUGLESg5Fo0eTCaEMXsuzg+NJA2AMHD3uZ+m5E3nsjd0M6p9F7sBstyQ4xjLUOMYY531G4TCg/Qm3kxNnyfxC5hSPYN6kEUwaMxhodzmMRsZOn64//zxM0eqqqcLChQsBKpIrRaesw8p/VUJ7Xq5TgEbgzqRJpUQkmgp0wbrNq0P21R53n4gVBOkNp7LUii0H2FFVx29vtarFhzPAhKr+5HD/qzv54TNlHPe1cOfVM8MacsJ5zHiTG5eW17rGF68RxXuMQTl9OxhhvEabH1xfwjlTR7thYNDRaBRsyNH8NnHjOqANqyT4BvtVmkR5FEVJIMaYg8mWQelI8P3Ds2v38Ny6vdSd8HPjeZPcCIGl5xazfd8xt1BL0ejBzJwwPKQBZkr+UO66ZhZ/en03500bzV9XV7r5PaOqmPvii5qnU4k7kQw3c7DKXO7Beso+F7gEq6rT7bSX4lZShLoTzdSd8DN53BDWl9dw/TkT3UpRAJPzh5DTN4urzywMyF/z1vZDLD3XCm167I1ylswvZOaE4QHGGJ+/zQ2NuuasYlfROXGfQwZkdwiPCudZE5W1GqykfIoSJcaYM+3FFfYrZcgUj75kEi7xILRXvGtpbeNCDjPFE3p0w3kTOe5rYc17h3h5YxWPrrJStzkhS5HyvQR7wHjxGkUcb5docseMHNKfH1xfAsAPri9xy3o/umo3jXYYldOn990bxvXbW+czckh/Jl821M1f8+b2g/z21vkBpcadBMup7mGTBh59wfwKKylxY7IFURRFUax5x9pdVlqIioMNQOC9gxMtMC5vIDl9+3Tq+f/W9oPufUW4xMRh0fmLkgAiGW6OGGPu964Qq57354Avo4ablGNFWTXPrdvrfv7NKzv5+kdPZfnmatbuPszOqnqWnlvMxopaSstrGT44myMNfvIG92PepBEMGZANtHvV1J1oduM/nQReDo7FubNYz3CeNVFZqwEywANK6VmC8nK9a4xZmVyJLDLFoy9VCNYhTljn+LyB3Lp7CP+xarfrVeKERYUywEST78VrnAkuNe59D5fXJhwvrt/HyxurOGfqaCZfNtTd3xv2FOwds3hOAW9uP+gaexxuOG+im6z4UXvsscqTbNLAoy+YL2Z6QlJFUZR0YkVZNdVHmsjPG8inL7RSG3rvF5wUEr6WVnZW1ePzt3Hj+ZM67bNo1GD3YfazayujK6KgoVJKAohkuGkVkc8bY37jrLBvUn4jIl9NrGhKLHjLf/v8rdTU+1i7u4aq2kbW7aohd2C2q8imFgzlL29XADB22ACONPipbWhm+eZqcgdmBxhtHK+cukY/FYcauHzu+A5hUJ15z0TtWROOhx8GrZKhRElvy8vV2+gsxLIgbxC3XzWDuhPNnFK1nJPP+2yH/UOFQDn5XpyEvk7IkWM0CS7ZHdyH97PXiBNNLpxQxh/nPVw5b6calmPs8crhlD0P7i9csmKl2+wWkRNYleycanbXGmM+lVSpFEVReimhogFCGVlqG6xURTuqjvHs2kq3vbd61KKSAnKys9x1b20/6EYeRHz4/MgjcM898R2c0uuJZLh5EfiliNyBNRl6F/gAKASGJ1g2JQa8E5przirm2bWVNGw5QO7AbKYWDGVw/76s2HKAqtpGfvXydqqPNDGjcBifvvBktxKUk4AYLMXnGG1mFA5jzfuHOHjMx6H6Jq4446SAY4fznolLafC5c7u2n9Jb6VV5uXob0RiCcwf0Y/51iyBKA4VjePGGGjllwh2vlR8+U8bd186OKvzJMeI4/Tn9QMfExp0ZgULhDZMKFfYUrr9QsihxYShWKPmlgPN41QBquFEURUkCkaIBHGPMoXofr2yoIitLeOyNcrfqlPfd8eR1DDbeAi0RmTMnIeNTejeRDDc/AK7BymnzSfvl8GqihFJiJ3hCc8H0sW4pu7+8XcGU/Fyqahvpl93HTco1JT+XIQOymZKfy5T8XGYV5QEV1J3w8+y/97hGmyn5ua5Bp/pIEyvKql0lGM444/XWgejK+ypKHNC8XGlGLAbeqEMsu4DXO8VJ9tud/DChqkp1N3QpkkdOLLIoceE94BJjTJOIFANXARclWSZFUZRej3c+FHyfccXphdSdaGbUkBzmTRrBW9sP4mtpc6tOHTjWFFCgxWuw6fKDaEWJA50abowxB+3Qg6exqrM4NADfTqBcSozkDujHBdPHBiimWROGU31kPz5/K3VNVrLLZn8bo4bmcP60MSwqKQjItL6xopbNlUfZXHnUrSA1JT/XDY2qb/JTVXuCeZNGuMd1PH18/lY3F45Tcaq0vJaS4ryuh0kBvPsuXH551/dXehualyvNiLrCXCcElwnf+sxyTl54cUzGDa+3St55OW7lJojNyBJcettLtAaUWCtVRUKrSCWMF4B/ich9wGrg51iGYkVRFCWJeB/0hPK+8W7Pyc7iqdWVlBTnAVBV28iMwmGMzxvI+LyBALEbbdavh6uvjtdwFAUIYbgRkZlYRpl1wLPGmB3AHBGZC5wDnAD+YYyp7lFJe4B0r/oSPAEa0t9KNLxzfz052Vluu/knj3KrP3kt0s/+ew8A08bn8ukLT3ZjPHMH9HPDr17esN+NGXVy6gD4/K0Bx/b2G07RRfWk/eabu3FGlHiTBlVfNC9XmtHtPFh0LBN+R9PkgOTE0eIYTI77WjoNSeqMzrxqojWghOsjmrw5So9yD3Ay8A+sECmAveGbK4qiKD1NpPuMeZNGsGLLAUrLa5k0ZjBLzy3G5291jTml5bXkZGfF9nDpxhvjIbqiBBDK4+YaYAkwBSvcYAeAMeZdETkKFGei0QbSv+rLBdPH4vO3Wh42J5oDkgifPXU0b20/CBCwPtDi3AeA08YPc6tL1Z/wByQ9XjK/0PXs8RpqvNWngvsNR1RP2h9/HO64I9ZToSSINKj6onm50oyuhD8FG32Db8oe+OANpnQhLChcVSeI3mgSj7CkcH2kW5WoTEFEJgAfAg4BLxlj/ADGmDbgJhH5M/AxIAerRLiiKIqSInjDo0JVhVq3yyrmUlKcx9lTR7NuVw1nTx3tJiY+dfzQ2B8uPfkkfPe7cR6J0tsJZbi5GPhPY8yvQ2zrD7woIt83xmiq7BQjd0A/crKzeOyNcteI4g1fWlRSwLNr9/CT57Zw8thcLpw+luWbq6k41MCnLzyZRSUF+Pxt7Kiqw+dv47l397oJjZ1kXUvPtTx1vEacSITzrInqSXtOTvdOitLb0LxcvYBgo2+w8efsmSdFnZzYyw3nTeS4r8Vd9hpoovWCiUdYUrg+OqsSpd44iUFETgdW0l6prkZErjXGrHDaGGNexDIaJ5WKigqWLVvGwoULHSO7oige0sBrOGWpqKpK66gECP/A2Dsfeal0H0+trsTnb3VTSnQpt43OX3odPaFfQhluBocx2mCM2SIi84DnReQdY8y/EimcEht1J5pDesU4+Wd8/laeW2d5cW/bW0fFwfYqUg8tf5//d9k03i2vpaq2EcB1DywpzuOm8ye6FmdvXhxHkXXmPRMuD05UT9o/8pE4niEl09G8XL2DiEbfLuqNkUP6MyinL995cgODcvoGGE/i4QXjGFeCS47HIl+4KlHqjZMw7iOwUt1IrAdYpxljdidJppAUFRWxbNmyZIuhKClLGngNpyxF+fksW7w42WJETaiHxuHuHbzzEZ+/zX0PN7eJKtVDGp0rJT70hH6JVFWqA8aYTSLyCeDrgBpuUgRvFSfHoOKETu2oqmNz5VEunzueU8fnUn20idqGZoYOzGZQ/yyON7UyMjeH7z65karaRsYO70/RqMGcccpIWlrbGD9iYMCxQim+eZNGsHXvMaYWDO3ghui0C86DExVPPAF6I6p0Qm/Oy9VbiWj07Ybe8Bpogr1YOvOCiSY0yjGuBJccj4XD9U0c97XwxQ9P5bivhcP1TYwc0l8rRyWO2UAd8CYwBJiPFRL1dawKdoqiKEqKEcroEtUDY2l/d+Y23qIs4fruwJNPQpp6JimpSyjDTa6I5BtjqsLtZIx5W0TyEyiXEiPBVZwcazDA5sqj5OcNBIGte+sYO9x6wrtzfx3Hm1oZO7w/h+t8VNU2kp83kLMmj+Sp1ZXsrW10q0ztrWkMKO0dHCu6bldNQOk8px20V7x6qXRf1OFVLgsWxOsUKZlLWuTlSvfk56lA1KXDu6E3vAaaUJ4tXmINTwouOd4VI8ujq3bzw2fKuHRWPi9vrOrgGRQvkhF6laJhDEOAKcaYCgARGQc8AZyeTKEURVGU8MRS+MB7b5HT18r3mdO3T8Dc5rZFUyJ67gQwf353xFeUkIQy3JQCvxeRK40xzZ3sq4k+U4jgKk5O6bsl8wvdkKezJo9k8rgh7NxfT97gfgzpn83BYz5a2wwfmTuevll9uOl8ayKxo6qOUbk5XDp7HFW1J1g027LTzZs0wlVwTsZ17/HDJfEKFV4VFQ0N3T01SuaTFnm50j35eSoQdenwEHojFkOEN6QJwnuxxBqe5BiFDtc3RWwbjnDGn3iHSt3/6k5++EwZy8v289AXzu4R402KhjG87xhtAIwx+22v47XJE0lRFEXpjFgKH3jvLRaVFAQUW3FyfK4oq47Nc0fnL0oCCGW4eQJ4DFgvIvcAzxhjAu4yReRDqOEmpQhWIl5DTv0JP2BVllq55QAAtQ3N1DY0M6h/FoeO+Xj+3b38v8umsaKsmrpGv+tpU1Kcx+bKo/TN6kNpeS2njh8K4BqFlp5b7BqLnOMX5A3qIF+XS/5u3gwf+1hs+yi9Dc3L1UsIpUdCeuGE0BvBho3ODDnRGkG6Gp7UHSOL1yNo8mVDuy1LJFZsOcCjXSitnkEUi8jzQJnntRU47G0kIr82xnwhCfIpiqIo3SD44bd3PnXboinuPUZMlJXFU0RFAUIbbp4EvgLMxTLgNIvIemAbVpz3BOAyrCoLSoriVTxOGNWp44eSN7gfh+t9bruRQ3I43tTI0IHZfOWP66hr9DNtfK67bVRuDkvmFzKrKA+AqQVD2VhRy5L5hSwqKQjrPRM8mepKyV8Abr019n0UxYPm5cocQumRkF44IfRGsGGjM+NJrEaQ2gYf97+6E4DPXjw5ondKpP67EqYUj2pWXj578WR3uZfnzekPfNh+ObQBfhH5PbAaeAc4LwmyKYqiKN2kszmKk+4hVJh2p+Hbt9ySSJGVXkqf4BXGmDbgY1i5IgQrCd984FPA/wOuBLKB+3tMyh7CyUGxcvPmZIsSVy6YPpYl8wvx+Vv52PwJDMzJcrcdrvdx+dzxbK+qo67Rz6D+WRSOHORue2XjfnKys9i+z3IVfHpNJU+trmTXgc5dAJ3JlJNnp8s88ED39lfiSormoMiNlHPLGPM2EFVeLhE5WUR+JyJbRKRNRIyItIRpe4uIbBSRJhE5JCKPishJXRiD0kUumD7W9fxzCaE3HMOGYwi54byJ3H3t7JBGieC24XCMP3c8XsoPnynjh8+U8eiqyIWGIvXv9BtNX4li5JD+3Hn1TO68eqaWF7fuhbyvLKx7o09h3QttAqZF1ZHITSLydxEpF5FGETkgIv8SkQtCtFX9oigZSiJ0gYgU2tsOicgJe5+QFgTVL9ETbk7T6VznwQd7SDqlNxGyqpQxplJE5gD/C1yHdZPibgb+2xjz9x6Qr0fJ1BwUuQP6kZOdxWNvlJM7MJtGX6u77XhTK3trG8kdkM3hOh9jhg6gX1b75R47vD/zJo1gyIBsADezeml5Lb96aUdAsi6v5Tk4pCGUVTqqRKMjRoRerySFFM1BEe+8XNOBz0RqJCLfBu72rMoBlgLni8jpqZAQOROJypsvCr0RDw8Vb76ZkuK8gHXx6LeXe7qkCluAy4ES+zXbfi8Iamei7O8OrETqDgOAC4ELReQ6Y8yfQfWLovQC4qoLRGQs8DaBumkm1v3RWGPMvc5K1S+R6WxO49BpGgidvygJoIPHjYMxpsYYcxMwHuvH/G2sEKpZxpg7ekg+pZvUnWjmL2+XU9foZ+zw/tQ1+hk8oN1elzswm5vOn8gp44YAMGHUICoOWd40Y4f3p/pIEw8tf5+XSvdxwfSxFOQN4rZFU9yExyvKqt3qUi+V7nMtz85kyjHIhLJKR+WVYxkKFKUzngAWYeXl+oSIdHAPiDEv1z7gB1iTtX+HaiAiE4Dv2h/XAOOAG+3P44Fl0QqvxEYq6Q3H+DN53NC4eqdE6/Gj9Aj/ZYypMMb83RjzXWPMR40xJwGjgUuBb2GFmDdG2d9RrPupIiAXS9c4fAdUvyhKL+Eo8dUFd9FutLnRbrvG/vw9ESnsQp+9Fu+9RvCcxiHcegDO0+hZJf6ENdw4GGMOGGOeMMb8wBjzM2OMZltKE+pONPOrl3bw1OpKnnt3LzMLh5OfN5BbLjiZyflDGD00h9suncK6XTWul83hOh+bK48yo3AYp08cyYzCYWyuPMpTqyvdiVLugH7ctmiKG57gKDefv40ZhcOoa/RTdyLQ8SFUOEPIEIdg/vY3Dtc38bMXtnarEouS0TwJvAucipWX64iIvCUivxeRn4rI34AX7DYRMcasNcbcaYx5HjgRptkSrJBRgJ8aY6qNMY9i5QID+ISIRNSvSuxEqzdipSt6Jpp9VH+lN8aYlWHWHzbGvGqM+ZEx5jqi90S8yBhzrzHmA2NMPdbErc7edrL9rvpFUTKfuOkCWx9ca6/bZox51Paa+am9LtvuK+o+4znQdCSqe43O+HvGBaYoKUDIUCklM3CSEs8oHMaUfCvhcFVtI8vLqtlZVQ/AH1a+T/WRJi6fN56l5xYzb9II3tp+kB1VdTz37l6WzC909/UqL294grPe5291q1HlDswOCF8IFc4QVcLihQvjXuZWySyMMW0i8jHgDeAk2vNyzQ9qGs+8XHM8yzuDlqcBQ4FiYFccj6kQvd6Ila7omUj7HK5v4nMPrObljVUx9aukJf8RTSNjTHCCuH60h6Pvs99VvyhKhhNnXdDHXg7VzqEkxj57tX7pclEVB/W4URKAGm4ymODydvtqj7PrQAPDBlqG9tFDc6g+Yj8FNlDX6Od3r+2kpc2ws6qeGYXDOq0c5eAot321x9my9yhgVZ96dm1l5/lroqGqihsut+bfmu9BCUcS8nKN9CzXhVkeTS+/8UkaVVUx79KVvDKR9nl01W5e3ljFpbPyVX9lOMaY1V3c9WvAIHvZyWap+kVReh/d0QXE0E71S0+wf3+yJVAyEDXcZDDB1uJ1u2pcDxyAkuI8yg82cPR4Mw1NflZsORCwf9GowSETCr9Uaj0IWFRihdI6bdbtqmHbXkvvP72mktLyWoCwZfSiYufOuJe5VTITY0wNcJOIfB0rwV8xVt6J1xIQ4ilRrA9IVupUrQNYOGMGC2fMiLNIisvOnZHbBNEVPRNpH69hR/PVhGflypVOxTpIvap1CUNEbgK+b39cAfzI2RRuF89yh2TIFRUVLFu2DLASyS/UHHGKAqS+jomDLggX2hRKZ3RNv1RV6T1MLLz3XrIlUHqIntQvarjJcJys6PMmjcDnb+XyeePx+VtpaWuj8vBxN2SqT59j7j4DcrIoGjUIBB57oxzANQCtKKvmqdWVAORkZ+Hzt/LU6kp8/lYWlRTg81sVq86eOppTxw8NyIHj7Sdqbr21W+NXeh/GmANYCYsTySHPcq5neUiYNhlbtS4l6WG9cbi+iUdX7e5goOmu0Tlcv5mG18hw1113VSRVmB5CRD4JPIQ14XoTuMIY47c3x6xfAIqKilzDjaIo7aSyjomTLugTZbtY+gygKD+fZYsXhxqCEopbQlZhVzKQntQvvT75lBfnifjKzZuTLUrccIwmf3p9N0+trmRvTSOvbNzPtr11bNtbx7TxucwoHMZtl07l8rnjGTkkhxO+VrbtraPiYANL5hcGlPSua/Rz6vhcLpk1Dp+/FV9Lm3us3AH9WFRSQE52FkMGZLuZ1ruV4OuBB+J1KpQ4YFuUi5IrRUqw3rM8OcTyMaC858RRAuhBveHksfnOkxt4dNXumPYLl7TY2Xb/qzsj9qvJj9MPEbmZ9onacmCRnZzUQfWLovQC4qgLdtnL4doBlMbYp9IdHnwwchtFiRE13HhwnohngvufU6J73qQRLD23mKvPLKSkOI+rzyxkyfxCLp87niXzC7n+HMuV/9/vHeaMU0bS2NwCwMCcLDZXHgUIKOn93Lt72bq3jkN1Pp5aXUlO3z4sPbc4IGwquFRvp+XyIjFuXDfOghJvbItyRXKlSCwiki0iI0VkJO2VF3DWiUgO8FfAeSL2ZREZKyJLsRL7AfzZGNOGkhx6UG+Ey2MTyaDiJDYOZZTxJj2++9rZnebH6awfJfUQkU9h5a/oA7wEXGaMOR7UTPWLomQ48dQFtj540l43TUSWisgY4Cv2Or/dV9R9xmmYvZexXaxGpSidkNBQKRE5GfgmcBaWQhCg1RjT4bgicgvwRWAKUA+8DNxujNkT1K4Q+AFwKTAYKwv6L4wxHUyb0faZiQSHJz271so5c+r4oVxzVrHb7u6nNrqVoN4tr6XRZ4U69c/OotHXypa9R6k70ex6zjihULOK8gArJKogb5Db37xJI9i69xjzJo2Iz0DmzYtPP4oSPWdjxZh7yaLddfhTxpiHReT7wN1Y1au8Wej2AcsSLaTSCQnQG+HClsLlsYlUcaqzxMbOusVzCnhx/b4O26PtR0lJvkf7Q7NFwAmRgJQTxcaYCtUvipLxxFsXfA+4DCgAHg061l3O3Mcu5qD6JdHMnZtsCZQMJNEeN9OBzwCnEj4ZFiLybeD3wEysUr4jgaXA2yIy1tNuLPC2vW0k0N/e5/cicmdX+sw0gj1tvJWlnM9Om9LyGnYdsDwyR+bm8JE5BQzMySJvcD9mTRgOwLa9dW4yYrDy2iwqKWBjRS2l5bW8tf1gwPGdBMhvbT/Is2srqTvR3L0BPfdc9/ZXlARhjLkHS79tAnxADfA4cJYxprqzfZUEkwC9Ec6zxcljE5yD5obzJnbqLRNuP++2F9fvC3lMrzdPcD8aOpUZqH5RFAWi1wX28ln2thq77SbgM8aYe7vSp9INXngh2RIoGUiikxPvw/KOeQf4DnBGcAMRmQB81/64BrgSuAh4BBiPZfn9nL39LixLMsCNwGvAM8CZwPdE5BHbkhxLnxlFuETA3gpTz66t5LE3yskdmO162Byu8/H8+n00+lpp9LVyuN7HtPG5bNtbx5a9R/nL2+X4/G089+5e1pfXuP3uqKpzPXKgvQS5z9/a9YTEXi65pOv7KkoXMMaspBNDc1DbB2kv26mkCgnQG7F6tsSjGl64Y3bmzRPJ00dJLsaYohjaqn5RlAwlEbrAGFOJ9ZA6bn0qXeSii5ItgZKBJNRwY4xZC6wFEJGvhWm2hPY8Ej+1Lb2PisgdWOFVnxCRL9jbr7XftxljHrX7/SlWXGe23ddPo+0zE2M4vR42kdoUjhzE//1zOw1NLcwoHMY1ZxXxl7craG1rY3PlUZbML6R/dl9Ky2vZtreOscOtJ7pOye/8vIFsrjzKirJq1zjjGIjqTjSTk53VtYTEXnbuhLPO6l4fiqL0LhKgN+JhiIlEcDhWuGNGE2aloVOKoiiKkiTeew/sSkOKEi9SoRz4HM/yzqDlacBQoBgrrGtomHYOJTH2uavLUqcoXs+aaNr87FOns6Ks2jWwzJwwnKkFQ3l6TSVnTx3NopICnl27h7W7DlN9pIkZhcMoGjWYnOw+zCrK4+k1lW4+G6f0+AXTx0YlR3D7kFRUxHYCFEVREqw3QuW7iUfp7mi9ZTozIvWEgQl6T6lyRVEURYmZDz5ItgRKBpIKVaVGepbrwiyPjqFdLH32Opz8Nk7uGW/FJyfM6jev7KS0vJZ1u2rIHdCP3AHZVB9poqQ4j09feDK5A7M5e+ponl5T6baD0BWlOiOq9rfe2u0xK4rSy0iw3giV7yYe1Z0i5cXxEmsum3jnvtFqVoqiKIoShltuSbYESgaSCh434XJJeNcbwhuZgtvF0mcAFQcPsuzxxwFYOGNGRpQFh0DPFm8OHOez4/Eyb9IIVmw5QFVtIzMKh+Hzt1J3otmtFHXT+RNZt6uGx94oZ+veY5SW11JSnNchPCva8KjO2jsyX/z2Ggbed088ToPSDVauXMnKlSudj0XJkyR9cfRLJumWlOWBB2DZsoR1HyocKdYQpVAeK7F4y8SayybeuW8SEZJl65iiuHWoKIqiKMngwQfhHp2/KPElFQw3hzzLuZ7lIUFt+kTZLpY+AygaPZpl11/fqbDpiNdY4y3XHWzEeWj5+67RZkp+Lk+triQnOwvALSXuGFnmTRrBpDGDA44TTXhUtO0d2QbMHo6mJ04+CxcuZKEdq3vXXXdVJFWYNCVT9UtKUlSU0O5DGVhiDVHqriElVsNJvA0tiQjJsnVMRVw7VRRFUZSeZsKEZEugZCCpYLhZDzizmcnABs8ywDGg3LM81LONoOXSLvSZ8XiNLX96fTel5bUA3HT+RHf7irJqNlceBaC1rY1ZRXnsOtDAvEkjGDIg223nNbbkZGfx2Bvl5GRndatyVKhcN47MZ5ynVVEURYmRyZMjt0kyXTGkBHvpxGI46ancN0ryqKioYNmyZQGGdkVR2lGvvq5TUVWlXsOxcMopyZZA6WF6Qr8kNMeNiGSLyEgRGUl7lSecdSKSA/wV8NubviwiY0VkKVYSYYA/G2Pa7ApQT9rrponIUhEZA3zFXue3+yLaPuM93lTFMbas21VDaXktuQOz3dw0jtFm3qQRTBtvOSdt3VvHX96uCMhz47RzcuOAZVxZem5xtytHhcp148g87M3Xu9W3oii9kFdeSbYEEXEMKbEk9tW8MkpnFBUVuYYbRVE6ol59XacoP59l11+vRptoee21ZEug9DA9oV8S7XFzNrAiaF0W7WFKnzLGPCwi3wfuBuYD+z1t9wHLPJ+/B1wGFACPBvV7lzFmD4AxpjKGPnsNF0wf2yE3jWM02fTBkYC2RaMGM3PCcNco47Tz+VvdMt/hQp06qxbVmXdNSAPQ5ZfHY+iKovQm0lRvRKrUpKW+FUVRFCUNuOyyZEugZCCpUFUKY8w9wGeATYAPqAEeB84yxlR72lUDZ9nbauy2m4DPGGPu7UqfvYncAf24bdEUlp5bzG2LprieNCXFeWyuPMq2vXVMG5/LjMJhnHHKyIB9He8aIGIlqM6qRXXmXROyJPi6dV0craIovZYU1BvRVHWK5FHTFS8dRVEURVF6mHffTbYESgaSUI8bY8xKwld4Cm77IPBgFO0qgaXx7LM34xhzXird5657anUlUMHmyqP4/K1cc5ZlsPH5W/H521gyv7DT8ChvTp1n11ZG710Tiv37I7dRFEXxkkC9EckrJhzRJCNWjxpFURRFyQCqe6WPgJJgUiE5sdKDeCtJOWFOuQP6saikgJdK9+Hzt3HJ7HzW764BCAihsgw6sGR+YdhQKKe/K04v5Nm1lSGPFVMi41tvjX2QiqL0bhKoN7paDSoao4wmEFYURVGUDOCWW5ItgZKBqOHGQ8XBgxmfMd3r8eLNN7OirNo1zOTnDeRwnQ+Anfvr2bm/nhmFw7h83nhy+lrRdcEGmUjH6jIPPADLlnV9fyWuaEUGJS1IoN7oqleMGmUURVEUpZfw4INwzz3JlkLJMNRw46Fo9GiWXX995IZpjNfjxesRc8H0sfj8rQDMKsrjL29XkJ83AICNHxxhc+VRZk4YzhWnF7qVpXz+VupONAd43QQnH+5OmXCgS2V9uxrKoERGKzIoaUECy4H3pAFGdZmiKIqipCFaDlxJAGq46cV4PWJyB/Rzc9kAfGfJLMAy7lQfaXKrUIFl/MnJzuKxN8rJyc4KMM6ECsWKRGdVqMjPj3lcXQ1lUJRE0hs8+lKGLuiNVKS36TL16FMURVEygnHjki2BkoGo4aYXE41HTLBxJ9R6r+Fl3qQRbN17jHmTRkQtR6fGnpUrwfLyiBpN8KmkIr3Boy9l6ILeSEV6my5Tjz5FURQlI1i1Ci65JNlSKBmGGm4Ul1CeL+GMO+FCrgBKy2s5dfxQCvIGRXXcTnPhfOxjsQ5Dc0koSm+nC3ojFVFdpiiKoihpyFVXJVsCJQNRw43iEinMKVxIU7DhxSob3jH/TTg69fxZuRI0rERRlFhQvaEoiqIoSrJYtQrmzk22FEqG0SfZAiipwwXTx7L03OKwVaAcw86KsmrAMuQ8u7bS3ddZn5OdxVOrK93P3aKmpvt9KIrSu1C9oSiKoihKstD7ECUBqMeN4hIp502wZ43XQwcIqFDlfe8Wt97a/T4UReldqN5QeiEVFRUsW7aMhQsXOvmCFEXxoAnQu05FVZUWWIiFW25JtgRKD9MT+kU9bjw4VV9Wbt6cbFFSkuC8NvMmjXA9dOZNGkFJcR7zJo1w20UTJhWRBx7ofh9K3NCbHiUtUL2h9EKKiopcw42iKB3RBOhdpyg/n2XXX69Gm2h58MFkS6D0MD2hX9TjxoNWfYmOULlwVpRVu0mJhwzIDl/eO1b0DyKl0JseJS1QvaEoiqIoSrKYPj3ZEigZiBpulKjwJiYOFQrlXRcuyXG45MadMnhwnEagKEqvQfWGoiiKoijJQu9DlASgoVJKVHgTEweHQgUbZMIlOQ5ObhwV77wTz2EoitIbUL2hKIqiKEqyWL062RIoGYh63ChR0VnC4WAPm1BJjutONOPzt7JkfmFsSYuvu67rQitKCuHk0NLEfj2A6o20RHNoKYqiKBnBtdcmWwIlA1GPGyUqOks4HKmMOFjGnadWV5KTnRVb3pvnn++KuIqScjg5tNRo0wOo3khLNIeWoiiKkhG8+GKyJVAyEPW4UbpNpDLi0I0S4T5fV8VSFKW3onpDURRFUZRkofchSgJQw43SI0Rj3AmJVvlSFCVWVG8oiqIoipIsNFRKSQAaKuXByUGxcvPmZIuiODz8cLIlUDxoDgolLVC9oSiKoihKsnjkkWRLoGQg6nHjwclBoaQQc+cmWwLFg+agUNIC1RuKoiiKoiSLOXOSLYGSgajHjaIoiqIoiqIoiqIoSoqihhsltXn33WRLoChKuqF6Q1EURVGUZLF+fbIlUDIQNdwoqc3NNydbAkVR0g3VG0ovpKKigmXLljm5yBRFCULz9HWdiqoqzQMaCzfemGwJlB6mJ/SLGm6U1Obxx5MtgaIo6YbqDaUXUlRUxLJly5xcZIqiBKF5+rpOUX4+y66/noUzZiRblPTgySeTLYHSw/SEflHDjZLa5OQkWwJFiQtata4HUb2RlujTcEVRFCUj0PsQJQFoVSkltfnIR5ItgaLEBa1a14Oo3khL9Gm4oiiKkhEsXpxsCZQMRD1uPOgT8RTkiSeSLYHiQZ+IK2mB6g1FURRFUZKFhkopCUA9bjzoE/EUZMGCZEugeNAn4kpaoHpDURRFUZRkMX9+siVQMhD1uFFSm4aGZEugKEq6oXpDURRFUZRkofchSgLIeMONiBSKyKMickhETojIRhG5JdlyKVGiYWtKCqP6JUVRvaFkCKpjFEVJFKpfEkhZWbIlUDKQjA6VEpGxwNtAgWf1TOD3IjLWGHNvciRToubWW5MtgaKERPVLCqN6Q8kAVMcoipIoVL8kmFvU/qXEn0z3uLmLdoV0IzAOWGN//p6IFCZFKiV6Hngg2RIoSjhUv6QqqjeUzEB1jKIoiUL1SyJ58MFkS6BkIBlruBGRPsC19sdtxphHjTHVwE/tddnAEu8+FQcP9qCEiSOTqmK9V1ubbBHigl2NKVMoSrYAyaY36xdIAx0zYkRUzTLpd5lBYylKtgCpQJd0TEVFzwmYoWTQ7ygp6PlLD7qkX6qqelDCxNIj9zBR3od0h0z6vWXQWIoS2XnGGm6AScBQe3mnZ713ucS7Q8WBA4mWqUdI+UlVDLzq9ydbhLiQQQoJdGIFvVi/QBroGKv6WUQy6XeZQWMpSrYAKULsOkYNN90mg35HSUHPX9oQu37Zty/RMvUYPXIPc955CT9EJv3eMmgsRYnsPJMNNyM9y3Vhlkd3peNof/DJahctsfSXrLGcum1bdP1F+YNPVrtoScRxkzWWDCdh+gV6n46Jd7uKn/wkqnbRkszfW6rrGNUvCSOhOiYakvXdS1Z/iSDVx9wbzyFqHIau6JfsbCgsjOq18sCBlG7H0KGJP+6zz3Y46Znyv5+oPpNx3HS7hxFjTLJlSAgichbwlv3xEWPMTfb6ScD79vqXjTGLPPu8A/jsjxX2KxRFnWzLxHbJPLa26167ePVZRPvNTo4xZkGUx85IEqxfIPW/V9ou9Y6dzu2KUP0SQA/omGgoikMf6dRfIvrU/lKjzyJUx7joPYy262a7ZB47FdsV0UP6JZOrSh3yLOd6loeEaUNvV+SKokSN6hdFURKJ6hhFURKF6hdFSUMyOVRqF3DMXp7sWe9dLu05cRRFySBUvyiKkkhUxyiKkihUvyhKGpKxhhtjTBvwpP1xmogsFZExwFfsdX7gr0kRTlGUtEb1i6IoiUR1jKIoiUL1i6KkJxmb4wZARMYC64CCEJu/bYy5t4dFUhQlQ1D9oihKIlEdoyhKolD9oijpR8Z63AAYY6qBs4DHgRqspFqbgM+oQlIUpTuoflEUJZGojlEUJVGoflGU9COjPW4URVEURVEURVEURVHSmYz2uFEURVEURVEURVEURUln1HCjKIqiKIqiKIqiKIqSoqjhRlEURVEURVEURVEUJUVRw42iKIqiKIqiKIqiKEqKooYbRVEURVEURVEURVGUFEUNN4qiKIqiKIqiKIqiKCmKGm4URVEURVEURVEURVFSFDXcKIqiKIqiKIqiKIqipCgZb7gRkUIReVREDonICRHZKCK3RLlvnoj8WkSqRMQnIttF5OsikpVoucPI06WxiMhCETFhXht6QHSvLCeLyO9EZIuItNkytMSwf8pck+6MJcWuyU0i8ncRKReRRhE5ICL/EpELotw/Za5JKtIdHdTTRPtdEJGKTr6/s5MkfgAicnMnMj4T1PYW+7o02dfpURE5KUmiB9DJGJzXQk/blLkusejHaM9/Ov2Weorefk5i+f9KxPcslXVHVxCRi4L0xjlB2/Uc9jJSVcck4j++J8aa7P/GeP3eoh2HxHBfkqRxpNd/iDEmY1/AWGAvYEK87oyw7wBgU5h9f5dmY1kYZj8DbOjhcVwZQoaWKPdNtWvSnbGk0jXZ3oksn0ina5Jqr+78bpMkb1TfBaCik3azkz0OW8abO5HxGU+7b4dpswcYmwLjCDcG5zUvFa9LtPox2vOfbr+lHjrHvf6cxKCz4v49S3Xd0YVzmQ1sCxrLOXoOe+8rlXUMcf6P76mxksT/xnj+3mIYR0Un12l2Cowjrf5Dkq4UEvkC7vechBvsk7na/twMFHay7+2efb8FjASe9qxbkEZjWejsmwLX5HTgXuAjwJpwP/Q0uSbdGUsqXZPVwJ3ABGCIPSbnnG5Jp2uSaq/u/G5T+btA+x/xzcmWuZOx3GzLWNFJmwn2dTD22Mfa18kZ82+TPY4QMvcF9tvy7QAkFa9LNPoxlvOfbr+lHjrHvf6cRKOzEvE9S0fdEcW5/IYt+3HPOM7Rc9h7X6msY4jzf3xPjZUk/TfG+/cWzTjsdhVEcV+SxHGk1X9I0pVCAn/QfYCj9uC3etZf4zkpX+lk/812mzogy153hmffX6TRWBY67ZJ9XYLkWhnuh57q1yQOY0mZawIMDvoswDFbPl+6XpNkv7r7u03l7wIpZCDoZCw3E/mm7quea3GNZ/1We91RoE+yxxIk85Jw359UvS7h9GO05z8df0s9cE71nJjodFYivmfpqDsinMd8oB44APzMMzbHcKPnsJe9Ul3HEMf/+GSNlR78b0zk7y3cOOxtFUS4L0nmOEiz/5BMznEzCRhqL+/0rPcul4TaUURygFPtj7uNMa3R7psgujyWYOzYvWY75vDnIjIsTjImlBS8JnEj2dfEGNMQtKof4OSn2Rduv0y+JnEibr/bnqIL34Uf29/dIyLyoogsSKyEXSJfRGpsOXeKyPft7y7AHE+7UNdoKFDcI1JGz2ft9ybg4TBt0uG6QPTnP+1+Sz2AnhOi1lmJ+J6lo+7ojJ8Ag4FvYk0YgtFz2PtIFx0Tj//4VBtrJv7eOrsvSdo40u0/JJMNNyM9y3VhlkeH2TeP9nMT676JoDtjCWY0VhzzBOCLwOseBZfKpNo1iSepdk2+Bgyylx/spF0mX5N4EM/fbbKI9F0YgfXdHQZ8GOu7e17PiBY12Vjf1WzgFOA7wLP2trS6RiJyMvAh++NfjDG1YZqmw3WB6M9/Wl2nHkLPSWhC6axEfM8y5vyLyPnAJ4C3gT+GaabnsPeRLtcnHv/xqTbWTPy9dXZfkkrjSOn/kEw23EgU600C9k0E3ZXnAJZB4BRgIFZc4g5720zguu4K2AOk2jXpLil5TUTkJuD79scVwI86ax7F+nS6JvEmrc9PhO/Cb4GzsZ4IjMWK6QXrT/n7pAbvAbcARVi/sQuwfncAl4pVjSndrtFnaZfttyG2p8N18RLt+U+369QT6DkJohOdlYjvWUacfxHpC/wSaAVuM7avfqimUazvlecwg0n16xPP//hUG2sm/d6iuS9JiXGkw39IJhtuDnmWcz3LQ8K08VIDtHVx30TQnbFgjNlmjPk/Y8z7xpgTxph1wF2eJqfHSc5EkmrXpFuk4jURkU8Cf8DSC28CVxhj/J3sklHXJAF063ebTCJ9F4wxPzTGvG2MqTPGHAD+E2i0N6eEPjHGvGWMecgY84H9G1sJ/NzT5HTS6BqJSD+smH6ATcaYd4LbpMN1CSLa858216kH0XPiIYLOSsT3LFPO/5XAdOCfAHZ53rGe7Sfbnn56DnsfKX194vwfn2pjzZjfW5T3JUkfR7r8h2Sy4WYXVnIhgMme9d7l0lA7GmN8WMmBACaKiBPrFnHfBNHlsQCISKjr7LXitYXYnlKk4DXpFql2TUTkZuAhLJ2wHFhkjKnvbJ9MuyYJoFu/22QR6bvQyXfX+f6mhD6J8je23vM51DU6BpTHWbSusoR219rfBG9Ml+sSRLTnPy1/SwlGz4lNFP9fifiepZPu6IzB9vtHsMZWSnseLbAmMr9Hz2FvJKV1TJz/41NtrBnxe4vhviSp40ir/5CuZI9OlxeBJbmWAmMILMl1kt3OafOwZ99IZY7np9FYHsKqFX8KkAPMJbBu/XU9OI5s+1yOBN6yj9/iWZeTRtekO2NJpWvyKSw3aYP11K1/mHYpf01S7RXt7zZVXtF8F4CPAk9iVUYbaI/pt55xvpzscdhyPo8VjlgI9LflrfbIucDe5pRjfAfrSfNST5uUKUcLvG7LVA8MSfXrEo1+jOX8p9tvqYfOca8/J1HqrLh/z9JJd0Q4fzd7ZA73WqnnsHe+UlnHEOf/+J4aK0n6b4z37y3KcUR9X5LEcaTVf0jSlUKCf9Rjgb2ek+B93elp56x72LNuALApzL6/S7OxPBNmP4M1GcjqwXEs7EQWg10uLk2uSXfGkkrXpCLCOIrS5Zqk2iva322qvKL5LmC51ofb3gCUJHsc9lg2dCLnY5523w7TZi8wNtnjsGWc5pEr9J95il2XGPRjVOc/3X5LPXSOe/05ieH/K+7fs3TQHV08p8s8Yzkn1vHqOcycVyrrGOL8H99TYyWJ/43x/L1FMw5iuC9J4jgqIoyjKJWuR9KVQg/8sAuBx4DDWOVTNwK3BLVxTtTDQetHAL8G9gM+rOSxX6cHJ9XxGAtwHpaHxw6sp7VNQJn9hQnpXZHAMUT8oafLNenOWFLsmkSrtFL+mqTiK5rfbaq8ovkuYD09WIb1BOEg4Aeq7DFOSfYYPGP5KPBn4H2seOrjwDrgNqBPUNtb7OvSZF+nx4DCZI/BI9/PPNdgdpg2KXVdotWPsZz/dPot9eB57tXnJNr/r0R9z1Jdd3TxnC7znL9zgrbpOexlr1TVMSTgP74nxprs/8Z4/d6iGQcx3pckaRwVEcZRlErXQ+ydFEVRFEVRFEVRFEVRlBQjk5MTK4qiKIqiKIqiKIqipDVquFEURVEURVEURVEURUlR1HCjKIqiKIqiKIqiKIqSoqjhRlEURUkIInKBiJSJiLFfJ+zPB0SkRUT2isj9IjIs2bIGIyJFHrkP2nIP6UI/FSKyw96/TET8nn7LPC9jt3/bPi9Om4XxHpuiKIqiKIqSXvRqw42ILBaRrZ4b5DtCtHlARPaLSK19cz2qB+W7yz6uEZG2VJzchMKerL0qIodEZJs9CfmPEO0uFJEqEflXMuSMBRG5RESW2a9+PXTMiBNHEbnGM8mr6KSvj9htPhYn2XRyqUTEGLMCuNyzaoUxZroxZgxW5YcC4FbgJ8mQLwZ+bctd38X9L7X3nw7UOCuddfZ6Z91ZWNXlFEWJEyIyRkR2ev6z/CKyUURyPG2uE5FyEWkUkb8nU15FUdID1S1KT9KrDTfGmBeBaz2rvisi04La3Ao8DvzYvsE+1IPyfQ9YY3/cYow52lPH7ioiMht4BbgIuMsYMw34EVaJ6GC+A4wDTheRrB4TsmvcBnwP+KwxpjkJxw+YOIrIYBF5zZZrQBT7X4FVqvvleAijk0slBko8y1s8y894lif3jChJ4SmgIYp2f0y0IIrSWzHGHABmAi2e1WcaY3yeNk8ArwHfMMZc1cMiKoqShqhuUXqSXm24sfFOKnKAB0Uk+LyUAKU9J1IAc+33t5N0/Fj5FNAXS4E9DGCM+YUxZlWIto8Ba4FPGmNae0zCrjHLft+QTCE8DAAeMsacD3TqBSAiguX18C9jTDQTSEWJJ+EMN971q3tIlrhge0Me9DxhKxKR0+wnas66mwGMMV8zxhyO1Kcx5uZEy60ovRljTBOw2f7Yl0AdhIicgXXP9ZseFk1RlDRGdYvSU6jhxvpx/Qlosj8vAP4rqM0sbMONWPzbDl0yIvKq00hE/miva3CMPyLyrIg02et3i8j3ReR1sXI9rBaRQjtk60XbhW6d4/UjIoWAE5qVJSJPi0iNPWH4UbCBSUQmiMjvReQDEdkl7fkjRtjbV3lk2SIiXxGRFbYsf+nsJNmugD8TkffscRwRkX+JyNmeNm8Bn7c/NgGrReSREH1NEpGdwO+A04E5nm3e87VTRL4mIm/a69aLyKl2u+/ZchsRed2zf5a932rPuu6cl6dtWSfY3c0WK2TpF9H2391zHwpjzCFjzONRNp8PjAGeDXOeY/peKkqMzPYsbwHXM+/n9rp/A/f2rEjdw/aG/HXQui1YhmtFUVKXNZ7l050F+37ql8AX0+BBkqIoqYfqFiXhqOHGmlQ8CSzzrLtXRIrBmpQDzcaYagBjjAEuBcRuuzGoL4DNxpg2u/0VwAl7/UDgAXv//sCZWG705cBH7HZzgW/a7R1vG4AWY8zVwKexjDlfxzNJEJHJwHrgFuDLxphJQBVW/ohHbVnO88gyCdhhjLkAS6E4luIOiEgRluHq/wE/N8ZMBG4HLgRes7cDnA+02ct/tkN7bgzuzxizCyvEx2GjZ5v3fA3DujbnA0ewjGzftdvdBThGsxIREXt9K/B7ZzxxOC+lQbLeZo/ri9H2H8Uxwp77OHEFYIDnPPJ053upKLHgPHkyWB6Nh7B+V+OB+4GLIoWBSoQcToqiKFHyb8/yGZ7l/wC2GWPe7GF5FEXJDNJGt4jIbNsJ4JMx7vdZ+2Fui4j0T5R8SnjUpaNxOQAADFxJREFUcGMZW0qBH2NNwAEGYXmDgDXpWB+0j9cFbgOAWAlrHY8EN6zKNgANsz/+1hizl8CcJJuMMduwJjXOeieh1Tz73WDlVwHY7dl3oWf5r0AeUGGMedpe5/yoTgohyxPGmBfs5W8DPyQ8T2Ploqmi/SnzHs8xrrCXp3pk39BJfxD4FN413ATJ+IAxZo9tjHHGMtCz37v2+xACc2RcB/zNXo7HefFe7+CQuYj9R3mMRHIF8G9jzP4w8sT6vVSUqBArmXuB/XGjMWamMWYUlmdjK/BZYLuIjEuWjIqi9Cq8k6vTAWzv2G8C33A2iMgAEflfEVlje8duEJFHpAuV5byIyCwRedL2Xl5le7h+X0SGdqdfRVGSTlJ0i4h8VKxoDCNWwZ3T7PV/sdeNsj//n22s+SowHWvuEjGM24sx5n6sVBi77PAwpYfp1YYbe/LqM8bst40Dt9CeXOpDIvIZ2g07XmZ7ljfY76cB2UHrgtu+Yb+fGmLdBNonyO/Z747HzRY7+RVYT6kdmu1xnI6VGAtgpYjkiMg3gBlYk6N7QsjyV2fBGOMzxvgJgYjMo91w8arjSQQUeZo5VZa8/Xs9kULhtG0A3g+xHmCFLUMB7UaG7Z7tXoPaXLvtx7CuwytxPC/O+I8aY8qd7TH0H80xEoLtETQVT5hUCHli/V4qSrR4jZ7uTY0xZjXt37F87MpTIjJCRF4Ske1ihWK+LiL/tNuNFZGVInKxiNznCfW70w4/fFisXDMr7dcuEXlGrDDPQrHKchsR2Wof67/Fqhh4YRzHK5GbKIqSRLbRnhfuFLGqdd6H5U18wNPuN8C5wNm2d+ylwCXA8a4eWEQuwNKDo4F5tifuNcBXgcFd7VdRlJQgKbrFGPMP4EWnb2PMFhGZAjhVZJ2HyP8EvmmM+Ykx5lEgz/MQOSrESuExBCjriqxK9+nVhhuCvGmMMRuwPG8cfgwsIrzhxke7IaGDF05QW2g3ZsyKsM7Z3zHceENpZniWnYTFZ3vWXWT3eRlWid3Zxpg/h5DFG4vZGed4lt/1LM8Nsd4ZgyF6w81mO/wseD20n4eZIdZ1kEdEcrGu2bdsA1O8zotzbTcErY+2/2iOkSgcb6jODDexfi8VJVq8etH93tsx39M92xzj7Y1YNzGfNMZ8CMvb73Z7W7UxZqEx5lVjzO1Atb1+g32ctVi658fGmIXA9Vjf/+8aYyrtvqH9hmMVcLsxZnl3B0m7N9qITlspipJU7HuDdfZHAb6AFdbwK6eNWFUur8P6XU+09zsAnOs8vBIrV98zIrJWrLx2PxCLYhH5m+1R8y8ReV5ERtlP0/+Glbj008aYY3a/lVgVOPfZBuYn7D63i8ijIpIrIjOlPfH5H+w2x0UkL5wcPXIyFUVxSZZusbt2oiAcI83XaL+vch743wg8IiJXiEgtcExETrZfjn75k+2pUyciX7Dl+bhYuUNfxzJEgRpukoYabjoaZe4CdtrLQ7HyfQS3OcV+f98Y43joOE9tWwk0tMy236s8lUUcQ4Qf2GoveyfIpWLl1hlpf97j2fYh+70Ba1ITzOeMMVPtakPfAC4RkfwgWfYZY2pC7BuKE57lD8CddF1ir9sCrAzqf7dTtjoUYsVFTrE/bgjaHErGgHPjLBhjqmifvM3HSjK93hjzfIjDdum8iMhA2q/3BntdoXSM7eys/06PkWCuwPqebg1a78gT0/cyIRIqmcxsz/K/AeynUL8BCu31f/cYT5xQ0FdE5Eksd9wNEY6x3BjTbIz5FVALXC4i62lPfjwVwBjzBpah/ioROQnr5inmxOBhcIxQS+LUn6IoicMb0vB9rKShLUFtjmL9rneIyA4R+SFWuDhihTW9hnUPeRbWPdDtWP+h/8IKYz/HNj4PABqxdMNwYI3XcxfAGPMj+8HTv7CexJ8H3AksBb5ijNlkHw+ssOzrsPL9tYaR47SunRZFUbpJMnQLeAw39vzR0F6tc7yIXAKsNMa0GGOeBQ7Y++42xrxPu355DUvvDAGuF5H5wBNYoe7nY+kcUMNN0ujthpvZBE1G7Zi9z2B96QGOYSVp9eL8UGwjqJxJ+w37DmOM19gx2373eqA4k+HtxpjmoHVOOM48T/tJ9sHOot1wc6sx5oi9/BLtP6Zz7LZ9gR8AFwNObpNQskTiH1hGImifaH0WK2/FIeATnvCpaEtmzwCywsgSSkbHoNBIu1HNwfG6OQvrnH3Wsy0e52U07b+TchEZj/WdyY6h/0jHSAi2JX4BHb1twskTzfdSUSIiIgtEpAz4qGf1X0RkN9YNxmVYbrufwmPssF1+zwf+iDWBeU6s8MewBOnbr2AlBr8d+IS9Lsuz/edYT7zvA44YYxqJD38QkVLaQ20B7hGRW50PIrLIPifeanNlYpcNVxSlx/BOrv5sjFnl3WiHzp+NpS92YOXQ+ybWfztY4U1FWOEJI7D+/w9hTaqKgb94QiOuMsYcp9172XkiH8w1wMlYhuwTWLkWob2i5Qys4g+/sGX8SSdy6H+1oiSHZOgWgL32+3is0Mv/od2YMx64GfgDgIjkYD2QLvPM35wUD0/T7qFTi1WcJYv2FA9Oagw13CSJXmm4EZEL7Rvoi4D/FhFv1SDnyez99scNQaE8YD0J2Y71J7say7Dgs7dNsp8UO0+WHWPHRnud0P50NtSkeYCIPEtgKFK5iGzCqqL0LnCFMeYJj7zbgSuxQgW+JCJbgOVAHfBRY4wJkmVT2JMThJ3Q9kP2se+2J15fxbp5mGGMKbPHlU976fINEbr1enHcISIL7T7Cyei07wsEe9M4hps6rPPiJtqK03nZg6XIjmNd9z9jhXHUR9N/hHF1C7HyeJRh5QgByLcngk4I3eVYv/Fng/bzyhPr91JRImKMecdY1dcGGWPEfp1qjJlojBlijBlvjFlsjHnYc+OAiHwJGGKsqm1fslcXYOnXbLGS+t3e4YDt5NnvB4GxIbb/GctLbyn2TUycmGmMKTHG3OAZ73hjzANOA2PMS/Y5yfa0mW6MeTiOciiKEhknbLMeq0JnAPZDslpjzJeMMVOxwjcBcu33+fb7QqwKjM/b65z/UDeM2xhTZy86D0Ocz86xLhaRqVje3d59nao0qz3/zzs8D+zCyuGZzCmK0rMkQ7dAu5HmVGCgMeY92o05Hwfe9jyQPQ3LGLPJlsnRL+8bYxpof1i+kXY95MwLZmDpMs15mST6JluAZGC75U+P0ObzwOfDbFtNewUph9+FaHeUoGSV9mS+Q+ZwY8zJIQ51h2e5gwII2v95Oho1OpUlWowx/6Y9NCocXg+htRH6+z1Wye7g9UcJIaMxJqzbrzHme7RX3Aq1vVvnxbaOh33iH6n/aI7RVYyVx6MzrsDKGP+2d2UcvpeKkih2AT8QkW9iPXV6FisE8hTg08CbwM9E5E5sw4yIvGKMcfTTg1hePg8Cf7fXzRaRW40xDxhjmkXkt1iG1eBqgcG0YrkTA9wgIldixaGHDQONByLyBpann3Ps5k6aK4oSI8aYfXT+n/xVrLAEp4rmECwvbOeB2UH7/R5jzGP2ZGwJ7ROlbADb426oMeZ/sMIyPwdcJCJ3GWP8dhjC/2J5xn5g73vCDsW+Ait09M9YXteD6PiUO5wcP4ruTCiKEk+SpFug3XAzDCvPpnfdYALnXI5hxknr4egX57OTS3UD7YYlv4hMx5r7bg4R/qX0EL3ScKPEF9tae7P9sQK7GpTSbbo7cXwLy1WzNWLLGNHJpZIIjDHPAc+F2PRf9svLvSH234TlTuxwt7MgVvWozVjhlh0MxyH62kNor52EYow5t6ePqShKABuA/xSRG7H+23KwwsJftrf/BOvJ9g9F5FNYk6qvYuWHmwLcaf9fb8YKg8AYs0JErsF6IPeeiOzAerDyYWNMvYj8HCts4vtYiUXfxqoAc0zaK98FG27CyaEoSmqygTjrFgBjTI2INAIvGGO22asdw83/mMDS3Y5h5jMi8mvP52DDzeewdNHDWIbntVhGKQ2TSiLSMQpIUWJDRP6GZYV9Hvg/e8KjKIqSMojIf2AZfyqBjwflxlEURVEURVGUlEUNN4qiKIqiKIqiKIqiKClKr0xOrCiKoiiKoiiKoiiKkg6o4UZRFEVRFEVRFEVRFCVFUcONoiiKoiiKoiiKoihKiqKGG0VRFEVRFEVRFEVRlBRFDTeKoiiKoiiKoiiKoigpihpuFEVRFEVRFEVRFEVRUpT/DwA2d++yuss5AAAAAElFTkSuQmCC\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 50\n", + " Kdiff : 24.4083%\n", + " Bstray : 51.4898\n", + "Accepted Soluntions : 4\n", + "Epoch: 60\n", + " Kdiff : 21.9842%\n", + " Bstray : 52.4551\n", + "Accepted Soluntions : 2\n", + "Epoch: 70\n", + " Kdiff : 22.9707%\n", + " Bstray : 51.9014\n", + "Accepted Soluntions : 1\n", + "Epoch: 80\n", + " Kdiff : 24.2745%\n", + " Bstray : 51.6732\n", + "Accepted Soluntions : 1\n", + "Epoch: 90\n", + " Kdiff : 23.6052%\n", + " Bstray : 51.9111\n", + "Accepted Soluntions : 4\n" + ] + }, + { + "data": { + "image/png": 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AxvuNUMNhjPkRVpTJbVheJv8O/Mv2RGnrmCIepoXXOpp63a6xeSOdIvVnjHkfmAhcheWLNBvLt+ZjEUnrxHF15fsDHf/8OpMCQtObvGQQmhZV4Nkeri1h2kfbd3toyfjb/x5H+kyjbQdqS6AoiqJ0AircKIqiKL0Nx2R1WpjXTrXvvVfVS4FwC/SORic44zglzGvhtoVjHNb/4r8Dn3huV9qv/wvYGn7XUIwx+40x/2eMuQbLJ2Q98H1P+tQ+IF1EhoTZ/VQggGWYGolS+z7a9zLaMtTQ9HmF+0ynYr1HbTJ8DhmIMRXGmL8YY/7TGDMNSzA7BWhWhczHPvvY4T5P52/tgH3fle+PMxaI/u++K/kQK/3sJO9G+3k6oR5PH9r3Z4TpZz7W+/BRO/vuaob6Kl45TAUOG2Mq7ectffbhIrLa+tkriqIoJzgq3CiKoii9jU1AHvBl76JKrNLOt2Mtil72tM8BpopIhqdtAtbivSO8Yh/rO+IpVy0ic4ALouzjD8DVYW7r7Ne/Qivlj8UqiR1S1toYU0NTes9Q+34l1v/9EFNeEfksljfKK8aYxhYOdQCox3dutkfP/DDtK7AWvq1GihhjDgPvAZeKyHRP30KTYfNfW+snHGGqk4H1NwThF9peVtr3P/Sehz3GzwPvGGOcakBd9v7YOH9vt3s/bxEZg2U8fRBoT4Wr9uCk9d3m2+48f9bZYIzZiyW2XC0ijlEx9uOrgTW+dKSo++4m/N+XK7DMwFd6NufY9/7P/os0mTN7qaD1vz1FURRFcdHwTUVRFKVXYYxpEJH/xFrIfygij2OVA78Wa4F8v6+i1K+xSoW/aVeQGYBVPalDXhnGmN12Jav/BNaIyF+wyoH/J7CFKIxijTFb7LYhiMjn7IevGmNaioIBWAQ8bh9/D9ai8DSsdKkPjDF77HZPAV8CfiAiWVgROZOwyhsXY3nBtDTWChF5Cvh3EfkTlrh0MpZosBWrZLeXDcDngF+LyHtYKSprbJEmHN/CKgf+tv2+Ftn7XwysCFNRKlp2icgG4AOsNJwxWCW0a4E/t7SjMeYNEXke6+9nqIj8jaZy4DVYVcWctl36/hhj9ojIL7DKga+3K1s55cBTgOtaSB1rFRGZiSVGgVV9DOAGj6nz/znm2caY1+z34jsiMhir1PcZWBFMy40x7/i6/xaWAfDbIvJ/9rb/hyUkftd3nm3tuys5Alxpi0zraCoHXoxVVcoZ8x67OtutthD3MVZK3hXAXiBEWMX67G8WkXuwBNZGrO96JYqiKIoSDmOM3vSmN73pTW8xcQMWYkUVfC+KtguwqjcFsBbRm4F/j9D2S1iiRi1WZMT3gfPsY93kaXeTvW1hmD7WAbm+bf2Au7CiHYJYJrHXYS3qDJDVzvfhKXv/4VG0HQ88irUADACV9uOfAYN9bZOxqkrtt9+Lw8AzwDhfuyz7+Mt821OwzJSPYglf72BVg3oKu0CP71hPYC1yG7zva7j29vZZWJEMpfb7ucv+rPqHe38ivB8GeMrz/A4skeqw3eenwAvAnCg/izjgB/ZYgvbYVgIzwrTtlPenlfH8B9bfeo39eb8BnBPN32sr/d5kjyHSLcvXPhGr0luu/b7sB34MxEfo/wzgn1jCYjmwOtJn0Na+w+zf7O830t+0/doy/zk67x9WmtvL9ntdbj+eFKaP0fbfVcA+x79jpdg1+xywBN6/2H9LjeHeX73pTW9605vevDcxRtNsFUVRFEVRFMVBRNZhiSlZPTwURVEURVGPG0VRFEVRFEVRFEVRlFhFhRtFURRFURRFURRFUZQYRYUbRVEURVEURVEURVGUGEU9bhRFURRFURRFURRFUWIUjbhRFEVRFEVRFEVRFEWJUVS4URRFURRFURRFURRFiVFUuFEURVEUpdMQkYUiYkTkpp4ei9I+RCTXLoetKIqiKEoMoMKNoiiKovQSPKKI91YhIptE5NsiEtfTY+wtiMilIvKSiBwSkaD9Pu4SkSdE5PyeHl9XIyLLROTynh6HoiiKoiitoz/wFEVRFKX38SdgFSDAaOBG4JfAKcAtPTiumEdEBgIrgMuBPcDTwH6gPzAZ+BzwFRFZaoz5U0+Nsxv4KfBHYGWY16YAWr1CURRFUWIEFW4URVEUpfexyRiz3HkiIr8BdgP/LiJ3GWNKem5oMc9vsUSbXwB3GGMavS+KyPeAK4Cq7h9a27GFqDpjTH1n9WmMCXZWX4qiKIqidBxNlVIURVGUXo4xphLYgBWBM9HZLiL9ROQuEVkvIkUiUisieSLyWxEZ5u1DRLLs1KtlIvI5EflQRGpEpFBEfhEuDUtELhORzXa7T0XkZ0B8uDGKyHARecRuV2vfPxJmHDfZ4zhfRH4iIgdFpFpEPhCR+XabBSLyjohU2uP7cTTvk4jMBL4EvAv8wC/a2O+lMca8ZIx53beviMjXROQjEakSkXIRWSsiizrhfTxZRJ6x29TaHjO/EJFkX7un7L5HiMiTIlIMVAJj7de/LiL/EJF8u59CEVkuIln+8dlPv+RNu/O0CetxIyKXi8i7dlpZhf34sjDtckVknYhMFZHX7PeqTEReFJHRzT8ZRVEURVFaQiNuFEVRFKVv4Ag2pZ5tA4Dbgb8AL2Mt8k8HbgbOFpHTjDG1vn6WAF8HHgWeBC4DvgccA+53GonIFXa/ucDPgHrgy1ipRiGIyGDgPWCS3ecmIBv4GnCeiHzGGFPu2+1BrPSlX9nn8V1gtYh8CXgCeBx4FrgG+JmIHPBGIUXg3+z7J4wxbU0Fegb4IvAi8AcgAbgOeENErjTGvOJrH+37eBqwBjgOPAbkA7OAbwJnicgCY0ydr+83gCLgHiAZqLC3fw9LwHsY6+9gOvDvWO/xDGPMUaAEuME+n7ex3sdWEZGvA49gRXbdi5VKdROwUkRuNcb4+8kA1gF/xfobnAXcCqQCF0VzTEVRFEVRbIwxetOb3vSmN73prRfcgIVYC+afAMOBEcAMrAW1Af7lay/AwDD93Gy3v8azLcveVglk+frYDhR6tvUH8oAjwHDP9sHAQbufmzzb77O3fd03jm/Y2+/xbLvJ3rYJGODZ/nl7ez1wumf7AKAQeD+K9+8vdh/ZYV5Ls99T55bqee0Ke79bfPvEARuBA4C09X20t2/BEkMG+bY7x/S+j0/Z25ZHOL/kMNvOt/f5vm+7AZ6K0E8usM7zfCiWOLTX976kAvuAcmCIb/+Qvy97u/N3OrWnv0t605ve9KY3vfWmm6ZKKYqiKErv426syInDwFasyI6XsMQNF2NRDSAi/UVkiIgMx4rwAJgXpu+Vxphcbx/AWmC0iKTYm08DTgL+YIw54mlbhhVh4ucKe7z+qIzHsMSfK8Ls81sTGg30tn2/wRjzoeeYtcC/gJPD9OEn1b4PhHktxx6jc1vhee16LHFipZ3yNdx+H4cAr2KJNf7jt/o+isgMYKZ9rARf3+9giT/holP+O9zJGStlzkmRG2z3swUoI/xnHS0XYkX2PGyMcd87+/H/ASnABb59Cowxz/u2OX93kzowFkVRFEU54dBUKUVRFEXpfTwOvIDlJzMD+AGWz0mNv6GIXIOVZpRNc/+ZoWH63h9m21H7fhhW5MUE+/nuMG13htk2HthofAa6xph6EdkDzGltHMaYYyICVnSLn2P22FrDER1Sw7x2JVb0DlipSF5OAQYBxS30PQpL/HGI5n08xX5+t32L1K+fnDDbEJHzsKKx5gGJvpfDfdbRMt6+3xHmte32/QTf9tbOX1EURVGUKFHhRlEURVF6H58YY960H/9dRN7BitB4FPiC00hErgSew4pI+RbwKZa40x94nfBFChpaOK747sP5xEiYbe0h0jhaGl9rbMcSaGYDm70vGGPWO49tgciLYEXhLG2lby9teR//B+vzCMcx/wZjTLOKVyJyOvAPrHSmO7AErmqsz+jPdKwgRXs+02jOX1EURVGUKFDhRlEURVF6OcaY90TkGeBGEXnYGPOe/dINWELNIu9iX0SmdvCQ++z7U8K8Fm7bfmCKiMR5o27sCkuTCR+d0RX8BSsi5WYRecpOX4qGT7DGucEYU9Fa4zbwiX3f4BHi2stSLEHus8YYNyrJrkzVkWgbaPq8pwH/9L12qn3fXZ+hoiiKopxwqMeNoiiKovQN7sGKcviZZ1sDVsSF+/9erHCSH3XwWB8Bh4Av2z4qTt+pwFfDtF+JZaT8777t/2Fv/2sHxxMVxpitwNPAWcCDItLsd5CECbex9+kHPBCuXxEJl84UDZuxInW+KiL+VCNEJE5E0qLsy4lw8Y//TsL/3qvAMmSOhjew/Hb+n4gM8oxvEPD/7L786WWKoiiKonQSGnGjKIqiKH0AY8xeEfkzcJ2InGOMeRurdPW/AWtE5Gksj5vLgaQOHqtBRL4NPA/8S0R+h1Xt6StYPiaZvl3+C7gaeERE5mAJFtlY1a322K93F1/Fqn71feAyEXkJK1okHmvcV9nt3KgVY8yLIvIH4D/t8f8Ny1R5LHAGltluM+GlNYwxRkRuwDLt3SoiT2L5yCTZfV4J/BCrmlRr/BX4NrBKRB4HarFMhWfaY/WzAbhARH6AVSHMGGP+HGGcx0Xk+1hVoT4QEWc8N9njvNU2plYURVEUpQtQ4UZRFEVR+g73AV/EirpZZIz5sx0V8W2sSkTHsKog3UGTUWy7sMWMq7BSj5ZhVbh6CliP5bXibVsmImdhGfB+HvgyltHvo8BPjTHlHRlLG8ddLSJX2OO4CfgSVtRPHZYH0NtYZb/X+vb7ioisBW7BElMGAEVYZct/2IHxfCwi2XYfn8cSlsqxSmo/RfPUpEj9vCsi/wb8GCv6qhp4E1iA9Zn4+TqWEHMXlvEyWF44kfr/jYgUArcDP7U3bwGuMMasjGaMiqIoiqK0D4k+vVtRFEVRFEVRFEVRFEXpTtTjRlEURVEURVEURVEUJUZR4UZRFEVRFEVRFEVRFCVGUeFGURRFURRFURRFURQlRlHhRlEURVEURVEURVEUJUZR4UZRFEVRFEVRFEVRFCVGUeFGURRFURRFURRFURQlRlHhRlEURVEURVEURVEUJUZR4UZRFEVRFEVRFEVRFCVG6VLhRkRuEhET4bbS1/ZmEdkiIjUiUiIiy0XkpDB9ZtqvlYhItb3PzRGOH1WfiqIo4RCRC3zz1tm+13WOURSlTYjIjSLyvj1nVIrIHhF5QESG+trp/KIofZzOng90naQofRcxxnRd5yI3AX+I8PLLxpjL7XY/Au4J0+YQcLoxpshuNxrYCGSEafsjY8x9nmNH1aeiKEo4RCQe2ApM9Ww+xxjzjv26zjGKorQJEbkd+K8IL39gjJlvt9P5RVH6OJ09H+g6SVH6Nt2VKnXQGCO+2+UAIjIO+Ind7gNgDHCD/XwssMzTz900TUY32G0/sJ//VEQy29GnoihKOL6NJdpU+V/QOUZRlHZyvX3fACwAhgP/srfNE5FTdX5RlBOGzp4PdJ2kKH2YWPC4uQqItx//0hhTZIxZDuyyt31BRPqJSD/gWnvbLmPMclsN/qW9Ld7uK+o+u+qEFEXp3YhIOvBj4DDwuzBNdI5RFKU9NNj3RcaY9caYo8AbntcHovOLopwodNp8oOskRen7dNeXMl1EjopIrYjkiMjPRCTBfm2Op11OmMeDgfHARPtxpHYA2W3sU1EUJRz/A6QAPwCOh3ld5xhFUdrD4/b9GBE5V0SGARfa2wqA7ej8oignCp05H+g6SVH6ON0l3MQDafb9yVhXsl+2XxvuaReI8HhkG9q1pU9FUZQQRGQB8AXgPeCPEZrpHKMoSpsxxjwK3AYI8BZwBPgMsBm4xBgTROcXRTkh6OT5QNdJitLHievi/j8Bbgb+iZVyMA/4MzAKuFhEFmJNVuHwbjdEFpn87fzbWmvrMn/+fJOYmAhAVlYWWVlZYTvJzc2N+Fp3ttuQU8KaHUWcN2008yePCNvOaTNp1CA+d9pYkhLiIvbXk+ei7bqmXWf1mZubS25uLgBvvfXWBmPMGVEdvJchInHAr7HCl79hjDEiYaeTNs8x0c4vEPt/V7m5uYwcM5atB48xc9xQd16pCtaHbGutP6f9EAmQmTmOv310iL3F5Zw3bTQzxw1t1v/uT/Zx3KSGbGvPcdtzvllZWc2O0xl9arsTZ34BEJGlwH/TfA4ZDcwCPg7zmru753Hz3zBTppjEeCv7IWvUKLJGdmzdlXv4cIf76E39tbXP3QVlbDt4nBnjhjA1fXDYNrF+zofKyxk7d26n9deW3ybd2WeszjGdPB906Tqpr/2G0Xbtb9eTx47Fdt05v3SpcGOMeRd417NpnYj8Crjffn46UOJ5PdXzeJDncQmhE1JL7bz30bR1SUxMZN26df7NzVi2bBnLli3r8XZHymtYvn4/1587AcB9/Ov/edBtl1NYxhcfepucwgDpZ8/mtktObfNxu+NctF3XtOuKPkUkGNWBeyeXA9OBvwGIyGysH1AOk0SkiHbMMdHOLxD7f1fLli1jyOnX8Nv8j1nsm1fa0t9Dr+3kt/kfc1b9u8w/eymf5n/M5y9O59Fb5jN8UGKz9ouXfoN3485qdkzvXDh8UCLf/+GPGHL6Ne7ztp7vkfIaHnvDihi/9cLJIXNqa/SGzy6W2/Xl+cX2jPg/rN9e+VgpEYfsbV8C/iAiO2jvb5j4eNY98ECnjXfZihUsW7r0hOmvrX0GqmtZu72IRdNHkzpwQIf76+zxRcPq//1fLo5ybouGtvw26ak+Y2WO6YL5oEvXSX3tN4y2a3+7njx2rLfr6vmlS4UbEelnjGn0bfYquI3AJsD5LzQZS112HgOUAQc8jwd7XsP3eLN935Y+28zChQtjot3wQYnu4uWh13by4+c+brbPqk355BQGWDRtFJXBeo6U17gLmWiP25ExdrRdtMTKZ9JRuuK4PXUuvZQU+/5z9s3PH7DCmV/lBJhjWsIRjJ379vTn7DtehnPWOU39RRJbvnb9ZSw2Y5sdc/n6/e78d9slp1KVOinkeVtZvn4/D67cDkByQpzOld3Yro8zEittHGC9MWYXgIiswFqoCbCILv4NEy0LZ8w4ofprK6kDB3DZ6Zktton1cz75pJM6tb+uoAvmjtzO7rCddMV8oOukTkTPo3v77Inj9rbfMGJMs2jbzutc5G/AP4CVWKlS82lKlQI4E0tl3ovlf7MBuAI4H1hut3nMGPNVu7/HgFvs7dcDb2J55cwD6oCJxphP7XJ3UfXp5aabbjJPPfVUJ5x59+O94rz9ow3uH1hOYRl3rtjMlPRUHv77bu65NvLV8Vhk3bp1MfNl6Qh95TwAROSPxpibenocXYGI3IQlzrTEW8CNtHGO6c3zi5+O/j37I2Q6un+k50vmZLBqU37E40Q6D3/ETXvG2J7z6Ah9ZY7p4/NLApbZeSLWb58L7HvnCjtYv3FW057fMOefb5667bauO4ETgHXbtvW4INStZGVBJ55vb5iHRORuY8yyGBhHp88HXblO0t8wsUdfOQ/oO+fS1b9hIuVDdhZjgV8BB4FqYC1Nos0KY8z7xpg84Gf2tvlAIU0TRz6wzNPfT+1t2G2KsCYjgLuNMZ8CtLFPl87Oy+1OnOib4YMSQ/7wV23KZ/WWApIS4rjn2tktXh2PRfrClxj6znnY5Pb0ALoKY8xTxhjx3oC7PU3OMcYsbM8c05vnFz8d/Xt2ImSWr9/fKft75z/v81Wb8t12R8preOi1nRwpr2n1PIYPSuSuK2dy15Uzu0y0CXceHaEPzTG5PT2ArsI2Gv2t/TQDq/RugKZFWjHwUrt/w3Sy38uJyAkl2gCsWNGp3fWheajL6aL5oMvWSfobJvboK+cBfepccruy8642J/4JVijeXCAdK01qF9YVbWeywhhzr4gUAt8EpgAVWArzD40xRZ52RSJyJvAAcDFWWsMe4GFjzBPeA0fbZ1/Hm87gXIl+6LWdnXKFV1FOZHSOiR5/ZEk0aVadsb+3nSOSVAbrSU6Ii4k5sK3vg9InuB3rYtaXsOaNAVgLtDXAMmPMUdD5RekmZs/u6RGc6HTqfKDrJEXp23S1OfErwCtRtn0CeCKKdnnAdZ3ZZ1/G64MDzb0gFEVpGTukelmE1074OSYa/POOf17qqv297RxxpDJYHzNzYFvfB6X3Y4xpwIpE/lUUbXV+UZQ+TFfMB7pOUpS+S1dH3Cg9jN/rYcmcDECv8CqK0n1cf+4EKoP1zQzSu2t/aBJJjpTXuBE3iqIoJzQffwzXXtvTo1AURVGioKs9bnoVubm5LFu2LOpyd7GI38fBuVJ954rN/Pi5j1m1KT/EC0JR2oL93cjq2VEovY3hgxJJTojjwZXb2+Xn0tH9/X3pHKgoigJ0cjl1RVEUpevQiBsPWVlZUdevj1X8KQXOVeUlczI4e+rIiFeZO7O6idJ3sc3Dcnt2FEpvwhv1B+2P9osVPxidKxVF6TO88EKnVpVSFEVRug6NuOmlhKuQAtai5p5rZ7NkTgYPvbYTsASctJQEKoP1PPZGTrN9AB57I4cfP/exWwJXUZTOpS9E9LUHR0xuLdrPP6f5n3dWpEykuTNa/JWgOtqf0oRG9CkdIVBdy8sf5hGoru3pofQeEhJ6egSKoihKlGjETS8lksmws7h56LWdIa8vX7+fB1duByA5IU4NMRWlm+kLEX3tIdpIGf+c1plG6t4omY726z8fNXzvPDSiT+kIa7cX8ezbBwC47PTMHh5NL2Hx4p4egaIoihIlKtz0UryLh3Ch+/7FhWPu6d3m5dYLJ6thp6IoPUa4Oct73xG84kpH+/VXgoqVFC5FOdFZNH10yL0SBS++CKed1tOjUBRFUaJAhZteyvBBie7V48pgvRtN4ywo/IuL4YMSuevKmS32p1eLFUXpDNoT4RJuzmrvnOQXsztLXAknkuvcqcQKuYcPs2zFChbOmMHCE9C3JHXgAI20aSunn97TI+hWNB2z/Tjp3gsXLnSiIxVF8dAd84sKN70YZ0F0x+XTuefa2a0uStRUU1GU7qAzI1w6enzHF8cRV/xppB3pV1FiiayRI1mmVYKUtlBZ2dMj6FY0HbP9nKjp3ooSLd0xv6hw04vxpz+1JsbookNRlO7AK9b0RESKXyzyitZtFZI6sq+iKEpMs3NnT49AURRFiRKtKuWht1V9GT4okeSEOB5cuT2qCidOxSlddCjtRcOMlWjorApQ4YimipP/+N5KUG0dW0f21YpTiqLENDfd1NMjUBRFUaJEI2489MYwwEgVTtZsL2TeySO49cLJIV4MjueEpksp7UHDjJWuwhvZAkScpx57I4cHV26nMlgf0berMz1uWore0ShHRVF6NU89BQ880NOjUBRFUaJAhZtejj8NYcmcDJ5Zv5+1O4pZu6O4WelvXUgoihJrHCmv4auPb2D1lgLe2X2Y7PFpzQzX24LfY6c9YrVXoIl2DvWLT5XBeu64fLpGOSqKEpukpfX0CBRFUZQoUeGmj/HC+wfJKQwwbkQyl809iSVzMnjotZ2dXl1FURSlPYSLWFm+fj+rtxQweUwqq7cUMCU9lYtnpbNkTkazfW69cDLJCXEtzmHeea69YnWk/VqaQ737ADy4cjv3XDtboxsVRSFQXcva7UUsmj6a1IEDeno4Fmef3dMjUBRFUaJEhZs+ysGSSkakJrJqUz4/fu5j18S4KlhPUoJ+7IrS3WgpTYtwgogjgiyZk8GqTflUButZvaWAs6eOJO3cBDcax9mnLVE0S+Zk8M7uw64IFC2RBJqWzJbD7aMiefSoh5bSl1m7vYhn3z4AEDtly195Bc44o6dHoSiKokSBruD7AP6r0Q7eBUNlsN5NPQCapVApitK19EYPra4gnLjh998C3KgaJxrn4lnpXH/uhJC0KggfReOPfHFEoMmXDI56nO2phuXfR+fYtqEeWkpfZtH00SH3MYFG3CiKovQaVLjpA/ivYPsNO2+75FS3qsnR8hr2FpW3+eqzoihKZxBJEPH70jh4o3EeeyOHDz4pYe2OYiaPSWXJnIywqVfXnzuBymA9lcF6rj5jXLM+W6It5sOKonQN7UkrislUJA+pAwfETqSNQ1FRT49AURRFiRItB94HcMp8O3424UrPDh+UyF1XziRzeAprdxSzalM+oOVqFUWJDZx5zOtL4y3BvWpTPg+u3O6KNjmFAVZtyg9p6zB8UCLJCXE8uHI7qzblt7v8NzSfI3XOVJSux0krWrs9emGhPfuc8Ozd29MjUBRFUaJEI276EC+8f9Atk+s17/SXxXWuRDtXlrXKlKIoPY03EieSV4zj1XX1GeNYtSm/RS+ZtnrbOPOh0947f3rnSJ0zFaXraU9aUU+nIsV6xE9Ybrqpp0egKIqiRIkKNx56q3mos5C44/Lp3HPtbCqD9SH+Dj9+7mPe2X2YR2+Z716J/vFzH4eIO2qgqUSDmocq3UG4dConatDB61cTriz3957eyNodxWSPT+OuK2e2mgIVbRUpv/it6VSK0vm0J62oM1OR2iPCxKT5cGs89RQ88EBPj0JRFEWJAhVuPPRW81DvgmL4oESOlNeEiDLv7D7M6i0FLF+/363I4iw8QK8aK9Gj5qFKZxNJUOmI18zy9ftZu6O42baWImWirSLlF791/lRihdzDh1m2YgULZ8xg4YwZPT2cXk17RJiejvhpF6NG9fQIuhW9+NR+euvFbUXpLrpjflHhpg8QyeyztCLIqk353L80m7OnjnQXJN6FBzRVb9Erx4qidDeRBBVnuzf1M5o56kh5DZXBer752akh21qLLmxLFSlvX2pmrMQKWSNHsmzp0p4eRpfRnalIrYkw4cYSk+bDrZGd3dMj6Fb04lP76a0XtxWlu+iO+UWFmz6Is+BxIm0gdEHkLGzuuHw6gPo1KIrSY0QSVJzn3tRPb8nwSCLJ8vX7eXDldu65djZASGRMZ81xXpHnodd26hyqKN1Ad6YitSbC9Mq0qHC8/josWNDTo1AURVGiQIWbPoiTClVdW0/2+DR3AeRcGa4M1rsLm+vPnRCSVqUoitIVRIpMiRTp4mz3pn5GYwwcydi4qyJjojFB1qgcRek47YmC6amx9BrOO6+nR6AoiqJEiZYD70PkFJZx1f+so7QiCMCvVu0GcBcK3kXPHZdPD/G40cWEoiidxZHyGu57aSv3vbTVLZvtzD9ffXxDm0ppOwKOUxXvnmtns2RORsSS3N723sfhyoa391y8rNqUz+otBazalB+xj/YeW1FOdALVtbz8YR6B6lo3CiaSKPP65nyeffsAr2+O/F1s77H9tDaWXoOWA1cURek1aMRNH+LOFZtZvaWAipo6io5XA1AVrOeh13Zy/bkT3CvDTild9bhRlO7jRDL2c9KVADdN6fpzJzQzSm8rjhATKT3JG9nijMObcnXH5dPbHF0Y7ly8RFOZr7U2GpFjocahip+eTEmKxXSoTo8qysvreB+KoihKt6DCTR/A+dF/+2XTAKioqWNfcQWTx6QiYnk8HCmvYevBY2553FsvnAyE+keoP4OidB0nkrGfk67pPAZLdHn0lvkhYkpLtCRmRDIH9kYVQpN/l5MeumjaKHfui1YsWTIngzXbC5k5bmi7U0pbMz6OJgXsRECNQxU/bUlJWpydQUJ8/05LX4rFdKhOF5NuuqnjfSiKoijdgqZKeXCuiNtX/XoNzo/+9/eU8OJ3F3L3tbOZPCaV3/zHPAYOsLQ5R7QBOFpe46YdAO26Cq2cmOgVcSUahg9K5K4rZ3LXlTPD+tlEE1USKb3IL7h42y2Zk8HFs9I5Y8qIsBE2a3cUu/099kYOP37uYx57I6fFcazalM/aHcVu6lWkcTopYEfKayKmcUXCSQHTeVhRQmlLSlJnpy/FYjrUoumjue6c8Z0nJj31VOf0oyiKonQ5GnHjobdeEXd+7Du+D5XBenIKA7y/p4RbL5xMckIcS+ZkcM0v32JfcQV7i8qB0OorJ3J4vhI9ekVc6S4ipRf5o1O885+TLnrgcAU5hQF3bnOibML158UvCnkr8EXaz58CBm2v1NeWUuSKopy4dHrJ8czYSAFTFEVRWqfbIm5E5AIRMZ7b2b7XbxaRLSJSIyIlIrJcRE4K00+m/VqJiFTb+9wc4ZhR9dlbca7sgrVA8PrWOFdvnciaVZvyeezWM1g0bRQzxw3lSHmNXuVVFKXbaGskir/ktrOff95y2jkmwcMHJZBTGODiWekhbfwRQLdeOJl7rp0dIup4o3eOlNfw1cc38ODK7SQnxEUUt50UMGdMOq8qSu8mkilxS2bFvZZJk3p6BIqiKEqUdItwIyLxwP+18PqPgN8DM4EEYDhwHfCeiIz2tBsNvGe/NhxItPf5vYjc1Z4+ezP+VAJnwXDrhZND0hG8qVTnTR/Dr1btZvn6/SFpC95FVXtC/RVFUVqivZWV/PuFS7fKKSxjzfZCzpoygiPlQS6elc6jt8xvMZLQEbUdkQZCRaHl6/ezektBiADknxv94rm/kpWiKB2ju8WSQHUtj7y+h2ffPsDa7UUhrzn+Mv7tvZo1a3p6BIqiKEqUdFeq1LeBqUAVkOR9QUTGAT+xn34AXA5cADwDjAWWAV+1X78byLAf3wC8CawE5gE/FZFnjDF5beyz1+JUiVoyx3pLvAsRb1WVM6aM4OJZ6SyZk0FaSgLQcvoBtD3UX1EUpSWiqb7U3v3uXLGZtTuKWTRtVEi0oZdwZsT+tCtvlI/3uJHaq6mwonQtXVHZyVuZyTmGU6Vp7fYiNh8oJXt8WjMfmVg0K+4wixf39AgURVGUKOly4UZE0oEfA4eBPwHf8jW5Coi3H//SGFMELBeRO4FTgC+IyNft16+173cZY5bb/f8SeM7u4yrgl9H2aYxp7Nyz7V6c1ICzp45k8iWDgaaFxTu7D5M9Po0HV27n4lnprN5SAMCjt8wPWz7XEX+8iyMN9VcUpbOI5OPSWnWnaPxf7l+a7d5PHjM4bJtwIktLolC44/rbt1eMUpSuIPfwYZatWMHCGTNYOGNGTw+nVaIpbd2aWNJaH+Fe94pBQIgw5D2ev79O95eJBTZvhgULenoU3YYWWGg/TgGXhQsXOn6HiqJ46I75pTsibv4HSAH+H+FPZo7ncY7v8SnAYGA8VlrX4AjtHLLb2Oe+aE4gVglXEteJwlm9pYDs8Wncc+1sV5RxzDOdqByvmSeEXjHWq8eKonQHfkEl2jLdYKVI3bliM/cvzXZLjaedmxB2v3AiS1tNgf3tW9q/LeehKJ1B1siRLFu6tKeHETXRRNO0Jpa01ke418OJQc7jtogz0QhPMU9xcU+PoFvRAgvtp7cWcFGU7qI75pcuFW5EZAHwBSxfmj8CPw3TbLjncSDC45G+fVprF22fvVq48Zt3OosfZwHjXTDcvzSbipo6Vm/JpyRQw8N/3+0KPJPHpLrijqIoSlfjFTX8KZ9eISdc6qd3XvMKz2dPHdli2lJnVm6KRpTRNCpFaZnOSD1qrY9wr/vFmfZG0XRFGle3c9NNPT0CRVEUJUq6TLgRkTjg10AD8A1jjBGRsE0jdeF5bIhspOxv15Y+Q3DCAIFeFwro92Nwrlw/9NpON7Lm3T0lbvuLZ6Vz+2XTACsSZ9WmfDfdSlH8rFu3zgkBBA0zbhd9Ncy4PZElfk8tb8qndy5ryXvrSHkNU9JTqa1v4PbLpvHm1sIWy3Z7oxJXbcqP6IPz2BtWkOatF052jdu9+7UUqeilpTQqjcYJj6YxnFi0Ft0STURLa320J4Jm7sRhbNx3tNVImj7hefPUU/DAAz09CkVRFCUKujLi5nJgOvA3ABGZDXj/u00SkSKgxLMt1fN4kOdxCaHCTUvtvPfRtHXpzWGA4a4me/1uVm8p4KwpIxgQ148ZmUN5+O+7OXvqyJDoHEWJhFdsuPvuu3N7dDC9lN48v7RES5ElkQSKcKKGv7y3v11pRZA12wvdynfL1+/n4b/v5p5rZ/P+nhIeXLmde66d3WoEzDPr95NTGAg73uXr9/Pgyu0AbD5Q6s6P3nnUufdWmwpHuDnZGXdlsN49jkbjNKFpDIqXtka0dDR1yTnezkNlbD5Q2upxI4lCvSqFSsuBK4qi9Bq6UrhJse8/Z9/8/AF4C3gVcJKyJwMfex4DlAEHPI8He17D93izfb+pDX32WZxFxZI5GZw9daS7eDpSXsOI1ET39cpgPY+9keNeYVYURYmW68+dQGWwnspgPUfKa9w55Eh5DV99fEPYyBS/qHH9uROaRbr42y1fv5+1O4pZu6PYraDn7Ovtp6Vx+kUXbzTNC+8fpLq2nlsumMQ/txWFeIKBNY9mj0+juraeKempJCW0/O+zpSpWd1w+3a1+pShKeNoa0dLR1KW5E4ex81AZV87L5NSxg9sdSdOrUqhG9+JoIUVRlBOM7ioH3hIvAA9gVYH6toisB87HMhEG+LNT/UlEngNuAU4RkeuwyoF/x25XZ/fVpj77Mt5FjzcNyu+N41z5TU6I06u/iqK0ieGDEklOiOPHz30cMocsX78/qsgUp22kecgrrlQG64HQlFCH1uau4YMSm/l/Od5gjqADVhrpvuIKd9ze4yQnxIVU6nPGGk2pcWfc3vErihKZaNKcvNEtHUldClTX8uSavWzLO87EUSlcc+b4do3Ze/xekUL1zjtwySU9PQpFURQlCrpMuDHGPAU85d0mIstoMig+xxjzjr39Z8A9wHyg0LNLPrDM8/ynwCVABrDcd8i7jTGf2sfOa0OffQ7/VWQIvYrt9XG4+oxxIYshRTmREZHzgdux0jyHY3lhHQBWAvcbYyo8bW8GvglMAcqB1cAPnXnoRKKl6JdoRAonase7X7i0oruunBnVeCKlaHlFmCPlNVQG67nj8ulcfcY4ssenAXDBzDGAZejuH7djpHz7ZdPcKEaAx97I4cGV26kM1rtjDBeJ1JkGyUrvRkRuAL4GzMBKBf8UeMUY831Pmz47x3RGOlGgupZHXt8TktbUEaPhbXnH27Wvf0y9Jk0K4POf7+kRKHTufCAimcD9wMVY2Q85wMPGmCfCHLfPzjGK0heJhYgbjDH3ikghTZNHBU2TR5GnXZGInIkVTeNMSHsIMyFF22dfxO/J4JCcENfMWDM5IS7qxZCinACcjjW3eDnFvmUDnwUQkR9hCcMOCcB1wAIROb2vzzF+wgkSLXm8hBNUbr1wMsvX73e3edOK7rh8uiuAOK9FW9HJW5nK296J8rnn2tlMHjPYnQcfem1niFmyl1Wb8t3XoonwcSKRHL8cjbJRAETk18A3fJunYKV4f99u06fnmEjpRH7ho6Xna7cXsflAKdnj0zoc3bJo+miCdQ0ALM5uf5XNaM8rZnjnHTjjjJ4exQlNZ84HIjIaq5Kv9494JvB7ERltjLnPc9xeM8eoob+iWHSrcGOMWUaEaBdbeGmmBodpl4c1sURzvKj67Gv4PRkcvGLOxFEpXDJnrEbZKEooHwNXY/3wOQZcCDyP9YNmsYikYZmc/8Ru/wGWEfsFwDPAWKw57qvdOOZeg1dUdoQMJwLwg09KWLujGLBSi7xROECIaXBLFZ2OlNdQEqhh0bRRLJmTEdE8OVLVp5aqQYV7zRGT7rh8OrdeODnkB6bXV2f5+v0abaMgIp+jaZH2AnAXcAiYACyw24yjj88xkdKJ/MJHS8+9fXRUDEkdOMBNjwpU1/Lyh3nt6jfa84oZSkt7egQnNF0wH9xNk2hzA5alxEpgHvBTEXnGzkroVXNMS0UQFOVEIiYibpTOxVsOHKC6th5jcFMCwCq/OyI1UZVrRfFgjHndt+kVEdkBzLGf1wFXYflnAfzSviq1XETuxIrM+YKIfP1E8NGKFm/6piNkfPXxDa7njCPKeP1wvBErjplvSaCG1VsKWDRtVMQy215DZKfsN0SfCtpSOpNjiuy98ueN3PH65oD1A1Mr9yk+vmnf5wLXG2Nq7ec77BucAHNMJP8av/DR0n1bSn23hY6ILNGeV8xw0009PYITnU6bD+zXr7XvdxljlgOIyC+B5+w+rgJ+GW2fsTLHtPX/uKL0VVS48ZCbm8uyZctCSh/3ZrwLIrCEm1Wb8rl/aTbZ49NCvBe8CytnsaOijuJn3bp1AFk9O4ruQUQGAhcB0+xNzxpjykVkjqdZju/xKViV78YD+7ploDGOX0x59Jb57vOvPr6B+5dmu5E1/sp2fp+c+17aCsC8k0eEnZ8cQ+RF00Yx7+QRYU2MvW3DXcFrbS586LWd/GrVbo6U13DvF+Y0+0EZztvGEXvOmDKCX7y8g/uXZjN5TGgaltL3EZH+wFn200PAX0XkLKAR+DvwPWNMIU1CMZxgc4xf+OgqcaYlIoksHUl36onziIqnnoIHHujpUZyQdMF80M9+HK6dQ7Z936vmGPWHUxQLFW48ZGVlsWzZsp4eRofwh+lXBuuprq1n4ADro3YWKt4qMNefO8FdSHl9cSKlIWie6YmLLWjm9uwouhYRScEy6fPyEvAV+/Fwz/ZAhMcjiZEfPD2Nv7qUU93JmXPOnjqymb+Ng//H2q0XTnbnrHB406scESjcnOUYE3/zs1OblTEP5xHmHcPWg8dC7v1jDFdly+lz8phUcgqtP5MXv7uwXe+n0qsZBiTZj8/2vbYUmCsi2egcE5HXN+fz4oY8gnUNYSs/tVdc8e8XTmRpLRKntWPHpM/NqboY7kE6ez6gDe3aPscYA40xEYDTO+jXr6dHoPRBVLjpY/ivIjsLouvPnUBpRZDNB0pZMieDtJQEoMm401lY3b80O6RaSmv9K0pPIyLntnGXWmPMhjbucyXwByx/LYk0FM9j43/RiegD+kxUXzSEqy7lL80d7bzirwrlF2T8oolXlPb27UQj+st6e8e7ZE5G2Lnwv2+cy50rNnP/0mwiES4KBwiJuFGaWLdunRPNB307oi/e9/xLwF+BXwC3ApPpyBxz+DDLVqwAYOGMGSycMaOj4+1SwgkZzra5E4excd9RN+rFadcajrjiGA2DZTbs798vnrQkynjHBJHTnVrrw6mAtfNQGd9YPCU2xJvk5J4eQbcQo3NMZ88HkZSCcPNGm+eYojffZN1f/sLOk07iwrg4Tk5JgYsvhtWrISsLEhNh924491z48EOoq4MFC+Cf/4RJk6xO9u6F88+Ht96C+Hg4/XRYvx6mToWaGsjNbeozNRWmT4f33oMZMyw/pvz8ptfT0qx+//UvyM6mOjeP/Vv3sW/2mVxcvI2EjHRIT4fNm+Ezn7GOXVratH9GhtXHtm1w5pmwfTsEAp13TmefDd/we04rfZHunF/EmGb/+09Yli1bZvpSxI2TWvDgyu3ccfl0APext5JUTmGZuxCZPGZwi1E1GnGjiMjdttF4TCAijYRZxLRAkTGm1bIhIpKEVWXqGeAke/Nc4DtYV8MAso0xH9vtVwKX2dsnGWNCrob3hfmlM4gUAePd5n0OVrltCE2lcrxk7rl2dthUJ68gdPGs9JCqTp2RGtrZc6HOrRaxNr90JvacUoG1ODpmjEmzt88EttjNHgGG0p455otfNMuWLqU34BUyrjtnvCt0vPxhHs++fYDs8Wnua4C77cYFE9i47yhzJw5jzfYicg9X8JXzJpGRluz2u3Z7EcG6Bl7ckAcQtn/vNu9+4aJhIu0T7pxa6yM9LYmC0qpW++o23nvvhEuVipU5prPnAyzhxkl7etkYc7ndbjaw2d6+3Bhzg4g8G02f3jlm2de+ZpYtWdLBs24/4URd7/fM+Y4BsfH9+uADuPfenh2D0u109fyiETd9BO+P/tYiYV54P5erzxhHWkoCy9fv50i5ZfiZPT6Nu66cyWNv5PDgyu1UBuublQrXPFMlRol09ajdbY0xVcBbIvIX4DZ788nAJpp+8EzGqkTlPAYoAw60YTwnFNFE13jbQFNFqXCRMS2ZFXrblFYEXU+dyWMGu/34S36HI5yoEq5CVqS24fryi0adFc2oAlDsYoypEpE9wFQii83VWPNHn55j/KW8/VEtcycO49Sxg93olp2Hyth8oJRTxw7mstMzefnDPF7deAiAp9/azw+vsKKLnDSnQHWte6xw/fujZlryoInWWNjbh1/Ecfb1RxL1OFdd1dMjOGHpovmgDMufxnkN32NHwOl1v2OciDZnLgDrO+l8zxZNH+1G2sXE9+uCC3p6BEofRIWbPkKkH/1+T4iXPsgjpzDAFx96myvnZfLgyu1MHJUCQFWwnode20mVpwSvovQC8oEfRdlWgB9EfFHk18DLWFe7AsBpwL95muzHKhX+AFaY87dFZD1wPpahH8CfY6USQywSTnDxi8V+wWXN9kJmZA4N2Sda02GnjTdlyvGXaSnSxyt6hJtfI5X6bk2AieSh01lVMzSdNeb5M1ap3TQRuRHLP+vrntffArbSh+aYcJEo/upQ3qvljvjhjaIZm5ZEfUNjiPASqK4j93AFNy5o/p3xlveG0KvxzmIv3JV7/1i9gs/rm/OB0NSrSOfpT5vyijrOeXX0PewUXn8dTjut8/pT2kqnzgci8hxwC3CKiFyHVQ78O3a7OqyS49j3vWqOcYSZQE09MzKHMHfisGbfs3C+Vz3GJ5/ACZISr3QfzYSbbvKLUDqZSD/6/YubP912Dtf88i1yCgMcLa9xzTIXTRvFtrxjrN1R7Jbe1bJ7Si8hzxjzx2gbi8hXWnj5eiBSUvIrxph/2X38DLgHmA8UetrkY/0IU3y0JSrQO28tX7+ftTuKOW/6mBAxJZLIEm4uPFJew5T0VGrrG0L8Zfwih/O8MljvCt5OVSh/n4Db55I5TZl3rQkwkTx0oo1mbC2iRsumdi5d8Jvof7BK9p4C/NG+ObwBvGaMMX1pjglnKOyPTgnWNXDV/MywV8rXbi/i1Y+s6JqN+46SkZZs738Sa7cXMWig3yqkOXMnDmPnobKQxZ73yr0zFv9CMFxbIOwC0btvZ5f/7kiJ8hYJBjuvL6U9dPZ88FPgEiADWO471t3GmE8BjDF5vW2OSR04gIT4/vzDToH0Rq7FRISNn4MHe3oESh8kXMTNOtroF4E1QSg9SEumnUfKa3jotZ1uXvh500ezr3gvOw+VkVMY4OJZ6WSPT3PNOv0leRUlljHGnNV6q5D2LS3EHsEqAT4eGIKVf74T66rYbz193CsihcA3gSl2u9XAD40xRW0Zz4lCuEgQZ666+oxxEatFXX/uBI6U17BmeyFnTBnB+3tKQvxr/CKLMxceKa/hvpe2Ul1bz9aDlig9eUxqs77D3VcG692xXn/uBNdjx8v//m0nD/99NwCrNuW7KVfe4z/02s5mAot3ro4mTSua99GLprN2OuvoxN9ExpgKEVkA3IvlJZEGfAqsAO41tvHgiTTHrN1exIsb8rjunPFho0kipUC0RczYuO+om2rl9Besb2TiqJSQPv0LQW+KU31DI9vyjkc8hj+KqDMFli5boF59def2p7SJzp4PjDFFInImVjTNxUAKsAd42BjzhO/YvWaOcSLOpmYMZkbmEMYMHRgyJ8RctTaAm2/u6REofZBIqVKd7hfRG3CqvvS2ii9eoaa0IsgXH3rbLTnrr6ry7p4SFk0bBcBpE4axeHaGu1jxLn4UJRy2a3pWz46idUQkHrgD68pTPLAW62qTv8x3CMaYu4C7ojmG/SPoiVYbKkD4SJBo0nqGD0pkd36AtTuKyS/9wJ3blszJYM32QtZuL+SDvUcpCdSQlGD9S3Oq6TneOABJCf3JKQxw54rNIRWt/CKL89yZD739ONseeyOHv/7Luuo3bkRyWMGpq1KWNKKmR+jU30TGmBKsqjG3ttKuT8wxi7MzSIjvH1F0aE2UcNKeWvKNefnDvIgpT0BIRI9z5T6cWOQXXLzPv3XJKWGrW3mP1VWGqJ0tBLmsWHHCmRPHGp09Hxhj8rCqUUVz7F4xxzgibfb4NLblHedoRS0FpVUkxPcH6JpotI7yxBNqTqx0OuGEmyLgh1Hu36JfRG8jKyuL3lj1xXvl2fGwmTgqhcpgPY+9kcPqLQVkjUhmaPIAkhPjuOvfZjLvZCsq0ivUOIsUxzSzIxVXlL6JLWjm9uwoouIO4G7P82xgOHBTj4xGaRYJcqS8hspgPXdcPr1VEcJJb7r9smm8ubWQymA9L7x/kLU7it02Tqqnl29+dioiuBE3E0elMCU91fXUWbO9kHknjwAsA2Sv0bDXy6bS9v1aMicjRAgHuGzuSWH9cZbMyeCd3YdZMiejUw2DNaKm2+k0D60TldZEh2hFCX/KlbNfOH8cbzQO4Io0YPndRDIpbslLJtw4vdWxnP5i8up/JGbP7ukRKEpYvN9Fr0gLhBibgyXMBusaCFTXxs73bty4nh6B0gcJJ9wUdqJfhNINeMP7cwoDTB6Tymez093S3xfPSmf1lgKmpA9m9ZYC3txayAeflLiLHKdylN8002+eqSixiIj0N8Y0+DafB1wFbMdKczgVeKibh3bCEo1Q4USy3HPt7FbFjLSUBM6eOpKJowbxfkIJP37uY+64fDp3XD6d6tp6Bg6I4+ozxvHC+0055U7fTgTN8vX7qQzWN5sXHV8v57nXaBgsocSZI+97aSurtxRw9tQRzBk/jKSEOG69cLJ7Pt4Im1Wb8u22IwHCpnUpvYLO9NBSugCvf41DuCiecKbBfsK93pKY46+O1VL/XWYwrCh9DK8gGqxrcCP2UgcO4BuLp7hpU4+8vocr52Wyr7iCzQdKSYjvHztRNyef3NMjUPog4YSbKSLyXeA3xpjq1jpoxS9C6QacK7A5hZZ5nnN1end+gAtmjgEge3waV58xjuzxaby9q5h395Q068dvmuk3z1SUGGWLiNxqjHnXs80AjfbNeG5KNxBNmpA/5aelCk/eqlOOUBJO/HAEFifVacmcjIgpUIDrXeMVX7z7+M2QP/jEmjfnjB/GiNTEZhGL3ntvxE1aSgIQ6p3jf1+0jHfM0mJ6pZ8T/TdRW8WJtrQ/a+pI9hVXcJYthjr7P/3Wfte/xqnY5I+OcR47Is/UjMHN0qsgvODTkhjj97VpKe2rywyGO8LHH8O11/b0KBQlBK8gGqxrbGZuDvD8e7lsyztO4fEqio7VMCNzSGyZFL/5plaVUjqdsBE3QDKwWUR+DzwSjYCjdD/+H/ovvH+Q1VsKyB6fRnJCHKu3FHDgcAU5hQHuuXY2k8cMJjkhzhVtFk0b5S5YvKSlJHTIPFNRupmHgJUishK43RhzHMvT5iVCxZpnun1kHnqrh1Z7iMaHxZ/y4xV7rj93Al/5zbus3VHspikBVAXroxI3nL7ve2lrSJlx/zEdocfhtktO5aHXdoYVV/73bztZu6OYs6aMICkhrlkbf9/eiBunLLlfOPKiZbxj1kNruoicD3xsjDna04OJZfxpQ9GIE/5KTOFKcTvPvQbDGWnJIcfzpk04YwknCDl9AFGPsyUxxhFrvMeK1F+kfno0Emfp0u49nqJEgfe78vrm/JDXnDnjolljOFpRy5T0QRQdsypHxlQk2yWX9PQIlD5IOOHmdmPMShF5CLgNFXBiFv8P/epaa4FTXVvP1WeM45n1+91S306FlavPGOcuhK4+Y1zIIkgXDkpvxBjze1u0+R9gj4h8D3gQ6IdlThyHVRlmWQ8NEei9HlrtoT0+LF6xxykB7nDrhZNJTohzI1b8KUftiVZpSzlxsDx0AIqOV7dYBaulflqq/qemwzHroZUO/ANARPKBTcBm5+aU1xWRt40x5/TYKGMAf9oQWKKEs/BanJ3RbGHlXaC1VIr7G4unNEuJ8h7vxgUTQsSPSNEtXq8Mp8KU/xz8+znijHMeZ00d6ZYibulYzvmHE3W828OVS+82XngBZszo3mMqSit4vyt+c3Ovr01BaRWNppFL545lVlYaD/x1GzcumOBG3vWoKPrRR3DGGd17TKXP00y4McastO8DwM9E5FdYAs7HIvI7VMCJGbyh+AADB8S596s25bsmxbX1jfxq1W53P2+1FMfT5tFb5uvCQem1GGOOAF8SkYVYZbu/AnzVGHN3S/spsYEjYiyZk+HeOwLzrRdODimx7RVwnLkrkujsCD7h0p8i7ROpnPd/3zjXrdi3alN+q8JUa+KV//hqOhzzCDAWq9T3pe5GkVIsEeeE/vAC1bVu5SavQOOU+gZa9Z8IV4p768FjbD5Q6oommw+UMnFUCtecOd4Vcm5cMIGN+466Is+NCyZQEqhh9NBEpmaERg17F4TO4i7cOfijd7yRRI6fBlhCTTTpUY745H1fvObJbaFTF6MJCR3bX1G6mEiV3gLVtbyXc4SC0ioOHa0i97CVOlXf0MiPr5oF9HB6YlFMVVRX+giRyoG7GGPKbMHmA+C/gNtF5BdYHjhVXT1AJTLeUPzJlwx2FymO8OKYC+8rrmDiqBQumTMWICQdwWnz0Gs72Z0f4P6l2c18HdR3QekNiMggrHlqJlZlvA228Hy/Maa2RwentIgjYqzZXuimR/nTmCBUVNl8oNQ1E/b6ynjFFqd9uPSnSEK138jY2WfymMGs/tEFIcfrCCqU9xp2AidjRdpMBfz5w8OAC4gBD63cw4dZtmIFC2fMYGE3R1E4Ao2/vPai6aMJ1jW4j6FJeJg7cZjrTwPWwsofkZI1IoVteccBCNY1htx7055uXDCBnYfKmqVBvfRBHj+8IvJ74RVBnHPIHp8W8roj2szIHMKU9FTOmjoyJFqntfQoZ1xrtxe57fxijzeioDVhplMXo4sXd2z/XkaMpmP2CnILCrp1fvF/D7zPwfoefO2iybz0QR43LpjAmu1FbMs7TmWwnnte3MI1Z2aFFWK7jZtv7v5jKj1Kd8wvzYQbEbkXyPTcMnztBPg58D0ghlygOk5v86Dw/vDPKSzjzhWbQ4SXR2+Zz//+bScvb/yUfcUViFj7OeV3hw9KdK9WOwsmgBe/uzDi4sVBBZ0Tk1j80SMiU4BnsUp+G+CfwHXAn4BHgO0i8jVjzD97bpRKS3gNiv0lvZ3t3vnGO3c528IJNN5IHu9xIHJEjCMi3XH5dO65djbXnzuBI+U1rpHxBTPH8NXHN3D/0mwmj2mfB1hLRsw6n8Ycs4E7gKuBK7FSubJ9tzE9NLYQskaOZFkPeZZEijpJHTjATf8JVNfy8odWStCLG/JcQcPvTwNN4sRV8zO57pzxIalKCfH93GM5fYwdlkR9QyMXzU4noX8/RqQmUHismhsXNH3nwwkifo8dp79HXt/jVq/xijZONNGggfFhxRX/MbxVcLznGMk82T+mcMJMSxE+flqNznnxRTjttFb76SvEaDpmryArPZ1lS5Z02/Gc74FV6ruRDXtLKCkLulWmnn37AOlpSdz++VMZNDCehLh+zMgc4gq9RytyKCitaiYmdxtPPAH33tv9x1V6jO6YX8JF3NxJ05UjibCfACO6ZEQ9SG/zoPAuPL76+AZWbymgtr6BeSdbH82tF05mRGoiB0sqAdiQU8IHe4+SNSKZqmA93/5cU3j+kjkZrvCTU1jmpgR4Fy9e1A/nxCRGf/T8BpgFFGHNTecD9xhjvgZcJCLXActF5J/GmOt7cJxKBLyRNI7fi1fMCDffhPOL8Qs07ZmnvIK4I6I89NpOV8B+6YM8cgoDHDhcweofXdAu4cU/Lp1PYxdjTD1wr4g8DzwKHAS+a4x5yWkjIqOAd3poiDFBS1EnDl4x5qr5mQTrGpk4KqVV75vUgQPIL61kT0GAS+eOZXF2httu4qgUxqYl8eG+IxQdq+FoRa27WPv380OLL4TzkvEf5xuLp/Cr13a56VnOsRyxyYmMiWTCHMkjpy2RMV4fnnCVr9rSX6vROaefHvW4FKU7mTtxGFsPHmPHoePsOhQIeW3R9NGs3VFMQWkVP395OyMGJbIt7zgzModw8ewxFJRWc82ZWezOL+u5SlNaDlzpAsIJN59ghQU74k0J8Knnlud5rMQITgnwKemp7gLDSZtyomni+ltXqXJLKnn477tJSohz0xEmjxkcEn2TUxhg8phU11/Cj4b5KzFEFjDCriaFiIzGqigFgDHmWRF5DcuwWIkx/KKHI1x4o2euP3cClcF6KoP1rrjjxe/X5ewfLtLGf2xvSXBvepUX5/hgRdx8/XcfkFMYYPn6/a0KL+FEHf/8qfNp7GOMyQHOE5H/AN4XkXuMMcvt14pF5O89O8LYx29E/OKGPK6an+mmTW3cd9S9XzR9dIjQ8PRb+9mWd5y4/v3ctAlvRamiYzWkpyXxtYsmsyW3lGBdA4Hq2lavtIfzz5iSnupetU8dOIC5E4fx5Jq9XDp3rDv2zQdKSU9Lco2Sw50jtM+PxhnTyx/mdTglqtXonMrKdvWrKF2F850J1jW438NTxqbS0Gjo3084a+pIUgcO4GsXTebnK7dTdKyGomM1pAyMY1vecWaOG8rN51mi7ZT0HqyMOyYmAjGVPkY44eYU4HrgLmAj8CNjTPsc1JRuY/KYwW6KU1KC9bE6C4Unv36WezX6j+v28dd/5fHp0Sq3CpWDs/g4a8oIFk0bxX/fODfi1WM10lRiCAF+JCK77MfZQIj/li3qfLX7h3Zi0JFUn0iihz/yJdkuwb35QCmP3jK/WSUox69r+fr9ACFCTqQxLV+/3xW6oak8eLjULK/njt/rJpzwEindVNOkejfGmN+JyCvAwyJyE3CrMWafMeabPTy0mMYvYDjGwsG6xpC0Kef+1Y8OcfvnpzElfTCB6lrGDkuipq6escOS3L4c8eTKeZkhnjMvfdBkHuxEyKQOHOBWp/FHsvgrX501dST7iiuYlZXGyx/msfXgMXcBuXZ7EVMzBpOelkRBaRUb9x0NMTn2C0HedA+vj01LlbYc2pISFYlWo3N27mx334rSFTjfmUtPG8spY1MBuOWCya4B+cZ9R6moqefnL2+noqZpHVNRXe+mXe4pKOO3/8jhaxdNblG86dKqU+vXw0UXdW6fyglPuKpSjcDTIrIcuAFYJSLvYaUe5Hbz+JROZkRqIotnp/O7f+7F+KwUvYufe66d3W7/BkXpZp4A7sGKEhT7/os9OqITjI6k+iyZk8Ga7YWUBGpComn84rB3frr43jf5023nkJaS4EbM3L80m7OnjuT6cydQWhHkmfX7Wb2lgMfeyAkpHe5wpLyGymA9Z00Zwbt7Stp0Pk46V7hIIX8f/nRTZ7tT0jySj5gSe4jIcOBcYAHWRa7pwDYRudIY83qPDi6GaMlLJlBVx6HSKsamJbkVoq47Z7xbnnvuxGF8UlhGoKqOh/++iweWznEja9LTknh14yES4qzoYcfPYktuKQnx/d3jOG0dUQisiBVHxHj+vQO8uCGPN7YWMG+SlVr+6keHAEvoCdY1sPlAKYdKKykpC7Jo+ihGD00kWN/As28fIHt8GgWlVc3Knnt9bLylw8FKtfJWkXLG5RwznAGrX3TpjEVmsz5uuqld/ShKVxCoriVQVceMzCEAboqUE4UHlpD5oz9vpqLaEm1OGZvKpFGpJMT346ypI3l9cz6vbymgorqe3/4jh59dO6vZ98Yb1eOdIzqVK67o3P4UhfDmxPHGmDpbwPmjLeDcCKwWkbeB+zQCJ3bxXkFOTojjtktO5bE3cnhw5XaeWreXfcUVLJo2CoCkhDhyCsv43tMbmZE5lC8tnEj2+DSmZqRGTElQlFjDGHOfiOzAWkzVAX8zxqzv4WGdUEST6hMpsmTVpnzW7ihm7Y5iRqRGjuRzDIkvvvdNcgoD3LliM9nj00IiZpLtaMNVm/LJKQxw8ax0gLAijDMvfvOzU1k8OyPEV8cpR97SPNiauOOtdLVqU36z7U5J83A+YhqFEzuIyNVYc8sCwpf8TgQmAyrc2Hh9VZw0ozFpSVw1P5Odh46z81CAmrp613TY2cdZWI0cPJCKmgoGJcaHCDGOWAKW8DEjcwhXzbcWW8++fYC1O4r52kWTXUHntAlptpdOaNqUU5XqcFmQVz865C4SZ2QOYdH00bz8L8sJoKQsCMCRQNBNx8gen8aV86xjXjmvKc3LWx3LGR80lUAPVNeGRNw4lbacsYO1cGzJk8bv0ZNfWsnTb+3nxgUTmpU2j+azuez0THjqKXjggaj2VZSuZu32IldEnZKe6n6/504c5ka7/eq1XSTa4u3w1ARuuWAyGWnJIemTACmJ/Zl+0mD3ewNN36lw5uedzvr1J5Txt9I9hEuV+hCrigIikgachOVz8wjwHeAGEXkGFXBiEq8Xg7MQqLKf7yuu4OJZ6dy/NJtVm/K5/twJfOU377qLptc2HXLbrN5S4Ao/ihLLiEiyMWYlsLIN7bs9sb+3Va1rC9GkTraUEuWfs1pi8ex0MtIGcv/SbF54/yCAK0Z7PXG8907EjVeY+eATK8omyTPPeX11vKlZ3jkzkk9NpPfE79XjHP+F9w9yx+XTw/qInahmxbFYtQ54jqZoPi8NwBZgPfCP7h5ULOIt8w3WYusXr+ykoLSKbXnHuWp+pmueOGlUqruIcrxcnHSik8cMYn9xBdPGDgkx6n1392EAZmWl8V7OEdfPYtH00byXc4SC0ip++48cThufxra84yTE9XOrzzgCCjRVpUpJ7M+ZU0by2ewM119n7fYi95M+dWwqE0dZqRpZI1JAICGuH1tyS0PKjjsmqd4IHH8JdG/0jF/E8T5uS3rUk2v2si3vOPUNjfz4qllRReQ06z8tLWw7RekJFk0f7X53nDRCryDjCLiA+/jpt/bzjcVTePlfn7L5QCmJCf2oCTZy0vBk/rGlkEtPG0v2+LQQPyq/KXmXcPRo1/SrnNCEE25OFpHdWIJNuEt9AnwZK40qoQvHprQRZ1HiXwg4njdnTRnBlPRUHn0jh72FAZbMyWBG5lC3/O6+4gomj0nl9sumuSkHTr9+A09FiSHeB2a22qqJt4E5XTSWiPS2qnWdhTN/HK0IsmjaKNcw2MHxj2kpysR5rSRQw8N/380dl09n8pjB3HrhZFeUAUIee/t3BBMnLemd3YdZu6OYi2elc+uFkyNWpXJSswD33psi5XjWPPTazojRMV5j5f/9204e/vtu1zD+nmtnhz3XymA9d1w+/YQzK47RqnUOdVi+f+vt2zvGmPKeHVJs4Y/mePnDPApKq0hJjONMO2Vo16EAMzKHkBDfz42CcRZrewoCbmWYq+Znugu3JsHFEmHe2XOYomM1boRM6sAB3P75U/n5y9spKK2irqGBS08LrT61aPpo18smUF3H6KGJFB2roSQQZNDA+BAz4FPGpjIjcwhfOW+S66tx3TlWFapn3z7gju+sqSOpb2h0x/yNxVPcRaBTtcrBK6o479POQ2V8Y/EUd1u49Cgvfo+eMWlJbMs7TtbIFCB81Sw/zfo/++y2fsyK0mWkDhzQ7G/XibrLHp/G4tnpPLHmE4YkJ3DDuRN46YM8SzzdXkRuSQUANUErok7AjdjZfKCUscOSyD1cQdaIFC77zEluJFy4ym2dws03d25/ikJ44WYgVlWpSKXAsV+L75IR9SC9/Yq49yqtd3HhLG68XgoAd67YzKO3zCcpIY7q2nq2HjzG2h3FvL+nJGRB4t1Po3BObGL0ivgoEflJG9qndNlITnDCiS9+A+AX3j8Y1nPGXxkq3GtOZI2DvyR4ZbDeFZmdY15/7gS++vgGVm8pcNOSlszJcMXp4YMSQ6JivPObU2lvyZwMssenuf2H69u7r/99cKJ3nPHXNTRGFGac9yucqBPN+610CXVYlereAzYDW4wxFd4GIvJlY8wfemJwsYI/mmPuxGFuNMqIQQnMnTiMfcUVjB2W5JbWdrxnEuL7sy3vOOm2GDFm6EB+9OfNnJIxmGEpCSzOzmDR9NGueTFASXkNhceqXdHj9InDeXXjIUrKghwqrXIXYt7IHq+3zIjBCW7Z72vOHN+s/yfX7CVrRAqXnjaWYF0DJ49JdSvXTElPJSMtmawRKZZ4MiLFPYZ/EZhfWulGHjnvj3Mcr89GS4ILNK825RW42s0rr8AZZ7R/f0XpIhyhNVjXyKVzx4KBv310iMNlQQ6XBXn+vVyyRqYwNi2JQHUdY4YO5FhlkOLjNaQkxrHzUIDs8cNYNH00CfH9XZPxbXnHSU2KD0lNdETUThVvnngC7r238/pTFMILN2AJM7VAPqGlwP23PkVvvyLuDd13FjolgRq25R1j5rih3LhgIgBHK4Ls/PQ4owYn8JXfvMt/3ziXyWMGhywCINRc847Lp4ccQzkxidEr4iOAn7ahfXFXDeREx5/i4zcAXjRtFFW2EFwZrA+p1OSvDOUVUJbMyeCd3Ye5/bJpzDu5EKCZ94xXIPL6xiy3TYod0cQROSZfMjjk2N57B38KmOOJ4+/74lnpIfv63wev382dKzbbqajhr320pTT4iZpS1c3swYowzrZvS4HpIlKIJeI4tzuAE1q48UdzbNx3NCSFyLly7hgSe9OBnCpTi2en8/rHBXycW8rhMstbxuGaM8e7KRFrdxRSdKyGh/++i5KyIIHqOvYWWUamwwcl2Iu50HLgTmTPpgOl7C+uoKHBStwK1je6gsvi2el8UhRgaNIAd5GXPT6NzQdKSU2Kdw1Ry2vqeOCv2xgx2J6DBDedw0n5mpoxmOffy6XweDVHAkFGDk4gUFXH65vzuXHBBNeM+ck1e6N+jwPVtQTrGlwfn5njhpI6cAD5pZXsKQhw6dyxbRNyNOJGiTHCGQc7f+9gmREfKQ+6309v+lT//tDQAMcq65gwKsX9vp01dSQl5UGKy6qZNnYIwboG8ksr3e+SI6J2qkHx9Omd15ei2IQTbvYC5xpjirp7MErH8KYEOCH/Tkj+2h3FISVtvVeY71yxmRe/uzDs1etIHgyKEmO0FCGodBN+0cERU+64fLprAOxExDh4BWMnwsUvWrzw/kFWbykge3xaxLLgXq8c75zlNQN+cOX2sFGD0Xj0OHg9cfwlyyO9D965+f6l2QDNBCrv+xDtWFoSeTQap9P4oTFmI1aaFAAiIsAUmsSc7wGdXJKk9+P3kfA/96YpbNx3NMQ3ZtH0UVQFjzI0ZQCfHqlia94xgnWNrj9NRY3lgzEoMZ6SsiDv7T7M0YpaAMYMHcirHx3iUGkVNy6Y4FakcUqCv7PH8sopragle3waCXH93BSvf24vpKK6niq7zPDIwQncuGCCO67RQxM5feJwcg9XhKR1OWlejnnys28fCFlQAgxJGhBSvWrR9NE88voed7/WBJdAdS2/em0X2/KOc+lpY11/H4Cn39rPtrzjxPXv17aogSL9qd+ZiMipwAFjTHVPj6W34jUOdgSbrBEpZI1IYW9xgIZG4xqHx8eJFdE3OIHhgxLcKlQAhcer2V9sBUY630+AjLQ61u4o5h9bCwlU1XHV/MyQ71KnkaLB3UrnE064uaAzRBsROR+4Hatc5nAsc78DWAai93vDjEXkZuCbWD+EyoHVWD+WPvX1mQncD1yMle6QAzxsjHkizPGj6rO34/9x7r8Cu2ROBt97eiMzxw0N+XF//bkT3Gic2y+bxkOv7XSrnzgLDG+4vi4ClBgmcmx5eBpab6K0h3AlvCG0spLfl8Y/Z7UkWjhG64umjWomfHiFaWguhBwprwnrgdMS3j5uvXCyuz2nsMydK8P53LRUHhwIK1C1J3rGL7a39L9AaR+28bl/mwF227c/AYjItu4dWXNyDx9m2YoVLJwxg4UzZnT78f3muKkDBzB34jB++eoODLD07NDvnrNAc8xIL51rpSSdOjaVgyWVVNTUkz50IAA5BeXkFFiWQqeMtQyD01IGUFZdB+CKNsNTE9y0pc0HStlXXE6gympz2emZvL45n6JjNYwcnED2+DQG9O9PsL6RS08bS6CqjuQB1s/iRttFed7JI8hIS+bGBROob2hkzNCBJMT145ozs4jrn8eNCybw7u7DIT43YAkzmcOT+cO6vSTG9Se3pJKsUYOYOW4oQEgEUvb4tJA0Da/Js2PIvDg7g9c357uLT/+lCkdccu6jZm/00T59ga5M9xaR+7Ai7w6LyBnAVcATxphjXXG87ia3oKBb5hdvqiVY1aUWZ2ewdntRiDDTvx/U1Vtf1JKyINPHDqGsqh5opKC0hgH9+1FNA0kJ/d2LOqeMTWVsWhKFx6soOlZDelqS66XV6WzYAJ/7XOf3q8Qs3WEn0Uy4McbkhWvYDk7HEli8nGLfsoHPAojIj4B7PG0SgOuABSJyuiMiichorPxy7yWJmcDvRWS0MeY+Z2O0ffYFIoXkO/eTxwzmlR+c32y/4YMSue+Llj+rE33jNeJ0UhOcyB1dBCixijHmYE+PQQlPuMpKfnHGO2dFMkL3+3Tdcfl0zps+JkT4yCks484Vm7n9smm8v6fENTIuCdQwIjWxTZEsDv55z4n2eemDPHIKA+52p90z6/fzp9vOYfKYwc368kfn+MfSlhSpaMba0f4UCxG5AlhtjKlqpel3u2M8LZE1ciTLli7tseN7zXGdhdbWg8fYaS+2Hlm92019uuz0THeBFqiq49WPDnHK2NSQhRlAfaNhRqaV2pBTWM6MzCFkjUhh16EApbZYk5zQn8qgJf6MSE3g1Y8OcdX8TI5W1FJQWsXooYkEqup4/r0DbinwUYMHMiCuP69utCJgvNExMzKHMGboQAqPVfOZScN5+UPrnJzUDLCKOfg9Maakp4b46rz8YR5Fx2oYPdSax1IT49z3BZpSt4J1jby+Od+NuHFSrrx+O15mZA4JiRK67PRMMtKS+eEV7VhM33RT2/fpxXRxuveNwArgbKwqvH8C/gf4Shcdr1vJSk9n2ZIlXXoMx9cG4N3dh3lxQx7XnTPeFYE3HThKQ4PhWFWtG3WTlNCfqmADH+47QkVNA0OTrTTk1IFxlFXVURVsYH9xBelpSUwancqrGw9x6dyxHDpa5QqdXWJQfO21ndeX0ivoDjuJZsJNW0vlttD+Y+BqLLHlGHAh8DyWiLLYLjU+CHBMRT8ALgcuAJ4BxgLLgK/ar99Nk2hzA/AmVvTOPOCnIvKMMSZPRMa1oc9eT7iQfO+CwL+Yca58nzFlBL94eQe3XzaNkkANZ00ZQeaIZNcw0/FuOHvqSCZfMlgXAYqitJuW5g/vnPXQaztDjIxvvXBy2KgZZ/5aMifDFXcc75gDhyvIKQy4njbb8o65lfPaKtz4BWyvD4/ja+OklU4clUJOYYBrfvkWV5+R1SzF1Jsu5Y1cbE+KVDha+1+gtJu/ADUi8k+s3xx/M8Y088kyxpyQJcG9UTZenGiaU8amMnxQAkfKgyQNiGNG5hD3SrrjifP8e5YAkVNgiTbJif2ptFOh4vqLW0r89EnDmTtxGGu2FTF5zCCOlgc5WlHLWaeMIjXRrp45daRb2jtY38iwlAEMt8UcsCJyRg5OaIpcwTIpLiit4pSxqUwbO8SNbvnHlkIg1z3+qWNT2XkowEjb1Ng572BdIzMyh3CWXTnLwWtCnJ6WxFlTRzar/JQQ39/18UiI7w/gRuE4UT7OWJ2qUv4y4uHKgEdTGhyAp56CBx6I9uNWWqYQuAkry+AFY8w3ReTunh1S72Lt9iL3+3DV/EzXCytQXcuTa/a6wu6Q5HiSEvozIjWBscOS2XLwmOs/FaxvDLlPSrC+VwWlVQxLGeAaHU8clcKggfFdZ1C8ahX0QOSj0rcJlyrVKaV1jTGv+za9IiI7PG3rsMIIHYfGX9qRMMtF5E6syJwviMjX7dcd6XKXMWY5gIj8EnjO7uMq4JfR9mmMaWzDOcYszo/zI+U13PfSViD0SrV/MeMsOiaOSmFfcQV7iwLss3NA391T4qZGeVMcnBQAXQQosY6I/C+WUPsRlifFRuBjY0xNizsqXUprIoK3HLfjywVNUSSVwXqSE+JcAeW+v2x127z43YUArneMX6T2poBGiyN4T81IdaMQHT8db5qTU5HqwZXbOWvKCMC6Eh/JS8d/Pl4jeehYNKMKNV3G54HLgM8BlwCNIvIvLBHnFWPM7h4cW4/jXfTcuGBCiLDgiBZXzc9kx6Hj7qLLqdaUEN+PxdkZnDzGSn1qaISE+H7ccfkMNzqnfz+xqzpZP9ne3X3YFWHAjj7pb/neeD1i3t19mFc3HuK6c8az9WBTpsqRgHWVPj0tiWvOzGLmuDJKyoP84+MCRg8ZyL7iCsqr69zjFR6vDiktvvNQgHmTRpCaFM+i6aN5fXO+O56N+46SkZbsHit14ACunJfJvuJyCkqreHf3YfYUhEYUOVE3zuPy6jp2HirjynmZvLv7MFkjU9xUEb/5s1PO2InQcbZ5PxfvtrCMGhX5NaWtFGD5hK4ChonIF7HsGpQo8X4fvClML3+YFyK2Hq+00h+PlAc5WGJFyiUn9icpIY6SsiCjhyYya9xQCkqr3f0S4vu5j5u29Xer33W6QXEw2Dn9KIqHcMJNp5fWFZGBwEXANHvTs8aYchHxCj45vsenAIOx/Cv62Y/DtXPItu+j7XNfa+PuLRwprwkpSetdENx+mfWWO4uZM+zFxcjBiewrruC8GWPIHB5g7Y5iJo5KcRdGkVIcFCXGuRIrsu5UrMg8gAYR2QV8iHW1fGUPjU2JgFe8ePLrZ7npUs58VBmsD0nn/OZnpzIgrj/3L80OSa9yxJV5k6x5zqkcNfmSwc18aCKRU1jGxfe+yZHyILX1DVw8Kz3ET8fvK1MSqGHiqBTe3VPiVt+D8NFFXqNk53y7KppRfck6B2PM34C/Adi+FZdhiTkPAg+IyF5sEQd4z/a/OWHwRpWcOnZwyKLnG4unuGkPDimJ/ZulHB0qtYK2ReA7nzuVMUMHcvaUka6haGJ8nCtMTB4ziLSUATQaw5CkARSXNS3MHFHEMe/NHp9G5vBkPtx7hKEp8VQF6wjWWVfgC0qr2J1fFhLxsyu/zE3lmjjK+ml7JBAkt6TCKktse+EA7uLSwYkk8qdcvPRBHoGqOtLTktyxeY2IUwcOCCkB7vjeAO69Y3gcDq9PjjfqyV+aPSLZ2S2/rrSFnwEvAl+zny/HmheUKPF/H5zIsakZg5mROYThgxLIL62ivtFQXlPnpkv17w+VNQ1U1jSQPT6NiaNSeHFDHjMyh3DR7HS2Hix10xaD9dZ31/nOPv3WfgpKq0hPS3KjATsFTZVSuoBwwk2nldYVkRQsY2AvL9GU7zncsz0Q4XFo7Gnr7aLts88IN95yt/NOHhFyFfeea2e7V6TnTRrBQ6/tZPWWAv7j/ElMHpPKtWdmMXHUIL7ym3dZu6OYP67b5/pBeCNvNEVK6SWMAnYCEwFntRqHZZI+HfiyiPzVGHNVD43vhCecoOBNSXK2OSlTd105002RWjIng7OnjgzZ15teFS7KxaG1yBZnXGu2F3KkPEhaygBmZA7lSwsnkj0+jcpgfUgJcr9gfvGs9GbpUf5zdebUx97IcdNSw0XKdIboor5knYOIiCPGGGPex4pKvkNETsZKxf48lr/N94AjIvJNY8xzPTXe7iZ14AC+sXhKs3QpZ8EF8OKGPC6dO5bE+DhGDk5g9ceFnDI2lbh+/dh8oJTJYwZRUhZk/MgUPikM8ElhgBc35DF5zCBGD01k8ex0xqYlsWFvCTmFTT8pnavuaSkDKK+pcwWc7PFpJMRbfTsmpF6qgg0hQoeTgjQ1YzAr3tlPsK6ejLQkhqcm0NhoCNaHL0vs7OuwZnsRr248FOLxc+U8S8i6ccEEBg2MdyOSIqVjeI1ZJ45KYU9BoMVIAH+lLu/nElXkwOuvw4IFrbdTouEt4BdYkTfzgcNYHjdKG/Cm+Tmphc73Lnt8GjmF5W4K1fPv5fLunsNU1jQwYnAC008awpFAkJPHpJKeluR+V2eNGwoco+hYjSvgXHNmFk+/td9NZSworWoWNdchnnkG7r23c/pSFJtwwg10bWndK4E/YJkFRzqOd7vBiriJpp1/W2ttQ8jNzWXZsmWAZTBkmwzFNOHKdnu3+QUXZ4G09eAxcgoD3PeXrbzyg/OZkTmUtTuKeXnjpxwssa5+ea8uR3u1WumbrFu3znFLhy52TO8gOcaYmSLSj6ZyvYuALwL1QCpwhYh8zr6S3m0480tvmVu6inCCwqpN+SGeWn684kbauQkhooa3DHhLAnNrIrQzLieaZ0p6Kg//fTcjUhNdU2KvMOQXzJ2qU94KfY6Zsvdc/RX7on2P2kpvFN27oyJDO9gInObfaIz5BGuR9gsRGY4l4HweOKl7h9fzOKW+vZ4qTqrORbPGkJ6WxGcmDeeGcweTX1rJ4bIgV87LZEtuKVPSUzlr6kh3AbW/uIKr5meSPT7NjTj5m52K5Fxdd8gakcyggfEUlVW71WXi44QhSfEE6xstg+LyoCvcDEzoR3Wwkcnpg7hxwQQ3GmhxdoabdnSkPEhJWZC9ReXU2n2WVtQSHyfU1RvS06wqV86CMHXgABLi+/Ps2wdcE+JgXaObvmR54wymoqaep9/az5XzMlv0o3FSpeZOHMY1Z44PeS2cb03UAk0kzjuv/fsqfl41xvzMfvz7Hh1JL8ab5ueQmhRPalI8p08cRn1DIyWBGl7fnE9dfaPrh1VSFuTD4FEqaurZV1xOVbCB+DgJifBLGRhHRXU9RcdqeOmDPDYfKHUNz08bn0awroFAdW3n+NzMaeYioigdJpxw02mlde2S3yIiSVhVpp7B+lGz1PanKfE0T/U8HuR5XEKocNNSO+99NG1DyMrKcoWb3kK4RUBLCwNngfQZOxxw0uhBIa8fLKlk0bRRzX7s69XbExuv2HD33Xfn9uhgWqZeRGYZY7YAu+zbChH5H+ALWCkPTpWHbhVueuP80hX4BYVwQrO/ZLgX/1zkLwPuxR+5EmnuiiSAO9GH/rH7z8Mb/eNN6Zo4KoVvLZkacb9o36P20Bs9b7qjIkM7mCIi3wV+Y4ypDtfAGHMEeNK+nZD4TXfnThzGzkNlHDpaSUFpFS99YJXO/sUrOykoraKmrp5dhwKcMtbyb3HSqhxD0RsXTGDk4AQ2HSilMljPftuLD5qqyDjixp6CMn712i6OVwWpqzeu/9VV8zM5Um6JPTMyhxCsbyCnoJwDhyt4ZeOnrN1utXOiYB55fY8rDtXWG1IGxnH6hGFs/fQ4R21vnLwjlUwalcqU9FRmZaVxz4tbXMPjomM1JCX05709hzlaUcuMzCEAPPv2AVKT4glU1fFJYYCKmnr3fYLQhaq3kpQ3kil14ABe/jAvOt+atrB3L1x4Yef0pdTYVW3/zxhT1tOD6Y0EqmsJ1jVw1fzMkAi+1z8uoKKmnj++tY9gXWOIEOOloqbe/a6BVS48Pk7IHp/G9rzjVFRbhQSSEuK4cp4lqgbrrIg6RyxOiO/fed8vRelkmkWyGGMOOjegxPs8wu1QmH79fVYZY97Cqs7gcDKwyfN8cpjHZcABrLSmshbaAWy276Pts09w/bkTuOfa2SE/8JfMyeDiWemuP4QTLXOkvMZt73jdDLMXKNvymsz7qoL1PPZGDkfKa1o8jqLEIKuA1SJynYg4JuXYBqILjTEbgSuw/K6ULsY79zg4goJfaE5OiHO3+dt48c9F4Y7h4Ig8X318AzmFZW47/z6tjcFf+cpJmfKO0RF/vvnZqUzNSHUN4HfnB5pVl4p0bm1po3QbhUAysFlEvmd79p3wBKprefnDPALVtfbzupD7jfuOsvlAKRNHpboVkhwviYT4flTbUXK7DgV45PU9AJaQYazUqjXbihg+KJGSsqAr2qQmWVP66CEDXcPiZ97ax5bcUhZOG4VtXWGnOQ4hWG8t8BwjYifWuq7e8OG+owBMTk9l68FjvPzhp1baVvoghg9KYFhqAhXV9ewpLGfIQOu4SQn92XUowKsfHeKdPYdZ8c5+tuUdZ+32Yg7bgk9VsIGjdplyx1R4RuYQAlV1pCTGUVFjnffOQ8d55q19PP/eAeZOHOamfty4YIL7fjmCziOv7yFQXcvcicPIHp/G1IzBIe99R6j6ZH/E+VNpMzdhVcA9IiLbReTPInJXD4+pV+FUlUqI7+9GvewrrqCipp6E+H4E6xpdsSZlYBw3L5rEsNQEEuKt5ewpY1O5+5pZrkcVWN/3j3NLqQpaE0RZlZVW+cxb+9h04ChHK4LMyBzClfOaqlh1Cps2td5GUdpIpFQphydF5Hzgy1jlt78N9Af+aIz5tKUdReTXwMvAFix/mdOAf/M02Y9VKvwBrCpQ3xaR9cD5NC2q/uxUfxKR54BbgFNE5Dp7PN+x29UBL9iPX4i2z75AuKuq/rQD/xVqJ/Vp+KBEzpgygovvfZOcwoC70Phg71E+2Hs0JC2gN169VU5IfgV8HXgaeExEPsSq8pCOne5gjCkVkYiRgkrnEU2kXmvRJd6oGadPb5SL9xj+cttL5mTw1Lq9rN5SQG19g3slHggZV7QRLi2djyP+OGbG31oyld35AbfaVVtwqlrdvzSbyWOap44p3crtxpiVIvIQcBuWgPN74JFIETgnAo6oEKxrICG+P58etVKsPz1aycsf5rkmn3MnDmPjvqMMGhjPjQsmsK+4nEBVHRX2ImpYqlVa+/6XtnG4rJo4u0JUbkkF15yZ5aY2ADQ0Wj/d9hdXkJwQF+I1c9X8TC49bSx7i5sMiqekp7pX0V/6IM/1x0lO7M/NiyaxZnsR+aVVlFbUUnC8mqvmZxKsa+TVAut6ZEJ8P7dMePb4NIYkx7tROkXHahiabAlETlpGv36QOSwJEMqq69iad4xZWU3GwpnDkxg7LJmPc0vZeSjATrvKlvcKf+rAAfzwCquEsBM94HjcACHGxc5770TkRF0C3MPzUxZqNHXnkYu1NpmJVYzlVOBq4L4eHFOvwuvZ5K2YlpzYn1GDBzLtpCFMGJnCo2/kUFFdz5rtRaQPGcg2OyLuSHmQNduKmDRmEPuKK0iIFxobobauySGjrMpq68wHTrW7mePKmqV8dogbbmi9jaK0kdaEm1nA9cBarAoK37K3f0dEzjHG7Ghh3+uBb0R47RVjzL8ARORnwD1YRl6Fnjb5wDLP859ileLMwHJq93K3IyQZY/La0GefxL8ICVfa21nUXHjPG5RW1DJxVArPf2cBf1y3j4/2H+W0CcOatVeUWMcYUyIiX8Cq5JAEnGvfwI7KE5E4moyLlS4kGkHE8amJZMbrLaG9+UCpawbsLDK885tjFvzO7sM8est8Vm3KZ599tX5G5lDOmz4mYupTpR1p6DcYhtBy5ZHOx9nmRDPeuGBii6JLSwbEd67Y7J6nYy4fzX5K5+NUoTPGBICficivsAScj0Xkd5ygAo6zwArWNfDs2wc4ZWyq5RUxMiUkncdJ79l68BhT0lP58sKJ/GHdPqakp3IkUMKQgfEE6xo8qVANpKcl8ZXzJvHkmr1UVNczLGUANfVWxZhTxqYybeyQZukSZ00dSUZasnu89LQkzpo6ksXZGVYKVl2jm56VOTyZNduLQoSfo4EgCfH93VStEYMTKCkLkpoUz9KzJzAlfbBbfWrymEH07y9MGpXKZZ85ieffP8g/Pi6gsREGxPenoqaBYxW1HKuo5bf/yHEr1uw8FKC0staNzgGYnD4ooq+GtyKV9/0O1jW6EQXe99pfmn3jvqOtLkCv2bOO49d+WaOpO4fLjDHbnCciMhGY0YPj6XV4PZte/tDyoHFSn/bXVDA4aQCHjlYRrGskNSne/Q6PHJxAZbCekrIgr350iJGDEwAIegQb5ztdF+ay3YzMIa5o4xWkOyTgPPcc/KQtRZoVpXVaE24qjTGrRUSwhBiwTH6HAD8Abmxh30ewSoCPt9tXYFV7+TPwW6eRMeZeESkEvollJloBrAZ+aIwp8rQrEpEzsaJpLsYqQ74HeNgY84T3wNH22dfwLy6g6crtLReezBcfepucQktZvu2SU7lzxWZK7ZDe86aPJi0lgT0FAd7dU8LcicOatVeU3oAx5h8isgj4DZb4DFAL/ND+IfUYcLSnxnciEW2kXkuRLN4S2qu3FHDxrPSQRYZzDKdi3sRRKazeUuCKMJXBeqqC9STZVakc0cNb0ttbHcobaejMqY7JsCMIedOj/B46zjgiGS1Hc85OlE64aB31G+tZjDFltmDzAfBfwO0i8gssD5yqnh1d9+EssPJLK3kv5wi7DgXcNIPUgfEhV843HTjqGoSOHppIoKqOUjs9wVl4JQ7oRz8R5p08nM/PPYmN+44yfJC1+Bo2KMG9Op6WksA/thby5YUTuWjWGN7LKaGi2jL+/cbiKcydOIy1O4rdCjGXnZ5JQnx/XtyQx1XzM4nr1889puOVk5YygLmThlslvu113vxJIzhUWsXmA6X89h853P75U5mVlcZ7OUe4YcFEdueXud4115wxjk37j3IkEORIIEhpRS3DByUwYnACl5+eyesfF3DlvEzXDBVgeGoCRwJB+vcTNzXEWbDml1by9Fv7WTw7HbD8fpzFo3MuznvtLC6d99rrkePct+TXkXTyBJ1HOo8DIvITYBxW9bnnHOFXaRuO182lp42ltqGBA4cr6N9PuHFB0//+xbPTefnDPIqO1zSJoQIYOFwWJL4/1DVY3/NxI5JZevYE/rB2L/vtSBxH1JmROYRvXXIKqQMHuN5cwbpGt5Jcu/1uEhI68hYoSlhaE25G2PezscpsG5oEnPtb2tEYcxcQVW6nLbw8EUW7PKxqVJ3WZ1/C+UHvGGMC7uOP9h/lSHmQyWNS3UXP/Uuz2VsUYF9xBcMGJfLYGznuwmfjvqPkFAZC2uuVXqW3YJftzRaRSVg/onbY4u8I4O9AcYsdKN1KS5E53sp2jmFxuIgYx1y4KljPw3/f7e5715UzXdPgcBE7TnWos6aMYEBcP049aTCf//k/mTluKAMHxPHgyu3ccfl0Fk0bxeotBfzv33a6psXhRJSWzsUrrkeq/AcweczgZpE20bxXSucjIvcCmZ5bBqG/nQT4OVY58E4yR+g9bNx3lILSKre8tiPoOB44i6aPJq6flf6UnNif5AFNb13WiBRXRKmptapAATy5Zi/b8o4zOd0qntDQaLj0tLEkxPfj9S0FVFTX8+gbOVx62lgqqpvSiV7fbEXYOREu3nStnYfKOGvqSKCpIpTjeRHXXygsreIfece5an6mO44bF0yg8HgVBaVVPLlmLwAFpVWseGc/08YO4aJZY9h84CglgRrSUgZwJBB0S5aOGJzAdy+dxtrtRWw+UMqpYwe7BswAwfpGXt14iEmjU5kzfliIr4ZTYQtw06YcvOXC124vYmrGYB55fQ83LphARlqya2g8d+IwTh07uHW/jkmTWvmElTbwW5rWKDcB3xeRC+y1i9IG/GXAHaHS+f44nllOuqGLp2awE1mTGG95U/1h7V6OV1oXq2dnpXHgcAVDkgfwlfOs78DLH1oRbpsPlDI2LanjfjdLlrR/X0WJQGvCTZWIPEFTec4SY8wKAFtVVmIIpyxuVbCe7PFpXH/uBDf65pYLT+bxNz7h/qXZDB+U6EbiPHbrGby51comq7LNAvcVV3D+jNGkJMa77QH+9287efjvuykJ1HDfF7XMnRL7GGP2YnncOM9LsKpKKTFENJE5/jZeEcRJLbrn2tnceuFkkhLi3DZOKhZY6VRnTx3ZzMz9nd2H3fLf+aXV5BQGWLujmDsunx5ihLx2RzHb8o6xdkexW4L8W0umUhmsd48V7lyc+dY5hiOoXzwrvUveK6VTuZOm5YBEaCM0XejqMXIPH2bZihUsnDGDhTO6LkPD66WyaPpoK1LF97rjTQHwlfMm8fOXt1N0rIZ9NRWkpyWx61AAASaMSqGsspZhgxIsfxm79Hf2+DQqayyj433FFVTXNXL7508lIy2JX6/eTbCukWBdI5PTU8kpsDz69hQE3KiegtIq3t19mGvOHO8aJX9SFGDWuKEMS03gaCBIWsoA+vcXDpcFOVwWdKtAOeMI1jWSmhhPETWMGToQxCot3NBoeHFDHulpSRSUVrmLx9FDE93S47sOBfjRnzczcdQgRgxOoKC0ipc//JSEuH4szrZ+lzmRSf5UjCvnZVJcVsOV85pf6XeEMSclzDlmfUMjP75qVkiqSUZacusf5po1J1RVqXXr1kHTmqazmQtchWVmPg/4PJao+8UuOl63kltQ0C3zC+CmLI5JS2LmuKGuaONEwbyXc8T1nyo+XuNmD/TrB42NMHxQAmmDBtC/n7geNt7KdIHqOjLSktl8oJSn39rPxFEprlAElr9VhytLPfccdPH7pMQWXTy/AK0LN3/G8pYx9u11z2sm7B69mNzcXJYtWxZS+rg3MXxQIskJcSGlwIcPSnSv3M4ZP4zl6/eTdm4C33t6I2t3FFNb38C8k0fw4MrtfPOzU1k0bRRrdxSTlpLID6+Y6bYfPijRrTz10f6j3PfSVoCwfhBK36U7JqWOICJTgZ9g+dvsBj7G8rbJAf5mjLmk50antIXWIvweeyOHB1duZ832QtbuKHZTqJx58MfPfeymPXnFDn/6kmPmnj0+za24d99ftjJz3NCQ+c0pUX7GlBEMiNvhRvY4ZsTeFCs/jrBUW9/APdfOdgX11VsKWL5+f4tCjEY69jifYFXBdH7zlACfem55nsc9StbIkSxburTLj+MtX+2kIj379gE33ceJMnGiXjLSkjl7ykh3YXTNmVn89h85IVfLj1bUkjUyxfLJGZHCeTNG89t/WNWmkhL6U1BaxdNv7efUsYNpsHWihPh+bmWlT0srqa0zZI9PY2xaEq9+dIg9BQEC1bUsmj6aVz86RKCqjnd3l7jHLK2o5ZSxqcw/eQQJcVZU0Isb8picngrAnsKAm6KVOjCecltIykhL4vSJw5maMZjn38slWNdATmE5Q5MHMDR5ALsOBRgQLxQdq3GFnLVlTUGezvsUaWG4O7+MgtIqdueXMWbowLBmqU4kQEmghqJjhWSNSAnbV6ssXty+/Xop9m/73C7qvhzrd0Yt8IyIfA94q4uO1e1kpaezrJuiSJzvY2piXLPvyfBUSwgdPiiBhgbjijbJif2prGlg9NBEZo0byuqPC0kc0A8RMPbs3U8gc3gymcOTWf1xIQPi+7H5QCn1DVbE38ljUjlaURtiKt5u5s/veB9Kr6KL5xegdeHmPqxF2nVYE9J/2x4RNxP5ylOvJSsri2XLlvX0MNrNkfIa8o5UMHFUimuQ6cWbSnXymEGs3VHMjMyh7utJCXE8+fWz3EWCszCqDNZz15Uz+e8b57pXjR9cuR2gxcWK0vfojkmpgzyLldoJcKlnexWtz3dKDBGtl8vMcU2mw46w4UQfeiNh/IQzHHbavfKD85u193vYOEJPuCgeP17PGsew+NFb5odUy+ro+6B0GadgpYjfBWwEfmSMOdCzQ+pZvJ4qke4dr5WN+46SkZbM4uwM149l7fYiCkqrQiJUwKpItetQgJLyGipq6sgpsESTYSkDSE0yXDkvk5TEODYdOEpDoyFY18iXF07ikdV7CFTVMXnMIA6VVjIkKd5NsXBEj/mTR/DxgaOcPCaV0oogDY2GnIJydh0KMGl0KsG6RvYWB7h07lj25JcBUFoR5NK5Y90omV+9tguAI4EgX7toKgA/vmoWgepafvXaLrblHefS08aSGB/H5gOlpAyMY9a4oZRWBDlpWDIJ8f2pqK7jn9sLyRyeTN6RyrARN9730S+SOXhT0kakJrY/pWPzZliwoH37Kn72AntF5CVgE1ANpPbskHonZ00dyb7iCs6aOtKN8JuVlcaeggCFxy0v+CPlQY6UW942IwcnuD43RcdqSBpgzR01taFFhBsNVNXWk3fEqoJX6zE5PlpRy+YDpW465N3XWP+321OtDYCKitbbKEobaW0hM8AY82UR+QZQa4ypF5HrscyB/9X1w1PawmNv5PC7f1pZIb94eQcvfndhyNXa68+d4Ibo+1MA8kurePyNHE6fNCzi4sDxXThSXuOmIqjPghJjnIrlX5OIZYrukEwPRwn29oi+rqClaJIlczJYs72QkkBNWPHFiYAJt6836uaDT0qYd/IIrj5jHKs25btzlteQ2PHQiaaKntdjxmmXdm5CiyXLw3nWOClcj72R455PONNjv6dNeyNwekPkTixG9BljGoGnRWQ5cAOwSkTeA+4xxuT26OB6CG86TqTnjteKU9bXu/Dx+rS8u/swgeo6Co9VM8ZOoSo6VkNFdZ3b36dHrYXa8+/lAk3le3MKyklNiufua2bx+Js57C0qp67esLasmKvmZzJz3FDmThzmpm1dd854gnUNvLu7hFPGpnLK2FR2HQqQe7jC9b0pq6pneuYQcgrLORIIkhDXj2vOHA9YKV9Pv7U/xCDVObeskZZfT0J8P76xeIor5DiiTeGxaj532lh++bedBOsaXbEJmpufet9PvyjW2mfRZorV7q0T+QGwBqswivN745meG07vxUlvnDgqhX3FFWw+UEr2+LRmxuLDByVwpDzItJOGUFV7lIrqegbE9+NIeZMgPDQ5npPHpLI17zg1tQ2UVjRVdktLGcDwQQnE9xMKSqsYmNC/2Vgiiaetsn17+98ARYlAa8LNkyJyPvBl4E0RuR3oD1zulN9Weh7nB3l1reW5MHFUinuF11tKNzkhjtsvmwbA1WeMCylV+9qmQ5RW1vIfj77Pwd9cBYQujLw4hp+KEoNsBBYYYxpFJB2YiVVZyrnvMXp7RF9n4BcPWoomWbUpn7U7ilm7o5gRqc19XcJ53jgiyNVnjKMyWO+mfq7dURxiTAyEVKjyV5bylieHUCEmnMdMpJLlLZU5P1Jew1d+8y5rd1gLJ2/0ov998Qo81bX1/GrVbjcSMlp6Q+ROLEb0iUi8MabOFnD+aAs4NwKrReRt4L4TPQInHP6yvt6Fj/e1xdkZPPL6HrblHWdKepOYcvqk4RQdr3b9K0YOTnAXbU4qxID+/W0h5rAr5gyI78fCU0exODuD1IED3JLCp4xNZevBY4xJSwIs8eeq+ZlMGpXKvuIAi6aNYseh4xSUVpGS0N8tHQyhV9z9ZsHOou6q+ZmeqloDmJKeyra84+w6FHDHdvBIJcG6RkvcuXiKG3HTo9x0U88evw8gIoOAFGPMpyIyB7gW+AxwEPhVjw6ul+IYigeq69zv79hhSQSqatlXXMGYIQPZV1xBalI80g+25B6joroexIqiaWhoirQ5f8YY9hVXUFNr5VjWNzRdw6uqqyensNZ9PjI1gboGWHp207qnNfE0Ijff3J5TV5QWaU24mYUVIrwWeBD4lr39OyJytjFmZ1cOTokO5wf5Nz87lYtnpYeE4zvGm1V2OVtHva6tb+DJr5/lLiYumTOWZ9bvZ9yIlGYmm0fKa9TTRuktrMSq7FJgjCkACgj15lJ6kHCCBIRG7nlTmEoCNWzLO+amM7XWt5PC+cEnJa658LyTrbTRq88Y1yylyRFUnPSni2els2RORoiIA4SMOVzkijPPVtfWuxWqnGghp8KVf9/H3shxRZtF00aFNUx2ztt7boumjYry3Q5Fq1G1mw+x0y9FJA04Ccvn5hHgO8ANIvIMKuBEpKWFj+OHkz0+jbOmjiRY10hcv36cN30Mu/OthdurGw/R2GhdlCqrrqPoWA1nT4kHLE+ai2aNYcKoFIqOVzNv0nCuOTOL1IEDCFTXUnCsipTEOKqDDeSWBAjWNbg+OmdNHckvXtlJQWkVpZW1DEqM53BZ0PW1SU9L4qypI5tdcXfKdd+4YELIuXnTKBZnZ7hmyZPHDCIhvj+fO20sr39c4FaAyh4/LOS9CJeS0e6r/dHy1FPwwAOd3++Jxfew1kV3G2P+G/i9LeoeMsZU9vDYeiVOxI1jFhzXrx+vbjzE6KHW/9wB8f3IHp/mGqC72JqMeMw8gnWNXDkvk0+KApa4AwyIh9o6qAk2CTypSfFMyRjCPz4u4Pn3ct0S4e2OanviCbj33rbvpygt0JpwU2mMWS0iQlMZcMFKQbgD66qT0sNcf+4ESgI1vLbpkCvKOKkBjinmlPRUJo9JJafQuvKzdkcxX318A4/eMp/hgxL52bWzOVxWw+otBSHbIXTRoJ42SoxzC3C7iDyOJTh/bIw51sNjUmz84kFr0StOBacX3j/YanTJ9edOcI2Kw/neOKHTpRXBkJSph17b6ZbnBnjsjT2u+OIXebzjg6bIFcfgeEp6KhfPSqeips4VZbwpqd59HRZNGxUionv7O3vqSCZfMtj17AFCUr76ctpUDHGyiOzGEmzCvVmCFZV8A5DQnQOLdbxChHfh493uXFm/cl5mSBnsuP5WpMxV8zNdLxzHzwJwI1cACo9VuxVj1u4oJj0tiUXTR4dUtnJWdP37W5WhZo4b6pYyT02KD/HaGTk4gVGDB7It7zgb9x1l0fTRlARq+Of2QteQeFvecWrq6t1S3l7Rxjm/a87MAnLJGpnCedNHs3HfUb6xeEpEnwxHpNl5qMxt500rc8qrt8lnozW0HHhncCFwqi8T4STgBRH5rDEmv4fG1Wvx/t1v3HfUvc8cnswf39rPRTPTWbO9iAmjUthfXEHSgP5U2RE18XFCXb1xK0whsCW3lIrqegRrJqhtysR0q8t987OnsCXXmi+25R3n9c35bppkuxg2rPU2itJGWhNuHIfb2cBwrL93R8C5v4vGpLSR4YMS2VMQYF9xBUOS493UAOeq88Wz0klKiCOnMMBZU0a4DuurtxSwaNlqMtKSePgrn+HRW+a7V5q9lU4cr4kZmUP1aq0S6zjVX+6yb4hIPrAF2GKM+VFLO4vIjcAVWHPeKCxT9u3AvcaYtb62N2Plsk+x260GfqhppJGJppS1M8dUButd8aO6tt4VWBzRIpyvjddc3f+615w9XDSN44mTlmItigbE9QsxO47kOeMf8+otBXzzs1NJSYzn9sum8f6epio23kiatJTIa/xwApdXuHIqYz302s5Wo4H85+8/bxXiW2Ug1rzSUkEGAeLb2rGIXAC84dl0jjHmHc/rvXqOCRct4i8VHqxrcKu6bMs7ziljU4nr148r52Vy6tjBdrlxqzz35DGD6N+/qbwvwFXzM5mVlUZ9434aGmHKmNQQX5vJ6akcr7Q8MIalJHDW1JHuQvDd3Ye5an4mZ00dyeNv5rDrUICRgxP44RUzGDQw3hWXADZ8coRAVR2//UcOp02wvDaOlAfDRsM45+14cmzLOx7ioxPp6r3X1Hnt9qKQtDJ/ulmnMbqHU7X6BrX+76Qx5k0RuQUrW+GGaDrprPlARDKx1mgXAylYVTUfNsY8EeaYMTnHhCtrn5GWzMsf5lFQWsWvV++mocFKm7z0tLHsKSxzzczr6i2RttEOptlbZBmQQ6jRYXx/4aThyQSq6ygpC7IltzQkUs5LuwyKzz23fSevKC3QmnBTJSJP0GQWWGKMWQEgIj/pyoEpbeP+pdnU1jewt6ic45WWlDwjc6ibJnDBzDF88EkJtfWNvLO7hG8tmcqeggC5JZXkllTyvac3ct70Mdx+2TRq6xs4Ut5kCOp4TZw3fQzDByWGLA6guRGnovQgQaxKDkM828batyVAi8INcCfWDxiHgcB5wHki8kVjzJ8BRORHwD2edglY1fcWiMjpxpiijpzEiYw3RbPKjroxxhIanlm/n5zCAO/sPhwSFeilMljPY2/kNDP7rQzWc8fl0yOmTJVWBN3+J49J5b9vnOu+7o+yCZdC6mzzGiY7worjMeYIO87xvX443v2iEbiccXvvW/KxaUlwUlpFgFogn9BS4P5b9B2KxAP/18LrvX6OCZci5aRGzcgcQrCugWCdtbrKGpHCzHFDCdY18OKGPGaOK3MXbpd95iRSk+Ldfl7+16d8uP8IRcdquO6c8ezOL2PXoQDXnTPeFTmc9CvHJPnZtw9w3TnjyUhLJiMtmeffO8CLG/K4an4mgwbGE9fPit6ZN2mEG2XjHP/59w4QqKojObE/008azHnTR3PoaBWbD5SSmhTP1Iwmv0DnfJ1zq6mrZ9ehgHt+Lflk+E2dW3svO4V33oFLLuncPk880kUkxRgTUkbIGLNBRH4aTQedNR+IyGjgPcCbXzwTK31rtDHmvrb22ZP4BZO5E4fx4oaDBOsaEbEqSL21q5hAVR0zModQ39jIrkMBhibHc8xeC+06FODTI1UkxPUjWN+UGpUyMN6N1APYUxBgcXYG37rklGbfwXalLP71r3DaaZ3wLihKE60JN38GfoolUhpCvSJ6tEJLV9Cbq75MHjOY86aPYe2OYiaOSuGSOWP59udOdX/IP/z3XVQFrTDCs6aMYOCAOI6UB8kakUxaygD2H65g7XMfux44a3cUu4uHM6aMYPKYVLfEuF65PXGJxaovPlYbYy4XkSysqBnvLZr/tsexxJ3lQClWSuid9ms/Bv4sIuMAR7j+ALgcuACresRYYBnw1Q6dxQmKP2JkRGoia3cUM+/kEVw8K53VWwqYPCa1WVSgs6/Xm8ZJ6/Ruv+fa2Uz+/+2deXxU5bn4v08WwhIChC0kMQZQNokSRAU3wLpQXNBel7rW1lbbem/b2127YWu1t73ttf1101avVdRqqXWpXlwqiBsqEPZNICGGkBBIQhKSDEl4f3+ccyYnk5nJTDKTWfJ8P5/5zJlz3vOe5z1n5pnzPudZJozweqxAp9568LVd7DrQ4Dd0yZ/Rw18Iqa/BxQlxcufccUKnlq3e682rA6HpUd/z4+94vnI6+LZVfR0yu4HzozCJ+U9gGtAMDHVvSBYd4y83hDMZcgw0vkl9G1qOkZGe2i00yN1PRW0zVXWt5IwajKetg9MKsymemM2cyaO7HMO3ipXz3tByjJ2VnV47K0r2s7m8nqKCkWSkpwScoE0aN5xXNx5gbNZg7lw0lR8+vZHK2maefb+8S9LirCGDyEhP9Y7PXzhVOOcs2Po+c8UVke9z4LEeeE5EPmeMKXdWikgaXR8EBSNS+uAeOo02NwOvY+X+Owv4kYg8bowpTwQd4/bO87R1kJGe6jWIZqSn4Gk7TmoqNDS3kZs9lK9eOh2wjCzOfUB6mjAoLYWm1vYueW8A6pqOeQ08o4ZZJcEdTzff35oT0unomJBQjxslCvRkuPkp1iTtRiwXuv8WkUnAbQR3G05IEr3qi78ytYtn53H/c5tp9nSQliK0HzdU1DZzzbwTvU94v/jQGtaX1jE0I5U9tvXZnd/hF89vZdeBBm+JcX1yO3CJx6ovACIixuJKALtMbxnWDYvTZoS/fX240P3UzH4i9e9AFuAkA7iazrCIX9kTumUicjcwHfi0iHzZrkKjhEFPyYudpMV/e2+fZRDZXcMvnt/KfTcUe/PCOIbreVPHcvUvVzE1N6tbBSl/JbgdigpGddte2+Txhji5w6ecvDOB9J9TltwJWb3joildxgGWjn7szT0sPGV80CTMvoYpf4aXUD11lLC4N9JGG7vi3Q+Ag8BTdBZ+cEhKHeN+eg6QkZ7azaARSmiQU447f/RQlq8p94Y2TB6fybVnT+yxXPnKLVVsLq+neGI2i4rzWFFipSCZmpvFouI8r+Ho8Tf3UFbTxLVnF3rXOd44WUMG8a0rZnQrD+4QKGlx3PH22zBvXqylSHTuAz4A9ojI+1jVLRuwDCE95tiLlD6wt19nv283xiyz+/8V8LTdx9XAr0LtM5Y6xu2d5/zGL56VS86owUzPHcHWinoOHvEwbHAqUycM5+fPbSEtVfjChVOoafTw6oZK2toNI4akcaz9OG3txpsqAiBzcBppaZanXXpaCpfPyWfTvjrmTB7tDc8C2F971JvEfEb+iC7bgnLgQKRPiaIEN9wYY9qBz4rInVgxnO0ichOwCEtJKXGEv/K4dz9ZQrOng6EZqVw4M4cX1u1nX81RXl6/39v2vhuKKT3YxK4DDZw4dhj7ao4yI3+Ed3JRMGYok8dnekuJ+3tye6ixlQde2qYhU0q/IyKvA6eLyK3AbDrz2ezxaXoasDpYX76uzsAgINVedhIMznZt3+WzPB0YAUwEfI+v9EBPyYsdXeOEGP3tvbIuVfKc/DVjswbzi+e38srGSo61d3i9XJzwJX/J1u+4aIo3nMl3u5PkHWD5NxZ4ZfNNmOwvx4wzlsWz81i2em+X/r926QweeGkbv37Zqjz18vr9XbyB3DgeOlMmZIVUZUuJGN8EHuupkYj8uzHmtyH2+Uus3BP/gX8PxqTUMb7hBsE8SIKFBuVlD+PORVNZUbKfq+cW4Gk73i0nRTB8jSqOscbt3fP8h+W8uK4CsBIlOx417klbXvawbuXBHaLmIRNpamt7bqMExRizSUQ+CfwdOBuYR+fD7WtC6CJS+iDFXvbXzqE4zD5jpmN8vfOKJ2ZzwPa2q6prZXp+FgePeBiSnubNhwfwvadKGJuVweisDMxxw6FGD5PGZ3Ko0UNDsxU+lZ4qnDF5NHOnjOUvb+7lSxdP8SYdv/8fmzl/+ngWFeeRNWQQj7yxm8raZjKHpDEtb0ToScI/+ihq50YZuPTkceMkuVoCDBKRdbYFd1nUJVN6jfNE2XHPd8KfJo4fzlc+OY3N5XXMmzrWa2iZMmEEr3z/Qu57dhN/W7MPgDe2VLGnuqlLIs/3dtZw1klj/R7TXQnGnatBUfqBGVgeMXcBZzgrRaQJ2AxswDLm3I11IxIO3wScO3Unsd8Y1/aGAMvjSLBJVTzgNtQESrTrDjE6cexQ9lTv5qSc4SxbvddrXF48O89r3LjvhmKmTOg0hgTylHHn1nG3dfpwvwfiwdd28bPntnDU0+416owZPtjr2fjKxkq+uniat+x4MHl8z8FN50/y6mO3gUcrREWdySIy3RizPVADEcnH0i89Gm5EZD7waaw8FH/BCkf3JSl1TDh5WnoyfKzcUsXyNeXceN5Ea4LlyoHT174dGRua2yirafLrUZM03HprrCVICowxK+350jXAOVg5sZ4xxqwKtl+E9QFhtItrHRPIO6+xpY1H3thN4dhMLijK6VKJzikZ1ezpYF9NMwBDM6znbtVHWjja2sGU3OFU1rXQ1NLurUD3wK3WbWPh2Ew2l1tePMvXlHuPO2GUVWGuqaWdZ98v9x6vR8PsbbdF+KwoSg+GGxE5GyvD+WDXui3Ap4PdxCixxZ17YeEp4ykqGMVv/m8HxuAtrwubWLm1msdX7+X3XziL93bW8NqmA9QfbWPIICtkasqELL615BSKJ2bT7GnnqKfdm7DYF3dVFc15o/QzN2HlpXkCONO1fjjWk69e+YHbFaZ+bH9cCfzc2RRoF9dytxxgTg4tICHzaPU3gYzBvl45J4zO9OodJw+Ok/z33GnWParbG9Cfp4yDYwRxQqTcnoSOp42/9j0ZTRxj08JTxrNpn6WDz502juzzM7zHc9q5+3LOwaHGVnbsb+BbS07pllg5WELi3srb36xatcrJnwXxl0OrDXhKROYZY1p8N4rI1cCDdE2I7hc758VvgQ7gTmOMEd/EC3bTQF24lrvrmIMHWfrkkwAsKCpiQZF/b5BYEUkvFHfOib726y/xaNaQQdw8f3JEZI1rHn0U7r8/1lJEnWjpGBG5GThkjPk/YJwx5n+B/3VtTwkUbhQFfZASYrtw+vRSVlnZb/rF/Zt0jDcH6lp48u29AFxQZK1r7zjO0IxUK4enn8yrzZ4OUlPgaKsVeXC46RhNLe0MG5zKudPG4WnroKHlmKVDzjzBmzvHeV++ppzLT8/n6rmWXjhn2jhvtbseefhhuPfeCJwNJd7pz3uYnjxufoxVVcVNEfCGiJxqjKnxs48SY246fxJvbDnAyq3VHGu3Mq9/98qZAF53wmZPO9mZg9h1oIEv/+l9dh1o4MzJoymrOcpls/PYuK+eXQcaeG9nDd/71Kne6iju0AJ/uHPnKEp/YIx5A3gDQES+DdxJ16TEU7HCnUJOqC4inwEewboRehtYYoxpsze79V6Wa3m4a7mbbkz0HFr9ja8x+I0tBzjr5LFcM+9Ebxu3l8ywjDQWz87zGjYcg4ZTKQoCGzYcg4ZvGFMwT0J/OWeumXciJaW1XDPvRG+fi2fncdTT7vV2dPLdLJ6dx+d+/w4rt1Z7DTO++Wucc+Doc6CbASlYQmJfwjHy9CduQ+Y999xTFlNhuvM74DDwB+BWZ6WIDMOadN2C91lvj1wJzAT+afcxC3DPAE4SkSp6q2PGjWPpDTeEIEbis3bPYUpKa8PLORGAqFVsSgRmxI8eiCZR1DH/DYwSkdHAbhE5iuXpuwnYAnwe6z7EH1cSWX2QEmI793sobQEozM1l6eLFvmOICu7fpGPEyc0eSmWt5Unz2Jt7ae+wQiRPHDuUmgYPLcc6MAZSU2HuyWOpa/JQ0+ih5ogHsIw4TpGWo60dlFY3setAI6u3V/Mfn5zOxrJaPO2W0cad+8r5vHJLFcOHpIduJM4ZgPpkgNKf9zA9GW5mA1/CKnNZCJwOXIyVsfwu4OvRFE7pGX9PUMcMH8wjXz7HO6l4Z2cNl5yWy303FNPsaeel9RW8v/swAFMmZPH7L5zF65sO0HKsnblTxrK5vI5dBxq8yTyha54Gf7ls4nVCoAw4PmmM2YtVSQEAEcnAMjifFkoHdq6ch7Fugt4ArjDGHHU1WQ84s6MpWKFYzjLAEaC0d+IrvjjGkFc2VrJya7U3BBTwGmicUCLHUOK8O6FF7sTEbn2568AR7n6yhKm5Wfzm/3aw8JTxfPfKmd30nmM8cpchd4dsOe2cBMmOp4+zj9POMdr88fa5LFu91zsOxwvH3Rd0GqYWz87j7idL/IZqhZOQOBwjj2JhjPkegIg8JyK3GmMeFZEzsTz8JtH5hPovIXSXab9fZr98+V/gTeBFVMcEJZLGloTJRxMNhvXN6KVwMTDTGNMoIvVAFVbI9lx7ezCDbjT0wRGs/DTONnyWS+z3uL6Pcf8mnd94wZhhPPzGR4wcNohPnVXAU7b3zVGXQSY1FTo6rEpTM/JHclphNo+u3M2B+hZmnjCSvdVNHGq0DDm1R48BcPCIh1+/tN27HvB63Vw9t8BrxFm+phxPWwfXnh1ixL2WAleiQE+GmzpjzIPuFWL58X0Rq3SdGm5iTKAnwmOGD+aPt8/lwdd28faOam+SzrNOHsue6qYuJcMBflG61Zv40jHaOBMU6JwcOJ43QMhlaBUlmojI6cAmY0ybbbTpgjHGg1XlYW0IfX0W+DOW0WYFcJUxptWn2d+A+7EqMvyniKwGPoGV0A/gr4lW7SVecBtW3MbgP94+1+udcqz9ON+9ciaLZ+d18XiBroYSZz+3oeanz27iZ89t8YZ8Op4sTa1tTJmQxcqt1Vwwc0I3vedOiOyUIQ9Uxc+pPpWdmeFdd+60cV28gZyQLye3zTXzTuTl9fsDhjBNmTCC5d9Y0GMSePf5g+6hV+7xaDL50BCRhcaYlcBngbdF5FQsr740LKNNPfBFY8wzETys6pgeGNDGlkjy4YfwqU/FWoqExRizESuHHsBXjTGPi8gg4BSsh0UzI3CYkPWBiDwN3A5MF5EbsR5iOXO1NruvsPqMNe6E4QePeMjLHsbGslq2VTSQNTSdQw2WwSU9VWjrMIzJymBzeT2by+t5d9chMu1QqrKapi7GmUGpnccwtvl97IgMao54KKtp8lae6zUvvaQV25SI05PhpkNEvmSM+YOzwhhjgD+IyDeiK1r/4+SgSKT8E8Fyy4wZPpg7LprC+x9Z3o4rt1ZTVDDKmwdiZ2UDtU0eb8WUQEYbf8fzNdBoGdqBgR3DWRhbKbrxIdBm599a53ptMsYccxqJyPnGmKBVpbASAzruxouAFp+Y84nGmDIR+THwE6ynau6aj/uBpX0Yy4DGbYgGvN4vjhehY7w5b/p4r3eLr5eKr4HEn15yPFy+ungag9JSmZaXxa9f3tGtL7chxG0EgsBV/F7ZWEnxxOwuhnQnkbC7YpRvrp1A1aT8nR/w79no3g4EbKsekmHxa+BUY0ydbdh9i84yum8DNxljykPpyBjzKPCoe52ILKUzIel5xpi37fWqY5Toc/XVsZYgmfiDiIwxxvwPlmdLSbDGUdIHPwIuxYqM8C0kc48x5mP72OWJoGPcSYoXzsxhW8URSkprmTw+kxn5WWyraGBKbhb1Rz0ctEOi6o86hhyorG0mPdW6h8tIsyw12ZmDqG06RmWd3S5NOPWEkVQfaSF/9DCrnX3bV1JayzPv7WPTvlounpXLouIwKjpeeGGEzoKidNKT4eZl4LcicjdWkuJ1wD6gABgVZdn6nUTMQeGb48HXoOK44+ePHkrF4WZa29r54+1zvU+qm1rbeGdnDedMHcs9183iF89v5b4birsZbdwTmJ6qvijJi23QLIutFH5JxypzOQtwUvm3i8hWLL21Fvgu4VeV8osx5l4ROQB8BSuHThPwCnCXMaYqEscYiLgN0T97bgs/uW4W0Jlc+KyTx3YJk4LOMttu3RTICOKU/F48O6+Lh8uuA0fYsb+hm+5zDBxOiFQwI4c7dAoCG03cuW9eXr+/myyhnJ9Ano3+tvtrqx6SYXGSiDxH50Tst1hle+8B7rMfZiEii40xL0fqoKpjlH5hxQoN6YgcL9hGm4gTqj4wxlTZhWXuBy7BCsfaCfzGGPNwb/qMJb6Jw+9cNNVryNlaUQ9AagqcdfJYXlxbAUC7FTVFhx2k1mYvHGqyDDUpIowbkeE19OSOGuK9r9hW0eDdNml8JkUFI9lQepiDRzykSD2fv+Dk0IX/6CNIECcAJXHoyXBzH3AtluX2M/bL4bVoCaWET6AnyzedP4mPDzfx2JtWBMnf3tvHHRdN9YZR/fUdSyEOSkvhvZ013vwMvhMff2Vu/a1TlBhQjeX2ezqW4cZJqJ5ufz4N+FwoHRljCkM9qH0T9HCPDZWQ8A3zcQzRbuOJb46XYOGbgDeHjVMS3K0n3TrO8d7x9ZRxl+B2QqQC4Q59uvDUCZSU1npLfrtxj+eVjZVdQrv89e87hmAy+P4PBGqrHpJhMRi43H45tAALgBEish7LoPNzrIddYWGMWUqAp9uqY5So4/H03EYJlVYR+T7w/4wxR3rTQST0ge0BeGOIx4tLHeN42syZPBpPW0eX6k8LZ+awomQ/HXYg10k5WSw54wQ+3HOIqrpWhgxKxdPWwfHjMCQjlczBadQc8dBi58E51Ohhen4WTa3tNHs6OHjEw+isDE4tGMmhBg+by+sB2FvdBMDFp01gy8dH+NLFU/yJGph9+yJ1OhTFS1DDjTHmoG25fZauWdGbsMrvKnHOmOGDWbX1IK1tloarO9rG3U+WsPwbCxiWkUZZzVEmj8/kpJzhHGps7ZKYU1EShLvtEpyISAowA5iDZciZg2W4GUwYVaWU/iFQTpuvXTqjS2Ulx7hx7rRxDMtI61LhLpj3iBO6BN2rMbmP7Rhd3ImPv3bpDG+uMLdBKRBjhg/2yubkw/FnBHd7CvnmvvFHsDH4ol6QUcO3dO4Q4AL7pSiJyzXXxFqCZOJWrPuMH4nITqyqUpuNMT+NqVQJiNvTJiM9lSfeKmVPdZPX42b5Gis6tahgJBlpKRyoayF72CBGDRvESeOzeHGd5X0zLiuDMyaPYdO+OnYdaAQgJQW2VzQwaXwme6ubaDnWQcuxDuqPtvHVS6fz/Acfs7u6gZwRQzjU6OGTxXl8/hNhGm0Abrut5zaKEiY9edxgjCkDZtsJQM/FetL0Qry40Sk9u97//gtncceD7zHzhJFs3FdHwZihHGps9U4Uahpa+c3/7QBg4SnjueOiKd0mAE6IgXty4buuLyEAitJbHKONvXwc62ZpC3bsuIikYiUKVH/wOMNtrAmWP8uf8SRQrhk3991QzLH2DqbmZnkTEvs79tcuncGwjDS/lZ188Wccces+IKgxxp/Xj7/QLqfPby05xTuWntDcNVFhK5a3TTGd4ZjFWJ7IbtQwrCQeTz4J998faymShTKsZMCnYt1zzACuAdRwEya+VeOc3DbPf/gxZQebuPi0CWQNsVKNLV9Tzts7D1JV10rOqMHMLBjJG1sPcLS1g0ONHpavKWd6vlXxPDUFr6fO8eOWyh41LJ30tBQWzcpl5ZYqlpx5AllDBvHMu6Ws3FrNOzsOdqsk5c69kzVkkP9BPPww3HtvpE+NMsDpZrhxV2hxrzfGOAk/lTjD1/V+xYb9vLOzhhUb9rPsK+dx1klj2fCLK3jgpW08v7aCP/1rN0MGpTE2ywoFePC1Xd6+Vm6tZtlqK6zKt/yt70TAd124IQCKEg1EJBNrYnUM2GhXhdpkv5Q4wrcyU6ihPe4y4L7GE/e6KRNGcMHMCfzg6Q0MzUjrFgblK4NbFgdfY4g/44i/dcGMMaEYs50+f3LdLL+eNv76cjyHjnrauxmqlF7zH/YDrDLgH85KERlDpzGnGP/lfBUlvpk1K9YSJBNXGGO2OB9EZDJQFEN5EhbfqnGOp82mfXXeUKaxWYOZM3k0GempVNY1U1XXSlVdK795eTtHPR2kpwpHWztITcFb5bHDVSfrYEOrt59dBxr531W7qaprZeXWaq6ZW8AKV8XKhpZjrCjZD8Ci4rxuuXf8cuKJkTodiuLFn8dNJCu0KP2AM+GYN3UsAHV2Aq53dtbw4Gu7vJOVm86f5C1/+9jqPdQfbeOop507LppCy7F21u45zIwTRnLU0841807kjS0HeGVjJQ++tqtbDptAkwa3HN9ackrQEABFiTQicgGwHHBmzUdFZDnwDWNMXewkS8yqddEm1FwrvvrGN2mwex3Qzcjj9OHOyeV7bPdntwfNocZWFp4y3utNEywBcE+6Lpy8YD316e8cuMO1nFCyQOewN0Q7FCseq9YZY1YFWH8IK9ffawAisrkfxVIUJU4QkUuAB61F+a0x5hcAxpg9wJ6YCpckOIacOZNH89ibe8kfPZQn3irF02blramqbwE6y3lbWB41HcdhY1kt0/OzONTooeaIh9RUaLZz3rTbnjdVda2k2pWo/vjaLjxtxxk2OJWdlQ0cbvKwcosVRp2RntrNI8gvJ4eRyFhRQiQlwHqnQsttwO+B94FGEVkvIn8SkTuAv/STjEoPOBMOJ7lweloK50wdy1c+OQ2wPGe++NAaAB758jlMmZBF/dG2LvsPGZTGOztr2PZxPT97bgvX/upNTp4wPOAxnUmD453jT473dtZ480QoSj/xADASKyeFYFVU+AywTkRyYydWZ9U6Ndp0cqixlQde2sahxtagbT73+3f4wdMbvN6BN50/iUtOy/UmDXbW/eS6WQHLeQ8Z1P05hfv47mVHv33zsbX8+uUdrNxazcvrradtjp4L5ukT7hj9tQeC6k9/58BZ/90rZ3q9bhz86exwiUQfwYjHqnUiMizEpl8Is72ixJ4NG2ItQTLwAFa13ROAn4nI52MrTvKSlz2Mu64qYskZJ3D13AJ2VjawfE052ysayBk1mCkTrJCoFIG2Dqv0d+bgVJpaO9he0cDM/JHkZg+lo6Ozz/qjlk9CWip0dFjhVEvOyCdraDrjRwxhc3k9H+45DEDOqMHe8KglZxQEDpMCeP31qJ0HZeDiz+MmYhVaEo1EfyLuTuL53Stnesvevv+RZUj53O/f4ZEvn8NTXzuPbz62llNPHMUdF1kJt5rtaijlNUcB2FPdRMGYxm4TIbAmFkc97QETGWup2eQlHp+I+3Ay8ALW5G8CMBOYhiXz/XStjKfEmFA8UJat3utNGPy398q48NQJvLezhvtuKO7i0efPe8ftheMvT5d7O1hGbqcy1HevnEmzp52VW6tZeMp4v/rsUGMrX3xojTck1Anhcuf36kkGf+MNJU9NoNw/gbxufMOonGOF4z0zQHX7e1g5K4JijFljL74FzI6qRIoSKW64IdYSJAM5WKGSzcC3gVuAP8dUoiQna8ggMtJT2VxeT86owd4wqUN2+JPtRMP4kYMpGDOMVzYcAGB75RG+s2Qmb2ypYuf+I9Q3H/OWBfeWET8OKzYcoKG5jfnTxzNi6CAWzcplxYZKbpk/qYuxJmium0svje5JUAYk/gw3A7ZCi/NEPNFwP/F1buSPetr5wdMbeGPLAY61W0GdTv6ar106gxe+84kufQzNsL4KH9c2kztqCOlpKXzv307lrJPGdjvestV7+dlzW/jJdbP83vBrqdnkJR6fiPvwgTHmSvcKEZkA/BC4OiYSKX3CMTj87b0y9lQ38eU/vc+uAw3ebcGMD8Fy6DgG6K8unuYNDwU46mn36rc7LprizQXmr/9lq/fyysZKb0JjxxDlhKS65XcMQr660ddjJxzjiL8xuZMkBzPoAGEnMh6gun28iPwwjPaZUZOkB8oOHmTpk0+yoKiIBUWaWkMJgb/9DQbQdyVKD5+2GmNeBhCRNVhRCtifxRiTFPOlssrKuNIvTpjSnMmj+cHTG2hqaae9AzIHp5KbPZTUFGF7RQNpKVZwSeaQNKrqWvmv57eQNSTdW2Vq3IgMr/EGID0VGprbGDcigz3VDVx/7iR27D/CnYumdjPOBM11s24dzJsXreErcUh/PNzuZriJZIUWEbkFuArLU2c80Gj3da8xZqVP29uArwBT7XavAHcZYz72aVcA3AdcgnWDtAv4jTHmYT/HD6nPRMdfGd1dB47w7Pvl3snDmSeNZlhGGjNOGMHVv1zFfTcUM2VCZ/LMOy6a4i2FW1lnxYq+t7Omm+HG19tGy88qccZrIjLHGLPWWWGMOQB8SUQ+GUO5FD+E4oEyZvhgvvepU7lm3onc/WQJ31pyCu/trOnizXLU094l8bB730B5XhwDtBNu5NDs0m/+DBW+ZcShe0LjkyZkMSgtlcWz84LmnYHu+rsvxpGePJiC5edRXR6QscCPwmhfHS1BeqJw3DiWqgeFEg4ZGbGWoF+J0sOns0XkELARqwhCuoichjXfWQFcFOHjxYTC3FyWLl4cazG8OOFKDS3HOHvKWNaX1pI9bBBTc0fw4roKpudnMT0/iwnZQykcmwkCH+455PXOAcuYc9sFJ/PRgQa2VtSzvaKBtg7IzR5KZW0zB494+MnfN3Gs7Tj/2nKA7yyZSV52ZzRs0Fw3VVp8eaDRHw+3Q64q5WCM6SD0Ci13YxlNHIYAFwAXiMj1xpi/2sf8PvATV7sM4EZgvoic4ZQeF5Ec4F26luE8FfiziOQYY7wl90LtMxnwdzP+8vr97DrQwOTxmeypbuJwo4cPdh9mf22L92m1281+zPDBPPLlc3jgpW2s3XOYOZNH+w2RcsICHG+bB17apuVnlXjih8APReQl4A1gA1AFnIRV1leJAYGMAuEYKaZMGOGtruQYlB0d5XgYQnA95DZsOGGiTulux9MG6OJN2FNiZPfx7rhoCrVNrSxfs4/apmOcO20cUy4dEdSLpj/Dj3zPt7+qgL7rFcDKl6UoyceiRbGWIBlow8qtt9B+AawHWknC6IR4wQlTamhp49WNBygqGMlXL50OQEVtMyWltQBsr2igqGAkm8vrmZI7nKaWNppaO8gcnEZTSzsPv/ER40cM4YZzJ7GxzNrnnGnjeGNzFSu3VdHUYnnLVtW18tibe7nrqqIuIVIBq0rddlv0T4Iy4Ih2Val64PvAMqAW+C6WMQfgB8BfReRErMkWWO6FVwIXAo8D+cBS4Iv29nvoNNrcDLwOPAecBfxIRB43xpSH2WfCE6gaCliTkq888gHv7KzhxLHDuOuqmdz/jy18a8kpfm/Ud+xv4J2dNSyaldftqatvWAB0TjYWz87jgZe26dNaJdY4Ou0K++VmlYh8F1gLrDXG1PenYAOZaBkFHN13qLHV63Gz68AR7n6ypJtX4aHGVt7/qKZbH9mZGd4+HPzlwXG8ehbPzvPmEnNCT93yrNp6kNqmY4wZnhE0/45DbZOHt3cc9Hrn9MXzJRQPpkAM0Pw1oTAxzPYdPTdRlDhh+XI4vUcHesVGRP5ujPk3n9WPY3n3n4aV36rYfp2ClR9UiQJOmNL0fCsh8ebyelZuqWLO5NG0dxxnSu5wdlVa4VATRg0hLTXFa8wZNjiVWYWj2F3dSFVdKwePeGg/vpdT8kfiaTvOOzsOsuTME7igKIdH3tjNmOEZHGr0cMt86/9xRcl+lq8px9PWwbVnB/iLePhhuPfe6J8IZUDhz3ADnVWlZmFVlgJoF5GtWEactVhGmJ5uaC40xjQ5H2wvmH8HsrCegIOVd8JRbL+yPWGWicjdwHTg0yLyZXv7dfb7dmPMMrvPXwFP231cDfwq1D7tULCkIpir/L6aozz25h52HWjg9U0HvE+cnRt1xzAzZngGM04Y0c0Q4y8swJmQ/PTZTSGXuVWUKNIIfIR1w+TrA77AfoH1FCyQ/lMiTLSNAm7DiDtZsOOhA51Jji85LZc7LpriN0QpWHiR26vHX1Jgh99/4Sy+/Kf3+f0XzgrJ8HL3kyVd5O2LkasvYVYDNH9Njxhj9sVaBkWJGmecEWsJEo1FIvIl4I9O7hpjjFNF6j37BYCIpANv9r+IAwMnPKmhuc3rVbNwZg6/W7HTm7TYMd58fPgoN5w7iQP1zVTVtXK0tYO3d9Rw9dwCb4gUwPI15d7+3955kO8smckPrj6tdwJqOXAlCkS1qpTbaGMzCEi1l/fb7+7qC7t8lqcDI7AMRCn2sr92DsVh9rmnx0EkKM2edq/h5fRJo3lnZw3nTB1LUcEob94bsCYjD762izsumsJN50/i0VW72VPdxLcfX8ee6qZuuSMC5YtQlDjhcWPMv4tIGlZidffTr9OAwDXulajRn0aB+24o7vLu4Gt4DtWY5M+rJ9h4zjppLOv+67Jey9ufni+a16bviMgMoNQY0xJrWRQlbI4ejbUEiUY11v3EhyLyH8aY9wI1NMa0icjN/SfawMLJcbO/9igVtc1eb5iRQ9MZlJ5CVV0rY+3/te0VDTz19l6q6lrJzhxEbdMxRmdlcFphNgCn5I/k5AlZ1B3dzeC0VMpqjnYJjfJlUXEeGemp/nPbOEyYEPlBKwOe/q4q9U3AyerkJBMe49reEGB5nE8/PbULtc+kMtw4rv7fvXIm0LW07cJTxvPft8whOzODoRlpNHva+dzv3/EacWqbWvnlLWfyiaIc9lTvJmtIOl9dPI23tlfzzs4aahpau1VXCbfMraJEG2PMv9vv7XTm4nrU2S4iJ9FpyFGSCLcRwu1p4+A2wDhG7UDGF38GjZ6MT701grhz9/g7Tij99vbY/rx71JgTOiLyUyzv44MiMg/L2/dhY0xdbCVTlBDZti3WEiQadxtj/ioiZwL/T0S2Ad82xnSPwwWMMUk1z4hH1u457A2Bmjw+0zuvyRqazmWn51PTaCUjdiatY7IyqG06xuEGD394dReVtc3ceN5EVmyopKqulcwh1tQ4Z9RgrzHIF8doFJTVq+HiiyMyRkVxiGpVKTd2hakf2x9XAj93NgXaxS0WlsdNKO3C6bMLVS+8wKrly9l2wglclJbGyZmZcMkl8MorUFgIgwfDjh1w/vnw4YfQ1gbz58O//gUn2ZFfu3fDJz4Bb74J6emWG+rq1TBtGrS2QllZZ59ZWTBzJrz7rlWOsbYW9u/v3J6dbfX7wQdQXAyVlVBd3bl9/HjIzYWSEjjzTCpe/4ApW/dRNfd8PtO6m0skk8bl73Ho3XW0jj2Zsn88QE5OBlmDT2LKmtVkZ45hbtogTq6vZPv+U2HVH7l1z0FaPRNYsHor9bkFDG/0cFtjNQeOzmNsyQdUTR7LmE9dBKtX84WJJ3F2ywFOe/Q5MmoW87VXXoEXIjsmFi+GqVN9L5XSz6xatcopcwdRLnUXTYwxu4HdWF6F/UpZWRlLly5lwYIFTuZ5xQ+RNEL0tp27zeLZeX5z5vT2+P441NjKg69ZzqF3XDSly7h7qpzVl2P78+6JZZLi/iilGWFuAZ4EzgW+DjwF/JIQvZIVJebcemusJUgonKIqxpgPRGQucDuwRkT+B/h9MqZgiGcaWo7haetgen4WJaW1tLa1s/CU8Xy49zANzW385U3Ly6Z4YjafOquAZ94to9l+oJ2dOYjK2mam52dRMGYYKzZWMjQjlaaWdoonZnPnImvu8fyH5SycmdOtFLhbBidRcZc2V10V9fErAw+/OR5ERJzYTV/CrCrl9PcZ4BEs48vbwBJX1Sq3lTrLtewOaaihq+EmWLtw+uxCztSpLLjhBm8SDC/u8pqz7Sis/Hz/288803q/9lr/288+u/u6wkLfI3bd7hiFpk/3v91ePzZ7Ao++sI3K6mYKzlvoLZO34sSTGVnZwJ/L63k5dSiV1c1MOfsSKuuaaWrtYMOoQhbOGM/zo4YyZ/5o5u84yI7KE60Y0ZzBjC44l1FpqbRPOoFtaSkUjB5H1g03MAw4c14AmSM0Jl59VQ03cYDb2HDPPfeUxVSYIIhIA7AZKMGqKLUB2GyM8YjIU8aY62MlW2FhIUuXLo3V4ROG3hoO5k0dy5QJWcybOjao8aenUKRDja3UNLSy8JTxXqPNKxsrOdbewQUzJ3TpM1Bp8HBxSpMD3UqGh1I5q7fH9udFFMskxf1RSjPCHABuxfLy/Zsx5isick9sRVKUMHj0Ubj//lhLkZDY86QHRWQ5cB+w1g6feifGog0YVm6pYvmacooKRgJWSNTgdKta1NgRGVTWNjNlwnDys4fyzLtlbC6v9+6blmo9yz/U6OE3/7edo61WXnnHaJM1ZBDPf1jOE2+VAgT0sHGSJHdrs3q1Jv5WIo6/cuCvA6eLyK1Y+SE2Aht93f1CrCqF3c/DWIaXN4ArjDHuoNr1gDNjn4I10XKWAY4Apa7lEa5t+CyX9KLPpGHtnsNe6/GmfXXMmTyavOxhLCrurJ6+ubye6flZ7Ks5SrPHUlJjR2QwKC2FJ94qZdO+Or566XQWFefx65e2s7m8nqbWGq8FuqS0loz01J5dBCPF4cP9cxwlWcgE5tovhw4R2QlMjo1ISjj01nDwi+e3sutAA794fivnThsX0Mjhz1jhNsAsW72X3/zfDsBKHPytJacAMC0vq1ufvkam3nqo3HT+JG9Yq7/KWL45dnzpaUzheC5pkuKw2I/lwfcyMFpErgf0SYOSOGRnx1qChEJE0l0PngEwxhwWkW8BdwIrReQprPCpar+dKBHDyTEzZ/Jo3tlxELBKeU8en8mb263T39DaxovrKgAr/KmqrpWxIzI45YSRGOqpOeLx9jd5fGYXT5s5k0d3OU4wGbq10fmLEgX8edzMwPJSuQvwppsXkSasJ9kbsIw5d9NDVSkR+SzwZyyjzQrgKmNMq0+zvwH3YyU//k8RWQ18AiuJMMBfHddDEXkayy1xuojciFUO/Ot2uzY6wx9C7jNZcNwFr55bwM7KBjaX13uTaj3/wce8uK6Ci0+bwKknjmJ96WGv0Qag5ojHGx/qlNNbckYBhWMzLcNNS7s31jM/e2gXo5D7+CtKrHzTi4rzAroUhs3tt0emH2Wg8DhWHq7pdFaWS8MK7+xNXi6ln+mt4cA3ye8bWw5QfvgoP312U7fwI1/c1fjuuGgKRz3tvP9RDa9srOTcaeNY/o0FHGps7ZLUGPCWBl88Oy9g34HwNc64K1r5q4wV7nmJZcjTAOIbwL+AL9mflwEvxE4cRQmTc8+NtQSJxgd2fpvZWHOkM+33k7HSMQhwE3AFMCpWQg4U3Llm3GW5M9JTqTniITd7KJ+ZP4l/rqugcGwmFxTl8NibeykprWXlkWqKCkZ2MdwUT8zu4mmzcms137piBo0tbfz6pe0Ujs1kyZkndJnjBMx3c9tt3dcpSh/xZ7i5Cfg+8ASWQnIYDsyzX6HyIzpDnBYBLSJd0s9MNMaUiciPgZ9gPSU/4Nq+H1jq09+lQB7WDZKbe4wxHwMYY8rD6DMpcNwFbzxvIp+74CQeeWM3+dlDaWg5RlmNVdyr4vBRsoak03Hcmr8OzUhlwsgh7Klu4uARDzPys0hNSfFamDPSO6PTzpg8hrzsYVTUNncxCjmxnZ62Dm8ZvYh65Dz0EGh4iRIixpjPgLcM5ylYSYgvwEoa6ms0VpIId5LfB17axsqt1d4khb7hR8G8UZyy4L5V8/wZTl5ev99r3JlyaeD8N/5wlwF3lxcfM3xwwMpY4RDLkKcBRBuWofharHuNg1g5bhQlMXjhBZgXzm39gOcUoInu8yfxWQ7vD0GJKG4vmJVbqthcXk9aqjWnmTw+k/zRQ72P8grHZgLWnGdRcZ73QXjOqMFU1jbz2Jt7AevB9ubyejLSU7wVpYI+pH74Ybj33ugNUhmQ+EtO/AZWSBMi8m0s179ZrtdUrJLeEXt6bYy5V0QOAF+x+28CXgHuMsZUudpVicjZWN40l2CFRewEfmOMebg3fSYLbiWVNWQQhWMzeXFdBZ72DiaMGkJNYysZ6Sle40pu9lAqa5sZmpHG5XPyyUizFNryNeWs3XPYG2LlaTtOWU0TF9j9OxnWnXcntvPquQVcPbegiywRoah7GT5F6QnblXmD/fpfEfktMDOWMvHWW3D99Vaeqy1boKEhoZKfs3u31YezPS/P6mPz5rgb0zUjc3h0fCZ7qptYeMr4bsYLX28Uf1XxAnm49JTXJtQQJbdxxlce30pTvUFDnvqFNXR6IX/HGBNT3/iqqipW3X8/w669ljOam6GuDm6+GR5/3MobN2wYrF1rJc187TXweOBTn4Knn4ZTbY+vTZvguuvg2WchIwMuugj+8Q+YM8cqHb19e2efo0ZZv/2XXrLeq6pg797O7ePHw2mnWbnqFiyAPXvg4487t59wAkyeDKtWWdVXNm60dJCzfdIkyMmxfu+XXmq965giO6Zdu/r/ixpDIpAAPQ1r/uNbBKURWAt8YL/e78Mx4pKyykqWPvkkC4qKWJBA9+ZzJo9m5dZqb2RBSWktN55neec88VYpudlD+dYV1n/l71bsZGxWBq9uPMDCU8aTIil86qwCMgen0dpmhTN72o5751JBH1LPjO0tp9L/9EeBBQmQg9jaKDLJGLPXZ10GUASc5mssSXSWXn+9WepOkJugNLQc4/t/LaGqrtUbzwmQmgodHdb70mtm8ez75ZSU1nqVVlNrO394dRdfungKU3OthwWOu+CN501kWt6IbtsDZlOPFIMGWZMyJW4QkXuMMUtjLYc/ROQVrEnUBvu1wxVq+Z4xJmaPFpNFv/QXPemWYNv/UXKAW3Zncslpudx3QzEvr9/vNa4sW72XxbPzvOvCLbH9wEvb+MHTG/jJdbP8GkZ++uwmfvbcFr575cwu4U/B+nfkCiUfTbKX645n/eKLiIwyxtSJyBnAD4EXjDF/ipU8S5cuNZoAXVGC0xcdIyLHgXas1BHv02mo2R6oqEuysPRLXzJLFy+OtRgh4cxdiidmkz96KC+urWB6fhYn5WSBsbxrzpk2jl+8sI3K2maKJ2bT3nGczeX1jBuRwcEjHqbnZ7G9ooGigpF89dLprCjZz/I15Vw+J5+sIenee4+A9yIicNllsTsJSkyI9j2M36pSDsaYvSJSACwBBgFrjTFvYlmV10ZLKKVvrNxSRVVdK7nZQ/nSxVPYWFbrzXvjGG+eebeMwnGZHKhvprK2mV+8sI3Rdmm8Z94t49QTR7FwZk6XxF8/emYjDc1t/OHVXTxwq5X+KGBsZ6R47z013CjhcBFwoeuzR0R2Yz0dOyk2Iim9IWClhhC2X9JSzk+uu8GbbNjxZgH4wdMbeHvHQa+nSyAjTqAcMX0JQfJNguz0H05fmrsmfjDG1NmLm7BCs/8iIp8wxnw6hmIpihI9PgKKjDHHYi2IEpiFM3PYVnHEWyLc4cW1Fd6Ig60V9WRmpDIldzhjszK8aSVmTRxN1uA0tlXUA525Px0y0lK63HMEvBdZs0YNN0rECWq4scOSXgMGu9ZtAT5tjNkeZdn6nbKDBxPSDdDBsfo6OWqm5Y3g2ffLuWX+JBYV57FySxUFY4bxu1d2emM1Lz89n3WltVTWNnP6xGzSUlMYm5XBE2+VcqixlcraFgrHZvLGlioamtvIHJLGly6eEvDYPXnehO2hc33MqjcrfugPN8A+4uvCPJjOEKmk01nJTMBKDSFsH3rrzXxtqlUZwtfQ8vaOg7yysZLSg03sOtDg/QydhhDfkuC+Xi7BDCb+wq4c/BlrfI04PRljwjUc9dazR+kZEXkCK8fNFKwQcrGX1XCjKMnJeWq0iX+yhgzizkVTWbmlioaWNrZXNHDS+CzSUlLYXF5PzqjBbK9osNoOTWdXZSMARQUjyRpsTY23VTSQM2owpxaMwtPWwTnTxnlz27gJeC9y3XVRHqUyEAlquAF+DAzxWVcEvCEipxpjaqIjVmwoHDeORA5lcFt9F87M4YdPb6SytpnqI638+LrTWHJGAff/Y7PXALNwRg5LzjyBJWee4DWmAPz6JWt+u760lpojHjaX11NUMNLqd0YOO/YfYcKoIV0MLz09HQ+3nZd//hOmanXVeGHBggUAZbGVIihrsfJfFdOZl+tkoBn4XsykUsKmJ2++oNtdesPX0PLH2+d6qzY5oVTnThvXxRDiLgn+8nqrWp7b4BLM+BHMsOM2urjbOeXAj3ravdWrAuHsd6ixlQde2tajEcafxxGot06EuB44jlUSfIP9KomhPIqiRBFjzMFYy6CEhnOP0NByzBvatKJkP5vL6xk1bBCnnTiKjfvqqKprpahgJFNzswAr1+fVcwsoKhhptz3K9ooGtlbUc0r+yIDH6cbLL2ueTiXi9GS4mY1V5vJjrKfspwMXY1V1uovOUtxKHOCbRb2ytplhg1OprG3mly9u5YZzJ9Ha1u6N38xIT/EabByl88y7pWwurydzSBpTJmRRc6SGsSMyuOz0fG9Gdn+GF1+LcyDPGne7kLxvPB7/6xXFD8aYs+zFlfYrbkh0j754xa8e8aM33J4nvlWcfCtCOYaUlmOWMeWaeSd61/t6xxxqbOXB16wEnz2VHQ9k1BkzfDDDMtL4wdMbulTACpbPJlQvHV8PnVANRLEgATz6fPkdVlLi5lgLoiiKonTHbVhZVJzHnuomSkprmT1xNPd+upiVW6q8EQqLZuVSPDGbc6aNA6wwqUON1v3E9ooGtlc0eCvn+t57dLsX0fmLEgV6MtzUGWMedK8Qq573F4H/RA03cYVbOTkGkg/3HGJXZSPbKxr4w6u7qKxtpqhgJBedmounraOLh86Kkv1stWM6m1raaWhuo3hiNiWltazYUElJaS2Tx2dy43kTu7kE+lqcA3nWuNs5ycN823QhgT2glNjgk5drnTFmVWwlskh0j754xa+uueGGbkYPX0OHr7HDt/0dF03xeua4jSm+hpBlq/fys+e2AN3LjoeDvxCoYMaZUEKm/Bl+/BmI4oUE8Ojz5SvJnpBUURQlWXBCqFaU7KehuY1n3i3jQF0L60sPs72igeojrVTWNjMjf0QXI0/OqMGcduIohg9O985/fO89ut2LaKiUEgV6Mtx0iMiXjDF/cFbYNyl/EJFvRFc0pS9kDRnEwpk5rC+1qpNOz8/iyjMK+Mube7ns9HzKDx3ltMJs9lQ3WaXytlR5y9uNGZ5BSgpcdno+6/YeJmfUYBbNymVG/oiQc9P0lJ8i1DY8+iholQwlRAZaXi4lgB559FGWnXFtF6NHT4YO3/wzn/v9O6zcWt2tnLiv14zjnROsb+i5GpRvv4caWznqaee7V870228o5b6dMb294yB/vH0uY4YP7lNyZaUbe0WkBauSnVPN7jpjzGdjKpWiKIriF2cO8+K6Cu+6cSMyKCoYybVnF7Jj/xHvvOiW+db/ZElpLRNGerhmXqF3f997j273Io8/Dvfe2y9jUgYOPRluXgZ+KyJ3Y02G1gH7gAJgVJRlU8LAX7jAyi1VbK9ooHhiNrfMn8Rjb+6lsrbZ6z3jeNNMHp/J4SYPwwankjNiCHuqrczq/1xXwebyegBWbKjkrquKuhxvRYmV+2FRcV6vSoGHVJHq9NPD7lcZ0AyovFwDGbfO66ZHTj+9m4GiJ0OHb7LglVurATjr5LE9hj+5y34HCp168LVd/Oy5LRz1tAcsE+5bdepnz23hJ9fN6nVI003nT/ImX162ei9fu3RGSAYfJWRGYIWSXwI4j1cNoIYbRVGUOGfKhOE0tLZRVdfKRafmMmHUEHbsP8Ibm6t4cV0FNY0e2juOMz0/i5LSWlZuqfLeb/jOYbrNaWbP7u/hKAOAngw39wHXYuW0+Yz9cngtWkIp4eMvXMBdyvuxN/d6jTW3zJ/E5PGZNLS0UVQwkoaWNlZusSYp5e1HGTM8g0ONHgrHZlI4NpOymiav1dmZLHnaOrweOk68Z0/yKEo/oHm5kohgebB60jHhGij8JQsGy/gSKocaW73hVRB+6JTjIeMcO5C3jft4PXnxuPP5KBHnI+BiY0yriEwErgIujLFMiqIoShAWFed1qRDl3Gc49xVOQZZN+2q9yYv9pYlQlP4mqOHGGHPQDj14Fqs6i0MT8P0oyqWEidtI8/yH5d6JzpIzCnjm3VJKSmspKhjJnYumkjVkEBnpqbxqG17ctLUbDjV6yBk1mDNPHsOO/Uf43AUnsXbPYYYPSfcqtavnFnD13AK/x3TL02clt24dXH553/pQBhKalyuJCGacCapjbL3Rk2EjEL5eNKHy4Gu7eGVjJWedNJqhGWksnp3n3RasTLiDO4Hwz57bwnevnBlQfl8jUSADkXrYRJWXgH+JyP3AGuDXWIZiRVEUJU5xe8c0tHRWd3fPpdbuOexNWnzL/EnkZQ8L7yDr18OnPhUxmRUF/BhuRORULKPMWuB5Y8xOYLaInA6cC7QALxhjqvpV0n4gkau+OErInfDXsR43trYBMCF7KCtK9uNpO46n4zhTcoeTmiJce3Yhh5usxMWD0lM41nacqrpWfvfKDqrqWlm5tZrK2mZvn867Y6R55t1Slq8px9PWwTnTxvHYm3u5Zf6kyHja3Hpr3/tQIkYCVH3RvFxJRDDjTNBQy1tvDdmwEQ2GZqSxcms1L6/f761YFY4B5Zp5JzIsI42jnvaAyYmXrd7rLWmu3jQx417gJOAFrBApgIrAzRVFUZR4YX/tUX7xwjYqa5vZtK+OqblZ3vQPednDaGg5xoz8EQwfkh5aJVw3N98c/QEoAw5/HjfXAlcDU7HCDXYCGGPWiUg9MDEZjTaQHFVfFs7MwdPWgaetg+c//JgX11YwJXc4AG9vr6bZ09Ftnx37j/Cli6fwh1d38Zn5k7y5barqWskamk5lbTO52UOZM3l0j3lpnJAsoEtOnF7z5JNw991970eJCAlQ9UXzciURIeXB8seTT7LstCuDGjaCeePsOnCEu58s4b4bipkyYUS3fX37cXLaOAaXxbPzeHn9/pDCnNxt/ZUaD+Sl487J48jvr894K/mdiIjIicAngBpghTGmDcAYcxy4RUT+CvwbkIFVIlxRFEWJYxpajnmNNllD09lcXs/m8vou6R/cXr9Ajykguhh3nn4afvjD6A9EGVD4M9xcBPy7Meb3frYNBl4WkR8bYzRVdhzihEG5YzQPNXgAvEab6flZdBw37KpsZMzwDBqa29hYVktlbTPlh47y1Uuns6JkPzsrG9hcXu813jz25l5vqJWbc6aNY091E+dMG8c508YBeHPi9JmMjMj0owwUNC+XAhkZfg0bDj1549z9ZIl32/JvLAh6qEDlwB1Pm2D7ORWfXtlYyds7DnLfDcVemcOtQuWvT39jU8JDRM4AVtFZqe6wiFxnjFnptDHGvIxlNI4pZWVlLF26lAULFjhGdkVRXCSA13DcUlZZmbBRCf5YuaXK+2D6SxdPYWOZ9dDZ7eHrz+t34cycgN43XcK7h+n8ZaDRH/rFn+EmM4DRBmPMVhGZA/xTRN4zxvwrmsIpvcNfUmKAoRmpnDh2GLdfOIV3dhxkV2Ujhxo9vLiugotn5VI8MdvrVXPt2RNpaDnG71bspKS0ltzsod0yqjus3XOYktJaZuSPYMkZBdy5aCort1QxfEh6SO6EQd0PL7ssMidFGRBoXi4FgMsuCxqa1FOY0X03FHOsvYOpuVkcamztYjjxNaiEWg7cF6etkwfnlY2VnDttnFfmB17aFjBMKpQ+z502TkOoIsP9dK1UNwbrAdYpxpi9MZLJL4WFhSxdujTWYihK3JIAXsNxS2FuLksXL461GBHDN/XD1NzuD1t8vX6dZXdaCvf2Loae6aEXNlCSg/7QLz1VleqGMWaTiHwa+Baghps4xK1o7lw0lb+9V8bGfXWcemI2r26o5J0dB/G0HWfKhOEcavRQ23SM9XsOc6jRQ372ULKGpjNn8mje2XGQ/NFDmTw+k3OmjWPtnsN+c034WqTDrSgVtP1TT4HeiCpBGMh5uZQA9KA3gnnjAEyZMIILZk7gB09vYGxWVwOQbzhTsETGwbxm3IYlf5WffMuZh4K7z548fsKVdwAzC2gA3gaGA3OxQqK+hVXBTlEURRlABMq/18XQ8/TTkASeSUp84c9wkyUiucaYykA7GWPeFZHcKMqlRIisIYMYPjidqrpWzpiUwo3nTcTT1sGL67rmTzzUaIVT7a5uYHtFA5v21bG5vB6Aq+cWMHxIepf2vl4yAS3OIRC0/bx5IfWhDGgSIi9XIic/j3e6ee31oDdCSRQcyHASjkHF18gTjjyxqAYVqrw90VsDUJyGMQwHphpjygBEZALwFHBGLIVSFEVRek+4D5ndhJR/b+7c3oqmKAHxZ7gpAf4sIlcaY4752e6giT7jHGcy42k/DkBGegpLzihgf+1R3t55kKq6VrIzB5GSIkwen8nu6iaq61sBaD9+nOn5WWyvaGBrRb033w1YCs5ReNsqjnTLe9OTQvOdZAVt39QUmZOhJDMJkZcrGZKfxyvdbsCC6I1QE/gGMpyEY1DpjddMLImUvL01AMVpGMNux2gDYIw5YHsdfxg7kRRFUZS+EO5D5rDR+YsSBVL8rHsKWASsF5FPi0i3O1oR+QRquIl7nMnMh3sOcfnp+ZwzbRzPvFvKI2/s9laMqm06xqEGDyVldRxusMKmMtJT2F7RQFpKCkUFI9leYRltnMpSDS3H8LR1UFQw0pv3JhgNLcd4/sNyGlqOdZGrp/0A2Lw5EqdCSW6C5uUC5gC323pLSUIWzszhxvMmdt6ABdEbjlHh7idL+MHTG1i2OjppSuIp7OhQYysPvLSNQ42tQbc5Rqm+ynvT+ZP47pUzOepp93vMBGOiiPxTRH4mIjeJyCygFjjkbiQifnWQoiiKEn84D41DKu3dG7ZsiU6/yoDGn8fN08DXgdOBJ4BjIrIe2I4V530icClWlQUljlk4M4eVW6uprG1mXWktnvYOXt14AICcUYOpqmtlen4WdUePUVVn3Vynp4Kn7Tg5owazubye6flZzMjPwgDbKxp47M29TB6fyfI15RQVjOTy0/PxtHXQ0HIsoPLzfRoelpX79tv7fiKUAY3m5Up+unntBdEbkUjgG4pRprdeJ9Ew+Dz42i5+9twWjnrau+XjiVR4lJsxwwczLCONHzy9oUulrQRlMPBJ++VwHGgTkT8Da4D3gPNjIJuiKIoSJYIWT+mJ226LjlDKgKabx40x5jjwb1i5IgQrCd9c4LPAV4ErgXTgwX6Tsp9wclCsSmAvD7d3S9aQQXzrihnkjBpMZW0z6+3qUgBnTB7DjedN5BuXn8K9ny7m4tMmkDkkjTarYjinFoyieGI22ysa2FbRwEk5WRRPzPZWqCqemM3m8noqaptZvqY8qPeM79PwsKzcDz3U+5OhRJw4zUGR1VPOLWPMu0BIeblE5CQR+ZOIbBWR4yJiRKQ9QNvbRGSjiLSKSI2ILBORE3oxBiWSBNEbjlfJlAkjeu1d4hg7gnnr3HT+JH5y3aywDUOh9B1JeitnrPqNEeLzSsW6N/os1r3QJmB6SB2J3CIi/xCRUhFpFpFqEfmXiCz001b1i6IkKdHQBSJSYG+rEZEWex+/FgTVLz0TVoSALw8/HHmBlAGP36pSxphyEZkN/A9wPdZNincz8F/GmH/0g3z9SjLkoFhRsp/la8rxtHVw7dkTycsexrlTx7F8TTkjhqRzqMHD5PGZZKSlsHBmDo0tbTz25l6OetpoamknPU1oazdkDUnn2rML+fVL29lcXk9GWoq3zPfCmTkcqGuh+kgri2blMnl8ZlCvm5CSeAVi9Og+nhElksRpDopI5+WaCXy+p0Yi8n3gJ65VGcCNwHwROSMeEiIPWKKsN0LJBdPb5MLRyItzx0VTGJaR5rfPaCVBjkVy5SixFbgcKLZfs+z3PJ92JsT+7sZKpO4wBLgAuEBErjfG/BVUvyjKACCiukBEcoB36aqbTsW6P8oxxvzUWan6JTT6lAdH5y9KFPCX4wYAY8xhY8wtQD7Wj/n7WCFUpxlj7u4n+ZQwcRIRO+8Ai4rzuHxOPvUtbQAcaWlj+ZpyVpTs57E391JSWkv9UWu+29ZuKCoYyaLiPLKGDOLaswvJGTWYhtZ2Gu39AZ59v5zK2mZWbKgkIz21m9eNb16bntYHxDIUKEowIp2Xaz9wH9Zk7QN/DUTkROCH9sf3gQnAzfbnfGBpqMIrUSDKeiNSuWD6q+9oyjsA+A9jTJkx5h/GmB8aY64wxpwAjAMuAb6LFWLeHGJ/9Vj3U4VAFpaucfgBqH5RlAFCPZHVBffQabS52W77vv35RyJS0Is+BzR9yoNzvkbPKpEnoOHGwRhTbYx5yhhznzHmAWOMZluKYzLSUrq8O/GZZQebONxglfw+ZL83trbR2tbOlNzhzCrMZnp+FgBTc7O8SuqZd8uoqmvl1Q2VPPLGbp54q5QVJfu5Zf4kiidmc8v8SUzLG0Fu9lCm5Y3wyuG4F/5uxc4uRpqw3Q7//ve+nRBlIPA0sA6YgZWXq05E3hGRP4vIr0Tk78BLdpseMcZ8aIz5njHmn0BLgGZXY4WMAvzKGFNljFmGlQsM4NMi0qN+VaJEmHojWPLeULYryYsxZlWA9YeMMa8ZY35ujLme0D0RLzTG/NQYs88Y04g1cWuwt51kv6t+UZTkJ2K6wNYH19nrthtjltleM7+y16XbfYXcZyQHOiD5R9IFpihxgP4wk4xFxXnceN5EFhVbRvcVJft54q1SJowawtgRGQBMHp/J9PwsSkpr2V7RQFOrlbQ4LSWFq+cWcM60cTz/YTk7K49QfcSat87Iz6JwbKb3OHnZw7jrqiLysod5vW+eebfM602zcGaONyfOyi1VXk+bOZNHd63+0hPqcaP0QIzycs12Le/yszwCmBjB4ynhEKbe6CmvjO/2eDXk+JMrXmVNQr4QSiNjjG+N2EF0hqPvt99VvyhKkhNhXTDZXg7UDqwQz3D6VPqCetwoUcBvjhslcQmUTyZrSDrzp49n+ZpyGlvbOFhted1kpKdwzdwCVm8/SElpLaeeOIp3dhxk+ZpyckYN5uARD7nZQ/n65adY/QxN9xpdHG+eT51VQHvHcTxtHTzxVqk3v447J45vZamQqazs4xlRBgIxyMs1xrXcEGB5HLAngsdUQiVMvdFTXhnf7b6VmA41tvLga9Y97x0XTYlZSJK/ClHRqBqldMcYs6aXu34TGGYvO9ksVb8oysCjL7qAMNqpfukPDhyItQRKEqKGmyTH8bwBOGfaOPZUN3krQ2UOSaOppZ3V2w9yy3xrQjJn8mje2XEQsCpLTRjp8W5zl8RraDnG71bspKS0lhvPm8jU3CyWrykHrHLi0NWIFCzBV9Bye7t2dWuvKP4wxhwGbhGRb2El+JuIlXfi9SiEeEoI67skK3Wq1gEsKCpiQVFRhEVSvISpN3pKpOu73Z8h52fPWV+xUMtfh1L2O9zS4P4MUItn5/H2joMsnu2bS7fv8kViH7Cq1dkV6yD+qtZFDRG5Bfix/XEl8HNnU6BdXMvdkiGXlZWxdOlSwEokv0A9VhUFiH8dEwFdECiCwp/O6J1+qazUe5hw+OijWEug9BP9qV/UcJPkZA0ZREZ6qtfbZfL4TPKzh5KRnsJphdk8+345i2bl8osXtlFZ28yM/BEsKs4jIz21iyHl+Q/LeeKtUrZVHOGW+ZO8SY2LJ2azcGYOK0r2e49ZVtPUrcJUsMpSQb1xbr89kqdDGQAYY6qxEhZHkxrXcpZreXiANklRtS5hiLLe8GfIOepp9y6HQiieMOF6y/gzQL28fj+vbKzk3GnjmHLpiAB79v3Yvd0HuhoZ7rnnnrKQd0xgROQzwCNYE663gSXGGKcCQNj6BaCwsNBruFEUpZN41jER0gUpIbYLp88uFObmsnTxYn9DUPxxm98q7EoS0p/6RQ03Lpwn4slmSXa8XDxtHSxfU86N5030GkjuuqqI+/+xmcraZnKzhzJn8uhu3i/7a4+yaV+dNy8OQElpLUUFI5k83sp743j27KxsYHN5PSu3VIUcEhW03N5DD4HeiMYNtkW5MLZSxAXrAccKMwXY4FoGOAKU9rNMikM/640xwwfzvU+dGtY+oZT9drfprTdLb8uL92a/aJQyT0ZE5FasUIgU4A3gCmPMUVcT1S+KMgCIsC44gpWfxtmGz3JJL/pUesvDD8O998ZaCiXJ0OTELpwn4slgtHGX3Xa8XZzExXMmj/Zua2g5xshh6WQOSeMTReP50TMbu1WDeuSN3Wwurwfg6rkFjB0xmKKCkRSOzfSWAc8aMohFxXlMzc3i6rkFoScfpodyexMmROJ0KBHCtiiXxVaK6CIi6SIyRkTG0Fl5AWediGQAfwOcJ2L/KSI5InIjMN1e91c7abISC2KoN0JNBhxKiW53m54SKPflOJHaT8uO94yIfJbOidoK4FKfiRqoflGUpCeSusDWB0/b66aLyI0iMh74ur2uze4r5D4jNMyBS07o8yBFCZWoGm5E5CQR+ZOIbBWR4yJiRKQ9QNvbRGSjiLSKSI2ILBORE/y0K7C31YhIi72PX3+0UPtMRpxqUu4QJsdAsnbPYW9JbutVTVNLO0++XUpDcxsZ6SldqkG1H7f09wmjh7GnuolXN1SyubyejPSULhWiVm6pYvmacjLSU/0bYXxwG5cCMmdO306EooTPOVhuwjXA2fa6VNe6640x5XTGo88FDgDL7M/7gaX9Jazihxjqjd4aWALhGIIWz87jJ9fNipk3i1anihg/ovPeaxHQYt8bOa9C1S+KMiCItC74EZ3VqJYBVcBZ9ud7jDEfg1XMIYw+ld5y+umxlkBJQqIdKjUT+HxPjUTk+8BPXKsygBuB+SJyhjGmym6XA7wLuLMsngr8WURyjDE/DbfPgYSTBHha3giKJ2YzZ/Johg9Jp6G5jQ/3HqKqrpWhGakMTk9l/ozR3mpQ2ysaKCoYyceHj7K9ooHp+Vmckj+SRcV5XgNNQ8sxPG0dYXnbhFRp6sUXVfkpcYkx5l4ROQB8BZgKNAGvAHcNRP0SV8RQb0Q6XCheqkLFixwDBdUviqJA6LrAGFMlImcD9wOXAJnATuA3xpiHe9On0gdeegnmzYu1FEqSEW3DzX7gPuA94AfAmb4NRORE4If2x/eBK4ELgceBfCzL7xft7ffQabS5GXgdeA7LovwjEXncLgscTp9JiTvBsINjKCmemE1JaS0z8kew5IwClpx5AhnpKXjajvOvzQeobTrGutJarj27EE9bB5efnk9ZTRPbK6xKgWkpKV2MNk7fTv6cULxtoIfcNg4XX9yL0StK7zHGrCJw1QXftg/TWbZTiRdiqDd6qlAVLsEMQb3NexNpOZTQMcYUhtFW9YuiJCnR0AW2N82NkexT6SUXXhhrCZQkJKqhUsaYD40x3zPG/BNoCdDsajrzSPzKGFNljFkGbLfXfVpEUkQkBbjOXrfdGLPMtgr/yl6XbvcVcp99H2H84q7i5IQjLZyZw43nTeSW+ZP8hjh9uPcQzcc6AGjxtLOiZD/L15RTUdvM5vJ6igpGUlQwks3l9fz6pe08826pN8zJ6dudPydUGYMaerQcuKIo4ZJEeiNY3phIh2X1Vg5FURRFUVxoOXAlCsRDVanZruVdPsvTsTKkT8QyMo0I0M6hOMw+9/Ra6gTBNxxpyRkF3rCmFSX7WVScx8KZOWzaV+dNQDwoXWj2WAYcxxgzI3+E19DzuxU7KSmttfPcpHqNL0vOKPCWDXeO12fKyvreh6IoA4sE1xuhetKoF4yiKIqixCH79sVaAiUJiQfDzRjXckOA5XE++/TULtQ+k95w4w5HcnLcOGXBAa/hZWpuFpvL65mRn0V2ZgYb9tVx8oQsiieOBiAve5i3zzsXTfUmPfYNcwop/Ckcbr89Mv0oijJwSHC9EWo+mUiHZSmKoiiKEgFu81s3R1H6RDyECwXKJeFeb8JoF06fXSg7eJClTz7J0iefZNXmzQG6SCzc4Uhu75ur5xZ0SSR8zrRxFE/MZvL4LN7eUUNTSzt/eTOw+31Geqo3z41v6XEnqXEo4VI9VpZ66KHwB61EnFWrVrF06VKWLl0KUBhbaRITR78ki26JaxJcb9x0/qSYVpCKFatWrQLVL4qiKEqi87CmD1IiTzx43NS4lrNcy8N92qSE2C6cPrtQOG4cS2+4IaiwiYzbG8Y3r8zaPYcpKa1l8vhMLpk1gY376vjSxVP89uMbftXT52D02LawMOTxKdFjwYIFLFiwAIB77rmnLKbCJCjJrl/iigTXG/3hSdOfiY1DxdYxZbGVQlEURVH6yIknxloCJQmJB4+b9a7lKX6WjwClWGFNR4K0AygJs88Bhb+ExdC1lPei4jxuu2AKv/nsWUzNHdHNI6ah5RgNzW0UFYxkzmQrjGrO5NEUFYykobmtSxLkUMKlemw7xb/xSFEUJSCqN3qkPxMbK/1DWVkZS5cudTyXFEXxQb36ek9ZZaV6DYfDySfHWgKln+kP/RJVjxsRSaczoXC6a72Tg6YR+Btwv739P0VkNfAJrCTCAH81xhy393sauB2YLiI3YpUD/7rdrs3ui3D6HIg4Xi6b9tUxNTcLT9txXlxXQVHBSBYV53Vp61SW2rSvjq9eOp2VW6p4cV0FYHnp5GUPY+2ew2wur2dzeT1ZQ9O7JEF+/sPyLtWrfJfdxiR/NL/4Mg/VjYyrp8KKosQ5r74KZ58dayniGk1snHwUFhY64ayKovhBvfp6T2FuLksXL461GInD66+D7amuDAz6Q79EO1TqHGClz7pUOsOUPmuMeVREfgz8BJgLHHC13Q8sdX3+EXApkAcs8+n3HmPMxwDGmPIw+kxanGTE7tAox7vGKevtlPkG2Fxez4qS/Vx79kRvH562495tTl+eNqvilJPw2NPWwSWzJlBZ2+L1woGuYVCA3+WeQqmeyzktpCSdiqIoXi6/PNYSxD2a2FhRFEVRosSll8ZaAiUJiYccNxhj7hWRA8BXgKlAE/AKcJcxpsrVrkpEzsbyprkEyAR2Ar8xxjzcmz6TGX/5Y1ZuqWL5mnKunmtVkgIrMfFDr+9ie0WD11DjkJFuRdNNz8/yGmzchp3nPyxn+Zpyiidms7m83uuFA/4rTAVaDsQVVHPwugv0qbCiKKGzdi2cfnqspVAURVEUZSCybh3MmxdrKZQkI6qGG2PMKgJXePJt+zDQYwpuY0w5cGMk+0xWghlOfBMUn5I/ku0VDWSkp3g9dRzvmavnFtDQ2s7yNeU0tLYzdniGd3+nvzmTRzMjf0SXY/mGQQVaDkZm3SF9KqwoSngcONBzG0VRFEVRlGhQNSB8BJR+Ji48bpTo4C9/jL91TuJhJzmx46mzreIIJaW13HjeRA5UNgCwaV8tVXWtbKs4wp2Lpnbpb/iQ9G6hWX3m9tsj04+iKAMH1RuKoiiKosSK226LtQRKEhIPVaXihrKDBwdkxnQnfMpZnjN5NDeeN5Fb5k/yVnz63AUnUTwxmzsvmUbxxGxKSmtZuaWqWz9PvFXKyi1VNLQc45l3S3nm3VKvYSgYvtWrvDz0EIcaW3ngpW0camyN2JiV3qEVGZSE4KGHYi2BoiiKoigDlYcHbMCHEkXU48ZF4bhxLL3hhliL0a+4S4FD96TBednDaGg5xto9h7ll/iTvuxMW5U6A7A7DchuDMtJTmTN5NI+9uZdb5k/y9un2zvGXjweAKVO8ZWtBExTHGq3IoCQEWg5cURRFUZRYoeXAlSighpsBjmNgcTxrMtJTuyUN9nrSbK2msrYZ6DSuPP9heReDi7Pet/rU71bspKS0FoC7rirqZqjxl48HgNxcbjpdy9YqiY/j0begqIgFRUWxFie5yc2NtQRKL1CPPkVRFCUpmDAh1hIoSYgabgY4joHFMbL4Sxq8cGaON99N8cTsgMmOfXGMQFlDBnHLfMvo4rz77ucv9w4Aq1YxZsEC9bRREp6B6NEXM1atAss7TEkg1KNPURRFSQpWr4aLL461FEqSoYabAU7WkEFkpKfyxFulZKSn+jWeZA0ZxJ2LpnpDm8DytHGMMv72WVGyn+VryvG0dXDt2RPJyx7GXVcVdekzpMpS//ZvvR+coigDE9UbiqIoiqLEiquuirUEShKiyYkVFs7M8YZKBcIxtGQNGcSKkv088VYpK0r2d2kTMMFwX7Bc5xVFUUJngOsNTeiuKIqiKDFk9epYS6AkIepxo3TzfnESB8+ZPJq1ew6HXN7bnbdmUXGe33w5YXP4cN/2VxRl4DHA9YYmdFcURVGUGDLA70OU6KCGG6UbjgHGyWsDVu4bx6BzzrRxfo0ycyaPZlvFEeZMHu03FMq3klRI3H57RMakKMoAYoDrDSeRuyZ0H1iUlZWxdOlSFixY4OQLUhTFhSZA7z1llZVaYCEcbrst1hIo/Ux/6BcNlXLhVH1ZtXlzrEWJKU7o1C3zJ3UJoXIMOmv3HPaGTblZu+cwJaW1rN3j38rsrU61pSp0YR56KOAmDQfof/SmR0kIguiNgcCY4YP52qUzGDN8cKxFUfqRwsJCr+FGUZTuaAL03lOYm8vSG25Qo02oPPxwrCVQ+pn+0C/qceNCq75YuL1l8rKHedc7Bpw5k0d3SU7su91feFRDyzEaWtooKhjJnMmjQxcmyB+EhgP0P3rToyQEemOpKIqiKEqsmDkz1hIoSYgabpSQcQw6z39Y7s1l4w6HClYpauWWKl5cWwFYnjlug1BQMjMDbtJwAEVR/BJEbyiKoiiKokQVvQ9RooAabpSwCeRZEyyHzcKZOXjaOvzuF5T33oNLLvG7yQkHUBRF6UIQvaEoiqIoihJV1qyByy6LtRRKkqGGGyVsAnnWuKtK+W7PGjKIa8+eGP7Brr++VzIqSrzh5NDSxH79gOqNhERzaCmKoihJwXXXxVoCJQnR5MRKxHCSGgfKcfP8h+U0tBwLr9N//jNC0ilKbHFyaKnRph9QvZGQaA4tRVEUJSl4+eVYS6AkIWq4USKG44njr9R3rypKAXg8EZJOUZQBg+oNRVEURVFihd6HKFFAQ6WUfiFYxamgaJUvRVHCRfWGoiiKoiixQkOllCigHjcunBwUqzZvjrUoSUcwb5ygPPpoVORReofmoFASAtUbiqIoiqLEiscfj7UEShKiHjcunBwUShxx+umxlkBxoTkolIRA9YaiKIqiKLFi9uxYS6AkIepxoyiKoiiKoiiKoiiKEqeo4UaJb9ati7UEiqIkGqo3FEVRFEWJFevXx1oCJQlRw40S39x6a6wlUBQl0VC9oQxAysrKWLp0qZOLTFEUHzRPX+8pq6zUPKDhcPPNsZZA6Wf6Q7+o4UaJb558MtYSKIqSaKjeUAYghYWFLF261MlFpiiKD5qnr/cU5uay9IYbWFBUFGtREoOnn461BEo/0x/6RQ03SnyTkRFrCRQlImjVun5E9UZCok/DFUVRlKRA70OUKKBVpZT45rLLYi2BokQErVrXj6jeSEj0abiiKIqSFCxeHGsJlCREPW5c6BPxOOSpp2ItgeJCn4grCYHqDUVRFEVRYoWGSilRQD1uXOgT8Thk3rxYS6C40CfiSkKgekNRFEVRlFgxd26sJVCSEPW4UeKbpqZYS6AoSqKhekNRFEVRlFih9yFKFEh6w42IFIjIMhGpEZEWEdkoIrfFWi4lRDRsTYljVL/EKao3lCRBdYyiKNFC9UsU2bIl1hIoSUhSh0qJSA7wLpDnWn0q8GcRyTHG/DQ2kikhc/vtsZZAUfyi+iWOUb2hJAGqYxRFiRaqX6LMbWr/UiJPsnvc3EOnQroZmAC8b3/+kYgUxEQqJXQeeijWEihKIFS/xCuqN5TkQHWMoijRQvVLNHn44VhLoCQhSWu4EZEU4Dr743ZjzDJjTBXwK3tdOnC1e5+ygwf7UcLokUxVsT6qrY21CBHBrsaULBTGWoBYM5D1CySAjhk9OqRmyfS7TKKxFMZagHigVzqmrKz/BExSkuh3FBP0/CUGvdIvlZX9KGF06Zd7mBDvQ/pCMv3ekmgshdHsPGkNN8BkYIS9vMu13r1c7N6hrLo62jL1C3E/qQqD19raYi1CREgihQQ6sYIBrF8gAXSMVf2sR5Lpd5lEYymMtQBxQvg6Rg03fSaJfkcxQc9fwhC+ftm/P9oy9Rv9cg9z/vlRP0Qy/d6SaCyF0ew8mQ03Y1zLDQGWx/Wm41B/8LFqFyrh9BersczYvj20/kL8wceqXahE47ixGkuSEzX9AgNPx0S6XdkvfxlSu1CJ5e8t3nWM6peoEVUdEwqx+u7Fqr9oEO9jHojnEDUOQ2/0S3o6FBSE9FpVXR3X7RgxIvrHff75bic9Wf73o9VnLI6baPcwYoyJtQxRQUTOBt6xPz5ujLnFXj8Z2G2vf8UYs8i1z3uAx/5YZr/8URhkWzK2i+WxtV3f2kWqz0I6b3YyjDHzQjx2UhJl/QLx/73SdvF37ERuV4jqly70g44JhcII9JFI/UWjT+0vPvosRHWMF72H0XZ9bBfLY8dju0L6Sb8kc1WpGtdylmt5eIA2DHRFrihKyKh+URQlmqiOURQlWqh+UZQEJJlDpfYAR+zlKa717uWS/hNHUZQkQvWLoijRRHWMoijRQvWLoiQgSWu4McYcB562P04XkRtFZDzwdXtdG/C3mAinKEpCo/pFUZRoojpGUZRoofpFURKTpM1xAyAiOcBaIM/P5u8bY37azyIpipIkqH5RFCWaqI5RFCVaqH5RlMQjaT1uAIwxVcDZwJPAYaykWpuAz6tCUhSlL6h+URQlmqiOURQlWqh+UZTEI6k9bhRFURRFURRFURRFURKZpPa4URRFURRFURRFURRFSWTUcKMoiqIoiqIoiqIoihKnqOFGURRFURRFURRFURQlTlHDjaIoiqIoiqIoiqIoSpyihhtFURRFURRFURRFUZQ4RQ03iqIoiqIoiqIoiqIocYoabhRFURRFURRFURRFUeIUNdwoiqIoiqIoiqIoiqLEKUlvuBGRAhFZJiI1ItIiIhtF5LYQ980Wkd+LSKWIeERkh4h8S0RSoy13AHl6NRYRWSAiJsBrQz+I7pblJBH5k4hsFZHjtgztYewfN9ekL2OJs2tyi4j8Q0RKRaRZRKpF5F8isjDE/ePmmsQjfdFB/U2o3wURKQvy/Z0VI/G7ICK3BpHxOZ+2t9nXpdW+TstE5IQYid6FIGNwXgtcbePmuoSjH0M9/4n0W+ovBvo5Cef/Kxrfs3jWHb1BRC700Rvn+mzXczjAiFcdE43/+P4Ya6z/GyP1ewt1HBLGfUmMxpFY/yHGmKR9ATlABWD8vL7Xw75DgE0B9v1Tgo1lQYD9DLChn8dxpR8Z2kPcN96uSV/GEk/XZEcQWT6dSNck3l59+d3GSN6QvgtAWZB2s2I9DlvGW4PI+Jyr3fcDtPkYyImDcQQag/OaE4/XJVT9GOr5T7TfUj+d4wF/TsLQWRH/nsW77ujFuUwHtvuM5Vw9hwP3Fc86hgj/x/fXWInhf2Mkf29hjKMsyHWaFQfjSKj/kJgrhWi+gAddJ+Em+2SusT8fAwqC7HuXa9/vAmOAZ13r5iXQWBY4+8bBNTkD+ClwGfB+oB96glyTvowlnq7JGuB7wInAcHtMzjndmkjXJN5effndxvN3gc4/4ltjLXOQsdxqy1gWpM2J9nUw9thz7OvkjPmPsR6HH5nTgAO2fDsBicfrEop+DOf8J9pvqZ/O8YA/J6HorGh8zxJRd4RwLr9ty37UNY5z9RwO3Fc86xgi/B/fX2MlRv+Nkf69hTIOu10ZIdyXxHAcCfUfEnOlEMUfdApQbw9+m2v9ta6T8vUg+2+22zQAqfa6M137/iaBxrLAaRfr6+Ij16pAP/R4vyYRGEvcXBMg0+ezAEds+TyJek1i/err7zaevwvEkYEgyFhupeebum+4rsW1rvXb7HX1QEqsx+Ij89WBvj/xel0C6cdQz38i/pb64ZzqOTGh6axofM8SUXf0cB5zgUagGnjANTbHcKPncIC94l3HEMH/+FiNlX78b4zm7y3QOOxtZfRwXxLLcZBg/yHJnONmMjDCXt7lWu9eLva3o4hkADPsj3uNMR2h7hslej0WX+zYvWN2zOGvRWRkhGSMKnF4TSJGrK+JMabJZ9UgwMlPsz/Qfsl8TSJExH63/UUvvgv/bX9360TkZRGZF10Je0WuiBy25dwlIj+2v7sAs13t/F2jEcDEfpEydO6w31uBRwO0SYTrAqGf/4T7LfUDek4IWWdF43uWiLojGL8EMoHvYE0YfNFzOPBIFB0Tif/4eBtrMv7egt2XxGwcifYfksyGmzGu5YYAy+MC7JtN57kJd99o0Jex+DIOK475ROArwJsuBRfPxNs1iSTxdk2+CQyzlx8O0i6Zr0kkiOTvNlb09F0YjfXdHQl8Euu7e37/iBYy6Vjf1XTgZOAHwPP2toS6RiJyEvAJ++MzxpjaAE0T4bpA6Oc/oa5TP6HnxD/+dFY0vmdJc/5FZD7waeBd4C8Bmuk5HHgkyvWJxH98vI01GX9vwe5L4mkccf0fksyGGwlhvYnCvtGgr/JUYxkETgaGYsUl7rS3nQpc31cB+4F4uyZ9JS6viYjcAvzY/rgS+Hmw5iGsT6RrEmkS+vz08F34I3AO1hOBHKyYXrD+lH9MfPARcBtQiPUbW4j1uwO4RKxqTIl2je6gU7Y/+tmeCNfFTajnP9GuU3+g58SHIDorGt+zpDj/IpIG/BboAO40tq++v6YhrB+Q5zCJiffrE8n/+HgbazL93kK5L4mLcSTCf0gyG25qXMtZruXhAdq4OQwc7+W+0aAvY8EYs90Y8/+MMbuNMS3GmLXAPa4mZ0RIzmgSb9ekT8TjNRGRzwD/i6UX3gaWGGPaguySVNckCvTpdxtLevouGGN+Zox51xjTYIypBv4daLY3x4U+Mca8Y4x5xBizz/6NrQJ+7WpyBgl0jURkEFZMP8AmY8x7vm0S4br4EOr5T5jr1I/oOXHRg86KxvcsWc7/lcBM4P8A7PK8Oa7tJ9mefnoOBx5xfX0i/B8fb2NNmt9biPclMR9HovyHJLPhZg9WciGAKa717uUSfzsaYzxYyYEAJomIE+vW475RotdjARARf9fZbcU77md7XBGH16RPxNs1EZFbgUewdMIbwCJjTGOwfZLtmkSBPv1uY0VP34Ug313n+xsX+iTE39h612d/1+gIUBph0XrL1XS61v7Bd2OiXBcfQj3/CflbijJ6TmxC+P+KxvcskXRHMDLt98uwxlZCZx4tsCYyf0bP4UAkrnVMhP/j422sSfF7C+O+JKbjSKj/kN5kj06UF11Lct0IjKdrSa4T7HZOm0dd+/ZU5nhuAo3lEaxa8ScDGcDpdK1bf30/jiPdPpdjgHfs47e71mUk0DXpy1ji6Zp8FstN2mA9dRscoF3cX5N4e4X6u42XVyjfBeAK4GmsymhD7TH90TXOV2I9DlvOf2KFIxYAg215q1xyzrO3OeUY38N60nyjq03clKMF3rRlagSGx/t1CUU/hnP+E+231E/neMCfkxB1VsS/Z4mkO3o4f7e6ZA70WqXncGC+4lnHEOH/+P4aKzH6b4z07y3EcYR8XxLDcSTUf0jMlUKUf9Q5QIXrJLhf33O1c9Y96lo3BNgUYN8/JdhYnguwn8GaDKT24zgWBJHFYJeLS5Br0pexxNM1KethHIWJck3i7RXq7zZeXqF8F7Bc6wNtbwKKYz0Oeywbgsj5hKvd9wO0qQByYj0OW8bpLrn8/5nH2XUJQz+GdP4T7bfUT+d4wJ+TMP6/Iv49SwTd0ctzutQ1lnPDHa+ew+R5xbOOIcL/8f01VmL43xjJ31so4yCM+5IYjqOsh3EUxtP1iLlS6IcfdgHwBHAIq3zqRuA2nzbOiXrUZ/1o4PfAAcCDlTz2W/TjpDoSYwHOx/Lw2In1tLYV2GJ/Yfx6V0RxDD3+0BPlmvRlLHF2TUJVWnF/TeLxFcrvNl5eoXwXsJ4eLMV6gnAQaAMq7TFOjfUYXGO5AvgrsBsrnvoosBa4E0jxaXubfV1a7ev0BFAQ6zG45HvAdQ1mBWgTV9clVP0YzvlPpN9SP57nAX1OQv3/itb3LN51Ry/P6VLX+TvXZ5uewwH2ilcdQxT+4/tjrLH+b4zU7y2UcRDmfUmMxlHWwzgK4+l6iL2ToiiKoiiKoiiKoiiKEmckc3JiRVEURVEURVEURVGUhEYNN4qiKIqiKIqiKIqiKHGKGm4URVEURVEURVEURVHiFDXcKIqiKFFBRBaKyBYRMfarxf5cLSLtIlIhIg+KyMhYy+qLiBS65D5oyz28F/2UichOe/8tItLm6neL62Xs9u/a58VpsyDSY1MURVEURVESiwFtuBGRxSKyzXWDfLefNg+JyAERqbVvrsf2o3z32Mc1InI8Hic3/rAna6+JSI2IbLcnIV/w0+4CEakUkX/FQs5wEJGLRWSp/RrUneyeMQAAEHtJREFUT8fsceIoIte6JnllQfq6zG7zbxGSTSeXSo8YY1YCl7tWrTTGzDTGjMeq/JAH3A78MhbyhcHvbbkbe7n/Jfb+M4HDzkpnnb3eWXc2VnU5RVEihIiMF5Fdrv+sNhHZKCIZrjbXi0ipiDSLyD9iKa+iKImB6halPxnQhhtjzMvAda5VPxSR6T5tbgeeBP7bvsGu6Uf5fgS8b3/caoyp769j9xYRmQW8ClwI3GOMmQ78HKtEtC8/ACYAZ4hIar8J2TvuBH4E3GGMORaD43eZOIpIpoi8bss1JIT9l2CV6n4lEsLo5FIJg2LX8lbX8nOu5Sn9I0pMWA40hdDuL9EWRFEGKsaYauBUoN21+ixjjMfV5ingdeDbxpir+llERVESENUtSn8yoA03Nu5JRQbwsIj4npdioKT/ROrC6fb7uzE6frh8FkjDUmCPAhhjfmOMWe2n7RPAh8BnjDEd/SZh7zjNft8QSyFcDAEeMcbMB4J6AYiIYHk9/MsYE8oEUlEiSSDDjXv9mn6SJSLY3pAHXU/YCkXkFPuJmrPuVgBjzDeNMYd66tMYc2u05VaUgYwxphXYbH9Mo6sOQkTOxLrn+kM/i6YoSgKjukXpL9RwY/24HgNa7c/zgP/waXMatuFGLD6wQ5eMiLzmNBKRv9jrmhzjj4g8LyKt9vq9IvJjEXlTrFwPa0SkwA7Zetl2oVvreP2ISAHghGalisizInLYnjD83NfAJCInisifRWSfiOyRzvwRo+3tq12ybBWRr4vISluWZ4KdJNsV8AER+cgeR52I/EtEznG1eQf4kv2xFVgjIo/76WuyiOwC/gScAcx2bXOfr10i8k0Redtet15EZtjtfmTLbUTkTdf+qfZ+a1zr+nJenrVlPdHubpZYIUu/CbX/vp57fxhjaowxT4bYfC4wHng+wHkO63upKGEyy7W8Fbyeeb+2130A/LR/Reobtjfk733WbcUyXCuKEr+871o+w1mw76d+C3wlAR4kKYoSf6huUaKOGm6sScXTwFLXup+KyESwJuXAMWNMFYAxxgCXAGK33ejTF8BmY8xxu/0SoMVePxR4yN5/MHAWlht9KXCZ3e504Dt2e8fbBqDdGPMp4HNYxpxv4ZokiMgUYD1wG/CfxpjJQCVW/ohltiznu2SZDOw0xizEUiiOpbgbIlKIZbj6KvBrY8wk4C7gAuB1ezvAfOC4vfxXO7TnZt/+jDF7sEJ8HDa6trnP10isazMfqMMysv3QbncP4BjNikVE7PUdwJ+d8UTgvJT4yHqnPa6vhNp/CMcIeO4jxBLAAC+65OnL91JRwsF58mSwPBprsH5X+cCDwIU9hYFKDzmcFEVRQuQD1/KZruUvANuNMW/3szyKoiQHCaNbRGSW7QTwmTD3u8N+mNsuIoOjJZ8SGDXcWMaWEuC/sSbgAMOwvEHAmnSs99nH7QK3AUCshLWOR4I3rMo2AI20P/7RGFNB15wkm4wx27EmNc56J6HVHPvdYOVXAdjr2neBa/lvQDZQZox51l7n/KhO8CPLU8aYl+zl7wM/IzDPYuWiqaTzKfPHrmMssZenuWTfEKQ/6PoU3mu48ZHxIWPMx7YxxhnLUNd+6+z34XTNkXE98Hd7ORLnxX29fUPmeuw/xGNEkyXAB8aYAwHkCfd7qSghIVYy9zz740ZjzKnGmLFYno0dwB3ADhGZECsZFUUZULgnV2cA2N6x3wG+7WwQkSEi8j8i8r7tHbtBRB6XXlSWcyMip4nI07b38mrbw/XHIjKiL/0qihJzYqJbROQKsaIxjFgFd06x1z9jrxtrf/5/trHmG8BMrLlLj2HcbowxD2Klwthjh4cp/cyANtzYk1ePMeaAbRy4jc7kUp8Qkc/TadhxM8u1vMF+PwVI91nn2/Yt+32Gn3Un0jlB/sh+dzxuttrJr8B6Su1wzB7HGViJsQBWiUiGiHwbKMKaHN3rR5a/OQvGGI8xpg0/iMgcOg0XrzmeREChq5lTZcndv9sTyR9O2yZgt5/1ACttGfLoNDLscG13G9ROt9v+G9Z1eDWC58UZf70xptTZHkb/oRwjKtgeQdNwhUn5kSfc76WihIrb6Om9qTHGrKHzO5aLXXlKREaLyAoR2SFWKOabIvJ/drscEVklIheJyP2uUL/v2eGHj4qVa2aV/dojIs+JFeZZIFZZbiMi2+xj/ZdYFQMviOB4pecmiqLEkO105oU7WaxqnfdjeRNXu9r9ATgPOMf2jr0EuBg42tsDi8hCLD04Dphje+JeC3wDyOxtv4qixAUx0S3GmBeAl52+jTFbRWQq4FSRdR4i/x/wHWPML40xy4Bs10PkkBArhcdwYEtvZFX6zoA23ODjTWOM2YDleePw38AiAhtuPHQaErp54fi0hU5jxmk9rHP2dww37lCaIteyk7D4HNe6C+0+L8UqsTvLGPNXP7K4YzGDca5reZ1r+XQ/650xGEI33Gy2w89810PneTjVz7pu8ohIFtY1+65tYIrUeXGu7Qaf9aH2H8oxooXjDRXMcBPu91JRQsWtF73fezvme6Zrm2O8vRnrJuYzxphPYHn73WVvqzLGLDDGvGaMuQuostdvsI/zIZbu+W9jzALgBqzv/w+NMeV239B5w7EauMsY80ZfB0mnN9rooK0URYkp9r3BWvujAF/GCmv4ndNGrCqX12P9rifZ+1UD5zkPr8TK1feciHwoVl67+8Riooj83fao+ZeI/FNExtpP0/+Olbj0c8aYI3a/5VgVOPfbBuan7D53iMgyEckSkVOlM/H5/9ptjopIdiA5+uVkKoriJVa6xe7aiYJwjDTfpPO+ynngfzPwuIgsEZFa4IiInGS/HP3ymO2p0yAiX7bluUas3KFvYhmiQA03MUMNN92NMvcAu+zlEVj5PnzbnGy/7zbGOB46zlPbDroaWmbZ75WuyiKOIaIN2GYvuyfIJWLl1hljf/7Yte0T9nsT1qTGly8aY6bZ1Ya+DVwsIrk+suw3xhz2s68/WlzL+8A76brYXrcVWOXT/16nbLU/xIqLnGp/3OCz2Z+MXc6Ns2CMqaRz8jYXK8n0emPMP/0ctlfnRUSG0nm9N9jrCqR7bGew/oMeI8oswfqebvNZ78gT1vcyKhIqycws1/IHAPZTqD8ABfb6f7iMJ04o6Ksi8jSWO+6GHo7xhjHmmDHmd0AtcLmIrKcz+fE0AGPMW1iG+qtE5ASsm6ewE4MHwDFCXR2h/hRFiR7ukIYfYyUNbfdpU4/1u94pIjtF5GdY4eKIFdb0OtY95NlY90B3Yf2H/gsrjP1c2/g8BGjG0g2jgPfdnrsAxpif2w+e/oX1JP584HvAjcDXjTGb7OOBFZZ9PVa+v44AcpzSu9OiKEofiYVuAZfhxp4/GjqrdeaLyMXAKmNMuzHmeaDa3nevMWY3nfrldSy9Mxy4QUTmAk9hhbrPx9I5oIabmDHQDTez8JmM2jF7n8f60gMcwUrS6sb5odhGUDmLzhv2ncYYt7Fjlv3u9kBxJsM7jDHHfNY54ThzXO0n2wc7m07Dze3GmDp7eQWdP6Zz7bZpwH3ARYCT28SfLD3xApaRCDonWndg5a2oAT7tCp8KtWR2EZAaQBZ/MjoGhWY6jWoOjtfN2Vjn7A7Xtkicl3F0/k5KRSQf6zuTHkb/PR0jKtiW+Hl097YJJE8o30tF6RERmSciW4ArXKufEZG9WDcYl2K57X4Wl7HDdvmdD/wFawLzoljhjwHx0bdfx0oMfhfwaXtdqmv7r7GeeN8P1BljmokM/ysiJXSG2gLcKyK3Ox9EZJF9TtzV5raIXTZcUZR+wz25+qsxZrV7ox06fw6WvtiJlUPvO1j/7WCFNxVihSeMxvr/r8GaVE0EnnGFRlxljDlKp/ey80Tel2uBk7AM2S1YuRahs6JlEVbxh9/YMv4yiBz6X60osSEWugWgwn7Pxwq9/AWdxpx84FbgfwFEJAPrgfQW1/zNSfHwLJ0eOrVYxVlS6Uzx4KTGUMNNjBiQhhsRucC+gb4Q+C8RcVcNcp7MPmh/3OATygPWk5AdWH+ya7AMCx5722T7SbHzZNkxdmy01wmdT2f9TZqHiMjzdA1FKhWRTVhVlNYBS4wxT7nk3QFciRUq8DUR2Qq8ATQAVxhjjI8smwKeHB/shLafsI/9E3vi9Q2sm4ciY8wWe1y5dJYu39BDt24vjrtFZIHdRyAZnfZpgK83jWO4acA6L95EWxE6Lx9jKbKjWNf9r1hhHI2h9N/DuPqEWHk8tmDlCAHItSeCTgjd5Vi/8ed99nPLE+73UlF6xBjznrGqrw0zxoj9mmGMmWSMGW6MyTfGLDbGPOq6cUBEvgYMN1bVtq/Zq/Ow9Gu6WEn97up2wE6y7feDQI6f7X/F8tK7EfsmJkKcaowpNsbc5BpvvjHmIaeBMWaFfU7SXW1mGmMejaAciqL0jBO22YhVobML9kOyWmPM14wx07DCNwGy7Pe59vsCrAqM/7TXOf+h3jBuY0yDveg8DHE+O8e6SESmYXl3u/d1qtKscf0/73Q9sAsoh2sypyhK/xIL3QKdRpoZwFBjzEd0GnOuAd51PZA9BcsYs8mWydEvu40xTXQ+LN9Ipx5y5gVFWLpMc17GiLRYCxALbLf8mT20+RLwpQDb1tBZQcrhT37a1eOTrNKezHfLHG6MOcnPoe52LXdTAD77/5PuRo2gsoSKMeYDOkOjAuH2EPqwh/7+jFWy23d9PX5kNMYEdPs1xvyIzopb/rb36bzY1vGAT/x76j+UY/QWY+XxCMYSrIzx77pXRuB7qSjRYg9wn4h8B+up0/NYIZAnA58D3gYeEJHvYRtmRORVY4yjnx7G8vJ5GPiHvW6WiNxujHnIGHNMRP6IZVj1rRboSweWOzHATSJyJVYcesAw0EggIm9hefo5xz4WpLmiKGFijNlP8P/kb2CFJThVNIdjeWE7D8wO2u/3GmOesCdjV9M5UUoHsD3uRhhjfoEVlvlF4EIRuccY02aHIfwPlmfsPnvfFjsUewlW6Ohfsbyuh9H9KXcgOX4e2plQFCWSxEi3QKfhZiRWnk33uky6zrkcw4yT1sPRL85nJ5fqBjoNS20iMhNr7rvZT/iX0k8MSMONEllsa+2t9scy7GpQSp/p68TxHSxXzY4eW4aJTi6VaGCMeRF40c+m/7Bfbn7qZ/9NWO7EDj9xFsSqHrUZK9yym+HYT18f499rJ6oYY87r72MqitKFDcC/i8jNWP9tGVhh4a/Y23+J9WT7ZyLyWaxJ1Tew8sNNBb5n/19vxgqDwBizUkSuxXog95GI7MR6sPJJY0yjiPwaK2zix1iJRd/FqgBzRDor3/kabgLJoShKfLKBCOsWAGPMYRFpBl4yxmy3VzuGm1+YrqW7HcPM50Xk967PvoabL2LpokexDM8fYhmlNEwqhkj3KCBFCQ8R+TuWFfafwP+zJzyKoihxg4h8Acv4Uw5c45MbR1EURVEURVHiFjXcKIqiKIqiKIqiKIqixCkDMjmxoiiKoiiKoiiKoihKIqCGG0VRFEVRFEVRFEVRlDhFDTeKoiiKoiiKoiiKoihxihpuFEVRFEVRFEVRFEVR4hQ13CiKoiiKoiiKoiiKosQparhRFEVRFEVRFEVRFEWJU/4/JfgMCcjgercAAAAASUVORK5CYII=\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 100\n", + " Kdiff : 26.2830%\n", + " Bstray : 51.3222\n", + "Accepted Soluntions : 2\n", + "Epoch: 110\n", + " Kdiff : 25.9717%\n", + " Bstray : 50.9896\n", + "Accepted Soluntions : 2\n", + "Epoch: 120\n", + " Kdiff : 23.3910%\n", + " Bstray : 51.8547\n", + "Accepted Soluntions : 6\n", + "Epoch: 130\n", + " Kdiff : 22.5816%\n", + " Bstray : 52.1134\n", + "Accepted Soluntions : 5\n", + "Epoch: 140\n", + " Kdiff : 24.2926%\n", + " Bstray : 51.6517\n", + "Accepted Soluntions : 2\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 150\n", + " Kdiff : 24.2922%\n", + " Bstray : 51.7053\n", + "Accepted Soluntions : 1\n", + "Epoch: 160\n", + " Kdiff : 25.5581%\n", + " Bstray : 51.3585\n", + "Accepted Soluntions : 2\n", + "Epoch: 170\n", + " Kdiff : 24.3688%\n", + " Bstray : 51.7545\n", + "Accepted Soluntions : 6\n", + "Epoch: 180\n", + " Kdiff : 22.4299%\n", + " Bstray : 52.3845\n", + "Accepted Soluntions : 3\n", + "Epoch: 190\n", + " Kdiff : 24.7253%\n", + " Bstray : 51.5587\n", + "Accepted Soluntions : 7\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 200\n", + " Kdiff : 23.9096%\n", + " Bstray : 51.6029\n", + "Accepted Soluntions : 4\n", + "Epoch: 210\n", + " Kdiff : 26.5590%\n", + " Bstray : 51.0290\n", + "Accepted Soluntions : 1\n", + "Epoch: 220\n", + " Kdiff : 25.2698%\n", + " Bstray : 51.4601\n", + "Accepted Soluntions : 4\n", + "Epoch: 230\n", + " Kdiff : 23.0832%\n", + " Bstray : 52.0352\n", + "Accepted Soluntions : 4\n", + "Epoch: 240\n", + " Kdiff : 23.0785%\n", + " Bstray : 52.1948\n", + "Accepted Soluntions : 5\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 250\n", + " Kdiff : 24.3623%\n", + " Bstray : 51.8402\n", + "Accepted Soluntions : 3\n", + "Epoch: 260\n", + " Kdiff : 23.1825%\n", + " Bstray : 51.9108\n", + "Accepted Soluntions : 7\n", + "Epoch: 270\n", + " Kdiff : 22.0265%\n", + " Bstray : 52.3280\n", + "Accepted Soluntions : 0\n", + "Epoch: 280\n", + " Kdiff : 28.3348%\n", + " Bstray : 50.5435\n", + "Accepted Soluntions : 7\n", + "Epoch: 290\n", + " Kdiff : 23.8794%\n", + " Bstray : 51.7952\n", + "Accepted Soluntions : 1\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 300\n", + " Kdiff : 24.2554%\n", + " Bstray : 51.6698\n", + "Accepted Soluntions : 1\n", + "Epoch: 310\n", + " Kdiff : 24.7515%\n", + " Bstray : 51.6509\n", + "Accepted Soluntions : 5\n", + "Epoch: 320\n", + " Kdiff : 21.9991%\n", + " Bstray : 52.3803\n", + "Accepted Soluntions : 6\n", + "Epoch: 330\n", + " Kdiff : 23.6891%\n", + " Bstray : 51.8475\n", + "Accepted Soluntions : 1\n", + "Epoch: 340\n", + " Kdiff : 26.6381%\n", + " Bstray : 50.9617\n", + "Accepted Soluntions : 1\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 350\n", + " Kdiff : 25.6490%\n", + " Bstray : 51.4557\n", + "Accepted Soluntions : 0\n", + "Epoch: 360\n", + " Kdiff : 25.6321%\n", + " Bstray : 51.1669\n", + "Accepted Soluntions : 7\n", + "Epoch: 370\n", + " Kdiff : 22.9627%\n", + " Bstray : 52.0905\n", + "Accepted Soluntions : 3\n", + "Epoch: 380\n", + " Kdiff : 23.3173%\n", + " Bstray : 52.1183\n", + "Accepted Soluntions : 4\n", + "Epoch: 390\n", + " Kdiff : 22.4830%\n", + " Bstray : 52.2079\n", + "Accepted Soluntions : 0\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 400\n", + " Kdiff : 26.4378%\n", + " Bstray : 51.0219\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pareto_bstray_points = []\n", + "pareto_kdiff_points = []\n", + "pareto_numinv_points = []\n", + "pareto_coilloss_points = []\n", + "pareto_V_SecCore_points = []\n", + "pareto_pave_post_points = []\n", + "\n", + "accepted_solutions = []\n", + "max_solution = -float('inf')\n", + "cumulative_solutions = [0]\n", + "i = 1\n", + "\n", + "kdiffs = []\n", + "bstrays = []\n", + "numinvs = []\n", + "coillosses = []\n", + "combined_losses = []\n", + "\n", + "#testing for graphs\n", + "kdiff_designs = []\n", + "bstray_designs = []\n", + "numinv_designs = []\n", + "coilloss_designs = []\n", + "V_SecCore_designs = []\n", + "pave_designs = []\n", + "\n", + "pareto_bstray_points2 = []\n", + "pareto_kdiff_points2 = []\n", + "\n", + "x_calc_numpy =[]\n", + "y_calc_numpy=[]\n", + "\n", + "#training parameters\n", + "n_epochs = 401\n", + "n_noise = 1000\n", + "\n", + "for epoch in range(n_epochs):\n", + " \n", + " # Clear old gradients in the GP model\n", + " for param in gp_model.parameters():\n", + " param.grad = None\n", + "\n", + " # Create GP parameters from noise\n", + " noise = generate_noise(shape=(n_noise, 10))\n", + " gp = gp_model(noise)\n", + "\n", + " # Unpack and filter GP parameters\n", + " a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + " gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + " extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + " V_PriCore, V_SecCore, V_PriWind,V_SecWind, V_PriCore_ave = core_losses(extra_parameters,gp_parameters)\n", + " numinv = number_of_inverters(gp_parameters)\n", + " \n", + " # Scale GP parameters - why?\n", + " min_x = torch.min(gp_parameters)\n", + " max_x = torch.max(gp_parameters)\n", + " gp_parameters = (gp_parameters - min_x) / (max_x - min_x)\n", + " \n", + " # Push GP parameters through MP model\n", + " mp_parameters = mp_model(gp_parameters)\n", + "\n", + " # Scale MP parameters\n", + " y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + " min_y = torch.min(y, 1).values\n", + " max_y = torch.max(y, 1).values\n", + " mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + " k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + " l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + " b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + " # Calculate losses\n", + " kdiff = kdiff_loss(k_parameters)\n", + " bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " # coilloss,pave = coil_loss_and_power_average(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " # print(pave)\n", + " coilloss,pave = calculate_coilloss_pave(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + " \n", + " #pmax - pave_kW, to get tensor to minimize, pavemin is tensor to minimize\n", + " pavemin = torch.sub(10000, pave)\n", + "\n", + " # # Call pairwise pareto and loss\n", + " # bstray_kdiff_loss, pareto_bstray, pareto_kdiff, bstray_numpy, kdiff_numpy, pareto_bstray_numpy, pareto_kdiff_numpy, pareto_bstray_forx_numpy, pareto_kdiff_forx_numpy = compute_pairloss_and_pareto(bstray, kdiff)\n", + " # numinv_coilloss_loss, pareto_numinv, pareto_coilloss, numinv_numpy, coilloss_numpy, pareto_numinv_numpy, pareto_coilloss_numpy, pareto_numinv_forx_numpy, pareto_coilloss_forx_numpy = compute_pairloss_and_pareto(numinv, coilloss)\n", + " # V_SecCore_pavemin_loss, pareto_V_SecCore, pareto_pavemin, V_SecCore_numpy, pavemin_numpy, pareto_V_SecCore_numpy, pareto_pavemin_numpy, pareto_V_SecCore_forx_numpy, pareto_pavemin_forx_numpy = compute_pairloss_and_pareto(V_SecCore, pavemin)\n", + " # V_PriCore_V_SecWind_loss, pareto_V_PriCore, pareto_V_SecWind, V_PriCore_numpy, V_SecWind_numpy, pareto_V_PriCore_numpy, pareto_V_SecWind_numpy, pareto_V_PriCore_forx_numpy, pareto_V_SecWind_forx_numpy = compute_pairloss_and_pareto(V_PriCore, V_SecWind)\n", + " # # #get pave points for plotting\n", + " # pareto_pave_post = pave_tomax(pareto_pavemin)\n", + " \n", + " if epoch%10 == 0:\n", + " kwargs = {}\n", + "\n", + " kwargs[\"kdiff\"] = kdiff.clone().detach().cpu().numpy()\n", + " kwargs[\"pave\"] = pave.clone().detach().cpu().numpy()\n", + " kwargs[\"coilloss\"] = coilloss.clone().detach().cpu().numpy()\n", + " kwargs[\"bstray\"] = bstray.clone().detach().cpu().numpy()\n", + " kwargs[\"vpricore\"] = V_PriCore_ave.clone().detach().cpu().numpy()\n", + " kwargs[\"vsecwind\"] = V_SecWind.clone().detach().cpu().numpy()\n", + " kwargs[\"n_inv\"] = numinv.clone().detach().cpu().numpy()\n", + " kwargs[\"vseccore\"] = V_SecCore.clone().detach().cpu().numpy()\n", + " kwargs[\"epoch\"] = epoch\n", + "\n", + " accepted_solution_so_far = accepted_solution(**kwargs)\n", + "\n", + " \n", + " accepted_solutions.append(accepted_solution_so_far)\n", + " print(\"Accepted Soluntions : \", accepted_solution_so_far)\n", + " cumulative_solutions.append(cumulative_solutions[i - 1] + accepted_solution_so_far)\n", + " i+=1\n", + " \n", + " \n", + " if max_solution < accepted_solution_so_far:\n", + " torch.save(gp_model.state_dict(), f\"./models/gp_model_{epoch:06}.pt\")\n", + " max_solution = accepted_solution_so_far\n", + " if epoch%50 == 0:\n", + " plot_(**kwargs)\n", + " generate_solution_plot(**kwargs)\n", + " \n", + " \n", + " #Spline Fit Loss\n", + " # combined_loss = (bstray_kdiff_loss * numinv_coilloss_loss * V_SecCore_pavemin_loss)\n", + " \n", + " #Spline Fit Loss with V_PriCore and V_SecWind\n", + " # combined_loss = (bstray_kdiff_loss * numinv_coilloss_loss * V_SecCore_pavemin_loss * V_PriCore_V_SecWind_loss)\n", + " # combined_loss = (bstray_kdiff_loss)\n", + " \n", + " # ParetoPointMeanLoss\n", + " # combined_loss = (torch.mean(pareto_bstray) + torch.mean(pareto_kdiff) + torch.mean(pareto_numinv) + torch.mean(pareto_coilloss) + torch.mean(pareto_V_SecCore) + torch.mean(pareto_pavemin))\n", + " # combined_loss = (torch.mean(pareto_bstray) + torch.mean(pareto_kdiff) + torch.mean(pareto_numinv) + torch.mean(pareto_coilloss) + torch.mean(pareto_V_SecCore)) \n", + " # ParetoPointMedianLoss\n", + " # combined_loss = (torch.median(pareto_bstray) + torch.median(pareto_kdiff) + torch.median(pareto_numinv) + torch.median(pareto_coilloss) + torch.median(pareto_V_SecCore) + torch.median(pareto_pavemin))\n", + " \n", + " # SimpleMeanLoss\n", + " # combined_loss = (torch.mean(bstray) * torch.mean(kdiff) * torch.mean(numinv) * torch.mean(coilloss) * torch.mean(V_SecCore) * torch.mean(pavemin))\n", + " #Mean for all 8\n", + " combined_loss = (torch.mean(bstray) * torch.mean(kdiff) * torch.mean(numinv) * torch.mean(coilloss) * torch.mean(V_SecCore) * torch.mean(pavemin) * torch.mean(V_PriCore_ave) * torch.mean(V_SecWind))\n", + " # Push loss through the optimizer and update the GP model\n", + " # try:\n", + " # combined_loss.backward(retain_graph=True)\n", + " # optimizer.step()\n", + " # except Exception as e:\n", + " # print(f\"Error on epoch {epoch} regarding\", e)\n", + " # break\n", + "\n", + " \n", + " if epoch % 10 == 0:\n", + " \n", + " # plt.plot(pareto_bstray_numpy[:,0], spl_y(pareto_bstray_numpy[:,0]), '--',lw=4)\n", + " # # plt.plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + " # plt.plot(bstray_numpy, kdiff_numpy, 'o', ms=2)\n", + " # # plt.plot(x_calc.cpu().data.numpy(), kdiff_numpy, 'o')\n", + " # plt.xlim([0,100])\n", + " # plt.ylim([0,0.5])\n", + " # plt.show()\n", + " \n", + "# plt.plot(pareto_numinv_numpy, pareto_coilloss_numpy)\n", + "# plt.plot(numinv_numpy, coilloss_numpy, 'o')\n", + "# plt.show()\n", + " \n", + "# plt.plot(pareto_V_SecCore_numpy, pareto_pavemin_numpy)\n", + "# plt.plot(V_SecCore_numpy, pavemin_numpy, 'o')\n", + "# plt.show()\n", + " \n", + "# plt.plot(pareto_V_PriCore_numpy, pareto_V_SecWind_numpy)\n", + "# plt.plot(V_PriCore_numpy, V_SecWind_numpy, 'o')\n", + "# plt.show()\n", + "\n", + " # #paretopoints graphing test\n", + " # pareto_bstray_test, pareto_kdiff_test = oldgraph_pareto_frontier(bstray.cpu().detach().numpy(), kdiff.cpu().detach().numpy())\n", + " # pareto_bstray_points2.append(pareto_bstray_test)\n", + " # pareto_kdiff_points2.append(pareto_kdiff_test)\n", + "\n", + " # Store metrics for plotting or further logging\n", + "# combined_losses.append(combined_loss.cpu().data.numpy())\n", + "# kdiffs.append(torch.mean(kdiff).cpu().data.numpy())\n", + "# bstrays.append(torch.mean(bstray).cpu().data.numpy())\n", + "# numinvs.append(torch.mean(numinv).cpu().data.numpy())\n", + "# coillosses.append(torch.mean(coilloss).cpu().data.numpy())\n", + " \n", + "# kdiff_designs.append(kdiff.cpu().data.numpy())\n", + "# bstray_designs.append(bstray.cpu().data.numpy())\n", + "# numinv_designs.append(numinv.cpu().data.numpy())\n", + "# coilloss_designs.append(coilloss.cpu().data.numpy())\n", + "# V_SecCore_designs.append(V_SecCore_numpy)\n", + "# pave_designs.append(pave.cpu().data.numpy())\n", + " \n", + " \n", + " # pareto_bstray_points.append(pareto_bstray[:].cpu().detach().numpy())\n", + " # pareto_kdiff_points.append(pareto_kdiff[:].cpu().detach().numpy())\n", + " # pareto_numinv_points.append(pareto_numinv[:].cpu().detach().numpy())\n", + " # pareto_coilloss_points.append(pareto_coilloss[:].cpu().detach().numpy())\n", + " # pareto_V_SecCore_points.append(pareto_V_SecCore[:].cpu().detach().numpy())\n", + " # pareto_pave_post_points.append(pareto_pave_post[:].cpu().detach().numpy())\n", + "\n", + "\n", + " print(f\"Epoch: {epoch:4d}\")\n", + " # # print(f\" Combined Loss: {combined_losses[-1]:.10f}\")\n", + " print(f\" Kdiff : {100 * torch.mean(kdiff):.4f}%\")\n", + " print(f\" Bstray : {torch.mean(bstray):.4f}\")\n", + " # # print(f\" Pareto Kdiff : {100 * torch.min(pareto_kdiff):.4f}%\")\n", + " # # print(f\" Pareto Bstray : {torch.min(pareto_bstray):.4f}\")\n", + " # print(f\" Coilloss : {torch.mean(coilloss):.4f}\")\n", + " # print(f\" num inv : {torch.mean(numinv):.4f}\")\n", + " # # print(f\" Pareto Coilloss : {torch.min(pareto_coilloss):.4f}\")\n", + " # # print(f\" Pareto num inv : {torch.min(pareto_numinv):.4f}\") \n", + " # print(f\" V_SecCore : {torch.mean(V_SecCore).cpu().data.numpy():.4f}\")\n", + " # print(f\" pave : {torch.mean(pave).cpu().data.numpy():.4f}\")\n", + " \n", + "\n", + " # Save model for further training or testing\n", + " # torch.save(gp_model, f\"./models/gp_model_{epoch:06}.pt\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "# Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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SClEb06yQI9so8bKvIvT37NbH88rFQV8yjk72pwXxIsHI98g35r15qplZCYcQ5pf7lXXNI1gtqhWbvW64gaoTZvYSIYb3ioTRooRPE5HMKdUIL5wM1Wf7SEbnyPY17nQhmNlsQiTOL6v7RBa9cS3hDyCv5vnvwK/MbG7Osd70eYvQST5Smbj2sU/rXrr3+/VGqbjx2Tjo0BV+OYntva6ks8voWq3Y7HXDDVR9OZ3wEv1EUlIdv4XwIJ6Q/BNLWgc4pQrXO5PQp3SapOEx72/Q3UAC/Yo7XRhm9r+Ee/VbSScr+ncpsJ7CpJ37EcLJfhDPmUZwiRgj6WiFmOxS8Do/idDH11+OJBjvCZJWjLqsClwJ/MXM+jLRwVWEWN0/VIgZPyj6PH0xR/ZNQmjdZNq0PQkd9uWoRmz2+lHrYcOBslAi/nOO3ES6hnaviGlbEGI9v0uogt8MHEVXnPM7olxr3DdCHPQJMf1Jusev/nnqemsQRubeI4ymXQtsRfe404um5HuNO52jxwWEIfF2QgdrR9zeimAY0nGuX+rDPf0q4YWaFvN7k9DkuopgZBfOOWcvgo/YW4TRqDuBrVPHl6FnXO+DCJ3HWf23z9zHKwj+RDMIvkMn0D1mdzY+fTuwQUa/RQiTj7ZF2ccJ/lv3l7jut+Mz8QZhNHJzwsQT2TKclDqnktjsR9AzPv6iBGOe/r2eq+V75DHJHcdpWLyJ5zhOw+IGynGchsUNlOM4DYsbKMdxGhY3UI7jNCxuoBzHaVjcQDmO07C4gXIcp2FxA+U4TsPSLwOlMAWQ4zhOoXT71EXS/hWe9zMzG9G7mOM4Tv/JGqhsoCojP+iVmVlzTrrjOE7VyDbx7jazJjNrIsSbvoAwZ9tCcVmHMP/YXjXV0nGcAUm2BjXSzB6L238zs7xgZUi61cwqndfMcRynX3SrQSXGKVJuBofl+3IRSXvHSQNNUlvO8SZJxypMADk9ztJxjaR1c2RHxGPtUXaqpGNi8PlC83Qcp8aUCQ42mRBcbEgqbQghmNZjFQYYG0oIj3o3IWCYAW05csncXdfH/U3j/jvA2im5EXQFRtsopt0U939bdJ6++OJLbZfSB+DrhJlSPwJeistHhEiMW1WUeYjNvF/cbsszUCnDYYQZOJL012PaDam0v8W06am0Q1PnjywqT1988aX2S8kmjJndQwh7ejYhDOkThJlp1zKze0udl8ljlpld0YtYOqh9eu6wmXG9o6RFJS1GV7D/PDkIMZqLytNxnBpTdtodM5tJTrB+SYub2ftV0iHdJ5SeduejuB4ErEVwd2guI5fOq4g8HcepMf2dWfgGep9NolKGprY7SmwvnjmnN7ki8uzGZpttZkOGhOnlWlpaaGlp6SHT1taWm94Icq5bsXKfd93a2tpoa2sD4O67737IzDbvNcP+UKrtR5gJ4nfAo4T+p5dTy0d9bUtSug/qerr6e9KzbdyXSt8A2DC1f29KbptU+nVF5ZldvvGNb1hvjB07tleZesm5bsXKDSTdgFYrqA+qXA3qMmAY3acUgtAsqvSTmEp4hjCVDoQpeBIWjesOwjQ7itvNJeSSvIrKs8+MHj26oeXqcc1K5Kqpf1/yGyhlqMdz1G9KWS5gCtBc4tgpfbWElK5BfZX8EbfXYtpfU2k303PE7ZDU+ZsUlWd2OeCAAyr6d2lU7rrrrnqrMN94GRoDYKLVehQPeN7MOkocu6wi61cBZvYwcEnc3RmCRzuwMsGl4fiU+PEE14fhkjaIabvE9QQze6SoPLNU0oZvZBri33E+8TI0DG1FZVzOQP1a0imShqem5U64JPeMHCS1SppCl2f6SpKmSHogJXYowVCsK2kaYfrv64FRZjaviWVmU4BR8ditkqYTRuOOAw7PXLqIPB3HqSHl+qBuJzRxxgH0tFGVYWajK5DpIPhYnVWB7BRg93rk6ThObSlnoJ4jzBmfRYRPYAY0bW1tjBs3jtGjR39equmO02daW1sBWorKv5yBusLMLs07IGnpgvRZYGhpaWHcuHH1VsNx6kr8c24rKv9yn7qcVua8qwrQxXEcpxv9DSdStVE8x3GcUpRs4kl6ucx5KxSgi+M4TjfK9UEJmJiRHU74Bu/c4lRyHMcJlDNQl5rZ+GyipNWAY4pTyXEcJ1Cuk3xcifRpwFeKUmhBIXEziMOsjjMgqaebQQ8kDQVGA6sUos0ChLsZOE7xbgblOsk76YpgkKYDOLoohRzHcRL64kluhLArj8dmnuM4TqGUM1AXlfIkdxzHqQUlDZSZ/QpA0iKEqZkAnjazj0ud4ziOU03KepJLGgfMAh6Oy6yY5jiOUzjlOsmPBQ4Dzif0RwlYGzhM0ntmdk5tVHQcZ6BSrg9qf+BrZvZqOlHSbwiTXQ5oA+XhVhynvn5QH2aNE4CZvSrpw6IUWlBwPyjHqWO4FWCopJWyiZKGA4sVpZDjOE5CuRrUH4FJki4mTNFkwDrAgYTp0B3HcQqlnJvBmZKWAo4FhsTkT4Bfege54zi1oJuBkvT1uPmkmc02sxMl/Rfd/aAGfP+T4zi1IdsH9WdCE27lJMHMPjSzSXFx4+Q4Ts3IGqhnzexgM5sq6RVJLydLXbRrYDzciuPU3s1gXvQCM1sdQNJdZvbNohRYUHE3A8epr5tBQo+QKzkzDc8XktokWZmlJcq1lpEZkslzhKRrJLVLmi5pqqRjJDVl5JokHRuPT4/y10hat5pldByn7/R3Vpd/VFWL3ulMbb8DvJWzzDOkkkYADwJ7ADuY2arAKwT3iF9n8r4I+CXwQpTbJZ73gKS1CymN4zgVkW3iLSVpK8J3d2XTCtBlJJD1XL8SWNrMpqflzKytl7zOApYAXjWzyTHtBmAn4HBJE8zsMUmbAj+Ix28EMLNJkmYAKwFnAt/ub4Ecx5k/sgZqQ6A1k6ZMmsiPtDk/XAxMM7O3511E2hDYGtg9I3uApC2BNQm1qTuAM83szXjeYsB2UfaN1HkzU9t7Ao8RakqUkF0J2FHSomb2UT/L5TjOfJA1UE/QezhfAb+qphJm9vOc5FOjPjek0t4nGI8dgWWAvwPHAftI+pqZtQNrAc1RPh27Km1k1s2sS8kOivk9UXFhHMepGlkDdZWZ3d3bSZIKnfpc0nrAbsBeZpYeWdwlJTZT0i+AvwCrAicDRwJDUzIdJbYXj+u+yHYjcTMAPKKBM+BobW1Nu9i0FHWdbgbKzH5RyUmVys0HpwJTgWt7kXs2tZ14wX+QSmsusf1+P2S74W4GzkAm/ac8fvz4tqKu099RvMKIw/vfAU5L154kDZE0LCOeHt1LjO0LdNWAFkkdXzS1/UxmXUq2I+bnOE4daDgDBZxCqBldnUnfjO5TsUOI8JkwCcKnOcBtMW251PH0dlIzu67E8WT7Fu8gd5z60VAGStKXgL2B082sM0dka0kbRdklgONj+mzg9JTc8YSm2XBJG8S0pP9qgpk9AmBmDwOXxPSdY74jCd8ivpfK33GcOtBQBgo4CXiR8NFylpeAy4FLJD0GvA6sBvwJ2NTM5jXFzGwKMAq4HrhV0nTCaNxxwOGZfA8lGKJ1JU0DbonnjTKzZ3Acp270aerzojGzA8oce5UwiUOleU2hpw9VnlwHwbHzrErzdhynNpSsQUnaVNKpkr4Y90+RNFvSfZJWrZ2KjYlHM3Cc+k6acAJhBGu2pPWB8cC58Zyzgb2KUmpBwN0MHKf4aAblDNQwM9sDQNLxwINmdkzcf7AohRzHcRLKdZLPSW3vTuiMTvChd8dxCqdcDWqQpK0JH+WuRvikhDiRQu7nH47jONWknIE6ifCh7lLAqWb2pqRtgQvp/RMUx3Gc+abctFP3SVoWWMLMZsfkB4BvArNqoJvjOAOcso6aZtaZMk6Y2QdmNo2uIG8DFnczcJz6uhkk05xvSGjmpSNqHgH8b1FKLQi4m4Hj1NHNQNIRwPl0Dz2SUO2Imo7jOD0o18Q7FtiXEBO8Kb0A99RGPcdxBjLlmnivmlk25EnC/kUo4ziOk6ZcDepxSSuXOHZwEco4juOkKVeD+gS4T9KdwGt0j9N9AOHbPMdxnMIoZ6COBtqBMTnHli9EmwWIxM3AJ0xwBjL1dDN4yMy+mXdA0l0F6bPA4G4GjlO8m0G5PqhyHeF7lDnmOI5TFcp96vIqgKTRwMYx+VEzazWzd4pXzXGcgU45R83Fgb/SNd8cgEm6G9jVzD7IP9NxHKc6lGvinUkYudseWB34ImHK8c54zHEcp1DKdZJvCYw0s3TgujZJrcAjhWrlOI5D+RrUhxnjBICZfYpH1PRoBo5D/aMZjDGzOzNp2+AfC7ubgeNQ30kTTgduk/QQ8DzBKK0DfBXYtSiFHMdxEko28czsJmAngmHaB/guodN8BzO7pZpKSBotyUos/52Sa5J0rKSpkqZLapd0jaR1c/IcEY+1R9mpko6R1JSRqzhPx3FqS9kmnpndCtxaI10+BfJcFz5MbV9EiOZ5g5ntJmlT4GFgjKTNzOw5CMYJeBBYgtDRP1nSTYT5/NYiBNzrU56O49SesiF/SyGpVBiW+eFKMxuWs5wWr7kpXaGGbwQws0nADELEz7Trw1kE4/SqmU2OaTfE9eGSRvYjT8dxaky3GpSknYDZZna/pIvLnDeqAF2Gx2tuACwNPA1cGJua0P3zmjdS2zOBlYAdJS1KCE28XQm5hD2BxyrN08wG/Kil49SDbBPvN8CzhBd8d+DxEucNqbIec4FFCCFcphNqNROAnSSdaGZnAOk+oY9T24nxGERovomuMMV5cqTyqjTPJ/pSGMdxqkPWQK1FMBYAj9cqmoGZ3Uf3WtnvJf0nMAIYL2kiMDR1vKPEdnZC0d7k+pMn0OUHBXjIFWfA0dramvYBbCnqOt0MlJl9ltrdNissScDawLeKUijFswQDNRjYnO4d6M0ltt+n++wz5eToQ549cD8oZyCT/lMeP358W1HXKddJfmlO2qKEWYXPq6YSklaQlK3Ndaa2BwHPpPYXyegEodbzQlw6ysiRyqvSPB3HqQPlDNSK2QQz+9DMRhDmyqsmV+bkuXZyWcK3f9elji2Xs32LmX1kZh8Ct5WRg66p2yvKs1ftHccphOwo3vp0GYrlJX2f7k0mgGWAYQXo8mNJB5vZHEl7A1+J6b81s5eBlyVdAhwE7AxcHN0FVgbeA45P5XU84WPn4ZI2MLMngF3isQlm9giAmT3chzwdx6kx2WbV7sDYuG30bOYZIU75yVXW4zJgb2CypCGEIf5HgT8AF6bkDiU0yw6WNI0wmng9cLKZzWuumdkUSaOA04BbJX1G6G86Djgnc+2K8nQcp/bILP+7X0l3lRrFc2DcuHHmneSOA5LGm9m4IvIu1wd1SBEX/Lzg4VYcp47hVszspVLHJF1gZkcWo9KCgbsZOE4dw61IOrXMeTsWoIvjOE43ykUzOIHQIZ6WXY7gaT4z9wzHcZwq0qeJOyUNBg4D3i5UK8dxHMp3ku+QTTCzOWb2a2Df4lRyHMcJlIuo+UleevRTWqswjRzHcSLlOsnz4kEtQYhJ/nBhGi0gJG4GHsnAGcjUc1aXbDwoA94lhMg9vyiFFhTczcBx6jury8Nmtl2Z447jOIVSrg+qpHGStEox6jiO43TRr0kTCB/3Oo7jFEo23MrLFZ63QgG6OI7jdCPbByVgYi/nCNi/EG0cx3FSZA3UrWY2vreTJA34GpS7GThOjd0MzOzwSk6qVO7zjLsZOE7xbgZlO8kljZHUKun9uLRKGlOUMo7jOGlKGihJOxAmH1gIuBq4Jm7fKmn72qjnOM5Appyj5qnADmZ2ezpR0raEGYD/XqRijuM45Zp4yhonADO7rZfzHMdxqkI5QzM0ZzJNJC1E90kwHcdxCqGcgbofuEXS1pJWlTRc0reAvwH31ka9xsUnTXCc+kYzOA64GbidEMkg4V58Qkt3M3Ac6uhmYGbvmdmWwLbAz+LyLTMbbWYfVFMJSSdIulfSFEnTJLVLukfS9yQpJdcqyUosQzJ5jpB0TcxruqSpko6R1JSRa5J0bDw+PcpfI2ndapbRcZy+U64GBYCZ3QHcUbAeBwAPAIeZ2dw4o8x4YCtCgLyjUrLvAJ15qiYbkkYADxIC7I00s8mSbgLOJkQDPSJ13kXAD4AbzGw3SZsSAvKNkbSZmT1XrUI6jtM3srWJQZKWkDQ0kz5K0tmSzpK0SQF6vAyMN7O5cf8M4LO4fbikxVKyI81sWM7yaUrmLIJxetXMJse0G1L5jYzl2pRgnABuBDCzScAMYCngzOoV0XGcvpJt4o0j1FDmuRdEz/F7gCOBg4EHJG1RTSXMbEczm5banwPMjruDCQ6iCQdIul3SK5Iek3SmpGEpfRcDklhWb6TOS0+VtWdc75FKy5PdUZKPWDpOncgaqC2BH5rZ5qm0nxNe3tXN7AuE5tEJRSolaUlg2bg7yczeidvvE4zHjsBmhMgKxwGPpj5gXgtojtsfp7L9KLW9bmZdSnYQPkGE49SNbB/UEDP7XbIjaVVgFDDOzGYAmNkfJB1SsF5HEYzPLLqaYJjZLimZmZJ+AfwFWBU4mVDLSzdPO0psLx7XfZHtRuJmAHhEA2fA0dramnaxaSnqOlkD9VFmf0dC5/NVmfSPKQhJ3yF8ZvMA8G9m1lZG/NnU9tfjOj3C2Fxi+/1+yHbD3QycgUz6T3n8+PFtRV0n28QbnNnfF3jRzJ7JpC9cbUUkNUsaSwiY91NgKzNri+4CC0kaku5riqRH8xJj+wJdNaBFUsfTfUnPZNalZDtifo7j1IGsgXpW0s8lrSvpWEKf1EVpAUnfB3In9ewvktYA7o7XW9/MzjGzxPjcDKxE6HOamDl17dT2JAAz+5AQhQFgudTx9Pa1cX1diePJ9i1mlq1VOo5TI7IGaixhPryphKH6O4DzACQtL+lZgpG4jepyP7AFsBEwSdKbyQIMT8ltLWmjqM8SdHm0zwZOT8kdT2iaDZe0QUxL+q8mmNkjAGb2MHBJTN855jsSWBl4D/eYd5y6ko2oOSO+0COAz8zs+dThN8kfvq8GiRf4F8rIvARcDlwiqZMwuvYB8CdCJ/6LiaCZTZE0CjiNEL/qsyh7HHBOJt9DCU29gyVNi7pcD5yc07R1HKeG9PAkj02rKTnpHcC0bHo1MLOlKhQ9rA95TiHUBnuT6yDUFs+qNG/HcWqDx3XqJx7NwHHqG83AKYO7GThOnSdNcBzHqSduoBzHaVj6ZaAKimjgOI7Tjf7WoDwMieM4hdOtkzz6F1kJWcdxnJqSHcV7DvjvXs4R4Vu5AU3iZuCRDJyBTK3dDP5sZpf2dpKklmLUWXBwNwPHqbGbgZmNq/C8qdVXxXEcpzu9OmrGSAMrEZp2CSfQM0aU4zhOVSlpoCStRpho4CtJUlx7J7rjODWhXA3qbMJMJ/sQQqx8lxCobl+6R5x0HMcphHIGakUz2xNA0sepWVfGS7q2zHmO4zhVoZyjZjpq5kKZGXlXK0gfx3GceZQzUEMkbRu3pwPnS9pS0ul0j+89IPFwK45T33ArE4ATYpjfs4BWwpx4ncD+RSm0oOB+UI5TvB9USQNlZhPpmqRguqQvA18FpprZc0Up5DiOk1CyiZdq3gFgZq8Bfyd0ko8sWjHHcZxyfVA/y0n7lDChwG8K0cZxHCdFn8KtmFmHmV1Jd69yx3GcQsiGW/kx8OO4u4Kkl3POWQp4smC9HMdxenSSPw4k0QwOTG0ndALt+Hd4Hm7Fcaixm4GZ3U2YghxJg8xsfFEXXtBxNwPHqeOsLmZ2SrIt6QuSys36u8AjaYSkayS1S5ouaaqkYzIe9I7j1JCy4VYkHQiMA4bH/VcJ04xPLFqxWiJpBPAgsAQw0swmS7qJ8MH0WgQHVcepCDOj03oG/ejoNOZ0GHPmdjKno5M5czvp6LR54UHM0ttd+yE/6OjoZG6n0dFpzI3bczs6mdthzInr5LpmYFjMB5IgJDlqAdBpIY/P5nYypyPk2xGv1WlGZ2fUodPo6Ow61tFZbHCTcuFW9gd+TQi58hxh5G5t4NeS5prZHwvVrLacRTBOr5rZ5Jh2A7ATcLikCWb2WPqEtra22mrYC8lLkTyQycMZjmX2gXvuuYdRW2zZ9RAmD6J1vRBJfmGdOtZpzO0ML9rczvBgd3TEF6czvChdD2/Mq9O6vTBG+kVKjnd/GeekX75Om3ftjviyvPxKGyuuPJy5UfazqE9n5gVK9E7KnpQpKXdHj6Vz3vFgDEJ5O7Iv7bwX1ubpkOg8wGgpKuNyNagfA5ubWbcRO0nrEzzMPxcGStJiwHZx943UoZmp7T2BbgbqH1Nn8aM//LPrwY8Pa/rBTv/7dMQXrCN5ueODnPxTdTcGzPt3nDO3k89S/7bZayTy/eLyq/t5YgPx5DP11sCpk4H6LGucAMzsSUmfFqVQHViLrvhWH6fSP0ptr5s9aeZnQ7js7peK1MtZgBEgdXcXlGChQU0Mbm5icFx/8P57LLXkkiSikuY5GUpd+59+8jFDhy7GoOYmBjWJ5iaF7WYxOElrbuKdt99i2LBh3XSQuuvz5puzWHbZZXvoO7i5iUHNTfN0bP/XDFYdvgpNTaJJoqkJmqR5129uEk1NTRx/eVF3sbyBWlzSEmb2XreCSEsBQ4tTqeaky9JRYnvx7Elznrz2oTlPXpsY6jbyRzJaSqQ3glw9rlmpXD2uWW25ivNqb2Ddysi10FVzWriCvPpFOQP1N+A+SecBLxCa7+sA/0GItPl54YPUdnOJ7fezJ5nZ5oVp5DgOUN5AnUwYvZtAVxxyEfqexhasVy15gVBbagYWSaWnY155R4fj1IFuPj6SXpH0sqSjzGyOme1H6KP5blzWMrP9zWxuPZQtAjP7ELgt7i6XOpTe9hDHjlMPLA4hx9Gou9L7A2UB1gPeI9QUN4hpN8T9i1JyI4BrCJ/7TCfMD3gM0FTvMkT99gZei3q35RxvAo6Nek+P5bgGWDdHtmZlJUxjdi8wBZgWr3kP8D1AC4D+hwD/AJ4i1LbfBZJAj8s2uv4lyrQRMDfvWaplObKZ3Vmh8qvV4ibVcolG6rrUjXwa+ElyI+ONfjf+YBvFtJvi/m/rrPtQ4A7CZ0pvlTFQv4/Hro/7m8b9d4C1Mw9VzcoaX+aLgUFx/9R4LQPOXwD0vz7qlug/hvDdqgEPN7r+OeVpBh5N/QZtmeM1K0dWsUoNVEVyn6eFMGhgwPRU2qGpH3FkHXVbFtgvbreVeKg2Tel6cCr99Zh2Q73KCtxM6k8PGEyIPWbAZ8BiDa7/GGD5TNqbKf3VyPrnlOcnhBrttOyzVOtyZDvJN5J0J72zYQUynxv668xZK8xsFnBFL2J7pLazZVgJ2FHSooSXqaZlNbMdM/tzJM0m9AMOBhaisfXv9s5I+h6QfLs6wcxMUsPqn0bS6sCJwBbALTkiNS1H3iieB6PrSb+cORuMtH55ZRhEKKeoc1klLUmoFQJMMrN3JDW8/pL2BH4HLBOvfSaQRARpeP0jFxKa1c9kHU1zrl14OXrEgzKzb/Z2kqS7epP5nNEvZ84Goz9lqFdZjyI84LOAH8S0htffzK6WdB2hc/8SgjvORpJ2YwHQX9L3gVWBXcqI1bQc2VAi1tsJkYE27VS/nDkbjErLUNeySvoOoZP8AeCrZvZUPLRA6G8hLPalhP4XgF2BHfugV130lzSMEL3jMDP7rIxoTcvRr1hHZvZqf85bgEmcOWHBdeZM65dXhg5COetSVknNksYSPkT/KbCVmbXFOF0L0cD6S2rJSX46tf1lGlj/yK7AHOB/JT0u6XFCnxLASjHt99S4HFkD9cXEUbO3EwcS9vlw5rwutZ1XhlvM7KN6lFXSGgQXiS2B9c3sHDPrjIdvJrwoDas/MFnScpm0VVLbM2ls/TGzi81sZTPbMFmAGfHwjJh2CLUuR5HDlZ+nhQqdOeu9UMLNIB67OB67Nu6PjPvvknKyq3VZCb5nRhiazy4dQEuD6z+b7n5Q36DLTeJZYLFG1r+vz1Ity1H3F2pBWujFmbPOurUSPLE/o8v/ZgrwQEqmGTiOULWeRtc/+5frWdb4gluZpaXB9R8f7/8zwEuEkaqpwP8AyzT6/c+59g9LPEs/rHU5FDNxHMdpOHxCAMdxGhY3UI7jNCxuoBzHaVjcQDmO07C4gXIcp2FxA+U4TsPiBsopFEkrS2qVNDsurZnlcUkTa6zTrpIekmSSRtfy2k7fKDv1uePML2b2OjBaUmvcH50+Hg3EgTXW6UZJTwKv1PK6Tt/xGpRTb54kfEXvOD1wA+XUjdi0W9/MnpJ0RGzumaSDJN0q6RFJz2aiUSbNxislPSXp0dhU3Cwjs6KkK6LM3VHuFEnZVkNLlHtI0hRJWxVcbKcPuIFyGgIz+y1wdNzdE9jFzDYB/gu4StIGADGcbCshNMgGZrYx8CfgTklfLiHzDeBHhABy2Vmx9wEOMrPNCBNPXFJQEZ1+4AbKqSUbpjvIge1LyJ1rXUHT/kgI+3Fs3N8PWBM4zbpCsvyBMKPIT+P+vsCXgNMTGTN7iGDsssHYrjSzZAr7O4E1YshhpwHwTnKnljye7iQvM3rXlmyYmUl6ifBVPITQHp2EqAGJTKekF4CNY9LGUebldKZmNjbnWq+ltt+L66UIoUOcOuMGyqkbZnZghaKiezjqvBAc2Qj/lYbp6MhJ84lDGgRv4jl1RdJQSTtlkltSxwV8kRBfCcKEks2EZl4i0xT3H83IrJG51tGS0pEunQbHDZRTb4YRgp+lOSzGIYcwQ8pKdLki/B/wPHByNEwQZn5ZmjDNU1rmpGjgkDSG0FHeXkQhnGLwJp5TKLHGcjVhGmwkPZQRWZie/T1XAtdKWoEwNdHeZvYEgJl9JOmbBIP1pKRPCTOIjDGzKSVk3ooyO5rZ3GisfhGvda6k8YSm3s+T60v6qZndXaXb4PQTj6jpNAzRq/wuYHUza6urMk5D4E08x3EaFjdQTkMg6Qjg3Lh7ZU7HuTMA8Sae4zgNi9egHMdpWNxAOY7TsPw/yfqH4u3B1uIAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot([i*10 for i in range(len(cumulative_solutions))], cumulative_solutions, lw=3)\n", + "plt.ylim(0,10000)\n", + "plt.xlim(0,400)\n", + "plt.title(\"Cumulative Solution Plot \\n Random Generation\", y = 1.10)\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Total Solutions Found\")\n", + "plt.savefig(\"RandomGen_400ep/\" + \"CumulativeSolution_RandomGen\"+\".png\")\n", + "plt.show()\n", + "plt.clf()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#use inside every so many epoch printing \n", + "\n", + "\n", + "# figure, axis = plt.subplots(2, 1, figsize=(10,10))\n", + "# plt.figure(figsize=(10, 5))\n", + " \n", + "# if normalize == True:\n", + "# axis[0].plot(normalized_pareto_bstray_numpy, normalized_pareto_kdiff_numpy)\n", + "# axis[0].scatter(x_calc.cpu().data.numpy(), normalized_kdiff_numpy)\n", + "# axis[0].scatter(normalized_bstray_numpy, normalized_kdiff_numpy)\n", + "# axis[0].set_xlim([0,1])\n", + "# axis[0].set_ylim([0,1])\n", + "# axis[0].set_xlabel(\"Bstray\")\n", + "# axis[0].set_ylabel(\"Kdiff %\")\n", + "# axis[0].set_title(f\"x_calc vs. designs, epoch: {epoch}\")\n", + " \n", + "# axis[1].plot(normalized_pareto_bstray_numpy, normalized_pareto_kdiff_numpy)\n", + "# axis[1].scatter(normalized_bstray_numpy, y_calc.cpu().data.numpy())\n", + "# axis[1].scatter(normalized_bstray_numpy, normalized_kdiff_numpy)\n", + "# axis[1].set_xlim([0,1])\n", + "# axis[1].set_ylim([0,1])\n", + "# axis[1].set_xlabel(\"Bstray\")\n", + "# axis[1].set_ylabel(\"Kdiff %\")\n", + "# axis[1].set_title(f\"y_calc vs. designs, epoch: {epoch}\")\n", + "# else:\n", + "# axis[0].plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# axis[0].scatter(x_calc.cpu().data.numpy(), kdiff_numpy)\n", + "# axis[0].scatter(bstray_numpy, kdiff_numpy)\n", + "# axis[0].set_xlim([0,100])\n", + "# axis[0].set_ylim([0,1])\n", + "# axis[0].set_xlabel(\"Bstray\")\n", + "# axis[0].set_ylabel(\"Kdiff %\")\n", + "# axis[0].set_title(f\"x_calc vs. designs, epoch: {epoch}\")\n", + " \n", + "# axis[1].plot(pareto_bstray_numpy, pareto_kdiff_numpy)\n", + "# axis[1].scatter(bstray_numpy, y_calc.cpu().data.numpy())\n", + "# axis[1].scatter(bstray_numpy, kdiff_numpy)\n", + "# axis[1].set_xlim([0,100])\n", + "# axis[1].set_ylim([0,1])\n", + "# axis[1].set_xlabel(\"Bstray\")\n", + "# axis[1].set_ylabel(\"Kdiff %\")\n", + "# axis[1].set_title(f\"y_calc vs. designs, epoch: {epoch}\")\n", + " \n", + " \n", + " # plt.xlim([0,100])\n", + " # plt.ylim([0,0.5])\n", + " # plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#scatter plot of final pareto points\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff Combined Final Frontier\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.5])\n", + "\n", + "print(len(pareto_bstray_points2[:]))\n", + "print(len(pareto_kdiff_points2[:]))\n", + "# print(pareto_bstray_points2[:])\n", + "final_x = []\n", + "for list in pareto_bstray_points2:\n", + " for item in list:\n", + " final_x.append(item)\n", + "\n", + "\n", + "final_y = []\n", + "for list in pareto_kdiff_points2:\n", + " for item in list:\n", + " final_y.append(item)\n", + "\n", + "final_x, final_y = oldgraph_pareto_frontier(final_x, final_y)\n", + "print(len(final_x),len(final_y))\n", + "\n", + "plt.scatter(final_x, final_y)\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#line plot of final pareto points\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff Combined Final Frontier\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points2[:]))\n", + "print(len(pareto_kdiff_points2[:]))\n", + "# print(pareto_bstray_points2[:])\n", + "final_x = []\n", + "for list in pareto_bstray_points2:\n", + " for item in list:\n", + " final_x.append(item)\n", + "\n", + "\n", + "final_y = []\n", + "for list in pareto_kdiff_points2:\n", + " for item in list:\n", + " final_y.append(item)\n", + "\n", + "final_x, final_y = oldgraph_pareto_frontier(final_x, final_y)\n", + "print(len(final_x),len(final_y))\n", + "\n", + "plt.plot(final_x, final_y)\n", + "plt.show() " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points), len(pareto_kdiff_points))\n", + "\n", + "while i < len(pareto_bstray_points):\n", + " plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Number of Inverters vs Coilloss During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Num Inverters\")\n", + "plt.ylabel(\"Coil loss\")\n", + "plt.xlim([0,2])\n", + "plt.ylim([0, 5000])\n", + "\n", + "print(len(pareto_numinv_points), len(pareto_coilloss_points))\n", + "\n", + "while i < len(pareto_numinv_points):\n", + " plt.plot(pareto_numinv_points[i], pareto_coilloss_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "# plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto V_SecCore vs Power Avg During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"V_SecCore\")\n", + "plt.ylabel(\"Pave\")\n", + "plt.xlim([0,3000])\n", + "plt.ylim([0, 200])\n", + "\n", + "while i < len(pareto_V_SecCore_points):\n", + " plt.plot(pareto_V_SecCore_points[i], pareto_pave_post_points[i], label = f\"epoch: {i*50}\")\n", + " # plt.plot(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "# plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i = 0\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Pareto Bstray vs Kdiff During Training\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "print(len(pareto_bstray_points), len(pareto_kdiff_points))\n", + "\n", + "while i < len(pareto_bstray_points):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i])\n", + " i+=1\n", + "plt.legend()\n", + "plt.savefig('Pareto_Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i = 0\n", + "print(len(bstray_designs))\n", + "#colors = numpy.arange(len(bstray_designs))\n", + "colors = numpy.arange(1000)\n", + "plt.figure(figsize=(10, 5))\n", + "plt.suptitle(f\"Bstray vs Kdiff During Training, Generated Designs\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Coupling %\")\n", + "plt.xlim([0,100])\n", + "plt.ylim([0,0.50])\n", + "\n", + "while i < len(bstray_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " fig = plt.scatter(bstray_designs[i], kdiff_designs[i], label = f\"epoch: {i*100}\")\n", + " i+=1\n", + " \n", + "# plt.colorbar()\n", + "plt.legend()\n", + "# from matplotlib.patches import Rectangle\n", + "# someX, someY = 45, 0.5\n", + "# fig,ax = plt.subplots()\n", + "# currentAxis = plt.gca()\n", + "# currentAxis.add_patch(Rectangle((someX -0.1, someY-0.1), 0.2, 0.2, alpha=0.5, facecolor='red'))\n", + "#plt.savefig('Bstray_Kdiff_1000Epoch_256Noise_linfit.png')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(bstray_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " ax.scatter(bstray_designs[i], kdiff_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,200)\n", + "plt.ylim(0, 0.2)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"Bstray\", fontsize = 18)\n", + "plt.ylabel('Coupling %', fontsize = 18)\n", + "plt.suptitle(f\"Bstray vs Kdiff During Training, \\n Generated Designs\" , fontsize = 20, y=1.01)\n", + "# plt.title(f\" {n_epochs} epochs, {n_noise} noise, normalized:{normalize}, Loss = curve fit with {chosen_curve_util.__name__}\")\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle(( 0,0 ), 45, 0.15, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(numinv_designs):\n", + " #plt.scatter(pareto_bstray_points[i], pareto_kdiff_points[i], label = f\"epoch: {i*100}\")\n", + " #fig = plt.scatter(bstray_designs[i], kdiff_designs[i],c=i, cmap='viridis')\n", + " ax.scatter(numinv_designs[i], coilloss_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,2)\n", + "plt.ylim(0, 10000)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"Num Inverters\", fontsize = 18)\n", + "plt.ylabel('Coil Loss', fontsize = 18)\n", + "plt.suptitle(f\"Number of Inverters vs Coil Loss During Training, \\n Generated Designs\" , fontsize = 20, y=1.01)\n", + "\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle(( 0,2 ), 1, 2000, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "i = 0\n", + "fig, ax = plt.subplots()\n", + "\n", + "while i < len(V_SecCore_designs):\n", + "\n", + " ax.scatter(V_SecCore_designs[i], pave_designs[i], label = f\"epoch: {i*50}\")\n", + " i+=1\n", + "\n", + "plt.xlim(0,5000)\n", + "plt.ylim(0, 200)\n", + "plt.legend(title='Training Iteration:')\n", + "plt.xlabel(\"V_SecCore\", fontsize = 18)\n", + "plt.ylabel('Pave', fontsize = 18)\n", + "plt.suptitle(f\"V_SecCore vs Power Average During Training \\n Generated Designs\" , fontsize = 20, y=1.01)\n", + "\n", + "plt.title(f\" {n_epochs} epochs, {n_noise} noise, Loss = spline fit\")\n", + "\n", + "rect = patches.Rectangle((0,30), 1500, 200, alpha = 0.2, ec = \"Crimson\", fc = \"Red\", visible = True)\n", + "ax.add_patch(rect)\n", + "plt.rcParams[\"figure.figsize\"] = (8,8)\n", + "plt.figure(figsize=(8, 8))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "noise = generate_noise(shape=(10_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "pareto_bstray, pareto_kdiff = pareto_frontier(bstray, kdiff)\n", + "pareto_kdiff = pareto_kdiff * 100\n", + "\n", + "plt.plot(pareto_bstray.detach().cpu(), pareto_kdiff.detach().cpu())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "noise = generate_noise(shape=(25_000, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "# Unpack and filter GP parameters\n", + "a, lpx, lpy, ls, p, wp, ws, ip, np, ns, ys = scale_and_extract(gp)\n", + "gp_parameters = stack_parameters(a, lpx, lpy, ls, p, wp, ws, *ys)\n", + "extra_parameters = stack_parameters(ip, np, ns).transpose(0, 1)\n", + "\n", + "# Push GP parameters through MP model\n", + "mp_parameters = mp_model(gp_parameters)\n", + "\n", + "# Scale MP parameters\n", + "y = torch.Tensor(df_mp.values).transpose(0, 1).to(\"cuda\")\n", + "min_y = torch.min(y, 1).values\n", + "max_y = torch.max(y, 1).values\n", + "mp_parameters = mp_parameters * (max_y - min_y) + min_y\n", + "\n", + "k_parameters = mp_parameters.transpose(0, 1)[0:6]\n", + "l_parameters = mp_parameters.transpose(0, 1)[6:18]\n", + "b_parameters = mp_parameters.transpose(0, 1)[18:]\n", + "\n", + "# Calculate losses\n", + "kdiff = kdiff_loss(k_parameters)\n", + "bstray = bstray_loss(k_parameters, l_parameters, b_parameters, extra_parameters)\n", + "\n", + "plt.scatter(bstray.detach().cpu(), kdiff.detach().cpu())\n", + "plt.title(\"Randomly generated points after GP reshaping\")\n", + "plt.xlabel(\"Bstray\")\n", + "plt.ylabel(\"Kdiff\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "noise = generate_noise(shape=(3, 10))\n", + "gp = gp_model(noise)\n", + "\n", + "noise = noise.cpu().numpy()\n", + "gp = gp.cpu().detach().numpy()\n", + "\n", + "for i in range(3):\n", + " print(f\"Noise {i}\")\n", + " print(noise[i])\n", + " print()\n", + " print(f\"Generated Parameters {i}\")\n", + " print(gp[i])\n", + " print()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"KDiff Error During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"Coupling %\")\n", + "\n", + "plt.plot(numpy.arange(len(kdiffs)), kdiffs)\n", + "plt.show()\n", + "\n", + "\n", + "plt.figure(figsize=(10, 5))\n", + "plt.title(f\"BStray Error During Training\")\n", + "plt.xlabel(\"Epoch\")\n", + "plt.ylabel(\"BStray\")\n", + "\n", + "plt.plot(numpy.arange(len(bstrays)), bstrays)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "accepted_solutions[0] = 0\n", + "max_value = max(accepted_solutions)\n", + "max_index = accepted_solutions.index(max_value)\n", + "\n", + "plt.plot([ i*10 for i in range(len(accepted_solutions))], accepted_solutions, lw=3)\n", + "# plt.plot(epoch, accepted_solutions)\n", + "\n", + "# # plt.scatter(mark, marked_points, marker='o',color='y')\n", + "# plt.scatter([max_index], [max_value], marker = 'o', color = 'r')\n", + "\n", + "# plt.annotate('Max number of solutions ('+ str(max_value)+') at epoch '+str(max_index), xy=(max_index,max_value), xycoords='data',\n", + "# xytext=(-90,60), textcoords='offset points',\n", + "# arrowprops=dict(arrowstyle='fancy',fc='0.2',\n", + "# connectionstyle=\"angle3,angleA=45,angleB=90\"))\n", + "plt.xlabel(\"Number of epochs\")\n", + "plt.ylabel(\"Accepted solutions \\n out of 1000\")\n", + "plt.ylim(0, 1000)\n", + "plt.xlim(0,400)\n", + "plt.title(\"Random Generation\")\n", + "plt.savefig(\"RandomGen_400ep/\" + \"soultions_per_epoch.png\")\n", + "plt.show()\n", + "plt.clf()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "name": "optimization_date4_22_22.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}